Merge remote-tracking branch 'spacebeaker/upstream' into master
Change-Id: Ieb5c6acc9bbafad3813c83058a9e40089fee2e3c
diff --git a/.bazelrc b/.bazelrc
index 55bf613..af0b2fd 100644
--- a/.bazelrc
+++ b/.bazelrc
@@ -15,6 +15,18 @@
#
# TFLM Bazel configuration file.
+# The semver-format version label embedded in build outputs when and where
+# stamping is used. Note TFLM does not currently publish semver-versioned
+# releases; however, this value is used where a version label is required, such
+# as in the Python distribution package.
+build --embed_label=0
+
+# Get stamp values from a script's output
+build --workspace_status_command=./tools/workspace_status.sh
+
+# TODO(b/315853820): Needed for Bazel 7.0, until migrated to bzlmod
+build --noenable_bzlmod
+
# Use the following C++ standard
build --cxxopt -std=c++17
diff --git a/.bazelversion b/.bazelversion
new file mode 100644
index 0000000..66ce77b
--- /dev/null
+++ b/.bazelversion
@@ -0,0 +1 @@
+7.0.0
diff --git a/.github/mergify.yml b/.github/mergify.yml
index 79b9fc9..4d3b6ad 100644
--- a/.github/mergify.yml
+++ b/.github/mergify.yml
@@ -1,6 +1,7 @@
queue_rules:
- name: default
checks_timeout: 2 h
+ branch_protection_injection_mode: queue
conditions:
- base=main
- label=ci:ready_to_merge
@@ -14,7 +15,6 @@
actions:
queue:
name: default
- require_branch_protection: true
method: squash
commit_message_template: |
{{ title }} (#{{ number }})
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 8728675..1386751 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -35,7 +35,6 @@
run: |
sudo ci/install_bazelisk.sh
pip3 install Pillow
- pip3 install Wave
pip3 install numpy
- name: Test
run: |
@@ -56,7 +55,6 @@
run: |
sudo ci/install_bazelisk.sh
pip3 install Pillow
- pip3 install Wave
pip3 install numpy
- name: Test
run: |
@@ -77,8 +75,12 @@
run: |
sudo ci/install_bazelisk.sh
pip3 install Pillow
- pip3 install Wave
pip3 install numpy
+ - name: Fix kernel mmap rnd bits
+ # Asan in llvm 14 provided in ubuntu 22.04 is incompatible with
+ # high-entropy ASLR in much newer kernels that GitHub runners are
+ # using leading to random crashes: https://reviews.llvm.org/D148280
+ run: sudo sysctl vm.mmap_rnd_bits=28
- name: Test
run: |
tensorflow/lite/micro/tools/ci_build/test_bazel_msan.sh
@@ -98,8 +100,12 @@
run: |
sudo ci/install_bazelisk.sh
pip3 install Pillow
- pip3 install Wave
pip3 install numpy
+ - name: Fix kernel mmap rnd bits
+ # Asan in llvm 14 provided in ubuntu 22.04 is incompatible with
+ # high-entropy ASLR in much newer kernels that GitHub runners are
+ # using leading to random crashes: https://reviews.llvm.org/D148280
+ run: sudo sysctl vm.mmap_rnd_bits=28
- name: Test
run: |
tensorflow/lite/micro/tools/ci_build/test_bazel_asan.sh
@@ -118,7 +124,6 @@
- name: Install dependencies
run: |
pip3 install Pillow
- pip3 install Wave
pip3 install numpy
- name: Test
run: |
@@ -139,7 +144,6 @@
- name: Install dependencies
run: |
pip3 install Pillow
- pip3 install Wave
pip3 install numpy
- name: Test
run: |
@@ -160,7 +164,6 @@
- name: Install dependencies
run: |
pip3 install Pillow
- pip3 install Wave
pip3 install numpy
- name: Test
run: |
@@ -213,7 +216,6 @@
- name: Install dependencies
run: |
pip3 install Pillow
- pip3 install Wave
pip3 install numpy
- name: Test
run: |
@@ -234,7 +236,6 @@
- name: Install dependencies
run: |
pip3 install Pillow
- pip3 install Wave
pip3 install numpy
- name: Test
run: |
@@ -256,7 +257,6 @@
- name: Install dependencies
run: |
pip3 install Pillow
- pip3 install Wave
pip3 install numpy
- name: Test
run: |
@@ -278,7 +278,6 @@
- name: Install dependencies
run: |
pip3 install Pillow
- pip3 install Wave
pip3 install numpy
- name: Test
run: |
@@ -300,7 +299,6 @@
- name: Install dependencies
run: |
pip3 install Pillow
- pip3 install Wave
pip3 install numpy
- name: Test
run: |
diff --git a/.github/workflows/cortex_m.yml b/.github/workflows/cortex_m.yml
index 26fe77d..a1e8168 100644
--- a/.github/workflows/cortex_m.yml
+++ b/.github/workflows/cortex_m.yml
@@ -14,24 +14,40 @@
# Allow manually triggering of the workflow.
workflow_dispatch: {}
+ pull_request_target:
+ types:
+ - closed
+ - labeled
+
+ workflow_call:
+ inputs:
+ trigger-sha:
+ required: true
+ type: string
+ secrets:
+ tflm-bot-token:
+ required: true
+
jobs:
cortex_m_generic:
runs-on: ubuntu-latest
if: |
github.event_name == 'workflow_dispatch' ||
- (github.event_name == 'schedule' && github.repository == 'tensorflow/tflite-micro')
+ (github.event_name == 'schedule' &&
+ github.repository == 'tensorflow/tflite-micro') ||
+ (github.event.action == 'labeled' &&
+ github.event.label.name == 'ci:run_full')
name: Cortex-M Generic
steps:
- uses: actions/setup-python@v4
- with:
+ with:
python-version: '3.10'
- uses: actions/checkout@v2
- name: Install dependencies
run: |
pip3 install Pillow
- pip3 install Wave
pip3 install numpy
- name: Test
run: |
@@ -42,18 +58,20 @@
if: |
github.event_name == 'workflow_dispatch' ||
- (github.event_name == 'schedule' && github.repository == 'tensorflow/tflite-micro')
+ (github.event_name == 'schedule' &&
+ github.repository == 'tensorflow/tflite-micro') ||
+ (github.event.action == 'labeled' &&
+ github.event.label.name == 'ci:run_full')
name: Cortex-M Corstone 300 (FVP)
steps:
- uses: actions/setup-python@v4
- with:
+ with:
python-version: '3.10'
- uses: actions/checkout@v2
- name: Install dependencies
run: |
pip3 install Pillow
- pip3 install Wave
pip3 install numpy
- name: Test
run: |
diff --git a/.github/workflows/generate_integration_tests.yml b/.github/workflows/generate_integration_tests.yml
index 91c8f18..74ed944 100644
--- a/.github/workflows/generate_integration_tests.yml
+++ b/.github/workflows/generate_integration_tests.yml
@@ -30,7 +30,6 @@
- name: Install dependencies
run: |
pip3 install Pillow
- pip3 install Wave
pip3 install numpy
- name: Test
run: |
diff --git a/.github/workflows/pypi_build.yml b/.github/workflows/pypi_build.yml
new file mode 100644
index 0000000..52a8075
--- /dev/null
+++ b/.github/workflows/pypi_build.yml
@@ -0,0 +1,54 @@
+# YAML schema for GitHub Actions:
+# https://help.github.com/en/actions/automating-your-workflow-with-github-actions/workflow-syntax-for-github-actions
+#
+# Helpful YAML parser to clarify YAML syntax:
+# https://yaml-online-parser.appspot.com/
+#
+
+name: PyPI Build
+
+on:
+ schedule:
+ # 1pm UTC is 6am or 7am PT depending on daylight savings.
+ - cron: '0 13 * * *'
+
+ workflow_dispatch:
+ inputs:
+ upload-type:
+ description: 'Upload type'
+ required: true
+ default: 'pypi'
+ type: choice
+ options:
+ - 'pypi'
+ - 'no upload'
+env:
+ TWINE_PASSWORD: ${{ secrets.PYPI_API_KEY }}
+
+jobs:
+ pypi-build:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v2
+ with:
+ token: ${{ secrets.TFLM_BOT_REPO_TOKEN }}
+ - name: Build Wheel 3.10
+ run: |
+ python/tflite_micro/pypi_build.sh cp310
+ - name: Build Wheel 3.11
+ run: |
+ python/tflite_micro/pypi_build.sh cp311
+ - name: Check Directory Output
+ run: |
+ ls -l bazel-pypi-out
+ - name: Install Twine
+ run: |
+ python -m pip install --upgrade pip setuptools wheel
+ pip install twine
+ - name: upload to pypi
+ if: |
+ github.event.inputs.upload-type == 'pypi' ||
+ github.event_name == 'schedule'
+ run: |
+ python/tflite_micro/pypi_upload.sh \
+ bazel-pypi-out/tflite_micro-*.whl
\ No newline at end of file
diff --git a/.github/workflows/sync.yml b/.github/workflows/sync.yml
index d505a9d..0c5434e 100644
--- a/.github/workflows/sync.yml
+++ b/.github/workflows/sync.yml
@@ -62,5 +62,5 @@
author: TFLM-bot <tflm-github-bot@google.com>
body: "BUG=automated sync from upstream\nNO_CHECK_TFLITE_FILES=automated sync from upstream"
labels: bot:sync-tf, ci:run
- reviewers: advaitjain
+ reviewers: rascani
diff --git a/.github/workflows/tests_entry.yml b/.github/workflows/tests_entry.yml
index 07cf617..1ed4de1 100644
--- a/.github/workflows/tests_entry.yml
+++ b/.github/workflows/tests_entry.yml
@@ -28,9 +28,10 @@
runs-on: ubuntu-latest
steps:
- name: fail-without-labels
- if: ${{ !(contains(github.event.pull_request.labels.*.name, 'ci:run') ||
- contains(github.event.pull_request.labels.*.name, 'ci:ready_to_merge') ||
- contains(github.event.pull_request.labels.*.name, 'ci:run_full')) }}
+ if: github.event.action == 'labeled' &&
+ !(github.event.label.name == 'ci:run' ||
+ github.event.label.name == 'ci:ready_to_merge' ||
+ github.event.label.name == 'ci:run_full')
run: exit 1
ci-ready-to-merge:
@@ -60,7 +61,8 @@
needs: ci-ready-to-merge
steps:
- name: remove-cirun
- if: ${{ contains(github.event.pull_request.labels.*.name, 'ci:run') }}
+ if: github.event.action == 'labeled' &&
+ github.event.label.name == 'ci:run'
uses: actions/github-script@v5
with:
github-token: ${{ secrets.TFLM_BOT_REPO_TOKEN }}
@@ -78,7 +80,8 @@
needs: ci-run
steps:
- name: remove-cirun-full
- if: ${{ contains(github.event.pull_request.labels.*.name, 'ci:run_full') }}
+ if: github.event.action == 'labeled' &&
+ github.event.label.name == 'ci:run_full'
uses: actions/github-script@v5
with:
github-token: ${{ secrets.TFLM_BOT_REPO_TOKEN }}
@@ -102,7 +105,7 @@
echo "PR description requires a BUG= line with issue number."
echo "See https://testing.googleblog.com/2017/09/code-health-providing-context-with.html for additional context"
exit 1
-
+
call-ci:
needs: ci-run
uses: ./.github/workflows/ci.yml
@@ -128,7 +131,7 @@
call-check-tflite-files:
needs: ci-run
uses: ./.github/workflows/check_tflite_files.yml
- with:
+ with:
trigger-sha: ${{ github.event.pull_request.head.sha }}
pr-number: ${{ github.event.pull_request.number }}
pr-body: ${{ github.event.pull_request.body }}
diff --git a/.github/workflows/tests_post.yml b/.github/workflows/tests_post.yml
index 719adaa..d15b092 100644
--- a/.github/workflows/tests_post.yml
+++ b/.github/workflows/tests_post.yml
@@ -11,46 +11,12 @@
- labeled
jobs:
- riscv_postmerge:
- if: ${{ github.event.pull_request.merged == true ||
- contains(github.event.pull_request.labels.*.name, 'ci:run_full') }}
- uses: ./.github/workflows/riscv_postmerge.yml
- with:
- trigger-sha: ${{ github.event.pull_request.head.sha }}
- secrets:
- tflm-bot-token: ${{ secrets.TFLM_BOT_PACKAGE_READ_TOKEN }}
-
- xtensa_postmerge:
- if: ${{ github.event.pull_request.merged == true ||
- contains(github.event.pull_request.labels.*.name, 'ci:run_full') }}
- uses: ./.github/workflows/xtensa_postmerge.yml
- with:
- trigger-sha: ${{ github.event.pull_request.head.sha }}
- secrets:
- tflm-bot-token: ${{ secrets.TFLM_BOT_PACKAGE_READ_TOKEN }}
-
- issue_on_error:
- needs: [riscv_postmerge,xtensa_postmerge]
- if: ${{ always() && contains(needs.*.result, 'failure') &&
- !contains(github.event.pull_request.labels.*.name, 'ci:run_full') }}
- uses: ./.github/workflows/issue_on_error.yml
- with:
- repo: ${{ github.repository}}
- workflow: ${{ github.workflow }}
- run_number: ${{ github.run_number }}
- run_id: ${{ github.run_id }}
- flag_label: ci:bot_issue
- pr_number: ${{ github.event.number }}
- pr_link: ${{ github.event.pull_request._links.html.href }}
- secrets:
- token: ${{ secrets.GITHUB_TOKEN }}
-
ci_run_full:
- needs: [issue_on_error]
runs-on: ubuntu-latest
steps:
- name: remove-cirun-full
- if: ${{ contains(github.event.pull_request.labels.*.name, 'ci:run_full') }}
+ if: github.event.action == 'labeled' &&
+ github.event.label.name == 'ci:run_full'
uses: actions/github-script@v5
with:
github-token: ${{ secrets.TFLM_BOT_REPO_TOKEN }}
@@ -63,3 +29,48 @@
})
continue-on-error: true
+ riscv_postmerge:
+ needs: [ci_run_full]
+ if: always() && github.event.pull_request.merged == true ||
+ (github.event.action == 'labeled' &&
+ github.event.label.name == 'ci:run_full')
+ uses: ./.github/workflows/riscv_postmerge.yml
+ with:
+ trigger-sha: ${{ github.event.pull_request.head.sha }}
+ secrets:
+ tflm-bot-token: ${{ secrets.TFLM_BOT_PACKAGE_READ_TOKEN }}
+
+ xtensa_postmerge:
+ needs: [ci_run_full]
+ if: always() && github.event.pull_request.merged == true ||
+ (github.event.action == 'labeled' &&
+ github.event.label.name == 'ci:run_full')
+ uses: ./.github/workflows/xtensa_postmerge.yml
+ with:
+ trigger-sha: ${{ github.event.pull_request.head.sha }}
+ secrets:
+ tflm-bot-token: ${{ secrets.TFLM_BOT_PACKAGE_READ_TOKEN }}
+
+ cortex_m_ci_full:
+ needs: [ci_run_full]
+ uses: ./.github/workflows/cortex_m.yml
+ with:
+ trigger-sha: ${{ github.event.pull_request.head.sha }}
+ secrets:
+ tflm-bot-token: ${{ secrets.TFLM_BOT_PACKAGE_READ_TOKEN }}
+
+ issue_on_error:
+ needs: [riscv_postmerge, xtensa_postmerge, cortex_m_ci_full]
+ if: ${{ always() && contains(needs.*.result, 'failure') &&
+ github.event.pull_request.merged == true }}
+ uses: ./.github/workflows/issue_on_error.yml
+ with:
+ repo: ${{ github.repository}}
+ workflow: ${{ github.workflow }}
+ run_number: ${{ github.run_number }}
+ run_id: ${{ github.run_id }}
+ flag_label: ci:bot_issue
+ pr_number: ${{ github.event.number }}
+ pr_link: ${{ github.event.pull_request._links.html.href }}
+ secrets:
+ token: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.github/workflows/xtensa_postmerge.yml b/.github/workflows/xtensa_postmerge.yml
index 8e1188e..d91cc94 100644
--- a/.github/workflows/xtensa_postmerge.yml
+++ b/.github/workflows/xtensa_postmerge.yml
@@ -31,7 +31,7 @@
- run: |
rm -rf .git
echo ${{ secrets.tflm-bot-token }} | docker login ghcr.io -u tflm-bot --password-stdin
- docker run --env XTENSA_TOOLS_VERSION=RI-2020.4-linux --rm -v `pwd`:/opt/tflite-micro ghcr.io/tflm-bot/xtensa_xplorer_13:0.1 \
+ docker run --env XTENSA_TOOLS_VERSION=RI-2020.4-linux --rm -v `pwd`:/opt/tflite-micro ghcr.io/tflm-bot/xtensa_xplorer_13:0.3 \
/bin/bash -c \
"cd /opt && tflite-micro/tensorflow/lite/micro/tools/ci_build/test_xtensa_fusion_f1.sh EXTERNAL tflite-micro/"
@@ -46,7 +46,7 @@
- run: |
rm -rf .git
echo ${{ secrets.tflm-bot-token }} | docker login ghcr.io -u tflm-bot --password-stdin
- docker run --env XTENSA_TOOLS_VERSION=RI-2020.4-linux --rm -v `pwd`:/opt/tflite-micro ghcr.io/tflm-bot/xtensa_xplorer_13:0.1 \
+ docker run --env XTENSA_TOOLS_VERSION=RI-2020.4-linux --rm -v `pwd`:/opt/tflite-micro ghcr.io/tflm-bot/xtensa_xplorer_13:0.3 \
/bin/bash -c \
"cd /opt && tflite-micro/tensorflow/lite/micro/tools/ci_build/test_xtensa_vision_p6.sh RUN_TESTS tflite-micro/"
@@ -61,6 +61,6 @@
- run: |
rm -rf .git
echo ${{ secrets.tflm-bot-token }} | docker login ghcr.io -u tflm-bot --password-stdin
- docker run --env XTENSA_TOOLS_VERSION=RI-2019.2-linux --rm -v `pwd`:/opt/tflite-micro ghcr.io/tflm-bot/xtensa_xplorer_11:0.1 \
+ docker run --env XTENSA_TOOLS_VERSION=RI-2019.2-linux --rm -v `pwd`:/opt/tflite-micro ghcr.io/tflm-bot/xtensa_xplorer_11:0.2 \
/bin/bash -c \
"cd /opt && tflite-micro/tensorflow/lite/micro/tools/ci_build/test_xtensa_hifimini.sh tflite-micro/"
diff --git a/.github/workflows/xtensa_presubmit.yml b/.github/workflows/xtensa_presubmit.yml
index 58c4258..519aff9 100644
--- a/.github/workflows/xtensa_presubmit.yml
+++ b/.github/workflows/xtensa_presubmit.yml
@@ -32,7 +32,7 @@
- run: |
rm -rf .git
echo ${{ secrets.tflm-bot-token }} | docker login ghcr.io -u tflm-bot --password-stdin
- docker run --env XTENSA_TOOLS_VERSION=RI-2020.4-linux --rm -v `pwd`:/opt/tflite-micro ghcr.io/tflm-bot/xtensa_xplorer_13:0.1 \
+ docker run --env XTENSA_TOOLS_VERSION=RI-2020.4-linux --rm -v `pwd`:/opt/tflite-micro ghcr.io/tflm-bot/xtensa_xplorer_13:0.3 \
/bin/bash -c \
"cd /opt && tflite-micro/tensorflow/lite/micro/tools/ci_build/test_xtensa_vision_p6.sh RUN_NO_TESTS tflite-micro/"
@@ -47,7 +47,7 @@
- run: |
rm -rf .git
echo ${{ secrets.tflm-bot-token }} | docker login ghcr.io -u tflm-bot --password-stdin
- docker run --env XTENSA_TOOLS_VERSION=RI-2022.9-linux --rm -v `pwd`:/opt/tflite-micro ghcr.io/tflm-bot/xtensa_hifi5:0.1 \
+ docker run --env XTENSA_TOOLS_VERSION=RI-2022.9-linux --rm -v `pwd`:/opt/tflite-micro ghcr.io/tflm-bot/xtensa_xplorer_hifi5:0.2 \
/bin/bash -c \
"cd /opt && tflite-micro/tensorflow/lite/micro/tools/ci_build/test_xtensa_hifi5.sh tflite-micro/"
@@ -62,6 +62,6 @@
- run: |
rm -rf .git
echo ${{ secrets.tflm-bot-token }} | docker login ghcr.io -u tflm-bot --password-stdin
- docker run --env XTENSA_TOOLS_VERSION=RI-2020.4-linux --rm -v `pwd`:/opt/tflite-micro ghcr.io/tflm-bot/xtensa_xplorer_13:0.1 \
+ docker run --env XTENSA_TOOLS_VERSION=RI-2020.4-linux --rm -v `pwd`:/opt/tflite-micro ghcr.io/tflm-bot/xtensa_xplorer_13:0.3 \
/bin/bash -c \
"cd /opt && tflite-micro/tensorflow/lite/micro/tools/ci_build/test_xtensa_hifi3z.sh EXTERNAL tflite-micro/"
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index a442570..e1410af 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -4,25 +4,27 @@
-->
<!--ts-->
- * [How to Contribute](#how-to-contribute)
- * [Contributor License Agreement](#contributor-license-agreement)
- * [Community Guidelines](#community-guidelines)
- * [Code Contribution Guidelines](#code-contribution-guidelines)
- * [General Pull Request Guidelines](#general-pull-request-guidelines)
- * [Guidelines for Specific Contribution Categories](#guidelines-for-specific-contribution-categories)
- * [Bug Fixes](#bug-fixes)
- * [Reference Kernel Implementations](#reference-kernel-implementations)
- * [Optimized Kernel Implementations](#optimized-kernel-implementations)
- * [New Target / Platform / IDE / Examples](#new-target--platform--ide--examples)
- * [Development Workflow Notes](#development-workflow-notes)
- * [Initial Setup](#initial-setup)
- * [Before submitting your PR](#before-submitting-your-pr)
- * [During the PR review](#during-the-pr-review)
- * [Reviewer notes](#reviewer-notes)
- * [Python notes](#python-notes)
- * [Continuous Integration System](#continuous-integration-system)
+* [How to Contribute](#how-to-contribute)
+ * [Contributor License Agreement](#contributor-license-agreement)
+ * [Community Guidelines](#community-guidelines)
+* [Code Contribution Guidelines](#code-contribution-guidelines)
+ * [General Pull Request Guidelines](#general-pull-request-guidelines)
+ * [Guidelines for Specific Contribution Categories](#guidelines-for-specific-contribution-categories)
+ * [Bug Fixes](#bug-fixes)
+ * [Reference Kernel Implementations](#reference-kernel-implementations)
+ * [Optimized Kernel Implementations](#optimized-kernel-implementations)
+ * [New Target / Platform / IDE / Examples](#new-target--platform--ide--examples)
+* [Development Environment](#development-environment)
+ * [Prerequisites](#prerequisites)
+ * [Recommendations](#recommendations)
+* [Development Workflow Notes](#development-workflow-notes)
+ * [Before submitting your PR](#before-submitting-your-pr)
+ * [During the PR review](#during-the-pr-review)
+ * [Reviewer notes](#reviewer-notes)
+ * [Python notes](#python-notes)
+* [Continuous Integration System](#continuous-integration-system)
-<!-- Added by: advaitjain, at: Thu 16 Sep 2021 11:43:42 AM PDT -->
+<!-- Added by: rkuester, at: Fri Dec 15 04:25:41 PM CST 2023 -->
<!--te-->
@@ -143,10 +145,20 @@
Please see the [new platform support guide](tensorflow/lite/micro/docs/new_platform_support.md)
for documentation on how to add TFLM support for your particular platform.
+# Development Environment
-# Development Workflow Notes
+We support amd64-architecture development and testing on Ubuntu 22.04, although
+other OSes may work.
-## Initial Setup
+## Prerequisites
+
+TFLM's primary build system is [Bazel](https://bazel.build). Add
+[Bazelisk](https://github.com/bazelbuild/bazelisk) as the `bazel` executable in
+your PATH ([e.g., copy it to `/usr/local/bin/bazel`](ci/install_bazelisk.sh)) to
+automatically download and run the correct Bazel version as specified in
+`//.bazelversion`.
+
+## Recommendations
Below are some tips that might be useful and improve the development experience.
@@ -156,7 +168,9 @@
* Code search the [TfLite Micro codebase](https://sourcegraph.com/github.com/tensorflow/tflite-micro@main)
on Sourcegraph. And optionally install the [plugin that enables GitHub integration](https://docs.sourcegraph.com/integration/github#github-integration-with-sourcegraph).
-* Install [bazel](ci/install_bazelisk.sh) and [buildifier](ci/install_buildifier.sh).
+* Install
+ [Buildifier](https://github.com/bazelbuild/buildtools/blob/master/buildifier/README.md)
+ ([e.g.](ci/install_buildifier.sh)) to format Bazel BUILD and .bzl files.
* Install the latest clang and clang-format. For example, [here](ci/Dockerfile.micro)
is the what we do for the TFLM continuous integration Docker container.
@@ -164,8 +178,8 @@
* Get a copy of [cpplint](https://github.com/google/styleguide/tree/gh-pages/cpplint)
or install it:
-* Install Pillow and Wave. For example, [here](ci/Dockerfile.micro) is what we
- do for the TFLM continuous integration Docker container.
+* Install Pillow. For example, [here](ci/Dockerfile.micro) is what we do for
+ the TFLM continuous integration Docker container.
```
pip install cpplint
@@ -184,6 +198,8 @@
cp tensorflow/lite/micro/tools/dev_setup/pre-push.tflm .git/hooks/pre-push
```
+# Development Workflow Notes
+
## Before submitting your PR
1. Run in-place clang-format on all the files that are modified in your git
diff --git a/WORKSPACE b/WORKSPACE
index f881df9..48202f5 100644
--- a/WORKSPACE
+++ b/WORKSPACE
@@ -15,54 +15,72 @@
workspace(name = "tflite_micro")
+load("//tensorflow:workspace.bzl", "workspace")
+
+workspace()
+
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
# compile_commands.json generator
http_archive(
name = "hedron_compile_commands",
- url = "https://github.com/hedronvision/bazel-compile-commands-extractor/archive/1266d6a25314d165ca78d0061d3399e909b7920e.tar.gz",
- strip_prefix = "bazel-compile-commands-extractor-1266d6a25314d165ca78d0061d3399e909b7920e",
sha256 = "bacabfe758676fdc19e4bea7c4a3ac99c7e7378d259a9f1054d341c6a6b44ff6",
+ strip_prefix = "bazel-compile-commands-extractor-1266d6a25314d165ca78d0061d3399e909b7920e",
+ url = "https://github.com/hedronvision/bazel-compile-commands-extractor/archive/1266d6a25314d165ca78d0061d3399e909b7920e.tar.gz",
)
+
load("@hedron_compile_commands//:workspace_setup.bzl", "hedron_compile_commands_setup")
+
hedron_compile_commands_setup()
+_rules_python_version = "0.26.0"
+
http_archive(
name = "rules_python",
- sha256 = "497ca47374f48c8b067d786b512ac10a276211810f4a580178ee9b9ad139323a",
- strip_prefix = "rules_python-0.16.1",
- url = "https://github.com/bazelbuild/rules_python/archive/refs/tags/0.16.1.tar.gz",
+ sha256 = "9d04041ac92a0985e344235f5d946f71ac543f1b1565f2cdbc9a2aaee8adf55b",
+ strip_prefix = "rules_python-{}".format(_rules_python_version),
+ url = "https://github.com/bazelbuild/rules_python/archive/refs/tags/{}.tar.gz".format(_rules_python_version),
)
+load("@rules_python//python:repositories.bzl", "py_repositories")
+
+py_repositories()
+
+# Read the Python package dependencies of the build environment. To modify
+# them, see //third_party:python_requirements.in.
load("@rules_python//python:pip.bzl", "pip_parse")
+
pip_parse(
name = "tflm_pip_deps",
requirements_lock = "//third_party:python_requirements.txt",
)
+# Create repositories for each Python package dependency.
load("@tflm_pip_deps//:requirements.bzl", "install_deps", "requirement")
+
install_deps()
-load("//tensorflow:workspace.bzl", "workspace")
-workspace()
-
http_archive(
- name = "pybind11_bazel",
- strip_prefix = "pybind11_bazel-faf56fb3df11287f26dbc66fdedf60a2fc2c6631",
- urls = ["https://github.com/pybind/pybind11_bazel/archive/faf56fb3df11287f26dbc66fdedf60a2fc2c6631.zip"],
- sha256 = "a185aa68c93b9f62c80fcb3aadc3c83c763854750dc3f38be1dadcb7be223837",
+ name = "pybind11_bazel",
+ sha256 = "a185aa68c93b9f62c80fcb3aadc3c83c763854750dc3f38be1dadcb7be223837",
+ strip_prefix = "pybind11_bazel-faf56fb3df11287f26dbc66fdedf60a2fc2c6631",
+ urls = ["https://github.com/pybind/pybind11_bazel/archive/faf56fb3df11287f26dbc66fdedf60a2fc2c6631.zip"],
)
http_archive(
- name = "pybind11",
- build_file = "@pybind11_bazel//:pybind11.BUILD",
- strip_prefix = "pybind11-2.10.0",
- urls = ["https://github.com/pybind/pybind11/archive/refs/tags/v2.10.0.tar.gz"],
- sha256 = "eacf582fa8f696227988d08cfc46121770823839fe9e301a20fbce67e7cd70ec",
+ name = "pybind11",
+ build_file = "@pybind11_bazel//:pybind11.BUILD",
+ sha256 = "eacf582fa8f696227988d08cfc46121770823839fe9e301a20fbce67e7cd70ec",
+ strip_prefix = "pybind11-2.10.0",
+ urls = ["https://github.com/pybind/pybind11/archive/refs/tags/v2.10.0.tar.gz"],
)
load("@pybind11_bazel//:python_configure.bzl", "python_configure")
-python_configure(name = "local_config_python", python_version = "3")
+
+python_configure(
+ name = "local_config_python",
+ python_version = "3",
+)
load("//python:py_pkg_cc_deps.bzl", "py_pkg_cc_deps")
@@ -76,5 +94,13 @@
name = "tensorflow_cc_deps",
includes = ["tensorflow/include"],
libs = ["tensorflow/libtensorflow_framework.so.2"],
- pkg = requirement("tensorflow-cpu"),
+ pkg = requirement("tensorflow"),
+)
+
+# Optimized kernel deps
+http_archive(
+ name = "nnlib_hifi4",
+ build_file = "@tflite_micro//third_party/xtensa/nnlib_hifi4:nnlib_hifi4.BUILD",
+ strip_prefix = "nnlib-hifi4-34f5f995f28d298ae2b6e2ba6e76c32a5cb34989",
+ urls = ["https://github.com/foss-xtensa/nnlib-hifi4/archive/34f5f995f28d298ae2b6e2ba6e76c32a5cb34989.zip"],
)
diff --git a/ci/Dockerfile.micro b/ci/Dockerfile.micro
index 6ce775e..0515cca 100644
--- a/ci/Dockerfile.micro
+++ b/ci/Dockerfile.micro
@@ -59,7 +59,6 @@
# https://github.com/tensorflow/tflite-micro/pull/337
# https://github.com/tensorflow/tflite-micro/pull/410
RUN pip install Pillow
-RUN pip install Wave
# necessary bits for create_size_log scripts
RUN pip install pandas
diff --git a/ci/Dockerfile.xtensa_xplorer_11 b/ci/Dockerfile.xtensa_xplorer_11
new file mode 100644
index 0000000..fa7f316
--- /dev/null
+++ b/ci/Dockerfile.xtensa_xplorer_11
@@ -0,0 +1,49 @@
+FROM python:3.10-bullseye
+ENV DEBIAN_FRONTEND noninterative
+
+RUN \
+ apt update && \
+ apt install -y \
+ automake \
+ build-essential \
+ curl \
+ git \
+ unzip \
+ wget
+
+WORKDIR /opt/xtensa
+
+COPY ./Xplorer-8.0.11-linux-x64-installer.bin .
+COPY ./mini1m1m_RI_2019_2_linux_w_keys.tgz .
+COPY ./XtensaTools_RI_2022_9_linux.tgz .
+COPY ci/install_cores_xplorer_11.sh .
+COPY ci/install_bazelisk.sh .
+
+RUN \
+ pip3 install Pillow
+
+RUN \
+ pip3 install numpy
+
+RUN \
+ chmod +x Xplorer-8.0.11-linux-x64-installer.bin && \
+ ./Xplorer-8.0.11-linux-x64-installer.bin --prefix /opt/xtensa --mode unattended
+
+ENV XTENSA_BASE "/opt/xtensa/XtDevTools/install/"
+
+RUN \
+ chmod +x install_cores_xplorer_11.sh && \
+ ./install_cores_xplorer_11.sh
+
+RUN ./install_bazelisk.sh
+
+RUN \
+ rm Xplorer-8.0.11-linux-x64-installer.bin && \
+ rm mini1m1m_RI_2019_2_linux_w_keys.tgz && \
+ rm XtensaTools_RI_2022_9_linux.tgz && \
+ rm -f install_cores_xplorer_11.sh
+
+ENV LIC_DIR "/opt/xtensa/licenses/RI-2020.4-linux"
+ENV LM_LICENSE_FILE "/opt/xtensa/licenses/RI-2019.2-linux/mini1m1m_RG/misc/license.dat"
+
+CMD /opt/xtensa/XtDevTools/install/tools/RI-2020.4-linux/XtensaTools/bin/xt-clang++ --xtensa-core=
diff --git a/ci/Dockerfile.xtensa_xplorer_13 b/ci/Dockerfile.xtensa_xplorer_13
new file mode 100644
index 0000000..d50be6c
--- /dev/null
+++ b/ci/Dockerfile.xtensa_xplorer_13
@@ -0,0 +1,51 @@
+FROM python:3.10-bullseye
+ENV DEBIAN_FRONTEND noninterative
+
+RUN \
+ apt update && \
+ apt install -y \
+ automake \
+ build-essential \
+ curl \
+ git \
+ unzip \
+ wget
+
+WORKDIR /opt/xtensa
+
+COPY ./Xplorer-8.0.13-linux-x64-installer.bin .
+COPY ./F1_190305_swupgrade_linux.tgz .
+COPY ./P6_200528_linux.tgz .
+COPY ./HIFI_190304_swupgrade_linux.tgz .
+COPY ci/install_cores_xplorer_13.sh .
+COPY ci/install_bazelisk.sh .
+
+RUN \
+ pip3 install Pillow
+
+RUN \
+ pip3 install numpy
+
+RUN \
+ chmod +x Xplorer-8.0.13-linux-x64-installer.bin && \
+ ./Xplorer-8.0.13-linux-x64-installer.bin --prefix /opt/xtensa --mode unattended
+
+ENV XTENSA_BASE "/opt/xtensa/XtDevTools/install/"
+
+RUN \
+ chmod +x install_cores_xplorer_13.sh && \
+ ./install_cores_xplorer_13.sh
+
+RUN ./install_bazelisk.sh
+
+RUN \
+ rm Xplorer-8.0.13-linux-x64-installer.bin && \
+ rm F1_190305_swupgrade_linux.tgz && \
+ rm P6_200528_linux.tgz && \
+ rm HIFI_190304_swupgrade_linux.tgz && \
+ rm -f install_cores_xplorer_13.sh
+
+ENV LIC_DIR "/opt/xtensa/licenses/RI-2020.4-linux"
+ENV LM_LICENSE_FILE "${LIC_DIR}/F1_190305_swupgrade/misc/license.dat:${LIC_DIR}/AE_HiFi5_LE5_AO_FP_XC/misc/license.dat:${LIC_DIR}/P6_200528/misc/license.dat:${LIC_DIR}/HIFI_190304_swupgrade/misc/license.dat"
+
+CMD /opt/xtensa/XtDevTools/install/tools/RI-2020.4-linux/XtensaTools/bin/xt-clang++ --xtensa-core=
diff --git a/ci/Dockerfile.xtensa_xplorer_solo b/ci/Dockerfile.xtensa_xplorer_solo
new file mode 100644
index 0000000..e7fe205
--- /dev/null
+++ b/ci/Dockerfile.xtensa_xplorer_solo
@@ -0,0 +1,49 @@
+FROM python:3.10-bullseye
+ENV DEBIAN_FRONTEND noninterative
+
+RUN \
+ apt update && \
+ apt install -y \
+ automake \
+ build-essential \
+ curl \
+ git \
+ unzip \
+ wget
+
+WORKDIR /opt/xtensa
+
+COPY ./Xplorer-solo-9.0.19-linux-x64-installer.bin .
+COPY ./PRD_H5_RDO_07_01_2022_linux.tgz .
+COPY ./XtensaTools_RI_2022_9_linux.tgz .
+COPY ci/install_cores_xplorer_solo.sh .
+COPY ci/install_bazelisk.sh .
+
+RUN \
+ pip3 install Pillow
+
+RUN \
+ pip3 install numpy
+
+RUN \
+ chmod +x Xplorer-solo-9.0.19-linux-x64-installer.bin && \
+ ./Xplorer-solo-9.0.19-linux-x64-installer.bin --prefix /opt/xtensa --mode unattended
+
+ENV XTENSA_BASE "/opt/xtensa/XtDevTools/install/"
+
+RUN \
+ chmod +x install_cores_xplorer_solo.sh && \
+ ./install_cores_xplorer_solo.sh
+
+RUN ./install_bazelisk.sh
+
+RUN \
+ rm Xplorer-solo-9.0.19-linux-x64-installer.bin && \
+ rm PRD_H5_RDO_07_01_2022_linux.tgz && \
+ rm XtensaTools_RI_2022_9_linux.tgz && \
+ rm -f install_cores_xplorer_solo.sh
+
+ENV LIC_DIR "/opt/xtensa/licenses/RI-2020.4-linux"
+ENV LM_LICENSE_FILE "/opt/xtensa/licenses/RI-2022.9-linux/PRD_H5_RDO_07_01_2022/misc/license.dat"
+
+CMD /opt/xtensa/XtDevTools/install/tools/RI-2020.4-linux/XtensaTools/bin/xt-clang++ --xtensa-core=
diff --git a/ci/flatbuffers_for_tf_sync/BUILD b/ci/flatbuffers_for_tf_sync/BUILD
deleted file mode 100644
index 82bab3f..0000000
--- a/ci/flatbuffers_for_tf_sync/BUILD
+++ /dev/null
@@ -1 +0,0 @@
-# This empty BUILD file is required to make Bazel treat this directory as a package.
diff --git a/ci/flatbuffers_for_tf_sync/BUILD.system b/ci/flatbuffers_for_tf_sync/BUILD.system
deleted file mode 100644
index 8fe4d7a..0000000
--- a/ci/flatbuffers_for_tf_sync/BUILD.system
+++ /dev/null
@@ -1,43 +0,0 @@
-licenses(["notice"]) # Apache 2.0
-
-filegroup(
- name = "LICENSE.txt",
- visibility = ["//visibility:public"],
-)
-
-# Public flatc library to compile flatbuffer files at runtime.
-cc_library(
- name = "flatbuffers",
- linkopts = ["-lflatbuffers"],
- visibility = ["//visibility:public"],
-)
-
-# Public flatc compiler library.
-cc_library(
- name = "flatc_library",
- linkopts = ["-lflatbuffers"],
- visibility = ["//visibility:public"],
-)
-
-genrule(
- name = "lnflatc",
- outs = ["flatc.bin"],
- cmd = "ln -s $$(which flatc) $@",
-)
-
-# Public flatc compiler.
-sh_binary(
- name = "flatc",
- srcs = ["flatc.bin"],
- visibility = ["//visibility:public"],
-)
-
-cc_library(
- name = "runtime_cc",
- visibility = ["//visibility:public"],
-)
-
-py_library(
- name = "runtime_py",
- visibility = ["//visibility:public"],
-)
diff --git a/ci/flatbuffers_for_tf_sync/build_defs.bzl b/ci/flatbuffers_for_tf_sync/build_defs.bzl
deleted file mode 100644
index eaefb01..0000000
--- a/ci/flatbuffers_for_tf_sync/build_defs.bzl
+++ /dev/null
@@ -1,640 +0,0 @@
-"""BUILD rules for generating flatbuffer files."""
-
-load("@build_bazel_rules_android//android:rules.bzl", "android_library")
-load("@rules_python//python:defs.bzl", "py_library")
-
-flatc_path = "@flatbuffers//:flatc"
-zip_files = "//tensorflow/lite/tools:zip_files"
-
-DEFAULT_INCLUDE_PATHS = [
- "./",
- "$(GENDIR)",
- "$(BINDIR)",
-]
-
-DEFAULT_FLATC_ARGS = [
- "--no-union-value-namespacing",
- "--gen-object-api",
-]
-
-def flatbuffer_library_public(
- name,
- srcs,
- outs,
- language_flag,
- out_prefix = "",
- includes = [],
- include_paths = [],
- compatible_with = [],
- flatc_args = DEFAULT_FLATC_ARGS,
- reflection_name = "",
- reflection_visibility = None,
- output_to_bindir = False):
- """Generates code files for reading/writing the given flatbuffers in the requested language using the public compiler.
-
- Outs:
- filegroup(name): all generated source files.
- Fileset([reflection_name]): (Optional) all generated reflection binaries.
-
- Args:
- name: Rule name.
- srcs: Source .fbs files. Sent in order to the compiler.
- outs: Output files from flatc.
- language_flag: Target language flag. One of [-c, -j, -js].
- out_prefix: Prepend this path to the front of all generated files except on
- single source targets. Usually is a directory name.
- includes: Optional, list of filegroups of schemas that the srcs depend on.
- include_paths: Optional, list of paths the includes files can be found in.
- compatible_with: Optional, passed to genrule for environments this rule
- can be built for.
- flatc_args: Optional, list of additional arguments to pass to flatc.
- reflection_name: Optional, if set this will generate the flatbuffer
- reflection binaries for the schemas.
- reflection_visibility: The visibility of the generated reflection Fileset.
- output_to_bindir: Passed to genrule for output to bin directory.
- """
- include_paths_cmd = ["-I %s" % (s) for s in include_paths]
-
- # '$(@D)' when given a single source target will give the appropriate
- # directory. Appending 'out_prefix' is only necessary when given a build
- # target with multiple sources.
- output_directory = (
- ("-o $(@D)/%s" % (out_prefix)) if len(srcs) > 1 else ("-o $(@D)")
- )
- genrule_cmd = " ".join([
- "for f in $(SRCS); do",
- "$(location %s)" % (flatc_path),
- " ".join(flatc_args),
- " ".join(include_paths_cmd),
- language_flag,
- output_directory,
- "$$f;",
- "done",
- ])
- native.genrule(
- name = name,
- srcs = srcs,
- outs = outs,
- output_to_bindir = output_to_bindir,
- compatible_with = compatible_with,
- tools = includes + [flatc_path],
- cmd = genrule_cmd,
- message = "Generating flatbuffer files for %s:" % (name),
- )
- if reflection_name:
- reflection_genrule_cmd = " ".join([
- "for f in $(SRCS); do",
- "$(location %s)" % (flatc_path),
- "-b --schema",
- " ".join(flatc_args),
- " ".join(include_paths_cmd),
- language_flag,
- output_directory,
- "$$f;",
- "done",
- ])
- reflection_outs = [
- (out_prefix + "%s.bfbs") % (s.replace(".fbs", "").split("/")[-1])
- for s in srcs
- ]
- native.genrule(
- name = "%s_srcs" % reflection_name,
- srcs = srcs,
- outs = reflection_outs,
- output_to_bindir = output_to_bindir,
- compatible_with = compatible_with,
- tools = includes + [flatc_path],
- cmd = reflection_genrule_cmd,
- message = "Generating flatbuffer reflection binary for %s:" % (name),
- )
- # TODO(b/114456773): Make bazel rules proper and supported by flatbuffer
- # Have to comment this since FilesetEntry is not supported in bazel
- # skylark.
- # native.Fileset(
- # name = reflection_name,
- # out = "%s_out" % reflection_name,
- # entries = [
- # native.FilesetEntry(files = reflection_outs),
- # ],
- # visibility = reflection_visibility,
- # compatible_with = compatible_with,
- # )
-
-def flatbuffer_cc_library(
- name,
- srcs,
- srcs_filegroup_name = "",
- out_prefix = "",
- includes = [],
- include_paths = [],
- compatible_with = [],
- flatc_args = DEFAULT_FLATC_ARGS,
- visibility = None,
- srcs_filegroup_visibility = None,
- gen_reflections = False):
- '''A cc_library with the generated reader/writers for the given flatbuffer definitions.
-
- Outs:
- filegroup([name]_srcs): all generated .h files.
- filegroup(srcs_filegroup_name if specified, or [name]_includes if not):
- Other flatbuffer_cc_library's can pass this in for their `includes`
- parameter, if they depend on the schemas in this library.
- Fileset([name]_reflection): (Optional) all generated reflection binaries.
- cc_library([name]): library with sources and flatbuffers deps.
-
- Remarks:
- ** Because the genrule used to call flatc does not have any trivial way of
- computing the output list of files transitively generated by includes and
- --gen-includes (the default) being defined for flatc, the --gen-includes
- flag will not work as expected. The way around this is to add a dependency
- to the flatbuffer_cc_library defined alongside the flatc included Fileset.
- For example you might define:
-
- flatbuffer_cc_library(
- name = "my_fbs",
- srcs = [ "schemas/foo.fbs" ],
- includes = [ "//third_party/bazz:bazz_fbs_includes" ],
- )
-
- In which foo.fbs includes a few files from the Fileset defined at
- //third_party/bazz:bazz_fbs_includes. When compiling the library that
- includes foo_generated.h, and therefore has my_fbs as a dependency, it
- will fail to find any of the bazz *_generated.h files unless you also
- add bazz's flatbuffer_cc_library to your own dependency list, e.g.:
-
- cc_library(
- name = "my_lib",
- deps = [
- ":my_fbs",
- "//third_party/bazz:bazz_fbs"
- ],
- )
-
- Happy dependent Flatbuffering!
-
- Args:
- name: Rule name.
- srcs: Source .fbs files. Sent in order to the compiler.
- srcs_filegroup_name: Name of the output filegroup that holds srcs. Pass this
- filegroup into the `includes` parameter of any other
- flatbuffer_cc_library that depends on this one's schemas.
- out_prefix: Prepend this path to the front of all generated files. Usually
- is a directory name.
- includes: Optional, list of filegroups of schemas that the srcs depend on.
- ** SEE REMARKS BELOW **
- include_paths: Optional, list of paths the includes files can be found in.
- compatible_with: Optional, passed to genrule for environments this rule
- can be built for
- flatc_args: Optional list of additional arguments to pass to flatc
- (e.g. --gen-mutable).
- visibility: The visibility of the generated cc_library. By default, use the
- default visibility of the project.
- srcs_filegroup_visibility: The visibility of the generated srcs filegroup.
- By default, use the value of the visibility parameter above.
- gen_reflections: Optional, if true this will generate the flatbuffer
- reflection binaries for the schemas.
- '''
- output_headers = [
- (out_prefix + "%s_generated.h") % (s.replace(".fbs", "").split("/")[-1])
- for s in srcs
- ]
- reflection_name = "%s_reflection" % name if gen_reflections else ""
-
- flatbuffer_library_public(
- name = "%s_srcs" % (name),
- srcs = srcs,
- outs = output_headers,
- language_flag = "-c",
- out_prefix = out_prefix,
- includes = includes,
- include_paths = include_paths,
- compatible_with = compatible_with,
- flatc_args = flatc_args,
- reflection_name = reflection_name,
- reflection_visibility = visibility,
- )
- native.cc_library(
- name = name,
- hdrs = output_headers,
- srcs = output_headers,
- features = [
- "-parse_headers",
- ],
- deps = [
- "@flatbuffers//:runtime_cc",
- ],
- includes = ["."],
- linkstatic = 1,
- visibility = visibility,
- compatible_with = compatible_with,
- )
-
- # A filegroup for the `srcs`. That is, all the schema files for this
- # Flatbuffer set.
- native.filegroup(
- name = srcs_filegroup_name if srcs_filegroup_name else "%s_includes" % (name),
- srcs = srcs,
- visibility = srcs_filegroup_visibility if srcs_filegroup_visibility != None else visibility,
- compatible_with = compatible_with,
- )
-
-# Custom provider to track dependencies transitively.
-FlatbufferInfo = provider(
- fields = {
- "transitive_srcs": "flatbuffer schema definitions.",
- },
-)
-
-def _flatbuffer_schemas_aspect_impl(target, ctx):
- _ignore = [target]
- transitive_srcs = depset()
- if hasattr(ctx.rule.attr, "deps"):
- for dep in ctx.rule.attr.deps:
- if FlatbufferInfo in dep:
- transitive_srcs = depset(dep[FlatbufferInfo].transitive_srcs, transitive = [transitive_srcs])
- if hasattr(ctx.rule.attr, "srcs"):
- for src in ctx.rule.attr.srcs:
- if FlatbufferInfo in src:
- transitive_srcs = depset(src[FlatbufferInfo].transitive_srcs, transitive = [transitive_srcs])
- for f in src.files:
- if f.extension == "fbs":
- transitive_srcs = depset([f], transitive = [transitive_srcs])
- return [FlatbufferInfo(transitive_srcs = transitive_srcs)]
-
-# An aspect that runs over all dependencies and transitively collects
-# flatbuffer schema files.
-_flatbuffer_schemas_aspect = aspect(
- attr_aspects = [
- "deps",
- "srcs",
- ],
- implementation = _flatbuffer_schemas_aspect_impl,
-)
-
-# Rule to invoke the flatbuffer compiler.
-def _gen_flatbuffer_srcs_impl(ctx):
- outputs = ctx.attr.outputs
- include_paths = ctx.attr.include_paths
- if ctx.attr.no_includes:
- no_includes_statement = ["--no-includes"]
- else:
- no_includes_statement = []
-
- # Need to generate all files in a directory.
- if not outputs:
- outputs = [ctx.actions.declare_directory("{}_all".format(ctx.attr.name))]
- output_directory = outputs[0].path
- else:
- outputs = [ctx.actions.declare_file(output) for output in outputs]
- output_directory = outputs[0].dirname
-
- deps = depset(ctx.files.srcs + ctx.files.deps, transitive = [
- dep[FlatbufferInfo].transitive_srcs
- for dep in ctx.attr.deps
- if FlatbufferInfo in dep
- ])
-
- include_paths_cmd_line = []
- for s in include_paths:
- include_paths_cmd_line.extend(["-I", s])
-
- for src in ctx.files.srcs:
- ctx.actions.run(
- inputs = deps,
- outputs = outputs,
- executable = ctx.executable._flatc,
- arguments = [
- ctx.attr.language_flag,
- "-o",
- output_directory,
- # Allow for absolute imports and referencing of generated files.
- "-I",
- "./",
- "-I",
- ctx.genfiles_dir.path,
- "-I",
- ctx.bin_dir.path,
- ] + no_includes_statement +
- include_paths_cmd_line + [
- "--no-union-value-namespacing",
- "--gen-object-api",
- src.path,
- ],
- progress_message = "Generating flatbuffer files for {}:".format(src),
- use_default_shell_env = True,
- )
- return [
- DefaultInfo(files = depset(outputs)),
- ]
-
-_gen_flatbuffer_srcs = rule(
- _gen_flatbuffer_srcs_impl,
- attrs = {
- "srcs": attr.label_list(
- allow_files = [".fbs"],
- mandatory = True,
- ),
- "outputs": attr.string_list(
- default = [],
- mandatory = False,
- ),
- "deps": attr.label_list(
- default = [],
- mandatory = False,
- aspects = [_flatbuffer_schemas_aspect],
- ),
- "include_paths": attr.string_list(
- default = [],
- mandatory = False,
- ),
- "language_flag": attr.string(
- mandatory = True,
- ),
- "no_includes": attr.bool(
- default = False,
- mandatory = False,
- ),
- "_flatc": attr.label(
- default = Label("@flatbuffers//:flatc"),
- executable = True,
- cfg = "exec",
- ),
- },
- output_to_genfiles = True,
-)
-
-def flatbuffer_py_strip_prefix_srcs(name, srcs = [], strip_prefix = ""):
- """Strips path prefix.
-
- Args:
- name: Rule name. (required)
- srcs: Source .py files. (required)
- strip_prefix: Path that needs to be stripped from the srcs filepaths. (required)
- """
- for src in srcs:
- native.genrule(
- name = name + "_" + src.replace(".", "_").replace("/", "_"),
- srcs = [src],
- outs = [src.replace(strip_prefix, "")],
- cmd = "cp $< $@",
- )
-
-def _concat_flatbuffer_py_srcs_impl(ctx):
- # Merge all generated python files. The files are concatenated and import
- # statements are removed. Finally we import the flatbuffer runtime library.
- # IMPORTANT: Our Windows shell does not support "find ... -exec" properly.
- # If you're changing the commandline below, please build wheels and run smoke
- # tests on all the three operating systems.
- command = "echo 'import flatbuffers\n' > %s; "
- command += "for f in $(find %s -name '*.py' | sort); do cat $f | sed '/import flatbuffers/d' >> %s; done "
- ctx.actions.run_shell(
- inputs = ctx.attr.deps[0].files,
- outputs = [ctx.outputs.out],
- command = command % (
- ctx.outputs.out.path,
- ctx.attr.deps[0].files.to_list()[0].path,
- ctx.outputs.out.path,
- ),
- use_default_shell_env = True,
- )
-
-_concat_flatbuffer_py_srcs = rule(
- _concat_flatbuffer_py_srcs_impl,
- attrs = {
- "deps": attr.label_list(mandatory = True),
- },
- output_to_genfiles = True,
- outputs = {"out": "%{name}.py"},
-)
-
-def flatbuffer_py_library(
- name,
- srcs,
- deps = [],
- include_paths = []):
- """A py_library with the generated reader/writers for the given schema.
-
- This rule assumes that the schema files define non-conflicting names, so that
- they can be merged in a single file. This is e.g. the case if only a single
- namespace is used.
- The rule call the flatbuffer compiler for all schema files and merges the
- generated python files into a single file that is wrapped in a py_library.
-
- Args:
- name: Rule name. (required)
- srcs: List of source .fbs files. (required)
- deps: List of dependencies.
- include_paths: Optional, list of paths the includes files can be found in.
- """
- all_srcs = "{}_srcs".format(name)
- _gen_flatbuffer_srcs(
- name = all_srcs,
- srcs = srcs,
- language_flag = "--python",
- deps = deps,
- include_paths = include_paths,
- )
- all_srcs_no_include = "{}_srcs_no_include".format(name)
- _gen_flatbuffer_srcs(
- name = all_srcs_no_include,
- srcs = srcs,
- language_flag = "--python",
- deps = deps,
- no_includes = True,
- include_paths = include_paths,
- )
- concat_py_srcs = "{}_generated".format(name)
- _concat_flatbuffer_py_srcs(
- name = concat_py_srcs,
- deps = [
- ":{}".format(all_srcs_no_include),
- ],
- )
- py_library(
- name = name,
- srcs = [
- ":{}".format(concat_py_srcs),
- ],
- srcs_version = "PY3",
- deps = deps + [
- "@flatbuffers//:runtime_py",
- ],
- )
-
-def flatbuffer_java_library(
- name,
- srcs,
- custom_package = "",
- package_prefix = "",
- include_paths = DEFAULT_INCLUDE_PATHS,
- flatc_args = DEFAULT_FLATC_ARGS,
- visibility = None):
- """A java library with the generated reader/writers for the given flatbuffer definitions.
-
- Args:
- name: Rule name. (required)
- srcs: List of source .fbs files including all includes. (required)
- custom_package: Package name of generated Java files. If not specified
- namespace in the schema files will be used. (optional)
- package_prefix: like custom_package, but prefixes to the existing
- namespace. (optional)
- include_paths: List of paths that includes files can be found in. (optional)
- flatc_args: List of additional arguments to pass to flatc. (optional)
- visibility: Visibility setting for the java_library rule. (optional)
- """
- out_srcjar = "java_%s_all.srcjar" % name
- flatbuffer_java_srcjar(
- name = "%s_srcjar" % name,
- srcs = srcs,
- out = out_srcjar,
- custom_package = custom_package,
- flatc_args = flatc_args,
- include_paths = include_paths,
- package_prefix = package_prefix,
- )
-
- native.filegroup(
- name = "%s.srcjar" % name,
- srcs = [out_srcjar],
- )
-
- native.java_library(
- name = name,
- srcs = [out_srcjar],
- javacopts = ["-source 7 -target 7"],
- deps = [
- "@flatbuffers//:runtime_java",
- ],
- visibility = visibility,
- )
-
-def flatbuffer_java_srcjar(
- name,
- srcs,
- out,
- custom_package = "",
- package_prefix = "",
- include_paths = DEFAULT_INCLUDE_PATHS,
- flatc_args = DEFAULT_FLATC_ARGS):
- """Generate flatbuffer Java source files.
-
- Args:
- name: Rule name. (required)
- srcs: List of source .fbs files including all includes. (required)
- out: Output file name. (required)
- custom_package: Package name of generated Java files. If not specified
- namespace in the schema files will be used. (optional)
- package_prefix: like custom_package, but prefixes to the existing
- namespace. (optional)
- include_paths: List of paths that includes files can be found in. (optional)
- flatc_args: List of additional arguments to pass to flatc. (optional)
- """
- command_fmt = """set -e
- tmpdir=$(@D)
- schemas=$$tmpdir/schemas
- java_root=$$tmpdir/java
- rm -rf $$schemas
- rm -rf $$java_root
- mkdir -p $$schemas
- mkdir -p $$java_root
-
- for src in $(SRCS); do
- dest=$$schemas/$$src
- rm -rf $$(dirname $$dest)
- mkdir -p $$(dirname $$dest)
- if [ -z "{custom_package}" ] && [ -z "{package_prefix}" ]; then
- cp -f $$src $$dest
- else
- if [ -z "{package_prefix}" ]; then
- sed -e "s/namespace\\s.*/namespace {custom_package};/" $$src > $$dest
- else
- sed -e "s/namespace \\([^;]\\+\\);/namespace {package_prefix}.\\1;/" $$src > $$dest
- fi
- fi
- done
-
- flatc_arg_I="-I $$tmpdir/schemas"
- for include_path in {include_paths}; do
- flatc_arg_I="$$flatc_arg_I -I $$schemas/$$include_path"
- done
-
- flatc_additional_args=
- for arg in {flatc_args}; do
- flatc_additional_args="$$flatc_additional_args $$arg"
- done
-
- for src in $(SRCS); do
- $(location {flatc_path}) $$flatc_arg_I --java $$flatc_additional_args -o $$java_root $$schemas/$$src
- done
-
- $(location {zip_files}) -export_zip_path=$@ -file_directory=$$java_root
- """
- genrule_cmd = command_fmt.format(
- package_name = native.package_name(),
- custom_package = custom_package,
- package_prefix = package_prefix,
- flatc_path = flatc_path,
- zip_files = zip_files,
- include_paths = " ".join(include_paths),
- flatc_args = " ".join(flatc_args),
- )
-
- native.genrule(
- name = name,
- srcs = srcs,
- outs = [out],
- tools = [flatc_path, zip_files],
- cmd = genrule_cmd,
- )
-
-def flatbuffer_android_library(
- name,
- srcs,
- custom_package = "",
- package_prefix = "",
- include_paths = DEFAULT_INCLUDE_PATHS,
- flatc_args = DEFAULT_FLATC_ARGS,
- visibility = None):
- """An android_library with the generated reader/writers for the given flatbuffer definitions.
-
- Args:
- name: Rule name. (required)
- srcs: List of source .fbs files including all includes. (required)
- custom_package: Package name of generated Java files. If not specified
- namespace in the schema files will be used. (optional)
- package_prefix: like custom_package, but prefixes to the existing
- namespace. (optional)
- include_paths: List of paths that includes files can be found in. (optional)
- flatc_args: List of additional arguments to pass to flatc. (optional)
- visibility: Visibility setting for the android_library rule. (optional)
- """
- out_srcjar = "android_%s_all.srcjar" % name
- flatbuffer_java_srcjar(
- name = "%s_srcjar" % name,
- srcs = srcs,
- out = out_srcjar,
- custom_package = custom_package,
- flatc_args = flatc_args,
- include_paths = include_paths,
- package_prefix = package_prefix,
- )
-
- native.filegroup(
- name = "%s.srcjar" % name,
- srcs = [out_srcjar],
- )
-
- # To support org.checkerframework.dataflow.qual.Pure.
- checkerframework_annotations = [
- "@org_checkerframework_qual",
- ] if "--java-checkerframework" in flatc_args else []
-
- android_library(
- name = name,
- srcs = [out_srcjar],
- javacopts = ["-source 7 -target 7"],
- visibility = visibility,
- deps = [
- "@flatbuffers//:runtime_android",
- ] + checkerframework_annotations,
- )
diff --git a/ci/flatbuffers_for_tf_sync/flatbuffers.BUILD b/ci/flatbuffers_for_tf_sync/flatbuffers.BUILD
deleted file mode 100644
index 108c0cd..0000000
--- a/ci/flatbuffers_for_tf_sync/flatbuffers.BUILD
+++ /dev/null
@@ -1,156 +0,0 @@
-load("@build_bazel_rules_android//android:rules.bzl", "android_library")
-load(":build_defs.bzl", "flatbuffer_py_strip_prefix_srcs")
-
-package(default_visibility = ["//visibility:public"])
-
-licenses(["notice"]) # Apache 2.0
-
-exports_files(["LICENSE.txt"])
-
-licenses(["notice"])
-
-config_setting(
- name = "freebsd",
- values = {"cpu": "freebsd"},
-)
-
-config_setting(
- name = "windows",
- values = {"cpu": "x64_windows"},
-)
-
-load("@rules_cc//cc:defs.bzl", "cc_binary", "cc_library")
-
-# Public flatc library to compile flatbuffer files at runtime.
-cc_library(
- name = "flatbuffers",
- hdrs = ["//:public_headers"],
- linkstatic = 1,
- strip_include_prefix = "/include",
- visibility = ["//visibility:public"],
- deps = ["//src:flatbuffers"],
-)
-
-# Public C++ headers for the Flatbuffers library.
-filegroup(
- name = "public_headers",
- srcs = [
- "include/flatbuffers/base.h",
- "include/flatbuffers/code_generators.h",
- "include/flatbuffers/flatbuffers.h",
- "include/flatbuffers/flexbuffers.h",
- "include/flatbuffers/hash.h",
- "include/flatbuffers/idl.h",
- "include/flatbuffers/minireflect.h",
- "include/flatbuffers/reflection.h",
- "include/flatbuffers/reflection_generated.h",
- "include/flatbuffers/registry.h",
- "include/flatbuffers/stl_emulation.h",
- "include/flatbuffers/util.h",
- ],
- visibility = ["//:__subpackages__"],
-)
-
-# Public flatc compiler library.
-cc_library(
- name = "flatc_library",
- linkstatic = 1,
- visibility = ["//visibility:public"],
- deps = [
- "@flatbuffers//src:flatc_library",
- ],
-)
-
-# Public flatc compiler.
-cc_binary(
- name = "flatc",
- linkopts = select({
- ":freebsd": [
- "-lm",
- ],
- ":windows": [],
- "//conditions:default": [
- "-lm",
- "-ldl",
- ],
- }),
- visibility = ["//visibility:public"],
- deps = [
- "@flatbuffers//src:flatc",
- ],
-)
-
-filegroup(
- name = "flatc_headers",
- srcs = [
- "include/flatbuffers/flatc.h",
- ],
- visibility = ["//:__subpackages__"],
-)
-
-# Library used by flatbuffer_cc_library rules.
-cc_library(
- name = "runtime_cc",
- hdrs = [
- "include/flatbuffers/base.h",
- "include/flatbuffers/flatbuffers.h",
- "include/flatbuffers/flexbuffers.h",
- "include/flatbuffers/stl_emulation.h",
- "include/flatbuffers/util.h",
- ],
- linkstatic = 1,
- strip_include_prefix = "/include",
- visibility = ["//visibility:public"],
-)
-
-flatbuffer_py_strip_prefix_srcs(
- name = "flatbuffer_py_strip_prefix",
- srcs = [
- "python/flatbuffers/__init__.py",
- "python/flatbuffers/builder.py",
- "python/flatbuffers/compat.py",
- "python/flatbuffers/encode.py",
- "python/flatbuffers/number_types.py",
- "python/flatbuffers/packer.py",
- "python/flatbuffers/table.py",
- "python/flatbuffers/util.py",
- ],
- strip_prefix = "python/flatbuffers/",
-)
-
-filegroup(
- name = "runtime_py_srcs",
- srcs = [
- "__init__.py",
- "builder.py",
- "compat.py",
- "encode.py",
- "number_types.py",
- "packer.py",
- "table.py",
- "util.py",
- ],
-)
-
-py_library(
- name = "runtime_py",
- srcs = [":runtime_py_srcs"],
- visibility = ["//visibility:public"],
-)
-
-filegroup(
- name = "runtime_java_srcs",
- srcs = glob(["java/com/google/flatbuffers/**/*.java"]),
-)
-
-java_library(
- name = "runtime_java",
- srcs = [":runtime_java_srcs"],
- visibility = ["//visibility:public"],
-)
-
-android_library(
- name = "runtime_android",
- srcs = [":runtime_java_srcs"],
- visibility = ["//visibility:public"],
-)
diff --git a/ci/flatbuffers_for_tf_sync/workspace.bzl b/ci/flatbuffers_for_tf_sync/workspace.bzl
deleted file mode 100644
index 59c1fd9..0000000
--- a/ci/flatbuffers_for_tf_sync/workspace.bzl
+++ /dev/null
@@ -1,16 +0,0 @@
-"""Loads the Flatbuffers library, used by TF Lite."""
-
-load("//third_party:repo.bzl", "tf_http_archive", "tf_mirror_urls")
-
-def repo():
- tf_http_archive(
- name = "flatbuffers",
- strip_prefix = "flatbuffers-1.12.0",
- sha256 = "62f2223fb9181d1d6338451375628975775f7522185266cd5296571ac152bc45",
- urls = tf_mirror_urls("https://github.com/google/flatbuffers/archive/v1.12.0.tar.gz"),
- build_file = "//third_party/flatbuffers:flatbuffers.BUILD",
- system_build_file = "//third_party/flatbuffers:BUILD.system",
- link_files = {
- "//third_party/flatbuffers:build_defs.bzl": "build_defs.bzl",
- },
- )
diff --git a/ci/install_bazelisk.sh b/ci/install_bazelisk.sh
index d2f8a13..db385e6 100755
--- a/ci/install_bazelisk.sh
+++ b/ci/install_bazelisk.sh
@@ -18,5 +18,5 @@
wget https://github.com/bazelbuild/bazelisk/releases/download/v1.16.0/bazelisk-linux-amd64
mv bazelisk-linux-amd64 bazel
chmod +x bazel
-sudo mv bazel /usr/local/bin
+mv bazel /usr/local/bin
diff --git a/ci/install_cores_xplorer_11.sh b/ci/install_cores_xplorer_11.sh
new file mode 100755
index 0000000..1df00cb
--- /dev/null
+++ b/ci/install_cores_xplorer_11.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+
+mkdir /opt/xtensa/licenses
+
+mkdir -p /opt/xtensa/XtDevTools/install/tools/
+tar xvzf XtensaTools_RI_2022_9_linux.tgz --dir /opt/xtensa/XtDevTools/install/tools/
+
+
+###########
+# Hifimini
+###########
+cd /opt/xtensa/
+tar xvzf mini1m1m_RI_2019_2_linux_w_keys.tgz --dir /opt/xtensa/licenses/
+cd /opt/xtensa/licenses/RI-2019.2-linux/mini1m1m_RG/
+
+./install --xtensa-tools \
+ /opt/xtensa/XtDevTools/install/tools/RI-2019.2-linux/XtensaTools/ \
+ --no-default \
+ --no-replace
diff --git a/ci/install_cores_xplorer_13.sh b/ci/install_cores_xplorer_13.sh
new file mode 100755
index 0000000..a24b1f3
--- /dev/null
+++ b/ci/install_cores_xplorer_13.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+
+mkdir /opt/xtensa/licenses
+
+##############
+# Fusion F1
+##############
+cd /opt/xtensa/
+tar xvzf F1_190305_swupgrade_linux.tgz --dir /opt/xtensa/licenses/
+cd /opt/xtensa/licenses/RI-2020.4-linux/F1_190305_swupgrade/
+
+./install --xtensa-tools \
+ /opt/xtensa/XtDevTools/install/tools/RI-2020.4-linux/XtensaTools/ \
+ --no-default \
+ --no-replace
+
+##############
+# Vision P6
+##############
+cd /opt/xtensa/
+tar xvzf P6_200528_linux.tgz --dir /opt/xtensa/licenses/
+cd /opt/xtensa/licenses/RI-2020.4-linux/P6_200528/
+
+./install --xtensa-tools \
+ /opt/xtensa/XtDevTools/install/tools/RI-2020.4-linux/XtensaTools/ \
+ --no-default \
+ --no-replace
+
+##############
+# Hifi3Z
+##############
+cd /opt/xtensa/
+tar xvzf HIFI_190304_swupgrade_linux.tgz --dir /opt/xtensa/licenses/
+cd /opt/xtensa/licenses/RI-2020.4-linux/HIFI_190304_swupgrade/
+
+./install --xtensa-tools \
+ /opt/xtensa/XtDevTools/install/tools/RI-2020.4-linux/XtensaTools/ \
+ --no-default \
+ --no-replace
diff --git a/ci/install_cores_xplorer_solo.sh b/ci/install_cores_xplorer_solo.sh
new file mode 100755
index 0000000..559c67a
--- /dev/null
+++ b/ci/install_cores_xplorer_solo.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+
+mkdir /opt/xtensa/licenses
+
+mkdir -p /opt/xtensa/XtDevTools/install/tools/
+tar xvzf XtensaTools_RI_2022_9_linux.tgz --dir /opt/xtensa/XtDevTools/install/tools/
+
+
+###########
+# Hifi5
+###########
+cd /opt/xtensa/
+tar xvzf PRD_H5_RDO_07_01_2022_linux.tgz --dir /opt/xtensa/licenses/
+cd /opt/xtensa/licenses/RI-2022.9-linux/PRD_H5_RDO_07_01_2022/
+
+./install --xtensa-tools \
+ /opt/xtensa/XtDevTools/install/tools/RI-2022.9-linux/XtensaTools/ \
+ --no-default \
+ --no-replace
diff --git a/ci/sync_from_upstream_tf.sh b/ci/sync_from_upstream_tf.sh
index 094df65..bb5d098 100755
--- a/ci/sync_from_upstream_tf.sh
+++ b/ci/sync_from_upstream_tf.sh
@@ -47,6 +47,7 @@
git checkout tensorflow/lite/kernels/internal/optimized/neon_check.h
# http://b/149862813
git checkout tensorflow/lite/kernels/internal/runtime_shape.h
+git checkout tensorflow/lite/kernels/internal/runtime_shape.cc
# http://b/187728891
git checkout tensorflow/lite/kernels/op_macros.h
# http://b/242077843
@@ -57,8 +58,8 @@
bazel build tensorflow/lite/python:schema_py
/bin/cp bazel-bin/tensorflow/lite/python/schema_py_generated.py tensorflow/lite/python/schema_py_generated.py
-bazel build tensorflow/lite/schema:schema_fbs_srcs
-/bin/cp ./bazel-bin/tensorflow/lite/schema/schema_generated.h tensorflow/lite/schema/schema_generated.h
+bazel build tensorflow/compiler/mlir/lite/schema:schema_fbs_srcs
+/bin/cp ./bazel-bin/tensorflow/compiler/mlir/lite/schema/schema_generated.h tensorflow/lite/schema/schema_generated.h
# Must clean the bazel directories out after building as we don't check these in.
bazel clean
diff --git a/ci/tflite_files.txt b/ci/tflite_files.txt
index 51381ce..6514137 100644
--- a/ci/tflite_files.txt
+++ b/ci/tflite_files.txt
@@ -1,3 +1,6 @@
+tensorflow/compiler/mlir/lite/schema/schema.fbs
+tensorflow/compiler/mlir/lite/schema/schema_utils.h
+tensorflow/compiler/mlir/lite/schema/schema_utils.cc
tensorflow/lite/array.h
tensorflow/lite/array.cc
tensorflow/lite/builtin_op_data.h
@@ -12,7 +15,6 @@
tensorflow/lite/kernels/internal/tensor_ctypes.cc
tensorflow/lite/kernels/internal/reference/comparisons.cc
tensorflow/lite/kernels/kernel_util.cc
-tensorflow/lite/schema/schema_utils.cc
tensorflow/lite/c/builtin_op_data.h
tensorflow/lite/c/c_api_types.h
tensorflow/lite/c/common.h
@@ -108,7 +110,6 @@
tensorflow/lite/portable_type_to_tflitetype.h
tensorflow/lite/python/schema_util.py
tensorflow/lite/schema/schema_utils.h
-tensorflow/lite/schema/schema.fbs
tensorflow/lite/tools/flatbuffer_utils.py
tensorflow/lite/tools/flatbuffer_utils_test.py
tensorflow/lite/tools/randomize_weights.py
diff --git a/codegen/BUILD b/codegen/BUILD
new file mode 100644
index 0000000..ae62c04
--- /dev/null
+++ b/codegen/BUILD
@@ -0,0 +1,71 @@
+load("@rules_python//python:defs.bzl", "py_binary", "py_library")
+load("@tflm_pip_deps//:requirements.bzl", "requirement")
+
+package(
+ default_visibility = ["//:__subpackages__"],
+ licenses = ["notice"],
+)
+
+py_library(
+ name = "graph",
+ srcs = [
+ "graph.py",
+ ],
+ deps = [
+ ":tensor",
+ ":utils",
+ "//codegen/operators:factory",
+ "//codegen/operators:operator",
+ "//tensorflow/lite/python:schema_py",
+ "//tensorflow/lite/tools:visualize",
+ ],
+)
+
+py_library(
+ name = "inference_generator",
+ srcs = [
+ "inference_generator.py",
+ ],
+ data = [
+ "templates/inference.cc.mako",
+ "templates/inference.h.mako",
+ ],
+ deps = [
+ ":graph",
+ requirement("mako"),
+ ],
+)
+
+py_library(
+ name = "tensor",
+ srcs = [
+ "tensor.py",
+ ],
+ deps = [
+ ":utils",
+ "//tensorflow/lite/python:schema_py",
+ ],
+)
+
+py_library(
+ name = "utils",
+ srcs = [
+ "utils.py",
+ ],
+)
+
+py_binary(
+ name = "code_generator",
+ srcs = [
+ "code_generator.py",
+ ],
+ python_version = "PY3",
+ srcs_version = "PY3",
+ deps = [
+ ":graph",
+ ":inference_generator",
+ "//tensorflow/lite/tools:flatbuffer_utils",
+ "@absl_py//absl:app",
+ "@absl_py//absl/flags",
+ ],
+)
diff --git a/codegen/README.md b/codegen/README.md
new file mode 100644
index 0000000..ff7e9d2
--- /dev/null
+++ b/codegen/README.md
@@ -0,0 +1,3 @@
+# TFLM Code Generator
+
+This is a work in progress experiment. It is not ready for use.
diff --git a/codegen/build_def.bzl b/codegen/build_def.bzl
new file mode 100644
index 0000000..28b6232
--- /dev/null
+++ b/codegen/build_def.bzl
@@ -0,0 +1,44 @@
+""" Build rule for generating ML inference code from TFLite model. """
+
+load("//tensorflow/lite/micro:build_def.bzl", "micro_copts")
+
+def tflm_inference_library(
+ name,
+ tflite_model,
+ visibility = None):
+ """Creates a C++ library capable of performing ML inference of the provided
+ model.
+
+ Args:
+ name: Target name.
+ tflite_model: TFLite Model to generate inference from.
+ visibility: Visibility for the C++ library.
+ """
+ generated_target = name + "_gen"
+ native.genrule(
+ name = generated_target,
+ srcs = [tflite_model],
+ outs = [name + ".h", name + ".cc"],
+ tools = ["//codegen:code_generator"],
+ cmd = "$(location //codegen:code_generator) " +
+ "--model=$< --output_dir=$(RULEDIR) --output_name=%s" % name,
+ visibility = ["//visibility:private"],
+ )
+
+ native.cc_library(
+ name = name,
+ hdrs = [name + ".h"],
+ srcs = [name + ".cc"],
+ deps = [
+ generated_target,
+ "//codegen/runtime:micro_codegen_context",
+ "//tensorflow/lite/c:common",
+ "//tensorflow/lite/c:c_api_types",
+ "//tensorflow/lite/kernels/internal:compatibility",
+ "//tensorflow/lite/micro/kernels:micro_ops",
+ "//tensorflow/lite/micro:micro_common",
+ "//tensorflow/lite/micro:micro_context",
+ ],
+ copts = micro_copts(),
+ visibility = visibility,
+ )
diff --git a/codegen/code_generator.py b/codegen/code_generator.py
new file mode 100644
index 0000000..91cab73
--- /dev/null
+++ b/codegen/code_generator.py
@@ -0,0 +1,66 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" Generates C/C++ source code capable of performing inference for a model. """
+
+import os
+
+from absl import app
+from absl import flags
+from collections.abc import Sequence
+
+from tflite_micro.codegen import inference_generator
+from tflite_micro.codegen import graph
+from tflite_micro.tensorflow.lite.tools import flatbuffer_utils
+
+# Usage information:
+# Default:
+# `bazel run codegen:code_generator -- \
+# --model=</path/to/my_model.tflite>
+# Output will be located at: /path/to/my_model.h|cc
+
+_MODEL_PATH = flags.DEFINE_string(name="model",
+ default=None,
+ help="Path to the TFLite model file.",
+ required=True)
+
+_OUTPUT_DIR = flags.DEFINE_string(
+ name="output_dir",
+ default=None,
+ help="Path to write generated source to. Leave blank to use 'model' path.",
+ required=False)
+
+_OUTPUT_NAME = flags.DEFINE_string(
+ name="output_name",
+ default=None,
+ help=("The output basename for the generated .h/.cc. Leave blank to use "
+ "'model' basename."),
+ required=False)
+
+
+def main(argv: Sequence[str]) -> None:
+ output_dir = _OUTPUT_DIR.value or os.path.dirname(_MODEL_PATH.value)
+ output_name = _OUTPUT_NAME.value or os.path.splitext(
+ os.path.basename(_MODEL_PATH.value))[0]
+
+ model = flatbuffer_utils.read_model(_MODEL_PATH.value)
+
+ print("Generating inference code for model: {}".format(_MODEL_PATH.value))
+
+ inference_generator.generate(output_dir, output_name,
+ graph.OpCodeTable([model]), graph.Graph(model))
+
+
+if __name__ == "__main__":
+ app.run(main)
diff --git a/codegen/examples/hello_world/BUILD b/codegen/examples/hello_world/BUILD
new file mode 100644
index 0000000..04425cb
--- /dev/null
+++ b/codegen/examples/hello_world/BUILD
@@ -0,0 +1,17 @@
+load("//codegen:build_def.bzl", "tflm_inference_library")
+
+package(default_visibility = ["//visibility:public"])
+
+tflm_inference_library(
+ name = "hello_world_model",
+ tflite_model = "//tensorflow/lite/micro/examples/hello_world/models:hello_world_int8.tflite",
+)
+
+cc_binary(
+ name = "hello_world",
+ srcs = ["hello_world.cc"],
+ deps = [
+ ":hello_world_model",
+ "//tensorflow/lite/c:c_api_types",
+ ],
+)
diff --git a/codegen/examples/hello_world/README.md b/codegen/examples/hello_world/README.md
new file mode 100644
index 0000000..62afee5
--- /dev/null
+++ b/codegen/examples/hello_world/README.md
@@ -0,0 +1,27 @@
+# Codegen Hello World Example
+
+This is a code-generated example of the hello world model. The generated source
+is checked in for now so that it can be reviewed during the prototyping stage.
+
+## Building the example executable
+Please note that this will execute Bazel from make as part of the process.
+
+```
+bazel build //codegen/examples/hello_world:hello_world
+```
+
+## Running the example
+
+TODO(rjascani): The command works, but it'll just crash as we don't have all of
+the data structures fully populated yet.
+
+```
+bazel run //codegen/examples/hello_world:hello_world
+```
+
+## Updating the generated sources
+To update the generated source, you can execute this make target:
+
+```
+./codegen/examples/hello_world/update_example_source.sh
+```
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc b/codegen/examples/hello_world/hello_world.cc
similarity index 62%
copy from tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc
copy to codegen/examples/hello_world/hello_world.cc
index e2cf661..70d665b 100644
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc
+++ b/codegen/examples/hello_world/hello_world.cc
@@ -1,4 +1,4 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -13,11 +13,16 @@
limitations under the License.
==============================================================================*/
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.h"
+#include "codegen/examples/hello_world/hello_world_model.h"
+#include "tensorflow/lite/c/c_api_types.h"
-const char* kCategoryLabels[kCategoryCount] = {
- "silence",
- "unknown",
- "yes",
- "no",
-};
+int main(int argc, char** argv) {
+ hello_world_model::Model hello_world{};
+
+ TfLiteStatus status = hello_world.Invoke();
+ if (status != kTfLiteOk) {
+ return -1;
+ }
+
+ return 0;
+}
diff --git a/codegen/examples/hello_world/hello_world_model.cc b/codegen/examples/hello_world/hello_world_model.cc
new file mode 100644
index 0000000..7d8290c
--- /dev/null
+++ b/codegen/examples/hello_world/hello_world_model.cc
@@ -0,0 +1,316 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+/* AUTOMATICALLY GENERATED DO NOT MODIFY */
+
+#include "hello_world_model.h"
+
+#include "codegen/runtime/micro_codegen_context.h"
+#include "tensorflow/lite/c/builtin_op_data.h"
+#include "tensorflow/lite/c/c_api_types.h"
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/kernels/internal/compatibility.h"
+#include "tensorflow/lite/micro/kernels/micro_ops.h"
+#include "tensorflow/lite/micro/micro_common.h"
+#include "tensorflow/lite/micro/micro_context.h"
+
+namespace hello_world_model {
+namespace {
+// TODO(rjascani): We should probably split out the OpTable to a separate file
+// once we start generating for multiple models.
+enum OpCode { kFullyConnected, kCount };
+
+TFLMInferenceRegistration op_table[OpCode::kCount] = {
+ tflite::RegisterInference_FULLY_CONNECTED(),
+};
+
+// buffer_1 is located in the arena
+
+alignas(16) uint8_t buffer_2[4] = {
+ 0xAD,
+ 0x01,
+ 0x00,
+ 0x00,
+};
+
+alignas(16) uint8_t buffer_3[16] = {
+ 0xD9, 0x3B, 0x27, 0x15, 0x1C, 0xE0, 0xDE, 0xDD,
+ 0x0F, 0x1B, 0xC5, 0xD7, 0x12, 0xDD, 0xF9, 0x7F,
+};
+
+alignas(16) uint8_t buffer_4[64] = {
+ 0x27, 0xFD, 0xFF, 0xFF, 0xA2, 0x07, 0x00, 0x00, 0x62, 0x02, 0x00,
+ 0x00, 0x00, 0x00, 0x00, 0x00, 0xF1, 0x00, 0x00, 0x00, 0x29, 0xFE,
+ 0xFF, 0xFF, 0xDD, 0xFF, 0xFF, 0xFF, 0x9D, 0xFC, 0xFF, 0xFF, 0x3B,
+ 0x02, 0x00, 0x00, 0x45, 0x02, 0x00, 0x00, 0xA4, 0x10, 0x00, 0x00,
+ 0x67, 0x0F, 0x00, 0x00, 0x4F, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00,
+ 0x00, 0x87, 0xFC, 0xFF, 0xFF, 0x11, 0xEC, 0xFF, 0xFF,
+};
+
+alignas(16) uint8_t buffer_5[256] = {
+ 0xF4, 0x1A, 0xED, 0x09, 0x19, 0x21, 0xF4, 0x24, 0xE0, 0x21, 0xEF, 0xBC,
+ 0xF7, 0xF5, 0xFA, 0x19, 0x03, 0xDC, 0xD2, 0x02, 0x06, 0xF9, 0xF4, 0x02,
+ 0xFF, 0xFA, 0xEF, 0xF1, 0xEF, 0xD3, 0x27, 0xE1, 0xFB, 0x27, 0xDD, 0xEB,
+ 0xDB, 0xE4, 0x05, 0x1A, 0x17, 0xFC, 0x24, 0x12, 0x15, 0xEF, 0x1E, 0xE4,
+ 0x10, 0xFE, 0x14, 0xDA, 0x1C, 0xF8, 0xF3, 0xF1, 0xEF, 0xE2, 0xF3, 0x09,
+ 0xE3, 0xE9, 0xED, 0xE3, 0xE4, 0x15, 0x07, 0x0B, 0x04, 0x1B, 0x1A, 0xFE,
+ 0xEB, 0x01, 0xDE, 0x21, 0xE6, 0x0B, 0xEC, 0x03, 0x23, 0x0A, 0x22, 0x24,
+ 0x1E, 0x27, 0x03, 0xE6, 0x03, 0x24, 0xFF, 0xC0, 0x11, 0xF8, 0xFC, 0xF1,
+ 0x11, 0x0C, 0xF5, 0xE0, 0xF3, 0x07, 0x17, 0xE5, 0xE8, 0xED, 0xFA, 0xDC,
+ 0xE8, 0x23, 0xFB, 0x07, 0xDD, 0xFB, 0xFD, 0x00, 0x14, 0x26, 0x11, 0x17,
+ 0xE7, 0xF1, 0x11, 0xEA, 0x02, 0x26, 0x04, 0x04, 0x25, 0x21, 0x1D, 0x0A,
+ 0xDB, 0x1D, 0xDC, 0x20, 0x01, 0xFA, 0xE3, 0x37, 0x0B, 0xF1, 0x1A, 0x16,
+ 0xEF, 0x1C, 0xE7, 0x03, 0xE0, 0x16, 0x02, 0x03, 0x21, 0x18, 0x09, 0x2E,
+ 0xD9, 0xE5, 0x14, 0x0B, 0xEA, 0x1A, 0xFC, 0xD8, 0x13, 0x00, 0xC4, 0xD8,
+ 0xEC, 0xD9, 0xFE, 0x0D, 0x19, 0x20, 0xD8, 0xD6, 0xE2, 0x1F, 0xE9, 0xD7,
+ 0xCA, 0xE2, 0xDD, 0xC6, 0x13, 0xE7, 0x04, 0x3E, 0x00, 0x01, 0x14, 0xC7,
+ 0xDB, 0xE7, 0x15, 0x15, 0xF5, 0x06, 0xD6, 0x1A, 0xDC, 0x09, 0x22, 0xFE,
+ 0x08, 0x02, 0x13, 0xEF, 0x19, 0x1E, 0xE2, 0x09, 0xFD, 0xF3, 0x14, 0xDD,
+ 0xDA, 0x20, 0xD9, 0x0F, 0xE3, 0xF9, 0xF7, 0xEE, 0xE9, 0x24, 0xE6, 0x29,
+ 0x00, 0x07, 0x16, 0xE2, 0x1E, 0x0D, 0x23, 0xD3, 0xDD, 0xF7, 0x14, 0xFA,
+ 0x08, 0x22, 0x26, 0x21, 0x09, 0x08, 0x0F, 0x0B, 0xE0, 0x12, 0xF4, 0x7F,
+ 0xDC, 0x58, 0xE5, 0x26,
+};
+
+alignas(16) uint8_t buffer_6[64] = {
+ 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xC2, 0xEA, 0xFF,
+ 0xFF, 0x75, 0xEA, 0xFF, 0xFF, 0xB8, 0xFA, 0xFF, 0xFF, 0x24, 0xFA,
+ 0xFF, 0xFF, 0xC8, 0xEF, 0xFF, 0xFF, 0xAC, 0xFF, 0xFF, 0xFF, 0x44,
+ 0x0D, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xBD, 0x07, 0x00, 0x00,
+ 0x33, 0xEA, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x00, 0xCC, 0xE4, 0xFF,
+ 0xFF, 0x4F, 0x0D, 0x00, 0x00, 0xCF, 0xE3, 0xFF, 0xFF,
+};
+
+alignas(16) uint8_t buffer_7[16] = {
+ 0xF7, 0xCA, 0x39, 0x47, 0x68, 0x73, 0x62, 0x63,
+ 0x40, 0xE6, 0x7F, 0x19, 0xAE, 0x44, 0x5F, 0x56,
+};
+
+// buffer_8 is located in the arena
+
+// buffer_9 is located in the arena
+
+// buffer_10 is located in the arena
+
+constexpr size_t kSubgraph0Inputs[1] = {0};
+
+constexpr size_t kSubgraph0Outputs[1] = {9};
+
+struct Node0_0 {
+ struct Inputs {
+ int size = 3;
+ int data[3] = {0, 6, 5};
+ } inputs;
+ struct Outputs {
+ int size = 1;
+ int data[1] = {7};
+ } outputs;
+ // No intermediates
+ TfLiteFullyConnectedParams builtin_data = {
+ .activation = kTfLiteActRelu,
+ .weights_format = kTfLiteFullyConnectedWeightsFormatDefault,
+ .keep_num_dims = false,
+ .asymmetric_quantize_inputs = false,
+ .quantized_bias_type = kTfLiteNoType};
+} node_0_0;
+
+struct Node0_1 {
+ struct Inputs {
+ int size = 3;
+ int data[3] = {7, 4, 3};
+ } inputs;
+ struct Outputs {
+ int size = 1;
+ int data[1] = {8};
+ } outputs;
+ // No intermediates
+ TfLiteFullyConnectedParams builtin_data = {
+ .activation = kTfLiteActRelu,
+ .weights_format = kTfLiteFullyConnectedWeightsFormatDefault,
+ .keep_num_dims = false,
+ .asymmetric_quantize_inputs = false,
+ .quantized_bias_type = kTfLiteNoType};
+} node_0_1;
+
+struct Node0_2 {
+ struct Inputs {
+ int size = 3;
+ int data[3] = {8, 2, 1};
+ } inputs;
+ struct Outputs {
+ int size = 1;
+ int data[1] = {9};
+ } outputs;
+ // No intermediates
+ TfLiteFullyConnectedParams builtin_data = {
+ .activation = kTfLiteActNone,
+ .weights_format = kTfLiteFullyConnectedWeightsFormatDefault,
+ .keep_num_dims = false,
+ .asymmetric_quantize_inputs = false,
+ .quantized_bias_type = kTfLiteNoType};
+} node_0_2;
+
+struct Tensor0_0Dims {
+ int size = 2;
+ int data[2] = {1, 1};
+} tensor0_0_dims;
+
+struct Tensor0_1Dims {
+ int size = 1;
+ int data[1] = {1};
+} tensor0_1_dims;
+
+struct Tensor0_2Dims {
+ int size = 2;
+ int data[2] = {1, 16};
+} tensor0_2_dims;
+
+struct Tensor0_3Dims {
+ int size = 1;
+ int data[1] = {16};
+} tensor0_3_dims;
+
+struct Tensor0_4Dims {
+ int size = 2;
+ int data[2] = {16, 16};
+} tensor0_4_dims;
+
+struct Tensor0_5Dims {
+ int size = 1;
+ int data[1] = {16};
+} tensor0_5_dims;
+
+struct Tensor0_6Dims {
+ int size = 2;
+ int data[2] = {16, 1};
+} tensor0_6_dims;
+
+struct Tensor0_7Dims {
+ int size = 2;
+ int data[2] = {1, 16};
+} tensor0_7_dims;
+
+struct Tensor0_8Dims {
+ int size = 2;
+ int data[2] = {1, 16};
+} tensor0_8_dims;
+
+struct Tensor0_9Dims {
+ int size = 2;
+ int data[2] = {1, 1};
+} tensor0_9_dims;
+
+TfLiteStatus InvokeSubgraph0(TfLiteContext* context,
+ tflite::Span<TfLiteNode> nodes) {
+ TFLITE_DCHECK(nodes.size() == 3);
+ TF_LITE_ENSURE_OK(
+ context, op_table[OpCode::kFullyConnected].invoke(context, &nodes[0]));
+ TF_LITE_ENSURE_OK(
+ context, op_table[OpCode::kFullyConnected].invoke(context, &nodes[1]));
+ TF_LITE_ENSURE_OK(
+ context, op_table[OpCode::kFullyConnected].invoke(context, &nodes[2]));
+
+ return kTfLiteOk;
+}
+
+} // namespace
+
+Model::Model()
+ : subgraphs_{
+ {.inputs = {&kSubgraph0Inputs[0], 1},
+ .outputs = {&kSubgraph0Outputs[0], 1},
+ .nodes = {&subgraph0_nodes_[0], 3},
+ .tensors = {&subgraph0_tensors_[0], 10},
+ .invoke = &InvokeSubgraph0},
+ },
+ micro_context_{&context_, {&subgraphs_[0], 1}} {
+ context_.impl_ = static_cast<void*>(µ_context_);
+ context_.ReportError = nullptr;
+ context_.GetTensor = nullptr;
+ context_.GetEvalTensor = tflite::MicroContextGetEvalTensor;
+ context_.profiler = nullptr;
+ context_.GetExternalContext = nullptr;
+ context_.GetScratchBuffer = nullptr;
+
+ subgraph0_nodes_[0] = TfLiteNode{
+ .inputs = reinterpret_cast<TfLiteIntArray*>(&node_0_0.inputs),
+ .outputs = reinterpret_cast<TfLiteIntArray*>(&node_0_0.outputs),
+ .intermediates = nullptr,
+ .user_data = nullptr,
+ .builtin_data = static_cast<void*>(&node_0_0.builtin_data),
+ .custom_initial_data = nullptr,
+ .custom_initial_data_size = 0};
+ subgraph0_nodes_[1] = TfLiteNode{
+ .inputs = reinterpret_cast<TfLiteIntArray*>(&node_0_1.inputs),
+ .outputs = reinterpret_cast<TfLiteIntArray*>(&node_0_1.outputs),
+ .intermediates = nullptr,
+ .user_data = nullptr,
+ .builtin_data = static_cast<void*>(&node_0_1.builtin_data),
+ .custom_initial_data = nullptr,
+ .custom_initial_data_size = 0};
+ subgraph0_nodes_[2] = TfLiteNode{
+ .inputs = reinterpret_cast<TfLiteIntArray*>(&node_0_2.inputs),
+ .outputs = reinterpret_cast<TfLiteIntArray*>(&node_0_2.outputs),
+ .intermediates = nullptr,
+ .user_data = nullptr,
+ .builtin_data = static_cast<void*>(&node_0_2.builtin_data),
+ .custom_initial_data = nullptr,
+ .custom_initial_data_size = 0};
+
+ subgraph0_tensors_[0] = TfLiteEvalTensor{
+ .data = {.data = static_cast<void*>(nullptr /* buffer_1 */)},
+ .dims = reinterpret_cast<TfLiteIntArray*>(&tensor0_0_dims),
+ .type = kTfLiteInt8};
+ subgraph0_tensors_[1] = TfLiteEvalTensor{
+ .data = {.data = static_cast<void*>(&buffer_2)},
+ .dims = reinterpret_cast<TfLiteIntArray*>(&tensor0_1_dims),
+ .type = kTfLiteInt32};
+ subgraph0_tensors_[2] = TfLiteEvalTensor{
+ .data = {.data = static_cast<void*>(&buffer_3)},
+ .dims = reinterpret_cast<TfLiteIntArray*>(&tensor0_2_dims),
+ .type = kTfLiteInt8};
+ subgraph0_tensors_[3] = TfLiteEvalTensor{
+ .data = {.data = static_cast<void*>(&buffer_4)},
+ .dims = reinterpret_cast<TfLiteIntArray*>(&tensor0_3_dims),
+ .type = kTfLiteInt32};
+ subgraph0_tensors_[4] = TfLiteEvalTensor{
+ .data = {.data = static_cast<void*>(&buffer_5)},
+ .dims = reinterpret_cast<TfLiteIntArray*>(&tensor0_4_dims),
+ .type = kTfLiteInt8};
+ subgraph0_tensors_[5] = TfLiteEvalTensor{
+ .data = {.data = static_cast<void*>(&buffer_6)},
+ .dims = reinterpret_cast<TfLiteIntArray*>(&tensor0_5_dims),
+ .type = kTfLiteInt32};
+ subgraph0_tensors_[6] = TfLiteEvalTensor{
+ .data = {.data = static_cast<void*>(&buffer_7)},
+ .dims = reinterpret_cast<TfLiteIntArray*>(&tensor0_6_dims),
+ .type = kTfLiteInt8};
+ subgraph0_tensors_[7] = TfLiteEvalTensor{
+ .data = {.data = static_cast<void*>(nullptr /* buffer_8 */)},
+ .dims = reinterpret_cast<TfLiteIntArray*>(&tensor0_7_dims),
+ .type = kTfLiteInt8};
+ subgraph0_tensors_[8] = TfLiteEvalTensor{
+ .data = {.data = static_cast<void*>(nullptr /* buffer_9 */)},
+ .dims = reinterpret_cast<TfLiteIntArray*>(&tensor0_8_dims),
+ .type = kTfLiteInt8};
+ subgraph0_tensors_[9] = TfLiteEvalTensor{
+ .data = {.data = static_cast<void*>(nullptr /* buffer_10 */)},
+ .dims = reinterpret_cast<TfLiteIntArray*>(&tensor0_9_dims),
+ .type = kTfLiteInt8};
+}
+
+TfLiteStatus Model::Invoke() { return micro_context_.InvokeSubgraph(0); }
+
+} // namespace hello_world_model
diff --git a/codegen/examples/hello_world/hello_world_model.h b/codegen/examples/hello_world/hello_world_model.h
new file mode 100644
index 0000000..80cfe2c
--- /dev/null
+++ b/codegen/examples/hello_world/hello_world_model.h
@@ -0,0 +1,40 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+/* AUTOMATICALLY GENERATED DO NOT MODIFY */
+
+#pragma once
+
+#include "codegen/runtime/micro_codegen_context.h"
+#include "tensorflow/lite/c/c_api_types.h"
+#include "tensorflow/lite/c/common.h"
+
+namespace hello_world_model {
+
+class Model {
+ public:
+ Model();
+
+ TfLiteStatus Invoke();
+
+ private:
+ TfLiteContext context_ = {};
+ tflite::Subgraph subgraphs_[1];
+ tflite::MicroCodegenContext micro_context_;
+ TfLiteNode subgraph0_nodes_[3] = {};
+ TfLiteEvalTensor subgraph0_tensors_[10] = {};
+};
+
+} // namespace hello_world_model
diff --git a/codegen/examples/hello_world/update_example_source.sh b/codegen/examples/hello_world/update_example_source.sh
new file mode 100755
index 0000000..a381fed
--- /dev/null
+++ b/codegen/examples/hello_world/update_example_source.sh
@@ -0,0 +1,32 @@
+#!/usr/bin/env bash
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+#
+# Syncs the generated example source code in the repository.
+#
+
+set -e
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+ROOT_DIR=${SCRIPT_DIR}/../../..
+cd "${ROOT_DIR}"
+
+bazel build //codegen/examples/hello_world:hello_world_model
+cp ./bazel-bin/codegen/examples/hello_world/hello_world_model.h ${SCRIPT_DIR}
+cp ./bazel-bin/codegen/examples/hello_world/hello_world_model.cc ${SCRIPT_DIR}
+clang-format --style=google -i \
+ ${SCRIPT_DIR}/hello_world_model.h \
+ ${SCRIPT_DIR}/hello_world_model.cc
diff --git a/codegen/graph.py b/codegen/graph.py
new file mode 100644
index 0000000..ad5a700
--- /dev/null
+++ b/codegen/graph.py
@@ -0,0 +1,262 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" Provides object representation for the model that is conducive to code
+ generation using templates. """
+
+from typing import Dict, List, Optional, Sequence
+import string
+import textwrap
+
+from tflite_micro.codegen.operators import factory
+from tflite_micro.codegen.operators import operator
+from tflite_micro.codegen import tensor
+from tflite_micro.codegen import utils
+from tflite_micro.tensorflow.lite.python import schema_py_generated as schema_fb
+from tflite_micro.tensorflow.lite.tools import visualize
+
+
+class OpCode(object):
+
+ def __init__(self, op_code: schema_fb.OperatorCodeT):
+ self._op_code: schema_fb.OperatorCodeT = op_code
+
+ @property
+ def name(self) -> str:
+ if self._op_code.customCode:
+ return self._op_code.customCode
+ return visualize.BuiltinCodeToName(self._op_code.builtinCode)
+
+ @property
+ def register_function(self) -> str:
+ return "tflite::RegisterInference_{}".format(self.name)
+
+ @property
+ def enum_name(self) -> str:
+ return "k{}".format(utils.to_pascal_case(self.name))
+
+ @property
+ def full_enum_name(self) -> str:
+ return "OpCode::" + self.enum_name
+
+
+class Subgraph(object):
+
+ def __init__(self, model: schema_fb.ModelT, buffers: Sequence[tensor.Buffer],
+ subgraph_idx: int, subgraph: schema_fb.SubGraphT):
+ self._subgraph_idx: int = subgraph_idx
+ self._subgraph: schema_fb.SubGraphT = subgraph
+ self._op_codes: List[OpCode] = [
+ OpCode(op_code) for op_code in model.operatorCodes
+ ]
+ self._tensors: List[Tensor] = []
+ for t in subgraph.tensors:
+ self._tensors.append(tensor.Tensor(buffers[t.buffer], t))
+
+ self._operators: List[operator.Operator] = []
+ for op in subgraph.operators:
+ op_code = model.operatorCodes[op.opcodeIndex]
+ self._operators.append(factory.create_operator(op_code, op))
+
+ @property
+ def index(self) -> int:
+ return self._subgraph_idx
+
+ @property
+ def inputs(self) -> Sequence[int]:
+ return self._subgraph.inputs
+
+ @property
+ def outputs(self) -> Sequence[int]:
+ return self._subgraph.outputs
+
+ @property
+ def operators(self) -> Sequence[operator.Operator]:
+ return self._operators
+
+ @property
+ def tensors(self) -> Sequence[tensor.Tensor]:
+ return self._tensors
+
+ @property
+ def needs_zero_length_int_array(self) -> bool:
+ return any(t.needs_zero_length_int_array for t in self.tensors)
+
+ @property
+ def invoke_fn_name(self) -> str:
+ return f"InvokeSubgraph{self.index}"
+
+ @property
+ def inputs_array_name(self) -> str:
+ return f"kSubgraph{self.index}Inputs"
+
+ @property
+ def outputs_array_name(self) -> str:
+ return f"kSubgraph{self.index}Outputs"
+
+ @property
+ def nodes_array(self) -> str:
+ return f"subgraph{self.index}_nodes_"
+
+ def nodes_element(self, operator_idx: int) -> str:
+ return self.nodes_array + f"[{operator_idx}]"
+
+ def node_data_type(self, operator_idx: int) -> str:
+ return f"Node{self.index}_{operator_idx}"
+
+ def node_data_name(self, operator_idx: int) -> str:
+ return f"node_{self.index}_{operator_idx}"
+
+ def generate_c_node_data(self, indent: str) -> str:
+ node_data_strs: List[str] = []
+ for op_idx, op in enumerate(self.operators):
+ type_name = self.node_data_type(op_idx)
+ node_name = self.node_data_name(op_idx)
+ node_data_strs.append(op.generate_c_node_data(type_name, node_name))
+ return textwrap.indent("\n\n".join(node_data_strs), indent)
+
+ def generate_c_node_init(self, indent: str) -> str:
+ node_init_strs: List[str] = []
+ for op_idx, op in enumerate(self.operators):
+ tflite_node_name = self.nodes_element(op_idx)
+ node_data_name = self.node_data_name(op_idx)
+ node_init_strs.append(
+ op.generate_c_node_init(tflite_node_name, node_data_name))
+ return textwrap.indent("\n".join(node_init_strs), indent)
+
+ def generate_c_invoke(self, indent: str) -> str:
+ function_template = string.Template(
+ "TfLiteStatus ${function_name}(TfLiteContext* context,\n"
+ " tflite::Span<TfLiteNode> nodes) {\n"
+ " TFLITE_DCHECK(nodes.size() == ${num_nodes});\n"
+ "${body}\n"
+ " return kTfLiteOk;\n"
+ "}")
+
+ body_template = string.Template(
+ " TF_LITE_ENSURE_OK(\n"
+ " context, op_table[${op_code}].invoke(context, &${node}));\n")
+ invoke_strs: List[str] = []
+ for op_idx, op in enumerate(self.operators):
+ invoke_strs.append(
+ body_template.substitute(
+ op_code=self._op_codes[op.op_code_index].full_enum_name,
+ node=f"nodes[{op_idx}]"))
+
+ invoke = function_template.substitute(function_name=self.invoke_fn_name,
+ num_nodes=len(self.operators),
+ body="".join(invoke_strs))
+ return textwrap.indent(invoke, indent)
+
+ def generate_c_input_array(self, indent: str) -> str:
+ return utils.generate_c_int_array(indent, "size_t", self.inputs_array_name,
+ self.inputs)
+
+ def generate_c_output_array(self, indent: str) -> str:
+ return utils.generate_c_int_array(indent, "size_t",
+ self.outputs_array_name, self.outputs)
+
+ def generate_c_subgraph_init(self, indent: str) -> str:
+ init_template = string.Template(
+ "{.inputs = {&${input_array}[0], ${input_size}},\n"
+ " .outputs = {&${output_array}[0], ${output_size}},\n"
+ " .nodes = {&${node_array}[0], ${node_size}},\n"
+ " .tensors = {&${tensor_array}[0], ${tensor_size}},\n"
+ " .invoke = &${invoke}},")
+ return textwrap.indent(
+ init_template.substitute(input_array=self.inputs_array_name,
+ input_size=len(self.inputs),
+ output_array=self.outputs_array_name,
+ output_size=len(self.outputs),
+ node_array=self.nodes_array,
+ node_size=len(self.operators),
+ tensor_array=self.tensors_array,
+ tensor_size=len(self.tensors),
+ invoke=self.invoke_fn_name), indent)
+
+ @property
+ def tensors_array(self) -> str:
+ return f"subgraph{self.index}_tensors_"
+
+ def tensors_element(self, tensor_idx: int) -> str:
+ return self.tensors_array + f"[{tensor_idx}]"
+
+ def tensor_data_type(self, tensor_idx: int) -> str:
+ return f"Tensor{self.index}_{tensor_idx}"
+
+ def tensor_data_name(self, tensor_idx: int) -> str:
+ return f"tensor{self.index}_{tensor_idx}"
+
+ def generate_c_tensor_data(self, indent: str) -> str:
+ tensor_dims_strs: List[str] = []
+ for tensor_idx, tensor in enumerate(self.tensors):
+ type_name = self.tensor_data_type(tensor_idx)
+ tensor_name = self.tensor_data_name(tensor_idx)
+ tensor_dims_strs.append(
+ tensor.generate_c_tensor_dims(type_name, tensor_name))
+ return textwrap.indent("\n\n".join(tensor_dims_strs), indent)
+
+ def generate_c_tensor_init(self, indent: str) -> str:
+ tensor_init_strs: List[str] = []
+ for tensor_idx, tensor in enumerate(self.tensors):
+ tflite_tensor_name = self.tensors_element(tensor_idx)
+ tensor_data_name = self.tensor_data_name(tensor_idx)
+ tensor_init_strs.append(
+ tensor.generate_c_tensor_init(tflite_tensor_name, tensor_data_name))
+ return textwrap.indent("\n".join(tensor_init_strs), indent)
+
+
+class Graph(object):
+
+ def __init__(self, model: schema_fb.ModelT):
+ buffers: List[tensor.Buffer] = [
+ tensor.Buffer("buffer_{}".format(idx), buffer)
+ for idx, buffer in enumerate(model.buffers)
+ ]
+ self._subgraphs: List[SubGraph] = [
+ Subgraph(model, buffers, idx, subgraph)
+ for idx, subgraph in enumerate(model.subgraphs)
+ ]
+
+ @property
+ def subgraphs(self) -> Sequence[Subgraph]:
+ return self._subgraphs
+
+ @property
+ def buffers(self) -> Sequence[tensor.Buffer]:
+ buffers: List[tensor.Buffer] = []
+ for subgraph in self.subgraphs:
+ for t in subgraph.tensors:
+ buffers.append(t.buffer)
+ return buffers
+
+ @property
+ def needs_zero_length_int_array(self) -> bool:
+ return any(subgraph.needs_zero_length_int_array
+ for subgraph in self.subgraphs)
+
+
+class OpCodeTable(object):
+
+ def __init__(self, models: Sequence[schema_fb.ModelT]):
+ op_codes = []
+ for model in models:
+ for op_code in model.operatorCodes:
+ op_codes.append(OpCode(op_code))
+
+ self._op_codes: List([OpCode]) = list(set(op_codes))
+
+ @property
+ def op_codes(self) -> Sequence[OpCode]:
+ return self._op_codes
diff --git a/codegen/inference_generator.py b/codegen/inference_generator.py
new file mode 100644
index 0000000..fe351f3
--- /dev/null
+++ b/codegen/inference_generator.py
@@ -0,0 +1,68 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" Generates C/C++ inference source code. """
+
+import pathlib
+
+from mako import template
+from typing import TypedDict
+
+from tflite_micro.codegen import graph
+
+_TEMPLATE_DIR = pathlib.Path(__file__).parent / 'templates'
+_HEADER_TEMPLATE = _TEMPLATE_DIR / 'inference.h.mako'
+_SOURCE_TEMPLATE = _TEMPLATE_DIR / 'inference.cc.mako'
+
+
+class ModelData(TypedDict):
+ header_file: str
+ model_name: str
+ op_code_table: graph.OpCodeTable
+ graph: graph.Graph
+
+
+def _render(output_file: pathlib.Path, template_file: pathlib.Path,
+ model_data: ModelData) -> None:
+ print("Generating {}".format(output_file))
+ t = template.Template(filename=str(template_file))
+ with output_file.open('w+') as file:
+ file.write(t.render(**model_data))
+
+
+def _generate_header(header_path: pathlib.Path, model_data: ModelData) -> None:
+ _render(header_path, _HEADER_TEMPLATE, model_data)
+
+
+def _generate_source(source_path: pathlib.Path, model_data: ModelData) -> None:
+ _render(source_path, _SOURCE_TEMPLATE, model_data)
+
+
+def generate(output_dir: str, output_name: str,
+ op_code_table: graph.OpCodeTable, graph: graph.Graph) -> None:
+ """ Generate C/C++ inference code. """
+ header_file = f"{output_name}.h"
+ model_data: ModelData = {
+ 'header_file': header_file,
+ 'model_name': output_name,
+ 'op_code_table': op_code_table,
+ 'graph': graph,
+ }
+
+ # Ensure output directory exists
+ output_path = pathlib.Path(output_dir)
+ output_path.mkdir(parents=True, exist_ok=True)
+
+ _generate_header(output_path / header_file, model_data)
+ _generate_source(output_path / f"{output_name}.cc", model_data)
diff --git a/codegen/operators/BUILD b/codegen/operators/BUILD
new file mode 100644
index 0000000..3a7ae29
--- /dev/null
+++ b/codegen/operators/BUILD
@@ -0,0 +1,52 @@
+load("@rules_python//python:defs.bzl", "py_library")
+
+package(
+ default_visibility = ["//:__subpackages__"],
+ licenses = ["notice"],
+)
+
+py_library(
+ name = "constants",
+ srcs = [
+ "constants.py",
+ ],
+ deps = [
+ "//tensorflow/lite/python:schema_py",
+ ],
+)
+
+py_library(
+ name = "factory",
+ srcs = [
+ "factory.py",
+ ],
+ deps = [
+ ":fully_connected",
+ ":operator",
+ "//tensorflow/lite/python:schema_py",
+ ],
+)
+
+py_library(
+ name = "fully_connected",
+ srcs = [
+ "fully_connected.py",
+ ],
+ deps = [
+ ":constants",
+ ":operator",
+ "//codegen:utils",
+ "//tensorflow/lite/python:schema_py",
+ ],
+)
+
+py_library(
+ name = "operator",
+ srcs = [
+ "operator.py",
+ ],
+ deps = [
+ "//codegen:utils",
+ "//tensorflow/lite/python:schema_py",
+ ],
+)
diff --git a/codegen/operators/constants.py b/codegen/operators/constants.py
new file mode 100644
index 0000000..b9ff17a
--- /dev/null
+++ b/codegen/operators/constants.py
@@ -0,0 +1,50 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" Operator Constants """
+
+from typing import Dict
+
+from tflite_micro.tensorflow.lite.python import schema_py_generated as schema_fb
+
+ACTIVATION_FUNCS: Dict[int, str] = {
+ schema_fb.ActivationFunctionType.NONE: "kTfLiteActNone",
+ schema_fb.ActivationFunctionType.RELU: "kTfLiteActRelu",
+ schema_fb.ActivationFunctionType.RELU_N1_TO_1: "kTfLiteActReluN1To1",
+ schema_fb.ActivationFunctionType.RELU6: "kTfLiteActRelu6",
+ schema_fb.ActivationFunctionType.TANH: "kTfLiteActTanh",
+ schema_fb.ActivationFunctionType.SIGN_BIT: "kTfLiteActSignBit",
+}
+
+TFLITE_TYPE: Dict[int, str] = {
+ 0: "kTfLiteNoType",
+ 1: "kTfLiteFloat32",
+ 2: "kTfLiteInt32",
+ 3: "kTfLiteUInt8",
+ 4: "kTfLiteInt64",
+ 5: "kTfLiteString",
+ 6: "kTfLiteBool",
+ 7: "kTfLiteInt16",
+ 8: "kTfLiteComplex64",
+ 9: "kTfLiteInt8",
+ 10: "kTfLiteFloat16",
+ 11: "kTfLiteFloat64",
+ 12: "kTfLiteComplex128",
+ 13: "kTfLiteUInt64",
+ 14: "kTfLiteResource",
+ 15: "kTfLiteVariant",
+ 16: "kTfLiteUInt32",
+ 17: "kTfLiteUInt16",
+ 18: "kTfLiteInt4",
+}
diff --git a/codegen/operators/factory.py b/codegen/operators/factory.py
new file mode 100644
index 0000000..f62cacb
--- /dev/null
+++ b/codegen/operators/factory.py
@@ -0,0 +1,28 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" A factory function for creating operators """
+
+from tflite_micro.codegen.operators import fully_connected
+from tflite_micro.codegen.operators import operator
+from tflite_micro.tensorflow.lite.python import schema_py_generated as schema_fb
+
+_BUILTIN_OPERATORS: dict[int, operator.Operator.__class__] = {
+ schema_fb.BuiltinOperator.FULLY_CONNECTED: fully_connected.FullyConnected,
+}
+
+
+def create_operator(op_code: schema_fb.OperatorCodeT, op: schema_fb.Operator):
+ operator_class = _BUILTIN_OPERATORS[op_code.builtinCode]
+ return operator_class(op)
diff --git a/codegen/operators/fully_connected.py b/codegen/operators/fully_connected.py
new file mode 100644
index 0000000..f756bef
--- /dev/null
+++ b/codegen/operators/fully_connected.py
@@ -0,0 +1,56 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" FullyConnected operator """
+
+from typing import Dict
+import string
+
+from tflite_micro.codegen.operators import constants
+from tflite_micro.codegen.operators import operator
+from tflite_micro.codegen import utils
+from tflite_micro.tensorflow.lite.python import schema_py_generated as schema_fb
+
+_WEIGHTS_FORMATS: Dict[int, str] = {
+ schema_fb.FullyConnectedOptionsWeightsFormat.DEFAULT:
+ "kTfLiteFullyConnectedWeightsFormatDefault",
+ schema_fb.FullyConnectedOptionsWeightsFormat.SHUFFLED4x16INT8:
+ "kTfLiteFullyConnectedWeightsFormatShuffled4x16Int8",
+}
+
+
+class FullyConnected(operator.Operator):
+
+ def __init__(self, op: schema_fb.OperatorT):
+ assert op.builtinOptionsType == schema_fb.BuiltinOptions.FullyConnectedOptions
+ super(FullyConnected, self).__init__(op)
+ self._builtin_options: schema_fb.FullyConnectedOptionsT = op.builtinOptions
+
+ def generate_c_builtin_data(self) -> str:
+ builtin_template = string.Template(
+ "TfLiteFullyConnectedParams builtin_data = {\n"
+ " .activation = ${activation},\n"
+ " .weights_format = ${weights_format},\n"
+ " .keep_num_dims = ${keep_num_dims},\n"
+ " .asymmetric_quantize_inputs = ${asymmetric_quantize_inputs},\n"
+ " .quantized_bias_type = ${quantized_bias_type}};")
+ return builtin_template.substitute(
+ activation=constants.ACTIVATION_FUNCS[
+ self._builtin_options.fusedActivationFunction],
+ weights_format=_WEIGHTS_FORMATS[self._builtin_options.weightsFormat],
+ keep_num_dims=utils.bool_to_c_str(self._builtin_options.keepNumDims),
+ asymmetric_quantize_inputs=utils.bool_to_c_str(
+ self._builtin_options.asymmetricQuantizeInputs),
+ quantized_bias_type=constants.TFLITE_TYPE[
+ self._builtin_options.quantizedBiasType])
diff --git a/codegen/operators/operator.py b/codegen/operators/operator.py
new file mode 100644
index 0000000..2879d37
--- /dev/null
+++ b/codegen/operators/operator.py
@@ -0,0 +1,92 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" Provides object representation for the model that is conducive to code
+ generation using templates. """
+
+import abc
+from typing import Optional
+import string
+import textwrap
+
+from tflite_micro.codegen import utils
+from tflite_micro.tensorflow.lite.python import schema_py_generated as schema_fb
+
+
+class Operator(abc.ABC):
+
+ def __init__(self, operator: schema_fb.OperatorT):
+ self._operator: schema_fb.OperatorT = operator
+ self._inputs: utils.IntArray = utils.IntArray(self._operator.inputs)
+ self._outputs: utils.IntArray = utils.IntArray(self._operator.outputs)
+ self._intermediates: Optional[utils.IntArray] = utils.IntArray(
+ self._operator.intermediates) if self._operator.intermediates else None
+
+ def generate_c_node_data(self, type_name: str, node_name: str) -> str:
+ struct_template = string.Template("struct ${type_name} {\n"
+ "${body}"
+ "} ${node_name};")
+ body_template = string.Template("${inputs}\n"
+ "${outputs}\n"
+ "${intermediates}\n"
+ "${builtin_data}\n")
+ if self._intermediates:
+ intermediates = self._intermediates.generate_c_struct(
+ "Intermediates", "intermediates")
+ else:
+ intermediates = "// No intermediates"
+
+ body = body_template.substitute(
+ inputs=self._inputs.generate_c_struct("Inputs", "inputs"),
+ outputs=self._outputs.generate_c_struct("Outputs", "outputs"),
+ intermediates=intermediates,
+ builtin_data=self.generate_c_builtin_data())
+
+ return struct_template.substitute(type_name=type_name,
+ node_name=node_name,
+ body=textwrap.indent(body, " "))
+
+ def generate_c_node_init(self, tflite_node_name: str,
+ node_data_name: str) -> str:
+ init_template = string.Template(
+ "${tflite_node_name} = TfLiteNode{\n"
+ " .inputs ="
+ " reinterpret_cast<TfLiteIntArray*>(&${node_data_name}.inputs),\n"
+ " .outputs ="
+ " reinterpret_cast<TfLiteIntArray*>(&${node_data_name}.outputs),\n"
+ " .intermediates = ${intermediates},\n"
+ " .user_data = nullptr,\n"
+ " .builtin_data ="
+ " static_cast<void*>(&${node_data_name}.builtin_data),\n"
+ " .custom_initial_data = nullptr,\n"
+ " .custom_initial_data_size = 0};")
+
+ if self._intermediates:
+ intermediates = (
+ "reinterpret_cast<TfLiteIntArray*>(&{}.intermediates)".format(
+ self._intermediates))
+ else:
+ intermediates = "nullptr"
+
+ return init_template.substitute(tflite_node_name=tflite_node_name,
+ node_data_name=node_data_name,
+ intermediates=intermediates)
+
+ @property
+ def op_code_index(self) -> int:
+ return self._operator.opcodeIndex
+
+ @abc.abstractmethod
+ def generate_c_builtin_data(self) -> str:
+ raise NotImplementedError(f"Generating builtin data in {self.__name__}")
diff --git a/codegen/runtime/BUILD b/codegen/runtime/BUILD
new file mode 100644
index 0000000..d23cb70
--- /dev/null
+++ b/codegen/runtime/BUILD
@@ -0,0 +1,19 @@
+load("//tensorflow/lite/micro:build_def.bzl", "micro_copts")
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+ name = "micro_codegen_context",
+ srcs = ["micro_codegen_context.cc"],
+ hdrs = ["micro_codegen_context.h"],
+ copts = micro_copts(),
+ deps = [
+ "//tensorflow/lite/c:common",
+ "//tensorflow/lite/kernels:op_macros",
+ "//tensorflow/lite/kernels/internal:compatibility",
+ "//tensorflow/lite/micro:micro_context",
+ "//tensorflow/lite/micro:micro_graph",
+ "//tensorflow/lite/micro:micro_log",
+ "//tensorflow/lite/micro:span",
+ ],
+)
diff --git a/codegen/runtime/micro_codegen_context.cc b/codegen/runtime/micro_codegen_context.cc
new file mode 100644
index 0000000..858c823
--- /dev/null
+++ b/codegen/runtime/micro_codegen_context.cc
@@ -0,0 +1,139 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "codegen/runtime/micro_codegen_context.h"
+
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/kernels/internal/compatibility.h"
+#include "tensorflow/lite/kernels/op_macros.h"
+#include "tensorflow/lite/micro/micro_log.h"
+
+namespace tflite {
+
+MicroCodegenContext::MicroCodegenContext(TfLiteContext* context,
+ Span<Subgraph> subgraphs)
+ : context_(context), subgraphs_(subgraphs) {}
+
+void* MicroCodegenContext::GetScratchBuffer(int buffer_idx) {
+ // TODO(rjascani): Implement scratch buffers
+ return nullptr;
+}
+
+TfLiteEvalTensor* MicroCodegenContext::GetEvalTensor(int tensor_idx) {
+ TFLITE_DCHECK(static_cast<size_t>(tensor_idx) <
+ subgraphs_[current_subgraph_idx_].tensors.size());
+ return &subgraphs_[current_subgraph_idx_].tensors[tensor_idx];
+}
+
+TfLiteStatus MicroCodegenContext::set_external_context(
+ void* external_context_payload) {
+ if (external_context_payload == nullptr ||
+ external_context_payload_ != nullptr) {
+ MicroPrintf(
+ "Attempting to set external context to %x but it was %x already",
+ external_context_payload, external_context_payload_);
+ return kTfLiteError;
+ }
+
+ external_context_payload_ = external_context_payload;
+ return kTfLiteOk;
+}
+
+void* MicroCodegenContext::external_context() {
+ return external_context_payload_;
+}
+
+MicroGraph& MicroCodegenContext::graph() { return *this; }
+
+void* MicroCodegenContext::AllocatePersistentBuffer(size_t) {
+ // Not allowed at Eval
+ TFLITE_ABORT;
+ return nullptr;
+}
+
+TfLiteStatus MicroCodegenContext::RequestScratchBufferInArena(size_t, int*) {
+ // Not allowed at Eval
+ TFLITE_ABORT;
+ return kTfLiteError;
+}
+
+TfLiteTensor* MicroCodegenContext::AllocateTempTfLiteTensor(int) {
+ // Not allowed at Eval
+ TFLITE_ABORT;
+ return nullptr;
+}
+
+void MicroCodegenContext::DeallocateTempTfLiteTensor(TfLiteTensor*) {
+ // Not allowed at Eval
+ TFLITE_ABORT;
+}
+
+uint8_t* MicroCodegenContext::AllocateTempBuffer(size_t, size_t) {
+ // Not allowed at Eval
+ TFLITE_ABORT;
+ return nullptr;
+}
+
+void MicroCodegenContext::DeallocateTempBuffer(uint8_t*) {
+ // Not allowed at Eval
+ TFLITE_ABORT;
+}
+
+TfLiteStatus MicroCodegenContext::InvokeSubgraph(int subgraph_idx) {
+ TF_LITE_ENSURE(context_,
+ static_cast<size_t>(subgraph_idx) < subgraphs_.size());
+ size_t previous_subgraph_idx = current_subgraph_idx_;
+ current_subgraph_idx_ = subgraph_idx;
+ TfLiteStatus status =
+ subgraphs_[subgraph_idx].invoke(context_, subgraphs_[subgraph_idx].nodes);
+ current_subgraph_idx_ = previous_subgraph_idx;
+ return status;
+}
+
+size_t MicroCodegenContext::NumSubgraphInputs(int subgraph_idx) {
+ TFLITE_DCHECK(static_cast<size_t>(subgraph_idx) < subgraphs_.size());
+ return subgraphs_[subgraph_idx].inputs.size();
+}
+
+TfLiteEvalTensor* MicroCodegenContext::GetSubgraphInput(int subgraph_idx,
+ int input_idx) {
+ TFLITE_DCHECK(static_cast<size_t>(subgraph_idx) < subgraphs_.size());
+ TFLITE_DCHECK(static_cast<size_t>(input_idx) <
+ subgraphs_[subgraph_idx].inputs.size());
+ const size_t tensor_idx = subgraphs_[subgraph_idx].inputs[input_idx];
+ return &subgraphs_[subgraph_idx].tensors[tensor_idx];
+}
+
+size_t MicroCodegenContext::NumSubgraphOutputs(int subgraph_idx) {
+ TFLITE_DCHECK(static_cast<size_t>(subgraph_idx) < subgraphs_.size());
+ return subgraphs_[subgraph_idx].outputs.size();
+}
+
+TfLiteEvalTensor* MicroCodegenContext::GetSubgraphOutput(int subgraph_idx,
+ int output_idx) {
+ TFLITE_DCHECK(static_cast<size_t>(subgraph_idx) < subgraphs_.size());
+ TFLITE_DCHECK(static_cast<size_t>(output_idx) <
+ subgraphs_[subgraph_idx].outputs.size());
+ const size_t tensor_idx = subgraphs_[subgraph_idx].outputs[output_idx];
+ return &subgraphs_[subgraph_idx].tensors[tensor_idx];
+}
+
+int MicroCodegenContext::NumSubgraphs() { return subgraphs_.size(); }
+
+MicroResourceVariables* MicroCodegenContext::GetResourceVariables() {
+ return nullptr;
+}
+
+} // namespace tflite
diff --git a/codegen/runtime/micro_codegen_context.h b/codegen/runtime/micro_codegen_context.h
new file mode 100644
index 0000000..3693ad2
--- /dev/null
+++ b/codegen/runtime/micro_codegen_context.h
@@ -0,0 +1,75 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef CODEGEN_RUNTIME_MICRO_CODEGEN_CONTEXT_H_
+#define CODEGEN_RUNTIME_MICRO_CODEGEN_CONTEXT_H_
+
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/micro/micro_context.h"
+#include "tensorflow/lite/micro/micro_graph.h"
+#include "tensorflow/lite/micro/span.h"
+
+namespace tflite {
+
+struct Subgraph {
+ Span<const size_t> inputs;
+ Span<const size_t> outputs;
+ Span<TfLiteNode> nodes;
+ Span<TfLiteEvalTensor> tensors;
+ TfLiteStatus (*invoke)(TfLiteContext*, Span<TfLiteNode>);
+};
+
+class MicroCodegenContext : public MicroContext, MicroGraph {
+ public:
+ MicroCodegenContext(TfLiteContext* context, Span<Subgraph> subgraphs);
+
+ ~MicroCodegenContext() = default;
+
+ // MicroContext API
+ void* AllocatePersistentBuffer(size_t bytes) override;
+ TfLiteStatus RequestScratchBufferInArena(size_t bytes,
+ int* buffer_idx) override;
+ void* GetScratchBuffer(int buffer_idx) override;
+ TfLiteTensor* AllocateTempTfLiteTensor(int tensor_idx) override;
+ void DeallocateTempTfLiteTensor(TfLiteTensor* tensor) override;
+ uint8_t* AllocateTempBuffer(size_t size, size_t alignment) override;
+ void DeallocateTempBuffer(uint8_t* buffer) override;
+ TfLiteEvalTensor* GetEvalTensor(int tensor_idx) override;
+ TfLiteStatus set_external_context(void* external_context_payload) override;
+ void* external_context() override;
+ MicroGraph& graph() override;
+
+ // MicroGraph API
+ TfLiteStatus InvokeSubgraph(int subgraph_idx) override;
+ size_t NumSubgraphInputs(int subgraph_idx) override;
+ TfLiteEvalTensor* GetSubgraphInput(int subgraph_idx, int input_idx) override;
+ size_t NumSubgraphOutputs(int subgraph_idx) override;
+ TfLiteEvalTensor* GetSubgraphOutput(int subgraph_idx,
+ int output_idx) override;
+ int NumSubgraphs() override;
+ MicroResourceVariables* GetResourceVariables() override;
+
+ private:
+ TfLiteContext* context_;
+ Span<Subgraph> subgraphs_;
+ size_t current_subgraph_idx_ = 0;
+ void* external_context_payload_ = nullptr;
+
+ TF_LITE_REMOVE_VIRTUAL_DELETE
+};
+
+} // namespace tflite
+
+#endif // CODEGEN_RUNTIME_MICRO_CODEGEN_CONTEXT_H_
diff --git a/codegen/templates/inference.cc.mako b/codegen/templates/inference.cc.mako
new file mode 100644
index 0000000..cb6e59a
--- /dev/null
+++ b/codegen/templates/inference.cc.mako
@@ -0,0 +1,93 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+/* AUTOMATICALLY GENERATED DO NOT MODIFY */
+
+#include "${header_file}"
+
+#include "codegen/runtime/micro_codegen_context.h"
+#include "tensorflow/lite/c/builtin_op_data.h"
+#include "tensorflow/lite/c/c_api_types.h"
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/kernels/internal/compatibility.h"
+#include "tensorflow/lite/micro/kernels/micro_ops.h"
+#include "tensorflow/lite/micro/micro_common.h"
+#include "tensorflow/lite/micro/micro_context.h"
+
+namespace ${model_name} {
+namespace {
+// TODO(rjascani): We should probably split out the OpTable to a separate file
+// once we start generating for multiple models.
+enum OpCode {
+% for op_code in op_code_table.op_codes:
+ ${op_code.enum_name},
+% endfor
+ kCount
+};
+
+TFLMInferenceRegistration op_table[OpCode::kCount] = {
+% for op_code in op_code_table.op_codes:
+ ${op_code.register_function}(),
+% endfor
+};
+
+% for buffer in graph.buffers:
+${buffer.generate_c_buffer_array("")}
+% endfor
+% for subgraph in graph.subgraphs:
+${subgraph.generate_c_input_array("")}
+
+${subgraph.generate_c_output_array("")}
+
+${subgraph.generate_c_node_data("")}
+
+${subgraph.generate_c_tensor_data("")}
+% endfor
+% if graph.needs_zero_length_int_array:
+
+TfLiteIntArray zero_length_int_array = {};
+% endif
+
+% for subgraph in graph.subgraphs:
+${subgraph.generate_c_invoke("")}
+% endfor
+
+} // namespace
+
+Model::Model()
+ : subgraphs_{
+%for subgraph in graph.subgraphs:
+${subgraph.generate_c_subgraph_init(" ")}
+%endfor
+ },
+ micro_context_{&context_, {&subgraphs_[0], ${len(graph.subgraphs)}}} {
+ context_.impl_ = static_cast<void*>(µ_context_);
+ context_.ReportError = nullptr;
+ context_.GetTensor = nullptr;
+ context_.GetEvalTensor = tflite::MicroContextGetEvalTensor;
+ context_.profiler = nullptr;
+ context_.GetExternalContext = nullptr;
+ context_.GetScratchBuffer = nullptr;
+
+% for subgraph in graph.subgraphs:
+${subgraph.generate_c_node_init(" ")}
+
+${subgraph.generate_c_tensor_init(" ")}
+% endfor
+}
+
+TfLiteStatus Model::Invoke() { return micro_context_.InvokeSubgraph(0); }
+
+} // namespace ${model_name}
diff --git a/codegen/templates/inference.h.mako b/codegen/templates/inference.h.mako
new file mode 100644
index 0000000..5ab64e1
--- /dev/null
+++ b/codegen/templates/inference.h.mako
@@ -0,0 +1,44 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+/* AUTOMATICALLY GENERATED DO NOT MODIFY */
+
+#pragma once
+
+#include "codegen/runtime/micro_codegen_context.h"
+#include "tensorflow/lite/c/c_api_types.h"
+#include "tensorflow/lite/c/common.h"
+
+namespace ${model_name} {
+
+class Model {
+ public:
+ Model();
+
+ TfLiteStatus Invoke();
+
+ private:
+ TfLiteContext context_ = {};
+ tflite::Subgraph subgraphs_[${len(graph.subgraphs)}];
+ tflite::MicroCodegenContext micro_context_;
+% for subgraph in graph.subgraphs:
+ TfLiteNode ${subgraph.nodes_array}[${len(subgraph.operators)}] = {};
+% endfor
+% for subgraph in graph.subgraphs:
+ TfLiteEvalTensor ${subgraph.tensors_array}[${len(subgraph.tensors)}] = {};
+% endfor
+};
+
+} // namespace ${model_name}
diff --git a/codegen/tensor.py b/codegen/tensor.py
new file mode 100644
index 0000000..83870fc
--- /dev/null
+++ b/codegen/tensor.py
@@ -0,0 +1,127 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" Tensor class """
+
+from typing import Dict, Optional
+import string
+import textwrap
+
+from tflite_micro.codegen import utils
+from tflite_micro.tensorflow.lite.python import schema_py_generated as schema_fb
+
+_TENSOR_TYPES: Dict[int, str] = {
+ schema_fb.TensorType.FLOAT16: "kTfLiteFloat16",
+ schema_fb.TensorType.FLOAT32: "kTfLiteFloat32",
+ schema_fb.TensorType.FLOAT64: "kTfLiteFloat64",
+ schema_fb.TensorType.INT16: "kTfLiteInt16",
+ schema_fb.TensorType.UINT16: "kTfLiteUInt16",
+ schema_fb.TensorType.INT32: "kTfLiteInt32",
+ schema_fb.TensorType.UINT32: "kTfLiteUInt32",
+ schema_fb.TensorType.UINT8: "kTfLiteUInt8",
+ schema_fb.TensorType.INT8: "kTfLiteInt8",
+ schema_fb.TensorType.INT64: "kTfLiteInt64",
+ schema_fb.TensorType.UINT64: "kTfLiteUInt64",
+ schema_fb.TensorType.STRING: "kTfLiteString",
+ schema_fb.TensorType.BOOL: "kTfLiteBool",
+ schema_fb.TensorType.COMPLEX64: "kTfLiteComplex64",
+ schema_fb.TensorType.COMPLEX128: "kTfLiteComplex128",
+ schema_fb.TensorType.RESOURCE: "kTfLiteResource",
+ schema_fb.TensorType.VARIANT: "kTfLiteVariant",
+ schema_fb.TensorType.INT4: "kTfLiteInt4",
+}
+
+
+class Buffer(object):
+ """ This buffer could be either a static array or a pointer into the arena """
+
+ def __init__(self, buffer_name: str, buffer: schema_fb.BufferT):
+ # TODO(rjascani): Get arena allocation offsets from preprocessor
+ self._buffer_name = buffer_name
+ self._buffer = buffer
+
+ @property
+ def address(self) -> str:
+ if self._buffer is None or self._buffer.data is None:
+ # TODO(rjascani): This needs to point into the arena
+ return f"nullptr /* {self._buffer_name} */"
+ return f"&{self._buffer_name}"
+
+ def generate_c_buffer_array(self, indent: str) -> str:
+ if self._buffer is None or self._buffer.data is None:
+ return f"// {self._buffer_name} is located in the arena\n"
+
+ buffer_template = string.Template(
+ "alignas(16) uint8_t ${buffer_name}[${size}] = {\n"
+ "${body}\n"
+ "};\n")
+
+ byte_strs = ['0x{:02X}'.format(b) for b in self._buffer.data]
+
+ lines = []
+ for byte_strs_for_line in utils.split_into_chunks(byte_strs, 12):
+ bytes_segment = ', '.join(byte_strs_for_line)
+ lines.append(f' {bytes_segment},')
+
+ return textwrap.indent(
+ buffer_template.substitute(buffer_name=self._buffer_name,
+ size=len(self._buffer.data),
+ body='\n'.join(lines)), indent)
+
+
+class Tensor(object):
+
+ def __init__(self, buffer: Buffer, tensor: schema_fb.TensorT):
+ self._buffer = buffer
+ self._tensor: schema_fb.TensorT = tensor
+
+ @property
+ def buffer_index(self) -> bool:
+ return self._tensor.buffer
+
+ @property
+ def buffer(self) -> Buffer:
+ return self._buffer
+
+ @property
+ def has_shape(self) -> bool:
+ return self._tensor.shape is not None
+
+ @property
+ def needs_zero_length_int_array(self) -> bool:
+ return not self.has_shape
+
+ def generate_c_tensor_dims(self, type_name: str, tensor_name: str) -> str:
+ if not self.has_shape:
+ return f"// No data dims necessary for {tensor_name}"
+ return utils.IntArray(self._tensor.shape).generate_c_struct(
+ type_name + "Dims", tensor_name + "_dims")
+
+ def generate_c_tensor_init(self, tflite_tensor_name: str,
+ tensor_name: str) -> str:
+ init_template = string.Template(
+ "${tflite_tensor_name} = TfLiteEvalTensor{\n"
+ " .data = {.data = static_cast<void*>(${data})},\n"
+ " .dims = ${dims},\n"
+ " .type = ${tflite_type}};")
+ dims = "reinterpret_cast<TfLiteIntArray*>(&{})".format(
+ f"{tensor_name}_dims" if self._tensor.
+ shape is not None else "zero_length_int_array")
+
+ return init_template.substitute(
+ tflite_tensor_name=tflite_tensor_name,
+ tensor_name=tensor_name,
+ data=self._buffer.address,
+ dims=dims,
+ tflite_type=_TENSOR_TYPES[self._tensor.type])
diff --git a/codegen/utils.py b/codegen/utils.py
new file mode 100644
index 0000000..c6c31c8
--- /dev/null
+++ b/codegen/utils.py
@@ -0,0 +1,101 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" Utility functions and classes for code generation. """
+
+from typing import Any, Generator, Iterable, List, Optional, Sequence, Tuple
+import string
+import textwrap
+import itertools
+
+
+def to_pascal_case(s: str) -> str:
+ """ Basic function for converting snake_case to PascalCase. """
+ # This isn't perfect, as there might be some cases where we want underscores
+ # to remain if they are used as number separators.
+ return s.title().replace('_', '')
+
+
+def bool_to_c_str(b: bool) -> str:
+ """ Convert a python bool value to a C bool string. Ie, False -> 'false' """
+ return str(b).lower()
+
+
+def split_into_chunks(
+ data: Iterable[Any],
+ chunk_size: int) -> Generator[Tuple[Any, ...], None, None]:
+ """Splits an iterable into chunks of a given size."""
+ data_iterator = iter(data)
+ while True:
+ chunk = tuple(itertools.islice(data_iterator, chunk_size))
+ if not chunk:
+ break
+ yield chunk
+
+
+def generate_c_int_array(indent: str, int_type: str, name: str,
+ ints: Sequence[int]) -> str:
+ int_strs = ['{}'.format(i) for i in ints]
+
+ # Try to do it on a single line first
+ single_line_array_template = string.Template(
+ "constexpr ${int_type} ${name}[${size}] = {${data}};")
+ single_line = textwrap.indent(
+ single_line_array_template.substitute(int_type=int_type,
+ name=name,
+ size=len(int_strs),
+ data=', '.join(int_strs)), indent)
+
+ if len(single_line) < 81:
+ return single_line
+
+ # Couldn't fit, so split it across multiple lines
+ multi_line_array_template = string.Template(
+ "constexpr ${int_type} ${name}[${size}] = {\n"
+ "${body}\n"
+ "};\n")
+
+ lines = []
+ for int_strs_for_line in split_into_chunks(int_strs, 12):
+ ints_segment = ', '.join(int_strs_for_line)
+ lines.append(f' {ints_segment},')
+
+ return textwrap.indent(
+ multi_line_array_template.substitute(int_type=int_type,
+ name=name,
+ size=len(ints),
+ body='\n'.join(lines)), indent)
+
+
+class IntArray(object):
+ """ A helper class for generating int arrays that can be used to provide the
+ backing storage for a TfLiteIntArray. """
+
+ def __init__(self, data: List[int]):
+ self._data = data
+
+ def generate_c_struct(self, type_name: str,
+ variable_name: Optional[str]) -> str:
+ struct_template = string.Template("struct ${type_name} {\n"
+ " int size = ${size};\n"
+ " int data[${size}] = {${data}};\n"
+ "}")
+ # TODO(rjascani): Make this pretty print in multi-line chunks
+ int_strs = ['{}'.format(i) for i in self._data]
+ c_struct_str = struct_template.substitute(type_name=type_name,
+ size=len(int_strs),
+ data=', '.join(int_strs))
+ if variable_name:
+ return c_struct_str + " {};".format(variable_name)
+ return c_struct_str + ";"
diff --git a/debugging_output.md b/debugging_output.md
new file mode 100644
index 0000000..59aa14e
--- /dev/null
+++ b/debugging_output.md
@@ -0,0 +1,76 @@
+# How to debug invalid output
+
+The TFLM debugging output tools allow TFLM users to easily debug their models
+by providing a tool that will compare the intermediate values(output of each OP/Kernel)
+from a model post invoke between the TFLM and TfLite. As well as a way to
+compare intermediate values between TFLM x86 implementations and Optimized
+Implementations.
+
+## How to debug TFLM Interpreter output on embedded targets
+
+First you call a C++ binary that takes a TfLite model and returns a file that has
+random inputs and their corresponding output values for each layer of the model
+it was provided.
+
+The second is you provide a TfLite model and file outputted by C++ binary above
+to a python script. The script runs TFLM x86 inference comparison to the
+expected output.
+
+## How to debug TFLM Python Interpreter output
+
+Using a python script mentioned in the section above when only a TfLite model is
+provided as input, the script generates random input and compares TFLM vs TfLite
+inference outputs for each layer of the model.
+
+## C++ Expected Layer by Layer Output Tool on TFLite Micro
+
+This C++ binary allows you to pass in a TfLite model and returns a flatbuffer
+file with input and the corresponding output values appended into it that can be
+passed into a python debugging tool which can compare those golden values vs
+the x86 TFLM reference kernel implementation.
+
+The C++ Tool/binary will write a debugging file to the path provide in
+2nd arg using the tflite_model provided in the 1st arg.
+
+##### Command bazel/blaze:
+
+```
+ bazel run tensorflow/lite/micro/tools:layer_cc -- \
+ </path/to/input_model.tflite>
+ </path/to/output.file_name>
+```
+
+##### How to Build using Makefile :
+
+```
+make -f tensorflow/lite/micro/tools/make/Makefile layer_by_layer_output_tool -j24
+```
+
+## Python Layer by Layer Debugging Tool
+
+The Python Tool/Script can first be used to compare TFLM vs Tflite outputs for
+random inputs by only providing a TfLite file.
+
+#### TfLite vs TFLM command:
+```
+ bazel run tensorflow/lite/micro/tools:layer_by_layer_debugger -- \
+ --input_tflite_file=</path/to/my_model.tflite>
+```
+
+The Python Tool/Script can also be used to compare TFLM's python x86 output
+vs expected output provided by the C++ Tool/binary.
+
+#### TFLM vs Expected Command:
+```
+ bazel run tensorflow/lite/micro/tools:layer_by_layer_debugger -- \
+ --input_tflite_file=</path/to/my_model.tflite> \
+ --layer_by_layer_data_file=</path/to/my_debug_flatbuffer_file>
+```
+
+#### Optional Flags:
+ ` --print_dump `
+When this flag is set, it will print the TFLM output for each layer that is
+compared.
+
+ ` --rng`
+Integer random number seed for generating input data for comparisons against TFLite. (Default: 42)
diff --git a/python/tflite_micro/BUILD b/python/tflite_micro/BUILD
index 68321fd..282e9db 100644
--- a/python/tflite_micro/BUILD
+++ b/python/tflite_micro/BUILD
@@ -1,5 +1,8 @@
+load("@bazel_skylib//rules:common_settings.bzl", "string_flag")
+load("@rules_python//python:defs.bzl", "py_library", "py_test")
load("@pybind11_bazel//:build_defs.bzl", "pybind_extension")
load("//python:py_namespace.bzl", "py_namespace")
+load("//tools:expand_stamp_vars.bzl", "expand_stamp_vars")
load("@rules_python//python:packaging.bzl", "py_package", "py_wheel")
load("@tflm_pip_deps//:requirements.bzl", "requirement")
load(
@@ -70,9 +73,7 @@
srcs = [
"runtime.py",
],
- data = [
- ":_runtime.so",
- ],
+ data = [":_runtime.so"],
srcs_version = "PY3",
visibility = ["//visibility:public"],
deps = [
@@ -91,9 +92,10 @@
"noubsan",
],
deps = [
- requirement("numpy"),
- requirement("tensorflow-cpu"),
":runtime",
+ requirement("numpy"),
+ requirement("tensorflow"),
+ "//tensorflow/lite/micro/examples/recipes:add_four_numbers",
"//tensorflow/lite/micro/testing:generate_test_models_lib",
],
)
@@ -108,6 +110,14 @@
],
)
+# Generate a version attribute, imported as tflite_micro.__version__, using
+# stamp (a.k.a. workspace status) variables.
+expand_stamp_vars(
+ name = "version",
+ out = "_version.py",
+ template = "_version.py.in",
+)
+
# Collect the `deps` and their transitive dependences together into a set of
# files to package. The files retain their full path relative to the workspace
# root, which determines the subpackage path at which they're located within
@@ -128,6 +138,7 @@
deps = [
":postinstall_check",
":runtime",
+ ":version",
],
)
@@ -150,16 +161,78 @@
],
)
+expand_stamp_vars(
+ name = "description_file",
+ out = "README.pypi.md",
+ template = "README.pypi.md.in",
+)
+
+# Building the :whl or its descendants requires the following build setting to
+# supply the Python compatibility tags for the wheel metadata.
+string_flag(
+ name = "compatibility_tag",
+ build_setting_default = "local",
+ values = [
+ "cp310_cp310_manylinux_2_28_x86_64",
+ "cp311_cp311_manylinux_2_28_x86_64",
+ "local",
+ ],
+)
+
+config_setting(
+ name = "cp310_cp310_manylinux_2_28_x86_64",
+ flag_values = {
+ ":compatibility_tag": "cp310_cp310_manylinux_2_28_x86_64",
+ },
+)
+
+config_setting(
+ name = "cp311_cp311_manylinux_2_28_x86_64",
+ flag_values = {
+ ":compatibility_tag": "cp311_cp311_manylinux_2_28_x86_64",
+ },
+)
+
+config_setting(
+ name = "local",
+ flag_values = {
+ ":compatibility_tag": "local",
+ },
+)
+
py_wheel(
name = "whl",
+ # This macro yields additional targets:
+ #
+ # - whl.dist: build a properly named file under whl_dist/
+ #
+ abi = select({
+ ":cp310_cp310_manylinux_2_28_x86_64": "cp310",
+ ":cp311_cp311_manylinux_2_28_x86_64": "cp311",
+ ":local": "none",
+ }),
+ description_file = ":description_file",
distribution = "tflite_micro",
+ platform = select({
+ ":cp310_cp310_manylinux_2_28_x86_64": "manylinux_2_28_x86_64",
+ ":cp311_cp311_manylinux_2_28_x86_64": "manylinux_2_28_x86_64",
+ ":local": "any",
+ }),
+ python_tag = select({
+ ":cp310_cp310_manylinux_2_28_x86_64": "cp310",
+ ":cp311_cp311_manylinux_2_28_x86_64": "cp311",
+ ":local": "py3",
+ }),
requires = [
"flatbuffers",
"numpy",
"tensorflow",
],
+ stamp = 1, # 1 == always stamp
strip_path_prefixes = [package_name()],
- version = "0.1.0",
+ summary = "TensorFlow Lite for Microcontrollers",
+ twine = "@tflm_pip_deps_twine//:pkg",
+ version = "{BUILD_EMBED_LABEL}.dev{STABLE_GIT_COMMIT_TIME}",
deps = [
":namespace",
],
@@ -176,4 +249,7 @@
data = [
":whl",
],
+ tags = [
+ "notap", # See http://b/294278650#comment4 for more details.
+ ],
)
diff --git a/python/tflite_micro/README.md b/python/tflite_micro/README.md
index cf5f638..927705f 100644
--- a/python/tflite_micro/README.md
+++ b/python/tflite_micro/README.md
@@ -1,25 +1,127 @@
-# TFLM Python Interpreter
+# The `tflite_micro` Python Package
-The TFLM interpreter can be invoked from Python by using the Python interpreter
-wrapper in this directory.
+This directory contains the `tflite_micro` Python package. The following is
+mainly documentation for its developers.
-## Usage
+The `tflite_micro` package contains a complete TFLM interpreter built as a
+CPython extension module. The build of simple Python packages may be driven by
+standard Python package builders such as `build`, `setuptools`, and `flit`;
+however, as TFLM is first and foremost a large C/C++ project, `tflite_micro`'s
+build is instead driven by its C/C++ build system Bazel.
-There are two ways to import the Python wrapper, either by using Bazel/Blaze, or
-in near future by installing a PyPi package.
+## Building and installing locally
-### Bazel
+### Building
-#### Build
+The Bazel target `//python/tflite_micro:whl.dist` builds a `tflite_micro`
+Python *.whl* under the output directory `bazel-bin/python/tflite_micro/whl_dist`. For example:
+```
+% bazel build //python/tflite_micro:whl.dist
+....
+Target //python/tflite_micro:whl.dist up-to-date:
+ bazel-bin/python/tflite_micro/whl_dist
+
+% tree bazel-bin/python/tflite_micro/whl_dist
+bazel-bin/python/tflite_micro/whl_dist
+└── tflite_micro-0.dev20230920161638-py3-none-any.whl
+```
+
+### Installing
+
+Install the resulting *.whl* via pip. For example, in a Python virtual
+environment:
+```
+% python3 -m venv ~/tmp/venv
+% source ~/tmp/venv/bin/activate
+(venv) $ pip install bazel-bin/python/tflite_micro/whl_dist/tflite_micro-0.dev20230920161638-py3-none-any.whl
+Processing ./bazel-bin/python/tflite_micro/whl_dist/tflite_micro-0.dev20230920161638-py3-none-any.whl
+....
+Installing collected packages: [....]
+```
+
+The package should now be importable and usable. For example:
+```
+(venv) $ python
+Python 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] on linux
+Type "help", "copyright", "credits" or "license" for more information.
+>>> import tflite_micro
+>>> tflite_micro.postinstall_check.passed()
+True
+>>> i = tflite_micro.runtime.Interpreter.from_file("foo.tflite")
+>>> # etc.
+```
+
+## Building and uploading to PyPI
+
+The *.whl* generated above is unsuitable for distribution to the wider world
+via PyPI. The extension module is inevitably compiled against a particular
+Python implementation and platform C library. The resulting package is only
+binary-compatible with a system running the same Python implementation and a
+compatible (typically the same or newer) C library.
+
+The solution is to distribute multiple *.whl*s, one built for each Python
+implementation and platform combination. TFLM accomplishes this by running
+Bazel builds from within multiple, uniquely configured Docker containers. The
+images used are based on standards-conforming images published by the Python
+Package Authority (PyPA) for exactly such use.
+
+Python *.whl*s contain metadata used by installers such as `pip` to determine
+which distributions (*.whl*s) are compatible with the target platform. See the PyPA
+specification for [platform compatibility
+tags](https://packaging.python.org/en/latest/specifications/platform-compatibility-tags/).
+
+### Building
+
+In an environment with a working Docker installation, run the script
+`python/tflite_micro/pypi_build.sh <python-tag>` once for each tag. The
+script's online help (`--help`) lists the available tags. The script builds an
+appropriate Docker container and invokes a Bazel build and test within it.
+For example:
+```
+% python/tflite_micro/pypi_build.sh cp310
+[+] Building 2.6s (7/7) FINISHED
+=> writing image sha256:900704dad7fa27938dcc1c5057c0e760fb4ab0dff676415182455ae66546bbd4
+bazel build //python/tflite_micro:whl.dist \
+ --//python/tflite_micro:compatibility_tag=cp310_cp310_manylinux_2_28_x86_64
+bazel test //python/tflite_micro:whl_test \
+ --//python/tflite_micro:compatibility_tag=cp310_cp310_manylinux_2_28_x86_64
+//python/tflite_micro:whl_test
+Executed 1 out of 1 test: 1 test passes.
+Output:
+bazel-pypi-out/tflite_micro-0.dev20230920031310-cp310-cp310-manylinux_2_28_x86_64.whl
+```
+
+By default, *.whl*s are generated under the output directory `bazel-pypi-out/`.
+
+### Uploading to PyPI
+
+Upload the generated *.whl*s to PyPI with the script
+`python/tflite_micro/pypi_upload.sh`. This script lightly wraps the standard
+upload tool `twine`. A PyPI authentication token must be assigned to
+`TWINE_PASSWORD` in the environment. For example:
+```
+% export TWINE_PASSWORD=pypi-AgENdGV[....]
+% ./python/tflite_micro/pypi_upload.sh --test-pypi bazel-pypi-out/tflite_micro-*.whl
+Uploading distributions to https://test.pypi.org/legacy/
+Uploading tflite_micro-0.dev20230920031310-cp310-cp310-manylinux_2_28_x86_64.whl
+Uploading tflite_micro-0.dev20230920031310-cp311-cp311-manylinux_2_28_x86_64.whl
+View at:
+https://test.pypi.org/project/tflite-micro/0.dev20230920031310/
+```
+
+See the script's online help (`--help`) for more.
+
+## Using `tflite_micro` from within the TFLM source tree
+
+:construction:
+*The remainder of this document is under construction and may contain some
+obsolete information.*
+:construction:
The only package that needs to be included in the `BUILD` file is
`//python/tflite_micro:runtime`. It contains all
the correct dependencies to build the Python interpreter.
-### PyPi
-
-Work in progress.
-
### Examples
Depending on the workflow, the package import path may be slightly different.
@@ -55,7 +157,7 @@
print(tflm_interpreter.get_output_details(0))
```
-## Technical Details
+### Technical Details
The Python interpreter uses [pybind11](https://github.com/pybind/pybind11) to
expose an evolving set of C++ APIs. The Bazel build leverages the
@@ -64,7 +166,7 @@
The most updated Python APIs can be found in
`python/tflite_micro/runtime.py`.
-## Custom Ops
+### Custom Ops
The Python interpreter works with models with
[custom ops](https://www.tensorflow.org/lite/guide/ops_custom) but special steps
@@ -126,7 +228,7 @@
properly included in TFLM's op resolver. This approach is very similar to
TFLite's custom op support.
-## Print Allocations
+### Print Allocations
The Python interpreter can also be used to print memory arena allocations. This
is very helpful to figure out actual memory arena usage.
diff --git a/python/tflite_micro/README.pypi.md.in b/python/tflite_micro/README.pypi.md.in
new file mode 100644
index 0000000..a04356e
--- /dev/null
+++ b/python/tflite_micro/README.pypi.md.in
@@ -0,0 +1,5 @@
+# TensorFlow Lite for Microcontrollers
+
+This package is built from commit
+[{STABLE_GIT_HASH}](https://github.com/tensorflow/tflite-micro/blob/{STABLE_GIT_HASH}/python/tflite_micro)
+of [github.com/tensorflow/tflite-micro](https://github.com/tensorflow/tflite-micro).
diff --git a/python/tflite_micro/__init__.py b/python/tflite_micro/__init__.py
index 940289c..9f15213 100644
--- a/python/tflite_micro/__init__.py
+++ b/python/tflite_micro/__init__.py
@@ -11,6 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
+# ----
# Define a public API for the package by providing aliases for modules which
# are otherwise deeply nested in subpackages determined by their location in
@@ -20,5 +21,8 @@
from tflite_micro.python.tflite_micro import runtime
+# Unambiguously identify the source used to build the package.
+from tflite_micro.python.tflite_micro._version import __version__
+
# Ordered after `runtime` to avoid a circular dependency
from tflite_micro.python.tflite_micro import postinstall_check
diff --git a/python/tflite_micro/_runtime.cc b/python/tflite_micro/_runtime.cc
index 824b3b4..246545f 100644
--- a/python/tflite_micro/_runtime.cc
+++ b/python/tflite_micro/_runtime.cc
@@ -24,14 +24,19 @@
PYBIND11_MODULE(_runtime, m) {
m.doc() = "TFLite Micro Runtime Extension";
-
+ py::enum_<tflite::InterpreterConfig>(m, "PythonInterpreterConfig")
+ .value("kAllocationRecording",
+ tflite::InterpreterConfig::kAllocationRecording)
+ .value("kPreserveAllTensors",
+ tflite::InterpreterConfig::kPreserveAllTensors);
py::class_<InterpreterWrapper>(m, "InterpreterWrapper")
.def(py::init([](const py::bytes& data,
const std::vector<std::string>& registerers_by_name,
- size_t arena_size, int num_resource_variables) {
+ size_t arena_size, int num_resource_variables,
+ tflite::InterpreterConfig config) {
return std::unique_ptr<InterpreterWrapper>(
new InterpreterWrapper(data.ptr(), registerers_by_name, arena_size,
- num_resource_variables));
+ num_resource_variables, config));
}))
.def("PrintAllocations", &InterpreterWrapper::PrintAllocations)
.def("Invoke", &InterpreterWrapper::Invoke)
@@ -55,6 +60,14 @@
},
py::arg("index"))
.def(
+ "GetTensor",
+ [](InterpreterWrapper& self, size_t tensor_index,
+ size_t subgraph_index = 0) {
+ return tflite::PyoOrThrow(
+ self.GetTensor(tensor_index, subgraph_index));
+ },
+ py::arg("tensor_index"), py::arg("subgraph_index"))
+ .def(
"GetOutputTensorDetails",
[](InterpreterWrapper& self, size_t index) {
return tflite::PyoOrThrow(self.GetOutputTensorDetails(index));
diff --git a/python/tflite_micro/_version.py.in b/python/tflite_micro/_version.py.in
new file mode 100644
index 0000000..77768f7
--- /dev/null
+++ b/python/tflite_micro/_version.py.in
@@ -0,0 +1 @@
+__version__ = "{BUILD_EMBED_LABEL}.dev{STABLE_GIT_COMMIT_TIME}-g{STABLE_GIT_HASH}"
diff --git a/python/tflite_micro/interpreter_wrapper.cc b/python/tflite_micro/interpreter_wrapper.cc
index 41c4f7a..53efe8e 100644
--- a/python/tflite_micro/interpreter_wrapper.cc
+++ b/python/tflite_micro/interpreter_wrapper.cc
@@ -15,6 +15,11 @@
#include "python/tflite_micro/interpreter_wrapper.h"
+#include <cstddef>
+
+#include "tensorflow/lite/micro/micro_allocator.h"
+#include "tensorflow/lite/micro/micro_utils.h"
+
// Disallow Numpy 1.7 deprecated symbols.
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
// See https://numpy.org/doc/1.16/reference/c-api.array.html#importing-the-api
@@ -187,6 +192,39 @@
return result;
}
+PyObject* GetEvalTensorDetails(const TfLiteEvalTensor* eval_tensor) {
+ PyObject* tensor_type =
+ PyArray_TypeObjectFromType(TfLiteTypeToPyArrayType(eval_tensor->type));
+ PyObject* np_size_array =
+ PyArrayFromIntVector(eval_tensor->dims->data, eval_tensor->dims->size);
+ PyObject* tensor_size =
+ PyArray_Return(reinterpret_cast<PyArrayObject*>(np_size_array));
+
+ size_t eval_tensor_bytes = tflite::EvalTensorBytes(eval_tensor);
+ void* data = malloc(eval_tensor_bytes);
+ memcpy(data, eval_tensor->data.data, eval_tensor_bytes);
+
+ std::vector<npy_intp> dims(eval_tensor->dims->data,
+ eval_tensor->dims->data + eval_tensor->dims->size);
+ int py_type_num = TfLiteTypeToPyArrayType(eval_tensor->type);
+ PyObject* np_array =
+ PyArray_SimpleNewFromData(dims.size(), dims.data(), py_type_num, data);
+
+ // Transfer ownership to Python so that there's Python will take care of
+ // releasing this buffer
+ PyArray_ENABLEFLAGS(reinterpret_cast<PyArrayObject*>(np_array),
+ NPY_ARRAY_OWNDATA);
+
+ PyObject* result = PyDict_New();
+ PyDict_SetItemString(result, "dtype", tensor_type);
+ PyDict_SetItemString(result, "shape", tensor_size);
+ PyDict_SetItemString(
+ result, "tensor_data",
+ PyArray_Return(reinterpret_cast<PyArrayObject*>(np_array)));
+
+ return result;
+}
+
} // namespace
InterpreterWrapper::~InterpreterWrapper() {
@@ -204,7 +242,7 @@
InterpreterWrapper::InterpreterWrapper(
PyObject* model_data, const std::vector<std::string>& registerers_by_name,
- size_t arena_size, int num_resource_variables) {
+ size_t arena_size, int num_resource_variables, InterpreterConfig config) {
interpreter_ = nullptr;
// `model_data` is used as a raw pointer beyond the scope of this
@@ -223,12 +261,6 @@
const Model* model = GetModel(buf);
model_ = model_data;
memory_arena_ = std::unique_ptr<uint8_t[]>(new uint8_t[arena_size]);
- allocator_ = RecordingMicroAllocator::Create(memory_arena_.get(), arena_size);
- MicroResourceVariables* resource_variables_ = nullptr;
- if (num_resource_variables > 0)
- resource_variables_ =
- MicroResourceVariables::Create(allocator_, num_resource_variables);
-
for (const std::string& registerer : registerers_by_name) {
if (!AddCustomOpRegistererByName(registerer.c_str(),
&python_ops_resolver_)) {
@@ -237,6 +269,24 @@
}
}
+ switch (config) {
+ case InterpreterConfig::kAllocationRecording: {
+ recording_allocator_ =
+ RecordingMicroAllocator::Create(memory_arena_.get(), arena_size);
+ allocator_ = recording_allocator_;
+ break;
+ }
+ case InterpreterConfig::kPreserveAllTensors: {
+ allocator_ = MicroAllocator::Create(memory_arena_.get(), arena_size,
+ MemoryPlannerType::kLinear);
+ break;
+ }
+ }
+ MicroResourceVariables* resource_variables_ = nullptr;
+ if (num_resource_variables > 0)
+ resource_variables_ =
+ MicroResourceVariables::Create(allocator_, num_resource_variables);
+
interpreter_ = new MicroInterpreter(model, python_ops_resolver_, allocator_,
resource_variables_);
@@ -250,7 +300,13 @@
ImportNumpy();
}
-void InterpreterWrapper::PrintAllocations() { allocator_->PrintAllocations(); }
+void InterpreterWrapper::PrintAllocations() {
+ if (!recording_allocator_) {
+ ThrowValueError("Cannot print allocations as they were not recorded");
+ return;
+ }
+ return recording_allocator_->PrintAllocations();
+}
int InterpreterWrapper::Invoke() {
TfLiteStatus status = interpreter_->Invoke();
@@ -358,6 +414,18 @@
return PyArray_Return(reinterpret_cast<PyArrayObject*>(np_array));
}
+PyObject* InterpreterWrapper::GetTensor(size_t tensor_index,
+ size_t subgraph_index) {
+ if (!interpreter_->preserve_all_tensors()) {
+ ThrowRuntimeError(
+ "TFLM only supports GetTensor() when using a python interpreter with "
+ "the InterpreterConfig.kPeserverAllTensors interpreter_config");
+ return nullptr;
+ }
+ return GetEvalTensorDetails(
+ interpreter_->GetTensor(tensor_index, subgraph_index));
+}
+
PyObject* InterpreterWrapper::GetInputTensorDetails(size_t index) const {
return GetTensorDetails(interpreter_->input(index));
}
diff --git a/python/tflite_micro/interpreter_wrapper.h b/python/tflite_micro/interpreter_wrapper.h
index 1ead5af..9bb31b0 100644
--- a/python/tflite_micro/interpreter_wrapper.h
+++ b/python/tflite_micro/interpreter_wrapper.h
@@ -18,16 +18,29 @@
#include <Python.h>
#include "python/tflite_micro/python_ops_resolver.h"
+#include "tensorflow/lite/micro/micro_allocator.h"
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "tensorflow/lite/micro/recording_micro_allocator.h"
namespace tflite {
+// Allocation Recording is mutually exclusive from the PreserveAllTensors
+// debugging feature because PreserveAllTensors uses the LinearMemoryPlanner.
+// This means that the Allocations recorded by the RecordingMicroAllocator
+// wouldn't be accurate because the GreedyMemoryPlanner would have to be used.
+// So this Enum was made to represent the two possible modes/configs you can use
+// the python interpreter for.
+enum InterpreterConfig {
+ kAllocationRecording = 0,
+ kPreserveAllTensors = 1,
+};
+
class InterpreterWrapper {
public:
- InterpreterWrapper(PyObject* model_data,
- const std::vector<std::string>& registerers_by_name,
- size_t arena_size, int num_resource_variables);
+ InterpreterWrapper(
+ PyObject* model_data, const std::vector<std::string>& registerers_by_name,
+ size_t arena_size, int num_resource_variables,
+ InterpreterConfig config = InterpreterConfig::kAllocationRecording);
~InterpreterWrapper();
void PrintAllocations();
@@ -37,9 +50,11 @@
PyObject* GetOutputTensor(size_t index) const;
PyObject* GetInputTensorDetails(size_t index) const;
PyObject* GetOutputTensorDetails(size_t index) const;
+ PyObject* GetTensor(size_t tensor_index, size_t subgraph_index = 0);
private:
- tflite::RecordingMicroAllocator* allocator_;
+ tflite::MicroAllocator* allocator_ = nullptr;
+ tflite::RecordingMicroAllocator* recording_allocator_ = nullptr;
const PyObject* model_;
std::unique_ptr<uint8_t[]> memory_arena_;
tflite::PythonOpsResolver python_ops_resolver_;
diff --git a/python/tflite_micro/numpy_utils.cc b/python/tflite_micro/numpy_utils.cc
index 4a4aad8..20f43c9 100644
--- a/python/tflite_micro/numpy_utils.cc
+++ b/python/tflite_micro/numpy_utils.cc
@@ -40,6 +40,9 @@
return NPY_FLOAT32;
case kTfLiteFloat16:
return NPY_FLOAT16;
+ case kTfLiteBFloat16:
+ // TODO(b/329491949): NPY_BFLOAT16 currently doesn't exist
+ return NPY_FLOAT16;
case kTfLiteFloat64:
return NPY_FLOAT64;
case kTfLiteInt32:
diff --git a/python/tflite_micro/pypi_build.dockerfile b/python/tflite_micro/pypi_build.dockerfile
new file mode 100644
index 0000000..a2ac320
--- /dev/null
+++ b/python/tflite_micro/pypi_build.dockerfile
@@ -0,0 +1,16 @@
+# Use the Python Packaging Authority's reference build environment
+# for binary extensions. Binary extensions are typically built and distributed
+# for each target Python version and OS platform. The reference build
+# environment contains Python installations for each version, and a C/C++
+# toolchain specified for maximum compatibility among x86_64 Linux paltforms.
+FROM quay.io/pypa/manylinux_2_28_x86_64
+
+# Install bazel (via bazelisk)
+ENV BAZELISK=https://github.com/bazelbuild/bazelisk/releases/download/v1.18.0/bazelisk-linux-amd64
+ENV BAZEL=/usr/local/bin/bazel
+RUN curl --output $BAZEL --location $BAZELISK && chmod 755 $BAZEL
+
+# Append the location of the C/C++ toolchain to the default PATH, where
+# bazel expects to find it. The reference environment provides the location
+# (typically somewhere under /opt) in DEVTOOLSET_ROOTPATH.
+RUN echo "PATH="${PATH}:/${DEVTOOLSET_ROOTPATH}"" >>/etc/environment
diff --git a/python/tflite_micro/pypi_build.sh b/python/tflite_micro/pypi_build.sh
new file mode 100755
index 0000000..0581225
--- /dev/null
+++ b/python/tflite_micro/pypi_build.sh
@@ -0,0 +1,119 @@
+#!/bin/sh
+
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+set -e
+
+OUT_DIR_DEFAULT=bazel-pypi-out
+
+USAGE="$(basename $0) <python-tag> [<output-directory>]
+
+Build a Python wheel for public release to PyPI using a special Docker build
+container. Uses bazel, but does not pollute the WORKSPACE's default cache.
+
+<python-tag> must be one of the supported interpreters:
+ cp310
+ cp311
+
+<output-directory> defaults to $OUT_DIR_DEFAULT.
+"
+
+case "$1" in
+ cp310|cp311)
+ PY_TAG=$1
+ OUTDIR=$(realpath ${2:-$OUT_DIR_DEFAULT})
+ mkdir -p $OUTDIR
+ break
+ ;;
+ *)
+ echo usage: "$USAGE" >&2
+ exit 1
+esac
+
+SRCDIR=$(realpath .)
+if ! test -f $SRCDIR/WORKSPACE; then
+ echo "error: must run from the top of the source tree" >&2
+ exit 1
+fi
+
+# Remove Bazel's workspace symlinks so they'll be rewritten below, pointing into
+# OUTDIR.
+find . -maxdepth 1 -type l -name bazel-\* | xargs rm -f
+
+# Build the Docker image from its source file. Don't pollute the public list of
+# images by tagging; just use the image's ID.
+DOCKERFILE=python/tflite_micro/pypi_build.dockerfile
+IMAGE_ID_FILE=$OUTDIR/image-id
+docker build - --iidfile $IMAGE_ID_FILE <$DOCKERFILE
+IMAGE_ID=$(cat $IMAGE_ID_FILE)
+
+# Build the Python package within an ephemeral container.
+docker run \
+ --rm \
+ --interactive \
+ --mount type=bind,source=$SRCDIR,destination=$SRCDIR \
+ --mount type=bind,source=$OUTDIR,destination=$OUTDIR \
+ --workdir $SRCDIR \
+ --env USER=$(id -un) \
+ $IMAGE_ID \
+ /bin/bash -s -e -x -u \
+<<EOF
+ # Setup the Python compatibility tags. The PY_ABI always matches the Python
+ # interpreter tag. The platform tag is supplied by the build image in the
+ # environment variable AUDITWHEEL_PLAT.
+ PY_ABI=$PY_TAG
+ PY_PLATFORM=\$AUDITWHEEL_PLAT
+ PY_COMPATIBILITY=${PY_TAG}_\${PY_ABI}_\${PY_PLATFORM}
+
+ # Link the desired Python version into the PATH, where bazel will find it.
+ # The build image contains many different Python versions as options.
+ ln -sf /opt/python/$PY_TAG-$PY_TAG/bin/* /usr/bin
+
+ # Bazelisk fails if it can't check HOME for a .rc file., and pip (in
+ # :whl_test) installation of some dependencies (e.g., wrapt) expects HOME.
+ export HOME=$OUTDIR
+
+ # Bazelisk, bazel, and pip all need a writable cache directory.
+ export XDG_CACHE_HOME=$OUTDIR/cache
+
+ # Relocate the bazel root to keep the cache used for each Python toolchain
+ # separate. Drop root privledges and run as the invoking user.
+ call_bazel() {
+ setpriv --reuid=$(id -u) --regid=$(id -g) --clear-groups \
+ bazel \
+ --output_user_root=$OUTDIR/$PY_TAG-out \
+ "\$@" \
+ --action_env=HOME `# help setuptools find HOME in container` \
+ --action_env=USER `# bazel reads USER via whoami` \
+ --action_env=XDG_CACHE_HOME `# locate pip's cache inside OUTDIR`
+ }
+
+ # Build the wheel via bazel, using the Python compatibility tag matching the
+ # build environment.
+ call_bazel build //python/tflite_micro:whl.dist \
+ --//python/tflite_micro:compatibility_tag=\$PY_COMPATIBILITY
+
+ # Test, in the container environment.
+ call_bazel test //python/tflite_micro:whl_test \
+ --//python/tflite_micro:compatibility_tag=\$PY_COMPATIBILITY
+EOF
+
+# Make the output directory tree writable so it can be removed easily by the
+# user with `rm -rf $OUTDIR`. Bazel leaves it write-protected.
+chmod -R +w $OUTDIR
+
+# Copy the generated wheel file to the root of the $OUTDIR.
+cp bazel-bin/python/tflite_micro/whl_dist/*.whl $OUTDIR
+echo "Output:\n$(ls $OUTDIR/*.whl)"
diff --git a/python/tflite_micro/pypi_upload.sh b/python/tflite_micro/pypi_upload.sh
new file mode 100755
index 0000000..a5194c4
--- /dev/null
+++ b/python/tflite_micro/pypi_upload.sh
@@ -0,0 +1,57 @@
+#!/bin/sh
+
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+set -e
+
+USAGE="$(basename $0) [--test-pypi] <whl>...
+
+Upload the given Python wheels to PyPI using the program twine. Requires an
+authentication token in the environment variable TWINE_PASSWORD. TWINE_USERNAME
+is set to \`__token__\` if not set in the environment.
+"
+
+die () {
+ echo "$*" >&2
+ exit 1
+}
+
+case "$1" in
+ --test-pypi)
+ export TWINE_REPOSITORY=testpypi
+ shift
+ ;;
+ -h|--help)
+ echo "$USAGE"
+ exit
+esac
+
+if [ ! "$#" -ge 1 ]; then
+ die "$USAGE"
+fi
+
+if [ ! -x $(command -v twine) ]; then
+ die "error: twine not found. On Debian and derivatives, try \`apt install twine\`."
+fi
+
+if [ ! "$TWINE_PASSWORD" ]; then
+ die "error: TWINE_PASSWORD is not set"
+fi
+
+: ${TWINE_USERNAME:="__token__"}
+
+export TWINE_PASSWORD
+export TWINE_USERNAME
+twine upload "$@"
diff --git a/python/tflite_micro/python_ops_resolver.cc b/python/tflite_micro/python_ops_resolver.cc
index a47a261..f5d6e63 100644
--- a/python/tflite_micro/python_ops_resolver.cc
+++ b/python/tflite_micro/python_ops_resolver.cc
@@ -28,6 +28,7 @@
AddArgMin();
AddAssignVariable();
AddAveragePool2D();
+ AddBatchMatMul();
AddBatchToSpaceNd();
AddBroadcastArgs();
AddBroadcastTo();
@@ -45,8 +46,9 @@
AddDequantize();
AddDetectionPostprocess();
AddDiv();
- AddEnergy();
AddElu();
+ AddEmbeddingLookup();
+ AddEnergy();
AddEqual();
AddEthosU();
AddExp();
@@ -55,8 +57,8 @@
AddFill();
AddFilterBank();
AddFilterBankLog();
- AddFilterBankSquareRoot();
AddFilterBankSpectralSubtraction();
+ AddFilterBankSquareRoot();
AddFloor();
AddFloorDiv();
AddFloorMod();
@@ -75,11 +77,11 @@
AddLess();
AddLessEqual();
AddLog();
+ AddLogSoftmax();
AddLogicalAnd();
AddLogicalNot();
AddLogicalOr();
AddLogistic();
- AddLogSoftmax();
AddMaxPool2D();
AddMaximum();
AddMean();
@@ -89,6 +91,7 @@
AddNeg();
AddNotEqual();
AddOverlapAdd();
+ AddPCAN();
AddPack();
AddPad();
AddPadV2();
@@ -117,8 +120,8 @@
AddSquare();
AddSquaredDifference();
AddSqueeze();
- AddStridedSlice();
AddStacker();
+ AddStridedSlice();
AddSub();
AddSum();
AddSvdf();
diff --git a/python/tflite_micro/runtime.py b/python/tflite_micro/runtime.py
index 06a62b0..fbf2f20 100644
--- a/python/tflite_micro/runtime.py
+++ b/python/tflite_micro/runtime.py
@@ -14,15 +14,64 @@
# ==============================================================================
"""Python package for TFLM Python Interpreter"""
+import enum
import os
-
-from tflite_micro.python.tflite_micro import _runtime
from tflite_micro.tensorflow.lite.tools import flatbuffer_utils
+from tflite_micro.python.tflite_micro import _runtime
+
+
+class InterpreterConfig(enum.Enum):
+ """There are two mutually exclusive types of way you could use the TFLM python
+
+ interpreter, this enum is made so that users can clearly choose between the
+ two
+ different usage method for the interpreter.
+
+ The first default way is kRecordingAllocation where all memory usage by the
+ interpreter is recorded on inference. When using this config the GetTensor()
+ api is disabled by the interpreter since this interpreter configuration
+ doesn’t
+ guarantee that the valid data for all tensors is available post inference.
+
+ The second way is kPreserveAllTensors where the GetTensor() api is disabled by
+ the interpreter since this interpreter configuration doesn’t guarantee that
+ the
+ valid data for all tensors is available post inference. But the memory usage
+ by
+ the interpreter won’t be recorded on inference.
+
+ Usage:
+
+ default_interpreter = Interpreter(…
+ intrepreter_config=InterpreterConfig.kAllocationRecording)
+
+ preserve_interpreter = Interpreter(…
+ intrepreter_config=InterpreterConfig.kPreserveAllTensors)
+ """
+
+ kAllocationRecording = 0
+ kPreserveAllTensors = 1
+
+
+#TODO(b/297118768): Once Korko Docker contrainer for ubuntu x86 has imutabledict
+# added to it, this should be turned into an immutabledict.
+_ENUM_TRANSLATOR = {
+ InterpreterConfig.kAllocationRecording:
+ (_runtime.PythonInterpreterConfig.kAllocationRecording),
+ InterpreterConfig.kPreserveAllTensors:
+ (_runtime.PythonInterpreterConfig.kPreserveAllTensors),
+}
class Interpreter(object):
- def __init__(self, model_data, custom_op_registerers, arena_size):
+ def __init__(
+ self,
+ model_data,
+ custom_op_registerers,
+ arena_size,
+ intrepreter_config=InterpreterConfig.kAllocationRecording,
+ ):
if model_data is None:
raise ValueError("Model must not be None")
@@ -33,20 +82,28 @@
# This is a heuristic to ensure that the arena is sufficiently sized.
if arena_size is None:
arena_size = len(model_data) * 10
-
# Some models make use of resource variables ops, get the count here
num_resource_variables = flatbuffer_utils.count_resource_variables(
model_data)
print("Number of resource variables the model uses = ",
num_resource_variables)
- self._interpreter = _runtime.InterpreterWrapper(model_data,
- custom_op_registerers,
- arena_size,
- num_resource_variables)
+ self._interpreter = _runtime.InterpreterWrapper(
+ model_data,
+ custom_op_registerers,
+ arena_size,
+ num_resource_variables,
+ _ENUM_TRANSLATOR[intrepreter_config],
+ )
@classmethod
- def from_file(self, model_path, custom_op_registerers=[], arena_size=None):
+ def from_file(
+ self,
+ model_path,
+ custom_op_registerers=[],
+ arena_size=None,
+ intrepreter_config=InterpreterConfig.kAllocationRecording,
+ ):
"""Instantiates a TFLM interpreter from a model .tflite filepath.
Args:
@@ -65,10 +122,21 @@
with open(model_path, "rb") as f:
model_data = f.read()
- return Interpreter(model_data, custom_op_registerers, arena_size)
+ return Interpreter(
+ model_data,
+ custom_op_registerers,
+ arena_size,
+ intrepreter_config,
+ )
@classmethod
- def from_bytes(self, model_data, custom_op_registerers=[], arena_size=None):
+ def from_bytes(
+ self,
+ model_data,
+ custom_op_registerers=[],
+ arena_size=None,
+ intrepreter_config=InterpreterConfig.kAllocationRecording,
+ ):
"""Instantiates a TFLM interpreter from a model in byte array.
Args:
@@ -82,7 +150,12 @@
An Interpreter instance
"""
- return Interpreter(model_data, custom_op_registerers, arena_size)
+ return Interpreter(
+ model_data,
+ custom_op_registerers,
+ arena_size,
+ intrepreter_config,
+ )
def print_allocations(self):
"""Invoke the RecordingMicroAllocator to print the arena usage.
@@ -157,6 +230,9 @@
return self._interpreter.GetOutputTensor(index)
+ def GetTensor(self, tensor_index, subgraph_index):
+ return self._interpreter.GetTensor(tensor_index, subgraph_index)
+
def get_input_details(self, index):
"""Get input tensor information
diff --git a/python/tflite_micro/runtime_test.py b/python/tflite_micro/runtime_test.py
index 6a127fc..2a9003c 100644
--- a/python/tflite_micro/runtime_test.py
+++ b/python/tflite_micro/runtime_test.py
@@ -21,13 +21,78 @@
import gc
import weakref
+
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import test_util
from tensorflow.python.platform import test
-from tflite_micro.tensorflow.lite.micro.testing import generate_test_models
from tflite_micro.python.tflite_micro import runtime
+from tflite_micro.tensorflow.lite.micro.examples.recipes import add_four_numbers
+from tflite_micro.tensorflow.lite.micro.testing import generate_test_models
+
+
+class PeserveAllTensorsTest(test_util.TensorFlowTestCase):
+
+ def AddFourNumbersTestInterpreterMaker(self, inputs):
+ """Returns a tflm interpreter with a simple model that loads 4 numbers loaded
+
+ into it and loads the 4 inputs into the model
+ """
+ model_data = add_four_numbers.generate_model(write_file=False)
+ tflm_interpreter = runtime.Interpreter.from_bytes(
+ model_data,
+ intrepreter_config=runtime.InterpreterConfig.kPreserveAllTensors,
+ )
+ self.assertEqual(len(inputs), 4)
+ tflm_interpreter.set_input(inputs[0], 0)
+ tflm_interpreter.set_input(inputs[1], 1)
+ tflm_interpreter.set_input(inputs[2], 2)
+ tflm_interpreter.set_input(inputs[3], 3)
+ return tflm_interpreter
+
+ def testGetTensorAccuratelyGetsAllTenors(self):
+ """Test checks that GetTensor() returns accurate values for each tensor in the
+
+ model based on inputs of 1 2 3 4 into the AddFourNumbers TfLite model
+ """
+ tflm_interpreter = self.AddFourNumbersTestInterpreterMaker(
+ [[np.float32(1)], [np.float32(2)], [np.float32(3)], [np.float32(4)]])
+
+ tflm_interpreter.invoke()
+
+ tflm_output = tflm_interpreter.get_output(0)
+ self.assertEqual(tflm_output[0].astype("float32"), 10.0)
+ self.assertEqual(tflm_interpreter.GetTensor(0, 0)["tensor_data"][0], 1.0)
+ self.assertEqual(tflm_interpreter.GetTensor(1, 0)["tensor_data"][0], 2.0)
+ self.assertEqual(tflm_interpreter.GetTensor(2, 0)["tensor_data"][0], 3.0)
+ self.assertEqual(tflm_interpreter.GetTensor(3, 0)["tensor_data"][0], 4.0)
+ self.assertEqual(tflm_interpreter.GetTensor(4, 0)["tensor_data"][0], 7.0)
+ self.assertEqual(tflm_interpreter.GetTensor(5, 0)["tensor_data"][0], 9.0)
+ self.assertEqual(tflm_interpreter.GetTensor(6, 0)["tensor_data"][0], 10.0)
+
+ def testGetTensorAllUniqueTensors(self):
+ """Test checks that GetTensor() returns all the tensors in the model.
+
+ Due to the values used as inputs all the Tensors have unique data values so
+ this test confirms that this is the case.
+ """
+ tflm_interpreter = self.AddFourNumbersTestInterpreterMaker(
+ [[np.float32(1)], [np.float32(2)], [np.float32(3)], [np.float32(4)]])
+
+ tflm_interpreter.invoke()
+ tensors = [
+ tflm_interpreter.GetTensor(0, 0)["tensor_data"][0],
+ tflm_interpreter.GetTensor(1, 0)["tensor_data"][0],
+ tflm_interpreter.GetTensor(2, 0)["tensor_data"][0],
+ tflm_interpreter.GetTensor(3, 0)["tensor_data"][0],
+ tflm_interpreter.GetTensor(4, 0)["tensor_data"][0],
+ tflm_interpreter.GetTensor(5, 0)["tensor_data"][0],
+ tflm_interpreter.GetTensor(6, 0)["tensor_data"][0],
+ ]
+
+ # Check that all tensors are unique
+ self.assertEqual(len(set(tensors)), 7)
class ConvModelTests(test_util.TensorFlowTestCase):
diff --git a/python/tflite_micro/signal/BUILD b/python/tflite_micro/signal/BUILD
index 2bf5a94..0fce266 100644
--- a/python/tflite_micro/signal/BUILD
+++ b/python/tflite_micro/signal/BUILD
@@ -1,8 +1,10 @@
+load("@rules_python//python:defs.bzl", "py_library", "py_test")
load("//python/tflite_micro/signal:tflm_signal.bzl", "py_tflm_signal_library")
load("//tensorflow:extra_rules.bzl", "tflm_signal_friends")
load("@tflm_pip_deps//:requirements.bzl", "requirement")
package(
+ default_visibility = [":__subpackages__"],
licenses = ["notice"],
)
@@ -21,6 +23,7 @@
":filter_bank_ops_cc",
":framer_op_cc",
":overlap_add_op_cc",
+ ":pcan_op_cc",
":stacker_op_cc",
":window_op_cc",
],
@@ -33,7 +36,7 @@
"ops/__init__.py",
],
srcs_version = "PY3",
- visibility = ["//python/tflite_micro/signal/utils:__subpackages__"],
+ visibility = ["//visibility:public"],
deps = [
":delay_op",
":energy_op",
@@ -41,6 +44,7 @@
":filter_bank_ops",
":framer_op",
":overlap_add_op",
+ ":pcan_op",
":stacker_op",
":window_op",
],
@@ -53,6 +57,9 @@
cc_op_kernels = [
"//signal/tensorflow_core/kernels:delay_kernel",
],
+ deps = [
+ "//python/tflite_micro/signal/utils:util",
+ ],
)
py_test(
@@ -63,9 +70,8 @@
srcs_version = "PY3",
deps = [
":delay_op",
- "//python/tflite_micro/signal/utils:util",
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
],
)
@@ -76,6 +82,9 @@
cc_op_kernels = [
"//signal/tensorflow_core/kernels:energy_kernel",
],
+ deps = [
+ "//python/tflite_micro/signal/utils:util",
+ ],
)
py_test(
@@ -89,9 +98,8 @@
srcs_version = "PY3",
deps = [
":energy_op",
- "//python/tflite_micro/signal/utils:util",
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
],
)
@@ -102,6 +110,9 @@
cc_op_kernels = [
"//signal/tensorflow_core/kernels:fft_kernel",
],
+ deps = [
+ "//python/tflite_micro/signal/utils:util",
+ ],
)
py_test(
@@ -115,9 +126,8 @@
srcs_version = "PY3",
deps = [
":fft_ops",
- "//python/tflite_micro/signal/utils:util",
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
],
)
@@ -130,6 +140,7 @@
],
deps = [
"//python/tflite_micro/signal/utils:freq_to_mel",
+ "//python/tflite_micro/signal/utils:util",
],
)
@@ -149,9 +160,8 @@
srcs_version = "PY3",
deps = [
":filter_bank_ops",
- "//python/tflite_micro/signal/utils:util",
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
],
)
@@ -162,6 +172,9 @@
cc_op_kernels = [
"//signal/tensorflow_core/kernels:framer_kernel",
],
+ deps = [
+ "//python/tflite_micro/signal/utils:util",
+ ],
)
py_test(
@@ -175,9 +188,8 @@
srcs_version = "PY3",
deps = [
":framer_op",
- "//python/tflite_micro/signal/utils:util",
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
],
)
@@ -188,6 +200,9 @@
cc_op_kernels = [
"//signal/tensorflow_core/kernels:overlap_add_kernel",
],
+ deps = [
+ "//python/tflite_micro/signal/utils:util",
+ ],
)
py_test(
@@ -198,10 +213,42 @@
srcs_version = "PY3",
deps = [
":overlap_add_op",
- "//python/tflite_micro/signal/utils:util",
"@absl_py//absl/testing:parameterized",
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
+ ],
+)
+
+py_tflm_signal_library(
+ name = "pcan_op",
+ srcs = ["ops/pcan_op.py"],
+ cc_op_defs = ["//signal/tensorflow_core/ops:pcan_op"],
+ cc_op_kernels = [
+ "//signal/tensorflow_core/kernels:pcan_kernel",
+ ],
+ deps = [
+ "//python/tflite_micro/signal/utils:util",
+ "//python/tflite_micro/signal/utils:wide_dynamic_func_lut",
+ ],
+)
+
+py_test(
+ name = "pcan_op_test",
+ srcs = ["ops/pcan_op_test.py"],
+ data = [
+ "//python/tflite_micro/signal/ops/testdata:pcan_op_test1.txt",
+ ],
+ python_version = "PY3",
+ srcs_version = "PY3",
+ tags = [
+ "noasan",
+ "nomsan",
+ "noubsan",
+ ],
+ deps = [
+ ":pcan_op",
+ requirement("numpy"),
+ requirement("tensorflow"),
],
)
@@ -212,6 +259,9 @@
cc_op_kernels = [
"//signal/tensorflow_core/kernels:stacker_kernel",
],
+ deps = [
+ "//python/tflite_micro/signal/utils:util",
+ ],
)
py_test(
@@ -225,9 +275,8 @@
srcs_version = "PY3",
deps = [
":stacker_op",
- "//python/tflite_micro/signal/utils:util",
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
],
)
@@ -238,6 +287,9 @@
cc_op_kernels = [
"//signal/tensorflow_core/kernels:window_kernel",
],
+ deps = [
+ "//python/tflite_micro/signal/utils:util",
+ ],
)
py_test(
@@ -250,8 +302,7 @@
srcs_version = "PY3",
deps = [
":window_op",
- "//python/tflite_micro/signal/utils:util",
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
],
)
diff --git a/python/tflite_micro/signal/ops/pcan_op.py b/python/tflite_micro/signal/ops/pcan_op.py
new file mode 100644
index 0000000..753e76b
--- /dev/null
+++ b/python/tflite_micro/signal/ops/pcan_op.py
@@ -0,0 +1,70 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import tensorflow as tf
+from tflite_micro.python.tflite_micro.signal.utils import util
+from tflite_micro.python.tflite_micro.signal.utils import wide_dynamic_func_lut_wrapper
+
+gen_pcan_op = util.load_custom_op("pcan_op.so")
+
+PCAN_SNR_BITS = 12
+
+
+def _pcan_wrapper(pcan_fn, default_name):
+ """Wrapper around gen_pcan.pcan*."""
+
+ def _pcan(input_tensor,
+ noise_estimate,
+ strength,
+ offset,
+ gain_bits,
+ smoothing_bits,
+ input_correction_bits,
+ name=default_name):
+ with tf.name_scope(name) as scope:
+ input_tensor = tf.convert_to_tensor(input_tensor, dtype=tf.uint32)
+ noise_estimate = tf.convert_to_tensor(noise_estimate, dtype=tf.uint32)
+
+ input_bits = smoothing_bits - input_correction_bits
+ snr_shift = gain_bits - input_correction_bits - PCAN_SNR_BITS
+ if snr_shift < 1:
+ raise ValueError("SNR shift must be non-negative: %d" % snr_shift)
+
+ lut = wide_dynamic_func_lut_wrapper.wide_dynamic_func_lut(
+ strength, offset, input_bits, gain_bits)
+
+ lut_tensor = tf.convert_to_tensor(lut, dtype=tf.int16)
+
+ dim_list = input_tensor.shape.as_list()
+ if len(dim_list) != 1:
+ raise ValueError("Input tensor must have a rank of 1")
+ dim_list = noise_estimate.shape.as_list()
+ if len(dim_list) != 1:
+ raise ValueError("Noise estimate must have a rank of 1")
+
+ snr_shift = 6
+ return pcan_fn(input_tensor,
+ noise_estimate,
+ lut_tensor,
+ snr_shift=snr_shift,
+ name=scope)
+
+ return _pcan
+
+
+# TODO(b/286250473): change back name after name clash resolved
+pcan = _pcan_wrapper(gen_pcan_op.signal_pcan, "signal_pcan")
+
+tf.no_gradient("pcan")
diff --git a/python/tflite_micro/signal/ops/pcan_op_test.py b/python/tflite_micro/signal/ops/pcan_op_test.py
new file mode 100644
index 0000000..1400bf3
--- /dev/null
+++ b/python/tflite_micro/signal/ops/pcan_op_test.py
@@ -0,0 +1,83 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import os
+
+import numpy as np
+import tensorflow as tf
+
+from tensorflow.python.platform import resource_loader
+from tflite_micro.python.tflite_micro.signal.ops import pcan_op
+from tflite_micro.python.tflite_micro.signal.utils import util
+
+
+class PcanOpTest(tf.test.TestCase):
+
+ _PREFIX_PATH = resource_loader.get_path_to_datafile('')
+
+ def GetResource(self, filepath):
+ full_path = os.path.join(self._PREFIX_PATH, filepath)
+ with open(full_path, 'rt') as f:
+ file_text = f.read()
+ return file_text
+
+ def SinglePcanOpTest(self, filename):
+ lines = self.GetResource(filename).splitlines()
+ args = lines[0].split()
+ strength = float(args[0])
+ offset = float(args[1])
+ gain_bits = int(args[2])
+ smoothing_bits = int(args[3])
+ input_correction_bits = int(args[4])
+
+ func = tf.function(pcan_op.pcan)
+ channel_num = len(lines[1].split())
+
+ concrete_function = func.get_concrete_function(
+ tf.TensorSpec(channel_num, dtype=tf.uint32),
+ tf.TensorSpec(channel_num, dtype=tf.uint32),
+ strength=strength,
+ offset=offset,
+ gain_bits=gain_bits,
+ smoothing_bits=smoothing_bits,
+ input_correction_bits=input_correction_bits)
+ interpreter = util.get_tflm_interpreter(concrete_function, func)
+
+ # Read lines in pairs <input, noise_estimate, expected>
+ for i in range(1, len(lines), 3):
+ in_frame = np.array([int(j) for j in lines[i + 0].split()],
+ dtype='uint32')
+ noise_estimate = np.array([int(j) for j in lines[i + 1].split()],
+ dtype='uint32')
+ output_expected = np.array([int(j) for j in lines[i + 2].split()],
+ dtype='uint32')
+ # TFLM
+ interpreter.set_input(in_frame, 0)
+ interpreter.set_input(noise_estimate, 1)
+ interpreter.invoke()
+ output = interpreter.get_output(0)
+ self.assertAllEqual(output_expected, output)
+ # TF
+ output = self.evaluate(
+ pcan_op.pcan(in_frame, noise_estimate, strength, offset, gain_bits,
+ smoothing_bits, input_correction_bits))
+ self.assertAllEqual(output_expected, output)
+
+ def testPcanOp(self):
+ self.SinglePcanOpTest('testdata/pcan_op_test1.txt')
+
+
+if __name__ == '__main__':
+ tf.test.main()
diff --git a/python/tflite_micro/signal/ops/testdata/BUILD b/python/tflite_micro/signal/ops/testdata/BUILD
index 87fd9f2..4c8d7bc 100644
--- a/python/tflite_micro/signal/ops/testdata/BUILD
+++ b/python/tflite_micro/signal/ops/testdata/BUILD
@@ -16,6 +16,7 @@
"filter_bank_square_root_test1.txt",
"filter_bank_spectral_subtraction_test1.txt",
"framer_test1.txt",
+ "pcan_op_test1.txt",
"rfft_test1.txt",
"stacker_test1.txt",
"window_test1.txt",
diff --git a/python/tflite_micro/signal/ops/testdata/pcan_op_test1.txt b/python/tflite_micro/signal/ops/testdata/pcan_op_test1.txt
new file mode 100644
index 0000000..68da771
--- /dev/null
+++ b/python/tflite_micro/signal/ops/testdata/pcan_op_test1.txt
@@ -0,0 +1,1348 @@
+0.950000 80.000000 21 10 3
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+1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 2 1 2 1 3 1 2 1 3 1 4 2 3 1 5 1 3 1 3 2 5 1 4 2
\ No newline at end of file
diff --git a/python/tflite_micro/signal/tflm_signal.bzl b/python/tflite_micro/signal/tflm_signal.bzl
index 9bf773e..1635b48 100644
--- a/python/tflite_micro/signal/tflm_signal.bzl
+++ b/python/tflite_micro/signal/tflm_signal.bzl
@@ -25,7 +25,7 @@
srcs: Python source files for the Python library.
deps: Dependencies for the Python library.
visibility: Visibility for the Python library.
- cc_op_defs: A list of c++ src files containing REGISTER_OP definitions.
+ cc_op_defs: A list of c++ libraries containing REGISTER_OP definitions.
cc_op_kernels: A list of c++ targets containing kernels that are used
by the Python library.
"""
@@ -39,12 +39,12 @@
library_name = name + "_cc"
native.cc_library(
name = library_name,
- srcs = cc_op_defs,
copts = select({
"//conditions:default": ["-pthread"],
}),
alwayslink = 1,
deps =
+ cc_op_defs +
cc_op_kernels +
["@tensorflow_cc_deps//:cc_library"] +
select({"//conditions:default": []}),
diff --git a/python/tflite_micro/signal/utils/BUILD b/python/tflite_micro/signal/utils/BUILD
index 32a26fb..58b7dcf 100644
--- a/python/tflite_micro/signal/utils/BUILD
+++ b/python/tflite_micro/signal/utils/BUILD
@@ -1,4 +1,5 @@
# Signal python utilities.
+load("@rules_python//python:defs.bzl", "py_library", "py_test")
load("@tflm_pip_deps//:requirements.bzl", "requirement")
load("@pybind11_bazel//:build_defs.bzl", "pybind_extension")
@@ -36,9 +37,39 @@
py_library(
name = "util",
srcs = ["util.py"],
+ visibility = ["//visibility:public"],
deps = [
+ requirement("tensorflow"),
"//python/tflite_micro:runtime",
- "//python/tflite_micro/signal:ops",
- requirement("tensorflow-cpu"),
],
)
+
+pybind_extension(
+ name = "wide_dynamic_func_lut_wrapper", # :wide_dynamic_func_lut_wrapper.so
+ srcs = [
+ "wide_dynamic_func_lut_wrapper.cc",
+ ],
+)
+
+py_library(
+ name = "wide_dynamic_func_lut",
+ data = [
+ ":wide_dynamic_func_lut_wrapper.so",
+ ],
+)
+
+py_test(
+ name = "wide_dynamic_func_lut_test",
+ srcs = ["wide_dynamic_func_lut_test.py"],
+ data = [
+ ":wide_dynamic_func_lut_wrapper.so",
+ ],
+ python_version = "PY3",
+ srcs_version = "PY3",
+ tags = [
+ "noasan",
+ "nomsan",
+ "noubsan",
+ ],
+ visibility = ["//visibility:public"],
+)
diff --git a/python/tflite_micro/signal/utils/wide_dynamic_func_lut_test.py b/python/tflite_micro/signal/utils/wide_dynamic_func_lut_test.py
new file mode 100644
index 0000000..5cbcdd5
--- /dev/null
+++ b/python/tflite_micro/signal/utils/wide_dynamic_func_lut_test.py
@@ -0,0 +1,157 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for signal.python.utils.wide_dynamic_func_lut"""
+import unittest
+from tflite_micro.python.tflite_micro.signal.utils import wide_dynamic_func_lut_wrapper
+
+
+class WideDynamicFuncLutTest(unittest.TestCase):
+
+ def testWideDynamicFuncLut(self):
+ self.maxDiff = None
+ expected_lut = [
+ 32636,
+ 32633,
+ 32630,
+ -6,
+ 0,
+ 0,
+ 32624,
+ -12,
+ 0,
+ 0,
+ 32612,
+ -23,
+ -2,
+ 0,
+ 32587,
+ -48,
+ 0,
+ 0,
+ 32539,
+ -96,
+ 0,
+ 0,
+ 32443,
+ -190,
+ 0,
+ 0,
+ 32253,
+ -378,
+ 4,
+ 0,
+ 31879,
+ -739,
+ 18,
+ 0,
+ 31158,
+ -1409,
+ 62,
+ 0,
+ 29811,
+ -2567,
+ 202,
+ 0,
+ 27446,
+ -4301,
+ 562,
+ 0,
+ 23707,
+ -6265,
+ 1230,
+ 0,
+ 18672,
+ -7458,
+ 1952,
+ 0,
+ 13166,
+ -7030,
+ 2212,
+ 0,
+ 8348,
+ -5342,
+ 1868,
+ 0,
+ 4874,
+ -3459,
+ 1282,
+ 0,
+ 2697,
+ -2025,
+ 774,
+ 0,
+ 1446,
+ -1120,
+ 436,
+ 0,
+ 762,
+ -596,
+ 232,
+ 0,
+ 398,
+ -313,
+ 122,
+ 0,
+ 207,
+ -164,
+ 64,
+ 0,
+ 107,
+ -85,
+ 34,
+ 0,
+ 56,
+ -45,
+ 18,
+ 0,
+ 29,
+ -22,
+ 8,
+ 0,
+ 15,
+ -13,
+ 6,
+ 0,
+ 8,
+ -8,
+ 4,
+ 0,
+ 4,
+ -2,
+ 0,
+ 0,
+ 2,
+ -3,
+ 2,
+ 0,
+ 1,
+ 0,
+ 0,
+ 0,
+ 1,
+ -3,
+ 2,
+ 0,
+ 0,
+ 0,
+ 0,
+ ]
+ lut = wide_dynamic_func_lut_wrapper.wide_dynamic_func_lut(
+ 0.95, 80.0, 7, 21)
+ self.assertEqual(lut, expected_lut)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/python/tflite_micro/signal/utils/wide_dynamic_func_lut_wrapper.cc b/python/tflite_micro/signal/utils/wide_dynamic_func_lut_wrapper.cc
new file mode 100644
index 0000000..4ecf161
--- /dev/null
+++ b/python/tflite_micro/signal/utils/wide_dynamic_func_lut_wrapper.cc
@@ -0,0 +1,96 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <pybind11/pybind11.h>
+#include <pybind11/pytypes.h>
+
+#include <cstddef>
+#include <cstdint>
+#include <limits>
+
+namespace py = pybind11;
+
+constexpr size_t kWideDynamicFunctionBits = 32;
+constexpr size_t kWideDynamicFunctionLUTSize =
+ (4 * kWideDynamicFunctionBits - 3);
+
+int16_t PcanGainLookupFunction(const float strength, const float offset,
+ const int gain_bits, int32_t input_bits,
+ uint32_t x) {
+ const float x_as_float =
+ static_cast<float>(x) / (static_cast<uint32_t>(1) << input_bits);
+ const float gain_as_float = (static_cast<uint32_t>(1) << gain_bits) *
+ powf(x_as_float + offset, -strength);
+
+ if (gain_as_float > std::numeric_limits<int16_t>::max()) {
+ return std::numeric_limits<int16_t>::max();
+ }
+ return static_cast<int16_t>(gain_as_float + 0.5f);
+}
+
+py::list WideDynamicFuncLut(float strength, float offset, int input_bits,
+ int gain_bits) {
+ // Avoid accessing outside of the buffer below gain_lut[4 * interval + 3].
+ int16_t gain_lut_storage[kWideDynamicFunctionLUTSize + 1];
+ int16_t* gain_lut = gain_lut_storage;
+
+ gain_lut[0] =
+ PcanGainLookupFunction(strength, offset, gain_bits, input_bits, 0);
+ gain_lut[1] =
+ PcanGainLookupFunction(strength, offset, gain_bits, input_bits, 1);
+ // This puts the pointer outside of the buffer making the calculation in the
+ // loop below a lot simpler.
+ gain_lut -= 6;
+
+ for (size_t interval = 2; interval <= kWideDynamicFunctionBits; ++interval) {
+ const uint32_t x0 = static_cast<uint32_t>(1) << (interval - 1);
+ const uint32_t x1 = x0 + (x0 >> 1);
+ const uint32_t x2 =
+ (interval == kWideDynamicFunctionBits) ? x0 + (x0 - 1) : 2 * x0;
+
+ const int16_t y0 =
+ PcanGainLookupFunction(strength, offset, gain_bits, input_bits, x0);
+ const int16_t y1 =
+ PcanGainLookupFunction(strength, offset, gain_bits, input_bits, x1);
+ const int16_t y2 =
+ PcanGainLookupFunction(strength, offset, gain_bits, input_bits, x2);
+
+ const int32_t diff1 = static_cast<int32_t>(y1 - y0);
+ const int32_t diff2 = static_cast<int32_t>(y2 - y0);
+ const int32_t a1 = 4 * diff1 - diff2;
+ const int32_t a2 = diff2 - a1;
+
+ gain_lut[4 * interval] = y0;
+ gain_lut[4 * interval + 1] = static_cast<int16_t>(a1);
+ gain_lut[4 * interval + 2] = static_cast<int16_t>(a2);
+ gain_lut[4 * interval + 3] = 0;
+ }
+ // Brings the pointer back to the start of the buffer post calculation for the
+ // lut
+ gain_lut += 6;
+
+ py::list lut_list = py::list();
+ for (size_t i = 0; i < kWideDynamicFunctionLUTSize; i++) {
+ lut_list.append(gain_lut[i]);
+ }
+
+ return lut_list;
+}
+
+PYBIND11_MODULE(wide_dynamic_func_lut_wrapper, m) {
+ m.doc() = "wide_dynamic_func_lut";
+ m.def("wide_dynamic_func_lut", &WideDynamicFuncLut, py::arg("strength"),
+ py::arg("offset"), py::arg("input_bits"), py::arg("gain_bits"));
+}
diff --git a/python/tflite_micro/whl_test.sh b/python/tflite_micro/whl_test.sh
index 8b6d97f..f3e8293 100755
--- a/python/tflite_micro/whl_test.sh
+++ b/python/tflite_micro/whl_test.sh
@@ -37,7 +37,7 @@
# Run the package's post-installation checks.
python3 << HEREDOC
-import sys
-from tflite_micro import postinstall_check
-sys.exit(0 if postinstall_check.passed() else 1)
+import sys, tflite_micro
+print(tflite_micro.__version__)
+sys.exit(0 if tflite_micro.postinstall_check.passed() else 1)
HEREDOC
diff --git a/signal/micro/kernels/BUILD b/signal/micro/kernels/BUILD
index 4d37f06..b7ac658 100644
--- a/signal/micro/kernels/BUILD
+++ b/signal/micro/kernels/BUILD
@@ -10,19 +10,24 @@
srcs = [
"delay.cc",
"energy.cc",
- "fft_auto_scale.cc",
+ "fft_auto_scale_common.cc",
+ "fft_auto_scale_kernel.cc",
"filter_bank.cc",
"filter_bank_log.cc",
"filter_bank_spectral_subtraction.cc",
"filter_bank_square_root.cc",
+ "filter_bank_square_root_common.cc",
"framer.cc",
"irfft.cc",
"overlap_add.cc",
+ "pcan.cc",
"rfft.cc",
"stacker.cc",
"window.cc",
],
hdrs = [
+ "fft_auto_scale_kernel.h",
+ "filter_bank_square_root.h",
"irfft.h",
"rfft.h",
],
@@ -40,9 +45,11 @@
"//signal/src:filter_bank_square_root",
"//signal/src:irfft",
"//signal/src:overlap_add",
+ "//signal/src:pcan_argc_fixed",
"//signal/src:rfft",
"//signal/src:window",
"//tensorflow/lite:type_to_tflitetype",
+ "//tensorflow/lite/c:common",
"//tensorflow/lite/kernels:kernel_util",
"//tensorflow/lite/kernels/internal:tensor",
"//tensorflow/lite/micro:flatbuffer_utils",
@@ -324,3 +331,28 @@
"//tensorflow/lite/micro/testing:micro_test",
],
)
+
+cc_library(
+ name = "pcan_flexbuffers_generated_data",
+ srcs = [
+ "pcan_flexbuffers_generated_data.cc",
+ ],
+ hdrs = [
+ "pcan_flexbuffers_generated_data.h",
+ ],
+)
+
+cc_test(
+ name = "pcan_test",
+ srcs = [
+ "pcan_test.cc",
+ ],
+ deps = [
+ ":pcan_flexbuffers_generated_data",
+ "//tensorflow/lite/c:common",
+ "//tensorflow/lite/micro:op_resolvers",
+ "//tensorflow/lite/micro:test_helpers",
+ "//tensorflow/lite/micro/kernels:kernel_runner",
+ "//tensorflow/lite/micro/testing:micro_test",
+ ],
+)
diff --git a/signal/micro/kernels/delay.cc b/signal/micro/kernels/delay.cc
index 155e198..33ef35e 100644
--- a/signal/micro/kernels/delay.cc
+++ b/signal/micro/kernels/delay.cc
@@ -44,7 +44,7 @@
tflm_signal::CircularBuffer** circular_buffers;
};
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* DelayInit(TfLiteContext* context, const char* buffer, size_t length) {
auto* params = static_cast<TFLMSignalFrontendDelayParams*>(
context->AllocatePersistentBuffer(context,
sizeof(TFLMSignalFrontendDelayParams)));
@@ -58,7 +58,7 @@
return params;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus DelayPrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
@@ -108,7 +108,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus DelayEval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TFLMSignalFrontendDelayParams*>(node->user_data);
const TfLiteEvalTensor* input =
@@ -132,7 +132,7 @@
return kTfLiteOk;
}
-void Reset(TfLiteContext* context, void* buffer) {
+void DelayReset(TfLiteContext* context, void* buffer) {
auto* params = static_cast<TFLMSignalFrontendDelayParams*>(buffer);
for (int i = 0; i < params->outer_dims; ++i) {
tflm_signal::CircularBufferReset(params->circular_buffers[i]);
@@ -145,8 +145,8 @@
namespace tflm_signal {
TFLMRegistration* Register_DELAY() {
- static TFLMRegistration r =
- micro::RegisterOp(Init, Prepare, Eval, nullptr, Reset);
+ static TFLMRegistration r = micro::RegisterOp(DelayInit, DelayPrepare,
+ DelayEval, nullptr, DelayReset);
return &r;
}
} // namespace tflm_signal
diff --git a/signal/micro/kernels/energy.cc b/signal/micro/kernels/energy.cc
index fa79bc8..6a86366 100644
--- a/signal/micro/kernels/energy.cc
+++ b/signal/micro/kernels/energy.cc
@@ -42,7 +42,7 @@
int32_t start_index;
};
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* EnergyInit(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
auto* data =
@@ -60,7 +60,7 @@
return data;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus EnergyPrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
@@ -83,7 +83,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus EnergyEval(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TFLMSignalEnergyParams*>(node->user_data);
const TfLiteEvalTensor* input =
@@ -104,7 +104,8 @@
namespace tflm_signal {
TFLMRegistration* Register_ENERGY() {
- static TFLMRegistration r = tflite::micro::RegisterOp(Init, Prepare, Eval);
+ static TFLMRegistration r =
+ tflite::micro::RegisterOp(EnergyInit, EnergyPrepare, EnergyEval);
return &r;
}
} // namespace tflm_signal
diff --git a/signal/micro/kernels/fft_auto_scale.cc b/signal/micro/kernels/fft_auto_scale_common.cc
similarity index 61%
rename from signal/micro/kernels/fft_auto_scale.cc
rename to signal/micro/kernels/fft_auto_scale_common.cc
index 8eb0d8f..8703ac6 100644
--- a/signal/micro/kernels/fft_auto_scale.cc
+++ b/signal/micro/kernels/fft_auto_scale_common.cc
@@ -12,26 +12,17 @@
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
-
-#include "signal/src/fft_auto_scale.h"
-
-#include <math.h>
-#include <stddef.h>
-#include <stdint.h>
-
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
+#include "signal/micro/kernels/fft_auto_scale_kernel.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/micro_context.h"
namespace tflite {
-namespace {
constexpr int kInputTensor = 0;
constexpr int kOutputTensor = 0;
constexpr int kScaleBitTensor = 1;
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FftAutoScalePrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 2);
@@ -60,32 +51,4 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
- const TfLiteEvalTensor* input =
- tflite::micro::GetEvalInput(context, node, kInputTensor);
- TfLiteEvalTensor* output =
- tflite::micro::GetEvalOutput(context, node, kOutputTensor);
- TfLiteEvalTensor* scale_bit =
- tflite::micro::GetEvalOutput(context, node, kScaleBitTensor);
-
- const int16_t* input_data = tflite::micro::GetTensorData<int16_t>(input);
- int16_t* output_data = tflite::micro::GetTensorData<int16_t>(output);
- int32_t* scale_bit_data = tflite::micro::GetTensorData<int32_t>(scale_bit);
-
- *scale_bit_data =
- tflm_signal::FftAutoScale(input_data, output->dims->data[0], output_data);
- return kTfLiteOk;
-}
-
-} // namespace
-
-// TODO(b/286250473): remove namespace once de-duped libraries
-namespace tflm_signal {
-
-TFLMRegistration* Register_FFT_AUTO_SCALE() {
- static TFLMRegistration r = tflite::micro::RegisterOp(nullptr, Prepare, Eval);
- return &r;
-}
-
-} // namespace tflm_signal
} // namespace tflite
diff --git a/signal/micro/kernels/fft_auto_scale_kernel.cc b/signal/micro/kernels/fft_auto_scale_kernel.cc
new file mode 100644
index 0000000..4946fb3
--- /dev/null
+++ b/signal/micro/kernels/fft_auto_scale_kernel.cc
@@ -0,0 +1,64 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "signal/micro/kernels/fft_auto_scale_kernel.h"
+
+#include <math.h>
+#include <stddef.h>
+#include <stdint.h>
+
+#include "signal/src/fft_auto_scale.h"
+#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/micro_context.h"
+
+namespace tflite {
+namespace {
+
+constexpr int kInputTensor = 0;
+constexpr int kOutputTensor = 0;
+constexpr int kScaleBitTensor = 1;
+
+TfLiteStatus FftAutoScaleEval(TfLiteContext* context, TfLiteNode* node) {
+ const TfLiteEvalTensor* input =
+ tflite::micro::GetEvalInput(context, node, kInputTensor);
+ TfLiteEvalTensor* output =
+ tflite::micro::GetEvalOutput(context, node, kOutputTensor);
+ TfLiteEvalTensor* scale_bit =
+ tflite::micro::GetEvalOutput(context, node, kScaleBitTensor);
+
+ const int16_t* input_data = tflite::micro::GetTensorData<int16_t>(input);
+ int16_t* output_data = tflite::micro::GetTensorData<int16_t>(output);
+ int32_t* scale_bit_data = tflite::micro::GetTensorData<int32_t>(scale_bit);
+
+ *scale_bit_data =
+ tflm_signal::FftAutoScale(input_data, output->dims->data[0], output_data);
+ return kTfLiteOk;
+}
+
+} // namespace
+
+// TODO(b/286250473): remove namespace once de-duped libraries
+namespace tflm_signal {
+
+TFLMRegistration* Register_FFT_AUTO_SCALE() {
+ static TFLMRegistration r =
+ tflite::micro::RegisterOp(nullptr, FftAutoScalePrepare, FftAutoScaleEval);
+ return &r;
+}
+
+} // namespace tflm_signal
+} // namespace tflite
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc b/signal/micro/kernels/fft_auto_scale_kernel.h
similarity index 60%
copy from tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc
copy to signal/micro/kernels/fft_auto_scale_kernel.h
index e2cf661..9461c90 100644
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc
+++ b/signal/micro/kernels/fft_auto_scale_kernel.h
@@ -1,4 +1,4 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -12,12 +12,15 @@
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
+#ifndef SIGNAL_MICRO_KERNELS_FFT_AUTO_SCALE_KERNEL_H_
+#define SIGNAL_MICRO_KERNELS_FFT_AUTO_SCALE_KERNEL_H_
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.h"
+#include "tensorflow/lite/c/common.h"
-const char* kCategoryLabels[kCategoryCount] = {
- "silence",
- "unknown",
- "yes",
- "no",
-};
+namespace tflite {
+
+TfLiteStatus FftAutoScalePrepare(TfLiteContext* context, TfLiteNode* node);
+
+} // namespace tflite
+
+#endif // SIGNAL_MICRO_KERNELS_FFT_AUTO_SCALE_KERNEL_H_
diff --git a/signal/micro/kernels/fft_test.cc b/signal/micro/kernels/fft_test.cc
index 9d6fd25..bf54d41 100644
--- a/signal/micro/kernels/fft_test.cc
+++ b/signal/micro/kernels/fft_test.cc
@@ -274,11 +274,11 @@
const TFLMRegistration* registration =
tflite::tflm_signal::Register_RFFT_INT16();
// See (b/287518815) for why this is needed.
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
int tolerance = 9;
-#else // defined(HIFI4) || defined(HIFI5)
+#else // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
int tolerance = 3;
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
TF_LITE_MICRO_EXPECT_EQ(
kTfLiteOk, tflite::testing::TestFFT<int16_t>(
input_shape, tflite::kRfftInt16Length512Input,
diff --git a/signal/micro/kernels/filter_bank.cc b/signal/micro/kernels/filter_bank.cc
index 7866ac3..1cf08d2 100644
--- a/signal/micro/kernels/filter_bank.cc
+++ b/signal/micro/kernels/filter_bank.cc
@@ -46,7 +46,8 @@
uint64_t* work_area;
};
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* FilterBankInit(TfLiteContext* context, const char* buffer,
+ size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
auto* params = static_cast<TFLMSignalFilterBankParams*>(
@@ -70,7 +71,7 @@
return params;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FilterBankPrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 6);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
@@ -122,7 +123,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FilterBankEval(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TFLMSignalFilterBankParams*>(node->user_data);
const TfLiteEvalTensor* input0 =
@@ -166,7 +167,8 @@
namespace tflm_signal {
TFLMRegistration* Register_FILTER_BANK() {
- static TFLMRegistration r = tflite::micro::RegisterOp(Init, Prepare, Eval);
+ static TFLMRegistration r = tflite::micro::RegisterOp(
+ FilterBankInit, FilterBankPrepare, FilterBankEval);
return &r;
}
diff --git a/signal/micro/kernels/filter_bank_log.cc b/signal/micro/kernels/filter_bank_log.cc
index ea0cdb7..3d38e61 100644
--- a/signal/micro/kernels/filter_bank_log.cc
+++ b/signal/micro/kernels/filter_bank_log.cc
@@ -42,7 +42,8 @@
int output_scale;
};
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* FilterBankLogInit(TfLiteContext* context, const char* buffer,
+ size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
auto* params = static_cast<TFLMSignalLogParams*>(
@@ -59,7 +60,7 @@
return params;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FilterBankLogPrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
@@ -82,7 +83,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FilterBankLogEval(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TFLMSignalLogParams*>(node->user_data);
const TfLiteEvalTensor* input =
@@ -103,7 +104,8 @@
namespace tflm_signal {
TFLMRegistration* Register_FILTER_BANK_LOG() {
- static TFLMRegistration r = tflite::micro::RegisterOp(Init, Prepare, Eval);
+ static TFLMRegistration r = tflite::micro::RegisterOp(
+ FilterBankLogInit, FilterBankLogPrepare, FilterBankLogEval);
return &r;
}
diff --git a/signal/micro/kernels/filter_bank_spectral_subtraction.cc b/signal/micro/kernels/filter_bank_spectral_subtraction.cc
index f5ea4d7..e069323 100644
--- a/signal/micro/kernels/filter_bank_spectral_subtraction.cc
+++ b/signal/micro/kernels/filter_bank_spectral_subtraction.cc
@@ -51,12 +51,14 @@
size_t noise_estimate_size;
};
-void ResetState(TFLMSignalSpectralSubtractionParams* params) {
+void FilterBankSpectralSubtractionResetState(
+ TFLMSignalSpectralSubtractionParams* params) {
memset(params->noise_estimate, 0,
sizeof(uint32_t) * params->config.num_channels);
}
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* FilterBankSpectralSubtractionInit(TfLiteContext* context,
+ const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
auto* params = static_cast<TFLMSignalSpectralSubtractionParams*>(
@@ -96,7 +98,8 @@
return params;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FilterBankSpectralSubtractionPrepare(TfLiteContext* context,
+ TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 2);
@@ -125,7 +128,7 @@
TfLiteTypeSizeOf(output->type, ¶ms->noise_estimate_size);
params->noise_estimate_size *= ElementCount(*noise_estimate->dims);
- ResetState(params);
+ FilterBankSpectralSubtractionResetState(params);
micro_context->DeallocateTempTfLiteTensor(input);
micro_context->DeallocateTempTfLiteTensor(output);
@@ -133,7 +136,8 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FilterBankSpectralSubtractionEval(TfLiteContext* context,
+ TfLiteNode* node) {
auto* params =
reinterpret_cast<TFLMSignalSpectralSubtractionParams*>(node->user_data);
@@ -158,8 +162,9 @@
return kTfLiteOk;
}
-void Reset(TfLiteContext* context, void* buffer) {
- ResetState(static_cast<TFLMSignalSpectralSubtractionParams*>(buffer));
+void FilterBankSpectralSubtractionReset(TfLiteContext* context, void* buffer) {
+ FilterBankSpectralSubtractionResetState(
+ static_cast<TFLMSignalSpectralSubtractionParams*>(buffer));
}
} // namespace
@@ -167,8 +172,10 @@
namespace tflm_signal {
TFLMRegistration* Register_FILTER_BANK_SPECTRAL_SUBTRACTION() {
- static TFLMRegistration r =
- tflite::micro::RegisterOp(Init, Prepare, Eval, /*Free*/ nullptr, Reset);
+ static TFLMRegistration r = tflite::micro::RegisterOp(
+ FilterBankSpectralSubtractionInit, FilterBankSpectralSubtractionPrepare,
+ FilterBankSpectralSubtractionEval,
+ /*Free*/ nullptr, FilterBankSpectralSubtractionReset);
return &r;
}
diff --git a/signal/micro/kernels/filter_bank_square_root.cc b/signal/micro/kernels/filter_bank_square_root.cc
index 8df2617..bd7eff9 100644
--- a/signal/micro/kernels/filter_bank_square_root.cc
+++ b/signal/micro/kernels/filter_bank_square_root.cc
@@ -17,6 +17,7 @@
#include <stdint.h>
+#include "signal/micro/kernels/filter_bank_square_root.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
@@ -30,37 +31,8 @@
constexpr int kScaleBitsTensor = 1;
constexpr int kOutputTensor = 0;
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
- TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
- TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
- MicroContext* micro_context = GetMicroContext(context);
-
- TfLiteTensor* input =
- micro_context->AllocateTempInputTensor(node, kInputTensor);
- TfLiteTensor* scale_bits =
- micro_context->AllocateTempInputTensor(node, kScaleBitsTensor);
- TfLiteTensor* output =
- micro_context->AllocateTempOutputTensor(node, kOutputTensor);
- TF_LITE_ENSURE(context, input != nullptr);
- TF_LITE_ENSURE(context, scale_bits != nullptr);
- TF_LITE_ENSURE(context, output != nullptr);
-
- TF_LITE_ENSURE_EQ(context, NumDimensions(input), 1);
- TF_LITE_ENSURE_EQ(context, NumDimensions(scale_bits), 0);
- TF_LITE_ENSURE_EQ(context, NumDimensions(output), 1);
-
- TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteUInt64);
- TF_LITE_ENSURE_TYPES_EQ(context, scale_bits->type, kTfLiteInt32);
- TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteUInt32);
-
- micro_context->DeallocateTempTfLiteTensor(input);
- micro_context->DeallocateTempTfLiteTensor(output);
- micro_context->DeallocateTempTfLiteTensor(scale_bits);
- return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FilterBankSquareRootEval(TfLiteContext* context,
+ TfLiteNode* node) {
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kInputTensor);
const TfLiteEvalTensor* scale_bits =
@@ -83,7 +55,8 @@
namespace tflm_signal {
TFLMRegistration* Register_FILTER_BANK_SQUARE_ROOT() {
- static TFLMRegistration r = tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ static TFLMRegistration r = tflite::micro::RegisterOp(
+ nullptr, FilterBankSquareRootPrepare, FilterBankSquareRootEval);
return &r;
}
diff --git a/signal/micro/kernels/filter_bank_square_root.h b/signal/micro/kernels/filter_bank_square_root.h
new file mode 100644
index 0000000..25b6779
--- /dev/null
+++ b/signal/micro/kernels/filter_bank_square_root.h
@@ -0,0 +1,27 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef SIGNAL_MICRO_KERNELS_FILTER_BANK_SQUARE_ROOT_H_
+#define SIGNAL_MICRO_KERNELS_FILTER_BANK_SQUARE_ROOT_H_
+
+#include "tensorflow/lite/c/common.h"
+
+namespace tflite {
+
+TfLiteStatus FilterBankSquareRootPrepare(TfLiteContext* context,
+ TfLiteNode* node);
+
+} // namespace tflite
+
+#endif // SIGNAL_MICRO_KERNELS_FILTER_BANK_SQUARE_ROOT_H_
diff --git a/signal/micro/kernels/filter_bank_square_root_common.cc b/signal/micro/kernels/filter_bank_square_root_common.cc
new file mode 100644
index 0000000..b430901
--- /dev/null
+++ b/signal/micro/kernels/filter_bank_square_root_common.cc
@@ -0,0 +1,56 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "signal/micro/kernels/filter_bank_square_root.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+
+namespace tflite {
+
+constexpr int kInputTensor = 0;
+constexpr int kScaleBitsTensor = 1;
+constexpr int kOutputTensor = 0;
+
+TfLiteStatus FilterBankSquareRootPrepare(TfLiteContext* context,
+ TfLiteNode* node) {
+ TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
+ TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
+
+ MicroContext* micro_context = GetMicroContext(context);
+
+ TfLiteTensor* input =
+ micro_context->AllocateTempInputTensor(node, kInputTensor);
+ TfLiteTensor* scale_bits =
+ micro_context->AllocateTempInputTensor(node, kScaleBitsTensor);
+ TfLiteTensor* output =
+ micro_context->AllocateTempOutputTensor(node, kOutputTensor);
+ TF_LITE_ENSURE(context, input != nullptr);
+ TF_LITE_ENSURE(context, scale_bits != nullptr);
+ TF_LITE_ENSURE(context, output != nullptr);
+
+ TF_LITE_ENSURE_EQ(context, NumDimensions(input), 1);
+ TF_LITE_ENSURE_EQ(context, NumDimensions(scale_bits), 0);
+ TF_LITE_ENSURE_EQ(context, NumDimensions(output), 1);
+
+ TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteUInt64);
+ TF_LITE_ENSURE_TYPES_EQ(context, scale_bits->type, kTfLiteInt32);
+ TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteUInt32);
+
+ micro_context->DeallocateTempTfLiteTensor(input);
+ micro_context->DeallocateTempTfLiteTensor(output);
+ micro_context->DeallocateTempTfLiteTensor(scale_bits);
+ return kTfLiteOk;
+}
+
+} // namespace tflite
diff --git a/signal/micro/kernels/framer.cc b/signal/micro/kernels/framer.cc
index 8437bd0..36f189c 100644
--- a/signal/micro/kernels/framer.cc
+++ b/signal/micro/kernels/framer.cc
@@ -48,7 +48,7 @@
tflite::tflm_signal::CircularBuffer** circular_buffers;
};
-void ResetState(TFLMSignalFramerParams* params) {
+void FramerResetState(TFLMSignalFramerParams* params) {
for (int i = 0; i < params->outer_dims; ++i) {
tflite::tflm_signal::CircularBufferReset(params->circular_buffers[i]);
if (params->prefill) {
@@ -58,7 +58,7 @@
}
}
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* FramerInit(TfLiteContext* context, const char* buffer, size_t length) {
const uint8_t* buffer_t = reinterpret_cast<const uint8_t*>(buffer);
auto* params =
@@ -76,7 +76,7 @@
return params;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FramerPrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 2);
@@ -132,7 +132,7 @@
capacity, params->state_buffers[i], state_size);
}
- ResetState(params);
+ FramerResetState(params);
micro_context->DeallocateTempTfLiteTensor(input);
micro_context->DeallocateTempTfLiteTensor(output);
@@ -141,7 +141,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FramerEval(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TFLMSignalFramerParams*>(node->user_data);
const TfLiteEvalTensor* input =
@@ -181,8 +181,8 @@
return kTfLiteOk;
}
-void Reset(TfLiteContext* context, void* buffer) {
- ResetState(static_cast<TFLMSignalFramerParams*>(buffer));
+void FramerReset(TfLiteContext* context, void* buffer) {
+ FramerResetState(static_cast<TFLMSignalFramerParams*>(buffer));
}
} // namespace
@@ -190,8 +190,8 @@
namespace tflm_signal {
// TODO(b/286250473): remove namespace once de-duped libraries above
TFLMRegistration* Register_FRAMER() {
- static TFLMRegistration r =
- tflite::micro::RegisterOp(Init, Prepare, Eval, nullptr, Reset);
+ static TFLMRegistration r = tflite::micro::RegisterOp(
+ FramerInit, FramerPrepare, FramerEval, nullptr, FramerReset);
return &r;
}
} // namespace tflm_signal
diff --git a/signal/micro/kernels/irfft.cc b/signal/micro/kernels/irfft.cc
index 6cb3d7c..b0d58d5 100644
--- a/signal/micro/kernels/irfft.cc
+++ b/signal/micro/kernels/irfft.cc
@@ -50,7 +50,7 @@
template <typename T, size_t (*get_needed_memory_func)(int32_t),
void* (*init_func)(int32_t, void*, size_t)>
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* IrfftInit(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
auto* params = static_cast<TfLiteAudioFrontendIrfftParams*>(
@@ -79,7 +79,7 @@
}
template <TfLiteType TfLiteTypeEnum>
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus IrfftPrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
@@ -113,7 +113,7 @@
}
template <typename T, void (*apply_func)(void*, const Complex<T>* input, T*)>
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus IrfftEval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteAudioFrontendIrfftParams*>(node->user_data);
@@ -133,61 +133,61 @@
return kTfLiteOk;
}
-void* InitAll(TfLiteContext* context, const char* buffer, size_t length) {
+void* IrfftInitAll(TfLiteContext* context, const char* buffer, size_t length) {
const uint8_t* buffer_t = reinterpret_cast<const uint8_t*>(buffer);
const flexbuffers::Map& m = flexbuffers::GetRoot(buffer_t, length).AsMap();
auto tensor_type = static_cast<tflite::TensorType>(m["T"].AsInt32());
switch (tensor_type) {
case TensorType_INT16: {
- return Init<int16_t, tflm_signal::IrfftInt16GetNeededMemory,
- tflm_signal::IrfftInt16Init>(context, buffer, length);
+ return IrfftInit<int16_t, tflm_signal::IrfftInt16GetNeededMemory,
+ tflm_signal::IrfftInt16Init>(context, buffer, length);
}
case TensorType_INT32: {
- return Init<int32_t, tflm_signal::IrfftInt32GetNeededMemory,
- tflm_signal::IrfftInt32Init>(context, buffer, length);
+ return IrfftInit<int32_t, tflm_signal::IrfftInt32GetNeededMemory,
+ tflm_signal::IrfftInt32Init>(context, buffer, length);
}
case TensorType_FLOAT32: {
- return Init<float, tflm_signal::IrfftFloatGetNeededMemory,
- tflm_signal::IrfftFloatInit>(context, buffer, length);
+ return IrfftInit<float, tflm_signal::IrfftFloatGetNeededMemory,
+ tflm_signal::IrfftFloatInit>(context, buffer, length);
}
default:
return nullptr;
}
}
-TfLiteStatus PrepareAll(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus IrfftPrepareAll(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteAudioFrontendIrfftParams*>(node->user_data);
switch (params->fft_type) {
case kTfLiteInt16: {
- return Prepare<kTfLiteInt16>(context, node);
+ return IrfftPrepare<kTfLiteInt16>(context, node);
}
case kTfLiteInt32: {
- return Prepare<kTfLiteInt32>(context, node);
+ return IrfftPrepare<kTfLiteInt32>(context, node);
}
case kTfLiteFloat32: {
- return Prepare<kTfLiteFloat32>(context, node);
+ return IrfftPrepare<kTfLiteFloat32>(context, node);
}
default:
return kTfLiteError;
}
}
-TfLiteStatus EvalAll(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus IrfftEvalAll(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteAudioFrontendIrfftParams*>(node->user_data);
switch (params->fft_type) {
case kTfLiteInt16: {
- return Eval<int16_t, tflm_signal::IrfftInt16Apply>(context, node);
+ return IrfftEval<int16_t, tflm_signal::IrfftInt16Apply>(context, node);
}
case kTfLiteInt32: {
- return Eval<int32_t, tflm_signal::IrfftInt32Apply>(context, node);
+ return IrfftEval<int32_t, tflm_signal::IrfftInt32Apply>(context, node);
}
case kTfLiteFloat32: {
- return Eval<float, tflm_signal::IrfftFloatApply>(context, node);
+ return IrfftEval<float, tflm_signal::IrfftFloatApply>(context, node);
}
default:
return kTfLiteError;
@@ -201,28 +201,28 @@
TFLMRegistration* Register_IRFFT() {
static TFLMRegistration r =
- tflite::micro::RegisterOp(InitAll, PrepareAll, EvalAll);
+ tflite::micro::RegisterOp(IrfftInitAll, IrfftPrepareAll, IrfftEvalAll);
return &r;
}
TFLMRegistration* Register_IRFFT_FLOAT() {
static TFLMRegistration r = tflite::micro::RegisterOp(
- Init<float, IrfftFloatGetNeededMemory, IrfftFloatInit>,
- Prepare<kTfLiteFloat32>, Eval<float, IrfftFloatApply>);
+ IrfftInit<float, IrfftFloatGetNeededMemory, IrfftFloatInit>,
+ IrfftPrepare<kTfLiteFloat32>, IrfftEval<float, IrfftFloatApply>);
return &r;
}
TFLMRegistration* Register_IRFFT_INT16() {
static TFLMRegistration r = tflite::micro::RegisterOp(
- Init<int16_t, IrfftInt16GetNeededMemory, IrfftInt16Init>,
- Prepare<kTfLiteInt16>, Eval<int16_t, IrfftInt16Apply>);
+ IrfftInit<int16_t, IrfftInt16GetNeededMemory, IrfftInt16Init>,
+ IrfftPrepare<kTfLiteInt16>, IrfftEval<int16_t, IrfftInt16Apply>);
return &r;
}
TFLMRegistration* Register_IRFFT_INT32() {
static TFLMRegistration r = tflite::micro::RegisterOp(
- Init<int32_t, IrfftInt32GetNeededMemory, IrfftInt32Init>,
- Prepare<kTfLiteInt32>, Eval<int32_t, IrfftInt32Apply>);
+ IrfftInit<int32_t, IrfftInt32GetNeededMemory, IrfftInt32Init>,
+ IrfftPrepare<kTfLiteInt32>, IrfftEval<int32_t, IrfftInt32Apply>);
return &r;
}
diff --git a/signal/micro/kernels/overlap_add.cc b/signal/micro/kernels/overlap_add.cc
index 8de7463..c365cd8 100644
--- a/signal/micro/kernels/overlap_add.cc
+++ b/signal/micro/kernels/overlap_add.cc
@@ -48,14 +48,15 @@
};
template <typename T>
-void ResetState(TFLMSignalOverlapAddParams<T>* params) {
+void OverlapAddResetState(TFLMSignalOverlapAddParams<T>* params) {
for (int i = 0; i < params->outer_dims; i++) {
memset(params->state_buffers[i], 0, sizeof(T) * params->frame_size);
}
}
template <typename T>
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* OverlapAddInit(TfLiteContext* context, const char* buffer,
+ size_t length) {
const uint8_t* buffer_t = reinterpret_cast<const uint8_t*>(buffer);
auto* params = static_cast<TFLMSignalOverlapAddParams<T>*>(
@@ -73,7 +74,7 @@
}
template <typename T, TfLiteType TfLiteTypeEnum>
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus OverlapAddPrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
@@ -112,7 +113,7 @@
static_cast<T*>(context->AllocatePersistentBuffer(
context, params->frame_size * sizeof(T)));
}
- ResetState(params);
+ OverlapAddResetState(params);
micro_context->DeallocateTempTfLiteTensor(input);
micro_context->DeallocateTempTfLiteTensor(output);
@@ -120,7 +121,7 @@
}
template <typename T>
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus OverlapAddEval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TFLMSignalOverlapAddParams<T>*>(node->user_data);
const TfLiteEvalTensor* input =
@@ -144,69 +145,70 @@
}
template <typename T>
-void Reset(TfLiteContext* context, void* buffer) {
- ResetState(static_cast<TFLMSignalOverlapAddParams<T>*>(buffer));
+void OverlapAddReset(TfLiteContext* context, void* buffer) {
+ OverlapAddResetState(static_cast<TFLMSignalOverlapAddParams<T>*>(buffer));
}
-void* InitAll(TfLiteContext* context, const char* buffer, size_t length) {
+void* OverlapAddInitAll(TfLiteContext* context, const char* buffer,
+ size_t length) {
const uint8_t* buffer_t = reinterpret_cast<const uint8_t*>(buffer);
const flexbuffers::Map& m = flexbuffers::GetRoot(buffer_t, length).AsMap();
auto tensor_type = static_cast<tflite::TensorType>(m["T"].AsInt32());
switch (tensor_type) {
case TensorType_INT16: {
- return Init<int16_t>(context, buffer, length);
+ return OverlapAddInit<int16_t>(context, buffer, length);
}
case TensorType_FLOAT32: {
- return Init<float>(context, buffer, length);
+ return OverlapAddInit<float>(context, buffer, length);
}
default:
return nullptr;
}
}
-TfLiteStatus PrepareAll(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus OverlapAddPrepareAll(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TFLMSignalOverlapAddParams<void>*>(node->user_data);
switch (params->type) {
case kTfLiteInt16: {
- return Prepare<int16_t, kTfLiteInt16>(context, node);
+ return OverlapAddPrepare<int16_t, kTfLiteInt16>(context, node);
}
case kTfLiteFloat32: {
- return Prepare<float, kTfLiteFloat32>(context, node);
+ return OverlapAddPrepare<float, kTfLiteFloat32>(context, node);
}
default:
return kTfLiteError;
}
}
-TfLiteStatus EvalAll(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus OverlapAddEvalAll(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TFLMSignalOverlapAddParams<void>*>(node->user_data);
switch (params->type) {
case kTfLiteInt16: {
- return Eval<int16_t>(context, node);
+ return OverlapAddEval<int16_t>(context, node);
}
case kTfLiteFloat32: {
- return Eval<float>(context, node);
+ return OverlapAddEval<float>(context, node);
}
default:
return kTfLiteError;
}
}
-void ResetAll(TfLiteContext* context, void* buffer) {
+void OverlapAddResetAll(TfLiteContext* context, void* buffer) {
auto* params = reinterpret_cast<TFLMSignalOverlapAddParams<void>*>(buffer);
switch (params->type) {
case kTfLiteInt16: {
- Reset<int16_t>(context, buffer);
+ OverlapAddReset<int16_t>(context, buffer);
break;
}
case kTfLiteFloat32: {
- Reset<float>(context, buffer);
+ OverlapAddReset<float>(context, buffer);
break;
}
default:
@@ -218,22 +220,23 @@
namespace tflm_signal {
TFLMRegistration* Register_OVERLAP_ADD() {
- static TFLMRegistration r = tflite::micro::RegisterOp(
- InitAll, PrepareAll, EvalAll, nullptr, ResetAll);
+ static TFLMRegistration r =
+ tflite::micro::RegisterOp(OverlapAddInitAll, OverlapAddPrepareAll,
+ OverlapAddEvalAll, nullptr, OverlapAddResetAll);
return &r;
}
TFLMRegistration* Register_OVERLAP_ADD_FLOAT() {
- static TFLMRegistration r =
- tflite::micro::RegisterOp(Init<float>, Prepare<float, kTfLiteFloat32>,
- Eval<float>, nullptr, Reset<float>);
+ static TFLMRegistration r = tflite::micro::RegisterOp(
+ OverlapAddInit<float>, OverlapAddPrepare<float, kTfLiteFloat32>,
+ OverlapAddEval<float>, nullptr, OverlapAddReset<float>);
return &r;
}
TFLMRegistration* Register_OVERLAP_ADD_INT16() {
- static TFLMRegistration r =
- tflite::micro::RegisterOp(Init<int16_t>, Prepare<int16_t, kTfLiteInt16>,
- Eval<int16_t>, nullptr, Reset<int16_t>);
+ static TFLMRegistration r = tflite::micro::RegisterOp(
+ OverlapAddInit<int16_t>, OverlapAddPrepare<int16_t, kTfLiteInt16>,
+ OverlapAddEval<int16_t>, nullptr, OverlapAddReset<int16_t>);
return &r;
}
} // namespace tflm_signal
diff --git a/signal/micro/kernels/pcan.cc b/signal/micro/kernels/pcan.cc
new file mode 100644
index 0000000..9473e1b
--- /dev/null
+++ b/signal/micro/kernels/pcan.cc
@@ -0,0 +1,135 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <stddef.h>
+#include <stdint.h>
+
+#include "signal/src/pcan_argc_fixed.h"
+#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/flatbuffer_utils.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/memory_helpers.h"
+#include "tensorflow/lite/micro/micro_context.h"
+
+namespace tflite {
+namespace tflm_signal {
+// TODO(b/286250473): remove namespace once de-duped libraries above
+
+constexpr int kInputTensor = 0;
+constexpr int kNoiseEstimateTensor = 1;
+constexpr int kGainLutTensor = 2;
+constexpr int kOutputTensor = 0;
+
+// Indices into the init flexbuffer's vector.
+// The parameter's name is in the comment that follows.
+// Elements in the vectors are ordered alphabetically by parameter name.
+constexpr int kSnrShiftIndex = 0; // 'snr_shift'
+
+struct TfLitePcanParams {
+ int snr_shift;
+};
+
+void* PcanInit(TfLiteContext* context, const char* buffer, size_t length) {
+ auto* params = static_cast<TfLitePcanParams*>(
+ context->AllocatePersistentBuffer(context, sizeof(TfLitePcanParams)));
+
+ tflite::FlexbufferWrapper fbw(reinterpret_cast<const uint8_t*>(buffer),
+ length);
+ params->snr_shift = fbw.ElementAsInt32(kSnrShiftIndex);
+ return params;
+}
+
+TfLiteStatus PcanPrepare(TfLiteContext* context, TfLiteNode* node) {
+ TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
+ TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
+
+ MicroContext* micro_context = GetMicroContext(context);
+
+ TfLiteTensor* input =
+ micro_context->AllocateTempInputTensor(node, kInputTensor);
+ TF_LITE_ENSURE(context, input != nullptr);
+ TfLiteTensor* noise_estimate =
+ micro_context->AllocateTempInputTensor(node, kNoiseEstimateTensor);
+ TF_LITE_ENSURE(context, noise_estimate != nullptr);
+ TfLiteTensor* gain_lut =
+ micro_context->AllocateTempInputTensor(node, kGainLutTensor);
+ TF_LITE_ENSURE(context, gain_lut != nullptr);
+ TfLiteTensor* output =
+ micro_context->AllocateTempOutputTensor(node, kOutputTensor);
+ TF_LITE_ENSURE(context, output != nullptr);
+
+ TF_LITE_ENSURE_EQ(context, NumDimensions(input), 1);
+ TF_LITE_ENSURE_EQ(context, NumDimensions(noise_estimate), 1);
+ TF_LITE_ENSURE_EQ(context, NumDimensions(gain_lut), 1);
+ TF_LITE_ENSURE_EQ(context, NumDimensions(output), 1);
+
+ TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteUInt32);
+ TF_LITE_ENSURE_TYPES_EQ(context, noise_estimate->type, kTfLiteUInt32);
+ TF_LITE_ENSURE_TYPES_EQ(context, gain_lut->type, kTfLiteInt16);
+ TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteUInt32);
+
+ micro_context->DeallocateTempTfLiteTensor(input);
+ micro_context->DeallocateTempTfLiteTensor(output);
+ micro_context->DeallocateTempTfLiteTensor(noise_estimate);
+ micro_context->DeallocateTempTfLiteTensor(gain_lut);
+ return kTfLiteOk;
+}
+
+TfLiteStatus PcanEval(TfLiteContext* context, TfLiteNode* node) {
+ auto* params = reinterpret_cast<TfLitePcanParams*>(node->user_data);
+
+ const TfLiteEvalTensor* input =
+ tflite::micro::GetEvalInput(context, node, kInputTensor);
+ TF_LITE_ENSURE(context, input != nullptr);
+ const TfLiteEvalTensor* noise_estimate =
+ tflite::micro::GetEvalInput(context, node, kNoiseEstimateTensor);
+ TF_LITE_ENSURE(context, noise_estimate != nullptr);
+ const TfLiteEvalTensor* gain_lut =
+ tflite::micro::GetEvalInput(context, node, kGainLutTensor);
+ TF_LITE_ENSURE(context, gain_lut != nullptr);
+ TfLiteEvalTensor* output =
+ tflite::micro::GetEvalOutput(context, node, kOutputTensor);
+ TF_LITE_ENSURE(context, output != nullptr);
+
+ const uint32_t* input_data = tflite::micro::GetTensorData<uint32_t>(input);
+ const uint32_t* noise_estimate_data =
+ tflite::micro::GetTensorData<uint32_t>(noise_estimate);
+ const int16_t* gain_lut_data =
+ tflite::micro::GetTensorData<int16_t>(gain_lut);
+ uint32_t* output_data = tflite::micro::GetTensorData<uint32_t>(output);
+
+ int num_channels = input->dims->data[0];
+
+ size_t output_byte_size;
+ TF_LITE_ENSURE_OK(
+ context, tflite::TfLiteEvalTensorByteLength(output, &output_byte_size));
+
+ memcpy(output_data, input_data, output_byte_size);
+
+ tflite::tflm_signal::ApplyPcanAutoGainControlFixed(
+ gain_lut_data, params->snr_shift, noise_estimate_data, output_data,
+ num_channels);
+ return kTfLiteOk;
+}
+
+TFLMRegistration* Register_PCAN() {
+ static TFLMRegistration r =
+ tflite::micro::RegisterOp(PcanInit, PcanPrepare, PcanEval);
+ return &r;
+}
+
+} // namespace tflm_signal
+} // namespace tflite
diff --git a/signal/micro/kernels/pcan_flexbuffers_generated_data.cc b/signal/micro/kernels/pcan_flexbuffers_generated_data.cc
new file mode 100644
index 0000000..5fc9a9f
--- /dev/null
+++ b/signal/micro/kernels/pcan_flexbuffers_generated_data.cc
@@ -0,0 +1,7 @@
+#include "signal/micro/kernels/pcan_flexbuffers_generated_data.h"
+
+const int g_gen_data_size_snr_shift_6_test = 20;
+const unsigned char g_gen_data_snr_shift_6_test[] = {
+ 0x73, 0x6e, 0x72, 0x5f, 0x73, 0x68, 0x69, 0x66, 0x74, 0x00,
+ 0x01, 0x0b, 0x01, 0x01, 0x01, 0x06, 0x04, 0x02, 0x24, 0x01,
+};
diff --git a/signal/micro/kernels/pcan_flexbuffers_generated_data.h b/signal/micro/kernels/pcan_flexbuffers_generated_data.h
new file mode 100644
index 0000000..32b4cd7
--- /dev/null
+++ b/signal/micro/kernels/pcan_flexbuffers_generated_data.h
@@ -0,0 +1,7 @@
+#ifndef SIGNAL_MICRO_KERNELS_PCAN_FLEXBUFFERS_GENERATED_DATA_H_
+#define SIGNAL_MICRO_KERNELS_PCAN_FLEXBUFFERS_GENERATED_DATA_H_
+
+extern const int g_gen_data_size_snr_shift_6_test;
+extern const unsigned char g_gen_data_snr_shift_6_test[];
+
+#endif // SIGNAL_MICRO_KERNELS_PCAN_FLEXBUFFERS_GENERATED_DATA_H_
diff --git a/signal/micro/kernels/pcan_test.cc b/signal/micro/kernels/pcan_test.cc
new file mode 100644
index 0000000..aecb5d1
--- /dev/null
+++ b/signal/micro/kernels/pcan_test.cc
@@ -0,0 +1,132 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "signal/micro/kernels/pcan_flexbuffers_generated_data.h"
+#include "tensorflow/lite/micro/kernels/kernel_runner.h"
+#include "tensorflow/lite/micro/test_helpers.h"
+#include "tensorflow/lite/micro/testing/micro_test.h"
+
+namespace tflite {
+namespace tflm_signal {
+namespace {
+
+TfLiteStatus TestPCAN(const unsigned char* init_data, int init_data_size,
+ int* input_dims_data, const uint32_t* input_data,
+ int* noise_estimate_dims_data,
+ const uint32_t* noise_estimate_data,
+ int* gain_lut_dims_data, const int16_t* gain_lut_data,
+ int* output_dims_data, const uint32_t* golden,
+ uint32_t* output_data) {
+ TfLiteIntArray* input_dims =
+ ::tflite::testing::IntArrayFromInts(input_dims_data);
+ TfLiteIntArray* noise_estimate_dims =
+ ::tflite::testing::IntArrayFromInts(noise_estimate_dims_data);
+ TfLiteIntArray* gain_lut_dims =
+ ::tflite::testing::IntArrayFromInts(gain_lut_dims_data);
+ TfLiteIntArray* output_dims =
+ ::tflite::testing::IntArrayFromInts(output_dims_data);
+ const int output_len = ElementCount(*output_dims);
+ constexpr int kInputsSize = 3;
+ constexpr int kOutputsSize = 1;
+ constexpr int kTensorsSize = kInputsSize + kOutputsSize;
+ TfLiteTensor tensors[kTensorsSize] = {
+ tflite::testing::CreateTensor(input_data, input_dims),
+ tflite::testing::CreateTensor(noise_estimate_data, noise_estimate_dims),
+ tflite::testing::CreateTensor(gain_lut_data, gain_lut_dims),
+ tflite::testing::CreateTensor(output_data, output_dims),
+ };
+ int inputs_array_data[] = {3, 0, 1, 2};
+ TfLiteIntArray* inputs_array =
+ ::tflite::testing::IntArrayFromInts(inputs_array_data);
+ int outputs_array_data[] = {1, 3};
+ TfLiteIntArray* outputs_array =
+ ::tflite::testing::IntArrayFromInts(outputs_array_data);
+
+ const TFLMRegistration* registration = tflite::tflm_signal::Register_PCAN();
+ micro::KernelRunner runner(*registration, tensors, kTensorsSize, inputs_array,
+ outputs_array,
+ /*builtin_data=*/nullptr);
+
+ // TfLite uses a char* for the raw bytes whereas flexbuffers use an unsigned
+ // char*. This small discrepancy results in compiler warnings unless we
+ // reinterpret_cast right before passing in the flexbuffer bytes to the
+ // KernelRunner.
+ TfLiteStatus status = runner.InitAndPrepare(
+ reinterpret_cast<const char*>(init_data), init_data_size);
+ if (status != kTfLiteOk) {
+ return status;
+ }
+ status = runner.Invoke();
+ if (status != kTfLiteOk) {
+ return status;
+ }
+ for (int i = 0; i < output_len; ++i) {
+ TF_LITE_MICRO_EXPECT_EQ(golden[i], output_data[i]);
+ }
+ return kTfLiteOk;
+}
+
+} // namespace
+} // namespace tflm_signal
+} // namespace tflite
+
+TF_LITE_MICRO_TESTS_BEGIN
+
+TF_LITE_MICRO_TEST(Mix1Ref1Case) {
+ int input_shape[] = {1, 40};
+ int noise_estimate_shape[] = {1, 40};
+ int gain_lut_shape[] = {1, 125};
+ int output_shape[] = {1, 40};
+ const uint32_t input[] = {286, 298, 305, 291, 290, 279, 273, 257, 250, 240,
+ 240, 233, 234, 230, 221, 205, 183, 159, 156, 188,
+ 239, 298, 345, 374, 380, 369, 359, 364, 372, 354,
+ 302, 243, 194, 135, 64, 72, 171, 245, 277, 304};
+ const uint32_t noise_estimate[] = {
+ 7310, 18308, 7796, 17878, 7413, 17141, 6978, 15789, 6390, 14745,
+ 6135, 14314, 5981, 14130, 5649, 12594, 4677, 9768, 3987, 11550,
+ 6109, 18308, 8819, 22977, 9713, 22670, 9176, 22363, 9509, 21748,
+ 7719, 14929, 4959, 8294, 1636, 4423, 4371, 15052, 7080, 18677};
+
+ const int16_t gain_lut[] = {
+ 32636, 32633, 32630, -6, 0, -21589, 32624, -12, 0, -21589,
+ 32612, -23, -2, -21589, 32587, -48, 0, -21589, 32539, -96,
+ 0, -21589, 32443, -190, 0, -21589, 32253, -378, 4, -21589,
+ 31879, -739, 18, -21589, 31158, -1409, 62, -21589, 29811, -2567,
+ 202, -21589, 27446, -4301, 562, -21589, 23707, -6265, 1230, -21589,
+ 18672, -7458, 1952, -21589, 13166, -7030, 2212, -21589, 8348, -5342,
+ 1868, -21589, 4874, -3459, 1282, -21589, 2697, -2025, 774, -21589,
+ 1446, -1120, 436, -21589, 762, -596, 232, -21589, 398, -313,
+ 122, -21589, 207, -164, 64, -21589, 107, -85, 34, -21589,
+ 56, -45, 18, -21589, 29, -22, 8, -21589, 15, -13,
+ 6, -21589, 8, -8, 4, -21589, 4, -2, 0, -21589,
+ 2, -3, 2, -21589, 1, 0, 0, -21589, 1, -3,
+ 2, -21589, 0, 0, 0};
+
+ uint32_t output[40];
+ const uint32_t golden[] = {1301, 836, 1354, 827, 1312, 811, 1263, 779,
+ 1192, 753, 1160, 743, 1140, 738, 1096, 698,
+ 956, 607, 845, 667, 1157, 836, 1461, 912,
+ 1546, 908, 1496, 904, 1527, 895, 1346, 758,
+ 999, 548, 378, 344, 908, 761, 1274, 843};
+ memset(output, 0, sizeof(output));
+ TF_LITE_MICRO_EXPECT_EQ(
+ kTfLiteOk,
+ tflite::tflm_signal::TestPCAN(
+ g_gen_data_snr_shift_6_test, g_gen_data_size_snr_shift_6_test,
+ input_shape, input, noise_estimate_shape, noise_estimate,
+ gain_lut_shape, gain_lut, output_shape, golden, output));
+}
+
+TF_LITE_MICRO_TESTS_END
diff --git a/signal/micro/kernels/rfft.cc b/signal/micro/kernels/rfft.cc
index fccc6eb..c9472b0 100644
--- a/signal/micro/kernels/rfft.cc
+++ b/signal/micro/kernels/rfft.cc
@@ -48,12 +48,13 @@
int32_t output_length;
TfLiteType fft_type;
T* work_area;
+ int scratch_buffer_index;
int8_t* state;
};
template <typename T, size_t (*get_needed_memory_func)(int32_t),
void* (*init_func)(int32_t, void*, size_t)>
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* RfftInit(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
const uint8_t* buffer_t = reinterpret_cast<const uint8_t*>(buffer);
@@ -65,9 +66,6 @@
params->fft_length = fbw.ElementAsInt32(kFftLengthIndex);
params->fft_type = typeToTfLiteType<T>();
- params->work_area = static_cast<T*>(context->AllocatePersistentBuffer(
- context, params->fft_length * sizeof(T)));
-
size_t state_size = (*get_needed_memory_func)(params->fft_length);
params->state = static_cast<int8_t*>(
context->AllocatePersistentBuffer(context, state_size * sizeof(int8_t)));
@@ -76,7 +74,7 @@
}
template <typename T, TfLiteType TfLiteTypeEnum>
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus RfftPrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
@@ -103,13 +101,15 @@
params->output_length =
output_shape.Dims(output_shape.DimensionsCount() - 1) / 2;
+ context->RequestScratchBufferInArena(context, params->fft_length * sizeof(T),
+ ¶ms->scratch_buffer_index);
micro_context->DeallocateTempTfLiteTensor(input);
micro_context->DeallocateTempTfLiteTensor(output);
return kTfLiteOk;
}
template <typename T, void (*apply_func)(void*, const T* input, Complex<T>*)>
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus RfftEval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteAudioFrontendRfftParams<T>*>(node->user_data);
@@ -122,74 +122,76 @@
tflite::micro::GetEvalOutput(context, node, kOutputTensor);
Complex<T>* output_data = tflite::micro::GetTensorData<Complex<T>>(output);
+ T* work_area = static_cast<T*>(
+ context->GetScratchBuffer(context, params->scratch_buffer_index));
+
for (int input_idx = 0, output_idx = 0; input_idx < params->input_size;
input_idx += params->input_length, output_idx += params->output_length) {
- memcpy(params->work_area, &input_data[input_idx],
- sizeof(T) * params->input_length);
+ memcpy(work_area, &input_data[input_idx], sizeof(T) * params->input_length);
// Zero pad input to FFT length
- memset(¶ms->work_area[params->input_length], 0,
+ memset(&work_area[params->input_length], 0,
sizeof(T) * (params->fft_length - params->input_length));
- (*apply_func)(params->state, params->work_area, &output_data[output_idx]);
+ (*apply_func)(params->state, work_area, &output_data[output_idx]);
}
return kTfLiteOk;
}
-void* InitAll(TfLiteContext* context, const char* buffer, size_t length) {
+void* RfftInitAll(TfLiteContext* context, const char* buffer, size_t length) {
const uint8_t* buffer_t = reinterpret_cast<const uint8_t*>(buffer);
const flexbuffers::Map& m = flexbuffers::GetRoot(buffer_t, length).AsMap();
auto tensor_type = static_cast<tflite::TensorType>(m["T"].AsInt32());
switch (tensor_type) {
case TensorType_INT16: {
- return Init<int16_t, ::tflm_signal::RfftInt16GetNeededMemory,
- ::tflm_signal::RfftInt16Init>(context, buffer, length);
+ return RfftInit<int16_t, ::tflm_signal::RfftInt16GetNeededMemory,
+ ::tflm_signal::RfftInt16Init>(context, buffer, length);
}
case TensorType_INT32: {
- return Init<int32_t, ::tflm_signal::RfftInt32GetNeededMemory,
- ::tflm_signal::RfftInt32Init>(context, buffer, length);
+ return RfftInit<int32_t, ::tflm_signal::RfftInt32GetNeededMemory,
+ ::tflm_signal::RfftInt32Init>(context, buffer, length);
}
case TensorType_FLOAT32: {
- return Init<float, ::tflm_signal::RfftFloatGetNeededMemory,
- ::tflm_signal::RfftFloatInit>(context, buffer, length);
+ return RfftInit<float, ::tflm_signal::RfftFloatGetNeededMemory,
+ ::tflm_signal::RfftFloatInit>(context, buffer, length);
}
default:
return nullptr;
}
}
-TfLiteStatus PrepareAll(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus RfftPrepareAll(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteAudioFrontendRfftParams<void>*>(node->user_data);
switch (params->fft_type) {
case kTfLiteInt16: {
- return Prepare<int16_t, kTfLiteInt16>(context, node);
+ return RfftPrepare<int16_t, kTfLiteInt16>(context, node);
}
case kTfLiteInt32: {
- return Prepare<int32_t, kTfLiteInt32>(context, node);
+ return RfftPrepare<int32_t, kTfLiteInt32>(context, node);
}
case kTfLiteFloat32: {
- return Prepare<float, kTfLiteFloat32>(context, node);
+ return RfftPrepare<float, kTfLiteFloat32>(context, node);
}
default:
return kTfLiteError;
}
}
-TfLiteStatus EvalAll(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus RfftEvalAll(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteAudioFrontendRfftParams<void>*>(node->user_data);
switch (params->fft_type) {
case kTfLiteInt16: {
- return Eval<int16_t, ::tflm_signal::RfftInt16Apply>(context, node);
+ return RfftEval<int16_t, ::tflm_signal::RfftInt16Apply>(context, node);
}
case kTfLiteInt32: {
- return Eval<int32_t, ::tflm_signal::RfftInt32Apply>(context, node);
+ return RfftEval<int32_t, ::tflm_signal::RfftInt32Apply>(context, node);
}
case kTfLiteFloat32: {
- return Eval<float, ::tflm_signal::RfftFloatApply>(context, node);
+ return RfftEval<float, ::tflm_signal::RfftFloatApply>(context, node);
}
default:
return kTfLiteError;
@@ -202,34 +204,34 @@
TFLMRegistration* Register_RFFT() {
static TFLMRegistration r =
- tflite::micro::RegisterOp(InitAll, PrepareAll, EvalAll);
+ tflite::micro::RegisterOp(RfftInitAll, RfftPrepareAll, RfftEvalAll);
return &r;
}
TFLMRegistration* Register_RFFT_FLOAT() {
static TFLMRegistration r = tflite::micro::RegisterOp(
- Init<float, ::tflm_signal::RfftFloatGetNeededMemory,
- ::tflm_signal::RfftFloatInit>,
- Prepare<float, kTfLiteFloat32>,
- Eval<float, ::tflm_signal::RfftFloatApply>);
+ RfftInit<float, ::tflm_signal::RfftFloatGetNeededMemory,
+ ::tflm_signal::RfftFloatInit>,
+ RfftPrepare<float, kTfLiteFloat32>,
+ RfftEval<float, ::tflm_signal::RfftFloatApply>);
return &r;
}
TFLMRegistration* Register_RFFT_INT16() {
static TFLMRegistration r = tflite::micro::RegisterOp(
- Init<int16_t, ::tflm_signal::RfftInt16GetNeededMemory,
- ::tflm_signal::RfftInt16Init>,
- Prepare<int16_t, kTfLiteInt16>,
- Eval<int16_t, ::tflm_signal::RfftInt16Apply>);
+ RfftInit<int16_t, ::tflm_signal::RfftInt16GetNeededMemory,
+ ::tflm_signal::RfftInt16Init>,
+ RfftPrepare<int16_t, kTfLiteInt16>,
+ RfftEval<int16_t, ::tflm_signal::RfftInt16Apply>);
return &r;
}
TFLMRegistration* Register_RFFT_INT32() {
static TFLMRegistration r = tflite::micro::RegisterOp(
- Init<int32_t, ::tflm_signal::RfftInt32GetNeededMemory,
- ::tflm_signal::RfftInt32Init>,
- Prepare<int32_t, kTfLiteInt32>,
- Eval<int32_t, ::tflm_signal::RfftInt32Apply>);
+ RfftInit<int32_t, ::tflm_signal::RfftInt32GetNeededMemory,
+ ::tflm_signal::RfftInt32Init>,
+ RfftPrepare<int32_t, kTfLiteInt32>,
+ RfftEval<int32_t, ::tflm_signal::RfftInt32Apply>);
return &r;
}
diff --git a/signal/micro/kernels/stacker.cc b/signal/micro/kernels/stacker.cc
index 42a2ee6..fc1a4a3 100644
--- a/signal/micro/kernels/stacker.cc
+++ b/signal/micro/kernels/stacker.cc
@@ -52,7 +52,7 @@
tflm_signal::CircularBuffer* circular_buffer;
};
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* StackerInit(TfLiteContext* context, const char* buffer, size_t length) {
const uint8_t* buffer_t = reinterpret_cast<const uint8_t*>(buffer);
auto* params =
@@ -88,7 +88,7 @@
return params;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus StackerPrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 2);
@@ -118,7 +118,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus StackerEval(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TFLMSignalStackerParams*>(node->user_data);
TF_LITE_ENSURE(context, params != nullptr);
@@ -157,7 +157,7 @@
return kTfLiteOk;
}
-void Reset(TfLiteContext* context, void* buffer) {
+void StackerReset(TfLiteContext* context, void* buffer) {
auto* params = static_cast<TFLMSignalStackerParams*>(buffer);
tflm_signal::CircularBufferReset(params->circular_buffer);
params->stacker_has_first_frame = false;
@@ -167,8 +167,8 @@
namespace tflm_signal {
TFLMRegistration* Register_STACKER() {
- static TFLMRegistration r =
- tflite::micro::RegisterOp(Init, Prepare, Eval, /*Free*/ nullptr, Reset);
+ static TFLMRegistration r = tflite::micro::RegisterOp(
+ StackerInit, StackerPrepare, StackerEval, /*Free*/ nullptr, StackerReset);
return &r;
}
} // namespace tflm_signal
diff --git a/signal/micro/kernels/window.cc b/signal/micro/kernels/window.cc
index e850898..cd9c462 100644
--- a/signal/micro/kernels/window.cc
+++ b/signal/micro/kernels/window.cc
@@ -41,7 +41,7 @@
int32_t input_size;
};
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* WindowInit(TfLiteContext* context, const char* buffer, size_t length) {
const uint8_t* buffer_t = reinterpret_cast<const uint8_t*>(buffer);
auto* params =
@@ -53,7 +53,7 @@
return params;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus WindowPrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
@@ -87,7 +87,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus WindowEval(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TFLMSignalWindowParams*>(node->user_data);
const TfLiteEvalTensor* input =
@@ -114,7 +114,8 @@
namespace tflm_signal {
TFLMRegistration* Register_WINDOW() {
- static TFLMRegistration r = tflite::micro::RegisterOp(Init, Prepare, Eval);
+ static TFLMRegistration r =
+ tflite::micro::RegisterOp(WindowInit, WindowPrepare, WindowEval);
return &r;
}
diff --git a/signal/micro/kernels/xtensa/fft_auto_scale_kernel.cc b/signal/micro/kernels/xtensa/fft_auto_scale_kernel.cc
new file mode 100644
index 0000000..fbd739b
--- /dev/null
+++ b/signal/micro/kernels/xtensa/fft_auto_scale_kernel.cc
@@ -0,0 +1,103 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "signal/micro/kernels/fft_auto_scale_kernel.h"
+
+#include <math.h>
+#include <stddef.h>
+#include <stdint.h>
+
+#include "signal/src/fft_auto_scale.h"
+#include "signal/src/max_abs.h"
+#include "signal/src/msb.h"
+#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/micro_context.h"
+
+#if XCHAL_HAVE_HIFI3
+#include <xtensa/tie/xt_hifi3.h>
+namespace {
+// Implementation for DSPs that support the Hifi3 ISA. Bit exact with the
+// portable version below.
+int XtensaFftAutoScale(const int16_t* input, int size, int16_t* output) {
+ const int16_t max = tflite::tflm_signal::MaxAbs16(input, size);
+ int scale_bits = (sizeof(int16_t) * 8) -
+ tflite::tflm_signal::MostSignificantBit32(max) - 1;
+ int i;
+ if (scale_bits > 0) {
+ const ae_int16x4* input_16x4_ptr =
+ reinterpret_cast<const ae_int16x4*>(input);
+ ae_int16x4* output_16x4_ptr = reinterpret_cast<ae_int16x4*>(output);
+ const int num_iterations = ((size + 3) >> 2);
+ for (i = 0; i < num_iterations; ++i) {
+ ae_int16x4 input_16x4;
+ AE_L16X4_IP(input_16x4, input_16x4_ptr, 8);
+ ae_f16x4 input_f16x4 = *reinterpret_cast<ae_f16x4*>(&input_16x4);
+ input_f16x4 = AE_SLAA16S(input_f16x4, scale_bits);
+ input_16x4 = *reinterpret_cast<ae_int16x4*>(&input_f16x4);
+ AE_S16X4_IP(input_16x4, output_16x4_ptr, 8);
+ }
+ } else {
+ memcpy(output, input, size * sizeof(output[0]));
+ scale_bits = 0;
+ }
+ return scale_bits;
+}
+} // namespace
+#endif
+
+namespace tflite {
+namespace {
+
+constexpr int kInputTensor = 0;
+constexpr int kOutputTensor = 0;
+constexpr int kScaleBitTensor = 1;
+
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+ const TfLiteEvalTensor* input =
+ tflite::micro::GetEvalInput(context, node, kInputTensor);
+ TfLiteEvalTensor* output =
+ tflite::micro::GetEvalOutput(context, node, kOutputTensor);
+ TfLiteEvalTensor* scale_bit =
+ tflite::micro::GetEvalOutput(context, node, kScaleBitTensor);
+
+ const int16_t* input_data = tflite::micro::GetTensorData<int16_t>(input);
+ int16_t* output_data = tflite::micro::GetTensorData<int16_t>(output);
+ int32_t* scale_bit_data = tflite::micro::GetTensorData<int32_t>(scale_bit);
+
+#if XCHAL_HAVE_HIFI3
+ *scale_bit_data =
+ XtensaFftAutoScale(input_data, output->dims->data[0], output_data);
+#else
+ *scale_bit_data =
+ tflm_signal::FftAutoScale(input_data, output->dims->data[0], output_data);
+#endif
+ return kTfLiteOk;
+}
+
+} // namespace
+
+// TODO(b/286250473): remove namespace once de-duped libraries
+namespace tflm_signal {
+
+TFLMRegistration* Register_FFT_AUTO_SCALE() {
+ static TFLMRegistration r =
+ tflite::micro::RegisterOp(nullptr, FftAutoScalePrepare, Eval);
+ return &r;
+}
+
+} // namespace tflm_signal
+} // namespace tflite
diff --git a/signal/micro/kernels/xtensa/filter_bank_square_root.cc b/signal/micro/kernels/xtensa/filter_bank_square_root.cc
new file mode 100644
index 0000000..60e4119
--- /dev/null
+++ b/signal/micro/kernels/xtensa/filter_bank_square_root.cc
@@ -0,0 +1,72 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "signal/micro/kernels/filter_bank_square_root.h"
+
+#include <stdint.h>
+
+#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/memory_helpers.h"
+#include "tensorflow/lite/micro/micro_utils.h"
+
+// Defined in square_root.S
+extern "C" uint32_t xtensa_sqrt_64(const uint64_t num);
+
+namespace tflite {
+namespace {
+
+constexpr int kInputTensor = 0;
+constexpr int kScaleBitsTensor = 1;
+constexpr int kOutputTensor = 0;
+
+void ApplyFilterbankSqrt(const uint64_t* input, int num_channels,
+ int scale_down_bits, uint32_t* output) {
+ for (int i = 0; i < num_channels; ++i) {
+ output[i] = xtensa_sqrt_64(input[i]) >> scale_down_bits;
+ }
+}
+
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+ const TfLiteEvalTensor* input =
+ tflite::micro::GetEvalInput(context, node, kInputTensor);
+ const TfLiteEvalTensor* scale_bits =
+ tflite::micro::GetEvalInput(context, node, kScaleBitsTensor);
+ TfLiteEvalTensor* output =
+ tflite::micro::GetEvalOutput(context, node, kOutputTensor);
+
+ const uint64_t* input_data = tflite::micro::GetTensorData<uint64_t>(input);
+ const int32_t* scale_bits_data =
+ tflite::micro::GetTensorData<int32_t>(scale_bits);
+ uint32_t* output_data = tflite::micro::GetTensorData<uint32_t>(output);
+ int32_t num_channels = input->dims->data[0];
+ ApplyFilterbankSqrt(input_data, num_channels, *scale_bits_data, output_data);
+ return kTfLiteOk;
+}
+
+} // namespace
+
+namespace tflm_signal {
+
+TFLMRegistration* Register_FILTER_BANK_SQUARE_ROOT() {
+ static TFLMRegistration r =
+ tflite::micro::RegisterOp(nullptr, FilterBankSquareRootPrepare, Eval);
+ return &r;
+}
+
+} // namespace tflm_signal
+
+} // namespace tflite
diff --git a/signal/micro/kernels/xtensa/xtensa_square_root.S b/signal/micro/kernels/xtensa/xtensa_square_root.S
new file mode 100644
index 0000000..054d72f
--- /dev/null
+++ b/signal/micro/kernels/xtensa/xtensa_square_root.S
@@ -0,0 +1,400 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+.section .note.GNU-stack,"",@progbits
+
+#include "xtensa/config/core-isa.h"
+
+#ifdef __XTENSA_CALL0_ABI__
+#define NO_REGISTER_WINDOW (1)
+#endif
+
+#if XCHAL_HAVE_WINDOWED == 0
+#define NO_REGISTER_WINDOW
+#endif
+
+// Since the 64 bit sqrt jumps into the middle of the 32 bit sqrt under certain
+// conditions, both functions should reserve the same amount of stack space.
+#define XTENSA_SQRT_STACK_SIZE 32
+
+.text
+.type xtensa_sqrt_64, @function
+.align 4
+.global xtensa_sqrt_64
+
+// Make macros for our 64 bit functions, since we don't have a carry/borrow bit
+// in the base ISA, these take up way more cycles than they should. These are
+// the "preferred instruction idioms" from 8.9.2 of the base ISA manual. Since
+// these macros define a jump (and I couldn't find a way to be clever and use
+// something like __LINE__/__FILE__ to define these automatically, you may also
+// have to provide an 'opname' that contains a unique string to define a label
+// for the macro.
+
+// dest must not be the same as num2, or this function will not work!
+#define ADD_64(dest, num1, num2, opname) \
+ add.n dest##_low, num1##_low, num2##_low; \
+ add.n dest##_high, num1##_high, num2##_high; \
+ bgeu dest##_low, num2##_low, .add_64_jump_##opname; \
+ addi.n dest##_high, dest##_high, 1; \
+ .add_64_jump_##opname:
+
+// All three registers must be unique, or this function will not work!
+#define SUB_64(dest, num1, num2, opname) \
+ sub dest##_low, num1##_low, num2##_low; \
+ sub dest##_high, num1##_high, num2##_high; \
+ bgeu num1##_low, num2##_low, .sub_64_jump_##opname; \
+ addi.n dest##_high, dest##_high, -1; \
+ .sub_64_jump_##opname:
+
+#define SRLI_64(dest, val, imm) \
+ slli scratch4, val##_high, (32 - imm); \
+ srli dest##_high, val##_high, imm; \
+ srli dest##_low, val##_low, imm; \
+ or dest##_low, dest##_low, scratch4;
+
+#define COND_MOV_64(op, dest, val, test) \
+ mov##op dest##_low, val##_low, test; \
+ mov##op dest##_high, val##_high, test
+
+#define num_low a2
+#define num_high a3
+#define bit_low a4
+#define bit_high a5
+#define res_low a6
+#define res_high a7
+#define temp1_low a8
+#define temp1_high a9
+#define temp2_low a10
+#define temp2_high a11
+#define scratch1 a12
+#define scratch2 a13
+#define scratch3 a14
+#define scratch4 a15
+#define temp3_low scratch1
+#define temp3_high scratch2
+
+.align 4
+xtensa_sqrt_64:
+#ifdef NO_REGISTER_WINDOW
+addi.n a1, a1, -XTENSA_SQRT_STACK_SIZE
+s32i.n a0, a1, 4
+s32i.n a11, a1, 8
+s32i.n a12, a1, 12
+s32i.n a13, a1, 16
+s32i.n a14, a1, 20
+s32i.n a15, a1, 24
+#else
+entry a1, XTENSA_SQRT_STACK_SIZE
+#endif
+// In the event that the upper word of the number is all zero, we can just
+// pretend that we're doing a 32 bit sqrt (but the rounding condition at the
+// end is slightly different, so we've got a bit of an anomly there. Such is
+// life)
+beqz.n num_high, .xtensa_sqrt_32_start
+// ** uint64 res= 0;
+movi.n res_low, 0
+movi.n res_high, 0
+
+movi.n scratch2, 1
+
+// Setup 'bit' - first we need to know what bit to set it to.
+// ** int max_bit_number = 64 - MostSignificantBit_64(num);
+movi.n bit_low, 0
+nsau scratch1, num_high
+
+// ** max_bit_number |= 1;
+or scratch1, scratch2, scratch1
+
+// The ammount we shift by is 31 - what's in scratch1 for the max bit number.
+// This is because we've got the two words, so we can't do a 64 bit shift.
+movi.n scratch3, 31
+sub scratch1, scratch3, scratch1
+
+// Do the shift
+// ** uint32 bit = 1 << (63 - max_bit_number);
+ssl scratch1
+sll bit_high, scratch2
+
+// Figure out how many iterations we're going to need. However, we already have
+// 31 - max_bit_number in scratch1, so just add 32 to that.
+// ** int iterations = (63 - max_bit_number) / 2 + 1;
+addi.n scratch1, scratch1, 32
+srli scratch1, scratch1, 1
+add scratch1, scratch1, scratch2
+
+// If the number of iterations is equal to 32, this means that we're likely in
+// an overflow spot if we try and do a subtraction (since the upper most bit is
+// going to be set since the bit had to be shifted up so high). We have to do
+// one iteration of the loop where we use the pipeline destroying branch call
+// that can compare two unsigned numbers. If we need less than 32 iterations,
+// we can skip this slow path and jump to the tight inner loop.
+blti scratch1, 32, .xtensa_sqrt_64_inner_loop_start
+
+// Cache bit + res.
+ADD_64(temp1, bit, res, temp1_bit_res)
+// Since we've stored a copy of bit + res, we can right shift res (since both
+// branches of the conditional are going to need it, one branch just needs to
+// perform an extra addition).
+// ** res <<= 1;
+SRLI_64(res, res, 1);
+
+// ** if (num >= res_plus_bit) {
+bltu num_high, temp1_high, .xtensa_sqrt_64_branch_skip
+bne num_high, temp1_high, .xtensa_sqrt_64_comparison_failed
+bltu num_low, temp1_low, .xtensa_sqrt_64_branch_skip
+.xtensa_sqrt_64_comparison_failed:
+
+// ** num -= res + bit;
+SUB_64(temp2, num, temp1, temp2_num_temp1_early_branch)
+// Since the sub can't use the same registers, we have to move it back to where
+// it belongs.
+mov.n num_low, temp2_low
+mov.n num_high, temp2_high
+// ** res += bit;
+ADD_64(res, res, bit, res_res_bit_early_branch)
+// ** }
+.xtensa_sqrt_64_branch_skip:
+
+// ** bit >>= 2;
+SRLI_64(bit, bit, 2)
+// Make sure we knock off this iteration when we fall into the inner loop.
+sub scratch1, scratch1, scratch2
+
+.xtensa_sqrt_64_inner_loop_start:
+loop scratch1, .xtensa_sqrt_64_round
+
+// We don't have enough registers to be as verbose as the 32 bit version, so
+// this version is not as easy to read. Instead of having the two operations in
+// the same style of conditional move, we sort of decide to do both branches at
+// the same time of the if, then fix up what was incorrect at the end.
+SRLI_64(temp1, res, 1)
+ADD_64(res, res, bit, res_res_bit)
+
+SUB_64(temp2, num, res, num_res_temp2)
+ADD_64(res, temp1, bit, res_temp1_bit)
+
+COND_MOV_64(gez, num, temp2, temp2_high)
+COND_MOV_64(ltz, res, temp1, temp2_high)
+
+// ** bit >>= 2;
+SRLI_64(bit, bit, 2)
+
+.xtensa_sqrt_64_round:
+
+// Need to do if (num > res) { ++res; }, but we'll do it with conditional moves
+// again. Except we're going to do it slightly backwards, since we need to move
+// the result into the num register to be returned. We'll do this by setting
+// the return value to res + 1, but in the event that it was a mistake, we'll
+// conditionally move the raw result back into place.
+SUB_64(temp1, res, num, res_num_temp1)
+addi.n num_low, res_low, 1
+movgez num_low, res_low, temp1_high
+
+// But we may have overflowed num_low - set it back to result_low if it's been
+// zeroed out.
+moveqz num_low, res_low, num_low
+
+#ifdef NO_REGISTER_WINDOW
+l32i.n a0, a1, 4
+l32i.n a11, a1, 8
+l32i.n a12, a1, 12
+l32i.n a13, a1, 16
+l32i.n a14, a1, 20
+l32i.n a15, a1, 24
+addi a1, a1, XTENSA_SQRT_STACK_SIZE
+ret.n
+#else
+retw.n
+#endif
+.xtensa_sqrt_64_end:
+ .size xtensa_sqrt_64, . - xtensa_sqrt_64
+
+
+#undef ADD_64
+#undef SUB_64
+#undef SRLI_64
+#undef COND_MOV_64
+
+#undef num_low
+#undef num_high
+#undef bit_low
+#undef bit_high
+#undef res_low
+#undef res_high
+#undef temp1_low
+#undef temp1_high
+#undef temp2_low
+#undef temp2_high
+#undef scratch1
+#undef scratch2
+#undef scratch3
+#undef scratch4
+#undef temp3_low
+#undef temp3_high
+.text
+.type xtensa_sqrt_32, @function
+.align 4
+.global xtensa_sqrt_32
+
+// Make the program more readable...
+#define num a2
+#define bit a4
+#define res a5
+#define one a6
+#define max_bit_number a7
+#define iterations max_bit_number
+#define bit_plus_res a8
+#define num_minus_bit_plus_res a9
+#define res_shift_left_plus_bit a10
+#define res_minus_num res_shift_left_plus_bit
+
+xtensa_sqrt_32:
+#ifdef NO_REGISTER_WINDOW
+addi.n a1, a1, -XTENSA_SQRT_STACK_SIZE
+s32i.n a0, a1, 4
+s32i.n a11, a1, 8
+s32i.n a12, a1, 12
+s32i.n a13, a1, 16
+s32i.n a14, a1, 20
+s32i.n a15, a1, 24
+#else
+entry a1, XTENSA_SQRT_STACK_SIZE
+#endif
+
+.xtensa_sqrt_32_start:
+// If the number is zero, just quickly exit without doing anything.
+beqz.n num, .xtensa_sqrt_32_return
+
+// ** uint32 res = 0;
+movi.n res, 0
+// Also, setup the handy constant we need a few times.
+movi.n one, 1
+
+// This will give us (32 - index of the first bit that is set).
+// ** int max_bit_number = 32 - MostSignificantBit_32(num);
+nsau max_bit_number, num
+
+// ** max_bit_number |= one;
+or max_bit_number, max_bit_number, one
+
+// The ammount we shift by is 31 - what we stored in max_bit_number.
+movi.n a15, 31
+sub max_bit_number, a15, max_bit_number
+
+// Do the shift.
+// ** uint32 bit = 1 << (31 - max_bit_number);
+ssl max_bit_number
+sll bit, one
+
+// Compute the number of iterations we're going to need.
+// ** int iterations = (31 - max_bit_number) / 2 + 1;
+srli iterations, max_bit_number, 1
+add iterations, iterations, one
+
+// If the number of iterations is equal to 16, this means that we're likely in
+// an overflow spot if we try and do a subtraction (since the upper most bit is
+// going to be set since the bit had to be shifted up so high). We have to do
+// one iteration of the loop where we use the pipeline destroying branch call
+// that can compare two unsigned numbers. If we need less than 16 iterations,
+// we can skip this slow path and jump to the tight inner loop.
+blti iterations, 16, .xtensa_sqrt_32_inner_loop_start
+
+// Cache bit + res into another register.
+add.n bit_plus_res, bit, res
+// Since we've stored a copy of bit + res, we can right shift res (since both
+// branches of the conditional are going to need it, one branch just needs to
+// perform an extra addition).
+// ** res <<= 1;
+srli res, res, 1
+// ** if (num >= res_plus_bit) {
+bltu num, bit_plus_res, .xtensa_sqrt_32_branch_skip
+// ** num -= res + bit;
+sub num, num, bit_plus_res
+// ** res += bit;
+add res, res, bit
+// ** }
+.xtensa_sqrt_32_branch_skip:
+
+// ** bit >>= 2;
+srli bit, bit, 2
+// Make sure we knock off this iteration when we fall into the inner loop.
+sub iterations, iterations, one
+
+.xtensa_sqrt_32_inner_loop_start:
+// Start a zero overhead loop for the number of remaining iterations.
+loop iterations, .xtensa_sqrt_32_round
+
+// Cache bit + res into another register.
+add.n bit_plus_res, bit, res
+// ** res <<= 1;
+srli res, res, 1
+
+// We can dodge a hefty branch penalty by doing conditional moves - so we need
+// to compute the values for num and res for what would happen if we took the
+// if part of the condition. If the condition is true, then we'll copy stuff
+// across.
+
+// compute num - bit_plus_res. We can use this for the conditional check
+// against zero.
+sub num_minus_bit_plus_res, num, bit_plus_res
+// compute the shifted res + bit.
+add res_shift_left_plus_bit, res, bit
+
+// Copy stuff if the condition is true.
+movgez num, num_minus_bit_plus_res, num_minus_bit_plus_res
+movgez res, res_shift_left_plus_bit, num_minus_bit_plus_res
+
+// ** bit >>= 2;
+srli bit, bit, 2
+
+.xtensa_sqrt_32_round:
+
+// Need to do if (num > res) { ++res; }, but we'll do it with conditional moves
+// again. Except we're going to do it slightly backwards, since we need to move
+// the result into the num register to be returned. We'll do this by setting
+// the return value to res + 1, but in the event that it was a mistake, we'll
+// conditionally move the raw result back into place.
+sub res_minus_num, res, num
+add.n num, res, one
+movgez num, res, res_minus_num
+
+// But we might have also pooched the rounding by adding an extra bit, make sure
+// we don't explode when we overflow.
+clamps num, num, 16
+
+.xtensa_sqrt_32_return:
+#ifdef NO_REGISTER_WINDOW
+l32i.n a0, a1, 4
+l32i.n a11, a1, 8
+l32i.n a12, a1, 12
+l32i.n a13, a1, 16
+l32i.n a14, a1, 20
+l32i.n a15, a1, 24
+addi a1, a1, XTENSA_SQRT_STACK_SIZE
+ret.n
+#else
+retw.n
+#endif
+
+#undef num
+#undef bit
+#undef res
+#undef one
+#undef max_bit_number
+#undef iterations
+#undef bit_plus_res
+#undef num_minus_bit_plus_res
+#undef res_shift_left_plus_bit
+#undef res_minus_num
diff --git a/signal/src/BUILD b/signal/src/BUILD
index c09f608..e5c1060 100644
--- a/signal/src/BUILD
+++ b/signal/src/BUILD
@@ -142,3 +142,13 @@
":square_root_64",
],
)
+
+cc_library(
+ name = "pcan_argc_fixed",
+ srcs = ["pcan_argc_fixed.cc"],
+ hdrs = ["pcan_argc_fixed.h"],
+ deps = [
+ ":msb_32",
+ "//tensorflow/lite/kernels/internal:compatibility",
+ ],
+)
diff --git a/signal/src/max_abs.cc b/signal/src/max_abs.cc
index 365938d..0ad117a 100644
--- a/signal/src/max_abs.cc
+++ b/signal/src/max_abs.cc
@@ -15,11 +15,58 @@
#include "signal/src/max_abs.h"
+#if defined(XTENSA)
+#include <stdint.h>
+#include <xtensa/tie/xt_misc.h>
+#endif
+#if XCHAL_HAVE_HIFI3
+#include <xtensa/tie/xt_hifi3.h>
+static inline ae_p24x2s MaxAbs16Single(ae_p24x2s max, ae_p24x2s current) {
+ return AE_MAXABSSP24S(max, current);
+}
+#elif XCHAL_HAVE_HIFI_MINI || XCHAL_HAVE_HIFI2 || XCHAL_HAVE_HIFI_EP
+#include <xtensa/tie/xt_hifi2.h>
+static inline ae_p24x2s MaxAbs16Single(ae_p24x2s max, ae_p24x2s current) {
+ current = AE_ABSSP24S(current);
+ return AE_MAXP24S(max, current);
+}
+#endif
+
// TODO(b/286250473): remove namespace once de-duped libraries
namespace tflite {
namespace tflm_signal {
+#if XCHAL_HAVE_HIFI_MINI || XCHAL_HAVE_HIFI2 || XCHAL_HAVE_HIFI_EP || \
+ XCHAL_HAVE_HIFI3
+int16_t XtensaMaxAbs16(const int16_t* input, int size) {
+ int i;
+ ae_p24x2s current_24x2;
+ // AE_LP16X2F_IU() effectively pre-increments the address in input_16x2 by 4
+ // bytes before loading, so we need to initialize it accordingly.
+ const ae_p16x2s* input_16x2 = (const ae_p16x2s*)(input - 2);
+ ae_p24x2s max = AE_ZEROP48();
+ const int num_iterations = size / 2;
+ for (i = 0; i < num_iterations; i++) {
+ // Advancing the pointer by 2 X 16-bits.
+ AE_LP16X2F_IU(current_24x2, input_16x2, 4);
+ max = MaxAbs16Single(max, current_24x2);
+ }
+ if (size & 1) { // n is odd
+ // Advancing the pointer by 2 X 16-bits.
+ current_24x2 = AE_LP16F_I((ae_p16s*)input_16x2, 4);
+ max = MaxAbs16Single(max, current_24x2);
+ }
+ const int max_L = AE_TRUNCA16P24S_L(max);
+ const int max_H = AE_TRUNCA16P24S_H(max);
+ return (max_L >= max_H) ? max_L : max_H;
+}
+#endif
+
int16_t MaxAbs16(const int16_t* input, int size) {
+#if XCHAL_HAVE_HIFI_MINI || XCHAL_HAVE_HIFI2 || XCHAL_HAVE_HIFI_EP || \
+ XCHAL_HAVE_HIFI3
+ return XtensaMaxAbs16(input, size);
+#else
int16_t max = 0;
for (int i = 0; i < size; i++) {
const int16_t value = input[i];
@@ -30,6 +77,8 @@
}
}
return max;
+#endif
}
+
} // namespace tflm_signal
} // namespace tflite
diff --git a/signal/src/msb_32.cc b/signal/src/msb_32.cc
index 6f12dc4..67f2664 100644
--- a/signal/src/msb_32.cc
+++ b/signal/src/msb_32.cc
@@ -15,13 +15,21 @@
#include "signal/src/msb.h"
+#if defined(XTENSA)
+#include <xtensa/tie/xt_misc.h>
+#endif
+
namespace tflite {
namespace tflm_signal {
// TODO(b/286250473): remove namespace once de-duped libraries above
// TODO(b/291167350): can allow __builtin_clz to be used in more cases here
uint32_t MostSignificantBit32(uint32_t x) {
-#if defined(__GNUC__)
+#if defined(XTENSA)
+ // XT_NSAU returns the number of left shifts needed to put the MSB in the
+ // leftmost position. Returns 32 if the argument is 0.
+ return 32 - XT_NSAU(x);
+#elif defined(__GNUC__)
if (x) {
return 32 - __builtin_clz(x);
}
diff --git a/signal/src/msb_64.cc b/signal/src/msb_64.cc
index 233985c..7416438 100644
--- a/signal/src/msb_64.cc
+++ b/signal/src/msb_64.cc
@@ -15,12 +15,25 @@
#include "signal/src/msb.h"
+#if defined(XTENSA)
+#include <xtensa/tie/xt_misc.h>
+#endif
+
namespace tflite {
namespace tflm_signal {
// TODO(b/286250473): remove namespace once de-duped libraries above
uint32_t MostSignificantBit64(uint64_t x) {
-#if defined(__GNUC__)
+#if defined(XTENSA)
+ // XT_NSAU returns the number of left shifts needed to put the MSB in the
+ // leftmost position. Returns 32 if the argument is 0.
+ uint32_t upper = 64 - XT_NSAU((uint32_t)(x >> 32));
+ if (upper != 32) {
+ return upper;
+ }
+ // Only if the upper bits are all clear do we want to look at the lower bits.
+ return 32 - XT_NSAU((uint32_t)x);
+#elif defined(__GNUC__)
if (x) {
return 64 - __builtin_clzll(x);
}
diff --git a/signal/src/pcan_argc_fixed.cc b/signal/src/pcan_argc_fixed.cc
new file mode 100644
index 0000000..2700b28
--- /dev/null
+++ b/signal/src/pcan_argc_fixed.cc
@@ -0,0 +1,75 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "pcan_argc_fixed.h"
+
+namespace tflite {
+namespace tflm_signal {
+
+int16_t WideDynamicFunction(const uint32_t x, const int16_t* lut) {
+ if (x <= 2) {
+ return lut[x];
+ }
+
+ const int16_t interval = MostSignificantBit32(x);
+ lut += 4 * interval - 6;
+
+ const int16_t frac =
+ ((interval < 11) ? (x << (11 - interval)) : (x >> (interval - 11))) &
+ 0x3FF;
+
+ int32_t result = ((int32_t)lut[2] * frac) >> 5;
+ result += (int32_t)((uint32_t)lut[1] << 5);
+ result *= frac;
+ result = (result + (1 << 14)) >> 15;
+ result += lut[0];
+ return (int16_t)result;
+}
+
+// Evaluate the piecewise polynomial "shrink" function defined by
+// shrink(x) = x^2 / 4 for x < 2,
+// shrink(x) = x - 1 for x >= 2.
+// The input x has kPcanSnrBits fractional bits, and the output has
+// kPcanOutputBits fractional bits.
+uint32_t PcanShrink(const uint32_t x) {
+ TFLITE_DCHECK(kPcanSnrBits >= kPcanOutputBits);
+ if (x < (2 << kPcanSnrBits)) {
+ // Compute x^2 / 4.
+ return (x * x) >> (2 + 2 * kPcanSnrBits - kPcanOutputBits);
+ } else {
+ // Compute x - 1.
+ return (x >> (kPcanSnrBits - kPcanOutputBits)) - (1 << kPcanOutputBits);
+ }
+}
+
+void ApplyPcanAutoGainControlFixed(const int16_t* gain_lut, int32_t snr_shift,
+ const uint32_t* noise_estimate,
+ uint32_t* filterbank_output,
+ int num_channels) {
+ int i;
+ for (i = 0; i < num_channels; ++i) {
+ // The gain has gain_bits fractional bits, and filterbank_output[i] has
+ // -input_correction_bits fractional bits. The product is shifted so that
+ // the resulting snr has kPcanSnrBits fractional bits.
+ const uint32_t gain = WideDynamicFunction(noise_estimate[i], gain_lut);
+ const uint32_t snr = ((uint64_t)filterbank_output[i] * gain) >> snr_shift;
+ // Result has kPcanOutputBits fractional bits.
+ // NOTE: This assumes filterbank_output_scale = 1 << kPcanOutputBits.
+ filterbank_output[i] = PcanShrink(snr);
+ }
+}
+
+} // namespace tflm_signal
+} // namespace tflite
diff --git a/signal/src/pcan_argc_fixed.h b/signal/src/pcan_argc_fixed.h
new file mode 100644
index 0000000..36eaf3d
--- /dev/null
+++ b/signal/src/pcan_argc_fixed.h
@@ -0,0 +1,41 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef SIGNAL_MICRO_KERNELS__SRC_PCAN_AGC_FIXED_H
+#define SIGNAL_MICRO_KERNELS__SRC_PCAN_AGC_FIXED_H
+#include <cstdint>
+
+#include "msb.h"
+#include "tensorflow/lite/kernels/internal/compatibility.h"
+
+namespace tflite {
+namespace tflm_signal {
+
+#define kPcanSnrBits 12
+#define kPcanOutputBits 6
+
+int16_t WideDynamicFunction(const uint32_t x, const int16_t* lut);
+
+uint32_t PcanShrink(const uint32_t x);
+
+void ApplyPcanAutoGainControlFixed(const int16_t* gain_lut, int32_t snr_shift,
+ const uint32_t* noise_estimate,
+ uint32_t* filterbank_output,
+ int num_channels);
+
+} // namespace tflm_signal
+} // namespace tflite
+
+#endif // SIGNAL_MICRO_KERNELS__PCAN_AGC_FIXED_H
diff --git a/signal/tensorflow_core/kernels/BUILD b/signal/tensorflow_core/kernels/BUILD
index 3b7d1be..33f5d08 100644
--- a/signal/tensorflow_core/kernels/BUILD
+++ b/signal/tensorflow_core/kernels/BUILD
@@ -66,6 +66,15 @@
)
tflm_signal_kernel_library(
+ name = "pcan_kernel",
+ srcs = ["pcan_kernel.cc"],
+ deps = [
+ "//signal/src:pcan_argc_fixed",
+ "@tensorflow_cc_deps//:cc_library",
+ ],
+)
+
+tflm_signal_kernel_library(
name = "stacker_kernel",
srcs = ["stacker_kernel.cc"],
deps = [
diff --git a/signal/tensorflow_core/kernels/pcan_kernel.cc b/signal/tensorflow_core/kernels/pcan_kernel.cc
new file mode 100644
index 0000000..a1adde0
--- /dev/null
+++ b/signal/tensorflow_core/kernels/pcan_kernel.cc
@@ -0,0 +1,53 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "signal/src/pcan_argc_fixed.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/types.h"
+
+namespace tensorflow {
+namespace signal {
+
+class PcanOp : public tensorflow::OpKernel {
+ public:
+ explicit PcanOp(tensorflow::OpKernelConstruction* context)
+ : tensorflow::OpKernel(context) {
+ OP_REQUIRES_OK(context, context->GetAttr("snr_shift", &snr_shift_));
+ }
+
+ void Compute(tensorflow::OpKernelContext* context) override {
+ tensorflow::Tensor* output_tensor = nullptr;
+ const uint32_t* input = context->input(0).flat<uint32_t>().data();
+ const uint32_t* noise_estimate = context->input(1).flat<uint32_t>().data();
+ const int16_t* gain_lut = context->input(2).flat<int16_t>().data();
+ int32_t num_channels = context->input(0).NumElements();
+ OP_REQUIRES_OK(context,
+ context->allocate_output(0, {num_channels}, &output_tensor));
+ uint32_t* output = output_tensor->flat<uint32_t>().data();
+
+ memcpy(output, input, sizeof(uint32_t) * num_channels);
+ tflite::tflm_signal::ApplyPcanAutoGainControlFixed(
+ gain_lut, snr_shift_, noise_estimate, output, num_channels);
+ }
+
+ private:
+ int snr_shift_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("SignalPCAN").Device(tensorflow::DEVICE_CPU),
+ PcanOp);
+
+} // namespace signal
+} // namespace tensorflow
diff --git a/signal/tensorflow_core/ops/BUILD b/signal/tensorflow_core/ops/BUILD
index 185c593..8ef47d8 100644
--- a/signal/tensorflow_core/ops/BUILD
+++ b/signal/tensorflow_core/ops/BUILD
@@ -54,6 +54,14 @@
)
tflm_signal_kernel_library(
+ name = "pcan_op",
+ srcs = ["pcan_op.cc"],
+ deps = [
+ "@tensorflow_cc_deps//:cc_library",
+ ],
+)
+
+tflm_signal_kernel_library(
name = "stacker_op",
srcs = ["stacker_op.cc"],
deps = [
diff --git a/signal/tensorflow_core/ops/delay_op.cc b/signal/tensorflow_core/ops/delay_op.cc
index dfee3d2..fb816a6 100644
--- a/signal/tensorflow_core/ops/delay_op.cc
+++ b/signal/tensorflow_core/ops/delay_op.cc
@@ -22,11 +22,11 @@
namespace tensorflow {
namespace signal {
-Status DelayShape(InferenceContext* c) {
+absl::Status DelayShape(InferenceContext* c) {
ShapeHandle out;
TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &out));
c->set_output(0, out);
- return OkStatus();
+ return absl::OkStatus();
}
// TODO(b/286250473): change back name after name clash resolved
diff --git a/signal/tensorflow_core/ops/energy_op.cc b/signal/tensorflow_core/ops/energy_op.cc
index 9ca74ed..d4f7cb7 100644
--- a/signal/tensorflow_core/ops/energy_op.cc
+++ b/signal/tensorflow_core/ops/energy_op.cc
@@ -22,7 +22,7 @@
namespace tensorflow {
namespace signal {
-Status EnergyShape(InferenceContext* c) {
+absl::Status EnergyShape(InferenceContext* c) {
ShapeHandle out;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &out));
@@ -30,7 +30,7 @@
TF_RETURN_IF_ERROR(c->ReplaceDim(out, 0, c->MakeDim(length), &out));
c->set_output(0, out);
- return OkStatus();
+ return absl::OkStatus();
}
// TODO(b/286250473): change back name after name clash resolved
diff --git a/signal/tensorflow_core/ops/fft_ops.cc b/signal/tensorflow_core/ops/fft_ops.cc
index 4f52a2b..63d8e78 100644
--- a/signal/tensorflow_core/ops/fft_ops.cc
+++ b/signal/tensorflow_core/ops/fft_ops.cc
@@ -22,7 +22,7 @@
namespace tensorflow {
namespace signal {
-Status RfftShape(InferenceContext* c) {
+absl::Status RfftShape(InferenceContext* c) {
ShapeHandle out;
int fft_length;
TF_RETURN_IF_ERROR(c->GetAttr<int>("fft_length", &fft_length));
@@ -30,17 +30,17 @@
auto dim = ((fft_length / 2) + 1) * 2; // * 2 for complex
TF_RETURN_IF_ERROR(c->ReplaceDim(out, -1, c->MakeDim(dim), &out));
c->set_output(0, out);
- return OkStatus();
+ return absl::OkStatus();
}
-Status IrfftShape(InferenceContext* c) {
+absl::Status IrfftShape(InferenceContext* c) {
ShapeHandle out;
int fft_length;
TF_RETURN_IF_ERROR(c->GetAttr<int>("fft_length", &fft_length));
TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &out));
TF_RETURN_IF_ERROR(c->ReplaceDim(out, -1, c->MakeDim(fft_length), &out));
c->set_output(0, out);
- return OkStatus();
+ return absl::OkStatus();
}
// TODO(b/286250473): change back name after name clash resolved
@@ -107,7 +107,7 @@
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &out));
c->set_output(0, out);
c->set_output(1, c->Scalar());
- return OkStatus();
+ return absl::OkStatus();
})
.Doc(R"doc(
Shifts the input left until the amplitude is maximized without clipping. Returns
diff --git a/signal/tensorflow_core/ops/filter_bank_ops.cc b/signal/tensorflow_core/ops/filter_bank_ops.cc
index de16715..7e021ff 100644
--- a/signal/tensorflow_core/ops/filter_bank_ops.cc
+++ b/signal/tensorflow_core/ops/filter_bank_ops.cc
@@ -22,7 +22,7 @@
namespace tensorflow {
namespace signal {
-Status FilterBankShape(InferenceContext* c) {
+absl::Status FilterBankShape(InferenceContext* c) {
ShapeHandle out;
shape_inference::DimensionHandle unused;
int num_channels;
@@ -49,7 +49,7 @@
c->WithValue(c->Dim(c->input(5), 0), num_channels + 1, &unused));
TF_RETURN_IF_ERROR(c->ReplaceDim(out, 0, c->MakeDim(num_channels), &out));
c->set_output(0, out);
- return OkStatus();
+ return absl::OkStatus();
}
// TODO(b/286250473): change back name after name clash resolved
@@ -92,7 +92,7 @@
ShapeHandle out;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &out));
c->set_output(0, out);
- return OkStatus();
+ return absl::OkStatus();
})
.Doc(R"doc(
Applies a square root to each element in the input then shift right by
@@ -122,7 +122,7 @@
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &out));
c->set_output(0, out);
c->set_output(1, out);
- return OkStatus();
+ return absl::OkStatus();
})
.Doc(R"doc(
Applies spectral subtraction to a filter bank output of size num_channels
@@ -152,7 +152,7 @@
ShapeHandle out;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &out));
c->set_output(0, out);
- return OkStatus();
+ return absl::OkStatus();
})
.Doc(R"doc(
Applies natural log to each element in input with pre-shift and post scaling.
diff --git a/signal/tensorflow_core/ops/framer_op.cc b/signal/tensorflow_core/ops/framer_op.cc
index ebac6e7..bf77699 100644
--- a/signal/tensorflow_core/ops/framer_op.cc
+++ b/signal/tensorflow_core/ops/framer_op.cc
@@ -22,7 +22,7 @@
namespace tensorflow {
namespace signal {
-Status FramerShape(InferenceContext* c) {
+absl::Status FramerShape(InferenceContext* c) {
ShapeHandle unused;
ShapeHandle in;
int frame_step, frame_size;
@@ -41,7 +41,7 @@
TF_RETURN_IF_ERROR(c->ReplaceDim(out, -1, c->MakeDim(frame_size), &out));
c->set_output(0, out);
c->set_output(1, c->Scalar());
- return OkStatus();
+ return absl::OkStatus();
}
// TODO(b/286250473): change back name after name clash resolved
diff --git a/signal/tensorflow_core/ops/overlap_add_op.cc b/signal/tensorflow_core/ops/overlap_add_op.cc
index 759faa9..74ccb1f 100644
--- a/signal/tensorflow_core/ops/overlap_add_op.cc
+++ b/signal/tensorflow_core/ops/overlap_add_op.cc
@@ -22,7 +22,7 @@
namespace tensorflow {
namespace signal {
-Status OverlapAddShape(InferenceContext* c) {
+absl::Status OverlapAddShape(InferenceContext* c) {
shape_inference::DimensionHandle unused;
ShapeHandle in;
ShapeHandle out;
@@ -39,7 +39,7 @@
c->ReplaceDim(out, -1, c->MakeDim(n_frames * frame_step), &out));
}
c->set_output(0, out);
- return OkStatus();
+ return absl::OkStatus();
}
// TODO(b/286250473): change back name after name clash resolved
diff --git a/signal/tensorflow_core/ops/pcan_op.cc b/signal/tensorflow_core/ops/pcan_op.cc
new file mode 100644
index 0000000..2a8490f
--- /dev/null
+++ b/signal/tensorflow_core/ops/pcan_op.cc
@@ -0,0 +1,60 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+
+using ::tensorflow::shape_inference::InferenceContext;
+using ::tensorflow::shape_inference::ShapeHandle;
+
+namespace tensorflow {
+namespace signal {
+
+namespace {
+
+absl::Status PcanShape(InferenceContext* c) {
+ ShapeHandle out, lut;
+ TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &out));
+ TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &out));
+ TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &lut));
+
+ c->set_output(0, out);
+ return absl::OkStatus();
+}
+
+} // namespace
+
+REGISTER_OP("SignalPCAN")
+ .Attr("snr_shift: int")
+ .Input("input: uint32")
+ .Input("noise_estimate: uint32")
+ .Input("gain_lut: int16")
+ .Output("output: uint32")
+ .SetShapeFn(PcanShape)
+ .Doc(R"doc(
+Determines whether per-channel amplitude-normalized (PCAN) auto gain control is
+applied, using either floating-point or fixed-point computation. If enabled,
+the dynamic range of the filterbank output is compressed by dividing by a power
+of the noise estimate.
+
+input: A 1-D array of mel-spectrum subband filter bank outputs.
+noise_estimate: A 1-D array of mel-spectrun subbabd noise estimates.
+gain_lut: A 1-D lookup table for gain calculation.
+output: A 1-D array of processed subband filter bank.
+snr_shift: Amount of right shift when calculcating the SNR.
+)doc");
+
+} // namespace signal
+} // namespace tensorflow
diff --git a/signal/tensorflow_core/ops/stacker_op.cc b/signal/tensorflow_core/ops/stacker_op.cc
index 2aa69c0..5fd2aca 100644
--- a/signal/tensorflow_core/ops/stacker_op.cc
+++ b/signal/tensorflow_core/ops/stacker_op.cc
@@ -22,7 +22,7 @@
namespace tensorflow {
namespace signal {
-Status StackerShape(InferenceContext* c) {
+absl::Status StackerShape(InferenceContext* c) {
int num_channels, stacker_left_context, stacker_right_context;
TF_RETURN_IF_ERROR(c->GetAttr<int>("num_channels", &num_channels));
TF_RETURN_IF_ERROR(
@@ -41,7 +41,7 @@
c->ReplaceDim(out, 0, c->MakeDim(num_channels * output_frames), &out));
c->set_output(0, out);
c->set_output(1, c->Scalar());
- return OkStatus();
+ return absl::OkStatus();
}
// TODO(b/286250473): change back name after name clash resolved
diff --git a/signal/tensorflow_core/ops/window_op.cc b/signal/tensorflow_core/ops/window_op.cc
index 24e51b2..5a08da8 100644
--- a/signal/tensorflow_core/ops/window_op.cc
+++ b/signal/tensorflow_core/ops/window_op.cc
@@ -22,7 +22,7 @@
namespace tensorflow {
namespace signal {
-Status WindowShape(InferenceContext* c) {
+absl::Status WindowShape(InferenceContext* c) {
ShapeHandle out;
TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &out));
TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &out));
@@ -33,7 +33,7 @@
TF_RETURN_IF_ERROR(c->WithValue(c->Dim(c->input(1), 0),
InferenceContext::Value(dim_in), &dim_in));
c->set_output(0, out);
- return OkStatus();
+ return absl::OkStatus();
}
// TODO(b/286250473): change back name to "Window" after name clash resolved
@@ -55,4 +55,4 @@
)doc");
} // namespace signal
-} // namespace tensorflow
\ No newline at end of file
+} // namespace tensorflow
diff --git a/tensorflow/compiler/mlir/lite/kernels/internal/BUILD b/tensorflow/compiler/mlir/lite/kernels/internal/BUILD
new file mode 100644
index 0000000..4ebbc21
--- /dev/null
+++ b/tensorflow/compiler/mlir/lite/kernels/internal/BUILD
@@ -0,0 +1,10 @@
+package(
+ default_visibility = ["//visibility:public"],
+ licenses = ["notice"],
+)
+
+cc_library(
+ name = "compatibility_macros",
+ hdrs = ["compatibility_macros.h"],
+ deps = ["//tensorflow/lite/kernels/internal:compatibility"],
+)
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc b/tensorflow/compiler/mlir/lite/kernels/internal/compatibility_macros.h
similarity index 60%
copy from tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc
copy to tensorflow/compiler/mlir/lite/kernels/internal/compatibility_macros.h
index e2cf661..5d07380 100644
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc
+++ b/tensorflow/compiler/mlir/lite/kernels/internal/compatibility_macros.h
@@ -1,4 +1,4 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -13,11 +13,9 @@
limitations under the License.
==============================================================================*/
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.h"
+#ifndef TENSORFLOW_COMPILER_MLIR_LITE_KERNELS_INTERNAL_COMPATABILITY_MACROS_H_
+#define TENSORFLOW_COMPILER_MLIR_LITE_KERNELS_INTERNAL_COMPATABILITY_MACROS_H_
-const char* kCategoryLabels[kCategoryCount] = {
- "silence",
- "unknown",
- "yes",
- "no",
-};
+#include "tensorflow/lite/kernels/internal/compatibility.h" // IWYU pragma: keep
+
+#endif // TENSORFLOW_COMPILER_MLIR_LITE_KERNELS_INTERNAL_COMPATABILITY_MACROS_H_
diff --git a/tensorflow/compiler/mlir/lite/schema/BUILD b/tensorflow/compiler/mlir/lite/schema/BUILD
new file mode 100644
index 0000000..f54726b
--- /dev/null
+++ b/tensorflow/compiler/mlir/lite/schema/BUILD
@@ -0,0 +1,41 @@
+load("@flatbuffers//:build_defs.bzl", "flatbuffer_cc_library")
+
+package(
+ default_visibility = [
+ "//visibility:public",
+ ],
+ licenses = ["notice"],
+)
+
+exports_files(
+ srcs = ["schema.fbs"],
+)
+
+flatbuffer_cc_library(
+ name = "schema_fbs",
+ srcs = ["schema.fbs"],
+)
+
+# Generic schema for inference on device (but with reflections makes bigger).
+flatbuffer_cc_library(
+ name = "schema_fbs_with_reflection",
+ srcs = ["schema.fbs"],
+ flatc_args = [
+ "--reflect-types",
+ "--reflect-names",
+ "--no-union-value-namespacing",
+ "--gen-object-api",
+ ],
+ out_prefix = "reflection/",
+)
+
+cc_library(
+ name = "schema_utils",
+ srcs = ["schema_utils.cc"],
+ hdrs = ["schema_utils.h"],
+ deps = [
+ ":schema_fbs",
+ "//tensorflow/compiler/mlir/lite/kernels/internal:compatibility_macros",
+ "@flatbuffers//:runtime_cc",
+ ],
+)
diff --git a/tensorflow/lite/schema/schema.fbs b/tensorflow/compiler/mlir/lite/schema/schema.fbs
similarity index 79%
rename from tensorflow/lite/schema/schema.fbs
rename to tensorflow/compiler/mlir/lite/schema/schema.fbs
index 4c84646..7ab78be 100644
--- a/tensorflow/lite/schema/schema.fbs
+++ b/tensorflow/compiler/mlir/lite/schema/schema.fbs
@@ -58,6 +58,7 @@
UINT32 = 15,
UINT16 = 16,
INT4 = 17,
+ BFLOAT16 = 18,
}
// Custom quantization parameters for experimenting with new quantization
@@ -235,8 +236,12 @@
// represented with -1.
shape_signature:[int]; // Optional.
- // If false, the rank or the number of tensor dimensions is unknown.
- // If false, "shape" must be [].
+ // This field is added to distinguish between scalars and tensors of unknown
+ // ranks (both of which shape is []).
+ // For scalars (rank = 0), shape = [] and has_rank = true.
+ // For tensors with known rank (rank > 0) and shape, shape = [...] and
+ // has_rank = true.
+ // For tensors with unknown rank and shape, shape = [] and has_rank = false.
has_rank: bool = false;
// The nested Tensor types for VARIANT type. This is always empty for
@@ -420,6 +425,53 @@
BITCAST = 159,
BITWISE_XOR = 160,
RIGHT_SHIFT = 161,
+ // All Operators start with STABLEHLO_ prefixes are subject to change
+ // Many of the ops below can not be executed by TFlite runtime
+ STABLEHLO_LOGISTIC = 162, // WARNING: Do not have runtime support
+ STABLEHLO_ADD = 163,
+ STABLEHLO_DIVIDE = 164, // WARNING: No runtime support yet
+ STABLEHLO_MULTIPLY = 165,
+ STABLEHLO_MAXIMUM = 166,
+ STABLEHLO_RESHAPE = 167, // WARNING: No runtime support yet
+ STABLEHLO_CLAMP = 168, // WARNING: No runtime support
+ STABLEHLO_CONCATENATE = 169, // WARNING: No runtime support
+ STABLEHLO_BROADCAST_IN_DIM = 170, // WARNING: No runtime support
+ STABLEHLO_CONVOLUTION = 171, // WARNING: No runtime support
+ STABLEHLO_SLICE = 172, // WARNING: No runtime support
+ STABLEHLO_CUSTOM_CALL = 173, // WARNING: No runtime support
+ STABLEHLO_REDUCE = 174, // WARNING: No runtime support
+ STABLEHLO_ABS = 175, // WARNING: No runtime support
+ STABLEHLO_AND = 176, // WARNING: No runtime support
+ STABLEHLO_COSINE = 177, // WARNING: No runtime support
+ STABLEHLO_EXPONENTIAL = 178, // WARNING: No runtime support
+ STABLEHLO_FLOOR = 179, // WARNING: No runtime support
+ STABLEHLO_LOG = 180, // WARNING: No runtime support
+ STABLEHLO_MINIMUM = 181,
+ STABLEHLO_NEGATE = 182, // WARNING: No runtime support
+ STABLEHLO_OR = 183, // WARNING: No runtime support
+ STABLEHLO_POWER = 184, // WARNING: No runtime support
+ STABLEHLO_REMAINDER = 185, // WARNING: No runtime support
+ STABLEHLO_RSQRT = 186, // WARNING: No runtime support
+ STABLEHLO_SELECT = 187, // WARNING: No runtime support
+ STABLEHLO_SUBTRACT = 188, // WARNING: No runtime support
+ STABLEHLO_TANH = 189, // WARNING: No runtime support
+ STABLEHLO_SCATTER = 190,
+ STABLEHLO_COMPARE = 191, // WARNING: No runtime support
+ STABLEHLO_CONVERT = 192, // WARNING: No runtime support
+ STABLEHLO_DYNAMIC_SLICE = 193, // WARNING: No runtime support
+ STABLEHLO_DYNAMIC_UPDATE_SLICE = 194, // WARNING: No runtime support
+ STABLEHLO_PAD = 195,
+ STABLEHLO_IOTA = 196, // WARNING: No runtime support
+ STABLEHLO_DOT_GENERAL = 197, // WARNING: No runtime support
+ STABLEHLO_REDUCE_WINDOW = 198,
+ STABLEHLO_SORT = 199, // WARNING: No runtime support
+ STABLEHLO_WHILE = 200, // WARNING: No runtime support
+ STABLEHLO_GATHER = 201,
+ STABLEHLO_TRANSPOSE = 202, // WARNING: No runtime support
+ DILATE = 203,
+ STABLEHLO_RNG_BIT_GENERATOR = 204,
+ REDUCE_WINDOW = 205 (deprecated),
+ STABLEHLO_COMPOSITE = 206, // WARNING: No runtime support
}
// LINT.ThenChange(nnapi_linter/linter.proto)
@@ -551,6 +603,192 @@
BitcastOptions,
BitwiseXorOptions,
RightShiftOptions,
+ // DO NOT add new options this union, will cause failure in Java api
+ // generation otherwise
+ // Add new builtin options into builtin options 2 instead
+}
+
+union BuiltinOptions2{
+ StablehloConcatenateOptions,
+ StablehloBroadcastInDimOptions,
+ StablehloSliceOptions,
+ StablehloConvolutionOptions,
+ StablehloCustomCallOptions,
+ StablehloReduceOptions,
+ StablehloScatterOptions,
+ StablehloCompareOptions,
+ StablehloDynamicSliceOptions,
+ StablehloPadOptions,
+ StablehloIotaOptions,
+ StablehloDotGeneralOptions,
+ StablehloReduceWindowOptions,
+ StablehloSortOptions,
+ StablehloWhileOptions,
+ StablehloGatherOptions,
+ StablehloTransposeOptions,
+ DilateOptions,
+ StablehloRngBitGeneratorOptions,
+ ReduceWindowOptions (deprecated),
+ StableHLOCompositeOptions,
+}
+
+table StablehloGatherOptions{
+ offset_dims : [long];
+ collapsed_slice_dims : [long];
+ start_index_map : [long];
+ index_vector_dim : long;
+ slice_sizes : [long];
+ indices_are_sorted : bool;
+}
+
+table StablehloTransposeOptions{
+ permutation : [long];
+}
+
+enum StablehloPrecisionConfig : uint {
+ DEFAULT,
+ HIGH,
+ HIGHEST,
+}
+
+table StablehloDotGeneralOptions{
+ lhs_batching_dimensions : [long];
+ rhs_batching_dimensions : [long];
+ lhs_contracting_dimensions : [long];
+ rhs_contracting_dimensions : [long];
+ precision_config : [StablehloPrecisionConfig];
+}
+
+table StablehloReduceWindowOptions{
+ window_dimensions : [long];
+ window_strides : [long];
+ base_dilations : [long];
+ window_dilations : [long];
+ padding : [long];
+ body_subgraph_index : int;
+}
+
+table StablehloWhileOptions{
+ cond_subgraph_index : int;
+ body_subgraph_index : int;
+}
+
+table StablehloSortOptions{
+ dimension : long;
+ is_stable : bool;
+ comparator_subgraph_index : int;
+}
+
+table StablehloConcatenateOptions {
+ dimension : long;
+}
+
+table StablehloBroadcastInDimOptions{
+ broadcast_dimensions : [long];
+}
+
+enum StablehloComparisonDirection : uint {
+ STABLEHLO_COMPARISON_DIRECTION_EQ,
+ STABLEHLO_COMPARISON_DIRECTION_NE,
+ STABLEHLO_COMPARISON_DIRECTION_GE,
+ STABLEHLO_COMPARISON_DIRECTION_GT,
+ STABLEHLO_COMPARISON_DIRECTION_LE,
+ STABLEHLO_COMPARISON_DIRECTION_LT,
+
+}
+
+enum StablehloComparisonType : uint {
+ STABLEHLO_COMPARISON_TYPE_NOTYPE,
+ STABLEHLO_COMPARISON_TYPE_FLOAT,
+ STABLEHLO_COMPARISON_TYPE_FLOAT_TOTAL_ORDER,
+ STABLEHLO_COMPARISON_TYPE_SIGNED,
+ STABLEHLO_COMPARISON_TYPE_UNSIGNED,
+}
+
+table StablehloCompareOptions{
+ comparison_direction : StablehloComparisonDirection;
+ compare_type : StablehloComparisonType;
+}
+
+table StablehloDynamicSliceOptions{
+ slice_sizes : [long];
+}
+
+table StablehloPadOptions{
+ edge_padding_low : [long];
+ edge_padding_high : [long];
+ interior_padding : [long];
+}
+
+table StablehloIotaOptions{
+ iota_dimension : long;
+}
+
+table StablehloCustomCallOptions {
+ call_target_name : string;
+ has_side_effect : bool;
+ backend_config: string;
+ api_version : int; // will be decprecated
+ called_computations: [int]; // should point to subgraphs of the computations
+ custom_attributes : [ubyte];
+}
+
+table StablehloReduceOptions {
+ dimensions : [long];
+ body_subgraph_index : int;
+}
+
+table StablehloSliceOptions{
+ start_indices : [long];
+ limit_indices : [long];
+ strides : [long];
+}
+
+table StablehloConvolutionOptions{
+ window_strides : [long];
+ padding : [long];
+ lhs_dilation : [long];
+ rhs_dilation : [long];
+ window_reversal : [bool];
+ input_batch_dimension : long;
+ input_feature_dimension : long;
+ input_spatial_dimensions : [long];
+ kernel_input_feature_dimension : long;
+ kernel_output_feature_dimension : long;
+ kernel_spatial_dimensions : [long];
+ output_batch_dimension : long;
+ output_feature_dimension : long;
+ output_spatial_dimensions : [long];
+ feature_group_count : long;
+ batch_group_count : long;
+ precision_config : [StablehloPrecisionConfig];
+}
+
+table StablehloScatterOptions {
+ indices_are_sorted: bool;
+ update_window_dims: [long];
+ inserted_window_dims: [long];
+ scatter_dims_to_operand_dims: [long];
+ index_vector_dim: long;
+ unique_indices: bool;
+ update_computation_subgraph_index: int;
+}
+
+enum RngAlgorithm : byte {
+ // An algorithm auto-selected by the system according to device type.
+ DEFAULT = 0,
+ // The Philox algorithm, as described in paper
+ // ['Parallel Random Numbers: As Easy as 1, 2, 3']
+ // (https://www.thesalmons.org/john/random123/papers/random123sc11.pdf)
+ PHILOX = 1,
+ // The ThreeFry algorithm, as described in paper
+ // ['Parallel Random Numbers: As Easy as 1, 2, 3']
+ // (https://www.thesalmons.org/john/random123/papers/random123sc11.pdf)
+ THREEFRY = 2,
+}
+
+table StablehloRngBitGeneratorOptions {
+ algorithm:RngAlgorithm;
}
// LINT.IfChange
@@ -575,6 +813,9 @@
fused_activation_function:ActivationFunctionType;
dilation_w_factor:int = 1;
dilation_h_factor:int = 1;
+ // Parameters for Conv2D version 8 or above.
+ // When set, quantized_bias_type defines the dtype for both bias and accumulator.
+ quantized_bias_type: TensorType;
}
// Options for both Conv3D and Conv3DTranspose.
@@ -682,6 +923,10 @@
// If set to true, then weights-only op will use asymmetric quantization for
// inputs.
asymmetric_quantize_inputs: bool;
+
+ // Parameters for FullyConnected version 11 or above.
+ // When set, quantized_bias_type defines the dtype for both bias and accumulator.
+ quantized_bias_type: TensorType;
}
table SoftmaxOptions {
@@ -941,6 +1186,11 @@
// Parameters supported by version 4:
fused_activation_function:ActivationFunctionType = NONE;
+
+ // Parameters for TransposeConv version 5 or above.
+ // If set, use this for bias and accumulator.
+ // When set, quantized_bias_type defines the dtype for both bias and accumulator.
+ quantized_bias_type: TensorType;
}
table ExpandDimsOptions {
@@ -1198,6 +1448,23 @@
table RightShiftOptions {
}
+table DilateOptions {
+}
+
+enum ReduceWindowFunction : int {
+ UNSUPPORTED,
+ ADD,
+ MUL,
+ MINIMUM,
+ MAXIMUM,
+ ALL,
+ ANY,
+}
+
+table ReduceWindowOptions (deprecated) {
+ reduce_function: ReduceWindowFunction;
+}
+
// An OperatorCode can be an enum value (BuiltinOperator) if the operator is a
// builtin, or a string if the operator is custom.
table OperatorCode {
@@ -1222,6 +1489,14 @@
FLEXBUFFERS = 0,
}
+table StableHLOCompositeOptions {
+ name:string;
+ decomposition_subgraph_index:int32;
+ composite_attributes:[ubyte];
+ composite_attributes_format:CustomOptionsFormat;
+ version:int32;
+}
+
// An operator takes tensors as inputs and outputs. The type of operation being
// performed is determined by an index into the list of valid OperatorCodes,
// while the specifics of each operations is configured using builtin_options
@@ -1264,6 +1539,11 @@
// beginning of the file and is only valid if > 1
large_custom_options_offset: ulong;
large_custom_options_size: ulong;
+
+ // Flatbuffers union struct has a 128 elements limit in JAVA, so a second
+ // union is added, in the case of where BuitlinOptions2 runs out, a third
+ // one can be added
+ builtin_options_2 : BuiltinOptions2;
}
// The root type, defining a subgraph, which typically represents an entire
diff --git a/tensorflow/compiler/mlir/lite/schema/schema_generated.h b/tensorflow/compiler/mlir/lite/schema/schema_generated.h
new file mode 100644
index 0000000..7eeedf8
--- /dev/null
+++ b/tensorflow/compiler/mlir/lite/schema/schema_generated.h
@@ -0,0 +1,22 @@
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef TENSORFLOW_COMPILER_MLIR_LITE_SCHEMA_SCHEMA_GENERATED_H_
+#define TENSORFLOW_COMPILER_MLIR_LITE_SCHEMA_SCHEMA_GENERATED_H_
+
+// This file should only be used by the make build to redirect schema_utils.cc
+// usage of the generated schema to the proper location.
+#include "tensorflow/lite/schema/schema_generated.h" // IWYU pragma: keep
+
+#endif // TENSORFLOW_LITE_SCHEMA_SCHEMA_UTILS_H_
diff --git a/tensorflow/lite/schema/schema_utils.cc b/tensorflow/compiler/mlir/lite/schema/schema_utils.cc
similarity index 89%
rename from tensorflow/lite/schema/schema_utils.cc
rename to tensorflow/compiler/mlir/lite/schema/schema_utils.cc
index fc19290..a173380 100644
--- a/tensorflow/lite/schema/schema_utils.cc
+++ b/tensorflow/compiler/mlir/lite/schema/schema_utils.cc
@@ -12,16 +12,16 @@
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
-#include "tensorflow/lite/schema/schema_utils.h"
+#include "tensorflow/compiler/mlir/lite/schema/schema_utils.h"
#include <algorithm>
-#include "tensorflow/lite/kernels/internal/compatibility.h"
+#include "tensorflow/compiler/mlir/lite/kernels/internal/compatibility_macros.h"
namespace tflite {
// The following GetBuiltinCode methods are the utility methods for reading
-// builtin operatore code, ensuring compatibility issues between v3 and v3a
+// builtin operator code, ensuring compatibility issues between v3 and v3a
// schema. Always the maximum value of the two fields always will be the correct
// value as follows:
//
@@ -29,7 +29,7 @@
//
// The `builtin_code` field is not available in the v3 models. Flatbuffer
// library will feed zero value, which is the default value in the v3a schema.
-// The actual builtin operatore code value will exist in the
+// The actual builtin operator code value will exist in the
// `deprecated_builtin_code` field. At the same time, it implies that
// `deprecated_builtin_code` >= `builtin_code` and the maximum value of the two
// fields will be same with `deprecated_builtin_code'.
diff --git a/tensorflow/compiler/mlir/lite/schema/schema_utils.h b/tensorflow/compiler/mlir/lite/schema/schema_utils.h
new file mode 100644
index 0000000..7498aa0
--- /dev/null
+++ b/tensorflow/compiler/mlir/lite/schema/schema_utils.h
@@ -0,0 +1,33 @@
+/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef TENSORFLOW_COMPILER_MLIR_LITE_SCHEMA_SCHEMA_UTILS_H_
+#define TENSORFLOW_COMPILER_MLIR_LITE_SCHEMA_SCHEMA_UTILS_H_
+
+#include "flatbuffers/flatbuffers.h"
+#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
+
+namespace tflite {
+
+// The following methods are introduced to resolve op builtin code shortage
+// problem. The new builtin operator will be assigned to the extended builtin
+// code field in the flatbuffer schema. Those methods helps to hide builtin code
+// details.
+BuiltinOperator GetBuiltinCode(const OperatorCode *op_code);
+
+BuiltinOperator GetBuiltinCode(const OperatorCodeT *op_code);
+
+} // namespace tflite
+
+#endif // TENSORFLOW_COMPILER_MLIR_LITE_SCHEMA_SCHEMA_UTILS_H_
diff --git a/tensorflow/extra_rules.bzl b/tensorflow/extra_rules.bzl
index 7a3523e..29e0bda 100644
--- a/tensorflow/extra_rules.bzl
+++ b/tensorflow/extra_rules.bzl
@@ -19,6 +19,10 @@
"""Config setting for all Fusion F1 based cores."""
return "//tensorflow/lite/micro/kernels:xtensa_fusion_f1_default"
+def xtensa_hifi_3_config():
+ """Config setting for all HiFi 3 based cores."""
+ return "//tensorflow/lite/micro/kernels:xtensa_hifi_3_default"
+
def xtensa_hifi_3z_config():
"""Config setting for all HiFi 3z based cores."""
return "//tensorflow/lite/micro/kernels:xtensa_hifi_3z_default"
diff --git a/tensorflow/lite/build_def.bzl b/tensorflow/lite/build_def.bzl
index e8fc49c..e65ec23 100644
--- a/tensorflow/lite/build_def.bzl
+++ b/tensorflow/lite/build_def.bzl
@@ -3,6 +3,7 @@
copts = [
"-DFARMHASH_NO_CXX_STRING",
"-Wno-sign-compare",
+ "-Wno-unused-parameter",
"-fno-exceptions", # Exceptions are unused in TFLite.
]
return copts
diff --git a/tensorflow/lite/builtin_ops.h b/tensorflow/lite/builtin_ops.h
index f9871ad..5dba0f6 100644
--- a/tensorflow/lite/builtin_ops.h
+++ b/tensorflow/lite/builtin_ops.h
@@ -189,6 +189,51 @@
kTfLiteBuiltinBitcast = 159,
kTfLiteBuiltinBitwiseXor = 160,
kTfLiteBuiltinRightShift = 161,
+ kTfLiteBuiltinStablehloLogistic = 162,
+ kTfLiteBuiltinStablehloAdd = 163,
+ kTfLiteBuiltinStablehloDivide = 164,
+ kTfLiteBuiltinStablehloMultiply = 165,
+ kTfLiteBuiltinStablehloMaximum = 166,
+ kTfLiteBuiltinStablehloReshape = 167,
+ kTfLiteBuiltinStablehloClamp = 168,
+ kTfLiteBuiltinStablehloConcatenate = 169,
+ kTfLiteBuiltinStablehloBroadcastInDim = 170,
+ kTfLiteBuiltinStablehloConvolution = 171,
+ kTfLiteBuiltinStablehloSlice = 172,
+ kTfLiteBuiltinStablehloCustomCall = 173,
+ kTfLiteBuiltinStablehloReduce = 174,
+ kTfLiteBuiltinStablehloAbs = 175,
+ kTfLiteBuiltinStablehloAnd = 176,
+ kTfLiteBuiltinStablehloCosine = 177,
+ kTfLiteBuiltinStablehloExponential = 178,
+ kTfLiteBuiltinStablehloFloor = 179,
+ kTfLiteBuiltinStablehloLog = 180,
+ kTfLiteBuiltinStablehloMinimum = 181,
+ kTfLiteBuiltinStablehloNegate = 182,
+ kTfLiteBuiltinStablehloOr = 183,
+ kTfLiteBuiltinStablehloPower = 184,
+ kTfLiteBuiltinStablehloRemainder = 185,
+ kTfLiteBuiltinStablehloRsqrt = 186,
+ kTfLiteBuiltinStablehloSelect = 187,
+ kTfLiteBuiltinStablehloSubtract = 188,
+ kTfLiteBuiltinStablehloTanh = 189,
+ kTfLiteBuiltinStablehloScatter = 190,
+ kTfLiteBuiltinStablehloCompare = 191,
+ kTfLiteBuiltinStablehloConvert = 192,
+ kTfLiteBuiltinStablehloDynamicSlice = 193,
+ kTfLiteBuiltinStablehloDynamicUpdateSlice = 194,
+ kTfLiteBuiltinStablehloPad = 195,
+ kTfLiteBuiltinStablehloIota = 196,
+ kTfLiteBuiltinStablehloDotGeneral = 197,
+ kTfLiteBuiltinStablehloReduceWindow = 198,
+ kTfLiteBuiltinStablehloSort = 199,
+ kTfLiteBuiltinStablehloWhile = 200,
+ kTfLiteBuiltinStablehloGather = 201,
+ kTfLiteBuiltinStablehloTranspose = 202,
+ kTfLiteBuiltinDilate = 203,
+ kTfLiteBuiltinStablehloRngBitGenerator = 204,
+ kTfLiteBuiltinReduceWindow = 205,
+ kTfLiteBuiltinStablehloComposite = 206,
} TfLiteBuiltinOperator;
#ifdef __cplusplus
diff --git a/tensorflow/lite/c/builtin_op_data.h b/tensorflow/lite/c/builtin_op_data.h
index 7628e5a..0606819 100644
--- a/tensorflow/lite/c/builtin_op_data.h
+++ b/tensorflow/lite/c/builtin_op_data.h
@@ -15,6 +15,9 @@
#ifndef TENSORFLOW_LITE_C_BUILTIN_OP_DATA_H_
#define TENSORFLOW_LITE_C_BUILTIN_OP_DATA_H_
+/// For documentation, see
+/// third_party/tensorflow/lite/core/c/builtin_op_data.h
+
#include "tensorflow/lite/core/c/builtin_op_data.h"
#endif // TENSORFLOW_LITE_C_BUILTIN_OP_DATA_H_
diff --git a/tensorflow/lite/c/c_api_types.h b/tensorflow/lite/c/c_api_types.h
index cdbf1fd..05cda07 100644
--- a/tensorflow/lite/c/c_api_types.h
+++ b/tensorflow/lite/c/c_api_types.h
@@ -15,6 +15,12 @@
#ifndef TENSORFLOW_LITE_C_C_API_TYPES_H_
#define TENSORFLOW_LITE_C_C_API_TYPES_H_
+/// \file
+///
+/// C API types for TensorFlow Lite.
+///
+/// For documentation, see tensorflow/lite/core/c/c_api_types.h
+
#include "tensorflow/lite/core/c/c_api_types.h"
#endif // TENSORFLOW_LITE_C_C_API_TYPES_H_
diff --git a/tensorflow/lite/c/common.h b/tensorflow/lite/c/common.h
index e3e8001..8a8b513 100644
--- a/tensorflow/lite/c/common.h
+++ b/tensorflow/lite/c/common.h
@@ -13,25 +13,17 @@
limitations under the License.
==============================================================================*/
-// This file defines common C types and APIs for implementing operations,
-// delegates and other constructs in TensorFlow Lite. The actual operations and
-// delegates can be defined using C++, but the interface between the interpreter
-// and the operations are C.
-//
-// Summary of abstractions
-// TF_LITE_ENSURE - Self-sufficient error checking
-// TfLiteStatus - Status reporting
-// TfLiteIntArray - stores tensor shapes (dims),
-// TfLiteContext - allows an op to access the tensors
-// TfLiteTensor - tensor (a multidimensional array)
-// TfLiteNode - a single node or operation
-// TfLiteRegistration - the implementation of a conceptual operation.
-// TfLiteDelegate - allows delegation of nodes to alternative backends.
-//
-// Some abstractions in this file are created and managed by Interpreter.
-//
-// NOTE: The order of values in these structs are "semi-ABI stable". New values
-// should be added only to the end of structs and never reordered.
+/// \file
+///
+/// This file defines common C types and APIs for implementing operations,
+/// delegates and other constructs in TensorFlow Lite. The actual operations and
+/// delegates can be defined using C++, but the interface between the
+/// interpreter and the operations are C.
+///
+/// For documentation, see tensorflow/lite/core/c/common.h.
+///
+/// See also c_api_opaque.h which has more ABI-stable variants of some of these
+/// APIs.
#ifndef TENSORFLOW_LITE_C_COMMON_H_
#define TENSORFLOW_LITE_C_COMMON_H_
diff --git a/tensorflow/lite/core/api/error_reporter.h b/tensorflow/lite/core/api/error_reporter.h
index 99ab8cf..1e0ef7d 100644
--- a/tensorflow/lite/core/api/error_reporter.h
+++ b/tensorflow/lite/core/api/error_reporter.h
@@ -61,9 +61,9 @@
// reduce binary size, define TF_LITE_STRIP_ERROR_STRINGS when compiling and
// every call will be stubbed out, taking no memory.
#ifndef TF_LITE_STRIP_ERROR_STRINGS
-#define TF_LITE_REPORT_ERROR(reporter, ...) \
- do { \
- static_cast<tflite::ErrorReporter*>(reporter)->Report(__VA_ARGS__); \
+#define TF_LITE_REPORT_ERROR(reporter, ...) \
+ do { \
+ static_cast<::tflite::ErrorReporter*>(reporter)->Report(__VA_ARGS__); \
} while (false)
#else // TF_LITE_STRIP_ERROR_STRINGS
#define TF_LITE_REPORT_ERROR(reporter, ...)
diff --git a/tensorflow/lite/core/api/flatbuffer_conversions.cc b/tensorflow/lite/core/api/flatbuffer_conversions.cc
index 9f955df..3526810 100644
--- a/tensorflow/lite/core/api/flatbuffer_conversions.cc
+++ b/tensorflow/lite/core/api/flatbuffer_conversions.cc
@@ -15,11 +15,13 @@
#include "tensorflow/lite/core/api/flatbuffer_conversions.h"
+#include <algorithm>
#include <cstddef>
#include <cstdint>
#include <memory>
#include "flatbuffers/flatbuffers.h" // from @flatbuffers
+#include "flatbuffers/vector.h" // from @flatbuffers
#include "tensorflow/lite/core/api/error_reporter.h"
#include "tensorflow/lite/core/c/builtin_op_data.h"
#include "tensorflow/lite/core/c/common.h"
@@ -76,9 +78,10 @@
// Copies the contents from the flatbuffer int vector `flatbuffer` into the
// int array `buffer`. `flat_vector` and `buffer` represent the same
// configuration operation for a given operation.
-TfLiteStatus FlatBufferIntVectorToArray(
- int max_size_of_buffer, const flatbuffers::Vector<int32_t>* flat_vector,
- int* buffer, ErrorReporter* error_reporter, const char* op_name) {
+template <typename DataType = int32_t>
+static TfLiteStatus FlatBufferIntVectorToArray(
+ int max_size_of_buffer, const flatbuffers::Vector<DataType>* flat_vector,
+ DataType* buffer, ErrorReporter* error_reporter, const char* op_name) {
if (!flat_vector) {
TF_LITE_REPORT_ERROR(error_reporter,
"Input array not provided for operation '%s'.\n",
@@ -86,7 +89,7 @@
return kTfLiteError;
} else {
size_t num_dimensions = flat_vector->size();
- if (num_dimensions > max_size_of_buffer / sizeof(int)) {
+ if (num_dimensions > max_size_of_buffer / sizeof(DataType)) {
TF_LITE_REPORT_ERROR(
error_reporter,
"Found too many dimensions in the input array of operation '%s'.\n",
@@ -142,6 +145,18 @@
return kTfLiteMirrorPaddingUnknown;
}
+TfLiteRngAlgorithm ConvertRngAlgorithm(RngAlgorithm algorithm) {
+ switch (algorithm) {
+ case RngAlgorithm_THREEFRY:
+ return kTfLiteRngAlgorithmThreefry;
+ case RngAlgorithm_PHILOX:
+ return kTfLiteRngAlgorithmPhilox;
+ case RngAlgorithm_DEFAULT:
+ return kTfLiteRngAlgorithmDefault;
+ }
+ return kTfLiteRngAlgorithmUnknown;
+}
+
#ifndef TF_LITE_STATIC_MEMORY
TfLiteStatus ParseOpDataTfLite(const Operator* op, BuiltinOperator op_type,
ErrorReporter* error_reporter,
@@ -857,6 +872,97 @@
*builtin_data = params.release();
return kTfLiteOk;
}
+ case BuiltinOperator_STABLEHLO_SCATTER: {
+ return ParseStablehloScatter(op, error_reporter, allocator, builtin_data);
+ }
+ case BuiltinOperator_STABLEHLO_RNG_BIT_GENERATOR: {
+ return ParseStablehloRngBitGenerator(op, error_reporter, allocator,
+ builtin_data);
+ }
+ case BuiltinOperator_STABLEHLO_GATHER: {
+ return ParseStablehloGather(op, error_reporter, allocator, builtin_data);
+ }
+ case BuiltinOperator_STABLEHLO_REDUCE_WINDOW: {
+ return ParseStablehloReduceWindow(op, error_reporter, allocator,
+ builtin_data);
+ }
+ case BuiltinOperator_REDUCE_WINDOW: {
+ auto params = safe_allocator.Allocate<TfLiteReduceWindowParams>();
+ TF_LITE_ENSURE(error_reporter, params != nullptr);
+ if (const auto* reduce_params =
+ op->builtin_options_2_as_ReduceWindowOptions()) {
+ switch (reduce_params->reduce_function()) {
+ case ReduceWindowFunction_ADD:
+ params->reduce_function = TfLiteReduceWindowFunctionAdd;
+ break;
+ case ReduceWindowFunction_MUL:
+ params->reduce_function = TfLiteReduceWindowFunctionMul;
+ break;
+ case ReduceWindowFunction_MINIMUM:
+ params->reduce_function = TfLiteReduceWindowFunctionMin;
+ break;
+ case ReduceWindowFunction_MAXIMUM:
+ params->reduce_function = TfLiteReduceWindowFunctionMax;
+ break;
+ case ReduceWindowFunction_ALL:
+ params->reduce_function = TfLiteReduceWindowFunctionAll;
+ break;
+ case ReduceWindowFunction_ANY:
+ params->reduce_function = TfLiteReduceWindowFunctionAny;
+ break;
+ case ReduceWindowFunction_UNSUPPORTED:
+ default:
+ return kTfLiteError;
+ }
+ }
+ *builtin_data = params.release();
+ return kTfLiteOk;
+ }
+ case BuiltinOperator_STABLEHLO_PAD: {
+ return ParseStablehloPad(op, error_reporter, allocator, builtin_data);
+ }
+ case BuiltinOperator_STABLEHLO_COMPOSITE: {
+ return ParseStablehloComposite(op, error_reporter, allocator,
+ builtin_data);
+ }
+ // TODO: skip param parsing for now since ops below don't have kernels
+ case BuiltinOperator_STABLEHLO_SLICE:
+ case BuiltinOperator_STABLEHLO_BROADCAST_IN_DIM:
+ case BuiltinOperator_STABLEHLO_CONVOLUTION:
+ case BuiltinOperator_STABLEHLO_LOGISTIC:
+ case BuiltinOperator_STABLEHLO_ADD:
+ case BuiltinOperator_STABLEHLO_DIVIDE:
+ case BuiltinOperator_STABLEHLO_MULTIPLY:
+ case BuiltinOperator_STABLEHLO_MAXIMUM:
+ case BuiltinOperator_STABLEHLO_RESHAPE:
+ case BuiltinOperator_STABLEHLO_CLAMP:
+ case BuiltinOperator_STABLEHLO_CONCATENATE:
+ case BuiltinOperator_STABLEHLO_CUSTOM_CALL:
+ case BuiltinOperator_STABLEHLO_REDUCE:
+ case BuiltinOperator_STABLEHLO_ABS:
+ case BuiltinOperator_STABLEHLO_AND:
+ case BuiltinOperator_STABLEHLO_COSINE:
+ case BuiltinOperator_STABLEHLO_EXPONENTIAL:
+ case BuiltinOperator_STABLEHLO_FLOOR:
+ case BuiltinOperator_STABLEHLO_LOG:
+ case BuiltinOperator_STABLEHLO_MINIMUM:
+ case BuiltinOperator_STABLEHLO_NEGATE:
+ case BuiltinOperator_STABLEHLO_OR:
+ case BuiltinOperator_STABLEHLO_POWER:
+ case BuiltinOperator_STABLEHLO_REMAINDER:
+ case BuiltinOperator_STABLEHLO_RSQRT:
+ case BuiltinOperator_STABLEHLO_SELECT:
+ case BuiltinOperator_STABLEHLO_SUBTRACT:
+ case BuiltinOperator_STABLEHLO_TANH:
+ case BuiltinOperator_STABLEHLO_DYNAMIC_SLICE:
+ case BuiltinOperator_STABLEHLO_DYNAMIC_UPDATE_SLICE:
+ case BuiltinOperator_STABLEHLO_IOTA:
+ case BuiltinOperator_STABLEHLO_COMPARE:
+ case BuiltinOperator_STABLEHLO_CONVERT:
+ case BuiltinOperator_STABLEHLO_DOT_GENERAL:
+ case BuiltinOperator_STABLEHLO_SORT:
+ case BuiltinOperator_STABLEHLO_WHILE:
+ case BuiltinOperator_STABLEHLO_TRANSPOSE:
// Below are the ops with no builtin_data structure.
// TODO(aselle): Implement call in BuiltinOptions, but nullptrs are
@@ -899,6 +1005,7 @@
case BuiltinOperator_SIGN:
case BuiltinOperator_BITCAST:
case BuiltinOperator_WHERE:
+ case BuiltinOperator_DILATE:
return kTfLiteOk;
case BuiltinOperator_PLACEHOLDER_FOR_GREATER_OP_CODES:
return kTfLiteError;
@@ -914,6 +1021,9 @@
case TensorType_FLOAT16:
*type = kTfLiteFloat16;
return kTfLiteOk;
+ case TensorType_BFLOAT16:
+ *type = kTfLiteBFloat16;
+ return kTfLiteOk;
case TensorType_FLOAT32:
*type = kTfLiteFloat32;
return kTfLiteOk;
@@ -999,7 +1109,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -1029,7 +1139,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -1054,7 +1164,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -1134,7 +1244,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -1192,7 +1302,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -1220,10 +1330,13 @@
params->dilation_width_factor = schema_params->dilation_w_factor();
params->dilation_height_factor = schema_params->dilation_h_factor();
+ TF_LITE_ENSURE_STATUS(
+ ConvertTensorType(schema_params->quantized_bias_type(),
+ ¶ms->quantized_bias_type, error_reporter));
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -1274,7 +1387,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -1310,7 +1423,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -1434,7 +1547,9 @@
params->keep_num_dims = schema_params->keep_num_dims();
params->asymmetric_quantize_inputs =
schema_params->asymmetric_quantize_inputs();
-
+ TF_LITE_ENSURE_STATUS(
+ ConvertTensorType(schema_params->quantized_bias_type(),
+ ¶ms->quantized_bias_type, error_reporter));
switch (schema_params->weights_format()) {
case FullyConnectedOptionsWeightsFormat_DEFAULT:
params->weights_format = kTfLiteFullyConnectedWeightsFormatDefault;
@@ -1451,7 +1566,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -1528,7 +1643,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -1555,7 +1670,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -1711,7 +1826,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -1735,7 +1850,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -1776,7 +1891,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -1822,7 +1937,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -1880,7 +1995,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -1933,7 +2048,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -1994,6 +2109,307 @@
return kTfLiteOk;
}
+TfLiteStatus ParseStablehloReduceWindow(const Operator* op,
+ ErrorReporter* error_reporter,
+ BuiltinDataAllocator* allocator,
+ void** builtin_data) {
+ CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
+
+ SafeBuiltinDataAllocator safe_allocator(allocator);
+ auto params = safe_allocator.Allocate<TfLiteStablehloReduceWindowParams>();
+
+ const StablehloReduceWindowOptions* schema_params =
+ op->builtin_options_2_as_StablehloReduceWindowOptions();
+ if (schema_params) {
+ if (!schema_params->window_dimensions() ||
+ schema_params->window_dimensions()->size() == 0) {
+ TF_LITE_REPORT_ERROR(error_reporter,
+ "'window_dimensions' attribute is not optional for "
+ "'stablehlo.reduce_window' and cannot be empty.");
+ return kTfLiteError;
+ }
+
+ const size_t rank = schema_params->window_dimensions()->size();
+
+ auto LoadAttr = [&error_reporter](
+ int64_t* params_array, size_t params_array_size_bytes,
+ const flatbuffers::Vector<int64_t>* flatbuffer_vector,
+ const char* attr_name, const size_t expected_size,
+ const int64_t fill_value) -> TfLiteStatus {
+ if (flatbuffer_vector && flatbuffer_vector->size()) {
+ if (expected_size != 0 && flatbuffer_vector->size() != expected_size) {
+ TF_LITE_REPORT_ERROR(
+ error_reporter,
+ "'%s' attribute of 'stablehlo.reduce_window' does not have the "
+ "expected size (%llu != %llu).",
+ attr_name, flatbuffer_vector->size(), expected_size);
+ return kTfLiteError;
+ }
+ TfLiteStatus status = FlatBufferIntVectorToArray(
+ params_array_size_bytes, flatbuffer_vector, params_array,
+ error_reporter, "stablehlo.reduce_window");
+ if (status != kTfLiteOk) {
+ TF_LITE_REPORT_ERROR(error_reporter, "Check the '%s' attribute.",
+ attr_name);
+ return status;
+ }
+ } else {
+ std::fill_n(params_array, params_array_size_bytes / sizeof(int64_t),
+ fill_value);
+ }
+ return kTfLiteOk;
+ };
+
+ TF_LITE_ENSURE_STATUS(
+ LoadAttr(params->window_dimensions, sizeof(params->window_dimensions),
+ schema_params->window_dimensions(), "window_dimensions",
+ /*expected_size=*/rank, /*fill_value=*/1));
+ TF_LITE_ENSURE_STATUS(
+ LoadAttr(params->window_strides, sizeof(params->window_strides),
+ schema_params->window_strides(), "window_strides",
+ /*expected_size=*/rank, /*fill_value=*/1));
+ TF_LITE_ENSURE_STATUS(
+ LoadAttr(params->base_dilations, sizeof(params->base_dilations),
+ schema_params->base_dilations(), "base_dilations",
+ /*expected_size=*/rank, /*fill_value=*/1));
+ TF_LITE_ENSURE_STATUS(
+ LoadAttr(params->window_dilations, sizeof(params->window_dilations),
+ schema_params->window_dilations(), "window_dilations",
+ /*expected_size=*/rank, /*fill_value=*/1));
+ TF_LITE_ENSURE_STATUS(LoadAttr(params->padding, sizeof(params->padding),
+ schema_params->padding(), "padding",
+ /*expected_size=*/2 * rank,
+ /*fill_value=*/0));
+
+ params->body_subgraph_index = schema_params->body_subgraph_index();
+ *builtin_data = params.release();
+ return kTfLiteOk;
+ }
+ TF_LITE_REPORT_ERROR(
+ error_reporter,
+ "Could not get 'stablehlo.reduce_window' operation parameters.");
+ return kTfLiteError;
+}
+
+TfLiteStatus ParseStablehloScatter(const Operator* op,
+ ErrorReporter* error_reporter,
+ BuiltinDataAllocator* allocator,
+ void** builtin_data) {
+ CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
+
+ SafeBuiltinDataAllocator safe_allocator(allocator);
+ std::unique_ptr<TfLiteStablehloScatterParams,
+ SafeBuiltinDataAllocator::BuiltinDataDeleter>
+ params = safe_allocator.Allocate<TfLiteStablehloScatterParams>();
+ TF_LITE_ENSURE(error_reporter, params != nullptr);
+
+ const StablehloScatterOptions* schema_params =
+ op->builtin_options_2_as_StablehloScatterOptions();
+ if (schema_params) {
+ params->indices_are_sorted = schema_params->indices_are_sorted();
+
+ if (schema_params->update_window_dims()) {
+ TF_LITE_ENSURE_STATUS(FlatBufferIntVectorToArray<int64_t>(
+ schema_params->update_window_dims()->size() * sizeof(int64_t),
+ schema_params->update_window_dims(), params->update_window_dims,
+ error_reporter, "stablehlo_scatter"));
+ params->num_update_window_dims =
+ schema_params->update_window_dims()->size();
+ }
+
+ if (schema_params->inserted_window_dims()) {
+ TF_LITE_ENSURE_STATUS(FlatBufferIntVectorToArray<int64_t>(
+ schema_params->inserted_window_dims()->size() * sizeof(int64_t),
+ schema_params->inserted_window_dims(), params->inserted_window_dims,
+ error_reporter, "stablehlo_scatter"));
+ params->num_inserted_window_dims =
+ schema_params->inserted_window_dims()->size();
+ }
+
+ if (schema_params->scatter_dims_to_operand_dims()) {
+ TF_LITE_ENSURE_STATUS(FlatBufferIntVectorToArray<int64_t>(
+ schema_params->scatter_dims_to_operand_dims()->size() *
+ sizeof(int64_t),
+ schema_params->scatter_dims_to_operand_dims(),
+ params->scatter_dims_to_operand_dims, error_reporter,
+ "stablehlo_scatter"));
+ params->num_scatter_dims_to_operand_dims =
+ schema_params->scatter_dims_to_operand_dims()->size();
+ }
+
+ params->index_vector_dim = schema_params->index_vector_dim();
+ params->unique_indices = schema_params->unique_indices();
+ params->update_computation_subgraph_index =
+ schema_params->update_computation_subgraph_index();
+ } else {
+ // TODO(b/157480169): We should either return kTfLiteError or fill in some
+ // reasonable defaults in the params struct. We are not doing so until we
+ // better understand the ramifications of changing the legacy behavior.
+ }
+ *builtin_data = params.release();
+ return kTfLiteOk;
+}
+
+TfLiteStatus ParseStablehloRngBitGenerator(const Operator* op,
+ ErrorReporter* error_reporter,
+ BuiltinDataAllocator* allocator,
+ void** builtin_data) {
+ CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
+
+ SafeBuiltinDataAllocator safe_allocator(allocator);
+ std::unique_ptr<TfLiteStablehloRngBitGeneratorParams,
+ SafeBuiltinDataAllocator::BuiltinDataDeleter>
+ params = safe_allocator.Allocate<TfLiteStablehloRngBitGeneratorParams>();
+ TF_LITE_ENSURE(error_reporter, params != nullptr);
+
+ const StablehloRngBitGeneratorOptions* schema_params =
+ op->builtin_options_2_as_StablehloRngBitGeneratorOptions();
+ if (schema_params != nullptr) {
+ params->algorithm = ConvertRngAlgorithm(schema_params->algorithm());
+ } else {
+ // TODO(b/157480169): We should either return kTfLiteError or fill in some
+ // reasonable defaults in the params struct. We are not doing so until we
+ // better understand the ramifications of changing the legacy behavior.
+ }
+
+ *builtin_data = params.release();
+ return kTfLiteOk;
+}
+
+TfLiteStatus ParseStablehloGather(const Operator* op,
+ ErrorReporter* error_reporter,
+ BuiltinDataAllocator* allocator,
+ void** builtin_data) {
+ CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
+
+ SafeBuiltinDataAllocator safe_allocator(allocator);
+ std::unique_ptr<TfLiteStablehloGatherParams,
+ SafeBuiltinDataAllocator::BuiltinDataDeleter>
+ params = safe_allocator.Allocate<TfLiteStablehloGatherParams>();
+ TF_LITE_ENSURE(error_reporter, params != nullptr);
+
+ const StablehloGatherOptions* schema_params =
+ op->builtin_options_2_as_StablehloGatherOptions();
+
+ if (schema_params != nullptr) {
+ TF_LITE_ENSURE_STATUS(FlatBufferIntVectorToArray<int64_t>(
+ /*max_size_of_buffer=*/schema_params->offset_dims()->size() *
+ sizeof(int64_t),
+ /*flat_vector=*/schema_params->offset_dims(),
+ /*buffer=*/params->offset_dims, /*error_reporter=*/error_reporter,
+ /*op_name=*/"stablehlo_gather"));
+ params->num_offset_dims = schema_params->offset_dims()->size();
+
+ TF_LITE_ENSURE_STATUS(FlatBufferIntVectorToArray<int64_t>(
+ schema_params->collapsed_slice_dims()->size() * sizeof(int64_t),
+ schema_params->collapsed_slice_dims(), params->collapsed_slice_dims,
+ error_reporter, "stablehlo_gather"));
+ params->num_collapsed_slice_dims =
+ schema_params->collapsed_slice_dims()->size();
+
+ TF_LITE_ENSURE_STATUS(FlatBufferIntVectorToArray<int64_t>(
+ schema_params->start_index_map()->size() * sizeof(int64_t),
+ schema_params->start_index_map(), params->start_index_map,
+ error_reporter, "stablehlo_gather"));
+ params->num_start_index_map = schema_params->start_index_map()->size();
+
+ params->index_vector_dim = schema_params->index_vector_dim();
+
+ TF_LITE_ENSURE_STATUS(FlatBufferIntVectorToArray<int64_t>(
+ schema_params->slice_sizes()->size() * sizeof(int64_t),
+ schema_params->slice_sizes(), params->slice_sizes, error_reporter,
+ "stablehlo_gather"));
+ params->num_slice_sizes = schema_params->slice_sizes()->size();
+
+ params->indices_are_sorted = schema_params->indices_are_sorted();
+ } else {
+ // TODO(b/157480169): We should either return kTfLiteError or fill in some
+ // reasonable defaults in the params struct. We are not doing so until we
+ // better understand the ramifications of changing the legacy behavior.
+ }
+
+ *builtin_data = params.release();
+ return kTfLiteOk;
+}
+
+TfLiteStatus ParseStablehloPad(const Operator* op,
+ ErrorReporter* error_reporter,
+ BuiltinDataAllocator* allocator,
+ void** builtin_data) {
+ CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
+
+ SafeBuiltinDataAllocator safe_allocator(allocator);
+ auto params = safe_allocator.Allocate<TfLiteStablehloPadParams>();
+ const StablehloPadOptions* schema_params =
+ op->builtin_options_2_as_StablehloPadOptions();
+
+ if (schema_params) {
+ auto LoadAttr =
+ [&error_reporter](
+ int64_t* params_array, const size_t params_array_size_bytes,
+ const flatbuffers::Vector<int64_t>* const flatbuffer_vector,
+ const char* const attr_name) -> TfLiteStatus {
+ TfLiteStatus status = FlatBufferIntVectorToArray(
+ params_array_size_bytes, flatbuffer_vector, params_array,
+ error_reporter, "stablehlo.pad");
+ if (status != kTfLiteOk) {
+ TF_LITE_REPORT_ERROR(error_reporter, "Check the '%s' attribute.",
+ attr_name);
+ }
+ return status;
+ };
+
+ TF_LITE_ENSURE_STATUS(
+ LoadAttr(params->edge_padding_low, sizeof(params->edge_padding_low),
+ schema_params->edge_padding_low(), "edge_padding_low"));
+ TF_LITE_ENSURE_STATUS(
+ LoadAttr(params->edge_padding_high, sizeof(params->edge_padding_high),
+ schema_params->edge_padding_high(), "edge_padding_high"));
+ TF_LITE_ENSURE_STATUS(
+ LoadAttr(params->interior_padding, sizeof(params->interior_padding),
+ schema_params->interior_padding(), "interior_padding"));
+ if (schema_params->edge_padding_low()->size() !=
+ schema_params->edge_padding_high()->size() ||
+ schema_params->edge_padding_low()->size() !=
+ schema_params->interior_padding()->size()) {
+ TF_LITE_REPORT_ERROR(error_reporter,
+ "'stablehlo.pad' operation parameter array sizes "
+ "are not consistent.");
+ return kTfLiteError;
+ }
+ *builtin_data = params.release();
+ return kTfLiteOk;
+ }
+ TF_LITE_REPORT_ERROR(error_reporter,
+ "Could not get 'stablehlo.pad' operation parameters.");
+ return kTfLiteError;
+}
+
+TfLiteStatus ParseStablehloComposite(const Operator* op,
+ ErrorReporter* error_reporter,
+ BuiltinDataAllocator* allocator,
+ void** builtin_data) {
+ CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
+
+ SafeBuiltinDataAllocator safe_allocator(allocator);
+ auto params = safe_allocator.Allocate<TfLiteStablehloCompositeParams>();
+ const StableHLOCompositeOptions* schema_params =
+ op->builtin_options_2_as_StableHLOCompositeOptions();
+ if (schema_params) {
+ params->name = schema_params->name()->c_str();
+ params->version = schema_params->version();
+ params->subgraph_index = schema_params->decomposition_subgraph_index();
+ params->attributes = schema_params->composite_attributes()->data();
+ params->attributes_size = schema_params->composite_attributes()->size();
+ *builtin_data = params.release();
+ return kTfLiteOk;
+ }
+ TF_LITE_REPORT_ERROR(
+ error_reporter,
+ "Could not get 'stablehlo.composite' operation parameters.");
+ return kTfLiteError;
+}
+
// We have this parse function instead of directly returning kTfLiteOk from the
// switch-case in ParseOpData because this function is used as part of the
// selective registration for the OpResolver implementation in micro.
@@ -2034,7 +2450,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -2075,7 +2491,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -2108,7 +2524,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -2132,7 +2548,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -2156,7 +2572,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -2214,7 +2630,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -2270,7 +2686,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -2295,7 +2711,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -2322,7 +2738,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -2365,10 +2781,13 @@
params->activation =
ConvertActivation(transpose_conv_params->fused_activation_function());
+ TF_LITE_ENSURE_STATUS(
+ ConvertTensorType(transpose_conv_params->quantized_bias_type(),
+ ¶ms->quantized_bias_type, error_reporter));
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
return kTfLiteOk;
@@ -2392,7 +2811,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -2423,7 +2842,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
@@ -2448,7 +2867,7 @@
} else {
// TODO(b/157480169): We should either return kTfLiteError or fill in some
// reasonable defaults in the params struct. We are not doing so until we
- // better undertand the ramifications of changing the legacy behavior.
+ // better understand the ramifications of changing the legacy behavior.
}
*builtin_data = params.release();
diff --git a/tensorflow/lite/core/api/flatbuffer_conversions.h b/tensorflow/lite/core/api/flatbuffer_conversions.h
index 9ffe397..c01e887 100644
--- a/tensorflow/lite/core/api/flatbuffer_conversions.h
+++ b/tensorflow/lite/core/api/flatbuffer_conversions.h
@@ -420,6 +420,36 @@
BuiltinDataAllocator* allocator,
void** builtin_data);
+TfLiteStatus ParseStablehloScatter(const Operator* op,
+ ErrorReporter* error_reporter,
+ BuiltinDataAllocator* allocator,
+ void** builtin_data);
+
+TfLiteStatus ParseStablehloRngBitGenerator(const Operator* op,
+ ErrorReporter* error_reporter,
+ BuiltinDataAllocator* allocator,
+ void** builtin_data);
+
+TfLiteStatus ParseStablehloGather(const Operator* op,
+ ErrorReporter* error_reporter,
+ BuiltinDataAllocator* allocator,
+ void** builtin_data);
+
+TfLiteStatus ParseStablehloReduceWindow(const Operator* op,
+ ErrorReporter* error_reporter,
+ BuiltinDataAllocator* allocator,
+ void** builtin_data);
+
+TfLiteStatus ParseStablehloPad(const Operator* op,
+ ErrorReporter* error_reporter,
+ BuiltinDataAllocator* allocator,
+ void** builtin_data);
+
+TfLiteStatus ParseStablehloComposite(const Operator* op,
+ ErrorReporter* error_reporter,
+ BuiltinDataAllocator* allocator,
+ void** builtin_data);
+
} // namespace tflite
#endif // TENSORFLOW_LITE_CORE_API_FLATBUFFER_CONVERSIONS_H_
diff --git a/tensorflow/lite/core/c/builtin_op_data.h b/tensorflow/lite/core/c/builtin_op_data.h
index e9c6eb3..e1428e7 100644
--- a/tensorflow/lite/core/c/builtin_op_data.h
+++ b/tensorflow/lite/core/c/builtin_op_data.h
@@ -21,6 +21,7 @@
#define TENSORFLOW_LITE_CORE_C_BUILTIN_OP_DATA_H_
#include <stdbool.h>
+#include <stddef.h>
#include <stdint.h>
#include "tensorflow/lite/core/c/common.h"
@@ -32,6 +33,10 @@
// TfLiteReshapeParams can't have dynamic data so we fix the maximum possible
// number of dimensions.
#define TFLITE_RESHAPE_PARAMS_MAX_DIMENSION_COUNT 8
+#define TFLITE_STABLEHLO_SCATTER_PARAMS_MAX_DIMENSION_COUNT 8
+#define TFLITE_STABLEHLO_GATHER_PARAMS_MAX_DIMENSION_COUNT 8
+#define TFLITE_STABLEHLO_REDUCE_WINDOW_PARAMS_MAX_DIMENSION_COUNT 8
+#define TFLITE_STABLEHLO_PAD_PARAMS_MAX_DIMENSION_COUNT 8
// TODO(aselle): Consider using "if this then that" for testing.
@@ -90,6 +95,10 @@
// Note: Version 2 supports dilation values not equal to 1.
int dilation_width_factor;
int dilation_height_factor;
+
+ // Parameters for CONV_2D version 7 or above.
+ // Used to determine the default value for the quantized bias.
+ TfLiteType quantized_bias_type;
} TfLiteConvParams;
typedef struct {
@@ -193,6 +202,10 @@
// If set to true and the weights are quantized, then non constant inputs
// are quantized at evaluation time with asymmetric quantization.
bool asymmetric_quantize_inputs;
+
+ // Parameters for FullyConnected version 10 or above.
+ // Used to determine the default value for the quantized bias.
+ TfLiteType quantized_bias_type;
} TfLiteFullyConnectedParams;
typedef enum {
@@ -341,7 +354,7 @@
// These fields are only used in old models for backward compatibility.
// In the current implementation, we use the 2nd input of the op as the shape,
// and these fields are unused.
- int shape[TFLITE_RESHAPE_PARAMS_MAX_DIMENSION_COUNT];
+ int32_t shape[TFLITE_RESHAPE_PARAMS_MAX_DIMENSION_COUNT];
int num_dimensions;
} TfLiteReshapeParams;
@@ -398,7 +411,7 @@
typedef struct {
// TODO(ahentz): We can't have dynamic data in this struct, at least not yet.
// For now we will fix the maximum possible number of dimensions.
- int squeeze_dims[8];
+ int32_t squeeze_dims[8];
int num_squeeze_dims;
} TfLiteSqueezeParams;
@@ -430,6 +443,10 @@
// Parameters supported by version 4:
TfLiteFusedActivation activation;
+
+ // Parameters for TransposeConv version 5 or above.
+ // Used to determine the default value for the quantized bias.
+ TfLiteType quantized_bias_type;
} TfLiteTransposeConvParams;
typedef struct {
@@ -535,6 +552,108 @@
bool approximate;
} TfLiteGeluParams;
+typedef struct {
+ int64_t dimension;
+} TfLiteStablehloConcatenateParams;
+
+typedef struct {
+ // See the stablehlo spec for the explanation of the attributes:
+ // https://github.com/openxla/stablehlo/blob/main/docs/spec.md#scatter
+ bool indices_are_sorted;
+ int64_t
+ update_window_dims[TFLITE_STABLEHLO_SCATTER_PARAMS_MAX_DIMENSION_COUNT];
+ int num_update_window_dims;
+ int64_t
+ inserted_window_dims[TFLITE_STABLEHLO_SCATTER_PARAMS_MAX_DIMENSION_COUNT];
+ int num_inserted_window_dims;
+ int64_t scatter_dims_to_operand_dims
+ [TFLITE_STABLEHLO_SCATTER_PARAMS_MAX_DIMENSION_COUNT];
+ int num_scatter_dims_to_operand_dims;
+ int64_t index_vector_dim;
+ bool unique_indices;
+ int update_computation_subgraph_index;
+} TfLiteStablehloScatterParams;
+
+typedef enum {
+ kTfLiteRngAlgorithmUnknown = 0,
+ // An algorithm auto-selected by the system according to device type.
+ kTfLiteRngAlgorithmDefault,
+ // The Philox algorithm, as described in paper
+ // ['Parallel Random Numbers: As Easy as 1, 2, 3']
+ // (https://www.thesalmons.org/john/random123/papers/random123sc11.pdf)
+ kTfLiteRngAlgorithmPhilox,
+ // The ThreeFry algorithm, as described in paper
+ // ['Parallel Random Numbers: As Easy as 1, 2, 3']
+ // (https://www.thesalmons.org/john/random123/papers/random123sc11.pdf)
+ kTfLiteRngAlgorithmThreefry,
+} TfLiteRngAlgorithm;
+
+typedef struct {
+ TfLiteRngAlgorithm algorithm;
+} TfLiteStablehloRngBitGeneratorParams;
+
+typedef struct {
+ // See the stablehlo spec for the explanation of the attributes:
+ // https://github.com/openxla/stablehlo/blob/main/docs/spec.md#gather
+ int64_t offset_dims[TFLITE_STABLEHLO_GATHER_PARAMS_MAX_DIMENSION_COUNT];
+ int num_offset_dims;
+ int64_t
+ collapsed_slice_dims[TFLITE_STABLEHLO_GATHER_PARAMS_MAX_DIMENSION_COUNT];
+ int num_collapsed_slice_dims;
+ int64_t start_index_map[TFLITE_STABLEHLO_GATHER_PARAMS_MAX_DIMENSION_COUNT];
+ int num_start_index_map;
+ int64_t index_vector_dim;
+ int64_t slice_sizes[TFLITE_STABLEHLO_GATHER_PARAMS_MAX_DIMENSION_COUNT];
+ int num_slice_sizes;
+ bool indices_are_sorted;
+} TfLiteStablehloGatherParams;
+
+typedef struct {
+ // See the stablehlo spec for the explanation of the attributes:
+ // https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce_window
+ int64_t window_dimensions
+ [TFLITE_STABLEHLO_REDUCE_WINDOW_PARAMS_MAX_DIMENSION_COUNT];
+ int64_t
+ window_strides[TFLITE_STABLEHLO_REDUCE_WINDOW_PARAMS_MAX_DIMENSION_COUNT];
+ int64_t
+ base_dilations[TFLITE_STABLEHLO_REDUCE_WINDOW_PARAMS_MAX_DIMENSION_COUNT];
+ int64_t window_dilations
+ [TFLITE_STABLEHLO_REDUCE_WINDOW_PARAMS_MAX_DIMENSION_COUNT];
+ int64_t
+ padding[2 * TFLITE_STABLEHLO_REDUCE_WINDOW_PARAMS_MAX_DIMENSION_COUNT];
+ int body_subgraph_index;
+} TfLiteStablehloReduceWindowParams;
+
+enum TfLiteReduceWindowFunction {
+ TfLiteReduceWindowFunctionUnsupported,
+ TfLiteReduceWindowFunctionAdd,
+ TfLiteReduceWindowFunctionMul,
+ TfLiteReduceWindowFunctionMin,
+ TfLiteReduceWindowFunctionMax,
+ TfLiteReduceWindowFunctionAll,
+ TfLiteReduceWindowFunctionAny
+};
+
+typedef struct {
+ enum TfLiteReduceWindowFunction reduce_function;
+} TfLiteReduceWindowParams;
+
+typedef struct {
+ // See the stablehlo spec for the explanation of the attributes:
+ // https://github.com/openxla/stablehlo/blob/main/docs/spec.md#pad
+ int64_t edge_padding_low[TFLITE_STABLEHLO_PAD_PARAMS_MAX_DIMENSION_COUNT];
+ int64_t edge_padding_high[TFLITE_STABLEHLO_PAD_PARAMS_MAX_DIMENSION_COUNT];
+ int64_t interior_padding[TFLITE_STABLEHLO_PAD_PARAMS_MAX_DIMENSION_COUNT];
+} TfLiteStablehloPadParams;
+
+typedef struct {
+ const char* name;
+ int32_t subgraph_index;
+ int32_t version;
+ const uint8_t* attributes;
+ size_t attributes_size;
+} TfLiteStablehloCompositeParams;
+
#ifdef __cplusplus
} // extern "C"
#endif // __cplusplus
diff --git a/tensorflow/lite/core/c/c_api_types.h b/tensorflow/lite/core/c/c_api_types.h
index 3a6594d..32cefa8 100644
--- a/tensorflow/lite/core/c/c_api_types.h
+++ b/tensorflow/lite/core/c/c_api_types.h
@@ -12,16 +12,24 @@
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
+// WARNING: Users of TensorFlow Lite should not include this file directly, but
+// should instead include "third_party/tensorflow/lite/c/c_api_types.h".
+// Only the TensorFlow Lite implementation itself should include this file
+// directly.
/// This file declares types used by the pure C inference API defined in
/// c_api.h, some of which are also used in the C++ and C kernel and interpreter
/// APIs.
-
-// WARNING: Users of TensorFlow Lite should not include this file directly,
-// but should instead include
-// "third_party/tensorflow/lite/c/c_api_types.h".
-// Only the TensorFlow Lite implementation itself should include this
-// file directly.
+///
+// clang-format off
+// NOLINTBEGIN(whitespace/line_length)
+/// \note Users of TensorFlow Lite should use
+/// \code
+/// #include "tensorflow/lite/c/c_api_types.h"
+/// \endcode
+/// to access the APIs documented on this page.
+// NOLINTEND(whitespace/line_length)
+// clang-format on
// IWYU pragma: private, include "third_party/tensorflow/lite/c/c_api_types.h"
@@ -34,9 +42,13 @@
extern "C" {
#endif
-/** \addtogroup c_api_types tensorflow/lite/c/c_api_types.h
+// clang-format off
+// NOLINTBEGIN(whitespace/line_length)
+/** \defgroup c_api_types lite/c/c_api_types.h
* @{
*/
+// NOLINTEND(whitespace/line_length)
+// clang-format on
// Define TFL_CAPI_EXPORT macro to export a function properly with a shared
// library.
@@ -121,14 +133,14 @@
kTfLiteUInt32 = 16,
kTfLiteUInt16 = 17,
kTfLiteInt4 = 18,
+ kTfLiteBFloat16 = 19,
} TfLiteType;
-/// Legacy. Will be deprecated in favor of TfLiteAffineQuantization.
+/// Legacy. Will be deprecated in favor of `TfLiteAffineQuantization`.
/// If per-layer quantization is specified this field will still be populated in
-/// addition to TfLiteAffineQuantization.
+/// addition to `TfLiteAffineQuantization`.
/// Parameters for asymmetric quantization. Quantized values can be converted
-/// back to float using:
-/// real_value = scale * (quantized_value - zero_point)
+/// back to float using: `real_value = scale * (quantized_value - zero_point)`
typedef struct TfLiteQuantizationParams {
float scale;
int32_t zero_point;
@@ -156,6 +168,7 @@
/// This is an abstract type that is intended to have the same
/// role as TfLiteDelegate, but without exposing the implementation
/// details of how delegates are implemented.
+///
/// WARNING: This is an experimental type and subject to change.
typedef struct TfLiteOpaqueDelegateStruct TfLiteOpaqueDelegateStruct;
@@ -163,6 +176,7 @@
/// TfLiteDelegate; allows delegation of nodes to alternative backends.
/// For TF Lite in Play Services, this is an opaque type,
/// but for regular TF Lite, this is just a typedef for TfLiteDelegate.
+///
/// WARNING: This is an experimental type and subject to change.
#if TFLITE_WITH_STABLE_ABI || TFLITE_USE_OPAQUE_DELEGATE
typedef TfLiteOpaqueDelegateStruct TfLiteOpaqueDelegate;
diff --git a/tensorflow/lite/core/c/common.cc b/tensorflow/lite/core/c/common.cc
index 367f175..7afecdb 100644
--- a/tensorflow/lite/core/c/common.cc
+++ b/tensorflow/lite/core/c/common.cc
@@ -370,6 +370,8 @@
return "STRING";
case kTfLiteFloat16:
return "FLOAT16";
+ case kTfLiteBFloat16:
+ return "BFLOAT16";
case kTfLiteFloat64:
return "FLOAT64";
case kTfLiteResource:
@@ -384,40 +386,128 @@
TfLiteDelegate TfLiteDelegateCreate() { return TfLiteDelegate{}; }
-#ifndef TF_LITE_STATIC_MEMORY
-TfLiteOpaqueDelegate* TfLiteOpaqueDelegateCreate(
- const TfLiteOpaqueDelegateBuilder* opaque_delegate_builder) {
- if (!opaque_delegate_builder) return nullptr;
-
- TfLiteDelegate* result = new TfLiteDelegate{};
- result->opaque_delegate_builder = new TfLiteOpaqueDelegateBuilder{};
- *(result->opaque_delegate_builder) = *opaque_delegate_builder;
-
- return reinterpret_cast<TfLiteOpaqueDelegate*>(result);
+// Returns a tensor data allocation strategy.
+TfLiteAllocationStrategy TfLiteTensorGetAllocationStrategy(
+ const TfLiteTensor* const t) {
+ switch (t->allocation_type) {
+ case kTfLiteMemNone:
+ return kTfLiteAllocationStrategyNone;
+ case kTfLiteMmapRo:
+ return kTfLiteAllocationStrategyMMap;
+ case kTfLiteArenaRw:
+ return kTfLiteAllocationStrategyArena;
+ case kTfLiteArenaRwPersistent:
+ return kTfLiteAllocationStrategyArena;
+ case kTfLiteDynamic:
+ return kTfLiteAllocationStrategyMalloc;
+ case kTfLitePersistentRo:
+ return kTfLiteAllocationStrategyUnknown;
+ case kTfLiteCustom:
+ return kTfLiteAllocationStrategyUnknown;
+ case kTfLiteVariantObject:
+ return kTfLiteAllocationStrategyNew;
+ }
+ return kTfLiteAllocationStrategyUnknown;
}
-void TfLiteOpaqueDelegateDelete(TfLiteOpaqueDelegate* opaque_delegate) {
- if (!opaque_delegate) return;
-
- const TfLiteDelegate* tflite_delegate =
- reinterpret_cast<const TfLiteDelegate*>(opaque_delegate);
- delete tflite_delegate->opaque_delegate_builder;
- delete tflite_delegate;
+// Returns how stable a tensor data buffer address is across runs.
+TfLiteRunStability TfLiteTensorGetBufferAddressStability(
+ const TfLiteTensor* const t) {
+ switch (t->allocation_type) {
+ case kTfLiteMemNone:
+ return kTfLiteRunStabilityAcrossRuns;
+ case kTfLiteMmapRo:
+ return kTfLiteRunStabilityAcrossRuns;
+ case kTfLiteArenaRw:
+ return kTfLiteRunStabilityUnstable;
+ case kTfLiteArenaRwPersistent:
+ return kTfLiteRunStabilityUnstable;
+ case kTfLiteDynamic:
+ return kTfLiteRunStabilitySingleRun;
+ case kTfLitePersistentRo:
+ return kTfLiteRunStabilitySingleRun;
+ case kTfLiteCustom:
+ return kTfLiteRunStabilityUnknown;
+ case kTfLiteVariantObject:
+ return kTfLiteRunStabilityAcrossRuns;
+ }
+ return kTfLiteRunStabilityUnknown;
}
-#endif // TF_LITE_STATIC_MEMORY
-void* TfLiteOpaqueDelegateGetData(const TfLiteOpaqueDelegate* delegate) {
- if (!delegate) return nullptr;
+// Returns how stable a tensor data values are across runs.
+TfLiteRunStability TfLiteTensorGetDataStability(const TfLiteTensor* const t) {
+ switch (t->allocation_type) {
+ case kTfLiteMemNone:
+ return kTfLiteRunStabilityAcrossRuns;
+ case kTfLiteMmapRo:
+ return kTfLiteRunStabilityAcrossRuns;
+ case kTfLiteArenaRw:
+ return kTfLiteRunStabilitySingleRun;
+ case kTfLiteArenaRwPersistent:
+ return kTfLiteRunStabilityAcrossRuns;
+ case kTfLiteDynamic:
+ return kTfLiteRunStabilitySingleRun;
+ case kTfLitePersistentRo:
+ return kTfLiteRunStabilitySingleRun;
+ case kTfLiteCustom:
+ return kTfLiteRunStabilityUnknown;
+ case kTfLiteVariantObject:
+ return kTfLiteRunStabilitySingleRun;
+ }
+ return kTfLiteRunStabilityUnknown;
+}
- // The following cast is safe only because this code is part of the
- // TF Lite runtime implementation. Apps using TF Lite should not rely on
- // 'TfLiteOpaqueDelegate' and 'TfLiteDelegate' being equivalent.
- const auto* tflite_delegate =
- reinterpret_cast<const TfLiteDelegate*>(delegate);
+// Returns the operation step when the data of a tensor is populated.
+//
+// Some operations can precompute their results before the evaluation step. This
+// makes the data available earlier for subsequent operations.
+TfLiteRunStep TfLiteTensorGetDataKnownStep(const TfLiteTensor* t) {
+ switch (t->allocation_type) {
+ case kTfLiteMemNone:
+ return kTfLiteRunStepInit;
+ case kTfLiteMmapRo:
+ return kTfLiteRunStepInit;
+ case kTfLiteArenaRw:
+ return kTfLiteRunStepEval;
+ case kTfLiteArenaRwPersistent:
+ return kTfLiteRunStepEval;
+ case kTfLiteDynamic:
+ return kTfLiteRunStepEval;
+ case kTfLitePersistentRo:
+ return kTfLiteRunStepPrepare;
+ case kTfLiteCustom:
+ return kTfLiteRunStepUnknown;
+ case kTfLiteVariantObject:
+ return kTfLiteRunStepEval;
+ }
+ return kTfLiteRunStepUnknown;
+}
- if (!tflite_delegate->opaque_delegate_builder) return tflite_delegate->data_;
-
- return tflite_delegate->opaque_delegate_builder->data;
+// Returns the operation steop when the shape of a tensor is computed.
+//
+// Some operations can precompute the shape of their results before the
+// evaluation step. This makes the shape available earlier for subsequent
+// operations.
+TfLiteRunStep TfLiteTensorGetShapeKnownStep(const TfLiteTensor* t) {
+ switch (t->allocation_type) {
+ case kTfLiteMemNone:
+ return kTfLiteRunStepInit;
+ case kTfLiteMmapRo:
+ return kTfLiteRunStepInit;
+ case kTfLiteArenaRw:
+ return kTfLiteRunStepPrepare;
+ case kTfLiteArenaRwPersistent:
+ return kTfLiteRunStepPrepare;
+ case kTfLiteDynamic:
+ return kTfLiteRunStepEval;
+ case kTfLitePersistentRo:
+ return kTfLiteRunStepPrepare;
+ case kTfLiteCustom:
+ return kTfLiteRunStepUnknown;
+ case kTfLiteVariantObject:
+ return kTfLiteRunStepEval;
+ }
+ return kTfLiteRunStepUnknown;
}
} // extern "C"
diff --git a/tensorflow/lite/core/c/common.h b/tensorflow/lite/core/c/common.h
index 19a74a7..648b862 100644
--- a/tensorflow/lite/core/c/common.h
+++ b/tensorflow/lite/core/c/common.h
@@ -12,32 +12,41 @@
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
+// WARNING: Users of TensorFlow Lite should not include this file directly, but
+// should instead include "third_party/tensorflow/lite/c/common.h".
+// Only the TensorFlow Lite implementation itself should include this file
+// directly.
-// This file defines common C types and APIs for implementing operations,
-// delegates and other constructs in TensorFlow Lite. The actual operations and
-// delegates can be defined using C++, but the interface between the interpreter
-// and the operations are C.
-//
-// Summary of abstractions
-// TF_LITE_ENSURE - Self-sufficient error checking
-// TfLiteStatus - Status reporting
-// TfLiteIntArray - stores tensor shapes (dims),
-// TfLiteContext - allows an op to access the tensors
-// TfLiteTensor - tensor (a multidimensional array)
-// TfLiteNode - a single node or operation
-// TfLiteRegistration - the implementation of a conceptual operation.
-// TfLiteDelegate - allows delegation of nodes to alternative backends.
-//
-// Some abstractions in this file are created and managed by Interpreter.
-//
-// NOTE: The order of values in these structs are "semi-ABI stable". New values
-// should be added only to the end of structs and never reordered.
+/// This file defines common C types and APIs for implementing operations,
+/// delegates and other constructs in TensorFlow Lite. The actual operations and
+/// delegates can be defined using C++, but the interface between the
+/// interpreter and the operations are C.
+///
+/// Summary of abstractions:
+/// * `TF_LITE_ENSURE` - self-sufficient error checking
+/// * `TfLiteStatus` - status reporting
+/// * `TfLiteIntArray` - stores tensor shapes (dims),
+/// * `TfLiteContext` - allows an op to access the tensors
+/// * `TfLiteTensor` - tensor (a multidimensional array)
+/// * `TfLiteNode` - a single node or operation
+/// * `TfLiteRegistration` - the implementation of a conceptual operation.
+/// * `TfLiteDelegate` - allows delegation of nodes to alternative backends.
+///
+/// Some abstractions in this file are created and managed by Interpreter.
+///
+/// NOTE: The order of values in these structs are "semi-ABI stable". New values
+/// should be added only to the end of structs and never reordered.
+///
+// clang-format off
+// NOLINTBEGIN(whitespace/line_length)
+/// \note Users of TensorFlow Lite should use
+/// \code
+/// #include "tensorflow/lite/c/common.h"
+/// \endcode
+/// to access the APIs documented on this page.
+// NOLINTEND(whitespace/line_length)
+// clang-format on
-/// WARNING: Users of TensorFlow Lite should not include this file directly,
-/// but should instead include
-/// "third_party/tensorflow/lite/c/common.h".
-/// Only the TensorFlow Lite implementation itself should include this
-/// file directly.
// IWYU pragma: private, include "third_party/tensorflow/lite/c/common.h"
#ifndef TENSORFLOW_LITE_CORE_C_COMMON_H_
@@ -54,15 +63,23 @@
extern "C" {
#endif // __cplusplus
-// The list of external context types known to TF Lite. This list exists solely
-// to avoid conflicts and to ensure ops can share the external contexts they
-// need. Access to the external contexts is controlled by one of the
-// corresponding support files.
+// clang-format off
+// NOLINTBEGIN(whitespace/line_length)
+/** \defgroup common lite/c/common.h
+ * @{
+ */
+// NOLINTEND(whitespace/line_length)
+// clang-format on
+
+/// The list of external context types known to TF Lite. This list exists solely
+/// to avoid conflicts and to ensure ops can share the external contexts they
+/// need. Access to the external contexts is controlled by one of the
+/// corresponding support files.
typedef enum TfLiteExternalContextType {
- kTfLiteEigenContext = 0, // include eigen_support.h to use.
- kTfLiteGemmLowpContext = 1, // include gemm_support.h to use.
- kTfLiteEdgeTpuContext = 2, // Placeholder for Edge TPU support.
- kTfLiteCpuBackendContext = 3, // include cpu_backend_context.h to use.
+ kTfLiteEigenContext = 0, /// include eigen_support.h to use.
+ kTfLiteGemmLowpContext = 1, /// include gemm_support.h to use.
+ kTfLiteEdgeTpuContext = 2, /// Placeholder for Edge TPU support.
+ kTfLiteCpuBackendContext = 3, /// include cpu_backend_context.h to use.
kTfLiteMaxExternalContexts = 4
} TfLiteExternalContextType;
@@ -73,20 +90,22 @@
struct TfLiteRegistration;
struct TfLiteOpaqueDelegateBuilder;
-// An external context is a collection of information unrelated to the TF Lite
-// framework, but useful to a subset of the ops. TF Lite knows very little
-// about the actual contexts, but it keeps a list of them, and is able to
-// refresh them if configurations like the number of recommended threads
-// change.
+/// An external context is a collection of information unrelated to the TF Lite
+/// framework, but useful to a subset of the ops. TF Lite knows very little
+/// about the actual contexts, but it keeps a list of them, and is able to
+/// refresh them if configurations like the number of recommended threads
+/// change.
typedef struct TfLiteExternalContext {
TfLiteExternalContextType type;
TfLiteStatus (*Refresh)(struct TfLiteContext* context);
} TfLiteExternalContext;
+// LINT.IfChange(optional_tensor)
#define kTfLiteOptionalTensor (-1)
+// LINT.ThenChange(//tensorflow/compiler/mlir/lite/flatbuffer_export.cc:optional_tensor)
-// Fixed size list of integers. Used for dimensions and inputs/outputs tensor
-// indices
+/// Fixed size list of integers. Used for dimensions and inputs/outputs tensor
+/// indices
typedef struct TfLiteIntArray {
int size;
@@ -105,33 +124,33 @@
#endif
} TfLiteIntArray;
-// Given the size (number of elements) in a TfLiteIntArray, calculate its size
-// in bytes.
+/// Given the size (number of elements) in a TfLiteIntArray, calculate its size
+/// in bytes.
size_t TfLiteIntArrayGetSizeInBytes(int size);
#ifndef TF_LITE_STATIC_MEMORY
-// Create a array of a given `size` (uninitialized entries).
-// This returns a pointer, that you must free using TfLiteIntArrayFree().
+/// Create a array of a given `size` (uninitialized entries).
+/// This returns a pointer, that you must free using TfLiteIntArrayFree().
TfLiteIntArray* TfLiteIntArrayCreate(int size);
#endif
-// Check if two intarrays are equal. Returns 1 if they are equal, 0 otherwise.
+/// Check if two intarrays are equal. Returns 1 if they are equal, 0 otherwise.
int TfLiteIntArrayEqual(const TfLiteIntArray* a, const TfLiteIntArray* b);
-// Check if an intarray equals an array. Returns 1 if equals, 0 otherwise.
+/// Check if an intarray equals an array. Returns 1 if equals, 0 otherwise.
int TfLiteIntArrayEqualsArray(const TfLiteIntArray* a, int b_size,
const int b_data[]);
#ifndef TF_LITE_STATIC_MEMORY
-// Create a copy of an array passed as `src`.
-// You are expected to free memory with TfLiteIntArrayFree
+/// Create a copy of an array passed as `src`.
+/// You are expected to free memory with TfLiteIntArrayFree
TfLiteIntArray* TfLiteIntArrayCopy(const TfLiteIntArray* src);
-// Free memory of array `a`.
+/// Free memory of array `a`.
void TfLiteIntArrayFree(TfLiteIntArray* a);
#endif // TF_LITE_STATIC_MEMORY
-// Fixed size list of floats. Used for per-channel quantization.
+/// Fixed size list of floats. Used for per-channel quantization.
typedef struct TfLiteFloatArray {
int size;
#if defined(_MSC_VER)
@@ -149,20 +168,20 @@
#endif
} TfLiteFloatArray;
-// Given the size (number of elements) in a TfLiteFloatArray, calculate its size
-// in bytes.
+/// Given the size (number of elements) in a TfLiteFloatArray, calculate its
+/// size in bytes.
int TfLiteFloatArrayGetSizeInBytes(int size);
#ifndef TF_LITE_STATIC_MEMORY
-// Create a array of a given `size` (uninitialized entries).
-// This returns a pointer, that you must free using TfLiteFloatArrayFree().
+/// Create a array of a given `size` (uninitialized entries).
+/// This returns a pointer, that you must free using TfLiteFloatArrayFree().
TfLiteFloatArray* TfLiteFloatArrayCreate(int size);
-// Create a copy of an array passed as `src`.
-// You are expected to free memory with TfLiteFloatArrayFree.
+/// Create a copy of an array passed as `src`.
+/// You are expected to free memory with TfLiteFloatArrayFree.
TfLiteFloatArray* TfLiteFloatArrayCopy(const TfLiteFloatArray* src);
-// Free memory of array `a`.
+/// Free memory of array `a`.
void TfLiteFloatArrayFree(TfLiteFloatArray* a);
#endif // TF_LITE_STATIC_MEMORY
@@ -191,18 +210,18 @@
#define TF_LITE_MAYBE_KERNEL_LOG(context, ...) ARGS_UNUSED(__VA_ARGS__)
#endif // TF_LITE_STRIP_ERROR_STRINGS
-// Check whether value is true, and if not return kTfLiteError from
-// the current function (and report the error string msg).
-#define TF_LITE_ENSURE_MSG(context, value, msg) \
- do { \
- if (!(value)) { \
- TF_LITE_KERNEL_LOG((context), __FILE__ " " msg); \
- return kTfLiteError; \
- } \
+/// Check whether value is true, and if not return kTfLiteError from
+/// the current function (and report the error string msg).
+#define TF_LITE_ENSURE_MSG(context, value, ...) \
+ do { \
+ if (!(value)) { \
+ TF_LITE_KERNEL_LOG((context), __FILE__ " " __VA_ARGS__); \
+ return kTfLiteError; \
+ } \
} while (0)
-// Check whether the value `a` is true, and if not return kTfLiteError from
-// the current function, while also reporting the location of the error.
+/// Check whether the value `a` is true, and if not return kTfLiteError from
+/// the current function, while also reporting the location of the error.
#define TF_LITE_ENSURE(context, a) \
do { \
if (!(a)) { \
@@ -220,11 +239,12 @@
} \
} while (0)
-// Check whether the value `a == b` is true, and if not return kTfLiteError from
-// the current function, while also reporting the location of the error.
-// `a` and `b` may be evaluated more than once, so no side effects or
-// extremely expensive computations should be done.
-// NOTE: Use TF_LITE_ENSURE_TYPES_EQ if comparing TfLiteTypes.
+/// Check whether the value `a == b` is true, and if not return kTfLiteError
+/// from the current function, while also reporting the location of the error.
+/// `a` and `b` may be evaluated more than once, so no side effects or
+/// extremely expensive computations should be done.
+///
+/// NOTE: Use TF_LITE_ENSURE_TYPES_EQ if comparing TfLiteTypes.
#define TF_LITE_ENSURE_EQ(context, a, b) \
do { \
if ((a) != (b)) { \
@@ -263,61 +283,69 @@
} \
} while (0)
-// Single-precision complex data type compatible with the C99 definition.
+/// Single-precision complex data type compatible with the C99 definition.
typedef struct TfLiteComplex64 {
- float re, im; // real and imaginary parts, respectively.
+ float re, im; /// real and imaginary parts, respectively.
} TfLiteComplex64;
-// Double-precision complex data type compatible with the C99 definition.
+/// Double-precision complex data type compatible with the C99 definition.
typedef struct TfLiteComplex128 {
- double re, im; // real and imaginary parts, respectively.
+ double re, im; /// real and imaginary parts, respectively.
} TfLiteComplex128;
-// Half precision data type compatible with the C99 definition.
+/// Half precision data type compatible with the C99 definition.
typedef struct TfLiteFloat16 {
uint16_t data;
} TfLiteFloat16;
-// Return the name of a given type, for error reporting purposes.
+/// bfloat16 data type compatible with the Google Brain definition.
+/// https://cloud.google.com/tpu/docs/bfloat16.
+/// This provides 1 bit of sign, 8 bits of exponent, and 7 bits of mantissa.
+typedef struct TfLiteBFloat16 {
+ uint16_t data;
+} TfLiteBFloat16;
+
+/// Return the name of a given type, for error reporting purposes.
const char* TfLiteTypeGetName(TfLiteType type);
-// SupportedQuantizationTypes.
+/// SupportedQuantizationTypes.
typedef enum TfLiteQuantizationType {
- // No quantization.
+ /// No quantization.
kTfLiteNoQuantization = 0,
- // Affine quantization (with support for per-channel quantization).
- // Corresponds to TfLiteAffineQuantization.
+ /// Affine quantization (with support for per-channel quantization).
+ /// Corresponds to TfLiteAffineQuantization.
kTfLiteAffineQuantization = 1,
} TfLiteQuantizationType;
-// Structure specifying the quantization used by the tensor, if-any.
+/// Structure specifying the quantization used by the tensor, if-any.
typedef struct TfLiteQuantization {
- // The type of quantization held by params.
+ /// The type of quantization held by params.
TfLiteQuantizationType type;
- // Holds an optional reference to a quantization param structure. The actual
- // type depends on the value of the `type` field (see the comment there for
- // the values and corresponding types).
+ /// Holds an optional reference to a quantization param structure. The actual
+ /// type depends on the value of the `type` field (see the comment there for
+ /// the values and corresponding types).
void* params;
} TfLiteQuantization;
-// Parameters for asymmetric quantization across a dimension (i.e per output
-// channel quantization).
-// quantized_dimension specifies which dimension the scales and zero_points
-// correspond to.
-// For a particular value in quantized_dimension, quantized values can be
-// converted back to float using:
-// real_value = scale * (quantized_value - zero_point)
+/// Parameters for asymmetric quantization across a dimension (i.e per output
+/// channel quantization).
+/// quantized_dimension specifies which dimension the scales and zero_points
+/// correspond to.
+/// For a particular value in quantized_dimension, quantized values can be
+/// converted back to float using:
+/// `real_value = scale * (quantized_value - zero_point)`
typedef struct TfLiteAffineQuantization {
TfLiteFloatArray* scale;
TfLiteIntArray* zero_point;
int32_t quantized_dimension;
} TfLiteAffineQuantization;
-/* A union of pointers that points to memory for a given tensor. */
+/// A union of pointers that points to memory for a given tensor.
+///
+/// Do not access these members directly, if possible, use
+/// `GetTensorData<TYPE>(tensor)` instead, otherwise only access `.data`, as
+/// other members are deprecated.
typedef union TfLitePtrUnion {
- /* Do not access these members directly, if possible, use
- * GetTensorData<TYPE>(tensor) instead, otherwise only access .data, as other
- * members are deprecated. */
int32_t* i32;
uint32_t* u32;
int64_t* i64;
@@ -334,24 +362,26 @@
TfLiteComplex64* c64;
TfLiteComplex128* c128;
int8_t* int8;
- /* Only use this member. */
+ /// Only use this member.
void* data;
} TfLitePtrUnion;
-// Memory allocation strategies.
-// * kTfLiteMmapRo: Read-only memory-mapped data, or data externally allocated.
-// * kTfLiteArenaRw: Arena allocated with no guarantees about persistence,
-// and available during eval.
-// * kTfLiteArenaRwPersistent: Arena allocated but persistent across eval, and
-// only available during eval.
-// * kTfLiteDynamic: Allocated during eval, or for string tensors.
-// * kTfLitePersistentRo: Allocated and populated during prepare. This is
-// useful for tensors that can be computed during prepare and treated
-// as constant inputs for downstream ops (also in prepare).
-// * kTfLiteCustom: Custom memory allocation provided by the user. See
-// TfLiteCustomAllocation below.
-// * kTfLiteVariantObject: Allocation is an arbitrary type-erased C++ object.
-// Allocation and deallocation are done through `new` and `delete`.
+/// Memory allocation strategies.
+/// * `kTfLiteMmapRo`: Read-only memory-mapped data, or data externally
+/// allocated.
+/// * `kTfLiteArenaRw`: Arena allocated with no guarantees about persistence,
+/// and available during eval.
+/// * `kTfLiteArenaRwPersistent`: Arena allocated but persistent across eval,
+/// and only available during eval.
+/// * `kTfLiteDynamic`: Allocated during eval, or for string tensors.
+/// * `kTfLitePersistentRo`: Allocated and populated during prepare. This is
+/// useful for tensors that can be computed during prepare and treated
+/// as constant inputs for downstream ops (also in prepare).
+/// * `kTfLiteCustom`: Custom memory allocation provided by the user. See
+/// TfLiteCustomAllocation below.
+/// * `kTfLiteVariantObject`: Allocation is an arbitrary type-erased C++
+/// object.
+/// Allocation and deallocation are done through `new` and `delete`.
typedef enum TfLiteAllocationType {
kTfLiteMemNone = 0,
kTfLiteMmapRo,
@@ -363,20 +393,51 @@
kTfLiteVariantObject,
} TfLiteAllocationType;
-// The delegates should use zero or positive integers to represent handles.
-// -1 is reserved from unallocated status.
+/// Memory allocation strategies.
+///
+/// TfLiteAllocationType values have been overloaded to mean more than their
+/// original intent. This enum should only be used to document the allocation
+/// strategy used by a tensor for it data.
+typedef enum TfLiteAllocationStrategy {
+ kTfLiteAllocationStrategyUnknown,
+ kTfLiteAllocationStrategyNone, /// No data is allocated.
+ kTfLiteAllocationStrategyMMap, /// Data is mmaped.
+ kTfLiteAllocationStrategyArena, /// Handled by the arena.
+ kTfLiteAllocationStrategyMalloc, /// Uses `malloc`/`free`.
+ kTfLiteAllocationStrategyNew /// Uses `new[]`/`delete[]`.
+} TfLiteAllocationStrategy;
+
+/// Describes how stable a tensor attribute is with regards to an interpreter
+/// runs.
+typedef enum TfLiteRunStability {
+ kTfLiteRunStabilityUnknown,
+ kTfLiteRunStabilityUnstable, /// May change at any time.
+ kTfLiteRunStabilitySingleRun, /// Will stay the same for one run.
+ kTfLiteRunStabilityAcrossRuns /// Will stay the same across all runs.
+} TfLiteRunStability;
+
+/// Describes the steps of a TFLite operation life cycle.
+typedef enum TfLiteRunStep {
+ kTfLiteRunStepUnknown,
+ kTfLiteRunStepInit,
+ kTfLiteRunStepPrepare,
+ kTfLiteRunStepEval
+} TfLiteRunStep;
+
+/// The delegates should use zero or positive integers to represent handles.
+/// -1 is reserved from unallocated status.
typedef int TfLiteBufferHandle;
enum {
kTfLiteNullBufferHandle = -1,
};
-// Storage format of each dimension in a sparse tensor.
+/// Storage format of each dimension in a sparse tensor.
typedef enum TfLiteDimensionType {
kTfLiteDimDense = 0,
kTfLiteDimSparseCSR,
} TfLiteDimensionType;
-// Metadata to encode each dimension in a sparse tensor.
+/// Metadata to encode each dimension in a sparse tensor.
typedef struct TfLiteDimensionMetadata {
TfLiteDimensionType format;
int dense_size;
@@ -384,8 +445,8 @@
TfLiteIntArray* array_indices;
} TfLiteDimensionMetadata;
-// Parameters used to encode a sparse tensor. For detailed explanation of each
-// field please refer to lite/schema/schema.fbs.
+/// Parameters used to encode a sparse tensor. For detailed explanation of each
+/// field please refer to lite/schema/schema.fbs.
typedef struct TfLiteSparsity {
TfLiteIntArray* traversal_order;
TfLiteIntArray* block_map;
@@ -393,133 +454,141 @@
int dim_metadata_size;
} TfLiteSparsity;
-// Defines a custom memory allocation not owned by the runtime.
-// `data` should be aligned to kDefaultTensorAlignment defined in
-// lite/util.h. (Currently 64 bytes)
-// NOTE: See Interpreter.SetCustomAllocationForTensor for details on usage.
+/// Defines a custom memory allocation not owned by the runtime.
+/// `data` should be aligned to kDefaultTensorAlignment defined in
+/// lite/util.h. (Currently 64 bytes)
+/// NOTE: See `Interpreter::SetCustomAllocationForTensor` for details on usage.
typedef struct TfLiteCustomAllocation {
void* data;
size_t bytes;
} TfLiteCustomAllocation;
-// The flags used in `Interpreter::SetCustomAllocationForTensor`.
-// Note that this is a bitmask, so the values should be 1, 2, 4, 8, ...etc.
+/// The flags used in `Interpreter::SetCustomAllocationForTensor`.
+/// Note that this is a bitmask, so the values should be 1, 2, 4, 8, ...etc.
typedef enum TfLiteCustomAllocationFlags {
kTfLiteCustomAllocationFlagsNone = 0,
- // Skips checking whether allocation.data points to an aligned buffer as
- // expected by the TFLite runtime.
- // NOTE: Setting this flag can cause crashes when calling Invoke().
- // Use with caution.
+ /// Skips checking whether allocation.data points to an aligned buffer as
+ /// expected by the TFLite runtime.
+ /// NOTE: Setting this flag can cause crashes when calling Invoke().
+ /// Use with caution.
kTfLiteCustomAllocationFlagsSkipAlignCheck = 1,
} TfLiteCustomAllocationFlags;
-// A tensor in the interpreter system which is a wrapper around a buffer of
-// data including a dimensionality (or NULL if not currently defined).
+enum { kTfLiteNoBufferIdentifier = SIZE_MAX };
+
+/// A tensor in the interpreter system which is a wrapper around a buffer of
+/// data including a dimensionality (or NULL if not currently defined).
#ifndef TF_LITE_STATIC_MEMORY
typedef struct TfLiteTensor {
- // The data type specification for data stored in `data`. This affects
- // what member of `data` union should be used.
+ /// The data type specification for data stored in `data`. This affects
+ /// what member of `data` union should be used.
TfLiteType type;
- // A union of data pointers. The appropriate type should be used for a typed
- // tensor based on `type`.
+ /// A union of data pointers. The appropriate type should be used for a typed
+ /// tensor based on `type`.
TfLitePtrUnion data;
- // A pointer to a structure representing the dimensionality interpretation
- // that the buffer should have. NOTE: the product of elements of `dims`
- // and the element datatype size should be equal to `bytes` below.
+ /// A pointer to a structure representing the dimensionality interpretation
+ /// that the buffer should have. NOTE: the product of elements of `dims`
+ /// and the element datatype size should be equal to `bytes` below.
TfLiteIntArray* dims;
- // Quantization information.
+ /// Quantization information.
TfLiteQuantizationParams params;
- // How memory is mapped
- // kTfLiteMmapRo: Memory mapped read only.
- // i.e. weights
- // kTfLiteArenaRw: Arena allocated read write memory
- // (i.e. temporaries, outputs).
+ /// How memory is mapped
+ /// kTfLiteMmapRo: Memory mapped read only.
+ /// i.e. weights
+ /// kTfLiteArenaRw: Arena allocated read write memory
+ /// (i.e. temporaries, outputs).
TfLiteAllocationType allocation_type;
- // The number of bytes required to store the data of this Tensor. I.e.
- // (bytes of each element) * dims[0] * ... * dims[n-1]. For example, if
- // type is kTfLiteFloat32 and dims = {3, 2} then
- // bytes = sizeof(float) * 3 * 2 = 4 * 3 * 2 = 24.
+ /// The number of bytes required to store the data of this Tensor. I.e.
+ /// (bytes of each element) * dims[0] * ... * dims[n-1]. For example, if
+ /// type is kTfLiteFloat32 and dims = {3, 2} then
+ /// bytes = sizeof(float) * 3 * 2 = 4 * 3 * 2 = 24.
size_t bytes;
- // An opaque pointer to a tflite::MMapAllocation
+ /// An opaque pointer to a tflite::MMapAllocation
const void* allocation;
- // Null-terminated name of this tensor.
+ /// Null-terminated name of this tensor.
const char* name;
- // The delegate which knows how to handle `buffer_handle`.
- // WARNING: This is an experimental interface that is subject to change.
+ /// The delegate which knows how to handle `buffer_handle`.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
struct TfLiteDelegate* delegate;
- // An integer buffer handle that can be handled by `delegate`.
- // The value is valid only when delegate is not null.
- // WARNING: This is an experimental interface that is subject to change.
+ /// An integer buffer handle that can be handled by `delegate`.
+ /// The value is valid only when delegate is not null.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
TfLiteBufferHandle buffer_handle;
- // If the delegate uses its own buffer (e.g. GPU memory), the delegate is
- // responsible to set data_is_stale to true.
- // `delegate->CopyFromBufferHandle` can be called to copy the data from
- // delegate buffer.
- // WARNING: This is an // experimental interface that is subject to change.
+ /// If the delegate uses its own buffer (e.g. GPU memory), the delegate is
+ /// responsible to set data_is_stale to true.
+ /// `delegate->CopyFromBufferHandle` can be called to copy the data from
+ /// delegate buffer.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
bool data_is_stale;
- // True if the tensor is a variable.
+ /// True if the tensor is a variable.
bool is_variable;
- // Quantization information. Replaces params field above.
+ /// Quantization information. Replaces params field above.
TfLiteQuantization quantization;
- // Parameters used to encode a sparse tensor.
- // This is optional. The field is NULL if a tensor is dense.
- // WARNING: This is an experimental interface that is subject to change.
+ /// Parameters used to encode a sparse tensor.
+ /// This is optional. The field is NULL if a tensor is dense.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
TfLiteSparsity* sparsity;
- // Optional. Encodes shapes with unknown dimensions with -1. This field is
- // only populated when unknown dimensions exist in a read-write tensor (i.e.
- // an input or output tensor). (e.g. `dims` contains [1, 1, 1, 3] and
- // `dims_signature` contains [1, -1, -1, 3]). If no unknown dimensions exist
- // then `dims_signature` is either null, or set to an empty array. Note that
- // this field only exists when TF_LITE_STATIC_MEMORY is not defined.
+ /// Optional. Encodes shapes with unknown dimensions with -1. This field is
+ /// only populated when unknown dimensions exist in a read-write tensor (i.e.
+ /// an input or output tensor). (e.g. `dims` contains [1, 1, 1, 3] and
+ /// `dims_signature` contains [1, -1, -1, 3]). If no unknown dimensions exist
+ /// then `dims_signature` is either null, or set to an empty array. Note that
+ /// this field only exists when TF_LITE_STATIC_MEMORY is not defined.
const TfLiteIntArray* dims_signature;
} TfLiteTensor;
-// A structure representing an instance of a node.
-// This structure only exhibits the inputs, outputs, user defined data and some
-// node properties (like statefulness), not other features like the type.
+/// A structure representing an instance of a node.
+/// This structure only exhibits the inputs, outputs, user defined data and some
+/// node properties (like statefulness), not other features like the type.
typedef struct TfLiteNode {
- // Inputs to this node expressed as indices into the simulator's tensors.
+ /// Inputs to this node expressed as indices into the simulator's tensors.
TfLiteIntArray* inputs;
- // Outputs to this node expressed as indices into the simulator's tensors.
+ /// Outputs to this node expressed as indices into the simulator's tensors.
TfLiteIntArray* outputs;
- // intermediate tensors to this node expressed as indices into the simulator's
- // tensors.
+ /// intermediate tensors to this node expressed as indices into the
+ /// simulator's tensors.
TfLiteIntArray* intermediates;
- // Temporary tensors uses during the computations. This usually contains no
- // tensors, but ops are allowed to change that if they need scratch space of
- // any sort.
+ /// Temporary tensors uses during the computations. This usually contains no
+ /// tensors, but ops are allowed to change that if they need scratch space of
+ /// any sort.
TfLiteIntArray* temporaries;
- // Opaque data provided by the node implementer through `Registration.init`.
+ /// Opaque data provided by the node implementer through `Registration.init`.
void* user_data;
- // Opaque data provided to the node if the node is a builtin. This is usually
- // a structure defined in builtin_op_data.h
+ /// Opaque data provided to the node if the node is a builtin. This is usually
+ /// a structure defined in builtin_op_data.h
void* builtin_data;
- // Custom initial data. This is the opaque data provided in the flatbuffer.
- // WARNING: This is an experimental interface that is subject to change.
+ /// Custom initial data. This is the opaque data provided in the flatbuffer.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
const void* custom_initial_data;
int custom_initial_data_size;
- // The pointer to the delegate. This is non-null only when the node is
- // created by calling `interpreter.ModifyGraphWithDelegate`.
- // WARNING: This is an experimental interface that is subject to change.
+ /// The pointer to the delegate. This is non-null only when the node is
+ /// created by calling `interpreter.ModifyGraphWithDelegate`.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
struct TfLiteDelegate* delegate;
- // Whether this op might have side effect (e.g. stateful op).
+ /// Whether this op might have side effect (e.g. stateful op).
bool might_have_side_effect;
} TfLiteNode;
#else // defined(TF_LITE_STATIC_MEMORY)?
@@ -602,90 +671,89 @@
void* builtin_data;
// Custom initial data. This is the opaque data provided in the flatbuffer.
+ //
// WARNING: This is an experimental interface that is subject to change.
const void* custom_initial_data;
int custom_initial_data_size;
} TfLiteNode;
#endif // TF_LITE_STATIC_MEMORY
-// Light-weight tensor struct for TF Micro runtime. Provides the minimal amount
-// of information required for a kernel to run during TfLiteRegistration::Eval.
+/// Light-weight tensor struct for TF Micro runtime. Provides the minimal amount
+/// of information required for a kernel to run during TfLiteRegistration::Eval.
// TODO(b/160955687): Move this field into TF_LITE_STATIC_MEMORY when TFLM
// builds with this flag by default internally.
typedef struct TfLiteEvalTensor {
- // A union of data pointers. The appropriate type should be used for a typed
- // tensor based on `type`.
+ /// A union of data pointers. The appropriate type should be used for a typed
+ /// tensor based on `type`.
TfLitePtrUnion data;
- // A pointer to a structure representing the dimensionality interpretation
- // that the buffer should have.
+ /// A pointer to a structure representing the dimensionality interpretation
+ /// that the buffer should have.
TfLiteIntArray* dims;
- // The data type specification for data stored in `data`. This affects
- // what member of `data` union should be used.
+ /// The data type specification for data stored in `data`. This affects
+ /// what member of `data` union should be used.
TfLiteType type;
} TfLiteEvalTensor;
#ifndef TF_LITE_STATIC_MEMORY
-// Free data memory of tensor `t`.
+/// Free data memory of tensor `t`.
void TfLiteTensorDataFree(TfLiteTensor* t);
-// Free quantization data.
+/// Free quantization data.
void TfLiteQuantizationFree(TfLiteQuantization* quantization);
-// Free sparsity parameters.
+/// Free sparsity parameters.
void TfLiteSparsityFree(TfLiteSparsity* sparsity);
-// Free memory of tensor `t`.
+/// Free memory of tensor `t`.
void TfLiteTensorFree(TfLiteTensor* t);
-// Set all of a tensor's fields (and free any previously allocated data).
+/// Set all of a tensor's fields (and free any previously allocated data).
void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims,
TfLiteQuantizationParams quantization, char* buffer,
size_t size, TfLiteAllocationType allocation_type,
const void* allocation, bool is_variable,
TfLiteTensor* tensor);
-// Copies the contents of 'src' in 'dst'.
-// Function does nothing if either 'src' or 'dst' is passed as nullptr and
-// return kTfLiteOk.
-// Returns kTfLiteError if 'src' and 'dst' doesn't have matching data size.
-// Note function copies contents, so it won't create new data pointer
-// or change allocation type.
-// All Tensor related properties will be copied from 'src' to 'dst' like
-// quantization, sparsity, ...
+/// Copies the contents of `src` in `dst`.
+/// Function does nothing if either `src` or `dst` is passed as nullptr and
+/// return `kTfLiteOk`.
+/// Returns `kTfLiteError` if `src` and `dst` doesn't have matching data size.
+/// Note function copies contents, so it won't create new data pointer
+/// or change allocation type.
+/// All Tensor related properties will be copied from `src` to `dst` like
+/// quantization, sparsity, ...
TfLiteStatus TfLiteTensorCopy(const TfLiteTensor* src, TfLiteTensor* dst);
-// Change the size of the memory block owned by `tensor` to `num_bytes`.
-// Tensors with allocation types other than `kTfLiteDynamic` will be ignored and
-// a kTfLiteOk will be returned.
-// `tensor`'s internal data buffer will be assigned a pointer
-// which can safely be passed to free or realloc if `num_bytes` is zero.
-// If `preserve_data` is true, tensor data will be unchanged in the range from
-// the start of the region up to the minimum of the old and new sizes. In the
-// case of NULL tensor, or an error allocating new memory, returns
-// `kTfLiteError`.
+/// Change the size of the memory block owned by `tensor` to `num_bytes`.
+/// Tensors with allocation types other than `kTfLiteDynamic` will be ignored
+/// and a `kTfLiteOk` will be returned. `tensor`'s internal data buffer will be
+/// assigned a pointer which can safely be passed to free or realloc if
+/// `num_bytes` is zero. If `preserve_data` is true, tensor data will be
+/// unchanged in the range from the start of the region up to the minimum of the
+/// old and new sizes. In the case of NULL tensor, or an error allocating new
+/// memory, returns `kTfLiteError`.
TfLiteStatus TfLiteTensorResizeMaybeCopy(size_t num_bytes, TfLiteTensor* tensor,
bool preserve_data);
-// Change the size of the memory block owned by `tensor` to `num_bytes`.
-// Tensors with allocation types other than kTfLiteDynamic will be ignored and
-// a kTfLiteOk will be returned.
-// `tensor`'s internal data buffer will be assigned a pointer
-// which can safely be passed to free or realloc if `num_bytes` is zero.
-// Tensor data will be unchanged in the range from the start of the region up to
-// the minimum of the old and new sizes. In the case
-// of NULL tensor, or an error allocating new memory, returns `kTfLiteError`.
+/// Change the size of the memory block owned by `tensor` to `num_bytes`.
+/// Tensors with allocation types other than `kTfLiteDynamic` will be ignored
+/// and a `kTfLiteOk` will be returned. `tensor`'s internal data buffer will be
+/// assigned a pointer which can safely be passed to free or realloc if
+/// `num_bytes` is zero. Tensor data will be unchanged in the range from the
+/// start of the region up to the minimum of the old and new sizes. In the case
+/// of NULL tensor, or an error allocating new memory, returns `kTfLiteError`.
TfLiteStatus TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor);
#endif // TF_LITE_STATIC_MEMORY
-// WARNING: This is an experimental interface that is subject to change.
-//
-// Currently, TfLiteDelegateParams has to be allocated in a way that it's
-// trivially destructable. It will be stored as `builtin_data` field in
-// `TfLiteNode` of the delegate node.
-//
-// See also the `CreateDelegateParams` function in `interpreter.cc` details.
+/// WARNING: This is an experimental interface that is subject to change.
+///
+/// Currently, TfLiteDelegateParams has to be allocated in a way that it's
+/// trivially destructable. It will be stored as `builtin_data` field in
+/// `TfLiteNode` of the delegate node.
+///
+/// See also the `CreateDelegateParams` function in `interpreter.cc` details.
typedef struct TfLiteDelegateParams {
struct TfLiteDelegate* delegate;
TfLiteIntArray* nodes_to_replace;
@@ -693,14 +761,14 @@
TfLiteIntArray* output_tensors;
} TfLiteDelegateParams;
-// WARNING: This is an experimental interface that is subject to change.
-//
-// Currently, TfLiteOpaqueDelegateParams has to be allocated in a way that it's
-// trivially destructable. It will be stored as `builtin_data` field in
-// `TfLiteNode` of the delegate node.
-//
-// See also the `CreateOpaqueDelegateParams` function in `subgraph.cc`
-// details.
+/// WARNING: This is an experimental interface that is subject to change.
+///
+/// Currently, TfLiteOpaqueDelegateParams has to be allocated in a way that it's
+/// trivially destructable. It will be stored as `builtin_data` field in
+/// `TfLiteNode` of the delegate node.
+///
+/// See also the `CreateOpaqueDelegateParams` function in `subgraph.cc`
+/// details.
typedef struct TfLiteOpaqueDelegateParams {
TfLiteOpaqueDelegate* delegate;
void* delegate_data;
@@ -709,371 +777,430 @@
TfLiteIntArray* output_tensors;
} TfLiteOpaqueDelegateParams;
+/// `TfLiteContext` allows an op to access the tensors.
+///
+/// `TfLiteContext` is a struct that is created by the TF Lite runtime
+/// and passed to the "methods" (C function pointers) in the
+/// `TfLiteRegistration` struct that are used to define custom ops and custom
+/// delegate kernels. It contains information and methods (C function pointers)
+/// that can be called by the code implementing a custom op or a custom delegate
+/// kernel. These methods provide access to the context in which that custom op
+/// or custom delegate kernel occurs, such as access to the input and output
+/// tensors for that op, as well as methods for allocating memory buffers
+/// and intermediate tensors, etc.
+///
+/// See also `TfLiteOpaqueContext`, which is an more ABI-stable equivalent.
typedef struct TfLiteContext {
- // Number of tensors in the context.
+ /// Number of tensors in the context.
size_t tensors_size;
- // The execution plan contains a list of the node indices in execution
- // order. execution_plan->size is the current number of nodes. And,
- // execution_plan->data[0] is the first node that needs to be run.
- // TfLiteDelegates can traverse the current execution plan by iterating
- // through each member of this array and using GetNodeAndRegistration() to
- // access details about a node. i.e.
- //
- // TfLiteIntArray* execution_plan;
- // TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &execution_plan));
- // for (int exec_index = 0; exec_index < execution_plan->size; exec_index++) {
- // int node_index = execution_plan->data[exec_index];
- // TfLiteNode* node;
- // TfLiteRegistration* reg;
- // context->GetNodeAndRegistration(context, node_index, &node, ®);
- // }
- // Note: the memory pointed by '`*execution_plan` is OWNED by TfLite runtime.
- // Future calls to GetExecutionPlan invalidates earlier outputs. The following
- // code snippet shows the issue of such an invocation pattern. After calling
- // CheckNode, subsequent access to `plan_1st` is undefined.
- //
- // void CheckNode(const TfLiteNode* node) {
- // ...
- // TfLiteIntArray* plan_2nd;
- // TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &plan_2nd));
- // ...
- // }
- //
- // TfLiteIntArray* plan_1st;
- // TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &plan_1st));
- // for (int exec_index = 0; exec_index < plan_1st->size; exec_index++) {
- // int node_index = plan_1st->data[exec_index];
- // TfLiteNode* node;
- // TfLiteRegistration* reg;
- // context->GetNodeAndRegistration(context, node_index, &node, ®);
- // CheckNode(node);
- // }
- //
- // WARNING: This is an experimental interface that is subject to change.
+ /// The execution plan contains a list of the node indices in execution
+ /// order. execution_plan->size is the current number of nodes. And,
+ /// execution_plan->data[0] is the first node that needs to be run.
+ /// TfLiteDelegates can traverse the current execution plan by iterating
+ /// through each member of this array and using GetNodeAndRegistration() to
+ /// access details about a node. i.e.
+ ///
+ ///
+ /// TfLiteIntArray* execution_plan;
+ /// TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context,
+ /// &execution_plan));
+ /// for (int exec_index = 0; exec_index < execution_plan->size;
+ /// exec_index++) {
+ /// int node_index = execution_plan->data[exec_index];
+ /// TfLiteNode* node;
+ /// TfLiteRegistration* reg;
+ /// context->GetNodeAndRegistration(context, node_index, &node, ®);
+ /// }
+ ///
+ /// Note: the memory pointed by '`*execution_plan` is OWNED by TfLite runtime.
+ /// Future calls to GetExecutionPlan invalidates earlier outputs. The
+ /// following code snippet shows the issue of such an invocation pattern.
+ /// After calling CheckNode, subsequent access to `plan_1st` is undefined.
+ ///
+ /// void CheckNode(const TfLiteNode* node) {
+ /// ...
+ /// TfLiteIntArray* plan_2nd;
+ /// TF_LITE_ENSURE_STATUS(
+ /// context->GetExecutionPlan(context, &plan_2nd)
+ /// );
+ /// ...
+ /// }
+ ///
+ /// TfLiteIntArray* plan_1st;
+ /// TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &plan_1st));
+ /// for (int exec_index = 0; exec_index < plan_1st->size; exec_index++) {
+ /// int node_index = plan_1st->data[exec_index];
+ /// TfLiteNode* node;
+ /// TfLiteRegistration* reg;
+ /// context->GetNodeAndRegistration(context, node_index, &node, ®);
+ /// CheckNode(node);
+ /// }
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*GetExecutionPlan)(struct TfLiteContext* context,
TfLiteIntArray** execution_plan);
- // An array of tensors in the interpreter context (of length `tensors_size`)
+ /// An array of tensors in the interpreter context (of length `tensors_size`)
TfLiteTensor* tensors;
- // opaque full context ptr (an opaque c++ data structure)
+ /// opaque full context ptr (an opaque c++ data structure)
void* impl_;
- // Request memory pointer be resized. Updates dimensions on the tensor.
- // NOTE: ResizeTensor takes ownership of newSize.
+ /// Request memory pointer be resized. Updates dimensions on the tensor.
+ /// NOTE: ResizeTensor takes ownership of newSize.
TfLiteStatus (*ResizeTensor)(struct TfLiteContext*, TfLiteTensor* tensor,
TfLiteIntArray* new_size);
- // Request that an error be reported with format string msg.
+ /// Request that an error be reported with format string msg.
void (*ReportError)(struct TfLiteContext*, const char* msg, ...);
- // Add `tensors_to_add` tensors, preserving pre-existing Tensor entries. If
- // non-null, the value pointed to by `first_new_tensor_index` will be set to
- // the index of the first new tensor.
+ /// Add `tensors_to_add` tensors, preserving pre-existing Tensor entries. If
+ /// non-null, the value pointed to by `first_new_tensor_index` will be set to
+ /// the index of the first new tensor.
TfLiteStatus (*AddTensors)(struct TfLiteContext*, int tensors_to_add,
int* first_new_tensor_index);
- // Get a Tensor node by node_index.
- // WARNING: This is an experimental interface that is subject to change.
+ /// Get a Tensor node by node_index.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*GetNodeAndRegistration)(
struct TfLiteContext*, int node_index, TfLiteNode** node,
struct TfLiteRegistration** registration);
- // Replace ops with one or more stub delegate operations. This function
- // does not take ownership of `nodes_to_replace`.
+ /// Replace ops with one or more stub delegate operations. This function
+ /// does not take ownership of `nodes_to_replace`.
TfLiteStatus (*ReplaceNodeSubsetsWithDelegateKernels)(
struct TfLiteContext*, struct TfLiteRegistration registration,
const TfLiteIntArray* nodes_to_replace, struct TfLiteDelegate* delegate);
- // Number of threads that are recommended to subsystems like gemmlowp and
- // eigen.
+ /// Number of threads that are recommended to subsystems like gemmlowp and
+ /// eigen.
int recommended_num_threads;
- // Access external contexts by type.
- // WARNING: This is an experimental interface that is subject to change.
+ /// Access external contexts by type.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
TfLiteExternalContext* (*GetExternalContext)(struct TfLiteContext*,
TfLiteExternalContextType);
- // Set the value of a external context. Does not take ownership of the
- // pointer.
- // WARNING: This is an experimental interface that is subject to change.
+ /// Set the value of a external context. Does not take ownership of the
+ /// pointer.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
void (*SetExternalContext)(struct TfLiteContext*, TfLiteExternalContextType,
TfLiteExternalContext*);
- // Flag for allowing float16 precision for FP32 calculation.
- // default: false.
- // WARNING: This is an experimental API and subject to change.
+ /// Flag for allowing float16 precision for FP32 calculation.
+ /// default: false.
+ ///
+ /// WARNING: This is an experimental API and subject to change.
bool allow_fp32_relax_to_fp16;
- // Pointer to the op-level profiler, if set; nullptr otherwise.
+ /// Pointer to the op-level profiler, if set; nullptr otherwise.
void* profiler;
- // Allocate persistent buffer which has the same life time as the interpreter.
- // Returns nullptr on failure.
- // The memory is allocated from heap for TFL, and from tail in TFLM.
- // This method is only available in Init or Prepare stage.
- // WARNING: This is an experimental interface that is subject to change.
+ /// Allocate persistent buffer which has the same life time as the
+ /// interpreter. Returns `nullptr` on failure. The memory is allocated from
+ /// heap for TFL, and from tail in TFLM. This method is only available in
+ /// `Init` or `Prepare` stage.
+ ///
+ /// WARNING: This is an experimental interface that is subject
+ /// to change.
void* (*AllocatePersistentBuffer)(struct TfLiteContext* ctx, size_t bytes);
- // Allocate a buffer which will be deallocated right after invoke phase.
- // The memory is allocated from heap in TFL, and from volatile arena in TFLM.
- // This method is only available in invoke stage.
- // NOTE: If possible use RequestScratchBufferInArena method to avoid memory
- // allocation during inference time.
- // WARNING: This is an experimental interface that is subject to change.
+ /// Allocate a buffer which will be deallocated right after invoke phase.
+ /// The memory is allocated from heap in TFL, and from volatile arena in TFLM.
+ /// This method is only available in invoke stage.
+ ///
+ /// NOTE: If possible use `RequestScratchBufferInArena` method to avoid memory
+ /// allocation during inference time.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*AllocateBufferForEval)(struct TfLiteContext* ctx, size_t bytes,
void** ptr);
- // Request a scratch buffer in the arena through static memory planning.
- // This method is only available in Prepare stage and the buffer is allocated
- // by the interpreter between Prepare and Eval stage. In Eval stage,
- // GetScratchBuffer API can be used to fetch the address.
- // WARNING: This is an experimental interface that is subject to change.
+ /// Request a scratch buffer in the arena through static memory planning.
+ /// This method is only available in `Prepare` stage and the buffer is
+ /// allocated by the interpreter between Prepare and Eval stage. In `Eval`
+ /// stage, `GetScratchBuffer` API can be used to fetch the address.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*RequestScratchBufferInArena)(struct TfLiteContext* ctx,
size_t bytes, int* buffer_idx);
- // Get the scratch buffer pointer.
- // This method is only available in Eval stage.
- // WARNING: This is an experimental interface that is subject to change.
+ /// Get the scratch buffer pointer.
+ /// This method is only available in Eval stage.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
void* (*GetScratchBuffer)(struct TfLiteContext* ctx, int buffer_idx);
- // Resize the memory pointer of the `tensor`. This method behaves the same as
- // `ResizeTensor`, except that it makes a copy of the shape array internally
- // so the shape array could be deallocated right afterwards.
- // WARNING: This is an experimental interface that is subject to change.
+ /// Resize the memory pointer of the `tensor`. This method behaves the same as
+ /// `ResizeTensor`, except that it makes a copy of the shape array internally
+ /// so the shape array could be deallocated right afterwards.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*ResizeTensorExplicit)(struct TfLiteContext* ctx,
TfLiteTensor* tensor, int dims,
const int* shape);
- // This method provides a preview of post-delegation partitioning. Each
- // TfLiteDelegateParams in the referenced array corresponds to one instance of
- // the delegate kernel.
- // Example usage:
- //
- // TfLiteIntArray* nodes_to_replace = ...;
- // TfLiteDelegateParams* params_array;
- // int num_partitions = 0;
- // TF_LITE_ENSURE_STATUS(context->PreviewDelegatePartitioning(
- // context, delegate, nodes_to_replace, ¶ms_array, &num_partitions));
- // for (int idx = 0; idx < num_partitions; idx++) {
- // const auto& partition_params = params_array[idx];
- // ...
- // }
- //
- // NOTE: The context owns the memory referenced by partition_params_array. It
- // will be cleared with another call to PreviewDelegatePartitioning, or after
- // TfLiteDelegateParams::Prepare returns.
- //
- // WARNING: This is an experimental interface that is subject to change.
+ /// This method provides a preview of post-delegation partitioning. Each
+ /// TfLiteDelegateParams in the referenced array corresponds to one instance
+ /// of the delegate kernel. Example usage:
+ ///
+ /// TfLiteIntArray* nodes_to_replace = ...;
+ /// TfLiteDelegateParams* params_array;
+ /// int num_partitions = 0;
+ /// TF_LITE_ENSURE_STATUS(context->PreviewDelegatePartitioning(
+ /// context, delegate, nodes_to_replace, ¶ms_array,
+ /// &num_partitions));
+ /// for (int idx = 0; idx < num_partitions; idx++) {
+ /// const auto& partition_params = params_array[idx];
+ /// ...
+ /// }
+ ///
+ /// NOTE: The context owns the memory referenced by partition_params_array. It
+ /// will be cleared with another call to PreviewDelegatePartitioning, or after
+ /// TfLiteDelegateParams::Prepare returns.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*PreviewDelegatePartitioning)(
struct TfLiteContext* context, const TfLiteIntArray* nodes_to_replace,
TfLiteDelegateParams** partition_params_array, int* num_partitions);
- // Returns a TfLiteTensor struct for a given index.
- // WARNING: This is an experimental interface that is subject to change.
- // WARNING: This method may not be available on all platforms.
+ /// Returns a TfLiteTensor struct for a given index.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
+ ///
+ /// WARNING: This method may not be available on all platforms.
TfLiteTensor* (*GetTensor)(const struct TfLiteContext* context,
int tensor_idx);
- // Returns a TfLiteEvalTensor struct for a given index.
- // WARNING: This is an experimental interface that is subject to change.
- // WARNING: This method may not be available on all platforms.
+ /// Returns a TfLiteEvalTensor struct for a given index.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
+ ///
+ /// WARNING: This method may not be available on all platforms.
TfLiteEvalTensor* (*GetEvalTensor)(const struct TfLiteContext* context,
int tensor_idx);
- // Retrieves named metadata buffer from the TFLite model.
- // Returns kTfLiteOk if metadata is successfully obtained from the flatbuffer
- // Model: that is, there exists a `metadata` entry with given `name` string.
- // (see TFLite's schema.fbs).
- // The corresponding `buffer` information is populated in `ptr` & `bytes`.
- // The data from `ptr` is valid for the lifetime of the Interpreter.
- //
- // WARNING: This is an experimental interface that is subject to change.
+ /// Retrieves named metadata buffer from the TFLite model.
+ /// Returns kTfLiteOk if metadata is successfully obtained from the flatbuffer
+ /// Model: that is, there exists a `metadata` entry with given `name` string.
+ /// (see TFLite's schema.fbs).
+ /// The corresponding `buffer` information is populated in `ptr` & `bytes`.
+ /// The data from `ptr` is valid for the lifetime of the Interpreter.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*GetModelMetadata)(const struct TfLiteContext* context,
const char* name, const char** ptr,
size_t* bytes);
- // Retrieves the corresponding TfLiteContext of a subgraph that the given
- // subgraph_index points to and switches to the delegate context for that
- // subgraph. If an invalid subgraph index is given, returns kTfLiteError.
- // NOTE: This function is expected to be paired with ReleaseSubgraphContext()
- // once the delegate preparation is done and/or the delegate context functions
- // are no longer needed.
- //
- // WARNING: This is an experimental interface that is subject to change.
+ /// Retrieves the corresponding TfLiteContext of a subgraph that the given
+ /// subgraph_index points to and switches to the delegate context for that
+ /// subgraph. If an invalid subgraph index is given, returns kTfLiteError.
+ ///
+ /// NOTE: This function is expected to be paired with ReleaseSubgraphContext()
+ /// once the delegate preparation is done and/or the delegate context
+ /// functions are no longer needed.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*AcquireSubgraphContext)(
struct TfLiteContext* context, int subgraph_index,
struct TfLiteContext** acquired_context);
- // Releases the subgraph context by switching back to the TFLite kernel
- // context for the subgraph that the given subgraph_index points to.
- // NOTE: This function is expected to be used after AcquireSubgraphContext()
- // once the delegate preparation is done and/or the delegate context functions
- // are no longer needed.
- //
- // WARNING: This is an experimental interface that is subject to change.
+ /// Releases the subgraph context by switching back to the TFLite kernel
+ /// context for the subgraph that the given subgraph_index points to.
+ ///
+ /// NOTE: This function is expected to be used after AcquireSubgraphContext()
+ /// once the delegate preparation is done and/or the delegate context
+ /// functions are no longer needed.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*ReleaseSubgraphContext)(struct TfLiteContext* context,
int subgraph_index);
} TfLiteContext;
-// `TfLiteRegistrationExternal` is an external version of `TfLiteRegistration`
-// for C API which doesn't use internal types (such as `TfLiteContext`) but only
-// uses stable API types (such as `TfLiteOpaqueContext`). The purpose of each
-// field is the exactly the same as with `TfLiteRegistration`.
-typedef struct TfLiteRegistrationExternal TfLiteRegistrationExternal;
+/// `TfLiteOperator` is an external version of `TfLiteRegistration`
+/// for C API which doesn't use internal types (such as `TfLiteContext`) but
+/// only uses stable API types (such as `TfLiteOpaqueContext`). The purpose of
+/// each field is the exactly the same as with `TfLiteRegistration`.
+typedef struct TfLiteOperator TfLiteOperator;
-// The valid values of the `inplace_operator` field in `TfLiteRegistration`.
-// This allow an op to signal to the runtime that the same data pointer
-// may be passed as an input and output without impacting the result.
-// This does not mean that the memory can safely be reused, it is up to the
-// runtime to determine this, e.g. if another op consumes the same input or not
-// or if an input tensor has sufficient memory allocated to store the output
-// data.
-//
-// Setting these flags authorizes the runtime to set the data pointers of an
-// input and output tensor to the same value. In such cases, the memory required
-// by the output must be less than or equal to that required by the shared
-// input, never greater. If kTfLiteInplaceOpDataUnmodified is set, then the
-// runtime can share the same input tensor with multiple operator's outputs,
-// provided that kTfLiteInplaceOpDataUnmodified is set for all of them.
-// Otherwise, if an input tensor is consumed by multiple operators, it may only
-// be shared with the operator which is the last to consume it.
-//
-// Note that this is a bitmask, so the values should be 1, 2, 4, 8, ...etc.
+#ifndef DOXYGEN_SKIP
+// For backwards compatibility.
+// Deprecated. Use TfLiteOperator instead.
+typedef TfLiteOperator TfLiteRegistrationExternal;
+#endif
+
+/// The valid values of the `inplace_operator` field in `TfLiteRegistration`.
+/// This allow an op to signal to the runtime that the same data pointer
+/// may be passed as an input and output without impacting the result.
+/// This does not mean that the memory can safely be reused, it is up to the
+/// runtime to determine this, e.g. if another op consumes the same input or not
+/// or if an input tensor has sufficient memory allocated to store the output
+/// data.
+///
+/// Setting these flags authorizes the runtime to set the data pointers of an
+/// input and output tensor to the same value. In such cases, the memory
+/// required by the output must be less than or equal to that required by the
+/// shared input, never greater. If kTfLiteInplaceOpDataUnmodified is set, then
+/// the runtime can share the same input tensor with multiple operator's
+/// outputs, provided that kTfLiteInplaceOpDataUnmodified is set for all of
+/// them. Otherwise, if an input tensor is consumed by multiple operators, it
+/// may only be shared with the operator which is the last to consume it.
+///
+/// Note that this is a bitmask, so the values should be 1, 2, 4, 8, ...etc.
typedef enum {
- // The default value. This indicates that the same data pointer cannot safely
- // be passed as an op's input and output.
+ /// The default value. This indicates that the same data pointer cannot safely
+ /// be passed as an op's input and output.
kTfLiteInplaceOpNone = 0,
- // This indicates that an op's first output's data is identical to its first
- // input's data, for example Reshape.
+ /// This indicates that an op's first output's data is identical to its first
+ /// input's data, for example Reshape.
kTfLiteInplaceOpDataUnmodified = 1,
- // Setting kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput means
- // that InputN may be shared with OutputN instead of with the first output.
- // This flag requires one or more of kTfLiteInplaceOpInputNShared to be set.
+ /// Setting kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput means
+ /// that InputN may be shared with OutputN instead of with the first output.
+ /// This flag requires one or more of kTfLiteInplaceOpInputNShared to be set.
kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput = 2,
- // kTfLiteInplaceOpInputNShared indicates that it is safe for an op to share
- // InputN's data pointer with an output tensor. If
- // kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput is set then
- // kTfLiteInplaceOpInputNShared indicates that InputN may be shared
- // with OutputN, otherwise kTfLiteInplaceOpInputNShared indicates that InputN
- // may be shared with the first output.
- //
- // Indicates that an op's first input may be shared with the first output
- // tensor. kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput has
- // no impact on the behavior allowed by this flag.
+ /// kTfLiteInplaceOpInputNShared indicates that it is safe for an op to share
+ /// InputN's data pointer with an output tensor. If
+ /// kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput is set then
+ /// kTfLiteInplaceOpInputNShared indicates that InputN may be shared
+ /// with OutputN, otherwise kTfLiteInplaceOpInputNShared indicates that InputN
+ /// may be shared with the first output.
+ ///
+ /// Indicates that an op's first input may be shared with the first output
+ /// tensor. kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput has
+ /// no impact on the behavior allowed by this flag.
kTfLiteInplaceOpInput0Shared = 4,
- // Indicates that an op's second input may be shared with the first output
- // if kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput is not set
- // or second output if kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput
- // is set.
+ /// Indicates that an op's second input may be shared with the first output
+ /// if kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput is not set
+ /// or second output if kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput
+ /// is set.
kTfLiteInplaceOpInput1Shared = 8,
- // Indicates that an op's third input may be shared with the first output
- // if kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput is not set
- // or third output if kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput is
- // set.
+ /// Indicates that an op's third input may be shared with the first output
+ /// if kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput is not set
+ /// or third output if kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput
+ /// is
+ /// set.
kTfLiteInplaceOpInput2Shared = 16,
- // Placeholder to ensure that enum can hold 64 bit values to accommodate
- // future fields.
+ /// Placeholder to ensure that enum can hold 64 bit values to accommodate
+ /// future fields.
kTfLiteInplaceOpMaxValue = UINT64_MAX,
} TfLiteInPlaceOp;
-// The number of shareable inputs supported.
+/// The number of shareable inputs supported.
static const int kTfLiteMaxSharableOpInputs = 3;
+/// `TfLiteRegistration` defines the implementation of an operation
+/// (a built-in op, custom op, or custom delegate kernel).
+///
+/// It is a struct containing "methods" (C function pointers) that will be
+/// invoked by the TF Lite runtime to evaluate instances of the operation.
+///
+/// See also `TfLiteOperator` which is a more ABI-stable equivalent.
typedef struct TfLiteRegistration {
- // Initializes the op from serialized data.
- // Called only *once* for the lifetime of the op, so any one-time allocations
- // should be made here (unless they depend on tensor sizes).
- //
- // If a built-in op:
- // `buffer` is the op's params data (TfLiteLSTMParams*).
- // `length` is zero.
- // If custom op:
- // `buffer` is the op's `custom_options`.
- // `length` is the size of the buffer.
- //
- // Returns a type-punned (i.e. void*) opaque data (e.g. a primitive pointer
- // or an instance of a struct).
- //
- // The returned pointer will be stored with the node in the `user_data` field,
- // accessible within prepare and invoke functions below.
- // NOTE: if the data is already in the desired format, simply implement this
- // function to return `nullptr` and implement the free function to be a no-op.
+ /// Initializes the op from serialized data.
+ /// Called only *once* for the lifetime of the op, so any one-time allocations
+ /// should be made here (unless they depend on tensor sizes).
+ ///
+ /// * If a built-in op:
+ /// * `buffer` is the op's params data (TfLiteLSTMParams*).
+ /// * `length` is zero.
+ /// * If custom op:
+ /// * `buffer` is the op's `custom_options`.
+ /// * `length` is the size of the buffer.
+ ///
+ /// Returns a type-punned (i.e. void*) opaque data (e.g. a primitive pointer
+ /// or an instance of a struct).
+ ///
+ /// The returned pointer will be stored with the node in the `user_data`
+ /// field, accessible within prepare and invoke functions below.
+ ///
+ /// NOTE: if the data is already in the desired format, simply implement this
+ /// function to return `nullptr` and implement the free function to be a
+ /// no-op.
void* (*init)(TfLiteContext* context, const char* buffer, size_t length);
- // The pointer `buffer` is the data previously returned by an init invocation.
+ /// The pointer `buffer` is the data previously returned by an init
+ /// invocation.
void (*free)(TfLiteContext* context, void* buffer);
- // prepare is called when the inputs this node depends on have been resized.
- // context->ResizeTensor() can be called to request output tensors to be
- // resized.
- // Can be called multiple times for the lifetime of the op.
- //
- // Returns kTfLiteOk on success.
+ /// prepare is called when the inputs this node depends on have been resized.
+ /// `context->ResizeTensor()` can be called to request output tensors to be
+ /// resized.
+ /// Can be called multiple times for the lifetime of the op.
+ ///
+ /// Returns `kTfLiteOk` on success.
TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node);
- // Execute the node (should read node->inputs and output to node->outputs).
- // Returns kTfLiteOk on success.
+ /// Execute the node (should read `node->inputs` and output to
+ /// `node->outputs`).
+ ///
+ /// Returns `kTfLiteOk` on success.
TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node);
- // profiling_string is called during summarization of profiling information
- // in order to group executions together. Providing a value here will cause a
- // given op to appear multiple times is the profiling report. This is
- // particularly useful for custom ops that can perform significantly
- // different calculations depending on their `user-data`.
+ /// `profiling_string` is called during summarization of profiling information
+ /// in order to group executions together. Providing a value here will cause a
+ /// given op to appear multiple times is the profiling report. This is
+ /// particularly useful for custom ops that can perform significantly
+ /// different calculations depending on their `user-data`.
const char* (*profiling_string)(const TfLiteContext* context,
const TfLiteNode* node);
- // Builtin codes. If this kernel refers to a builtin this is the code
- // of the builtin. This is so we can do marshaling to other frameworks like
- // NN API.
- // Note: It is the responsibility of the registration binder to set this
- // properly.
+ /// Builtin codes. If this kernel refers to a builtin this is the code
+ /// of the builtin. This is so we can do marshaling to other frameworks like
+ /// NN API.
+ ///
+ /// Note: It is the responsibility of the registration binder to set this
+ /// properly.
int32_t builtin_code;
- // Custom op name. If the op is a builtin, this will be null.
- // Note: It is the responsibility of the registration binder to set this
- // properly.
- // WARNING: This is an experimental interface that is subject to change.
+ /// Custom op name. If the op is a builtin, this will be `null`.
+ ///
+ /// Note: It is the responsibility of the registration binder to set this
+ /// properly.
+ ///
+ /// WARNING: This is an experimental interface that is subject to change.
const char* custom_name;
- // The version of the op.
- // Note: It is the responsibility of the registration binder to set this
- // properly.
+ /// The version of the op.
+ /// Note: It is the responsibility of the registration binder to set this
+ /// properly.
int version;
- // The external version of `TfLiteRegistration`. Since we can't use internal
- // types (such as `TfLiteContext`) for C API to maintain ABI stability.
- // C API user will provide `TfLiteRegistrationExternal` to implement custom
- // ops. We keep it inside of `TfLiteRegistration` and use it to route
- // callbacks properly.
- TfLiteRegistrationExternal* registration_external;
+ /// The external (i.e. ABI-stable) version of `TfLiteRegistration`.
+ /// Since we can't use internal types (such as `TfLiteContext`) for C API to
+ /// maintain ABI stability. C API user will provide `TfLiteOperator` to
+ /// implement custom ops. We keep it inside of `TfLiteRegistration` and use
+ /// it to route callbacks properly.
+ TfLiteOperator* registration_external;
- // Retrieves asynchronous kernel.
- //
- // If the `async_kernel` field is nullptr, it means the operation described by
- // this TfLiteRegistration object does not support asynchronous execution.
- // Otherwise, the function that the field points to should only be called for
- // delegate kernel nodes, i.e. `node` should be a delegate kernel node created
- // by applying a delegate.
- // If the function returns nullptr, that means that the underlying delegate
- // does not support asynchronous execution for this `node`.
+ /// Retrieves asynchronous kernel.
+ ///
+ /// If the `async_kernel` field is nullptr, it means the operation described
+ /// by this TfLiteRegistration object does not support asynchronous execution.
+ /// Otherwise, the function that the field points to should only be called for
+ /// delegate kernel nodes, i.e. `node` should be a delegate kernel node
+ /// created by applying a delegate. If the function returns nullptr, that
+ /// means that the underlying delegate does not support asynchronous execution
+ /// for this `node`.
struct TfLiteAsyncKernel* (*async_kernel)(TfLiteContext* context,
TfLiteNode* node);
- // Indicates if an operator's output may safely overwrite its inputs.
- // See the comments in `TfLiteInPlaceOp`.
+ /// Indicates if an operator's output may safely overwrite its inputs.
+ /// See the comments in `TfLiteInPlaceOp`.
uint64_t inplace_operator;
} TfLiteRegistration;
/// \private
-// Old version of `TfLiteRegistration` to maintain binary backward
-// compatibility.
-// The legacy registration type must be a POD struct type whose field types must
-// be a prefix of the field types in TfLiteRegistration, and offset of the first
-// field in TfLiteRegistration that is not present in the legacy registration
-// type must be greater than or equal to the size of the legacy registration
-// type.
-// WARNING: This structure is deprecated / not an official part of the
-// API. It should be only used for binary backward compatibility.
+/// Old version of `TfLiteRegistration` to maintain binary backward
+/// compatibility.
+/// The legacy registration type must be a POD struct type whose field types
+/// must be a prefix of the field types in TfLiteRegistration, and offset of the
+/// first field in TfLiteRegistration that is not present in the legacy
+/// registration type must be greater than or equal to the size of the legacy
+/// registration type.
+///
+/// WARNING: This structure is deprecated / not an official part of the
+/// API. It should be only used for binary backward compatibility.
typedef struct TfLiteRegistration_V3 {
void* (*init)(TfLiteContext* context, const char* buffer, size_t length);
void (*free)(TfLiteContext* context, void* buffer);
@@ -1084,21 +1211,22 @@
int32_t builtin_code;
const char* custom_name;
int version;
- TfLiteRegistrationExternal* registration_external;
+ TfLiteOperator* registration_external;
struct TfLiteAsyncKernel* (*async_kernel)(TfLiteContext* context,
TfLiteNode* node);
} TfLiteRegistration_V3;
/// \private
-// Old version of `TfLiteRegistration` to maintain binary backward
-// compatibility.
-// The legacy registration type must be a POD struct type whose field types must
-// be a prefix of the field types in TfLiteRegistration, and offset of the first
-// field in TfLiteRegistration that is not present in the legacy registration
-// type must be greater than or equal to the size of the legacy registration
-// type.
-// WARNING: This structure is deprecated / not an official part of the
-// API. It should be only used for binary backward compatibility.
+/// Old version of `TfLiteRegistration` to maintain binary backward
+/// compatibility.
+/// The legacy registration type must be a POD struct type whose field types
+/// must be a prefix of the field types in TfLiteRegistration, and offset of the
+/// first field in TfLiteRegistration that is not present in the legacy
+/// registration type must be greater than or equal to the size of the legacy
+/// registration type.
+///
+/// WARNING: This structure is deprecated / not an official part of the
+/// API. It should be only used for binary backward compatibility.
typedef struct TfLiteRegistration_V2 {
void* (*init)(TfLiteContext* context, const char* buffer, size_t length);
void (*free)(TfLiteContext* context, void* buffer);
@@ -1109,19 +1237,20 @@
int32_t builtin_code;
const char* custom_name;
int version;
- TfLiteRegistrationExternal* registration_external;
+ TfLiteOperator* registration_external;
} TfLiteRegistration_V2;
/// \private
-// Old version of `TfLiteRegistration` to maintain binary backward
-// compatibility.
-// The legacy registration type must be a POD struct type whose field types must
-// be a prefix of the field types in TfLiteRegistration, and offset of the first
-// field in TfLiteRegistration that is not present in the legacy registration
-// type must be greater than or equal to the size of the legacy registration
-// type.
-// WARNING: This structure is deprecated / not an official part of the
-// API. It should be only used for binary backward compatibility.
+/// Old version of `TfLiteRegistration` to maintain binary backward
+/// compatibility.
+/// The legacy registration type must be a POD struct type whose field types
+/// must be a prefix of the field types in TfLiteRegistration, and offset of the
+/// first field in TfLiteRegistration that is not present in the legacy
+/// registration type must be greater than or equal to the size of the legacy
+/// registration type.
+///
+/// WARNING: This structure is deprecated / not an official part of the
+/// API. It should be only used for binary backward compatibility.
typedef struct TfLiteRegistration_V1 {
void* (*init)(TfLiteContext* context, const char* buffer, size_t length);
void (*free)(TfLiteContext* context, void* buffer);
@@ -1134,184 +1263,202 @@
int version;
} TfLiteRegistration_V1;
-// The flags used in `TfLiteDelegate`. Note that this is a bitmask, so the
-// values should be 1, 2, 4, 8, ...etc.
+/// The flags used in `TfLiteDelegate`. Note that this is a bitmask, so the
+/// values should be 1, 2, 4, 8, ...etc.
typedef enum TfLiteDelegateFlags {
kTfLiteDelegateFlagsNone = 0,
- // The flag is set if the delegate can handle dynamic sized tensors.
- // For example, the output shape of a `Resize` op with non-constant shape
- // can only be inferred when the op is invoked.
- // In this case, the Delegate is responsible for calling
- // `SetTensorToDynamic` to mark the tensor as a dynamic tensor, and calling
- // `ResizeTensor` when invoking the op.
- //
- // If the delegate isn't capable to handle dynamic tensors, this flag need
- // to be set to false.
+ /// The flag is set if the delegate can handle dynamic sized tensors.
+ /// For example, the output shape of a `Resize` op with non-constant shape
+ /// can only be inferred when the op is invoked.
+ /// In this case, the Delegate is responsible for calling
+ /// `SetTensorToDynamic` to mark the tensor as a dynamic tensor, and calling
+ /// `ResizeTensor` when invoking the op.
+ ///
+ /// If the delegate isn't capable to handle dynamic tensors, this flag need
+ /// to be set to false.
kTfLiteDelegateFlagsAllowDynamicTensors = 1,
- // This flag can be used by delegates (that allow dynamic tensors) to ensure
- // applicable tensor shapes are automatically propagated in the case of tensor
- // resizing.
- // This means that non-dynamic (allocation_type != kTfLiteDynamic) I/O tensors
- // of a delegate kernel will have correct shapes before its Prepare() method
- // is called. The runtime leverages TFLite builtin ops in the original
- // execution plan to propagate shapes.
- //
- // A few points to note:
- // 1. This requires kTfLiteDelegateFlagsAllowDynamicTensors. If that flag is
- // false, this one is redundant since the delegate kernels are re-initialized
- // every time tensors are resized.
- // 2. Enabling this flag adds some overhead to AllocateTensors(), since extra
- // work is required to prepare the original execution plan.
- // 3. This flag requires that the original execution plan only have ops with
- // valid registrations (and not 'dummy' custom ops like with Flex).
- // WARNING: This feature is experimental and subject to change.
+ /// This flag can be used by delegates (that allow dynamic tensors) to ensure
+ /// applicable tensor shapes are automatically propagated in the case of
+ /// tensor resizing. This means that non-dynamic (allocation_type !=
+ /// kTfLiteDynamic) I/O tensors of a delegate kernel will have correct shapes
+ /// before its Prepare() method is called. The runtime leverages TFLite
+ /// builtin ops in the original execution plan to propagate shapes.
+ ///
+ /// A few points to note:
+ /// 1. This requires kTfLiteDelegateFlagsAllowDynamicTensors. If that flag is
+ /// false, this one is redundant since the delegate kernels are re-initialized
+ /// every time tensors are resized.
+ /// 2. Enabling this flag adds some overhead to AllocateTensors(), since extra
+ /// work is required to prepare the original execution plan.
+ /// 3. This flag requires that the original execution plan only have ops with
+ /// valid registrations (and not 'dummy' custom ops like with Flex).
+ ///
+ /// WARNING: This feature is experimental and subject to change.
kTfLiteDelegateFlagsRequirePropagatedShapes = 2,
- // This flag can be used by delegates to request per-operator profiling. If a
- // node is a delegate node, this flag will be checked before profiling. If
- // set, then the node will not be profiled. The delegate will then add per
- // operator information using Profiler::EventType::OPERATOR_INVOKE_EVENT and
- // the results will appear in the operator-wise Profiling section and not in
- // the Delegate internal section.
+ /// This flag can be used by delegates to request per-operator profiling. If a
+ /// node is a delegate node, this flag will be checked before profiling. If
+ /// set, then the node will not be profiled. The delegate will then add per
+ /// operator information using `Profiler::EventType::OPERATOR_INVOKE_EVENT`
+ /// and the results will appear in the operator-wise Profiling section and not
+ /// in the Delegate internal section.
kTfLiteDelegateFlagsPerOperatorProfiling = 4
} TfLiteDelegateFlags;
-// WARNING: This is an experimental interface that is subject to change.
+/// WARNING: This is an experimental interface that is subject to change.
typedef struct TfLiteDelegate {
- // Data that delegate needs to identify itself. This data is owned by the
- // delegate. The delegate is owned in the user code, so the delegate is
- // responsible for deallocating this when it is destroyed.
+ /// Data that delegate needs to identify itself. This data is owned by the
+ /// delegate. The delegate is owned in the user code, so the delegate is
+ /// responsible for deallocating this when it is destroyed.
void* data_;
- // Invoked by ModifyGraphWithDelegate. This prepare is called, giving the
- // delegate a view of the current graph through TfLiteContext*. It typically
- // will look at the nodes and call ReplaceNodeSubsetsWithDelegateKernels()
- // to ask the TensorFlow lite runtime to create macro-nodes to represent
- // delegated subgraphs of the original graph.
+ /// Invoked by `ModifyGraphWithDelegate`. This prepare is called, giving the
+ /// delegate a view of the current graph through `TfLiteContext*`. It
+ /// typically will look at the nodes and call
+ /// `ReplaceNodeSubsetsWithDelegateKernels()` to ask the TensorFlow lite
+ /// runtime to create macro-nodes to represent delegated subgraphs of the
+ /// original graph.
TfLiteStatus (*Prepare)(TfLiteContext* context,
struct TfLiteDelegate* delegate);
- // Copy the data from delegate buffer handle into raw memory of the given
- // 'tensor'. Note that the delegate is allowed to allocate the raw bytes as
- // long as it follows the rules for kTfLiteDynamic tensors, in which case this
- // cannot be null.
+ /// Copy the data from delegate buffer handle into raw memory of the given
+ /// `tensor`. Note that the delegate is allowed to allocate the raw bytes as
+ /// long as it follows the rules for `kTfLiteDynamic` tensors, in which case
+ /// this cannot be null.
TfLiteStatus (*CopyFromBufferHandle)(TfLiteContext* context,
struct TfLiteDelegate* delegate,
TfLiteBufferHandle buffer_handle,
TfLiteTensor* tensor);
- // Copy the data from raw memory of the given 'tensor' to delegate buffer
- // handle. This can be null if the delegate doesn't use its own buffer.
+ /// Copy the data from raw memory of the given `tensor` to delegate buffer
+ /// handle. This can be null if the delegate doesn't use its own buffer.
TfLiteStatus (*CopyToBufferHandle)(TfLiteContext* context,
struct TfLiteDelegate* delegate,
TfLiteBufferHandle buffer_handle,
TfLiteTensor* tensor);
- // Free the Delegate Buffer Handle. Note: This only frees the handle, but
- // this doesn't release the underlying resource (e.g. textures). The
- // resources are either owned by application layer or the delegate.
- // This can be null if the delegate doesn't use its own buffer.
+ /// Free the Delegate Buffer Handle. Note: This only frees the handle, but
+ /// this doesn't release the underlying resource (e.g. textures). The
+ /// resources are either owned by application layer or the delegate.
+ /// This can be null if the delegate doesn't use its own buffer.
void (*FreeBufferHandle)(TfLiteContext* context,
struct TfLiteDelegate* delegate,
TfLiteBufferHandle* handle);
- // Bitmask flags. See the comments in `TfLiteDelegateFlags`.
+ /// Bitmask flags. See the comments in `TfLiteDelegateFlags`.
int64_t flags;
- // The opaque delegate builder associated with this object. If set then the
- // TF Lite runtime will give precedence to this field. E.g. instead of
- // invoking 'Prepare' via the function pointer inside the 'TfLiteDelegate'
- // object, the runtime will first check if the corresponding function
- // pointer inside 'opaque_delegate_builder' is set and if so invoke that.
- //
- // If this field is non-null, then the 'Prepare' field (of the
- // 'TfLiteDelegate') should be null.
+ /// The opaque delegate builder associated with this object. If set then the
+ /// TF Lite runtime will give precedence to this field. E.g. instead of
+ /// invoking `Prepare` via the function pointer inside the `TfLiteDelegate`
+ /// object, the runtime will first check if the corresponding function
+ /// pointer inside `opaque_delegate_builder` is set and if so invoke that.
+ ///
+ /// If this field is non-null, then the `Prepare` field (of the
+ /// `TfLiteDelegate`) should be null.
struct TfLiteOpaqueDelegateBuilder* opaque_delegate_builder;
} TfLiteDelegate;
-// Build a 'null' delegate, with all the fields properly set to their default
-// values.
+/// Build a `null` delegate, with all the fields properly set to their default
+/// values.
TfLiteDelegate TfLiteDelegateCreate(void);
-// `TfLiteOpaqueDelegateBuilder` is used for constructing
-// `TfLiteOpaqueDelegate`, see `TfLiteOpaqueDelegateCreate` below. Note:
-// This struct is not ABI stable.
-//
-// For forward source compatibility `TfLiteOpaqueDelegateBuilder` objects should
-// be brace-initialized, so that all fields (including any that might be added
-// in the future) get zero-initialized. The purpose of each field is exactly
-// the same as with `TfLiteDelegate`.
-//
-// WARNING: This is an experimental interface that is subject to change.
+/// `TfLiteOpaqueDelegateBuilder` is used for constructing
+/// `TfLiteOpaqueDelegate`, see `TfLiteOpaqueDelegateCreate` in c_api_opaque.h.
+/// NOTE: This struct is not ABI stable.
+///
+/// For forward source compatibility `TfLiteOpaqueDelegateBuilder` objects
+/// should be brace-initialized, so that all fields (including any that might be
+/// added in the future) get zero-initialized. The purpose of each field is
+/// exactly the same as with `TfLiteDelegate`.
+///
+/// NOTE: This type is part of the TensorFlow Lite Extension APIs.
+/// We reserve the right to make changes to this API in future releases,
+/// potentially including non-backwards-compatible changes, on a different
+/// schedule than for the other TensorFlow Lite APIs. See
+/// https://www.tensorflow.org/guide/versions#separate_version_number_for_tensorflow_lite_extension_apis.
typedef struct TfLiteOpaqueDelegateBuilder {
- // Data that delegate needs to identify itself. This data is owned by the
- // delegate. The delegate is owned in the user code, so the delegate is
- // responsible for deallocating this when it is destroyed.
+ /// Data that delegate needs to identify itself. This data is owned by the
+ /// delegate. The delegate is owned in the user code, so the delegate is
+ /// responsible for deallocating this when it is destroyed.
void* data;
- // Invoked by ModifyGraphWithDelegate. This prepare is called, giving the
- // delegate a view of the current graph through TfLiteContext*. It typically
- // will look at the nodes and call ReplaceNodeSubsetsWithDelegateKernels()
- // to ask the TensorFlow lite runtime to create macro-nodes to represent
- // delegated subgraphs of the original graph.
+ /// Invoked by ModifyGraphWithDelegate. This prepare is called, giving the
+ /// delegate a view of the current graph through `TfLiteContext*`. It
+ /// typically will look at the nodes and call
+ /// `ReplaceNodeSubsetsWithDelegateKernels()` to ask the TensorFlow lite
+ /// runtime to create macro-nodes to represent delegated subgraphs of the
+ /// original graph.
TfLiteStatus (*Prepare)(TfLiteOpaqueContext* context, // NOLINT
TfLiteOpaqueDelegate* delegate, void* data);
- // Copies the data from delegate buffer handle into raw memory of the given
- // 'tensor'. Note that the delegate is allowed to allocate the raw bytes as
- // long as it follows the rules for kTfLiteDynamic tensors, in which case this
- // cannot be null.
+ /// Copies the data from delegate buffer handle into raw memory of the given
+ /// `tensor`. Note that the delegate is allowed to allocate the raw bytes as
+ /// long as it follows the rules for kTfLiteDynamic tensors, in which case
+ /// this cannot be null.
TfLiteStatus (*CopyFromBufferHandle)( // NOLINT
TfLiteOpaqueContext* context, TfLiteOpaqueDelegate* delegate, void* data,
TfLiteBufferHandle buffer_handle, TfLiteOpaqueTensor* tensor);
- // Copies the data from raw memory of the given 'tensor' to delegate buffer
- // handle. This can be null if the delegate doesn't use its own buffer.
+ /// Copies the data from raw memory of the given `tensor` to delegate buffer
+ /// handle. This can be null if the delegate doesn't use its own buffer.
TfLiteStatus (*CopyToBufferHandle)( // NOLINT
TfLiteOpaqueContext* context, TfLiteOpaqueDelegate* delegate, void* data,
TfLiteBufferHandle buffer_handle, TfLiteOpaqueTensor* tensor);
- // Frees the Delegate Buffer Handle. Note: This only frees the handle, but
- // this doesn't release the underlying resource (e.g. textures). The
- // resources are either owned by application layer or the delegate.
- // This can be null if the delegate doesn't use its own buffer.
+ /// Frees the Delegate Buffer Handle. Note: This only frees the handle, but
+ /// this doesn't release the underlying resource (e.g. textures). The
+ /// resources are either owned by application layer or the delegate.
+ /// This can be null if the delegate doesn't use its own buffer.
void (*FreeBufferHandle)(TfLiteOpaqueContext* context, // NOLINT
TfLiteOpaqueDelegate* delegate, void* data,
TfLiteBufferHandle* handle);
- // Bitmask flags. See the comments in `TfLiteDelegateFlags`.
+ /// Bitmask flags. See the comments in `TfLiteDelegateFlags`.
int64_t flags;
} TfLiteOpaqueDelegateBuilder;
#ifndef TF_LITE_STATIC_MEMORY
-// Creates an opaque delegate and returns its address. The opaque delegate will
-// behave according to the provided 'opaque_delegate_builder'. The lifetime of
-// the objects pointed to by any of the fields within the
-// 'opaque_delegate_builder' must outlive the returned
-// 'TfLiteOpaqueDelegate' and any 'TfLiteInterpreter',
-// 'TfLiteInterpreterOptions', 'tflite::Interpreter', or
-// 'tflite::InterpreterBuilder' that the delegate is added to. The returned
-// address should be passed to 'TfLiteOpaqueDelegateDelete' for deletion. If
-// 'opaque_delegate_builder' is a null pointer, then a null pointer will be
-// returned.
+// See c_api_opaque.h.
+// This declaration in common.h is only for backwards compatibility.
+// NOTE: This function is part of the TensorFlow Lite Extension APIs, see above.
TfLiteOpaqueDelegate* TfLiteOpaqueDelegateCreate(
const TfLiteOpaqueDelegateBuilder* opaque_delegate_builder);
-// Deletes the provided opaque 'delegate'. This function has no effect if the
-// 'delegate' is a null pointer.
+// See c_api_opaque.h.
+// This declaration in common.h is only for backwards compatibility.
+// NOTE: This function is part of the TensorFlow Lite Extension APIs, see above.
void TfLiteOpaqueDelegateDelete(TfLiteOpaqueDelegate* delegate);
#endif // TF_LITE_STATIC_MEMORY
-// Returns a pointer to the data associated with the provided opaque 'delegate'.
-//
-// A null pointer will be returned when:
-// - The 'delegate' is null.
-// - The 'data' field of the 'TfLiteOpaqueDelegateBuilder' used to construct the
-// 'delegate' was null.
-// - Or in case of any other error.
-// - The 'delegate' has been constructed via a 'TfLiteOpaqueDelegateBuilder',
-// but the 'data' field of the 'TfLiteOpaqueDelegateBuilder' is null.
-//
-// The data_ field of 'delegate' will be returned if the
-// 'opaque_delegate_builder' field is null.
+// See c_api_opaque.h.
+// This declaration in common.h is only for backwards compatibility.
+// NOTE: This function is part of the TensorFlow Lite Extension APIs, see above.
void* TfLiteOpaqueDelegateGetData(const TfLiteOpaqueDelegate* delegate);
+/// Returns a tensor data allocation strategy.
+TfLiteAllocationStrategy TfLiteTensorGetAllocationStrategy(
+ const TfLiteTensor* t);
+
+/// Returns how stable a tensor data buffer address is across runs.
+TfLiteRunStability TfLiteTensorGetBufferAddressStability(const TfLiteTensor* t);
+
+/// Returns how stable a tensor data values are across runs.
+TfLiteRunStability TfLiteTensorGetDataStability(const TfLiteTensor* t);
+
+/// Returns the operation step when the data of a tensor is populated.
+///
+/// Some operations can precompute their results before the evaluation step.
+/// This makes the data available earlier for subsequent operations.
+TfLiteRunStep TfLiteTensorGetDataKnownStep(const TfLiteTensor* t);
+
+/// Returns the operation steop when the shape of a tensor is computed.
+///
+/// Some operations can precompute the shape of their results before the
+/// evaluation step. This makes the shape available earlier for subsequent
+/// operations.
+TfLiteRunStep TfLiteTensorGetShapeKnownStep(const TfLiteTensor* t);
+
+/** @} */
+// Ends `\addtogroup`, it's important for the doc generator that this doesn't
+// include the CC code below.
+
#ifdef __cplusplus
} // extern "C"
diff --git a/tensorflow/lite/core/macros.h b/tensorflow/lite/core/macros.h
index d329ded..86de4da 100644
--- a/tensorflow/lite/core/macros.h
+++ b/tensorflow/lite/core/macros.h
@@ -65,14 +65,4 @@
#define TFLITE_HAS_ATTRIBUTE_WEAK 0
#endif
-#ifndef TF_LITE_STATIC_MEMORY
-// maximum size of a valid flatbuffer
-inline constexpr unsigned int flatbuffer_size_max = 2147483648;
-// If none zero then the buffer is stored outside of the flatbuffers, string
-inline constexpr char tflite_metadata_buffer_location[] = "buffer_location";
-// field for minimum runtime version, string
-inline constexpr char tflite_metadata_min_runtime_version[] =
- "min_runtime_version";
-#endif
-
#endif // TENSORFLOW_LITE_CORE_MACROS_H_
diff --git a/tensorflow/lite/experimental/microfrontend/lib/filterbank_io.c b/tensorflow/lite/experimental/microfrontend/lib/filterbank_io.c
index 6ce4c7c..ea45d1b 100644
--- a/tensorflow/lite/experimental/microfrontend/lib/filterbank_io.c
+++ b/tensorflow/lite/experimental/microfrontend/lib/filterbank_io.c
@@ -17,7 +17,7 @@
static void PrintArray(FILE* fp, const char* name, const int16_t* values,
size_t size) {
fprintf(fp, "static int16_t filterbank_%s[] = {", name);
- int i;
+ size_t i;
for (i = 0; i < size; ++i) {
fprintf(fp, "%d", values[i]);
if (i < size - 1) {
diff --git a/tensorflow/lite/experimental/microfrontend/lib/filterbank_util.c b/tensorflow/lite/experimental/microfrontend/lib/filterbank_util.c
index f18ebf5..430fc6e 100644
--- a/tensorflow/lite/experimental/microfrontend/lib/filterbank_util.c
+++ b/tensorflow/lite/experimental/microfrontend/lib/filterbank_util.c
@@ -28,7 +28,7 @@
config->output_scale_shift = 7;
}
-static float FreqToMel(float freq) { return 1127.0 * log1p(freq / 700.0); }
+static float FreqToMel(float freq) { return 1127.0f * log1pf(freq / 700.0f); }
static void CalculateCenterFrequencies(const int num_channels,
const float lower_frequency_limit,
@@ -49,8 +49,8 @@
static void QuantizeFilterbankWeights(const float float_weight, int16_t* weight,
int16_t* unweight) {
- *weight = floor(float_weight * (1 << kFilterbankBits) + 0.5);
- *unweight = floor((1.0 - float_weight) * (1 << kFilterbankBits) + 0.5);
+ *weight = floorf(float_weight * (1 << kFilterbankBits) + 0.5f);
+ *unweight = floorf((1.0f - float_weight) * (1 << kFilterbankBits) + 0.5f);
}
int FilterbankPopulateState(const struct FilterbankConfig* config,
@@ -95,8 +95,8 @@
config->upper_band_limit, center_mel_freqs);
// Always exclude DC.
- const float hz_per_sbin = 0.5 * sample_rate / ((float)spectrum_size - 1);
- state->start_index = 1.5 + config->lower_band_limit / hz_per_sbin;
+ const float hz_per_sbin = 0.5f * sample_rate / ((float)spectrum_size - 1);
+ state->start_index = 1.5f + config->lower_band_limit / hz_per_sbin;
state->end_index = 0; // Initialized to zero here, but actually set below.
// For each channel, we need to figure out what frequencies belong to it, and
diff --git a/tensorflow/lite/experimental/microfrontend/lib/frontend_main.c b/tensorflow/lite/experimental/microfrontend/lib/frontend_main.c
index 861778c..60868f8 100644
--- a/tensorflow/lite/experimental/microfrontend/lib/frontend_main.c
+++ b/tensorflow/lite/experimental/microfrontend/lib/frontend_main.c
@@ -56,7 +56,7 @@
audio_file_size -= num_samples_read;
if (output.values != NULL) {
- int i;
+ size_t i;
for (i = 0; i < output.size; ++i) {
printf("%d ", output.values[i]);
}
diff --git a/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_io.c b/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_io.c
index 19c32b3..8a5d7a4 100644
--- a/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_io.c
+++ b/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_io.c
@@ -16,7 +16,7 @@
void NoiseReductionWriteMemmapPreamble(
FILE* fp, const struct NoiseReductionState* state) {
- fprintf(fp, "static uint32_t noise_reduction_estimate[%zu];\n",
+ fprintf(fp, "static uint32_t noise_reduction_estimate[%d];\n",
state->num_channels);
fprintf(fp, "\n");
}
diff --git a/tensorflow/lite/experimental/microfrontend/lib/window_io.c b/tensorflow/lite/experimental/microfrontend/lib/window_io.c
index d12cac2..5a7b047 100644
--- a/tensorflow/lite/experimental/microfrontend/lib/window_io.c
+++ b/tensorflow/lite/experimental/microfrontend/lib/window_io.c
@@ -16,7 +16,7 @@
void WindowWriteMemmapPreamble(FILE* fp, const struct WindowState* state) {
fprintf(fp, "static int16_t window_coefficients[] = {\n");
- int i;
+ size_t i;
for (i = 0; i < state->size; ++i) {
fprintf(fp, "%d", state->coefficients[i]);
if (i < state->size - 1) {
diff --git a/tensorflow/lite/experimental/microfrontend/lib/window_util.c b/tensorflow/lite/experimental/microfrontend/lib/window_util.c
index eee6e7b..6fdffdc 100644
--- a/tensorflow/lite/experimental/microfrontend/lib/window_util.c
+++ b/tensorflow/lite/experimental/microfrontend/lib/window_util.c
@@ -41,13 +41,13 @@
}
// Populate the window values.
- const float arg = M_PI * 2.0 / ((float)state->size);
- int i;
+ const float arg = (float)M_PI * 2.0f / ((float)state->size);
+ size_t i;
for (i = 0; i < state->size; ++i) {
- float float_value = 0.5 - (0.5 * cos(arg * (i + 0.5)));
+ float float_value = 0.5f - (0.5f * cosf(arg * (i + 0.5f)));
// Scale it to fixed point and round it.
state->coefficients[i] =
- floor(float_value * (1 << kFrontendWindowBits) + 0.5);
+ floorf(float_value * (1 << kFrontendWindowBits) + 0.5f);
}
state->input_used = 0;
diff --git a/tensorflow/lite/kernels/internal/common.cc b/tensorflow/lite/kernels/internal/common.cc
index 1654ab8..fabb020 100644
--- a/tensorflow/lite/kernels/internal/common.cc
+++ b/tensorflow/lite/kernels/internal/common.cc
@@ -17,6 +17,53 @@
namespace tflite {
+// Single-rounding MultiplyByQuantizedMultiplier
+#if TFLITE_SINGLE_ROUNDING
+int32_t MultiplyByQuantizedMultiplier(int32_t x, int32_t quantized_multiplier,
+ int shift) {
+ TFLITE_DCHECK(quantized_multiplier >= 0);
+ TFLITE_DCHECK(shift >= -31 && shift <= 30);
+
+ const int64_t total_shift = 31 - shift;
+ const int64_t round = static_cast<int64_t>(1) << (total_shift - 1);
+ int64_t result = x * static_cast<int64_t>(quantized_multiplier) + round;
+ result = result >> total_shift;
+
+ TFLITE_DCHECK(result >= std::numeric_limits<int32_t>::min() &&
+ result <= std::numeric_limits<int32_t>::max());
+ return static_cast<int32_t>(result);
+}
+
+int32_t MultiplyByQuantizedMultiplier(int64_t x, int32_t quantized_multiplier,
+ int shift) {
+ // Inputs:
+ // - quantized_multiplier has fixed point at bit 31
+ // - shift is -31 to +7 (negative for right shift)
+ //
+ // Assumptions: The following input ranges are assumed
+ // - quantize_scale>=0 (the usual range is (1<<30) to (1>>31)-1)
+ // - scaling is chosen so final scaled result fits in int32_t
+ // - input x is in the range -(1<<47) <= x < (1<<47)
+ TFLITE_DCHECK(quantized_multiplier >= 0);
+ TFLITE_DCHECK(shift >= -31 && shift < 8);
+ TFLITE_DCHECK(x >= -(static_cast<int64_t>(1) << 47) &&
+ x < (static_cast<int64_t>(1) << 47));
+
+ const int32_t reduced_multiplier =
+ (quantized_multiplier < 0x7FFF0000)
+ ? ((quantized_multiplier + (1 << 15)) >> 16)
+ : 0x7FFF;
+ const int64_t total_shift = 15 - shift;
+ const int64_t round = static_cast<int64_t>(1) << (total_shift - 1);
+ int64_t result = x * static_cast<int64_t>(reduced_multiplier) + round;
+ result = result >> total_shift;
+
+ TFLITE_DCHECK(result >= std::numeric_limits<int32_t>::min() &&
+ result <= std::numeric_limits<int32_t>::max());
+ return static_cast<int32_t>(result);
+}
+// Double-rounding MultiplyByQuantizedMultiplier
+#else
int32_t MultiplyByQuantizedMultiplier(int32_t x, int32_t quantized_multiplier,
int shift) {
using gemmlowp::RoundingDivideByPOT;
@@ -51,5 +98,6 @@
int32_t result = x >> total_shift;
return result;
}
+#endif // TFLITE_SINGLE_ROUNDING
} // namespace tflite
diff --git a/tensorflow/lite/kernels/internal/common.h b/tensorflow/lite/kernels/internal/common.h
index 05184df..9761a8c 100644
--- a/tensorflow/lite/kernels/internal/common.h
+++ b/tensorflow/lite/kernels/internal/common.h
@@ -16,6 +16,10 @@
#define TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_
#include <algorithm>
+#include <cstddef>
+#include <cstdint>
+
+#include "tensorflow/lite/kernels/internal/runtime_shape.h"
#ifndef ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK
#ifdef GEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK
#define ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK
@@ -35,6 +39,117 @@
constexpr int kReverseShift = -1;
+// Reduces and compresses dimensions so that broadcast handling becomes more
+// efficient. Returns true if the output shape is broadcastable; it doesn't
+// contain any degenerate dimension, i.e. shape dimension = 0. False otherwise.
+template <int MAX_DIM = 6>
+bool ReduceDimensionsForBroadcast(const RuntimeShape& input1_shape,
+ const RuntimeShape& input2_shape,
+ size_t* compressed_input1_stride,
+ size_t* compressed_input2_stride,
+ size_t* compressed_output_shape) {
+ size_t num_compressed_dims = 0;
+ size_t compressed_input1_shape[MAX_DIM];
+ size_t compressed_input2_shape[MAX_DIM];
+ std::fill(compressed_input1_shape, compressed_input1_shape + MAX_DIM, 1);
+ std::fill(compressed_input2_shape, compressed_input2_shape + MAX_DIM, 1);
+ std::fill(compressed_output_shape, compressed_output_shape + MAX_DIM, 1);
+ bool broadcast_input1 = false;
+ bool broadcast_input2 = false;
+ bool first_nonunit = true;
+ const size_t num_input1_dims = input1_shape.DimensionsCount();
+ const size_t num_input2_dims = input2_shape.DimensionsCount();
+ const int32_t* input1_dims = input1_shape.DimsData();
+ const int32_t* input2_dims = input2_shape.DimsData();
+ const size_t num_common_dims = std::min(num_input1_dims, num_input2_dims);
+ for (size_t i = 1; i <= num_common_dims; i++) {
+ const size_t input1_dim = input1_dims[num_input1_dims - i];
+ const size_t input2_dim = input2_dims[num_input2_dims - i];
+ if (input1_dim == 0 || input2_dim == 0) {
+ return false;
+ }
+ if (input1_dim == 1 && input2_dim == 1) {
+ continue;
+ }
+ assert(!broadcast_input1 || !broadcast_input2);
+
+ if (input1_dim == 1) {
+ if (!broadcast_input1) {
+ broadcast_input1 = true;
+ broadcast_input2 = false;
+ num_compressed_dims++;
+ }
+ compressed_input2_shape[num_compressed_dims - 1] *= input2_dim;
+ compressed_output_shape[num_compressed_dims - 1] *= input2_dim;
+ } else if (input2_dim == 1) {
+ if (!broadcast_input2) {
+ broadcast_input1 = false;
+ broadcast_input2 = true;
+ num_compressed_dims++;
+ }
+ compressed_input1_shape[num_compressed_dims - 1] *= input1_dim;
+ compressed_output_shape[num_compressed_dims - 1] *= input1_dim;
+ } else {
+ TFLITE_DCHECK(input1_dim == input2_dim);
+ if (broadcast_input1 || broadcast_input2 || first_nonunit) {
+ broadcast_input1 = false;
+ broadcast_input2 = false;
+ num_compressed_dims++;
+ }
+ compressed_input1_shape[num_compressed_dims - 1] *= input1_dim;
+ compressed_input2_shape[num_compressed_dims - 1] *= input1_dim;
+ compressed_output_shape[num_compressed_dims - 1] *= input1_dim;
+ }
+ first_nonunit = false;
+ }
+ if (num_input1_dims > num_input2_dims) {
+ if (!broadcast_input2) {
+ num_compressed_dims++;
+ }
+ for (size_t i = 0; i < num_input1_dims - num_input2_dims; i++) {
+ const size_t input1_dim = input1_dims[i];
+ if (input1_dim == 0) {
+ return false;
+ }
+ compressed_input1_shape[num_compressed_dims - 1] *= input1_dim;
+ compressed_output_shape[num_compressed_dims - 1] *= input1_dim;
+ }
+ } else if (num_input2_dims > num_input1_dims) {
+ if (!broadcast_input1) {
+ num_compressed_dims++;
+ }
+ for (size_t i = 0; i < num_input2_dims - num_input1_dims; i++) {
+ const size_t input2_dim = input2_dims[i];
+ if (input2_dim == 0) {
+ return false;
+ }
+ compressed_input2_shape[num_compressed_dims - 1] *= input2_dim;
+ compressed_output_shape[num_compressed_dims - 1] *= input2_dim;
+ }
+ }
+ num_compressed_dims = (num_compressed_dims > 1) ? num_compressed_dims : 1;
+
+ int input1_stride = 1;
+ int input2_stride = 1;
+ for (int i = 0; i < MAX_DIM; ++i) {
+ compressed_input1_stride[i] = input1_stride;
+ input1_stride *= compressed_input1_shape[i];
+ compressed_input2_stride[i] = input2_stride;
+ input2_stride *= compressed_input2_shape[i];
+ }
+ for (int i = 0; i < MAX_DIM; ++i) {
+ if (compressed_input1_shape[i] != compressed_input2_shape[i]) {
+ if (compressed_input1_shape[i] == 1) {
+ compressed_input1_stride[i] = 0;
+ } else {
+ TFLITE_DCHECK_EQ(compressed_input2_shape[i], 1);
+ compressed_input2_stride[i] = 0;
+ }
+ }
+ }
+ return true;
+}
+
inline void GetActivationMinMax(FusedActivationFunctionType ac,
float* output_activation_min,
float* output_activation_max) {
@@ -142,24 +257,14 @@
#endif
}
+TFLITE_NOINLINE int32_t MultiplyByQuantizedMultiplier(
+ int32_t x, int32_t quantized_multiplier, int shift);
+
+TFLITE_NOINLINE int32_t MultiplyByQuantizedMultiplier(
+ int64_t x, int32_t quantized_multiplier, int shift);
+
// Single-rounding MultiplyByQuantizedMultiplier
#if TFLITE_SINGLE_ROUNDING
-inline int32_t MultiplyByQuantizedMultiplier(int32_t x,
- int32_t quantized_multiplier,
- int shift) {
- TFLITE_DCHECK(quantized_multiplier >= 0);
- TFLITE_DCHECK(shift >= -31 && shift <= 30);
-
- const int64_t total_shift = 31 - shift;
- const int64_t round = static_cast<int64_t>(1) << (total_shift - 1);
- int64_t result = x * static_cast<int64_t>(quantized_multiplier) + round;
- result = result >> total_shift;
-
- TFLITE_DCHECK(result >= std::numeric_limits<int32_t>::min() &&
- result <= std::numeric_limits<int32_t>::max());
- return static_cast<int32_t>(result);
-}
-
inline int32_t MultiplyByQuantizedMultiplierSmallerThanOneExp(
int32_t x, int32_t quantized_multiplier, int shift) {
TFLITE_DCHECK_LE(shift, 0);
@@ -172,36 +277,6 @@
return MultiplyByQuantizedMultiplier(x, quantized_multiplier, shift);
}
-inline int32_t MultiplyByQuantizedMultiplier(int64_t x,
- int32_t quantized_multiplier,
- int shift) {
- // Inputs:
- // - quantized_multiplier has fixed point at bit 31
- // - shift is -31 to +7 (negative for right shift)
- //
- // Assumptions: The following input ranges are assumed
- // - quantize_scale>=0 (the usual range is (1<<30) to (1>>31)-1)
- // - scaling is chosen so final scaled result fits in int32_t
- // - input x is in the range -(1<<47) <= x < (1<<47)
- TFLITE_DCHECK(quantized_multiplier >= 0);
- TFLITE_DCHECK(shift >= -31 && shift < 8);
- TFLITE_DCHECK(x >= -(static_cast<int64_t>(1) << 47) &&
- x < (static_cast<int64_t>(1) << 47));
-
- const int32_t reduced_multiplier =
- (quantized_multiplier < 0x7FFF0000)
- ? ((quantized_multiplier + (1 << 15)) >> 16)
- : 0x7FFF;
- const int64_t total_shift = 15 - shift;
- const int64_t round = static_cast<int64_t>(1) << (total_shift - 1);
- int64_t result = x * static_cast<int64_t>(reduced_multiplier) + round;
- result = result >> total_shift;
-
- TFLITE_DCHECK(result >= std::numeric_limits<int32_t>::min() &&
- result <= std::numeric_limits<int32_t>::max());
- return static_cast<int32_t>(result);
-}
-
#ifdef USE_NEON
inline int32x4x4_t MultiplyByQuantizedMultiplier4Rows(
int32x4x4_t input_val, int32_t quantized_multiplier, int shift) {
@@ -251,12 +326,6 @@
quantized_multiplier);
}
-TFLITE_NOINLINE int32_t MultiplyByQuantizedMultiplier(
- int32_t x, int32_t quantized_multiplier, int shift);
-
-TFLITE_NOINLINE int32_t MultiplyByQuantizedMultiplier(
- int64_t x, int32_t quantized_multiplier, int shift);
-
#ifdef USE_NEON
// Round uses ARM's rounding shift right.
inline int32x4x4_t MultiplyByQuantizedMultiplier4Rows(
diff --git a/tensorflow/lite/kernels/internal/cppmath.h b/tensorflow/lite/kernels/internal/cppmath.h
index c97cc31..67ab461 100644
--- a/tensorflow/lite/kernels/internal/cppmath.h
+++ b/tensorflow/lite/kernels/internal/cppmath.h
@@ -32,8 +32,8 @@
return TF_LITE_GLOBAL_STD_PREFIX::std_name(x); \
}
-DECLARE_STD_GLOBAL_SWITCH1(TfLiteRound, round);
-DECLARE_STD_GLOBAL_SWITCH1(TfLiteExpm1, expm1);
+DECLARE_STD_GLOBAL_SWITCH1(TfLiteRound, round)
+DECLARE_STD_GLOBAL_SWITCH1(TfLiteExpm1, expm1)
} // namespace tflite
diff --git a/tensorflow/lite/kernels/internal/portable_tensor.h b/tensorflow/lite/kernels/internal/portable_tensor.h
index 1eee621..a9f9551 100644
--- a/tensorflow/lite/kernels/internal/portable_tensor.h
+++ b/tensorflow/lite/kernels/internal/portable_tensor.h
@@ -15,6 +15,7 @@
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_PORTABLE_TENSOR_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_PORTABLE_TENSOR_H_
+#include <cstddef>
#include <vector>
#include "tensorflow/lite/core/c/common.h"
@@ -50,6 +51,26 @@
all_shape_ptr_.push_back(&all_shape_[i]);
}
}
+
+ explicit VectorOfTensors(const std::vector<TfLiteTensor*>& tensors) {
+ int num_tensors = tensors.size();
+
+ all_data_.reserve(num_tensors);
+ all_shape_.reserve(num_tensors);
+ all_shape_ptr_.reserve(num_tensors);
+
+ for (auto* t : tensors) {
+ all_data_.push_back(GetTensorData<T>(t));
+ all_shape_.push_back(GetTensorShape(t));
+ }
+
+ // Taking the pointer from inside a std::vector is only OK if the vector is
+ // never modified, so we populate all_shape in the previous loop and then we
+ // are free to grab iterators here.
+ for (int i = 0; i < num_tensors; ++i) {
+ all_shape_ptr_.push_back(&all_shape_[i]);
+ }
+ }
// Return a pointer to the data pointers of all tensors in the list. For
// example:
// float* const* f = v.data();
@@ -62,6 +83,8 @@
// dims[1] are the dimensions of the second tensor in the list.
const RuntimeShape* const* shapes() const { return all_shape_ptr_.data(); }
+ size_t size() const { return all_data_.size(); }
+
private:
std::vector<T*> all_data_;
std::vector<RuntimeShape> all_shape_;
diff --git a/tensorflow/lite/kernels/internal/portable_tensor_utils.cc b/tensorflow/lite/kernels/internal/portable_tensor_utils.cc
index 024043d..577fc6b 100644
--- a/tensorflow/lite/kernels/internal/portable_tensor_utils.cc
+++ b/tensorflow/lite/kernels/internal/portable_tensor_utils.cc
@@ -70,6 +70,12 @@
void UnpackDenseInt4IntoInt8(const int8_t* src_buffer, int num_elements,
int8_t* dst_buffer) {
+ // num_elements means the number of elements regardless of packed or unpacked.
+ // For example, 3 elements means both
+ // 1) Packed: 3 int4's = 12 bit -> 16 bits (padded) = 2 bytes.
+ // stored in src_buffer[0] and src_buffer[1] (i = 0..1)
+ // 2) Unpacked: 3 int8's = 3 bytes.
+ //. stored in dst_buffer[0], dst_buffer[1] and dst_buffer[2] (j = 0..2)
for (int i = 0; i < num_elements / 2; i++) {
int8_t byte = src_buffer[i];
// Shift left first so that sign is properly extended when shifted right
diff --git a/tensorflow/lite/kernels/internal/portable_tensor_utils.h b/tensorflow/lite/kernels/internal/portable_tensor_utils.h
index c28892c..ed59fd0 100644
--- a/tensorflow/lite/kernels/internal/portable_tensor_utils.h
+++ b/tensorflow/lite/kernels/internal/portable_tensor_utils.h
@@ -241,7 +241,8 @@
const int32_t* __restrict__ indices, int m_rows, int m_cols,
const int8_t* __restrict__ vector, const int32_t* __restrict__ bias_vector,
int n_batch, const int32_t input_offset, const int32_t output_multiplier,
- const int32_t output_shift, const int32_t output_offset,
+ int32_t output_shift, const int32_t* per_channel_scale,
+ const int32_t* per_channel_shift, int32_t output_offset,
const int32_t output_activation_min, const int32_t output_activation_max,
int8_t* __restrict__ result);
@@ -259,7 +260,7 @@
const int8_t* __restrict__ matrix, const uint8_t* __restrict__ ledger,
const int m_rows, const int m_cols, const int8_t* __restrict__ vectors,
const float* __restrict__ scaling_factors, int n_batch,
- float* __restrict__ result);
+ float* __restrict__ result, const float* per_channel_scale = nullptr);
// Same as the above 8, 8, 8 integer matmul except for the presence of zero
// point and non-accumulative.
@@ -316,7 +317,7 @@
void ApplySigmoid(const int16_t* input, int32_t n_batch, int32_t n_input,
int16_t* output);
-// Same as above but the internal calcualtion is float.
+// Same as above but the internal calculation is float.
void ApplySigmoidFloat(const int16_t* input, int32_t n_batch, int32_t n_input,
int16_t* output);
@@ -332,7 +333,7 @@
void ApplyTanh(int32_t intger_bits, const int16_t* input, int32_t n_batch,
int32_t n_input, int16_t* output);
-// Apply Tanh to a quantized vector. Tbe internal calculation is in float.
+// Apply Tanh to a quantized vector. The internal calculation is in float.
// - Input has 2^(integer_bits) as scale.
// - Output has Q0.15 as scale.
void ApplyTanhFloat(const int16_t* input, int32_t n_batch, int32_t n_input,
diff --git a/tensorflow/lite/kernels/internal/quantization_util.h b/tensorflow/lite/kernels/internal/quantization_util.h
index 0ee914b..eb4e840 100644
--- a/tensorflow/lite/kernels/internal/quantization_util.h
+++ b/tensorflow/lite/kernels/internal/quantization_util.h
@@ -20,7 +20,6 @@
#include <limits>
#include "tensorflow/lite/kernels/internal/compatibility.h"
-#include "tensorflow/lite/kernels/internal/cppmath.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
@@ -103,6 +102,7 @@
return ChooseQuantizationParams<T>(rmin, rmax, false);
}
+// LINT.IfChange
// Converts a floating-point number to an integer. For all inputs x where
// static_cast<IntOut>(x) is legal according to the C++ standard, the result
// is identical to that cast (i.e. the result is x with its fractional part
@@ -167,6 +167,7 @@
return x < 0 ? std::numeric_limits<IntOut>::min()
: std::numeric_limits<IntOut>::max();
}
+// LINT.ThenChange(//tensorflow/compiler/mlir/lite/kernels/internal/quantization_util.h)
// Decompose a double multiplier into a Q0.31 int32 representation of its
// significand, and shift representation of NEGATIVE its exponent ---
diff --git a/tensorflow/lite/kernels/internal/reference/add.h b/tensorflow/lite/kernels/internal/reference/add.h
index b89a57b..5b520bd 100644
--- a/tensorflow/lite/kernels/internal/reference/add.h
+++ b/tensorflow/lite/kernels/internal/reference/add.h
@@ -16,10 +16,13 @@
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_H_
#include <algorithm>
+#include <cstddef>
+#include <cstdint>
#include <type_traits>
#include "fixedpoint/fixedpoint.h"
#include "tensorflow/lite/kernels/internal/common.h"
+#include "tensorflow/lite/kernels/internal/compatibility.h"
namespace tflite {
@@ -194,21 +197,135 @@
}
}
+template <typename T>
+inline void AddBroadcast(const T* input_data, const T* broadcast_data,
+ T* output_data, size_t size, T activation_min,
+ T activation_max) {
+ for (size_t c = 0; c < size; ++c) {
+ output_data[c] = ActivationFunctionWithMinMax<T>(
+ input_data[c] + broadcast_data[0], activation_min, activation_max);
+ }
+}
+
+template <>
+inline void AddBroadcast<int32_t>(const int32_t* input_data,
+ const int32_t* broadcast_data,
+ int32_t* output_data, size_t size,
+ int32_t activation_min,
+ int32_t activation_max) {
+ size_t c = 0;
+#ifdef USE_NEON
+ const int32x4_t vmax = vdupq_n_s32(activation_max);
+ const int32x4_t vmin = vdupq_n_s32(activation_min);
+ const int32x4_t vb = vdupq_n_s32(broadcast_data[0]);
+ for (; c + 4 <= size; c += 4) {
+ const int32x4_t va = vld1q_s32(&input_data[c]);
+ int32x4_t vres = vaddq_s32(va, vb);
+ vres = vmaxq_s32(vmin, vres);
+ vres = vminq_s32(vmax, vres);
+ vst1q_s32(&output_data[c], vres);
+ }
+#endif
+ for (; c < size; ++c) {
+ output_data[c] = ActivationFunctionWithMinMax<int32_t>(
+ input_data[c] + broadcast_data[0], activation_min, activation_max);
+ }
+}
+
+template <typename T>
+void AddElementwise(const T* input1_data, const T* input2_data, T* output_data,
+ size_t size, T activation_min, T activation_max) {
+ for (size_t c = 0; c < size; ++c) {
+ output_data[c] = ActivationFunctionWithMinMax<T>(
+ input1_data[c] + input2_data[c], activation_min, activation_max);
+ }
+}
+
+template <>
+inline void AddElementwise<int32_t>(const int32_t* input1_data,
+ const int32_t* input2_data,
+ int32_t* output_data, size_t size,
+ int32_t activation_min,
+ int32_t activation_max) {
+ size_t c = 0;
+#ifdef USE_NEON
+ const int32x4_t vmax = vdupq_n_s32(activation_max);
+ const int32x4_t vmin = vdupq_n_s32(activation_min);
+ for (; c + 4 <= size; c += 4) {
+ const int32x4_t va = vld1q_s32(&input1_data[c]);
+ const int32x4_t vb = vld1q_s32(&input2_data[c]);
+ int32x4_t vres = vaddq_s32(va, vb);
+ vres = vmaxq_s32(vmin, vres);
+ vres = vminq_s32(vmax, vres);
+ vst1q_s32(&output_data[c], vres);
+ }
+#endif
+ for (; c < size; ++c) {
+ output_data[c] = ActivationFunctionWithMinMax<int32_t>(
+ input1_data[c] + input2_data[c], activation_min, activation_max);
+ }
+}
+
+template <typename T>
+inline void BroadcastAddRecursiveDimensions(
+ int dimension, size_t* input1_offset_p, size_t* input2_offset_p,
+ size_t* output_offset, size_t* compressed_input1_stride,
+ size_t* compressed_input2_stride, size_t* compressed_output_shape,
+ T activation_min, T activation_max, const T* input1_data,
+ const T* input2_data, T* output_data) {
+ if (dimension > 0) {
+ for (size_t c = 0; c < compressed_output_shape[dimension]; ++c) {
+ size_t input1_offset_c = *input1_offset_p;
+ size_t input2_offset_c = *input2_offset_p;
+ BroadcastAddRecursiveDimensions(
+ dimension - 1, &input1_offset_c, &input2_offset_c, output_offset,
+ compressed_input1_stride, compressed_input2_stride,
+ compressed_output_shape, activation_min, activation_max, input1_data,
+ input2_data, output_data);
+ *input1_offset_p += compressed_input1_stride[dimension];
+ *input2_offset_p += compressed_input2_stride[dimension];
+ }
+ } else {
+ TFLITE_DCHECK(dimension == 0);
+ bool input1_is_broadcast = compressed_input1_stride[dimension] == 0;
+ bool input2_is_broadcast = compressed_input2_stride[dimension] == 0;
+ TFLITE_DCHECK(!(input1_is_broadcast && input2_is_broadcast));
+ const T* input1_data_ptr = input1_data + *input1_offset_p;
+ const T* input2_data_ptr = input2_data + *input2_offset_p;
+ T* output_data_ptr = output_data + *output_offset;
+ if (input1_is_broadcast) {
+ // input1 is broadcast.
+ AddBroadcast<T>(input2_data_ptr, input1_data_ptr, output_data_ptr,
+ compressed_output_shape[dimension], activation_min,
+ activation_max);
+ *input2_offset_p += compressed_output_shape[dimension];
+ } else if (input2_is_broadcast) {
+ // input2 is broadcast.
+ AddBroadcast<T>(input1_data_ptr, input2_data_ptr, output_data_ptr,
+ compressed_output_shape[dimension], activation_min,
+ activation_max);
+ *input1_offset_p += compressed_output_shape[dimension];
+ } else {
+ // Add element-wise.
+ AddElementwise<T>(input1_data_ptr, input2_data_ptr, output_data_ptr,
+ compressed_output_shape[dimension], activation_min,
+ activation_max);
+ *input1_offset_p += compressed_output_shape[dimension];
+ *input2_offset_p += compressed_output_shape[dimension];
+ }
+ *output_offset += compressed_output_shape[dimension];
+ }
+}
+
template <typename T,
- // For unquantized add for small integers, explictly set to true.
+ // For unquantized add for small integers, explicitly set to true.
bool dummy = false>
inline typename std::enable_if<!is_small_integer<T>::value || dummy, void>::type
BroadcastAdd6DSlow(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const T* input1_data,
const RuntimeShape& input2_shape, const T* input2_data,
const RuntimeShape& output_shape, T* output_data) {
- NdArrayDesc<6> desc1;
- NdArrayDesc<6> desc2;
- NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
- &desc2);
- const RuntimeShape extended_output_shape =
- RuntimeShape::ExtendedShape(6, output_shape);
-
+ constexpr int kMaxBroadcastDim = 6;
T activation_min, activation_max;
GetActivationParams(params, &activation_min, &activation_max);
@@ -223,64 +340,74 @@
// We name our variables by their Tensorflow convention, but generate C code
// nesting loops such that the innermost loop has the smallest stride for the
// best cache behavior.
- size_t input1_offset_a = 0;
- size_t input2_offset_a = 0;
- size_t output_offset_a = 0;
- for (int a = 0; a < extended_output_shape.Dims(0); ++a) {
- size_t input1_offset_d = input1_offset_a;
- size_t input2_offset_d = input2_offset_a;
- size_t output_offset_d = output_offset_a;
- for (int d = 0; d < extended_output_shape.Dims(1); ++d) {
- size_t input1_offset_b = input1_offset_d;
- size_t input2_offset_b = input2_offset_d;
- size_t output_offset_b = output_offset_d;
- for (int b = 0; b < extended_output_shape.Dims(2); ++b) {
- size_t input1_offset_y = input1_offset_b;
- size_t input2_offset_y = input2_offset_b;
- size_t output_offset_y = output_offset_b;
- for (int y = 0; y < extended_output_shape.Dims(3); ++y) {
- size_t input1_offset_x = input1_offset_y;
- size_t input2_offset_x = input2_offset_y;
- size_t output_offset_x = output_offset_y;
- for (int x = 0; x < extended_output_shape.Dims(4); ++x) {
- size_t input1_offset_c = input1_offset_x;
- size_t input2_offset_c = input2_offset_x;
- size_t output_offset_c = output_offset_x;
- for (int c = 0; c < extended_output_shape.Dims(5); ++c) {
- output_data[output_offset_c] = ActivationFunctionWithMinMax<T>(
- input1_data[input1_offset_c] + input2_data[input2_offset_c],
- activation_min, activation_max);
- input1_offset_c += desc1.strides[5];
- input2_offset_c += desc2.strides[5];
- ++output_offset_c;
- }
- input1_offset_x += desc1.strides[4];
- input2_offset_x += desc2.strides[4];
- output_offset_x += extended_output_shape.Dims(5);
- }
- input1_offset_y += desc1.strides[3];
- input2_offset_y += desc2.strides[3];
- output_offset_y +=
- extended_output_shape.Dims(4) * extended_output_shape.Dims(5);
- }
- input1_offset_b += desc1.strides[2];
- input2_offset_b += desc2.strides[2];
- output_offset_b += extended_output_shape.Dims(3) *
- extended_output_shape.Dims(4) *
- extended_output_shape.Dims(5);
- }
- input1_offset_d += desc1.strides[1];
- input2_offset_d += desc2.strides[1];
- output_offset_d +=
- extended_output_shape.Dims(2) * extended_output_shape.Dims(3) *
- extended_output_shape.Dims(4) * extended_output_shape.Dims(5);
+ size_t compressed_input1_stride[kMaxBroadcastDim];
+ size_t compressed_input2_stride[kMaxBroadcastDim];
+ size_t compressed_output_shape[kMaxBroadcastDim];
+ bool broadcastable_shape = ReduceDimensionsForBroadcast<kMaxBroadcastDim>(
+ input1_shape, input2_shape, compressed_input1_stride,
+ compressed_input2_stride, compressed_output_shape);
+ // Skip broadcasting for degenerate shapes.
+ if (!broadcastable_shape) {
+ return;
+ }
+
+ size_t input1_offset = 0;
+ size_t input2_offset = 0;
+ size_t output_offset = 0;
+ BroadcastAddRecursiveDimensions<T>(
+ kMaxBroadcastDim - 1, &input1_offset, &input2_offset, &output_offset,
+ compressed_input1_stride, compressed_input2_stride,
+ compressed_output_shape, activation_min, activation_max, input1_data,
+ input2_data, output_data);
+}
+
+// This function is used for 8-bit as well as for 16-bit, but the accumulator
+// is 32-bit for both cases. The overflow does not happen due to the
+// choice of the shift (20 or 15, accordingly - see add.cc for more comments).
+template <typename T>
+inline void BroadcastAddRecursiveDimensions(
+ const ArithmeticParams& params, int dimension, size_t* input1_offset_p,
+ size_t* input2_offset_p, size_t* output_offset,
+ size_t* compressed_input1_stride, size_t* compressed_input2_stride,
+ size_t* compressed_output_shape, const T* input1_data, const T* input2_data,
+ T* output_data) {
+ for (size_t c = 0; c < compressed_output_shape[dimension]; ++c) {
+ if (dimension > 0) {
+ size_t input1_offset_c = *input1_offset_p;
+ size_t input2_offset_c = *input2_offset_p;
+ BroadcastAddRecursiveDimensions(
+ params, dimension - 1, &input1_offset_c, &input2_offset_c,
+ output_offset, compressed_input1_stride, compressed_input2_stride,
+ compressed_output_shape, input1_data, input2_data, output_data);
+ } else {
+ TFLITE_DCHECK(dimension == 0);
+ const int32_t input1_val =
+ params.input1_offset + input1_data[*input1_offset_p];
+ const int32_t input2_val =
+ params.input2_offset + input2_data[*input2_offset_p];
+ const int32_t shifted_input1_val = input1_val * (1 << params.left_shift);
+ const int32_t shifted_input2_val = input2_val * (1 << params.left_shift);
+ const int32_t scaled_input1_val =
+ MultiplyByQuantizedMultiplierSmallerThanOneExp(
+ shifted_input1_val, params.input1_multiplier,
+ params.input1_shift);
+ const int32_t scaled_input2_val =
+ MultiplyByQuantizedMultiplierSmallerThanOneExp(
+ shifted_input2_val, params.input2_multiplier,
+ params.input2_shift);
+ const int32_t raw_sum = scaled_input1_val + scaled_input2_val;
+ const int32_t raw_output =
+ MultiplyByQuantizedMultiplierSmallerThanOneExp(
+ raw_sum, params.output_multiplier, params.output_shift) +
+ params.output_offset;
+ const int32_t clamped_output =
+ std::min(params.quantized_activation_max,
+ std::max(params.quantized_activation_min, raw_output));
+ output_data[*output_offset] = static_cast<T>(clamped_output);
+ ++(*output_offset);
}
- input1_offset_a += desc1.strides[0];
- input2_offset_a += desc2.strides[0];
- output_offset_a +=
- extended_output_shape.Dims(1) * extended_output_shape.Dims(2) *
- extended_output_shape.Dims(3) * extended_output_shape.Dims(4) *
- extended_output_shape.Dims(5);
+ *input1_offset_p += compressed_input1_stride[dimension];
+ *input2_offset_p += compressed_input2_stride[dimension];
}
}
@@ -293,12 +420,7 @@
const RuntimeShape& input1_shape, const T* input1_data,
const RuntimeShape& input2_shape, const T* input2_data,
const RuntimeShape& output_shape, T* output_data) {
- NdArrayDesc<6> desc1;
- NdArrayDesc<6> desc2;
- NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
- &desc2);
- const RuntimeShape extended_output_shape =
- RuntimeShape::ExtendedShape(6, output_shape);
+ constexpr int kMaxBroadcastDim = 6;
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
@@ -311,87 +433,24 @@
// We name our variables by their Tensorflow convention, but generate C code
// nesting loops such that the innermost loop has the smallest stride for the
// best cache behavior.
- size_t input1_offset_a = 0;
- size_t input2_offset_a = 0;
- size_t output_offset_a = 0;
- for (int a = 0; a < extended_output_shape.Dims(0); ++a) {
- size_t input1_offset_d = input1_offset_a;
- size_t input2_offset_d = input2_offset_a;
- size_t output_offset_d = output_offset_a;
- for (int d = 0; d < extended_output_shape.Dims(1); ++d) {
- size_t input1_offset_b = input1_offset_d;
- size_t input2_offset_b = input2_offset_d;
- size_t output_offset_b = output_offset_d;
- for (int b = 0; b < extended_output_shape.Dims(2); ++b) {
- size_t input1_offset_y = input1_offset_b;
- size_t input2_offset_y = input2_offset_b;
- size_t output_offset_y = output_offset_b;
- for (int y = 0; y < extended_output_shape.Dims(3); ++y) {
- size_t input1_offset_x = input1_offset_y;
- size_t input2_offset_x = input2_offset_y;
- size_t output_offset_x = output_offset_y;
- for (int x = 0; x < extended_output_shape.Dims(4); ++x) {
- size_t input1_offset_c = input1_offset_x;
- size_t input2_offset_c = input2_offset_x;
- size_t output_offset_c = output_offset_x;
- for (int c = 0; c < extended_output_shape.Dims(5); ++c) {
- const int32_t input1_val =
- params.input1_offset + input1_data[input1_offset_c];
- const int32_t input2_val =
- params.input2_offset + input2_data[input2_offset_c];
- const int32_t shifted_input1_val =
- input1_val * (1 << params.left_shift);
- const int32_t shifted_input2_val =
- input2_val * (1 << params.left_shift);
- const int32_t scaled_input1_val =
- MultiplyByQuantizedMultiplierSmallerThanOneExp(
- shifted_input1_val, params.input1_multiplier,
- params.input1_shift);
- const int32_t scaled_input2_val =
- MultiplyByQuantizedMultiplierSmallerThanOneExp(
- shifted_input2_val, params.input2_multiplier,
- params.input2_shift);
- const int32_t raw_sum = scaled_input1_val + scaled_input2_val;
- const int32_t raw_output =
- MultiplyByQuantizedMultiplierSmallerThanOneExp(
- raw_sum, params.output_multiplier, params.output_shift) +
- params.output_offset;
- const int32_t clamped_output = std::min(
- params.quantized_activation_max,
- std::max(params.quantized_activation_min, raw_output));
- output_data[output_offset_c] = static_cast<T>(clamped_output);
- input1_offset_c += desc1.strides[5];
- input2_offset_c += desc2.strides[5];
- ++output_offset_c;
- }
- input1_offset_x += desc1.strides[4];
- input2_offset_x += desc2.strides[4];
- output_offset_x += extended_output_shape.Dims(5);
- }
- input1_offset_y += desc1.strides[3];
- input2_offset_y += desc2.strides[3];
- output_offset_y +=
- extended_output_shape.Dims(4) * extended_output_shape.Dims(5);
- }
- input1_offset_b += desc1.strides[2];
- input2_offset_b += desc2.strides[2];
- output_offset_b += extended_output_shape.Dims(3) *
- extended_output_shape.Dims(4) *
- extended_output_shape.Dims(5);
- }
- input1_offset_d += desc1.strides[1];
- input2_offset_d += desc2.strides[1];
- output_offset_d +=
- extended_output_shape.Dims(2) * extended_output_shape.Dims(3) *
- extended_output_shape.Dims(4) * extended_output_shape.Dims(5);
- }
- input1_offset_a += desc1.strides[0];
- input2_offset_a += desc2.strides[0];
- output_offset_a +=
- extended_output_shape.Dims(1) * extended_output_shape.Dims(2) *
- extended_output_shape.Dims(3) * extended_output_shape.Dims(4) *
- extended_output_shape.Dims(5);
+ size_t compressed_input1_stride[kMaxBroadcastDim];
+ size_t compressed_input2_stride[kMaxBroadcastDim];
+ size_t compressed_output_shape[kMaxBroadcastDim];
+ bool broadcastable_shape = ReduceDimensionsForBroadcast<kMaxBroadcastDim>(
+ input1_shape, input2_shape, compressed_input1_stride,
+ compressed_input2_stride, compressed_output_shape);
+ // Skip broadcasting for degenerate shapes.
+ if (!broadcastable_shape) {
+ return;
}
+
+ size_t input1_offset = 0;
+ size_t input2_offset = 0;
+ size_t output_offset = 0;
+ BroadcastAddRecursiveDimensions(
+ params, kMaxBroadcastDim - 1, &input1_offset, &input2_offset,
+ &output_offset, compressed_input1_stride, compressed_input2_stride,
+ compressed_output_shape, input1_data, input2_data, output_data);
}
template <typename T>
diff --git a/tensorflow/lite/kernels/internal/reference/comparisons.h b/tensorflow/lite/kernels/internal/reference/comparisons.h
index 3558319..366b378 100644
--- a/tensorflow/lite/kernels/internal/reference/comparisons.h
+++ b/tensorflow/lite/kernels/internal/reference/comparisons.h
@@ -257,12 +257,12 @@
op_params, input1_shape, input1_data, input2_shape, input2_data, \
output_shape, output_data); \
}
-TFLITE_COMPARISON_OP(Equal);
-TFLITE_COMPARISON_OP(NotEqual);
-TFLITE_COMPARISON_OP(Greater);
-TFLITE_COMPARISON_OP(GreaterEqual);
-TFLITE_COMPARISON_OP(Less);
-TFLITE_COMPARISON_OP(LessEqual);
+TFLITE_COMPARISON_OP(Equal)
+TFLITE_COMPARISON_OP(NotEqual)
+TFLITE_COMPARISON_OP(Greater)
+TFLITE_COMPARISON_OP(GreaterEqual)
+TFLITE_COMPARISON_OP(Less)
+TFLITE_COMPARISON_OP(LessEqual)
#undef TFLITE_COMPARISON_OP
} // namespace reference_ops
diff --git a/tensorflow/lite/kernels/internal/reference/integer_ops/add.h b/tensorflow/lite/kernels/internal/reference/integer_ops/add.h
index 579964d..c2a0e0f 100644
--- a/tensorflow/lite/kernels/internal/reference/integer_ops/add.h
+++ b/tensorflow/lite/kernels/internal/reference/integer_ops/add.h
@@ -16,6 +16,7 @@
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_
#include <algorithm>
+#include <cstddef>
#include <limits>
#include "tensorflow/lite/kernels/internal/common.h"
@@ -35,7 +36,30 @@
TFLITE_DCHECK_LE(-params.input2_offset, std::numeric_limits<int8_t>::max());
}
-// TODO(b/270589088): move to a more appropriate file (b/270589088#comment2)
+// TODO: b/270589088 - move to a more appropriate file (b/270589088#comment2)
+template <typename T>
+void BroadcastInput1(int size, const ArithmeticParams& params,
+ const T* input1_data, const T* input2_data, T* output_data,
+ void (*check_arithmetic_params)(const ArithmeticParams&),
+ T (*binary_func)(T, T, const ArithmeticParams&)) {
+ CheckArithmeticParams(params);
+ for (int i = 0; i < size; ++i) {
+ output_data[i] = binary_func(input1_data[0], input2_data[i], params);
+ }
+}
+
+template <typename T>
+void BroadcastInput2(int size, const ArithmeticParams& params,
+ const T* input1_data, const T* input2_data, T* output_data,
+ void (*check_arithmetic_params)(const ArithmeticParams&),
+ T (*binary_func)(T, T, const ArithmeticParams&)) {
+ CheckArithmeticParams(params);
+ for (int i = 0; i < size; ++i) {
+ output_data[i] = binary_func(input1_data[i], input2_data[0], params);
+ }
+}
+
+// TODO: b/270589088 - move to a more appropriate file (b/270589088#comment2)
template <typename T>
void ElementWise(int size, const ArithmeticParams& params, const T* input1_data,
const T* input2_data, T* output_data,
@@ -46,7 +70,60 @@
output_data[i] = binary_func(input1_data[i], input2_data[i], params);
}
}
-// TODO(b/270589088): move to a more appropriate file. (b/270589088#comment2)
+
+template <typename T>
+inline void BroadcastAddRecursiveDimensions(
+ const ArithmeticParams& params, int dimension, size_t* input1_offset_p,
+ size_t* input2_offset_p, size_t* output_offset,
+ size_t* compressed_input1_stride, size_t* compressed_input2_stride,
+ size_t* compressed_output_shape, const T* input1_data, const T* input2_data,
+ T* output_data, void (*check_arithmetic_params)(const ArithmeticParams&),
+ T (*binary_func)(T, T, const ArithmeticParams&)) {
+ if (dimension > 0) {
+ for (size_t c = 0; c < compressed_output_shape[dimension]; ++c) {
+ size_t input1_offset_c = *input1_offset_p;
+ size_t input2_offset_c = *input2_offset_p;
+ BroadcastAddRecursiveDimensions(
+ params, dimension - 1, &input1_offset_c, &input2_offset_c,
+ output_offset, compressed_input1_stride, compressed_input2_stride,
+ compressed_output_shape, input1_data, input2_data, output_data,
+ check_arithmetic_params, binary_func);
+ *input1_offset_p += compressed_input1_stride[dimension];
+ *input2_offset_p += compressed_input2_stride[dimension];
+ }
+ } else {
+ TFLITE_DCHECK(dimension == 0);
+ bool input1_is_broadcast = compressed_input1_stride[dimension] == 0;
+ bool input2_is_broadcast = compressed_input2_stride[dimension] == 0;
+ TFLITE_DCHECK(!(input1_is_broadcast && input2_is_broadcast));
+ const T* input1_data_ptr = input1_data + *input1_offset_p;
+ const T* input2_data_ptr = input2_data + *input2_offset_p;
+ T* output_data_ptr = output_data + *output_offset;
+ if (input1_is_broadcast) {
+ // input1 is broadcast.
+ BroadcastInput1<T>(compressed_output_shape[dimension], params,
+ input1_data_ptr, input2_data_ptr, output_data_ptr,
+ check_arithmetic_params, binary_func);
+ *input2_offset_p += compressed_output_shape[dimension];
+ } else if (input2_is_broadcast) {
+ // input2 is broadcast.
+ BroadcastInput2<T>(compressed_output_shape[dimension], params,
+ input1_data_ptr, input2_data_ptr, output_data_ptr,
+ check_arithmetic_params, binary_func);
+ *input1_offset_p += compressed_output_shape[dimension];
+ } else {
+ // Add element-wise.
+ ElementWise<T>(compressed_output_shape[dimension], params,
+ input1_data_ptr, input2_data_ptr, output_data_ptr,
+ check_arithmetic_params, binary_func);
+ *input1_offset_p += compressed_output_shape[dimension];
+ *input2_offset_p += compressed_output_shape[dimension];
+ }
+ *output_offset += compressed_output_shape[dimension];
+ }
+}
+
+// TODO: b/270589088 - move to a more appropriate file. (b/270589088#comment2)
template <typename T>
void BroadcastBinaryFunction6DSlow(
const ArithmeticParams& params, const RuntimeShape& input1_shape,
@@ -54,12 +131,7 @@
const T* input2_data, const RuntimeShape& output_shape, T* output_data,
void (*check_arithmetic_params)(const ArithmeticParams&),
T (*binary_func)(T, T, const ArithmeticParams&)) {
- NdArrayDesc<6> desc1;
- NdArrayDesc<6> desc2;
- NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
- &desc2);
- const RuntimeShape extended_output_shape =
- RuntimeShape::ExtendedShape(6, output_shape);
+ constexpr int kMaxBroadcastDim = 6;
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
@@ -72,65 +144,25 @@
// We name our variables by their Tensorflow convention, but generate C code
// nesting loops such that the innermost loop has the smallest stride for the
// best cache behavior.
- size_t input1_offset_a = 0;
- size_t input2_offset_a = 0;
- size_t output_offset_a = 0;
- for (int a = 0; a < extended_output_shape.Dims(0); ++a) {
- size_t input1_offset_d = input1_offset_a;
- size_t input2_offset_d = input2_offset_a;
- size_t output_offset_d = output_offset_a;
- for (int d = 0; d < extended_output_shape.Dims(1); ++d) {
- size_t input1_offset_b = input1_offset_d;
- size_t input2_offset_b = input2_offset_d;
- size_t output_offset_b = output_offset_d;
- for (int b = 0; b < extended_output_shape.Dims(2); ++b) {
- size_t input1_offset_y = input1_offset_b;
- size_t input2_offset_y = input2_offset_b;
- size_t output_offset_y = output_offset_b;
- for (int y = 0; y < extended_output_shape.Dims(3); ++y) {
- size_t input1_offset_x = input1_offset_y;
- size_t input2_offset_x = input2_offset_y;
- size_t output_offset_x = output_offset_y;
- for (int x = 0; x < extended_output_shape.Dims(4); ++x) {
- size_t input1_offset_c = input1_offset_x;
- size_t input2_offset_c = input2_offset_x;
- size_t output_offset_c = output_offset_x;
- for (int c = 0; c < extended_output_shape.Dims(5); ++c) {
- output_data[output_offset_c] =
- binary_func(input1_data[input1_offset_c],
- input2_data[input2_offset_c], params);
- input1_offset_c += desc1.strides[5];
- input2_offset_c += desc2.strides[5];
- ++output_offset_c;
- }
- input1_offset_x += desc1.strides[4];
- input2_offset_x += desc2.strides[4];
- output_offset_x += extended_output_shape.Dims(5);
- }
- input1_offset_y += desc1.strides[3];
- input2_offset_y += desc2.strides[3];
- output_offset_y +=
- extended_output_shape.Dims(4) * extended_output_shape.Dims(5);
- }
- input1_offset_b += desc1.strides[2];
- input2_offset_b += desc2.strides[2];
- output_offset_b += extended_output_shape.Dims(3) *
- extended_output_shape.Dims(4) *
- extended_output_shape.Dims(5);
- }
- input1_offset_d += desc1.strides[1];
- input2_offset_d += desc2.strides[1];
- output_offset_d +=
- extended_output_shape.Dims(2) * extended_output_shape.Dims(3) *
- extended_output_shape.Dims(4) * extended_output_shape.Dims(5);
- }
- input1_offset_a += desc1.strides[0];
- input2_offset_a += desc2.strides[0];
- output_offset_a +=
- extended_output_shape.Dims(1) * extended_output_shape.Dims(2) *
- extended_output_shape.Dims(3) * extended_output_shape.Dims(4) *
- extended_output_shape.Dims(5);
+ size_t compressed_input1_stride[kMaxBroadcastDim];
+ size_t compressed_input2_stride[kMaxBroadcastDim];
+ size_t compressed_output_shape[kMaxBroadcastDim];
+ bool broadcastable_shape = ReduceDimensionsForBroadcast<kMaxBroadcastDim>(
+ input1_shape, input2_shape, compressed_input1_stride,
+ compressed_input2_stride, compressed_output_shape);
+ // Skip broadcasting for degenerate shapes.
+ if (!broadcastable_shape) {
+ return;
}
+
+ size_t input1_offset = 0;
+ size_t input2_offset = 0;
+ size_t output_offset = 0;
+ BroadcastAddRecursiveDimensions(
+ params, kMaxBroadcastDim - 1, &input1_offset, &input2_offset,
+ &output_offset, compressed_input1_stride, compressed_input2_stride,
+ compressed_output_shape, input1_data, input2_data, output_data,
+ check_arithmetic_params, binary_func);
}
template <typename T>
diff --git a/tensorflow/lite/kernels/internal/reference/integer_ops/mul.h b/tensorflow/lite/kernels/internal/reference/integer_ops/mul.h
index 0506618..a57056d 100644
--- a/tensorflow/lite/kernels/internal/reference/integer_ops/mul.h
+++ b/tensorflow/lite/kernels/internal/reference/integer_ops/mul.h
@@ -24,6 +24,9 @@
namespace tflite {
namespace reference_integer_ops {
+// Maximum dimension supported by the broadcast mul operation.
+constexpr int kMaxMulBroadcastDim = 6;
+
template <typename InputType, typename OutputType>
void MulElementwise(int size, const ArithmeticParams& params,
const InputType* input1_data, const InputType* input2_data,
@@ -88,46 +91,104 @@
}
template <typename T>
-inline void BroadcastMul4DSlow(
+inline void BroadcastMul6DSlow(
const ArithmeticParams& params, const RuntimeShape& input1_shape,
const T* input1_data, const RuntimeShape& input2_shape,
const T* input2_data, const RuntimeShape& output_shape, T* output_data) {
- ruy::profiler::ScopeLabel label("BroadcastMul4DSlow");
+ ruy::profiler::ScopeLabel label("BroadcastMul6DSlow");
- NdArrayDesc<4> desc1;
- NdArrayDesc<4> desc2;
+ NdArrayDesc<kMaxMulBroadcastDim> desc1;
+ NdArrayDesc<kMaxMulBroadcastDim> desc2;
// The input shapes are extended as part of NdArrayDesc initialization.
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
&desc2);
const RuntimeShape extended_output_shape =
- RuntimeShape::ExtendedShape(4, output_shape);
+ RuntimeShape::ExtendedShape(kMaxMulBroadcastDim, output_shape);
+ // Cache output shape dimensions.
+ int32_t extended_output_shape_dims[kMaxMulBroadcastDim];
+ std::memcpy(extended_output_shape_dims, extended_output_shape.DimsData(),
+ sizeof(extended_output_shape_dims));
- for (int b = 0; b < extended_output_shape.Dims(0); ++b) {
- for (int y = 0; y < extended_output_shape.Dims(1); ++y) {
- for (int x = 0; x < extended_output_shape.Dims(2); ++x) {
- for (int c = 0; c < extended_output_shape.Dims(3); ++c) {
- const int32_t input1_val =
- params.input1_offset +
- input1_data[SubscriptToIndex(desc1, b, y, x, c)];
- const int32_t input2_val =
- params.input2_offset +
- input2_data[SubscriptToIndex(desc2, b, y, x, c)];
- const int32_t unclamped_result =
- params.output_offset +
- MultiplyByQuantizedMultiplier(input1_val * input2_val,
- params.output_multiplier,
- params.output_shift);
- const int32_t clamped_output = std::min(
- params.quantized_activation_max,
- std::max(params.quantized_activation_min, unclamped_result));
- output_data[Offset(extended_output_shape, b, y, x, c)] =
- static_cast<T>(clamped_output);
+ size_t input1_offset_a = 0;
+ size_t input2_offset_a = 0;
+ size_t output_offset_a = 0;
+ for (int a = 0; a < extended_output_shape_dims[0]; ++a) {
+ size_t input1_offset_d = input1_offset_a;
+ size_t input2_offset_d = input2_offset_a;
+ size_t output_offset_d = output_offset_a;
+ for (int d = 0; d < extended_output_shape_dims[1]; ++d) {
+ size_t input1_offset_b = input1_offset_d;
+ size_t input2_offset_b = input2_offset_d;
+ size_t output_offset_b = output_offset_d;
+ for (int b = 0; b < extended_output_shape_dims[2]; ++b) {
+ size_t input1_offset_y = input1_offset_b;
+ size_t input2_offset_y = input2_offset_b;
+ size_t output_offset_y = output_offset_b;
+ for (int y = 0; y < extended_output_shape_dims[3]; ++y) {
+ size_t input1_offset_x = input1_offset_y;
+ size_t input2_offset_x = input2_offset_y;
+ size_t output_offset_x = output_offset_y;
+ for (int x = 0; x < extended_output_shape_dims[4]; ++x) {
+ size_t input1_offset_c = input1_offset_x;
+ size_t input2_offset_c = input2_offset_x;
+ size_t output_offset_c = output_offset_x;
+ for (int c = 0; c < extended_output_shape_dims[5]; ++c) {
+ const int32_t input1_val =
+ params.input1_offset + input1_data[input1_offset_c];
+ const int32_t input2_val =
+ params.input2_offset + input2_data[input2_offset_c];
+ const int32_t unclamped_result =
+ params.output_offset +
+ MultiplyByQuantizedMultiplier(input1_val * input2_val,
+ params.output_multiplier,
+ params.output_shift);
+ const int32_t clamped_output = std::min(
+ params.quantized_activation_max,
+ std::max(params.quantized_activation_min, unclamped_result));
+ output_data[output_offset_c] = static_cast<T>(clamped_output);
+ input1_offset_c += desc1.strides[5];
+ input2_offset_c += desc2.strides[5];
+ ++output_offset_c;
+ }
+ input1_offset_x += desc1.strides[4];
+ input2_offset_x += desc2.strides[4];
+ output_offset_x += extended_output_shape_dims[5];
+ }
+ input1_offset_y += desc1.strides[3];
+ input2_offset_y += desc2.strides[3];
+ output_offset_y +=
+ extended_output_shape_dims[4] * extended_output_shape_dims[5];
}
+ input1_offset_b += desc1.strides[2];
+ input2_offset_b += desc2.strides[2];
+ output_offset_b += extended_output_shape_dims[3] *
+ extended_output_shape_dims[4] *
+ extended_output_shape_dims[5];
}
+ input1_offset_d += desc1.strides[1];
+ input2_offset_d += desc2.strides[1];
+ output_offset_d +=
+ extended_output_shape_dims[2] * extended_output_shape_dims[3] *
+ extended_output_shape_dims[4] * extended_output_shape_dims[5];
}
+ input1_offset_a += desc1.strides[0];
+ input2_offset_a += desc2.strides[0];
+ output_offset_a +=
+ extended_output_shape_dims[1] * extended_output_shape_dims[2] *
+ extended_output_shape_dims[3] * extended_output_shape_dims[4] *
+ extended_output_shape_dims[5];
}
}
+template <typename T>
+inline void BroadcastMul4DSlow(
+ const ArithmeticParams& params, const RuntimeShape& input1_shape,
+ const T* input1_data, const RuntimeShape& input2_shape,
+ const T* input2_data, const RuntimeShape& output_shape, T* output_data) {
+ BroadcastMul6DSlow(params, input1_shape, input1_data, input2_shape,
+ input2_data, output_shape, output_data);
+}
+
} // namespace reference_integer_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_
diff --git a/tensorflow/lite/kernels/internal/reference/mul.h b/tensorflow/lite/kernels/internal/reference/mul.h
index 2767fef..fca74a3 100644
--- a/tensorflow/lite/kernels/internal/reference/mul.h
+++ b/tensorflow/lite/kernels/internal/reference/mul.h
@@ -24,6 +24,9 @@
namespace reference_ops {
+// Maximum dimension supported by the broadcast mul operation.
+constexpr int kMaxMulBroadcastDim = 6;
+
// Element-wise mul that can often be used for inner loop of broadcast Mul as
// well as the non-broadcast Mul.
inline void MulElementwise(int size, const ArithmeticParams& params,
@@ -88,128 +91,174 @@
MulElementwise(flat_size, params, input1_data, input2_data, output_data);
}
-inline void BroadcastMul4DSlow(const ArithmeticParams& params,
+template <typename T, typename F>
+void BroadcastMulRecursiveDimensions(
+ const ArithmeticParams& params, int dimension, const T* input1_data,
+ const T* input2_data, T* output_data, size_t* input1_offset_p,
+ size_t* input2_offset_p, size_t* output_offset,
+ const NdArrayDesc<kMaxMulBroadcastDim>& desc1,
+ const NdArrayDesc<kMaxMulBroadcastDim>& desc2,
+ const int32_t extended_output_shape_dims[kMaxMulBroadcastDim],
+ F binary_func) {
+ if (dimension == kMaxMulBroadcastDim - 1) {
+ for (int c = 0; c < extended_output_shape_dims[dimension]; ++c) {
+ const T input1_val = input1_data[*input1_offset_p];
+ const T input2_val = input2_data[*input2_offset_p];
+ output_data[*output_offset] = binary_func(params, input1_val, input2_val);
+ *input1_offset_p += desc1.strides[dimension];
+ *input2_offset_p += desc2.strides[dimension];
+ ++(*output_offset);
+ }
+ } else {
+ for (int a = 0; a < extended_output_shape_dims[dimension]; ++a) {
+ size_t input1_offset_c = *input1_offset_p;
+ size_t input2_offset_c = *input2_offset_p;
+ BroadcastMulRecursiveDimensions(
+ params, dimension + 1, input1_data, input2_data, output_data,
+ &input1_offset_c, &input2_offset_c, output_offset, desc1, desc2,
+ extended_output_shape_dims, binary_func);
+ *input1_offset_p += desc1.strides[dimension];
+ *input2_offset_p += desc2.strides[dimension];
+ }
+ }
+}
+
+inline void BroadcastMul6DSlow(const ArithmeticParams& params,
const RuntimeShape& input1_shape,
const uint8_t* input1_data,
const RuntimeShape& input2_shape,
const uint8_t* input2_data,
const RuntimeShape& output_shape,
uint8_t* output_data) {
- NdArrayDesc<4> desc1;
- NdArrayDesc<4> desc2;
+ NdArrayDesc<kMaxMulBroadcastDim> desc1;
+ NdArrayDesc<kMaxMulBroadcastDim> desc2;
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
&desc2);
const RuntimeShape extended_output_shape =
- RuntimeShape::ExtendedShape(4, output_shape);
+ RuntimeShape::ExtendedShape(kMaxMulBroadcastDim, output_shape);
+ // Cache output shape dimensions.
+ int32_t extended_output_shape_dims[kMaxMulBroadcastDim];
+ std::memcpy(extended_output_shape_dims, extended_output_shape.DimsData(),
+ sizeof(extended_output_shape_dims));
- for (int b = 0; b < extended_output_shape.Dims(0); ++b) {
- for (int y = 0; y < extended_output_shape.Dims(1); ++y) {
- for (int x = 0; x < extended_output_shape.Dims(2); ++x) {
- for (int c = 0; c < extended_output_shape.Dims(3); ++c) {
- const int32_t input1_val =
- params.input1_offset +
- input1_data[SubscriptToIndex(desc1, b, y, x, c)];
- const int32_t input2_val =
- params.input2_offset +
- input2_data[SubscriptToIndex(desc2, b, y, x, c)];
- const int32_t unclamped_result =
- params.output_offset +
- MultiplyByQuantizedMultiplier(input1_val * input2_val,
- params.output_multiplier,
- params.output_shift);
- const int32_t clamped_output = std::min(
- params.quantized_activation_max,
- std::max(params.quantized_activation_min, unclamped_result));
- output_data[Offset(extended_output_shape, b, y, x, c)] =
- static_cast<uint8_t>(clamped_output);
- }
- }
- }
- }
+ size_t input1_offset = 0;
+ size_t input2_offset = 0;
+ size_t output_offset = 0;
+ BroadcastMulRecursiveDimensions(
+ params, 0, input1_data, input2_data, output_data, &input1_offset,
+ &input2_offset, &output_offset, desc1, desc2, extended_output_shape_dims,
+ [](const ArithmeticParams& params, const uint8_t input1_val,
+ const uint8_t input2_val) {
+ const int32_t offsetted_input1_val = params.input1_offset + input1_val;
+ const int32_t offsetted_input2_val = params.input2_offset + input2_val;
+ const int32_t unclamped_result =
+ params.output_offset +
+ MultiplyByQuantizedMultiplier(
+ offsetted_input1_val * offsetted_input2_val,
+ params.output_multiplier, params.output_shift);
+ const int32_t clamped_output = std::min(
+ params.quantized_activation_max,
+ std::max(params.quantized_activation_min, unclamped_result));
+ return static_cast<uint8_t>(clamped_output);
+ });
}
template <typename T,
- // For unquantized mul on small integers, explictly set to true.
+ // For unquantized mul on small integers, explicitly set to true.
bool enable_for_short_integers = false>
inline typename std::enable_if<
!is_small_integer<T>::value || enable_for_short_integers, void>::type
-BroadcastMul4DSlow(const ArithmeticParams& params,
+BroadcastMul6DSlow(const ArithmeticParams& params,
const RuntimeShape& unextended_input1_shape,
const T* input1_data,
const RuntimeShape& unextended_input2_shape,
const T* input2_data,
const RuntimeShape& unextended_output_shape,
T* output_data) {
- T output_activation_min;
- T output_activation_max;
- GetActivationParams(params, &output_activation_min, &output_activation_max);
-
- TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4);
- TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 4);
- TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4);
- const RuntimeShape output_shape =
- RuntimeShape::ExtendedShape(4, unextended_output_shape);
-
- NdArrayDesc<4> desc1;
- NdArrayDesc<4> desc2;
+ TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 6);
+ TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 6);
+ TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 6);
+ NdArrayDesc<kMaxMulBroadcastDim> desc1;
+ NdArrayDesc<kMaxMulBroadcastDim> desc2;
NdArrayDescsForElementwiseBroadcast(unextended_input1_shape,
unextended_input2_shape, &desc1, &desc2);
+ const RuntimeShape extended_output_shape =
+ RuntimeShape::ExtendedShape(kMaxMulBroadcastDim, unextended_output_shape);
+ // Cache output shape dimensions.
+ int32_t extended_output_shape_dims[kMaxMulBroadcastDim];
+ std::memcpy(extended_output_shape_dims, extended_output_shape.DimsData(),
+ sizeof(extended_output_shape_dims));
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
- // trailing dimension changing most rapidly (channels has the smallest stride,
- // typically 1 element).
+ // trailing dimension changing most rapidly (channels has the smallest
+ // stride, typically 1 element).
//
// In generated C code, we store arrays with the dimensions reversed. The
// first dimension has smallest stride.
//
// We name our variables by their Tensorflow convention, but generate C code
- // nesting loops such that the innermost loop has the smallest stride for the
- // best cache behavior.
- for (int b = 0; b < output_shape.Dims(0); ++b) {
- for (int y = 0; y < output_shape.Dims(1); ++y) {
- for (int x = 0; x < output_shape.Dims(2); ++x) {
- for (int c = 0; c < output_shape.Dims(3); ++c) {
- output_data[Offset(output_shape, b, y, x, c)] =
- ActivationFunctionWithMinMax<T>(
- input1_data[SubscriptToIndex(desc1, b, y, x, c)] *
- input2_data[SubscriptToIndex(desc2, b, y, x, c)],
- output_activation_min, output_activation_max);
- }
- }
- }
- }
+ // nesting loops such that the innermost loop has the smallest stride for
+ // the best cache behavior.
+ size_t input1_offset = 0;
+ size_t input2_offset = 0;
+ size_t output_offset = 0;
+ BroadcastMulRecursiveDimensions(
+ params, 0, input1_data, input2_data, output_data, &input1_offset,
+ &input2_offset, &output_offset, desc1, desc2, extended_output_shape_dims,
+ [](const ArithmeticParams& params, const T input1_val,
+ const T input2_val) {
+ T output_activation_min;
+ T output_activation_max;
+ GetActivationParams(params, &output_activation_min,
+ &output_activation_max);
+ return ActivationFunctionWithMinMax<T>(input1_val * input2_val,
+ output_activation_min,
+ output_activation_max);
+ });
}
-inline void BroadcastMul4DSlow(const ArithmeticParams& params,
+inline void BroadcastMul6DSlow(const ArithmeticParams& params,
const RuntimeShape& unextended_input1_shape,
const std::complex<float>* input1_data,
const RuntimeShape& unextended_input2_shape,
const std::complex<float>* input2_data,
const RuntimeShape& unextended_output_shape,
std::complex<float>* output_data) {
- TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4);
- TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 4);
- TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4);
- const RuntimeShape output_shape =
- RuntimeShape::ExtendedShape(4, unextended_output_shape);
+ TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 6);
+ TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 6);
+ TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 6);
- NdArrayDesc<4> desc1;
- NdArrayDesc<4> desc2;
+ NdArrayDesc<kMaxMulBroadcastDim> desc1;
+ NdArrayDesc<kMaxMulBroadcastDim> desc2;
NdArrayDescsForElementwiseBroadcast(unextended_input1_shape,
unextended_input2_shape, &desc1, &desc2);
+ const RuntimeShape extended_output_shape =
+ RuntimeShape::ExtendedShape(kMaxMulBroadcastDim, unextended_output_shape);
+ // Cache output shape dimensions.
+ int32_t extended_output_shape_dims[kMaxMulBroadcastDim];
+ std::memcpy(extended_output_shape_dims, extended_output_shape.DimsData(),
+ sizeof(extended_output_shape_dims));
- for (int b = 0; b < output_shape.Dims(0); ++b) {
- for (int y = 0; y < output_shape.Dims(1); ++y) {
- for (int x = 0; x < output_shape.Dims(2); ++x) {
- for (int c = 0; c < output_shape.Dims(3); ++c) {
- output_data[Offset(output_shape, b, y, x, c)] =
- input1_data[SubscriptToIndex(desc1, b, y, x, c)] *
- input2_data[SubscriptToIndex(desc2, b, y, x, c)];
- }
- }
- }
- }
+ size_t input1_offset = 0;
+ size_t input2_offset = 0;
+ size_t output_offset = 0;
+ BroadcastMulRecursiveDimensions(
+ params, 0, input1_data, input2_data, output_data, &input1_offset,
+ &input2_offset, &output_offset, desc1, desc2, extended_output_shape_dims,
+ [](const ArithmeticParams& params, const std::complex<float> input1_val,
+ const std::complex<float> input2_val) {
+ return input1_val * input2_val;
+ });
+}
+
+template <typename T>
+inline void BroadcastMul4DSlow(
+ const ArithmeticParams& params, const RuntimeShape& input1_shape,
+ const T* input1_data, const RuntimeShape& input2_shape,
+ const T* input2_data, const RuntimeShape& output_shape, T* output_data) {
+ return BroadcastMul6DSlow(params, input1_shape, input1_data, input2_shape,
+ input2_data, output_shape, output_data);
}
} // namespace reference_ops
diff --git a/tensorflow/lite/kernels/internal/reference/portable_tensor_utils.cc b/tensorflow/lite/kernels/internal/reference/portable_tensor_utils.cc
index d386203..d529463 100644
--- a/tensorflow/lite/kernels/internal/reference/portable_tensor_utils.cc
+++ b/tensorflow/lite/kernels/internal/reference/portable_tensor_utils.cc
@@ -37,6 +37,7 @@
const int32_t kInt16Min = std::numeric_limits<int16_t>::min();
} // namespace
+// LINT.IfChange(portable_symmetric_quantize_floats)
void PortableSymmetricQuantizeFloats(const float* values, const int size,
int8_t* quantized_values, float* min_value,
float* max_value, float* scaling_factor) {
@@ -68,6 +69,7 @@
std::min(kScale, std::max(-kScale, quantized_value)));
}
}
+// LINT.ThenChange(//tensorflow/compiler/mlir/lite/quantization/lite/toco_legacy/portable_tensor_utils.cc:portable_symmetric_quantize_floats)
void PortableAsymmetricQuantizeFloats(const float* values, const int size,
int8_t* quantized_values,
@@ -157,7 +159,7 @@
*result += dotprod * batch_scaling_factor;
++result;
} // for row
- } // for batch
+ } // for batch
}
void PortableMatrixBatchVectorMultiplyAccumulate(
@@ -200,7 +202,7 @@
*result += dotprod * scale;
++result;
} // for row
- } // for batch
+ } // for batch
}
void PortableSparseMatrixBatchVectorMultiplyAccumulate1x4(
@@ -232,7 +234,8 @@
const int32_t* __restrict__ indices, int m_rows, int m_cols,
const int8_t* __restrict__ vector, const int32_t* __restrict__ bias_vector,
int n_batch, const int32_t input_offset, const int32_t output_multiplier,
- const int32_t output_shift, const int32_t output_offset,
+ const int32_t output_shift, const int32_t* per_channel_scale,
+ const int32_t* per_channel_shift, const int32_t output_offset,
const int32_t output_activation_min, const int32_t output_activation_max,
int8_t* __restrict__ result) {
const int kBlockSize = 16;
@@ -252,8 +255,10 @@
}
}
const int32_t bias_value = bias_vector != nullptr ? bias_vector[row] : 0;
- dot_prod = MultiplyByQuantizedMultiplier(dot_prod + bias_value,
- output_multiplier, output_shift);
+ dot_prod = MultiplyByQuantizedMultiplier(
+ dot_prod + bias_value,
+ per_channel_scale ? per_channel_scale[row] : output_multiplier,
+ per_channel_shift ? per_channel_shift[row] : output_shift);
dot_prod += output_offset;
result[batch * m_rows + row] =
static_cast<int8_t>(ActivationFunctionWithMinMax(
@@ -294,7 +299,8 @@
void PortableSparseMatrixBatchVectorMultiplyAccumulate(
const int8_t* __restrict__ matrix, const uint8_t* ledger, const int m_rows,
const int m_cols, const int8_t* __restrict__ vectors,
- const float* scaling_factors, int n_batch, float* __restrict__ result) {
+ const float* scaling_factors, int n_batch, float* __restrict__ result,
+ const float* per_channel_scale) {
static const int kBlockSize = 16;
TFLITE_DCHECK_EQ( // NOLINT
m_cols % kBlockSize, 0);
@@ -318,10 +324,14 @@
for (int c = 0; c < kBlockSize; c++) {
dotprod += (*row_ptr++) * (*vector_block_ptr++);
} // for block
- } // for num_nonzero_blocks
- result[batch * m_rows + row] += dotprod * batch_scaling_factor;
+ } // for num_nonzero_blocks
+ float scaling_factor = batch_scaling_factor;
+ if (per_channel_scale) {
+ scaling_factor *= per_channel_scale[row];
+ }
+ result[batch * m_rows + row] += dotprod * scaling_factor;
} // for row
- } // for batch
+ } // for batch
}
template <typename T>
diff --git a/tensorflow/lite/kernels/internal/reference/portable_tensor_utils.h b/tensorflow/lite/kernels/internal/reference/portable_tensor_utils.h
index 0416db0..7c623f7 100644
--- a/tensorflow/lite/kernels/internal/reference/portable_tensor_utils.h
+++ b/tensorflow/lite/kernels/internal/reference/portable_tensor_utils.h
@@ -116,23 +116,26 @@
const int32_t* __restrict__ indices, int m_rows, int m_cols,
const int8_t* __restrict__ vector, const int32_t* __restrict__ bias_vector,
int n_batch, const int32_t input_offset, const int32_t output_multiplier,
- const int32_t output_shift, const int32_t output_offset,
+ const int32_t output_shift, const int32_t* per_channel_scale,
+ const int32_t* per_channel_shift, const int32_t output_offset,
const int32_t output_activation_min, const int32_t output_activation_max,
int8_t* __restrict__ result) {
PortableSparseMatrixBatchVectorMultiplyAccumulate1x16(
matrix, segments, indices, m_rows, m_cols, vector, bias_vector, n_batch,
- input_offset, output_multiplier, output_shift, output_offset,
- output_activation_min, output_activation_max, result);
+ input_offset, output_multiplier, output_shift, per_channel_scale,
+ per_channel_shift, output_offset, output_activation_min,
+ output_activation_max, result);
}
void SparseMatrixBatchVectorMultiplyAccumulate(
const int8_t* __restrict__ matrix, const uint8_t* ledger, const int m_rows,
const int m_cols, const int8_t* __restrict__ vectors,
- const float* scaling_factors, int n_batch, float* __restrict__ result) {
+ const float* scaling_factors, int n_batch, float* __restrict__ result,
+ const float* per_channel_scale) {
PortableSparseMatrixBatchVectorMultiplyAccumulate(
- matrix, ledger, m_rows, m_cols, vectors, scaling_factors, n_batch,
- result);
+ matrix, ledger, m_rows, m_cols, vectors, scaling_factors, n_batch, result,
+ per_channel_scale);
}
void MatrixBatchVectorMultiplyAccumulate(
diff --git a/tensorflow/lite/kernels/internal/reference/portable_tensor_utils_impl.h b/tensorflow/lite/kernels/internal/reference/portable_tensor_utils_impl.h
index 6c404d5..5e228bb 100644
--- a/tensorflow/lite/kernels/internal/reference/portable_tensor_utils_impl.h
+++ b/tensorflow/lite/kernels/internal/reference/portable_tensor_utils_impl.h
@@ -40,6 +40,7 @@
return true;
}
+// LINT.IfChange(portable_symmetric_quantize_floats)
void PortableSymmetricQuantizeFloats(const float* values, const int size,
int8_t* quantized_values, float* min_value,
float* max_value, float* scaling_factor);
@@ -47,6 +48,7 @@
void PortableSymmetricQuantizeFloats(const float* values, const int size,
int8_t* quantized_values, float min_value,
float max_value, float* scaling_factor);
+// LINT.ThenChange(//tensorflow/compiler/mlir/lite/quantization/lite/toco_legacy/portable_tensor_utils.h:portable_symmetric_quantize_floats)
void PortableAsymmetricQuantizeFloats(const float* values, const int size,
int8_t* quantized_values,
@@ -92,14 +94,16 @@
const int32_t* __restrict__ indices, int m_rows, int m_cols,
const int8_t* __restrict__ vector, const int32_t* __restrict__ bias_vector,
int n_batch, const int32_t input_offset, const int32_t output_multiplier,
- const int32_t output_shift, const int32_t output_offset,
+ int32_t output_shift, const int32_t* per_channel_scale,
+ const int32_t* per_channel_shift, int32_t output_offset,
const int32_t output_activation_min, const int32_t output_activation_max,
int8_t* __restrict__ result);
void PortableSparseMatrixBatchVectorMultiplyAccumulate(
const int8_t* __restrict__ matrix, const uint8_t* ledger, const int m_rows,
const int m_cols, const int8_t* __restrict__ vectors,
- const float* scaling_factors, int n_batch, float* __restrict__ result);
+ const float* scaling_factors, int n_batch, float* __restrict__ result,
+ const float* per_channel_scale);
// Dot product of two vectors.
float PortableVectorVectorDotProduct(const float* vector1, const float* vector2,
diff --git a/tensorflow/lite/kernels/internal/reference/softmax.h b/tensorflow/lite/kernels/internal/reference/softmax.h
index c09a7ea..2930217 100644
--- a/tensorflow/lite/kernels/internal/reference/softmax.h
+++ b/tensorflow/lite/kernels/internal/reference/softmax.h
@@ -115,6 +115,9 @@
FixedPoint0 shifted_scale = FixedPoint0::FromRaw(GetReciprocal(
sum_of_exps.raw(), kAccumulationIntegerBits, &num_bits_over_unit));
+ const int exponent = num_bits_over_unit + 31 - (sizeof(OutputT) * 8);
+ TFLITE_CHECK(0 <= exponent && exponent <= 31);
+
for (int c = 0; c < depth; ++c) {
int32_t input_diff =
static_cast<int32_t>(input_data[i * depth + c]) - max_in_row;
@@ -127,8 +130,7 @@
FixedPoint0 exp_in_0 = exp_on_negative_values(scaled_diff_f8);
int32_t unsat_output = gemmlowp::RoundingDivideByPOT(
- (shifted_scale * exp_in_0).raw(),
- num_bits_over_unit + 31 - (sizeof(OutputT) * 8));
+ (shifted_scale * exp_in_0).raw(), exponent);
const int32_t shifted_output =
unsat_output +
diff --git a/tensorflow/lite/kernels/internal/reference/sub.h b/tensorflow/lite/kernels/internal/reference/sub.h
index d0ebc95..1a74aeb 100644
--- a/tensorflow/lite/kernels/internal/reference/sub.h
+++ b/tensorflow/lite/kernels/internal/reference/sub.h
@@ -18,6 +18,7 @@
#include <stdint.h>
#include <algorithm>
+#include <cstddef>
#include <limits>
#include "ruy/profiler/instrumentation.h" // from @ruy
@@ -29,199 +30,245 @@
namespace reference_ops {
-inline void SubNonBroadcast(const ArithmeticParams& params,
- const RuntimeShape& input1_shape,
- const float* input1_data,
- const RuntimeShape& input2_shape,
- const float* input2_data,
- const RuntimeShape& output_shape,
- float* output_data) {
- const int flat_size =
- MatchingElementsSize(input1_shape, input2_shape, output_shape);
- for (int i = 0; i < flat_size; ++i) {
- output_data[i] = ActivationFunctionWithMinMax(
- input1_data[i] - input2_data[i], params.float_activation_min,
- params.float_activation_max);
+template <class T>
+struct SubImpl {
+ template <class F>
+ static void BroadcastInput1(const ArithmeticParams& params,
+ const T* input1_data, const T* input2_data,
+ T* output_data, size_t size, F binary_func) {
+ for (size_t c = 0; c < size; ++c) {
+ output_data[c] = binary_func(input1_data[0], input2_data[c], params);
+ }
+ }
+
+ template <class F>
+ static void BroadcastInput2(const ArithmeticParams& params,
+ const T* input1_data, const T* input2_data,
+ T* output_data, size_t size, F binary_func) {
+ for (size_t c = 0; c < size; ++c) {
+ output_data[c] = binary_func(input1_data[c], input2_data[0], params);
+ }
+ }
+
+ template <class F>
+ static void ElementWise(const ArithmeticParams& params, const T* input1_data,
+ const T* input2_data, T* output_data, size_t size,
+ F binary_func) {
+ for (size_t c = 0; c < size; ++c) {
+ output_data[c] = binary_func(input1_data[c], input2_data[c], params);
+ }
+ }
+};
+
+template <>
+struct SubImpl<int32_t> {
+ template <class F>
+ static void BroadcastInput1(const ArithmeticParams& params,
+ const int32_t* input1_data,
+ const int32_t* input2_data, int32_t* output_data,
+ size_t size, F binary_func) {
+ size_t c = 0;
+ int32_t activation_min, activation_max;
+ GetActivationParams(params, &activation_min, &activation_max);
+#ifdef USE_NEON
+ const int32x4_t vmax = vdupq_n_s32(activation_max);
+ const int32x4_t vmin = vdupq_n_s32(activation_min);
+ const int32x4_t va = vdupq_n_s32(input1_data[0]);
+ for (; c + 4 <= size; c += 4) {
+ const int32x4_t vb = vld1q_s32(&input2_data[c]);
+ int32x4_t vres = vsubq_s32(va, vb);
+ vres = vmaxq_s32(vmin, vres);
+ vres = vminq_s32(vmax, vres);
+ vst1q_s32(&output_data[c], vres);
+ }
+#endif
+ for (; c < size; ++c) {
+ output_data[c] = binary_func(input1_data[0], input2_data[c], params);
+ }
+ }
+
+ template <class F>
+ static void BroadcastInput2(const ArithmeticParams& params,
+ const int32_t* input1_data,
+ const int32_t* input2_data, int32_t* output_data,
+ size_t size, F binary_func) {
+ size_t c = 0;
+ int32_t activation_min, activation_max;
+ GetActivationParams(params, &activation_min, &activation_max);
+#ifdef USE_NEON
+ const int32x4_t vmax = vdupq_n_s32(activation_max);
+ const int32x4_t vmin = vdupq_n_s32(activation_min);
+ const int32x4_t vb = vdupq_n_s32(input2_data[0]);
+ for (; c + 4 <= size; c += 4) {
+ const int32x4_t va = vld1q_s32(&input1_data[c]);
+ int32x4_t vres = vsubq_s32(va, vb);
+ vres = vmaxq_s32(vmin, vres);
+ vres = vminq_s32(vmax, vres);
+ vst1q_s32(&output_data[c], vres);
+ }
+#endif
+ for (; c < size; ++c) {
+ output_data[c] = binary_func(input1_data[c], input2_data[0], params);
+ }
+ }
+
+ template <class F>
+ static void ElementWise(const ArithmeticParams& params,
+ const int32_t* input1_data,
+ const int32_t* input2_data, int32_t* output_data,
+ size_t size, F binary_func) {
+ size_t c = 0;
+ int32_t activation_min, activation_max;
+ GetActivationParams(params, &activation_min, &activation_max);
+#ifdef USE_NEON
+ int32x4_t vmax = vdupq_n_s32(activation_max);
+ int32x4_t vmin = vdupq_n_s32(activation_min);
+ for (; c + 4 <= size; c += 4) {
+ const int32x4_t va = vld1q_s32(&input1_data[c]);
+ const int32x4_t vb = vld1q_s32(&input2_data[c]);
+ int32x4_t vres = vsubq_s32(va, vb);
+ vres = vmaxq_s32(vmin, vres);
+ vres = vminq_s32(vmax, vres);
+ vst1q_s32(&output_data[c], vres);
+ }
+#endif
+ for (; c < size; ++c) {
+ output_data[c] = binary_func(input1_data[c], input2_data[c], params);
+ }
+ }
+};
+
+template <typename T, typename F>
+inline void BroadcastSubRecursiveDimensions(
+ int dimension, const ArithmeticParams& params, const T* input1_data,
+ const T* input2_data, T* output_data, size_t* input1_offset_p,
+ size_t* input2_offset_p, size_t* output_offset,
+ size_t* compressed_input1_stride, size_t* compressed_input2_stride,
+ size_t* compressed_output_shape, F binary_func) {
+ if (dimension > 0) {
+ for (size_t c = 0; c < compressed_output_shape[dimension]; ++c) {
+ size_t input1_offset_c = *input1_offset_p;
+ size_t input2_offset_c = *input2_offset_p;
+ BroadcastSubRecursiveDimensions(
+ dimension - 1, params, input1_data, input2_data, output_data,
+ &input1_offset_c, &input2_offset_c, output_offset,
+ compressed_input1_stride, compressed_input2_stride,
+ compressed_output_shape, binary_func);
+ *input1_offset_p += compressed_input1_stride[dimension];
+ *input2_offset_p += compressed_input2_stride[dimension];
+ }
+ } else {
+ TFLITE_DCHECK(dimension == 0);
+ bool input1_is_broadcast = compressed_input1_stride[dimension] == 0;
+ bool input2_is_broadcast = compressed_input2_stride[dimension] == 0;
+ TFLITE_DCHECK(!(input1_is_broadcast && input2_is_broadcast));
+ const T* input1_data_ptr = input1_data + *input1_offset_p;
+ const T* input2_data_ptr = input2_data + *input2_offset_p;
+ T* output_data_ptr = output_data + *output_offset;
+ if (input1_is_broadcast) {
+ // input1 is broadcast.
+ SubImpl<T>::BroadcastInput1(
+ params, input1_data_ptr, input2_data_ptr, output_data_ptr,
+ compressed_output_shape[dimension], binary_func);
+ *input2_offset_p += compressed_output_shape[dimension];
+ } else if (input2_is_broadcast) {
+ // input2 is broadcast.
+ SubImpl<T>::BroadcastInput2(
+ params, input1_data_ptr, input2_data_ptr, output_data_ptr,
+ compressed_output_shape[dimension], binary_func);
+ *input1_offset_p += compressed_output_shape[dimension];
+ } else {
+ // Add element-wise.
+ SubImpl<T>::ElementWise(params, input1_data_ptr, input2_data_ptr,
+ output_data_ptr,
+ compressed_output_shape[dimension], binary_func);
+ *input1_offset_p += compressed_output_shape[dimension];
+ *input2_offset_p += compressed_output_shape[dimension];
+ }
+ *output_offset += compressed_output_shape[dimension];
}
}
-inline void SubNonBroadcast(const ArithmeticParams& params,
- const RuntimeShape& input1_shape,
- const int32_t* input1_data,
- const RuntimeShape& input2_shape,
- const int32_t* input2_data,
- const RuntimeShape& output_shape,
- int32_t* output_data) {
- const int flat_size =
- MatchingElementsSize(input1_shape, input2_shape, output_shape);
- for (int i = 0; i < flat_size; ++i) {
- output_data[i] = ActivationFunctionWithMinMax(
- input1_data[i] - input2_data[i], params.quantized_activation_min,
- params.quantized_activation_max);
+// TODO: b/296510380 - we may be able to factor out this to common.h for all
+// binary arithmetic ops (add, sub, mul).
+template <typename T, typename F>
+inline void BroadcastSubCommon(const ArithmeticParams& params,
+ const RuntimeShape& input1_shape,
+ const T* input1_data,
+ const RuntimeShape& input2_shape,
+ const T* input2_data,
+ const RuntimeShape& output_shape, T* output_data,
+ F binary_func) {
+ constexpr int kMaxBroadcastDim = 6;
+ TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), kMaxBroadcastDim);
+ TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), kMaxBroadcastDim);
+ TFLITE_DCHECK_LE(output_shape.DimensionsCount(), kMaxBroadcastDim);
+
+ // In Tensorflow, the dimensions are canonically named (batch_number, row,
+ // col, channel), with extents (batches, height, width, depth), with the
+ // trailing dimension changing most rapidly (channels has the smallest stride,
+ // typically 1 element).
+ //
+ // In generated C code, we store arrays with the dimensions reversed. The
+ // first dimension has smallest stride.
+ //
+ // We name our variables by their Tensorflow convention, but generate C code
+ // nesting loops such that the innermost loop has the smallest stride for the
+ // best cache behavior.
+
+ // In Tensorflow, the dimensions are canonically named (batch_number, row,
+ // col, channel), with extents (batches, height, width, depth), with the
+ // trailing dimension changing most rapidly (channels has the smallest stride,
+ // typically 1 element).
+ //
+ // In generated C code, we store arrays with the dimensions reversed. The
+ // first dimension has smallest stride.
+ //
+ // We name our variables by their Tensorflow convention, but generate C code
+ // nesting loops such that the innermost loop has the smallest stride for the
+ // best cache behavior.
+
+ size_t compressed_input1_stride[kMaxBroadcastDim];
+ size_t compressed_input2_stride[kMaxBroadcastDim];
+ size_t compressed_output_shape[kMaxBroadcastDim];
+ bool broadcastable_shape = ReduceDimensionsForBroadcast<kMaxBroadcastDim>(
+ input1_shape, input2_shape, compressed_input1_stride,
+ compressed_input2_stride, compressed_output_shape);
+ // Skip broadcasting for degenerate shapes.
+ if (!broadcastable_shape) {
+ return;
}
+
+ size_t input1_offset = 0;
+ size_t input2_offset = 0;
+ size_t output_offset = 0;
+ BroadcastSubRecursiveDimensions(
+ kMaxBroadcastDim - 1, params, input1_data, input2_data, output_data,
+ &input1_offset, &input2_offset, &output_offset, compressed_input1_stride,
+ compressed_input2_stride, compressed_output_shape, binary_func);
}
// TODO(b/151345304): We can implement BroadcastSub on buffers of arbitrary
// dimensionality if the runtime code does a single loop over one dimension
// that handles broadcasting as the base case. The code generator would then
// generate max(D1, D2) nested for loops.
-template <int N = 5>
-inline void BroadcastSubSlow(const ArithmeticParams& params,
- const RuntimeShape& input1_shape,
- const float* input1_data,
- const RuntimeShape& input2_shape,
- const float* input2_data,
- const RuntimeShape& output_shape,
- float* output_data) {
- ruy::profiler::ScopeLabel label("BroadcastSubSlow/float");
- TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N);
- TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N);
- TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N);
- NdArrayDesc<N> desc1;
- NdArrayDesc<N> desc2;
- NdArrayDesc<N> output_desc;
- NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
- &desc2);
- CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc);
-
- // In Tensorflow, the dimensions are canonically named (batch_number, row,
- // col, channel), with extents (batches, height, width, depth), with the
- // trailing dimension changing most rapidly (channels has the smallest stride,
- // typically 1 element).
- //
- // In generated C code, we store arrays with the dimensions reversed. The
- // first dimension has smallest stride.
- //
- // We name our variables by their Tensorflow convention, but generate C code
- // nesting loops such that the innermost loop has the smallest stride for the
- // best cache behavior.
- auto sub_func = [&](int indexes[N]) {
- output_data[SubscriptToIndex(output_desc, indexes)] =
- ActivationFunctionWithMinMax(
- input1_data[SubscriptToIndex(desc1, indexes)] -
- input2_data[SubscriptToIndex(desc2, indexes)],
- params.float_activation_min, params.float_activation_max);
- };
- NDOpsHelper<N>(output_desc, sub_func);
-}
-
-template <int N = 5>
-inline void BroadcastSubSlow(const ArithmeticParams& params,
- const RuntimeShape& input1_shape,
- const int32_t* input1_data,
- const RuntimeShape& input2_shape,
- const int32_t* input2_data,
- const RuntimeShape& output_shape,
- int32_t* output_data) {
- ruy::profiler::ScopeLabel label("BroadcastSubSlow/int32_t");
- TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N);
- TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N);
- TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N);
- NdArrayDesc<N> desc1;
- NdArrayDesc<N> desc2;
- NdArrayDesc<N> output_desc;
- NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
- &desc2);
- CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc);
-
- // In Tensorflow, the dimensions are canonically named (batch_number, row,
- // col, channel), with extents (batches, height, width, depth), with the
- // trailing dimension changing most rapidly (channels has the smallest stride,
- // typically 1 element).
- //
- // In generated C code, we store arrays with the dimensions reversed. The
- // first dimension has smallest stride.
- //
- // We name our variables by their Tensorflow convention, but generate C code
- // nesting loops such that the innermost loop has the smallest stride for the
- // best cache behavior.
- auto sub_func = [&](int indexes[N]) {
- output_data[SubscriptToIndex(output_desc, indexes)] =
- ActivationFunctionWithMinMax(
- input1_data[SubscriptToIndex(desc1, indexes)] -
- input2_data[SubscriptToIndex(desc2, indexes)],
- params.quantized_activation_min, params.quantized_activation_max);
- };
- NDOpsHelper<N>(output_desc, sub_func);
-}
-
-template <int N = 5>
-void BroadcastSubSlow(const ArithmeticParams& params,
- const RuntimeShape& input1_shape,
- const int64_t* input1_data,
- const RuntimeShape& input2_shape,
- const int64_t* input2_data,
- const RuntimeShape& output_shape, int64_t* output_data) {
- ruy::profiler::ScopeLabel label("BroadcastSubSlow/int64_t");
- TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N);
- TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N);
- TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N);
- NdArrayDesc<N> desc1;
- NdArrayDesc<N> desc2;
- NdArrayDesc<N> output_desc;
- NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
- &desc2);
- CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc);
-
- // In Tensorflow, the dimensions are canonically named (batch_number, row,
- // col, channel), with extents (batches, height, width, depth), with the
- // trailing dimension changing most rapidly (channels has the smallest stride,
- // typically 1 element).
- //
- // In generated C code, we store arrays with the dimensions reversed. The
- // first dimension has smallest stride.
- //
- // We name our variables by their Tensorflow convention, but generate C code
- // nesting loops such that the innermost loop has the smallest stride for the
- // best cache behavior.
- auto sub_func = [&](int indexes[N]) {
- output_data[SubscriptToIndex(output_desc, indexes)] =
- ActivationFunctionWithMinMax(
- input1_data[SubscriptToIndex(desc1, indexes)] -
- input2_data[SubscriptToIndex(desc2, indexes)],
- params.int64_activation_min, params.int64_activation_max);
- };
- NDOpsHelper<N>(output_desc, sub_func);
-}
-
-template <typename T, int N = 5>
+template <typename T>
void BroadcastSubSlow(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const T* input1_data,
const RuntimeShape& input2_shape, const T* input2_data,
const RuntimeShape& output_shape, T* output_data) {
- ruy::profiler::ScopeLabel label("BroadcastSubSlow/templated");
- TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N);
- TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N);
- TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N);
- NdArrayDesc<N> desc1;
- NdArrayDesc<N> desc2;
- NdArrayDesc<N> output_desc;
- NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
- &desc2);
- CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc);
-
- // In Tensorflow, the dimensions are canonically named (batch_number, row,
- // col, channel), with extents (batches, height, width, depth), with the
- // trailing dimension changing most rapidly (channels has the smallest stride,
- // typically 1 element).
- //
- // In generated C code, we store arrays with the dimensions reversed. The
- // first dimension has smallest stride.
- //
- // We name our variables by their Tensorflow convention, but generate C code
- // nesting loops such that the innermost loop has the smallest stride for the
- // best cache behavior.
- auto sub_func = [&](int indexes[N]) {
- output_data[SubscriptToIndex(output_desc, indexes)] =
- ActivationFunctionWithMinMax(
- input1_data[SubscriptToIndex(desc1, indexes)] -
- input2_data[SubscriptToIndex(desc2, indexes)],
- params.quantized_activation_min, params.quantized_activation_max);
- };
- NDOpsHelper<N>(output_desc, sub_func);
+ ruy::profiler::ScopeLabel label("BroadcastSubSlow/T");
+ BroadcastSubCommon<T>(
+ params, input1_shape, input1_data, input2_shape, input2_data,
+ output_shape, output_data,
+ [](T input1_val, T input2_val, const ArithmeticParams& params) {
+ T activation_min, activation_max;
+ GetActivationParams(params, &activation_min, &activation_max);
+ return ActivationFunctionWithMinMax(input1_val - input2_val,
+ activation_min, activation_max);
+ });
}
-template <int N = 5>
inline void BroadcastSub16POTSlow(const ArithmeticParams& params,
const RuntimeShape& input1_shape,
const int16_t* input1_data,
@@ -230,42 +277,24 @@
const RuntimeShape& output_shape,
int16_t* output_data) {
ruy::profiler::ScopeLabel label("BroadcastSub16POTSlow/int16_t");
- NdArrayDesc<N> desc1;
- NdArrayDesc<N> desc2;
- NdArrayDesc<N> output_desc;
- NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
- &desc2);
- CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc);
-
- // In Tensorflow, the dimensions are canonically named (batch_number, row,
- // col, channel), with extents (batches, height, width, depth), with the
- // trailing dimension changing most rapidly (channels has the smallest stride,
- // typically 1 element).
- //
- // In generated C code, we store arrays with the dimensions reversed. The
- // first dimension has smallest stride.
- //
- // We name our variables by their Tensorflow convention, but generate C code
- // nesting loops such that the innermost loop has the smallest stride for the
- // best cache behavior.
- auto sub_func = [&](int indexes[N]) {
- const int32_t input1_val = input1_data[SubscriptToIndex(desc1, indexes)];
- const int32_t input2_val = input2_data[SubscriptToIndex(desc2, indexes)];
- const int32_t scaled_input1_val =
- gemmlowp::RoundingDivideByPOT(input1_val, -params.input1_shift);
- const int32_t scaled_input2_val =
- gemmlowp::RoundingDivideByPOT(input2_val, -params.input2_shift);
- const int32_t raw_output = scaled_input1_val - scaled_input2_val;
- const int32_t clamped_output =
- std::min(params.quantized_activation_max,
- std::max(params.quantized_activation_min, raw_output));
- output_data[SubscriptToIndex(output_desc, indexes)] =
- static_cast<int16_t>(clamped_output);
- };
- NDOpsHelper<N>(output_desc, sub_func);
+ BroadcastSubCommon<int16_t>(
+ params, input1_shape, input1_data, input2_shape, input2_data,
+ output_shape, output_data,
+ [](int16_t input1_val, int16_t input2_val,
+ const ArithmeticParams& params) {
+ const int32_t scaled_input1_val =
+ gemmlowp::RoundingDivideByPOT(input1_val, -params.input1_shift);
+ const int32_t scaled_input2_val =
+ gemmlowp::RoundingDivideByPOT(input2_val, -params.input2_shift);
+ const int32_t raw_output = scaled_input1_val - scaled_input2_val;
+ const int32_t clamped_output =
+ std::min(params.quantized_activation_max,
+ std::max(params.quantized_activation_min, raw_output));
+ return static_cast<int16_t>(clamped_output);
+ });
}
-template <typename T, int N = 5>
+template <typename T>
void BroadcastQuantSubSlow(const ArithmeticParams& params,
const RuntimeShape& input1_shape,
const T* input1_data,
@@ -273,52 +302,32 @@
const T* input2_data,
const RuntimeShape& output_shape, T* output_data) {
ruy::profiler::ScopeLabel label("BroadcastQuantSubSlow/T");
- TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N);
- TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N);
- TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N);
- NdArrayDesc<N> desc1;
- NdArrayDesc<N> desc2;
- NdArrayDesc<N> output_desc;
- NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
- &desc2);
- CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc);
-
- // In Tensorflow, the dimensions are canonically named (batch_number, row,
- // col, channel), with extents (batches, height, width, depth), with the
- // trailing dimension changing most rapidly (channels has the smallest stride,
- // typically 1 element).
- //
- // In generated C code, we store arrays with the dimensions reversed. The
- // first dimension has smallest stride.
- //
- // We name our variables by their Tensorflow convention, but generate C code
- // nesting loops such that the innermost loop has the smallest stride for the
- // best cache behavior.
- auto sub_func = [&](int indexes[N]) {
- const int32_t input1_val =
- params.input1_offset + input1_data[SubscriptToIndex(desc1, indexes)];
- const int32_t input2_val =
- params.input2_offset + input2_data[SubscriptToIndex(desc2, indexes)];
- const int32_t shifted_input1_val = input1_val * (1 << params.left_shift);
- const int32_t shifted_input2_val = input2_val * (1 << params.left_shift);
- const int32_t scaled_input1_val =
- MultiplyByQuantizedMultiplierSmallerThanOneExp(
- shifted_input1_val, params.input1_multiplier, params.input1_shift);
- const int32_t scaled_input2_val =
- MultiplyByQuantizedMultiplierSmallerThanOneExp(
- shifted_input2_val, params.input2_multiplier, params.input2_shift);
- const int32_t raw_sub = scaled_input1_val - scaled_input2_val;
- const int32_t raw_output =
- MultiplyByQuantizedMultiplierSmallerThanOneExp(
- raw_sub, params.output_multiplier, params.output_shift) +
- params.output_offset;
- const int32_t clamped_output =
- std::min(params.quantized_activation_max,
- std::max(params.quantized_activation_min, raw_output));
- output_data[SubscriptToIndex(output_desc, indexes)] =
- static_cast<T>(clamped_output);
- };
- NDOpsHelper<N>(output_desc, sub_func);
+ BroadcastSubCommon<T>(
+ params, input1_shape, input1_data, input2_shape, input2_data,
+ output_shape, output_data,
+ [](T input1_val, T input2_val, const ArithmeticParams& params) {
+ const int32_t shifted_input1_val =
+ (params.input1_offset + input1_val) * (1 << params.left_shift);
+ const int32_t shifted_input2_val =
+ (params.input2_offset + input2_val) * (1 << params.left_shift);
+ const int32_t scaled_input1_val =
+ MultiplyByQuantizedMultiplierSmallerThanOneExp(
+ shifted_input1_val, params.input1_multiplier,
+ params.input1_shift);
+ const int32_t scaled_input2_val =
+ MultiplyByQuantizedMultiplierSmallerThanOneExp(
+ shifted_input2_val, params.input2_multiplier,
+ params.input2_shift);
+ const int32_t raw_sub = scaled_input1_val - scaled_input2_val;
+ const int32_t raw_output =
+ MultiplyByQuantizedMultiplierSmallerThanOneExp(
+ raw_sub, params.output_multiplier, params.output_shift) +
+ params.output_offset;
+ const int32_t clamped_output =
+ std::min(params.quantized_activation_max,
+ std::max(params.quantized_activation_min, raw_output));
+ return static_cast<T>(clamped_output);
+ });
}
// Element-wise add that can often be used for inner loop of broadcast add as
@@ -405,35 +414,12 @@
const T* input1_data, const RuntimeShape& input2_shape,
const T* input2_data, const RuntimeShape& output_shape,
T* output_data) {
- NdArrayDesc<4> desc1;
- NdArrayDesc<4> desc2;
- NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
- &desc2);
- const RuntimeShape extended_output_shape =
- RuntimeShape::ExtendedShape(4, output_shape);
-
- // In Tensorflow, the dimensions are canonically named (batch_number, row,
- // col, channel), with extents (batches, height, width, depth), with the
- // trailing dimension changing most rapidly (channels has the smallest stride,
- // typically 1 element).
- //
- // In generated C code, we store arrays with the dimensions reversed. The
- // first dimension has smallest stride.
- //
- // We name our variables by their Tensorflow convention, but generate C code
- // nesting loops such that the innermost loop has the smallest stride for the
- // best cache behavior.
- for (int b = 0; b < extended_output_shape.Dims(0); ++b) {
- for (int y = 0; y < extended_output_shape.Dims(1); ++y) {
- for (int x = 0; x < extended_output_shape.Dims(2); ++x) {
- for (int c = 0; c < extended_output_shape.Dims(3); ++c) {
- output_data[Offset(extended_output_shape, b, y, x, c)] =
- input1_data[SubscriptToIndex(desc1, b, y, x, c)] -
- input2_data[SubscriptToIndex(desc2, b, y, x, c)];
- }
- }
- }
- }
+ BroadcastSubCommon<T>(
+ params, input1_shape, input1_data, input2_shape, input2_data,
+ output_shape, output_data,
+ [](T input1_val, T input2_val, const ArithmeticParams& params) {
+ return input1_val - input2_val;
+ });
}
inline void SetActivationMinMax(const ArithmeticParams& params,
diff --git a/tensorflow/lite/kernels/internal/reference/transpose_conv.h b/tensorflow/lite/kernels/internal/reference/transpose_conv.h
index 8a51e0f..744ed0f 100644
--- a/tensorflow/lite/kernels/internal/reference/transpose_conv.h
+++ b/tensorflow/lite/kernels/internal/reference/transpose_conv.h
@@ -219,6 +219,103 @@
}
}
+inline void HybridTransposeConv(
+ const ConvParams& params, float* scaling_factors_ptr,
+ const RuntimeShape& input_shape, const int8_t* input_data,
+ const RuntimeShape& filter_shape, const int8_t* filter_data,
+ const RuntimeShape& bias_shape, const float* bias_data,
+ const RuntimeShape& output_shape, float* output_data,
+ const float* per_channel_scale, int32_t* input_offset) {
+ const int stride_width = params.stride_width;
+ const int stride_height = params.stride_height;
+ const int pad_width = params.padding_values.width;
+ const int pad_height = params.padding_values.height;
+ TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
+ TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
+ TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
+
+ const int batches = MatchingDim(input_shape, 0, output_shape, 0);
+ const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3);
+ const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3);
+ const int input_height = input_shape.Dims(1);
+ const int input_width = input_shape.Dims(2);
+ const int filter_height = filter_shape.Dims(1);
+ const int filter_width = filter_shape.Dims(2);
+ const int output_height = output_shape.Dims(1);
+ const int output_width = output_shape.Dims(2);
+ const float output_activation_min = params.float_activation_min;
+ const float output_activation_max = params.float_activation_max;
+ if (bias_data) {
+ TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
+ }
+
+ // Although transpose convolution simplifies to convolution with transposed
+ // weights for strides of 1, non-unitary striding complicates matters. To
+ // keep this reference implementation as clear as possible, we use a
+ // "scatter" access pattern, where we loop through all the input elements,
+ // computing their influence on the output, rather than looping through the
+ // output elements in the typical "gather" access pattern of a conv. We
+ // therefore must initialize the output array to zero.
+ const int num_elements = output_shape.FlatSize();
+ for (int i = 0; i < num_elements; i++) {
+ output_data[i] = 0.0f;
+ }
+
+ // Loop through input elements one at a time.
+ for (int batch = 0; batch < batches; ++batch) {
+ const float scaling_factor = scaling_factors_ptr[batch];
+ for (int in_y = 0; in_y < input_height; ++in_y) {
+ for (int in_x = 0; in_x < input_width; ++in_x) {
+ for (int in_channel = 0; in_channel < input_depth; ++in_channel) {
+ // Loop through the output elements it will influence
+ const int out_x_origin = (in_x * stride_width) - pad_width;
+ const int out_y_origin = (in_y * stride_height) - pad_height;
+ for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
+ for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
+ for (int out_channel = 0; out_channel < output_depth;
+ ++out_channel) {
+ // Compute output element location
+ const int out_x = out_x_origin + filter_x;
+ const int out_y = out_y_origin + filter_y;
+ // We cannot accumulate out of bounds
+ if ((out_x >= 0) && (out_x < output_width) && (out_y >= 0) &&
+ (out_y < output_height)) {
+ int32_t input_value = input_data[Offset(
+ input_shape, batch, in_y, in_x, in_channel)];
+ int32_t filter_value =
+ filter_data[Offset(filter_shape, out_channel, filter_y,
+ filter_x, in_channel)];
+ int32_t acc =
+ (input_value - input_offset[batch]) * filter_value;
+ output_data[Offset(output_shape, batch, out_y, out_x,
+ out_channel)] +=
+ acc * per_channel_scale[out_channel] * scaling_factor;
+ }
+ }
+ }
+ }
+ }
+ }
+ }
+ }
+
+ for (int batch = 0; batch < batches; ++batch) {
+ for (int out_y = 0; out_y < output_height; ++out_y) {
+ for (int out_x = 0; out_x < output_width; ++out_x) {
+ for (int out_channel = 0; out_channel < output_depth; ++out_channel) {
+ float acc = output_data[Offset(output_shape, batch, out_y, out_x,
+ out_channel)];
+ if (bias_data) acc += bias_data[out_channel];
+
+ output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] =
+ ActivationFunctionWithMinMax(acc, output_activation_min,
+ output_activation_max);
+ }
+ }
+ }
+ }
+}
+
} // namespace reference_ops
} // namespace tflite
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc b/tensorflow/lite/kernels/internal/runtime_shape.cc
similarity index 67%
copy from tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc
copy to tensorflow/lite/kernels/internal/runtime_shape.cc
index e2cf661..dd12278 100644
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc
+++ b/tensorflow/lite/kernels/internal/runtime_shape.cc
@@ -1,4 +1,4 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -13,11 +13,11 @@
limitations under the License.
==============================================================================*/
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.h"
+#include "tensorflow/lite/kernels/internal/runtime_shape.h"
-const char* kCategoryLabels[kCategoryCount] = {
- "silence",
- "unknown",
- "yes",
- "no",
-};
+namespace tflite {
+
+// Defining a constexpr static class member is necessary in C++11
+constexpr int tflite::RuntimeShape::kMaxSmallSize;
+
+} // namespace tflite
diff --git a/tensorflow/lite/kernels/internal/runtime_shape.h b/tensorflow/lite/kernels/internal/runtime_shape.h
index 0e4df2c..bc786bd 100644
--- a/tensorflow/lite/kernels/internal/runtime_shape.h
+++ b/tensorflow/lite/kernels/internal/runtime_shape.h
@@ -15,6 +15,8 @@
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_RUNTIME_SHAPE_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_RUNTIME_SHAPE_H_
+#include <cstring>
+
#include "tensorflow/lite/kernels/internal/compatibility.h"
namespace tflite {
diff --git a/tensorflow/lite/kernels/internal/types.h b/tensorflow/lite/kernels/internal/types.h
index b775ca8..f2cc160 100644
--- a/tensorflow/lite/kernels/internal/types.h
+++ b/tensorflow/lite/kernels/internal/types.h
@@ -157,7 +157,8 @@
};
// Gets next index to iterate through a multidimensional array.
-inline bool NextIndex(const int num_dims, const int* dims, int* current) {
+template <typename IndexType = int>
+inline bool NextIndex(const int num_dims, const int* dims, IndexType* current) {
if (num_dims == 0) {
return false;
}
@@ -165,7 +166,7 @@
TFLITE_DCHECK(current != nullptr);
int carry = 1;
for (int idx = num_dims - 1; idx >= 0; --idx) {
- int current_val = current[idx] + carry;
+ IndexType current_val = current[idx] + carry;
TFLITE_DCHECK_GE(dims[idx], current_val);
if (dims[idx] == current_val) {
current[idx] = 0;
diff --git a/tensorflow/lite/kernels/kernel_util.cc b/tensorflow/lite/kernels/kernel_util.cc
index 58fd99f..39f7bc7 100644
--- a/tensorflow/lite/kernels/kernel_util.cc
+++ b/tensorflow/lite/kernels/kernel_util.cc
@@ -572,12 +572,11 @@
bool IsMobilePlatform() {
#if defined(ANDROID) || defined(__ANDROID__)
return true;
-#elif defined(__APPLE__)
-#if TARGET_IPHONE_SIMULATOR || TARGET_OS_IPHONE
+#elif defined(__APPLE__) && (TARGET_IPHONE_SIMULATOR || TARGET_OS_IPHONE)
return true;
-#endif
-#endif
+#else
return false;
+#endif
}
bool HasUnspecifiedDimension(const TfLiteTensor* tensor) {
diff --git a/tensorflow/lite/micro/BUILD b/tensorflow/lite/micro/BUILD
index 13cb8de..58ea22d 100644
--- a/tensorflow/lite/micro/BUILD
+++ b/tensorflow/lite/micro/BUILD
@@ -39,8 +39,8 @@
deps = [
":memory_helpers",
":micro_allocator",
- ":micro_context",
- ":micro_graph",
+ ":micro_interpreter_context",
+ ":micro_interpreter_graph",
":micro_profiler_interface",
":op_resolvers",
"//tensorflow/lite:type_to_tflitetype",
@@ -63,9 +63,27 @@
],
copts = micro_copts(),
deps = [
+ ":micro_common",
+ ":micro_graph",
+ ":micro_log",
+ "//tensorflow/lite/c:common",
+ ],
+)
+
+cc_library(
+ name = "micro_interpreter_context",
+ srcs = [
+ "micro_interpreter_context.cc",
+ ],
+ hdrs = [
+ "micro_interpreter_context.h",
+ ],
+ copts = micro_copts(),
+ deps = [
":memory_helpers",
":micro_allocator",
- ":micro_graph",
+ ":micro_context",
+ ":micro_interpreter_graph",
":micro_log",
":micro_profiler_interface",
"//tensorflow/lite/c:common",
@@ -94,22 +112,36 @@
copts = micro_copts(),
deps = [
":memory_helpers",
- ":micro_allocator",
+ ":micro_arena_constants",
":micro_context",
":micro_log",
":mock_micro_graph",
"//tensorflow/lite/c:common",
+ "//tensorflow/lite/micro/arena_allocator:simple_memory_allocator",
],
)
cc_library(
name = "micro_graph",
- srcs = ["micro_graph.cc"],
hdrs = ["micro_graph.h"],
+ copts = micro_copts(),
+ deps = [
+ ":micro_common",
+ ":micro_resource_variable",
+ "//tensorflow/lite/kernels/internal:compatibility",
+ ],
+)
+
+cc_library(
+ name = "micro_interpreter_graph",
+ srcs = ["micro_interpreter_graph.cc"],
+ hdrs = ["micro_interpreter_graph.h"],
+ copts = micro_copts(),
deps = [
":memory_helpers",
":micro_allocator",
":micro_common",
+ ":micro_graph",
":micro_log",
":micro_profiler",
":micro_resource_variable",
@@ -124,6 +156,7 @@
name = "mock_micro_graph",
srcs = ["mock_micro_graph.cc"],
hdrs = ["mock_micro_graph.h"],
+ copts = micro_copts(),
deps = [
":micro_allocator",
":micro_graph",
@@ -158,6 +191,7 @@
"//tensorflow/lite/micro/arena_allocator:persistent_arena_buffer_allocator",
"//tensorflow/lite/micro/arena_allocator:simple_memory_allocator",
"//tensorflow/lite/micro/memory_planner:greedy_memory_planner",
+ "//tensorflow/lite/micro/memory_planner:linear_memory_planner",
"//tensorflow/lite/micro/memory_planner:micro_memory_planner",
"//tensorflow/lite/micro/tflite_bridge:flatbuffer_conversions_bridge",
"//tensorflow/lite/schema:schema_fbs",
@@ -179,6 +213,7 @@
name = "flatbuffer_utils",
srcs = ["flatbuffer_utils.cc"],
hdrs = ["flatbuffer_utils.h"],
+ copts = micro_copts(),
deps = [
"//tensorflow/lite/c:common",
"//tensorflow/lite/schema:schema_fbs",
@@ -190,6 +225,7 @@
name = "memory_helpers",
srcs = ["memory_helpers.cc"],
hdrs = ["memory_helpers.h"],
+ copts = micro_copts(),
deps = [
"//tensorflow/lite/c:common",
"//tensorflow/lite/kernels/internal:reference",
@@ -369,6 +405,21 @@
)
cc_library(
+ name = "span",
+ hdrs = ["span.h"],
+ copts = micro_copts(),
+)
+
+cc_library(
+ name = "static_vector",
+ hdrs = ["static_vector.h"],
+ copts = micro_copts(),
+ deps = [
+ "//tensorflow/lite/kernels:op_macros",
+ ],
+)
+
+cc_library(
name = "system_setup",
srcs = [
"system_setup.cc",
@@ -387,6 +438,7 @@
deps = [
":micro_log",
":system_setup",
+ "//tensorflow/lite/micro/testing:micro_test",
],
)
@@ -403,13 +455,14 @@
)
cc_test(
- name = "micro_context_test",
+ name = "micro_interpreter_context_test",
srcs = [
- "micro_context_test.cc",
+ "micro_interpreter_context_test.cc",
],
deps = [
":micro_allocator",
- ":micro_context",
+ ":micro_interpreter_context",
+ ":micro_interpreter_graph",
":test_helpers",
"//tensorflow/lite/micro/testing:micro_test",
],
@@ -423,6 +476,7 @@
deps = [
":fake_micro_context",
":micro_allocator",
+ ":mock_micro_graph",
":test_helpers",
"//tensorflow/lite/micro/testing:micro_test",
],
@@ -518,6 +572,18 @@
)
cc_test(
+ name = "span_test",
+ size = "small",
+ srcs = [
+ "span_test.cc",
+ ],
+ deps = [
+ ":span",
+ "//tensorflow/lite/micro/testing:micro_test",
+ ],
+)
+
+cc_test(
name = "testing_helpers_test",
srcs = [
"testing_helpers_test.cc",
@@ -574,6 +640,18 @@
],
)
+cc_test(
+ name = "static_vector_test",
+ size = "small",
+ srcs = [
+ "static_vector_test.cc",
+ ],
+ deps = [
+ ":static_vector",
+ "//tensorflow/lite/micro/testing:micro_test",
+ ],
+)
+
bzl_library(
name = "build_def_bzl",
srcs = ["build_def.bzl"],
diff --git a/tensorflow/lite/micro/arc_emsdp/debug_log.cc b/tensorflow/lite/micro/arc_emsdp/debug_log.cc
index 55cf420..a61e3c2 100644
--- a/tensorflow/lite/micro/arc_emsdp/debug_log.cc
+++ b/tensorflow/lite/micro/arc_emsdp/debug_log.cc
@@ -122,4 +122,12 @@
vsnprintf_(log_buffer, kMaxLogLen, format, args);
LogDebugString(log_buffer);
#endif
-}
\ No newline at end of file
+}
+
+#ifndef TF_LITE_STRIP_ERROR_STRINGS
+// Only called from MicroVsnprintf (micro_log.h)
+extern "C" int DebugVsnprintf(char* buffer, size_t buf_size, const char* format,
+ va_list vlist) {
+ return vsnprintf_(buffer, buf_size, format, vlist);
+}
+#endif
diff --git a/tensorflow/lite/micro/benchmarks/BUILD b/tensorflow/lite/micro/benchmarks/BUILD
index 808f6ac..d0f67b6 100644
--- a/tensorflow/lite/micro/benchmarks/BUILD
+++ b/tensorflow/lite/micro/benchmarks/BUILD
@@ -65,9 +65,7 @@
hdrs = [
"//tensorflow/lite/micro/models:generated_keyword_scrambled_8bit_model_hdr",
],
- visibility = [
- ":micro_top_level",
- ],
+ visibility = ["//visibility:private"],
)
cc_binary(
diff --git a/tensorflow/lite/micro/benchmarks/README.md b/tensorflow/lite/micro/benchmarks/README.md
index 1031a58..1ac5d25 100644
--- a/tensorflow/lite/micro/benchmarks/README.md
+++ b/tensorflow/lite/micro/benchmarks/README.md
@@ -70,29 +70,20 @@
For more info about the Corstone-300 software see:
[tensorflow/lite/micro/cortex_m_corstone_300/README.md](../cortex_m_corstone_300/README.md).
-Disclaimer: Executing the benchmark test on the Corstone-300 software will
-provide a general metric of instructions executed. The estimates are not cycle
-accurate, however it aligns to instruction per cycle, and is a consistent
-environment. This means it can detect if code changes changed performance.
+Disclaimer: The FVP can not be used to measure CPU performance.
+The results are not reliable, not even for relative measurements.
+FVP may however be used for performance measurements when running on NPU and only NPU PMU numbers can be used. The NPU model is cycle accurate within approximately +-10%.
-The person detection benchmark can also run with Ethos-U enabled, as the
-downloaded model will be optimized for Ethos-U. For more info see:
+As an example, the person detect downloaded model will be optimized for Ethos-U. For more info see:
[tensorflow/lite/micro/kernels/ethos_u/README.md](../kernels/ethos_u/README.md).
+And since it only makes sense to measure performance on the NPU, only the person detection benchmark should be run and only with Ethos-U enabled.
+See also network tester example, where person detect model is used in the same way when Ethos-U is enabled:
+[tensorflow/lite/micro/examples/network_tester/README.md](../examples/network_tester/README.md).
-To run the keyword benchmark on FVP:
-
-```
-make -j -f tensorflow/lite/micro/tools/make/Makefile TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 run_keyword_benchmark
-```
-
-To run the person detection benchmark on FVP:
-
-```
-make -j -f tensorflow/lite/micro/tools/make/Makefile TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 run_person_detection_benchmark
-```
+The person detect model is not an optimial model for Ethos-U since it quite small. Also note that only the NPU PMU cycles are logged even though the CPU is setting up the Ethos-U driver in each iteration.
To run the person detection benchmark on FVP with Ethos-U:
```
-make -j -f tensorflow/lite/micro/tools/make/Makefile CO_PROCESSOR=ethos_u TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 run_person_detection_benchmark
+make -f tensorflow/lite/micro/tools/make/Makefile CO_PROCESSOR=ethos_u TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 run_person_detection_benchmark
```
diff --git a/tensorflow/lite/micro/benchmarks/person_detection_benchmark.cc b/tensorflow/lite/micro/benchmarks/person_detection_benchmark.cc
index e21789b..29d30ee 100644
--- a/tensorflow/lite/micro/benchmarks/person_detection_benchmark.cc
+++ b/tensorflow/lite/micro/benchmarks/person_detection_benchmark.cc
@@ -1,4 +1,4 @@
-/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -37,7 +37,11 @@
namespace tflite {
+#ifdef ETHOS_U
+using PersonDetectionOpResolver = MicroMutableOpResolver<1>;
+#else
using PersonDetectionOpResolver = MicroMutableOpResolver<6>;
+#endif
using PersonDetectionBenchmarkRunner = MicroBenchmarkRunner<int8_t>;
// Create an area of memory to use for input, output, and intermediate arrays.
@@ -57,12 +61,16 @@
// PersonDetectionBenchmarkRunner object.
PersonDetectionOpResolver* op_resolver =
new (op_resolver_buffer) PersonDetectionOpResolver();
+#ifdef ETHOS_U
+ op_resolver->AddEthosU();
+#else
op_resolver->AddFullyConnected(tflite::Register_FULLY_CONNECTED_INT8());
op_resolver->AddConv2D(tflite::Register_CONV_2D_INT8REF());
op_resolver->AddDepthwiseConv2D();
op_resolver->AddSoftmax();
op_resolver->AddAveragePool2D(tflite::Register_AVERAGE_POOL_2D_INT8());
op_resolver->AddReshape();
+#endif
return new (benchmark_runner_buffer)
PersonDetectionBenchmarkRunner(g_person_detect_model_data, op_resolver,
tensor_arena, kTensorArenaSize, profiler);
diff --git a/tensorflow/lite/micro/bluepill/debug_log.cc b/tensorflow/lite/micro/bluepill/debug_log.cc
index 6d0d53b..720410b 100644
--- a/tensorflow/lite/micro/bluepill/debug_log.cc
+++ b/tensorflow/lite/micro/bluepill/debug_log.cc
@@ -45,3 +45,11 @@
SysWriteDebugConsole(log_buffer);
#endif // TF_LITE_STRIP_ERROR_STRINGS
}
+
+#ifndef TF_LITE_STRIP_ERROR_STRINGS
+// Only called from MicroVsnprintf (micro_log.h)
+extern "C" int DebugVsnprintf(char* buffer, size_t buf_size, const char* format,
+ va_list vlist) {
+ return vsnprintf_(buffer, buf_size, format, vlist);
+}
+#endif
diff --git a/tensorflow/lite/micro/build_def.bzl b/tensorflow/lite/micro/build_def.bzl
index b9ecf77..82768f1 100644
--- a/tensorflow/lite/micro/build_def.bzl
+++ b/tensorflow/lite/micro/build_def.bzl
@@ -1,6 +1,7 @@
def micro_copts():
return [
"-Wall",
+ "-Wno-unused-parameter",
"-Wnon-virtual-dtor",
"-DFLATBUFFERS_LOCALE_INDEPENDENT=0",
]
diff --git a/tensorflow/lite/micro/chre/debug_log.cc b/tensorflow/lite/micro/chre/debug_log.cc
index b599ebe..99f7c67 100644
--- a/tensorflow/lite/micro/chre/debug_log.cc
+++ b/tensorflow/lite/micro/chre/debug_log.cc
@@ -29,4 +29,12 @@
vsnprintf_(log_buffer, kMaxLogLen, format, args);
chreLog(CHRE_LOG_DEBUG, "[TFL_MICRO] %s", log_buffer);
#endif
-}
\ No newline at end of file
+}
+
+#ifndef TF_LITE_STRIP_ERROR_STRINGS
+// Only called from MicroVsnprintf (micro_log.h)
+extern "C" int DebugVsnprintf(char* buffer, size_t buf_size, const char* format,
+ va_list vlist) {
+ return vsnprintf_(buffer, buf_size, format, vlist);
+}
+#endif
diff --git a/tensorflow/lite/micro/compression.h b/tensorflow/lite/micro/compression.h
new file mode 100644
index 0000000..43965c2
--- /dev/null
+++ b/tensorflow/lite/micro/compression.h
@@ -0,0 +1,70 @@
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_MICRO_MICRO_COMPRESSION_H_
+#define TENSORFLOW_LITE_MICRO_MICRO_COMPRESSION_H_
+
+#ifdef USE_TFLM_COMPRESSION
+
+#include "tensorflow/lite/c/common.h"
+
+namespace tflite {
+
+//
+// Compressed tensors
+//
+
+static constexpr const char* kCompressionMetadataString = "TFLM_COMPRESSION";
+
+enum class CompressionScheme : uint8_t {
+ kBinQuant,
+};
+
+// TODO(ddavis-2015): pack struct
+struct LookupTableData {
+ static constexpr size_t kMaxBitWidth = 7;
+ static constexpr size_t kMaxValueTableChannelStride = 128;
+
+ const void* value_table; // Pointer into FlatBuffer Values.
+ uint8_t value_table_channel_stride; // elements per channel
+ uint8_t compressed_bit_width : 3; // 1 to 7 bits
+ bool is_per_channel_quantized : 1; // tensor is per-channel quantized
+ bool use_alternate_axis : 1; // shape default channel:
+ // 0 = first, 1 = last
+ uint8_t reserved : 3;
+};
+
+union CompressionData {
+ LookupTableData* lut_data;
+};
+
+// TODO(ddavis-2015): pack struct
+struct CompressionTensorData {
+ CompressionScheme scheme;
+ CompressionData data;
+};
+
+// TODO(ddavis-2015): pack struct
+struct CompressedTensorList {
+ // Sparsely populated array with the same number of elements as there are
+ // tensors in the Subgraph. An alternative would include a tensor index in
+ // the struct for each and walk the list on look up. This could be slow.
+ CompressionTensorData** tensors;
+};
+
+} // namespace tflite
+
+#endif // USE_TFLM_COMPRESSION
+#endif // TENSORFLOW_LITE_MICRO_MICRO_COMPRESSION_H_
diff --git a/tensorflow/lite/micro/cortex_m_corstone_300/README.md b/tensorflow/lite/micro/cortex_m_corstone_300/README.md
index 94935ac..663f636 100644
--- a/tensorflow/lite/micro/cortex_m_corstone_300/README.md
+++ b/tensorflow/lite/micro/cortex_m_corstone_300/README.md
@@ -37,12 +37,12 @@
Some examples:
```
-make -j -f tensorflow/lite/micro/tools/make/Makefile CO_PROCESSOR=ethos_u TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 test_network_tester_test
-make -j -f tensorflow/lite/micro/tools/make/Makefile OPTIMIZED_KERNEL_DIR=cmsis_nn TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 test_network_tester_test
-make -j -f tensorflow/lite/micro/tools/make/Makefile CO_PROCESSOR=ethos_u OPTIMIZED_KERNEL_DIR=cmsis_nn TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 test_network_tester_test
-make -j -f tensorflow/lite/micro/tools/make/Makefile TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 test_network_tester_test
-make -j -f tensorflow/lite/micro/tools/make/Makefile TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 test_kernel_fully_connected_test
-make -j -f tensorflow/lite/micro/tools/make/Makefile OPTIMIZED_KERNEL_DIR=cmsis_nn TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m7+fp test_kernel_fully_connected_test
-make -j -f tensorflow/lite/micro/tools/make/Makefile TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m3 test_kernel_fully_connected_test
-make -j -f tensorflow/lite/micro/tools/make/Makefile TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 BUILD_TYPE=release_with_logs TOOLCHAIN=armclang test_network_tester_test
+make -f tensorflow/lite/micro/tools/make/Makefile CO_PROCESSOR=ethos_u TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 test_network_tester_test
+make -f tensorflow/lite/micro/tools/make/Makefile OPTIMIZED_KERNEL_DIR=cmsis_nn TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 test_network_tester_test
+make -f tensorflow/lite/micro/tools/make/Makefile CO_PROCESSOR=ethos_u OPTIMIZED_KERNEL_DIR=cmsis_nn TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 test_network_tester_test
+make -f tensorflow/lite/micro/tools/make/Makefile TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 test_network_tester_test
+make -f tensorflow/lite/micro/tools/make/Makefile TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 test_kernel_fully_connected_test
+make -f tensorflow/lite/micro/tools/make/Makefile OPTIMIZED_KERNEL_DIR=cmsis_nn TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m7+fp test_kernel_fully_connected_test
+make -f tensorflow/lite/micro/tools/make/Makefile TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m3 test_kernel_fully_connected_test
+make -f tensorflow/lite/micro/tools/make/Makefile TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 BUILD_TYPE=release_with_logs TOOLCHAIN=armclang test_network_tester_test
```
diff --git a/tensorflow/lite/micro/cortex_m_corstone_300/system_setup.cc b/tensorflow/lite/micro/cortex_m_corstone_300/system_setup.cc
index 95a11b2..6473340 100644
--- a/tensorflow/lite/micro/cortex_m_corstone_300/system_setup.cc
+++ b/tensorflow/lite/micro/cortex_m_corstone_300/system_setup.cc
@@ -1,4 +1,4 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -14,33 +14,90 @@
==============================================================================*/
#ifdef ETHOS_U
+#include <inttypes.h>
+
+#include <algorithm>
+
#include "ethosu_driver.h"
+#include "pmu_ethosu.h"
#endif
// This is set in micro/tools/make/targets/cortex_m_corstone_300_makefile.inc.
-// It is needed for the calls to NVIC_SetVector()/NVIC_EnableIR() and for the
-// DWT and PMU counters.
+// It is needed for the calls to NVIC_SetVector()/NVIC_EnableIR(),
#include CMSIS_DEVICE_ARM_CORTEX_M_XX_HEADER_FILE
#include "tensorflow/lite/micro/micro_log.h"
#include "tensorflow/lite/micro/micro_time.h"
#include "tensorflow/lite/micro/system_setup.h"
+#ifdef ETHOS_U
+
+bool npuPmuCycleCounterIsSet;
+uint64_t npuPmuCycleCounter;
+
+extern "C" {
+void ethosu_inference_begin(struct ethosu_driver* drv, void* userArg) {
+ // Enable PMU
+ ETHOSU_PMU_Enable(drv);
+
+ // Enable cycle counter
+ ETHOSU_PMU_PMCCNTR_CFG_Set_Stop_Event(drv, ETHOSU_PMU_NPU_IDLE);
+ ETHOSU_PMU_PMCCNTR_CFG_Set_Start_Event(drv, ETHOSU_PMU_NPU_ACTIVE);
+ ETHOSU_PMU_CNTR_Enable(drv, ETHOSU_PMU_CCNT_Msk);
+ ETHOSU_PMU_CYCCNT_Reset(drv);
+
+ // Reset all counters
+ ETHOSU_PMU_EVCNTR_ALL_Reset(drv);
+}
+
+void ethosu_inference_end(struct ethosu_driver* drv, void* userArg) {
+ // Save cycle counter
+ npuPmuCycleCounter += ETHOSU_PMU_Get_CCNTR(drv);
+ npuPmuCycleCounterIsSet = true;
+
+ // Disable PMU
+ ETHOSU_PMU_Disable(drv);
+}
+}
+#endif
+
namespace tflite {
namespace {
+#ifdef ETHOS_U
+constexpr uint32_t kClocksPerSecond = 200e6;
+#else
constexpr uint32_t kClocksPerSecond = 25e6;
+#endif
} // namespace
uint32_t ticks_per_second() { return kClocksPerSecond; }
uint32_t GetCurrentTimeTicks() {
-#if (!defined(TF_LITE_STRIP_ERROR_STRINGS) && !defined(ARMCM0))
+#if (!defined(TF_LITE_STRIP_ERROR_STRINGS))
+#ifdef ETHOS_U
+ uint32_t ticks = static_cast<uint32_t>(npuPmuCycleCounter);
+
+ // Note cycle counter will be reset here for next iteration
+ if (npuPmuCycleCounterIsSet) {
+ npuPmuCycleCounter = 0;
+ npuPmuCycleCounterIsSet = false;
+ }
+
+ return ticks;
+#else
+
+#if defined(ARMCM0)
+ return 0;
+#else
#ifdef ARMCM55
return ARM_PMU_Get_CCNTR();
#else
return DWT->CYCCNT;
#endif
+#endif
+
+#endif
#else
return 0;
#endif
@@ -88,14 +145,17 @@
#ifdef ETHOS_U
constexpr int ethosu_base_address = 0x48102000;
constexpr int ethosu_irq = 56;
+ constexpr int ethosu_irq_priority = 5;
// Initialize Ethos-U NPU driver.
if (ethosu_init(ðosu0_driver, reinterpret_cast<void*>(ethosu_base_address),
ethosu0_scratch, ETHOSU_FAST_MEMORY_SIZE, 1, 1)) {
MicroPrintf("Failed to initialize Ethos-U driver");
+ return;
}
NVIC_SetVector(static_cast<IRQn_Type>(ethosu_irq),
(uint32_t)ðosuIrqHandler0);
+ NVIC_SetPriority(static_cast<IRQn_Type>(ethosu_irq), ethosu_irq_priority);
NVIC_EnableIRQ(static_cast<IRQn_Type>(ethosu_irq));
#endif
}
diff --git a/tensorflow/lite/micro/cortex_m_generic/debug_log.cc b/tensorflow/lite/micro/cortex_m_generic/debug_log.cc
index 2c237fe..b7182a5 100644
--- a/tensorflow/lite/micro/cortex_m_generic/debug_log.cc
+++ b/tensorflow/lite/micro/cortex_m_generic/debug_log.cc
@@ -16,16 +16,16 @@
// Implementation for the DebugLog() function that prints to the debug logger on
// an generic Cortex-M device.
+#include "tensorflow/lite/micro/debug_log.h"
+
#ifdef __cplusplus
extern "C" {
#endif // __cplusplus
-#include "tensorflow/lite/micro/debug_log.h"
-
#include "tensorflow/lite/micro/cortex_m_generic/debug_log_callback.h"
#ifndef TF_LITE_STRIP_ERROR_STRINGS
-#include "eyalroz_printf/src/printf/printf.h"
+#include <stdio.h>
#endif
static DebugLogCallback debug_log_callback = nullptr;
@@ -49,11 +49,19 @@
constexpr int kMaxLogLen = 256;
char log_buffer[kMaxLogLen];
- vsnprintf_(log_buffer, kMaxLogLen, format, args);
+ vsnprintf(log_buffer, kMaxLogLen, format, args);
InvokeDebugLogCallback(log_buffer);
#endif
}
+#ifndef TF_LITE_STRIP_ERROR_STRINGS
+// Only called from MicroVsnprintf (micro_log.h)
+int DebugVsnprintf(char* buffer, size_t buf_size, const char* format,
+ va_list vlist) {
+ return vsnprintf(buffer, buf_size, format, vlist);
+}
+#endif
+
#ifdef __cplusplus
} // extern "C"
#endif // __cplusplus
diff --git a/tensorflow/lite/micro/debug_log.cc b/tensorflow/lite/micro/debug_log.cc
index a8ef36b..9823196 100644
--- a/tensorflow/lite/micro/debug_log.cc
+++ b/tensorflow/lite/micro/debug_log.cc
@@ -44,3 +44,11 @@
vfprintf(stderr, format, args);
#endif
}
+
+#ifndef TF_LITE_STRIP_ERROR_STRINGS
+// Only called from MicroVsnprintf (micro_log.h)
+extern "C" int DebugVsnprintf(char* buffer, size_t buf_size, const char* format,
+ va_list vlist) {
+ return vsnprintf(buffer, buf_size, format, vlist);
+}
+#endif
diff --git a/tensorflow/lite/micro/debug_log.h b/tensorflow/lite/micro/debug_log.h
index c0cf699..6e2e69e 100644
--- a/tensorflow/lite/micro/debug_log.h
+++ b/tensorflow/lite/micro/debug_log.h
@@ -17,19 +17,23 @@
#ifdef __cplusplus
#include <cstdarg>
+#include <cstddef>
#else
#include <stdarg.h>
+#include <stddef.h>
#endif // __cplusplus
#ifdef __cplusplus
extern "C" {
#endif // __cplusplus
-// This function should be implemented by each target platform, and provide a
+// These functions should be implemented by each target platform, and provide a
// way for strings to be output to some text stream. For more information, see
-// the tensorflow/lite/micro/debug_log.cc file. This function should support
+// the tensorflow/lite/micro/debug_log.cc file. These functions should support
// standard C/C++ stdio style formatting operations.
void DebugLog(const char* format, va_list args);
+int DebugVsnprintf(char* buffer, size_t buf_size, const char* format,
+ va_list vlist);
#ifdef __cplusplus
} // extern "C"
diff --git a/tensorflow/lite/micro/docs/new_platform_support.md b/tensorflow/lite/micro/docs/new_platform_support.md
index 692d98e..8752c8b 100644
--- a/tensorflow/lite/micro/docs/new_platform_support.md
+++ b/tensorflow/lite/micro/docs/new_platform_support.md
@@ -25,7 +25,7 @@
Prior to integrating TFLM with a specific hardware involves tasks that is
outside the scope of the TFLM project, including:
- * Toolchain setup - TFLM requires support for C++11
+ * Toolchain setup - TFLM requires support for C++17
* Set up and installation of board-specific SDKs and IDEs
* Compiler flags and Linker setup
* Integrating peripherals such as cameras, microphones and accelerometers to
@@ -77,7 +77,7 @@
that when linking TFLM into a binary, the implementations of the functions in
[debug\_log.h](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/debug_log.h),
[micro\_time.h](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/micro_time.h)
-and [system\_setup.h](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/debug_log.h)
+and [system\_setup.h](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/system_setup.h)
can be found.
For example, the implementations of these functions for:
diff --git a/tensorflow/lite/micro/docs/optimized_kernel_implementations.md b/tensorflow/lite/micro/docs/optimized_kernel_implementations.md
index 4a5c81a..8eefb55 100644
--- a/tensorflow/lite/micro/docs/optimized_kernel_implementations.md
+++ b/tensorflow/lite/micro/docs/optimized_kernel_implementations.md
@@ -169,6 +169,12 @@
* Build a static libtensorflow-microlite.a using the TFLM makefile with:
`make -f tensorflow/lite/micro/tools/make/Makefile TARGET=<target>
OPTIMIZED_KERNEL_DIR=<optimize_dir> microlite`
+ * Optionally build for size or speed. Translated to a valid make command it will be any of these two:
+ `make -f tensorflow/lite/micro/tools/make/Makefile TARGET=<target>
+ OPTIMIZED_KERNEL_DIR=<optimize_dir> OPTIMIZE_KERNELS_FOR=KERNELS_OPTIMIZED_FOR_SIZE microlite`
+ `make -f tensorflow/lite/micro/tools/make/Makefile TARGET=<target>
+ OPTIMIZED_KERNEL_DIR=<optimize_dir> OPTIMIZE_KERNELS_FOR=KERNELS_OPTIMIZED_FOR_SPEED microlite`
+ Check relevant README for given optimization library if this is applicable.
* Use the static library and any TFLM headers as part of the overall
application (with its own build system).
diff --git a/tensorflow/lite/micro/examples/dtln/Makefile.inc b/tensorflow/lite/micro/examples/dtln/Makefile.inc
new file mode 100644
index 0000000..e88cf09
--- /dev/null
+++ b/tensorflow/lite/micro/examples/dtln/Makefile.inc
@@ -0,0 +1,30 @@
+
+DTLN_TEST_SRCS := \
+$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/dtln/dtln_test.cc \
+$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/dtln/dtln_inout_data.cc
+
+DTLN_TEST_HDRS := \
+$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/dtln/dtln_inout_data.h
+
+DTLN_GENERATOR_INPUTS := \
+$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/dtln/dtln_noise_suppression.tflite
+
+DTLN_GENERATED_SRCS := \
+$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/dtln/dtln_noise_suppression_model_data.cc
+
+DTLN_GENERATED_HDRS := \
+$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/dtln/dtln_noise_suppression_model_data.h
+
+#Find any platform - specific rules for this example.
+include $(wildcard $(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/dtln/*/Makefile.inc)
+
+# TODO(b/161489252): Disabling warnings for this example until we have a better
+# way to build third_party code with a reduced list of CFLAGS.
+CCFLAGS := $(filter-out $(CC_WARNINGS),$(CCFLAGS))
+
+# Tests loading and running a dtln model.
+# TODO(b/319712246): Re-enable tests once the dtln_test works on HiFi Mini & VP6
+ifneq ($(TARGET_ARCH), $(filter $(TARGET_ARCH), hifimini vision_p6))
+ $(eval $(call microlite_test,dtln_test,\
+ $(DTLN_TEST_SRCS),$(DTLN_TEST_HDRS),$(DTLN_GENERATOR_INPUTS)))
+endif
diff --git a/tensorflow/lite/micro/examples/dtln/README.md b/tensorflow/lite/micro/examples/dtln/README.md
new file mode 100644
index 0000000..eb96b0c
--- /dev/null
+++ b/tensorflow/lite/micro/examples/dtln/README.md
@@ -0,0 +1,23 @@
+# DTLN example
+The DTLN example is a demonstration of DTLN network running on HiFi DSP for Noise suppression in speech.
+It uses feature_data as input and provides noise suppressed speech as output.
+It is based on the paper(https://github.com/breizhn/DTLN).
+While paper presents 2 parts, one for noise suppression and the other for speech enhancement,
+the example presented here follows the noise suppression part only.
+The model was re-trained by Cadence using the DNS challenge data (https://github.com/microsoft/DNS-Challenge)
+and the noise suppression part was 8-bit quantized.
+This example is not to be used to evaluate the network quality or quality of noise suppression, but only as a demonstration as stated above.
+
+## Run the tests on a development machine
+
+```
+make -f tensorflow/lite/micro/tools/make/Makefile third_party_downloads
+make -f tensorflow/lite/micro/tools/make/Makefile test_dtln_test
+```
+
+You should see a series of files get compiled, followed by some logging output
+from a test, which should conclude with `~~~ALL TESTS PASSED~~~`. If you see
+this, it means that a small program has been built and run that loads a trained
+TensorFlow model, runs with features data, and got the expected
+outputs. This particular test runs with a feature data as input,
+and validate the output with golden reference output.
diff --git a/tensorflow/lite/micro/examples/dtln/dtln_inout_data.cc b/tensorflow/lite/micro/examples/dtln/dtln_inout_data.cc
new file mode 100644
index 0000000..a4652a7
--- /dev/null
+++ b/tensorflow/lite/micro/examples/dtln/dtln_inout_data.cc
@@ -0,0 +1,60 @@
+/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/lite/c/common.h"
+
+int8_t feature_data[] = {
+ -127, -126, -115, -82, -90, -113, -15, 13, -87, -105, -77, -106,
+ -113, -81, -90, -123, -113, -112, -124, -120, -114, -123, -123, -112,
+ -115, -125, -120, -120, -126, -126, -126, -127, -128, -127, -128, -128,
+ -127, -127, -128, -126, -125, -126, -127, -127, -127, -128, -126, -127,
+ -128, -127, -127, -128, -127, -126, -127, -128, -127, -127, -127, -124,
+ -124, -127, -126, -126, -127, -126, -124, -125, -128, -126, -125, -127,
+ -126, -126, -127, -127, -126, -126, -127, -126, -126, -127, -126, -125,
+ -127, -126, -123, -124, -126, -126, -126, -128, -127, -127, -127, -128,
+ -127, -127, -128, -128, -128, -128, -128, -127, -128, -128, -127, -128,
+ -128, -127, -127, -128, -128, -128, -128, -128, -128, -128, -128, -128,
+ -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
+ -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
+ -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
+ -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
+ -128, -128, -128, -128, -128, -128, -128, -127, -127, -127, -127, -127,
+ -127, -127, -127, -128, -127, -127, -127, -127, -126, -127, -127, -127,
+ -127, -127, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
+ -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
+ -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
+ -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
+ -128, -127, -128, -128, -127, -127, -128, -128, -128, -128, -128, -128,
+ -128, -128, -128, -128, -128};
+
+int8_t golden_ref[] = {
+ 119, 82, 72, 116, -5, 10, -2, 0, -2, 36, 116, 125, 123, 124, 126,
+ 124, 115, 116, 124, 126, 121, 124, 121, 104, 104, 113, 113, 110, 115, 101,
+ 96, 119, 120, 117, 118, 114, 104, 110, 118, 119, 111, 114, 119, 117, 114,
+ 110, 117, 112, 115, 120, 119, 118, 119, 116, 117, 120, 121, 121, 121, 119,
+ 117, 120, 121, 120, 118, 115, 114, 114, 117, 119, 113, 108, 108, 111, 112,
+ 114, 114, 116, 115, 112, 110, 113, 113, 110, 107, 98, 102, 101, 101, 103,
+ 92, 98, 101, 102, 102, 101, 104, 102, 101, 101, 100, 102, 98, 104, 100,
+ 99, 92, 96, 87, 97, 96, 96, 96, 95, 92, 98, 95, 90, 85, 82,
+ 87, 82, 82, 89, 90, 83, 86, 85, 80, 86, 87, 91, 89, 87, 87,
+ 85, 82, 74, 80, 80, 72, 79, 74, 79, 82, 83, 77, 85, 71, 76,
+ 72, 76, 76, 77, 56, 74, 74, 69, 69, 69, 65, 56, 60, 67, 71,
+ 69, 74, 67, 71, 65, 77, 76, 79, 67, 72, 61, 60, 67, 69, 71,
+ 77, 63, 63, 60, 63, 71, 80, 80, 74, 76, 67, 74, 63, 67, 69,
+ 72, 77, 71, 72, 82, 65, 49, 67, 58, 71, 65, 63, 69, 61, 77,
+ 63, 65, 65, 69, 69, 65, 72, 77, 80, 60, 79, 77, 71, 67, 79,
+ 69, 67, 65, 74, 69, 71, 67, 76, 77, 77, 77, 83, 67, 65, 79,
+ 77, 60, 71, 86, 86, 63, 74, 63, 63, 63, 69, 79, 63, 52, 85,
+ 87, 86};
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc b/tensorflow/lite/micro/examples/dtln/dtln_inout_data.h
similarity index 69%
rename from tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc
rename to tensorflow/lite/micro/examples/dtln/dtln_inout_data.h
index e2cf661..c5fde46 100644
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc
+++ b/tensorflow/lite/micro/examples/dtln/dtln_inout_data.h
@@ -1,4 +1,4 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -13,11 +13,7 @@
limitations under the License.
==============================================================================*/
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.h"
+#include "tensorflow/lite/c/common.h"
-const char* kCategoryLabels[kCategoryCount] = {
- "silence",
- "unknown",
- "yes",
- "no",
-};
+extern int8_t feature_data[];
+extern int8_t golden_ref[];
diff --git a/tensorflow/lite/micro/examples/dtln/dtln_noise_suppression.tflite b/tensorflow/lite/micro/examples/dtln/dtln_noise_suppression.tflite
new file mode 100644
index 0000000..143ef8d
--- /dev/null
+++ b/tensorflow/lite/micro/examples/dtln/dtln_noise_suppression.tflite
Binary files differ
diff --git a/tensorflow/lite/micro/examples/dtln/dtln_test.cc b/tensorflow/lite/micro/examples/dtln/dtln_test.cc
new file mode 100644
index 0000000..1077195
--- /dev/null
+++ b/tensorflow/lite/micro/examples/dtln/dtln_test.cc
@@ -0,0 +1,100 @@
+/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/micro/examples/dtln/dtln_inout_data.h"
+#include "tensorflow/lite/micro/examples/dtln/dtln_noise_suppression_model_data.h"
+#include "tensorflow/lite/micro/micro_interpreter.h"
+#include "tensorflow/lite/micro/micro_log.h"
+#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
+#include "tensorflow/lite/micro/testing/micro_test.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+
+TF_LITE_MICRO_TESTS_BEGIN
+
+MicroPrintf(
+ "\nThis example demonstrates LSTM layers on HiFi DSP, NOT for evaluating "
+ "noise suppression quality.\n");
+TF_LITE_MICRO_TEST(TestInvoke) {
+ // Map the model into a usable data structure. This doesn't involve any
+ // copying or parsing, it's a very lightweight operation.
+ const tflite::Model* model =
+ ::tflite::GetModel(g_dtln_noise_suppression_model_data);
+ if (model->version() != TFLITE_SCHEMA_VERSION) {
+ MicroPrintf(
+ "Model provided is schema version %d not equal "
+ "to supported version %d.\n",
+ model->version(), TFLITE_SCHEMA_VERSION);
+ }
+
+ // Pull in only the operation implementations we need.
+ // This relies on a complete list of all the ops needed by this graph.
+
+ tflite::MicroMutableOpResolver<3> micro_op_resolver;
+ micro_op_resolver.AddUnidirectionalSequenceLSTM();
+ micro_op_resolver.AddFullyConnected();
+ micro_op_resolver.AddLogistic();
+
+ // Create an area of memory to use for input, output, and intermediate arrays.
+ constexpr int tensor_arena_size = 16 * 1024;
+ alignas(16) uint8_t tensor_arena[tensor_arena_size];
+
+ // Build an interpreter to run the model with.
+ tflite::MicroInterpreter interpreter(model, micro_op_resolver, tensor_arena,
+ tensor_arena_size);
+ interpreter.AllocateTensors();
+
+ // Get information about the memory area to use for the model's input.
+ TfLiteTensor* input = interpreter.input(0);
+
+ // Make sure the input has the properties we expect.
+ TF_LITE_MICRO_EXPECT(input != nullptr);
+ TF_LITE_MICRO_EXPECT_EQ(3, input->dims->size);
+ TF_LITE_MICRO_EXPECT_EQ(1, input->dims->data[0]);
+ TF_LITE_MICRO_EXPECT_EQ(1, input->dims->data[1]);
+ TF_LITE_MICRO_EXPECT_EQ(257, input->dims->data[2]);
+ TF_LITE_MICRO_EXPECT_EQ(kTfLiteInt8, input->type);
+
+ // Copy a spectrogram created from a noisy.wav audio file,
+ // into the memory area used for the input.
+ for (size_t i = 0; i < input->bytes; ++i) {
+ input->data.int8[i] = feature_data[i];
+ }
+
+ // Run the model on this input and make sure it succeeds.
+ TfLiteStatus invoke_status = interpreter.Invoke();
+ if (invoke_status != kTfLiteOk) {
+ MicroPrintf("Invoke failed\n");
+ }
+ TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, invoke_status);
+
+ // Get the output from the model, and make sure it's the expected size and
+ // type.
+ TfLiteTensor* output = interpreter.output(0);
+ TF_LITE_MICRO_EXPECT_EQ(3, output->dims->size);
+ TF_LITE_MICRO_EXPECT_EQ(1, output->dims->data[0]);
+ TF_LITE_MICRO_EXPECT_EQ(1, output->dims->data[1]);
+ TF_LITE_MICRO_EXPECT_EQ(257, output->dims->data[2]);
+ TF_LITE_MICRO_EXPECT_EQ(kTfLiteInt8, output->type);
+
+ int output_size =
+ output->dims->data[0] * output->dims->data[1] * output->dims->data[2];
+ for (int i = 0; i < output_size; i++)
+ TF_LITE_MICRO_EXPECT_EQ(output->data.int8[i], golden_ref[i]);
+
+ MicroPrintf("Ran successfully\n");
+}
+
+TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/examples/hello_world/BUILD b/tensorflow/lite/micro/examples/hello_world/BUILD
index f2b41b3..988b7dd 100644
--- a/tensorflow/lite/micro/examples/hello_world/BUILD
+++ b/tensorflow/lite/micro/examples/hello_world/BUILD
@@ -1,5 +1,6 @@
# Description:
# TensorFlow Lite for Microcontrollers "hello world" example.
+load("@rules_python//python:defs.bzl", "py_binary")
load("@tflm_pip_deps//:requirements.bzl", "requirement")
load(
"//tensorflow/lite/micro:build_def.bzl",
@@ -53,7 +54,7 @@
"@absl_py//absl/flags",
"@absl_py//absl/logging",
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
"//python/tflite_micro:runtime",
],
)
@@ -78,6 +79,6 @@
srcs_version = "PY3",
deps = [
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
],
)
diff --git a/tensorflow/lite/micro/examples/hello_world/models/BUILD b/tensorflow/lite/micro/examples/hello_world/models/BUILD
index 4f025b0..4c9441b 100644
--- a/tensorflow/lite/micro/examples/hello_world/models/BUILD
+++ b/tensorflow/lite/micro/examples/hello_world/models/BUILD
@@ -9,7 +9,10 @@
"hello_world_float.tflite",
"hello_world_int8.tflite",
],
- visibility = ["//tensorflow/lite/micro/examples/hello_world:__subpackages__"],
+ visibility = [
+ "//codegen/examples/hello_world:__subpackages__",
+ "//tensorflow/lite/micro/examples/hello_world:__subpackages__",
+ ],
)
generate_cc_arrays(
diff --git a/tensorflow/lite/micro/examples/hello_world/quantization/BUILD b/tensorflow/lite/micro/examples/hello_world/quantization/BUILD
index ecba316..9a204f2 100644
--- a/tensorflow/lite/micro/examples/hello_world/quantization/BUILD
+++ b/tensorflow/lite/micro/examples/hello_world/quantization/BUILD
@@ -1,3 +1,4 @@
+load("@rules_python//python:defs.bzl", "py_binary")
load("@tflm_pip_deps//:requirements.bzl", "requirement")
py_binary(
@@ -11,7 +12,7 @@
"@absl_py//absl/flags",
"@absl_py//absl/logging",
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
"//python/tflite_micro:runtime",
],
)
diff --git a/tensorflow/lite/micro/examples/hello_world/quantization/ptq.py b/tensorflow/lite/micro/examples/hello_world/quantization/ptq.py
index bfab0d0..f1c4210 100644
--- a/tensorflow/lite/micro/examples/hello_world/quantization/ptq.py
+++ b/tensorflow/lite/micro/examples/hello_world/quantization/ptq.py
@@ -16,7 +16,7 @@
Run:
Build the train.py script
-`bazel build tensorflow/lite/micro/examples/hello_world/quantization:train`
+`bazel build tensorflow/lite/micro/examples/hello_world:train`
The following command first creates the trained TF float model that we will quantize later
`bazel-bin/tensorflow/lite/micro/examples/hello_world/train --save_tf_model --save_dir=/tmp/float_model/`
@@ -113,4 +113,4 @@
if __name__ == "__main__":
- app.run(main)
\ No newline at end of file
+ app.run(main)
diff --git a/tensorflow/lite/micro/examples/micro_speech/BUILD b/tensorflow/lite/micro/examples/micro_speech/BUILD
index 71741f3..10d1c10 100644
--- a/tensorflow/lite/micro/examples/micro_speech/BUILD
+++ b/tensorflow/lite/micro/examples/micro_speech/BUILD
@@ -1,5 +1,7 @@
# Description:
# TensorFlow Lite microcontroller example.
+load("@rules_python//python:defs.bzl", "py_binary", "py_test")
+load("@tflm_pip_deps//:requirements.bzl", "requirement")
load("//tensorflow/lite/micro:build_def.bzl", "generate_cc_arrays")
package(
@@ -9,16 +11,6 @@
licenses = ["notice"],
)
-cc_library(
- name = "simple_model_settings",
- srcs = [
- "simple_features/simple_model_settings.cc",
- ],
- hdrs = [
- "simple_features/simple_model_settings.h",
- ],
-)
-
generate_cc_arrays(
name = "generated_yes_1000ms_wav_cc",
src = "testdata/yes_1000ms.wav",
@@ -44,6 +36,30 @@
)
generate_cc_arrays(
+ name = "generated_noise_1000ms_wav_cc",
+ src = "testdata/noise_1000ms.wav",
+ out = "testdata/noise_1000ms_audio_data.cc",
+)
+
+generate_cc_arrays(
+ name = "generated_noise_1000ms_wav_hdr",
+ src = "testdata/noise_1000ms.wav",
+ out = "testdata/noise_1000ms_audio_data.h",
+)
+
+generate_cc_arrays(
+ name = "generated_silence_1000ms_wav_cc",
+ src = "testdata/silence_1000ms.wav",
+ out = "testdata/silence_1000ms_audio_data.cc",
+)
+
+generate_cc_arrays(
+ name = "generated_silence_1000ms_wav_hdr",
+ src = "testdata/silence_1000ms.wav",
+ out = "testdata/silence_1000ms_audio_data.h",
+)
+
+generate_cc_arrays(
name = "generated_yes_30ms_wav_cc",
src = "testdata/yes_30ms.wav",
out = "testdata/yes_30ms_audio_data.cc",
@@ -69,14 +85,26 @@
generate_cc_arrays(
name = "generated_micro_speech_model_cc",
- src = "micro_speech.tflite",
- out = "micro_speech_model_data.cc",
+ src = "models/micro_speech_quantized.tflite",
+ out = "models/micro_speech_quantized_model_data.cc",
)
generate_cc_arrays(
name = "generated_micro_speech_model_hdr",
- src = "micro_speech.tflite",
- out = "micro_speech_model_data.h",
+ src = "models/micro_speech_quantized.tflite",
+ out = "models/micro_speech_quantized_model_data.h",
+)
+
+generate_cc_arrays(
+ name = "generated_audio_preprocessor_model_cc",
+ src = "models/audio_preprocessor_int8.tflite",
+ out = "models/audio_preprocessor_int8_model_data.cc",
+)
+
+generate_cc_arrays(
+ name = "generated_audio_preprocessor_model_hdr",
+ src = "models/audio_preprocessor_int8.tflite",
+ out = "models/audio_preprocessor_int8_model_data.h",
)
cc_library(
@@ -90,35 +118,17 @@
)
cc_library(
- name = "simple_features_test_data",
+ name = "audio_preprocessor_model_data",
srcs = [
- "simple_features/no_simple_features_data.cc",
- "simple_features/yes_simple_features_data.cc",
+ ":generated_audio_preprocessor_model_cc",
],
hdrs = [
- "simple_features/no_simple_features_data.h",
- "simple_features/yes_simple_features_data.h",
- ],
-)
-
-cc_test(
- name = "micro_speech_test",
- srcs = [
- "micro_speech_test.cc",
- ],
- deps = [
- ":micro_speech_model_data",
- "//tensorflow/lite/micro:micro_framework",
- "//tensorflow/lite/micro:micro_log",
- "//tensorflow/lite/micro:op_resolvers",
- "//tensorflow/lite/micro/examples/micro_speech/micro_features:micro_features_test_data",
- "//tensorflow/lite/micro/testing:micro_test",
- "//tensorflow/lite/schema:schema_fbs",
+ ":generated_audio_preprocessor_model_hdr",
],
)
cc_library(
- name = "audio_sample_test_data",
+ name = "audio_sample_test_data_30ms",
srcs = [
":generated_no_30ms_wav_cc",
":generated_yes_30ms_wav_cc",
@@ -130,324 +140,128 @@
)
cc_library(
- name = "audio_large_sample_test_data",
+ name = "audio_sample_test_data_1000ms",
srcs = [
":generated_no_1000ms_wav_cc",
+ ":generated_noise_1000ms_wav_cc",
+ ":generated_silence_1000ms_wav_cc",
":generated_yes_1000ms_wav_cc",
],
hdrs = [
":generated_no_1000ms_wav_hdr",
+ ":generated_noise_1000ms_wav_hdr",
+ ":generated_silence_1000ms_wav_hdr",
":generated_yes_1000ms_wav_hdr",
],
)
cc_library(
- name = "simple_features_generator_test_data",
- srcs = [
- "simple_features/no_power_spectrum_data.cc",
- "simple_features/yes_power_spectrum_data.cc",
- ],
+ name = "micro_model_settings",
hdrs = [
- "simple_features/no_power_spectrum_data.h",
- "simple_features/yes_power_spectrum_data.h",
- ],
-)
-
-cc_library(
- name = "simple_features_generator_reference",
- srcs = [
- "simple_features/simple_features_generator.cc",
- ],
- hdrs = [
- "simple_features/simple_features_generator.h",
- ],
- deps = [
- ":simple_model_settings",
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/micro:micro_log",
+ "micro_model_settings.h",
],
)
cc_test(
- name = "simple_features_generator_reference_test",
+ name = "micro_speech_test",
srcs = [
- "simple_features/simple_features_generator_test.cc",
+ "micro_speech_test.cc",
],
deps = [
- ":audio_sample_test_data",
- ":simple_features_generator_reference",
- ":simple_features_generator_test_data",
- ":simple_model_settings",
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/micro:micro_framework",
- "//tensorflow/lite/micro:micro_log",
- "//tensorflow/lite/micro/testing:micro_test",
- ],
-)
-
-cc_library(
- name = "simple_features_generator_fixed",
- srcs = [
- "simple_features/fixed_point/simple_features_generator.cc",
- ],
- hdrs = [
- "simple_features/simple_features_generator.h",
- ],
- deps = [
- ":simple_model_settings",
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/micro:micro_log",
- ],
-)
-
-cc_test(
- name = "simple_features_generator_fixed_test",
- srcs = [
- "simple_features/simple_features_generator_test.cc",
- ],
- deps = [
- ":audio_sample_test_data",
- ":simple_features_generator_fixed",
- ":simple_features_generator_test_data",
- ":simple_model_settings",
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/micro:micro_framework",
- "//tensorflow/lite/micro:micro_log",
- "//tensorflow/lite/micro/testing:micro_test",
- ],
-)
-
-cc_library(
- name = "audio_provider",
- srcs = [
- "audio_provider.cc",
- ],
- hdrs = [
- "audio_provider.h",
- ],
- deps = [
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/micro/examples/micro_speech/micro_features:micro_model_settings",
- ],
-)
-
-cc_library(
- name = "audio_provider_mock",
- srcs = [
- "audio_provider_mock.cc",
- ],
- hdrs = [
- "audio_provider.h",
- ],
- deps = [
- ":audio_large_sample_test_data",
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/micro/examples/micro_speech/micro_features:micro_model_settings",
- ],
-)
-
-cc_test(
- name = "audio_provider_test",
- srcs = [
- "audio_provider_test.cc",
- ],
- deps = [
- ":audio_provider",
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/micro:micro_framework",
- "//tensorflow/lite/micro/examples/micro_speech/micro_features:micro_model_settings",
- "//tensorflow/lite/micro/testing:micro_test",
- ],
-)
-
-cc_test(
- name = "audio_provider_mock_test",
- srcs = [
- "audio_provider_mock_test.cc",
- ],
- deps = [
- ":audio_large_sample_test_data",
- ":audio_provider_mock",
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/micro:micro_framework",
- "//tensorflow/lite/micro/examples/micro_speech/micro_features:micro_model_settings",
- "//tensorflow/lite/micro/testing:micro_test",
- ],
-)
-
-cc_library(
- name = "feature_provider",
- srcs = [
- "feature_provider.cc",
- ],
- hdrs = [
- "feature_provider.h",
- ],
- deps = [
- ":audio_provider",
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/micro:micro_log",
- "//tensorflow/lite/micro/examples/micro_speech/micro_features:micro_features_generator",
- "//tensorflow/lite/micro/examples/micro_speech/micro_features:micro_model_settings",
- ],
-)
-
-cc_test(
- name = "feature_provider_test",
- srcs = [
- "feature_provider_test.cc",
- ],
- deps = [
- ":audio_provider",
- ":feature_provider",
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/micro:micro_framework",
- "//tensorflow/lite/micro/examples/micro_speech/micro_features:micro_model_settings",
- "//tensorflow/lite/micro/testing:micro_test",
- ],
-)
-
-cc_library(
- name = "feature_provider_mock",
- srcs = [
- "feature_provider.cc",
- ],
- hdrs = [
- "feature_provider.h",
- ],
- deps = [
- ":audio_provider_mock",
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/micro:micro_log",
- "//tensorflow/lite/micro/examples/micro_speech/micro_features:micro_features_generator",
- "//tensorflow/lite/micro/examples/micro_speech/micro_features:micro_model_settings",
- ],
-)
-
-cc_test(
- name = "feature_provider_mock_test",
- size = "small",
- srcs = [
- "feature_provider_mock_test.cc",
- ],
- tags = [
- "noasan", # TODO(b/179930607): Fix with asan.
- ],
- deps = [
- ":feature_provider_mock",
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/micro:micro_framework",
- "//tensorflow/lite/micro/examples/micro_speech/micro_features:micro_features_test_data",
- "//tensorflow/lite/micro/examples/micro_speech/micro_features:micro_model_settings",
- "//tensorflow/lite/micro/testing:micro_test",
- ],
-)
-
-cc_library(
- name = "recognize_commands",
- srcs = [
- "recognize_commands.cc",
- ],
- hdrs = [
- "recognize_commands.h",
- ],
- deps = [
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/micro:micro_log",
- "//tensorflow/lite/micro/examples/micro_speech/micro_features:micro_model_settings",
- ],
-)
-
-cc_test(
- name = "recognize_commands_test",
- srcs = [
- "recognize_commands_test.cc",
- ],
- tags = [
- "no_oss", # TODO(122853023): Resolve issues and re-enable.
- ],
- deps = [
- ":recognize_commands",
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/micro:micro_framework",
- "//tensorflow/lite/micro:micro_log",
- "//tensorflow/lite/micro:test_helpers",
- "//tensorflow/lite/micro/testing:micro_test",
- ],
-)
-
-cc_library(
- name = "command_responder",
- srcs = [
- "command_responder.cc",
- ],
- hdrs = [
- "command_responder.h",
- ],
- deps = [
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/micro:micro_log",
- ],
-)
-
-cc_test(
- name = "command_responder_test",
- srcs = [
- "command_responder_test.cc",
- ],
- deps = [
- ":command_responder",
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/micro:micro_framework",
- "//tensorflow/lite/micro/testing:micro_test",
- ],
-)
-
-cc_binary(
- name = "micro_speech",
- srcs = [
- "main.cc",
- "main_functions.cc",
- "main_functions.h",
- ],
- deps = [
- ":audio_provider",
- ":command_responder",
- ":feature_provider",
+ ":audio_preprocessor_model_data",
+ ":audio_sample_test_data_1000ms",
+ ":audio_sample_test_data_30ms",
+ ":micro_model_settings",
":micro_speech_model_data",
- ":recognize_commands",
"//tensorflow/lite/micro:micro_framework",
"//tensorflow/lite/micro:micro_log",
"//tensorflow/lite/micro:op_resolvers",
- "//tensorflow/lite/micro:system_setup",
- "//tensorflow/lite/micro/examples/micro_speech/micro_features:micro_model_settings",
+ "//tensorflow/lite/micro/testing:micro_test",
"//tensorflow/lite/schema:schema_fbs",
],
)
-cc_binary(
- name = "micro_speech_mock",
- srcs = [
- "main.cc",
- "main_functions.cc",
- "main_functions.h",
+filegroup(
+ name = "samples_30ms",
+ srcs = glob(["testdata/*_30ms.wav"]),
+)
+
+filegroup(
+ name = "samples_1000ms",
+ srcs = glob(["testdata/*_1000ms.wav"]),
+)
+
+filegroup(
+ name = "models_tflite",
+ srcs = glob(["models/*.tflite"]),
+)
+
+py_binary(
+ name = "audio_preprocessor",
+ srcs = ["audio_preprocessor.py"],
+ data = [
+ ":samples_30ms",
],
+ python_version = "PY3",
+ srcs_version = "PY3",
deps = [
- ":audio_provider_mock",
- ":command_responder",
- ":feature_provider",
- ":micro_speech_model_data",
- ":recognize_commands",
- "//tensorflow/lite/micro:micro_framework",
- "//tensorflow/lite/micro:op_resolvers",
- "//tensorflow/lite/micro:system_setup",
- "//tensorflow/lite/micro/examples/micro_speech/micro_features:micro_model_settings",
- "//tensorflow/lite/schema:schema_fbs",
+ "@absl_py//absl:app",
+ "@absl_py//absl/flags",
+ requirement("numpy"),
+ requirement("tensorflow"),
+ "//python/tflite_micro:runtime",
+ "//python/tflite_micro/signal:ops",
+ "//python/tflite_micro/signal/utils:util",
],
)
-sh_test(
- name = "micro_speech_binary_mock_test",
- srcs = ["micro_speech_binary_mock_test.sh"],
- data = [":micro_speech_mock"],
+py_binary(
+ name = "evaluate",
+ srcs = ["evaluate.py"],
+ data = [
+ ":models_tflite",
+ ],
+ python_version = "PY3",
+ srcs_version = "PY3",
+ deps = [
+ ":audio_preprocessor",
+ ],
+)
+
+py_test(
+ name = "evaluate_test",
+ srcs = ["evaluate_test.py"],
+ data = [
+ ":models_tflite",
+ ":samples_1000ms",
+ ],
+ main = "evaluate_test.py",
+ python_version = "PY3",
+ tags = [
+ "noasan",
+ "nomsan", # Python doesn't like these symbols
+ "noubsan",
+ ],
+ deps = [
+ ":evaluate",
+ ],
+)
+
+py_test(
+ name = "audio_preprocessor_test",
+ srcs = ["audio_preprocessor_test.py"],
+ data = [
+ ":models_tflite",
+ ":samples_30ms",
+ ],
+ main = "audio_preprocessor_test.py",
+ python_version = "PY3",
+ tags = [
+ "noasan",
+ "nomsan", # Python doesn't like these symbols
+ "noubsan",
+ ],
+ deps = [
+ ":audio_preprocessor",
+ ],
)
diff --git a/tensorflow/lite/micro/examples/micro_speech/Makefile.inc b/tensorflow/lite/micro/examples/micro_speech/Makefile.inc
index d2ceab5..a1b5b56 100644
--- a/tensorflow/lite/micro/examples/micro_speech/Makefile.inc
+++ b/tensorflow/lite/micro/examples/micro_speech/Makefile.inc
@@ -1,299 +1,64 @@
-INCLUDES += \
- -I$(MAKEFILE_DIR)/downloads/kissfft
-
-KISSFFT_LIB_SRCS :=
-
-KISSFFT_LIB_HDRS := \
-$(MAKEFILE_DIR)/downloads/kissfft/COPYING \
-$(MAKEFILE_DIR)/downloads/kissfft/kiss_fft.c \
-$(MAKEFILE_DIR)/downloads/kissfft/kiss_fft.h \
-$(MAKEFILE_DIR)/downloads/kissfft/_kiss_fft_guts.h \
-$(MAKEFILE_DIR)/downloads/kissfft/tools/kiss_fftr.c \
-$(MAKEFILE_DIR)/downloads/kissfft/tools/kiss_fftr.h
-
MICRO_SPEECH_TEST_SRCS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_speech_test.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/no_micro_features_data.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/yes_micro_features_data.cc
+$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_speech_test.cc
MICRO_SPEECH_TEST_HDRS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/no_micro_features_data.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/yes_micro_features_data.h \
+$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_model_settings.h \
+$(TENSORFLOW_ROOT)tensorflow/lite/micro/testing/micro_test.h
-SIMPLE_FEATURES_GENERATOR_TEST_SRCS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/simple_features/simple_features_generator.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/simple_features/simple_features_generator_test.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/simple_features/no_power_spectrum_data.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/simple_features/yes_power_spectrum_data.cc
+MICRO_SPEECH_SRCS := $(MICRO_SPEECH_TEST_SRCS)
-SIMPLE_FEATURES_GENERATOR_TEST_HDRS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/simple_features/simple_features_generator.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/simple_features/no_power_spectrum_data.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/simple_features/yes_power_spectrum_data.h
-
-MICRO_FEATURES_LIB_SRCS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/fft.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/fft_util.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/filterbank.c \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/filterbank_util.c \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/frontend.c \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/frontend_util.c \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/kiss_fft_int16.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/log_lut.c \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/log_scale.c \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/log_scale_util.c \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/noise_reduction.c \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/noise_reduction_util.c \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control.c \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_util.c \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/window.c \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/window_util.c \
-$(KISSFFT_LIB_SRCS)
-
-MICRO_FEATURES_LIB_HDRS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/bits.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/fft.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/fft_util.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/filterbank.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/filterbank_util.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/frontend.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/frontend_util.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/kiss_fft_common.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/kiss_fft_int16.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/log_lut.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/log_scale.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/log_scale_util.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/noise_reduction.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/noise_reduction_util.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_util.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/window.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/experimental/microfrontend/lib/window_util.h \
-$(KISSFFT_LIB_HDRS)
-
-MICRO_FEATURES_GENERATOR_SRCS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/micro_features_generator.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.cc \
-$(MICRO_FEATURES_LIB_SRCS)
-
-MICRO_FEATURES_GENERATOR_HDRS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/micro_features_generator.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h \
-$(MICRO_FEATURES_LIB_HDRS)
-
-MICRO_FEATURES_GENERATOR_TEST_SRCS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/micro_features_generator_test.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/no_feature_data_slice.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/yes_feature_data_slice.cc \
-$(MICRO_FEATURES_GENERATOR_SRCS)
-
-MICRO_FEATURES_GENERATOR_TEST_HDRS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/no_feature_data_slice.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/yes_feature_data_slice.h \
-$(MICRO_FEATURES_GENERATOR_HDRS)
-
-AUDIO_PROVIDER_TEST_SRCS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/audio_provider_test.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/audio_provider.cc
-
-AUDIO_PROVIDER_TEST_HDRS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/audio_provider.h \
-
-AUDIO_PROVIDER_MOCK_TEST_SRCS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/audio_provider_mock_test.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/audio_provider_mock.cc
-
-AUDIO_PROVIDER_MOCK_TEST_HDRS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/audio_provider.h \
-
-FEATURE_PROVIDER_TEST_SRCS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/feature_provider_test.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/audio_provider.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/feature_provider.cc \
-$(MICRO_FEATURES_GENERATOR_SRCS)
-
-FEATURE_PROVIDER_TEST_HDRS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/audio_provider.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/feature_provider.h \
-$(MICRO_FEATURES_GENERATOR_HDRS)
-
-FEATURE_PROVIDER_MOCK_TEST_SRCS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/feature_provider_test.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/audio_provider_mock.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/feature_provider.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/no_micro_features_data.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/yes_micro_features_data.cc \
-$(MICRO_FEATURES_GENERATOR_SRCS)
-
-FEATURE_PROVIDER_MOCK_TEST_HDRS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/audio_provider.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/feature_provider.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/no_micro_features_data.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/yes_micro_features_data.h \
-$(MICRO_FEATURES_GENERATOR_HDRS)
-
-RECOGNIZE_COMMANDS_TEST_SRCS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/recognize_commands_test.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/recognize_commands.cc
-
-RECOGNIZE_COMMANDS_TEST_HDRS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/recognize_commands.h
-
-COMMAND_RESPONDER_TEST_SRCS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/command_responder_test.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/command_responder.cc
-
-COMMAND_RESPONDER_TEST_HDRS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/command_responder.h
-
-MICRO_SPEECH_SRCS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/main.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/main_functions.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/audio_provider.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/feature_provider.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/no_micro_features_data.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/yes_micro_features_data.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/recognize_commands.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/command_responder.cc \
-$(MICRO_FEATURES_GENERATOR_SRCS)
-
-MICRO_SPEECH_HDRS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/audio_provider.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/feature_provider.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/no_micro_features_data.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/yes_micro_features_data.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/recognize_commands.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/command_responder.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/main_functions.h \
-$(MICRO_FEATURES_GENERATOR_HDRS)
-
-MICRO_SPEECH_MOCK_SRCS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/main.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/main_functions.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/audio_provider_mock.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/feature_provider.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/no_micro_features_data.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/yes_micro_features_data.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/recognize_commands.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/command_responder.cc \
-$(MICRO_FEATURES_GENERATOR_SRCS)
-
-MICRO_SPEECH_MOCK_HDRS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/audio_provider.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/feature_provider.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/micro_features_generator.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/no_micro_features_data.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_features/yes_micro_features_data.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/recognize_commands.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/command_responder.h \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/main_functions.h \
-$(MICRO_FEATURES_GENERATOR_HDRS)
+MICRO_SPEECH_HDRS := $(MICRO_SPEECH_TEST_HDRS)
MICRO_SPEECH_GENERATOR_INPUTS := \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_speech.tflite \
+$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/models/micro_speech_quantized.tflite \
+$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/models/audio_preprocessor_int8.tflite \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/no_1000ms.wav \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/no_30ms.wav \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/yes_1000ms.wav \
+$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/silence_1000ms.wav \
+$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/noise_1000ms.wav \
+$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/no_30ms.wav \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/yes_30ms.wav
MICRO_SPEECH_GENERATED_SRCS := \
-$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_speech_model_data.cc \
+$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/models/micro_speech_quantized_model_data.cc \
+$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/models/audio_preprocessor_int8_model_data.cc \
$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/no_1000ms_audio_data.cc \
-$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/no_30ms_audio_data.cc \
$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/yes_1000ms_audio_data.cc \
+$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/silence_1000ms_audio_data.cc \
+$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/noise_1000ms_audio_data.cc \
+$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/no_30ms_audio_data.cc \
$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/yes_30ms_audio_data.cc
MICRO_SPEECH_GENERATED_HDRS := \
-$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/micro_speech_model_data.h \
+$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/models/micro_speech_quantized_model_data.h \
+$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/models/audio_preprocessor_int8_model_data.h \
$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/no_1000ms_audio_data.h \
-$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/no_30ms_audio_data.h \
$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/yes_1000ms_audio_data.h \
+$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/silence_1000ms_audio_data.h \
+$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/noise_1000ms_audio_data.h \
+$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/no_30ms_audio_data.h \
$(GENERATED_SRCS_DIR)$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/testdata/yes_30ms_audio_data.h
-#Find any platform - specific rules for this example.
-include $(wildcard $(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/*/Makefile.inc)
-
# TODO(b/161489252): Disabling warnings for this example until we have a better
# way to build third_party code with a reduced list of CFLAGS.
+# Xtensa hifi5/hifi4 xa_nnlib
CCFLAGS := $(filter-out $(CC_WARNINGS),$(CCFLAGS))
-# Test the code for feature generation.
-ifneq ($(TARGET_ARCH), $(filter $(TARGET_ARCH), hifi5 hifi3z))
- $(eval $(call microlite_test,micro_features_generator_test,\
- $(MICRO_FEATURES_GENERATOR_TEST_SRCS),$(MICRO_FEATURES_GENERATOR_TEST_HDRS),$(MICRO_SPEECH_GENERATOR_INPUTS)))
-endif
-
# Tests loading and running a speech model.
$(eval $(call microlite_test,micro_speech_test,\
$(MICRO_SPEECH_TEST_SRCS),$(MICRO_SPEECH_TEST_HDRS),$(MICRO_SPEECH_GENERATOR_INPUTS)))
-# TODO(b/268568089): This test is taking very long time to finish; causing the
-# CI to run for a long time to finish.
-ifneq ($(TARGET_ARCH), $(filter $(TARGET_ARCH), hifimini hifi5 hifi3z))
- # Test the code for feature generation.
- $(eval $(call microlite_test,simple_features_generator_test,\
- $(SIMPLE_FEATURES_GENERATOR_TEST_SRCS),$(SIMPLE_FEATURES_GENERATOR_TEST_HDRS),$(MICRO_SPEECH_GENERATOR_INPUTS)))
-endif
-
-# Tests the audio provider module.
-$(eval $(call microlite_test,audio_provider_test,\
-$(AUDIO_PROVIDER_TEST_SRCS),$(AUDIO_PROVIDER_TEST_HDRS)))
-
-# Tests the audio provider mock module.
-$(eval $(call microlite_test,audio_provider_mock_test,\
-$(AUDIO_PROVIDER_MOCK_TEST_SRCS),$(AUDIO_PROVIDER_MOCK_TEST_HDRS),$(MICRO_SPEECH_GENERATOR_INPUTS)))
-
-# Tests the feature provider module.
-ifneq ($(TARGET_ARCH), hifi3z)
- $(eval $(call microlite_test,feature_provider_test,\
- $(FEATURE_PROVIDER_TEST_SRCS),$(FEATURE_PROVIDER_TEST_HDRS)))
-endif
-
-# Tests the feature provider module using the mock audio provider.
-ifneq ($(TARGET_ARCH), hifi3z)
- $(eval $(call microlite_test,feature_provider_mock_test,\
- $(FEATURE_PROVIDER_MOCK_TEST_SRCS),$(FEATURE_PROVIDER_MOCK_TEST_HDRS),$(MICRO_SPEECH_GENERATOR_INPUTS)))
-endif
-
-# Tests the command recognizer module.
-$(eval $(call microlite_test,recognize_commands_test,\
-$(RECOGNIZE_COMMANDS_TEST_SRCS),$(RECOGNIZE_COMMANDS_TEST_HDRS)))
-
-# Tests responding to a command.
-$(eval $(call microlite_test,command_responder_test,\
-$(COMMAND_RESPONDER_TEST_SRCS),$(COMMAND_RESPONDER_TEST_HDRS)))
-
-# Builds a standalone speech command recognizer binary.
+# Builds a standalone binary.
$(eval $(call microlite_test,micro_speech,\
$(MICRO_SPEECH_SRCS),$(MICRO_SPEECH_HDRS),$(MICRO_SPEECH_GENERATOR_INPUTS)))
-# Builds a standalone speech command recognizer binary using fake audio input.
-$(eval $(call microlite_test,micro_speech_mock,\
-$(MICRO_SPEECH_MOCK_SRCS),$(MICRO_SPEECH_MOCK_HDRS),$(MICRO_SPEECH_GENERATOR_INPUTS)))
-
# Add sources and headers generated from $(MICRO_SPEECH_GENERATOR_INPUTS).
MICRO_SPEECH_SRCS += $(MICRO_SPEECH_GENERATED_SRCS)
MICRO_SPEECH_HDRS += $(MICRO_SPEECH_GENERATED_HDRS)
-MICRO_SPEECH_MOCK_SRCS += $(MICRO_SPEECH_GENERATED_SRCS)
-MICRO_SPEECH_MOCK_HDRS += $(MICRO_SPEECH_GENERATED_HDRS)
-
list_micro_speech_example_sources:
@echo $(MICRO_SPEECH_SRCS)
list_micro_speech_example_headers:
@echo $(MICRO_SPEECH_HDRS)
-
-list_micro_speech_mock_example_sources:
- @echo $(MICRO_SPEECH_MOCK_SRCS)
-
-list_micro_speech_mock_example_headers:
- @echo $(MICRO_SPEECH_MOCK_HDRS)
diff --git a/tensorflow/lite/micro/examples/micro_speech/README.md b/tensorflow/lite/micro/examples/micro_speech/README.md
index 8a4aa77..84aaec1 100644
--- a/tensorflow/lite/micro/examples/micro_speech/README.md
+++ b/tensorflow/lite/micro/examples/micro_speech/README.md
@@ -2,321 +2,275 @@
# Micro Speech Example
-This example shows how to run a 20 kB model that can recognize 2 keywords,
-"yes" and "no", from speech data.
+This example shows how to run inference using TensorFlow Lite Micro (TFLM)
+on two models for wake-word recognition.
+The first model is an audio preprocessor that generates spectrogram data
+from raw audio samples.
+The second is the Micro Speech model, a less than 20 kB model
+that can recognize 2 keywords, "yes" and "no", from speech data.
+The Micro Speech model takes the spectrogram data as input and produces
+category probabilities.
-The application listens to its surroundings with a microphone and indicates
-when it has detected a word by lighting an LED or displaying data on a
-screen, depending on the capabilities of the device.
-
-
-
-The code has a small footprint (for example, around 22 kilobytes on a Cortex
-M3) and only uses about 10 kilobytes of RAM for working memory, so it's able to
-run on systems like an STM32F103 with only 20 kilobytes of total SRAM and 64
-kilobytes of Flash.
## Table of contents
-- [Deploy to STM32F746](#deploy-to-STM32F746)
-- [Deploy to NXP FRDM K66F](#deploy-to-nxp-frdm-k66f)
-- [Deploy to CEVA BX1/SP500](#deploy-to-ceva-bx1)
-- [Run on macOS](#run-on-macos)
-- [Run the tests on a development machine](#run-the-tests-on-a-development-machine)
+- [Audio Preprocessor](#audio-preprocessor)
+- [Micro Speech Model Architecture](#micro-speech-model-architecture)
+- [Run the C++ tests on a development machine](#run-the-c-tests-on-a-development-machine)
+- [Run the evaluate.py script on a development machine](#run-the-evaluatepy-script-on-a-development-machine)
+- [Run the evaluate_test.py script on a development machine](#run-the-evaluate_testpy-script-on-a-development-machine)
+- [Converting models or audio samples to C++](#converting-models-or-audio-samples-to-c)
- [Train your own model](#train-your-own-model)
-## Deploy to STM32F746
+## Audio Preprocessor
-The following instructions will help you build and deploy the example to the
-[STM32F7 discovery kit](https://os.mbed.com/platforms/ST-Discovery-F746NG/)
-using [ARM Mbed](https://github.com/ARMmbed/mbed-cli).
+The Audio Preprocessor model converts raw audio samples into a spectrographic feature.
+Audio samples are input to the model in windowed frames, each window overlapping
+the previous. When sufficient features have been accumulated, those features can
+be provided as input to the Micro Speech model.
-Before we begin, you'll need the following:
+This model provides a replication of the legacy preprocessing used during training
+of the Micro Speech model. For additional information on audio preprocessing during training,
+please refer to the [training README](train/README.md#preprocessing-speech-input) documentation.
-- STM32F7 discovery kit board
-- Mini-USB cable
-- ARM Mbed CLI ([installation instructions](https://os.mbed.com/docs/mbed-os/v6.9/quick-start/build-with-mbed-cli.html). Check it out for MacOS Catalina - [mbed-cli is broken on MacOS Catalina #930](https://github.com/ARMmbed/mbed-cli/issues/930#issuecomment-660550734))
-- Python 3 and pip3
+Audio Preprocessing models providing `int8` and `float32` output, ready for use
+with the Micro Speech model, are provided in the [models](models/) directory.
+These models expect the audio input to conform to:
+* 30ms window frame
+* 20ms window stride
+* 16KHz sample rate
+* 16-bit signed PCM data
+* single channel (mono)
-Since Mbed requires a special folder structure for projects, we'll first run a
-command to generate a subfolder containing the required source files in this
-structure:
+### Model Architecture
-```
-make -f tensorflow/lite/micro/tools/make/Makefile TARGET=disco_f746ng OPTIMIZED_KERNEL_DIR=cmsis_nn generate_micro_speech_mbed_project
-```
+This model consists primarily of [Signal Library](https://github.com/tensorflow/tflite-micro/blob/main/python/tflite_micro/signal) operations.
+The library is a set of Python methods, and bindings to `C++` library code.
+To allow for use with the `TFLM MicroInterpreter`, a set of [Signal Library kernels](https://github.com/tensorflow/tflite-micro/blob/main/signal/micro/kernels)
+is also provided.
-Running the make command will result in the creation of a new folder:
+The [audio_preprocessor.py](audio_preprocessor.py) script provides a complete example
+of how to use the `Signal Library` within your own Python application. This script
+has support for TensorFlow eager-execution mode, graph-execution mode, and
+`TFLM MicroInterpreter` inference operations.
-```
-gen/disco_f746ng_cortex-m4_default/prj/micro_speech/mbed
-```
+[<img src="images/audio_preprocessor_int8.png" width="900" alt="model architecture"/>](images/audio_preprocessor_int8.png)
-This folder contains all of the example's dependencies structured in the correct
-way for Mbed to be able to build it.
+*This image was derived from visualizing the 'models/audio_preprocessor_int8.tflite' file in
+[Netron](https://github.com/lutzroeder/netron)*
-Change into the directory and run the following commands.
+Each of the steps performed by the model are outlined as follows:
+1) Audio frame input with shape `(1, 480)`
+1) Apply `Hann Window` smoothing using `SignalWindow`
+1) Reshape tensor to match the input of `SignalFftAutoScale`
+1) Rescale tensor data using `SignalFftAutoScale` and calculate one of the input
+parameters to `SignalFilterBankSquareRoot`
+1) Compute FFT using `SignalRfft`
+1) Compute power spectrum using `SignalEnergy`. The tensor data is only updated
+for elements between `[start_index, end_index)`.
+1) The `Cast`, `StridedSlice`, and `Concatenation` operations are used to fill
+the tensor data with zeros, for elements outside of `[start_index, end_index)`
+1) Compress the power spectrum tensor data into just 40 channels (frequency bands)
+using `SignalFilterBank`
+1) Scale down the tensor data using `SignalFilterBankSquareRoot`
+1) Apply noise reduction using `SignalFilterBankSpectralSubtraction`
+1) Apply gain control using `SignalPCAN`
+1) Scale down the tensor data using `SignalFilterBankLog`
+1) The remaining operations perform additional legacy down-scaling and convert
+the tensor data to `int8`
+1) Model output has shape `(40,)`
-First, tell Mbed that the current directory is the root of an Mbed project:
+### The `FeatureParams` Python Class
-```
-mbed config root .
-```
+The `FeatureParams` class is located within the [audio_preprocessor.py](audio_preprocessor.py#L260)
+script. This class allows for custom configuration of the `AudioPreprocessor` class.
+Parameters such as sample rate, window size, window stride, number of output channels,
+and many more can be configured. The parameters to be changed must be set during
+class instantiation, and are frozen thereafter. The defaults for `FeatureParams`
+match those of the legacy audio preprocessing used during Micro Speech model training.
-Next, tell Mbed to download the dependencies and prepare to build:
+### The `AudioPreprocessor` Python Class
-```
-mbed deploy
-```
+The `AudioPreprocessor` class in the [audio_preprocessor.py](audio_preprocessor.py#L338)
+script provides easy to use convenience methods for creating
+and using an audio preprocessing model. This class is configured through use of
+a `FeatureParams` object, allowing some flexibility in how the audio preprocessing
+model works.
-Older versions of Mbed will build the project using C++98. However, TensorFlow Lite
-requires C++11. If needed, run the following Python snippet to modify the Mbed
-configuration files so that it uses C++11:
+A short summary of the available methods and properties:
+* `load_samples`: load audio samples from a `WAV` format file and prepare
+the samples for use by other `AudioPreprocessor` methods
+* `samples`: tensor containing previously loaded audio samples
+* `params`: the `FeatureParams` object the class was instantiated with
+* `generate_feature`: generate a single feature using TensorFlow eager-execution
+* `generate_feature_using_graph`: generate a single feature using TensorFlow graph-execution
+* `generate_feature_using_tflm`: generate a single feature using the `TFLM MicroInterpreter`
+* `reset_tflm`: reset the internal state of the `TFLM MicroInterpreter` and the
+`Signal Library` operations
+* `generate_tflite_file`: create a `.tflite` format file for the preprocessor model
-```
-python -c 'import fileinput, glob;
-for filename in glob.glob("mbed-os/tools/profiles/*.json"):
- for line in fileinput.input(filename, inplace=True):
- print(line.replace("\"-std=gnu++98\"","\"-std=c++11\", \"-fpermissive\""))'
-```
+### Run the audio_preprocessor.py script on a development machine
-Note: Mbed has a dependency to an old version of arm_math.h and cmsis_gcc.h (adapted from the general [CMSIS-NN MBED example](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/kernels/cmsis_nn#example-2---mbed)). Therefore you need to copy the newer version as follows:
+The [audio_preprocessor.py](audio_preprocessor.py#L532) script generates a `.tflite`
+file for the preprocessing model, ready for use with the Micro Speech model.
+
+To generate a `.tflite` model file with `int8` output:
```bash
-cp tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/DSP/Include/\
-arm_math.h mbed-os/cmsis/TARGET_CORTEX_M/arm_math.h
-cp tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/Core/Include/\
-cmsis_gcc.h mbed-os/cmsis/TARGET_CORTEX_M/cmsis_gcc.h
+bazel build tensorflow/lite/micro/examples/micro_speech:audio_preprocessor
+bazel-bin/tensorflow/lite/micro/examples/micro_speech/audio_preprocessor --output_type=int8
```
-Finally, run the following command to compile:
-
-```
-mbed compile -m DISCO_F746NG -t GCC_ARM
+To generate a `.tflite` model file with `float32` output:
+```bash
+bazel build tensorflow/lite/micro/examples/micro_speech:audio_preprocessor
+bazel-bin/tensorflow/lite/micro/examples/micro_speech/audio_preprocessor --output_type=float32
```
-This should result in a binary at the following path:
+### Run the audio_preprocessor_test.py script on a development machine
-```
-./BUILD/DISCO_F746NG/GCC_ARM/mbed.bin
+The [audio_preprocessor_test.py](audio_preprocessor_test.py) script performs
+several tests to ensure correct inference operations occur across all execution modes.
+The tests are:
+* cross-check inference results between eager, graph, and `TFLM MicroInterpreter`
+execution modes
+* check the `yes` and `no` 30ms samples in the [testdata](testdata/) directory for
+correct generation of the feature tensor
+* compare the preprocessor `int8` model against the same model in the [models](models/) directory
+* compare the preprocessor `float32` model against the same model in the [models](models/) directory
+
+```bash
+bazel build tensorflow/lite/micro/examples/micro_speech:audio_preprocessor_test
+bazel-bin/tensorflow/lite/micro/examples/micro_speech/audio_preprocessor_test
```
-To deploy, plug in your STM board and copy the file to it. On macOS, you can do
-this with the following command:
+## Micro Speech Model Architecture
-```
-cp ./BUILD/DISCO_F746NG/GCC_ARM/mbed.bin /Volumes/DIS_F746NG/
+This is a simple model comprised of a Convolutional 2D layer, a Fully Connected
+Layer or a MatMul Layer (output: logits) and a Softmax layer
+(output: probabilities) as shown below. Refer to the [`tiny_conv`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/speech_commands/models.py#L673)
+model architecture. The output probabilities are in four categories:
+`silence`, `unknown`, `yes`, `no`.
+
+The input to the model is 49 spectrographic features, each feature
+consisting of 40 channels of data. The features are generated by the
+Audio Preprocessor model. For more information, please see the
+[training README](train/README.md#preprocessing-speech-input) documentation.
+
+[<img src="images/micro_speech_quantized.png" width="900" alt="model architecture"/>](images/micro_speech_quantized.png)
+
+*This image was derived from visualizing the 'models/micro_speech_quantized.tflite' file in
+[Netron](https://github.com/lutzroeder/netron)*
+
+## Run the C++ tests on a development machine
+
+To compile and test this example on a desktop Linux or macOS machine, download the
+[TFLM source code](https://github.com/tensorflow/tflite-micro). Then switch
+into the source directory from a terminal using the `cd` command.
+
+Compile and run a native binary using Bazel:
+```bash
+bazel run tensorflow/lite/micro/examples/micro_speech:micro_speech_test
```
-Copying the file will initiate the flashing process.
-
-The inference results are logged by the board while the program is running.
-To view it, establish a serial connection to the board
-using a baud rate of `9600`. On OSX and Linux, the following command should
-work, replacing `/dev/tty.devicename` with the name of your device as it appears
-in `/dev`:
-
-```
-screen /dev/tty.devicename 9600
-```
-
-You will see a line output for every word that is detected:
-
-```
-Heard yes (201) @4056ms
-Heard no (205) @6448ms
-Heard unknown (201) @13696ms
-Heard yes (205) @15000ms
-```
-
-The number after each detected word is its score. By default, the program only
-considers matches as valid if their score is over 200, so all of the scores you
-see will be at least 200.
-
-To stop viewing the debug output with `screen`, hit `Ctrl+A`, immediately
-followed by the `K` key, then hit the `Y` key.
-
-## Deploy to NXP FRDM K66F
-
-The following instructions will help you build and deploy the example to the
-[NXP FRDM K66F](https://www.nxp.com/design/development-boards/freedom-development-boards/mcu-boards/freedom-development-platform-for-kinetis-k66-k65-and-k26-mcus:FRDM-K66F)
-using [ARM Mbed](https://github.com/ARMmbed/mbed-cli).
-
-1. Download
- [the TensorFlow source code](https://github.com/tensorflow/tensorflow).
-2. Follow instructions from
- [mbed website](https://os.mbed.com/docs/mbed-os/v5.13/tools/installation-and-setup.html)
- to setup and install mbed CLI.
-3. Compile TensorFlow with the following command to generate mbed project:
-
- ```
- make -f tensorflow/lite/micro/tools/make/Makefile TARGET=mbed TAGS="nxp_k66f" generate_micro_speech_mbed_project
- ```
-
-4. Change into the following directory that has been generated:
- `gen/mbed_cortex-m4/prj/micro_speech/mbed`
-
-5. Create an Mbed project using the generated files, run ensuring your
- environment is using Python 2.7: `mbed config root .`
-
-6. Next, tell Mbed to download the dependencies and prepare to build: `mbed
- deploy`
-
-7. Finally, we can run the following command to compile the code: `mbed compile
- -m K66F -t GCC_ARM`
-
-8. For some Mbed compilers (such as GCC), you may get compile error in
- mbed_rtc_time.cpp. Go to `mbed-os/platform/mbed_rtc_time.h` and comment line
- 32 and line 37:
-
- ```
- //#if !defined(__GNUC__) || defined(__CC_ARM) || defined(__clang__)
- struct timeval {
- time_t tv_sec;
- int32_t tv_usec;
- };
- //#endif
- ```
-
-9. If your system does not recognize the board with the `mbed detect` command.
- Follow the instructions for setting up
- [DAPLink](https://armmbed.github.io/DAPLink/?board=FRDM-K66F) for the
- [K66F](https://os.mbed.com/platforms/FRDM-K66F/).
-
-10. Connect the USB cable to the micro USB port. When the Ethernet port is
- facing towards you, the micro USB port is left of the Ethernet port.
-
-11. To compile and flash in a single step, add the `--flash` option:
-
- ```
- mbed compile -m K66F -t GCC_ARM --flash
- ```
-
-12. Disconnect USB cable from the device to power down the device and connect
- back the power cable to start running the model.
-
-13. Connect to serial port with baud rate of 9600 and correct serial device to
- view the output from the MCU. In linux, you can run the following screen
- command if the serial device is `/dev/ttyACM0`:
-
- ```
- sudo screen /dev/ttyACM0 9600
- ```
-
-14. Saying "Yes" will print "Yes" and "No" will print "No" on the serial port.
-
-15. A loopback path from microphone to headset jack is enabled. Headset jack is
- in black color. If there is no output on the serial port, you can connect
- headphone to headphone port to check if audio loopback path is working.
-
-## Deploy to CEVA-BX1
-
-The following instructions will help you build and deploy the sample to the
-[CEVA-BX1](https://www.ceva-dsp.com/product/ceva-bx1-sound/) or [CEVA-SP500](https://www.ceva-dsp.com/product/ceva-senspro/)
-
-1. Contact CEVA at [sales@ceva-dsp.com](mailto:sales@ceva-dsp.com)
-2. For BX1:
-2.1. Download and install CEVA-BX Toolbox v18.0.2
-2.2. Set the TARGET_TOOLCHAIN_ROOT variable in
- /tensorflow/lite/micro/tools/make/templates/ceva_bx1/ceva_app_makefile.tpl
- To your installation location. For example: TARGET_TOOLCHAIN_ROOT :=
- /home/myuser/work/CEVA-ToolBox/V18/BX
-2.3. Generate the Makefile for the project: /tensorflow$ make -f
- tensorflow/lite/micro/tools/make/Makefile TARGET=ceva TARGET_ARCH=CEVA_BX1
- generate_micro_speech_make_project
-3. For SensPro (SP500):
-3.1. Download and install CEVA-SP Toolbox v20
-3.2. Set the TARGET_TOOLCHAIN_ROOT variable in
- /tensorflow/lite/micro/tools/make/templates/ceva_SP500/ceva_app_makefile.tpl
- To your installation location. For example: TARGET_TOOLCHAIN_ROOT :=
- /home/myuser/work/CEVA-ToolBox/V20/SensPro
-3.3. Generate the Makefile for the project: /tensorflow$ make -f
- tensorflow/lite/micro/tools/make/Makefile TARGET=ceva TARGET_ARCH=CEVA_SP500
- generate_micro_speech_make_project
-5. Build the project:
- /gen/ceva_bx1/prj/micro_speech/make$ make
-6. This should build the project and create a file called micro_speech.elf.
-7. The supplied configuration reads input from a files and expects a file
- called input.wav (easily changed in audio_provider.cc) to be placed in the
- same directory of the .elf file
-8. We used Google's speech command dataset: V0.0.2:
- http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz V0.0.1:
- http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz
-9. Follow CEVA Toolbox instructions for creating a debug target and running the
- project.
-10. Output should look like: Heard silence (208) @352ms Heard no (201) @1696ms
- Heard yes (203) @3904ms
-
-## Run on macOS
-
-The example contains an audio provider compatible with macOS. If you have access
-to a Mac, you can run the example on your development machine.
-
-First, use the following command to build it:
-
-```
-make -f tensorflow/lite/micro/tools/make/Makefile micro_speech
-```
-
-Once the build completes, you can run the example with the following command:
-
-```
-gen/osx_x86_64/bin/micro_speech
-```
-
-You might see a pop-up asking for microphone access. If so, grant it, and the
-program will start.
-
-Try saying "yes" and "no". You should see output that looks like the following:
-
-```
-Heard yes (201) @4056ms
-Heard no (205) @6448ms
-Heard unknown (201) @13696ms
-Heard yes (205) @15000ms
-Heard yes (205) @16856ms
-Heard unknown (204) @18704ms
-Heard no (206) @21000ms
-```
-
-The number after each detected word is its score. By default, the recognize
-commands component only considers matches as valid if their score is over 200,
-so all of the scores you see will be at least 200.
-
-The number after the score is the number of milliseconds since the program was
-started.
-
-If you don't see any output, make sure your Mac's internal microphone is
-selected in the Mac's *Sound* menu, and that its input volume is turned up high
-enough.
-
-## Run the tests on a development machine
-
-To compile and test this example on a desktop Linux or macOS machine, download
-[the TensorFlow source code](https://github.com/tensorflow/tensorflow), `cd`
-into the source directory from a terminal, and then run the following command:
-
-```
+For a native binary using `make`, run the following command:
+```bash
make -f tensorflow/lite/micro/tools/make/Makefile test_micro_speech_test
```
+For an Arm Cortex-M0 binary running in the QEMU emulator:
+```bash
+make -f tensorflow/lite/micro/tools/make/Makefile TARGET=cortex_m_qemu TARGET_ARCH=cortex-m0 OPTIMIZED_KERNEL_DIR=cmsis_nn BUILD_TYPE=default test_micro_speech_test
+```
+
This will take a few minutes, and downloads frameworks the code uses like
[CMSIS](https://developer.arm.com/embedded/cmsis) and
[flatbuffers](https://google.github.io/flatbuffers/). Once that process has
finished, you should see a series of files get compiled, followed by some
logging output from a test, which should conclude with `~~~ALL TESTS PASSED~~~`.
-If you see this, it means that a small program has been built and run that loads
-the trained TensorFlow model, runs some example inputs through it, and got the
+If you see this, it means that a small program has been built and executed that loads
+the trained TensorFlow Lite model, runs some example inputs through it, and got the
expected outputs.
-To understand how TensorFlow Lite does this, you can look at the source in
-[micro_speech_test.cc](micro_speech_test.cc).
-It's a fairly small amount of code that creates an interpreter, gets a handle to
-a model that's been compiled into the program, and then invokes the interpreter
-with the model and sample inputs.
+To understand how TFLM does this, you can look at the source in the
+[micro_speech_test.cc](micro_speech_test.cc) file.
+It's a fairly small amount of code that executes the following steps:
+1) Create a `TFLM MicroInterpreter` with a handle to the Audio Preprocessor model
+that has been compiled into the program
+1) Repeatedly execute inference operations using `MicroInterpreter::invoke`,
+with audio samples as input, and spectrogram features as output
+1) Create a new `TFLM MicroInterpreter` with a handle to the Micro Speech model
+that has been compiled into the program
+1) Execute a single inference operation using `MicroInterpreter::invoke`,
+with the spectrogram features as input, and category probabilities as output
+1) Check the largest category probability for a match with the speech sample label.
+
+## Run the evaluate.py script on a development machine
+The [evaluate.py](evaluate.py#L166) script predicts the category of a single audio sample
+given by the `sample_path` argument. The output consists of the predictions for
+the accumulated spectrogram features across (at most) 49 audio sample window frames.
+
+```bash
+bazel build tensorflow/lite/micro/examples/micro_speech:evaluate
+bazel-bin/tensorflow/lite/micro/examples/micro_speech/evaluate --sample_path=tensorflow/lite/micro/examples/micro_speech/testdata/no_1000ms.wav
+```
+
+The output looks like this:
+```bash
+Frame #0: [0.0000, 0.0273, 0.0312, 0.9414]
+Frame #1: [0.0000, 0.0273, 0.0312, 0.9414]
+Frame #2: [0.0000, 0.0273, 0.0312, 0.9414]
+Frame #3: [0.0000, 0.0273, 0.0273, 0.9414]
+Frame #4: [0.0000, 0.0273, 0.0273, 0.9414]
+Frame #5: [0.0000, 0.0273, 0.0273, 0.9414]
+Frame #6: [0.0000, 0.0273, 0.0273, 0.9453]
+Frame #7: [0.0000, 0.0273, 0.0273, 0.9453]
+Frame #8: [0.0000, 0.0273, 0.0273, 0.9453]
+
+...
+
+Frame #40: [0.0000, 0.0312, 0.0000, 0.9648]
+Frame #41: [0.0000, 0.0273, 0.0000, 0.9727]
+Frame #42: [0.0000, 0.0312, 0.0000, 0.9688]
+Frame #43: [0.0000, 0.0273, 0.0000, 0.9727]
+Frame #44: [0.0000, 0.0273, 0.0000, 0.9727]
+Frame #45: [0.0000, 0.0352, 0.0000, 0.9648]
+Frame #46: [0.0000, 0.0391, 0.0000, 0.9609]
+Frame #47: [0.0000, 0.0469, 0.0000, 0.9531]
+Frame #48: [0.0000, 0.0547, 0.0000, 0.9453]
+Model predicts the audio sample as <no> with probability 0.95
+```
+
+## Run the evaluate_test.py script on a development machine
+The [evaluate_test.py](evaluate_test.py) script verifies the combination of the
+Audio Preprocessor model and the Micro Speech model to generate correct inference results.
+Four audio samples from the [testdata](testdata/) directory are used as input to
+the Audio Preprocessor model.
+The Audio Preprocessor model is tested with both `int8` and `float32` outputs.
+The results of the audio preprocessing are then used to check predictions by the
+Micro Speech model.
+
+```bash
+bazel build tensorflow/lite/micro/examples/micro_speech:evaluate_test
+bazel-bin/tensorflow/lite/micro/examples/micro_speech/evaluate_test
+```
+
+## Converting models or audio samples to C++
+A tool is available to convert your custom model or audio samples into `C++` data
+structures that you can then use in your own wake-word application.
+Keep in mind that audio samples for use with Audio Preprocessor and Micro Speech models
+must be 1000ms in length, 16-bit PCM samples, and single channel (mono).
+The tool can be found here: [generate_cc_arrays.py](../../tools/generate_cc_arrays.py)
+
+The following commands show how to use the tool:
+```bash
+bazel build tensorflow/lite/micro/tools:generate_cc_arrays
+bazel-bin/tensorflow/lite/micro/tools/generate_cc_arrays /tmp/data.cc path_to_custom_sample.wav
+bazel-bin/tensorflow/lite/micro/tools/generate_cc_arrays /tmp/header.h path_to_custom_sample.wav
+```
## Train your own model
So far you have used an existing trained model to run inference on
microcontrollers. If you wish to train your own model, follow the instructions
-given in the [train/](train/) directory.
+given in the [train](train/README.md) directory.
diff --git a/tensorflow/lite/micro/examples/micro_speech/audio_preprocessor.py b/tensorflow/lite/micro/examples/micro_speech/audio_preprocessor.py
new file mode 100644
index 0000000..0379ec6
--- /dev/null
+++ b/tensorflow/lite/micro/examples/micro_speech/audio_preprocessor.py
@@ -0,0 +1,551 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Audio Sample Preprocessor
+
+When this module is run, feature generation models are created in the .tflite
+format.
+
+Run:
+bazel build tensorflow/lite/micro/examples/micro_speech:audio_preprocessor
+bazel-bin/tensorflow/lite/micro/examples/micro_speech/audio_preprocessor
+"""
+
+from __future__ import annotations
+from pathlib import Path
+from dataclasses import dataclass
+import tempfile
+
+from absl import app
+from absl import flags
+
+import tensorflow as tf
+from tensorflow.python.platform import resource_loader
+from tflite_micro.python.tflite_micro.signal.ops import window_op
+from tflite_micro.python.tflite_micro.signal.ops import fft_ops
+from tflite_micro.python.tflite_micro.signal.ops import energy_op
+from tflite_micro.python.tflite_micro.signal.ops import filter_bank_ops
+from tflite_micro.python.tflite_micro.signal.ops import pcan_op
+from tflite_micro.python.tflite_micro import runtime
+
+_ENABLE_DEBUG = flags.DEFINE_enum(
+ 'debug_mode',
+ 'off',
+ ['off', 'all'],
+ 'Enable debug output',
+)
+
+_FILE_TO_TEST = flags.DEFINE_enum('file_to_test', 'no', ['no', 'yes'],
+ 'File to test')
+
+_OUTPUT_TYPE = flags.DEFINE_enum(
+ 'output_type', 'int8', ['int8', 'float32'],
+ 'Type of TfLite output file (.tflite) to generate')
+
+
+def _debug_print(*args):
+ if _ENABLE_DEBUG.value != 'off':
+ print(*args)
+
+
+class _GenerateFeature(tf.Module):
+ """Generate feature tensor from audio window samples"""
+
+ def __init__(self, name: str, params: FeatureParams, detail: str):
+ super().__init__(name=name)
+ self._params = params
+ window_sample_count: int = int(params.window_size_ms * params.sample_rate /
+ 1000)
+ hann_window_weights = window_op.hann_window_weights(
+ window_sample_count, params.window_scaling_bits)
+ self._hann_window_weights_tensor = tf.constant(hann_window_weights,
+ name='hann_window_weights')
+ self._fft_size, self._fft_size_log2 = fft_ops.get_pow2_fft_length(
+ window_sample_count)
+ self._filter_bank_index_start, self._filter_bank_index_end = \
+ filter_bank_ops.calc_start_end_indices(
+ self._fft_size,
+ params.sample_rate,
+ params.filter_bank_number_of_channels,
+ params.filter_bank_lower_band_limit_hz,
+ params.filter_bank_upper_band_limit_hz)
+ self._detail = detail
+
+ def generate_feature_for_frame(self, audio_frame: tf.Tensor) -> tf.Tensor:
+ # Graph execution does not handle global variables. Instead, capture the
+ # global variable(s) within a closure (_debug_print_internal).
+ def _debug_print_internal(*args):
+ if _ENABLE_DEBUG.value != 'off' and tf.executing_eagerly():
+ print(*args)
+
+ _debug_print('*** generate_feature_for_frame ***')
+ params = self._params
+ detail = self._detail
+
+ # update filter_bank_ops constants
+ filter_bank_ops.FILTER_BANK_WEIGHT_SCALING_BITS = \
+ params.filter_bank_scaling_bits
+ filter_bank_ops.FILTER_BANK_ALIGNMENT = params.filter_bank_alignment
+ filter_bank_ops.FILTER_BANK_CHANNEL_BLOCK_SIZE = \
+ params.filter_bank_channel_block_size
+
+ _debug_print_internal(f'audio frame output [{detail}]: {audio_frame!r}')
+
+ # apply window to audio frame
+ weights = self._hann_window_weights_tensor
+ _debug_print_internal(f'window weights output [{detail}]: {weights!r}')
+ window_output: tf.Tensor = window_op.window(audio_frame, weights,
+ params.window_scaling_bits)
+ _debug_print_internal(f'window output [{detail}]: {window_output!r}')
+
+ # pre-scale window output
+ window_output = tf.reshape(window_output, [-1])
+ window_scaled_output, scaling_shift = fft_ops.fft_auto_scale(window_output)
+ _debug_print_internal(f'scaling shift [{detail}]: {scaling_shift!r}')
+
+ # compute FFT on scaled window output
+ _debug_print_internal(
+ f'fft size, log2 [{detail}]: {self._fft_size}, {self._fft_size_log2}')
+ fft_output: tf.Tensor = fft_ops.rfft(window_scaled_output, self._fft_size)
+ _debug_print_internal(f'fft output [{detail}]: {fft_output!r}')
+
+ index_start = self._filter_bank_index_start
+ index_end = self._filter_bank_index_end
+ # convert fft output complex numbers to energy values
+ _debug_print_internal(
+ f'index start, end [{detail}]: {index_start}, {index_end}')
+ energy_output: tf.Tensor = energy_op.energy(fft_output, index_start,
+ index_end)
+ # Energy op does not zero indices outside [index_start,index_end).
+ # The following operations to zero portions of the energy op output
+ # could be much more efficiently performed inside the energy op C++
+ # code.
+ # Need to convert to tf.int32 or the TfLite converter will not use
+ # the correct ops.
+ energy_output = tf.cast(energy_output, tf.int32) # type: ignore
+ zeros_head = tf.zeros(index_start, dtype=tf.int32)
+ number_of_elements = energy_output.shape.num_elements()
+ zeros_tail = tf.zeros(
+ number_of_elements - index_end, # type: ignore
+ dtype=tf.int32)
+ energy_slice = energy_output[index_start:index_end]
+ energy_output = tf.concat([zeros_head, energy_slice, zeros_tail],
+ 0) # type: ignore
+ energy_output = tf.cast(energy_output, dtype=tf.uint32) # type: ignore
+ _debug_print_internal(f'energy output [{detail}]: {energy_output!r}')
+
+ # compress energy output into 40 channels
+ filter_output: tf.Tensor = filter_bank_ops.filter_bank(
+ energy_output, params.sample_rate,
+ params.filter_bank_number_of_channels,
+ params.filter_bank_lower_band_limit_hz,
+ params.filter_bank_upper_band_limit_hz)
+ _debug_print_internal(f'filterbank output [{detail}]: {filter_output!r}')
+
+ # scale down filter_output
+ filter_scaled_output: tf.Tensor = filter_bank_ops.filter_bank_square_root(
+ filter_output, scaling_shift)
+ _debug_print_internal(
+ f'scaled filterbank output [{detail}]: {filter_scaled_output!r}')
+
+ # noise reduction
+ spectral_sub_bits: int = params.filter_bank_spectral_subtraction_bits
+ filter_noise_output: tf.Tensor
+ filter_noise_estimate: tf.Tensor
+ filter_noise_output, filter_noise_estimate = \
+ filter_bank_ops.filter_bank_spectral_subtraction(
+ filter_scaled_output,
+ num_channels=params.filter_bank_number_of_channels,
+ smoothing=params.filter_bank_even_smoothing,
+ alternate_smoothing=params.filter_bank_odd_smoothing,
+ smoothing_bits=params.filter_bank_smoothing_bits,
+ min_signal_remaining=params.filter_bank_min_signal_remaining,
+ clamping=params.filter_bank_clamping,
+ spectral_subtraction_bits=spectral_sub_bits,
+ )
+ _debug_print_internal(f'noise output [{detail}]: {filter_noise_output!r}')
+
+ # automatic gain control (PCAN)
+ correction_bits: int = self._fft_size_log2 - \
+ int(params.filter_bank_scaling_bits / 2)
+ filter_agc_output: tf.Tensor = pcan_op.pcan(
+ filter_noise_output,
+ filter_noise_estimate,
+ strength=params.pcan_strength,
+ offset=params.pcan_offset,
+ gain_bits=params.pcan_gain_bits,
+ smoothing_bits=params.pcan_smoothing_bits,
+ input_correction_bits=correction_bits)
+ _debug_print_internal(
+ f'AGC Noise output [{detail}]: {filter_agc_output!r}')
+
+ # re-scale features from UINT32 to INT16
+ feature_post_scale: int = 1 << params.filter_bank_post_scaling_bits
+ feature_pre_scale_shift: int = correction_bits
+ feature_rescaled_output: tf.Tensor = filter_bank_ops.filter_bank_log(
+ filter_agc_output,
+ output_scale=feature_post_scale,
+ input_correction_bits=feature_pre_scale_shift)
+ _debug_print_internal(
+ f'scaled noise output [{detail}]: {feature_rescaled_output!r}')
+
+ # These scaling values are derived from those used in input_data.py in the
+ # training pipeline.
+ # The feature pipeline outputs 16-bit signed integers in roughly a 0 to 670
+ # range. In training, these are then arbitrarily divided by 25.6 to get
+ # float values in the rough range of 0.0 to 26.0. This scaling is performed
+ # for historical reasons, to match up with the output of other feature
+ # generators.
+ # The process is then further complicated when we quantize the model. This
+ # means we have to scale the 0.0 to 26.0 real values to the -128 to 127
+ # signed integer numbers.
+ # All this means that to get matching values from our integer feature
+ # output into the tensor input, we have to perform:
+ # input = (((feature / 25.6) / 26.0) * 256) - 128
+ # To simplify this and perform it in 32-bit integer math, we rearrange to:
+ # input = (feature * 256) / (25.6 * 26.0) - 128
+ # constexpr int32_t value_scale = 256;
+ # constexpr int32_t value_div =
+ # static_cast<int32_t>((25.6f * 26.0f) + 0.5f);
+ # int32_t value =
+ # ((frontend_output.values[i] * value_scale) + (value_div / 2)) /
+ # value_div;
+ # value -= 128;
+ # if (value < -128) {
+ # value = -128;
+ # }
+ # if (value > 127) {
+ # value = 127;
+ # }
+ # output[i] = value;
+
+ feature_output: tf.Tensor
+ if self._params.use_float_output:
+ # feature_rescaled_output is INT16, cast to FLOAT32
+ feature_output = tf.cast(feature_rescaled_output,
+ tf.float32) # type: ignore
+ # feature_output will be FLOAT32
+ feature_output /= self._params.legacy_output_scaling
+ else:
+ value_scale = tf.constant(256, dtype=tf.int32)
+ value_div = tf.constant(int((25.6 * 26) + 0.5), dtype=tf.int32)
+ feature_output = tf.cast(feature_rescaled_output,
+ tf.int32) # type: ignore
+ feature_output = (feature_output * value_scale) + int(value_div / 2)
+ feature_output = tf.truncatediv(feature_output,
+ value_div) # type: ignore
+ feature_output += tf.constant(-128, dtype=tf.int32)
+ feature_output = tf.clip_by_value(feature_output,
+ clip_value_min=-128,
+ clip_value_max=127) # type: ignore
+ feature_output = tf.cast(feature_output, tf.int8) # type: ignore
+
+ _debug_print_internal(f'feature output [{detail}]: {feature_output!r}')
+
+ return feature_output
+
+
+@dataclass(kw_only=True, frozen=True)
+class FeatureParams:
+ """
+ Feature generator parameters
+
+ Defaults are configured to work with the micro_speech_quantized.tflite model
+ """
+
+ sample_rate: int = 16000
+ """audio sample rate"""
+
+ window_size_ms: int = 30
+ """input window size in milliseconds"""
+
+ window_stride_ms: int = 20
+ """input window stride in milliseconds"""
+
+ window_scaling_bits: int = 12
+ """input window shaping: scaling bits"""
+
+ filter_bank_number_of_channels: int = 40
+ """filter bank channel count"""
+
+ filter_bank_lower_band_limit_hz: float = 125.0
+ """filter bank lower band limit"""
+
+ filter_bank_upper_band_limit_hz: float = 7500.0
+ """filter bank upper band limit"""
+
+ filter_bank_scaling_bits: int = \
+ filter_bank_ops.FILTER_BANK_WEIGHT_SCALING_BITS
+ """filter bank weight scaling bits, updates filter bank constant"""
+
+ filter_bank_alignment: int = 4
+ """filter bank alignment, updates filter bank constant"""
+
+ filter_bank_channel_block_size: int = 4
+ """filter bank channel block size, updates filter bank constant"""
+
+ filter_bank_post_scaling_bits: int = 6
+ """filter bank output log-scaling bits"""
+
+ filter_bank_spectral_subtraction_bits: int = 14
+ """filter bank noise reduction spectral subtration bits"""
+
+ filter_bank_smoothing_bits: int = 10
+ """filter bank noise reduction smoothing bits"""
+
+ filter_bank_even_smoothing: float = 0.025
+ """filter bank noise reduction even smoothing"""
+
+ filter_bank_odd_smoothing: float = 0.06
+ """filter bank noise reduction odd smoothing"""
+
+ filter_bank_min_signal_remaining: float = 0.05
+ """filter bank noise reduction minimum signal remaining"""
+
+ filter_bank_clamping: bool = False
+ """filter bank noise reduction clamping"""
+
+ pcan_strength: float = 0.95
+ """PCAN gain control strength"""
+
+ pcan_offset: float = 80.0
+ """PCAN gain control offset"""
+
+ pcan_gain_bits: int = 21
+ """PCAN gain control bits"""
+
+ pcan_smoothing_bits = 10
+ """PCAN gain control smoothing bits"""
+
+ legacy_output_scaling: float = 25.6
+ """Final output scaling, legacy from training"""
+
+ use_float_output: bool = False
+ """Use float output if True, otherwise int8 output"""
+
+
+class AudioPreprocessor:
+ """
+ Audio Preprocessor
+
+ Args:
+ params: FeatureParams, an immutable object supplying parameters for
+ the AudioPreprocessor instance
+ detail: str, used for debug output (optional, for debugging only)
+ """
+
+ def __init__(self, params: FeatureParams, detail: str = 'unknown'):
+ self._detail = detail
+ self._params = params
+ self._samples_per_window = int(params.window_size_ms * params.sample_rate /
+ 1000)
+ self._tflm_interpreter = None
+ self._feature_generator = None
+ self._feature_generator_concrete_function = None
+ self._model = None
+ self._samples = None
+
+ def _get_feature_generator(self):
+ if self._feature_generator is None:
+ self._feature_generator = _GenerateFeature(name='GenerateFeature',
+ params=self._params,
+ detail=self._detail)
+ return self._feature_generator
+
+ def _get_concrete_function(self):
+ if self._feature_generator_concrete_function is None:
+ shape = [1, self._samples_per_window]
+ fg = self._get_feature_generator()
+ func = tf.function(func=fg.generate_feature_for_frame)
+ self._feature_generator_concrete_function = func.get_concrete_function(
+ tf.TensorSpec(shape=shape, dtype=tf.int16)) # type: ignore
+ return self._feature_generator_concrete_function
+
+ def _get_model(self):
+ if self._model is None:
+ cf = self._get_concrete_function()
+ converter = tf.lite.TFLiteConverter.from_concrete_functions(
+ [cf], self._get_feature_generator())
+ converter.allow_custom_ops = True
+ self._model = converter.convert()
+ if _ENABLE_DEBUG.value != 'off':
+ tf.lite.experimental.Analyzer.analyze(model_content=self._model)
+ return self._model
+
+ def load_samples(self, filename: Path, use_rounding: bool = False):
+ """
+ Load audio samples from file.
+
+ Loads INT16 audio samples from a WAV file.
+ Supports single channel at 16KHz.
+ The audio samples are accessible through the 'samples' property.
+
+ Args:
+ filename: a Path object
+ use_rounding: bool, if True, convert the normalized FLOAT data that
+ has been loaded into INT16, using a standard rounding algorithm.
+ Otherwise use a simple conversion to INT16.
+ """
+ file_data = tf.io.read_file(str(filename))
+ samples: tf.Tensor
+ samples, sample_rate = tf.audio.decode_wav(file_data, desired_channels=1)
+ sample_rate = int(sample_rate)
+ _debug_print(f'Loaded {filename.name}'
+ f' sample-rate={sample_rate}'
+ f' sample-count={len(samples)}')
+ assert sample_rate == self._params.sample_rate, 'mismatched sample rate'
+ # convert samples to INT16
+ # i = (((int) ((x * 32767) + 32768.5f)) - 32768);
+ max_value = tf.dtypes.int16.max
+ min_value = tf.dtypes.int16.min
+ if use_rounding:
+ samples = ((samples * max_value) + (-min_value + 0.5)) + min_value
+ else:
+ samples *= -min_value
+ samples = tf.cast(samples, tf.int16) # type: ignore
+ samples = tf.reshape(samples, [1, -1])
+
+ self._samples = samples
+
+ @property
+ def samples(self) -> tf.Tensor:
+ """
+ Audio Samples previously decoded using load_samples method.
+
+ Returns:
+ tf.Tensor containing INT16 audio samples
+ """
+ return self._samples
+
+ @property
+ def params(self) -> FeatureParams:
+ """
+ Feature Paramters being used by the AudioPreprocessor object
+
+ Returns:
+ FeatureParams object which is immutable
+ """
+ return self._params
+
+ def generate_feature(self, audio_frame: tf.Tensor) -> tf.Tensor:
+ """
+ Generate a single feature for a single audio frame. Uses TensorFlow
+ eager execution.
+
+ Args:
+ audio_frame: tf.Tensor, a single audio frame (self.params.window_size_ms)
+ with shape (1, audio_samples_count)
+
+ Returns:
+ tf.Tensor, a tensor containing a single audio feature with shape
+ (self.params.filter_bank_number_of_channels,)
+ """
+ fg = self._get_feature_generator()
+ feature = fg.generate_feature_for_frame(audio_frame=audio_frame)
+ return feature
+
+ def generate_feature_using_graph(self, audio_frame: tf.Tensor) -> tf.Tensor:
+ """
+ Generate a single feature for a single audio frame. Uses TensorFlow
+ graph execution.
+
+ Args:
+ audio_frame: tf.Tensor, a single audio frame (self.params.window_size_ms)
+ with shape (1, audio_samples_count)
+
+ Returns:
+ tf.Tensor, a tensor containing a single audio feature with shape
+ (self.params.filter_bank_number_of_channels,)
+ """
+ cf = self._get_concrete_function()
+ feature: tf.Tensor = cf(audio_frame=audio_frame) # type: ignore
+ return feature
+
+ def generate_feature_using_tflm(self, audio_frame: tf.Tensor) -> tf.Tensor:
+ """
+ Generate a single feature for a single audio frame. Uses TensorFlow
+ graph execution and the TensorFlow model converter to generate a
+ TFLM compatible model. This model is then used by the TFLM
+ MicroInterpreter to execute a single inference operation.
+
+ Args:
+ audio_frame: tf.Tensor, a single audio frame (self.params.window_size_ms)
+ with shape (1, audio_samples_count)
+
+ Returns:
+ tf.Tensor, a tensor containing a single audio feature with shape
+ (self.params.filter_bank_number_of_channels,)
+ """
+ if self._tflm_interpreter is None:
+ model = self._get_model()
+ self._tflm_interpreter = runtime.Interpreter.from_bytes(model)
+
+ self._tflm_interpreter.set_input(audio_frame, 0)
+ self._tflm_interpreter.invoke()
+ result = self._tflm_interpreter.get_output(0)
+ return tf.convert_to_tensor(result)
+
+ def reset_tflm(self):
+ """
+ Reset TFLM interpreter state
+
+ Re-initializes TFLM interpreter state and the internal state
+ of all TFLM kernel operators. Useful for resetting Signal
+ library operator noise estimation and other internal state.
+ """
+ if self._tflm_interpreter is not None:
+ self._tflm_interpreter.reset()
+
+ def generate_tflite_file(self) -> Path:
+ """
+ Create a .tflite model file
+
+ The model output tensor type will depend on the
+ 'FeatureParams.use_float_output' parameter.
+
+ Returns:
+ Path object for the created model file
+ """
+ model = self._get_model()
+ if self._params.use_float_output:
+ type_name = 'float'
+ else:
+ type_name = 'int8'
+ fname = Path(tempfile.gettempdir(),
+ 'audio_preprocessor_' + type_name + '.tflite')
+ with open(fname, mode='wb') as file_handle:
+ file_handle.write(model)
+ return fname
+
+
+def _main(_):
+ prefix_path = resource_loader.get_path_to_datafile('testdata')
+
+ fname = _FILE_TO_TEST.value
+ audio_30ms_path = Path(prefix_path, f'{fname}_30ms.wav')
+
+ use_float_output = _OUTPUT_TYPE.value == 'float32'
+ params = FeatureParams(use_float_output=use_float_output)
+ pp = AudioPreprocessor(params=params, detail=fname)
+
+ if _ENABLE_DEBUG.value != 'off':
+ pp.load_samples(audio_30ms_path)
+ _ = pp.generate_feature(pp.samples)
+
+ output_file_path: Path = pp.generate_tflite_file()
+ print('\nOutput file:', str(output_file_path), '\n')
+
+
+if __name__ == '__main__':
+ app.run(_main)
diff --git a/tensorflow/lite/micro/examples/micro_speech/audio_preprocessor_test.py b/tensorflow/lite/micro/examples/micro_speech/audio_preprocessor_test.py
new file mode 100644
index 0000000..e723a72
--- /dev/null
+++ b/tensorflow/lite/micro/examples/micro_speech/audio_preprocessor_test.py
@@ -0,0 +1,102 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+"""
+Audio feature generation testing, using the AudioPreprocessor class
+
+Run:
+bazel build tensorflow/lite/micro/examples/micro_speech:audio_preprocessor_test
+bazel-bin/tensorflow/lite/micro/examples/micro_speech/audio_preprocessor_test
+"""
+
+from pathlib import Path
+import filecmp
+
+from tensorflow.python.framework import test_util
+from tensorflow.python.platform import resource_loader
+from tensorflow.python.platform import test
+
+import tensorflow as tf
+from tflite_micro.tensorflow.lite.micro.examples.micro_speech import audio_preprocessor
+
+
+class AudioPreprocessorTest(test_util.TensorFlowTestCase):
+
+ def setUp(self):
+ self.sample_prefix_path = resource_loader.get_path_to_datafile('testdata')
+
+ def testFeatureGeneration(self):
+ feature_params = audio_preprocessor.FeatureParams()
+ audio_pp = audio_preprocessor.AudioPreprocessor(feature_params)
+ window_size = int(feature_params.window_size_ms *
+ feature_params.sample_rate / 1000)
+ data: tf.Tensor = tf.random.uniform(minval=int(tf.dtypes.int16.min),
+ maxval=tf.dtypes.int16.max,
+ seed=42,
+ shape=(1, window_size),
+ dtype=tf.int32)
+ data = tf.cast(data, dtype=tf.int16) # type: ignore
+
+ # test signal ops internal state retained and features do not match
+ feature_eager1 = audio_pp.generate_feature(data)
+ feature_eager2 = audio_pp.generate_feature(data)
+ self.assertNotAllEqual(feature_eager1, feature_eager2)
+
+ # test eager vs graph execution feature match
+ _ = audio_pp.generate_feature_using_graph(data)
+ feature_graph = audio_pp.generate_feature_using_graph(data)
+ self.assertAllEqual(feature_graph, feature_eager2)
+
+ # test eager vs MicroInterpreter execution feature match
+ feature_tflm = audio_pp.generate_feature_using_tflm(data)
+ self.assertAllEqual(feature_tflm, feature_eager1)
+
+ # test signal ops internal state reset
+ audio_pp.reset_tflm()
+ feature_tflm = audio_pp.generate_feature_using_tflm(data)
+ self.assertAllEqual(feature_tflm, feature_eager1)
+
+ # test signal ops internal state retained
+ feature_tflm = audio_pp.generate_feature_using_tflm(data)
+ self.assertAllEqual(feature_tflm, feature_eager2)
+
+ def testFeatureOutputYes(self):
+ feature_params = audio_preprocessor.FeatureParams()
+ audio_pp = audio_preprocessor.AudioPreprocessor(feature_params)
+ audio_pp.load_samples(Path(self.sample_prefix_path, 'yes_30ms.wav'))
+ feature = audio_pp.generate_feature_using_tflm(audio_pp.samples)
+ feature_list = feature.numpy().tolist()
+ expected = [
+ 124, 105, 126, 103, 125, 101, 123, 100, 116, 98, 115, 97, 113, 90, 91,
+ 82, 104, 96, 117, 97, 121, 103, 126, 101, 125, 104, 126, 104, 125, 101,
+ 116, 90, 81, 74, 80, 71, 83, 76, 82, 71
+ ]
+ self.assertSequenceEqual(feature_list, expected)
+
+ def testFeatureOutputNo(self):
+ feature_params = audio_preprocessor.FeatureParams()
+ audio_pp = audio_preprocessor.AudioPreprocessor(feature_params)
+ audio_pp.load_samples(Path(self.sample_prefix_path, 'no_30ms.wav'))
+ feature = audio_pp.generate_feature_using_tflm(audio_pp.samples)
+ feature_list = feature.numpy().tolist()
+ expected = [
+ 126, 103, 124, 102, 124, 102, 123, 100, 118, 97, 118, 100, 118, 98,
+ 121, 100, 121, 98, 117, 91, 96, 74, 54, 87, 100, 87, 109, 92, 91, 80,
+ 64, 55, 83, 74, 74, 78, 114, 95, 101, 81
+ ]
+ self.assertSequenceEqual(feature_list, expected)
+
+
+if __name__ == '__main__':
+ test.main()
diff --git a/tensorflow/lite/micro/examples/micro_speech/audio_provider.cc b/tensorflow/lite/micro/examples/micro_speech/audio_provider.cc
deleted file mode 100644
index 5ca425d..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/audio_provider.cc
+++ /dev/null
@@ -1,38 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/audio_provider.h"
-
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h"
-
-namespace {
-int16_t g_dummy_audio_data[kMaxAudioSampleSize];
-int32_t g_latest_audio_timestamp = 0;
-} // namespace
-
-TfLiteStatus GetAudioSamples(int start_ms, int duration_ms,
- int* audio_samples_size, int16_t** audio_samples) {
- for (int i = 0; i < kMaxAudioSampleSize; ++i) {
- g_dummy_audio_data[i] = 0;
- }
- *audio_samples_size = kMaxAudioSampleSize;
- *audio_samples = g_dummy_audio_data;
- return kTfLiteOk;
-}
-
-int32_t LatestAudioTimestamp() {
- g_latest_audio_timestamp += 100;
- return g_latest_audio_timestamp;
-}
diff --git a/tensorflow/lite/micro/examples/micro_speech/audio_provider.h b/tensorflow/lite/micro/examples/micro_speech/audio_provider.h
deleted file mode 100644
index d3aab2c..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/audio_provider.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_
-
-#include "tensorflow/lite/c/common.h"
-
-// This is an abstraction around an audio source like a microphone, and is
-// expected to return 16-bit PCM sample data for a given point in time. The
-// sample data itself should be used as quickly as possible by the caller, since
-// to allow memory optimizations there are no guarantees that the samples won't
-// be overwritten by new data in the future. In practice, implementations should
-// ensure that there's a reasonable time allowed for clients to access the data
-// before any reuse.
-// The reference implementation can have no platform-specific dependencies, so
-// it just returns an array filled with zeros. For real applications, you should
-// ensure there's a specialized implementation that accesses hardware APIs.
-TfLiteStatus GetAudioSamples(int start_ms, int duration_ms,
- int* audio_samples_size, int16_t** audio_samples);
-
-// Returns the time that audio data was last captured in milliseconds. There's
-// no contract about what time zero represents, the accuracy, or the granularity
-// of the result. Subsequent calls will generally not return a lower value, but
-// even that's not guaranteed if there's an overflow wraparound.
-// The reference implementation of this function just returns a constantly
-// incrementing value for each call, since it would need a non-portable platform
-// call to access time information. For real applications, you'll need to write
-// your own platform-specific implementation.
-int32_t LatestAudioTimestamp();
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/audio_provider_mock.cc b/tensorflow/lite/micro/examples/micro_speech/audio_provider_mock.cc
deleted file mode 100644
index fe3ad16..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/audio_provider_mock.cc
+++ /dev/null
@@ -1,54 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/audio_provider.h"
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h"
-#include "tensorflow/lite/micro/examples/micro_speech/testdata/no_1000ms_audio_data.h"
-#include "tensorflow/lite/micro/examples/micro_speech/testdata/yes_1000ms_audio_data.h"
-
-namespace {
-int16_t g_dummy_audio_data[kMaxAudioSampleSize];
-int32_t g_latest_audio_timestamp = 0;
-} // namespace
-
-TfLiteStatus GetAudioSamples(int start_ms, int duration_ms,
- int* audio_samples_size, int16_t** audio_samples) {
- const int yes_start = (0 * kAudioSampleFrequency) / 1000;
- const int yes_end = (1000 * kAudioSampleFrequency) / 1000;
- const int no_start = (4000 * kAudioSampleFrequency) / 1000;
- const int no_end = (5000 * kAudioSampleFrequency) / 1000;
- const int wraparound = (8000 * kAudioSampleFrequency) / 1000;
- const int start_sample = (start_ms * kAudioSampleFrequency) / 1000;
- for (int i = 0; i < kMaxAudioSampleSize; ++i) {
- const int sample_index = (start_sample + i) % wraparound;
- int16_t sample;
- if ((sample_index >= yes_start) && (sample_index < yes_end)) {
- sample = g_yes_1000ms_audio_data[sample_index - yes_start];
- } else if ((sample_index >= no_start) && (sample_index < no_end)) {
- sample = g_no_1000ms_audio_data[sample_index - no_start];
- } else {
- sample = 0;
- }
- g_dummy_audio_data[i] = sample;
- }
- *audio_samples_size = kMaxAudioSampleSize;
- *audio_samples = g_dummy_audio_data;
- return kTfLiteOk;
-}
-
-int32_t LatestAudioTimestamp() {
- g_latest_audio_timestamp += 100;
- return g_latest_audio_timestamp;
-}
diff --git a/tensorflow/lite/micro/examples/micro_speech/audio_provider_mock_test.cc b/tensorflow/lite/micro/examples/micro_speech/audio_provider_mock_test.cc
deleted file mode 100644
index b15749e..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/audio_provider_mock_test.cc
+++ /dev/null
@@ -1,68 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include <limits>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/micro/examples/micro_speech/audio_provider.h"
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h"
-#include "tensorflow/lite/micro/examples/micro_speech/testdata/no_1000ms_audio_data.h"
-#include "tensorflow/lite/micro/examples/micro_speech/testdata/yes_1000ms_audio_data.h"
-#include "tensorflow/lite/micro/testing/micro_test.h"
-
-TF_LITE_MICRO_TESTS_BEGIN
-
-TF_LITE_MICRO_TEST(TestAudioProviderMock) {
- int audio_samples_size = 0;
- int16_t* audio_samples = nullptr;
- TfLiteStatus get_status = GetAudioSamples(
- 0, kFeatureSliceDurationMs, &audio_samples_size, &audio_samples);
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, get_status);
- TF_LITE_MICRO_EXPECT_LE(audio_samples_size, kMaxAudioSampleSize);
- TF_LITE_MICRO_EXPECT(audio_samples != nullptr);
- for (int i = 0; i < audio_samples_size; ++i) {
- TF_LITE_MICRO_EXPECT_EQ(g_yes_1000ms_audio_data[i], audio_samples[i]);
- }
-
- get_status = GetAudioSamples(500, kFeatureSliceDurationMs,
- &audio_samples_size, &audio_samples);
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, get_status);
- TF_LITE_MICRO_EXPECT_LE(audio_samples_size, kMaxAudioSampleSize);
- TF_LITE_MICRO_EXPECT(audio_samples != nullptr);
- for (int i = 0; i < audio_samples_size; ++i) {
- TF_LITE_MICRO_EXPECT_EQ(g_yes_1000ms_audio_data[i + 8000],
- audio_samples[i]);
- }
-
- get_status = GetAudioSamples(1500, kFeatureSliceDurationMs,
- &audio_samples_size, &audio_samples);
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, get_status);
- TF_LITE_MICRO_EXPECT_LE(audio_samples_size, kMaxAudioSampleSize);
- TF_LITE_MICRO_EXPECT(audio_samples != nullptr);
- for (int i = 0; i < audio_samples_size; ++i) {
- TF_LITE_MICRO_EXPECT_EQ(0, audio_samples[i]);
- }
-
- get_status = GetAudioSamples(12250, kFeatureSliceDurationMs,
- &audio_samples_size, &audio_samples);
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, get_status);
- TF_LITE_MICRO_EXPECT_LE(audio_samples_size, kMaxAudioSampleSize);
- TF_LITE_MICRO_EXPECT(audio_samples != nullptr);
- for (int i = 0; i < audio_samples_size; ++i) {
- TF_LITE_MICRO_EXPECT_EQ(g_no_1000ms_audio_data[i + 4000], audio_samples[i]);
- }
-}
-
-TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/examples/micro_speech/audio_provider_test.cc b/tensorflow/lite/micro/examples/micro_speech/audio_provider_test.cc
deleted file mode 100644
index fb403c0..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/audio_provider_test.cc
+++ /dev/null
@@ -1,66 +0,0 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/audio_provider.h"
-
-#include <limits>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h"
-#include "tensorflow/lite/micro/testing/micro_test.h"
-
-TF_LITE_MICRO_TESTS_BEGIN
-
-TF_LITE_MICRO_TEST(TestAudioProvider) {
- int audio_samples_size = 0;
- int16_t* audio_samples = nullptr;
- TfLiteStatus get_status = GetAudioSamples(
- 0, kFeatureSliceDurationMs, &audio_samples_size, &audio_samples);
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, get_status);
- TF_LITE_MICRO_EXPECT_LE(audio_samples_size, kMaxAudioSampleSize);
- TF_LITE_MICRO_EXPECT(audio_samples != nullptr);
-
- // Make sure we can read all of the returned memory locations.
- int total = 0;
- for (int i = 0; i < audio_samples_size; ++i) {
- total += audio_samples[i];
- }
- (void)total;
-}
-
-TF_LITE_MICRO_TEST(TestTimer) {
- // Make sure that the technically-undefined overflow behavior we rely on below
- // works on this platform. It's still not guaranteed, but at least this is a
- // smoke check. Turn off when running with ASan, as it will complain about
- // the following undefined behavior.
-#ifndef ADDRESS_SANITIZER
- int32_t overflow_value = std::numeric_limits<int32_t>::max();
- overflow_value += 1;
- TF_LITE_MICRO_EXPECT_EQ(std::numeric_limits<int32_t>::min(), overflow_value);
-#endif
-
- const int32_t first_time = LatestAudioTimestamp();
- const int32_t second_time = LatestAudioTimestamp();
-
- // It's possible that the timer may have wrapped around from +BIG_NUM to
- // -BIG_NUM between the first and second calls, since we're storing
- // milliseconds in a 32-bit integer. It's not reasonable that the call itself
- // would have taken more than 2^31 milliseconds though, so look at the
- // difference and rely on integer overflow to ensure it's accurate.
- const int32_t time_delta = (second_time - first_time);
- TF_LITE_MICRO_EXPECT_LE(0, time_delta);
-}
-
-TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/examples/micro_speech/command_responder.cc b/tensorflow/lite/micro/examples/micro_speech/command_responder.cc
deleted file mode 100644
index 2184478..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/command_responder.cc
+++ /dev/null
@@ -1,28 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/command_responder.h"
-
-#include "tensorflow/lite/micro/micro_log.h"
-
-// The default implementation writes out the name of the recognized command
-// to the error console. Real applications will want to take some custom
-// action instead, and should implement their own versions of this function.
-void RespondToCommand(int32_t current_time, const char* found_command,
- uint8_t score, bool is_new_command) {
- if (is_new_command) {
- MicroPrintf("Heard %s (%d) @%dms", found_command, score, current_time);
- }
-}
diff --git a/tensorflow/lite/micro/examples/micro_speech/command_responder.h b/tensorflow/lite/micro/examples/micro_speech/command_responder.h
deleted file mode 100644
index a1acb99..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/command_responder.h
+++ /dev/null
@@ -1,30 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Provides an interface to take an action based on an audio command.
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_
-
-#include "tensorflow/lite/c/common.h"
-
-// Called every time the results of an audio recognition run are available. The
-// human-readable name of any recognized command is in the `found_command`
-// argument, `score` has the numerical confidence, and `is_new_command` is set
-// if the previous command was different to this one.
-void RespondToCommand(int32_t current_time, const char* found_command,
- uint8_t score, bool is_new_command);
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/command_responder_test.cc b/tensorflow/lite/micro/examples/micro_speech/command_responder_test.cc
deleted file mode 100644
index e02f7ae..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/command_responder_test.cc
+++ /dev/null
@@ -1,29 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/command_responder.h"
-
-#include "tensorflow/lite/micro/testing/micro_test.h"
-
-TF_LITE_MICRO_TESTS_BEGIN
-
-TF_LITE_MICRO_TEST(TestCallability) {
- // This will have external side-effects (like printing to the debug console
- // or lighting an LED) that are hard to observe, so the most we can do is
- // make sure the call doesn't crash.
- RespondToCommand(0, "foo", 0, true);
-}
-
-TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/examples/micro_speech/evaluate.py b/tensorflow/lite/micro/examples/micro_speech/evaluate.py
new file mode 100644
index 0000000..81f74b1
--- /dev/null
+++ b/tensorflow/lite/micro/examples/micro_speech/evaluate.py
@@ -0,0 +1,203 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+"""
+Wake-word model evaluation, with audio preprocessing using MicroInterpreter
+
+Run:
+bazel build tensorflow/lite/micro/examples/micro_speech:evaluate
+bazel-bin/tensorflow/lite/micro/examples/micro_speech/evaluate
+ --sample_path="path to 1 second audio sample in WAV format"
+"""
+
+from absl import app
+from absl import flags
+import numpy as np
+from pathlib import Path
+
+from tflite_micro.python.tflite_micro import runtime
+from tensorflow.python.platform import resource_loader
+import tensorflow as tf
+from tflite_micro.tensorflow.lite.micro.examples.micro_speech import audio_preprocessor
+
+_SAMPLE_PATH = flags.DEFINE_string(
+ name='sample_path',
+ default='',
+ help='path for the audio sample to be predicted.',
+)
+
+_FEATURES_SHAPE = (49, 40)
+
+
+def quantize_input_data(data, input_details):
+ """quantize the input data using scale and zero point
+
+ Args:
+ data (np.array in float): input data for the interpreter
+ input_details : output of get_input_details from the tflm interpreter.
+
+ Returns:
+ np.ndarray: quantized data as int8 dtype
+ """
+ # Get input quantization parameters
+ data_type = input_details['dtype']
+ input_quantization_parameters = input_details['quantization_parameters']
+ input_scale, input_zero_point = input_quantization_parameters['scales'][
+ 0], input_quantization_parameters['zero_points'][0]
+ # quantize the input data
+ data = data / input_scale + input_zero_point
+ return data.astype(data_type)
+
+
+def dequantize_output_data(data: np.ndarray,
+ output_details: dict) -> np.ndarray:
+ """Dequantize the model output
+
+ Args:
+ data: integer data to be dequantized
+ output_details: TFLM interpreter model output details
+
+ Returns:
+ np.ndarray: dequantized data as float32 dtype
+ """
+ output_quantization_parameters = output_details['quantization_parameters']
+ output_scale = output_quantization_parameters['scales'][0]
+ output_zero_point = output_quantization_parameters['zero_points'][0]
+ # Caveat: tflm_output_quant need to be converted to float to avoid integer
+ # overflow during dequantization
+ # e.g., (tflm_output_quant -output_zero_point) and
+ # (tflm_output_quant + (-output_zero_point))
+ # can produce different results (int8 calculation)
+ return output_scale * (data.astype(np.float32) - output_zero_point)
+
+
+def predict(interpreter: runtime.Interpreter,
+ features: np.ndarray) -> np.ndarray:
+ """
+ Use TFLM interpreter to predict wake-word from audio sample features
+
+ Args:
+ interpreter: TFLM python interpreter instance
+ features: wake-word model feature data, with shape _FEATURES_SHAPE
+
+ Returns:
+ np.ndarray: predicted probability (softmax) for each model category
+ """
+
+ input_details = interpreter.get_input_details(0)
+ # Quantize the input if the model is quantized
+ # and our features are np.float32
+ if input_details['dtype'] != np.float32 and features.dtype == np.float32:
+ features = quantize_input_data(features, input_details)
+ flattened_features = features.flatten().reshape([1, -1])
+ interpreter.set_input(flattened_features, 0)
+ interpreter.invoke()
+ tflm_output = interpreter.get_output(0)
+
+ output_details = interpreter.get_output_details(0)
+ if output_details['dtype'] == np.float32:
+ return tflm_output[0].astype(np.float32)
+ # Dequantize the output for quantized model
+ return dequantize_output_data(tflm_output[0], output_details)
+
+
+def generate_features(
+ audio_pp: audio_preprocessor.AudioPreprocessor) -> np.ndarray:
+ """
+ Generate audio sample features
+
+ Args:
+ audio_pp: AudioPreprocessor instance
+
+ Returns:
+ np.ndarray: generated audio sample features with shape _FEATURES_SHAPE
+ """
+ if audio_pp.params.use_float_output:
+ dtype = np.float32
+ else:
+ dtype = np.int8
+ features = np.zeros(_FEATURES_SHAPE, dtype=dtype)
+ start_index = 0
+ window_size = int(audio_pp.params.window_size_ms *
+ audio_pp.params.sample_rate / 1000)
+ window_stride = int(audio_pp.params.window_stride_ms *
+ audio_pp.params.sample_rate / 1000)
+ samples = audio_pp.samples[0]
+ frame_number = 0
+ end_index = start_index + window_size
+
+ # reset audio preprocessor noise estimates
+ audio_pp.reset_tflm()
+
+ while end_index <= len(samples):
+ frame_tensor: tf.Tensor = tf.convert_to_tensor(
+ samples[start_index:end_index])
+ frame_tensor = tf.reshape(frame_tensor, [1, -1])
+ feature_tensor = audio_pp.generate_feature_using_tflm(frame_tensor)
+ features[frame_number] = feature_tensor.numpy()
+ start_index += window_stride
+ end_index += window_stride
+ frame_number += 1
+
+ return features
+
+
+def get_category_names() -> list[str]:
+ """
+ Get the list of model output category names
+
+ Returns:
+ list[str]: model output category names
+ """
+ return ['silence', 'unknown', 'yes', 'no']
+
+
+def _main(_):
+ sample_path = Path(_SAMPLE_PATH.value)
+ assert sample_path.exists() and sample_path.is_file(), \
+ 'Audio sample file does not exist. Please check the path.'
+ model_prefix_path = resource_loader.get_path_to_datafile('models')
+ model_path = Path(model_prefix_path, 'micro_speech_quantized.tflite')
+
+ feature_params = audio_preprocessor.FeatureParams()
+ audio_pp = audio_preprocessor.AudioPreprocessor(feature_params)
+ audio_pp.load_samples(sample_path)
+ features = generate_features(audio_pp)
+
+ tflm_interpreter = runtime.Interpreter.from_file(model_path)
+
+ frame_number = 0
+ test_features = np.zeros(_FEATURES_SHAPE, dtype=np.int8)
+ for feature in features:
+ test_features[frame_number] = feature
+ category_probabilities = predict(tflm_interpreter, test_features)
+ category_probabilities_str = '['
+ for i in range(len(category_probabilities)):
+ if i > 0:
+ category_probabilities_str += ', '
+ category_probabilities_str += f'{category_probabilities[i]:.4f}'
+ category_probabilities_str += ']'
+ print(f'Frame #{frame_number}: {category_probabilities_str}')
+ frame_number += 1
+
+ category_probabilities = predict(tflm_interpreter, features)
+ predicted_category = np.argmax(category_probabilities)
+ category_names = get_category_names()
+ print('Model predicts the audio sample as'
+ f' <{category_names[predicted_category]}>'
+ f' with probability {category_probabilities[predicted_category]:.2f}')
+
+
+if __name__ == '__main__':
+ app.run(_main)
diff --git a/tensorflow/lite/micro/examples/micro_speech/evaluate_test.py b/tensorflow/lite/micro/examples/micro_speech/evaluate_test.py
new file mode 100644
index 0000000..d5d6ac0
--- /dev/null
+++ b/tensorflow/lite/micro/examples/micro_speech/evaluate_test.py
@@ -0,0 +1,96 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+"""
+Wake-word model testing, with audio preprocessing using MicroInterpreter
+
+Run:
+bazel build tensorflow/lite/micro/examples/micro_speech:evaluate_test
+bazel-bin/tensorflow/lite/micro/examples/micro_speech/evaluate_test
+"""
+
+import numpy as np
+from pathlib import Path
+
+from tensorflow.python.framework import test_util
+from tensorflow.python.platform import resource_loader
+from tensorflow.python.platform import test
+from tflite_micro.python.tflite_micro import runtime
+from tflite_micro.tensorflow.lite.micro.examples.micro_speech import audio_preprocessor
+from tflite_micro.tensorflow.lite.micro.examples.micro_speech import evaluate
+
+
+class MicroSpeechTest(test_util.TensorFlowTestCase):
+
+ def setUp(self):
+ model_prefix_path = resource_loader.get_path_to_datafile('models')
+ self.sample_prefix_path = resource_loader.get_path_to_datafile('testdata')
+ model_path = Path(model_prefix_path, 'micro_speech_quantized.tflite')
+ self.tflm_interpreter = runtime.Interpreter.from_file(model_path)
+ self.test_data = [
+ ('no', 'no_1000ms.wav'),
+ ('yes', 'yes_1000ms.wav'),
+ ('silence', 'noise_1000ms.wav'),
+ ('silence', 'silence_1000ms.wav'),
+ ]
+
+ def testModelAccuracyWithInt8Features(self):
+ feature_params = audio_preprocessor.FeatureParams()
+ audio_pp = audio_preprocessor.AudioPreprocessor(feature_params)
+ for label, sample_name in self.test_data:
+ # Load audio sample data
+ sample_path = Path(self.sample_prefix_path, sample_name)
+ audio_pp.load_samples(sample_path)
+
+ # Generate feature data from audio samples.
+ # Note that the noise estimate is reset each time generate_features()
+ # is called.
+ features = evaluate.generate_features(audio_pp)
+
+ # Run model inference (quantized) on the feature data
+ category_probabilities = evaluate.predict(self.tflm_interpreter,
+ features)
+
+ # Check the prediction result
+ predicted_category = np.argmax(category_probabilities)
+ category_names = evaluate.get_category_names()
+ # Check the prediction
+ self.assertEqual(category_names[predicted_category], label)
+
+ def testModelAccuracyWithFloatFeatures(self):
+ feature_params = audio_preprocessor.FeatureParams(use_float_output=True)
+ audio_pp = audio_preprocessor.AudioPreprocessor(feature_params)
+ for label, sample_name in self.test_data:
+ # Load audio sample data
+ sample_path = Path(self.sample_prefix_path, sample_name)
+ audio_pp.load_samples(sample_path)
+
+ # Generate feature data from audio samples.
+ # Note that the noise estimate is reset each time generate_features()
+ # is called.
+ features = evaluate.generate_features(audio_pp)
+
+ # Run model inference (quantized) on the feature data
+ category_probabilities = evaluate.predict(self.tflm_interpreter,
+ features)
+
+ # Check the prediction result
+ predicted_category = np.argmax(category_probabilities)
+ category_names = evaluate.get_category_names()
+ # Check the prediction
+ self.assertEqual(category_names[predicted_category], label)
+
+
+if __name__ == '__main__':
+ test.main()
diff --git a/tensorflow/lite/micro/examples/micro_speech/feature_provider.cc b/tensorflow/lite/micro/examples/micro_speech/feature_provider.cc
deleted file mode 100644
index a4a6635..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/feature_provider.cc
+++ /dev/null
@@ -1,119 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/feature_provider.h"
-
-#include "tensorflow/lite/micro/examples/micro_speech/audio_provider.h"
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/micro_features_generator.h"
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h"
-#include "tensorflow/lite/micro/micro_log.h"
-
-FeatureProvider::FeatureProvider(int feature_size, int8_t* feature_data)
- : feature_size_(feature_size),
- feature_data_(feature_data),
- is_first_run_(true) {
- // Initialize the feature data to default values.
- for (int n = 0; n < feature_size_; ++n) {
- feature_data_[n] = 0;
- }
-}
-
-FeatureProvider::~FeatureProvider() {}
-
-TfLiteStatus FeatureProvider::PopulateFeatureData(int32_t last_time_in_ms,
- int32_t time_in_ms,
- int* how_many_new_slices) {
- if (feature_size_ != kFeatureElementCount) {
- MicroPrintf("Requested feature_data_ size %d doesn't match %d",
- feature_size_, kFeatureElementCount);
- return kTfLiteError;
- }
-
- // Quantize the time into steps as long as each window stride, so we can
- // figure out which audio data we need to fetch.
- const int last_step = (last_time_in_ms / kFeatureSliceStrideMs);
- const int current_step = (time_in_ms / kFeatureSliceStrideMs);
-
- int slices_needed = current_step - last_step;
- // If this is the first call, make sure we don't use any cached information.
- if (is_first_run_) {
- TfLiteStatus init_status = InitializeMicroFeatures();
- if (init_status != kTfLiteOk) {
- return init_status;
- }
- is_first_run_ = false;
- slices_needed = kFeatureSliceCount;
- }
- if (slices_needed > kFeatureSliceCount) {
- slices_needed = kFeatureSliceCount;
- }
- *how_many_new_slices = slices_needed;
-
- const int slices_to_keep = kFeatureSliceCount - slices_needed;
- const int slices_to_drop = kFeatureSliceCount - slices_to_keep;
- // If we can avoid recalculating some slices, just move the existing data
- // up in the spectrogram, to perform something like this:
- // last time = 80ms current time = 120ms
- // +-----------+ +-----------+
- // | data@20ms | --> | data@60ms |
- // +-----------+ -- +-----------+
- // | data@40ms | -- --> | data@80ms |
- // +-----------+ -- -- +-----------+
- // | data@60ms | -- -- | <empty> |
- // +-----------+ -- +-----------+
- // | data@80ms | -- | <empty> |
- // +-----------+ +-----------+
- if (slices_to_keep > 0) {
- for (int dest_slice = 0; dest_slice < slices_to_keep; ++dest_slice) {
- int8_t* dest_slice_data =
- feature_data_ + (dest_slice * kFeatureSliceSize);
- const int src_slice = dest_slice + slices_to_drop;
- const int8_t* src_slice_data =
- feature_data_ + (src_slice * kFeatureSliceSize);
- for (int i = 0; i < kFeatureSliceSize; ++i) {
- dest_slice_data[i] = src_slice_data[i];
- }
- }
- }
- // Any slices that need to be filled in with feature data have their
- // appropriate audio data pulled, and features calculated for that slice.
- if (slices_needed > 0) {
- for (int new_slice = slices_to_keep; new_slice < kFeatureSliceCount;
- ++new_slice) {
- const int new_step = (current_step - kFeatureSliceCount + 1) + new_slice;
- const int32_t slice_start_ms = (new_step * kFeatureSliceStrideMs);
- int16_t* audio_samples = nullptr;
- int audio_samples_size = 0;
- // TODO(petewarden): Fix bug that leads to non-zero slice_start_ms
- GetAudioSamples((slice_start_ms > 0 ? slice_start_ms : 0),
- kFeatureSliceDurationMs, &audio_samples_size,
- &audio_samples);
- if (audio_samples_size < kMaxAudioSampleSize) {
- MicroPrintf("Audio data size %d too small, want %d", audio_samples_size,
- kMaxAudioSampleSize);
- return kTfLiteError;
- }
- int8_t* new_slice_data = feature_data_ + (new_slice * kFeatureSliceSize);
- size_t num_samples_read;
- TfLiteStatus generate_status = GenerateMicroFeatures(
- audio_samples, audio_samples_size, kFeatureSliceSize, new_slice_data,
- &num_samples_read);
- if (generate_status != kTfLiteOk) {
- return generate_status;
- }
- }
- }
- return kTfLiteOk;
-}
diff --git a/tensorflow/lite/micro/examples/micro_speech/feature_provider.h b/tensorflow/lite/micro/examples/micro_speech/feature_provider.h
deleted file mode 100644
index 2a2ef8f..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/feature_provider.h
+++ /dev/null
@@ -1,50 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_
-
-#include "tensorflow/lite/c/common.h"
-
-// Binds itself to an area of memory intended to hold the input features for an
-// audio-recognition neural network model, and fills that data area with the
-// features representing the current audio input, for example from a microphone.
-// The audio features themselves are a two-dimensional array, made up of
-// horizontal slices representing the frequencies at one point in time, stacked
-// on top of each other to form a spectrogram showing how those frequencies
-// changed over time.
-class FeatureProvider {
- public:
- // Create the provider, and bind it to an area of memory. This memory should
- // remain accessible for the lifetime of the provider object, since subsequent
- // calls will fill it with feature data. The provider does no memory
- // management of this data.
- FeatureProvider(int feature_size, int8_t* feature_data);
- ~FeatureProvider();
-
- // Fills the feature data with information from audio inputs, and returns how
- // many feature slices were updated.
- TfLiteStatus PopulateFeatureData(int32_t last_time_in_ms, int32_t time_in_ms,
- int* how_many_new_slices);
-
- private:
- int feature_size_;
- int8_t* feature_data_;
- // Make sure we don't try to use cached information if this is the first call
- // into the provider.
- bool is_first_run_;
-};
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/feature_provider_mock_test.cc b/tensorflow/lite/micro/examples/micro_speech/feature_provider_mock_test.cc
deleted file mode 100644
index 6fe5e43..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/feature_provider_mock_test.cc
+++ /dev/null
@@ -1,58 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/micro/examples/micro_speech/feature_provider.h"
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h"
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/no_micro_features_data.h"
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/yes_micro_features_data.h"
-#include "tensorflow/lite/micro/testing/micro_test.h"
-
-TF_LITE_MICRO_TESTS_BEGIN
-
-TF_LITE_MICRO_TEST(TestFeatureProviderMockYes) {
- int8_t feature_data[kFeatureElementCount];
- FeatureProvider feature_provider(kFeatureElementCount, feature_data);
-
- int how_many_new_slices = 0;
- TfLiteStatus populate_status = feature_provider.PopulateFeatureData(
- /* last_time_in_ms= */ 0, /* time_in_ms= */ 970, &how_many_new_slices);
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, populate_status);
- TF_LITE_MICRO_EXPECT_EQ(kFeatureSliceCount, how_many_new_slices);
-
- for (int i = 0; i < kFeatureElementCount; ++i) {
- TF_LITE_MICRO_EXPECT_EQ(g_yes_micro_f2e59fea_nohash_1_data[i],
- feature_data[i]);
- }
-}
-
-TF_LITE_MICRO_TEST(TestFeatureProviderMockNo) {
- int8_t feature_data[kFeatureElementCount];
- FeatureProvider feature_provider(kFeatureElementCount, feature_data);
-
- int how_many_new_slices = 0;
- TfLiteStatus populate_status = feature_provider.PopulateFeatureData(
- /* last_time_in_ms= */ 4000,
- /* time_in_ms= */ 4970, &how_many_new_slices);
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, populate_status);
- TF_LITE_MICRO_EXPECT_EQ(kFeatureSliceCount, how_many_new_slices);
-
- for (int i = 0; i < kFeatureElementCount; ++i) {
- TF_LITE_MICRO_EXPECT_EQ(g_no_micro_f9643d42_nohash_4_data[i],
- feature_data[i]);
- }
-}
-
-TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/examples/micro_speech/feature_provider_test.cc b/tensorflow/lite/micro/examples/micro_speech/feature_provider_test.cc
deleted file mode 100644
index 2582e8c..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/feature_provider_test.cc
+++ /dev/null
@@ -1,35 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/feature_provider.h"
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h"
-#include "tensorflow/lite/micro/testing/micro_test.h"
-
-TF_LITE_MICRO_TESTS_BEGIN
-
-TF_LITE_MICRO_TEST(TestFeatureProvider) {
- int8_t feature_data[kFeatureElementCount];
- FeatureProvider feature_provider(kFeatureElementCount, feature_data);
-
- int how_many_new_slices = 0;
- TfLiteStatus populate_status = feature_provider.PopulateFeatureData(
- /* last_time_in_ms= */ 0, /* time_in_ms= */ 10000, &how_many_new_slices);
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, populate_status);
- TF_LITE_MICRO_EXPECT_EQ(kFeatureSliceCount, how_many_new_slices);
-}
-
-TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/examples/micro_speech/images/animation_on_arduino.gif b/tensorflow/lite/micro/examples/micro_speech/images/animation_on_arduino.gif
deleted file mode 100644
index 66ab9c1..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/images/animation_on_arduino.gif
+++ /dev/null
Binary files differ
diff --git a/tensorflow/lite/micro/examples/micro_speech/images/audio_preprocessor_int8.png b/tensorflow/lite/micro/examples/micro_speech/images/audio_preprocessor_int8.png
new file mode 100644
index 0000000..a5c91fa
--- /dev/null
+++ b/tensorflow/lite/micro/examples/micro_speech/images/audio_preprocessor_int8.png
Binary files differ
diff --git a/tensorflow/lite/micro/examples/micro_speech/images/micro_speech_quantized.png b/tensorflow/lite/micro/examples/micro_speech/images/micro_speech_quantized.png
new file mode 100644
index 0000000..59e98c6
--- /dev/null
+++ b/tensorflow/lite/micro/examples/micro_speech/images/micro_speech_quantized.png
Binary files differ
diff --git a/tensorflow/lite/micro/examples/micro_speech/images/model_architecture.png b/tensorflow/lite/micro/examples/micro_speech/images/model_architecture.png
deleted file mode 100644
index ce91fad..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/images/model_architecture.png
+++ /dev/null
Binary files differ
diff --git a/tensorflow/lite/micro/examples/micro_speech/main.cc b/tensorflow/lite/micro/examples/micro_speech/main.cc
deleted file mode 100644
index f35c472..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/main.cc
+++ /dev/null
@@ -1,27 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/main_functions.h"
-
-// This is the default main used on systems that have the standard C entry
-// point. Other devices (for example FreeRTOS or ESP32) that have different
-// requirements for entry code (like an app_main function) should specialize
-// this main.cc file in a target-specific subfolder.
-int main(int argc, char* argv[]) {
- setup();
- while (true) {
- loop();
- }
-}
diff --git a/tensorflow/lite/micro/examples/micro_speech/main_functions.cc b/tensorflow/lite/micro/examples/micro_speech/main_functions.cc
deleted file mode 100644
index c92636a..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/main_functions.cc
+++ /dev/null
@@ -1,163 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/main_functions.h"
-
-#include "tensorflow/lite/micro/examples/micro_speech/audio_provider.h"
-#include "tensorflow/lite/micro/examples/micro_speech/command_responder.h"
-#include "tensorflow/lite/micro/examples/micro_speech/feature_provider.h"
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h"
-#include "tensorflow/lite/micro/examples/micro_speech/micro_speech_model_data.h"
-#include "tensorflow/lite/micro/examples/micro_speech/recognize_commands.h"
-#include "tensorflow/lite/micro/micro_interpreter.h"
-#include "tensorflow/lite/micro/micro_log.h"
-#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
-#include "tensorflow/lite/micro/system_setup.h"
-#include "tensorflow/lite/schema/schema_generated.h"
-
-// Globals, used for compatibility with Arduino-style sketches.
-namespace {
-const tflite::Model* model = nullptr;
-tflite::MicroInterpreter* interpreter = nullptr;
-TfLiteTensor* model_input = nullptr;
-FeatureProvider* feature_provider = nullptr;
-RecognizeCommands* recognizer = nullptr;
-int32_t previous_time = 0;
-
-// Create an area of memory to use for input, output, and intermediate arrays.
-// The size of this will depend on the model you're using, and may need to be
-// determined by experimentation.
-constexpr int kTensorArenaSize = 10 * 1024;
-uint8_t tensor_arena[kTensorArenaSize];
-int8_t feature_buffer[kFeatureElementCount];
-int8_t* model_input_buffer = nullptr;
-} // namespace
-
-// The name of this function is important for Arduino compatibility.
-void setup() {
- tflite::InitializeTarget();
-
- // Map the model into a usable data structure. This doesn't involve any
- // copying or parsing, it's a very lightweight operation.
- model = tflite::GetModel(g_micro_speech_model_data);
- if (model->version() != TFLITE_SCHEMA_VERSION) {
- MicroPrintf(
- "Model provided is schema version %d not equal "
- "to supported version %d.",
- model->version(), TFLITE_SCHEMA_VERSION);
- return;
- }
-
- // Pull in only the operation implementations we need.
- // This relies on a complete list of all the ops needed by this graph.
-
- // NOLINTNEXTLINE(runtime-global-variables)
- static tflite::MicroMutableOpResolver<4> micro_op_resolver;
- if (micro_op_resolver.AddDepthwiseConv2D() != kTfLiteOk) {
- return;
- }
- if (micro_op_resolver.AddFullyConnected() != kTfLiteOk) {
- return;
- }
- if (micro_op_resolver.AddSoftmax() != kTfLiteOk) {
- return;
- }
- if (micro_op_resolver.AddReshape() != kTfLiteOk) {
- return;
- }
-
- // Build an interpreter to run the model with.
- static tflite::MicroInterpreter static_interpreter(
- model, micro_op_resolver, tensor_arena, kTensorArenaSize);
- interpreter = &static_interpreter;
-
- // Allocate memory from the tensor_arena for the model's tensors.
- TfLiteStatus allocate_status = interpreter->AllocateTensors();
- if (allocate_status != kTfLiteOk) {
- MicroPrintf("AllocateTensors() failed");
- return;
- }
-
- // Get information about the memory area to use for the model's input.
- model_input = interpreter->input(0);
- if ((model_input->dims->size != 2) || (model_input->dims->data[0] != 1) ||
- (model_input->dims->data[1] !=
- (kFeatureSliceCount * kFeatureSliceSize)) ||
- (model_input->type != kTfLiteInt8)) {
- MicroPrintf("Bad input tensor parameters in model");
- return;
- }
- model_input_buffer = model_input->data.int8;
-
- // Prepare to access the audio spectrograms from a microphone or other source
- // that will provide the inputs to the neural network.
- // NOLINTNEXTLINE(runtime-global-variables)
- static FeatureProvider static_feature_provider(kFeatureElementCount,
- feature_buffer);
- feature_provider = &static_feature_provider;
-
- static RecognizeCommands static_recognizer;
- recognizer = &static_recognizer;
-
- previous_time = 0;
-}
-
-// The name of this function is important for Arduino compatibility.
-void loop() {
- // Fetch the spectrogram for the current time.
- const int32_t current_time = LatestAudioTimestamp();
- int how_many_new_slices = 0;
- TfLiteStatus feature_status = feature_provider->PopulateFeatureData(
- previous_time, current_time, &how_many_new_slices);
- if (feature_status != kTfLiteOk) {
- MicroPrintf("Feature generation failed");
- return;
- }
- previous_time = current_time;
- // If no new audio samples have been received since last time, don't bother
- // running the network model.
- if (how_many_new_slices == 0) {
- return;
- }
-
- // Copy feature buffer to input tensor
- for (int i = 0; i < kFeatureElementCount; i++) {
- model_input_buffer[i] = feature_buffer[i];
- }
-
- // Run the model on the spectrogram input and make sure it succeeds.
- TfLiteStatus invoke_status = interpreter->Invoke();
- if (invoke_status != kTfLiteOk) {
- MicroPrintf("Invoke failed");
- return;
- }
-
- // Obtain a pointer to the output tensor
- TfLiteTensor* output = interpreter->output(0);
- // Determine whether a command was recognized based on the output of inference
- const char* found_command = nullptr;
- uint8_t score = 0;
- bool is_new_command = false;
- TfLiteStatus process_status = recognizer->ProcessLatestResults(
- output, current_time, &found_command, &score, &is_new_command);
- if (process_status != kTfLiteOk) {
- MicroPrintf("RecognizeCommands::ProcessLatestResults() failed");
- return;
- }
- // Do something based on the recognized command. The default implementation
- // just prints to the error console, but you should replace this with your
- // own function for a real application.
- RespondToCommand(current_time, found_command, score, is_new_command);
-}
diff --git a/tensorflow/lite/micro/examples/micro_speech/main_functions.h b/tensorflow/lite/micro/examples/micro_speech/main_functions.h
deleted file mode 100644
index 0ac0677..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/main_functions.h
+++ /dev/null
@@ -1,37 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_
-
-// Expose a C friendly interface for main functions.
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-// Initializes all data needed for the example. The name is important, and needs
-// to be setup() for Arduino compatibility.
-void setup();
-
-// Runs one iteration of data gathering and inference. This should be called
-// repeatedly from the application code. The name needs to be loop() for Arduino
-// compatibility.
-void loop();
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_features/BUILD b/tensorflow/lite/micro/examples/micro_speech/micro_features/BUILD
deleted file mode 100644
index 1077435..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/micro_features/BUILD
+++ /dev/null
@@ -1,78 +0,0 @@
-# Library for generating feature vectors from audio data
-package(
- default_visibility = ["//visibility:public"],
- # Disabling layering_check because of http://b/177257332
- features = ["-layering_check"],
- licenses = ["notice"],
-)
-
-cc_library(
- name = "micro_model_settings",
- srcs = [
- "micro_model_settings.cc",
- ],
- hdrs = [
- "micro_model_settings.h",
- ],
-)
-
-cc_library(
- name = "micro_features_test_data",
- srcs = [
- "no_micro_features_data.cc",
- "yes_micro_features_data.cc",
- ],
- hdrs = [
- "no_micro_features_data.h",
- "yes_micro_features_data.h",
- ],
-)
-
-cc_library(
- name = "micro_features_generator",
- srcs = [
- "micro_features_generator.cc",
- ],
- hdrs = [
- "micro_features_generator.h",
- ],
- deps = [
- ":micro_model_settings",
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/experimental/microfrontend/lib:frontend",
- "//tensorflow/lite/micro:micro_log",
- ],
-)
-
-cc_library(
- name = "micro_features_generator_test_data",
- srcs = [
- "no_feature_data_slice.cc",
- "yes_feature_data_slice.cc",
- ],
- hdrs = [
- "no_feature_data_slice.h",
- "yes_feature_data_slice.h",
- ],
-)
-
-cc_test(
- name = "micro_features_generator_test",
- size = "small",
- srcs = [
- "micro_features_generator_test.cc",
- ],
- tags = [
- "noasan", # TODO(b/179930607): Fix with asan.
- ],
- deps = [
- ":micro_features_generator",
- ":micro_features_generator_test_data",
- ":micro_model_settings",
- "//tensorflow/lite/c:common",
- "//tensorflow/lite/micro:micro_framework",
- "//tensorflow/lite/micro:micro_log",
- "//tensorflow/lite/micro/examples/micro_speech:audio_sample_test_data",
- "//tensorflow/lite/micro/testing:micro_test",
- ],
-)
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_features/micro_features_generator.cc b/tensorflow/lite/micro/examples/micro_speech/micro_features/micro_features_generator.cc
deleted file mode 100644
index 3dbb5d3..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/micro_features/micro_features_generator.cc
+++ /dev/null
@@ -1,113 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/micro_features_generator.h"
-
-#include <cmath>
-#include <cstring>
-
-#include "tensorflow/lite/experimental/microfrontend/lib/frontend.h"
-#include "tensorflow/lite/experimental/microfrontend/lib/frontend_util.h"
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h"
-#include "tensorflow/lite/micro/micro_log.h"
-
-namespace {
-
-FrontendState g_micro_features_state;
-bool g_is_first_time = true;
-
-} // namespace
-
-TfLiteStatus InitializeMicroFeatures() {
- FrontendConfig config;
- config.window.size_ms = kFeatureSliceDurationMs;
- config.window.step_size_ms = kFeatureSliceStrideMs;
- config.noise_reduction.smoothing_bits = 10;
- config.filterbank.num_channels = kFeatureSliceSize;
- config.filterbank.lower_band_limit = 125.0;
- config.filterbank.upper_band_limit = 7500.0;
- config.noise_reduction.smoothing_bits = 10;
- config.noise_reduction.even_smoothing = 0.025;
- config.noise_reduction.odd_smoothing = 0.06;
- config.noise_reduction.min_signal_remaining = 0.05;
- config.pcan_gain_control.enable_pcan = 1;
- config.pcan_gain_control.strength = 0.95;
- config.pcan_gain_control.offset = 80.0;
- config.pcan_gain_control.gain_bits = 21;
- config.log_scale.enable_log = 1;
- config.log_scale.scale_shift = 6;
- if (!FrontendPopulateState(&config, &g_micro_features_state,
- kAudioSampleFrequency)) {
- MicroPrintf("FrontendPopulateState() failed");
- return kTfLiteError;
- }
- g_is_first_time = true;
- return kTfLiteOk;
-}
-
-// This is not exposed in any header, and is only used for testing, to ensure
-// that the state is correctly set up before generating results.
-void SetMicroFeaturesNoiseEstimates(const uint32_t* estimate_presets) {
- for (int i = 0; i < g_micro_features_state.filterbank.num_channels; ++i) {
- g_micro_features_state.noise_reduction.estimate[i] = estimate_presets[i];
- }
-}
-
-TfLiteStatus GenerateMicroFeatures(const int16_t* input, int input_size,
- int output_size, int8_t* output,
- size_t* num_samples_read) {
- const int16_t* frontend_input;
- if (g_is_first_time) {
- frontend_input = input;
- g_is_first_time = false;
- } else {
- frontend_input = input + 160;
- }
- FrontendOutput frontend_output = FrontendProcessSamples(
- &g_micro_features_state, frontend_input, input_size, num_samples_read);
-
- for (size_t i = 0; i < frontend_output.size; ++i) {
- // These scaling values are derived from those used in input_data.py in the
- // training pipeline.
- // The feature pipeline outputs 16-bit signed integers in roughly a 0 to 670
- // range. In training, these are then arbitrarily divided by 25.6 to get
- // float values in the rough range of 0.0 to 26.0. This scaling is performed
- // for historical reasons, to match up with the output of other feature
- // generators.
- // The process is then further complicated when we quantize the model. This
- // means we have to scale the 0.0 to 26.0 real values to the -128 to 127
- // signed integer numbers.
- // All this means that to get matching values from our integer feature
- // output into the tensor input, we have to perform:
- // input = (((feature / 25.6) / 26.0) * 256) - 128
- // To simplify this and perform it in 32-bit integer math, we rearrange to:
- // input = (feature * 256) / (25.6 * 26.0) - 128
- constexpr int32_t value_scale = 256;
- constexpr int32_t value_div = static_cast<int32_t>((25.6f * 26.0f) + 0.5f);
- int32_t value =
- ((frontend_output.values[i] * value_scale) + (value_div / 2)) /
- value_div;
- value -= 128;
- if (value < -128) {
- value = -128;
- }
- if (value > 127) {
- value = 127;
- }
- output[i] = value;
- }
-
- return kTfLiteOk;
-}
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_features/micro_features_generator.h b/tensorflow/lite/micro/examples/micro_speech/micro_features/micro_features_generator.h
deleted file mode 100644
index 7ee0d2b..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/micro_features/micro_features_generator.h
+++ /dev/null
@@ -1,30 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_
-
-#include "tensorflow/lite/c/common.h"
-
-// Sets up any resources needed for the feature generation pipeline.
-TfLiteStatus InitializeMicroFeatures();
-
-// Converts audio sample data into a more compact form that's appropriate for
-// feeding into a neural network.
-TfLiteStatus GenerateMicroFeatures(const int16_t* input, int input_size,
- int output_size, int8_t* output,
- size_t* num_samples_read);
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_features/micro_features_generator_test.cc b/tensorflow/lite/micro/examples/micro_speech/micro_features/micro_features_generator_test.cc
deleted file mode 100644
index e41c44f..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/micro_features/micro_features_generator_test.cc
+++ /dev/null
@@ -1,95 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/micro_features_generator.h"
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/no_feature_data_slice.h"
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/yes_feature_data_slice.h"
-#include "tensorflow/lite/micro/examples/micro_speech/testdata/no_30ms_audio_data.h"
-#include "tensorflow/lite/micro/examples/micro_speech/testdata/yes_30ms_audio_data.h"
-#include "tensorflow/lite/micro/micro_log.h"
-#include "tensorflow/lite/micro/testing/micro_test.h"
-
-// This is a test-only API, not exposed in any public headers, so declare it.
-void SetMicroFeaturesNoiseEstimates(const uint32_t* estimate_presets);
-
-TF_LITE_MICRO_TESTS_BEGIN
-
-TF_LITE_MICRO_TEST(TestMicroFeaturesGeneratorYes) {
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, InitializeMicroFeatures());
-
- // The micro features pipeline retains state from previous calls to help
- // estimate the background noise. Unfortunately this makes it harder to
- // exactly reproduce results in a test environment, so use a known snapshot
- // of the parameters at the point that the golden feature values were
- // created.
- const uint32_t yes_estimate_presets[] = {
- 1062898, 2644477, 1257642, 1864718, 412722, 725703, 395721, 474082,
- 173046, 255856, 158966, 153736, 69181, 199100, 144493, 227740,
- 110573, 164330, 79666, 144650, 122947, 476799, 398553, 497493,
- 322152, 1140005, 566716, 690605, 308902, 347481, 109891, 170457,
- 73901, 100975, 42963, 72325, 34183, 20207, 6640, 9468,
- };
- SetMicroFeaturesNoiseEstimates(yes_estimate_presets);
-
- int8_t yes_calculated_data[g_yes_feature_data_slice_size] = {};
- size_t num_samples_read = 0;
- TfLiteStatus yes_status = GenerateMicroFeatures(
- g_yes_30ms_audio_data, g_yes_30ms_audio_data_size,
- g_yes_feature_data_slice_size, yes_calculated_data, &num_samples_read);
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, yes_status);
-
- for (int i = 0; i < g_yes_feature_data_slice_size; ++i) {
- const int expected = g_yes_feature_data_slice[i];
- const int actual = yes_calculated_data[i];
- TF_LITE_MICRO_EXPECT_EQ(expected, actual);
- if (expected != actual) {
- MicroPrintf("Expected value %d but found %d", expected, actual);
- }
- }
-}
-
-TF_LITE_MICRO_TEST(TestMicroFeaturesGeneratorNo) {
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, InitializeMicroFeatures());
- // As we did for the previous features, set known good noise state
- // parameters.
- const uint32_t no_estimate_presets[] = {
- 2563964, 1909393, 559801, 538670, 203643, 175959, 75088, 139491,
- 59691, 95307, 43865, 129263, 52517, 80058, 51330, 100731,
- 76674, 76262, 15497, 22598, 13778, 21460, 8946, 17806,
- 10023, 18810, 8002, 10842, 7578, 9983, 6267, 10759,
- 8946, 18488, 9691, 39785, 9939, 17835, 9671, 18512,
- };
- SetMicroFeaturesNoiseEstimates(no_estimate_presets);
-
- int8_t no_calculated_data[g_no_feature_data_slice_size] = {};
- size_t num_samples_read = 0;
- TfLiteStatus no_status = GenerateMicroFeatures(
- g_no_30ms_audio_data, g_no_30ms_audio_data_size,
- g_no_feature_data_slice_size, no_calculated_data, &num_samples_read);
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, no_status);
-
- for (size_t i = 0; i < g_no_feature_data_slice_size; ++i) {
- const int expected = g_no_feature_data_slice[i];
- const int actual = no_calculated_data[i];
- TF_LITE_MICRO_EXPECT_EQ(expected, actual);
- if (expected != actual) {
- MicroPrintf("Expected value %d but found %d", expected, actual);
- }
- }
-}
-
-TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.cc b/tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.cc
deleted file mode 100644
index 47d12ba..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.cc
+++ /dev/null
@@ -1,23 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h"
-
-const char* kCategoryLabels[kCategoryCount] = {
- "silence",
- "unknown",
- "yes",
- "no",
-};
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h b/tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h
deleted file mode 100644
index e542213..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h
+++ /dev/null
@@ -1,43 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_
-
-// Keeping these as constant expressions allow us to allocate fixed-sized arrays
-// on the stack for our working memory.
-
-// The size of the input time series data we pass to the FFT to produce the
-// frequency information. This has to be a power of two, and since we're dealing
-// with 30ms of 16KHz inputs, which means 480 samples, this is the next value.
-constexpr int kMaxAudioSampleSize = 512;
-constexpr int kAudioSampleFrequency = 16000;
-
-// The following values are derived from values used during model training.
-// If you change the way you preprocess the input, update all these constants.
-constexpr int kFeatureSliceSize = 40;
-constexpr int kFeatureSliceCount = 49;
-constexpr int kFeatureElementCount = (kFeatureSliceSize * kFeatureSliceCount);
-constexpr int kFeatureSliceStrideMs = 20;
-constexpr int kFeatureSliceDurationMs = 30;
-
-// Variables for the model's output categories.
-constexpr int kSilenceIndex = 0;
-constexpr int kUnknownIndex = 1;
-// If you modify the output categories, you need to update the following values.
-constexpr int kCategoryCount = 4;
-extern const char* kCategoryLabels[kCategoryCount];
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_features/no_feature_data_slice.cc b/tensorflow/lite/micro/examples/micro_speech/micro_features/no_feature_data_slice.cc
deleted file mode 100644
index 684f702..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/micro_features/no_feature_data_slice.cc
+++ /dev/null
@@ -1,25 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// See the header for documentation on the meaning of this data.
-
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/no_feature_data_slice.h"
-
-alignas(16) const int8_t
- g_no_feature_data_slice[g_no_feature_data_slice_size] = {
- 89, 68, 96, 83, 111, 96, 115, 87, 99, 76, 105, 84, 105, 86,
- 113, 91, 108, 87, 110, 78, 80, 46, 22, 74, 88, 72, 103, 86,
- 80, 68, 48, 24, 68, 48, 55, 36, 108, 90, 90, 63,
-};
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_features/no_feature_data_slice.h b/tensorflow/lite/micro/examples/micro_speech/micro_features/no_feature_data_slice.h
deleted file mode 100644
index 01e6605..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/micro_features/no_feature_data_slice.h
+++ /dev/null
@@ -1,29 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// This data was extracted from the larger feature data held in
-// no_features_data.cc and consists of the 29th spectrogram slice of 43 values.
-// This is the expected result of running the sample data in
-// no_30ms_sample_data.cc through the preprocessing pipeline.
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_FEATURE_DATA_SLICE_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_FEATURE_DATA_SLICE_H_
-
-#include <cstdint>
-
-constexpr int g_no_feature_data_slice_size = 40;
-extern const int8_t g_no_feature_data_slice[];
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_FEATURE_DATA_SLICE_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_features/no_micro_features_data.cc b/tensorflow/lite/micro/examples/micro_speech/micro_features/no_micro_features_data.cc
deleted file mode 100644
index f481486..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/micro_features/no_micro_features_data.cc
+++ /dev/null
@@ -1,188 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/no_micro_features_data.h"
-
-// Golden test values for the expected spectrogram from a "no" sample file
-// speech_commands_test_set_v0.02/no/f9643d42_nohash_4.wav.
-
-const int g_no_micro_f9643d42_nohash_4_width = 40;
-const int g_no_micro_f9643d42_nohash_4_height = 49;
-alignas(16) const signed char g_no_micro_f9643d42_nohash_4_data[] = {
- 103, 78, 64, 76, 75, 54, 53, 67, 77, 60, 56, 70,
- 76, 71, 68, 58, 74, 32, 23, -2, -18, 11, 13, 15,
- 9, 20, 5, -7, -18, -2, -10, -18, -10, -12, 9, 7,
- -33, -12, -4, -18, 57, 17, 55, 62, 70, 45, 61, 37,
- 67, 52, 48, 47, 55, 46, 57, 47, 73, 17, 27, 20,
- 19, 8, 15, -6, -1, 10, -12, -29, -6, -23, -18, -3,
- -1, 5, 3, -4, -12, -8, -1, -14, 65, 48, 58, 43,
- 48, 19, 39, 39, 57, 57, 58, 55, 67, 58, 49, 50,
- 70, 27, 9, 16, 37, 4, 25, 4, 11, 9, 7, -33,
- -7, -12, 3, -6, -29, -7, -7, -18, -12, -18, -2, -1,
- 0, 31, 60, -8, 51, 59, 70, 40, 71, 57, 52, 38,
- 66, 48, 17, 6, 59, 8, 15, 7, 18, 4, 18, -23,
- -8, -4, -3, -12, -3, -26, 1, 10, 2, -29, -29, -37,
- -7, -4, 6, -33, 67, 44, 59, -4, 64, 51, 68, 55,
- 74, 9, 40, 15, 57, 33, 60, 18, 40, 25, 27, -20,
- 25, -16, 6, 17, -10, -12, -23, -43, -23, -23, -29, -37,
- -4, -16, -16, -60, -20, -23, -10, -29, -12, 15, 12, -37,
- 27, 15, 61, 44, 50, 8, 48, 22, 49, -18, 46, 33,
- 42, 34, 46, -8, 4, -18, -43, -43, -10, 1, -10, -16,
- -10, -77, -16, -33, 11, -26, -23, -37, 0, -8, -16, -29,
- 42, 40, 68, 24, 47, 46, 53, -128, 30, 2, 42, 21,
- 21, -4, 43, 2, 43, 5, 32, -26, 7, -37, -43, -23,
- -2, -8, 2, -37, -50, -60, -1, -7, -33, -77, -6, -18,
- -16, -50, -12, -33, 53, 8, 52, 18, 51, 35, 69, 26,
- 44, 8, 27, -128, 21, -33, 17, -14, 38, -128, -14, -18,
- 17, -20, -14, -37, 8, -60, -33, -33, -33, -43, -12, -29,
- -12, -128, -33, -60, -26, -77, -26, -50, 57, 29, 11, 30,
- 53, -10, 45, 15, 18, -10, 42, 2, 31, -29, 10, -4,
- 42, -37, -50, -128, -4, -43, -20, -77, -14, -26, -33, -128,
- -12, -43, -8, -33, -33, -60, -43, -77, -12, -60, -26, -50,
- 40, -23, 36, 35, 50, -2, 37, 27, 26, -77, 49, -7,
- 28, -43, 6, 11, 41, -37, 33, -26, -14, -12, -6, -33,
- -16, -26, -20, -77, -14, -43, -8, -50, -14, -37, -26, -77,
- -26, -77, -14, -29, 50, -60, 25, -26, 57, 38, 51, 1,
- 50, 1, 53, -18, 30, -23, 11, -128, 18, -43, 20, -26,
- -10, -26, -12, -128, -50, -60, -37, -77, -20, -43, -50, -128,
- -77, -128, -77, -128, -33, -77, -20, -60, 53, -10, -37, -128,
- 10, -128, 60, 18, -8, 13, 37, -37, 8, -128, 3, -77,
- 32, -29, 14, 10, -12, -77, -37, -77, -37, -60, -23, -128,
- -43, -50, -16, -77, -6, -33, 0, -60, -43, -128, -16, -60,
- 20, -2, 51, 19, 43, 2, 63, 20, 60, -4, 42, -50,
- 4, -128, 2, -3, 32, -33, -26, -128, -18, -128, -33, -43,
- -7, -60, -50, -77, -29, -77, -23, -128, -16, -26, -23, -60,
- -37, -77, -37, -128, -1, -33, 39, 48, 60, 5, 8, -128,
- 44, 11, 4, 0, 13, -77, -2, -20, 33, -128, -33, -77,
- -8, -128, -14, -128, -33, -18, -12, -77, -16, -128, -37, -128,
- -12, -77, -60, -128, -23, -60, -23, -128, 36, -50, 46, -128,
- 66, 39, 18, -14, -12, -77, -20, -6, 24, -128, 28, -26,
- 21, -77, -6, -33, 1, -128, -43, -128, -1, -50, -37, -128,
- -50, -128, -33, -128, -18, -128, -60, -8, -7, -60, -60, -128,
- -6, -29, 20, -1, 73, 40, -43, -14, 33, -43, 33, -3,
- 15, -29, 29, -43, 20, -60, -29, -128, -20, -26, 4, -77,
- -16, -60, -33, -50, -29, -128, -60, -128, -77, -128, -37, -50,
- 0, -77, -33, -128, 39, 8, 47, 10, 62, 16, 2, 1,
- 10, 7, 4, -7, 6, -128, -77, -50, 19, -77, -77, -128,
- -77, -128, -50, -128, -60, -60, -33, -50, -37, -128, -128, -128,
- -60, -128, -37, -60, -18, -128, -33, -77, 37, 23, 29, -128,
- -128, -128, -16, -128, -16, -33, 21, -20, -8, -60, -2, -60,
- 11, -128, -50, -128, -50, -128, -29, -77, -16, -128, -26, -128,
- -50, -77, -43, -128, -128, -128, -50, -128, -33, -128, -33, -50,
- -23, -128, 24, -128, -128, -77, 4, -23, 32, -128, 1, -26,
- -14, -128, 10, -77, -4, -128, 1, -50, -8, -77, -77, -77,
- -23, -128, -50, -43, -33, -128, -43, -128, -128, -128, -43, -128,
- -50, -128, -128, -128, 44, 15, 14, -128, 9, -128, 21, 0,
- 29, -7, 18, -7, -7, -128, -33, -50, 14, -60, -60, -128,
- -60, -128, -37, -128, -43, -128, -20, -128, -50, -128, -43, -77,
- -26, -128, -60, -50, -60, -128, -77, -128, -3, -128, 14, -77,
- -26, 11, 47, -77, -7, -77, 45, -43, -12, 14, 37, -60,
- 22, -4, 5, -77, -14, -128, -10, -60, 22, -77, -12, -60,
- -50, -128, -60, -128, -60, -128, -43, -128, -50, -128, -77, -50,
- 27, -37, 33, -128, 4, -29, -4, -50, -20, -128, 6, -37,
- -33, -128, -50, -128, 34, 15, -43, -128, -20, -50, -3, -37,
- -37, -77, -77, -128, -43, -128, -128, -128, 4, -26, -26, 27,
- 0, -128, -29, -60, 35, -26, 23, -128, -29, -77, 19, 14,
- 28, -128, -16, -7, 31, -1, 17, 11, 60, 44, 8, 11,
- 18, -128, -33, -60, -1, -128, -43, -128, -23, -128, -128, -128,
- 59, 43, 35, 61, 37, -77, -77, -50, 116, 88, 98, 69,
- 78, 53, 78, 40, 48, 7, 29, -18, -2, -14, 5, 12,
- 65, 35, 31, -12, 33, -2, -6, -1, 44, -29, -14, -60,
- -4, -43, -37, -128, 29, 18, 38, 51, 8, -128, -12, -37,
- 115, 91, 113, 77, 89, 36, 60, 44, 49, 36, 27, 31,
- 63, 30, 62, 14, 55, 49, 42, 0, 45, 17, -23, 1,
- 30, -37, -50, -77, -8, -60, 9, -60, -12, -50, 13, 4,
- 23, -6, 28, 13, 107, 78, 101, 73, 89, 46, 63, 17,
- 34, -43, -6, 30, 67, 40, 77, 21, 53, 39, 38, 12,
- -6, 5, 28, -2, 18, -43, 0, -128, -29, -77, 18, -128,
- -2, -77, 39, 35, 38, 35, 50, 29, 100, 70, 94, 69,
- 86, 50, 45, 38, 45, 12, 58, 64, 74, 36, 77, 45,
- 78, 62, 8, -60, 38, 6, 21, 7, 8, -37, -1, -20,
- 48, -37, 8, -10, 8, 13, 45, 39, 38, 22, 49, 25,
- 94, 63, 87, 66, 84, -128, 29, 20, 55, 51, 80, 36,
- 62, 30, 81, 72, 68, 37, 51, 27, 54, 22, 16, -29,
- 4, 9, 57, 15, 35, -43, -77, -20, 4, 6, 37, -1,
- 40, 31, 47, 14, 89, 68, 96, 83, 111, 96, 115, 87,
- 99, 76, 105, 84, 105, 86, 113, 91, 108, 87, 110, 78,
- 80, 46, 22, 74, 88, 72, 103, 86, 80, 68, 48, 24,
- 68, 48, 55, 36, 108, 90, 90, 63, 83, 63, 87, 64,
- 90, 92, 113, 88, 102, 79, 109, 83, 100, 89, 109, 60,
- 56, 21, 75, 62, 81, 45, 63, 73, 93, 65, 94, 80,
- 89, 81, 73, 3, 43, 60, 102, 70, 84, 67, 99, 74,
- 78, 57, 79, 50, 93, 82, 98, 56, 77, 70, 91, 71,
- 85, 82, 86, 13, 45, -18, 48, 40, 53, 28, 85, 60,
- 65, 52, 86, 78, 76, 46, 73, 19, 35, 54, 75, 40,
- 71, 60, 82, 37, 69, 42, 62, 40, 96, 70, 85, 77,
- 70, 68, 103, 84, 94, 69, 81, -128, -128, -128, -43, -37,
- 40, 2, 48, 45, 76, 37, 65, 16, 43, 18, 58, 20,
- 27, 12, 71, 31, 53, 44, 88, 47, 50, 33, 39, 8,
- 89, 57, 88, 69, 72, 63, 100, 68, 81, -77, -10, -128,
- -128, -128, -128, -128, 13, -77, 8, 27, 60, 28, 41, -128,
- -37, -128, 28, -43, -18, -128, 47, -37, 45, 27, 51, -29,
- 15, 39, 52, 30, 49, -33, 65, 15, 76, 71, 90, 19,
- 46, -128, -16, -128, -128, -128, -128, -128, -128, -128, -18, -128,
- -20, -128, 32, -128, 21, -33, 45, -128, -128, -128, -12, -128,
- -6, -14, 43, -128, -128, -128, -128, -128, 52, -18, 69, -43,
- 78, 55, 42, -128, -29, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, 14, -128, -16, -128, -128, -128, 7, -128,
- -128, -128, -128, -128, -128, -128, 12, -128, -128, -128, -128, -16,
- 59, -50, 35, -128, 42, 0, 47, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -33, -128, -23, -128,
- -128, -128, -23, -128, -128, -128, -128, -128, -128, -128, -33, -128,
- -128, -128, -128, -128, -128, -128, -8, -128, 36, -50, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -37, -128, -128, -60, -10, -128, -128, -128, -128, -128,
- -128, -128, 21, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -12, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -77, -128, -128, -128, -29, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -29, -128, -128, -128, -128, -128, -128, -128, -128, -128, -50, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128,
-};
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_features/no_micro_features_data.h b/tensorflow/lite/micro/examples/micro_speech/micro_features/no_micro_features_data.h
deleted file mode 100644
index 8c1b6d5..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/micro_features/no_micro_features_data.h
+++ /dev/null
@@ -1,23 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_
-
-extern const int g_no_micro_f9643d42_nohash_4_width;
-extern const int g_no_micro_f9643d42_nohash_4_height;
-extern const signed char g_no_micro_f9643d42_nohash_4_data[];
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_features/yes_feature_data_slice.cc b/tensorflow/lite/micro/examples/micro_speech/micro_features/yes_feature_data_slice.cc
deleted file mode 100644
index e3d006a..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/micro_features/yes_feature_data_slice.cc
+++ /dev/null
@@ -1,25 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// See the header for documentation on the meaning of this data.
-
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/yes_feature_data_slice.h"
-
-alignas(16) const int8_t
- g_yes_feature_data_slice[g_yes_feature_data_slice_size] = {
- 86, 88, 108, 75, 108, 76, 98, 64, 75, 61, 71, 66, 85, -1,
- -77, -128, 46, 61, 92, 69, 100, 93, 113, 80, 108, 93, 113, 91,
- 110, 80, 85, 15, -33, -128, 12, -50, 34, 50, 70, 55,
-};
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_features/yes_feature_data_slice.h b/tensorflow/lite/micro/examples/micro_speech/micro_features/yes_feature_data_slice.h
deleted file mode 100644
index 18faadc..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/micro_features/yes_feature_data_slice.h
+++ /dev/null
@@ -1,29 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// This data was extracted from the larger feature data held in
-// no_micro_features_data.cc and consists of the 26th spectrogram slice of 40
-// values. This is the expected result of running the sample data in
-// yes_30ms_sample_data.cc through the preprocessing pipeline.
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_FEATURE_DATA_SLICE_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_FEATURE_DATA_SLICE_H_
-
-#include <cstdint>
-
-constexpr int g_yes_feature_data_slice_size = 40;
-extern const int8_t g_yes_feature_data_slice[];
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_FEATURE_DATA_SLICE_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_features/yes_micro_features_data.cc b/tensorflow/lite/micro/examples/micro_speech/micro_features/yes_micro_features_data.cc
deleted file mode 100644
index 7ee5387..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/micro_features/yes_micro_features_data.cc
+++ /dev/null
@@ -1,188 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/yes_micro_features_data.h"
-
-// Golden test values for the expected spectrogram from a "yes" sample file
-// speech_commands_test_set_v0.02/yes/f2e59fea_nohash_1.wav.
-
-const int g_yes_micro_f2e59fea_nohash_1_width = 40;
-const int g_yes_micro_f2e59fea_nohash_1_height = 49;
-alignas(16) const signed char g_yes_micro_f2e59fea_nohash_1_data[] = {
- 116, 98, 118, 95, 106, 85, 101, 81, 67, -18, -33, -12,
- -26, -128, 9, 34, 56, 45, 9, -12, 5, 30, 23, 28,
- 0, -18, 0, -128, -60, -50, -50, -37, -60, -60, -50, -26,
- -33, -50, -33, -50, 83, 61, 81, 55, 76, 61, 73, 64,
- 38, -8, -37, -20, -18, -20, 48, 29, 52, 41, 55, 18,
- 25, 37, 44, 37, 8, 15, -6, -60, -128, -50, -37, -37,
- -18, -37, -26, -29, -37, -60, -50, -60, 95, 59, 52, -4,
- 54, -18, 68, 43, 31, -18, -26, -33, -37, -29, 33, 7,
- -3, 8, 26, 24, 36, 6, 36, 23, 14, 8, -29, -37,
- -37, -37, -50, -50, -26, -8, -26, -37, -18, -37, -60, -77,
- 50, 48, 83, 44, 56, -128, -33, -60, 1, -26, -60, -43,
- -14, -23, -18, -43, -26, -33, 13, -77, -43, -77, -33, -37,
- 16, -12, -37, -50, -50, -77, -20, -43, -60, -128, -60, -77,
- -37, -77, -60, -128, 37, -10, 65, -7, 28, -128, 10, -77,
- -37, -128, -77, -128, -77, -43, -128, -128, -77, -128, -128, -128,
- -128, -128, -14, -128, -43, -50, -37, -77, -128, -128, -77, -43,
- -29, -43, -20, -60, -37, -43, -50, -128, -77, -128, -18, -128,
- -60, -128, -128, -128, -77, -128, -77, -128, -128, -128, -60, -37,
- -20, -128, -60, -128, -128, -128, -60, -128, -77, -60, -128, -50,
- -60, -128, -77, -128, -50, -60, -37, -60, -50, -77, -77, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -37, -128,
- -128, -128, -128, -128, -77, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -77, -60, -128, -128, -50, -128, -50, -128,
- -50, -128, -77, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -77, -128, -77, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -77, -128, -77, -128, -77, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -77, -128, -128, -128,
- -128, -77, -50, -128, -128, -77, -77, -128, -128, -128, -50, -128,
- 85, 43, 65, 53, 69, 60, 45, 3, 46, -12, 9, -23,
- 32, -1, -128, -128, -128, -128, -1, 37, 38, 33, 43, 36,
- 58, 70, 68, 39, 6, 10, 32, 6, 8, -23, -77, -128,
- -29, -128, -77, -128, 101, 87, 102, 91, 110, 88, 101, 83,
- 110, 95, 111, 83, 81, 84, 106, 90, 93, 82, 98, 91,
- 108, 95, 118, 97, 118, 97, 116, 96, 113, 90, 110, 96,
- 107, 85, 94, 66, 69, 36, 29, 0, 100, 60, 105, 68,
- 92, 93, 113, 92, 107, 85, 107, 83, 104, 91, 105, 85,
- 112, 88, 101, 80, 101, 79, 96, 80, 98, 80, 105, 83,
- 98, 81, 103, 71, 100, 79, 83, 78, 91, 47, 50, 13,
- 108, 81, 93, 78, 98, 76, 105, 76, 98, 40, 77, 72,
- 81, 62, 93, 77, 96, 80, 98, 61, 97, 69, 88, 61,
- 71, 56, 98, 68, 97, 72, 89, 51, 81, 61, 88, 75,
- 86, 56, 48, 13, 71, 22, 84, 66, 76, -7, 48, 61,
- 77, 62, 91, 65, 95, 74, 88, 59, 75, 58, 83, 55,
- 87, 55, 76, 43, 76, -3, 56, 60, 79, 57, 71, 54,
- 82, 33, 74, 71, 91, 45, 18, -7, 61, 56, 77, 41,
- 73, 42, 82, 49, 59, 63, 82, 65, 66, 38, 83, 34,
- 48, -8, 46, 20, 54, 33, 54, 6, 48, 16, 60, 37,
- 58, 22, 58, 14, 65, 53, 75, -4, 42, 16, 16, -50,
- 22, -128, 80, 54, 43, -50, 42, -128, -10, -77, 28, -29,
- 68, 43, 73, 2, 25, -60, 47, 14, 45, 7, 66, 4,
- 62, 37, 71, 7, 46, -10, 44, 22, 55, 53, 57, -29,
- 26, -10, -3, -128, 38, -128, 46, -10, 16, -128, -10, -26,
- 60, -7, 65, 38, 70, -60, 35, -8, 42, -29, 6, -128,
- 34, -128, 36, -60, 44, -12, -2, -128, -7, -60, -60, -128,
- -23, -128, 31, -33, 22, -77, -37, -43, -128, -128, 3, -128,
- -23, -128, 17, -77, 43, -77, -7, -128, -20, -128, 17, -43,
- 32, -128, -43, -128, -128, -77, 21, -128, -50, -128, -128, -128,
- -128, -128, -128, -128, -37, -128, -16, -128, -50, -26, -6, -128,
- -128, -128, -128, -128, -23, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -16, -128, 36, -7, 16, -128, -128, -128, -128, -128,
- -77, -128, -37, -128, -50, -128, -128, -128, -128, -128, -18, -128,
- 11, -128, -16, -77, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -26, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -20, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -50, -128, -77, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -77, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -1, -18, 5, -128,
- 40, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, 4, -128, 63, 66, 75, -128,
- 70, 60, 34, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- 87, 86, 95, 76, 91, 62, 72, -6, -50, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, 64, 83, 104, 70,
- 98, 90, 111, 89, 109, 80, 71, -128, -128, -128, -128, -128,
- -20, -6, 27, 33, 86, 88, 108, 75, 108, 76, 98, 64,
- 75, 61, 71, 66, 85, -1, -77, -128, 46, 61, 92, 69,
- 100, 93, 113, 80, 108, 93, 113, 91, 110, 80, 85, 15,
- -33, -128, 12, -50, 34, 50, 70, 55, 84, 72, 108, 81,
- 111, 88, 100, 80, 84, 73, 97, 86, 99, 65, 85, 43,
- 96, 78, 107, 94, 118, 98, 115, 92, 118, 94, 111, 93,
- 111, 86, 99, 52, 32, -16, 48, 31, 81, 74, 85, 64,
- 78, 64, 98, 70, 110, 92, 96, 73, 100, 72, 94, 73,
- 98, 76, 85, 67, 101, 83, 101, 83, 112, 89, 98, 85,
- 105, 78, 98, 72, 102, 80, 95, 23, 19, -8, 52, 57,
- 103, 91, 95, 65, 74, 8, 77, 49, 96, 76, 100, 87,
- 105, 81, 94, 62, 94, 78, 81, 72, 99, 82, 101, 78,
- 108, 65, 82, 70, 100, 63, 79, 58, 80, 59, 87, 48,
- 50, 57, 93, 67, 86, 80, 103, 56, 77, 31, 81, 57,
- 62, 41, 96, 85, 91, 71, 101, 76, 89, 78, 95, 76,
- 96, 79, 103, 81, 103, 48, 70, 57, 88, 66, 84, 11,
- 85, 67, 104, 37, 38, 67, 90, 54, 81, 62, 90, 52,
- 78, -60, 54, -8, 68, 40, 55, 8, 77, 52, 66, 31,
- 55, 13, 60, 26, 69, 42, 63, -29, 57, -128, -3, -128,
- 3, -128, -29, -60, 52, -43, 63, 56, 86, 75, 95, 75,
- 85, 63, 82, 10, 50, -128, 31, -77, 0, -77, -23, -128,
- 12, -77, 51, -3, 58, -14, 44, 0, 48, 4, 53, 47,
- 28, -128, -128, -128, -37, -128, -3, -128, 49, 61, 100, 90,
- 117, 88, 107, 94, 112, 64, 96, 83, -128, -128, 7, -128,
- -77, -128, -23, -128, -23, -128, 16, -37, 65, -8, 48, 20,
- 14, -77, 57, -18, -43, -128, -128, -128, -128, -128, -128, -128,
- 24, 12, 74, 76, 105, 76, 99, 80, 108, 79, 103, 85,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- 42, -128, -8, -128, -50, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -60, -128, -128, 5, 73, 53, 93, 70, 101, 73,
- 94, 57, 86, 66, -18, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -50, -128, 36, -128, -128, -128, -128, -128, -20, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, 23, 37,
- 75, 54, 97, 70, 83, 52, 85, 65, 7, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -43, -128, 23, -128, -43, -128,
- -33, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -26, -37, 65, 33, 76, 37, 73, 50, 77, 47,
- -12, -128, -128, -128, -128, -128, -128, -128, -128, -128, -7, -14,
- -4, -128, -14, -128, 18, -60, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -26, -60, 71, 42, 68, 53,
- 81, 49, 73, 36, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -18, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, 15, -26,
- 44, -18, 59, 39, 57, 20, 62, 26, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, 49, -128, 30, 8, 69, 27, 62, 38,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -43, -128, 28, -37, 48, -10,
- 48, 11, 74, 37, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -77, -128, 11, -128, -7, -60, -77, -4, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -8, -128, -50, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128,
- -128, -128, -128, -128,
-};
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_features/yes_micro_features_data.h b/tensorflow/lite/micro/examples/micro_speech/micro_features/yes_micro_features_data.h
deleted file mode 100644
index cd1ad10..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/micro_features/yes_micro_features_data.h
+++ /dev/null
@@ -1,23 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_MICRO_FEATURES_DATA_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_MICRO_FEATURES_DATA_H_
-
-extern const int g_yes_micro_f2e59fea_nohash_1_width;
-extern const int g_yes_micro_f2e59fea_nohash_1_height;
-extern const signed char g_yes_micro_f2e59fea_nohash_1_data[];
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_MICRO_FEATURES_DATA_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_model_settings.h b/tensorflow/lite/micro/examples/micro_speech/micro_model_settings.h
new file mode 100644
index 0000000..9d5b046
--- /dev/null
+++ b/tensorflow/lite/micro/examples/micro_speech/micro_model_settings.h
@@ -0,0 +1,37 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_MODEL_SETTINGS_H_
+#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_MODEL_SETTINGS_H_
+
+// The following values are derived from values used during model training.
+// If you change the way you preprocess the input, update all these constants.
+constexpr int kAudioSampleFrequency = 16000;
+constexpr int kFeatureSize = 40;
+constexpr int kFeatureCount = 49;
+constexpr int kFeatureElementCount = (kFeatureSize * kFeatureCount);
+constexpr int kFeatureStrideMs = 20;
+constexpr int kFeatureDurationMs = 30;
+
+// Variables for the model's output categories.
+constexpr int kCategoryCount = 4;
+constexpr const char* kCategoryLabels[kCategoryCount] = {
+ "silence",
+ "unknown",
+ "yes",
+ "no",
+};
+
+#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_MODEL_SETTINGS_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_speech_binary_mock_test.sh b/tensorflow/lite/micro/examples/micro_speech/micro_speech_binary_mock_test.sh
deleted file mode 100755
index 0515d7c..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/micro_speech_binary_mock_test.sh
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/bin/bash
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-#
-# Bash unit tests for the example binary.
-
-set -e
-
-OUTPUT_LOG_FILE=${TEST_TMPDIR}/output_log.txt
-${TEST_SRCDIR}/${TEST_WORKSPACE}/tensorflow/lite/micro/examples/micro_speech/micro_speech_mock 2>&1 | head > ${OUTPUT_LOG_FILE}
-
-if ! grep -q 'Heard ' ${OUTPUT_LOG_FILE}; then
- echo "ERROR: Expected logs not found in output '${OUTPUT_LOG_FILE}'"
- exit 1
-fi
-
-echo
-echo "SUCCESS: micro_speech_binary_mock_test PASSED"
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_speech_test.cc b/tensorflow/lite/micro/examples/micro_speech/micro_speech_test.cc
index 56cb156..0191958 100644
--- a/tensorflow/lite/micro/examples/micro_speech/micro_speech_test.cc
+++ b/tensorflow/lite/micro/examples/micro_speech/micro_speech_test.cc
@@ -1,4 +1,4 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -13,132 +13,268 @@
limitations under the License.
==============================================================================*/
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/no_micro_features_data.h"
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/yes_micro_features_data.h"
-#include "tensorflow/lite/micro/examples/micro_speech/micro_speech_model_data.h"
+#include <algorithm>
+#include <cstdint>
+#include <iterator>
+
+#include "tensorflow/lite/core/c/common.h"
+#include "tensorflow/lite/micro/examples/micro_speech/micro_model_settings.h"
+#include "tensorflow/lite/micro/examples/micro_speech/models/audio_preprocessor_int8_model_data.h"
+#include "tensorflow/lite/micro/examples/micro_speech/models/micro_speech_quantized_model_data.h"
+#include "tensorflow/lite/micro/examples/micro_speech/testdata/no_1000ms_audio_data.h"
+#include "tensorflow/lite/micro/examples/micro_speech/testdata/no_30ms_audio_data.h"
+#include "tensorflow/lite/micro/examples/micro_speech/testdata/noise_1000ms_audio_data.h"
+#include "tensorflow/lite/micro/examples/micro_speech/testdata/silence_1000ms_audio_data.h"
+#include "tensorflow/lite/micro/examples/micro_speech/testdata/yes_1000ms_audio_data.h"
+#include "tensorflow/lite/micro/examples/micro_speech/testdata/yes_30ms_audio_data.h"
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "tensorflow/lite/micro/micro_log.h"
#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
#include "tensorflow/lite/micro/testing/micro_test.h"
-#include "tensorflow/lite/schema/schema_generated.h"
+
+namespace {
+
+// Arena size is a guesstimate, followed by use of
+// MicroInterpreter::arena_used_bytes() on both the AudioPreprocessor and
+// MicroSpeech models and using the larger of the two results.
+constexpr size_t kArenaSize = 28584; // xtensa p6
+alignas(16) uint8_t g_arena[kArenaSize];
+
+using Features = int8_t[kFeatureCount][kFeatureSize];
+Features g_features;
+
+constexpr int kAudioSampleDurationCount =
+ kFeatureDurationMs * kAudioSampleFrequency / 1000;
+constexpr int kAudioSampleStrideCount =
+ kFeatureStrideMs * kAudioSampleFrequency / 1000;
+
+using MicroSpeechOpResolver = tflite::MicroMutableOpResolver<4>;
+using AudioPreprocessorOpResolver = tflite::MicroMutableOpResolver<18>;
+
+TfLiteStatus RegisterOps(MicroSpeechOpResolver& op_resolver) {
+ TF_LITE_ENSURE_STATUS(op_resolver.AddReshape());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddFullyConnected());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddDepthwiseConv2D());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddSoftmax());
+ return kTfLiteOk;
+}
+
+TfLiteStatus RegisterOps(AudioPreprocessorOpResolver& op_resolver) {
+ TF_LITE_ENSURE_STATUS(op_resolver.AddReshape());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddCast());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddStridedSlice());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddConcatenation());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddMul());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddAdd());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddDiv());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddMinimum());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddMaximum());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddWindow());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddFftAutoScale());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddRfft());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddEnergy());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddFilterBank());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddFilterBankSquareRoot());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddFilterBankSpectralSubtraction());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddPCAN());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddFilterBankLog());
+ return kTfLiteOk;
+}
+
+TfLiteStatus LoadMicroSpeechModelAndPerformInference(
+ const Features& features, const char* expected_label) {
+ // Map the model into a usable data structure. This doesn't involve any
+ // copying or parsing, it's a very lightweight operation.
+ const tflite::Model* model =
+ tflite::GetModel(g_micro_speech_quantized_model_data);
+ TF_LITE_MICRO_EXPECT(model->version() == TFLITE_SCHEMA_VERSION);
+ TF_LITE_MICRO_CHECK_FAIL();
+
+ MicroSpeechOpResolver op_resolver;
+ TF_LITE_MICRO_EXPECT(RegisterOps(op_resolver) == kTfLiteOk);
+ TF_LITE_MICRO_CHECK_FAIL();
+
+ tflite::MicroInterpreter interpreter(model, op_resolver, g_arena, kArenaSize);
+
+ TF_LITE_MICRO_EXPECT(interpreter.AllocateTensors() == kTfLiteOk);
+ TF_LITE_MICRO_CHECK_FAIL();
+
+ MicroPrintf("MicroSpeech model arena size = %u",
+ interpreter.arena_used_bytes());
+
+ TfLiteTensor* input = interpreter.input(0);
+ TF_LITE_MICRO_EXPECT(input != nullptr);
+ TF_LITE_MICRO_CHECK_FAIL();
+ // check input shape is compatible with our feature data size
+ TF_LITE_MICRO_EXPECT_EQ(kFeatureElementCount,
+ input->dims->data[input->dims->size - 1]);
+ TF_LITE_MICRO_CHECK_FAIL();
+
+ TfLiteTensor* output = interpreter.output(0);
+ TF_LITE_MICRO_EXPECT(output != nullptr);
+ TF_LITE_MICRO_CHECK_FAIL();
+ // check output shape is compatible with our number of prediction categories
+ TF_LITE_MICRO_EXPECT_EQ(kCategoryCount,
+ output->dims->data[output->dims->size - 1]);
+ TF_LITE_MICRO_CHECK_FAIL();
+
+ float output_scale = output->params.scale;
+ int output_zero_point = output->params.zero_point;
+
+ std::copy_n(&features[0][0], kFeatureElementCount,
+ tflite::GetTensorData<int8_t>(input));
+ TF_LITE_MICRO_EXPECT(interpreter.Invoke() == kTfLiteOk);
+ TF_LITE_MICRO_CHECK_FAIL();
+
+ // Dequantize output values
+ float category_predictions[kCategoryCount];
+ MicroPrintf("MicroSpeech category predictions for <%s>", expected_label);
+ for (int i = 0; i < kCategoryCount; i++) {
+ category_predictions[i] =
+ (tflite::GetTensorData<int8_t>(output)[i] - output_zero_point) *
+ output_scale;
+ MicroPrintf(" %.4f %s", static_cast<double>(category_predictions[i]),
+ kCategoryLabels[i]);
+ }
+ int prediction_index =
+ std::distance(std::begin(category_predictions),
+ std::max_element(std::begin(category_predictions),
+ std::end(category_predictions)));
+ TF_LITE_MICRO_EXPECT_STRING_EQ(expected_label,
+ kCategoryLabels[prediction_index]);
+ TF_LITE_MICRO_CHECK_FAIL();
+
+ return kTfLiteOk;
+}
+
+TfLiteStatus GenerateSingleFeature(const int16_t* audio_data,
+ const int audio_data_size,
+ int8_t* feature_output,
+ tflite::MicroInterpreter* interpreter) {
+ TfLiteTensor* input = interpreter->input(0);
+ TF_LITE_MICRO_EXPECT(input != nullptr);
+ TF_LITE_MICRO_CHECK_FAIL();
+ // check input shape is compatible with our audio sample size
+ TF_LITE_MICRO_EXPECT_EQ(kAudioSampleDurationCount, audio_data_size);
+ TF_LITE_MICRO_CHECK_FAIL();
+ TF_LITE_MICRO_EXPECT_EQ(kAudioSampleDurationCount,
+ input->dims->data[input->dims->size - 1]);
+ TF_LITE_MICRO_CHECK_FAIL();
+
+ TfLiteTensor* output = interpreter->output(0);
+ TF_LITE_MICRO_EXPECT(output != nullptr);
+ TF_LITE_MICRO_CHECK_FAIL();
+ // check output shape is compatible with our feature size
+ TF_LITE_MICRO_EXPECT_EQ(kFeatureSize,
+ output->dims->data[output->dims->size - 1]);
+ TF_LITE_MICRO_CHECK_FAIL();
+
+ std::copy_n(audio_data, audio_data_size,
+ tflite::GetTensorData<int16_t>(input));
+ TF_LITE_MICRO_EXPECT(interpreter->Invoke() == kTfLiteOk);
+ TF_LITE_MICRO_CHECK_FAIL();
+ std::copy_n(tflite::GetTensorData<int8_t>(output), kFeatureSize,
+ feature_output);
+
+ return kTfLiteOk;
+}
+
+TfLiteStatus GenerateFeatures(const int16_t* audio_data,
+ const size_t audio_data_size,
+ Features* features_output) {
+ // Map the model into a usable data structure. This doesn't involve any
+ // copying or parsing, it's a very lightweight operation.
+ const tflite::Model* model =
+ tflite::GetModel(g_audio_preprocessor_int8_model_data);
+ TF_LITE_MICRO_EXPECT(model->version() == TFLITE_SCHEMA_VERSION);
+ TF_LITE_MICRO_CHECK_FAIL();
+
+ AudioPreprocessorOpResolver op_resolver;
+ TF_LITE_MICRO_EXPECT(RegisterOps(op_resolver) == kTfLiteOk);
+ TF_LITE_MICRO_CHECK_FAIL();
+
+ tflite::MicroInterpreter interpreter(model, op_resolver, g_arena, kArenaSize);
+
+ TF_LITE_MICRO_EXPECT(interpreter.AllocateTensors() == kTfLiteOk);
+ TF_LITE_MICRO_CHECK_FAIL();
+
+ MicroPrintf("AudioPreprocessor model arena size = %u",
+ interpreter.arena_used_bytes());
+
+ size_t remaining_samples = audio_data_size;
+ size_t feature_index = 0;
+ while (remaining_samples >= kAudioSampleDurationCount &&
+ feature_index < kFeatureCount) {
+ TF_LITE_ENSURE_STATUS(
+ GenerateSingleFeature(audio_data, kAudioSampleDurationCount,
+ (*features_output)[feature_index], &interpreter));
+ feature_index++;
+ audio_data += kAudioSampleStrideCount;
+ remaining_samples -= kAudioSampleStrideCount;
+ }
+
+ return kTfLiteOk;
+}
+
+TfLiteStatus TestAudioSample(const char* label, const int16_t* audio_data,
+ const size_t audio_data_size) {
+ TF_LITE_ENSURE_STATUS(
+ GenerateFeatures(audio_data, audio_data_size, &g_features));
+ TF_LITE_ENSURE_STATUS(
+ LoadMicroSpeechModelAndPerformInference(g_features, label));
+ return kTfLiteOk;
+}
+
+} // namespace
TF_LITE_MICRO_TESTS_BEGIN
-TF_LITE_MICRO_TEST(TestInvoke) {
- // Map the model into a usable data structure. This doesn't involve any
- // copying or parsing, it's a very lightweight operation.
- const tflite::Model* model = ::tflite::GetModel(g_micro_speech_model_data);
- if (model->version() != TFLITE_SCHEMA_VERSION) {
- MicroPrintf(
- "Model provided is schema version %d not equal "
- "to supported version %d.\n",
- model->version(), TFLITE_SCHEMA_VERSION);
+TF_LITE_MICRO_TEST(NoFeatureTest) {
+ int8_t expected_feature[kFeatureSize] = {
+ 126, 103, 124, 102, 124, 102, 123, 100, 118, 97, 118, 100, 118, 98,
+ 121, 100, 121, 98, 117, 91, 96, 74, 54, 87, 100, 87, 109, 92,
+ 91, 80, 64, 55, 83, 74, 74, 78, 114, 95, 101, 81,
+ };
+
+ TF_LITE_ENSURE_STATUS(GenerateFeatures(
+ g_no_30ms_audio_data, g_no_30ms_audio_data_size, &g_features));
+ for (size_t i = 0; i < kFeatureSize; i++) {
+ TF_LITE_MICRO_EXPECT_EQ(g_features[0][i], expected_feature[i]);
+ TF_LITE_MICRO_CHECK_FAIL();
}
+}
- // Pull in only the operation implementations we need.
- // This relies on a complete list of all the ops needed by this graph.
+TF_LITE_MICRO_TEST(YesFeatureTest) {
+ int8_t expected_feature[kFeatureSize] = {
+ 124, 105, 126, 103, 125, 101, 123, 100, 116, 98, 115, 97, 113, 90,
+ 91, 82, 104, 96, 117, 97, 121, 103, 126, 101, 125, 104, 126, 104,
+ 125, 101, 116, 90, 81, 74, 80, 71, 83, 76, 82, 71,
+ };
- tflite::MicroMutableOpResolver<4> micro_op_resolver;
- micro_op_resolver.AddDepthwiseConv2D();
- micro_op_resolver.AddFullyConnected();
- micro_op_resolver.AddReshape();
- micro_op_resolver.AddSoftmax();
-
- // Create an area of memory to use for input, output, and intermediate arrays.
-#if (defined(XTENSA) && defined(VISION_P6))
- constexpr int tensor_arena_size = 28 * 1024;
-#elif defined(XTENSA)
- constexpr int tensor_arena_size = 15 * 1024;
-#elif defined(HEXAGON)
- constexpr int tensor_arena_size = 25 * 1024;
-#else
- constexpr int tensor_arena_size = 10 * 1024;
-#endif
- alignas(16) uint8_t tensor_arena[tensor_arena_size];
-
- // Build an interpreter to run the model with.
- tflite::MicroInterpreter interpreter(model, micro_op_resolver, tensor_arena,
- tensor_arena_size);
- interpreter.AllocateTensors();
-
- // Get information about the memory area to use for the model's input.
- TfLiteTensor* input = interpreter.input(0);
-
- // Make sure the input has the properties we expect.
- TF_LITE_MICRO_EXPECT(input != nullptr);
- TF_LITE_MICRO_EXPECT_EQ(2, input->dims->size);
- TF_LITE_MICRO_EXPECT_EQ(1, input->dims->data[0]);
- TF_LITE_MICRO_EXPECT_EQ(1960, input->dims->data[1]);
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteInt8, input->type);
-
- // Copy a spectrogram created from a .wav audio file of someone saying "Yes",
- // into the memory area used for the input.
- const int8_t* yes_features_data = g_yes_micro_f2e59fea_nohash_1_data;
- for (size_t i = 0; i < input->bytes; ++i) {
- input->data.int8[i] = yes_features_data[i];
+ TF_LITE_ENSURE_STATUS(GenerateFeatures(
+ g_yes_30ms_audio_data, g_yes_30ms_audio_data_size, &g_features));
+ for (size_t i = 0; i < kFeatureSize; i++) {
+ TF_LITE_MICRO_EXPECT_EQ(g_features[0][i], expected_feature[i]);
+ TF_LITE_MICRO_CHECK_FAIL();
}
+}
- // Run the model on this input and make sure it succeeds.
- TfLiteStatus invoke_status = interpreter.Invoke();
- if (invoke_status != kTfLiteOk) {
- MicroPrintf("Invoke failed\n");
- }
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, invoke_status);
+TF_LITE_MICRO_TEST(NoTest) {
+ TestAudioSample("no", g_no_1000ms_audio_data, g_no_1000ms_audio_data_size);
+}
- // Get the output from the model, and make sure it's the expected size and
- // type.
- TfLiteTensor* output = interpreter.output(0);
- TF_LITE_MICRO_EXPECT_EQ(2, output->dims->size);
- TF_LITE_MICRO_EXPECT_EQ(1, output->dims->data[0]);
- TF_LITE_MICRO_EXPECT_EQ(4, output->dims->data[1]);
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteInt8, output->type);
+TF_LITE_MICRO_TEST(YesTest) {
+ TestAudioSample("yes", g_yes_1000ms_audio_data, g_yes_1000ms_audio_data_size);
+}
- // There are four possible classes in the output, each with a score.
- const int kSilenceIndex = 0;
- const int kUnknownIndex = 1;
- const int kYesIndex = 2;
- const int kNoIndex = 3;
+TF_LITE_MICRO_TEST(SilenceTest) {
+ TestAudioSample("silence", g_silence_1000ms_audio_data,
+ g_silence_1000ms_audio_data_size);
+}
- // Make sure that the expected "Yes" score is higher than the other classes.
- uint8_t silence_score = output->data.int8[kSilenceIndex] + 128;
- uint8_t unknown_score = output->data.int8[kUnknownIndex] + 128;
- uint8_t yes_score = output->data.int8[kYesIndex] + 128;
- uint8_t no_score = output->data.int8[kNoIndex] + 128;
- TF_LITE_MICRO_EXPECT_GT(yes_score, silence_score);
- TF_LITE_MICRO_EXPECT_GT(yes_score, unknown_score);
- TF_LITE_MICRO_EXPECT_GT(yes_score, no_score);
-
- // Now test with a different input, from a recording of "No".
- const int8_t* no_features_data = g_no_micro_f9643d42_nohash_4_data;
- for (size_t i = 0; i < input->bytes; ++i) {
- input->data.int8[i] = no_features_data[i];
- }
-
- // Run the model on this "No" input.
- invoke_status = interpreter.Invoke();
- if (invoke_status != kTfLiteOk) {
- MicroPrintf("Invoke failed\n");
- }
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, invoke_status);
-
- // Get the output from the model, and make sure it's the expected size and
- // type.
- output = interpreter.output(0);
- TF_LITE_MICRO_EXPECT_EQ(2, output->dims->size);
- TF_LITE_MICRO_EXPECT_EQ(1, output->dims->data[0]);
- TF_LITE_MICRO_EXPECT_EQ(4, output->dims->data[1]);
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteInt8, output->type);
-
- // Make sure that the expected "No" score is higher than the other classes.
- silence_score = output->data.int8[kSilenceIndex] + 128;
- unknown_score = output->data.int8[kUnknownIndex] + 128;
- yes_score = output->data.int8[kYesIndex] + 128;
- no_score = output->data.int8[kNoIndex] + 128;
- TF_LITE_MICRO_EXPECT_GT(no_score, silence_score);
- TF_LITE_MICRO_EXPECT_GT(no_score, unknown_score);
- TF_LITE_MICRO_EXPECT_GT(no_score, yes_score);
-
- MicroPrintf("Ran successfully\n");
+TF_LITE_MICRO_TEST(NoiseTest) {
+ TestAudioSample("silence", g_noise_1000ms_audio_data,
+ g_noise_1000ms_audio_data_size);
}
TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/examples/micro_speech/models/audio_preprocessor_float.tflite b/tensorflow/lite/micro/examples/micro_speech/models/audio_preprocessor_float.tflite
new file mode 100644
index 0000000..8f91ec7
--- /dev/null
+++ b/tensorflow/lite/micro/examples/micro_speech/models/audio_preprocessor_float.tflite
Binary files differ
diff --git a/tensorflow/lite/micro/examples/micro_speech/models/audio_preprocessor_int8.tflite b/tensorflow/lite/micro/examples/micro_speech/models/audio_preprocessor_int8.tflite
new file mode 100644
index 0000000..790087b
--- /dev/null
+++ b/tensorflow/lite/micro/examples/micro_speech/models/audio_preprocessor_int8.tflite
Binary files differ
diff --git a/tensorflow/lite/micro/examples/micro_speech/micro_speech.tflite b/tensorflow/lite/micro/examples/micro_speech/models/micro_speech_quantized.tflite
similarity index 100%
rename from tensorflow/lite/micro/examples/micro_speech/micro_speech.tflite
rename to tensorflow/lite/micro/examples/micro_speech/models/micro_speech_quantized.tflite
Binary files differ
diff --git a/tensorflow/lite/micro/examples/micro_speech/recognize_commands.cc b/tensorflow/lite/micro/examples/micro_speech/recognize_commands.cc
deleted file mode 100644
index 99edb47..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/recognize_commands.cc
+++ /dev/null
@@ -1,139 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/recognize_commands.h"
-
-#include <limits>
-
-#include "tensorflow/lite/micro/micro_log.h"
-
-RecognizeCommands::RecognizeCommands(int32_t average_window_duration_ms,
- uint8_t detection_threshold,
- int32_t suppression_ms,
- int32_t minimum_count)
- : average_window_duration_ms_(average_window_duration_ms),
- detection_threshold_(detection_threshold),
- suppression_ms_(suppression_ms),
- minimum_count_(minimum_count),
- previous_results_() {
- previous_top_label_ = "silence";
- previous_top_label_time_ = std::numeric_limits<int32_t>::min();
-}
-
-TfLiteStatus RecognizeCommands::ProcessLatestResults(
- const TfLiteTensor* latest_results, const int32_t current_time_ms,
- const char** found_command, uint8_t* score, bool* is_new_command) {
- if ((latest_results->dims->size != 2) ||
- (latest_results->dims->data[0] != 1) ||
- (latest_results->dims->data[1] != kCategoryCount)) {
- MicroPrintf(
- "The results for recognition should contain %d elements, but there are "
- "%d in an %d-dimensional shape",
- kCategoryCount, latest_results->dims->data[1],
- latest_results->dims->size);
- return kTfLiteError;
- }
-
- if (latest_results->type != kTfLiteInt8) {
- MicroPrintf(
- "The results for recognition should be int8_t elements, but are %d",
- latest_results->type);
- return kTfLiteError;
- }
-
- if ((!previous_results_.empty()) &&
- (current_time_ms < previous_results_.front().time_)) {
- MicroPrintf(
- "Results must be fed in increasing time order, but received a "
- "timestamp of %d that was earlier than the previous one of %d",
- current_time_ms, previous_results_.front().time_);
- return kTfLiteError;
- }
-
- // Add the latest results to the head of the queue.
- previous_results_.push_back({current_time_ms, latest_results->data.int8});
-
- // Prune any earlier results that are too old for the averaging window.
- const int64_t time_limit = current_time_ms - average_window_duration_ms_;
- while ((!previous_results_.empty()) &&
- previous_results_.front().time_ < time_limit) {
- previous_results_.pop_front();
- }
-
- // If there are too few results, assume the result will be unreliable and
- // bail.
- const int64_t how_many_results = previous_results_.size();
- const int64_t earliest_time = previous_results_.front().time_;
- const int64_t samples_duration = current_time_ms - earliest_time;
- if ((how_many_results < minimum_count_) ||
- (samples_duration < (average_window_duration_ms_ / 4))) {
- *found_command = previous_top_label_;
- *score = 0;
- *is_new_command = false;
- return kTfLiteOk;
- }
-
- // Calculate the average score across all the results in the window.
- int32_t average_scores[kCategoryCount];
- for (int offset = 0; offset < previous_results_.size(); ++offset) {
- PreviousResultsQueue::Result previous_result =
- previous_results_.from_front(offset);
- const int8_t* scores = previous_result.scores;
- for (int i = 0; i < kCategoryCount; ++i) {
- if (offset == 0) {
- average_scores[i] = scores[i] + 128;
- } else {
- average_scores[i] += scores[i] + 128;
- }
- }
- }
- for (int i = 0; i < kCategoryCount; ++i) {
- average_scores[i] /= how_many_results;
- }
-
- // Find the current highest scoring category.
- int current_top_index = 0;
- int32_t current_top_score = 0;
- for (int i = 0; i < kCategoryCount; ++i) {
- if (average_scores[i] > current_top_score) {
- current_top_score = average_scores[i];
- current_top_index = i;
- }
- }
- const char* current_top_label = kCategoryLabels[current_top_index];
-
- // If we've recently had another label trigger, assume one that occurs too
- // soon afterwards is a bad result.
- int64_t time_since_last_top;
- if ((previous_top_label_ == kCategoryLabels[0]) ||
- (previous_top_label_time_ == std::numeric_limits<int32_t>::min())) {
- time_since_last_top = std::numeric_limits<int32_t>::max();
- } else {
- time_since_last_top = current_time_ms - previous_top_label_time_;
- }
- if ((current_top_score > detection_threshold_) &&
- ((current_top_label != previous_top_label_) ||
- (time_since_last_top > suppression_ms_))) {
- previous_top_label_ = current_top_label;
- previous_top_label_time_ = current_time_ms;
- *is_new_command = true;
- } else {
- *is_new_command = false;
- }
- *found_command = current_top_label;
- *score = current_top_score;
-
- return kTfLiteOk;
-}
diff --git a/tensorflow/lite/micro/examples/micro_speech/recognize_commands.h b/tensorflow/lite/micro/examples/micro_speech/recognize_commands.h
deleted file mode 100644
index 8a5a895..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/recognize_commands.h
+++ /dev/null
@@ -1,151 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_
-
-#include <cstdint>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h"
-#include "tensorflow/lite/micro/micro_log.h"
-
-// Partial implementation of std::dequeue, just providing the functionality
-// that's needed to keep a record of previous neural network results over a
-// short time period, so they can be averaged together to produce a more
-// accurate overall prediction. This doesn't use any dynamic memory allocation
-// so it's a better fit for microcontroller applications, but this does mean
-// there are hard limits on the number of results it can store.
-class PreviousResultsQueue {
- public:
- PreviousResultsQueue() : front_index_(0), size_(0) {}
-
- // Data structure that holds an inference result, and the time when it
- // was recorded.
- struct Result {
- Result() : time_(0), scores() {}
- Result(int32_t time, int8_t* input_scores) : time_(time) {
- for (int i = 0; i < kCategoryCount; ++i) {
- scores[i] = input_scores[i];
- }
- }
- int32_t time_;
- int8_t scores[kCategoryCount];
- };
-
- int size() { return size_; }
- bool empty() { return size_ == 0; }
- Result& front() { return results_[front_index_]; }
- Result& back() {
- int back_index = front_index_ + (size_ - 1);
- if (back_index >= kMaxResults) {
- back_index -= kMaxResults;
- }
- return results_[back_index];
- }
-
- void push_back(const Result& entry) {
- if (size() >= kMaxResults) {
- MicroPrintf("Couldn't push_back latest result, too many already!");
- return;
- }
- size_ += 1;
- back() = entry;
- }
-
- Result pop_front() {
- if (size() <= 0) {
- MicroPrintf("Couldn't pop_front result, none present!");
- return Result();
- }
- Result result = front();
- front_index_ += 1;
- if (front_index_ >= kMaxResults) {
- front_index_ = 0;
- }
- size_ -= 1;
- return result;
- }
-
- // Most of the functions are duplicates of dequeue containers, but this
- // is a helper that makes it easy to iterate through the contents of the
- // queue.
- Result& from_front(int offset) {
- if ((offset < 0) || (offset >= size_)) {
- MicroPrintf("Attempt to read beyond the end of the queue!");
- offset = size_ - 1;
- }
- int index = front_index_ + offset;
- if (index >= kMaxResults) {
- index -= kMaxResults;
- }
- return results_[index];
- }
-
- private:
- static constexpr int kMaxResults = 50;
- Result results_[kMaxResults];
-
- int front_index_;
- int size_;
-};
-
-// This class is designed to apply a very primitive decoding model on top of the
-// instantaneous results from running an audio recognition model on a single
-// window of samples. It applies smoothing over time so that noisy individual
-// label scores are averaged, increasing the confidence that apparent matches
-// are real.
-// To use it, you should create a class object with the configuration you
-// want, and then feed results from running a TensorFlow model into the
-// processing method. The timestamp for each subsequent call should be
-// increasing from the previous, since the class is designed to process a stream
-// of data over time.
-class RecognizeCommands {
- public:
- // labels should be a list of the strings associated with each one-hot score.
- // The window duration controls the smoothing. Longer durations will give a
- // higher confidence that the results are correct, but may miss some commands.
- // The detection threshold has a similar effect, with high values increasing
- // the precision at the cost of recall. The minimum count controls how many
- // results need to be in the averaging window before it's seen as a reliable
- // average. This prevents erroneous results when the averaging window is
- // initially being populated for example. The suppression argument disables
- // further recognitions for a set time after one has been triggered, which can
- // help reduce spurious recognitions.
- explicit RecognizeCommands(int32_t average_window_duration_ms = 1000,
- uint8_t detection_threshold = 200,
- int32_t suppression_ms = 1500,
- int32_t minimum_count = 3);
-
- // Call this with the results of running a model on sample data.
- TfLiteStatus ProcessLatestResults(const TfLiteTensor* latest_results,
- const int32_t current_time_ms,
- const char** found_command, uint8_t* score,
- bool* is_new_command);
-
- private:
- // Configuration
- int32_t average_window_duration_ms_;
- uint8_t detection_threshold_;
- int32_t suppression_ms_;
- int32_t minimum_count_;
-
- // Working variables
- PreviousResultsQueue previous_results_;
- const char* previous_top_label_;
- int32_t previous_top_label_time_;
-};
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/recognize_commands_test.cc b/tensorflow/lite/micro/examples/micro_speech/recognize_commands_test.cc
deleted file mode 100644
index 7c1e4c6..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/recognize_commands_test.cc
+++ /dev/null
@@ -1,199 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/recognize_commands.h"
-
-#include "tensorflow/lite/micro/test_helpers.h"
-#include "tensorflow/lite/micro/testing/micro_test.h"
-
-TF_LITE_MICRO_TESTS_BEGIN
-
-TF_LITE_MICRO_TEST(PreviousResultsQueueBasic) {
- PreviousResultsQueue queue;
- TF_LITE_MICRO_EXPECT_EQ(0, queue.size());
-
- int8_t scores_a[4] = {0, 0, 0, 1};
- queue.push_back({0, scores_a});
- TF_LITE_MICRO_EXPECT_EQ(1, queue.size());
- TF_LITE_MICRO_EXPECT_EQ(0, queue.front().time_);
- TF_LITE_MICRO_EXPECT_EQ(0, queue.back().time_);
-
- int8_t scores_b[4] = {0, 0, 1, 0};
- queue.push_back({1, scores_b});
- TF_LITE_MICRO_EXPECT_EQ(2, queue.size());
- TF_LITE_MICRO_EXPECT_EQ(0, queue.front().time_);
- TF_LITE_MICRO_EXPECT_EQ(1, queue.back().time_);
-
- PreviousResultsQueue::Result pop_result = queue.pop_front();
- TF_LITE_MICRO_EXPECT_EQ(0, pop_result.time_);
- TF_LITE_MICRO_EXPECT_EQ(1, queue.size());
- TF_LITE_MICRO_EXPECT_EQ(1, queue.front().time_);
- TF_LITE_MICRO_EXPECT_EQ(1, queue.back().time_);
-
- int8_t scores_c[4] = {0, 1, 0, 0};
- queue.push_back({2, scores_c});
- TF_LITE_MICRO_EXPECT_EQ(2, queue.size());
- TF_LITE_MICRO_EXPECT_EQ(1, queue.front().time_);
- TF_LITE_MICRO_EXPECT_EQ(2, queue.back().time_);
-}
-
-TF_LITE_MICRO_TEST(PreviousResultsQueuePushPop) {
- PreviousResultsQueue queue;
- TF_LITE_MICRO_EXPECT_EQ(0, queue.size());
-
- for (int i = 0; i < 123; ++i) {
- int8_t scores[4] = {0, 0, 0, 1};
- queue.push_back({i, scores});
- TF_LITE_MICRO_EXPECT_EQ(1, queue.size());
- TF_LITE_MICRO_EXPECT_EQ(i, queue.front().time_);
- TF_LITE_MICRO_EXPECT_EQ(i, queue.back().time_);
-
- PreviousResultsQueue::Result pop_result = queue.pop_front();
- TF_LITE_MICRO_EXPECT_EQ(i, pop_result.time_);
- TF_LITE_MICRO_EXPECT_EQ(0, queue.size());
- }
-}
-
-TF_LITE_MICRO_TEST(RecognizeCommandsTestBasic) {
- RecognizeCommands recognize_commands;
-
- const int8_t result_data[] = {127, -128, -128, -128};
- int result_dims[] = {2, 1, 4};
- TfLiteTensor results = tflite::testing::CreateQuantizedTensor(
- result_data, tflite::testing::IntArrayFromInts(result_dims), -128.0f,
- 127.0f);
-
- const char* found_command;
- uint8_t score;
- bool is_new_command;
- TF_LITE_MICRO_EXPECT_EQ(
- kTfLiteOk, recognize_commands.ProcessLatestResults(
- &results, 0, &found_command, &score, &is_new_command));
-}
-
-TF_LITE_MICRO_TEST(RecognizeCommandsTestFindCommands) {
- RecognizeCommands recognize_commands(1000, 51);
-
- const int8_t yes_data[] = {-128, -128, 127, -128};
- int yes_dims[] = {2, 1, 4};
- TfLiteTensor yes_results = tflite::testing::CreateQuantizedTensor(
- yes_data, tflite::testing::IntArrayFromInts(yes_dims), -128.0f, 127.0f);
-
- bool has_found_new_command = false;
- const char* new_command;
- for (int i = 0; i < 10; ++i) {
- const char* found_command;
- uint8_t score;
- bool is_new_command;
- int32_t current_time_ms = 0 + (i * 100);
- TF_LITE_MICRO_EXPECT_EQ(
- kTfLiteOk, recognize_commands.ProcessLatestResults(
- &yes_results, current_time_ms, &found_command, &score,
- &is_new_command));
- if (is_new_command) {
- TF_LITE_MICRO_EXPECT(!has_found_new_command);
- has_found_new_command = true;
- new_command = found_command;
- }
- }
- TF_LITE_MICRO_EXPECT(has_found_new_command);
- if (has_found_new_command) {
- TF_LITE_MICRO_EXPECT_EQ(0, tflite::testing::TestStrcmp("yes", new_command));
- }
-
- const int8_t no_data[] = {-128, -128, -128, 127};
- int no_dims[] = {2, 1, 4};
- TfLiteTensor no_results = tflite::testing::CreateQuantizedTensor(
- no_data, tflite::testing::IntArrayFromInts(no_dims), -128.0f, 127.0f);
- has_found_new_command = false;
- new_command = "";
- uint8_t score;
- for (int i = 0; i < 10; ++i) {
- const char* found_command;
- bool is_new_command;
- int32_t current_time_ms = 1000 + (i * 100);
- TF_LITE_MICRO_EXPECT_EQ(
- kTfLiteOk, recognize_commands.ProcessLatestResults(
- &no_results, current_time_ms, &found_command, &score,
- &is_new_command));
- if (is_new_command) {
- TF_LITE_MICRO_EXPECT(!has_found_new_command);
- has_found_new_command = true;
- new_command = found_command;
- }
- }
- TF_LITE_MICRO_EXPECT(has_found_new_command);
- if (has_found_new_command) {
- TF_LITE_MICRO_EXPECT_EQ(231, score);
- TF_LITE_MICRO_EXPECT_EQ(0, tflite::testing::TestStrcmp("no", new_command));
- }
-}
-
-TF_LITE_MICRO_TEST(RecognizeCommandsTestBadInputLength) {
- RecognizeCommands recognize_commands(1000, 51);
-
- const int8_t bad_data[] = {-128, -128, 127};
- int bad_dims[] = {2, 1, 3};
- TfLiteTensor bad_results = tflite::testing::CreateQuantizedTensor(
- bad_data, tflite::testing::IntArrayFromInts(bad_dims), -128.0f, 127.0f);
-
- const char* found_command;
- uint8_t score;
- bool is_new_command;
- TF_LITE_MICRO_EXPECT_NE(
- kTfLiteOk, recognize_commands.ProcessLatestResults(
- &bad_results, 0, &found_command, &score, &is_new_command));
-}
-
-TF_LITE_MICRO_TEST(RecognizeCommandsTestBadInputTimes) {
- RecognizeCommands recognize_commands(1000, 51);
-
- const int8_t result_data[] = {-128, -128, 127, -128};
- int result_dims[] = {2, 1, 4};
- TfLiteTensor results = tflite::testing::CreateQuantizedTensor(
- result_data, tflite::testing::IntArrayFromInts(result_dims), -128.0f,
- 127.0f);
-
- const char* found_command;
- uint8_t score;
- bool is_new_command;
- TF_LITE_MICRO_EXPECT_EQ(
- kTfLiteOk, recognize_commands.ProcessLatestResults(
- &results, 100, &found_command, &score, &is_new_command));
- TF_LITE_MICRO_EXPECT_NE(
- kTfLiteOk, recognize_commands.ProcessLatestResults(
- &results, 0, &found_command, &score, &is_new_command));
-}
-
-TF_LITE_MICRO_TEST(RecognizeCommandsTestTooFewInputs) {
- RecognizeCommands recognize_commands(1000, 51);
-
- const int8_t result_data[] = {-128, -128, 127, -128};
- int result_dims[] = {2, 1, 4};
- TfLiteTensor results = tflite::testing::CreateQuantizedTensor(
- result_data, tflite::testing::IntArrayFromInts(result_dims), -128.0f,
- 127.0f);
-
- const char* found_command;
- uint8_t score;
- bool is_new_command;
- TF_LITE_MICRO_EXPECT_EQ(
- kTfLiteOk, recognize_commands.ProcessLatestResults(
- &results, 100, &found_command, &score, &is_new_command));
- TF_LITE_MICRO_EXPECT_EQ(0, score);
- TF_LITE_MICRO_EXPECT_EQ(false, is_new_command);
-}
-
-TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/CMSIS/simple_features_generator.cc b/tensorflow/lite/micro/examples/micro_speech/simple_features/CMSIS/simple_features_generator.cc
deleted file mode 100644
index 33c1e24..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/CMSIS/simple_features_generator.cc
+++ /dev/null
@@ -1,96 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/simple_features_generator.h"
-
-#include "tensorflow/lite/micro/micro_log.h"
-
-extern "C" {
-#define IFFT_FLAG_R 0
-#define BIT_REVERSE_FLAG 1
-#define FFT_SIZE 512
-#define FFT_SIZE_DIV2 256
-#include <arm_math.h>
-
-#include "arm_cmplx_mag_squared_q10p6.h"
-#include "tensorflow/lite/micro/examples/micro_speech/CMSIS/hanning.h"
-}
-
-void quantize(q15_t* bufA, q15_t* bufB, uint8_t* output);
-
-q15_t bufA[FFT_SIZE];
-q15_t bufB[FFT_SIZE];
-arm_rfft_instance_q15 S_arm_fft;
-arm_status arm_math_status;
-
-namespace {
-// These constants allow us to allocate fixed-sized arrays on the stack for our
-// working memory.
-constexpr int kInputSize = 512;
-constexpr int kAverageWindowSize = 6;
-constexpr int kOutputSize =
- ((kInputSize / 2) + (kAverageWindowSize - 1)) / kAverageWindowSize;
-} // namespace
-
-TfLiteStatus GenerateSimpleFeatures(const int16_t* input, int input_size,
- int output_size, uint8_t* output) {
- if (input_size > kInputSize) {
- MicroPrintf("Input size %d larger than %d", input_size, kInputSize);
- return kTfLiteError;
- }
- if (output_size != kOutputSize) {
- MicroPrintf("Requested output size %d doesn't match %d", output_size,
- kOutputSize);
- return kTfLiteError;
- }
-
- // 30ms at 16 kHz = 480 samples
- // We want to pad the rest of the 512-sample buffer with zeros
- arm_mult_q15((q15_t*)input, g_hanning, bufB, 480);
- int i;
- for (i = 480; i < 512; i++) {
- bufB[i] = 0;
- }
-
- // Should move init code outside of Preprocess() function
- arm_math_status =
- arm_rfft_init_q15(&S_arm_fft, FFT_SIZE, IFFT_FLAG_R, BIT_REVERSE_FLAG);
- arm_rfft_q15(&S_arm_fft, bufB, bufA);
-
- // The rfft function packs data as follows:
- // {real[0], real[N/2], real[1], imag[1], ..., real[N/2-1], imag[N/2-1]}
- // Below we pack as follows:
- // {real[0], 0, real[1], imag[1], ..., real[N/2-1], imag[N/2-1, real[N/2], 0}
- bufA[FFT_SIZE_DIV2] = bufA[1];
- bufA[FFT_SIZE_DIV2 + 1] = 0;
- bufA[1] = 0;
- arm_cmplx_mag_squared_q10p6(bufA, bufB, FFT_SIZE_DIV2 + 1);
-
- quantize(bufA, bufB, output);
-
- return kTfLiteOk;
-}
-
-void quantize(q15_t* bufA, q15_t* bufB, uint8_t* output) {
- int i;
- for (i = 0; i < 42; i++) {
- arm_mean_q15(bufB + 6 * i, 6, bufA + i);
- }
- arm_mean_q15(bufB + 252, 5, bufA + 42);
-
- for (i = 0; i < 43; i++) {
- output[i] = (uint8_t)(bufA[i] >> 5);
- }
-}
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/fixed_point/simple_features_generator.cc b/tensorflow/lite/micro/examples/micro_speech/simple_features/fixed_point/simple_features_generator.cc
deleted file mode 100644
index 03e8b27..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/fixed_point/simple_features_generator.cc
+++ /dev/null
@@ -1,212 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Reference implementation of the preprocessing pipeline, with the same
-// results as the audio tutorial at
-// https://www.tensorflow.org/tutorials/sequences/audio_recognition
-// This module takes 30ms of PCM-encoded signed 16-bit audio samples (at 16KHz,
-// so 480 values), and extracts a power spectrum of frequencies. There are 43
-// frequency bands in the result, derived from the original 256 output from the
-// discrete Fourier transform, and averaged together in groups of 6.
-// It's expected that most platforms will have optimized versions of the
-// functions used here, for example replacing the DFT with an FFT, so this
-// version shouldn't be used where performance is critical.
-// This implementation uses fixed point for any non-constant calculations,
-// instead of floating point, to help show how this can work on platforms that
-// don't have good float support.
-
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/simple_features_generator.h"
-
-#include <cmath>
-
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.h"
-#include "tensorflow/lite/micro/micro_log.h"
-
-namespace {
-
-// q format notation: qx.y => 1 sign bit, x-1 integer bits, y fraction bits.
-// Use standard (non-saturating) arithmetic with signed ints of size x+y bits.
-// Sacrifice some precision to avoid use of 64-bit ints.
-
-// q1.15 * q1.15 => q2.30
-inline int32_t Q1_15_FixedMultiply_Q2_30(int16_t a, int16_t b) {
- int32_t big_a = a;
- int32_t big_b = b;
- return big_a * big_b;
-}
-
-// q2.30 * q2.30 => q10.22
-inline int32_t Q2_30_FixedMultiply_Q10_22(int32_t a, int32_t b) {
- // q2.30 result
- int32_t tmp = (a >> 15) * (b >> 15);
- // q10.22 result
- return tmp >> 8;
-}
-
-// q10.22 * q10.22 => q10.22
-// Will overflow if product is >= 512.
-// Largest product in small test set is 465.25
-inline int32_t Q10_22_FixedMultiply_Q10_22(int32_t a, int32_t b) {
- // q10.22 result
- return (a >> 11) * (b >> 11);
-}
-
-// float => q2.30
-// No checking for saturation. Only used for inputs in range [-1, 1].
-inline int32_t FloatToFixed_Q2_30(float input) {
- return static_cast<int32_t>(roundf(input * (1 << 30)));
-}
-
-// Performs a discrete Fourier transform on the real inputs. This corresponds to
-// rdft() in the FFT package at http://www.kurims.kyoto-u.ac.jp/~ooura/fft.html,
-// and to kiss_fftr() in KISSFFT at https://github.com/mborgerding/kissfft.
-// It takes in an array of float real values, and returns a result of the same
-// length with q10.22 fixed point real and imaginary components interleaved, so
-// fourier_output[0] is the first real value, fourier_output[1] is the first
-// imaginary, fourier_output[2] is the second real, and so on.
-// The calling function should ensure that the array passed in as fourier_output
-// is at least time_series_size in length. Most optimized FFT implementations
-// require the length to be a power of two as well, but this version doesn't
-// enforce that.
-
-// input: q2.30 fixed point. output: q10.22 fixed point.
-// Outputs interpreted as q10.22 fixed point are un-scaled.
-void CalculateDiscreteFourierTransform(int32_t* time_series,
- int time_series_size,
- int32_t* fourier_output) {
- for (int i = 0; i < time_series_size / 2; ++i) {
- int32_t real = 0;
- for (int j = 0; j < time_series_size; ++j) {
- const int32_t real_scale =
- FloatToFixed_Q2_30(cos(j * i * M_PI * 2 / time_series_size));
- real += Q2_30_FixedMultiply_Q10_22(time_series[j], real_scale);
- }
- int32_t imaginary = 0;
- for (int j = 0; j < time_series_size; ++j) {
- const int32_t imaginary_scale =
- FloatToFixed_Q2_30(sin(j * i * M_PI * 2 / time_series_size));
- imaginary -= Q2_30_FixedMultiply_Q10_22(time_series[j], imaginary_scale);
- }
- fourier_output[(i * 2) + 0] = real;
- fourier_output[(i * 2) + 1] = imaginary;
- }
-}
-
-// Produces a simple sine curve that is used to ensure frequencies at the center
-// of the current sample window are weighted more heavily than those at the end.
-// q1.15 output format.
-void CalculatePeriodicHann(int window_length, int16_t* window_function) {
- for (int i = 0; i < window_length; ++i) {
- const float real_value = (0.5 - 0.5 * cos((2 * M_PI * i) / window_length));
- int tmp = static_cast<int32_t>(roundf(real_value * (1 << 15)));
- // Saturate the 0x8000 value to 0x7fff
- if (tmp > 0x7fff) tmp = 0x7fff;
- window_function[i] = tmp;
- }
-}
-
-} // namespace
-
-TfLiteStatus GenerateSimpleFeatures(const int16_t* input, int input_size,
- int output_size, uint8_t* output) {
- // Ensure our input and output data arrays are valid.
- if (input_size > kMaxAudioSampleSize) {
- MicroPrintf("Input size %d larger than %d", input_size,
- kMaxAudioSampleSize);
- return kTfLiteError;
- }
- if (output_size != kFeatureSliceSize) {
- MicroPrintf("Requested output size %d doesn't match %d", output_size,
- kFeatureSliceSize);
- return kTfLiteError;
- }
-
- // Pre-calculate the window function we'll be applying to the input data.
- // In a real application, we'd calculate this table once in an initialization
- // function and store it for repeated reuse.
- // q1.15 format.
- int16_t window_function[kMaxAudioSampleSize];
- CalculatePeriodicHann(input_size, window_function);
-
- // Apply the window function to our time series input, and pad it with zeroes
- // to the next power of two.
- int32_t fixed_input[kMaxAudioSampleSize];
- for (int i = 0; i < kMaxAudioSampleSize; ++i) {
- if (i < input_size) {
- // input is int16_t. Treat as q1.15 fixed point value in range [-1,1)
- // window_function is also q1.15 fixed point number
- fixed_input[i] = Q1_15_FixedMultiply_Q2_30(input[i], window_function[i]);
- } else {
- fixed_input[i] = 0;
- }
- }
-
- // Pull the frequency data from the time series sample.
- // Calculated in q10.22 format from q2.30 inputs.
- int32_t fourier_values[kMaxAudioSampleSize];
- CalculateDiscreteFourierTransform(fixed_input, kMaxAudioSampleSize,
- fourier_values);
-
- // We have the complex numbers giving us information about each frequency
- // band, but all we want to know is how strong each frequency is, so calculate
- // the squared magnitude by adding together the squares of each component.
- int32_t power_spectrum[kMaxAudioSampleSize / 2];
- for (int i = 0; i < (kMaxAudioSampleSize / 2); ++i) {
- const int32_t real = fourier_values[(i * 2) + 0];
- const int32_t imaginary = fourier_values[(i * 2) + 1];
- // q10.22 results
- power_spectrum[i] = Q10_22_FixedMultiply_Q10_22(real, real) +
- Q10_22_FixedMultiply_Q10_22(imaginary, imaginary);
- }
-
- // Finally, reduce the size of the output by averaging together six adjacent
- // frequencies into each slot, producing an array of 43 values.
- // Power_spectrum numbers are q10.22. Divide by kAverageWindowSize inside
- // loop to prevent overflow.
- for (int i = 0; i < kFeatureSliceSize; ++i) {
- int32_t average = 0;
- for (int j = 0; j < kAverageWindowSize; ++j) {
- const int index = (i * kAverageWindowSize) + j;
- if (index < (kMaxAudioSampleSize / 2)) {
- average += power_spectrum[index] / kAverageWindowSize;
- }
- }
- // Quantize the result into eight bits, effectively multiplying by two.
- // The 127.5 constant here has to match the features_max value defined in
- // tensorflow/examples/speech_commands/input_data.py, and this also assumes
- // that features_min is zero.
- //
- // q10.22 input
- // integer output
- //
- // output = (input - features_min) *
- // (output_max - output_min) / (features_max - features_min)
- // == (input) * (255) / (127.5)
- // == input * 2
- // == input << 1
- // Also want to round to nearest integer and only keep integer bits
- // => ((input << 1) + 0x200000) >> 22
- // == (input + 0x100000) >> 21
- int32_t quantized_average = (average + 0x100000) >> 21;
- if (quantized_average < 0) {
- quantized_average = 0;
- }
- if (quantized_average > 255) {
- quantized_average = 255;
- }
- output[i] = quantized_average;
- }
- return kTfLiteOk;
-}
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/model.cc b/tensorflow/lite/micro/examples/micro_speech/simple_features/model.cc
deleted file mode 100644
index e8fea5b..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/model.cc
+++ /dev/null
@@ -1,1674 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// This is a standard TensorFlow Lite FlatBuffer model file that has been
-// converted into a C data array, so it can be easily compiled into a binary
-// for devices that don't have a file system. It was created using the command:
-// xxd -i model.tflite > model.cc
-
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/model.h"
-
-const unsigned char g_model[] = {
- 0x18, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x0e, 0x00,
- 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00,
- 0x0e, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x08, 0x4d, 0x00, 0x00,
- 0x0c, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00,
- 0x01, 0x00, 0x00, 0x00, 0xf4, 0x47, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00,
- 0x54, 0x4f, 0x43, 0x4f, 0x20, 0x43, 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74,
- 0x65, 0x64, 0x2e, 0x00, 0x09, 0x00, 0x00, 0x00, 0xd4, 0x47, 0x00, 0x00,
- 0xb4, 0x47, 0x00, 0x00, 0xe4, 0x02, 0x00, 0x00, 0xb4, 0x02, 0x00, 0x00,
- 0xac, 0x02, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00,
- 0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xb8, 0xb3, 0xff, 0xff,
- 0xbc, 0xb3, 0xff, 0xff, 0xc0, 0xb3, 0xff, 0xff, 0x1e, 0xb4, 0xff, 0xff,
- 0x04, 0x00, 0x00, 0x00, 0x80, 0x02, 0x00, 0x00, 0x89, 0xa5, 0xe8, 0xc1,
- 0xb1, 0x89, 0x5b, 0xc6, 0x4f, 0x9b, 0xd3, 0x74, 0x93, 0x88, 0xff, 0xaf,
- 0x89, 0xff, 0xf4, 0x70, 0xcc, 0x75, 0x78, 0xbf, 0x92, 0xcd, 0xa9, 0xa8,
- 0xd6, 0x6a, 0x6f, 0x7b, 0x7f, 0xd8, 0xa8, 0xb1, 0xe6, 0x32, 0x21, 0x70,
- 0xa0, 0x9c, 0x6f, 0xc8, 0xc6, 0x59, 0x67, 0x93, 0x97, 0xca, 0x3f, 0xde,
- 0xcb, 0x74, 0x7c, 0xb5, 0xa4, 0xd9, 0x66, 0xc6, 0x87, 0x98, 0xa5, 0xd0,
- 0xbb, 0xb9, 0xc2, 0xb2, 0xaa, 0x79, 0x25, 0xb9, 0x6d, 0x5a, 0xc8, 0x7f,
- 0x70, 0x85, 0x79, 0xbc, 0x6a, 0x9b, 0xd1, 0x9a, 0x9c, 0x51, 0x53, 0x71,
- 0x89, 0xc0, 0xb4, 0xac, 0xae, 0x47, 0x67, 0x70, 0x79, 0xd2, 0x81, 0xa5,
- 0xd2, 0x09, 0x38, 0x82, 0x74, 0xc9, 0x5d, 0xaf, 0xc1, 0x4f, 0x53, 0x99,
- 0xcb, 0xb7, 0x3a, 0xba, 0xe8, 0x7f, 0x76, 0xb9, 0xb3, 0xd3, 0x60, 0xc0,
- 0x93, 0x9f, 0x87, 0xbd, 0xd0, 0xb8, 0xca, 0xc1, 0xb6, 0x6c, 0x01, 0xc1,
- 0x5c, 0x5d, 0xb2, 0x82, 0x76, 0x77, 0x39, 0xbc, 0x72, 0x6a, 0xc3, 0xb4,
- 0x79, 0x21, 0x48, 0x42, 0x86, 0xa6, 0xbd, 0xaf, 0xae, 0x23, 0x9c, 0x69,
- 0x78, 0xc3, 0x6b, 0xb3, 0xab, 0x43, 0xb2, 0x88, 0x71, 0xc6, 0x6b, 0xbe,
- 0xc3, 0x75, 0xc2, 0xc3, 0xa5, 0xcf, 0x32, 0xbe, 0xcb, 0xb0, 0xb8, 0xc1,
- 0x9c, 0xcf, 0x64, 0xc4, 0xb4, 0x96, 0xa8, 0xb9, 0xcb, 0xc0, 0xc0, 0xb8,
- 0xb8, 0x77, 0x65, 0xc0, 0xc4, 0xb3, 0xc5, 0x77, 0x9b, 0x61, 0xd4, 0xac,
- 0x7e, 0x36, 0xb1, 0xae, 0x36, 0x36, 0xb8, 0x39, 0x6b, 0x70, 0x9c, 0xb5,
- 0x88, 0x5c, 0xb3, 0x6a, 0xad, 0xc5, 0x7b, 0xb4, 0xad, 0xaa, 0xc4, 0x84,
- 0x5e, 0xc4, 0x67, 0xc1, 0xde, 0xba, 0xcf, 0xbd, 0xa0, 0xd3, 0x35, 0xb3,
- 0xe7, 0xc8, 0xb8, 0xb8, 0xaf, 0xb4, 0x59, 0xb8, 0xb4, 0xac, 0xac, 0xaa,
- 0xc7, 0xad, 0xc8, 0xb6, 0xac, 0x99, 0xa0, 0xcb, 0xc1, 0xc8, 0xcb, 0x89,
- 0xc3, 0xac, 0xca, 0x8b, 0x97, 0x1f, 0xbd, 0xbf, 0x13, 0xad, 0xc8, 0x41,
- 0x56, 0x3c, 0x86, 0xb2, 0x61, 0xc4, 0xbb, 0x71, 0xba, 0x92, 0x8d, 0xc3,
- 0x86, 0xcb, 0xc5, 0x8d, 0x88, 0xc8, 0x6a, 0xbf, 0x9c, 0xcd, 0xcd, 0xc0,
- 0x81, 0xb1, 0x47, 0xb5, 0xf0, 0xce, 0xb1, 0xc1, 0xaa, 0xa8, 0x54, 0xcb,
- 0xbc, 0xc7, 0xc5, 0x8e, 0xc3, 0xce, 0xc7, 0xb9, 0xb9, 0xa1, 0xc5, 0xbd,
- 0xb8, 0xb8, 0xb7, 0x81, 0xb6, 0xba, 0xd2, 0x90, 0xbc, 0x96, 0xbe, 0xba,
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- 0x03, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00,
- 0x04, 0x00, 0x00, 0x00, 0xfa, 0xff, 0xff, 0xff, 0x00, 0x19, 0x06, 0x00,
- 0x06, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0x00, 0x09, 0x06, 0x00,
- 0x08, 0x00, 0x07, 0x00, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04};
-const int g_model_len = 19800;
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/model.h b/tensorflow/lite/micro/examples/micro_speech/simple_features/model.h
deleted file mode 100644
index b3e705e..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/model.h
+++ /dev/null
@@ -1,27 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// This is a standard TensorFlow Lite FlatBuffer model file that has been
-// converted into a C data array, so it can be easily compiled into a binary
-// for devices that don't have a file system. It was created using the command:
-// xxd -i model.tflite > model.cc
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_MODEL_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_MODEL_H_
-
-extern const unsigned char g_model[];
-extern const int g_model_len;
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_MODEL_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/no_power_spectrum_data.cc b/tensorflow/lite/micro/examples/micro_speech/simple_features/no_power_spectrum_data.cc
deleted file mode 100644
index aff0242..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/no_power_spectrum_data.cc
+++ /dev/null
@@ -1,23 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// See the header for documentation on the meaning of this data.
-
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/no_power_spectrum_data.h"
-
-const uint8_t g_no_power_spectrum_data[g_no_power_spectrum_data_size] = {
- 255, 7, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-};
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/no_power_spectrum_data.h b/tensorflow/lite/micro/examples/micro_speech/simple_features/no_power_spectrum_data.h
deleted file mode 100644
index f203623..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/no_power_spectrum_data.h
+++ /dev/null
@@ -1,29 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// This data was extracted from the larger feature data held in
-// no_features_data.cc and consists of the 29th spectrogram slice of 43 values.
-// This is the expected result of running the sample data in
-// no_30ms_sample_data.cc through the preprocessing pipeline.
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_NO_POWER_SPECTRUM_DATA_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_NO_POWER_SPECTRUM_DATA_H_
-
-#include <cstdint>
-
-constexpr int g_no_power_spectrum_data_size = 43;
-extern const uint8_t g_no_power_spectrum_data[];
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_NO_POWER_SPECTRUM_DATA_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/no_simple_features_data.cc b/tensorflow/lite/micro/examples/micro_speech/simple_features/no_simple_features_data.cc
deleted file mode 100644
index 2d7ae62..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/no_simple_features_data.cc
+++ /dev/null
@@ -1,152 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/no_simple_features_data.h"
-
-/* File automatically created by
- * tensorflow/examples/speech_commands/wav_to_features.py \
- * --sample_rate=16000 \
- * --clip_duration_ms=1000 \
- * --window_size_ms=30 \
- * --window_stride_ms=20 \
- * --feature_bin_count=40 \
- * --quantize=1 \
- * --preprocess="average" \
- * --input_wav="speech_commands_test_set_v0.02/no/f9643d42_nohash_4.wav" \
- * --output_c_file="no_simple_features_data.cc" \
- */
-
-const int g_no_simple_f9643d42_nohash_4_width = 43;
-const int g_no_simple_f9643d42_nohash_4_height = 49;
-const unsigned char g_no_simple_f9643d42_nohash_4_data[] = {
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 67, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 2, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 195, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 230, 2, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 255, 7,
- 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 255, 7, 16, 1, 1, 0, 2, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 255, 7, 22, 0, 1, 0,
- 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 238, 5, 20, 3, 4, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 144, 4, 19, 3, 5, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 42, 6, 3,
- 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 3, 1, 5, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 1, 3, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0,
-};
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/no_simple_features_data.h b/tensorflow/lite/micro/examples/micro_speech/simple_features/no_simple_features_data.h
deleted file mode 100644
index ff46134..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/no_simple_features_data.h
+++ /dev/null
@@ -1,23 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_NO_SIMPLE_FEATURES_DATA_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_NO_SIMPLE_FEATURES_DATA_H_
-
-extern const int g_no_simple_f9643d42_nohash_4_width;
-extern const int g_no_simple_f9643d42_nohash_4_height;
-extern const unsigned char g_no_simple_f9643d42_nohash_4_data[];
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_NO_SIMPLE_FEATURES_DATA_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_features_generator.cc b/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_features_generator.cc
deleted file mode 100644
index 3733912..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_features_generator.cc
+++ /dev/null
@@ -1,149 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Reference implementation of the preprocessing pipeline, with the same
-// results as the audio tutorial at
-// https://www.tensorflow.org/tutorials/sequences/audio_recognition
-// This module takes 30ms of PCM-encoded signed 16-bit audio samples (at 16KHz,
-// so 480 values), and extracts a power spectrum of frequencies. There are 43
-// frequency bands in the result, derived from the original 256 output from the
-// discrete Fourier transform, and averaged together in groups of 6.
-// It's expected that most platforms will have optimized versions of the
-// functions used here, for example replacing the DFT with an FFT, so this
-// version shouldn't be used where performance is critical.
-
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/simple_features_generator.h"
-
-#include <cmath>
-
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.h"
-#include "tensorflow/lite/micro/micro_log.h"
-
-namespace {
-
-// Needed because some platforms don't have M_PI defined.
-constexpr float kPi = 3.14159265358979323846f;
-
-// Performs a discrete Fourier transform on the real inputs. This corresponds to
-// rdft() in the FFT package at http://www.kurims.kyoto-u.ac.jp/~ooura/fft.html,
-// and to kiss_fftr() in KISSFFT at https://github.com/mborgerding/kissfft.
-// It takes in an array of float real values, and returns a result of the same
-// length with float real and imaginary components interleaved, so
-// fourier_output[0] is the first real value, fourier_output[1] is the first
-// imaginary, fourier_output[2] is the second real, and so on.
-// The calling function should ensure that the array passed in as fourier_output
-// is at least time_series_size in length. Most optimized FFT implementations
-// require the length to be a power of two as well, but this version doesn't
-// enforce that.
-void CalculateDiscreteFourierTransform(float* time_series, int time_series_size,
- float* fourier_output) {
- for (int i = 0; i < time_series_size / 2; ++i) {
- float real = 0;
- for (int j = 0; j < time_series_size; ++j) {
- real += time_series[j] * std::cos(j * i * kPi * 2 / time_series_size);
- }
- float imaginary = 0;
- for (int j = 0; j < time_series_size; ++j) {
- imaginary -=
- time_series[j] * std::sin(j * i * kPi * 2 / time_series_size);
- }
- fourier_output[(i * 2) + 0] = real;
- fourier_output[(i * 2) + 1] = imaginary;
- }
-}
-
-// Produces a simple sine curve that is used to ensure frequencies at the center
-// of the current sample window are weighted more heavily than those at the end.
-void CalculatePeriodicHann(int window_length, float* window_function) {
- for (int i = 0; i < window_length; ++i) {
- window_function[i] = 0.5f - 0.5f * std::cos((2 * kPi * i) / window_length);
- }
-}
-
-} // namespace
-
-TfLiteStatus GenerateSimpleFeatures(const int16_t* input, int input_size,
- int output_size, uint8_t* output) {
- // Ensure our input and output data arrays are valid.
- if (input_size > kMaxAudioSampleSize) {
- MicroPrintf("Input size %d larger than %d", input_size,
- kMaxAudioSampleSize);
- return kTfLiteError;
- }
- if (output_size != kFeatureSliceSize) {
- MicroPrintf("Requested output size %d doesn't match %d", output_size,
- kFeatureSliceSize);
- return kTfLiteError;
- }
-
- // Pre-calculate the window function we'll be applying to the input data.
- // In a real application, we'd calculate this table once in an initialization
- // function and store it for repeated reuse.
- float window_function[kMaxAudioSampleSize];
- CalculatePeriodicHann(input_size, window_function);
-
- // Apply the window function to our time series input, and pad it with zeroes
- // to the next power of two.
- float float_input[kMaxAudioSampleSize];
- for (int i = 0; i < kMaxAudioSampleSize; ++i) {
- if (i < input_size) {
- float_input[i] =
- (input[i] * window_function[i]) / static_cast<float>(1 << 15);
- } else {
- float_input[i] = 0.0f;
- }
- }
-
- // Pull the frequency data from the time series sample.
- float fourier_values[kMaxAudioSampleSize];
- CalculateDiscreteFourierTransform(float_input, kMaxAudioSampleSize,
- fourier_values);
-
- // We have the complex numbers giving us information about each frequency
- // band, but all we want to know is how strong each frequency is, so calculate
- // the squared magnitude by adding together the squares of each component.
- float power_spectrum[kMaxAudioSampleSize / 2];
- for (int i = 0; i < (kMaxAudioSampleSize / 2); ++i) {
- const float real = fourier_values[(i * 2) + 0];
- const float imaginary = fourier_values[(i * 2) + 1];
- power_spectrum[i] = (real * real) + (imaginary * imaginary);
- }
-
- // Finally, reduce the size of the output by averaging together six adjacent
- // frequencies into each slot, producing an array of 43 values.
- for (int i = 0; i < kFeatureSliceSize; ++i) {
- float total = 0.0f;
- for (int j = 0; j < kAverageWindowSize; ++j) {
- const int index = (i * kAverageWindowSize) + j;
- if (index < (kMaxAudioSampleSize / 2)) {
- total += power_spectrum[index];
- }
- }
- const float average = total / kAverageWindowSize;
- // Quantize the result into eight bits, effectively multiplying by two.
- // The 127.5 constant here has to match the features_max value defined in
- // tensorflow/examples/speech_commands/input_data.py, and this also assumes
- // that features_min is zero. If it wasn't, we'd have to subtract it first.
- int quantized_average = roundf(average * (255.0f / 127.5f));
- if (quantized_average < 0) {
- quantized_average = 0;
- }
- if (quantized_average > 255) {
- quantized_average = 255;
- }
- output[i] = quantized_average;
- }
- return kTfLiteOk;
-}
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_features_generator.h b/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_features_generator.h
deleted file mode 100644
index 7beccea..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_features_generator.h
+++ /dev/null
@@ -1,29 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_SIMPLE_FEATURES_GENERATOR_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_SIMPLE_FEATURES_GENERATOR_H_
-
-#include "tensorflow/lite/c/common.h"
-
-// Converts audio sample data into a more compact form that's appropriate for
-// feeding into a neural network. There are reference implementations that use
-// both floating point and fixed point available, but because the calculations
-// involved can be time-consuming, it's recommended that you use or write
-// specialized versions for your platform.
-TfLiteStatus GenerateSimpleFeatures(const int16_t* input, int input_size,
- int output_size, uint8_t* output);
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_SIMPLE_FEATURES_GENERATOR_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_features_generator_test.cc b/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_features_generator_test.cc
deleted file mode 100644
index f3babd1..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_features_generator_test.cc
+++ /dev/null
@@ -1,59 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/simple_features_generator.h"
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/no_power_spectrum_data.h"
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/yes_power_spectrum_data.h"
-#include "tensorflow/lite/micro/examples/micro_speech/testdata/no_30ms_audio_data.h"
-#include "tensorflow/lite/micro/examples/micro_speech/testdata/yes_30ms_audio_data.h"
-#include "tensorflow/lite/micro/micro_log.h"
-#include "tensorflow/lite/micro/testing/micro_test.h"
-
-TF_LITE_MICRO_TESTS_BEGIN
-
-TF_LITE_MICRO_TEST(TestSimpleFeaturesGenerator) {
- uint8_t yes_calculated_data[g_yes_power_spectrum_data_size];
- TfLiteStatus yes_status = GenerateSimpleFeatures(
- g_yes_30ms_audio_data, g_yes_30ms_audio_data_size,
- g_yes_power_spectrum_data_size, yes_calculated_data);
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, yes_status);
-
- for (int i = 0; i < g_yes_power_spectrum_data_size; ++i) {
- TF_LITE_MICRO_EXPECT_EQ(g_yes_power_spectrum_data[i],
- yes_calculated_data[i]);
- if (g_yes_power_spectrum_data[i] != yes_calculated_data[i]) {
- MicroPrintf("Expected value %d but found %d",
- g_yes_power_spectrum_data[i], yes_calculated_data[i]);
- }
- }
-
- uint8_t no_calculated_data[g_yes_power_spectrum_data_size];
- TfLiteStatus no_status =
- GenerateSimpleFeatures(g_no_30ms_audio_data, g_no_30ms_audio_data_size,
- g_no_power_spectrum_data_size, no_calculated_data);
- TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, no_status);
-
- for (int i = 0; i < g_no_power_spectrum_data_size; ++i) {
- TF_LITE_MICRO_EXPECT_EQ(g_no_power_spectrum_data[i], no_calculated_data[i]);
- if (g_no_power_spectrum_data[i] != no_calculated_data[i]) {
- MicroPrintf("Expected value %d but found %d", g_no_power_spectrum_data[i],
- no_calculated_data[i]);
- }
- }
-}
-
-TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.h b/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.h
deleted file mode 100644
index 9d129c8..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.h
+++ /dev/null
@@ -1,43 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_SIMPLE_MODEL_SETTINGS_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_SIMPLE_MODEL_SETTINGS_H_
-
-// Keeping these as constant expressions allow us to allocate fixed-sized arrays
-// on the stack for our working memory.
-
-// The size of the input time series data we pass to the FFT to produce the
-// frequency information. This has to be a power of two, and since we're dealing
-// with 30ms of 16KHz inputs, which means 480 samples, this is the next value.
-constexpr int kMaxAudioSampleSize = 512;
-constexpr int kAudioSampleFrequency = 16000;
-
-// All of these values are derived from the values used during model training,
-// if you change your model you'll need to update these constants.
-constexpr int kAverageWindowSize = 6;
-constexpr int kFeatureSliceSize =
- ((kMaxAudioSampleSize / 2) + (kAverageWindowSize - 1)) / kAverageWindowSize;
-constexpr int kFeatureSliceCount = 49;
-constexpr int kFeatureElementCount = (kFeatureSliceSize * kFeatureSliceCount);
-constexpr int kFeatureSliceStrideMs = 20;
-constexpr int kFeatureSliceDurationMs = 30;
-
-constexpr int kCategoryCount = 4;
-constexpr int kSilenceIndex = 0;
-constexpr int kUnknownIndex = 1;
-extern const char* kCategoryLabels[kCategoryCount];
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_SIMPLE_MODEL_SETTINGS_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/yes_power_spectrum_data.cc b/tensorflow/lite/micro/examples/micro_speech/simple_features/yes_power_spectrum_data.cc
deleted file mode 100644
index 96a7c9a..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/yes_power_spectrum_data.cc
+++ /dev/null
@@ -1,23 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// See the header for documentation on the meaning of this data.
-
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/yes_power_spectrum_data.h"
-
-const uint8_t g_yes_power_spectrum_data[g_yes_power_spectrum_data_size] = {
- 8, 89, 8, 0, 0, 0, 0, 0, 0, 0, 0, 4, 13, 1, 6, 23, 20, 6, 4, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-};
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/yes_power_spectrum_data.h b/tensorflow/lite/micro/examples/micro_speech/simple_features/yes_power_spectrum_data.h
deleted file mode 100644
index 5264e62..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/yes_power_spectrum_data.h
+++ /dev/null
@@ -1,29 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// This data was extracted from the larger feature data held in
-// no_features_data.cc and consists of the 26th spectrogram slice of 43 values.
-// This is the expected result of running the sample data in
-// yes_30ms_sample_data.cc through the preprocessing pipeline.
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_YES_POWER_SPECTRUM_DATA_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_YES_POWER_SPECTRUM_DATA_H_
-
-#include <cstdint>
-
-constexpr int g_yes_power_spectrum_data_size = 43;
-extern const uint8_t g_yes_power_spectrum_data[];
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_YES_POWER_SPECTRUM_DATA_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/yes_simple_features_data.cc b/tensorflow/lite/micro/examples/micro_speech/simple_features/yes_simple_features_data.cc
deleted file mode 100644
index 078f78d..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/yes_simple_features_data.cc
+++ /dev/null
@@ -1,158 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/yes_simple_features_data.h"
-
-/* File automatically created by
- * tensorflow/examples/speech_commands/wav_to_features.py \
- * --sample_rate=16000 \
- * --clip_duration_ms=1000 \
- * --window_size_ms=30 \
- * --window_stride_ms=20 \
- * --feature_bin_count=40 \
- * --quantize=1 \
- * --preprocess="average" \
- * --input_wav="speech_commands_test_set_v0.02/yes/f2e59fea_nohash_1.wav" \
- * --output_c_file="yes_simple_features_data.cc" \
- */
-
-const int g_yes_simple_f2e59fea_nohash_1_width = 43;
-const int g_yes_simple_f2e59fea_nohash_1_height = 49;
-const unsigned char g_yes_simple_f2e59fea_nohash_1_data[] = {
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 4, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 19, 1, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 3, 3, 1, 1, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 8, 89, 8, 0, 0, 0, 0, 0, 0, 0, 0, 4, 13,
- 1, 6, 23, 20, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 19, 177, 42, 1,
- 1, 0, 0, 0, 0, 2, 3, 119, 51, 5, 139, 92, 58, 58, 15, 2, 1,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 13, 165, 176, 3, 1, 1, 0, 0, 1, 1, 32, 214,
- 26, 19, 113, 103, 28, 22, 27, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12, 55, 128,
- 27, 1, 1, 0, 1, 4, 2, 52, 93, 10, 28, 156, 10, 21, 21, 3, 3,
- 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 14, 99, 32, 65, 7, 1, 2, 2, 6, 13, 121,
- 36, 15, 11, 112, 125, 14, 5, 13, 4, 4, 2, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 25,
- 32, 5, 1, 0, 0, 0, 1, 0, 7, 5, 1, 1, 3, 3, 0, 3, 3,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 13, 13, 5, 1, 0, 0, 0, 0, 0, 3,
- 4, 1, 0, 1, 2, 3, 1, 1, 1, 4, 8, 1, 2, 1, 3, 1, 1,
- 0, 1, 1, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
- 8, 2, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 2, 0, 2,
- 1, 0, 2, 0, 2, 2, 3, 1, 1, 0, 1, 1, 4, 5, 1, 0, 1,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 2, 1, 0, 1, 3, 1,
- 1, 3, 1, 1, 6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 2, 6, 2, 4, 2, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
- 0, 0, 1, 2, 1, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 2, 3, 5, 2, 0,
- 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 2, 3,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-};
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/yes_simple_features_data.h b/tensorflow/lite/micro/examples/micro_speech/simple_features/yes_simple_features_data.h
deleted file mode 100644
index 98c7e42..0000000
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/yes_simple_features_data.h
+++ /dev/null
@@ -1,23 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_YES_SIMPLE_FEATURES_DATA_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_YES_SIMPLE_FEATURES_DATA_H_
-
-extern const int g_yes_simple_f2e59fea_nohash_1_width;
-extern const int g_yes_simple_f2e59fea_nohash_1_height;
-extern const unsigned char g_yes_simple_f2e59fea_nohash_1_data[];
-
-#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_SIMPLE_FEATURES_YES_SIMPLE_FEATURES_DATA_H_
diff --git a/tensorflow/lite/micro/examples/micro_speech/testdata/noise_1000ms.wav b/tensorflow/lite/micro/examples/micro_speech/testdata/noise_1000ms.wav
new file mode 100644
index 0000000..8e0896a
--- /dev/null
+++ b/tensorflow/lite/micro/examples/micro_speech/testdata/noise_1000ms.wav
Binary files differ
diff --git a/tensorflow/lite/micro/examples/micro_speech/testdata/silence_1000ms.wav b/tensorflow/lite/micro/examples/micro_speech/testdata/silence_1000ms.wav
new file mode 100644
index 0000000..294dfc6
--- /dev/null
+++ b/tensorflow/lite/micro/examples/micro_speech/testdata/silence_1000ms.wav
Binary files differ
diff --git a/tensorflow/lite/micro/examples/micro_speech/train/README.md b/tensorflow/lite/micro/examples/micro_speech/train/README.md
index 18a6846..636f70c 100644
--- a/tensorflow/lite/micro/examples/micro_speech/train/README.md
+++ b/tensorflow/lite/micro/examples/micro_speech/train/README.md
@@ -1,7 +1,7 @@
# Micro Speech Training
-This example shows how to train a 20 kB model that can recognize 2 keywords,
+This example shows how to train a less than 20 kB model that can recognize 2 keywords,
"yes" and "no", from speech data.
If the input does not belong to either categories, it is classified as "unknown"
@@ -89,19 +89,17 @@
**Fully quantized implies that the model is **strictly int8** quantized
**including** the input(s) and output(s).*
-<!-- **Fully quantized implies that the model is **strictly int8** except the
-input(s) and output(s) which remain float.* -->
## Model Architecture
-This is a simple model comprising of a Convolutional 2D layer, a Fully Connected
+This is a simple model comprised of a Convolutional 2D layer, a Fully Connected
Layer or a MatMul Layer (output: logits) and a Softmax layer
-(output: probabilities) as shown below. Refer to the [`tiny_conv`](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/examples/speech_commands/models.py#L673)
+(output: probabilities) as shown below. Refer to the [`tiny_conv`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/speech_commands/models.py#L673)
model architecture.
-
+[<img src="../images/micro_speech_quantized.png" width="900" alt="model architecture"/>](../images/micro_speech_quantized.png)
-*This image was derived from visualizing the 'model.tflite' file in
+*This image was derived from visualizing the 'models/micro_speech_quantized.tflite' file in
[Netron](https://github.com/lutzroeder/netron)*
This doesn't produce a highly accurate model, but it's designed to be used as
@@ -109,7 +107,7 @@
can always be on, and then wake higher-power chips when a possible utterance has
been found, so that more accurate analysis can be done. Additionally, the model
takes in preprocessed speech input as a result of which we can leverage a
-simpler model for accurate results.
+simpler model for inference results.
## Dataset
@@ -124,49 +122,32 @@
## Preprocessing Speech Input
In this section we discuss spectrograms, the preprocessed speech input to the
-model. Here's an illustration of the process:
-
-
+model.
The model doesn't take in raw audio sample data, instead it works with
spectrograms which are two dimensional arrays that are made up of slices of
frequency information, each taken from a different time window.
The recipe for creating the spectrogram data is that each frequency slice is
-created by running an FFT across a 30ms section of the audio sample data. The
+created by running an FFT across a 30ms window of the audio sample data. The
input samples are treated as being between -1 and +1 as real values (encoded as
--32,768 and 32,767 in 16-bit signed integer samples).
+-32,768 and 32,767 in 16-bit signed integer samples). The audio sampling window
+stride is 20ms, thus every window overlaps by 10ms.
-This results in an FFT with 256 entries. Every sequence of six entries is
-averaged together, giving a total of 43 frequency buckets in the final slice.
-The results are stored as unsigned eight-bit values, where 0 represents a real
-number of zero, and 255 represents 127.5 as a real number.
+This results in an FFT with 257 entries. Every sequence of approximately six entries is
+averaged together, giving a total of 40 frequency buckets in the slice.
+The results are further processed by down-scaling, noise reduction, automatic
+gain control, and a final down-scaling.
Each adjacent frequency entry is stored in ascending memory order (frequency
bucket 0 at data[0], bucket 1 at data[1], etc). The window for the frequency
analysis is then moved forward by 20ms, and the process repeated, storing the
-results in the next memory row (for example bucket 0 in this moved window would
-be in data[43 + 0], etc). This process happens 49 times in total, producing a
-single channel image that is 43 pixels wide, and 49 rows high.
-
-In a complete application these spectrograms would be calculated at runtime from
-microphone inputs, but the code for doing that is not yet included in this
-sample code. The test uses spectrograms that have been pre-calculated from
-one-second WAV files in the test dataset generated by running the following
-commands:
-
-```
-python tensorflow/tensorflow/examples/speech_commands/wav_to_features.py \
---input_wav=/tmp/speech_dataset/yes/f2e59fea_nohash_1.wav \
---output_c_file=/tmp/yes_features_data.cc \
---window_stride=20 --preprocess=average --quantize=1
-
-python tensorflow/tensorflow/examples/speech_commands/wav_to_features.py \
---input_wav=/tmp/speech_dataset/no/f9643d42_nohash_4.wav \
---output_c_file=/tmp/no_features_data.cc \
---window_stride=20 --preprocess=average --quantize=1
-```
-
+results of the new frequency slice in the next memory row.
+The training is configured for raw audio samples of 1000ms in length.
+With a window size of 30ms and stride of 20ms, some 49 frequency slices can
+be created from 1000ms of audio data.
+Thus, the preprocessing produces a
+single channel image that is 40 pixels wide, and 49 rows high.
## Other Training Methods
diff --git a/tensorflow/lite/micro/examples/mnist_lstm/BUILD b/tensorflow/lite/micro/examples/mnist_lstm/BUILD
index 6df2eef..7d818b2 100644
--- a/tensorflow/lite/micro/examples/mnist_lstm/BUILD
+++ b/tensorflow/lite/micro/examples/mnist_lstm/BUILD
@@ -1,3 +1,4 @@
+load("@rules_python//python:defs.bzl", "py_binary", "py_test")
load("@tflm_pip_deps//:requirements.bzl", "requirement")
py_binary(
@@ -6,7 +7,7 @@
srcs_version = "PY3",
deps = [
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
],
)
@@ -17,6 +18,7 @@
deps = [
"//python/tflite_micro:runtime",
"@absl_py//absl:app",
+ requirement("pillow"),
],
)
diff --git a/tensorflow/lite/micro/examples/network_tester/network_tester_test.cc b/tensorflow/lite/micro/examples/network_tester/network_tester_test.cc
index e62e0c4..23e50c9 100644
--- a/tensorflow/lite/micro/examples/network_tester/network_tester_test.cc
+++ b/tensorflow/lite/micro/examples/network_tester/network_tester_test.cc
@@ -1,4 +1,4 @@
-/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -40,7 +40,7 @@
#define NUM_INFERENCES 1
#endif
-uint8_t tensor_arena[TENSOR_ARENA_SIZE];
+alignas(16) uint8_t tensor_arena[TENSOR_ARENA_SIZE];
#ifdef NUM_BYTES_TO_PRINT
inline void print_output_data(TfLiteTensor* output) {
@@ -92,15 +92,19 @@
model->version(), TFLITE_SCHEMA_VERSION);
return kTfLiteError;
}
+#ifdef ETHOS_U
+ tflite::MicroMutableOpResolver<1> resolver;
+ resolver.AddEthosU();
- tflite::MicroMutableOpResolver<6> resolver;
+#else
+ tflite::MicroMutableOpResolver<5> resolver;
resolver.AddAveragePool2D(tflite::Register_AVERAGE_POOL_2D_INT8());
resolver.AddConv2D(tflite::Register_CONV_2D_INT8());
resolver.AddDepthwiseConv2D(tflite::Register_DEPTHWISE_CONV_2D_INT8());
- resolver.AddEthosU();
resolver.AddReshape();
resolver.AddSoftmax(tflite::Register_SOFTMAX_INT8());
+#endif
tflite::MicroInterpreter interpreter(model, resolver, tensor_arena,
TENSOR_ARENA_SIZE);
@@ -152,7 +156,8 @@
}
#endif
}
- MicroPrintf("Ran successfully\n");
+
+ MicroPrintf("~~~ALL TESTS PASSED~~~\n");
}
TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/examples/person_detection/README.md b/tensorflow/lite/micro/examples/person_detection/README.md
index a490629..2b33334 100644
--- a/tensorflow/lite/micro/examples/person_detection/README.md
+++ b/tensorflow/lite/micro/examples/person_detection/README.md
@@ -7,6 +7,7 @@
- [Run the tests on a development machine](#run-the-tests-on-a-development-machine)
- [Training your own model](#training-your-own-model)
+- [Additional makefile targets](#additional-makefile-targets)
## Run the tests on a development machine
@@ -26,6 +27,19 @@
To understand how TensorFlow Lite does this, you can look at
[person_detection_test.cc](person_detection_test.cc).
+## Additional makefile targets
+```
+make -f tensorflow/lite/micro/tools/make/Makefile person_detection
+make -f tensorflow/lite/micro/tools/make/Makefile person_detection_bin
+make -f tensorflow/lite/micro/tools/make/Makefile run_person_detection
+```
+
+The `run_person_detection` target will produce continuous output similar
+to the following:
+```
+person score:-72 no person score 72
+```
+
## Training your own model
You can train your own model with some easy-to-use scripts. See
diff --git a/tensorflow/lite/micro/examples/person_detection/utils/BUILD b/tensorflow/lite/micro/examples/person_detection/utils/BUILD
index 980d803..7f5c815 100644
--- a/tensorflow/lite/micro/examples/person_detection/utils/BUILD
+++ b/tensorflow/lite/micro/examples/person_detection/utils/BUILD
@@ -1,4 +1,5 @@
load("@tflm_pip_deps//:requirements.bzl", "requirement")
+load("@rules_python//python:defs.bzl", "py_binary", "py_library", "py_test")
package(
features = ["-layering_check"],
@@ -36,7 +37,7 @@
],
deps = [
":raw_to_bitmap_lib",
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
requirement("numpy"),
],
)
diff --git a/tensorflow/lite/micro/examples/recipes/BUILD b/tensorflow/lite/micro/examples/recipes/BUILD
index 475e552..3572fdb 100644
--- a/tensorflow/lite/micro/examples/recipes/BUILD
+++ b/tensorflow/lite/micro/examples/recipes/BUILD
@@ -1,3 +1,4 @@
+load("@rules_python//python:defs.bzl", "py_library", "py_test")
load("@tflm_pip_deps//:requirements.bzl", "requirement")
package(
@@ -5,13 +6,24 @@
)
py_library(
+ name = "add_four_numbers",
+ srcs = ["add_four_numbers.py"],
+ srcs_version = "PY3",
+ visibility = ["//:__subpackages__"],
+ deps = [
+ requirement("numpy"),
+ requirement("tensorflow"),
+ ],
+)
+
+py_library(
name = "resource_variables_lib",
srcs = ["resource_variables_lib.py"],
srcs_version = "PY3",
visibility = ["//:__subpackages__"],
deps = [
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
],
)
@@ -26,8 +38,6 @@
],
deps = [
":resource_variables_lib",
- # TODO(b/286456378): update tflm_runtime to runtime when we are ready to
- # remove the alias.
- "//tensorflow/lite/micro/python/interpreter/src:tflm_runtime",
+ "//python/tflite_micro:runtime",
],
)
diff --git a/tensorflow/lite/micro/examples/recipes/add_four_numbers.py b/tensorflow/lite/micro/examples/recipes/add_four_numbers.py
new file mode 100644
index 0000000..f564141
--- /dev/null
+++ b/tensorflow/lite/micro/examples/recipes/add_four_numbers.py
@@ -0,0 +1,62 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+"""Simple TF model creation using resource variables."""
+
+import numpy as np
+import tensorflow as tf
+
+
+"""
+Generates a simple TfLite model that adds 4 numbers.
+
+Basic Usage:
+
+ model = generate_model(False)
+
+Usage where you want model written to file:
+
+ file_path = "some file path"
+ model = generate_model(True, file_path)
+"""
+
+class AddFourNumbers(tf.Module):
+ @tf.function(
+ input_signature=[
+ tf.TensorSpec(shape=[1], dtype=tf.float32, name="a"),
+ tf.TensorSpec(shape=[1], dtype=tf.float32, name="b"),
+ tf.TensorSpec(shape=[1], dtype=tf.float32, name="c"),
+ tf.TensorSpec(shape=[1], dtype=tf.float32, name="d"),
+ ]
+ )
+ def __call__(self, a, b, c, d):
+ return a + b + c + d
+
+
+def get_model_from_concrete_function():
+ """Accumulator model built via TF concrete functions."""
+ model = AddFourNumbers("AddFourNumbers")
+ concrete_func = model.__call__.get_concrete_function()
+ converter = tf.lite.TFLiteConverter.from_concrete_functions(
+ [concrete_func], model
+ )
+ return converter.convert()
+
+
+def generate_model(write_file=True, filename="/tmp/add.tflite"):
+ model = get_model_from_concrete_function()
+ if write_file:
+ with open(filename, "wb") as f:
+ f.write(model)
+ return model
diff --git a/tensorflow/lite/micro/examples/recipes/resource_variables_test.py b/tensorflow/lite/micro/examples/recipes/resource_variables_test.py
index ad8c79e..686a650 100644
--- a/tensorflow/lite/micro/examples/recipes/resource_variables_test.py
+++ b/tensorflow/lite/micro/examples/recipes/resource_variables_test.py
@@ -18,9 +18,7 @@
from tensorflow.python.platform import test
from tflite_micro.tensorflow.lite.micro.examples.recipes import resource_variables_lib
-# TODO(b/286456378): change tflm_runtime to runtime when we all other usage has
-# been updated.
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import tflm_runtime
+from tflite_micro.python.tflite_micro import runtime as tflm_runtime
class ResourceVariablesTest(test_util.TensorFlowTestCase):
diff --git a/tensorflow/lite/micro/fake_micro_context.cc b/tensorflow/lite/micro/fake_micro_context.cc
index 03ea6df..8874798 100644
--- a/tensorflow/lite/micro/fake_micro_context.cc
+++ b/tensorflow/lite/micro/fake_micro_context.cc
@@ -1,4 +1,4 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -15,28 +15,31 @@
#include "tensorflow/lite/micro/fake_micro_context.h"
+#include "tensorflow/lite/c/c_api_types.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h"
-#include "tensorflow/lite/micro/micro_allocator.h"
#include "tensorflow/lite/micro/micro_arena_constants.h"
#include "tensorflow/lite/micro/micro_log.h"
namespace tflite {
-namespace {
-// Dummy static variables to allow creation of dummy MicroAllocator.
-// All tests are guarateed to run serially.
-static constexpr int KDummyTensorArenaSize = 256;
-static uint8_t dummy_tensor_arena[KDummyTensorArenaSize];
-} // namespace
-FakeMicroContext::FakeMicroContext(TfLiteTensor* tensors,
- SingleArenaBufferAllocator* allocator,
- MicroGraph* micro_graph)
- : MicroContext(
- MicroAllocator::Create(dummy_tensor_arena, KDummyTensorArenaSize),
- nullptr, micro_graph),
+FakeMicroContext::FakeMicroContext(
+ TfLiteTensor* tensors, SingleArenaBufferAllocator* allocator,
+ MicroGraph* micro_graph
+#ifdef USE_TFLM_COMPRESSION
+ ,
+ const CompressedTensorList* compressed_tensors
+#endif // USE_TFLM_COMPRESSION
+ )
+ : graph_(*micro_graph),
tensors_(tensors),
- allocator_(allocator) {}
+ allocator_(allocator)
+#ifdef USE_TFLM_COMPRESSION
+ ,
+ compressed_tensors_(compressed_tensors)
+#endif // USE_TFLM_COMPRESSION
+{
+}
TfLiteTensor* FakeMicroContext::AllocateTempTfLiteTensor(int tensor_index) {
allocated_temp_count_++;
@@ -113,4 +116,69 @@
return scratch_buffers_[buffer_index];
}
+TfLiteStatus FakeMicroContext::set_external_context(
+ void* external_context_payload) {
+ return kTfLiteError;
+}
+
+void* FakeMicroContext::external_context() { return nullptr; }
+
+MicroGraph& FakeMicroContext::graph() { return graph_; }
+
+#ifdef USE_TFLM_COMPRESSION
+
+// Available during Prepare & Eval. Returns false if tensor is not
+// compressed.
+bool FakeMicroContext::IsTensorCompressed(const TfLiteNode* node,
+ int tensor_idx) {
+ if (compressed_tensors_ != nullptr && tensor_idx < node->inputs->size) {
+ int index = node->inputs->data[tensor_idx];
+ if (index >= 0 && compressed_tensors_->tensors[index] != nullptr) {
+ return true;
+ }
+ }
+
+ return false;
+}
+
+// Only available during Prepare. The kernel is responsible for storing the
+// scratch buffer handle.
+int FakeMicroContext::AllocateDecompressionScratchBuffer(const TfLiteNode* node,
+ int tensor_idx) {
+ if (compressed_tensors_ == nullptr || tensor_idx >= node->inputs->size) {
+ return -1;
+ }
+ int index = node->inputs->data[tensor_idx];
+ if (index < 0 || compressed_tensors_->tensors[index] == nullptr) {
+ return -1;
+ }
+ TfLiteTensor* tensor = &tensors_[index];
+ int scratch_index = -1;
+ TfLiteStatus result =
+ RequestScratchBufferInArena(tensor->bytes, &scratch_index);
+ if (result != kTfLiteOk) {
+ return -1;
+ }
+
+ return scratch_index;
+}
+
+// Available during Prepare & Eval. Returns nullptr if tensor is not
+// compressed.
+const CompressionTensorData* FakeMicroContext::GetTensorCompressionData(
+ const TfLiteNode* node, int tensor_idx) {
+ if (compressed_tensors_ == nullptr || tensor_idx >= node->inputs->size) {
+ return nullptr;
+ }
+
+ int index = node->inputs->data[tensor_idx];
+ if (index < 0) {
+ return nullptr;
+ }
+
+ return compressed_tensors_->tensors[index];
+}
+
+#endif // USE_TFLM_COMPRESSION
+
} // namespace tflite
diff --git a/tensorflow/lite/micro/fake_micro_context.h b/tensorflow/lite/micro/fake_micro_context.h
index b068f32..7cf9c68 100644
--- a/tensorflow/lite/micro/fake_micro_context.h
+++ b/tensorflow/lite/micro/fake_micro_context.h
@@ -1,4 +1,4 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -27,8 +27,15 @@
class FakeMicroContext : public MicroContext {
public:
+ ~FakeMicroContext() = default;
+
FakeMicroContext(TfLiteTensor* tensors, SingleArenaBufferAllocator* allocator,
- MicroGraph* micro_graph);
+ MicroGraph* micro_graph
+#ifdef USE_TFLM_COMPRESSION
+ ,
+ const CompressedTensorList* compressed_tensors = nullptr
+#endif // USE_TFLM_COMPRESSION
+ );
void* AllocatePersistentBuffer(size_t bytes) override;
TfLiteStatus RequestScratchBufferInArena(size_t bytes,
@@ -44,9 +51,32 @@
TfLiteEvalTensor* GetEvalTensor(int tensor_index) override;
+ TfLiteStatus set_external_context(void* external_context_payload) override;
+ void* external_context() override;
+ MicroGraph& graph() override;
+
+#ifdef USE_TFLM_COMPRESSION
+
+ // Available during Prepare & Eval. Returns false if tensor is not
+ // compressed.
+ bool IsTensorCompressed(const TfLiteNode* node, int tensor_idx) override;
+
+ // Only available during Prepare. The kernel is responsible for storing the
+ // scratch buffer handle.
+ int AllocateDecompressionScratchBuffer(const TfLiteNode* node,
+ int tensor_idx) override;
+
+ // Available during Prepare & Eval. Returns nullptr if tensor is not
+ // compressed.
+ const CompressionTensorData* GetTensorCompressionData(
+ const TfLiteNode* node, int tensor_idx) override;
+
+#endif // USE_TFLM_COMPRESSION
+
private:
static constexpr int kNumScratchBuffers_ = 12;
+ MicroGraph& graph_;
int scratch_buffer_count_ = 0;
uint8_t* scratch_buffers_[kNumScratchBuffers_];
@@ -55,6 +85,15 @@
SingleArenaBufferAllocator* allocator_;
+#ifdef USE_TFLM_COMPRESSION
+
+ //
+ // Compression
+ //
+ const CompressedTensorList* compressed_tensors_;
+
+#endif // USE_TFLM_COMPRESSION
+
TF_LITE_REMOVE_VIRTUAL_DELETE
};
diff --git a/tensorflow/lite/micro/fake_micro_context_test.cc b/tensorflow/lite/micro/fake_micro_context_test.cc
index 264b7e7..e792238 100644
--- a/tensorflow/lite/micro/fake_micro_context_test.cc
+++ b/tensorflow/lite/micro/fake_micro_context_test.cc
@@ -18,6 +18,7 @@
#include "tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h"
#include "tensorflow/lite/micro/micro_allocator.h"
+#include "tensorflow/lite/micro/mock_micro_graph.h"
#include "tensorflow/lite/micro/test_helpers.h"
#include "tensorflow/lite/micro/testing/micro_test.h"
@@ -58,7 +59,7 @@
uint8_t arena_buffer[kArenaSize];
tflite::SingleArenaBufferAllocator simple_memory_allocator(arena_buffer,
kArenaSize);
- tflite::MicroGraph dummy_micro_graph(nullptr, nullptr, nullptr, nullptr);
+ tflite::MockMicroGraph dummy_micro_graph{&simple_memory_allocator};
tflite::FakeMicroContext micro_context = tflite::CreateFakeMicroContext(
&simple_memory_allocator, &dummy_micro_graph);
@@ -71,7 +72,7 @@
uint8_t arena_buffer[kArenaSize];
tflite::SingleArenaBufferAllocator simple_memory_allocator(arena_buffer,
kArenaSize);
- tflite::MicroGraph dummy_micro_graph(nullptr, nullptr, nullptr, nullptr);
+ tflite::MockMicroGraph dummy_micro_graph{&simple_memory_allocator};
tflite::FakeMicroContext micro_context = tflite::CreateFakeMicroContext(
&simple_memory_allocator, &dummy_micro_graph);
diff --git a/tensorflow/lite/micro/integration_tests/BUILD b/tensorflow/lite/micro/integration_tests/BUILD
index 1e96ba8..5b90e5d 100644
--- a/tensorflow/lite/micro/integration_tests/BUILD
+++ b/tensorflow/lite/micro/integration_tests/BUILD
@@ -1,3 +1,4 @@
+load("@rules_python//python:defs.bzl", "py_binary")
load("@tflm_pip_deps//:requirements.bzl", "requirement")
package(licenses = ["notice"])
@@ -18,7 +19,7 @@
"@absl_py//absl:app",
"@absl_py//absl/flags",
requirement("mako"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
"//tensorflow/lite/micro/tools:generate_test_for_model",
"//tensorflow/lite/python:schema_py",
"//tensorflow/lite/python:schema_util",
diff --git a/tensorflow/lite/micro/kernels/BUILD b/tensorflow/lite/micro/kernels/BUILD
index 065c8f5..22254d9 100644
--- a/tensorflow/lite/micro/kernels/BUILD
+++ b/tensorflow/lite/micro/kernels/BUILD
@@ -1,8 +1,10 @@
+load("@bazel_skylib//rules:common_settings.bzl", "string_flag")
load("//tensorflow/lite/micro:build_def.bzl", "micro_copts", "tflm_kernel_cc_library")
load(
"//tensorflow:extra_rules.bzl",
"tflm_kernel_friends",
"xtensa_fusion_f1_config",
+ "xtensa_hifi_3_config",
"xtensa_hifi_3z_config",
"xtensa_hifi_5_config",
"xtensa_vision_p6_config",
@@ -44,6 +46,7 @@
cc_library(
name = "activation_utils",
hdrs = ["activation_utils.h"],
+ copts = micro_copts(),
deps = [
"//tensorflow/lite/c:common",
"//tensorflow/lite/kernels/internal:cppmath",
@@ -58,6 +61,7 @@
hdrs = [
"circular_buffer_flexbuffers_generated_data.h",
],
+ copts = micro_copts(),
)
cc_library(
@@ -71,6 +75,7 @@
visibility = [
"//visibility:public",
],
+ copts = micro_copts(),
deps = [
":kernel_runner",
":micro_ops",
@@ -88,6 +93,7 @@
hdrs = [
"detection_postprocess_flexbuffers_generated_data.h",
],
+ copts = micro_copts(),
)
cc_library(
@@ -96,6 +102,7 @@
"kernel_runner.cc",
],
hdrs = ["kernel_runner.h"],
+ copts = micro_copts(),
visibility = [
"//visibility:public",
],
@@ -116,6 +123,7 @@
"kernel_util.cc",
],
hdrs = ["kernel_util.h"],
+ copts = micro_copts(),
visibility = [
":kernel_friends",
":tflite_micro",
@@ -135,6 +143,7 @@
hdrs = [
"lstm_shared.h",
],
+ copts = micro_copts(),
visibility = ["//tensorflow/lite/micro/kernels/testdata:__pkg__"],
)
@@ -143,6 +152,7 @@
hdrs = [
"lstm_eval_test.h",
],
+ copts = micro_copts(),
deps = [
":kernel_util",
":micro_ops",
@@ -158,6 +168,7 @@
"micro_tensor_utils.cc",
],
hdrs = ["micro_tensor_utils.h"],
+ copts = micro_copts(),
deps = [
"//tensorflow/lite/c:common",
"//tensorflow/lite/core:macros",
@@ -169,6 +180,11 @@
],
)
+HIFI3_COPTS = [
+ "-DXTENSA=1",
+ "-DHIFI3=1",
+]
+
HIFI4_COPTS = [
"-DXTENSA=1",
"-DHIFI4=1",
@@ -197,6 +213,7 @@
"add_n.cc",
"arg_min_max.cc",
"assign_variable.cc",
+ "batch_matmul.cc",
"batch_to_space_nd.cc",
"broadcast_args.cc",
"broadcast_to.cc",
@@ -278,6 +295,7 @@
"squared_difference.cc",
"squeeze.cc",
"strided_slice.cc",
+ "strided_slice_common.cc",
"sub.cc",
"sub_common.cc",
"svdf.cc",
@@ -315,10 +333,13 @@
"reduce.h",
"reshape.h",
"softmax.h",
+ "strided_slice.h",
"sub.h",
"svdf.h",
+ "transpose_conv.h",
] + select({
xtensa_fusion_f1_config(): glob(["xtensa/**/*.h"]),
+ xtensa_hifi_3_config(): glob(["xtensa/**/*.h"]),
xtensa_hifi_3z_config(): glob(["xtensa/**/*.h"]),
xtensa_hifi_5_config(): glob(["xtensa/**/*.h"]),
xtensa_vision_p6_config(): glob(["xtensa/**/*.h"]),
@@ -327,6 +348,7 @@
}),
accelerated_srcs = {
xtensa_fusion_f1_config(): glob(["xtensa/**/*.cc"]),
+ xtensa_hifi_3_config(): glob(["xtensa/**/*.cc"]),
xtensa_hifi_3z_config(): glob(["xtensa/**/*.cc"]),
xtensa_hifi_5_config(): glob(["xtensa/**/*.cc"]),
xtensa_vision_p6_config(): glob(["xtensa/**/*.cc"]),
@@ -334,6 +356,7 @@
},
copts = micro_copts() + select({
xtensa_fusion_f1_config(): HIFI4_COPTS,
+ xtensa_hifi_3_config(): HIFI3_COPTS,
xtensa_hifi_3z_config(): HIFI4_COPTS,
xtensa_hifi_5_config(): HIFI5_COPTS,
xtensa_vision_p6_config(): VP6_COPTS,
@@ -370,6 +393,7 @@
"@flatbuffers//:runtime_cc",
] + select({
xtensa_fusion_f1_config(): ["//third_party/xtensa/nnlib_hifi4:nnlib_hifi4_lib"],
+ xtensa_hifi_3_config(): ["//third_party/xtensa/nnlib_hifi4:nnlib_hifi4_lib"],
xtensa_hifi_3z_config(): ["//third_party/xtensa/nnlib_hifi4:nnlib_hifi4_lib"],
xtensa_hifi_5_config(): ["//third_party/xtensa/nnlib_hifi5:nnlib_hifi5_lib"],
xtensa_vision_p6_config(): ["//third_party/xtensa/xi_tflmlib_vision_p6:xi_tflmlib_vision_p6_lib"],
@@ -440,6 +464,20 @@
)
cc_test(
+ name = "batch_matmul_test",
+ srcs = [
+ "batch_matmul_test.cc",
+ ],
+ deps = [
+ ":kernel_runner",
+ "//tensorflow/lite/c:common",
+ "//tensorflow/lite/micro:op_resolvers",
+ "//tensorflow/lite/micro:test_helpers",
+ "//tensorflow/lite/micro/testing:micro_test",
+ ],
+)
+
+cc_test(
name = "batch_to_space_nd_test",
srcs = [
"batch_to_space_nd_test.cc",
@@ -1442,31 +1480,55 @@
# Bazel config settings.
####################################
+# Command line flag to select which set of optimized kernels to use.
+# Each value should have a `config_setting` which is selected on in the
+# `micro_ops` target to pickup optimized kernel sources. An empty value
+# indicates only reference kernels should be used.
+string_flag(
+ name = "optimized_kernels",
+ build_setting_default = "",
+ values = [
+ "",
+ "xtensa_fusion_f1",
+ "xtensa_hifi_3",
+ "xtensa_hifi_3z",
+ "xtensa_hifi_5",
+ "xtensa_vision_p6",
+ ],
+)
+
config_setting(
name = "xtensa_fusion_f1_default",
- values = {
- "cpu": "F1_190305_swupgrade",
+ flag_values = {
+ ":optimized_kernels": "xtensa_fusion_f1",
+ },
+)
+
+config_setting(
+ name = "xtensa_hifi_3_default",
+ flag_values = {
+ ":optimized_kernels": "xtensa_hifi_3",
},
)
config_setting(
name = "xtensa_hifi_3z_default",
- values = {
- "cpu": "HIFI_190304_swupgrade",
+ flag_values = {
+ ":optimized_kernels": "xtensa_hifi_3z",
},
)
config_setting(
name = "xtensa_hifi_5_default",
- values = {
- "cpu": "AE_HiFi5_LE5_AO_FP_XC",
+ flag_values = {
+ ":optimized_kernels": "xtensa_hifi_5",
},
)
config_setting(
name = "xtensa_vision_p6_default",
- values = {
- "cpu": "P6_200528",
+ flag_values = {
+ ":optimized_kernels": "xtensa_vision_p6",
},
)
diff --git a/tensorflow/lite/micro/kernels/Makefile.inc b/tensorflow/lite/micro/kernels/Makefile.inc
index 384fcb3..0bd846b 100644
--- a/tensorflow/lite/micro/kernels/Makefile.inc
+++ b/tensorflow/lite/micro/kernels/Makefile.inc
@@ -1,4 +1,4 @@
-# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -92,6 +92,11 @@
$(TENSORFLOW_ROOT)signal/micro/kernels/overlap_add_flexbuffers_generated_data.cc, \
$(TENSORFLOW_ROOT)signal/micro/kernels/overlap_add_flexbuffers_generated_data.h))
+$(eval $(call microlite_test,kernel_signal_pcan_test,\
+ $(TENSORFLOW_ROOT)signal/micro/kernels/pcan_test.cc \
+ $(TENSORFLOW_ROOT)signal/micro/kernels/pcan_flexbuffers_generated_data.cc, \
+ $(TENSORFLOW_ROOT)signal/micro/kernels/pcan_flexbuffers_generated_data.h))
+
$(eval $(call microlite_test,kernel_signal_stacker_test,\
$(TENSORFLOW_ROOT)signal/micro/kernels/stacker_test.cc \
$(TENSORFLOW_ROOT)signal/micro/kernels/stacker_flexbuffers_generated_data.cc, \
@@ -109,6 +114,7 @@
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/add_test.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/add_n_test.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/arg_min_max_test.cc \
+$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/batch_matmul_test.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/batch_to_space_nd_test.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/broadcast_args_test.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/broadcast_to_test.cc \
diff --git a/tensorflow/lite/micro/kernels/add.cc b/tensorflow/lite/micro/kernels/add.cc
index b27206c..fde047a 100644
--- a/tensorflow/lite/micro/kernels/add.cc
+++ b/tensorflow/lite/micro/kernels/add.cc
@@ -38,7 +38,7 @@
const TfLiteEvalTensor* input2, TfLiteEvalTensor* output) {
switch (output->type) {
case kTfLiteFloat32: {
- tflite::ArithmeticParams op_params;
+ tflite::ArithmeticParams op_params = {};
SetActivationParams(data->output_activation_min_f32,
data->output_activation_max_f32, &op_params);
if (data->requires_broadcast) {
@@ -59,7 +59,7 @@
}
} break;
case kTfLiteInt32: {
- tflite::ArithmeticParams op_params;
+ tflite::ArithmeticParams op_params = {};
SetActivationParams(std::numeric_limits<int32_t>::lowest(),
std::numeric_limits<int32_t>::max(), &op_params);
if (data->requires_broadcast) {
@@ -93,7 +93,7 @@
const TfLiteEvalTensor* input1,
const TfLiteEvalTensor* input2,
TfLiteEvalTensor* output) {
- tflite::ArithmeticParams op_params;
+ tflite::ArithmeticParams op_params = {};
op_params.left_shift = data->left_shift;
op_params.input1_offset = data->input1_offset;
op_params.input1_multiplier = data->input1_multiplier;
diff --git a/tensorflow/lite/micro/kernels/batch_matmul.cc b/tensorflow/lite/micro/kernels/batch_matmul.cc
new file mode 100644
index 0000000..bd621f4
--- /dev/null
+++ b/tensorflow/lite/micro/kernels/batch_matmul.cc
@@ -0,0 +1,558 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/lite/kernels/internal/reference/batch_matmul.h"
+
+#include <algorithm>
+#include <cstdint>
+#include <limits>
+
+#include "tensorflow/lite/core/c/common.h"
+#include "tensorflow/lite/kernels/internal/quantization_util.h"
+#include "tensorflow/lite/kernels/internal/reference/transpose.h"
+#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
+#include "tensorflow/lite/kernels/internal/types.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/micro_log.h"
+
+namespace tflite {
+namespace {
+
+constexpr int kInputLhsTensor = 0;
+constexpr int kInputRhsTensor = 1;
+constexpr int kOutputTensor = 0;
+
+struct QuantizationOpData {
+ // The scaling factor from input to output (aka the 'real multiplier') can
+ // be represented as a fixed point multiplier plus a left shift.
+ int32_t output_multiplier;
+ int output_shift; // exponent
+
+ // The range of the fused activation layer. For example for kNone and
+ // int8_t these would be -128 and 127.
+ int32_t output_activation_min;
+ int32_t output_activation_max;
+
+ int32_t lhs_zero_point;
+ int32_t rhs_zero_point;
+ int32_t output_zero_point;
+};
+
+struct OpData {
+ QuantizationOpData* quantization;
+
+ // Transpose tensors and state
+ TfLiteEvalTensor* lhs_transposed_tensor;
+ TfLiteEvalTensor* rhs_transposed_tensor;
+ bool rhs_is_transposed;
+ bool lhs_is_constant_tensor;
+ bool rhs_is_constant_tensor;
+};
+
+struct OpContext {
+ OpContext(TfLiteContext* context, TfLiteNode* node)
+ : params(static_cast<TfLiteBatchMatMulParams*>(node->builtin_data)),
+ op_data(static_cast<OpData*>(node->user_data)) {}
+
+ TfLiteBatchMatMulParams* params;
+ OpData* op_data;
+};
+
+struct PrepareOpContext : OpContext {
+ PrepareOpContext(TfLiteContext* context, TfLiteNode* node)
+ : OpContext(context, node),
+ micro_context_(GetMicroContext(context)),
+ lhs(micro_context_->AllocateTempInputTensor(node, kInputLhsTensor)),
+ rhs(micro_context_->AllocateTempInputTensor(node, kInputRhsTensor)),
+ output(micro_context_->AllocateTempOutputTensor(node, kOutputTensor)) {}
+
+ ~PrepareOpContext() {
+ if (lhs != nullptr) {
+ micro_context_->DeallocateTempTfLiteTensor(lhs);
+ }
+ if (rhs != nullptr) {
+ micro_context_->DeallocateTempTfLiteTensor(rhs);
+ }
+ if (output != nullptr) {
+ micro_context_->DeallocateTempTfLiteTensor(output);
+ }
+ }
+
+ private:
+ MicroContext* micro_context_;
+
+ public:
+ TfLiteTensor* lhs;
+ TfLiteTensor* rhs;
+ TfLiteTensor* output;
+};
+
+struct EvalOpContext : OpContext {
+ EvalOpContext(TfLiteContext* context, TfLiteNode* node)
+ : OpContext(context, node),
+ lhs(tflite::micro::GetEvalInput(context, node, kInputLhsTensor)),
+ rhs(tflite::micro::GetEvalInput(context, node, kInputRhsTensor)),
+ output(tflite::micro::GetEvalOutput(context, node, kOutputTensor)) {}
+
+ const TfLiteEvalTensor* lhs;
+ const TfLiteEvalTensor* rhs;
+ TfLiteEvalTensor* output;
+};
+
+TfLiteStatus ReshapeOutputTensor(TfLiteContext* context, TfLiteNode* node,
+ const RuntimeShape& extended_lhs_shape,
+ const RuntimeShape& extended_rhs_shape,
+ bool adj_x, bool adj_y, int output_rank,
+ TfLiteTensor* output) {
+ int64_t orig_size = NumElements(output);
+
+ // make sure the new output dims rank does not exceed the original rank
+ TF_LITE_ENSURE(context, output_rank <= NumDimensions(output));
+
+ // make sure output tensor dims are not in the FlatBuffer
+ TfLiteEvalTensor* output_eval =
+ tflite::micro::GetEvalOutput(context, node, kOutputTensor);
+ TF_LITE_ENSURE_OK(context, tflite::micro::CreateWritableTensorDimsWithCopy(
+ context, output, output_eval));
+
+ // Fill in any broadcast dimensions.
+ for (int i = 0; i < output_rank - 2; ++i) {
+ const int lhs_dim = extended_lhs_shape.Dims(i);
+ const int rhs_dim = extended_rhs_shape.Dims(i);
+ int broadcast_dim = lhs_dim;
+ if ((lhs_dim != rhs_dim) && (lhs_dim == 1)) {
+ broadcast_dim = rhs_dim;
+ }
+ output->dims->data[i] = broadcast_dim;
+ }
+ // Fill in the matmul dimensions.
+ int lhs_rows_index = adj_x ? output_rank - 1 : output_rank - 2;
+ int rhs_cols_index = adj_y ? output_rank - 2 : output_rank - 1;
+
+ output->dims->data[output_rank - 2] = extended_lhs_shape.Dims(lhs_rows_index);
+ output->dims->data[output_rank - 1] = extended_rhs_shape.Dims(rhs_cols_index);
+ output->dims->size = output_rank;
+
+ // Check that output tensor has not been resized
+ // since TFLM doesn't support tensor resizing.
+ TF_LITE_ENSURE_EQ(context, orig_size, NumElements(output));
+
+ return kTfLiteOk;
+}
+
+TfLiteEvalTensor* AllocInitTransposeTensorFromTfLiteTensor(
+ TfLiteContext* context, const TfLiteTensor& tensor) {
+ MicroContext* micro_context = GetMicroContext(context);
+ TfLiteEvalTensor* eval_tensor = static_cast<TfLiteEvalTensor*>(
+ micro_context->AllocatePersistentBuffer(sizeof(TfLiteEvalTensor)));
+ if (eval_tensor == nullptr) {
+ return nullptr;
+ }
+
+ eval_tensor->type = tensor.type;
+
+ const int tensor_rank = NumDimensions(&tensor);
+ const size_t eval_dims_size = TfLiteIntArrayGetSizeInBytes(tensor_rank);
+ eval_tensor->dims = static_cast<TfLiteIntArray*>(
+ micro_context->AllocatePersistentBuffer(eval_dims_size));
+ if (eval_tensor->dims == nullptr) {
+ return nullptr;
+ }
+ eval_tensor->dims->size = tensor_rank;
+ for (int i = 0; i < tensor_rank - 2; ++i) {
+ eval_tensor->dims->data[i] = tensor.dims->data[i];
+ }
+ // Swap last two dimensions.
+ eval_tensor->dims->data[tensor_rank - 2] = tensor.dims->data[tensor_rank - 1];
+ eval_tensor->dims->data[tensor_rank - 1] = tensor.dims->data[tensor_rank - 2];
+
+ const size_t eval_data_size = static_cast<size_t>(NumElements(&tensor)) *
+ TfLiteTypeGetSize(tensor.type);
+ eval_tensor->data.data =
+ micro_context->AllocatePersistentBuffer(eval_data_size);
+ if (eval_tensor->data.data == nullptr) {
+ return nullptr;
+ }
+
+ return eval_tensor;
+}
+
+// Initializes tensors to store transposed operands.
+// Allocate storage for hybrid quantization if needed.
+// Allocate normal quantization data if needed.
+TfLiteStatus InitializeTemporaries(TfLiteContext* context, TfLiteNode* node,
+ const PrepareOpContext& op_context) {
+ OpData* op_data = op_context.op_data;
+ const TfLiteTensor* lhs = op_context.lhs;
+ const TfLiteTensor* rhs = op_context.rhs;
+ MicroContext* micro_context = GetMicroContext(context);
+
+ op_data->quantization = nullptr;
+ op_data->lhs_transposed_tensor = nullptr;
+ op_data->rhs_transposed_tensor = nullptr;
+
+ if (lhs->type == kTfLiteInt8 || lhs->type == kTfLiteInt16) {
+ op_data->quantization = static_cast<decltype(op_data->quantization)>(
+ micro_context->AllocatePersistentBuffer(
+ sizeof(*op_data->quantization)));
+ TF_LITE_ENSURE(context, op_data->quantization != nullptr);
+ }
+
+ // tensor for Transposed LHS;
+ if (op_context.params->adj_x) {
+ op_data->lhs_transposed_tensor =
+ AllocInitTransposeTensorFromTfLiteTensor(context, *lhs);
+ TF_LITE_ENSURE(context, op_data->lhs_transposed_tensor != nullptr);
+ }
+
+ // We need a buffer for the RHS if we need to transpose the RHS. We
+ // transpose by default, so that the two inputs (LHS and RHS) are in a proper
+ // layout for our fast matrix multiplication routines. If the transpose flag
+ // is set by the caller, the data is already in the desired layout.
+ if (!op_context.params->adj_y) {
+ op_data->rhs_transposed_tensor =
+ AllocInitTransposeTensorFromTfLiteTensor(context, *rhs);
+ TF_LITE_ENSURE(context, op_data->rhs_transposed_tensor != nullptr);
+ }
+
+ return kTfLiteOk;
+}
+
+template <typename Scalar>
+void TransposeRowsColumnsImpl(const TfLiteEvalTensor& tensor_in,
+ TfLiteEvalTensor* tensor_out) {
+ const Scalar* input = tflite::micro::GetTensorData<Scalar>(&tensor_in);
+ Scalar* output = tflite::micro::GetTensorData<Scalar>(tensor_out);
+ RuntimeShape transposed_shape(tflite::micro::GetTensorShape(&tensor_in));
+ RuntimeShape shape(transposed_shape);
+ TransposeParams params;
+ const int rank = shape.DimensionsCount();
+ params.perm_count = rank;
+ for (int i = 0; i < rank - 2; ++i) {
+ params.perm[i] = i;
+ }
+ // Transpose the last two dimensions.
+ params.perm[rank - 2] = rank - 1;
+ params.perm[rank - 1] = rank - 2;
+ transposed_shape.SetDim(rank - 1, shape.Dims(rank - 2));
+ transposed_shape.SetDim(rank - 2, shape.Dims(rank - 1));
+ reference_ops::Transpose(params, shape, input, transposed_shape, output);
+}
+
+TfLiteStatus TransposeRowsColumns(const TfLiteEvalTensor& tensor_in,
+ TfLiteEvalTensor* tensor_out) {
+ if (tensor_in.type == kTfLiteFloat32) {
+ TransposeRowsColumnsImpl<float>(tensor_in, tensor_out);
+ return kTfLiteOk;
+ } else if (tensor_in.type == kTfLiteInt8) {
+ TransposeRowsColumnsImpl<int8_t>(tensor_in, tensor_out);
+ return kTfLiteOk;
+ } else if (tensor_in.type == kTfLiteInt16) {
+ TransposeRowsColumnsImpl<int16_t>(tensor_in, tensor_out);
+ return kTfLiteOk;
+ } else {
+ MicroPrintf(
+ "BATCH_MATMUL can only transpose tensors with FLOAT32, INT8, INT16 "
+ "type.");
+ }
+ return kTfLiteError;
+}
+
+RuntimeShape SwapRowColumnDims(const RuntimeShape& shape) {
+ RuntimeShape swapped_shape(shape);
+ const int32_t dims = shape.DimensionsCount();
+ swapped_shape.SetDim(dims - 2, shape.Dims(dims - 1));
+ swapped_shape.SetDim(dims - 1, shape.Dims(dims - 2));
+ return swapped_shape;
+}
+
+void* BatchMatMulInit(TfLiteContext* context, const char* buffer,
+ size_t length) {
+ // This is a builtin op, so we don't use the contents in 'buffer', if any.
+ // Instead, we allocate a new object to carry information from Prepare() to
+ // Eval().
+ TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
+ MicroContext* micro_context = GetMicroContext(context);
+ return micro_context->AllocatePersistentBuffer(sizeof(OpData));
+}
+
+TfLiteStatus BatchMatMulPrepare(TfLiteContext* context, TfLiteNode* node) {
+ TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
+ TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
+
+ PrepareOpContext op_context(context, node);
+ const TfLiteTensor* lhs_data = op_context.lhs;
+ TF_LITE_ENSURE(context, lhs_data != nullptr);
+ const TfLiteTensor* rhs_data = op_context.rhs;
+ TF_LITE_ENSURE(context, rhs_data != nullptr);
+ TfLiteTensor* output = op_context.output;
+ TF_LITE_ENSURE(context, output != nullptr);
+
+ TF_LITE_ENSURE(context, lhs_data->type == kTfLiteFloat32 ||
+ lhs_data->type == kTfLiteInt8 ||
+ lhs_data->type == kTfLiteInt16);
+ TF_LITE_ENSURE(context, rhs_data->type == kTfLiteFloat32 ||
+ rhs_data->type == kTfLiteInt8 ||
+ rhs_data->type == kTfLiteInt16);
+ // Both inputs should be of the same type.
+ // Hybrid input (FLOAT32 LHS, INT8 RHS) is not supported.
+ TF_LITE_ENSURE(context, lhs_data->type == rhs_data->type);
+ // LHS input must match output type. INT32 output not supported.
+ TF_LITE_ENSURE(context, lhs_data->type == output->type);
+
+ const int lhs_rank = NumDimensions(lhs_data);
+ const int rhs_rank = NumDimensions(rhs_data);
+ // Support dimensions between 2 and 5, inclusive.
+ TF_LITE_ENSURE(context, lhs_rank >= 2);
+ TF_LITE_ENSURE(context, lhs_rank <= 5);
+ TF_LITE_ENSURE(context, rhs_rank >= 2);
+ TF_LITE_ENSURE(context, rhs_rank <= 5);
+
+ TF_LITE_ENSURE_OK(context, InitializeTemporaries(context, node, op_context));
+
+ OpData* op_data = op_context.op_data;
+ // If the RHS is constant, we only transpose once.
+ op_data->rhs_is_transposed = false;
+ op_data->lhs_is_constant_tensor = IsConstantTensor(lhs_data);
+ op_data->rhs_is_constant_tensor = IsConstantTensor(rhs_data);
+
+ // Note that quantized inference requires that all tensors have their
+ // parameters set. This is usually done during quantized training.
+ if (lhs_data->type == kTfLiteInt8 || lhs_data->type == kTfLiteInt16) {
+ TF_LITE_ENSURE(context, op_data->quantization != nullptr);
+ double real_multiplier = 0.0;
+ TF_LITE_ENSURE_STATUS(GetQuantizedConvolutionMultipler(
+ context, lhs_data, rhs_data, output, &real_multiplier));
+ QuantizeMultiplier(real_multiplier,
+ &op_data->quantization->output_multiplier,
+ &op_data->quantization->output_shift);
+ // BatchMatMul has no fused activation functions. Therefore, set
+ // output activation min and max to min and max of int8_t or int16_t type.
+ if (lhs_data->type == kTfLiteInt8) {
+ op_data->quantization->output_activation_min =
+ std::numeric_limits<int8_t>::min();
+ op_data->quantization->output_activation_max =
+ std::numeric_limits<int8_t>::max();
+ } else {
+ op_data->quantization->output_activation_min =
+ std::numeric_limits<int16_t>::min();
+ op_data->quantization->output_activation_max =
+ std::numeric_limits<int16_t>::max();
+
+ TF_LITE_ENSURE_EQ(context, lhs_data->params.zero_point, 0);
+ TF_LITE_ENSURE_EQ(context, rhs_data->params.zero_point, 0);
+ TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0);
+ }
+
+ op_data->quantization->lhs_zero_point = lhs_data->params.zero_point;
+ op_data->quantization->rhs_zero_point = rhs_data->params.zero_point;
+ op_data->quantization->output_zero_point = output->params.zero_point;
+ }
+
+ const int output_rank = std::max(lhs_rank, rhs_rank);
+ const RuntimeShape extended_lhs_shape =
+ RuntimeShape::ExtendedShape(output_rank, GetTensorShape(lhs_data));
+ const RuntimeShape extended_rhs_shape =
+ RuntimeShape::ExtendedShape(output_rank, GetTensorShape(rhs_data));
+
+ // Ensure any batch dimensions obey broacasting rules.
+ for (int i = 0; i < output_rank - 2; ++i) {
+ const int lhs_dim = extended_lhs_shape.Dims(i);
+ const int rhs_dim = extended_rhs_shape.Dims(i);
+ if (lhs_dim != rhs_dim) {
+ if (lhs_dim != 1) {
+ TF_LITE_ENSURE_EQ(context, rhs_dim, 1);
+ }
+ }
+ }
+ bool adj_x = op_context.params->adj_x;
+ bool adj_y = op_context.params->adj_y;
+ // Ensure other dimensions work for matrix multiplication.
+ int accum_dim_lhs = adj_x ? extended_lhs_shape.Dims(output_rank - 2)
+ : extended_lhs_shape.Dims(output_rank - 1);
+ int accum_dim_rhs = adj_y ? extended_rhs_shape.Dims(output_rank - 1)
+ : extended_rhs_shape.Dims(output_rank - 2);
+
+ TF_LITE_ENSURE_EQ(context, accum_dim_lhs, accum_dim_rhs);
+ TfLiteStatus status =
+ ReshapeOutputTensor(context, node, extended_lhs_shape, extended_rhs_shape,
+ adj_x, adj_y, output_rank, output);
+ return status;
+}
+
+TfLiteStatus EvalInt8(TfLiteContext* context, const OpData& data,
+ const RuntimeShape& lhs_shape,
+ const TfLiteEvalTensor& lhs,
+ const RuntimeShape& rhs_shape,
+ const TfLiteEvalTensor& rhs,
+ const RuntimeShape& output_shape,
+ TfLiteEvalTensor* output) {
+ TF_LITE_ENSURE(context, data.quantization != nullptr);
+ // Reuse params struct from FullyConnected Op.
+ FullyConnectedParams op_params;
+ op_params.input_offset = -data.quantization->lhs_zero_point;
+ op_params.weights_offset =
+ -data.quantization->rhs_zero_point; // filter offset
+ op_params.output_offset = data.quantization->output_zero_point;
+ op_params.output_multiplier = data.quantization->output_multiplier;
+ op_params.output_shift = data.quantization->output_shift;
+ op_params.quantized_activation_min = data.quantization->output_activation_min;
+ op_params.quantized_activation_max = data.quantization->output_activation_max;
+ op_params.lhs_cacheable = data.lhs_is_constant_tensor;
+ op_params.rhs_cacheable = data.rhs_is_constant_tensor;
+
+ // Note we pass RHS args first, LHS args second. See note for Eval.
+ reference_ops::BatchMatMul<int8_t, int32_t>(
+ op_params, rhs_shape, tflite::micro::GetTensorData<int8_t>(&rhs),
+ lhs_shape, tflite::micro::GetTensorData<int8_t>(&lhs), output_shape,
+ tflite::micro::GetTensorData<int8_t>(output));
+
+ return kTfLiteOk;
+}
+
+TfLiteStatus EvalInt16(TfLiteContext* context, const OpData& data,
+ const RuntimeShape& lhs_shape,
+ const TfLiteEvalTensor& lhs,
+ const RuntimeShape& rhs_shape,
+ const TfLiteEvalTensor& rhs,
+ const RuntimeShape& output_shape,
+ TfLiteEvalTensor* output) {
+ TF_LITE_ENSURE(context, data.quantization != nullptr);
+ // Reuse params struct from FullyConnected Op.
+ FullyConnectedParams op_params;
+ op_params.input_offset = -data.quantization->lhs_zero_point;
+ op_params.weights_offset =
+ -data.quantization->rhs_zero_point; // filter offset
+ op_params.output_offset = data.quantization->output_zero_point;
+ op_params.output_multiplier = data.quantization->output_multiplier;
+ op_params.output_shift = data.quantization->output_shift;
+ op_params.quantized_activation_min = data.quantization->output_activation_min;
+ op_params.quantized_activation_max = data.quantization->output_activation_max;
+ op_params.lhs_cacheable = data.lhs_is_constant_tensor;
+ op_params.rhs_cacheable = data.rhs_is_constant_tensor;
+
+ // Note we pass RHS args first, LHS args second. See note for Eval.
+ reference_ops::BatchMatMul<int16_t, int64_t>(
+ op_params, rhs_shape, tflite::micro::GetTensorData<int16_t>(&rhs),
+ lhs_shape, tflite::micro::GetTensorData<int16_t>(&lhs), output_shape,
+ tflite::micro::GetTensorData<int16_t>(output));
+
+ return kTfLiteOk;
+}
+
+// Perform a batch matrix multiply on
+// LHS <..., A, B> X RHS<..., B, C>
+// where the leading dimensions of LHS and RHS obey broadcasting rules
+// (this Op will apply broadcasting rules).
+// We assume that LHS and RHS are both row oriented (adjacent values in memory
+// are in the same row) and will output in the same memory layout. However,
+// our fast GEMM libraries assume RCC layout (LHS row oriented,
+// RHS column oriented, output column oriented). Therefore, we perform
+// RHS <..., C, B> X LHS <..., B, A>
+// where output is a C X A column-oriented, which is equivalent to
+// A X C row-oriented.
+TfLiteStatus BatchMatMulEval(TfLiteContext* context, TfLiteNode* node) {
+ EvalOpContext op_context(context, node);
+ OpData* op_data = op_context.op_data;
+ const TfLiteEvalTensor* lhs = op_context.lhs;
+ const TfLiteEvalTensor* rhs = op_context.rhs;
+ TfLiteEvalTensor* output = op_context.output;
+ RuntimeShape orig_lhs_shape = tflite::micro::GetTensorShape(lhs);
+ RuntimeShape orig_rhs_shape = tflite::micro::GetTensorShape(rhs);
+
+ bool adj_y = op_context.params->adj_y;
+ bool adj_x = op_context.params->adj_x;
+
+ // Compress BatchMatMul when third from last RHS dimension is one.
+ int32_t rhs_dims_count = orig_rhs_shape.DimensionsCount();
+ int32_t lhs_dims_count = orig_lhs_shape.DimensionsCount();
+ // Compress ops where rhs shape is [..., 1, X, Y] and lhs shape is
+ // [..., Q, R, S] which is equivalent to rhs: [..., X, Y] and
+ // lhs: [..., Q * R, S].
+ if (rhs_dims_count > 2 && lhs_dims_count > 2) {
+ int rhs_one = orig_rhs_shape.DimsData()[rhs_dims_count - 3];
+ if (rhs_one == 1) {
+ int32_t* lhs_dims = orig_lhs_shape.DimsData();
+ int32_t* rhs_dims = orig_rhs_shape.DimsData();
+ RuntimeShape tmp_l(lhs_dims_count - 1, lhs_dims);
+ tmp_l.SetDim(lhs_dims_count - 3,
+ lhs_dims[lhs_dims_count - 3] * lhs_dims[lhs_dims_count - 2]);
+ tmp_l.SetDim(lhs_dims_count - 2, lhs_dims[lhs_dims_count - 1]);
+ orig_lhs_shape.ReplaceWith(tmp_l.DimensionsCount(), tmp_l.DimsData());
+ RuntimeShape tmp_r(rhs_dims_count - 1, orig_rhs_shape.DimsData());
+ tmp_r.SetDim(rhs_dims_count - 3, rhs_dims[rhs_dims_count - 2]);
+ tmp_r.SetDim(rhs_dims_count - 2, rhs_dims[rhs_dims_count - 1]);
+ orig_rhs_shape.ReplaceWith(tmp_r.DimensionsCount(), tmp_r.DimsData());
+ rhs_dims_count = orig_rhs_shape.DimensionsCount();
+ lhs_dims_count = orig_lhs_shape.DimensionsCount();
+ }
+ }
+
+ TfLiteEvalTensor* rhs_tensor = adj_y ? const_cast<TfLiteEvalTensor*>(rhs)
+ : op_data->rhs_transposed_tensor;
+ TfLiteEvalTensor* lhs_tensor = adj_x ? op_data->lhs_transposed_tensor
+ : const_cast<TfLiteEvalTensor*>(lhs);
+ TF_LITE_ENSURE(context, rhs_tensor != nullptr);
+ TF_LITE_ENSURE(context, lhs_tensor != nullptr);
+ if (!adj_y) {
+ // TODO(b/154760341): Constant tensors should already be transposed, but
+ // we transpose once if necessary for now.
+ if (!(op_data->rhs_is_constant_tensor && op_data->rhs_is_transposed)) {
+ TransposeRowsColumns(*rhs, rhs_tensor);
+ op_data->rhs_is_transposed = true;
+ }
+ }
+ if (adj_x) {
+ TransposeRowsColumns(*lhs, lhs_tensor);
+ }
+ RuntimeShape rhs_shape =
+ adj_y ? orig_rhs_shape : SwapRowColumnDims(orig_rhs_shape);
+ RuntimeShape lhs_shape =
+ adj_x ? orig_lhs_shape : SwapRowColumnDims(orig_lhs_shape);
+
+ switch (lhs->type) {
+ case kTfLiteFloat32:
+ // Note we pass RHS args first, LHS args second. See note above.
+ reference_ops::BatchMatMul(
+ rhs_shape, tflite::micro::GetTensorData<float>(rhs_tensor), lhs_shape,
+ tflite::micro::GetTensorData<float>(lhs_tensor),
+ tflite::micro::GetTensorShape(output),
+ tflite::micro::GetTensorData<float>(output));
+ break;
+ case kTfLiteInt8:
+ return EvalInt8(context, *op_data, lhs_shape, *lhs_tensor, rhs_shape,
+ *rhs_tensor, tflite::micro::GetTensorShape(output),
+ output);
+ case kTfLiteInt16:
+ return EvalInt16(context, *op_data, lhs_shape, *lhs_tensor, rhs_shape,
+ *rhs_tensor, tflite::micro::GetTensorShape(output),
+ output);
+ default:
+ MicroPrintf("BATCH_MATMUL doesn't support input type %s",
+ TfLiteTypeGetName(lhs->type));
+ return kTfLiteError;
+ }
+ return kTfLiteOk;
+}
+
+} // namespace
+
+TFLMRegistration Register_BATCH_MATMUL() {
+ return tflite::micro::RegisterOp(BatchMatMulInit, BatchMatMulPrepare,
+ BatchMatMulEval);
+}
+
+} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/batch_matmul_test.cc b/tensorflow/lite/micro/kernels/batch_matmul_test.cc
new file mode 100644
index 0000000..abba757
--- /dev/null
+++ b/tensorflow/lite/micro/kernels/batch_matmul_test.cc
@@ -0,0 +1,736 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <algorithm>
+#include <iterator>
+#include <numeric>
+#include <type_traits>
+
+#include "tensorflow/lite/c/builtin_op_data.h"
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/micro/kernels/kernel_runner.h"
+#include "tensorflow/lite/micro/test_helpers.h"
+#include "tensorflow/lite/micro/testing/micro_test.h"
+
+namespace tflite {
+namespace testing {
+namespace {
+
+constexpr float kFloatTolerance = 1e-5;
+
+constexpr int kNumInputs = 2;
+constexpr int kNumOutputs = 1;
+constexpr int kLhsInputTensorIndex = 0;
+constexpr int kRhsInputTensorIndex = 1;
+constexpr int kOutputTensorIndex = 2;
+
+// data_min/data_max are used to compute symmetric scale, zero-point is 0
+// scale should be 0 to use data_min/data_max
+template <typename T, size_t kNumElements>
+struct TestQuantizationParams {
+ // quantization parameters
+ float scale; // if 0, use data_min and data_max
+ int zero_point;
+ float data_min; // input data minimum value
+ float data_max; // input data maximum value
+
+ T quantized_data[kNumElements]; // quantized storage
+};
+
+micro::KernelRunner* GetKernelRunnerInstance(
+ TfLiteTensor* tensors, int tensors_count,
+ const TfLiteBatchMatMulParams& params, bool need_init_prepare) {
+ static int kInputArrayData[] = {kNumInputs, kLhsInputTensorIndex,
+ kRhsInputTensorIndex};
+ TfLiteIntArray* inputs_array = IntArrayFromInts(kInputArrayData);
+ static int kOutputArrayData[] = {kNumOutputs, kOutputTensorIndex};
+ TfLiteIntArray* outputs_array = IntArrayFromInts(kOutputArrayData);
+
+ static const TFLMRegistration registration = tflite::Register_BATCH_MATMUL();
+
+ alignas(micro::KernelRunner) static char
+ kernel_runner_buffer[sizeof(micro::KernelRunner)] = {};
+
+ static micro::KernelRunner* runner = nullptr;
+ if (runner == nullptr || need_init_prepare) {
+ runner = new (kernel_runner_buffer)
+ micro::KernelRunner(registration, tensors, tensors_count, inputs_array,
+ outputs_array, ¶ms);
+
+ TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner->InitAndPrepare());
+ }
+
+ return runner;
+}
+
+void TestBatchMatMulFloat(const TfLiteBatchMatMulParams& params,
+ const int* input_dims_data[kNumInputs],
+ const float* input_data_lhs,
+ const float* input_data_rhs, const int* expected_dims,
+ const float* expected_data, float* output_data,
+ bool need_constant_rhs = false,
+ bool need_init_prepare = true) {
+ TfLiteIntArray* input_dims_lhs = IntArrayFromInts(input_dims_data[0]);
+ TfLiteIntArray* input_dims_rhs = IntArrayFromInts(input_dims_data[1]);
+ TfLiteIntArray* output_dims = IntArrayFromInts(expected_dims);
+ const int kOutputCount = ElementCount(*output_dims);
+
+ static TfLiteTensor tensors[kNumInputs + kNumOutputs];
+
+ if (need_init_prepare) {
+ tensors[kLhsInputTensorIndex] =
+ CreateTensor(input_data_lhs, input_dims_lhs);
+ tensors[kRhsInputTensorIndex] =
+ CreateTensor(input_data_rhs, input_dims_rhs);
+ if (need_constant_rhs) {
+ tensors[kRhsInputTensorIndex].allocation_type = kTfLiteMmapRo;
+ }
+ tensors[kOutputTensorIndex] = CreateTensor(output_data, output_dims);
+ }
+
+ constexpr int kTensorCount = std::extent<decltype(tensors)>::value;
+ micro::KernelRunner* runner =
+ GetKernelRunnerInstance(tensors, kTensorCount, params, need_init_prepare);
+ TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner->Invoke());
+
+ // check output data against expected
+ for (int i = 0; i < kOutputCount; i++) {
+ TF_LITE_MICRO_EXPECT_NEAR(expected_data[i], output_data[i],
+ kFloatTolerance);
+ }
+
+ // check output dimensions (relocated) against original dimensions
+ TF_LITE_MICRO_EXPECT_EQ(output_dims->size,
+ tensors[kOutputTensorIndex].dims->size);
+ for (int i = 0; i < output_dims->size; i++) {
+ TF_LITE_MICRO_EXPECT_EQ(output_dims->data[i],
+ tensors[kOutputTensorIndex].dims->data[i]);
+ }
+}
+
+template <typename T, size_t kNumElements>
+void SetScaleAndZeroPoint(TestQuantizationParams<T, kNumElements>* q_params) {
+ if (q_params->scale == 0.0f || q_params->data_max != 0 ||
+ q_params->data_min != 0) {
+ q_params->scale =
+ ScaleFromMinMax<T>(q_params->data_min, q_params->data_max);
+ q_params->zero_point =
+ ZeroPointFromMinMax<T>(q_params->data_min, q_params->data_max);
+ }
+}
+
+template <typename T, size_t kNumLhs, size_t kNumRhs, size_t kNumOutput>
+void TestBatchMatMulQuantized(
+ const TfLiteBatchMatMulParams& params,
+ TestQuantizationParams<T, kNumLhs>* quantization_lhs,
+ TestQuantizationParams<T, kNumRhs>* quantization_rhs,
+ TestQuantizationParams<T, kNumOutput>* quantization_output,
+ const int* input_dims_data[kNumInputs], const float* input_data_lhs,
+ const float* input_data_rhs, const int* expected_dims,
+ const T* expected_data, const float* output_data) {
+ TfLiteIntArray* input_dims_lhs = IntArrayFromInts(input_dims_data[0]);
+ TfLiteIntArray* input_dims_rhs = IntArrayFromInts(input_dims_data[1]);
+ TfLiteIntArray* output_dims = IntArrayFromInts(expected_dims);
+ const int kOutputCount = ElementCount(*output_dims);
+
+ static TfLiteTensor tensors[kNumInputs + kNumOutputs];
+
+ SetScaleAndZeroPoint<T, kNumLhs>(quantization_lhs);
+ tensors[kLhsInputTensorIndex] = CreateQuantizedTensor(
+ input_data_lhs, quantization_lhs->quantized_data, input_dims_lhs,
+ quantization_lhs->scale, quantization_lhs->zero_point);
+ SetScaleAndZeroPoint<T, kNumRhs>(quantization_rhs);
+ tensors[kRhsInputTensorIndex] = CreateQuantizedTensor(
+ input_data_rhs, quantization_rhs->quantized_data, input_dims_rhs,
+ quantization_rhs->scale, quantization_rhs->zero_point);
+ SetScaleAndZeroPoint<T, kNumOutput>(quantization_output);
+ tensors[kOutputTensorIndex] = CreateQuantizedTensor(
+ quantization_output->quantized_data, output_dims,
+ quantization_output->scale, quantization_output->zero_point);
+
+ constexpr int kTensorCount = std::extent<decltype(tensors)>::value;
+ micro::KernelRunner* runner =
+ GetKernelRunnerInstance(tensors, kTensorCount, params, true);
+ TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner->Invoke());
+
+ // check output data against expected
+ for (int i = 0; i < kOutputCount; i++) {
+ TF_LITE_MICRO_EXPECT_EQ(expected_data[i],
+ quantization_output->quantized_data[i]);
+ }
+ // check dequantized output data against expected
+ for (int i = 0; i < kOutputCount; i++) {
+ float dequantized_value = (quantization_output->quantized_data[i] -
+ quantization_output->zero_point) *
+ quantization_output->scale;
+ TF_LITE_MICRO_EXPECT_NEAR(output_data[i], dequantized_value,
+ kFloatTolerance);
+ }
+
+ // check output dimensions (relocated) against original dimensions
+ TF_LITE_MICRO_EXPECT_EQ(output_dims->size,
+ tensors[kOutputTensorIndex].dims->size);
+ for (int i = 0; i < output_dims->size; i++) {
+ TF_LITE_MICRO_EXPECT_EQ(output_dims->data[i],
+ tensors[kOutputTensorIndex].dims->data[i]);
+ }
+}
+
+} // namespace
+} // namespace testing
+} // namespace tflite
+
+TF_LITE_MICRO_TESTS_BEGIN
+
+TF_LITE_MICRO_TEST(BatchMatMulOpTestFloat32Test_Ones) {
+ constexpr int kLhsInputDims[] = {4, 3, 2, 1, 4};
+ constexpr int kRhsInputDims[] = {4, 3, 1, 4, 1};
+ const int* kInputDims[tflite::testing::kNumInputs] = {kLhsInputDims,
+ kRhsInputDims};
+
+ constexpr size_t kLhsInputSize = 24;
+ float lhs_input[kLhsInputSize];
+ std::iota(std::begin(lhs_input), std::end(lhs_input), 1);
+
+ constexpr size_t kRhsInputSize = 12;
+ float rhs_input[kRhsInputSize];
+ std::iota(std::begin(rhs_input), std::end(rhs_input), 1);
+
+ constexpr float kExpect[] = {30, 70, 278, 382, 782, 950};
+ constexpr int kOutputDims[] = {4, 3, 2, 1, 1};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ float output_data[kOutputCount];
+
+ constexpr TfLiteBatchMatMulParams params = {
+ false, // adj_x
+ false, // adj_y
+ false // asymmetric_quantize_inputs
+ };
+
+ tflite::testing::TestBatchMatMulFloat(params, kInputDims, lhs_input,
+ rhs_input, kOutputDims, kExpect,
+ output_data);
+}
+
+TF_LITE_MICRO_TEST(BatchMatMulOpTestFloat32Test_Flatten) {
+ constexpr int kLhsInputDims[] = {4, 3, 2, 2, 4};
+ constexpr int kRhsInputDims[] = {4, 3, 1, 4, 1};
+ const int* kInputDims[tflite::testing::kNumInputs] = {kLhsInputDims,
+ kRhsInputDims};
+
+ constexpr size_t kLhsInputSize = 48;
+ float lhs_input[kLhsInputSize];
+ std::iota(std::begin(lhs_input), std::end(lhs_input), 1);
+
+ constexpr size_t kRhsInputSize = 12;
+ float rhs_input[kRhsInputSize];
+ std::iota(std::begin(rhs_input), std::end(rhs_input), 1);
+
+ constexpr float kExpect[] = {30, 70, 110, 150, 486, 590,
+ 694, 798, 1454, 1622, 1790, 1958};
+ constexpr int kOutputDims[] = {4, 3, 2, 2, 1};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ float output_data[kOutputCount];
+
+ constexpr TfLiteBatchMatMulParams params = {
+ false, // adj_x
+ false, // adj_y
+ false // asymmetric_quantize_inputs
+ };
+
+ tflite::testing::TestBatchMatMulFloat(params, kInputDims, lhs_input,
+ rhs_input, kOutputDims, kExpect,
+ output_data);
+}
+
+TF_LITE_MICRO_TEST(BatchMatMulOpTestFloat32Test_Simple) {
+ constexpr int kLhsInputDims[] = {3, 1, 2, 3};
+ constexpr int kRhsInputDims[] = {3, 1, 3, 4};
+ const int* kInputDims[tflite::testing::kNumInputs] = {kLhsInputDims,
+ kRhsInputDims};
+
+ constexpr size_t kLhsInputSize = 6;
+ float lhs_input[kLhsInputSize];
+ std::iota(std::begin(lhs_input), std::end(lhs_input), 1);
+
+ constexpr size_t kRhsInputSize = 12;
+ float rhs_input[kRhsInputSize];
+ std::iota(std::begin(rhs_input), std::end(rhs_input), 7);
+
+ constexpr float kExpect[] = {74., 80., 86., 92., 173., 188., 203., 218.};
+ constexpr int kOutputDims[] = {3, 1, 2, 4};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ float output_data[kOutputCount];
+
+ constexpr TfLiteBatchMatMulParams params = {
+ false, // adj_x
+ false, // adj_y
+ false // asymmetric_quantize_inputs
+ };
+
+ tflite::testing::TestBatchMatMulFloat(params, kInputDims, lhs_input,
+ rhs_input, kOutputDims, kExpect,
+ output_data);
+}
+
+TF_LITE_MICRO_TEST(BatchMatMulOpTestFloat32Test_SimpleRHSAdjoint) {
+ constexpr int kLhsInputDims[] = {3, 1, 2, 3};
+ constexpr int kRhsInputDims[] = {3, 1, 4, 3};
+ const int* kInputDims[tflite::testing::kNumInputs] = {kLhsInputDims,
+ kRhsInputDims};
+
+ constexpr size_t kLhsInputSize = 6;
+ float lhs_input[kLhsInputSize];
+ std::iota(std::begin(lhs_input), std::end(lhs_input), 1);
+
+ constexpr float kRhsInput[] = {7, 11, 15, 8, 12, 16, 9, 13, 17, 10, 14, 18};
+
+ constexpr float kExpect[] = {74., 80., 86., 92., 173., 188., 203., 218.};
+ constexpr int kOutputDims[] = {3, 1, 2, 4};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ float output_data[kOutputCount];
+
+ constexpr TfLiteBatchMatMulParams params = {
+ false, // adj_x
+ true, // adj_y
+ false // asymmetric_quantize_inputs
+ };
+
+ tflite::testing::TestBatchMatMulFloat(params, kInputDims, lhs_input,
+ kRhsInput, kOutputDims, kExpect,
+ output_data);
+}
+
+TF_LITE_MICRO_TEST(BatchMatMulOpTestFloat32Test_SimpleLHSAdjoint) {
+ constexpr int kLhsInputDims[] = {3, 1, 3, 2};
+ constexpr int kRhsInputDims[] = {3, 1, 3, 4};
+ const int* kInputDims[tflite::testing::kNumInputs] = {kLhsInputDims,
+ kRhsInputDims};
+ constexpr float kLhsInput[] = {1, 4, 2, 5, 3, 6};
+
+ constexpr size_t kRhsInputSize = 12;
+ float rhs_input[kRhsInputSize];
+ std::iota(std::begin(rhs_input), std::end(rhs_input), 7);
+
+ constexpr float kExpect[] = {74., 80., 86., 92., 173., 188., 203., 218.};
+ constexpr int kOutputDims[] = {3, 1, 2, 4};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ float output_data[kOutputCount];
+
+ constexpr TfLiteBatchMatMulParams params = {
+ true, // adj_x
+ false, // adj_y
+ false // asymmetric_quantize_inputs
+ };
+
+ tflite::testing::TestBatchMatMulFloat(params, kInputDims, kLhsInput,
+ rhs_input, kOutputDims, kExpect,
+ output_data);
+}
+
+TF_LITE_MICRO_TEST(BatchMatMulOpTestFloat32Test_BatchSizeTwo) {
+ constexpr int kLhsInputDims[] = {3, 2, 2, 3};
+ constexpr int kRhsInputDims[] = {3, 2, 3, 4};
+ const int* kInputDims[tflite::testing::kNumInputs] = {kLhsInputDims,
+ kRhsInputDims};
+ constexpr size_t kLhsInputSize = 12;
+ float lhs_input[kLhsInputSize];
+ std::iota(std::begin(lhs_input), std::end(lhs_input), 1);
+
+ constexpr size_t kRhsInputSize = 24;
+ float rhs_input[kRhsInputSize];
+ std::iota(std::begin(rhs_input), std::end(rhs_input), 7);
+
+ constexpr float kExpect[] = {74., 80., 86., 92., 173., 188., 203., 218.,
+ 560., 584., 608., 632., 767., 800., 833., 866.};
+ constexpr int kOutputDims[] = {3, 2, 2, 4};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ float output_data[kOutputCount];
+
+ constexpr TfLiteBatchMatMulParams params = {
+ false, // adj_x
+ false, // adj_y
+ false // asymmetric_quantize_inputs
+ };
+
+ tflite::testing::TestBatchMatMulFloat(params, kInputDims, lhs_input,
+ rhs_input, kOutputDims, kExpect,
+ output_data);
+}
+
+TF_LITE_MICRO_TEST(BatchMatMulOpTestFloat32Test_Broadcast) {
+ constexpr int kLhsInputDims[] = {3, 2, 2, 3};
+ constexpr int kRhsInputDims[] = {2, 3, 4};
+ const int* kInputDims[tflite::testing::kNumInputs] = {kLhsInputDims,
+ kRhsInputDims};
+ constexpr size_t kLhsInputSize = 12;
+ float lhs_input[kLhsInputSize];
+ std::iota(std::begin(lhs_input), std::end(lhs_input), 1);
+
+ constexpr size_t kRhsInputSize = 12;
+ float rhs_input[kRhsInputSize];
+ std::iota(std::begin(rhs_input), std::end(rhs_input), 7);
+
+ constexpr float kExpect[] = {74., 80., 86., 92., 173., 188., 203., 218.,
+ 272., 296., 320., 344., 371., 404., 437., 470.};
+ constexpr int kOutputDims[] = {3, 2, 2, 4};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ float output_data[kOutputCount];
+
+ constexpr TfLiteBatchMatMulParams params = {
+ false, // adj_x
+ false, // adj_y
+ false // asymmetric_quantize_inputs
+ };
+
+ tflite::testing::TestBatchMatMulFloat(params, kInputDims, lhs_input,
+ rhs_input, kOutputDims, kExpect,
+ output_data);
+}
+
+TF_LITE_MICRO_TEST(BatchMatMulOpTestFloat32Test_BroadcastLHSAdjoint) {
+ constexpr int kLhsInputDims[] = {3, 2, 3, 2};
+ constexpr int kRhsInputDims[] = {2, 3, 4};
+ const int* kInputDims[tflite::testing::kNumInputs] = {kLhsInputDims,
+ kRhsInputDims};
+
+ constexpr float kLhsInput[] = {1, 4, 2, 5, 3, 6, 7, 10, 8, 11, 9, 12};
+
+ constexpr size_t kRhsInputSize = 12;
+ float rhs_input[kRhsInputSize];
+ std::iota(std::begin(rhs_input), std::end(rhs_input), 7);
+
+ constexpr float kExpect[] = {74., 80., 86., 92., 173., 188., 203., 218.,
+ 272., 296., 320., 344., 371., 404., 437., 470.};
+ constexpr int kOutputDims[] = {3, 2, 2, 4};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ float output_data[kOutputCount];
+
+ constexpr TfLiteBatchMatMulParams params = {
+ true, // adj_x
+ false, // adj_y
+ false // asymmetric_quantize_inputs
+ };
+
+ tflite::testing::TestBatchMatMulFloat(params, kInputDims, kLhsInput,
+ rhs_input, kOutputDims, kExpect,
+ output_data);
+}
+
+TF_LITE_MICRO_TEST(BatchMatMulOpTestFloat32Test_Broadcast2) {
+ constexpr int kLhsInputDims[] = {4, 2, 1, 3, 2};
+ constexpr int kRhsInputDims[] = {3, 3, 2, 4};
+ const int* kInputDims[tflite::testing::kNumInputs] = {kLhsInputDims,
+ kRhsInputDims};
+
+ constexpr size_t kLhsInputSize = 12;
+ float lhs_input[kLhsInputSize];
+ std::iota(std::begin(lhs_input), std::end(lhs_input), 1);
+
+ constexpr size_t kRhsInputSize = 24;
+ float rhs_input[kRhsInputSize];
+ std::iota(std::begin(rhs_input), std::end(rhs_input), 7);
+
+ constexpr float kExpect[] = {
+ 29., 32., 35., 38., 65., 72., 79., 86., 101., 112., 123., 134.,
+ 53., 56., 59., 62., 121., 128., 135., 142., 189., 200., 211., 222.,
+ 77., 80., 83., 86., 177., 184., 191., 198., 277., 288., 299., 310.,
+ 137., 152., 167., 182., 173., 192., 211., 230., 209., 232., 255., 278.,
+ 257., 272., 287., 302., 325., 344., 363., 382., 393., 416., 439., 462.,
+ 377., 392., 407., 422., 477., 496., 515., 534., 577., 600., 623., 646.};
+ constexpr int kOutputDims[] = {4, 2, 3, 3, 4};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ float output_data[kOutputCount];
+
+ constexpr TfLiteBatchMatMulParams params = {
+ false, // adj_x
+ false, // adj_y
+ false // asymmetric_quantize_inputs
+ };
+
+ tflite::testing::TestBatchMatMulFloat(params, kInputDims, lhs_input,
+ rhs_input, kOutputDims, kExpect,
+ output_data);
+}
+
+TF_LITE_MICRO_TEST(BatchMatMulOpTestFloat32Test_Broadcast2LHSAdjoint) {
+ constexpr int kLhsInputDims[] = {4, 2, 1, 2, 3};
+ constexpr int kRhsInputDims[] = {3, 3, 2, 4};
+ const int* kInputDims[tflite::testing::kNumInputs] = {kLhsInputDims,
+ kRhsInputDims};
+
+ constexpr float kLhsInput[] = {1, 3, 5, 2, 4, 6, 7, 9, 11, 8, 10, 12};
+
+ constexpr size_t kRhsInputSize = 24;
+ float rhs_input[kRhsInputSize];
+ std::iota(std::begin(rhs_input), std::end(rhs_input), 7);
+
+ constexpr float kExpect[] = {
+ 29., 32., 35., 38., 65., 72., 79., 86., 101., 112., 123., 134.,
+ 53., 56., 59., 62., 121., 128., 135., 142., 189., 200., 211., 222.,
+ 77., 80., 83., 86., 177., 184., 191., 198., 277., 288., 299., 310.,
+ 137., 152., 167., 182., 173., 192., 211., 230., 209., 232., 255., 278.,
+ 257., 272., 287., 302., 325., 344., 363., 382., 393., 416., 439., 462.,
+ 377., 392., 407., 422., 477., 496., 515., 534., 577., 600., 623., 646.};
+ constexpr int kOutputDims[] = {4, 2, 3, 3, 4};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ float output_data[kOutputCount];
+
+ constexpr TfLiteBatchMatMulParams params = {
+ true, // adj_x
+ false, // adj_y
+ false // asymmetric_quantize_inputs
+ };
+
+ tflite::testing::TestBatchMatMulFloat(params, kInputDims, kLhsInput,
+ rhs_input, kOutputDims, kExpect,
+ output_data);
+}
+
+TF_LITE_MICRO_TEST(BatchMatMulOpTestFloat32Test_Broadcast2RHSAdjoint) {
+ constexpr int kLhsInputDims[] = {4, 2, 1, 3, 2};
+ constexpr int kRhsInputDims[] = {3, 3, 4, 2};
+ const int* kInputDims[tflite::testing::kNumInputs] = {kLhsInputDims,
+ kRhsInputDims};
+
+ constexpr size_t kLhsInputSize = 12;
+ float lhs_input[kLhsInputSize];
+ std::iota(std::begin(lhs_input), std::end(lhs_input), 1);
+
+ constexpr float kRhsInput[] = {7, 11, 8, 12, 9, 13, 10, 14,
+ 15, 19, 16, 20, 17, 21, 18, 22,
+ 23, 27, 24, 28, 25, 29, 26, 30};
+
+ constexpr float kExpect[] = {
+ 29., 32., 35., 38., 65., 72., 79., 86., 101., 112., 123., 134.,
+ 53., 56., 59., 62., 121., 128., 135., 142., 189., 200., 211., 222.,
+ 77., 80., 83., 86., 177., 184., 191., 198., 277., 288., 299., 310.,
+ 137., 152., 167., 182., 173., 192., 211., 230., 209., 232., 255., 278.,
+ 257., 272., 287., 302., 325., 344., 363., 382., 393., 416., 439., 462.,
+ 377., 392., 407., 422., 477., 496., 515., 534., 577., 600., 623., 646.};
+ constexpr int kOutputDims[] = {4, 2, 3, 3, 4};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ float output_data[kOutputCount];
+
+ constexpr TfLiteBatchMatMulParams params = {
+ false, // adj_x
+ true, // adj_y
+ false // asymmetric_quantize_inputs
+ };
+
+ tflite::testing::TestBatchMatMulFloat(params, kInputDims, lhs_input,
+ kRhsInput, kOutputDims, kExpect,
+ output_data);
+}
+
+TF_LITE_MICRO_TEST(BatchMatMulOpTestFloat32Test_Broadcast2BothAdjoint) {
+ constexpr int kLhsInputDims[] = {4, 2, 1, 2, 3};
+ constexpr int kRhsInputDims[] = {3, 3, 4, 2};
+ const int* kInputDims[tflite::testing::kNumInputs] = {kLhsInputDims,
+ kRhsInputDims};
+
+ constexpr float kLhsInput[] = {1, 3, 5, 2, 4, 6, 7, 9, 11, 8, 10, 12};
+
+ constexpr float kRhsInput[] = {7, 11, 8, 12, 9, 13, 10, 14,
+ 15, 19, 16, 20, 17, 21, 18, 22,
+ 23, 27, 24, 28, 25, 29, 26, 30};
+
+ constexpr float kExpect[] = {
+ 29., 32., 35., 38., 65., 72., 79., 86., 101., 112., 123., 134.,
+ 53., 56., 59., 62., 121., 128., 135., 142., 189., 200., 211., 222.,
+ 77., 80., 83., 86., 177., 184., 191., 198., 277., 288., 299., 310.,
+ 137., 152., 167., 182., 173., 192., 211., 230., 209., 232., 255., 278.,
+ 257., 272., 287., 302., 325., 344., 363., 382., 393., 416., 439., 462.,
+ 377., 392., 407., 422., 477., 496., 515., 534., 577., 600., 623., 646.};
+ constexpr int kOutputDims[] = {4, 2, 3, 3, 4};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ float output_data[kOutputCount];
+
+ constexpr TfLiteBatchMatMulParams params = {
+ true, // adj_x
+ true, // adj_y
+ false // asymmetric_quantize_inputs
+ };
+
+ tflite::testing::TestBatchMatMulFloat(params, kInputDims, kLhsInput,
+ kRhsInput, kOutputDims, kExpect,
+ output_data);
+}
+
+TF_LITE_MICRO_TEST(BatchMatMulOpTestFloat32Test_BroadcastFromRHS) {
+ constexpr int kLhsInputDims[] = {2, 4, 5};
+ constexpr int kRhsInputDims[] = {4, 3, 1, 5, 2};
+ const int* kInputDims[tflite::testing::kNumInputs] = {kLhsInputDims,
+ kRhsInputDims};
+
+ constexpr size_t kLhsInputSize = 20;
+ float lhs_input[kLhsInputSize];
+ std::iota(std::begin(lhs_input), std::end(lhs_input), 1);
+
+ constexpr size_t kRhsInputSize = 30;
+ float rhs_input[kRhsInputSize];
+ std::iota(std::begin(rhs_input), std::end(rhs_input), 7);
+
+ constexpr float kExpect[] = {185., 200., 460., 500., 735., 800.,
+ 1010., 1100., 335., 350., 860., 900.,
+ 1385., 1450., 1910., 2000., 485., 500.,
+ 1260., 1300., 2035., 2100., 2810., 2900.};
+ constexpr int kOutputDims[] = {4, 3, 1, 4, 2};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ float output_data[kOutputCount];
+
+ constexpr TfLiteBatchMatMulParams params = {
+ false, // adj_x
+ false, // adj_y
+ false // asymmetric_quantize_inputs
+ };
+
+ tflite::testing::TestBatchMatMulFloat(params, kInputDims, lhs_input,
+ rhs_input, kOutputDims, kExpect,
+ output_data);
+}
+
+TF_LITE_MICRO_TEST(ConstRHSBatchMatMulOpModelRHSNotAdjoint) {
+ constexpr int kLhsInputDims[] = {3, 1, 6, 2};
+ constexpr int kRhsInputDims[] = {2, 2, 3};
+ const int* kInputDims[tflite::testing::kNumInputs] = {kLhsInputDims,
+ kRhsInputDims};
+
+ constexpr float kLhsInput[] = {6, 3, 7, 4, 6, 9, 2, 6, 7, 4, 3, 7};
+
+ constexpr float kRhsInput[] = {6, 3, 7, 4, 6, 9};
+
+ constexpr float kExpect[] = {48, 36, 69, 58, 45, 85, 72, 72, 123,
+ 36, 42, 68, 58, 45, 85, 46, 51, 84};
+ constexpr int kOutputDims[] = {3, 1, 6, 3};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ float output_data[kOutputCount];
+
+ constexpr TfLiteBatchMatMulParams params = {
+ false, // adj_x
+ false, // adj_y
+ false // asymmetric_quantize_inputs
+ };
+
+ tflite::testing::TestBatchMatMulFloat(params, kInputDims, kLhsInput,
+ kRhsInput, kOutputDims, kExpect,
+ output_data, true);
+ // Eval twice to make sure constant transposed RHS is persistent.
+ tflite::testing::TestBatchMatMulFloat(params, kInputDims, kLhsInput,
+ kRhsInput, kOutputDims, kExpect,
+ output_data, true, false);
+}
+
+TF_LITE_MICRO_TEST(QuantizedBatchMatMulOpTestSimpleTestQuantizedInt8) {
+ constexpr int kLhsInputDims[] = {2, 2, 10};
+ constexpr int kRhsInputDims[] = {2, 10, 3};
+ const int* kInputDims[tflite::testing::kNumInputs] = {kLhsInputDims,
+ kRhsInputDims};
+
+ constexpr float kLhsInput[] = {
+ 1, 2, 3, 4, 5, 6, 7, 8, -9, -10, // b = 0
+ 1, 2, 3, 4, 5, 6, 7, -8, 9, -10, // b = 1
+ };
+ constexpr int kLhsInputCount = std::extent<decltype(kLhsInput)>::value;
+
+ constexpr float kRhsInput[] = {
+ 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5,
+ 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 10,
+ };
+ constexpr int kRhsInputCount = std::extent<decltype(kRhsInput)>::value;
+
+ constexpr int8_t kExpect[] = {22, 22, 22, 56, 56, 56};
+ constexpr int kOutputDims[] = {2, 2, 3};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ constexpr float output_data[kOutputCount] = {23, 23, 23, 57, 57, 57};
+
+ constexpr TfLiteBatchMatMulParams params = {
+ false, // adj_x
+ false, // adj_y
+ false // asymmetric_quantize_inputs
+ };
+
+ tflite::testing::TestQuantizationParams<int8_t, kLhsInputCount>
+ quantization_params_lhs = {0.0f, // scale
+ 0, // zero_point
+ -63.5f, // data_min
+ 64.0f, // data_max
+ {}};
+ tflite::testing::TestQuantizationParams<int8_t, kRhsInputCount>
+ quantization_params_rhs = {0.0f, // scale
+ 0, // zero_point
+ -63.5f, // data_min
+ 64.0f, // data_max
+ {}};
+ tflite::testing::TestQuantizationParams<int8_t, kOutputCount>
+ quantization_params_output = {0.0f, // scale
+ 0, // zero_point
+ -127.0f, // data_min
+ 128.0f, // data_max
+ {}};
+
+ tflite::testing::TestBatchMatMulQuantized<int8_t>(
+ params, &quantization_params_lhs, &quantization_params_rhs,
+ &quantization_params_output, kInputDims, kLhsInput, kRhsInput,
+ kOutputDims, kExpect, output_data);
+}
+
+TF_LITE_MICRO_TEST(QuantizedBatchMatMulOpTestSimpleTestQuantizedInt16) {
+ constexpr int kLhsInputDims[] = {2, 2, 10};
+ constexpr int kRhsInputDims[] = {2, 10, 3};
+ const int* kInputDims[tflite::testing::kNumInputs] = {kLhsInputDims,
+ kRhsInputDims};
+
+ constexpr float kLhsInput[] = {
+ 1, 2, 3, 4, 5, 6, 7, 8, -9, -10, // b = 0
+ 1, 2, 3, 4, 5, 6, 7, -8, 9, -10, // b = 1
+ };
+ constexpr int kLhsInputCount = std::extent<decltype(kLhsInput)>::value;
+
+ constexpr float kRhsInput[] = {
+ 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5,
+ 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 10,
+ };
+ constexpr int kRhsInputCount = std::extent<decltype(kRhsInput)>::value;
+
+ constexpr int16_t kExpect[] = {23, 23, 23, 57, 57, 57};
+ constexpr int kOutputDims[] = {2, 2, 3};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ constexpr float output_data[kOutputCount] = {23, 23, 23, 57, 57, 57};
+
+ constexpr TfLiteBatchMatMulParams params = {
+ false, // adj_x
+ false, // adj_y
+ false // asymmetric_quantize_inputs
+ };
+
+ tflite::testing::TestQuantizationParams<int16_t, kLhsInputCount>
+ quantization_params_lhs = {};
+ quantization_params_lhs.scale = 10.0f / std::numeric_limits<int16_t>::max();
+ tflite::testing::TestQuantizationParams<int16_t, kRhsInputCount>
+ quantization_params_rhs = {};
+ quantization_params_rhs.scale = 10.0f / std::numeric_limits<int16_t>::max();
+
+ tflite::testing::TestQuantizationParams<int16_t, kOutputCount>
+ quantization_params_output = {};
+ quantization_params_output.scale = 1.0f;
+
+ tflite::testing::TestBatchMatMulQuantized<int16_t>(
+ params, &quantization_params_lhs, &quantization_params_rhs,
+ &quantization_params_output, kInputDims, kLhsInput, kRhsInput,
+ kOutputDims, kExpect, output_data);
+}
+
+TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/kernels/batch_to_space_nd.cc b/tensorflow/lite/micro/kernels/batch_to_space_nd.cc
index 090a040..31a1c28 100644
--- a/tensorflow/lite/micro/kernels/batch_to_space_nd.cc
+++ b/tensorflow/lite/micro/kernels/batch_to_space_nd.cc
@@ -38,7 +38,7 @@
const int kInputOutputMinDimensionNum = 3;
const int kInputOutputMaxDimensionNum = 4;
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus BatchToSpaceNDPrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
@@ -62,7 +62,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus BatchToSpaceNDEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kInputTensor);
const TfLiteEvalTensor* block_shape =
@@ -106,7 +106,8 @@
} // namespace.
TFLMRegistration Register_BATCH_TO_SPACE_ND() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, BatchToSpaceNDPrepare,
+ BatchToSpaceNDEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/call_once.cc b/tensorflow/lite/micro/kernels/call_once.cc
index 8ad1c20..65857ef 100644
--- a/tensorflow/lite/micro/kernels/call_once.cc
+++ b/tensorflow/lite/micro/kernels/call_once.cc
@@ -36,12 +36,12 @@
bool has_run;
};
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* CallOnceInit(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(OpData));
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus CallOncePrepare(TfLiteContext* context, TfLiteNode* node) {
OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
const auto* params =
reinterpret_cast<const TfLiteCallOnceParams*>(node->builtin_data);
@@ -60,7 +60,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus CallOnceEval(TfLiteContext* context, TfLiteNode* node) {
OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
// Call once only runs one time then is a no-op for every subsequent call.
@@ -82,7 +82,7 @@
} // namespace.
TFLMRegistration Register_CALL_ONCE() {
- return tflite::micro::RegisterOp(Init, Prepare, Eval);
+ return tflite::micro::RegisterOp(CallOnceInit, CallOncePrepare, CallOnceEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/cast.cc b/tensorflow/lite/micro/kernels/cast.cc
index a493618..0b450d6 100644
--- a/tensorflow/lite/micro/kernels/cast.cc
+++ b/tensorflow/lite/micro/kernels/cast.cc
@@ -1,4 +1,4 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -25,7 +25,7 @@
constexpr int kInputTensor = 0;
constexpr int kOutputTensor = 0;
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus CastPrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
@@ -63,6 +63,9 @@
case kTfLiteInt32:
copyCast(in, out->data.i32, num_elements);
break;
+ case kTfLiteUInt32:
+ copyCast(in, out->data.u32, num_elements);
+ break;
case kTfLiteFloat32:
copyCast(in, tflite::micro::GetTensorData<float>(out), num_elements);
break;
@@ -74,7 +77,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus CastEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kInputTensor);
TfLiteEvalTensor* output =
@@ -98,6 +101,9 @@
case kTfLiteFloat32:
return copyToTensor(context, tflite::micro::GetTensorData<float>(input),
output, num_elements);
+ case kTfLiteBool:
+ return copyToTensor(context, tflite::micro::GetTensorData<bool>(input),
+ output, num_elements);
default:
// Unsupported type.
MicroPrintf("Input type %s (%d) not supported.",
@@ -108,7 +114,7 @@
} // namespace
TFLMRegistration Register_CAST() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, CastPrepare, CastEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/cast_test.cc b/tensorflow/lite/micro/kernels/cast_test.cc
index f5ab660..8625572 100644
--- a/tensorflow/lite/micro/kernels/cast_test.cc
+++ b/tensorflow/lite/micro/kernels/cast_test.cc
@@ -1,4 +1,4 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -113,4 +113,28 @@
tflite::testing::TestCast(input_dims, input_values, golden, output_data);
}
+TF_LITE_MICRO_TEST(CastUInt32ToInt32) {
+ int32_t output_data[6];
+ int input_dims[] = {2, 2, 3};
+ const uint32_t input_values[] = {100, 200, 300, 400, 500, 600};
+ const int32_t golden[] = {100, 200, 300, 400, 500, 600};
+ tflite::testing::TestCast(input_dims, input_values, golden, output_data);
+}
+
+TF_LITE_MICRO_TEST(CastUInt32ToInt32) {
+ uint32_t output_data[6];
+ int input_dims[] = {2, 2, 3};
+ const int32_t input_values[] = {100, 200, 300, 400, 500, 600};
+ const uint32_t golden[] = {100, 200, 300, 400, 500, 600};
+ tflite::testing::TestCast(input_dims, input_values, golden, output_data);
+}
+
+TF_LITE_MICRO_TEST(CastBoolToFloat) {
+ float output_data[6];
+ int input_dims[] = {2, 2, 3};
+ const bool input_values[] = {true, true, false, true, false, true};
+ const float golden[] = {1.f, 1.0f, 0.f, 1.0f, 0.0f, 1.0f};
+ tflite::testing::TestCast(input_dims, input_values, golden, output_data);
+}
+
TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/kernels/ceil.cc b/tensorflow/lite/micro/kernels/ceil.cc
index 46b55e7..36139f9 100644
--- a/tensorflow/lite/micro/kernels/ceil.cc
+++ b/tensorflow/lite/micro/kernels/ceil.cc
@@ -27,7 +27,7 @@
constexpr int kInputTensor = 0;
constexpr int kOutputTensor = 0;
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus CeilPrepare(TfLiteContext* context, TfLiteNode* node) {
MicroContext* micro_context = GetMicroContext(context);
TfLiteTensor* input =
@@ -50,7 +50,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus CeilEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kInputTensor);
TfLiteEvalTensor* output =
@@ -67,7 +67,7 @@
} // namespace
TFLMRegistration Register_CEIL() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, CeilPrepare, CeilEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/cmsis_nn/README.md b/tensorflow/lite/micro/kernels/cmsis_nn/README.md
index e4a4de3..dc531b7 100644
--- a/tensorflow/lite/micro/kernels/cmsis_nn/README.md
+++ b/tensorflow/lite/micro/kernels/cmsis_nn/README.md
@@ -1,12 +1,14 @@
<!-- mdformat off(b/169948621#comment2) -->
-# Info
+# General Info
CMSIS-NN is a library containing kernel optimizations for Arm(R) Cortex(R)-M
processors. To use CMSIS-NN optimized kernels instead of reference kernels, add
`OPTIMIZED_KERNEL_DIR=cmsis_nn` to the make command line. See examples below.
For more information about the optimizations, check out
-[CMSIS-NN documentation](https://github.com/ARM-software/CMSIS_5/blob/develop/CMSIS/NN/README.md).
+[CMSIS-NN documentation](https://github.com/ARM-software/CMSIS-NN/blob/main/README.md),
+
+# Specifying path to CMSIS-NN
By default CMSIS-NN is built by code that is downloaded to the TFLM tree.
It also possible to build CMSIS-NN code from an external path by specifying
@@ -14,7 +16,7 @@
since CMSIS-NN has a dependency to CMSIS-Core. As a third option CMSIS-NN can be provided manually as an external library.
The examples below will illustrate this.
-# Example - FVP based on Arm Corstone-300 software.
+## Example - FVP based on Arm Corstone-300 software.
In this example, the kernel conv unit test is built. For more information about
this specific target, check out the [Corstone-300 readme](https://github.com/tensorflow/tflite-micro/tree/main/tensorflow/lite/micro/cortex_m_corstone_300/README.md).
@@ -39,3 +41,22 @@
Also note that if specifying CMSIS_NN_LIBS but not CMSIS_PATH and or CMSIS_NN_PATH, headers and
system/startup code from the default downloaded path of CMSIS would be used.
So CMSIS_NN_LIBS, CMSIS_NN_PATH and CMSIS_PATH should have the same base path and if not there will be a build error.
+
+# Build for speed or size
+It is possible to build for speed or size. The size option may be required for a large model on an embedded system with limited memory. Where applicable, building for size would result in higher latency paired with a smaller scratch buffer, whereas building for speed would result in lower latency with a larger scratch buffer. Currently only transpose conv supports this. See examples below.
+
+## Example - building a static library with CMSIS-NN optimized kernels
+More info on the target used in this example: https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/cortex_m_generic/README.md
+
+Bulding for speed (default):
+Note that speed is default so if leaving out OPTIMIZE_KERNELS_FOR completely that will be the default.
+```
+make -f tensorflow/lite/micro/tools/make/Makefile TARGET=cortex_m_generic TARGET_ARCH=cortex-m55 OPTIMIZED_KERNEL_DIR=cmsis_nn OPTIMIZE_KERNELS_FOR=KERNELS_OPTIMIZED_FOR_SPEED microlite
+
+```
+
+Bulding for size:
+```
+make -f tensorflow/lite/micro/tools/make/Makefile TARGET=cortex_m_generic TARGET_ARCH=cortex-m55 OPTIMIZED_KERNEL_DIR=cmsis_nn OPTIMIZE_KERNELS_FOR=KERNELS_OPTIMIZED_FOR_SIZE microlite
+
+```
diff --git a/tensorflow/lite/micro/kernels/cmsis_nn/add.cc b/tensorflow/lite/micro/kernels/cmsis_nn/add.cc
index 898410a..fb166a1 100644
--- a/tensorflow/lite/micro/kernels/cmsis_nn/add.cc
+++ b/tensorflow/lite/micro/kernels/cmsis_nn/add.cc
@@ -1,4 +1,4 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -301,6 +301,15 @@
micro_context->AllocateTempOutputTensor(node, kOutputTensor);
TF_LITE_ENSURE(context, output != nullptr);
+ TF_LITE_ENSURE_EQ(context, input1->type, output->type);
+ TF_LITE_ENSURE_MSG(
+ context,
+ input1->type == kTfLiteFloat32 || input1->type == kTfLiteInt32 ||
+ input1->type == kTfLiteInt16 || input1->type == kTfLiteInt8,
+ "Input data type not supported");
+ TF_LITE_ENSURE_MSG(context, input1->type == input2->type,
+ "Hybrid models are not supported on TFLite Micro.");
+
if (input1->type == kTfLiteInt16) {
TF_LITE_ENSURE_EQ(context, input1->params.zero_point, 0);
TF_LITE_ENSURE_EQ(context, input2->params.zero_point, 0);
diff --git a/tensorflow/lite/micro/kernels/cmsis_nn/conv.cc b/tensorflow/lite/micro/kernels/cmsis_nn/conv.cc
index 8b6928b..cae68c7 100644
--- a/tensorflow/lite/micro/kernels/cmsis_nn/conv.cc
+++ b/tensorflow/lite/micro/kernels/cmsis_nn/conv.cc
@@ -1,4 +1,4 @@
-/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -21,7 +21,6 @@
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/reference/conv.h"
-#include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/kernels/padding.h"
@@ -63,39 +62,62 @@
TfLiteTensor* output =
micro_context->AllocateTempOutputTensor(node, kConvOutputTensor);
TF_LITE_ENSURE(context, output != nullptr);
+ TfLiteTensor* bias =
+ micro_context->AllocateTempOutputTensor(node, kConvBiasTensor);
+ TfLiteType bias_type = bias != nullptr ? bias->type : kTfLiteNoType;
- RuntimeShape input_shape = GetTensorShape(input);
- RuntimeShape output_shape = GetTensorShape(output);
+ TF_LITE_ENSURE_EQ(context, input->type, output->type);
+ TF_LITE_ENSURE_MSG(context,
+ input->type == kTfLiteFloat32 ||
+ input->type == kTfLiteInt16 ||
+ input->type == kTfLiteInt8,
+ "Input data type not supported");
+ TF_LITE_ENSURE_MSG(
+ context,
+ (input->type == kTfLiteFloat32 && filter->type == kTfLiteFloat32) ||
+ (input->type == kTfLiteInt16 && filter->type == kTfLiteInt8) ||
+ (input->type == kTfLiteInt8 &&
+ (filter->type == kTfLiteInt4 || filter->type == kTfLiteInt8)),
+ "Hybrid models are not supported on TFLite Micro.");
- // Initialize cmsis_nn input dimensions
+ // Consistency check tensor dims
+ // Dimensionality
+ TF_LITE_ENSURE_EQ(context, input->dims->size, 4);
+ TF_LITE_ENSURE_EQ(context, filter->dims->size, 4);
+ TF_LITE_ENSURE_EQ(context, output->dims->size, 4);
+ // Equal batch size in input and output
+ TF_LITE_ENSURE_EQ(context, input->dims->data[0], output->dims->data[0]);
+ // Input channels should be an even multiple of filter channels
+ TF_LITE_ENSURE(context, filter->dims->data[3] > 0);
+ TF_LITE_ENSURE_EQ(context, input->dims->data[3] % filter->dims->data[3], 0);
+ // Output channels should be an even multiple of the number of groups
+ const int groups = input->dims->data[3] / filter->dims->data[3];
+ TFLITE_DCHECK_EQ(output->dims->data[3] % groups, 0);
+ // Bias size equal to output channels
+ if (bias != nullptr) {
+ TF_LITE_ENSURE_EQ(context, bias->dims->size, 4);
+ const int bias_size = NumElements(bias->dims);
+ TFLITE_DCHECK_EQ(bias_size, output->dims->data[3]);
+ }
+
+ // Initialize cmsis_nn dimensions
cmsis_nn_dims input_dims;
- input_dims.n = MatchingDim(input_shape, 0, output_shape, 0);
+ input_dims.n = input->dims->data[0];
input_dims.h = input->dims->data[1];
input_dims.w = input->dims->data[2];
- input_dims.c = input_shape.Dims(3);
+ input_dims.c = input->dims->data[3];
- // Initialize cmsis_nn filter dimensions
cmsis_nn_dims filter_dims;
- filter_dims.n = output_shape.Dims(3);
+ filter_dims.n = 1;
filter_dims.h = filter->dims->data[1];
filter_dims.w = filter->dims->data[2];
- filter_dims.c = input_dims.c;
+ filter_dims.c = filter->dims->data[3];
- // Initialize cmsis_nn output dimensions
cmsis_nn_dims output_dims;
- output_dims.n = input_dims.n;
+ output_dims.n = output->dims->data[0];
output_dims.h = output->dims->data[1];
output_dims.w = output->dims->data[2];
- output_dims.c = output_shape.Dims(3);
-
- if (filter->type == kTfLiteInt4) {
- int filter_size =
- RuntimeShape(filter->dims->size,
- reinterpret_cast<const int32_t*>(filter->dims->data))
- .FlatSize();
- context->RequestScratchBufferInArena(
- context, filter_size, &data->reference_op_data.filter_buffer_index);
- }
+ output_dims.c = output->dims->data[3];
if (input->type == kTfLiteInt8 || input->type == kTfLiteInt16) {
const int num_channels = filter->dims->data[kConvQuantizedDimension];
@@ -112,7 +134,10 @@
filter_dims.h, output_dims.w, output_dims.h, input->type,
&data->reference_op_data));
- if (input->type == kTfLiteInt8 || input->type == kTfLiteInt16) {
+ // CMSIS_NN allows INT64 or nullptr bias data pointer
+ if (input->type == kTfLiteInt8 ||
+ (input->type == kTfLiteInt16 &&
+ (bias_type == kTfLiteInt64 || bias_type == kTfLiteNoType))) {
// Initialize cmsis_nn convolution parameters
cmsis_nn_conv_params conv_params;
conv_params.input_offset = -input->params.zero_point;
@@ -147,10 +172,76 @@
micro_context->DeallocateTempTfLiteTensor(output);
micro_context->DeallocateTempTfLiteTensor(input);
micro_context->DeallocateTempTfLiteTensor(filter);
+ if (bias != nullptr) {
+ micro_context->DeallocateTempTfLiteTensor(bias);
+ }
return kTfLiteOk;
}
+template <class ActType, class BiasType, class WeigthsType>
+arm_cmsis_nn_status convolve_wrapper(
+ const cmsis_nn_context* ctx, const cmsis_nn_conv_params* conv_params,
+ const cmsis_nn_per_channel_quant_params* quant_params,
+ const cmsis_nn_dims* input_dims, const ActType* input,
+ const cmsis_nn_dims* filter_dims, const int8_t* filter,
+ const cmsis_nn_dims* bias_dims, const BiasType* bias,
+ const cmsis_nn_dims* output_dims, ActType* output, WeigthsType weightsT) {
+ return ARM_CMSIS_NN_ARG_ERROR;
+}
+
+template <>
+arm_cmsis_nn_status convolve_wrapper(
+ const cmsis_nn_context* ctx, const cmsis_nn_conv_params* conv_params,
+ const cmsis_nn_per_channel_quant_params* quant_params,
+ const cmsis_nn_dims* input_dims, const int8_t* input,
+ const cmsis_nn_dims* filter_dims, const int8_t* filter,
+ const cmsis_nn_dims* bias_dims, const int32_t* bias,
+ const cmsis_nn_dims* output_dims, int8_t* output, TfLiteType weightsT) {
+ if (weightsT == kTfLiteInt8) {
+ return arm_convolve_wrapper_s8(ctx, conv_params, quant_params, input_dims,
+ input, filter_dims, filter, bias_dims, bias,
+ output_dims, output);
+ } else if (weightsT == kTfLiteInt4) {
+ return arm_convolve_wrapper_s4(ctx, conv_params, quant_params, input_dims,
+ input, filter_dims, filter, bias_dims, bias,
+ output_dims, output);
+ } else {
+ return ARM_CMSIS_NN_ARG_ERROR;
+ }
+}
+
+template <>
+arm_cmsis_nn_status convolve_wrapper(
+ const cmsis_nn_context* ctx, const cmsis_nn_conv_params* conv_params,
+ const cmsis_nn_per_channel_quant_params* quant_params,
+ const cmsis_nn_dims* input_dims, const int16_t* input,
+ const cmsis_nn_dims* filter_dims, const int8_t* filter,
+ const cmsis_nn_dims* bias_dims, const int64_t* bias,
+ const cmsis_nn_dims* output_dims, int16_t* output, TfLiteType weightsT) {
+ const cmsis_nn_bias_data bias_data = {bias, false};
+
+ return arm_convolve_wrapper_s16(ctx, conv_params, quant_params, input_dims,
+ input, filter_dims, filter, bias_dims,
+ &bias_data, output_dims, output);
+}
+
+template <>
+arm_cmsis_nn_status convolve_wrapper(
+ const cmsis_nn_context* ctx, const cmsis_nn_conv_params* conv_params,
+ const cmsis_nn_per_channel_quant_params* quant_params,
+ const cmsis_nn_dims* input_dims, const int16_t* input,
+ const cmsis_nn_dims* filter_dims, const int8_t* filter,
+ const cmsis_nn_dims* bias_dims, const int32_t* bias,
+ const cmsis_nn_dims* output_dims, int16_t* output, TfLiteType weightsT) {
+ const cmsis_nn_bias_data bias_data = {bias, true};
+
+ return arm_convolve_wrapper_s16(ctx, conv_params, quant_params, input_dims,
+ input, filter_dims, filter, bias_dims,
+ &bias_data, output_dims, output);
+}
+
+template <typename ActType, typename BiasType, TfLiteType type>
TfLiteStatus EvalQuantizedPerChannel(TfLiteContext* context, TfLiteNode* node,
const TfLiteConvParams& params,
const OpData& data,
@@ -179,51 +270,31 @@
quant_params.shift =
const_cast<int32_t*>(data.reference_op_data.per_channel_output_shift);
- RuntimeShape filter_shape = tflite::micro::GetTensorShape(filter);
- RuntimeShape input_shape = tflite::micro::GetTensorShape(input);
- RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
- RuntimeShape bias_shape = tflite::micro::GetTensorShape(bias);
-
- // Consistency check.
- TFLITE_DCHECK_LE(conv_params.activation.min, conv_params.activation.max);
- TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
- TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
- TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
- const int batch_size = MatchingDim(input_shape, 0, output_shape, 0);
- const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3);
- const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3);
- if (tflite::micro::GetOptionalTensorData<int8_t>(bias)) {
- TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
- }
-
- // Initialize cmsis_nn dimensions
- // Input
+ // Initialize cmsis_nn dimension structs, consistency is checked in the
+ // prepare stage
cmsis_nn_dims input_dims;
- input_dims.n = batch_size;
- input_dims.h = input_shape.Dims(1);
- input_dims.w = input_shape.Dims(2);
- input_dims.c = input_depth;
+ input_dims.n = input->dims->data[0];
+ input_dims.h = input->dims->data[1];
+ input_dims.w = input->dims->data[2];
+ input_dims.c = input->dims->data[3];
- // Filter
cmsis_nn_dims filter_dims;
- filter_dims.n = output_depth;
- filter_dims.h = filter_shape.Dims(1);
- filter_dims.w = filter_shape.Dims(2);
- filter_dims.c = input_depth;
+ filter_dims.n = 1;
+ filter_dims.h = filter->dims->data[1];
+ filter_dims.w = filter->dims->data[2];
+ filter_dims.c = filter->dims->data[3];
- // Bias
cmsis_nn_dims bias_dims;
bias_dims.n = 1;
bias_dims.h = 1;
bias_dims.w = 1;
- bias_dims.c = output_depth;
+ bias_dims.c = output->dims->data[3];
- // Output
cmsis_nn_dims output_dims;
- output_dims.n = batch_size;
- output_dims.h = output_shape.Dims(1);
- output_dims.w = output_shape.Dims(2);
- output_dims.c = output_depth;
+ output_dims.n = output->dims->data[0];
+ output_dims.h = output->dims->data[1];
+ output_dims.w = output->dims->data[2];
+ output_dims.c = output->dims->data[3];
// Initialize cmsis_nn context
cmsis_nn_context ctx;
@@ -233,118 +304,44 @@
if (data.buffer_idx > -1) {
ctx.buf = context->GetScratchBuffer(context, data.buffer_idx);
// Note: ctx.size is currently not used in cmsis_nn.
- // The buffer should be allocated in the Prepare function through
- // arm_convolve_wrapper_s8_get_buffer_size
+ // The buffer should be allocated in the prepare function through
+ // the corresponding arm_convolve_wrapper_[type]_get_buffer_size
}
- // arm_convolve_wrapper_s8 dispatches the optimized kernel accordingly with
- // the parameters passed
+ // arm_convolve_wrapper_[type] dispatches the optimized kernel accordingly
+ // with the parameters passed
TFLITE_DCHECK_EQ(
- arm_convolve_wrapper_s8(
+ convolve_wrapper(
&ctx, &conv_params, &quant_params, &input_dims,
- tflite::micro::GetTensorData<int8_t>(input), &filter_dims,
+ tflite::micro::GetTensorData<ActType>(input), &filter_dims,
tflite::micro::GetTensorData<int8_t>(filter), &bias_dims,
- tflite::micro::GetOptionalTensorData<int32_t>(bias), &output_dims,
- tflite::micro::GetTensorData<int8_t>(output)),
+ tflite::micro::GetOptionalTensorData<BiasType>(bias), &output_dims,
+ tflite::micro::GetTensorData<ActType>(output), type),
ARM_CMSIS_NN_SUCCESS);
return kTfLiteOk;
}
-TfLiteStatus EvalQuantizedPerChannel16x8(
- TfLiteContext* context, TfLiteNode* node, const TfLiteConvParams& params,
- const OpData& data, const TfLiteEvalTensor* input,
- const TfLiteEvalTensor* filter, const TfLiteEvalTensor* bias,
- TfLiteEvalTensor* output) {
- cmsis_nn_conv_params conv_params;
- conv_params.dilation.h = params.dilation_height_factor;
- conv_params.dilation.w = params.dilation_width_factor;
+TfLiteStatus EvalInt4(TfLiteContext* context, TfLiteNode* node) {
+ const TfLiteEvalTensor* input =
+ tflite::micro::GetEvalInput(context, node, kConvInputTensor);
+ const TfLiteEvalTensor* filter =
+ tflite::micro::GetEvalInput(context, node, kConvWeightsTensor);
+ const TfLiteEvalTensor* bias =
+ (NumInputs(node) == 3)
+ ? tflite::micro::GetEvalInput(context, node, kConvBiasTensor)
+ : nullptr;
+ TfLiteEvalTensor* output =
+ tflite::micro::GetEvalOutput(context, node, kConvOutputTensor);
- // Initialize cmsis_nn convolution parameters
- conv_params.input_offset = -data.reference_op_data.input_zero_point;
- conv_params.output_offset = data.reference_op_data.output_zero_point;
- conv_params.stride.h = params.stride_height;
- conv_params.stride.w = params.stride_width;
- conv_params.padding.h = data.reference_op_data.padding.height;
- conv_params.padding.w = data.reference_op_data.padding.width;
- conv_params.activation.min = data.reference_op_data.output_activation_min;
- conv_params.activation.max = data.reference_op_data.output_activation_max;
+ TFLITE_DCHECK(node->builtin_data != nullptr);
+ const auto& params =
+ *(reinterpret_cast<TfLiteConvParams*>(node->builtin_data));
+ TFLITE_DCHECK(node->user_data != nullptr);
+ const OpData& data = *(static_cast<const OpData*>(node->user_data));
- // Initialize cmsis_nn per channel quantization parameters
- cmsis_nn_per_channel_quant_params quant_params;
- quant_params.multiplier = const_cast<int32_t*>(
- data.reference_op_data.per_channel_output_multiplier);
- quant_params.shift =
- const_cast<int32_t*>(data.reference_op_data.per_channel_output_shift);
-
- RuntimeShape filter_shape = tflite::micro::GetTensorShape(filter);
- RuntimeShape input_shape = tflite::micro::GetTensorShape(input);
- RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
- RuntimeShape bias_shape = tflite::micro::GetTensorShape(bias);
-
- // Consistency check.
- TFLITE_DCHECK_LE(conv_params.activation.min, conv_params.activation.max);
- TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
- TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
- TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
- const int batch_size = MatchingDim(input_shape, 0, output_shape, 0);
- const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3);
- const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3);
- if (tflite::micro::GetOptionalTensorData<int8_t>(bias)) {
- TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
- }
-
- // Initialize cmsis_nn dimensions
- // Input
- cmsis_nn_dims input_dims;
- input_dims.n = batch_size;
- input_dims.h = input_shape.Dims(1);
- input_dims.w = input_shape.Dims(2);
- input_dims.c = input_depth;
-
- // Filter
- cmsis_nn_dims filter_dims;
- filter_dims.n = output_depth;
- filter_dims.h = filter_shape.Dims(1);
- filter_dims.w = filter_shape.Dims(2);
- filter_dims.c = input_depth;
-
- // Bias
- cmsis_nn_dims bias_dims;
- bias_dims.n = 1;
- bias_dims.h = 1;
- bias_dims.w = 1;
- bias_dims.c = output_depth;
-
- // Output
- cmsis_nn_dims output_dims;
- output_dims.n = batch_size;
- output_dims.h = output_shape.Dims(1);
- output_dims.w = output_shape.Dims(2);
- output_dims.c = output_depth;
-
- // Initialize cmsis_nn context
- cmsis_nn_context ctx;
- ctx.buf = nullptr;
- ctx.size = 0;
-
- if (data.buffer_idx > -1) {
- ctx.buf = context->GetScratchBuffer(context, data.buffer_idx);
- // Note: ctx.size is currently not used in cmsis_nn.
- // The buffer should be allocated in the Prepare function through
- // arm_convolve_wrapper_s8_get_buffer_size
- }
-
- TFLITE_DCHECK_EQ(
- arm_convolve_wrapper_s16(
- &ctx, &conv_params, &quant_params, &input_dims,
- tflite::micro::GetTensorData<int16_t>(input), &filter_dims,
- tflite::micro::GetTensorData<int8_t>(filter), &bias_dims,
- tflite::micro::GetOptionalTensorData<int64_t>(bias), &output_dims,
- tflite::micro::GetTensorData<int16_t>(output)),
- ARM_CMSIS_NN_SUCCESS);
-
- return kTfLiteOk;
+ return EvalQuantizedPerChannel<int8_t, int32_t, kTfLiteInt4>(
+ context, node, params, data, input, filter, bias, output);
}
TfLiteStatus EvalInt8(TfLiteContext* context, TfLiteNode* node) {
@@ -364,11 +361,9 @@
*(reinterpret_cast<TfLiteConvParams*>(node->builtin_data));
TFLITE_DCHECK(node->user_data != nullptr);
const OpData& data = *(static_cast<const OpData*>(node->user_data));
- TfLiteEvalTensor filter_int8 = tflite::micro::MakeUnpackedInt4Tensor(
- context, data.reference_op_data.filter_buffer_index, filter);
- return EvalQuantizedPerChannel(context, node, params, data, input,
- &filter_int8, bias, output);
+ return EvalQuantizedPerChannel<int8_t, int32_t, kTfLiteInt8>(
+ context, node, params, data, input, filter, bias, output);
}
TfLiteStatus EvalInt16x8(TfLiteContext* context, TfLiteNode* node) {
@@ -389,8 +384,17 @@
TFLITE_DCHECK(node->user_data != nullptr);
const OpData& data = *(static_cast<const OpData*>(node->user_data));
- return EvalQuantizedPerChannel16x8(context, node, params, data, input, filter,
- bias, output);
+ if (bias == nullptr || bias->type == kTfLiteInt32) {
+ return EvalQuantizedPerChannel<int16_t, int32_t, kTfLiteInt16>(
+ context, node, params, data, input, filter, bias, output);
+ } else if (bias->type == kTfLiteInt64) {
+ return EvalQuantizedPerChannel<int16_t, int64_t, kTfLiteInt16>(
+ context, node, params, data, input, filter, bias, output);
+ } else {
+ MicroPrintf("Bias type %s (%d) not supported.",
+ TfLiteTypeGetName(bias->type), bias->type);
+ return kTfLiteError;
+ }
}
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
@@ -419,9 +423,6 @@
(input->type == kTfLiteInt8 && filter->type == kTfLiteInt4),
"Hybrid models are not supported on TFLite Micro.");
- TfLiteEvalTensor filter_int8 = tflite::micro::MakeUnpackedInt4Tensor(
- context, data.reference_op_data.filter_buffer_index, filter);
-
switch (input->type) { // Already know in/out types are same.
case kTfLiteFloat32: {
tflite::reference_ops::Conv(
@@ -437,30 +438,44 @@
tflite::micro::GetTensorShape(nullptr), nullptr);
break;
}
- case kTfLiteInt8:
- switch (filter_int8.type) {
- case kTfLiteInt8: {
- return EvalQuantizedPerChannel(context, node, params, data, input,
- &filter_int8, bias, output);
+ case kTfLiteInt8: {
+ switch (filter->type) {
+ case kTfLiteInt4: {
+ return EvalQuantizedPerChannel<int8_t, int32_t, kTfLiteInt4>(
+ context, node, params, data, input, filter, bias, output);
}
-
+ case kTfLiteInt8: {
+ return EvalQuantizedPerChannel<int8_t, int32_t, kTfLiteInt8>(
+ context, node, params, data, input, filter, bias, output);
+ }
default: {
MicroPrintf("Filter type %s (%d) not supported.",
TfLiteTypeGetName(filter->type), filter->type);
return kTfLiteError;
}
}
-
break;
- case kTfLiteInt16:
- return EvalQuantizedPerChannel16x8(context, node, params, data, input,
- filter, bias, output);
+ }
+ case kTfLiteInt16: {
+ if (bias == nullptr || bias->type == kTfLiteInt32) {
+ return EvalQuantizedPerChannel<int16_t, int32_t, kTfLiteInt16>(
+ context, node, params, data, input, filter, bias, output);
+ } else if (bias->type == kTfLiteInt64) {
+ return EvalQuantizedPerChannel<int16_t, int64_t, kTfLiteInt16>(
+ context, node, params, data, input, filter, bias, output);
+ } else {
+ MicroPrintf("Bias type %s (%d) not supported.",
+ TfLiteTypeGetName(bias->type), bias->type);
+ return kTfLiteError;
+ }
break;
+ }
default:
MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
input->type);
return kTfLiteError;
}
+
return kTfLiteOk;
}
@@ -470,6 +485,10 @@
return tflite::micro::RegisterOp(Init, Prepare, Eval);
}
+TFLMRegistration Register_CONV_2D_INT4() {
+ return tflite::micro::RegisterOp(Init, Prepare, EvalInt4);
+}
+
TFLMRegistration Register_CONV_2D_INT8() {
return tflite::micro::RegisterOp(Init, Prepare, EvalInt8);
}
diff --git a/tensorflow/lite/micro/kernels/cmsis_nn/depthwise_conv.cc b/tensorflow/lite/micro/kernels/cmsis_nn/depthwise_conv.cc
index 7b733b7..7183a28 100644
--- a/tensorflow/lite/micro/kernels/cmsis_nn/depthwise_conv.cc
+++ b/tensorflow/lite/micro/kernels/cmsis_nn/depthwise_conv.cc
@@ -1,4 +1,4 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -75,6 +75,20 @@
micro_context->AllocateTempOutputTensor(node, kDepthwiseConvOutputTensor);
TF_LITE_ENSURE(context, output != nullptr);
+ TF_LITE_ENSURE_EQ(context, input->type, output->type);
+ TF_LITE_ENSURE_MSG(context,
+ input->type == kTfLiteFloat32 ||
+ input->type == kTfLiteInt16 ||
+ input->type == kTfLiteInt8,
+ "Input data type not supported");
+ TF_LITE_ENSURE_MSG(
+ context,
+ (input->type == kTfLiteFloat32 && filter->type == kTfLiteFloat32) ||
+ (input->type == kTfLiteInt16 && filter->type == kTfLiteInt8) ||
+ (input->type == kTfLiteInt8 &&
+ (filter->type == kTfLiteInt4 || filter->type == kTfLiteInt8)),
+ "Hybrid models are not supported on TFLite Micro.");
+
const TfLiteType data_type = input->type;
int input_width = SizeOfDimension(input, 2);
int input_height = SizeOfDimension(input, 1);
@@ -118,15 +132,6 @@
context, num_channels * sizeof(int32_t)));
}
- if (filter->type == kTfLiteInt4) {
- int filter_size =
- RuntimeShape(filter->dims->size,
- reinterpret_cast<const int32_t*>(filter->dims->data))
- .FlatSize();
- context->RequestScratchBufferInArena(
- context, filter_size, &data->reference_op_data.filter_buffer_index);
- }
-
TF_LITE_ENSURE_STATUS(CalculateOpDataDepthwiseConv(
context, node, params, input_width, input_height, filter_width,
filter_height, output_width, output_height, data_type,
@@ -168,8 +173,18 @@
dw_conv_params.dilation.h = params.dilation_height_factor;
dw_conv_params.dilation.w = params.dilation_width_factor;
- const int32_t buf_size = arm_depthwise_conv_wrapper_s8_get_buffer_size(
- &dw_conv_params, &input_dims, &filter_dims, &output_dims);
+ int32_t buf_size = 0;
+ if (filter->type == kTfLiteInt8) {
+ buf_size = arm_depthwise_conv_wrapper_s8_get_buffer_size(
+ &dw_conv_params, &input_dims, &filter_dims, &output_dims);
+ } else if (filter->type == kTfLiteInt4) {
+ buf_size = arm_depthwise_conv_wrapper_s4_get_buffer_size(
+ &dw_conv_params, &input_dims, &filter_dims, &output_dims);
+ } else {
+ MicroPrintf("Filter type %s (%d) not supported.",
+ TfLiteTypeGetName(filter->type), filter->type);
+ return kTfLiteError;
+ }
if (buf_size > 0) {
TF_LITE_ENSURE_STATUS(context->RequestScratchBufferInArena(
@@ -285,6 +300,43 @@
ARM_CMSIS_NN_SUCCESS);
}
+void EvalQuantizedPerChannelInt4(TfLiteContext* context, TfLiteNode* node,
+ const TfLiteDepthwiseConvParams& params,
+ const OpData& data,
+ const TfLiteEvalTensor* input,
+ const TfLiteEvalTensor* filter,
+ const TfLiteEvalTensor* bias,
+ TfLiteEvalTensor* output) {
+ cmsis_nn_dw_conv_params dw_conv_params;
+ cmsis_nn_per_channel_quant_params quant_params;
+ cmsis_nn_dims input_dims;
+ cmsis_nn_dims filter_dims;
+ cmsis_nn_dims bias_dims;
+ cmsis_nn_dims output_dims;
+
+ PopulateDwConvParams(&dw_conv_params, &quant_params, &input_dims,
+ &filter_dims, &bias_dims, &output_dims, params, data,
+ input, filter, bias, output);
+
+ cmsis_nn_context ctx;
+ ctx.buf = nullptr;
+ /* 'size' is unused */
+ ctx.size = 0;
+
+ if (data.buffer_idx > -1) {
+ ctx.buf = context->GetScratchBuffer(context, data.buffer_idx);
+ }
+
+ TFLITE_DCHECK_EQ(
+ arm_depthwise_conv_wrapper_s4(
+ &ctx, &dw_conv_params, &quant_params, &input_dims,
+ tflite::micro::GetTensorData<int8_t>(input), &filter_dims,
+ tflite::micro::GetTensorData<int8_t>(filter), &bias_dims,
+ tflite::micro::GetOptionalTensorData<int32_t>(bias), &output_dims,
+ tflite::micro::GetTensorData<int8_t>(output)),
+ ARM_CMSIS_NN_SUCCESS);
+}
+
void EvalQuantizedPerChannel16x8(TfLiteContext* context, TfLiteNode* node,
const TfLiteDepthwiseConvParams& params,
const OpData& data,
@@ -337,9 +389,6 @@
? tflite::micro::GetEvalInput(context, node, kDepthwiseConvBiasTensor)
: nullptr;
- TfLiteEvalTensor filter_int8 = tflite::micro::MakeUnpackedInt4Tensor(
- context, data.reference_op_data.filter_buffer_index, filter);
-
switch (input->type) { // Already know in/out types are same.
case kTfLiteFloat32: {
tflite::reference_ops::DepthwiseConv(
@@ -355,10 +404,15 @@
break;
}
case kTfLiteInt8:
- switch (filter_int8.type) {
+ switch (filter->type) {
case kTfLiteInt8: {
- EvalQuantizedPerChannel(context, node, params, data, input,
- &filter_int8, bias, output);
+ EvalQuantizedPerChannel(context, node, params, data, input, filter,
+ bias, output);
+ break;
+ }
+ case kTfLiteInt4: {
+ EvalQuantizedPerChannelInt4(context, node, params, data, input,
+ filter, bias, output);
break;
}
default: {
@@ -399,11 +453,8 @@
? tflite::micro::GetEvalInput(context, node, kDepthwiseConvBiasTensor)
: nullptr;
- TfLiteEvalTensor filter_int8 = tflite::micro::MakeUnpackedInt4Tensor(
- context, data.reference_op_data.filter_buffer_index, filter);
-
- EvalQuantizedPerChannel(context, node, params, data, input, &filter_int8,
- bias, output);
+ EvalQuantizedPerChannel(context, node, params, data, input, filter, bias,
+ output);
return kTfLiteOk;
}
@@ -431,6 +482,30 @@
return kTfLiteOk;
}
+TfLiteStatus EvalInt4(TfLiteContext* context, TfLiteNode* node) {
+ TFLITE_DCHECK(node->user_data != nullptr);
+ TFLITE_DCHECK(node->builtin_data != nullptr);
+
+ const auto& params =
+ *(reinterpret_cast<TfLiteDepthwiseConvParams*>(node->builtin_data));
+ const OpData& data = *(static_cast<OpData*>(node->user_data));
+
+ TfLiteEvalTensor* output =
+ tflite::micro::GetEvalOutput(context, node, kDepthwiseConvOutputTensor);
+ const TfLiteEvalTensor* input =
+ tflite::micro::GetEvalInput(context, node, kDepthwiseConvInputTensor);
+ const TfLiteEvalTensor* filter =
+ tflite::micro::GetEvalInput(context, node, kDepthwiseConvWeightsTensor);
+ const TfLiteEvalTensor* bias =
+ (NumInputs(node) == 3)
+ ? tflite::micro::GetEvalInput(context, node, kDepthwiseConvBiasTensor)
+ : nullptr;
+
+ EvalQuantizedPerChannelInt4(context, node, params, data, input, filter, bias,
+ output);
+ return kTfLiteOk;
+}
+
} // namespace
TFLMRegistration Register_DEPTHWISE_CONV_2D() {
@@ -445,4 +520,8 @@
return tflite::micro::RegisterOp(Init, Prepare, EvalInt16x8);
}
+TFLMRegistration Register_DEPTHWISE_CONV_2D_INT4() {
+ return tflite::micro::RegisterOp(Init, Prepare, EvalInt4);
+}
+
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/cmsis_nn/fully_connected.cc b/tensorflow/lite/micro/kernels/cmsis_nn/fully_connected.cc
index a7ab8f1..7c373b5 100644
--- a/tensorflow/lite/micro/kernels/cmsis_nn/fully_connected.cc
+++ b/tensorflow/lite/micro/kernels/cmsis_nn/fully_connected.cc
@@ -1,4 +1,4 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -26,6 +26,7 @@
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/micro_arena_constants.h"
#include "tensorflow/lite/micro/micro_log.h"
namespace tflite {
@@ -42,6 +43,8 @@
// Index to buffer for optimizations if applicable.
int buffer_idx;
+ int32_t* kernel_sums;
+
int32_t batches;
int32_t accum_depth;
int32_t output_depth;
@@ -73,7 +76,19 @@
node, kFullyConnectedOutputTensor);
TF_LITE_ENSURE(context, output != nullptr);
- TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
+ TF_LITE_ENSURE_EQ(context, input->type, output->type);
+ TF_LITE_ENSURE_MSG(context,
+ input->type == kTfLiteFloat32 ||
+ input->type == kTfLiteInt16 ||
+ input->type == kTfLiteInt8,
+ "Input data type not supported");
+ TF_LITE_ENSURE_MSG(
+ context,
+ (input->type == kTfLiteFloat32 && filter->type == kTfLiteFloat32) ||
+ (input->type == kTfLiteInt16 && filter->type == kTfLiteInt8) ||
+ (input->type == kTfLiteInt8 &&
+ (filter->type == kTfLiteInt4 || filter->type == kTfLiteInt8)),
+ "Hybrid models are not supported on TFLite Micro.");
const RuntimeShape filter_shape = GetTensorShape(filter);
const RuntimeShape output_shape = GetTensorShape(output);
@@ -101,7 +116,7 @@
TF_LITE_ENSURE_EQ(context, input->params.zero_point, 0);
TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0);
buf_size = arm_fully_connected_s16_get_buffer_size(&filter_dims);
- } else if (input->type == kTfLiteInt8) {
+ } else if (input->type == kTfLiteInt8 && filter->type != kTfLiteInt4) {
const RuntimeShape input_shape = GetTensorShape(input);
TFLITE_DCHECK_GE(output_dim_count, 2);
@@ -122,18 +137,23 @@
input_dims.c = data->accum_depth;
buf_size = arm_convolve_1x1_s8_fast_get_buffer_size(&input_dims);
- } else {
+ } else if (input->type == kTfLiteInt8) {
buf_size = arm_fully_connected_s8_get_buffer_size(&filter_dims);
- }
- }
- if (filter->type == kTfLiteInt4) {
- int filter_size =
- RuntimeShape(filter->dims->size,
- reinterpret_cast<const int32_t*>(filter->dims->data))
- .FlatSize();
- context->RequestScratchBufferInArena(
- context, filter_size, &data->reference_op_data.filter_buffer_index);
+ int8_t* filter_data = GetTensorData<int8_t>(filter);
+ data->kernel_sums = nullptr;
+
+ if (buf_size > 0 && filter_data != nullptr) {
+ data->kernel_sums = static_cast<int32_t*>(
+ context->AllocatePersistentBuffer(context, buf_size));
+
+ arm_vector_sum_s8(data->kernel_sums, filter_dims.n, data->output_depth,
+ filter_data, 1, nullptr);
+
+ // Do not request a scratch buffer since using persistent memory
+ buf_size = 0;
+ }
+ }
}
if (buf_size > 0) {
@@ -188,6 +208,49 @@
}
}
+TfLiteStatus EvalQuantizedInt4(TfLiteContext* context, TfLiteNode* node,
+ const OpData& data,
+ const TfLiteEvalTensor* input,
+ const TfLiteEvalTensor* filter,
+ const TfLiteEvalTensor* bias,
+ TfLiteEvalTensor* output) {
+ const RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
+ const int output_dim_count = output_shape.DimensionsCount();
+ TFLITE_DCHECK_GE(output_dim_count, 2);
+ TFLITE_DCHECK_LE(output_dim_count, 4);
+
+ cmsis_nn_per_tensor_quant_params quant_params;
+ cmsis_nn_dims input_dims;
+ cmsis_nn_dims filter_dims;
+ cmsis_nn_dims bias_dims;
+ cmsis_nn_dims output_dims;
+ cmsis_nn_context ctx;
+
+ PopulateCommonParams(context, &quant_params, &input_dims, &filter_dims,
+ &bias_dims, &output_dims, &ctx, data);
+
+ const int32_t* bias_data =
+ tflite::micro::GetOptionalTensorData<int32_t>(bias);
+
+ cmsis_nn_fc_params fc_params;
+ fc_params.input_offset = -data.reference_op_data.input_zero_point;
+ fc_params.output_offset = data.reference_op_data.output_zero_point;
+ fc_params.filter_offset = 0;
+ fc_params.activation.min = data.reference_op_data.output_activation_min;
+ fc_params.activation.max = data.reference_op_data.output_activation_max;
+
+ TF_LITE_ENSURE_EQ(
+ context,
+ arm_fully_connected_s4(
+ &ctx, &fc_params, &quant_params, &input_dims,
+ tflite::micro::GetTensorData<int8_t>(input), &filter_dims,
+ tflite::micro::GetTensorData<int8_t>(filter), &bias_dims, bias_data,
+ &output_dims, tflite::micro::GetTensorData<int8_t>(output)),
+ ARM_CMSIS_NN_SUCCESS);
+
+ return kTfLiteOk;
+}
+
TfLiteStatus EvalQuantizedInt8(TfLiteContext* context, TfLiteNode* node,
const OpData& data,
const TfLiteEvalTensor* input,
@@ -247,11 +310,20 @@
} else {
cmsis_nn_fc_params fc_params;
fc_params.input_offset = -data.reference_op_data.input_zero_point;
+ fc_params.filter_offset = -data.reference_op_data.filter_zero_point;
fc_params.output_offset = data.reference_op_data.output_zero_point;
- fc_params.filter_offset = 0;
fc_params.activation.min = data.reference_op_data.output_activation_min;
fc_params.activation.max = data.reference_op_data.output_activation_max;
+ if (data.kernel_sums != nullptr) {
+ ctx.buf = data.kernel_sums;
+ } else if (ctx.buf != nullptr) {
+ // If behaving like batch matmul we calculate kernel sums in eval.
+ arm_vector_sum_s8(
+ static_cast<int32_t*>(ctx.buf), filter_dims.n, data.output_depth,
+ tflite::micro::GetTensorData<int8_t>(filter), 1, nullptr);
+ }
+
TF_LITE_ENSURE_EQ(
context,
arm_fully_connected_s8(
@@ -319,9 +391,6 @@
TFLITE_DCHECK(node->user_data != nullptr);
const OpData& data = *(static_cast<const OpData*>(node->user_data));
- TfLiteEvalTensor filter_int8 = tflite::micro::MakeUnpackedInt4Tensor(
- context, data.reference_op_data.filter_buffer_index, filter);
-
// Checks in Prepare ensure input, output and filter types are all the same.
switch (input->type) {
case kTfLiteFloat32: {
@@ -339,10 +408,13 @@
break;
}
case kTfLiteInt8: {
- switch (filter_int8.type) {
+ switch (filter->type) {
+ case kTfLiteInt4:
+ return EvalQuantizedInt4(context, node, data, input, filter, bias,
+ output);
case kTfLiteInt8:
- return EvalQuantizedInt8(context, node, data, input, &filter_int8,
- bias, output);
+ return EvalQuantizedInt8(context, node, data, input, filter, bias,
+ output);
default:
MicroPrintf("Filter Type %s (%d) not supported.",
TfLiteTypeGetName(filter->type), filter->type);
@@ -363,6 +435,29 @@
return kTfLiteOk;
}
+TfLiteStatus EvalInt4(TfLiteContext* context, TfLiteNode* node) {
+ const TfLiteEvalTensor* input =
+ tflite::micro::GetEvalInput(context, node, kFullyConnectedInputTensor);
+ const TfLiteEvalTensor* filter =
+ tflite::micro::GetEvalInput(context, node, kFullyConnectedWeightsTensor);
+ const TfLiteEvalTensor* bias =
+ tflite::micro::GetEvalInput(context, node, kFullyConnectedBiasTensor);
+ TfLiteEvalTensor* output =
+ tflite::micro::GetEvalOutput(context, node, kFullyConnectedOutputTensor);
+
+ TFLITE_DCHECK(node->user_data != nullptr);
+ const OpData& data = *(static_cast<const OpData*>(node->user_data));
+
+ // Checks in Prepare ensure input, output and filter types are all the same.
+ if (input->type != kTfLiteInt8 && filter->type != kTfLiteInt4) {
+ MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
+ input->type);
+ return kTfLiteError;
+ }
+
+ return EvalQuantizedInt4(context, node, data, input, filter, bias, output);
+}
+
// Note that the current function names are not ideal at all (this EvalInt8
// function internally calls EvalQuantizedInt8, and there is similar name
// aliasing in the Eval function too). We will be attempting to have a more
@@ -389,11 +484,7 @@
return kTfLiteError;
}
- TfLiteEvalTensor filter_int8 = tflite::micro::MakeUnpackedInt4Tensor(
- context, data.reference_op_data.filter_buffer_index, filter);
-
- return EvalQuantizedInt8(context, node, data, input, &filter_int8, bias,
- output);
+ return EvalQuantizedInt8(context, node, data, input, filter, bias, output);
}
TfLiteStatus EvalInt16(TfLiteContext* context, TfLiteNode* node) {
@@ -425,6 +516,10 @@
return tflite::micro::RegisterOp(Init, Prepare, Eval);
}
+TFLMRegistration Register_FULLY_CONNECTED_INT4() {
+ return tflite::micro::RegisterOp(Init, Prepare, EvalInt4);
+}
+
TFLMRegistration Register_FULLY_CONNECTED_INT8() {
return tflite::micro::RegisterOp(Init, Prepare, EvalInt8);
}
@@ -433,4 +528,8 @@
return tflite::micro::RegisterOp(Init, Prepare, EvalInt16);
}
+TFLMInferenceRegistration RegisterInference_FULLY_CONNECTED() {
+ return tflite::micro::RegisterOp(Eval);
+}
+
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/cmsis_nn/softmax.cc b/tensorflow/lite/micro/kernels/cmsis_nn/softmax.cc
index f83a090..6515691 100644
--- a/tensorflow/lite/micro/kernels/cmsis_nn/softmax.cc
+++ b/tensorflow/lite/micro/kernels/cmsis_nn/softmax.cc
@@ -1,4 +1,4 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -52,6 +52,17 @@
TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0);
TF_LITE_ENSURE(context, output != nullptr);
+ TF_LITE_ENSURE_MSG(
+ context,
+ input->type == output->type ||
+ (input->type == kTfLiteInt8 && output->type == kTfLiteInt16),
+ "Input and output data types are not supported together.");
+ TF_LITE_ENSURE_MSG(context,
+ input->type == kTfLiteFloat32 ||
+ input->type == kTfLiteInt16 ||
+ input->type == kTfLiteInt8,
+ "Input data type not supported");
+
TF_LITE_ENSURE(context, node->user_data != nullptr);
CMSISNNSoftmaxParams* op_data =
static_cast<CMSISNNSoftmaxParams*>(node->user_data);
diff --git a/tensorflow/lite/micro/kernels/cmsis_nn/svdf.cc b/tensorflow/lite/micro/kernels/cmsis_nn/svdf.cc
index 03dbaee..bf64016 100644
--- a/tensorflow/lite/micro/kernels/cmsis_nn/svdf.cc
+++ b/tensorflow/lite/micro/kernels/cmsis_nn/svdf.cc
@@ -1,4 +1,4 @@
-/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -31,9 +31,195 @@
namespace tflite {
namespace {
+struct CmsisNnOpDataSvdf {
+ int32_t effective_scale_1_a;
+ int32_t effective_scale_2_a;
+ // b versions of each scale are kept at int since the numbers are just the
+ // shift value - typically between [-32, 32].
+ int effective_scale_1_b;
+ int effective_scale_2_b;
+ int scratch_tensor_index;
+ int scratch_output_tensor_index;
+
+ // Cached tensor zero point values for quantized operations.
+ int input_zero_point;
+ int output_zero_point;
+ int activation_state_zero_point;
+ int32_t* kernel_sums;
+};
+
void* Init(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
- return context->AllocatePersistentBuffer(context, sizeof(OpDataSvdf));
+ return context->AllocatePersistentBuffer(context, sizeof(CmsisNnOpDataSvdf));
+}
+
+TfLiteStatus CmsisNnPrepareSvdf(TfLiteContext* context, TfLiteNode* node) {
+ TFLITE_DCHECK(node->builtin_data != nullptr);
+
+ const auto* params = static_cast<const TfLiteSVDFParams*>(node->builtin_data);
+
+ MicroContext* micro_context = GetMicroContext(context);
+
+ // Validate Tensor Inputs (dtype depends on quantization):
+ // [0] = Input, {2, batch_size, input_size}
+ // [1] = Weights Feature, {2, num_filters, input_size}
+ // [2] = Weights Time, {2, num_filters, memory_size}
+ // [3] = Bias (optional), {1, num_units}
+ // [4] = Activation State (variable),
+ // {2, batch_size, memory_size * num_filters}
+ TfLiteTensor* input =
+ micro_context->AllocateTempInputTensor(node, kSvdfInputTensor);
+ TF_LITE_ENSURE(context, input != nullptr);
+ TfLiteTensor* weights_feature =
+ micro_context->AllocateTempInputTensor(node, kSvdfWeightsFeatureTensor);
+ TF_LITE_ENSURE(context, weights_feature != nullptr);
+ TfLiteTensor* weights_time =
+ micro_context->AllocateTempInputTensor(node, kSvdfWeightsTimeTensor);
+ TF_LITE_ENSURE(context, weights_time != nullptr);
+ TfLiteTensor* bias =
+ micro_context->AllocateTempInputTensor(node, kSvdfBiasTensor);
+ TfLiteTensor* activation_state = micro_context->AllocateTempInputTensor(
+ node, kSvdfInputActivationStateTensor);
+ TF_LITE_ENSURE(context, activation_state != nullptr);
+
+ // Define input constants based on input tensor definition above:
+ const int rank = params->rank;
+ const int input_size = input->dims->data[1];
+ const int batch_size = input->dims->data[0];
+ const int num_filters = weights_feature->dims->data[0];
+ TF_LITE_ENSURE_EQ(context, num_filters % rank, 0);
+ const int num_units = num_filters / rank;
+ const int memory_size = weights_time->dims->data[1];
+
+ // Validate Input Tensor:
+ TF_LITE_ENSURE(context,
+ input->type == kTfLiteFloat32 || input->type == kTfLiteInt8);
+ TF_LITE_ENSURE_EQ(context, NumDimensions(input), 2);
+
+ // Validate Tensor Output:
+ // [0] = float/int8_t, {2, batch_size, num_units}
+ TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);
+ TfLiteTensor* output =
+ micro_context->AllocateTempOutputTensor(node, kSvdfOutputTensor);
+ TF_LITE_ENSURE(context, output != nullptr);
+ TF_LITE_ENSURE_EQ(context, NumDimensions(output), 2);
+ TF_LITE_ENSURE_EQ(context, output->dims->data[0], batch_size);
+ TF_LITE_ENSURE_EQ(context, output->dims->data[1], num_units);
+
+ // Validate Weights Feature Input Tensor:
+ TF_LITE_ENSURE_EQ(context, NumDimensions(weights_feature), 2);
+ TF_LITE_ENSURE_EQ(context, weights_feature->dims->data[1], input_size);
+
+ // Validate Weights Time Input Tensor:
+ TF_LITE_ENSURE_EQ(context, NumDimensions(weights_time), 2);
+ TF_LITE_ENSURE_EQ(context, weights_time->dims->data[0], num_filters);
+ TF_LITE_ENSURE_EQ(context, weights_time->dims->data[1], memory_size);
+
+ // Validate Optional Bias Input Tensor:
+ if (bias != nullptr) {
+ TF_LITE_ENSURE_EQ(context, bias->dims->data[0], num_units);
+ }
+
+ // Validate Activation State Input Tensor:
+ TF_LITE_ENSURE_EQ(context, NumDimensions(activation_state), 2);
+ TF_LITE_ENSURE_EQ(context, activation_state->dims->data[0], batch_size);
+ TF_LITE_ENSURE_EQ(context, activation_state->dims->data[1],
+ memory_size * num_filters);
+ // Since is_variable is not part of TFLiteEvalTensor, check is_variable here.
+ TF_LITE_ENSURE_EQ(context, activation_state->is_variable, true);
+
+ TF_LITE_ENSURE_EQ(context, node->inputs->size, 5);
+
+ TFLITE_DCHECK(node->user_data != nullptr);
+ CmsisNnOpDataSvdf* data = static_cast<CmsisNnOpDataSvdf*>(node->user_data);
+
+ if (input->type == kTfLiteInt8) {
+ TF_LITE_ENSURE_EQ(context, weights_feature->type, kTfLiteInt8);
+ TF_LITE_ENSURE(context, (weights_time->type == kTfLiteInt16) ||
+ (weights_time->type == kTfLiteInt8));
+ TF_LITE_ENSURE(context, (activation_state->type == kTfLiteInt16) ||
+ (activation_state->type == kTfLiteInt8));
+ if (bias != nullptr) {
+ TF_LITE_ENSURE_EQ(context, bias->type, kTfLiteInt32);
+ }
+
+ TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteInt8);
+
+ const double effective_scale_1 = static_cast<double>(
+ input->params.scale * weights_feature->params.scale /
+ activation_state->params.scale);
+ const double effective_scale_2 =
+ static_cast<double>(activation_state->params.scale *
+ weights_time->params.scale / output->params.scale);
+
+ // TODO(b/162018098): Use TF_LITE_ENSURE_NEAR when it is ready.
+ // TODO(#1751): account for optional bias tensor
+ TF_LITE_ENSURE(
+ context,
+ std::abs(static_cast<double>(bias->params.scale) -
+ static_cast<double>(activation_state->params.scale *
+ weights_time->params.scale)) < 1e-5);
+
+ QuantizeMultiplier(effective_scale_1, &(data->effective_scale_1_a),
+ &(data->effective_scale_1_b));
+ QuantizeMultiplier(effective_scale_2, &(data->effective_scale_2_a),
+ &(data->effective_scale_2_b));
+
+ data->input_zero_point = input->params.zero_point;
+ data->output_zero_point = output->params.zero_point;
+ data->activation_state_zero_point = activation_state->params.zero_point;
+
+ TFLITE_DCHECK(context->RequestScratchBufferInArena != nullptr);
+
+ const TfLiteStatus scratch_status = context->RequestScratchBufferInArena(
+ context, batch_size * num_filters * sizeof(int32_t),
+ &(data->scratch_tensor_index));
+ TF_LITE_ENSURE_OK(context, scratch_status);
+
+ const TfLiteStatus scratch_output_status =
+ context->RequestScratchBufferInArena(
+ context, batch_size * num_units * sizeof(int32_t),
+ &(data->scratch_output_tensor_index));
+ TF_LITE_ENSURE_OK(context, scratch_output_status);
+
+ cmsis_nn_dims weights_feature_dims;
+ weights_feature_dims.n = num_filters;
+ weights_feature_dims.h = input_size;
+
+ const int32_t buf_size = arm_svdf_s8_get_buffer_size(&weights_feature_dims);
+
+ if (buf_size > 0) {
+ data->kernel_sums = static_cast<int32_t*>(
+ context->AllocatePersistentBuffer(context, buf_size));
+
+ arm_vector_sum_s8(data->kernel_sums, input_size, num_filters,
+ GetTensorData<int8_t>(weights_feature), 1, nullptr);
+ }
+
+ } else {
+ TF_LITE_ENSURE_EQ(context, weights_feature->type, kTfLiteFloat32);
+ TF_LITE_ENSURE_EQ(context, weights_time->type, kTfLiteFloat32);
+ TF_LITE_ENSURE_EQ(context, activation_state->type, kTfLiteFloat32);
+ if (bias != nullptr) {
+ TF_LITE_ENSURE_EQ(context, bias->type, kTfLiteFloat32);
+ }
+ TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteFloat32);
+
+ TFLITE_DCHECK(context->RequestScratchBufferInArena != nullptr);
+ const TfLiteStatus scratch_status = context->RequestScratchBufferInArena(
+ context, batch_size * num_filters * sizeof(float),
+ &(data->scratch_tensor_index));
+ TF_LITE_ENSURE_OK(context, scratch_status);
+ }
+
+ micro_context->DeallocateTempTfLiteTensor(input);
+ micro_context->DeallocateTempTfLiteTensor(weights_feature);
+ micro_context->DeallocateTempTfLiteTensor(weights_time);
+ micro_context->DeallocateTempTfLiteTensor(activation_state);
+ micro_context->DeallocateTempTfLiteTensor(output);
+ // TODO(#1751): account for optional bias tensor
+ micro_context->DeallocateTempTfLiteTensor(bias);
+ return kTfLiteOk;
}
TfLiteStatus EvalIntegerSVDF(TfLiteContext* context, TfLiteNode* node,
@@ -44,7 +230,7 @@
const TfLiteSVDFParams* params,
TfLiteEvalTensor* activation_state_tensor,
TfLiteEvalTensor* output_tensor,
- const OpDataSvdf& data) {
+ const CmsisNnOpDataSvdf& data) {
cmsis_nn_dims input_dims;
input_dims.n = input_tensor->dims->data[0];
input_dims.h = input_tensor->dims->data[1];
@@ -102,9 +288,12 @@
switch (weights_time_tensor->type) {
case kTfLiteInt8: {
+ cmsis_nn_context ctx;
+ ctx.buf = data.kernel_sums;
+
arm_svdf_s8(
- &scratch_ctx, &scratch_output_ctx, &svdf_params, &in_quant_params,
- &out_quant_params, &input_dims,
+ &ctx, &scratch_ctx, &scratch_output_ctx, &svdf_params,
+ &in_quant_params, &out_quant_params, &input_dims,
tflite::micro::GetTensorData<int8_t>(input_tensor), &state_dims,
tflite::micro::GetTensorData<int8_t>(activation_state_tensor),
&weights_feature_dims,
@@ -141,7 +330,8 @@
TfLiteStatus EvalSvdf(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteSVDFParams*>(node->builtin_data);
TFLITE_DCHECK(node->user_data != nullptr);
- const OpDataSvdf& data = *(static_cast<const OpDataSvdf*>(node->user_data));
+ const CmsisNnOpDataSvdf& data =
+ *(static_cast<const CmsisNnOpDataSvdf*>(node->user_data));
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kSvdfInputTensor);
@@ -184,7 +374,8 @@
TfLiteStatus EvalSvdfInt8(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteSVDFParams*>(node->builtin_data);
TFLITE_DCHECK(node->user_data != nullptr);
- const OpDataSvdf& data = *(static_cast<const OpDataSvdf*>(node->user_data));
+ const CmsisNnOpDataSvdf& data =
+ *(static_cast<const CmsisNnOpDataSvdf*>(node->user_data));
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kSvdfInputTensor);
@@ -213,11 +404,11 @@
} // namespace
TFLMRegistration Register_SVDF() {
- return tflite::micro::RegisterOp(Init, PrepareSvdf, EvalSvdf);
+ return tflite::micro::RegisterOp(Init, CmsisNnPrepareSvdf, EvalSvdf);
}
TFLMRegistration Register_SVDF_INT8() {
- return tflite::micro::RegisterOp(Init, PrepareSvdf, EvalSvdfInt8);
+ return tflite::micro::RegisterOp(Init, CmsisNnPrepareSvdf, EvalSvdfInt8);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/cmsis_nn/transpose_conv.cc b/tensorflow/lite/micro/kernels/cmsis_nn/transpose_conv.cc
new file mode 100644
index 0000000..20cf0e1
--- /dev/null
+++ b/tensorflow/lite/micro/kernels/cmsis_nn/transpose_conv.cc
@@ -0,0 +1,556 @@
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/lite/micro/kernels/transpose_conv.h"
+
+#include "Include/arm_nnfunctions.h"
+#include "tensorflow/lite/c/builtin_op_data.h"
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/kernels/internal/common.h"
+#include "tensorflow/lite/kernels/internal/quantization_util.h"
+#include "tensorflow/lite/kernels/internal/reference/integer_ops/transpose_conv.h"
+#include "tensorflow/lite/kernels/internal/reference/transpose_conv.h"
+#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/kernels/padding.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/micro_log.h"
+
+namespace tflite {
+namespace {
+
+// For the TfLite transpose_conv implementation, input tensor 0 corresponds to
+// the OutputShapeTensor. However, since TFLM does not support dynamic tensors,
+// the TFLM implementation ignores input tensor 0 and the only inputs we care
+// about are kFilterTensor, kInputTensor and kBiasTensor.
+constexpr int kFilterTensor = 1;
+constexpr int kInputTensor = 2;
+constexpr int kBiasTensor = 3;
+constexpr int kOutputTensor = 0;
+
+// Conv is quantized along dimension 0:
+// https://www.tensorflow.org/lite/performance/quantization_spec
+constexpr int kConvQuantizedDimension = 0;
+
+struct OpData {
+ ConvParams params;
+
+ // Scratch buffers are required for quantized implementations.
+ int scratch_buffer_index;
+ int scratch_buffer_output_index;
+
+ // TODO(b/192090531): Remove this once all 8x16 transpose conv models use
+ // 64-bit biases.
+ int bias_converted_buffer_index;
+
+ // Multiplier and shift arrays are required for the int8 implementation.
+ int32_t* per_channel_output_multiplier;
+ int32_t* per_channel_output_shift;
+};
+
+inline PaddingType RuntimePaddingType(TfLitePadding padding) {
+ switch (padding) {
+ case TfLitePadding::kTfLitePaddingSame:
+ return PaddingType::kSame;
+ case TfLitePadding::kTfLitePaddingValid:
+ return PaddingType::kValid;
+ case TfLitePadding::kTfLitePaddingUnknown:
+ default:
+ return PaddingType::kNone;
+ }
+}
+
+TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node,
+ const TfLiteTransposeConvParams* params, int width,
+ int height, int filter_width, int filter_height,
+ const TfLiteType data_type, OpData* data) {
+ bool has_bias = node->inputs->size == 4;
+ // Check number of inputs/outputs
+ TF_LITE_ENSURE(context, has_bias || node->inputs->size == 3);
+ TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);
+
+ // Matching GetWindowedOutputSize in TensorFlow.
+ auto padding = params->padding;
+ int pad_output_width;
+ int pad_output_height;
+
+ TfLitePaddingValues padding_values = ComputePaddingHeightWidth(
+ params->stride_height, params->stride_width, 1,
+ 1, // Dilation height and width are always 1 for transpose_conv.
+ height, width, filter_height, filter_width, padding, &pad_output_height,
+ &pad_output_width);
+
+ data->params.padding_type = RuntimePaddingType(padding);
+ data->params.padding_values.width = padding_values.width;
+ data->params.padding_values.height = padding_values.height;
+ data->params.padding_values.width_offset =
+ padding_values.width_offset + padding_values.width;
+ data->params.padding_values.height_offset =
+ padding_values.height_offset + padding_values.height;
+
+ // Note that quantized inference requires that all tensors have their
+ // parameters set. This is usually done during quantized training.
+ if (data_type != kTfLiteFloat32) {
+ MicroContext* micro_context = GetMicroContext(context);
+
+ TfLiteTensor* input =
+ micro_context->AllocateTempInputTensor(node, kInputTensor);
+ TF_LITE_ENSURE(context, input != nullptr);
+ TfLiteTensor* filter =
+ micro_context->AllocateTempInputTensor(node, kFilterTensor);
+ TF_LITE_ENSURE(context, filter != nullptr);
+ TfLiteTensor* bias =
+ micro_context->AllocateTempInputTensor(node, kBiasTensor);
+ TfLiteTensor* output =
+ micro_context->AllocateTempOutputTensor(node, kOutputTensor);
+ TF_LITE_ENSURE(context, output != nullptr);
+ int output_channels = filter->dims->data[kConvQuantizedDimension];
+
+ TF_LITE_ENSURE_STATUS(tflite::PopulateConvolutionQuantizationParams(
+ context, input, filter, bias, output, kTfLiteActNone,
+ &data->params.output_multiplier, &data->params.output_shift,
+ &data->params.quantized_activation_min,
+ &data->params.quantized_activation_max,
+ data->per_channel_output_multiplier, data->per_channel_output_shift,
+ output_channels));
+
+ // TODO(b/192090531): Remove this once all 8x16 transpose conv models use
+ // 64-bit biases.
+ if (input->type == kTfLiteInt16) {
+ TFLITE_DCHECK(filter->type == kTfLiteInt8);
+ TFLITE_DCHECK(output->type == kTfLiteInt16);
+ if (bias->type == kTfLiteInt16) {
+ TFLITE_DCHECK(
+ context->RequestScratchBufferInArena(
+ context, GetTensorShape(bias).FlatSize() * sizeof(std::int64_t),
+ &(data->bias_converted_buffer_index)) == kTfLiteOk);
+ }
+ }
+
+ micro_context->DeallocateTempTfLiteTensor(input);
+ micro_context->DeallocateTempTfLiteTensor(filter);
+ micro_context->DeallocateTempTfLiteTensor(output);
+ if (bias != nullptr) {
+ micro_context->DeallocateTempTfLiteTensor(bias);
+ }
+ }
+ return kTfLiteOk;
+}
+
+void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+ TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
+ return context->AllocatePersistentBuffer(context, sizeof(OpData));
+}
+
+TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+ TFLITE_DCHECK(node->user_data != nullptr);
+ TFLITE_DCHECK(node->builtin_data != nullptr);
+
+ OpData* data = static_cast<OpData*>(node->user_data);
+ const auto params =
+ static_cast<const TfLiteTransposeConvParams*>(node->builtin_data);
+
+ MicroContext* micro_context = GetMicroContext(context);
+
+ TfLiteTensor* output =
+ micro_context->AllocateTempOutputTensor(node, kOutputTensor);
+ TF_LITE_ENSURE(context, output != nullptr);
+ TfLiteTensor* input =
+ micro_context->AllocateTempInputTensor(node, kInputTensor);
+ TF_LITE_ENSURE(context, input != nullptr);
+ TfLiteTensor* filter =
+ micro_context->AllocateTempInputTensor(node, kFilterTensor);
+ TF_LITE_ENSURE(context, filter != nullptr);
+
+ TF_LITE_ENSURE_EQ(context, input->type, output->type);
+ TF_LITE_ENSURE_MSG(context,
+ input->type == kTfLiteFloat32 ||
+ input->type == kTfLiteInt16 ||
+ input->type == kTfLiteInt8,
+ "Input data type not supported");
+ TF_LITE_ENSURE_MSG(
+ context,
+ (input->type == kTfLiteFloat32 && filter->type == kTfLiteFloat32) ||
+ (input->type == kTfLiteInt16 && filter->type == kTfLiteInt8) ||
+ (input->type == kTfLiteInt8 && filter->type == kTfLiteInt8),
+ "Hybrid models are not supported on TFLite Micro.");
+
+ // Get height and width of the output.
+ const int width = SizeOfDimension(output, 2);
+ const int height = SizeOfDimension(output, 1);
+ const int filter_width = SizeOfDimension(filter, 2);
+ const int filter_height = SizeOfDimension(filter, 1);
+
+ // Dynamically allocate per-channel quantization parameters.
+ const int num_channels = filter->dims->data[kConvQuantizedDimension];
+ data->per_channel_output_multiplier =
+ static_cast<int32_t*>(context->AllocatePersistentBuffer(
+ context, num_channels * sizeof(int32_t)));
+ data->per_channel_output_shift =
+ static_cast<int32_t*>(context->AllocatePersistentBuffer(
+ context, num_channels * sizeof(int32_t)));
+
+ if (input->type == kTfLiteInt8) {
+ TFLITE_DCHECK(context->RequestScratchBufferInArena != nullptr);
+
+ RuntimeShape input_shape = GetTensorShape(input);
+ RuntimeShape output_shape = GetTensorShape(output);
+ RuntimeShape filter_shape = GetTensorShape(filter);
+
+ const int batch_size = MatchingDim(input_shape, 0, output_shape, 0);
+ const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3);
+
+ cmsis_nn_dims output_dims;
+ output_dims.n = batch_size;
+ output_dims.h = output_shape.Dims(1);
+ output_dims.w = output_shape.Dims(2);
+ output_dims.c = output_depth;
+
+#if defined(KERNELS_OPTIMIZED_FOR_SPEED)
+ const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3);
+
+ cmsis_nn_dims input_dims;
+ input_dims.n = batch_size;
+ input_dims.h = input_shape.Dims(1);
+ input_dims.w = input_shape.Dims(2);
+ input_dims.c = input_depth;
+
+ cmsis_nn_dims filter_dims;
+ filter_dims.n = output_depth;
+ filter_dims.h = filter_shape.Dims(1);
+ filter_dims.w = filter_shape.Dims(2);
+ filter_dims.c = input_depth;
+
+ const size_t buf_size = arm_transpose_conv_s8_get_buffer_size(
+ &input_dims, &filter_dims, &output_dims);
+ TFLITE_DCHECK(context->RequestScratchBufferInArena(
+ context, buf_size, &(data->scratch_buffer_index)) ==
+ kTfLiteOk);
+#endif
+
+ // Quantized 8-bit kernels use an int32 scratch buffer.
+ TFLITE_DCHECK(
+ context->RequestScratchBufferInArena(
+ context,
+ output_dims.h * output_dims.w * output_dims.c * sizeof(int32_t),
+ &(data->scratch_buffer_output_index)) == kTfLiteOk);
+ }
+
+ // Quantized 16x8 kernels use an int64 scratch buffer.
+ if (input->type == kTfLiteInt16) {
+ TFLITE_DCHECK(context->RequestScratchBufferInArena != nullptr);
+ TFLITE_DCHECK(context->RequestScratchBufferInArena(
+ context,
+ GetTensorShape(output).FlatSize() * sizeof(std::int64_t),
+ &(data->scratch_buffer_index)) == kTfLiteOk);
+ }
+
+ // All per-channel quantized tensors need valid zero point and scale arrays.
+ if (input->type == kTfLiteInt8 || input->type == kTfLiteInt16) {
+ TF_LITE_ENSURE_EQ(context, filter->quantization.type,
+ kTfLiteAffineQuantization);
+
+ const auto* affine_quantization =
+ static_cast<TfLiteAffineQuantization*>(filter->quantization.params);
+ TF_LITE_ENSURE(context, affine_quantization);
+ TF_LITE_ENSURE(context, affine_quantization->scale);
+ TF_LITE_ENSURE(context, affine_quantization->zero_point);
+
+ TF_LITE_ENSURE(context,
+ affine_quantization->scale->size == 1 ||
+ affine_quantization->scale->size ==
+ filter->dims->data[kConvQuantizedDimension]);
+ TF_LITE_ENSURE_EQ(context, affine_quantization->scale->size,
+ affine_quantization->zero_point->size);
+ }
+
+ TF_LITE_ENSURE_STATUS(CalculateOpData(context, node, params, width, height,
+ filter_width, filter_height,
+ input->type, data));
+
+ // Offsets (zero points)
+ data->params.input_offset = -input->params.zero_point;
+ data->params.weights_offset = -filter->params.zero_point;
+ data->params.output_offset = output->params.zero_point;
+
+ // Stride
+ data->params.stride_width = params->stride_width;
+ data->params.stride_height = params->stride_height;
+
+ micro_context->DeallocateTempTfLiteTensor(output);
+ micro_context->DeallocateTempTfLiteTensor(input);
+ micro_context->DeallocateTempTfLiteTensor(filter);
+ return kTfLiteOk;
+}
+
+#if defined(KERNELS_OPTIMIZED_FOR_SPEED)
+TfLiteStatus EvalQuantizedPerChannel(TfLiteContext* context, TfLiteNode* node,
+ const TfLiteConvParams& params,
+ const OpData& data,
+ const TfLiteEvalTensor* input,
+ const TfLiteEvalTensor* filter,
+ const TfLiteEvalTensor* bias,
+ TfLiteEvalTensor* output) {
+ cmsis_nn_transpose_conv_params conv_params;
+ conv_params.dilation.h = 1;
+ conv_params.dilation.w = 1;
+
+ // Initialize cmsis_nn convolution parameters
+ conv_params.input_offset = data.params.input_offset;
+ conv_params.output_offset = data.params.output_offset;
+ conv_params.stride.h = params.stride_height;
+ conv_params.stride.w = params.stride_width;
+ conv_params.padding.h = data.params.padding_values.height;
+ conv_params.padding.w = data.params.padding_values.width;
+ conv_params.padding_offsets.h = data.params.padding_values.height_offset;
+ conv_params.padding_offsets.w = data.params.padding_values.width_offset;
+ conv_params.activation.min = data.params.quantized_activation_min;
+ conv_params.activation.max = data.params.quantized_activation_max;
+
+ // Initialize cmsis_nn per channel quantization parameters
+ cmsis_nn_per_channel_quant_params quant_params;
+ quant_params.multiplier =
+ const_cast<int32_t*>(data.per_channel_output_multiplier);
+ quant_params.shift = const_cast<int32_t*>(data.per_channel_output_shift);
+
+ RuntimeShape filter_shape = tflite::micro::GetTensorShape(filter);
+ RuntimeShape input_shape = tflite::micro::GetTensorShape(input);
+ RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
+ RuntimeShape bias_shape = tflite::micro::GetTensorShape(bias);
+
+ // Consistency check.
+ TFLITE_DCHECK_LE(conv_params.activation.min, conv_params.activation.max);
+ TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
+ TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
+ TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
+ const int batch_size = MatchingDim(input_shape, 0, output_shape, 0);
+ const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3);
+ const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3);
+ if (tflite::micro::GetOptionalTensorData<int32_t>(bias)) {
+ TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
+ }
+
+ cmsis_nn_dims input_dims;
+ input_dims.n = batch_size;
+ input_dims.h = input_shape.Dims(1);
+ input_dims.w = input_shape.Dims(2);
+ input_dims.c = input_depth;
+
+ cmsis_nn_dims filter_dims;
+ filter_dims.n = output_depth;
+ filter_dims.h = filter_shape.Dims(1);
+ filter_dims.w = filter_shape.Dims(2);
+ filter_dims.c = input_depth;
+
+ cmsis_nn_dims bias_dims;
+ bias_dims.n = 1;
+ bias_dims.h = 1;
+ bias_dims.w = 1;
+ bias_dims.c = output_depth;
+
+ cmsis_nn_dims output_dims;
+ output_dims.n = batch_size;
+ output_dims.h = output_shape.Dims(1);
+ output_dims.w = output_shape.Dims(2);
+ output_dims.c = output_depth;
+
+ cmsis_nn_context ctx;
+ ctx.size = 0; // Note: ctx.size is currently not used in cmsis_nn.
+ ctx.buf = context->GetScratchBuffer(context, data.scratch_buffer_index);
+
+ cmsis_nn_context scratch_output_ctx;
+ scratch_output_ctx.size =
+ 0; // Note: ctx.size is currently not used in cmsis_nn.
+ scratch_output_ctx.buf =
+ context->GetScratchBuffer(context, data.scratch_buffer_output_index);
+
+ TFLITE_DCHECK_EQ(
+ arm_transpose_conv_s8(
+ &ctx, &scratch_output_ctx, &conv_params, &quant_params, &input_dims,
+ tflite::micro::GetTensorData<int8_t>(input), &filter_dims,
+ tflite::micro::GetTensorData<int8_t>(filter), &bias_dims,
+ tflite::micro::GetOptionalTensorData<int32_t>(bias), &output_dims,
+ tflite::micro::GetTensorData<int8_t>(output)),
+ ARM_CMSIS_NN_SUCCESS);
+
+ return kTfLiteOk;
+}
+#endif
+
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+ const TfLiteEvalTensor* input =
+ tflite::micro::GetEvalInput(context, node, kInputTensor);
+ const TfLiteEvalTensor* filter =
+ tflite::micro::GetEvalInput(context, node, kFilterTensor);
+ const TfLiteEvalTensor* bias =
+ (NumInputs(node) == 4)
+ ? tflite::micro::GetEvalInput(context, node, kBiasTensor)
+ : nullptr;
+ TfLiteEvalTensor* output =
+ tflite::micro::GetEvalOutput(context, node, kOutputTensor);
+
+ TFLITE_DCHECK(node->user_data != nullptr);
+ const OpData& data = *(static_cast<const OpData*>(node->user_data));
+
+ TF_LITE_ENSURE_EQ(context, input->type, output->type);
+ const auto& params =
+ *(reinterpret_cast<TfLiteConvParams*>(node->builtin_data));
+
+ switch (input->type) { // Already know in/out types are same.
+ case kTfLiteFloat32: {
+ ConvParams op_params = data.params;
+ CalculateActivationRange(params.activation,
+ &op_params.float_activation_min,
+ &op_params.float_activation_max);
+
+ reference_ops::TransposeConv(
+ op_params, tflite::micro::GetTensorShape(input),
+ tflite::micro::GetTensorData<float>(input),
+ tflite::micro::GetTensorShape(filter),
+ tflite::micro::GetTensorData<float>(filter),
+ tflite::micro::GetTensorShape(bias),
+ tflite::micro::GetOptionalTensorData<float>(bias),
+ tflite::micro::GetTensorShape(output),
+ tflite::micro::GetTensorData<float>(output),
+ tflite::micro::GetTensorShape(nullptr), nullptr);
+ break;
+ }
+ case kTfLiteInt8: {
+#if defined(KERNELS_OPTIMIZED_FOR_SIZE)
+ int32_t* scratch_buffer = static_cast<int32_t*>(
+ context->GetScratchBuffer(context, data.scratch_buffer_index));
+ reference_integer_ops::TransposeConv(
+ data.params, data.per_channel_output_multiplier,
+ data.per_channel_output_shift, tflite::micro::GetTensorShape(input),
+ tflite::micro::GetTensorData<int8_t>(input),
+ tflite::micro::GetTensorShape(filter),
+ tflite::micro::GetTensorData<int8_t>(filter),
+ tflite::micro::GetTensorShape(bias),
+ tflite::micro::GetOptionalTensorData<int32_t>(bias),
+ tflite::micro::GetTensorShape(output),
+ tflite::micro::GetTensorData<int8_t>(output),
+ tflite::micro::GetTensorShape(nullptr), nullptr, scratch_buffer);
+#elif defined(KERNELS_OPTIMIZED_FOR_SPEED)
+ return EvalQuantizedPerChannel(context, node, params, data, input, filter,
+ bias, output);
+#else
+ MicroPrintf(
+ "Either KERNELS_OPTIMIZED_FOR_SIZE or KERNELS_OPTIMIZED_FOR_SPEED "
+ "must be defined");
+ return kTfLiteError;
+#endif
+ break;
+ }
+ case kTfLiteInt16: {
+ std::int64_t* scratch_buffer = static_cast<int64_t*>(
+ context->GetScratchBuffer(context, data.scratch_buffer_index));
+ // TODO(b/192090531): Remove this once all 8x16 transpose conv models use
+ // 64-bit biases.
+ if (bias != nullptr && bias->type == kTfLiteInt16) {
+ std::int64_t* bias_converted_buffer =
+ static_cast<int64_t*>(context->GetScratchBuffer(
+ context, data.bias_converted_buffer_index));
+ for (int i = 0; i < tflite::micro::GetTensorShape(bias).FlatSize();
+ i++) {
+ bias_converted_buffer[i] = bias->data.i16[i];
+ }
+ reference_integer_ops::TransposeConv(
+ data.params, data.per_channel_output_multiplier,
+ data.per_channel_output_shift, tflite::micro::GetTensorShape(input),
+ tflite::micro::GetTensorData<int16_t>(input),
+ tflite::micro::GetTensorShape(filter),
+ tflite::micro::GetTensorData<int8_t>(filter),
+ tflite::micro::GetTensorShape(bias), bias_converted_buffer,
+ tflite::micro::GetTensorShape(output),
+ tflite::micro::GetTensorData<int16_t>(output),
+ tflite::micro::GetTensorShape(nullptr), nullptr, scratch_buffer);
+ } else {
+ reference_integer_ops::TransposeConv(
+ data.params, data.per_channel_output_multiplier,
+ data.per_channel_output_shift, tflite::micro::GetTensorShape(input),
+ tflite::micro::GetTensorData<int16_t>(input),
+ tflite::micro::GetTensorShape(filter),
+ tflite::micro::GetTensorData<int8_t>(filter),
+ tflite::micro::GetTensorShape(bias),
+ tflite::micro::GetOptionalTensorData<std::int64_t>(bias),
+ tflite::micro::GetTensorShape(output),
+ tflite::micro::GetTensorData<int16_t>(output),
+ tflite::micro::GetTensorShape(nullptr), nullptr, scratch_buffer);
+ }
+ break;
+ }
+ default:
+ MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
+ input->type);
+ return kTfLiteError;
+ }
+ return kTfLiteOk;
+}
+
+TfLiteStatus EvalInt8(TfLiteContext* context, TfLiteNode* node) {
+ const TfLiteEvalTensor* input =
+ tflite::micro::GetEvalInput(context, node, kInputTensor);
+ const TfLiteEvalTensor* filter =
+ tflite::micro::GetEvalInput(context, node, kFilterTensor);
+ const TfLiteEvalTensor* bias =
+ (NumInputs(node) == 4)
+ ? tflite::micro::GetEvalInput(context, node, kBiasTensor)
+ : nullptr;
+ TfLiteEvalTensor* output =
+ tflite::micro::GetEvalOutput(context, node, kOutputTensor);
+
+ TFLITE_DCHECK(node->user_data != nullptr);
+ const OpData& data = *(static_cast<const OpData*>(node->user_data));
+
+#if defined(KERNELS_OPTIMIZED_FOR_SIZE)
+ int32_t* scratch_buffer = static_cast<int32_t*>(
+ context->GetScratchBuffer(context, data.scratch_buffer_index));
+ reference_integer_ops::TransposeConv(
+ data.params, data.per_channel_output_multiplier,
+ data.per_channel_output_shift, tflite::micro::GetTensorShape(input),
+ tflite::micro::GetTensorData<int8_t>(input),
+ tflite::micro::GetTensorShape(filter),
+ tflite::micro::GetTensorData<int8_t>(filter),
+ tflite::micro::GetTensorShape(bias),
+ tflite::micro::GetOptionalTensorData<int32_t>(bias),
+ tflite::micro::GetTensorShape(output),
+ tflite::micro::GetTensorData<int8_t>(output),
+ tflite::micro::GetTensorShape(nullptr), nullptr, scratch_buffer);
+#elif defined(KERNELS_OPTIMIZED_FOR_SPEED)
+ const auto& params =
+ *(reinterpret_cast<TfLiteConvParams*>(node->builtin_data));
+
+ return EvalQuantizedPerChannel(context, node, params, data, input, filter,
+ bias, output);
+#else
+ MicroPrintf(
+ "Either KERNELS_OPTIMIZED_FOR_SIZE or KERNELS_OPTIMIZED_FOR_SPEED must "
+ "be defined");
+ return kTfLiteError;
+#endif
+ return kTfLiteOk;
+}
+
+} // namespace
+
+TFLMRegistration Register_TRANSPOSE_CONV() {
+ return tflite::micro::RegisterOp(Init, Prepare, Eval);
+}
+
+TFLMRegistration Register_TRANSPOSE_CONV_INT8() {
+ return tflite::micro::RegisterOp(Init, Prepare, EvalInt8);
+}
+
+} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/cmsis_nn/unidirectional_sequence_lstm.cc b/tensorflow/lite/micro/kernels/cmsis_nn/unidirectional_sequence_lstm.cc
index f66ce80..49da4d9 100644
--- a/tensorflow/lite/micro/kernels/cmsis_nn/unidirectional_sequence_lstm.cc
+++ b/tensorflow/lite/micro/kernels/cmsis_nn/unidirectional_sequence_lstm.cc
@@ -1,4 +1,4 @@
-/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -28,477 +28,300 @@
#include "tensorflow/lite/micro/kernels/lstm_eval.h"
#include "tensorflow/lite/micro/kernels/lstm_shared.h"
#include "tensorflow/lite/micro/kernels/micro_tensor_utils.h"
-
namespace tflite {
namespace {
struct OpData {
- OpDataLSTM params_ref;
- cmsis_nn_lstm_params params_cmsis_nn;
+ OpDataLSTM params_ref; // Used for fallback implementation
+ cmsis_nn_lstm_params params_cmsis_nn; // Used for CMSIS-NN implementation
};
-/*Helper Functions*/
-TfLiteStatus PrecomputeZeroPointTimesWeightWithBias(
- TfLiteContext* context, int32_t zero_point,
- const TfLiteTensor* weight_tensor, const TfLiteTensor* bias_tensor,
- int32_t** output) {
- if (weight_tensor == nullptr) {
- return kTfLiteOk;
- }
+LSTMBuffers<int16_t> CMSIS_NN_CreateLSTMBuffers(TfLiteContext* context,
+ const int* buffer_indices) {
+ LSTMBuffers<int16_t> buffers;
+ buffers.buffer0 = reinterpret_cast<int16_t*>(
+ context->GetScratchBuffer(context, buffer_indices[0]));
+ buffers.buffer1 = reinterpret_cast<int16_t*>(
+ context->GetScratchBuffer(context, buffer_indices[1]));
+ buffers.buffer2 = reinterpret_cast<int16_t*>(
+ context->GetScratchBuffer(context, buffer_indices[2]));
- const RuntimeShape& weight_shape = GetTensorShape(weight_tensor);
- TF_LITE_ENSURE_EQ(context, weight_shape.DimensionsCount(), 2);
- const int row = weight_shape.Dims(0);
- const int col = weight_shape.Dims(1);
- TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
- *output = static_cast<int32_t*>(
- context->AllocatePersistentBuffer(context, row * sizeof(int32_t)));
-
- if (bias_tensor == nullptr) {
- memset(*output, 0, row * sizeof(int32_t));
- } else {
- const int32_t* bias = GetTensorData<int32_t>(bias_tensor);
- memcpy(*output, bias, row * sizeof(int32_t));
- }
-
- if (zero_point != 0) {
- const int8_t* weight = GetTensorData<int8_t>(weight_tensor);
- tflite::tensor_utils::MatrixScalarMultiplyAccumulate(weight, zero_point,
- row, col, *output);
- }
- return kTfLiteOk;
+ return buffers;
}
-TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node,
- const LstmTensors& lstm_tensors, OpData* op_data) {
- const TfLiteTensor* input = lstm_tensors.GetInternalTensor(kLstmInputTensor);
- const TfLiteTensor* output_state =
- lstm_tensors.GetInternalTensor(tflite::kLstmOutputStateTensor);
+void CMSIS_NN_VectorSum(int32_t* kernel_sum, const int32_t size1,
+ const int32_t size2, const int8_t* weights,
+ const int32_t offset, const int32_t* biases) {
+ arm_vector_sum_s8(kernel_sum, size1, size2, weights, offset, biases);
+}
- TF_LITE_ENSURE(context, input->type == kTfLiteInt8);
+void CMSIS_NN_VectorSum(int64_t* kernel_sum, const int32_t size1,
+ const int32_t size2, const int8_t* weights,
+ const int32_t offset, const int64_t* biases) {
+ arm_vector_sum_s8_s64(kernel_sum, size1, size2, weights, offset, biases);
+}
- op_data->params_cmsis_nn.output_state_offset =
- output_state->params.zero_point;
+template <typename BiasType>
+TfLiteStatus CMSIS_NN_PortOpData(TfLiteContext* context, OpDataLSTM* params_ref,
+ const LSTMKernelContents& kernel_content,
+ cmsis_nn_lstm_params* params_cmsis_nn) {
+ // Unwrap pointers
+ const BiasType* input_gate_bias =
+ tflite::micro::GetOptionalTensorData<BiasType>(
+ kernel_content.GetInternalTensor(tflite::kLstmInputGateBiasTensor));
+ const BiasType* forget_gate_bias =
+ tflite::micro::GetOptionalTensorData<BiasType>(
+ kernel_content.GetInternalTensor(tflite::kLstmForgetGateBiasTensor));
+ const BiasType* cell_gate_bias =
+ tflite::micro::GetOptionalTensorData<BiasType>(
+ kernel_content.GetInternalTensor(tflite::kLstmCellGateBiasTensor));
+ const BiasType* output_gate_bias =
+ tflite::micro::GetOptionalTensorData<BiasType>(
+ kernel_content.GetInternalTensor(tflite::kLstmOutputGateBiasTensor));
- const TfLiteTensor* input_to_forget_weights =
- lstm_tensors.GetInternalTensor(kLstmInputToForgetWeightsTensor);
- const TfLiteTensor* input_to_input_weights =
- lstm_tensors.GetInternalTensor(kLstmInputToInputWeightsTensor);
- const TfLiteTensor* input_to_output_weights =
- lstm_tensors.GetInternalTensor(kLstmInputToOutputWeightsTensor);
- const TfLiteTensor* input_to_cell_weights =
- lstm_tensors.GetInternalTensor(kLstmInputToCellWeightsTensor);
- const TfLiteTensor* forget_gate_bias =
- lstm_tensors.GetInternalTensor(kLstmForgetGateBiasTensor);
- const TfLiteTensor* cell_state =
- lstm_tensors.GetInternalTensor(kLstmCellStateTensor);
+ const int8_t* input_to_input_weights =
+ tflite::micro::GetOptionalTensorData<int8_t>(
+ kernel_content.GetInternalTensor(
+ tflite::kLstmInputToInputWeightsTensor));
+ const int8_t* input_to_forget_weights =
+ tflite::micro::GetOptionalTensorData<int8_t>(
+ kernel_content.GetInternalTensor(
+ tflite::kLstmInputToForgetWeightsTensor));
+ const int8_t* input_to_cell_weights =
+ tflite::micro::GetOptionalTensorData<int8_t>(
+ kernel_content.GetInternalTensor(
+ tflite::kLstmInputToCellWeightsTensor));
+ const int8_t* input_to_output_weights =
+ tflite::micro::GetOptionalTensorData<int8_t>(
+ kernel_content.GetInternalTensor(
+ tflite::kLstmInputToOutputWeightsTensor));
- const TfLiteTensor* cell_gate_bias =
- lstm_tensors.GetInternalTensor(kLstmCellGateBiasTensor);
- const TfLiteTensor* output_gate_bias =
- lstm_tensors.GetInternalTensor(kLstmOutputGateBiasTensor);
- const TfLiteTensor* input_gate_bias =
- lstm_tensors.GetInternalTensor(kLstmInputGateBiasTensor);
- const TfLiteTensor* recurrent_to_forget_weights =
- lstm_tensors.GetInternalTensor(kLstmRecurrentToForgetWeightsTensor);
- const TfLiteTensor* recurrent_to_cell_weights =
- lstm_tensors.GetInternalTensor(kLstmRecurrentToCellWeightsTensor);
- const TfLiteTensor* recurrent_to_output_weights =
- lstm_tensors.GetInternalTensor(kLstmRecurrentToOutputWeightsTensor);
- const TfLiteTensor* recurrent_to_input_weights =
- lstm_tensors.GetInternalTensor(kLstmRecurrentToInputWeightsTensor);
- const TfLiteTensor* cell_to_output_weights =
- lstm_tensors.GetInternalTensor(kLstmCellToOutputWeightsTensor);
- const TfLiteTensor* forget_layer_norm_coefficients =
- lstm_tensors.GetInternalTensor(kLstmForgetLayerNormCoefficientsTensor);
- const TfLiteTensor* projection_weights =
- lstm_tensors.GetInternalTensor(kLstmProjectionWeightsTensor);
+ const int8_t* recurrent_to_input_weights =
+ tflite::micro::GetOptionalTensorData<int8_t>(
+ kernel_content.GetInternalTensor(
+ tflite::kLstmRecurrentToInputWeightsTensor));
+ const int8_t* recurrent_to_forget_weights =
+ tflite::micro::GetOptionalTensorData<int8_t>(
+ kernel_content.GetInternalTensor(
+ tflite::kLstmRecurrentToForgetWeightsTensor));
+ const int8_t* recurrent_to_cell_weights =
+ tflite::micro::GetOptionalTensorData<int8_t>(
+ kernel_content.GetInternalTensor(
+ tflite::kLstmRecurrentToCellWeightsTensor));
+ const int8_t* recurrent_to_output_weights =
+ tflite::micro::GetOptionalTensorData<int8_t>(
+ kernel_content.GetInternalTensor(
+ tflite::kLstmRecurrentToOutputWeightsTensor));
- const bool use_layer_norm = (forget_layer_norm_coefficients != nullptr);
- const bool use_peephole = (cell_to_output_weights != nullptr);
- const bool use_projection = (projection_weights != nullptr);
- const bool use_cifg = (input_to_input_weights == nullptr);
- const bool lstm_unsupported_config =
- use_layer_norm || use_peephole || use_projection || use_cifg;
- TFLITE_DCHECK(!lstm_unsupported_config);
+ int32_t size_data = params_ref->size_info.input_dimension;
+ int32_t size_hidden = params_ref->size_info.state_dimension;
- // Pre-calculate bias + zero_point * weight.
- int32_t* input_to_forget_effective_bias = nullptr;
- int32_t* recurrent_to_forget_effective_bias = nullptr;
- int32_t* input_to_cell_effective_bias = nullptr;
- int32_t* recurrent_to_cell_effective_bias = nullptr;
- int32_t* input_to_output_effective_bias = nullptr;
- int32_t* recurrent_to_output_effective_bias = nullptr;
- int32_t* input_to_input_effective_bias = nullptr;
- int32_t* recurrent_to_input_effective_bias = nullptr;
+ BiasType* input_data_kernel_sum{
+ static_cast<BiasType*>(context->AllocatePersistentBuffer(
+ context, size_hidden * sizeof(BiasType)))};
+ BiasType* forget_data_kernel_sum{
+ static_cast<BiasType*>(context->AllocatePersistentBuffer(
+ context, size_hidden * sizeof(BiasType)))};
+ BiasType* cell_data_kernel_sum{
+ static_cast<BiasType*>(context->AllocatePersistentBuffer(
+ context, size_hidden * sizeof(BiasType)))};
+ BiasType* output_data_kernel_sum{
+ static_cast<BiasType*>(context->AllocatePersistentBuffer(
+ context, size_hidden * sizeof(BiasType)))};
- const int32_t output_state_zero_point =
- -op_data->params_cmsis_nn.output_state_offset;
- const int32_t input_zero_point = -input->params.zero_point;
+ BiasType* input_hidden_kernel_sum{
+ static_cast<BiasType*>(context->AllocatePersistentBuffer(
+ context, size_hidden * sizeof(BiasType)))};
+ BiasType* forget_hidden_kernel_sum{
+ static_cast<BiasType*>(context->AllocatePersistentBuffer(
+ context, size_hidden * sizeof(BiasType)))};
+ BiasType* cell_hidden_kernel_sum = {
+ static_cast<BiasType*>(context->AllocatePersistentBuffer(
+ context, size_hidden * sizeof(BiasType)))};
+ BiasType* output_hidden_kernel_sum = {
+ static_cast<BiasType*>(context->AllocatePersistentBuffer(
+ context, size_hidden * sizeof(BiasType)))};
- TF_LITE_ENSURE_OK(context,
- PrecomputeZeroPointTimesWeightWithBias(
- context, input_zero_point, input_to_forget_weights,
- forget_gate_bias, &input_to_forget_effective_bias));
+ // Compute effective biases
+ CMSIS_NN_VectorSum(
+ input_data_kernel_sum, size_data, size_hidden, input_to_input_weights,
+ params_ref->input_gate_parameters.input_fc_params.input_offset,
+ input_gate_bias);
- TF_LITE_ENSURE_OK(context, PrecomputeZeroPointTimesWeightWithBias(
- context, output_state_zero_point,
- recurrent_to_forget_weights, nullptr,
- &recurrent_to_forget_effective_bias));
+ CMSIS_NN_VectorSum(
+ forget_data_kernel_sum, size_data, size_hidden, input_to_forget_weights,
+ params_ref->forget_gate_parameters.input_fc_params.input_offset,
+ forget_gate_bias);
- // Modulation gate.
- TF_LITE_ENSURE_OK(context,
- PrecomputeZeroPointTimesWeightWithBias(
- context, input_zero_point, input_to_cell_weights,
- cell_gate_bias, &input_to_cell_effective_bias));
- TF_LITE_ENSURE_OK(
- context, PrecomputeZeroPointTimesWeightWithBias(
- context, output_state_zero_point, recurrent_to_cell_weights,
- nullptr, &recurrent_to_cell_effective_bias));
+ CMSIS_NN_VectorSum(
+ cell_data_kernel_sum, size_data, size_hidden, input_to_cell_weights,
+ params_ref->cell_gate_parameters.input_fc_params.input_offset,
+ cell_gate_bias);
- // Output gate.
- TF_LITE_ENSURE_OK(context,
- PrecomputeZeroPointTimesWeightWithBias(
- context, input_zero_point, input_to_output_weights,
- output_gate_bias, &input_to_output_effective_bias));
+ CMSIS_NN_VectorSum(
+ output_data_kernel_sum, size_data, size_hidden, input_to_output_weights,
+ params_ref->output_gate_parameters.input_fc_params.input_offset,
+ output_gate_bias);
- TF_LITE_ENSURE_OK(context, PrecomputeZeroPointTimesWeightWithBias(
- context, output_state_zero_point,
- recurrent_to_output_weights, nullptr,
- &recurrent_to_output_effective_bias));
+ CMSIS_NN_VectorSum(
+ input_hidden_kernel_sum, size_hidden, size_hidden,
+ recurrent_to_input_weights,
+ -params_ref->inter_gate_parameters.output_mul_params.output_offset,
+ nullptr);
- // Input gate. The calculation is only meaningful for non-cifg case.
- TF_LITE_ENSURE_OK(context,
- PrecomputeZeroPointTimesWeightWithBias(
- context, input_zero_point, input_to_input_weights,
- input_gate_bias, &input_to_input_effective_bias));
- TF_LITE_ENSURE_OK(
- context, PrecomputeZeroPointTimesWeightWithBias(
- context, output_state_zero_point, recurrent_to_input_weights,
- nullptr, &recurrent_to_input_effective_bias));
+ CMSIS_NN_VectorSum(
+ forget_hidden_kernel_sum, size_hidden, size_hidden,
+ recurrent_to_forget_weights,
+ -params_ref->inter_gate_parameters.output_mul_params.output_offset,
+ nullptr);
- op_data->params_cmsis_nn.i2f_effective_bias = input_to_forget_effective_bias;
- op_data->params_cmsis_nn.r2f_effective_bias =
- recurrent_to_forget_effective_bias;
- op_data->params_cmsis_nn.i2c_effective_bias = input_to_cell_effective_bias;
- op_data->params_cmsis_nn.r2c_effective_bias =
- recurrent_to_cell_effective_bias;
- op_data->params_cmsis_nn.i2o_effective_bias = input_to_output_effective_bias;
- op_data->params_cmsis_nn.r2o_effective_bias =
- recurrent_to_output_effective_bias;
- op_data->params_cmsis_nn.i2i_effective_bias = input_to_input_effective_bias;
- op_data->params_cmsis_nn.r2i_effective_bias =
- recurrent_to_input_effective_bias;
+ CMSIS_NN_VectorSum(
+ cell_hidden_kernel_sum, size_hidden, size_hidden,
+ recurrent_to_cell_weights,
+ -params_ref->inter_gate_parameters.output_mul_params.output_offset,
+ nullptr);
- // Get intermediate scales and zero points.
- float intermediate_scale[5];
- int32_t intermediate_zp[5];
- for (int i = 0; i < 4; ++i) {
- // Q3.12 for activation functions.
- intermediate_scale[i] = std::pow(2.0f, -12.0f);
- intermediate_zp[i] = 0;
- }
+ CMSIS_NN_VectorSum(
+ output_hidden_kernel_sum, size_hidden, size_hidden,
+ recurrent_to_output_weights,
+ -params_ref->inter_gate_parameters.output_mul_params.output_offset,
+ nullptr);
- MicroContext* micro_context = GetMicroContext(context);
- // In the absence of projection, hidden becomes otuput and this intermediate
- // is ignored.
- TfLiteTensor* hidden = micro_context->AllocateTempIntermediateTensor(node, 4);
- TF_LITE_ENSURE(context, hidden->quantization.type != kTfLiteNoQuantization);
- auto* hidden_params =
- static_cast<TfLiteAffineQuantization*>(hidden->quantization.params);
- intermediate_scale[4] = hidden_params->scale->data[0];
- intermediate_zp[4] = hidden_params->zero_point->data[0];
- if (hidden != nullptr) {
- micro_context->DeallocateTempTfLiteTensor(hidden);
- }
+ // Create input gate parameters
+ cmsis_nn_lstm_gate gate_input{
+ params_ref->input_gate_parameters.input_fc_params.output_multiplier,
+ params_ref->input_gate_parameters.input_fc_params.output_shift,
+ input_to_input_weights,
+ input_data_kernel_sum,
+ params_ref->input_gate_parameters.recurrent_fc_params.output_multiplier,
+ params_ref->input_gate_parameters.recurrent_fc_params.output_shift,
+ recurrent_to_input_weights,
+ input_hidden_kernel_sum,
+ input_gate_bias,
+ ARM_SIGMOID};
- // Scales.
- const float default_scale = 1.0;
- float input_scale = default_scale;
- float input_to_input_weight_scale = default_scale;
- float recurrent_to_input_weight_scale = default_scale;
- float input_to_forget_weight_scale = default_scale;
- float recurrent_to_forget_weight_scale = default_scale;
- float input_to_cell_weight_scale = default_scale;
- float recurrent_to_cell_weight_scale = default_scale;
- float input_to_output_weight_scale = default_scale;
- float recurrent_to_output_weight_scale = default_scale;
- float output_state_scale = default_scale;
- int cell_scale = 1;
+ // Create forget gate parameters
+ cmsis_nn_lstm_gate gate_forget{
+ params_ref->forget_gate_parameters.input_fc_params.output_multiplier,
+ params_ref->forget_gate_parameters.input_fc_params.output_shift,
+ input_to_forget_weights,
+ forget_data_kernel_sum,
+ params_ref->forget_gate_parameters.recurrent_fc_params.output_multiplier,
+ params_ref->forget_gate_parameters.recurrent_fc_params.output_shift,
+ recurrent_to_forget_weights,
+ forget_hidden_kernel_sum,
+ forget_gate_bias,
+ ARM_SIGMOID};
- // Effective scales.
- float effective_input_to_input_scale = default_scale;
- float effective_recurrent_to_input_scale = default_scale;
- float effective_cell_to_input_scale = default_scale;
- float effective_input_to_forget_scale = default_scale;
- float effective_recurrent_to_forget_scale = default_scale;
- float effective_cell_to_forget_scale = default_scale;
- float effective_input_to_cell_scale = default_scale;
- float effective_recurrent_to_cell_scale = default_scale;
- float effective_input_to_output_scale = default_scale;
- float effective_recurrent_to_output_scale = default_scale;
- float effective_cell_to_output_scale = default_scale;
- float effective_hidden_scale = default_scale;
+ auto cell_gate_nonlinear_type =
+ (params_ref->cell_gate_nonlinear_type == kTfLiteActTanh) ? ARM_TANH
+ : ARM_SIGMOID;
+ // Create cell gate parameters
+ cmsis_nn_lstm_gate gate_cell{
+ params_ref->cell_gate_parameters.input_fc_params.output_multiplier,
+ params_ref->cell_gate_parameters.input_fc_params.output_shift,
+ input_to_cell_weights,
+ cell_data_kernel_sum,
+ params_ref->cell_gate_parameters.recurrent_fc_params.output_multiplier,
+ params_ref->cell_gate_parameters.recurrent_fc_params.output_shift,
+ recurrent_to_cell_weights,
+ cell_hidden_kernel_sum,
+ cell_gate_bias,
+ cell_gate_nonlinear_type};
- // Populate scales.
- input_to_input_weight_scale = input_to_input_weights->params.scale;
- recurrent_to_input_weight_scale = recurrent_to_input_weights->params.scale;
+ // Create output gate parameters
+ cmsis_nn_lstm_gate gate_output{
+ params_ref->output_gate_parameters.input_fc_params.output_multiplier,
+ params_ref->output_gate_parameters.input_fc_params.output_shift,
+ input_to_output_weights,
+ output_data_kernel_sum,
+ params_ref->output_gate_parameters.recurrent_fc_params.output_multiplier,
+ params_ref->output_gate_parameters.recurrent_fc_params.output_shift,
+ recurrent_to_output_weights,
+ output_hidden_kernel_sum,
+ output_gate_bias,
+ ARM_SIGMOID};
- output_state_scale = output_state->params.scale;
-
- input_to_forget_weight_scale = input_to_forget_weights->params.scale;
- input_to_cell_weight_scale = input_to_cell_weights->params.scale;
- input_to_output_weight_scale = input_to_output_weights->params.scale;
- recurrent_to_forget_weight_scale = recurrent_to_forget_weights->params.scale;
- recurrent_to_cell_weight_scale = recurrent_to_cell_weights->params.scale;
- recurrent_to_output_weight_scale = recurrent_to_output_weights->params.scale;
-
- // Check cell state (already used above)
- TF_LITE_ENSURE(context, CheckedLog2(cell_state->params.scale, &cell_scale));
- TF_LITE_ENSURE(context, cell_scale <= -9);
-
- op_data->params_cmsis_nn.cell_state_shift = cell_scale;
- input_scale = input->params.scale;
-
- // Calculate effective scales.
- effective_input_to_input_scale =
- input_to_input_weight_scale * input_scale / intermediate_scale[0];
- effective_recurrent_to_input_scale = recurrent_to_input_weight_scale *
- output_state_scale /
- intermediate_scale[0];
-
- effective_input_to_forget_scale =
- input_to_forget_weight_scale * input_scale / intermediate_scale[1];
- effective_recurrent_to_forget_scale = recurrent_to_forget_weight_scale *
- output_state_scale /
- intermediate_scale[1];
-
- effective_input_to_cell_scale =
- input_to_cell_weight_scale * input_scale / intermediate_scale[2];
- effective_recurrent_to_cell_scale = recurrent_to_cell_weight_scale *
- output_state_scale /
- intermediate_scale[2];
-
- effective_input_to_output_scale =
- input_to_output_weight_scale * input_scale / intermediate_scale[3];
- effective_recurrent_to_output_scale = recurrent_to_output_weight_scale *
- output_state_scale /
- intermediate_scale[3];
-
- effective_hidden_scale =
- std::pow(2.0f, -15.0f) / intermediate_scale[4] * std::pow(2.0f, -15.0f);
-
- // Decompose scales.
- int shift_output;
- QuantizeMultiplier(
- static_cast<double>(effective_input_to_input_scale),
- &op_data->params_cmsis_nn.input_to_input_scaling.multiplier,
- &shift_output);
- op_data->params_cmsis_nn.input_to_input_scaling.shift =
- static_cast<int32_t>(shift_output);
-
- QuantizeMultiplier(
- static_cast<double>(effective_recurrent_to_input_scale),
- &op_data->params_cmsis_nn.recurrent_to_input_scaling.multiplier,
- &shift_output);
- op_data->params_cmsis_nn.recurrent_to_input_scaling.shift =
- static_cast<int32_t>(shift_output);
- QuantizeMultiplier(static_cast<double>(effective_cell_to_input_scale),
- &op_data->params_cmsis_nn.cell_to_input_scaling.multiplier,
- &shift_output);
- op_data->params_cmsis_nn.cell_to_input_scaling.shift =
- static_cast<int32_t>(shift_output);
- QuantizeMultiplier(
- static_cast<double>(effective_input_to_forget_scale),
- &op_data->params_cmsis_nn.input_to_forget_scaling.multiplier,
- &shift_output);
- op_data->params_cmsis_nn.input_to_forget_scaling.shift =
- static_cast<int32_t>(shift_output);
- QuantizeMultiplier(
- static_cast<double>(effective_recurrent_to_forget_scale),
- &op_data->params_cmsis_nn.recurrent_to_forget_scaling.multiplier,
- &shift_output);
- op_data->params_cmsis_nn.recurrent_to_forget_scaling.shift =
- static_cast<int32_t>(shift_output);
- QuantizeMultiplier(
- static_cast<double>(effective_cell_to_forget_scale),
- &op_data->params_cmsis_nn.cell_to_forget_scaling.multiplier,
- &shift_output);
- // ok
- op_data->params_cmsis_nn.cell_to_forget_scaling.shift =
- static_cast<int32_t>(shift_output);
- QuantizeMultiplier(static_cast<double>(effective_input_to_cell_scale),
- &op_data->params_cmsis_nn.input_to_cell_scaling.multiplier,
- &shift_output);
- op_data->params_cmsis_nn.input_to_cell_scaling.shift =
- static_cast<int32_t>(shift_output);
- QuantizeMultiplier(
- static_cast<double>(effective_recurrent_to_cell_scale),
- &op_data->params_cmsis_nn.recurrent_to_cell_scaling.multiplier,
- &shift_output);
- op_data->params_cmsis_nn.recurrent_to_cell_scaling.shift =
- static_cast<int32_t>(shift_output);
- QuantizeMultiplier(
- static_cast<double>(effective_input_to_output_scale),
- &op_data->params_cmsis_nn.input_to_output_scaling.multiplier,
- &shift_output);
- op_data->params_cmsis_nn.input_to_output_scaling.shift =
- static_cast<int32_t>(shift_output);
- QuantizeMultiplier(
- static_cast<double>(effective_recurrent_to_output_scale),
- &op_data->params_cmsis_nn.recurrent_to_output_scaling.multiplier,
- &shift_output);
- op_data->params_cmsis_nn.recurrent_to_output_scaling.shift =
- static_cast<int32_t>(shift_output);
- QuantizeMultiplier(
- static_cast<double>(effective_cell_to_output_scale),
- &op_data->params_cmsis_nn.cell_to_output_scaling.multiplier,
- &shift_output);
- op_data->params_cmsis_nn.cell_to_output_scaling.shift =
- static_cast<int32_t>(shift_output);
-
- op_data->params_cmsis_nn.projection_scaling.shift =
- static_cast<int32_t>(shift_output);
-
- QuantizeMultiplier(static_cast<double>(effective_hidden_scale),
- &op_data->params_cmsis_nn.hidden_scaling.multiplier,
- &shift_output);
- op_data->params_cmsis_nn.hidden_scaling.shift =
- static_cast<int32_t>(shift_output);
-
- op_data->params_cmsis_nn.hidden_offset = intermediate_zp[4];
-
- op_data->params_cmsis_nn.activation.min = std::numeric_limits<int16_t>::min();
- op_data->params_cmsis_nn.activation.max = std::numeric_limits<int16_t>::max();
+ // Create the complete lstm data struct
+ *params_cmsis_nn = {
+ params_ref->size_info.time_major,
+ params_ref->size_info.batch_size,
+ params_ref->size_info.time_steps,
+ params_ref->size_info.input_dimension,
+ params_ref->size_info.state_dimension,
+ params_ref->forget_gate_parameters.input_fc_params.input_offset,
+ params_ref->inter_gate_parameters.forget_cell_mul_params
+ .output_multiplier,
+ params_ref->inter_gate_parameters.forget_cell_mul_params.output_shift,
+ params_ref->inter_gate_parameters.input_mul_params.output_multiplier,
+ params_ref->inter_gate_parameters.input_mul_params.output_shift,
+ params_ref->cell_state_info.quantized_cell_clip,
+ params_ref->cell_state_info.cell_state_scale_power,
+ params_ref->inter_gate_parameters.output_mul_params.output_multiplier,
+ params_ref->inter_gate_parameters.output_mul_params.output_shift,
+ params_ref->inter_gate_parameters.output_mul_params.output_offset,
+ gate_forget,
+ gate_input,
+ gate_cell,
+ gate_output};
return kTfLiteOk;
}
-template <typename CellType>
TfLiteStatus CMSIS_NN_EvalInteger8x8_16Lstm(
const OpData& op_data, const LSTMKernelContents& kernel_content,
- const LSTMBuffers<CellType>& buffers) {
- const OpDataLSTM& op_data_lstm = op_data.params_ref;
- const TfLiteEvalTensor* input =
- kernel_content.GetInternalTensor(tflite::kLstmInputTensor);
- const TfLiteEvalTensor* input_gate_bias =
- kernel_content.GetInternalTensor(tflite::kLstmInputGateBiasTensor);
- const TfLiteEvalTensor* forget_gate_bias =
- kernel_content.GetInternalTensor(tflite::kLstmForgetGateBiasTensor);
- const TfLiteEvalTensor* cell_gate_bias =
- kernel_content.GetInternalTensor(tflite::kLstmCellGateBiasTensor);
- const TfLiteEvalTensor* output_gate_bias =
- kernel_content.GetInternalTensor(tflite::kLstmOutputGateBiasTensor);
- const TfLiteEvalTensor* input_to_output_weights =
- kernel_content.GetInternalTensor(tflite::kLstmInputToOutputWeightsTensor);
- const TfLiteEvalTensor* recurrent_to_output_weights =
- kernel_content.GetInternalTensor(
- tflite::kLstmRecurrentToOutputWeightsTensor);
- const TfLiteEvalTensor* input_to_input_weights =
- kernel_content.GetInternalTensor(tflite::kLstmInputToInputWeightsTensor);
- const TfLiteEvalTensor* input_to_forget_weights =
- kernel_content.GetInternalTensor(tflite::kLstmInputToForgetWeightsTensor);
- const TfLiteEvalTensor* input_to_cell_weights =
- kernel_content.GetInternalTensor(tflite::kLstmInputToCellWeightsTensor);
- const TfLiteEvalTensor* recurrent_to_input_weights =
- kernel_content.GetInternalTensor(
- tflite::kLstmRecurrentToInputWeightsTensor);
- const TfLiteEvalTensor* recurrent_to_forget_weights =
- kernel_content.GetInternalTensor(
- tflite::kLstmRecurrentToForgetWeightsTensor);
- const TfLiteEvalTensor* recurrent_to_cell_weights =
- kernel_content.GetInternalTensor(
- tflite::kLstmRecurrentToCellWeightsTensor);
- const TfLiteEvalTensor* cell_to_input_weights =
- kernel_content.GetInternalTensor(tflite::kLstmCellToInputWeightsTensor);
- const TfLiteEvalTensor* cell_to_forget_weights =
- kernel_content.GetInternalTensor(tflite::kLstmCellToForgetWeightsTensor);
- const TfLiteEvalTensor* cell_to_output_weights =
- kernel_content.GetInternalTensor(tflite::kLstmCellToOutputWeightsTensor);
- const TfLiteEvalTensor* cell_state =
- kernel_content.GetInternalTensor(tflite::kLstmCellStateTensor);
- const TfLiteEvalTensor* output_state =
- kernel_content.GetInternalTensor(tflite::kLstmOutputStateTensor);
- const TfLiteEvalTensor* output = kernel_content.output_tensor;
+ const LSTMBuffers<int16_t>& buffers) {
+ TFLITE_DCHECK(
+ kernel_content.GetInternalTensor(tflite::kLstmInputTensor)->dims->size >=
+ 2 &&
+ kernel_content.GetInternalTensor(tflite::kLstmInputTensor)->dims->size <=
+ 3);
- TFLITE_DCHECK(input->dims->size >= 2 && input->dims->size <= 3);
+ const int8_t* input = tflite::micro::GetOptionalTensorData<int8_t>(
+ kernel_content.GetInternalTensor(tflite::kLstmInputTensor));
+ int8_t* output =
+ tflite::micro::GetTensorData<int8_t>(kernel_content.output_tensor);
- cmsis_nn_lstm_context scratch_buffers;
- scratch_buffers.input_gate = reinterpret_cast<int16_t*>(buffers.buffer0);
- scratch_buffers.forget_gate = reinterpret_cast<int16_t*>(buffers.buffer1);
- scratch_buffers.cell_gate = reinterpret_cast<int16_t*>(buffers.buffer2);
- scratch_buffers.output_gate = reinterpret_cast<int16_t*>(buffers.buffer3);
+ // Create lstm buffer struct
+ cmsis_nn_lstm_context cmsis_buffers;
+ cmsis_buffers.temp1 = reinterpret_cast<int16_t*>(buffers.buffer0);
+ cmsis_buffers.temp2 = reinterpret_cast<int16_t*>(buffers.buffer1);
+ cmsis_buffers.cell_state = reinterpret_cast<int16_t*>(buffers.buffer2);
- cmsis_nn_lstm_params cmsis_lstm_params = op_data.params_cmsis_nn;
- cmsis_lstm_params.time_major = op_data_lstm.size_info.time_major;
- cmsis_lstm_params.clip.cell =
- op_data_lstm.cell_state_info.quantized_cell_clip;
+ arm_lstm_unidirectional_s8(input, output, &op_data.params_cmsis_nn,
+ &cmsis_buffers);
- cmsis_lstm_params.input_gate_bias = const_cast<int32_t*>(
- tflite::micro::GetOptionalTensorData<int32_t>(input_gate_bias));
- cmsis_lstm_params.forget_gate_bias = const_cast<int32_t*>(
- tflite::micro::GetOptionalTensorData<int32_t>(forget_gate_bias));
- cmsis_lstm_params.cell_gate_bias = const_cast<int32_t*>(
- tflite::micro::GetOptionalTensorData<int32_t>(cell_gate_bias));
- cmsis_lstm_params.output_gate_bias = const_cast<int32_t*>(
- tflite::micro::GetOptionalTensorData<int32_t>(output_gate_bias));
+ return kTfLiteOk;
+}
- const bool time_major = op_data_lstm.size_info.time_major;
- const int n_input = input->dims->data[input->dims->size - 1];
- const int n_output = recurrent_to_output_weights->dims->data[1];
+TfLiteStatus CMSIS_NN_EvalInteger16x8_16Lstm(
+ const OpData& op_data, const LSTMKernelContents& kernel_content,
+ const LSTMBuffers<int16_t>& buffers) {
+ TFLITE_DCHECK(
+ kernel_content.GetInternalTensor(tflite::kLstmInputTensor)->dims->size >=
+ 2 &&
+ kernel_content.GetInternalTensor(tflite::kLstmInputTensor)->dims->size <=
+ 3);
- int max_time, n_batch;
- if (input->dims->size == 2) {
- max_time = 1;
- n_batch = input->dims->data[0];
- } else {
- max_time = (time_major) ? input->dims->data[0] : input->dims->data[1];
- n_batch = (time_major) ? input->dims->data[1] : input->dims->data[0];
- }
+ const int16_t* input = tflite::micro::GetOptionalTensorData<int16_t>(
+ kernel_content.GetInternalTensor(tflite::kLstmInputTensor));
+ int16_t* output =
+ tflite::micro::GetTensorData<int16_t>(kernel_content.output_tensor);
- cmsis_nn_lstm_dims lstm_dims;
- lstm_dims.num_inputs = n_input;
- lstm_dims.num_outputs = n_output;
- lstm_dims.num_batches = n_batch;
- lstm_dims.max_time = max_time;
+ // Create lstm buffer struct
+ cmsis_nn_lstm_context cmsis_buffers;
+ cmsis_buffers.temp1 = reinterpret_cast<int16_t*>(buffers.buffer0);
+ cmsis_buffers.temp2 = reinterpret_cast<int16_t*>(buffers.buffer1);
+ cmsis_buffers.cell_state = reinterpret_cast<int16_t*>(buffers.buffer2);
- arm_lstm_unidirectional_s16_s8(
- &scratch_buffers,
- const_cast<int8_t*>(tflite::micro::GetTensorData<int8_t>(input)),
- &lstm_dims,
- const_cast<int8_t*>(
- tflite::micro::GetOptionalTensorData<int8_t>(input_to_input_weights)),
- const_cast<int8_t*>(tflite::micro::GetOptionalTensorData<int8_t>(
- input_to_forget_weights)),
- const_cast<int8_t*>(
- tflite::micro::GetOptionalTensorData<int8_t>(input_to_cell_weights)),
- const_cast<int8_t*>(tflite::micro::GetOptionalTensorData<int8_t>(
- input_to_output_weights)),
- const_cast<int8_t*>(tflite::micro::GetOptionalTensorData<int8_t>(
- recurrent_to_input_weights)),
- const_cast<int8_t*>(tflite::micro::GetOptionalTensorData<int8_t>(
- recurrent_to_forget_weights)),
- const_cast<int8_t*>(tflite::micro::GetOptionalTensorData<int8_t>(
- recurrent_to_cell_weights)),
- const_cast<int8_t*>(tflite::micro::GetOptionalTensorData<int8_t>(
- recurrent_to_output_weights)),
- const_cast<int16_t*>(
- tflite::micro::GetOptionalTensorData<int16_t>(cell_to_input_weights)),
- const_cast<int16_t*>(tflite::micro::GetOptionalTensorData<int16_t>(
- cell_to_forget_weights)),
- const_cast<int16_t*>(tflite::micro::GetOptionalTensorData<int16_t>(
- cell_to_output_weights)),
- nullptr, &cmsis_lstm_params,
- const_cast<int8_t*>(tflite::micro::GetTensorData<int8_t>(output_state)),
- const_cast<int16_t*>(tflite::micro::GetTensorData<int16_t>(cell_state)),
- const_cast<int8_t*>(tflite::micro::GetTensorData<int8_t>(output)));
+ arm_lstm_unidirectional_s16(input, output, &op_data.params_cmsis_nn,
+ &cmsis_buffers);
return kTfLiteOk;
}
/*Kernel functions*/
-
void* UnidirectionalSequenceLstmInit(TfLiteContext* context, const char* buffer,
size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
@@ -531,15 +354,9 @@
const TfLiteTensor* input = lstm_tensors.GetInternalTensor(kLstmInputTensor);
const auto activation_type = input->type;
- if (kTfLiteInt8 == activation_type) {
- TF_LITE_ENSURE_STATUS(
- CalculateOpData(context, node, lstm_tensors, op_data));
- }
-
TF_LITE_ENSURE_OK(context, ValidateTensorSize(context, lstm_tensors,
op_data_lstm->size_info));
- // Create cell state information and gate parameters (Fully Connected and Mul)
auto cell_state_type =
lstm_tensors.GetInternalTensor(kLstmCellStateTensor)->type;
if (cell_state_type == kTfLiteFloat32) {
@@ -559,8 +376,24 @@
TfLiteTypeGetName(cell_state_type), cell_state_type);
return kTfLiteError;
}
- // request buffers (four buffers)
- for (size_t i = 0; i < 4; i++) {
+
+ size_t number_of_buffers;
+ if (activation_type == kTfLiteInt8 && cell_state_type == kTfLiteInt16) {
+ auto kernel_content = CreateLSTMKernelContent(context, node);
+ number_of_buffers = 3;
+ CMSIS_NN_PortOpData<int32_t>(context, op_data_lstm, kernel_content,
+ &op_data->params_cmsis_nn);
+ } else if (activation_type == kTfLiteInt16 &&
+ cell_state_type == kTfLiteInt16) {
+ auto kernel_content = CreateLSTMKernelContent(context, node);
+ number_of_buffers = 3;
+ CMSIS_NN_PortOpData<int64_t>(context, op_data_lstm, kernel_content,
+ &op_data->params_cmsis_nn);
+ } else {
+ number_of_buffers = 4;
+ }
+
+ for (size_t i = 0; i < number_of_buffers; i++) {
TF_LITE_ENSURE_OK(context, context->RequestScratchBufferInArena(
context,
op_data_lstm->size_info.batch_size *
@@ -598,9 +431,8 @@
case kTfLiteInt8: {
// 8(activation)x8(weight)->16(cell) LSTM with 32 bits bias
LSTMBuffers<int16_t> buffers =
- CreateLSTMBuffers<int16_t>(context, op_data_lstm.buffer_indices);
- return CMSIS_NN_EvalInteger8x8_16Lstm<int16_t>(
- op_data, kernel_content, buffers);
+ CMSIS_NN_CreateLSTMBuffers(context, op_data_lstm.buffer_indices);
+ CMSIS_NN_EvalInteger8x8_16Lstm(op_data, kernel_content, buffers);
break;
}
default: {
@@ -616,9 +448,8 @@
case kTfLiteInt8: {
// 16(activation)x8(weight)->16(cell) LSTM with 64 bits bias
LSTMBuffers<int16_t> buffers =
- CreateLSTMBuffers<int16_t>(context, op_data_lstm.buffer_indices);
- EvalLstm<int16_t, int8_t, int16_t, int64_t>(op_data_lstm,
- kernel_content, buffers);
+ CMSIS_NN_CreateLSTMBuffers(context, op_data_lstm.buffer_indices);
+ CMSIS_NN_EvalInteger16x8_16Lstm(op_data, kernel_content, buffers);
break;
}
default: {
@@ -654,10 +485,36 @@
if (activation_type == kTfLiteInt8) {
LSTMBuffers<int16_t> buffers =
- CreateLSTMBuffers<int16_t>(context, op_data_lstm.buffer_indices);
+ CMSIS_NN_CreateLSTMBuffers(context, op_data_lstm.buffer_indices);
- return CMSIS_NN_EvalInteger8x8_16Lstm<int16_t>(op_data, kernel_content,
- buffers);
+ return CMSIS_NN_EvalInteger8x8_16Lstm(op_data, kernel_content, buffers);
+ } else {
+ MicroPrintf("Input type %s (%d) not supported.",
+ TfLiteTypeGetName(activation_type), activation_type);
+ return kTfLiteError;
+ }
+ return kTfLiteOk;
+}
+
+TfLiteStatus UnidirectionalSequenceLstmEvalInt16(TfLiteContext* context,
+ TfLiteNode* node) {
+ TFLITE_DCHECK(node->user_data != nullptr);
+ const OpData& op_data = *reinterpret_cast<const OpData*>(node->user_data);
+ const OpDataLSTM& op_data_lstm = op_data.params_ref;
+ auto kernel_content = CreateLSTMKernelContent(context, node);
+ const auto activation_type =
+ kernel_content.internal_tensors[kLstmInputTensor]->type;
+ const auto weight_type =
+ kernel_content.internal_tensors[kLstmInputToInputWeightsTensor]->type;
+
+ TFLITE_DCHECK(weight_type == kTfLiteInt16 &&
+ "Only int16 filter type supported.");
+
+ if (activation_type == kTfLiteInt16) {
+ LSTMBuffers<int16_t> buffers =
+ CMSIS_NN_CreateLSTMBuffers(context, op_data_lstm.buffer_indices);
+
+ return CMSIS_NN_EvalInteger16x8_16Lstm(op_data, kernel_content, buffers);
} else {
MicroPrintf("Input type %s (%d) not supported.",
TfLiteTypeGetName(activation_type), activation_type);
@@ -680,4 +537,10 @@
UnidirectionalSequenceLstmEvalInt8);
}
+TFLMRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM_INT16() {
+ return tflite::micro::RegisterOp(UnidirectionalSequenceLstmInit,
+ UnidirectionalSequenceLstmPrepare,
+ UnidirectionalSequenceLstmEvalInt16);
+}
+
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/comparisons.cc b/tensorflow/lite/micro/kernels/comparisons.cc
index 4056316..69b3c61 100644
--- a/tensorflow/lite/micro/kernels/comparisons.cc
+++ b/tensorflow/lite/micro/kernels/comparisons.cc
@@ -533,7 +533,7 @@
return context->AllocatePersistentBuffer(context, sizeof(OpData));
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus ComparisonsPrepare(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
OpData* data = static_cast<OpData*>(node->user_data);
@@ -580,27 +580,27 @@
} // namespace
TFLMRegistration Register_EQUAL() {
- return tflite::micro::RegisterOp(Init, Prepare, EqualEval);
+ return tflite::micro::RegisterOp(Init, ComparisonsPrepare, EqualEval);
}
TFLMRegistration Register_NOT_EQUAL() {
- return tflite::micro::RegisterOp(Init, Prepare, NotEqualEval);
+ return tflite::micro::RegisterOp(Init, ComparisonsPrepare, NotEqualEval);
}
TFLMRegistration Register_GREATER() {
- return tflite::micro::RegisterOp(Init, Prepare, GreaterEval);
+ return tflite::micro::RegisterOp(Init, ComparisonsPrepare, GreaterEval);
}
TFLMRegistration Register_GREATER_EQUAL() {
- return tflite::micro::RegisterOp(Init, Prepare, GreaterEqualEval);
+ return tflite::micro::RegisterOp(Init, ComparisonsPrepare, GreaterEqualEval);
}
TFLMRegistration Register_LESS() {
- return tflite::micro::RegisterOp(Init, Prepare, LessEval);
+ return tflite::micro::RegisterOp(Init, ComparisonsPrepare, LessEval);
}
TFLMRegistration Register_LESS_EQUAL() {
- return tflite::micro::RegisterOp(Init, Prepare, LessEqualEval);
+ return tflite::micro::RegisterOp(Init, ComparisonsPrepare, LessEqualEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/concatenation.cc b/tensorflow/lite/micro/kernels/concatenation.cc
index b4a838f..57d63a9 100644
--- a/tensorflow/lite/micro/kernels/concatenation.cc
+++ b/tensorflow/lite/micro/kernels/concatenation.cc
@@ -103,12 +103,13 @@
tflite::micro::GetTensorData<data_type>(output));
}
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* ConcatenationInit(TfLiteContext* context, const char* buffer,
+ size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(OpData));
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus ConcatenationPrepare(TfLiteContext* context, TfLiteNode* node) {
// This function only checks the types. Additional shape validations are
// performed in the reference implementation called during Eval().
const TfLiteConcatenationParams* params =
@@ -214,7 +215,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus ConcatenationEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* output_tensor =
tflite::micro::GetEvalOutput(context, node, kOutputTensor);
TF_LITE_ENSURE(context, output_tensor != nullptr);
@@ -252,7 +253,8 @@
} // namespace
TFLMRegistration Register_CONCATENATION() {
- return tflite::micro::RegisterOp(Init, Prepare, Eval);
+ return tflite::micro::RegisterOp(ConcatenationInit, ConcatenationPrepare,
+ ConcatenationEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/conv.cc b/tensorflow/lite/micro/kernels/conv.cc
index 550f5b0..0df35fc 100644
--- a/tensorflow/lite/micro/kernels/conv.cc
+++ b/tensorflow/lite/micro/kernels/conv.cc
@@ -1,4 +1,4 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -27,12 +27,7 @@
namespace tflite {
namespace {
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
- TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
- return context->AllocatePersistentBuffer(context, sizeof(OpDataConv));
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus ConvEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kConvInputTensor);
const TfLiteEvalTensor* filter =
@@ -50,14 +45,6 @@
TFLITE_DCHECK(node->user_data != nullptr);
const auto& data = *(static_cast<const OpDataConv*>(node->user_data));
- TF_LITE_ENSURE_EQ(context, input->type, output->type);
- TF_LITE_ENSURE_MSG(
- context,
- input->type == filter->type ||
- (input->type == kTfLiteInt16 && filter->type == kTfLiteInt8) ||
- (input->type == kTfLiteInt8 && filter->type == kTfLiteInt4),
- "Hybrid models are not supported on TFLite Micro.");
-
switch (input->type) { // Already know in/out types are same.
case kTfLiteFloat32: {
tflite::reference_ops::Conv(
@@ -73,39 +60,34 @@
break;
}
case kTfLiteInt16: {
- switch (bias->type) {
- case kTfLiteInt32: {
- reference_integer_ops::ConvPerChannel(
- ConvParamsQuantized(params, data),
- data.per_channel_output_multiplier, data.per_channel_output_shift,
- tflite::micro::GetTensorShape(input),
- tflite::micro::GetTensorData<int16_t>(input),
- tflite::micro::GetTensorShape(filter),
- tflite::micro::GetTensorData<int8_t>(filter),
- tflite::micro::GetTensorShape(bias),
- tflite::micro::GetOptionalTensorData<std::int32_t>(bias),
- tflite::micro::GetTensorShape(output),
- tflite::micro::GetTensorData<int16_t>(output));
- break;
- }
- case kTfLiteInt64: {
- reference_integer_ops::ConvPerChannel(
- ConvParamsQuantized(params, data),
- data.per_channel_output_multiplier, data.per_channel_output_shift,
- tflite::micro::GetTensorShape(input),
- tflite::micro::GetTensorData<int16_t>(input),
- tflite::micro::GetTensorShape(filter),
- tflite::micro::GetTensorData<int8_t>(filter),
- tflite::micro::GetTensorShape(bias),
- tflite::micro::GetOptionalTensorData<std::int64_t>(bias),
- tflite::micro::GetTensorShape(output),
- tflite::micro::GetTensorData<int16_t>(output));
- break;
- }
- default:
- MicroPrintf("Bias type %s (%d) not supported.",
- TfLiteTypeGetName(bias->type), bias->type);
- return kTfLiteError;
+ if (bias == nullptr || bias->type == kTfLiteInt32) {
+ reference_integer_ops::ConvPerChannel(
+ ConvParamsQuantized(params, data),
+ data.per_channel_output_multiplier, data.per_channel_output_shift,
+ tflite::micro::GetTensorShape(input),
+ tflite::micro::GetTensorData<int16_t>(input),
+ tflite::micro::GetTensorShape(filter),
+ tflite::micro::GetTensorData<int8_t>(filter),
+ tflite::micro::GetTensorShape(bias),
+ tflite::micro::GetOptionalTensorData<std::int32_t>(bias),
+ tflite::micro::GetTensorShape(output),
+ tflite::micro::GetTensorData<int16_t>(output));
+ } else if (bias->type == kTfLiteInt64) {
+ reference_integer_ops::ConvPerChannel(
+ ConvParamsQuantized(params, data),
+ data.per_channel_output_multiplier, data.per_channel_output_shift,
+ tflite::micro::GetTensorShape(input),
+ tflite::micro::GetTensorData<int16_t>(input),
+ tflite::micro::GetTensorShape(filter),
+ tflite::micro::GetTensorData<int8_t>(filter),
+ tflite::micro::GetTensorShape(bias),
+ tflite::micro::GetOptionalTensorData<std::int64_t>(bias),
+ tflite::micro::GetTensorShape(output),
+ tflite::micro::GetTensorData<int16_t>(output));
+ } else {
+ MicroPrintf("Bias type %s (%d) not supported.",
+ TfLiteTypeGetName(bias->type), bias->type);
+ return kTfLiteError;
}
break;
}
@@ -162,7 +144,7 @@
} // namespace
TFLMRegistration Register_CONV_2D() {
- return tflite::micro::RegisterOp(Init, ConvPrepare, Eval);
+ return tflite::micro::RegisterOp(ConvInit, ConvPrepare, ConvEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/conv.h b/tensorflow/lite/micro/kernels/conv.h
index 3b122ad..0c8073f 100644
--- a/tensorflow/lite/micro/kernels/conv.h
+++ b/tensorflow/lite/micro/kernels/conv.h
@@ -1,4 +1,4 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -74,6 +74,8 @@
int out_height, const TfLiteType data_type,
OpDataConv* data);
+void* ConvInit(TfLiteContext* context, const char* buffer, size_t length);
+
TfLiteStatus ConvPrepare(TfLiteContext* context, TfLiteNode* node);
// This is the most generic TFLMRegistration. The actual supported types
@@ -86,14 +88,24 @@
// int8 activations and int8 weights and always calls the reference
// implementation.
TFLMRegistration Register_CONV_2D_INT8REF();
+
#else
inline TFLMRegistration Register_CONV_2D_INT8REF() {
return Register_CONV_2D();
}
-#endif
+#endif // defined(XTENSA)
#if defined(CMSIS_NN)
// Returns a TFLMRegistration struct for kernel variant that only supports
+// int8 activations and int4 weights and uses the latency optimized
+// implementations.
+TFLMRegistration Register_CONV_2D_INT4();
+#else
+inline TFLMRegistration Register_CONV_2D_INT4() { return Register_CONV_2D(); }
+#endif // defined(CMSIS_NN)
+
+#if defined(CMSIS_NN) || defined(XTENSA)
+// Returns a TFLMRegistration struct for kernel variant that only supports
// int8 activations and int8 weights and uses the latency optimized
// implementations.
TFLMRegistration Register_CONV_2D_INT8();
@@ -107,7 +119,7 @@
inline TFLMRegistration Register_CONV_2D_INT8() { return Register_CONV_2D(); }
inline TFLMRegistration Register_CONV_2D_INT16() { return Register_CONV_2D(); }
-#endif
+#endif // defined(CMSIS_NN) || defined(XTENSA)
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/conv_common.cc b/tensorflow/lite/micro/kernels/conv_common.cc
index c548c93..51c7a6f 100644
--- a/tensorflow/lite/micro/kernels/conv_common.cc
+++ b/tensorflow/lite/micro/kernels/conv_common.cc
@@ -1,4 +1,4 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -71,6 +71,11 @@
return op_params;
}
+void* ConvInit(TfLiteContext* context, const char* buffer, size_t length) {
+ TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
+ return context->AllocatePersistentBuffer(context, sizeof(OpDataConv));
+}
+
TfLiteStatus CalculateOpDataConv(TfLiteContext* context, TfLiteNode* node,
const TfLiteConvParams& params, int width,
int height, int filter_width,
@@ -120,10 +125,12 @@
data->filter_zero_point = filter->params.zero_point;
data->output_zero_point = output->params.zero_point;
+ micro_context->DeallocateTempTfLiteTensor(output);
micro_context->DeallocateTempTfLiteTensor(input);
micro_context->DeallocateTempTfLiteTensor(filter);
- micro_context->DeallocateTempTfLiteTensor(output);
- micro_context->DeallocateTempTfLiteTensor(bias);
+ if (bias != nullptr) {
+ micro_context->DeallocateTempTfLiteTensor(bias);
+ }
return kTfLiteOk;
}
@@ -147,6 +154,15 @@
micro_context->AllocateTempInputTensor(node, kConvWeightsTensor);
TF_LITE_ENSURE(context, filter != nullptr);
+ TF_LITE_ENSURE_EQ(context, input->type, output->type);
+ TF_LITE_ENSURE_MSG(
+ context,
+ (input->type == kTfLiteFloat32 && filter->type == kTfLiteFloat32) ||
+ (input->type == kTfLiteInt16 && filter->type == kTfLiteInt8) ||
+ (input->type == kTfLiteInt8 &&
+ (filter->type == kTfLiteInt4 || filter->type == kTfLiteInt8)),
+ "Hybrid models are not supported on TFLite Micro.");
+
const int input_width = input->dims->data[2];
const int input_height = input->dims->data[1];
const int filter_width = filter->dims->data[2];
@@ -196,7 +212,6 @@
micro_context->DeallocateTempTfLiteTensor(filter);
micro_context->DeallocateTempTfLiteTensor(input);
micro_context->DeallocateTempTfLiteTensor(output);
-
return kTfLiteOk;
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/conv_test.cc b/tensorflow/lite/micro/kernels/conv_test.cc
index 98c2615..0fb9411 100644
--- a/tensorflow/lite/micro/kernels/conv_test.cc
+++ b/tensorflow/lite/micro/kernels/conv_test.cc
@@ -1,4 +1,4 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -53,6 +53,7 @@
kTfLiteActNone, // activation
1, // dilation_width_factor
1, // dilation_height_factor
+ kTfLiteNoType // quantized_bias_type
};
} // namespace
@@ -61,10 +62,6 @@
TF_LITE_MICRO_TESTS_BEGIN
-#if !defined(VISION_P6) // TODO(b/270720625): disabled int8 and int4 test for
-// conv for fully connected vision p6 kernels, because vision p6 conv doesn't
-// work with per channel quantization
-
TF_LITE_MICRO_TEST(SimpleTestQuantized4bitPerChannel) {
const int output_dims_count = 12;
int8_t output_data[output_dims_count];
@@ -125,12 +122,6 @@
output_data));
}
-#endif // !defined(VISION_P6)
-
-#if !defined(XTENSA) // TODO(b/170321206): xtensa kernels are less general than
- // reference kernels and we ifdef out test cases that are
- // currently known to fail.
-
TF_LITE_MICRO_TEST(SimpleTestFloat) {
float output_data[tflite::testing::kOutputElements];
@@ -255,7 +246,6 @@
output_data));
}
-#if !defined(CMSIS_NN)
TF_LITE_MICRO_TEST(SimpleTestQuantized16x8PerChannel32bBias) {
const int output_dims_count = 12;
int16_t output_data[output_dims_count];
@@ -285,7 +275,6 @@
&tflite::testing::common_conv_params, tflite::Register_CONV_2D(),
output_data));
}
-#endif
TF_LITE_MICRO_TEST(SimpleTestDilatedQuantizedPerChannel) {
const int output_dims_count = 24;
@@ -396,7 +385,6 @@
&conv_params, tflite::Register_CONV_2D(), output_data));
}
-#if !defined(CMSIS_NN)
TF_LITE_MICRO_TEST(SimpleTestQuantized16x8PerChannelRelu632bBias) {
const int output_dims_count = 12;
int16_t output_data[output_dims_count];
@@ -429,13 +417,12 @@
golden_data, golden_quantized, output_scale, output_zero_point,
&conv_params, tflite::Register_CONV_2D(), output_data));
}
-#endif
TF_LITE_MICRO_TEST(Kernel1x1QuantizedPerChannel) {
// conv params:
// padding, stride_<width,height>, activation, dilation_<width, height>
- TfLiteConvParams conv_params = {kTfLitePaddingValid, 1, 1,
- kTfLiteActNone, 1, 1};
+ TfLiteConvParams conv_params = {
+ kTfLitePaddingValid, 1, 1, kTfLiteActNone, 1, 1, kTfLiteNoType};
int input_shape[] = {4, 1, 2, 2, 4}; // [len,N,H,W,C]
constexpr int input_elements =
@@ -487,8 +474,8 @@
TF_LITE_MICRO_TEST(Kernel1x1QuantizedPerChannelRelu6) {
// conv params:
// padding, stride_<width,height>, activation, dilation_<width, height>
- TfLiteConvParams conv_params = {kTfLitePaddingValid, 1, 1,
- kTfLiteActRelu6, 1, 1};
+ TfLiteConvParams conv_params = {
+ kTfLitePaddingValid, 1, 1, kTfLiteActRelu6, 1, 1, kTfLiteNoType};
int input_shape[] = {4, 1, 2, 2, 4}; // [len,N,H,W,C]
constexpr int input_elements =
@@ -540,8 +527,8 @@
TF_LITE_MICRO_TEST(Kernel1x1Quantized16x8PerChannelRelu6) {
// conv params:
// padding, stride_<width,height>, activation, dilation_<width, height>
- TfLiteConvParams conv_params = {kTfLitePaddingValid, 1, 1,
- kTfLiteActRelu6, 1, 1};
+ TfLiteConvParams conv_params = {
+ kTfLitePaddingValid, 1, 1, kTfLiteActRelu6, 1, 1, kTfLiteNoType};
int input_shape[] = {4, 1, 2, 2, 4}; // [len,N,H,W,C]
const int input_elements = 1 * 2 * 2 * 4;
@@ -673,8 +660,6 @@
tflite::Register_CONV_2D(), output_data));
}
-#endif // !defined(XTENSA)
-
TF_LITE_MICRO_TEST(Int8Filter1x3x3x1ShouldMatchGoldenEvenInputPaddingSame) {
using tflite::ElementCount;
using tflite::kConvFilter1x3x3x1;
diff --git a/tensorflow/lite/micro/kernels/conv_test.h b/tensorflow/lite/micro/kernels/conv_test.h
index 39d3fa7..c655f04 100644
--- a/tensorflow/lite/micro/kernels/conv_test.h
+++ b/tensorflow/lite/micro/kernels/conv_test.h
@@ -34,10 +34,6 @@
int output_length, TfLiteConvParams* conv_params,
TFLMRegistration registration, int8_t* output_data);
-TfLiteStatus InvokeConv(TfLiteTensor* tensors, int tensors_size,
- int output_length, TfLiteConvParams* conv_params,
- TFLMRegistration registration, uint8_t* output_data);
-
TfLiteStatus ValidateConvGoldens(TfLiteTensor* tensors, int tensors_size,
const float* expected_output_data,
int output_length,
@@ -52,13 +48,6 @@
TFLMRegistration registration,
int8_t* output_data, float tolerance = 1e-5);
-TfLiteStatus ValidateConvGoldens(TfLiteTensor* tensors, int tensors_size,
- const uint8_t* expected_output_data,
- int output_length,
- TfLiteConvParams* conv_params,
- TFLMRegistration registration,
- uint8_t* output_data, float tolerance = 1e-5);
-
TfLiteStatus TestConvFloat(int* input_dims_data, const float* input_data,
int* filter_dims_data, const float* filter_data,
int* bias_dims_data, const float* bias_data,
@@ -67,15 +56,6 @@
TfLiteConvParams* conv_params,
TFLMRegistration registration, float* output_data);
-TfLiteStatus TestConvQuantizedPerLayer(
- int* input_dims_data, const float* input_data, uint8_t* input_quantized,
- float input_scale, int* filter_dims_data, const float* filter_data,
- uint8_t* filter_quantized, float filter_scale, int* bias_dims_data,
- const float* bias_data, int32_t* bias_quantized, int* output_dims_data,
- const float* expected_output_data, uint8_t* expected_output_quantized,
- float output_scale, TfLiteConvParams* conv_params,
- TFLMRegistration registration, uint8_t* output_data);
-
TfLiteStatus TestConvQuantizedPerChannel(
int* input_dims_data, const float* input_data, int8_t* input_quantized,
float input_scale, int input_zero_point, int* filter_dims_data,
diff --git a/tensorflow/lite/micro/kernels/conv_test_common.cc b/tensorflow/lite/micro/kernels/conv_test_common.cc
index bdc9466..a0f733b 100644
--- a/tensorflow/lite/micro/kernels/conv_test_common.cc
+++ b/tensorflow/lite/micro/kernels/conv_test_common.cc
@@ -1,4 +1,4 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -145,8 +145,8 @@
input_data, input_quantized, input_dims, input_scale, input_zero_point);
TfLiteTensor filter_tensor = CreateSymmetricPerChannelQuantizedTensor(
filter_data, filter_data_quantized, filter_dims, filter_scales,
- filter_zero_points, &filter_quant, 0, false,
- tensor_weight_type /* quantized dimension */);
+ filter_zero_points, &filter_quant, 0 /* quantized dimension */, false,
+ tensor_weight_type);
TfLiteTensor bias_tensor = CreatePerChannelQuantizedBiasTensor(
bias_data, bias_data_quantized, bias_dims, input_scale, &filter_scales[1],
bias_scales, bias_zero_points, &bias_quant, 0 /* quantized dimension */);
diff --git a/tensorflow/lite/micro/kernels/cumsum.cc b/tensorflow/lite/micro/kernels/cumsum.cc
index f62f2a5..258cf8d 100644
--- a/tensorflow/lite/micro/kernels/cumsum.cc
+++ b/tensorflow/lite/micro/kernels/cumsum.cc
@@ -104,11 +104,11 @@
return kTfLiteOk;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus CumSumPrepare(TfLiteContext* context, TfLiteNode* node) {
return CalculateOpData(context, node);
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus CumSumEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kInputTensor);
const TfLiteEvalTensor* axis_tensor =
@@ -169,7 +169,7 @@
} // namespace
TFLMRegistration Register_CUMSUM() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, CumSumPrepare, CumSumEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/depth_to_space.cc b/tensorflow/lite/micro/kernels/depth_to_space.cc
index 7e0a8fa..d4faf7c 100644
--- a/tensorflow/lite/micro/kernels/depth_to_space.cc
+++ b/tensorflow/lite/micro/kernels/depth_to_space.cc
@@ -93,11 +93,11 @@
return kTfLiteOk;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus DepthToSpacePrepare(TfLiteContext* context, TfLiteNode* node) {
return CalculateOpData(context, node);
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus DepthToSpaceEval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteDepthToSpaceParams*>(node->builtin_data);
@@ -136,7 +136,8 @@
} // namespace
TFLMRegistration Register_DEPTH_TO_SPACE() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, DepthToSpacePrepare,
+ DepthToSpaceEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/depthwise_conv.cc b/tensorflow/lite/micro/kernels/depthwise_conv.cc
index 398f8cd..fa55a70 100644
--- a/tensorflow/lite/micro/kernels/depthwise_conv.cc
+++ b/tensorflow/lite/micro/kernels/depthwise_conv.cc
@@ -27,12 +27,13 @@
namespace tflite {
namespace {
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* DepthwiseConvInit(TfLiteContext* context, const char* buffer,
+ size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(OpDataConv));
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus DepthwiseConvEval(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
TFLITE_DCHECK(node->builtin_data != nullptr);
@@ -143,7 +144,8 @@
} // namespace
TFLMRegistration Register_DEPTHWISE_CONV_2D() {
- return tflite::micro::RegisterOp(Init, DepthwiseConvPrepare, Eval);
+ return tflite::micro::RegisterOp(DepthwiseConvInit, DepthwiseConvPrepare,
+ DepthwiseConvEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/depthwise_conv.h b/tensorflow/lite/micro/kernels/depthwise_conv.h
index d8cc78d..5f2d87e 100644
--- a/tensorflow/lite/micro/kernels/depthwise_conv.h
+++ b/tensorflow/lite/micro/kernels/depthwise_conv.h
@@ -1,4 +1,4 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -65,6 +65,11 @@
// implementations.
TFLMRegistration Register_DEPTHWISE_CONV_2D_INT16();
+// Returns a TFLMRegistration struct for kernel variant that only supports
+// int8 activations and int4 weights and uses the latency optimized
+// implementations.
+TFLMRegistration Register_DEPTHWISE_CONV_2D_INT4();
+
#else
inline TFLMRegistration Register_DEPTHWISE_CONV_2D_INT8() {
return Register_DEPTHWISE_CONV_2D();
@@ -73,6 +78,11 @@
inline TFLMRegistration Register_DEPTHWISE_CONV_2D_INT16() {
return Register_DEPTHWISE_CONV_2D();
}
+
+inline TFLMRegistration Register_DEPTHWISE_CONV_2D_INT4() {
+ return Register_DEPTHWISE_CONV_2D();
+}
+
#endif
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/detection_postprocess.cc b/tensorflow/lite/micro/kernels/detection_postprocess.cc
index e807f35..fa2d4ca 100644
--- a/tensorflow/lite/micro/kernels/detection_postprocess.cc
+++ b/tensorflow/lite/micro/kernels/detection_postprocess.cc
@@ -117,7 +117,8 @@
TfLiteQuantizationParams input_anchors;
};
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* DetectionPostProcessInit(TfLiteContext* context, const char* buffer,
+ size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
OpData* op_data = nullptr;
@@ -149,7 +150,8 @@
return op_data;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus DetectionPostProcessPrepare(TfLiteContext* context,
+ TfLiteNode* node) {
auto* op_data = static_cast<OpData*>(node->user_data);
MicroContext* micro_context = GetMicroContext(context);
@@ -774,7 +776,8 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus DetectionPostProcessEval(TfLiteContext* context,
+ TfLiteNode* node) {
TF_LITE_ENSURE(context, (kBatchSize == 1));
auto* op_data = static_cast<OpData*>(node->user_data);
@@ -800,7 +803,9 @@
} // namespace
TFLMRegistration* Register_DETECTION_POSTPROCESS() {
- static TFLMRegistration r = tflite::micro::RegisterOp(Init, Prepare, Eval);
+ static TFLMRegistration r = tflite::micro::RegisterOp(
+ DetectionPostProcessInit, DetectionPostProcessPrepare,
+ DetectionPostProcessEval);
return &r;
}
diff --git a/tensorflow/lite/micro/kernels/div.cc b/tensorflow/lite/micro/kernels/div.cc
index cc90e22..a80b3f2 100644
--- a/tensorflow/lite/micro/kernels/div.cc
+++ b/tensorflow/lite/micro/kernels/div.cc
@@ -1,4 +1,4 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -65,12 +65,12 @@
return kTfLiteOk;
}
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* DivInit(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(OpDataDiv));
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus DivPrepare(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
TFLITE_DCHECK(node->builtin_data != nullptr);
@@ -91,12 +91,21 @@
TF_LITE_ENSURE_STATUS(
CalculateOpDataDiv(context, input1, input2, output, params, data));
+ if (output->type == kTfLiteInt32) {
+ // Only support int32 unquantized DIV for now.
+ TF_LITE_ENSURE_EQ(context, input1->quantization.type,
+ kTfLiteNoQuantization);
+ TF_LITE_ENSURE_EQ(context, input2->quantization.type,
+ kTfLiteNoQuantization);
+ }
+
micro_context->DeallocateTempTfLiteTensor(input1);
micro_context->DeallocateTempTfLiteTensor(input2);
micro_context->DeallocateTempTfLiteTensor(output);
return kTfLiteOk;
}
+template <typename T>
void EvalDiv(TfLiteContext* context, TfLiteNode* node, TfLiteDivParams* params,
const OpDataDiv* data, const TfLiteEvalTensor* input1,
const TfLiteEvalTensor* input2, TfLiteEvalTensor* output) {
@@ -120,9 +129,9 @@
tflite::micro::GetTensorShape(input2), &op_params);
if (requires_broadcast) {
- TF_LITE_DIV(reference_ops, BroadcastDivSlow, float);
+ TF_LITE_DIV(reference_ops, BroadcastDivSlow, T);
} else {
- TF_LITE_DIV(reference_ops, Div, float);
+ TF_LITE_DIV(reference_ops, Div, T);
}
#undef TF_LITE_DIV
}
@@ -170,7 +179,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus DivEval(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->builtin_data != nullptr);
auto* params = static_cast<TfLiteDivParams*>(node->builtin_data);
TFLITE_DCHECK(node->user_data != nullptr);
@@ -184,13 +193,15 @@
tflite::micro::GetEvalOutput(context, node, kOutputTensor);
if (output->type == kTfLiteFloat32) {
- EvalDiv(context, node, params, data, input1, input2, output);
+ EvalDiv<float>(context, node, params, data, input1, input2, output);
+ } else if (output->type == kTfLiteInt32) {
+ EvalDiv<int32_t>(context, node, params, data, input1, input2, output);
} else if (output->type == kTfLiteInt8) {
TF_LITE_ENSURE_OK(context, EvalQuantized(context, node, params, data,
input1, input2, output));
} else {
MicroPrintf(
- "DIV only supports FLOAT32, quantized INT8 "
+ "DIV only supports FLOAT32, INT32, quantized INT8 "
"now, got type %s (%d).",
TfLiteTypeGetName(output->type), output->type);
return kTfLiteError;
@@ -202,7 +213,7 @@
} // namespace
TFLMRegistration Register_DIV() {
- return tflite::micro::RegisterOp(Init, Prepare, Eval);
+ return tflite::micro::RegisterOp(DivInit, DivPrepare, DivEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/div_test.cc b/tensorflow/lite/micro/kernels/div_test.cc
index e020255..ef35d0c 100644
--- a/tensorflow/lite/micro/kernels/div_test.cc
+++ b/tensorflow/lite/micro/kernels/div_test.cc
@@ -1,4 +1,4 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -374,4 +374,65 @@
kTfLiteActNone, ¶ms);
}
+TF_LITE_MICRO_TEST(IntegerDivOpTestNoActivation) {
+ int kDims[] = {4, 1, 2, 2, 1};
+ constexpr int32_t kInput1[] = {-2, 2, -15, 8};
+ constexpr int32_t kInput2[] = {5, -2, -3, 5};
+ constexpr int32_t kExpect[] = {0, -1, 5, 1};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ int32_t output_data[kOutputCount];
+
+ tflite::testing::TestDiv(kDims, kInput1, kDims, kInput2, kDims, kExpect,
+ output_data, kTfLiteActNone);
+}
+
+TF_LITE_MICRO_TEST(IntegerDivOpTestActivationRELU_N1_TO_1) {
+ int kDims[] = {4, 1, 2, 2, 1};
+ constexpr int32_t kInput1[] = {-2, 2, -12, 8};
+ constexpr int32_t kInput2[] = {1, 2, -15, 5};
+ constexpr int32_t kExpect[] = {-1, 1, 0, 1};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ int32_t output_data[kOutputCount];
+
+ tflite::testing::TestDiv(kDims, kInput1, kDims, kInput2, kDims, kExpect,
+ output_data, kTfLiteActReluN1To1);
+}
+
+TF_LITE_MICRO_TEST(IntegerDivOpTestVariousInputShapes) {
+ int kShape1[] = {1, 6};
+ int kShape2[] = {2, 2, 3};
+ int kShape3[] = {3, 2, 1, 3};
+ int kShape4[] = {4, 1, 3, 1, 2};
+ int* kDims[] = {kShape1, kShape2, kShape3, kShape4};
+ constexpr int kDimsCount = std::extent<decltype(kDims)>::value;
+
+ constexpr int32_t kInput1[] = {-20, 2, 3, 8, 11, -20};
+ constexpr int32_t kInput2[] = {1, 2, 6, 5, -11, -1};
+ constexpr int32_t kExpect[] = {-20, 1, 0, 1, -1, 20};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ int32_t output_data[kOutputCount];
+
+ tflite::testing::TestDivMultiShape(kDims, kDimsCount, kInput1, kInput2,
+ kExpect, output_data, kTfLiteActNone);
+}
+
+TF_LITE_MICRO_TEST(IntegerDivOpTestWithBroadcast) {
+ int kShape1[] = {1, 8};
+ int kShape2[] = {2, 2, 4};
+ int kShape3[] = {3, 2, 1, 4};
+ int kShape4[] = {4, 1, 4, 1, 2};
+ int kShape5[] = {5, 1, 2, 1, 2, 2};
+ int* kDims[] = {kShape1, kShape2, kShape3, kShape4, kShape5};
+ constexpr int kDimsCount = std::extent<decltype(kDims)>::value;
+
+ constexpr int32_t kInput1[] = {-20, 21, 7, 8, 11, -123, -42, -48};
+ constexpr int32_t kInput2[] = {3};
+ constexpr int32_t kExpect[] = {-6, 7, 2, 2, 3, -41, -14, -16};
+ constexpr int kOutputCount = std::extent<decltype(kExpect)>::value;
+ int32_t output_data[kOutputCount];
+
+ tflite::testing::TestDivMultiBroadcast(kDims, kDimsCount, kInput1, kInput2,
+ kExpect, output_data, kTfLiteActNone);
+}
+
TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/kernels/embedding_lookup.cc b/tensorflow/lite/micro/kernels/embedding_lookup.cc
index 77ac0e0..6a4be87 100644
--- a/tensorflow/lite/micro/kernels/embedding_lookup.cc
+++ b/tensorflow/lite/micro/kernels/embedding_lookup.cc
@@ -65,7 +65,7 @@
return kTfLiteOk;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus EmbeddingLookUpPrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
@@ -178,7 +178,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus EmbeddingLookUpEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* lookup =
tflite::micro::GetEvalInput(context, node, kInputTensor_0);
const TfLiteEvalTensor* value =
@@ -207,7 +207,8 @@
} // namespace
TFLMRegistration Register_EMBEDDING_LOOKUP() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, EmbeddingLookUpPrepare,
+ EmbeddingLookUpEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/exp.cc b/tensorflow/lite/micro/kernels/exp.cc
index 1a2e00c..8d1da8f 100644
--- a/tensorflow/lite/micro/kernels/exp.cc
+++ b/tensorflow/lite/micro/kernels/exp.cc
@@ -27,7 +27,7 @@
constexpr int kInputTensor = 0;
constexpr int kOutputTensor = 0;
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus ExpPrepare(TfLiteContext* context, TfLiteNode* node) {
MicroContext* micro_context = GetMicroContext(context);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
@@ -51,7 +51,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus ExpEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kInputTensor);
TfLiteEvalTensor* output =
@@ -73,7 +73,7 @@
} // namespace
TFLMRegistration Register_EXP() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, ExpPrepare, ExpEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/expand_dims.cc b/tensorflow/lite/micro/kernels/expand_dims.cc
index 0c4c6ff..d47b42c 100644
--- a/tensorflow/lite/micro/kernels/expand_dims.cc
+++ b/tensorflow/lite/micro/kernels/expand_dims.cc
@@ -13,6 +13,8 @@
limitations under the License.
==============================================================================*/
+#include <cstdint>
+
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
@@ -82,7 +84,7 @@
return kTfLiteOk;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus ExpandDimsPrepare(TfLiteContext* context, TfLiteNode* node) {
MicroContext* micro_context = GetMicroContext(context);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
@@ -116,7 +118,7 @@
}
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus ExpandDimsEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kInputTensor);
TfLiteEvalTensor* output =
@@ -128,13 +130,18 @@
memCopyN(tflite::micro::GetTensorData<float>(output),
tflite::micro::GetTensorData<float>(input), flat_size);
} break;
+ case kTfLiteInt16: {
+ memCopyN(tflite::micro::GetTensorData<int16_t>(output),
+ tflite::micro::GetTensorData<int16_t>(input), flat_size);
+ } break;
case kTfLiteInt8: {
memCopyN(tflite::micro::GetTensorData<int8_t>(output),
tflite::micro::GetTensorData<int8_t>(input), flat_size);
} break;
default:
MicroPrintf(
- "Expand_Dims only currently supports int8 and float32, got %d.",
+ "Expand_Dims only currently supports int8, int16 and float32, got "
+ "%d.",
input->type);
return kTfLiteError;
}
@@ -143,7 +150,7 @@
} // namespace
TFLMRegistration Register_EXPAND_DIMS() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, ExpandDimsPrepare, ExpandDimsEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/expand_dims_test.cc b/tensorflow/lite/micro/kernels/expand_dims_test.cc
index d8e217e..39a83b5 100644
--- a/tensorflow/lite/micro/kernels/expand_dims_test.cc
+++ b/tensorflow/lite/micro/kernels/expand_dims_test.cc
@@ -13,6 +13,8 @@
limitations under the License.
==============================================================================*/
+#include <cstdint>
+
#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/micro/kernels/kernel_runner.h"
@@ -138,6 +140,20 @@
golden_data, output_data);
}
+TF_LITE_MICRO_TEST(ExpandDimsPositiveAxisTest3) {
+ int16_t output_data[6];
+ int input_dims[] = {3, 3, 1, 2};
+ const int16_t input_data[] = {-1, 1, 2, -2, 0, 3};
+ const int16_t golden_data[] = {-1, 1, 2, -2, 0, 3};
+ int axis_dims[] = {1, 1};
+ const int32_t axis_data[] = {3};
+ int golden_dims[] = {1, 3, 1, 2};
+ int output_dims[] = {4, 3, 1, 2, 1};
+ tflite::testing::TestExpandDims<int16_t>(input_dims, input_data, axis_dims,
+ axis_data, golden_dims, output_dims,
+ golden_data, output_data);
+}
+
TF_LITE_MICRO_TEST(ExpandDimsNegativeAxisTest4) {
int8_t output_data[6];
int input_dims[] = {3, 3, 1, 2};
diff --git a/tensorflow/lite/micro/kernels/fill.cc b/tensorflow/lite/micro/kernels/fill.cc
index b1b366e..1486fcb 100644
--- a/tensorflow/lite/micro/kernels/fill.cc
+++ b/tensorflow/lite/micro/kernels/fill.cc
@@ -64,7 +64,7 @@
constexpr int kValueTensor = 1;
constexpr int kOutputTensor = 0;
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FillPrepare(TfLiteContext* context, TfLiteNode* node) {
MicroContext* micro_context = GetMicroContext(context);
// Ensure inputs and outputs exist.
@@ -107,7 +107,7 @@
micro::GetTensorShape(output), micro::GetTensorData<T>(output));
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FillEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* value =
micro::GetEvalInput(context, node, kValueTensor);
TfLiteEvalTensor* output = micro::GetEvalOutput(context, node, kOutputTensor);
@@ -134,7 +134,7 @@
} // namespace
TFLMRegistration Register_FILL() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, FillPrepare, FillEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/floor.cc b/tensorflow/lite/micro/kernels/floor.cc
index 094c8b5..f92b7e0 100644
--- a/tensorflow/lite/micro/kernels/floor.cc
+++ b/tensorflow/lite/micro/kernels/floor.cc
@@ -26,7 +26,7 @@
constexpr int kInputTensor = 0;
constexpr int kOutputTensor = 0;
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FloorEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kInputTensor);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteFloat32);
@@ -42,7 +42,7 @@
} // namespace
TFLMRegistration Register_FLOOR() {
- return tflite::micro::RegisterOp(nullptr, nullptr, Eval);
+ return tflite::micro::RegisterOp(nullptr, nullptr, FloorEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/floor_div.cc b/tensorflow/lite/micro/kernels/floor_div.cc
index 5c00808..9adf614 100644
--- a/tensorflow/lite/micro/kernels/floor_div.cc
+++ b/tensorflow/lite/micro/kernels/floor_div.cc
@@ -57,11 +57,11 @@
return kTfLiteOk;
}
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* FloorDivInit(TfLiteContext* context, const char* buffer, size_t length) {
return nullptr;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FloorDivPrepare(TfLiteContext* context, TfLiteNode* node) {
return CalculateOpData(context, node);
}
@@ -101,7 +101,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FloorDivEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input1 =
tflite::micro::GetEvalInput(context, node, kInputTensor1);
const TfLiteEvalTensor* input2 =
@@ -124,7 +124,7 @@
} // namespace
TFLMRegistration Register_FLOOR_DIV() {
- return tflite::micro::RegisterOp(Init, Prepare, Eval);
+ return tflite::micro::RegisterOp(FloorDivInit, FloorDivPrepare, FloorDivEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/floor_mod.cc b/tensorflow/lite/micro/kernels/floor_mod.cc
index f459892..da2a7c9 100644
--- a/tensorflow/lite/micro/kernels/floor_mod.cc
+++ b/tensorflow/lite/micro/kernels/floor_mod.cc
@@ -62,11 +62,11 @@
return kTfLiteOk;
}
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* FloorModInit(TfLiteContext* context, const char* buffer, size_t length) {
return nullptr;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FloorModPrepare(TfLiteContext* context, TfLiteNode* node) {
return CalculateOpData(context, node);
}
@@ -96,7 +96,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FloorModEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input1 =
tflite::micro::GetEvalInput(context, node, kInputTensor1);
const TfLiteEvalTensor* input2 =
@@ -122,7 +122,7 @@
} // namespace
TFLMRegistration Register_FLOOR_MOD() {
- return tflite::micro::RegisterOp(Init, Prepare, Eval);
+ return tflite::micro::RegisterOp(FloorModInit, FloorModPrepare, FloorModEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/fully_connected.cc b/tensorflow/lite/micro/kernels/fully_connected.cc
index f732b29..65c8379 100644
--- a/tensorflow/lite/micro/kernels/fully_connected.cc
+++ b/tensorflow/lite/micro/kernels/fully_connected.cc
@@ -26,13 +26,14 @@
namespace tflite {
namespace {
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* FullyConnectedInit(TfLiteContext* context, const char* buffer,
+ size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context,
sizeof(OpDataFullyConnected));
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FullyConnectedPrepare(TfLiteContext* context, TfLiteNode* node) {
MicroContext* micro_context = GetMicroContext(context);
TFLITE_DCHECK(node->user_data != nullptr);
@@ -87,7 +88,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus FullyConnectedEval(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->builtin_data != nullptr);
const auto* params =
static_cast<const TfLiteFullyConnectedParams*>(node->builtin_data);
@@ -200,7 +201,12 @@
} // namespace
TFLMRegistration Register_FULLY_CONNECTED() {
- return tflite::micro::RegisterOp(Init, Prepare, Eval);
+ return tflite::micro::RegisterOp(FullyConnectedInit, FullyConnectedPrepare,
+ FullyConnectedEval);
+}
+
+TFLMInferenceRegistration RegisterInference_FULLY_CONNECTED() {
+ return tflite::micro::RegisterOp(FullyConnectedEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/fully_connected.h b/tensorflow/lite/micro/kernels/fully_connected.h
index 3fa6060..8308838 100644
--- a/tensorflow/lite/micro/kernels/fully_connected.h
+++ b/tensorflow/lite/micro/kernels/fully_connected.h
@@ -1,4 +1,4 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -95,6 +95,10 @@
// int16.
TFLMRegistration Register_FULLY_CONNECTED_INT16();
+// Returns a TFLMRegistration struct for kernel variant that only supports
+// int8 and int4 packed kernels.
+TFLMRegistration Register_FULLY_CONNECTED_INT4();
+
#else
// Note that while this block gets used for both reference and optimized kernels
// that do not have any specialized implementations, the only goal here is to
@@ -105,6 +109,10 @@
return Register_FULLY_CONNECTED();
}
+inline TFLMRegistration Register_FULLY_CONNECTED_INT4() {
+ return Register_FULLY_CONNECTED();
+}
+
#endif
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/fully_connected_common.cc b/tensorflow/lite/micro/kernels/fully_connected_common.cc
index 5a8d312..66f8787 100644
--- a/tensorflow/lite/micro/kernels/fully_connected_common.cc
+++ b/tensorflow/lite/micro/kernels/fully_connected_common.cc
@@ -57,6 +57,24 @@
TfLiteType data_type, const TfLiteTensor* input, const TfLiteTensor* filter,
const TfLiteTensor* bias, TfLiteTensor* output,
OpDataFullyConnected* data) {
+ // TODO(b/324385802): Support per-channel quantization for FullyConnected.
+ // If you have hit this failure message, you will need to disable this
+ // behavior. This can be done by setting the following flag to true:
+ // TfLiteConverter._experimental_disable_per_channel_quantization_for_dense_layers
+ // https://github.com/tensorflow/tensorflow/blob/377f47694fa790e98db6665b9adecde00b5e0d68/tensorflow/lite/python/lite.py#L674
+ if (filter->quantization.type == kTfLiteAffineQuantization &&
+ filter->quantization.params != nullptr) {
+ TfLiteAffineQuantization* affine_quantization =
+ reinterpret_cast<TfLiteAffineQuantization*>(
+ filter->quantization.params);
+ TF_LITE_ENSURE(context, affine_quantization->scale);
+ TF_LITE_ENSURE_MSG(
+ context, affine_quantization->scale->size == 1,
+ "FullyConnected per-channel quantization not yet supported. Please set "
+ "converter._experimental_disable_per_channel_quantization_for_dense_"
+ "layers = True.");
+ }
+
if (data_type != kTfLiteFloat32) {
double real_multiplier = 0.0;
TF_LITE_ENSURE_STATUS(GetQuantizedConvolutionMultipler(
diff --git a/tensorflow/lite/micro/kernels/fully_connected_test.cc b/tensorflow/lite/micro/kernels/fully_connected_test.cc
index 2e9206a..2ad1320 100644
--- a/tensorflow/lite/micro/kernels/fully_connected_test.cc
+++ b/tensorflow/lite/micro/kernels/fully_connected_test.cc
@@ -247,7 +247,8 @@
const TfLiteFusedActivation activation, const float tolerance,
const int output_len, const T* golden, T* output_data) {
TfLiteFullyConnectedParams builtin_data = {
- activation, kTfLiteFullyConnectedWeightsFormatDefault, false, false};
+ activation, kTfLiteFullyConnectedWeightsFormatDefault, false, false,
+ kTfLiteNoType};
// Avoid variable length array warning.
constexpr int inputs_array_len = 4;
diff --git a/tensorflow/lite/micro/kernels/gather.cc b/tensorflow/lite/micro/kernels/gather.cc
index 9955601..a0af4c0 100644
--- a/tensorflow/lite/micro/kernels/gather.cc
+++ b/tensorflow/lite/micro/kernels/gather.cc
@@ -97,7 +97,7 @@
return kTfLiteOk;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus GatherPrepare(TfLiteContext* context, TfLiteNode* node) {
MicroContext* micro_context = GetMicroContext(context);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
@@ -188,7 +188,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus GatherEval(TfLiteContext* context, TfLiteNode* node) {
const auto* params =
reinterpret_cast<const TfLiteGatherParams*>(node->builtin_data);
const TfLiteEvalTensor* input =
@@ -218,7 +218,7 @@
} // namespace
TFLMRegistration Register_GATHER() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, GatherPrepare, GatherEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/gather_nd.cc b/tensorflow/lite/micro/kernels/gather_nd.cc
index 3774ddd..d01af7c 100644
--- a/tensorflow/lite/micro/kernels/gather_nd.cc
+++ b/tensorflow/lite/micro/kernels/gather_nd.cc
@@ -28,7 +28,7 @@
constexpr int kOutputTensor = 0;
constexpr int MAX_INDICES_ND = 5;
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus GatherNdPrepare(TfLiteContext* context, TfLiteNode* node) {
MicroContext* micro_context = GetMicroContext(context);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
@@ -185,7 +185,7 @@
return status;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus GatherNdEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* params =
tflite::micro::GetEvalInput(context, node, kParams);
const TfLiteEvalTensor* indices =
@@ -206,7 +206,7 @@
} // namespace
TFLMRegistration Register_GATHER_ND() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, GatherNdPrepare, GatherNdEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/if.cc b/tensorflow/lite/micro/kernels/if.cc
index 9143c9c..029846b 100644
--- a/tensorflow/lite/micro/kernels/if.cc
+++ b/tensorflow/lite/micro/kernels/if.cc
@@ -38,12 +38,12 @@
int else_subgraph_index;
};
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* IfInit(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(OpData));
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus IfPrepare(TfLiteContext* context, TfLiteNode* node) {
OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
const auto* params =
reinterpret_cast<const TfLiteIfParams*>(node->builtin_data);
@@ -67,7 +67,7 @@
// passed to the branch subgraphs. Therefore, the number of subgraph inputs
// will be the number of node inputs - 1.
size_t num_inputs = node->inputs->size - 1;
- size_t num_outputs = node->outputs->size;
+ size_t num_outputs = NumOutputs(node);
MicroGraph& graph_info = micro_context->graph();
@@ -85,7 +85,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus IfEval(TfLiteContext* context, TfLiteNode* node) {
const OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
tflite::MicroContext* micro_context = tflite::GetMicroContext(context);
@@ -117,7 +117,7 @@
} // namespace.
TFLMRegistration Register_IF() {
- return tflite::micro::RegisterOp(Init, Prepare, Eval);
+ return tflite::micro::RegisterOp(IfInit, IfPrepare, IfEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/kernel_runner.cc b/tensorflow/lite/micro/kernels/kernel_runner.cc
index d5112a1..79824ef 100644
--- a/tensorflow/lite/micro/kernels/kernel_runner.cc
+++ b/tensorflow/lite/micro/kernels/kernel_runner.cc
@@ -1,4 +1,4 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -18,7 +18,6 @@
#include "tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h"
#include "tensorflow/lite/micro/micro_arena_constants.h"
#include "tensorflow/lite/micro/micro_log.h"
-#include "tensorflow/lite/micro/test_helpers.h"
namespace tflite {
namespace micro {
@@ -37,12 +36,23 @@
KernelRunner::KernelRunner(const TFLMRegistration& registration,
TfLiteTensor* tensors, int tensors_size,
TfLiteIntArray* inputs, TfLiteIntArray* outputs,
- void* builtin_data, TfLiteIntArray* intermediates)
+ const void* builtin_data,
+ TfLiteIntArray* intermediates
+#ifdef USE_TFLM_COMPRESSION
+ ,
+ const CompressedTensorList* compressed_tensors
+#endif // USE_TFLM_COMPRESSION
+ )
: registration_(registration),
allocator_(SingleArenaBufferAllocator::Create(kKernelRunnerBuffer_,
kKernelRunnerBufferSize_)),
mock_micro_graph_(allocator_),
- fake_micro_context_(tensors, allocator_, &mock_micro_graph_) {
+ fake_micro_context_(tensors, allocator_, &mock_micro_graph_
+#ifdef USE_TFLM_COMPRESSION
+ ,
+ compressed_tensors
+#endif // USE_TFLM_COMPRESSION
+ ) {
// Prepare TfLiteContext:
context_.impl_ = static_cast<void*>(&fake_micro_context_);
context_.ReportError = MicroContextReportOpError;
@@ -57,7 +67,7 @@
// Prepare TfLiteNode:
node_.inputs = inputs;
node_.outputs = outputs;
- node_.builtin_data = builtin_data;
+ node_.builtin_data = const_cast<void*>(builtin_data);
node_.intermediates = intermediates;
}
diff --git a/tensorflow/lite/micro/kernels/kernel_runner.h b/tensorflow/lite/micro/kernels/kernel_runner.h
index d617c44..8dbd7f8 100644
--- a/tensorflow/lite/micro/kernels/kernel_runner.h
+++ b/tensorflow/lite/micro/kernels/kernel_runner.h
@@ -1,4 +1,4 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -35,8 +35,13 @@
public:
KernelRunner(const TFLMRegistration& registration, TfLiteTensor* tensors,
int tensors_size, TfLiteIntArray* inputs,
- TfLiteIntArray* outputs, void* builtin_data,
- TfLiteIntArray* intermediates = nullptr);
+ TfLiteIntArray* outputs, const void* builtin_data,
+ TfLiteIntArray* intermediates = nullptr
+#ifdef USE_TFLM_COMPRESSION
+ ,
+ const CompressedTensorList* compressed_tensors = nullptr
+#endif // USE_TFLM_COMPRESSION
+ );
// Calls init and prepare on the kernel (i.e. TFLMRegistration) struct.
// Any exceptions will be DebugLog'd and returned as a status code.
diff --git a/tensorflow/lite/micro/kernels/kernel_util.cc b/tensorflow/lite/micro/kernels/kernel_util.cc
index ffffa08..a509f5d 100644
--- a/tensorflow/lite/micro/kernels/kernel_util.cc
+++ b/tensorflow/lite/micro/kernels/kernel_util.cc
@@ -53,6 +53,15 @@
/*custom_name=*/nullptr};
}
+TFLMInferenceRegistration RegisterOp(
+ TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node),
+ void (*reset)(TfLiteContext* context, void* buffer)) {
+ return {
+ /*invoke=*/invoke,
+ /*reset*/ reset,
+ };
+}
+
// Returns a mutable tensor for a given input index. is_variable must be checked
// during prepare when the full TfLiteTensor is available.
TfLiteEvalTensor* GetMutableEvalInput(const TfLiteContext* context,
@@ -243,6 +252,7 @@
TfLiteNode* node,
MicroGraph* graph_info,
int subgraph_idx) {
+ if (graph_info->NumSubgraphOutputs(subgraph_idx) == 0) return kTfLiteOk;
TF_LITE_ENSURE(context, static_cast<size_t>(node->outputs->size) ==
graph_info->NumSubgraphOutputs(subgraph_idx));
for (int i = 0; i < node->outputs->size; i++) {
diff --git a/tensorflow/lite/micro/kernels/kernel_util.h b/tensorflow/lite/micro/kernels/kernel_util.h
index 080a0b3..977ed95 100644
--- a/tensorflow/lite/micro/kernels/kernel_util.h
+++ b/tensorflow/lite/micro/kernels/kernel_util.h
@@ -1,4 +1,4 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -35,6 +35,10 @@
void (*free)(TfLiteContext* context, void* buffer) = nullptr,
void (*reset)(TfLiteContext* context, void* buffer) = nullptr);
+TFLMInferenceRegistration RegisterOp(
+ TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node),
+ void (*reset)(TfLiteContext* context, void* buffer) = nullptr);
+
// Prints out n bytes in a int8_t buffer as hex
void PrintNBytes(const int8_t* tensor_data, int n_bytes,
const char* prefix = nullptr);
@@ -87,6 +91,31 @@
: reinterpret_cast<const T*>(tensor->data.raw);
}
+#ifdef USE_TFLM_COMPRESSION
+
+// Overloads existing GetTensorData. If not compressed, this will return
+// tensor->data.
+//
+// TODO(ddavis-2015): make micro_context a const pointer
+template <typename T>
+const T* GetTensorData(MicroContext* micro_context,
+ const TfLiteEvalTensor* tensor,
+ const CompressionTensorData* compression_data,
+ int scratch_buffer_handle) {
+ if (tensor == nullptr) {
+ return nullptr;
+ }
+ if (compression_data == nullptr) {
+ return reinterpret_cast<const T*>(tensor->data.data);
+ }
+
+ void* uncompressed_data = micro_context->DecompressTensorToScratchBuffer(
+ *tensor, *compression_data, scratch_buffer_handle);
+ return reinterpret_cast<const T*>(uncompressed_data);
+}
+
+#endif // USE_TFLM_COMPRESSION
+
// Returns the shape of a TfLiteEvalTensor struct.
const RuntimeShape GetTensorShape(const TfLiteEvalTensor* tensor);
diff --git a/tensorflow/lite/micro/kernels/l2norm.cc b/tensorflow/lite/micro/kernels/l2norm.cc
index fa3601b..bde38de 100644
--- a/tensorflow/lite/micro/kernels/l2norm.cc
+++ b/tensorflow/lite/micro/kernels/l2norm.cc
@@ -33,7 +33,7 @@
constexpr int kInputTensor = 0;
constexpr int kOutputTensor = 0;
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus L2NormPrepare(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
TFLITE_DCHECK(node->builtin_data != nullptr);
@@ -72,13 +72,13 @@
return kTfLiteOk;
}
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* L2NormInit(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context,
sizeof(L2NormalizationParams));
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus L2NormEval(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
const L2NormalizationParams& data =
*(static_cast<const L2NormalizationParams*>(node->user_data));
@@ -132,7 +132,7 @@
} // namespace
TFLMRegistration Register_L2NORM_REF() {
- return tflite::micro::RegisterOp(Init, Prepare, Eval);
+ return tflite::micro::RegisterOp(L2NormInit, L2NormPrepare, L2NormEval);
}
TFLMRegistration Register_L2_NORMALIZATION() { return Register_L2NORM_REF(); }
diff --git a/tensorflow/lite/micro/kernels/lstm_eval_test.cc b/tensorflow/lite/micro/kernels/lstm_eval_test.cc
index 53c0d7c..eaba2c4 100644
--- a/tensorflow/lite/micro/kernels/lstm_eval_test.cc
+++ b/tensorflow/lite/micro/kernels/lstm_eval_test.cc
@@ -454,6 +454,6 @@
cell_state_tolerance,
int16_node_contents);
}
-
#endif // !defined(XTENSA)
+
TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/kernels/micro_ops.h b/tensorflow/lite/micro/kernels/micro_ops.h
index 5bffa09..2e33a67 100644
--- a/tensorflow/lite/micro/kernels/micro_ops.h
+++ b/tensorflow/lite/micro/kernels/micro_ops.h
@@ -40,6 +40,7 @@
TFLMRegistration Register_ARG_MIN();
TFLMRegistration Register_ASSIGN_VARIABLE();
TFLMRegistration Register_AVERAGE_POOL_2D();
+TFLMRegistration Register_BATCH_MATMUL();
TFLMRegistration Register_BATCH_TO_SPACE_ND();
TFLMRegistration Register_BROADCAST_ARGS();
TFLMRegistration Register_BROADCAST_TO();
@@ -133,6 +134,9 @@
TFLMRegistration Register_WHILE();
TFLMRegistration Register_ZEROS_LIKE();
+// TODO(b/295174388): Add the rest of inference only registration functions.
+TFLMInferenceRegistration RegisterInference_FULLY_CONNECTED();
+
// TODO(b/160234179): Change custom OPs to also return by value.
namespace tflm_signal {
TFLMRegistration* Register_DELAY();
@@ -144,6 +148,7 @@
TFLMRegistration* Register_ENERGY();
TFLMRegistration* Register_FRAMER();
TFLMRegistration* Register_OVERLAP_ADD();
+TFLMRegistration* Register_PCAN();
TFLMRegistration* Register_STACKER();
TFLMRegistration* Register_WINDOW();
} // namespace tflm_signal
diff --git a/tensorflow/lite/micro/kernels/mirror_pad.cc b/tensorflow/lite/micro/kernels/mirror_pad.cc
index 4cbaf52..aa94e1b 100644
--- a/tensorflow/lite/micro/kernels/mirror_pad.cc
+++ b/tensorflow/lite/micro/kernels/mirror_pad.cc
@@ -100,7 +100,7 @@
}
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus MirrorPadEval(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
TfLiteStatus status = kTfLiteOk;
const OpDataMirrorPad* data =
@@ -161,12 +161,12 @@
return status;
}
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* MirrorPadInit(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(OpDataMirrorPad));
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus MirrorPadPrepare(TfLiteContext* context, TfLiteNode* node) {
MicroContext* micro_context = GetMicroContext(context);
TFLITE_DCHECK(node->user_data != nullptr);
@@ -209,7 +209,8 @@
} // namespace
TFLMRegistration Register_MIRROR_PAD() {
- return tflite::micro::RegisterOp(Init, Prepare, Eval);
+ return tflite::micro::RegisterOp(MirrorPadInit, MirrorPadPrepare,
+ MirrorPadEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/neg.cc b/tensorflow/lite/micro/kernels/neg.cc
index c80a809..a76ac01 100644
--- a/tensorflow/lite/micro/kernels/neg.cc
+++ b/tensorflow/lite/micro/kernels/neg.cc
@@ -27,7 +27,7 @@
constexpr int kInputTensor = 0;
constexpr int kOutputTensor = 0;
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus NegEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kInputTensor);
TfLiteEvalTensor* output =
@@ -51,7 +51,7 @@
} // namespace
TFLMRegistration Register_NEG() {
- return tflite::micro::RegisterOp(nullptr, nullptr, Eval);
+ return tflite::micro::RegisterOp(nullptr, nullptr, NegEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/pack.cc b/tensorflow/lite/micro/kernels/pack.cc
index 5ee2759..0cfd91b 100644
--- a/tensorflow/lite/micro/kernels/pack.cc
+++ b/tensorflow/lite/micro/kernels/pack.cc
@@ -69,7 +69,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus PackEval(TfLiteContext* context, TfLiteNode* node) {
const TfLitePackParams* data =
reinterpret_cast<TfLitePackParams*>(node->builtin_data);
@@ -109,7 +109,7 @@
} // namespace
TFLMRegistration Register_PACK() {
- return tflite::micro::RegisterOp(nullptr, nullptr, Eval);
+ return tflite::micro::RegisterOp(nullptr, nullptr, PackEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/pad.cc b/tensorflow/lite/micro/kernels/pad.cc
index f8d40ad..29f08fa 100644
--- a/tensorflow/lite/micro/kernels/pad.cc
+++ b/tensorflow/lite/micro/kernels/pad.cc
@@ -32,12 +32,12 @@
int32_t output_zero_point;
};
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* PadInit(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(OpData));
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus PadEval(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
const OpData* data = static_cast<const OpData*>(node->user_data);
@@ -218,12 +218,12 @@
}
TFLMRegistration Register_PAD() {
- return tflite::micro::RegisterOp(Init, PadPrepare, Eval);
+ return tflite::micro::RegisterOp(PadInit, PadPrepare, PadEval);
}
// Also register Pad as PadV2.
TFLMRegistration Register_PADV2() {
- return tflite::micro::RegisterOp(Init, PadPrepare, Eval);
+ return tflite::micro::RegisterOp(PadInit, PadPrepare, PadEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/pooling.cc b/tensorflow/lite/micro/kernels/pooling.cc
index e03f72e..c178d9b 100644
--- a/tensorflow/lite/micro/kernels/pooling.cc
+++ b/tensorflow/lite/micro/kernels/pooling.cc
@@ -91,7 +91,7 @@
return kTfLiteOk;
}
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* PoolInit(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(OpDataPooling));
}
@@ -99,11 +99,11 @@
} // namespace
TFLMRegistration Register_AVERAGE_POOL_2D() {
- return tflite::micro::RegisterOp(Init, PoolingPrepare, AverageEval);
+ return tflite::micro::RegisterOp(PoolInit, PoolingPrepare, AverageEval);
}
TFLMRegistration Register_MAX_POOL_2D() {
- return tflite::micro::RegisterOp(Init, PoolingPrepare, MaxEval);
+ return tflite::micro::RegisterOp(PoolInit, PoolingPrepare, MaxEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/quantize.cc b/tensorflow/lite/micro/kernels/quantize.cc
index 1ac6942..ba11f19 100644
--- a/tensorflow/lite/micro/kernels/quantize.cc
+++ b/tensorflow/lite/micro/kernels/quantize.cc
@@ -25,7 +25,8 @@
namespace tflite {
namespace {
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* InitQuantizeReference(TfLiteContext* context, const char* buffer,
+ size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context,
sizeof(OpDataQuantizeReference));
@@ -34,8 +35,8 @@
} // namespace
TFLMRegistration Register_QUANTIZE() {
- return tflite::micro::RegisterOp(Init, PrepareQuantizeReference,
- EvalQuantizeReference);
+ return tflite::micro::RegisterOp(
+ InitQuantizeReference, PrepareQuantizeReference, EvalQuantizeReference);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/reduce.cc b/tensorflow/lite/micro/kernels/reduce.cc
index ab24a82..a689d3b 100644
--- a/tensorflow/lite/micro/kernels/reduce.cc
+++ b/tensorflow/lite/micro/kernels/reduce.cc
@@ -29,7 +29,9 @@
namespace tflite {
void* InitReduce(TfLiteContext* context, const char* buffer, size_t length) {
- return context->AllocatePersistentBuffer(context, sizeof(OpDataReduce));
+ void* op_data =
+ context->AllocatePersistentBuffer(context, sizeof(OpDataReduce));
+ return new (op_data) OpDataReduce();
}
TfLiteStatus PrepareMax(TfLiteContext* context, TfLiteNode* node) {
diff --git a/tensorflow/lite/micro/kernels/reduce_common.cc b/tensorflow/lite/micro/kernels/reduce_common.cc
index 0dab49c..2c1a92a 100644
--- a/tensorflow/lite/micro/kernels/reduce_common.cc
+++ b/tensorflow/lite/micro/kernels/reduce_common.cc
@@ -74,7 +74,9 @@
TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0);
TfLiteTensor* axis = micro_context->AllocateTempInputTensor(node, 1);
+ op_data->input_zp = input->params.zero_point;
op_data->input_scale = input->params.scale;
+ op_data->output_zp = output->params.zero_point;
op_data->output_scale = output->params.scale;
op_data->num_output_elements = NumElements(output);
diff --git a/tensorflow/lite/micro/kernels/reshape.cc b/tensorflow/lite/micro/kernels/reshape.cc
index 4e6c530..5527798 100644
--- a/tensorflow/lite/micro/kernels/reshape.cc
+++ b/tensorflow/lite/micro/kernels/reshape.cc
@@ -29,7 +29,7 @@
namespace tflite {
namespace {
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus EvalReshapeReference(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kReshapeInputTensor);
TfLiteEvalTensor* output =
@@ -52,7 +52,8 @@
} // namespace
TFLMRegistration Register_RESHAPE() {
- return tflite::micro::RegisterOp(nullptr, PrepareReshapeReference, Eval);
+ return tflite::micro::RegisterOp(nullptr, PrepareReshapeReference,
+ EvalReshapeReference);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/resize_bilinear.cc b/tensorflow/lite/micro/kernels/resize_bilinear.cc
index e701e03..ab54e81 100644
--- a/tensorflow/lite/micro/kernels/resize_bilinear.cc
+++ b/tensorflow/lite/micro/kernels/resize_bilinear.cc
@@ -30,7 +30,7 @@
constexpr int kSizeTensor = 1;
constexpr int kOutputTensor = 0;
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus ResizeBilinearPrepare(TfLiteContext* context, TfLiteNode* node) {
MicroContext* micro_context = GetMicroContext(context);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
@@ -66,7 +66,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus ResizeBilinearEval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteResizeBilinearParams*>(node->builtin_data);
@@ -110,7 +110,8 @@
} // namespace
TFLMRegistration Register_RESIZE_BILINEAR() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, ResizeBilinearPrepare,
+ ResizeBilinearEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/resize_nearest_neighbor.cc b/tensorflow/lite/micro/kernels/resize_nearest_neighbor.cc
index 46b6ea1..ef2d35d 100644
--- a/tensorflow/lite/micro/kernels/resize_nearest_neighbor.cc
+++ b/tensorflow/lite/micro/kernels/resize_nearest_neighbor.cc
@@ -31,7 +31,8 @@
constexpr int kSizeTensor = 1;
constexpr int kOutputTensor = 0;
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus ResizeNearestNeighborPrepare(TfLiteContext* context,
+ TfLiteNode* node) {
MicroContext* micro_context = GetMicroContext(context);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
@@ -65,7 +66,8 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus ResizeNearestNeighborEval(TfLiteContext* context,
+ TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteResizeNearestNeighborParams*>(node->builtin_data);
@@ -117,7 +119,8 @@
} // namespace
TFLMRegistration Register_RESIZE_NEAREST_NEIGHBOR() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, ResizeNearestNeighborPrepare,
+ ResizeNearestNeighborEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/round.cc b/tensorflow/lite/micro/kernels/round.cc
index a42349e..ae8e354 100644
--- a/tensorflow/lite/micro/kernels/round.cc
+++ b/tensorflow/lite/micro/kernels/round.cc
@@ -26,7 +26,7 @@
constexpr int kInputTensor = 0;
constexpr int kOutputTensor = 0;
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus RoundPrepare(TfLiteContext* context, TfLiteNode* node) {
MicroContext* micro_context = GetMicroContext(context);
TfLiteTensor* input =
@@ -50,7 +50,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus RoundEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kInputTensor);
TfLiteEvalTensor* output =
@@ -66,7 +66,7 @@
} // namespace
TFLMRegistration Register_ROUND() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, RoundPrepare, RoundEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/shape.cc b/tensorflow/lite/micro/kernels/shape.cc
index a39bfc0..d95e450 100644
--- a/tensorflow/lite/micro/kernels/shape.cc
+++ b/tensorflow/lite/micro/kernels/shape.cc
@@ -35,14 +35,14 @@
}
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus ShapePrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus ShapeEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kInputTensor);
TfLiteEvalTensor* output =
@@ -61,7 +61,7 @@
} // namespace
TFLMRegistration Register_SHAPE() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, ShapePrepare, ShapeEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/slice.cc b/tensorflow/lite/micro/kernels/slice.cc
index 973da18..7963425 100644
--- a/tensorflow/lite/micro/kernels/slice.cc
+++ b/tensorflow/lite/micro/kernels/slice.cc
@@ -44,7 +44,7 @@
}
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus SlicePrepare(TfLiteContext* context, TfLiteNode* node) {
MicroContext* micro_context = GetMicroContext(context);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
@@ -81,7 +81,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus SliceEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kInputTensor);
const TfLiteEvalTensor* begin =
@@ -158,7 +158,7 @@
} // namespace
TFLMRegistration Register_SLICE() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, SlicePrepare, SliceEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/space_to_batch_nd.cc b/tensorflow/lite/micro/kernels/space_to_batch_nd.cc
index 6b536ee..f8df149 100644
--- a/tensorflow/lite/micro/kernels/space_to_batch_nd.cc
+++ b/tensorflow/lite/micro/kernels/space_to_batch_nd.cc
@@ -39,12 +39,13 @@
const int kInputOutputMinDimensionNum = 3;
const int kInputOutputMaxDimensionNum = 4;
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* SpaceToBatchNDInit(TfLiteContext* context, const char* buffer,
+ size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(SpaceToBatchParams));
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus SpaceToBatchNDPrepare(TfLiteContext* context, TfLiteNode* node) {
MicroContext* micro_context = GetMicroContext(context);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
@@ -67,7 +68,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus SpaceToBatchNDEval(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
const SpaceToBatchParams& params =
*(static_cast<const SpaceToBatchParams*>(node->user_data));
@@ -115,7 +116,8 @@
} // namespace.
TFLMRegistration Register_SPACE_TO_BATCH_ND() {
- return tflite::micro::RegisterOp(Init, Prepare, Eval);
+ return tflite::micro::RegisterOp(SpaceToBatchNDInit, SpaceToBatchNDPrepare,
+ SpaceToBatchNDEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/split.cc b/tensorflow/lite/micro/kernels/split.cc
index aa87720..cae7074 100644
--- a/tensorflow/lite/micro/kernels/split.cc
+++ b/tensorflow/lite/micro/kernels/split.cc
@@ -67,7 +67,7 @@
return kTfLiteOk;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus SplitPrepare(TfLiteContext* context, TfLiteNode* node) {
MicroContext* micro_context = GetMicroContext(context);
TfLiteTensor* axis = micro_context->AllocateTempInputTensor(node, 0);
TF_LITE_ENSURE(context, axis != nullptr);
@@ -82,7 +82,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus SplitEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* axis = tflite::micro::GetEvalInput(context, node, 0);
const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 1);
@@ -119,7 +119,7 @@
} // namespace
TFLMRegistration Register_SPLIT() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, SplitPrepare, SplitEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/split_v.cc b/tensorflow/lite/micro/kernels/split_v.cc
index 6aed6f7..ad96a20 100644
--- a/tensorflow/lite/micro/kernels/split_v.cc
+++ b/tensorflow/lite/micro/kernels/split_v.cc
@@ -71,7 +71,7 @@
return kTfLiteOk;
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus SplitVPrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
MicroContext* micro_context = GetMicroContext(context);
@@ -85,7 +85,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus SplitVEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
const TfLiteEvalTensor* axis = tflite::micro::GetEvalInput(context, node, 2);
@@ -121,7 +121,7 @@
} // namespace
TFLMRegistration Register_SPLIT_V() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, SplitVPrepare, SplitVEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/squeeze.cc b/tensorflow/lite/micro/kernels/squeeze.cc
index e52ccab..3df1363 100644
--- a/tensorflow/lite/micro/kernels/squeeze.cc
+++ b/tensorflow/lite/micro/kernels/squeeze.cc
@@ -44,7 +44,7 @@
TfLiteTensor* output;
};
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus SqueezePrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
@@ -55,7 +55,7 @@
// Determines number of dimensions of output tensor after squeeze.
const TfLiteIntArray* input_dims = op_context.input->dims;
const TfLiteIntArray* output_dims = op_context.output->dims;
- const int* squeeze_dims = op_context.params->squeeze_dims;
+ const int32_t* squeeze_dims = op_context.params->squeeze_dims;
constexpr int max_squeeze_dims = 8;
TF_LITE_ENSURE(context, input_num_dims <= max_squeeze_dims);
@@ -87,7 +87,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus SqueezeEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
if (input->type == kTfLiteString) {
@@ -112,7 +112,7 @@
} // namespace
TFLMRegistration Register_SQUEEZE() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, SqueezePrepare, SqueezeEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/strided_slice.cc b/tensorflow/lite/micro/kernels/strided_slice.cc
index 4e60e6b..78507a7 100644
--- a/tensorflow/lite/micro/kernels/strided_slice.cc
+++ b/tensorflow/lite/micro/kernels/strided_slice.cc
@@ -14,146 +14,30 @@
==============================================================================*/
#include "tensorflow/lite/kernels/internal/reference/strided_slice.h"
-#include <cmath>
+#include <cstdint>
#include <cstring>
-#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/kernels/op_macros.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/strided_slice.h"
#include "tensorflow/lite/micro/micro_log.h"
namespace tflite {
namespace {
-constexpr int kInputTensor = 0;
-constexpr int kBeginTensor = 1;
-constexpr int kEndTensor = 2;
-constexpr int kStridesTensor = 3;
-constexpr int kOutputTensor = 0;
-
-struct StridedSliceContext {
- StridedSliceContext(TfLiteContext* context, TfLiteNode* node) {
- params = reinterpret_cast<TfLiteStridedSliceParams*>(node->builtin_data);
- micro_context = GetMicroContext(context);
- input = micro_context->AllocateTempInputTensor(node, kInputTensor);
- begin = micro_context->AllocateTempInputTensor(node, kBeginTensor);
- end = micro_context->AllocateTempInputTensor(node, kEndTensor);
- strides = micro_context->AllocateTempInputTensor(node, kStridesTensor);
- output = micro_context->AllocateTempOutputTensor(node, kOutputTensor);
- dims = NumDimensions(input);
- }
- ~StridedSliceContext() {
- micro_context->DeallocateTempTfLiteTensor(input);
- micro_context->DeallocateTempTfLiteTensor(begin);
- micro_context->DeallocateTempTfLiteTensor(end);
- micro_context->DeallocateTempTfLiteTensor(strides);
- micro_context->DeallocateTempTfLiteTensor(output);
- }
- const TfLiteStridedSliceParams* params;
- MicroContext* micro_context;
- TfLiteTensor* input;
- TfLiteTensor* begin;
- TfLiteTensor* end;
- TfLiteTensor* strides;
- TfLiteTensor* output;
- int dims;
-};
-
-// This Op only supports 1-4D cases and since we use the reference 4D
-// implementation, the 1-3D tensors are mapped to 4D.
-const int kMaxDim = 4;
-
-tflite::StridedSliceParams BuildStridedSliceParams(
- StridedSliceContext* op_context) {
- tflite::StridedSliceParams op_params{};
- op_params.start_indices_count = op_context->dims;
- op_params.stop_indices_count = op_context->dims;
- op_params.strides_count = op_context->dims;
-
- for (int i = 0; i < op_context->dims; ++i) {
- op_params.start_indices[i] = GetTensorData<int32_t>(op_context->begin)[i];
- op_params.stop_indices[i] = GetTensorData<int32_t>(op_context->end)[i];
- op_params.strides[i] = GetTensorData<int32_t>(op_context->strides)[i];
- }
-
- op_params.begin_mask = op_context->params->begin_mask;
- op_params.ellipsis_mask = 0;
- op_params.end_mask = op_context->params->end_mask;
- op_params.new_axis_mask = 0;
- op_params.shrink_axis_mask = op_context->params->shrink_axis_mask;
- return op_params;
-}
-
-// Processes the indexing tensors (begin, end and strides) to resize the
-// output tensor. This function is callable from both Prepare() and Eval() as
-// long as the caller ensures the indexing tensors are present.
-TfLiteStatus CheckOutputSize(TfLiteContext* context,
- StridedSliceContext* op_context) {
- using ::tflite::strided_slice::StartForAxis;
- using ::tflite::strided_slice::StopForAxis;
- TfLiteIntArray* output_shape = op_context->output->dims;
- int shape_size = 0;
- auto op_params = BuildStridedSliceParams(op_context);
- auto input_shape = GetTensorShape(op_context->input);
- for (int idx = 0; idx < op_context->dims; ++idx) {
- int32_t stride = GetTensorData<int32_t>(op_context->strides)[idx];
- TF_LITE_ENSURE_MSG(context, stride != 0, "stride value has to be non-zero");
- int32_t begin = StartForAxis(op_params, input_shape, idx);
- int32_t end = StopForAxis(op_params, input_shape, idx, begin);
-
- // When shrinking an axis, the end position does not matter (and can be
- // incorrect when negative indexing is used, see Issue #19260). Always use
- // begin + 1 to generate a length 1 slice, since begin has
- // already been adjusted for negative indices by StartForAxis.
- const bool shrink_axis = op_context->params->shrink_axis_mask & (1 << idx);
- if (shrink_axis) {
- end = begin + 1;
- }
-
- // This is valid for both positive and negative strides
- int32_t dim_shape = std::ceil((end - begin) / static_cast<float>(stride));
- dim_shape = dim_shape < 0 ? 0 : dim_shape;
- if (!shrink_axis) {
- TF_LITE_ENSURE_EQ(context, output_shape->data[shape_size], dim_shape);
- shape_size++;
- }
- }
- TF_LITE_ENSURE_EQ(context, output_shape->size, shape_size);
- return kTfLiteOk;
-}
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
- TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
- return context->AllocatePersistentBuffer(context, sizeof(StridedSliceParams));
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
- TFLITE_DCHECK(node->user_data != nullptr);
- StridedSliceParams* op_params =
- static_cast<StridedSliceParams*>(node->user_data);
- TF_LITE_ENSURE_EQ(context, NumInputs(node), 4);
- TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
- StridedSliceContext op_context(context, node);
- TF_LITE_ENSURE_MSG(context, op_context.dims <= kMaxDim,
- "input dim should not exceed 4");
- auto params = BuildStridedSliceParams(&op_context);
- memcpy(op_params, ¶ms, sizeof(StridedSliceParams));
- return CheckOutputSize(context, &op_context);
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus StridedSliceEval(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
const StridedSliceParams& op_params =
*(static_cast<const StridedSliceParams*>(node->user_data));
const TfLiteEvalTensor* input =
- tflite::micro::GetEvalInput(context, node, kInputTensor);
+ tflite::micro::GetEvalInput(context, node, kStridedSliceInputTensor);
TfLiteEvalTensor* output =
- tflite::micro::GetEvalOutput(context, node, kOutputTensor);
+ tflite::micro::GetEvalOutput(context, node, kStridedSliceOutputTensor);
switch (output->type) {
case kTfLiteFloat32:
reference_ops::StridedSlice(op_params,
@@ -201,7 +85,8 @@
} // namespace
TFLMRegistration Register_STRIDED_SLICE() {
- return tflite::micro::RegisterOp(Init, Prepare, Eval);
+ return tflite::micro::RegisterOp(StridedSliceInit, StridedSlicePrepare,
+ StridedSliceEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/strided_slice.h b/tensorflow/lite/micro/kernels/strided_slice.h
new file mode 100644
index 0000000..ea9413f
--- /dev/null
+++ b/tensorflow/lite/micro/kernels/strided_slice.h
@@ -0,0 +1,40 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_MICRO_KERNELS_STRIDED_SLICE_H_
+#define TENSORFLOW_LITE_MICRO_KERNELS_STRIDED_SLICE_H_
+
+#include <cstdint>
+
+#include "tensorflow/lite/c/builtin_op_data.h"
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/micro/micro_common.h"
+
+namespace tflite {
+
+constexpr int kStridedSliceInputTensor = 0;
+constexpr int kStridedSliceBeginTensor = 1;
+constexpr int kStridedSliceEndTensor = 2;
+constexpr int kStridedSliceStridesTensor = 3;
+constexpr int kStridedSliceOutputTensor = 0;
+
+void* StridedSliceInit(TfLiteContext* context, const char* buffer,
+ size_t length);
+
+TfLiteStatus StridedSlicePrepare(TfLiteContext* context, TfLiteNode* node);
+
+} // namespace tflite
+
+#endif // TENSORFLOW_LITE_MICRO_KERNELS_STRIDED_SLICE_H_
diff --git a/tensorflow/lite/micro/kernels/strided_slice_common.cc b/tensorflow/lite/micro/kernels/strided_slice_common.cc
new file mode 100644
index 0000000..165e1f3
--- /dev/null
+++ b/tensorflow/lite/micro/kernels/strided_slice_common.cc
@@ -0,0 +1,149 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include <cmath>
+#include <cstring>
+
+#include "tensorflow/lite/c/builtin_op_data.h"
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/kernels/internal/reference/strided_slice.h"
+#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/kernels/op_macros.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/strided_slice.h"
+#include "tensorflow/lite/micro/micro_log.h"
+
+namespace tflite {
+
+namespace {
+
+struct StridedSliceContext {
+ StridedSliceContext(TfLiteContext* context, TfLiteNode* node) {
+ params = reinterpret_cast<TfLiteStridedSliceParams*>(node->builtin_data);
+ micro_context = GetMicroContext(context);
+ input =
+ micro_context->AllocateTempInputTensor(node, kStridedSliceInputTensor);
+ begin =
+ micro_context->AllocateTempInputTensor(node, kStridedSliceBeginTensor);
+ end = micro_context->AllocateTempInputTensor(node, kStridedSliceEndTensor);
+ strides = micro_context->AllocateTempInputTensor(
+ node, kStridedSliceStridesTensor);
+ output = micro_context->AllocateTempOutputTensor(node,
+ kStridedSliceOutputTensor);
+ dims = NumDimensions(input);
+ }
+ ~StridedSliceContext() {
+ micro_context->DeallocateTempTfLiteTensor(input);
+ micro_context->DeallocateTempTfLiteTensor(begin);
+ micro_context->DeallocateTempTfLiteTensor(end);
+ micro_context->DeallocateTempTfLiteTensor(strides);
+ micro_context->DeallocateTempTfLiteTensor(output);
+ }
+ const TfLiteStridedSliceParams* params;
+ MicroContext* micro_context;
+ TfLiteTensor* input;
+ TfLiteTensor* begin;
+ TfLiteTensor* end;
+ TfLiteTensor* strides;
+ TfLiteTensor* output;
+ int dims;
+};
+
+// This Op only supports 1-4D cases and since we use the reference 4D
+// implementation, the 1-3D tensors are mapped to 4D.
+const int kMaxDim = 4;
+
+tflite::StridedSliceParams BuildStridedSliceParams(
+ StridedSliceContext* op_context) {
+ tflite::StridedSliceParams op_params{};
+ op_params.start_indices_count = op_context->dims;
+ op_params.stop_indices_count = op_context->dims;
+ op_params.strides_count = op_context->dims;
+
+ for (int i = 0; i < op_context->dims; ++i) {
+ op_params.start_indices[i] = GetTensorData<int32_t>(op_context->begin)[i];
+ op_params.stop_indices[i] = GetTensorData<int32_t>(op_context->end)[i];
+ op_params.strides[i] = GetTensorData<int32_t>(op_context->strides)[i];
+ }
+
+ op_params.begin_mask = op_context->params->begin_mask;
+ op_params.ellipsis_mask = 0;
+ op_params.end_mask = op_context->params->end_mask;
+ op_params.new_axis_mask = 0;
+ op_params.shrink_axis_mask = op_context->params->shrink_axis_mask;
+ return op_params;
+}
+
+// Processes the indexing tensors (begin, end and strides) to resize the
+// output tensor. This function is callable from both Prepare() and Eval() as
+// long as the caller ensures the indexing tensors are present.
+TfLiteStatus CheckOutputSize(TfLiteContext* context,
+ StridedSliceContext* op_context) {
+ using ::tflite::strided_slice::StartForAxis;
+ using ::tflite::strided_slice::StopForAxis;
+ TfLiteIntArray* output_shape = op_context->output->dims;
+ int shape_size = 0;
+ auto op_params = BuildStridedSliceParams(op_context);
+ auto input_shape = GetTensorShape(op_context->input);
+ for (int idx = 0; idx < op_context->dims; ++idx) {
+ int32_t stride = GetTensorData<int32_t>(op_context->strides)[idx];
+ TF_LITE_ENSURE_MSG(context, stride != 0, "stride value has to be non-zero");
+ int32_t begin = StartForAxis(op_params, input_shape, idx);
+ int32_t end = StopForAxis(op_params, input_shape, idx, begin);
+
+ // When shrinking an axis, the end position does not matter (and can be
+ // incorrect when negative indexing is used, see Issue #19260). Always use
+ // begin + 1 to generate a length 1 slice, since begin has
+ // already been adjusted for negative indices by StartForAxis.
+ const bool shrink_axis = op_context->params->shrink_axis_mask & (1 << idx);
+ if (shrink_axis) {
+ end = begin + 1;
+ }
+
+ // This is valid for both positive and negative strides
+ int32_t dim_shape = std::ceil((end - begin) / static_cast<float>(stride));
+ dim_shape = dim_shape < 0 ? 0 : dim_shape;
+ if (!shrink_axis) {
+ TF_LITE_ENSURE_EQ(context, output_shape->data[shape_size], dim_shape);
+ shape_size++;
+ }
+ }
+ TF_LITE_ENSURE_EQ(context, output_shape->size, shape_size);
+ return kTfLiteOk;
+}
+
+} // namespace
+
+void* StridedSliceInit(TfLiteContext* context, const char* buffer,
+ size_t length) {
+ TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
+ return context->AllocatePersistentBuffer(context, sizeof(StridedSliceParams));
+}
+
+TfLiteStatus StridedSlicePrepare(TfLiteContext* context, TfLiteNode* node) {
+ TFLITE_DCHECK(node->user_data != nullptr);
+ StridedSliceParams* op_params =
+ static_cast<StridedSliceParams*>(node->user_data);
+ TF_LITE_ENSURE_EQ(context, NumInputs(node), 4);
+ TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
+ StridedSliceContext op_context(context, node);
+ TF_LITE_ENSURE_MSG(context, op_context.dims <= kMaxDim,
+ "input dim should not exceed 4");
+ auto params = BuildStridedSliceParams(&op_context);
+ memcpy(op_params, ¶ms, sizeof(StridedSliceParams));
+ return CheckOutputSize(context, &op_context);
+}
+
+} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/svdf.cc b/tensorflow/lite/micro/kernels/svdf.cc
index 0ffb4b0..9e85c6b 100644
--- a/tensorflow/lite/micro/kernels/svdf.cc
+++ b/tensorflow/lite/micro/kernels/svdf.cc
@@ -32,12 +32,12 @@
namespace tflite {
namespace {
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* InitSvdf(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(OpDataSvdf));
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus EvalSvdf(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteSVDFParams*>(node->builtin_data);
TFLITE_DCHECK(node->user_data != nullptr);
const OpDataSvdf& data = *(static_cast<const OpDataSvdf*>(node->user_data));
@@ -99,7 +99,7 @@
} // namespace
TFLMRegistration Register_SVDF() {
- return tflite::micro::RegisterOp(Init, PrepareSvdf, Eval);
+ return tflite::micro::RegisterOp(InitSvdf, PrepareSvdf, EvalSvdf);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/testdata/BUILD b/tensorflow/lite/micro/kernels/testdata/BUILD
index 16df712..c93bc7d 100644
--- a/tensorflow/lite/micro/kernels/testdata/BUILD
+++ b/tensorflow/lite/micro/kernels/testdata/BUILD
@@ -1,3 +1,4 @@
+load("@rules_python//python:defs.bzl", "py_binary", "py_test")
load("@tflm_pip_deps//:requirements.bzl", "requirement")
package(
@@ -45,7 +46,7 @@
deps = [
"@absl_py//absl:app",
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
],
)
diff --git a/tensorflow/lite/micro/kernels/testdata/lstm_test_data.cc b/tensorflow/lite/micro/kernels/testdata/lstm_test_data.cc
index 4d7d9d9..0fc010b 100644
--- a/tensorflow/lite/micro/kernels/testdata/lstm_test_data.cc
+++ b/tensorflow/lite/micro/kernels/testdata/lstm_test_data.cc
@@ -251,11 +251,12 @@
// state quantization parameters
quantization_settings.input = {/*scale=*/3.0518044e-5, /*zp=*/0,
- /*symmetry=*/false};
- quantization_settings.output = {/*scale=*/1.8310826e-5, /*zp=*/-5461,
- /*symmetry=*/false};
- quantization_settings.hidden_state = {/*scale=*/1.8310826e-5, /*zp=*/-5461,
- /*symmetry=*/false};
+ /*symmetry=*/true};
+ quantization_settings.output = {/*scale=*/2.1362956633198035e-05, /*zp=*/0,
+ /*symmetry=*/true};
+ quantization_settings.hidden_state = {/*scale=*/2.1362956633198035e-05,
+ /*zp=*/0,
+ /*symmetry=*/true};
quantization_settings.cell_state = {/*scale=*/0.00024414062, /*zp=*/0,
/*symmetry=*/true};
diff --git a/tensorflow/lite/micro/kernels/testdata/lstm_test_data.h b/tensorflow/lite/micro/kernels/testdata/lstm_test_data.h
index 3edf420..932b832 100644
--- a/tensorflow/lite/micro/kernels/testdata/lstm_test_data.h
+++ b/tensorflow/lite/micro/kernels/testdata/lstm_test_data.h
@@ -390,9 +390,9 @@
int state_size_[3] = {2, batch_size, state_dimension};
// see lstm_shared.h for tensor names, the last tensor is the output tensor
- TfLiteTensor tensors_[24 + 1];
+ TfLiteTensor tensors_[24 + 1] = {};
// Use for internel kernel testing
- TfLiteEvalTensor eval_tensors_[24 + 1];
+ TfLiteEvalTensor eval_tensors_[24 + 1] = {};
// indices for the tensors inside the node (required by kernel runner)
int input_tensor_indices_[1 + 24] = {};
// single output (last in the tensors array)
diff --git a/tensorflow/lite/micro/kernels/testdata/lstm_test_data_generator.py b/tensorflow/lite/micro/kernels/testdata/lstm_test_data_generator.py
index 97c8798..c6553fe 100644
--- a/tensorflow/lite/micro/kernels/testdata/lstm_test_data_generator.py
+++ b/tensorflow/lite/micro/kernels/testdata/lstm_test_data_generator.py
@@ -17,15 +17,15 @@
2. Print the intermediate step outputs inside the LSTM for a single step LSTM invocation (Get2X2GateOutputCheckData in .cc)
3. Print the outputs for multi-step LSTM invocation (Get2X2LstmEvalCheckData in .cc)
-Every invocation gives three types information:
-1. Quantized output: kernel output in integer
+Every invocation gives three types information:
+1. Quantized output: kernel output in integer
2. Dequantized output: Quantized output in floating point representation
3. Float output: output from the floating point computation (i.e., float kernel)
-Note:
+Note:
1. Change quantization settings in _KERNEL_CONFIG to see the outcomes from various quantization schema (e.g., 8x8 Vs. 16x8)
2. Only single batch inference is supporte here. Change _GATE_TEST_DATA or _MULTISTEP_TEST_DATA to see kernel outputs on different input data
-3. The quantization computation here is not the exact as the c++ implementation. The integer calculation is mimiced here using floating point.
+3. The quantization computation here is not the exact as the c++ implementation. The integer calculation is emulated here using floating point.
No fixed point math is implemented here. The purpose is to illustrate the computation procedure and possible quantization error accumulation, not for bit exactness.
"""
from absl import app
@@ -88,7 +88,7 @@
_MULTISTEP_TEST_DATA = {
'init_hidden_state_vals': [0, 0],
'init_cell_state_vals': [0, 0],
- 'input_data': [0.2, 0.3, 0.2, 0.3, 0.2, 0.3], # three time steps
+ 'input_data': [0.2, 0.3, 0.2, 0.3, 0.2, 0.3], # three time steps
'hidden_state_range': (-0.5, 0.7),
'cell_state_range': [-8, 8],
'input_data_range': [-1, 1]
diff --git a/tensorflow/lite/micro/kernels/testdata/lstm_test_data_utils.py b/tensorflow/lite/micro/kernels/testdata/lstm_test_data_utils.py
index 345b143..142a58c 100644
--- a/tensorflow/lite/micro/kernels/testdata/lstm_test_data_utils.py
+++ b/tensorflow/lite/micro/kernels/testdata/lstm_test_data_utils.py
@@ -92,14 +92,14 @@
def quantized_sigmoid(input, input_scale, output_scale, num_bits=16):
- """Sigmoid (interger)"""
+ """Sigmoid (integer)"""
float_input = input * input_scale
float_result = sigmoid(float_input)
return quantize_data(float_result, output_scale, bit_width=num_bits)
def quantized_tanh(input, input_scale, output_scale, num_bits=16):
- """Tanh (interger)"""
+ """Tanh (integer)"""
float_input = input * input_scale
float_result = np.tanh(float_input)
return quantize_data(float_result, output_scale, bit_width=num_bits)
diff --git a/tensorflow/lite/micro/kernels/transpose.cc b/tensorflow/lite/micro/kernels/transpose.cc
index c57812b..915def5 100644
--- a/tensorflow/lite/micro/kernels/transpose.cc
+++ b/tensorflow/lite/micro/kernels/transpose.cc
@@ -46,7 +46,7 @@
TfLiteTensor* output;
};
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus TransposePrepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
@@ -72,7 +72,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus TransposeEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* perm_tensor =
tflite::micro::GetEvalInput(context, node, kPermTensor);
const int32_t* perm_data = perm_tensor->data.i32;
@@ -123,6 +123,6 @@
} // namespace
TFLMRegistration Register_TRANSPOSE() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, TransposePrepare, TransposeEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/transpose_conv.cc b/tensorflow/lite/micro/kernels/transpose_conv.cc
index a2ac2b4..ea0efae 100644
--- a/tensorflow/lite/micro/kernels/transpose_conv.cc
+++ b/tensorflow/lite/micro/kernels/transpose_conv.cc
@@ -15,6 +15,9 @@
#include "tensorflow/lite/kernels/internal/reference/transpose_conv.h"
+#include <cstddef>
+#include <cstdint>
+
#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/common.h"
@@ -48,8 +51,9 @@
// A scratch buffer is required for quantized implementations.
int scratch_buffer_index;
- // TODO(b/192090531): Remove this once all 8x16 transpose conv models use
- // 64-bit biases.
+ // Index to the converted 64-bit bias buffer from 16-bit bias. This is
+ // required to handle 16x8 transpose convolutions where a 16-bit bias is
+ // provided, whereas the kernel expects 64-bit biases.
int bias_converted_buffer_index;
// Multiplier and shift arrays are required for the int8 implementation.
@@ -123,7 +127,9 @@
if (input->type == kTfLiteInt16) {
TFLITE_DCHECK(filter->type == kTfLiteInt8);
TFLITE_DCHECK(output->type == kTfLiteInt16);
- if (bias->type == kTfLiteInt16) {
+ // Handle the case where the bias is 16 bits for 16x8 transpose
+ // convolution where the kernel actually expects 64-bit biases.
+ if (bias != nullptr && bias->type == kTfLiteInt16) {
TFLITE_DCHECK(
context->RequestScratchBufferInArena(
context, GetTensorShape(bias).FlatSize() * sizeof(std::int64_t),
@@ -141,12 +147,13 @@
return kTfLiteOk;
}
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* TransposeConvInit(TfLiteContext* context, const char* buffer,
+ size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(OpData));
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus TransposeConvPrepare(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
TFLITE_DCHECK(node->builtin_data != nullptr);
@@ -243,7 +250,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus TransposeConvEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kInputTensor);
const TfLiteEvalTensor* filter =
@@ -298,12 +305,10 @@
break;
}
case kTfLiteInt16: {
- std::int64_t* scratch_buffer = static_cast<int64_t*>(
+ auto* scratch_buffer = static_cast<int64_t*>(
context->GetScratchBuffer(context, data.scratch_buffer_index));
- // TODO(b/192090531): Remove this once all 8x16 transpose conv models use
- // 64-bit biases.
if (bias != nullptr && bias->type == kTfLiteInt16) {
- std::int64_t* bias_converted_buffer =
+ auto* bias_converted_buffer =
static_cast<int64_t*>(context->GetScratchBuffer(
context, data.bias_converted_buffer_index));
for (int i = 0; i < tflite::micro::GetTensorShape(bias).FlatSize();
@@ -346,7 +351,8 @@
} // namespace
TFLMRegistration Register_TRANSPOSE_CONV() {
- return tflite::micro::RegisterOp(Init, Prepare, Eval);
+ return tflite::micro::RegisterOp(TransposeConvInit, TransposeConvPrepare,
+ TransposeConvEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/transpose_conv.h b/tensorflow/lite/micro/kernels/transpose_conv.h
new file mode 100644
index 0000000..3a99ccb
--- /dev/null
+++ b/tensorflow/lite/micro/kernels/transpose_conv.h
@@ -0,0 +1,50 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef TENSORFLOW_LITE_MICRO_KERNELS_TRANSPOSE_CONV_H_
+#define TENSORFLOW_LITE_MICRO_KERNELS_TRANSPOSE_CONV_H_
+
+#include <cstdint>
+
+#include "tensorflow/lite/c/builtin_op_data.h"
+#include "tensorflow/lite/kernels/internal/types.h"
+#include "tensorflow/lite/micro/micro_common.h"
+
+namespace tflite {
+
+// This is the most generic TFLMRegistration. The actual supported types
+// may still be target dependent. The only requirement is that every
+// implementation (reference or optimized) must define this function.
+TFLMRegistration Register_TRANSPOSE_CONV();
+
+#if defined(CMSIS_NN)
+// Returns a TFLMRegistration struct for kernel variant that only supports
+// int8.
+TFLMRegistration Register_TRANSPOSE_CONV_INT8();
+
+#else
+// Note that while this block gets used for both reference and optimized kernels
+// that do not have any specialized implementations, the only goal here is to
+// define fallback implementation that allow reference kernels to still be used
+// from applications that call a more specific kernel variant.
+
+inline TFLMRegistration Register_TRANSPOSE_CONV_INT8() {
+ return Register_TRANSPOSE_CONV();
+}
+
+#endif
+
+} // namespace tflite
+
+#endif // TENSORFLOW_LITE_MICRO_KERNELS_TRANSPOSE_CONV_H_
diff --git a/tensorflow/lite/micro/kernels/transpose_conv_test.cc b/tensorflow/lite/micro/kernels/transpose_conv_test.cc
index 0ddb3b2..49d2c90 100644
--- a/tensorflow/lite/micro/kernels/transpose_conv_test.cc
+++ b/tensorflow/lite/micro/kernels/transpose_conv_test.cc
@@ -53,7 +53,8 @@
1, // stride_height
kTfLiteActNone,
1,
- 1};
+ 1,
+ kTfLiteNoType};
template <typename T>
TfLiteStatus InvokeTransposeConv(TfLiteTensor* tensors, int tensors_size,
@@ -253,7 +254,8 @@
1, // stride_height
kTfLiteActRelu,
1,
- 1};
+ 1,
+ kTfLiteNoType};
TF_LITE_MICRO_EXPECT_EQ(
kTfLiteOk, tflite::testing::TestTransposeConvFloat(
@@ -276,7 +278,8 @@
3, // stride_height
kTfLiteActRelu,
1,
- 1};
+ 1,
+ kTfLiteNoType};
TF_LITE_MICRO_EXPECT_EQ(
kTfLiteOk, tflite::testing::TestTransposeConvFloat(
@@ -304,7 +307,8 @@
2, // stride_height
kTfLiteActRelu,
1,
- 1};
+ 1,
+ kTfLiteNoType};
TF_LITE_MICRO_EXPECT_EQ(
kTfLiteOk,
diff --git a/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm.h b/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm.h
index 16aa23b..46f6b2d 100644
--- a/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm.h
+++ b/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm.h
@@ -1,4 +1,4 @@
-/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -36,10 +36,19 @@
// implementations.
TFLMRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM_INT8();
+// Returns a TFLMRegistration struct for kernel variant that only supports
+// int16 activations and int8 weights and uses the latency optimized
+// implementations.
+TFLMRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM_INT16();
+
#else
inline TFLMRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM_INT8() {
return Register_UNIDIRECTIONAL_SEQUENCE_LSTM();
}
+
+inline TFLMRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM_INT16() {
+ return Register_UNIDIRECTIONAL_SEQUENCE_LSTM();
+}
#endif
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm_test.cc b/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm_test.cc
index c85e56f..1e5a868 100644
--- a/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm_test.cc
+++ b/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm_test.cc
@@ -1,4 +1,4 @@
-/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -28,7 +28,6 @@
namespace {
constexpr int kLstmMaxNumInputOutputTensors = 24 + 1;
-constexpr int kLstmIntermediateTensorBase = kLstmMaxNumInputOutputTensors + 1;
// Validate the output result array with golden values
template <typename T>
@@ -50,42 +49,20 @@
LstmNodeContent<ActivationType, WeightType, BiasType, CellType, batch_size,
time_steps, input_dimension, state_dimension>&
node_contents) {
- TfLiteTensor tensors[kLstmMaxNumInputOutputTensors + 1 + 5];
- memcpy(tensors, node_contents.GetTensors(),
- kLstmMaxNumInputOutputTensors * sizeof(TfLiteTensor));
-
- // Provide also intermediate tensors needed by older LSTM implementations
- int intermediate_array_data[6] = {5,
- kLstmIntermediateTensorBase,
- kLstmIntermediateTensorBase + 1,
- kLstmIntermediateTensorBase + 2,
- kLstmIntermediateTensorBase + 3,
- kLstmIntermediateTensorBase + 4};
- int input_zero_points[2] = {1, -21};
- float input_scales[2] = {1, 0.004705882165580988};
- TfLiteAffineQuantization input_quant = {
- tflite::testing::FloatArrayFromFloats(input_scales),
- tflite::testing::IntArrayFromInts(input_zero_points), 0};
- int intermediate_dim[2] = {1, 0};
- for (int i = 0; i < 5; ++i) {
- tensors[kLstmIntermediateTensorBase + i] =
- CreateTensor<int16_t>(nullptr, IntArrayFromInts(intermediate_dim));
- tensors[kLstmIntermediateTensorBase + i].quantization = {
- kTfLiteAffineQuantization, &input_quant};
- }
-
const TFLMRegistration registration = Register_UNIDIRECTIONAL_SEQUENCE_LSTM();
auto buildin_data = node_contents.BuiltinData();
micro::KernelRunner runner(
- registration, tensors, kLstmMaxNumInputOutputTensors + 1 + 5,
+ registration, node_contents.GetTensors(), kLstmMaxNumInputOutputTensors,
node_contents.KernelInputs(), node_contents.KernelOutputs(),
- reinterpret_cast<void*>(&buildin_data),
- IntArrayFromInts(intermediate_array_data));
+ reinterpret_cast<void*>(&buildin_data));
TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.InitAndPrepare());
TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.Invoke());
const auto& quantization_settings = node_contents.QuantizationSettings();
+// CMSIS-NN does not use the hidden state and cell state tensors so these tests
+// fail.
+#if !defined(CMSIS_NN)
float dequantized_hidden_state[batch_size * state_dimension] = {};
Dequantize(node_contents.GetHiddenStateData(), batch_size * state_dimension,
quantization_settings.hidden_state.scale,
@@ -104,6 +81,7 @@
ValidateResultGoldens(eval_check_data.expected_cell_state,
dequantized_cell_state, batch_size * state_dimension,
cell_state_tolerance);
+#endif
float dequantized_output[batch_size * state_dimension * time_steps] = {};
Dequantize(node_contents.GetOutputData(),
@@ -150,7 +128,6 @@
TF_LITE_MICRO_TESTS_BEGIN
// TODO(b/230666079) enable below tests for xtensa when the xtensa
// kernel is reconciled with reference kernel
-#if !defined(XTENSA)
TF_LITE_MICRO_TEST(TestUnidirectionalLSTMFloat) {
const tflite::testing::LstmEvalCheckData<12, 4, 12> kernel_eval_data =
tflite::testing::Get2X2LstmEvalCheckData();
@@ -193,5 +170,4 @@
kernel_eval_data, hidden_state_tolerance, cell_state_tolerance,
int16_node_contents);
}
-#endif // !defined(XTENSA)
TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/kernels/unpack.cc b/tensorflow/lite/micro/kernels/unpack.cc
index 3ce4c33..9ce1683 100644
--- a/tensorflow/lite/micro/kernels/unpack.cc
+++ b/tensorflow/lite/micro/kernels/unpack.cc
@@ -72,7 +72,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus UnpackEval(TfLiteContext* context, TfLiteNode* node) {
TfLiteUnpackParams* data =
reinterpret_cast<TfLiteUnpackParams*>(node->builtin_data);
@@ -102,7 +102,7 @@
} // namespace
TFLMRegistration Register_UNPACK() {
- return tflite::micro::RegisterOp(nullptr, nullptr, Eval);
+ return tflite::micro::RegisterOp(nullptr, nullptr, UnpackEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/var_handle.cc b/tensorflow/lite/micro/kernels/var_handle.cc
index 06087f7..0efb28c 100644
--- a/tensorflow/lite/micro/kernels/var_handle.cc
+++ b/tensorflow/lite/micro/kernels/var_handle.cc
@@ -36,12 +36,12 @@
int32_t resource_id;
};
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* VarHandleInit(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(OpData));
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus VarHandlePrepare(TfLiteContext* context, TfLiteNode* node) {
OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
const auto* params =
reinterpret_cast<const TfLiteVarHandleParams*>(node->builtin_data);
@@ -72,7 +72,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus VarHandleEval(TfLiteContext* context, TfLiteNode* node) {
OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);
@@ -87,7 +87,8 @@
} // namespace.
TFLMRegistration Register_VAR_HANDLE() {
- return tflite::micro::RegisterOp(Init, Prepare, Eval);
+ return tflite::micro::RegisterOp(VarHandleInit, VarHandlePrepare,
+ VarHandleEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/while.cc b/tensorflow/lite/micro/kernels/while.cc
index 097a342..a11adeb 100644
--- a/tensorflow/lite/micro/kernels/while.cc
+++ b/tensorflow/lite/micro/kernels/while.cc
@@ -36,12 +36,12 @@
int body_subgraph_index;
};
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+void* WhileInit(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(OpData));
}
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus WhilePrepare(TfLiteContext* context, TfLiteNode* node) {
OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
const auto* params =
reinterpret_cast<const TfLiteWhileParams*>(node->builtin_data);
@@ -74,7 +74,7 @@
return kTfLiteOk;
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus WhileEval(TfLiteContext* context, TfLiteNode* node) {
const OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
tflite::MicroContext* micro_context = tflite::GetMicroContext(context);
@@ -127,7 +127,7 @@
} // namespace.
TFLMRegistration Register_WHILE() {
- return tflite::micro::RegisterOp(Init, Prepare, Eval);
+ return tflite::micro::RegisterOp(WhileInit, WhilePrepare, WhileEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/xtensa/add.cc b/tensorflow/lite/micro/kernels/xtensa/add.cc
index 4e4f805..d3c2371 100644
--- a/tensorflow/lite/micro/kernels/xtensa/add.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/add.cc
@@ -113,11 +113,11 @@
op_params.output_shift = data->output_shift;
SetActivationParams(data->output_activation_min, data->output_activation_max,
&op_params);
-#if !(defined(HIFI4))
+#if !(defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
bool need_broadcast = reference_ops::ProcessBroadcastShapes(
tflite::micro::GetTensorShape(input1),
tflite::micro::GetTensorShape(input2), &op_params);
-#endif // !defined(HIFI4)
+#endif // !defined(HIFI3) && !defined(HIFI4) && !defined(HIFI5)
switch (output->type) {
case kTfLiteInt8: {
@@ -126,7 +126,7 @@
*(reinterpret_cast<XtensaAddOpData*>(node->user_data));
AddEvalQuantizedVision(context, node, *params, op_data, input1, input2,
output);
-#elif defined(HIFI4) // defined(VISION_P6)
+#elif defined(HIFI3) || defined(HIFI4) || defined(HIFI5) // defined(VISION_P6)
int err;
const RuntimeShape extended_input1_shape =
RuntimeShape::ExtendedShape(4, tflite::micro::GetTensorShape(input1));
@@ -150,7 +150,7 @@
op_params.left_shift);
TF_LITE_ENSURE(context, err == 0);
-#else // defined(VISION_P6)
+#else // defined(VISION_P6)
if (need_broadcast) {
reference_integer_ops::BroadcastAdd4DSlow(
op_params, tflite::micro::GetTensorShape(input1),
@@ -168,11 +168,11 @@
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int8_t>(output));
}
-#endif // defined(VISION_P6)
+#endif // defined(VISION_P6)
break;
}
case kTfLiteInt16: {
-#if defined(HIFI4)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
int err;
const RuntimeShape extended_input1_shape =
RuntimeShape::ExtendedShape(4, tflite::micro::GetTensorShape(input1));
@@ -196,7 +196,7 @@
op_params.left_shift);
TF_LITE_ENSURE(context, err == 0);
-#else // defined(HIFI4)
+#else // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
if (need_broadcast) {
reference_ops::BroadcastAdd4DSlow(
op_params, tflite::micro::GetTensorShape(input1),
@@ -214,7 +214,7 @@
tflite::micro::GetTensorData<int16_t>(output),
false);
}
-#endif // defined(HIFI4)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
break;
}
default:
diff --git a/tensorflow/lite/micro/kernels/xtensa/conv.cc b/tensorflow/lite/micro/kernels/xtensa/conv.cc
index 59e576c..384dba9 100644
--- a/tensorflow/lite/micro/kernels/xtensa/conv.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/conv.cc
@@ -1,4 +1,4 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -32,31 +32,6 @@
namespace tflite {
namespace {
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
- TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
- void* data =
- context->AllocatePersistentBuffer(context, sizeof(XtensaConvOpData));
-#if defined(VISION_P6)
- if (InitXtensaContext()) {
- return nullptr;
- }
-#endif // defined(VISION_P6)
-
- return data;
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
- TF_LITE_ENSURE_OK(context, ConvPrepare(context, node));
-
-#if defined(HIFI4) || defined(HIFI5)
- TF_LITE_ENSURE_OK(context, ConvPrepareHifi(context, node));
-#endif
-#if defined(VISION_P6)
- TF_LITE_ENSURE_OK(context, ConvPrepareVision(context, node));
-#endif // VISION_P6
- return kTfLiteOk;
-}
-
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
TFLITE_DCHECK(node->builtin_data != nullptr);
@@ -73,68 +48,76 @@
const TfLiteEvalTensor* filter =
tflite::micro::GetEvalInput(context, node, kConvWeightsTensor);
const TfLiteEvalTensor* bias =
- (NumInputs(node) == 3)
- ? tflite::micro::GetEvalInput(context, node, kConvBiasTensor)
- : nullptr;
-
- TfLiteEvalTensor filter_int8 = tflite::micro::MakeUnpackedInt4Tensor(
- context, op_data.reference_op_data.filter_buffer_index, filter);
+ tflite::micro::GetEvalInput(context, node, kConvBiasTensor);
switch (input->type) {
+ case kTfLiteFloat32: {
+ tflite::reference_ops::Conv(
+ ConvParamsFloat(params, op_data.reference_op_data),
+ tflite::micro::GetTensorShape(input),
+ tflite::micro::GetTensorData<float>(input),
+ tflite::micro::GetTensorShape(filter),
+ tflite::micro::GetTensorData<float>(filter),
+ tflite::micro::GetTensorShape(bias),
+ tflite::micro::GetOptionalTensorData<float>(bias),
+ tflite::micro::GetTensorShape(output),
+ tflite::micro::GetTensorData<float>(output),
+ tflite::micro::GetTensorShape(nullptr), nullptr);
+ break;
+ }
case kTfLiteInt8: {
- switch (filter_int8.type) {
- case kTfLiteInt8: {
-#if defined(HIFI4) || defined(HIFI5)
- ConvEvalHifi(context, node, params, op_data, input, &filter_int8,
- bias, output);
-#elif defined(VISION_P6)
- return ConvEvalVision(context, node, params, op_data, input,
- &filter_int8, bias, output);
-#else
- reference_integer_ops::ConvPerChannel(
- ConvParamsQuantized(params, op_data.reference_op_data),
- op_data.reference_op_data.per_channel_output_multiplier,
- op_data.reference_op_data.per_channel_output_shift,
- tflite::micro::GetTensorShape(input),
- tflite::micro::GetTensorData<int8_t>(input),
- tflite::micro::GetTensorShape(filter),
- tflite::micro::GetTensorData<int8_t>(&filter_int8),
- tflite::micro::GetTensorShape(bias),
- tflite::micro::GetOptionalTensorData<int32_t>(bias),
- tflite::micro::GetTensorShape(output),
- tflite::micro::GetTensorData<int8_t>(output));
- return kTfLiteOk;
-#endif
- break;
- }
-
- default:
- MicroPrintf("Filter type %s (%d) not supported.",
- TfLiteTypeGetName(filter->type), filter->type);
- return kTfLiteError;
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+ if (params.dilation_width_factor == 1 &&
+ params.dilation_height_factor == 1) {
+ return ConvEvalHifiInt8(context, node, params, op_data, input, filter,
+ bias, output);
+ } else {
+ return ConvReferenceEvalInt8(context, node);
}
- return kTfLiteOk;
+#elif defined(VISION_P6)
+ // At this time the optimized implementation is failing the unit tests in
+ // ways that are not entirely clear why. For now, we have identified some
+ // of the problem cases and are manually inserting a reference fallback.
+ // See http://b/270720625 for more details.
+ if (op_data.is_per_channel_quantized ||
+ input->dims->data[1] != input->dims->data[2]) {
+ return ConvReferenceEvalInt8(context, node);
+ } else {
+ return ConvEvalVision(context, node, params, op_data, input, filter,
+ bias, output);
+ }
+#else
+ return ConvReferenceEvalInt8(context, node);
+#endif
}
case kTfLiteInt16: {
-#if defined(HIFI4)
- ConvEvalHifi16(context, node, params, op_data, input, filter, bias,
- output);
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+ // Note that int32 bias is not widely supported and might be risky (e.g.
+ // http://b/262003750). As such, while we have a fallback to the reference
+ // implementation, production use-cases should only have int64 bias.
+ if (bias->type == kTfLiteInt32) {
+ return ConvReferenceEvalInt16(context, node);
+ } else {
+ return ConvEvalHifiInt16(context, node, params, op_data, input, filter,
+ bias, output);
+ }
#else
return ConvReferenceEvalInt16(context, node);
-#endif // defined(HIFI4)
- break;
+#endif
}
default:
MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
input->type);
return kTfLiteError;
}
+
return kTfLiteOk;
}
+
} // namespace
TFLMRegistration Register_CONV_2D() {
- return tflite::micro::RegisterOp(Init, Prepare, Eval);
+ return tflite::micro::RegisterOp(ConvInitXtensa, ConvPrepareXtensa, Eval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/xtensa/conv_common_xtensa.cc b/tensorflow/lite/micro/kernels/xtensa/conv_common_xtensa.cc
new file mode 100644
index 0000000..3063e77
--- /dev/null
+++ b/tensorflow/lite/micro/kernels/xtensa/conv_common_xtensa.cc
@@ -0,0 +1,56 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/lite/c/builtin_op_data.h"
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/kernels/internal/common.h"
+#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/conv.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/xtensa/xtensa.h"
+#include "tensorflow/lite/micro/kernels/xtensa/xtensa_conv.h"
+
+namespace tflite {
+
+void* ConvInitXtensa(TfLiteContext* context, const char* buffer,
+ size_t length) {
+ TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
+ void* data =
+ context->AllocatePersistentBuffer(context, sizeof(XtensaConvOpData));
+#if defined(VISION_P6)
+ if (InitXtensaContext()) {
+ return nullptr;
+ }
+#endif // defined(VISION_P6)
+
+ return data;
+}
+
+TfLiteStatus ConvPrepareXtensa(TfLiteContext* context, TfLiteNode* node) {
+ TF_LITE_ENSURE_OK(context, ConvPrepare(context, node));
+
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+ TF_LITE_ENSURE_OK(context, ConvPrepareHifi(context, node));
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+
+#if defined(VISION_P6)
+ TF_LITE_ENSURE_OK(context, ConvPrepareVision(context, node));
+#endif // defined(VISION_P6)
+
+ return kTfLiteOk;
+}
+
+} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/xtensa/conv_hifi.cc b/tensorflow/lite/micro/kernels/xtensa/conv_hifi.cc
index 487c84a..1d2d7ec 100644
--- a/tensorflow/lite/micro/kernels/xtensa/conv_hifi.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/conv_hifi.cc
@@ -1,4 +1,4 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -13,14 +13,14 @@
limitations under the License.
==============================================================================*/
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
#include <cstdint>
#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/common.h"
-#include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h"
+#include "tensorflow/lite/kernels/internal/portable_tensor_utils.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/micro/kernels/conv.h"
@@ -39,17 +39,43 @@
// Calculate scratch memory requirements and request scratch buffer
TfLiteTensor* output =
micro_context->AllocateTempOutputTensor(node, kConvOutputTensor);
- TF_LITE_ENSURE(context, output != nullptr);
TfLiteTensor* input =
micro_context->AllocateTempInputTensor(node, kConvInputTensor);
- TF_LITE_ENSURE(context, input != nullptr);
TfLiteTensor* filter =
micro_context->AllocateTempInputTensor(node, kConvWeightsTensor);
- TF_LITE_ENSURE(context, filter != nullptr);
+ TfLiteTensor* bias =
+ micro_context->AllocateTempInputTensor(node, kConvBiasTensor);
const RuntimeShape& input_shape = GetTensorShape(input);
const RuntimeShape& filter_shape = GetTensorShape(filter);
const RuntimeShape& output_shape = GetTensorShape(output);
+
+ // Check if the Xtensa optimized code can be used
+ // HIFI4 and HIFI5 do not allow bias data pointer to be nullptr
+ /* TODO(b/277112516): Dilation is currently not supported on HiFi 4 NN Library
+ */
+ bool inputs_and_bias_ok = bias != nullptr;
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+ inputs_and_bias_ok =
+ inputs_and_bias_ok &&
+ (input->type == kTfLiteInt8 ||
+ (input->type == kTfLiteInt16 && bias->type == kTfLiteInt64));
+#else
+ inputs_and_bias_ok = inputs_and_bias_ok && (input->type == kTfLiteInt8);
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+ if (!(inputs_and_bias_ok && params->dilation_width_factor == 1 &&
+ params->dilation_height_factor == 1 &&
+ input_shape.Dims(1) >= filter_shape.Dims(1) &&
+ input_shape.Dims(2) >= filter_shape.Dims(2))) {
+ micro_context->DeallocateTempTfLiteTensor(input);
+ micro_context->DeallocateTempTfLiteTensor(filter);
+ micro_context->DeallocateTempTfLiteTensor(output);
+ if (bias != nullptr) {
+ micro_context->DeallocateTempTfLiteTensor(bias);
+ }
+ return kTfLiteOk;
+ }
+
const int input_height = input_shape.Dims(1);
const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3);
const int filter_height = filter_shape.Dims(1);
@@ -60,7 +86,7 @@
const int pad_height = data->reference_op_data.padding.height;
int required_scratch = 0;
- // Dilation is currently not supported on HiFi 4 NN Library
+ // TODO(b/277112516): Dilation is currently not supported on HiFi 4 NN Library
if ((params->dilation_width_factor == 1) &&
(params->dilation_height_factor == 1)) {
if (input->type == kTfLiteInt8) {
@@ -83,245 +109,220 @@
micro_context->DeallocateTempTfLiteTensor(input);
micro_context->DeallocateTempTfLiteTensor(filter);
micro_context->DeallocateTempTfLiteTensor(output);
+ if (bias != nullptr) {
+ micro_context->DeallocateTempTfLiteTensor(bias);
+ }
return kTfLiteOk;
}
-#if defined(HIFI4)
-TfLiteStatus ConvEvalHifi16(TfLiteContext* context, TfLiteNode* node,
- const TfLiteConvParams& params,
- const XtensaConvOpData& data,
- const TfLiteEvalTensor* input,
- const TfLiteEvalTensor* filter,
- const TfLiteEvalTensor* bias,
- TfLiteEvalTensor* output) {
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+TfLiteStatus ConvEvalHifiInt16(TfLiteContext* context, TfLiteNode* node,
+ const TfLiteConvParams& params,
+ const XtensaConvOpData& data,
+ const TfLiteEvalTensor* input,
+ const TfLiteEvalTensor* filter,
+ const TfLiteEvalTensor* bias,
+ TfLiteEvalTensor* output) {
const RuntimeShape& input_shape = tflite::micro::GetTensorShape(input);
const RuntimeShape& filter_shape = tflite::micro::GetTensorShape(filter);
- /* TODO(b/277112516):Dilation is currently not supported on HiFi 4 NN Library
- */
- if ((params.dilation_width_factor == 1) &&
- (params.dilation_height_factor == 1) &&
- input_shape.Dims(1) >= filter_shape.Dims(1) &&
- input_shape.Dims(2) >= filter_shape.Dims(2)) {
- const int stride_width = params.stride_width;
- const int stride_height = params.stride_height;
- const int pad_width = data.reference_op_data.padding.width;
- const int pad_height = data.reference_op_data.padding.height;
- const int32_t output_activation_min =
- data.reference_op_data.output_activation_min;
- const int32_t output_activation_max =
- data.reference_op_data.output_activation_max;
+ const int stride_width = params.stride_width;
+ const int stride_height = params.stride_height;
+ const int pad_width = data.reference_op_data.padding.width;
+ const int pad_height = data.reference_op_data.padding.height;
+ const int32_t output_activation_min =
+ data.reference_op_data.output_activation_min;
+ const int32_t output_activation_max =
+ data.reference_op_data.output_activation_max;
- const RuntimeShape& output_shape = tflite::micro::GetTensorShape(output);
- const int batches = MatchingDim(input_shape, 0, output_shape, 0);
- const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3);
- const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3);
- const int input_height = input_shape.Dims(1);
- const int input_width = input_shape.Dims(2);
- const int filter_height = filter_shape.Dims(1);
- const int filter_width = filter_shape.Dims(2);
- const int output_height = output_shape.Dims(1);
- const int output_width = output_shape.Dims(2);
+ const RuntimeShape& output_shape = tflite::micro::GetTensorShape(output);
+ const int batches = MatchingDim(input_shape, 0, output_shape, 0);
+ const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3);
+ const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3);
+ const int input_height = input_shape.Dims(1);
+ const int input_width = input_shape.Dims(2);
+ const int filter_height = filter_shape.Dims(1);
+ const int filter_width = filter_shape.Dims(2);
+ const int output_height = output_shape.Dims(1);
+ const int output_width = output_shape.Dims(2);
- const int16_t* input_data = tflite::micro::GetTensorData<int16_t>(input);
- const int8_t* filter_data = tflite::micro::GetTensorData<int8_t>(filter);
- const int64_t* bias_data = tflite::micro::GetTensorData<int64_t>(bias);
- int16_t* output_data = tflite::micro::GetTensorData<int16_t>(output);
+ const int16_t* input_data = tflite::micro::GetTensorData<int16_t>(input);
+ const int8_t* filter_data = tflite::micro::GetTensorData<int8_t>(filter);
+ const int64_t* bias_data = tflite::micro::GetTensorData<int64_t>(bias);
+ int16_t* output_data = tflite::micro::GetTensorData<int16_t>(output);
- int output_data_format = 0;
- int out_length = output_height * output_width * output_depth;
- if (filter_height == 1 && filter_width == 1) {
- for (int batch = 0; batch < batches; ++batch) {
- int16_t* p_out_temp;
- p_out_temp = &output_data[batch * out_length];
+ int output_data_format = 0;
+ int out_length = output_height * output_width * output_depth;
+ if (filter_height == 1 && filter_width == 1) {
+ for (int batch = 0; batch < batches; ++batch) {
+ int16_t* p_out_temp;
+ p_out_temp = &output_data[batch * out_length];
+ TF_LITE_ENSURE_EQ(
+ context,
+ xa_nn_conv2d_pointwise_per_chan_sym8sxsym16s(
+ p_out_temp, const_cast<WORD8*>(filter_data),
+ const_cast<WORD16*>(&input_data[batch * input_height *
+ input_width * input_depth]),
+ const_cast<WORD64*>(bias_data), input_height, input_width,
+ input_depth, output_depth, 0,
+ data.reference_op_data.per_channel_output_multiplier,
+ data.reference_op_data.per_channel_output_shift, 0,
+ output_data_format),
+ 0);
+
+ TF_LITE_ENSURE_EQ(context,
+ xa_nn_vec_activation_min_max_16_16(
+ p_out_temp, p_out_temp, output_activation_min,
+ output_activation_max, out_length),
+ 0);
+ }
+ } else {
+ void* p_scratch = static_cast<void*>(
+ context->GetScratchBuffer(context, data.scratch_tensor_index));
+
+ for (int batch = 0; batch < batches; ++batch) {
+ int16_t* p_out_temp;
+ p_out_temp = &output_data[batch * out_length];
+
+ {
TF_LITE_ENSURE_EQ(
context,
- xa_nn_conv2d_pointwise_per_chan_sym8sxsym16s(
- p_out_temp, const_cast<WORD8*>(filter_data),
- const_cast<WORD16*>(&input_data[batch * input_height *
- input_width * input_depth]),
- const_cast<WORD64*>(bias_data), input_height, input_width,
- input_depth, output_depth, 0,
+ xa_nn_conv2d_std_per_chan_sym8sxsym16s(
+ p_out_temp,
+ &input_data[batch * input_height * input_width * input_depth],
+ const_cast<int8_t*>(filter_data), // filter_data,
+ bias_data, input_height, input_width, input_depth,
+ filter_height, filter_width, output_depth, stride_width,
+ stride_height, pad_width, pad_height, output_height,
+ output_width, 0,
data.reference_op_data.per_channel_output_multiplier,
data.reference_op_data.per_channel_output_shift, 0,
- output_data_format),
+ output_data_format, static_cast<void*>(p_scratch)),
0);
-
- TF_LITE_ENSURE_EQ(context,
- xa_nn_vec_activation_min_max_16_16(
- p_out_temp, p_out_temp, output_activation_min,
- output_activation_max, out_length),
- 0);
}
- } else {
- void* p_scratch = static_cast<void*>(
- context->GetScratchBuffer(context, data.scratch_tensor_index));
-
- for (int batch = 0; batch < batches; ++batch) {
- int16_t* p_out_temp;
- p_out_temp = &output_data[batch * out_length];
-
- {
- TF_LITE_ENSURE_EQ(
- context,
- xa_nn_conv2d_std_per_chan_sym8sxsym16s(
- p_out_temp,
- &input_data[batch * input_height * input_width * input_depth],
- const_cast<int8_t*>(filter_data), // filter_data,
- bias_data, input_height, input_width, input_depth,
- filter_height, filter_width, output_depth, stride_width,
- stride_height, pad_width, pad_height, output_height,
- output_width, 0,
- data.reference_op_data.per_channel_output_multiplier,
- data.reference_op_data.per_channel_output_shift, 0,
- output_data_format, static_cast<void*>(p_scratch)),
- 0);
- }
- TF_LITE_ENSURE_EQ(context,
- xa_nn_vec_activation_min_max_16_16(
- p_out_temp, p_out_temp, output_activation_min,
- output_activation_max, out_length),
- 0);
- }
+ TF_LITE_ENSURE_EQ(context,
+ xa_nn_vec_activation_min_max_16_16(
+ p_out_temp, p_out_temp, output_activation_min,
+ output_activation_max, out_length),
+ 0);
}
- return kTfLiteOk;
}
- reference_integer_ops::ConvPerChannel(
- ConvParamsQuantized(params, data.reference_op_data),
- data.reference_op_data.per_channel_output_multiplier,
- data.reference_op_data.per_channel_output_shift,
- tflite::micro::GetTensorShape(input),
- tflite::micro::GetTensorData<int16_t>(input),
- tflite::micro::GetTensorShape(filter),
- tflite::micro::GetTensorData<int8_t>(filter),
- tflite::micro::GetTensorShape(bias),
- tflite::micro::GetTensorData<int64_t>(bias),
- tflite::micro::GetTensorShape(output),
- tflite::micro::GetTensorData<int16_t>(output));
+
return kTfLiteOk;
}
-#endif // defined(HIFI4)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
-TfLiteStatus ConvEvalHifi(TfLiteContext* context, TfLiteNode* node,
- const TfLiteConvParams& params,
- const XtensaConvOpData& data,
- const TfLiteEvalTensor* input,
- const TfLiteEvalTensor* filter,
- const TfLiteEvalTensor* bias,
- TfLiteEvalTensor* output) {
+TfLiteStatus ConvEvalHifiInt8(TfLiteContext* context, TfLiteNode* node,
+ const TfLiteConvParams& params,
+ const XtensaConvOpData& data,
+ const TfLiteEvalTensor* input,
+ const TfLiteEvalTensor* filter,
+ const TfLiteEvalTensor* bias,
+ TfLiteEvalTensor* output) {
const RuntimeShape& input_shape = tflite::micro::GetTensorShape(input);
const RuntimeShape& filter_shape = tflite::micro::GetTensorShape(filter);
- /* TODO(b/277112516):Dilation is currently not supported on HiFi 4 NN
- Library */
- if ((params.dilation_width_factor == 1) &&
- (params.dilation_height_factor == 1) &&
- input_shape.Dims(1) >= filter_shape.Dims(1) &&
- input_shape.Dims(2) >= filter_shape.Dims(2)) {
- const int32_t input_offset = -data.reference_op_data.input_zero_point;
- const int32_t output_offset = data.reference_op_data.output_zero_point;
- const int stride_width = params.stride_width;
- const int stride_height = params.stride_height;
- const int pad_width = data.reference_op_data.padding.width;
- const int pad_height = data.reference_op_data.padding.height;
- const int32_t output_activation_min =
- data.reference_op_data.output_activation_min;
- const int32_t output_activation_max =
- data.reference_op_data.output_activation_max;
+ const int32_t input_offset = -data.reference_op_data.input_zero_point;
+ const int32_t output_offset = data.reference_op_data.output_zero_point;
+ const int stride_width = params.stride_width;
+ const int stride_height = params.stride_height;
+ const int pad_width = data.reference_op_data.padding.width;
+ const int pad_height = data.reference_op_data.padding.height;
+ const int32_t output_activation_min =
+ data.reference_op_data.output_activation_min;
+ const int32_t output_activation_max =
+ data.reference_op_data.output_activation_max;
- const RuntimeShape& output_shape = tflite::micro::GetTensorShape(output);
- const int batches = MatchingDim(input_shape, 0, output_shape, 0);
- const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3);
- const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3);
- const int input_height = input_shape.Dims(1);
- const int input_width = input_shape.Dims(2);
- const int filter_height = filter_shape.Dims(1);
- const int filter_width = filter_shape.Dims(2);
- const int output_height = output_shape.Dims(1);
- const int output_width = output_shape.Dims(2);
+ const RuntimeShape& output_shape = tflite::micro::GetTensorShape(output);
+ const int batches = MatchingDim(input_shape, 0, output_shape, 0);
+ const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3);
+ const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3);
+ const int input_height = input_shape.Dims(1);
+ const int input_width = input_shape.Dims(2);
+ const int filter_height = filter_shape.Dims(1);
+ const int filter_width = filter_shape.Dims(2);
+ const int output_height = output_shape.Dims(1);
+ const int output_width = output_shape.Dims(2);
- const int8_t* input_data = tflite::micro::GetTensorData<int8_t>(input);
- const int8_t* filter_data = tflite::micro::GetTensorData<int8_t>(filter);
- const int32_t* bias_data = tflite::micro::GetTensorData<int32_t>(bias);
- int8_t* output_data = tflite::micro::GetTensorData<int8_t>(output);
+ const int8_t* input_data = tflite::micro::GetTensorData<int8_t>(input);
+ const int32_t* bias_data = tflite::micro::GetTensorData<int32_t>(bias);
+ int8_t* output_data = tflite::micro::GetTensorData<int8_t>(output);
- int output_data_format = 0;
- int out_length = output_height * output_width * output_depth;
-
- if (filter_height == 1 && filter_width == 1) {
- for (int batch = 0; batch < batches; ++batch) {
- int8_t* p_out_temp;
- p_out_temp = &output_data[batch * out_length];
-
- TF_LITE_ENSURE_EQ(
- context,
-
- xa_nn_conv2d_pointwise_per_chan_sym8sxasym8s(
- p_out_temp, const_cast<WORD8*>(filter_data),
- const_cast<WORD8*>(&input_data[batch * input_height *
- input_width * input_depth]),
- const_cast<WORD32*>(bias_data), input_height, input_width,
- input_depth, output_depth, input_offset,
- data.reference_op_data.per_channel_output_multiplier,
- data.reference_op_data.per_channel_output_shift, output_offset,
- output_data_format),
- 0);
-
- TF_LITE_ENSURE_EQ(context,
- xa_nn_vec_activation_min_max_8_8(
- p_out_temp, p_out_temp, output_activation_min,
- output_activation_max, out_length),
- 0);
- }
- } else {
- void* p_scratch = static_cast<void*>(
- context->GetScratchBuffer(context, data.scratch_tensor_index));
-
- for (int batch = 0; batch < batches; ++batch) {
- int8_t* p_out_temp;
- p_out_temp = &output_data[batch * out_length];
-
- {
- TF_LITE_ENSURE_EQ(
- context,
- xa_nn_conv2d_std_per_chan_sym8sxasym8s(
- p_out_temp,
- &input_data[batch * input_height * input_width * input_depth],
- const_cast<int8_t*>(filter_data), // filter_data,
- bias_data, input_height, input_width, input_depth,
- filter_height, filter_width, output_depth, stride_width,
- stride_height, pad_width, pad_height, output_height,
- output_width, input_offset,
- data.reference_op_data.per_channel_output_multiplier,
- data.reference_op_data.per_channel_output_shift,
- output_offset, output_data_format,
- static_cast<void*>(p_scratch)),
- 0);
- }
-
- TF_LITE_ENSURE_EQ(context,
- xa_nn_vec_activation_min_max_8_8(
- p_out_temp, p_out_temp, output_activation_min,
- output_activation_max, out_length),
- 0);
- }
- }
- return kTfLiteOk;
+ const int8_t* filter_data;
+ if (filter->type == kTfLiteInt4) {
+ int8_t* unpacked_filter_data =
+ static_cast<int8_t*>(context->GetScratchBuffer(
+ context, data.reference_op_data.filter_buffer_index));
+ tflite::tensor_utils::UnpackDenseInt4IntoInt8(
+ tflite::micro::GetTensorData<int8_t>(filter),
+ tflite::micro::GetTensorShape(filter).FlatSize(), unpacked_filter_data);
+ filter_data = unpacked_filter_data;
+ } else {
+ filter_data = tflite::micro::GetTensorData<int8_t>(filter);
}
- reference_integer_ops::ConvPerChannel(
- ConvParamsQuantized(params, data.reference_op_data),
- data.reference_op_data.per_channel_output_multiplier,
- data.reference_op_data.per_channel_output_shift,
- tflite::micro::GetTensorShape(input),
- tflite::micro::GetTensorData<int8_t>(input),
- tflite::micro::GetTensorShape(filter),
- tflite::micro::GetTensorData<int8_t>(filter),
- tflite::micro::GetTensorShape(bias),
- tflite::micro::GetTensorData<int32_t>(bias),
- tflite::micro::GetTensorShape(output),
- tflite::micro::GetTensorData<int8_t>(output));
+ int output_data_format = 0;
+ int out_length = output_height * output_width * output_depth;
+
+ if (filter_height == 1 && filter_width == 1) {
+ for (int batch = 0; batch < batches; ++batch) {
+ int8_t* p_out_temp;
+ p_out_temp = &output_data[batch * out_length];
+
+ TF_LITE_ENSURE_EQ(
+ context,
+
+ xa_nn_conv2d_pointwise_per_chan_sym8sxasym8s(
+ p_out_temp, const_cast<WORD8*>(filter_data),
+ const_cast<WORD8*>(&input_data[batch * input_height *
+ input_width * input_depth]),
+ const_cast<WORD32*>(bias_data), input_height, input_width,
+ input_depth, output_depth, input_offset,
+ data.reference_op_data.per_channel_output_multiplier,
+ data.reference_op_data.per_channel_output_shift, output_offset,
+ output_data_format),
+ 0);
+
+ TF_LITE_ENSURE_EQ(context,
+ xa_nn_vec_activation_min_max_8_8(
+ p_out_temp, p_out_temp, output_activation_min,
+ output_activation_max, out_length),
+ 0);
+ }
+ } else {
+ void* p_scratch = static_cast<void*>(
+ context->GetScratchBuffer(context, data.scratch_tensor_index));
+
+ for (int batch = 0; batch < batches; ++batch) {
+ int8_t* p_out_temp;
+ p_out_temp = &output_data[batch * out_length];
+
+ {
+ TF_LITE_ENSURE_EQ(
+ context,
+ xa_nn_conv2d_std_per_chan_sym8sxasym8s(
+ p_out_temp,
+ &input_data[batch * input_height * input_width * input_depth],
+ const_cast<int8_t*>(filter_data), // filter_data,
+ bias_data, input_height, input_width, input_depth,
+ filter_height, filter_width, output_depth, stride_width,
+ stride_height, pad_width, pad_height, output_height,
+ output_width, input_offset,
+ data.reference_op_data.per_channel_output_multiplier,
+ data.reference_op_data.per_channel_output_shift, output_offset,
+ output_data_format, static_cast<void*>(p_scratch)),
+ 0);
+ }
+
+ TF_LITE_ENSURE_EQ(context,
+ xa_nn_vec_activation_min_max_8_8(
+ p_out_temp, p_out_temp, output_activation_min,
+ output_activation_max, out_length),
+ 0);
+ }
+ }
+
return kTfLiteOk;
}
} // namespace tflite
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
diff --git a/tensorflow/lite/micro/kernels/xtensa/conv_int16_reference.cc b/tensorflow/lite/micro/kernels/xtensa/conv_int16_reference.cc
index 0d3c4a3..2492d4b 100644
--- a/tensorflow/lite/micro/kernels/xtensa/conv_int16_reference.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/conv_int16_reference.cc
@@ -1,4 +1,4 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -23,16 +23,9 @@
#include "tensorflow/lite/kernels/padding.h"
#include "tensorflow/lite/micro/kernels/conv.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/micro_log.h"
namespace tflite {
-namespace {
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
- TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
- return context->AllocatePersistentBuffer(context, sizeof(OpDataConv));
-}
-
-} // namespace.
TfLiteStatus ConvReferenceEvalInt16(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
@@ -52,25 +45,37 @@
? tflite::micro::GetEvalInput(context, node, kConvBiasTensor)
: nullptr;
- reference_integer_ops::ConvPerChannel(
- ConvParamsQuantized(params, op_data),
- op_data.per_channel_output_multiplier, op_data.per_channel_output_shift,
- tflite::micro::GetTensorShape(input),
- tflite::micro::GetTensorData<int16_t>(input),
- tflite::micro::GetTensorShape(filter),
- tflite::micro::GetTensorData<int8_t>(filter),
- tflite::micro::GetTensorShape(bias),
- tflite::micro::GetTensorData<std::int64_t>(bias),
- tflite::micro::GetTensorShape(output),
- tflite::micro::GetTensorData<int16_t>(output));
- return kTfLiteOk;
-}
+ if (bias == nullptr || bias->type == kTfLiteInt32) {
+ reference_integer_ops::ConvPerChannel(
+ ConvParamsQuantized(params, op_data),
+ op_data.per_channel_output_multiplier, op_data.per_channel_output_shift,
+ tflite::micro::GetTensorShape(input),
+ tflite::micro::GetTensorData<int16_t>(input),
+ tflite::micro::GetTensorShape(filter),
+ tflite::micro::GetTensorData<int8_t>(filter),
+ tflite::micro::GetTensorShape(bias),
+ tflite::micro::GetOptionalTensorData<std::int32_t>(bias),
+ tflite::micro::GetTensorShape(output),
+ tflite::micro::GetTensorData<int16_t>(output));
+ } else if (bias->type == kTfLiteInt64) {
+ reference_integer_ops::ConvPerChannel(
+ ConvParamsQuantized(params, op_data),
+ op_data.per_channel_output_multiplier, op_data.per_channel_output_shift,
+ tflite::micro::GetTensorShape(input),
+ tflite::micro::GetTensorData<int16_t>(input),
+ tflite::micro::GetTensorShape(filter),
+ tflite::micro::GetTensorData<int8_t>(filter),
+ tflite::micro::GetTensorShape(bias),
+ tflite::micro::GetOptionalTensorData<std::int64_t>(bias),
+ tflite::micro::GetTensorShape(output),
+ tflite::micro::GetTensorData<int16_t>(output));
+ } else {
+ MicroPrintf("Bias type %s (%d) not supported.",
+ TfLiteTypeGetName(bias->type), bias->type);
+ return kTfLiteError;
+ }
-// TODO(b/189981943): This variant can be used for a smaller binary
-// since the optimized conv implementation currently adds a lot to
-// the binary size (~30KB to text section).
-TFLMRegistration Register_CONV_2D_INT16REF() {
- return tflite::micro::RegisterOp(Init, ConvPrepare, ConvReferenceEvalInt16);
+ return kTfLiteOk;
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/xtensa/conv_int8_int16.cc b/tensorflow/lite/micro/kernels/xtensa/conv_int8_int16.cc
new file mode 100644
index 0000000..ed64f01
--- /dev/null
+++ b/tensorflow/lite/micro/kernels/xtensa/conv_int8_int16.cc
@@ -0,0 +1,89 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/lite/c/builtin_op_data.h"
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/kernels/internal/common.h"
+#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/xtensa/xtensa.h"
+#include "tensorflow/lite/micro/kernels/xtensa/xtensa_conv.h"
+
+namespace tflite {
+namespace {
+
+TfLiteStatus EvalInt8(TfLiteContext* context, TfLiteNode* node) {
+#if defined(HIFIMINI)
+ return ConvReferenceEvalInt8(context, node);
+#else
+ const auto& op_data = *(reinterpret_cast<XtensaConvOpData*>(node->user_data));
+ const auto& params =
+ *(reinterpret_cast<TfLiteConvParams*>(node->builtin_data));
+
+ const TfLiteEvalTensor* input =
+ tflite::micro::GetEvalInput(context, node, kConvInputTensor);
+ TfLiteEvalTensor* output =
+ tflite::micro::GetEvalOutput(context, node, kConvOutputTensor);
+ const TfLiteEvalTensor* filter =
+ tflite::micro::GetEvalInput(context, node, kConvWeightsTensor);
+ const TfLiteEvalTensor* bias =
+ tflite::micro::GetEvalInput(context, node, kConvBiasTensor);
+
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+ return ConvEvalHifiInt8(context, node, params, op_data, input, filter, bias,
+ output);
+#elif defined(VISION_P6)
+ return ConvEvalVision(context, node, params, op_data, input, filter, bias,
+ output);
+#endif
+
+#endif // defined(HIFIMINI)
+}
+
+TfLiteStatus EvalInt16(TfLiteContext* context, TfLiteNode* node) {
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+ const auto& op_data = *(reinterpret_cast<XtensaConvOpData*>(node->user_data));
+ const auto& params =
+ *(reinterpret_cast<TfLiteConvParams*>(node->builtin_data));
+
+ const TfLiteEvalTensor* input =
+ tflite::micro::GetEvalInput(context, node, kConvInputTensor);
+ TfLiteEvalTensor* output =
+ tflite::micro::GetEvalOutput(context, node, kConvOutputTensor);
+ const TfLiteEvalTensor* filter =
+ tflite::micro::GetEvalInput(context, node, kConvWeightsTensor);
+ const TfLiteEvalTensor* bias =
+ tflite::micro::GetEvalInput(context, node, kConvBiasTensor);
+
+ return ConvEvalHifiInt16(context, node, params, op_data, input, filter, bias,
+ output);
+#else
+ return ConvReferenceEvalInt16(context, node);
+#endif
+}
+
+} // namespace
+
+TFLMRegistration Register_CONV_2D_INT8() {
+ return tflite::micro::RegisterOp(ConvInitXtensa, ConvPrepareXtensa, EvalInt8);
+}
+
+TFLMRegistration Register_CONV_2D_INT16() {
+ return tflite::micro::RegisterOp(ConvInitXtensa, ConvPrepareXtensa,
+ EvalInt16);
+}
+
+} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/xtensa/conv_int8_reference.cc b/tensorflow/lite/micro/kernels/xtensa/conv_int8_reference.cc
index 80a42d9..6ac07ba 100644
--- a/tensorflow/lite/micro/kernels/xtensa/conv_int8_reference.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/conv_int8_reference.cc
@@ -1,4 +1,4 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -16,6 +16,7 @@
#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/common.h"
+#include "tensorflow/lite/kernels/internal/portable_tensor_utils.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
@@ -25,14 +26,6 @@
#include "tensorflow/lite/micro/kernels/kernel_util.h"
namespace tflite {
-namespace {
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
- TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
- return context->AllocatePersistentBuffer(context, sizeof(OpDataConv));
-}
-
-} // namespace.
TfLiteStatus ConvReferenceEvalInt8(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
@@ -52,17 +45,29 @@
? tflite::micro::GetEvalInput(context, node, kConvBiasTensor)
: nullptr;
+ const int8_t* filter_data;
+ if (filter->type == kTfLiteInt4) {
+ int8_t* unpacked_filter_data = static_cast<int8_t*>(
+ context->GetScratchBuffer(context, op_data.filter_buffer_index));
+ tflite::tensor_utils::UnpackDenseInt4IntoInt8(
+ tflite::micro::GetTensorData<int8_t>(filter),
+ tflite::micro::GetTensorShape(filter).FlatSize(), unpacked_filter_data);
+ filter_data = unpacked_filter_data;
+ } else {
+ filter_data = tflite::micro::GetTensorData<int8_t>(filter);
+ }
+
reference_integer_ops::ConvPerChannel(
ConvParamsQuantized(params, op_data),
op_data.per_channel_output_multiplier, op_data.per_channel_output_shift,
tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<int8_t>(input),
- tflite::micro::GetTensorShape(filter),
- tflite::micro::GetTensorData<int8_t>(filter),
+ tflite::micro::GetTensorShape(filter), filter_data,
tflite::micro::GetTensorShape(bias),
- tflite::micro::GetTensorData<int32_t>(bias),
+ tflite::micro::GetOptionalTensorData<int32_t>(bias),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int8_t>(output));
+
return kTfLiteOk;
}
@@ -70,7 +75,8 @@
// since the optimized conv implementation currently adds a lot to
// the binary size (~30KB to text section).
TFLMRegistration Register_CONV_2D_INT8REF() {
- return tflite::micro::RegisterOp(Init, ConvPrepare, ConvReferenceEvalInt8);
+ return tflite::micro::RegisterOp(ConvInit, ConvPrepare,
+ ConvReferenceEvalInt8);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/xtensa/conv_vision.cc b/tensorflow/lite/micro/kernels/xtensa/conv_vision.cc
index e4f0d49..812ab60 100644
--- a/tensorflow/lite/micro/kernels/xtensa/conv_vision.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/conv_vision.cc
@@ -1,4 +1,4 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -21,6 +21,7 @@
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/portable_tensor_utils.h"
+#include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/micro/kernels/conv.h"
@@ -32,29 +33,22 @@
namespace tflite {
TfLiteStatus ConvPrepareVision(TfLiteContext* context, TfLiteNode* node) {
- TFLITE_DCHECK(node->user_data != nullptr);
- TFLITE_DCHECK(node->builtin_data != nullptr);
+ MicroContext* micro_context = GetMicroContext(context);
+ TfLiteTensor* input =
+ micro_context->AllocateTempInputTensor(node, kConvInputTensor);
+ TfLiteTensor* bias =
+ micro_context->AllocateTempInputTensor(node, kConvBiasTensor);
+ const uint32_t input_height = SizeOfDimension(input, 1);
+ const uint32_t input_width = SizeOfDimension(input, 2);
XtensaConvOpData* data = reinterpret_cast<XtensaConvOpData*>(node->user_data);
const auto& params =
*(reinterpret_cast<const TfLiteConvParams*>(node->builtin_data));
- MicroContext* micro_context = GetMicroContext(context);
TfLiteTensor* output =
micro_context->AllocateTempOutputTensor(node, kConvOutputTensor);
- TF_LITE_ENSURE(context, output != nullptr);
- TfLiteTensor* input =
- micro_context->AllocateTempInputTensor(node, kConvInputTensor);
- TF_LITE_ENSURE(context, input != nullptr);
TfLiteTensor* filter =
micro_context->AllocateTempInputTensor(node, kConvWeightsTensor);
- TF_LITE_ENSURE(context, filter != nullptr);
- TfLiteTensor* bias =
- micro_context->AllocateTempInputTensor(node, kConvBiasTensor);
- TF_LITE_ENSURE(context, bias != nullptr);
-
- const uint32_t input_height = SizeOfDimension(input, 1);
- const uint32_t input_width = SizeOfDimension(input, 2);
const uint32_t output_height = SizeOfDimension(output, 1);
const uint32_t output_width = SizeOfDimension(output, 2);
@@ -62,6 +56,15 @@
const uint32_t filter_height = SizeOfDimension(filter, 1);
const uint32_t filter_width = SizeOfDimension(filter, 2);
+ // At this time it is unclear if per channel quantization is correctly
+ // supported by the optimized vision P6 implementation or not. For now, we are
+ // manually adding a flag to switch to the reference implementation for
+ // per-channel conv.
+ // See http://b/270720625 for more details.
+ data->is_per_channel_quantized =
+ reinterpret_cast<TfLiteAffineQuantization*>(filter->quantization.params)
+ ->scale->size > 1;
+
// Dynamically allocate per-channel quantization parameters.
const int num_channels = SizeOfDimension(filter, kConvQuantizedDimension);
data->per_channel_output_shift_int8 = static_cast<int8_t*>(
@@ -97,7 +100,6 @@
tflite::tensor_utils::UnpackDenseInt4IntoInt8(
GetTensorData<int8_t>(filter), GetTensorShape(filter).FlatSize(),
GetTensorData<int8_t>(&filter_int8));
-
} else {
filter_int8 = *filter;
}
@@ -142,14 +144,17 @@
if (status) {
return kTfLiteError;
}
+
if (filter->type == kTfLiteInt4) {
micro_context->DeallocateTempBuffer(GetTensorData<uint8_t>(&filter_int8));
}
+
micro_context->DeallocateTempTfLiteTensor(output);
micro_context->DeallocateTempTfLiteTensor(input);
micro_context->DeallocateTempTfLiteTensor(filter);
- micro_context->DeallocateTempTfLiteTensor(bias);
-
+ if (bias != nullptr) {
+ micro_context->DeallocateTempTfLiteTensor(bias);
+ }
return kTfLiteOk;
}
@@ -170,7 +175,9 @@
data.reorder_coefficient_bias, data.reorder_coefficient_bias_size,
data.reference_op_data.per_channel_output_multiplier,
data.per_channel_output_shift_int8, num_channels);
+
return kTfLiteOk;
}
+
} // namespace tflite
#endif // defined(VISION_P6)
diff --git a/tensorflow/lite/micro/kernels/xtensa/depthwise_conv.cc b/tensorflow/lite/micro/kernels/xtensa/depthwise_conv.cc
index f3acb8d..8536ff7 100644
--- a/tensorflow/lite/micro/kernels/xtensa/depthwise_conv.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/depthwise_conv.cc
@@ -61,9 +61,9 @@
}
micro_context->DeallocateTempTfLiteTensor(input);
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
TF_LITE_ENSURE_OK(context, DepthwiseConvPrepareHifi(context, node));
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
#if defined(VISION_P6)
TF_LITE_ENSURE_OK(context, DepthwiseConvPrepareVision(context, node));
@@ -97,7 +97,7 @@
case kTfLiteInt8: {
switch (filter_int8.type) {
case kTfLiteInt8: {
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
DepthwiseConvEvalHifi(context, node, params, op_data, input,
&filter_int8, bias, output);
#elif defined(VISION_P6)
@@ -116,7 +116,7 @@
tflite::micro::GetOptionalTensorData<int32_t>(bias),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int8_t>(output));
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
break;
}
default:
diff --git a/tensorflow/lite/micro/kernels/xtensa/depthwise_conv_hifi.cc b/tensorflow/lite/micro/kernels/xtensa/depthwise_conv_hifi.cc
index 05dab48..8c2052b 100644
--- a/tensorflow/lite/micro/kernels/xtensa/depthwise_conv_hifi.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/depthwise_conv_hifi.cc
@@ -28,7 +28,7 @@
#include "tensorflow/lite/micro/kernels/xtensa/xtensa.h"
#include "tensorflow/lite/micro/kernels/xtensa/xtensa_depthwise_conv.h"
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
namespace tflite {
TfLiteStatus DepthwiseConvPrepareHifi(TfLiteContext* context,
TfLiteNode* node) {
@@ -187,4 +187,4 @@
return kTfLiteOk;
}
} // namespace tflite
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) ||defined(HIFI4) || defined(HIFI5)
diff --git a/tensorflow/lite/micro/kernels/xtensa/dequantize.cc b/tensorflow/lite/micro/kernels/xtensa/dequantize.cc
new file mode 100644
index 0000000..f2f4a7d
--- /dev/null
+++ b/tensorflow/lite/micro/kernels/xtensa/dequantize.cc
@@ -0,0 +1,118 @@
+/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/lite/kernels/internal/reference/dequantize.h"
+
+#include "tensorflow/lite/c/builtin_op_data.h"
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/kernels/internal/quantization_util.h"
+#include "tensorflow/lite/kernels/internal/reference/quantize.h"
+#include "tensorflow/lite/kernels/internal/reference/requantize.h"
+#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/dequantize.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/xtensa/xtensa.h"
+#include "tensorflow/lite/micro/micro_log.h"
+
+namespace tflite {
+
+void* DequantizeInit(TfLiteContext* context, const char* buffer,
+ size_t length) {
+ TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
+ return context->AllocatePersistentBuffer(context, sizeof(DequantizeOpData));
+}
+
+TfLiteStatus DequantizeEval(TfLiteContext* context, TfLiteNode* node) {
+ TFLITE_DCHECK(node->user_data != nullptr);
+ DequantizeOpData* data = static_cast<DequantizeOpData*>(node->user_data);
+
+ const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
+ TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);
+
+ // Output type ensured to be kTfLiteFloat32 at the Prepare stage
+ TFLITE_DCHECK(output->type == kTfLiteFloat32);
+
+ switch (input->type) {
+ case kTfLiteInt8: {
+#if HIFI_VFPU && (defined(HIFI5) || defined(HIFI4) || defined(HIFI3))
+ int err;
+ const int8_t* input_data_ptr;
+ float* output_data_ptr;
+ const int flat_size =
+ MatchingFlatSize(tflite::micro::GetTensorShape(input),
+ tflite::micro::GetTensorShape(output));
+ input_data_ptr = tflite::micro::GetTensorData<int8_t>(input);
+ output_data_ptr = tflite::micro::GetTensorData<float>(output);
+
+ err = xa_nn_elm_dequantize_asym8s_f32(
+ output_data_ptr, input_data_ptr, data->quantization_params.zero_point,
+ data->quantization_params.scale, flat_size);
+ TF_LITE_ENSURE(context, (err == 0));
+#else // HIFI_VFPU && (defined(HIFI5) || defined(HIFI4) || defined(HIFI3))
+ reference_ops::Dequantize(data->quantization_params,
+ tflite::micro::GetTensorShape(input),
+ tflite::micro::GetTensorData<int8_t>(input),
+ tflite::micro::GetTensorShape(output),
+ tflite::micro::GetTensorData<float>(output));
+#endif // HIFI_VFPU && (defined(HIFI5) || defined(HIFI4) || defined(HIFI3))
+ break;
+ }
+ case kTfLiteInt16: {
+#if HIFI_VFPU && (defined(HIFI5) || defined(HIFI4) || defined(HIFI3))
+ int err;
+ const int16_t* input_data_ptr;
+ float* output_data_ptr;
+ const RuntimeShape& input_shape = tflite::micro::GetTensorShape(input);
+ const RuntimeShape& output_shape = tflite::micro::GetTensorShape(output);
+ const int flat_size = MatchingFlatSize(input_shape, output_shape);
+ input_data_ptr = tflite::micro::GetTensorData<int16_t>(input);
+ output_data_ptr = tflite::micro::GetTensorData<float>(output);
+ err = xa_nn_elm_dequantize_asym16s_f32(
+ output_data_ptr, input_data_ptr, data->quantization_params.zero_point,
+ data->quantization_params.scale, flat_size);
+ TF_LITE_ENSURE(context, (err == 0));
+#else // HIFI_VFPU && (defined(HIFI5) || defined(HIFI4) || defined(HIFI3))
+ reference_ops::Dequantize(data->quantization_params,
+ tflite::micro::GetTensorShape(input),
+ tflite::micro::GetTensorData<int16_t>(input),
+ tflite::micro::GetTensorShape(output),
+ tflite::micro::GetTensorData<float>(output));
+#endif // HIFI_VFPU && (defined(HIFI5) || defined(HIFI4) || defined(HIFI3))
+ break;
+ }
+ case kTfLiteUInt8:
+ reference_ops::Dequantize(data->quantization_params,
+ tflite::micro::GetTensorShape(input),
+ tflite::micro::GetTensorData<uint8_t>(input),
+ tflite::micro::GetTensorShape(output),
+ tflite::micro::GetTensorData<float>(output));
+ break;
+ default:
+ MicroPrintf("Input %s, output %s not supported.",
+ TfLiteTypeGetName(input->type),
+ TfLiteTypeGetName(output->type));
+ return kTfLiteError;
+ }
+
+ return kTfLiteOk;
+}
+
+TFLMRegistration Register_DEQUANTIZE() {
+ return tflite::micro::RegisterOp(DequantizeInit, DequantizePrepare,
+ DequantizeEval);
+}
+
+} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/xtensa/fully_connected.cc b/tensorflow/lite/micro/kernels/xtensa/fully_connected.cc
index 1395fc3..df54580 100644
--- a/tensorflow/lite/micro/kernels/xtensa/fully_connected.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/fully_connected.cc
@@ -125,4 +125,8 @@
XtensaPrepareFullyConnected, Eval);
}
+TFLMInferenceRegistration RegisterInference_FULLY_CONNECTED() {
+ return tflite::micro::RegisterOp(Eval);
+}
+
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/xtensa/fully_connected_int8.cc b/tensorflow/lite/micro/kernels/xtensa/fully_connected_int8.cc
index b53afa4..f850c0c 100644
--- a/tensorflow/lite/micro/kernels/xtensa/fully_connected_int8.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/fully_connected_int8.cc
@@ -57,7 +57,7 @@
tflite::micro::GetTensorShape(bias), bias_data,
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int8_t>(output));
-#elif defined(HIFI4) || defined(HIFI5)
+#elif defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
const RuntimeShape& output_shape = tflite::micro::GetTensorShape(output);
const int num_batches =
FlatSizeSkipDim(output_shape, output_shape.DimensionsCount() - 1);
@@ -103,7 +103,7 @@
tflite::micro::GetTensorShape(bias), bias_data,
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int8_t>(output));
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
return kTfLiteOk;
}
diff --git a/tensorflow/lite/micro/kernels/xtensa/leaky_relu.cc b/tensorflow/lite/micro/kernels/xtensa/leaky_relu.cc
index 857a488..c1ed1d6 100644
--- a/tensorflow/lite/micro/kernels/xtensa/leaky_relu.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/leaky_relu.cc
@@ -76,7 +76,7 @@
return kTfLiteOk;
} break;
case kTfLiteInt16: {
-#if defined(HIFI4)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
const RuntimeShape& input_shape = tflite::micro::GetTensorShape(input);
const RuntimeShape& output_shape = tflite::micro::GetTensorShape(output);
const int flat_size = MatchingFlatSize(input_shape, output_shape);
@@ -89,7 +89,7 @@
if (err != 0) return kTfLiteError;
#else
QuantizeLeakyRelu<int16_t>(data, input, output);
-#endif // defined(HIFI4)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
return kTfLiteOk;
} break;
default:
diff --git a/tensorflow/lite/micro/kernels/xtensa/logistic.cc b/tensorflow/lite/micro/kernels/xtensa/logistic.cc
index 41e6f3d..2ddf82e 100644
--- a/tensorflow/lite/micro/kernels/xtensa/logistic.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/logistic.cc
@@ -54,7 +54,7 @@
switch (input->type) {
case kTfLiteFloat32: {
-#if HIFI_VFPU && (defined(HIFI4) || defined(HIFI5))
+#if HIFI_VFPU && (defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
const RuntimeShape& input_shape = tflite::micro::GetTensorShape(input);
const RuntimeShape& output_shape = tflite::micro::GetTensorShape(output);
const int flat_size = MatchingFlatSize(input_shape, output_shape);
@@ -70,11 +70,11 @@
tflite::micro::GetTensorData<float>(input),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<float>(output));
-#endif // HIFI_VFPU && (defined(HIFI4) || defined(HIFI5))
+#endif // HIFI_VFPU && (defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
break;
}
case kTfLiteInt8: {
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
const RuntimeShape& input_shape = tflite::micro::GetTensorShape(input);
const RuntimeShape& output_shape = tflite::micro::GetTensorShape(output);
const int flat_size = MatchingFlatSize(input_shape, output_shape);
@@ -96,7 +96,7 @@
data->input_multiplier, data->input_left_shift,
NumElements(input->dims), tflite::micro::GetTensorData<int8_t>(input),
tflite::micro::GetTensorData<int8_t>(output));
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
break;
}
case kTfLiteInt16: {
diff --git a/tensorflow/lite/micro/kernels/xtensa/lstm_eval.cc b/tensorflow/lite/micro/kernels/xtensa/lstm_eval.cc
index 9065388..94e76a1 100644
--- a/tensorflow/lite/micro/kernels/xtensa/lstm_eval.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/lstm_eval.cc
@@ -1,4 +1,4 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -14,1204 +14,469 @@
==============================================================================*/
#include "tensorflow/lite/micro/kernels/xtensa/lstm_eval.h"
-#include <math.h>
-#include <string.h>
+#include <limits>
-#include <algorithm>
-#include <cstdint>
-#include <memory>
-#include <vector>
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/compatibility.h"
-#include "tensorflow/lite/kernels/internal/portable_tensor_utils.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/op_macros.h"
+#include "tensorflow/lite/kernels/internal/reference/fully_connected.h"
+#include "tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h"
+#include "tensorflow/lite/kernels/internal/reference/integer_ops/logistic.h"
+#include "tensorflow/lite/kernels/internal/reference/integer_ops/mul.h"
+#include "tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h"
+#include "tensorflow/lite/kernels/internal/reference/logistic.h"
+#include "tensorflow/lite/kernels/internal/reference/mul.h"
+#include "tensorflow/lite/kernels/internal/reference/tanh.h"
+#include "tensorflow/lite/kernels/internal/types.h"
#include "tensorflow/lite/micro/kernels/xtensa/xtensa.h"
namespace tflite {
-namespace ops {
-namespace micro {
-namespace lstm_eval {
-namespace {
-// Calculates a single LSTM gate, int8x8_16 version.
-// Implements the same functionality as CalculateLstmGateFloat.
-void CalculateLstmGateInteger8x8_16(
- // Input and weights
- const int8_t* input, const int8_t* input_to_gate_weights,
- const int32_t* input_to_gate_bias, const int32_t input_to_gate_scale_a,
- const int32_t input_to_gate_scale_b,
- // Output state and weights
- const int8_t* output_state, const int8_t* recurrent_to_gate_weights,
- const int32_t* recurrent_to_gate_bias,
- const int32_t recurrent_to_gate_scale_a,
- const int32_t recurrent_to_gate_scale_b,
- // Cell state and weights
- const int16_t* cell_state, const int16_t* cell_to_gate_weights,
- const int32_t cell_to_gate_scale_a, const int32_t cell_to_gate_scale_b,
- // Layer normalization parameters (layer norm LSTM)
- const int16_t* layer_norm_coefficients, const int32_t* layer_norm_bias,
- const int32_t layer_norm_input_scale_a,
- const int32_t layer_norm_input_scale_b,
- const int32_t layer_norm_variance_guard,
- // Array sizes
- const int n_batch, const int n_input, const int n_output, const int n_cell,
- const TfLiteFusedActivation activation,
- // Output
- int16_t* gate,
- // Parameters for performance optimizations
- // CpuBackendContext* context,
- // Scratch arrays
- int32_t* scratch5) {
- const bool use_peephole = (cell_to_gate_weights != nullptr);
- const bool use_layer_norm = (layer_norm_coefficients != nullptr);
-
- // Initialize scratch buffers with zeros. Note that unlike float and hybrid
- // versions, bias is only used in layer normalization.
- std::fill_n(gate, n_batch * n_cell, 0);
-#if !defined(HIFI5)
- // For each batch and cell: compute input_weight * input.
- tensor_utils::PortableMatrixBatchVectorMultiplyAccumulate(
- input, input_to_gate_bias, input_to_gate_weights, input_to_gate_scale_a,
- input_to_gate_scale_b, n_batch, n_input, n_cell, 0, scratch5, gate, NULL);
-#else
- {
- xa_nn_matXvec_acc_batch_sym8sx8_asym16s(
- gate, input_to_gate_weights, input, input_to_gate_bias, n_cell, n_input,
- n_input, input_to_gate_scale_a, input_to_gate_scale_b, 0, n_batch);
+LstmTensors::LstmTensors(TfLiteContext* context, TfLiteNode* node) {
+ micro_context_ = GetMicroContext(context);
+ // 24 internal tensors. see lstm_shared.h for tensor names
+ for (size_t i = 0; i < 24; i++) {
+ internal_tensors_[i] = micro_context_->AllocateTempInputTensor(node, i);
}
-#endif // !defined(HIFI5)
-// Note: no aux_input.
-
-// For each batch and cell: compute recurrent_weight * output_state.
-#if !defined(HIFI5)
- tensor_utils::PortableMatrixBatchVectorMultiplyAccumulate(
- output_state, recurrent_to_gate_bias, recurrent_to_gate_weights,
- recurrent_to_gate_scale_a, recurrent_to_gate_scale_b, n_batch, n_output,
- n_cell, 0, scratch5, gate, NULL);
-#else
- {
- xa_nn_matXvec_acc_batch_sym8sx8_asym16s(
- gate, recurrent_to_gate_weights, output_state, recurrent_to_gate_bias,
- n_cell, n_output, n_output, recurrent_to_gate_scale_a,
- recurrent_to_gate_scale_b, 0, n_batch);
- }
-#endif // !defined(HIFI5)
- // For each batch and cell: compute cell_weight * cell_state (peephole LSTM)
- if (use_peephole) {
- tensor_utils::PortableVectorBatchVectorCwiseProductAccumulate(
- cell_to_gate_weights, n_output, cell_state, n_batch,
- cell_to_gate_scale_a, cell_to_gate_scale_b, gate);
- }
- // Do layer normalization (if layer norm LSTM)
- if (use_layer_norm) {
- tensor_utils::PortableApplyLayerNorm(
- gate, layer_norm_coefficients, layer_norm_bias,
- layer_norm_input_scale_a, layer_norm_input_scale_b,
- layer_norm_variance_guard, n_batch, n_cell, gate);
- }
- // Apply activation
- switch (activation) {
- case kTfLiteActSigmoid:
-#if !defined(HIFI5)
- tensor_utils::PortableApplySigmoid(gate, n_batch, n_cell, gate);
-#else
- xa_nn_vec_sigmoid_16_16(gate, gate, n_batch * n_cell);
-#endif // !defined(HIFI5)
- break;
- case kTfLiteActTanh:
-#if !defined(HIFI5)
- tensor_utils::PortableApplyTanh(3, gate, n_batch, n_cell, gate);
-#else
- xa_nn_vec_tanh_16_16(gate, gate, 3, n_batch * n_cell);
-#endif // !defined(HIFI5)
- break;
- default:
- // Only Sigmoid or Tanh is used.
- TFLITE_ASSERT_FALSE;
- }
+ output_tensor_ =
+ micro_context_->AllocateTempOutputTensor(node, kLstmOutputTensor);
}
-// Updates the LSTM cell state, used by both integer LSTM versions.
-// Also see UpdateLstmCellFloat.
-//
-// Parameters:
-// - n_batch, n_cell: sizes of vectors
-// - cell_state: input/output vector, size n_batch*n_cell
-// - cell_state_scale: scaling factor of cell state.
-// - input_gate: input vector, size n_batch*n_cell.
-// - forget_gate: input/scratch vector, size n_batch*n_cell, always modified.
-// - cell_gate: input vector, size n_batch*n_cell.
-// - use_cifg: use 1-forget_gate instead of input_gate.
-// - clip: if > 0, clip the resulting cell state to [-clip, +clip].
-void UpdateLstmCellInteger(int n_batch, int n_cell, int16_t* cell_state,
- int32_t cell_state_scale, const int16_t* input_gate,
- int16_t* forget_gate, const int16_t* cell_gate,
- bool use_cifg, int16_t clip) {
-#if !defined(HIFI5)
- // Use the forget_gate array as scratch, as input_gate array is not allocated
- // in CIFG case. (Be careful not to write to the scratch before reading the
- // forget gate data.)
- int16_t* scratch = forget_gate;
-
- tensor_utils::PortableCwiseMul(forget_gate, cell_state, n_batch, n_cell, 15,
- cell_state);
- if (use_cifg) {
- tensor_utils::PortableSub1Vector(forget_gate, n_batch * n_cell, scratch);
- tensor_utils::PortableCwiseMul(scratch, cell_gate, n_batch, n_cell,
- 30 + cell_state_scale, scratch);
- } else {
- tensor_utils::PortableCwiseMul(input_gate, cell_gate, n_batch, n_cell,
- 30 + cell_state_scale, scratch);
- }
- tensor_utils::PortableCwiseAdd(cell_state, scratch, n_batch, n_cell,
- cell_state);
-
- if (clip > 0) {
- tensor_utils::PortableCwiseClipping(cell_state, n_batch * n_cell, clip);
- }
-#else
- if (use_cifg) {
- calc_cell_state_with_cifg(cell_state, forget_gate, cell_gate, 15,
- 30 + cell_state_scale, clip, n_batch * n_cell);
- } else {
- calc_cell_state_without_cifg(cell_state, forget_gate, cell_gate, input_gate,
- 15, 30 + cell_state_scale, clip,
- n_batch * n_cell);
- }
-
-#endif // !defined(HIFI5)
-}
-
-// Calculates the output state tensor of an LSTM step. See Float and hybrid
-// versions as well.
-//
-// Parameters:
-// - n_batch: batches: the number of distinct vectors in each array.
-// - n_cell, n_output: sizes of vectors.
-// - cell_state, output_gate: input vectors, size n_batch*n_cell.
-// - cell_state_scale: scaling of cell_state.
-// - hidden_scale_[a|b]: effective scale of cell_state.*output_gate
-// - hidden_zp: zero_point for cell_state.*output_gate
-// - projection_weights, proj_scale_[a|b], projection_bias:
-// constant inputs, describing projection matrix and bias.
-// - output_state_zp: zero point of output_state. (Input, calibrated value.)
-// - quantized_proj_clip: if > 0, clip the output of the projection.
-// - output_state: output vector, size n_batch*n_output. Must be contiguous.
-// - context: data for optimized MatrixBatchVectorMultiplyAccumulate.
-// - scratch0: scratch area of size n_batch*n_cell
-// - scratch1: scratch area of size n_batch*n_cell
-// - scratch2: scratch area used by MatrixBatchVectorMultiplyAccumulate
-void CalculateLstmOutputInteger8x8_16(
- int n_batch, int n_cell, int n_output, const int16_t* cell_state,
- int32_t cell_state_scale, const int16_t* output_gate,
- int32_t hidden_scale_a, int32_t hidden_scale_b, int32_t hidden_zp,
- const int8_t* projection_weights, int32_t proj_scale_a,
- int32_t proj_scale_b, const int32_t* projection_bias,
- int32_t output_state_zp, int8_t quantized_proj_clip, int8_t* output_state,
- int16_t* scratch0, int8_t* scratch1, int32_t* scratch2) {
-// Note: unlike float/hybrid, the activation is always Tanh.
-#if !defined(HIFI5)
- tensor_utils::PortableApplyTanh(15 + cell_state_scale, cell_state, n_batch,
- n_cell, scratch0);
-#else
- xa_nn_vec_tanh_16_16(scratch0, cell_state, (15 + cell_state_scale),
- n_batch * n_cell);
-#endif // !defined(HIFI5)
-
-#if !defined(HIFI5)
- tensor_utils::PortableCwiseMul(output_gate, scratch0, hidden_scale_a,
- hidden_scale_b, n_batch, n_cell, hidden_zp,
- scratch1);
-#else
- xa_nn_elm_mul_16x16_asym8s(scratch1, output_gate, scratch0, hidden_scale_a,
- hidden_scale_b, hidden_zp, n_batch * n_cell);
-#endif // !defined(HIFI5)
-
- const bool use_projection = (projection_weights != nullptr);
-
- if (use_projection) {
- // Note: no bias like in float/hybrid
- std::fill_n(output_state, n_batch * n_output, 0);
- tensor_utils::PortableMatrixBatchVectorMultiplyAccumulate(
- scratch1, projection_bias, projection_weights, proj_scale_a,
- proj_scale_b, n_batch, n_cell, n_output, output_state_zp, scratch2,
- output_state, NULL);
- if (quantized_proj_clip > 0) {
- tensor_utils::PortableCwiseClipping(output_state, n_batch * n_output,
- quantized_proj_clip);
- }
- } else {
- std::copy_n(scratch1, n_batch * n_output, output_state);
- }
-}
-
-// Calculates a single LSTM gate, int8x8_8 version.
-// Implements the same functionality as CalculateLstmGateFloat.
-void CalculateLstmGateInteger8x8_8(
- // Inputs and weights
- const int8_t* input, int32_t input_zp, const int8_t* input_to_gate_weight,
- const int32_t input_to_gate_scale_a, const int32_t input_to_gate_scale_b,
- const int32_t input_times_weights_scale_a,
- const int32_t input_times_weights_scale_b,
- const int32_t input_times_weights_zp,
- // Output state and weights
- const int8_t* output_state, const int32_t output_state_zp,
- const int8_t* recurrent_to_gate_weight,
- const int32_t recurrent_to_gate_scale_a,
- const int32_t recurrent_to_gate_scale_b,
- const int32_t output_state_times_weights_scale_a,
- const int32_t output_state_times_weights_scale_b,
- const int32_t output_state_times_weights_zp,
- // Layer normalization parameters (layer norm LSTM)
- const int16_t* layer_norm_gate_weight,
- const int32_t layer_norm_gate_scale_a,
- const int32_t layer_norm_gate_scale_b, const int32_t* gate_bias,
- // Array sizes
- const int n_batch, const int n_input, const int n_output, const int n_cell,
- const TfLiteFusedActivation activation,
- // Output
- int16_t* gate,
- // Scratch arrays, both sized n_batch*n_cell
- int8_t* scratch0, int8_t* scratch1) {
- // Multiply input * input_weights => scratch0
- tensor_utils::PortableMatrixBatchVectorMultiply(
- input, input_zp, input_to_gate_weight, input_to_gate_scale_a,
- input_to_gate_scale_b, n_batch, n_input, n_cell, scratch0,
- input_times_weights_zp);
- // Multiply output_state * recurrent_weights => scratch1
- tensor_utils::PortableMatrixBatchVectorMultiply(
- output_state, output_state_zp, recurrent_to_gate_weight,
- recurrent_to_gate_scale_a, recurrent_to_gate_scale_b, n_batch, n_output,
- n_cell, scratch1, output_state_times_weights_zp);
- // Add scratch0 + scratch1 => gate
- tensor_utils::PortableTwoGateSaturatingAdd(
- scratch0, input_times_weights_zp, scratch1, output_state_times_weights_zp,
- input_times_weights_scale_a, input_times_weights_scale_b,
- output_state_times_weights_scale_a, output_state_times_weights_scale_b,
- n_batch, n_cell, gate);
- // Apply layer normalization.
- tensor_utils::PortableApplyLayerNormFloat(
- gate, layer_norm_gate_weight, layer_norm_gate_scale_a,
- layer_norm_gate_scale_b, gate_bias, n_batch, n_cell, gate);
- // Apply activation.
- switch (activation) {
- case kTfLiteActSigmoid:
- tensor_utils::PortableApplySigmoidFloat(gate, n_batch, n_cell, gate);
- break;
- case kTfLiteActTanh:
- tensor_utils::PortableApplyTanhFloat(gate, n_batch, n_cell, -12, gate);
- break;
- default:
- // Only Sigmoid or Tanh is used.
- TFLITE_ASSERT_FALSE;
- }
-}
-
-// Calculates the output state tensor of an LSTM step. See Float and hybrid
-// versions as well.
-//
-// Parameters:
-// - n_batch: batches: the number of distinct vectors in each array.
-// - n_cell, n_output: sizes of vectors.
-// - cell_state, output_gate: input vectors, size n_batch*n_cell.
-// - projection_weights, proj_scale_[a|b], projection_bias:
-// constant inputs, describing projection matrix and bias.
-// - output_state_zp: zero point of the output state.
-// - quantized_proj_clip: if > 0, clip the output of the projection.
-// - output_state: output vector, size n_batch*n_output. Must be contiguous.
-// - scratch: scratch area of size n_batch*n_cell
-void CalculateLstmOutputInteger8x8_8(
- int n_batch, int n_cell, int n_output, const int16_t* cell_state,
- const int16_t* output_gate, const int8_t* projection_weights,
- int32_t proj_scale_a, int32_t proj_scale_b, const int32_t* projection_bias,
- int32_t output_state_zp, int32_t quantized_proj_clip, int8_t* output_state,
- int16_t* scratch) {
- // Note: unlike float/hybrid, the activation is always Tanh.
- tensor_utils::PortableApplyTanhFloat(cell_state, n_batch, n_cell, -15,
- scratch);
- tensor_utils::PortableCwiseMul(output_gate, scratch, n_batch, n_cell,
- 15 + 15 - 15, scratch);
- // Note: no bias like in float/hybrid
- tensor_utils::PortableMatrixBatchVectorMultiply(
- scratch, projection_weights, proj_scale_a, proj_scale_b, projection_bias,
- n_batch, n_cell, n_output, output_state_zp, output_state);
- if (quantized_proj_clip > 0) {
- tensor_utils::PortableCwiseClipping(output_state, n_batch * n_output,
- (int8_t)quantized_proj_clip);
- }
-}
-
-// Fully quantized lstm kernel for 16 bit gate matmul output.
-//
-// Input tensor of size n_batch * n_input:
-// input_ptr
-//
-// LSTM weights:
-// Quantized input weights of size 'n_cell * n_input':
-// input_to_input_weight_ptr - optional
-// input_to_forget_weight_ptr - optional
-// input_to_cell_weight_ptr - optional
-// input_to_output_weight_ptr - optional
-//
-// Quantized recurrent weights of size 'n_cell * n_output':
-// recurrent_to_input_weight_ptr - optional
-// recurrent_to_forget_weights_ptr
-// recurrent_to_cell_weights_ptr
-// recurrent_to_input_weights_ptr
-//
-// Quantized peephole weights of size 'n_cell', representing diagonal matrices.
-// cell_to_input_weights - optional
-// cell_to_cell_weights - optional
-// cell_to_output_weights - optional
-//
-// Quantized projection weights of size 'n_output * n_cell'
-// projection_weight_ptr - optional
-//
-// Weight scales (scalars) for each of the weights above.
-// effective_input_to_input_scale_a - optional
-// effective_input_to_input_scale_b - optional
-// effective_input_to_forget_scale_a
-// effective_input_to_forget_scale_b
-// effective_input_to_cell_scale_a
-// effective_input_to_cell_scale_b
-// effective_input_to_output_scale_a
-// effective_input_to_output_scale_b
-// effective_recurrent_to_input_scale_a - optional
-// effective_recurrent_to_input_scale_b - optional
-// effective_recurrent_to_forget_scale_a
-// effective_recurrent_to_forget_scale_b
-// effective_recurrent_to_cell_scale_a
-// effective_recurrent_to_cell_scale_b
-// effective_recurrent_to_output_scale_a
-// effective_recurrent_to_output_scale_b
-// effective_proj_scale_a - optional
-// effective_proj_scale_b - optional
-//
-// Gate biases of size 'n_cell':
-// input_gate_bias_ptr - optional
-// forget_gate_bias_ptr
-// cell_gate_bias_ptr
-// output_gate_bias_ptr
-//
-// Layer norm coefficients of size 'n_cell', representing diagonal matrices.
-// layer_norm_input_weight_ptr - optional
-// layer_norm_forget_weight_ptr - optional
-// layer_norm_cell_weight_ptr - optional
-// layer_norm_output_weight_ptr - optional
-//
-// Layer norm scales of size 'n_cell'.
-// layer_norm_input_scale_a - optional
-// layer_norm_input_scale_b - optional
-// layer_norm_forget_scale_a - optional
-// layer_norm_forget_scale_b - optional
-// layer_norm_cell_scale_a - optional
-// layer_norm_cell_scale_b - optional
-// layer_norm_output_scale_a - optional
-// layer_norm_output_scale_b - optional
-//
-// Scalar values:
-// quantized_cell_clip: quantized clip value for cell.
-// quantized_proj_clip: quantized clip value for projection.
-// cell_state_scale: the power of two scale for cell state.
-//
-// Zero points:
-// output_state_zp: zero point of output state
-// hidden_zp: zero point for hidden state.
-//
-// Temporary pre-allocated storage for the calculation. Each is of size n_cell *
-// n_batch.
-// scratch0
-// scratch1
-// scratch2
-// scratch3
-// scratch4
-// scratch5: this scratch buffer is created purely for optimizing the
-// MatrixBatchVectorMultiplyAccumulate.
-//
-// Outputs:
-// output_state_ptr - size 'n_batch * n_output'
-// cell_state_ptr - size 'n_batch * n_cell'
-// output_ptr - size 'n_batch * n_output'
-// TODO(b/159947023): scratch0 is not used if (!cifg). Don't allocate then.
-inline void LstmStepInteger8x8_16(
- const int8_t* input_ptr, const int8_t* input_to_input_weight_ptr,
- int32_t effective_input_to_input_scale_a,
- int32_t effective_input_to_input_scale_b,
- const int8_t* input_to_forget_weight_ptr,
- int32_t effective_input_to_forget_scale_a,
- int32_t effective_input_to_forget_scale_b,
- const int8_t* input_to_cell_weight_ptr,
- int32_t effective_input_to_cell_scale_a,
- int32_t effective_input_to_cell_scale_b,
- const int8_t* input_to_output_weight_ptr,
- int32_t effective_input_to_output_scale_a,
- int32_t effective_input_to_output_scale_b,
- const int8_t* recurrent_to_input_weight_ptr,
- int32_t effective_recurrent_to_input_scale_a,
- int32_t effective_recurrent_to_input_scale_b,
- const int8_t* recurrent_to_forget_weight_ptr,
- int32_t effective_recurrent_to_forget_scale_a,
- int32_t effective_recurrent_to_forget_scale_b,
- const int8_t* recurrent_to_cell_weight_ptr,
- int32_t effective_recurrent_to_cell_scale_a,
- int32_t effective_recurrent_to_cell_scale_b,
- const int8_t* recurrent_to_output_weight_ptr,
- int32_t effective_recurrent_to_output_scale_a,
- int32_t effective_recurrent_to_output_scale_b,
- const int16_t* cell_to_input_weight_ptr,
- int32_t effective_cell_to_input_scale_a,
- int32_t effective_cell_to_input_scale_b,
- const int16_t* cell_to_forget_weight_ptr,
- int32_t effective_cell_to_forget_scale_a,
- int32_t effective_cell_to_forget_scale_b,
- const int16_t* cell_to_output_weight_ptr,
- int32_t effective_cell_to_output_scale_a,
- int32_t effective_cell_to_output_scale_b,
- const int8_t* projection_weight_ptr, int32_t effective_proj_scale_a,
- int32_t effective_proj_scale_b, int32_t hidden_zp,
- int32_t effective_hidden_scale_a, int32_t effective_hidden_scale_b,
- const int16_t* layer_norm_input_weight_ptr,
- int32_t layer_norm_input_scale_a, int32_t layer_norm_input_scale_b,
- const int16_t* layer_norm_forget_weight_ptr,
- int32_t layer_norm_forget_scale_a, int32_t layer_norm_forget_scale_b,
- const int16_t* layer_norm_cell_weight_ptr, int32_t layer_norm_cell_scale_a,
- int32_t layer_norm_cell_scale_b,
- const int16_t* layer_norm_output_weight_ptr,
- int32_t layer_norm_output_scale_a, int32_t layer_norm_output_scale_b,
- const int32_t* input_gate_bias_ptr, const int32_t* forget_gate_bias_ptr,
- const int32_t* cell_gate_bias_ptr, const int32_t* output_gate_bias_ptr,
- int16_t quantized_cell_clip, int8_t quantized_proj_clip,
- int32_t cell_state_scale, int32_t input_variance_guard,
- int32_t forget_variance_guard, int32_t cell_variance_guard,
- int32_t output_variance_guard,
- const int32_t* input_to_forget_effective_bias,
- const int32_t* recurrent_to_forget_effective_bias,
- const int32_t* input_to_cell_effective_bias,
- const int32_t* recurrent_to_cell_effective_bias,
- const int32_t* input_to_output_effective_bias,
- const int32_t* recurrent_to_output_effective_bias,
- const int32_t* input_to_input_effective_bias,
- const int32_t* recurrent_to_input_effective_bias,
- const int32_t* projection_effective_bias, int n_batch, int n_cell,
- int n_input, int n_output, int8_t* output_state_ptr,
- int32_t output_state_zp, int16_t* cell_state_ptr, int8_t* output_ptr,
- int16_t* scratch0, int16_t* scratch1, int16_t* scratch2, int16_t* scratch3,
- int8_t* scratch4, int32_t* scratch5) {
- // ruy::profiler::ScopeLabel label("LstmStepInteger8x8_16");
- // Make named scratch buffers for the different gates.
- int16_t* input_gate_scratch = scratch0;
- int16_t* forget_gate_scratch = scratch1;
- int16_t* cell_gate_scratch = scratch2;
- int16_t* output_gate_scratch = scratch3;
-
- // Since we have already checked that weights are all there or none, we
- // can check the existence of only one to the get the condition.
- const bool use_cifg = (input_to_input_weight_ptr == nullptr);
-
- // Check for nullptrs.
- TFLITE_DCHECK(input_to_forget_effective_bias);
- TFLITE_DCHECK(recurrent_to_forget_effective_bias);
- TFLITE_DCHECK(input_to_cell_effective_bias);
- TFLITE_DCHECK(recurrent_to_cell_effective_bias);
- TFLITE_DCHECK(input_to_output_effective_bias);
- TFLITE_DCHECK(recurrent_to_output_effective_bias);
- if (!use_cifg) {
- TFLITE_DCHECK(input_to_input_effective_bias);
- TFLITE_DCHECK(recurrent_to_input_effective_bias);
- }
- const bool use_projection = (projection_weight_ptr != nullptr);
- if (use_projection) {
- TFLITE_DCHECK(projection_effective_bias);
- }
- if (!use_cifg) {
- // Calculate the input gate. (If not CIFG.)
- CalculateLstmGateInteger8x8_16(
- input_ptr, input_to_input_weight_ptr, input_to_input_effective_bias,
- effective_input_to_input_scale_a, effective_input_to_input_scale_b,
- output_state_ptr, recurrent_to_input_weight_ptr,
- recurrent_to_input_effective_bias, effective_recurrent_to_input_scale_a,
- effective_recurrent_to_input_scale_b, cell_state_ptr,
- cell_to_input_weight_ptr, effective_cell_to_input_scale_a,
- effective_cell_to_input_scale_b, layer_norm_input_weight_ptr,
- input_gate_bias_ptr, layer_norm_input_scale_a, layer_norm_input_scale_b,
- input_variance_guard, n_batch, n_input, n_output, n_cell,
- kTfLiteActSigmoid, input_gate_scratch, scratch5);
- }
- // Calculate the forget gate.
- CalculateLstmGateInteger8x8_16(
- input_ptr, input_to_forget_weight_ptr, input_to_forget_effective_bias,
- effective_input_to_forget_scale_a, effective_input_to_forget_scale_b,
- output_state_ptr, recurrent_to_forget_weight_ptr,
- recurrent_to_forget_effective_bias, effective_recurrent_to_forget_scale_a,
- effective_recurrent_to_forget_scale_b, cell_state_ptr,
- cell_to_forget_weight_ptr, effective_cell_to_forget_scale_a,
- effective_cell_to_forget_scale_b, layer_norm_forget_weight_ptr,
- forget_gate_bias_ptr, layer_norm_forget_scale_a,
- layer_norm_forget_scale_b, forget_variance_guard, n_batch, n_input,
- n_output, n_cell, kTfLiteActSigmoid, forget_gate_scratch, scratch5);
- // Calculate the cell update gate.
- CalculateLstmGateInteger8x8_16(
- input_ptr, input_to_cell_weight_ptr, input_to_cell_effective_bias,
- effective_input_to_cell_scale_a, effective_input_to_cell_scale_b,
- output_state_ptr, recurrent_to_cell_weight_ptr,
- recurrent_to_cell_effective_bias, effective_recurrent_to_cell_scale_a,
- effective_recurrent_to_cell_scale_b, cell_state_ptr,
- /*cell_to_gate_weights=*/nullptr, /*cell_to_gate_scale_a=*/0,
- /*cell_to_gate_scale_b=*/0, layer_norm_cell_weight_ptr,
- cell_gate_bias_ptr, layer_norm_cell_scale_a, layer_norm_cell_scale_b,
- cell_variance_guard, n_batch, n_input, n_output, n_cell, kTfLiteActTanh,
- cell_gate_scratch, scratch5);
- // Update the cell state.
- UpdateLstmCellInteger(n_batch, n_cell, cell_state_ptr, cell_state_scale,
- input_gate_scratch, forget_gate_scratch,
- cell_gate_scratch, use_cifg, quantized_cell_clip);
- // Calculate the output gate.
- CalculateLstmGateInteger8x8_16(
- input_ptr, input_to_output_weight_ptr, input_to_output_effective_bias,
- effective_input_to_output_scale_a, effective_input_to_output_scale_b,
- output_state_ptr, recurrent_to_output_weight_ptr,
- recurrent_to_output_effective_bias, effective_recurrent_to_output_scale_a,
- effective_recurrent_to_output_scale_b, cell_state_ptr,
- cell_to_output_weight_ptr, effective_cell_to_output_scale_a,
- effective_cell_to_output_scale_b, layer_norm_output_weight_ptr,
- output_gate_bias_ptr, layer_norm_output_scale_a,
- layer_norm_output_scale_b, output_variance_guard, n_batch, n_input,
- n_output, n_cell, kTfLiteActSigmoid, output_gate_scratch, scratch5);
- // Update the output state.
- CalculateLstmOutputInteger8x8_16(
- n_batch, n_cell, n_output, cell_state_ptr, cell_state_scale,
- output_gate_scratch, effective_hidden_scale_a, effective_hidden_scale_b,
- hidden_zp, projection_weight_ptr, effective_proj_scale_a,
- effective_proj_scale_b, projection_effective_bias, output_state_zp,
- quantized_proj_clip, output_state_ptr, scratch0, scratch4, scratch5);
- // Copy output state to the output. Note that unlike float or hybrid, output
- // is always contiguous.
- std::copy_n(output_state_ptr, n_batch * n_output, output_ptr);
-}
-
-// Fully quantized lstm kernel for 8 bit gate matmul output.
-//
-// Input tensor of size n_batch * n_input:
-// input_ptr
-//
-// LSTM weights:
-// Quantized input weights of size 'n_cell * n_input':
-// input_to_input_weight_ptr - optional
-// input_to_forget_weight_ptr - optional
-// input_to_cell_weight_ptr - optional
-// input_to_output_weight_ptr - optional
-//
-// Quantized recurrent weights of size 'n_cell * n_output':
-// recurrent_to_input_weight_ptr - optional
-// recurrent_to_forget_weights_ptr
-// recurrent_to_cell_weights_ptr
-// recurrent_to_input_weights_ptr
-//
-// Quantized peephole weights of size 'n_cell', representing diagonal matrices.
-// cell_to_input_weights - optional
-// cell_to_cell_weights - optional
-// cell_to_output_weights - optional
-//
-// Quantized projection weights of size 'n_output * n_cell'
-// projection_weight_ptr - optional
-//
-// Weight scales (scalars) for each of the weights above.
-// effective_input_to_input_scale_a - optional
-// effective_input_to_input_scale_b - optional
-// effective_input_to_forget_scale_a
-// effective_input_to_forget_scale_b
-// effective_input_to_cell_scale_a
-// effective_input_to_cell_scale_b
-// effective_input_to_output_scale_a
-// effective_input_to_output_scale_b
-// effective_recurrent_to_input_scale_a - optional
-// effective_recurrent_to_input_scale_b - optional
-// effective_recurrent_to_forget_scale_a
-// effective_recurrent_to_forget_scale_b
-// effective_recurrent_to_cell_scale_a
-// effective_recurrent_to_cell_scale_b
-// effective_recurrent_to_output_scale_a
-// effective_recurrent_to_output_scale_b
-// effective_proj_scale_a - optional
-// effective_proj_scale_b - optional
-//
-// Gate biases of size 'n_cell':
-// input_gate_bias_ptr - optional
-// forget_gate_bias_ptr
-// cell_gate_bias_ptr
-// output_gate_bias_ptr
-//
-// Layer norm coefficients of size 'n_cell', representing diagonal matrices.
-// layer_norm_input_weight_ptr - optional
-// layer_norm_forget_weight_ptr - optional
-// layer_norm_cell_weight_ptr - optional
-// layer_norm_output_weight_ptr - optional
-//
-// Layer norm scales of size 'n_cell'.
-// layer_norm_input_scale_a - optional
-// layer_norm_input_scale_b - optional
-// layer_norm_forget_scale_a - optional
-// layer_norm_forget_scale_b - optional
-// layer_norm_cell_scale_a - optional
-// layer_norm_cell_scale_b - optional
-// layer_norm_output_scale_a - optional
-// layer_norm_output_scale_b - optional
-//
-// Scalar values:
-// quantized_cell_clip: quantized clip value for cell.
-// quantized_proj_clip: quantized clip value for projection.
-// cell_state_scale: the power of two scale for cell state.
-//
-// Zero points:
-// output_state_zp: zero point of output state.
-// hidden_zp: zero point for hidden state.
-//
-// Temporary pre-allocated storage for the calculation. Each is of size n_cell *
-// n_batch.
-// scratch0
-// scratch1
-// scratch2
-// scratch3
-// scratch4
-// scratch5
-// scratch6
-// scratch7
-//
-// Outputs:
-// output_state_ptr - size 'n_batch * n_output'
-// cell_state_ptr - size 'n_batch * n_cell'
-// output_ptr - size 'n_batch * n_output'
-// TODO(b/148688698): Move zero point calculation into Prepare().
-// TODO(b/159947023): scratch5 is unused, remove.
-inline void LstmStepInteger8x8_8(
- const int8_t* input_ptr, int32_t input_zp,
- const int8_t* input_to_input_weight_ptr,
- int32_t effective_input_to_input_scale_a,
- int32_t effective_input_to_input_scale_b,
- const int8_t* input_to_forget_weight_ptr,
- int32_t effective_input_to_forget_scale_a,
- int32_t effective_input_to_forget_scale_b,
- const int8_t* input_to_cell_weight_ptr,
- int32_t effective_input_to_cell_scale_a,
- int32_t effective_input_to_cell_scale_b,
- const int8_t* input_to_output_weight_ptr,
- int32_t effective_input_to_output_scale_a,
- int32_t effective_input_to_output_scale_b,
- const int8_t* recurrent_to_input_weight_ptr,
- int32_t effective_recurrent_to_input_scale_a,
- int32_t effective_recurrent_to_input_scale_b,
- const int8_t* recurrent_to_forget_weight_ptr,
- int32_t effective_recurrent_to_forget_scale_a,
- int32_t effective_recurrent_to_forget_scale_b,
- const int8_t* recurrent_to_cell_weight_ptr,
- int32_t effective_recurrent_to_cell_scale_a,
- int32_t effective_recurrent_to_cell_scale_b,
- const int8_t* recurrent_to_output_weight_ptr,
- int32_t effective_recurrent_to_output_scale_a,
- int32_t effective_recurrent_to_output_scale_b,
- const int8_t* cell_to_input_weight_ptr,
- int32_t effective_cell_to_input_scale_a,
- int32_t effective_cell_to_input_scale_b,
- const int8_t* cell_to_forget_weight_ptr,
- int32_t effective_cell_to_forget_scale_a,
- int32_t effective_cell_to_forget_scale_b,
- const int8_t* cell_to_output_weight_ptr,
- int32_t effective_cell_to_output_scale_a,
- int32_t effective_cell_to_output_scale_b,
- const int8_t* projection_weight_ptr, int32_t effective_proj_scale_a,
- int32_t effective_proj_scale_b, const int16_t* layer_norm_input_weight_ptr,
- int32_t layer_norm_input_scale_a, int32_t layer_norm_input_scale_b,
- const int16_t* layer_norm_forget_weight_ptr,
- int32_t layer_norm_forget_scale_a, int32_t layer_norm_forget_scale_b,
- const int16_t* layer_norm_cell_weight_ptr, int32_t layer_norm_cell_scale_a,
- int32_t layer_norm_cell_scale_b,
- const int16_t* layer_norm_output_weight_ptr,
- int32_t layer_norm_output_scale_a, int32_t layer_norm_output_scale_b,
- const int32_t* input_gate_bias_ptr, const int32_t* forget_gate_bias_ptr,
- const int32_t* cell_gate_bias_ptr, const int32_t* output_gate_bias_ptr,
- const int32_t* projection_bias_ptr, const TfLiteLSTMParams* params,
- const int32_t* intermediate_scale_a, const int32_t* intermediate_scale_b,
- const int32_t* intermediate_zp, int16_t quantized_cell_clip,
- int8_t quantized_proj_clip, int n_batch, int n_cell, int n_input,
- int n_output, int output_batch_leading_dim, int8_t* output_state_ptr,
- int32_t output_state_zp, int16_t* cell_state_ptr, int8_t* output_ptr,
- int8_t* scratch0, int8_t* scratch1, int16_t* scratch2, int16_t* scratch3,
- int16_t* scratch4, int16_t* scratch5, int16_t* scratch6,
- int16_t* scratch7) {
- // TODO(b/159066113): scratch5 is unused, remove.
-
- // ruy::profiler::ScopeLabel label("LstmStepInteger8x8_8");
- // Make named scratch buffers for the different gates.
- int16_t* forget_gate_scratch = scratch2;
- int16_t* cell_gate_scratch = scratch3;
- int16_t* output_gate_scratch = scratch4;
- // no-CIFG is not supported here
-
- // Calculate the forget gate.
- CalculateLstmGateInteger8x8_8(
- input_ptr, input_zp, input_to_forget_weight_ptr,
- effective_input_to_forget_scale_a, effective_input_to_forget_scale_b,
- intermediate_scale_a[2], intermediate_scale_b[2], intermediate_zp[4],
- output_state_ptr, output_state_zp, recurrent_to_forget_weight_ptr,
- effective_recurrent_to_forget_scale_a,
- effective_recurrent_to_forget_scale_b, intermediate_scale_a[3],
- intermediate_scale_b[3], intermediate_zp[5], layer_norm_forget_weight_ptr,
- layer_norm_forget_scale_a, layer_norm_forget_scale_b,
- forget_gate_bias_ptr, n_batch, n_input, n_output, n_cell,
- kTfLiteActSigmoid, forget_gate_scratch, scratch0, scratch1);
- // Calculate the cell update gate.
- CalculateLstmGateInteger8x8_8(
- input_ptr, input_zp, input_to_cell_weight_ptr,
- effective_input_to_cell_scale_a, effective_input_to_cell_scale_b,
- intermediate_scale_a[4], intermediate_scale_b[4], intermediate_zp[7],
- output_state_ptr, output_state_zp, recurrent_to_cell_weight_ptr,
- effective_recurrent_to_cell_scale_a, effective_recurrent_to_cell_scale_b,
- intermediate_scale_a[5], intermediate_scale_b[5], intermediate_zp[8],
- layer_norm_cell_weight_ptr, layer_norm_cell_scale_a,
- layer_norm_cell_scale_b, cell_gate_bias_ptr, n_batch, n_input, n_output,
- n_cell, kTfLiteActTanh, cell_gate_scratch, scratch0, scratch1);
- // Update the cell state.
- UpdateLstmCellInteger(n_batch, n_cell, cell_state_ptr,
- /*cell_state_scale=*/-15, /*input_gate=*/nullptr,
- forget_gate_scratch, cell_gate_scratch,
- /*use_cifg=*/true, quantized_cell_clip);
- // Calculate the output gate.
- CalculateLstmGateInteger8x8_8(
- input_ptr, input_zp, input_to_output_weight_ptr,
- effective_input_to_output_scale_a, effective_input_to_output_scale_b,
- intermediate_scale_a[6], intermediate_scale_b[6], intermediate_zp[10],
- output_state_ptr, output_state_zp, recurrent_to_output_weight_ptr,
- effective_recurrent_to_output_scale_a,
- effective_recurrent_to_output_scale_b, intermediate_scale_a[11],
- intermediate_scale_b[7], intermediate_zp[7], layer_norm_output_weight_ptr,
- layer_norm_output_scale_a, layer_norm_output_scale_b,
- output_gate_bias_ptr, n_batch, n_input, n_output, n_cell,
- kTfLiteActSigmoid, output_gate_scratch, scratch0, scratch1);
- // Update the output state.
- CalculateLstmOutputInteger8x8_8(
- n_batch, n_cell, n_output, cell_state_ptr, output_gate_scratch,
- projection_weight_ptr, effective_proj_scale_a, effective_proj_scale_b,
- projection_bias_ptr, output_state_zp, quantized_proj_clip,
- output_state_ptr, scratch2);
- // Copy output state to the output. Note that unlike float or hybrid, output
- // is always contiguous.
- std::copy_n(output_state_ptr, n_batch * n_output, output_ptr);
-}
-
-} // namespace
-
-// LINT.ThenChange(//tensorflow/lite/tools/optimize/calibration/builtin_logging_ops/lstm.cc)
-TfLiteStatus EvalInteger8x8_16(
- TfLiteContext* context, TfLiteNode* node, const TfLiteEvalTensor* input,
- const TfLiteEvalTensor* input_to_input_weights,
- const TfLiteEvalTensor* input_to_forget_weights,
- const TfLiteEvalTensor* input_to_cell_weights,
- const TfLiteEvalTensor* input_to_output_weights,
- const TfLiteEvalTensor* recurrent_to_input_weights,
- const TfLiteEvalTensor* recurrent_to_forget_weights,
- const TfLiteEvalTensor* recurrent_to_cell_weights,
- const TfLiteEvalTensor* recurrent_to_output_weights,
- const TfLiteEvalTensor* cell_to_input_weights,
- const TfLiteEvalTensor* cell_to_forget_weights,
- const TfLiteEvalTensor* cell_to_output_weights,
- const TfLiteEvalTensor* input_layer_norm_coefficients,
- const TfLiteEvalTensor* forget_layer_norm_coefficients,
- const TfLiteEvalTensor* cell_layer_norm_coefficients,
- const TfLiteEvalTensor* output_layer_norm_coefficients,
- const TfLiteEvalTensor* input_gate_bias,
- const TfLiteEvalTensor* forget_gate_bias,
- const TfLiteEvalTensor* cell_gate_bias,
- const TfLiteEvalTensor* output_gate_bias,
- const TfLiteEvalTensor* projection_weights,
- const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params,
- bool forward_sequence, bool time_major,
- const lstm_eval::IntegerLstmParameter* integer_lstm_param,
- TfLiteEvalTensor* output_state, TfLiteEvalTensor* cell_state,
- TfLiteEvalTensor* output, TfLiteEvalTensor* scratch0,
- TfLiteEvalTensor* scratch1, TfLiteEvalTensor* scratch2,
- TfLiteEvalTensor* scratch3, TfLiteEvalTensor* scratch4,
- TfLiteEvalTensor* scratch5) {
- TFLITE_DCHECK(input->dims->size >= 2 && input->dims->size <= 3);
- const int n_input = input->dims->data[input->dims->size - 1];
- int max_time, n_batch;
- if (input->dims->size == 2) {
- max_time = 1;
- n_batch = input->dims->data[0];
- } else {
- max_time = (time_major) ? input->dims->data[0] : input->dims->data[1];
- n_batch = (time_major) ? input->dims->data[1] : input->dims->data[0];
- }
-
- // n_cell and n_output will be the same size when there is no projection.
- const int n_cell = input_to_output_weights->dims->data[0];
- const int n_output = recurrent_to_output_weights->dims->data[1];
-
- // Activation zero point
- // TODO@is data.output_zero_point equal to output_state->params.zero_point
- // int output_state_zp = output_state->params.zero_point;
- int output_state_zp = 0;
-
- // Get params for time/batch/sequence.
- const int output_batch_leading_dim =
- output->dims->data[output->dims->size - 1];
-
- if (time_major) {
- const int input_step = n_batch * n_input;
- const int output_step = n_batch * output_batch_leading_dim;
- for (int t = 0; t < max_time; t++) {
- const int t_rel = t;
- int8_t* output_ptr =
- tflite::micro::GetTensorData<int8_t>(output) + t_rel * output_step;
- const int8_t* input_ptr =
- tflite::micro::GetTensorData<int8_t>(input) + t_rel * input_step;
- LstmStepInteger8x8_16(
- input_ptr,
- tflite::micro::GetTensorData<int8_t>(input_to_input_weights),
- integer_lstm_param->effective_input_to_input_scale_a,
- integer_lstm_param->effective_input_to_input_scale_b,
- tflite::micro::GetTensorData<int8_t>(input_to_forget_weights),
- integer_lstm_param->effective_input_to_forget_scale_a,
- integer_lstm_param->effective_input_to_forget_scale_b,
- tflite::micro::GetTensorData<int8_t>(input_to_cell_weights),
- integer_lstm_param->effective_input_to_cell_scale_a,
- integer_lstm_param->effective_input_to_cell_scale_b,
- tflite::micro::GetTensorData<int8_t>(input_to_output_weights),
- integer_lstm_param->effective_input_to_output_scale_a,
- integer_lstm_param->effective_input_to_output_scale_b,
- tflite::micro::GetTensorData<int8_t>(recurrent_to_input_weights),
- integer_lstm_param->effective_recurrent_to_input_scale_a,
- integer_lstm_param->effective_recurrent_to_input_scale_b,
- tflite::micro::GetTensorData<int8_t>(recurrent_to_forget_weights),
- integer_lstm_param->effective_recurrent_to_forget_scale_a,
- integer_lstm_param->effective_recurrent_to_forget_scale_b,
- tflite::micro::GetTensorData<int8_t>(recurrent_to_cell_weights),
- integer_lstm_param->effective_recurrent_to_cell_scale_a,
- integer_lstm_param->effective_recurrent_to_cell_scale_b,
- tflite::micro::GetTensorData<int8_t>(recurrent_to_output_weights),
- integer_lstm_param->effective_recurrent_to_output_scale_a,
- integer_lstm_param->effective_recurrent_to_output_scale_b,
- tflite::micro::GetTensorData<int16_t>(cell_to_input_weights),
- integer_lstm_param->effective_cell_to_input_scale_a,
- integer_lstm_param->effective_cell_to_input_scale_b,
- tflite::micro::GetTensorData<int16_t>(cell_to_forget_weights),
- integer_lstm_param->effective_cell_to_forget_scale_a,
- integer_lstm_param->effective_cell_to_forget_scale_b,
- tflite::micro::GetTensorData<int16_t>(cell_to_output_weights),
- integer_lstm_param->effective_cell_to_output_scale_a,
- integer_lstm_param->effective_cell_to_output_scale_b,
- tflite::micro::GetTensorData<int8_t>(projection_weights),
- integer_lstm_param->effective_proj_scale_a,
- integer_lstm_param->effective_proj_scale_b,
- integer_lstm_param->hidden_zp,
- integer_lstm_param->effective_hidden_scale_a,
- integer_lstm_param->effective_hidden_scale_b,
- tflite::micro::GetTensorData<int16_t>(input_layer_norm_coefficients),
- integer_lstm_param->layer_norm_input_scale_a,
- integer_lstm_param->layer_norm_input_scale_b,
- tflite::micro::GetTensorData<int16_t>(forget_layer_norm_coefficients),
- integer_lstm_param->layer_norm_forget_scale_a,
- integer_lstm_param->layer_norm_forget_scale_b,
- tflite::micro::GetTensorData<int16_t>(cell_layer_norm_coefficients),
- integer_lstm_param->layer_norm_cell_scale_a,
- integer_lstm_param->layer_norm_cell_scale_b,
- tflite::micro::GetTensorData<int16_t>(output_layer_norm_coefficients),
- integer_lstm_param->layer_norm_output_scale_a,
- integer_lstm_param->layer_norm_output_scale_b,
- tflite::micro::GetTensorData<int32_t>(input_gate_bias),
- tflite::micro::GetTensorData<int32_t>(forget_gate_bias),
- tflite::micro::GetTensorData<int32_t>(cell_gate_bias),
- tflite::micro::GetTensorData<int32_t>(output_gate_bias),
- integer_lstm_param->quantized_cell_clip,
- integer_lstm_param->quantized_proj_clip,
- integer_lstm_param->cell_scale,
- integer_lstm_param->input_variance_guard,
- integer_lstm_param->forget_variance_guard,
- integer_lstm_param->cell_variance_guard,
- integer_lstm_param->output_variance_guard,
- integer_lstm_param->input_to_forget_effective_bias.get(),
- integer_lstm_param->recurrent_to_forget_effective_bias.get(),
- integer_lstm_param->input_to_cell_effective_bias.get(),
- integer_lstm_param->recurrent_to_cell_effective_bias.get(),
- integer_lstm_param->input_to_output_effective_bias.get(),
- integer_lstm_param->recurrent_to_output_effective_bias.get(),
- integer_lstm_param->input_to_input_effective_bias.get(),
- integer_lstm_param->recurrent_to_input_effective_bias.get(),
- integer_lstm_param->projection_effective_bias.get(), n_batch, n_cell,
- n_input, n_output, tflite::micro::GetTensorData<int8_t>(output_state),
- output_state_zp, tflite::micro::GetTensorData<int16_t>(cell_state),
- output_ptr, (int16_t*)(scratch0), (int16_t*)(scratch1),
- (int16_t*)(scratch2), (int16_t*)(scratch3), (int8_t*)(scratch4),
- (int32_t*)(scratch5));
- }
- } else {
- for (int b = 0; b < n_batch; b++) {
- const int input_step = n_input;
- const int output_step = output_batch_leading_dim;
- for (int t = 0; t < max_time; t++) {
- // If this is the forward_sequence, step forward, otherwise step
- // backwards.
- const int t_rel = forward_sequence ? t : max_time - t - 1;
- const int time_offset = b * max_time + t_rel;
- const int8_t* input_ptr = tflite::micro::GetTensorData<int8_t>(input) +
- time_offset * input_step;
- int8_t* output_ptr = tflite::micro::GetTensorData<int8_t>(output) +
- time_offset * output_step;
-
- // Offset the {output,cell}_state pointers to the right batch.
- int8_t* output_state_ptr =
- tflite::micro::GetTensorData<int8_t>(output_state) +
- b * output_batch_leading_dim;
- int16_t* cell_state_ptr =
- tflite::micro::GetTensorData<int16_t>(cell_state) + b * n_cell;
-
- LstmStepInteger8x8_16(
- input_ptr,
- tflite::micro::GetTensorData<int8_t>(input_to_input_weights),
- integer_lstm_param->effective_input_to_input_scale_a,
- integer_lstm_param->effective_input_to_input_scale_b,
- tflite::micro::GetTensorData<int8_t>(input_to_forget_weights),
- integer_lstm_param->effective_input_to_forget_scale_a,
- integer_lstm_param->effective_input_to_forget_scale_b,
- tflite::micro::GetTensorData<int8_t>(input_to_cell_weights),
- integer_lstm_param->effective_input_to_cell_scale_a,
- integer_lstm_param->effective_input_to_cell_scale_b,
- tflite::micro::GetTensorData<int8_t>(input_to_output_weights),
- integer_lstm_param->effective_input_to_output_scale_a,
- integer_lstm_param->effective_input_to_output_scale_b,
- tflite::micro::GetTensorData<int8_t>(recurrent_to_input_weights),
- integer_lstm_param->effective_recurrent_to_input_scale_a,
- integer_lstm_param->effective_recurrent_to_input_scale_b,
- tflite::micro::GetTensorData<int8_t>(recurrent_to_forget_weights),
- integer_lstm_param->effective_recurrent_to_forget_scale_a,
- integer_lstm_param->effective_recurrent_to_forget_scale_b,
- tflite::micro::GetTensorData<int8_t>(recurrent_to_cell_weights),
- integer_lstm_param->effective_recurrent_to_cell_scale_a,
- integer_lstm_param->effective_recurrent_to_cell_scale_b,
- tflite::micro::GetTensorData<int8_t>(recurrent_to_output_weights),
- integer_lstm_param->effective_recurrent_to_output_scale_a,
- integer_lstm_param->effective_recurrent_to_output_scale_b,
- tflite::micro::GetTensorData<int16_t>(cell_to_input_weights),
- integer_lstm_param->effective_cell_to_input_scale_a,
- integer_lstm_param->effective_cell_to_input_scale_b,
- tflite::micro::GetTensorData<int16_t>(cell_to_forget_weights),
- integer_lstm_param->effective_cell_to_forget_scale_a,
- integer_lstm_param->effective_cell_to_forget_scale_b,
- tflite::micro::GetTensorData<int16_t>(cell_to_output_weights),
- integer_lstm_param->effective_cell_to_output_scale_a,
- integer_lstm_param->effective_cell_to_output_scale_b,
- tflite::micro::GetTensorData<int8_t>(projection_weights),
- integer_lstm_param->effective_proj_scale_a,
- integer_lstm_param->effective_proj_scale_b,
- integer_lstm_param->hidden_zp,
- integer_lstm_param->effective_hidden_scale_a,
- integer_lstm_param->effective_hidden_scale_b,
- tflite::micro::GetTensorData<int16_t>(
- input_layer_norm_coefficients),
- integer_lstm_param->layer_norm_input_scale_a,
- integer_lstm_param->layer_norm_input_scale_b,
- tflite::micro::GetTensorData<int16_t>(
- forget_layer_norm_coefficients),
- integer_lstm_param->layer_norm_forget_scale_a,
- integer_lstm_param->layer_norm_forget_scale_b,
- tflite::micro::GetTensorData<int16_t>(cell_layer_norm_coefficients),
- integer_lstm_param->layer_norm_cell_scale_a,
- integer_lstm_param->layer_norm_cell_scale_b,
- tflite::micro::GetTensorData<int16_t>(
- output_layer_norm_coefficients),
- integer_lstm_param->layer_norm_output_scale_a,
- integer_lstm_param->layer_norm_output_scale_b,
- tflite::micro::GetTensorData<int32_t>(input_gate_bias),
- tflite::micro::GetTensorData<int32_t>(forget_gate_bias),
- tflite::micro::GetTensorData<int32_t>(cell_gate_bias),
- tflite::micro::GetTensorData<int32_t>(output_gate_bias),
- integer_lstm_param->quantized_cell_clip,
- integer_lstm_param->quantized_proj_clip,
- integer_lstm_param->cell_scale,
- integer_lstm_param->input_variance_guard,
- integer_lstm_param->forget_variance_guard,
- integer_lstm_param->cell_variance_guard,
- integer_lstm_param->output_variance_guard,
- integer_lstm_param->input_to_forget_effective_bias.get(),
- integer_lstm_param->recurrent_to_forget_effective_bias.get(),
- integer_lstm_param->input_to_cell_effective_bias.get(),
- integer_lstm_param->recurrent_to_cell_effective_bias.get(),
- integer_lstm_param->input_to_output_effective_bias.get(),
- integer_lstm_param->recurrent_to_output_effective_bias.get(),
- integer_lstm_param->input_to_input_effective_bias.get(),
- integer_lstm_param->recurrent_to_input_effective_bias.get(),
- integer_lstm_param->projection_effective_bias.get(), /*n_batch=*/1,
- n_cell, n_input, n_output, output_state_ptr, output_state_zp,
- cell_state_ptr, output_ptr, (int16_t*)(scratch0),
- (int16_t*)(scratch1), (int16_t*)(scratch2), (int16_t*)(scratch3),
- (int8_t*)(scratch4), (int32_t*)(scratch5));
- }
+LstmTensors::~LstmTensors() {
+ for (size_t i = 0; i < 24; i++) {
+ if (internal_tensors_[i] != nullptr) {
+ micro_context_->DeallocateTempTfLiteTensor(internal_tensors_[i]);
}
}
+ micro_context_->DeallocateTempTfLiteTensor(output_tensor_);
+}
+// Verify the LSTM internal tensor properties (e.g., type checks)
+// Input/output/states/fc weights tensors are required for kernel evaulation.
+// The state tensors should be variables. Variants of the standard LSTM
+// are not supported here, therefore their corresponding tensors should be
+// invalid
+TfLiteStatus LstmTensors::ValidateTensorStatus(TfLiteContext* context) const {
+ // Verify certain tensor properties
+ // input tensor
+ TF_LITE_ENSURE(context, internal_tensors_[kLstmInputTensor] != nullptr);
+ // hidden state
+ TF_LITE_ENSURE(context, internal_tensors_[kLstmOutputStateTensor] != nullptr);
+ TF_LITE_ENSURE(context,
+ internal_tensors_[kLstmOutputStateTensor]->is_variable);
+ // hidden state becomes input so they must have the same type
+ TF_LITE_ENSURE_EQ(context, internal_tensors_[kLstmOutputStateTensor]->type,
+ internal_tensors_[kLstmInputTensor]->type);
+ // cell state
+ TF_LITE_ENSURE(context, internal_tensors_[kLstmCellStateTensor] != nullptr);
+ TF_LITE_ENSURE(context, internal_tensors_[kLstmCellStateTensor]->is_variable);
+ // output
+ TF_LITE_ENSURE(context, output_tensor_ != nullptr);
+ // output type is the same as the input type (activations)
+ TF_LITE_ENSURE_EQ(context, output_tensor_->type,
+ internal_tensors_[kLstmInputTensor]->type);
+
+ // weight tensors (1-9, see lstm_shared for index definition)
+ const auto weight_type =
+ internal_tensors_[kLstmInputToForgetWeightsTensor]->type;
+ for (size_t i = 1; i < 9; i++) {
+ TF_LITE_ENSURE(context, internal_tensors_[i] != nullptr);
+ TF_LITE_ENSURE_EQ(context, internal_tensors_[i]->type, weight_type);
+ }
+
+ // bias tensors (12-15, see lstm_shared for index definition)
+ const auto bias_type = internal_tensors_[kLstmForgetGateBiasTensor]->type;
+ for (size_t i = 12; i < 16; i++) {
+ TF_LITE_ENSURE(context, internal_tensors_[i] != nullptr);
+ TF_LITE_ENSURE_EQ(context, internal_tensors_[i]->type, bias_type);
+ }
+ // Tensors from LSTM variants are invalid
+ // No peephole
+ for (size_t i = 9; i < 12; i++) {
+ TF_LITE_ENSURE(context, internal_tensors_[i] == nullptr);
+ }
+ // No projection
+ for (size_t i = 16; i < 18; i++) {
+ TF_LITE_ENSURE(context, internal_tensors_[i] == nullptr);
+ }
+ // No internal layer norm
+ for (size_t i = 20; i < 24; i++) {
+ TF_LITE_ENSURE(context, internal_tensors_[i] == nullptr);
+ }
return kTfLiteOk;
}
-TfLiteStatus EvalInteger8x8_8(
- const TfLiteEvalTensor* input,
- const TfLiteEvalTensor* input_to_input_weights,
- const TfLiteEvalTensor* input_to_forget_weights,
- const TfLiteEvalTensor* input_to_cell_weights,
- const TfLiteEvalTensor* input_to_output_weights,
- const TfLiteEvalTensor* recurrent_to_input_weights,
- const TfLiteEvalTensor* recurrent_to_forget_weights,
- const TfLiteEvalTensor* recurrent_to_cell_weights,
- const TfLiteEvalTensor* recurrent_to_output_weights,
- const TfLiteEvalTensor* cell_to_input_weights,
- const TfLiteEvalTensor* cell_to_forget_weights,
- const TfLiteEvalTensor* cell_to_output_weights,
- const TfLiteEvalTensor* input_layer_norm_coefficients,
- const TfLiteEvalTensor* forget_layer_norm_coefficients,
- const TfLiteEvalTensor* cell_layer_norm_coefficients,
- const TfLiteEvalTensor* output_layer_norm_coefficients,
- const TfLiteEvalTensor* input_gate_bias,
- const TfLiteEvalTensor* forget_gate_bias,
- const TfLiteEvalTensor* cell_gate_bias,
- const TfLiteEvalTensor* output_gate_bias,
- const TfLiteEvalTensor* projection_weights,
- const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params,
- TfLiteEvalTensor* output_state, TfLiteEvalTensor* cell_state,
- TfLiteEvalTensor* output,
- const lstm_eval::IntegerLstmParameter* integer_lstm_param,
- TfLiteEvalTensor* scratch0, TfLiteEvalTensor* scratch1,
- TfLiteEvalTensor* scratch2, TfLiteEvalTensor* scratch3,
- TfLiteEvalTensor* scratch4, TfLiteEvalTensor* scratch5,
- TfLiteEvalTensor* scratch6, TfLiteEvalTensor* scratch7) {
- TFLITE_DCHECK(input->dims->size >= 2 && input->dims->size <= 3);
- const int n_input = input->dims->data[input->dims->size - 1];
- int max_time, n_batch;
- if (input->dims->size == 2) {
- max_time = 1;
- n_batch = input->dims->data[0];
- } else {
- max_time = input->dims->data[0];
- n_batch = input->dims->data[1];
+namespace lstm_internal {
+
+#if !(defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
+const int32_t kInt16Max = std::numeric_limits<int16_t>::max();
+const int32_t kInt16Min = std::numeric_limits<int16_t>::min();
+#endif
+
+void AddElementWise(const int16_t* input_1, const int16_t* input_2, int n_batch,
+ int n_input, int16_t* output) {
+#if !(defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
+ for (int batch = 0; batch < n_batch; ++batch) {
+ for (int i = 0; i < n_input; ++i) {
+ const int index = batch * n_input + i;
+ int32_t sum = input_1[index] + input_2[index];
+ const int32_t sum_clamped = std::min(kInt16Max, std::max(kInt16Min, sum));
+ output[index] = static_cast<int16_t>(sum_clamped);
+ }
}
-
- // n_cell and n_output will be the same size when there is no projection.
- const int n_cell = input_to_output_weights->dims->data[0];
- const int n_output = recurrent_to_output_weights->dims->data[1];
- //@TODO input zero point and output zeropoint
- // const int32_t input_zp = input->params.zero_point;
- /// const int32_t output_state_zp = output_state->params.zero_point;
- const int32_t input_zp = 0;
- const int32_t output_state_zp = 0;
-
- // Get params for time/batch/sequence.
- const int output_batch_leading_dim =
- output->dims->data[output->dims->size - 1];
- const int input_step = n_batch * n_input;
- const int output_step = n_batch * output_batch_leading_dim;
-
- for (int t = 0; t < max_time; t++) {
- const int t_rel = t;
- int8_t* output_ptr =
- tflite::micro::GetTensorData<int8_t>(output) + t_rel * output_step;
- // Input can be int8 asymmetric or int16 symmetric.
- const int8_t* input_ptr =
- tflite::micro::GetTensorData<int8_t>(input) + t_rel * input_step;
- lstm_eval::LstmStepInteger8x8_8(
- input_ptr, input_zp,
-
- tflite::micro::GetTensorData<int8_t>(input_to_input_weights),
- integer_lstm_param->effective_input_to_input_scale_a,
- integer_lstm_param->effective_input_to_input_scale_b,
-
- tflite::micro::GetTensorData<int8_t>(input_to_forget_weights),
- integer_lstm_param->effective_input_to_forget_scale_a,
- integer_lstm_param->effective_input_to_forget_scale_b,
-
- tflite::micro::GetTensorData<int8_t>(input_to_cell_weights),
- integer_lstm_param->effective_input_to_cell_scale_a,
- integer_lstm_param->effective_input_to_cell_scale_b,
-
- tflite::micro::GetTensorData<int8_t>(input_to_output_weights),
- integer_lstm_param->effective_input_to_output_scale_a,
- integer_lstm_param->effective_input_to_output_scale_b,
-
- tflite::micro::GetTensorData<int8_t>(recurrent_to_input_weights),
- integer_lstm_param->effective_recurrent_to_input_scale_a,
- integer_lstm_param->effective_recurrent_to_input_scale_b,
-
- tflite::micro::GetTensorData<int8_t>(recurrent_to_forget_weights),
- integer_lstm_param->effective_recurrent_to_forget_scale_a,
- integer_lstm_param->effective_recurrent_to_forget_scale_b,
-
- tflite::micro::GetTensorData<int8_t>(recurrent_to_cell_weights),
- integer_lstm_param->effective_recurrent_to_cell_scale_a,
- integer_lstm_param->effective_recurrent_to_cell_scale_b,
-
- tflite::micro::GetTensorData<int8_t>(recurrent_to_output_weights),
- integer_lstm_param->effective_recurrent_to_output_scale_a,
- integer_lstm_param->effective_recurrent_to_output_scale_b,
-
- tflite::micro::GetTensorData<int8_t>(cell_to_input_weights),
- integer_lstm_param->effective_cell_to_input_scale_a,
- integer_lstm_param->effective_cell_to_input_scale_b,
-
- tflite::micro::GetTensorData<int8_t>(cell_to_forget_weights),
- integer_lstm_param->effective_cell_to_forget_scale_a,
- integer_lstm_param->effective_cell_to_forget_scale_b,
-
- tflite::micro::GetTensorData<int8_t>(cell_to_output_weights),
- integer_lstm_param->effective_cell_to_output_scale_a,
- integer_lstm_param->effective_cell_to_output_scale_b,
-
- tflite::micro::GetTensorData<int8_t>(projection_weights),
- integer_lstm_param->effective_proj_scale_a,
- integer_lstm_param->effective_proj_scale_b,
-
- tflite::micro::GetTensorData<int16_t>(input_layer_norm_coefficients),
- integer_lstm_param->layer_norm_input_scale_a,
- integer_lstm_param->layer_norm_input_scale_b,
-
- tflite::micro::GetTensorData<int16_t>(forget_layer_norm_coefficients),
- integer_lstm_param->layer_norm_forget_scale_a,
- integer_lstm_param->layer_norm_forget_scale_b,
-
- tflite::micro::GetTensorData<int16_t>(cell_layer_norm_coefficients),
- integer_lstm_param->layer_norm_cell_scale_a,
- integer_lstm_param->layer_norm_cell_scale_b,
-
- tflite::micro::GetTensorData<int16_t>(output_layer_norm_coefficients),
- integer_lstm_param->layer_norm_output_scale_a,
- integer_lstm_param->layer_norm_output_scale_b,
-
- tflite::micro::GetTensorData<int32_t>(input_gate_bias),
- tflite::micro::GetTensorData<int32_t>(forget_gate_bias),
- tflite::micro::GetTensorData<int32_t>(cell_gate_bias),
- tflite::micro::GetTensorData<int32_t>(output_gate_bias),
- tflite::micro::GetTensorData<int32_t>(projection_bias),
-
- params, integer_lstm_param->intermediate_scale_a,
- integer_lstm_param->intermediate_scale_b,
- integer_lstm_param->intermediate_zp,
- integer_lstm_param->quantized_cell_clip,
- integer_lstm_param->quantized_proj_clip, n_batch, n_cell, n_input,
- n_output, output_batch_leading_dim,
- tflite::micro::GetTensorData<int8_t>(output_state), output_state_zp,
- tflite::micro::GetTensorData<int16_t>(cell_state), output_ptr,
- tflite::micro::GetTensorData<int8_t>(scratch0),
- tflite::micro::GetTensorData<int8_t>(scratch1),
- tflite::micro::GetTensorData<int16_t>(scratch2),
- tflite::micro::GetTensorData<int16_t>(scratch3),
- tflite::micro::GetTensorData<int16_t>(scratch4),
- tflite::micro::GetTensorData<int16_t>(scratch5),
- tflite::micro::GetTensorData<int16_t>(scratch6),
- tflite::micro::GetTensorData<int16_t>(scratch7));
- }
-
- return kTfLiteOk;
+#else
+ xa_nn_elm_add_16x16_16(output, input_1, input_2, n_batch * n_input);
+#endif
}
-} // namespace lstm_eval
-} // namespace micro
-} // namespace ops
+void AddElementWise(const float* input_1, const float* input_2, int n_batch,
+ int n_input, float* output) {
+ for (int batch = 0; batch < n_batch; ++batch) {
+ for (int i = 0; i < n_input; ++i) {
+ const int index = batch * n_input + i;
+ output[index] = input_1[index] + input_2[index];
+ }
+ }
+}
+
+#if !(defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
+void Sigmoid(const RuntimeShape& data_shape, int16_t* data) {
+ reference_integer_ops::Logistic(
+ 0 /*data->input_multiplier*/, 0 /*data->input_left_shift */,
+ data_shape.FlatSize() /*NumElements(input->dims)*/,
+ data /* tflite::micro::GetTensorData<int16_t>(input) */,
+ data /*tflite::micro::GetTensorData<int16_t>(output) */);
+}
+
+void Sigmoid(const RuntimeShape& data_shape, float* data) {
+ reference_ops::Logistic(data_shape, data, data_shape, data);
+}
+
+void Tanh(int32_t cell_state_scale_power, const RuntimeShape& input_data_shape,
+ int16_t* input_data, const RuntimeShape& output_data_shape,
+ int16_t* output_data) {
+ int32_t tanh_input_left_shift = (15 + cell_state_scale_power) - 3;
+ int32_t input_multiplier = 0;
+ if (tanh_input_left_shift < 0) /* handling negative shift value */
+ {
+ tanh_input_left_shift = -tanh_input_left_shift;
+ input_multiplier = 3;
+ }
+ reference_integer_ops::Tanh(input_multiplier, tanh_input_left_shift,
+ input_data_shape, input_data, output_data_shape,
+ output_data);
+}
+
+void Tanh(int32_t cell_state_scale_power, const RuntimeShape& input_data_shape,
+ float* input_data, const RuntimeShape& output_data_shape,
+ float* output_data) {
+ reference_ops::Tanh(input_data_shape, input_data, output_data_shape,
+ output_data);
+}
+
+// Input and output have the same shape in LSTM
+void Mul(const RuntimeShape& shape, const ArithmeticParams& params,
+ const int16_t* input1_data, const int16_t* input2_data,
+ int8_t* output_data) {
+ return reference_integer_ops::MulElementwise(
+ shape.FlatSize(), params, input1_data, input2_data, output_data);
+}
+
+// Input and output have the same shape in LSTM
+void Mul(const RuntimeShape& shape, const ArithmeticParams& params,
+ const int16_t* input1_data, const int16_t* input2_data,
+ int16_t* output_data) {
+ return reference_integer_ops::MulElementwise(
+ shape.FlatSize(), params, input1_data, input2_data, output_data);
+}
+
+// Input and output have the same shape in LSTM
+void Mul(const RuntimeShape& shape, const ArithmeticParams& params,
+ const float* input1_data, const float* input2_data,
+ float* output_data) {
+ return reference_ops::Mul(params, shape, input1_data, shape, input2_data,
+ shape, output_data);
+}
+
+void FullyConnected(const FullyConnectedParams& params,
+ const RuntimeShape& input_shape, const int8_t* input_data,
+ const RuntimeShape& filter_shape, const int8_t* filter_data,
+ const RuntimeShape& bias_shape, const int32_t* bias_data,
+ const RuntimeShape& output_shape, int16_t* output_data) {
+ return tflite::reference_integer_ops::FullyConnected(
+ params, input_shape, input_data, filter_shape, filter_data, bias_shape,
+ bias_data, output_shape, output_data);
+}
+
+void FullyConnected(const FullyConnectedParams& params,
+ const RuntimeShape& input_shape, const int16_t* input_data,
+ const RuntimeShape& filter_shape, const int8_t* filter_data,
+ const RuntimeShape& bias_shape, const int64_t* bias_data,
+ const RuntimeShape& output_shape, int16_t* output_data) {
+ return tflite::reference_integer_ops::FullyConnected(
+ params, input_shape, input_data, filter_shape, filter_data, bias_shape,
+ bias_data, output_shape, output_data);
+}
+
+void FullyConnected(const FullyConnectedParams& params,
+ const RuntimeShape& input_shape, const float* input_data,
+ const RuntimeShape& filter_shape, const float* filter_data,
+ const RuntimeShape& bias_shape, const float* bias_data,
+ const RuntimeShape& output_shape, float* output_data) {
+ return tflite::reference_ops::FullyConnected(
+ params, input_shape, input_data, filter_shape, filter_data, bias_shape,
+ bias_data, output_shape, output_data);
+}
+#else // #if !(defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
+void Sigmoid(int16_t* data, int32_t data_size) {
+ xa_nn_vec_sigmoid_sym16s_sym16s(data, data, 0, 0, data_size);
+}
+
+void Sigmoid(float* data, int32_t data_size) {
+ int data_dims[2] = {1, data_size};
+ RuntimeShape data_shape(2, reinterpret_cast<const int32_t*>(data_dims));
+ reference_ops::Logistic(data_shape, data, data_shape, data);
+}
+
+void Tanh(int32_t cell_state_scale_power, int16_t* input_data,
+ int16_t* output_data, int32_t data_size) {
+ int32_t tanh_input_left_shift = (15 + cell_state_scale_power) - 3;
+ int32_t input_multiplier = 0;
+ if (tanh_input_left_shift < 0) /* handling negative shift value */
+ {
+ tanh_input_left_shift = -tanh_input_left_shift;
+#if (defined(USE_HIFI_ACT_TIE) && \
+ (defined(AE_TANH16X4X2) || defined(AE_TANH16X4)))
+ input_multiplier = 1;
+#else
+ input_multiplier = 3;
+#endif
+ }
+ xa_nn_vec_tanh_sym16s_sym16s(output_data, input_data, input_multiplier,
+ tanh_input_left_shift, data_size);
+}
+
+void Tanh(int32_t cell_state_scale_power, float* input_data, float* output_data,
+ int32_t data_size) {
+ int data_dims[2] = {1, data_size};
+ RuntimeShape data_shape(2, reinterpret_cast<const int32_t*>(data_dims));
+ reference_ops::Tanh(data_shape, input_data, data_shape, output_data);
+}
+
+// Input and output have the same shape in LSTM
+void Mul(const ArithmeticParams& params, const int16_t* input1_data,
+ const int16_t* input2_data, int8_t* output_data, int32_t data_size) {
+ xa_nn_elm_mul_sym16sxsym16s_asym8s(
+ output_data, params.output_offset, params.output_shift,
+ params.output_multiplier, params.quantized_activation_min,
+ params.quantized_activation_max, input1_data, input2_data, data_size);
+}
+
+// Input and output have the same shape in LSTM
+void Mul(const ArithmeticParams& params, const int16_t* input1_data,
+ const int16_t* input2_data, int16_t* output_data, int32_t data_size) {
+ int dims_4D[4] = {1, 1, 1, data_size};
+ xa_nn_elm_mul_broadcast_4D_sym16sxsym16s_sym16s(
+ output_data, dims_4D, params.output_shift, params.output_multiplier,
+ params.quantized_activation_min, params.quantized_activation_max,
+ input1_data, dims_4D, input2_data, dims_4D);
+ return;
+}
+
+// Input and output have the same shape in LSTM
+void Mul(const ArithmeticParams& params, const float* input1_data,
+ const float* input2_data, float* output_data, int32_t data_size) {
+ int dims_2D[2] = {1, data_size};
+ RuntimeShape data_shape(2, reinterpret_cast<const int32_t*>(dims_2D));
+ return reference_ops::Mul(params, data_shape, input1_data, data_shape,
+ input2_data, data_shape, output_data);
+}
+
+void FullyConnected(const FullyConnectedParams& params,
+ const int8_t* input_data, const int8_t* filter_data,
+ const int32_t* bias_data, int16_t* output_data,
+ const int num_batches, const int output_depth,
+ const int accum_depth) {
+#pragma loop_count min = 1
+ for (int b = 0; b < num_batches; b++) {
+ xa_nn_matXvec_out_stride_sym8sxasym8s_16(
+ output_data + b * output_depth, filter_data,
+ input_data + b * accum_depth, bias_data, output_depth, accum_depth,
+ accum_depth, 1, params.input_offset, params.output_multiplier,
+ params.output_shift);
+ }
+ return;
+}
+
+void FullyConnected(const FullyConnectedParams& params,
+ const int16_t* input_data, const int8_t* filter_data,
+ const int64_t* bias_data, int16_t* output_data,
+ const int num_batches, const int output_depth,
+ const int accum_depth) {
+ xa_nn_matmul_sym8sxsym16s_sym16s(
+ output_data, filter_data, input_data, bias_data, output_depth,
+ accum_depth, accum_depth, num_batches, accum_depth, output_depth, 1,
+ params.input_offset, params.output_multiplier, params.output_shift,
+ params.output_offset);
+ return;
+}
+
+void FullyConnected(const FullyConnectedParams& params, const float* input_data,
+ const float* filter_data, const float* bias_data,
+ float* output_data, const int num_batches,
+ const int output_depth, const int accum_depth) {
+ int input_dims[2] = {num_batches, output_depth};
+ RuntimeShape input_shape(2, reinterpret_cast<const int32_t*>(input_dims));
+ RuntimeShape bias_shape(1, bias_data == NULL ? 0 : output_depth);
+ int filter_dims[2] = {output_depth, accum_depth};
+ RuntimeShape filter_shape(2, reinterpret_cast<const int32_t*>(filter_dims));
+ int output_dims[2] = {num_batches, output_depth};
+ RuntimeShape output_shape(2, reinterpret_cast<const int32_t*>(output_dims));
+ return tflite::reference_ops::FullyConnected(
+ params, input_shape, input_data, filter_shape, filter_data, bias_shape,
+ bias_data, output_shape, output_data);
+}
+#endif // #if !(defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
+
+void Clipping(const int v_size, const CellStateInfo& cell_state_info,
+ int16_t* vector) {
+ for (int i = 0; i < v_size; i++) {
+ vector[i] =
+ std::max(std::min(cell_state_info.quantized_cell_clip, vector[i]),
+ static_cast<int16_t>(-cell_state_info.quantized_cell_clip));
+ }
+}
+
+void Clipping(const int v_size, const CellStateInfo& cell_state_info,
+ float* vector) {
+ for (int i = 0; i < v_size; i++) {
+ vector[i] = std::max(std::min(cell_state_info.cell_clip, vector[i]),
+ -cell_state_info.cell_clip);
+ }
+}
+
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+void UpdateLstmCell(const LstmStepManager& step_info,
+ TfLiteEvalTensor* cell_state,
+ // Gate outputs
+ int16_t* forget_gate_output,
+ const int16_t* input_gate_output,
+ const int16_t* cell_gate_output,
+ // Mul parameters
+ const ArithmeticParams& forget_cell_mul_params,
+ const ArithmeticParams& input_mul_params,
+ const CellStateInfo& cell_state_info, int16_t* buffer) {
+ auto cell_state_shape = step_info.StateShape();
+ // Check offset validity to avoid memory overflow
+ TFLITE_DCHECK_LE(step_info.CellStateOffset() + cell_state_shape.FlatSize(),
+ tflite::micro::GetTensorShape(cell_state).FlatSize());
+
+ // Multiplier is equivalent to 0.5 here so adding 1 to shifts
+ xa_nn_lstm_cell_state_update_16(
+ tflite::micro::GetTensorData<int16_t>(cell_state) +
+ step_info.CellStateOffset(),
+ forget_gate_output, cell_gate_output, input_gate_output,
+ forget_cell_mul_params.output_shift - 1,
+ input_mul_params.output_shift - 1, cell_state_info.quantized_cell_clip,
+ cell_state_shape.FlatSize());
+}
+
+void UpdateLstmCell(const LstmStepManager& step_info,
+ TfLiteEvalTensor* cell_state,
+ // Gate outputs
+ float* forget_gate_output, const float* input_gate_output,
+ const float* cell_gate_output,
+ // Mul parameters
+ const ArithmeticParams& forget_cell_mul_params,
+ const ArithmeticParams& input_mul_params,
+ const CellStateInfo& cell_state_info, float* buffer) {
+ // Check offset validity to avoid memory overflow
+ TFLITE_DCHECK_LE(
+ step_info.CellStateOffset() + step_info.StateShape().FlatSize(),
+ tflite::micro::GetTensorShape(cell_state).FlatSize());
+
+ auto cell_state_shape = step_info.StateShape();
+ // Forget Gate x Cell State
+ Mul(forget_cell_mul_params, forget_gate_output,
+ tflite::micro::GetTensorData<float>(cell_state) +
+ step_info.CellStateOffset(),
+ tflite::micro::GetTensorData<float>(cell_state) +
+ step_info.CellStateOffset(),
+ cell_state_shape.FlatSize());
+ // Input Gate x Cell Gate
+ Mul(input_mul_params, input_gate_output, cell_gate_output, buffer,
+ cell_state_shape.FlatSize());
+
+ // Update the cell state
+ AddElementWise(tflite::micro::GetTensorData<float>(cell_state) +
+ step_info.CellStateOffset(),
+ buffer,
+ /*n_batch=*/cell_state_shape.DimsData()[0],
+ /*n_state=*/cell_state_shape.DimsData()[1],
+ tflite::micro::GetTensorData<float>(cell_state) +
+ step_info.CellStateOffset());
+
+ if (cell_state_info.cell_clip > 0) {
+ Clipping(cell_state_shape.FlatSize(), cell_state_info,
+ tflite::micro::GetTensorData<float>(cell_state) +
+ step_info.CellStateOffset());
+ }
+}
+#endif // #if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+
+// Increment the data offset so the sigle time step invocation call can access
+// the corresponding input/output tensor data at the time step
+void LstmStepManager::UpdateTime() {
+ current_time_ += 1;
+ TFLITE_DCHECK_LE(current_time_, size_info_.time_steps);
+ // default as one batch per inference
+ int input_step = size_info_.input_dimension;
+ int output_step = size_info_.state_dimension;
+ // time major: batch inference
+ if (size_info_.time_major) {
+ input_step = input_step * size_info_.batch_size;
+ output_step = output_step * size_info_.batch_size;
+ }
+
+ input_offset_ += input_step;
+ output_offset_ += output_step;
+}
+
+// Increment the data offset so the sigle time step invocation call can access
+// the corresponding hidden/cell state tensor data at the time step (for single
+// batch inference only)
+void LstmStepManager::UpdateBatch() {
+ current_batch_ += 1;
+ TFLITE_DCHECK_LE(current_batch_, size_info_.batch_size);
+ // batch inference for time major: no action needed
+ if (size_info_.time_major) {
+ return;
+ }
+ // otherwise: singe batch inference, go to the next batch
+ hidden_state_offset_ += size_info_.state_dimension;
+ cell_state_offset_ += size_info_.state_dimension;
+}
+
+// Input shape for each single time LSTM invocation.
+// Multi-batch for time_major input
+RuntimeShape LstmStepManager::InputShape() const {
+ int batch_size = 1;
+ if (size_info_.time_major) {
+ batch_size = size_info_.batch_size;
+ }
+ const int dims[2] = {batch_size, size_info_.input_dimension};
+ const int32_t* dims_data = reinterpret_cast<const int32_t*>(dims);
+ return RuntimeShape(2, dims_data);
+}
+
+// State shape (both hidden and cell) for each single time LSTM invocation.
+// Multi-batch for time_major input
+RuntimeShape LstmStepManager::StateShape() const {
+ int batch_size = 1;
+ if (size_info_.time_major) {
+ batch_size = size_info_.batch_size;
+ }
+ const int dims[2] = {batch_size, size_info_.state_dimension};
+ const int32_t* dims_data = reinterpret_cast<const int32_t*>(dims);
+ return RuntimeShape(2, dims_data);
+}
+
+} // namespace lstm_internal
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/xtensa/lstm_eval.h b/tensorflow/lite/micro/kernels/xtensa/lstm_eval.h
index 5dd746a..0ba5e22 100644
--- a/tensorflow/lite/micro/kernels/xtensa/lstm_eval.h
+++ b/tensorflow/lite/micro/kernels/xtensa/lstm_eval.h
@@ -1,4 +1,4 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -12,205 +12,813 @@
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
-#ifndef TENSORFLOW_LITE_KERNELS_LSTM_EVAL_H_
-#define TENSORFLOW_LITE_KERNELS_LSTM_EVAL_H_
+// Functions to perform integer evaulation for standard LSTM (e.g., defined in
+// the keras lstm layer, no peephole etc.). Currently used by the 16 bits
+// activation case only
+
+#ifndef TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_GENERAL_H_
+#define TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_GENERAL_H_
+#include <algorithm>
#include <cstdint>
-#include <memory>
#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/portable_tensor_utils.h"
-#include "tensorflow/lite/kernels/internal/reference/portable_tensor_utils_impl.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/lstm_shared.h"
+#include "tensorflow/lite/micro/kernels/xtensa/xtensa.h"
+#include "tensorflow/lite/micro/micro_log.h"
namespace tflite {
-namespace ops {
-namespace micro {
-namespace lstm_eval {
-#if defined(HIFI5)
-void calc_cell_state_without_cifg(int16_t* cell_state,
- const int16_t* forget_gate,
- const int16_t* cell_gate,
- const int16_t* input_gate, int shift1,
- int shift2, int clip, int num_elms);
+// Interface to access all the TempTfLiteTensors of the LSTM kernel during the
+// preparation phase. Can only be constructed through the constructor to avoid
+// memory leakage. All TempTfLiteTensors will be deallocated through the
+// destructor.
+class LstmTensors {
+ public:
+ LstmTensors(const LstmTensors& other) = delete;
+ LstmTensors& operator=(const LstmTensors& other) = delete;
-void calc_cell_state_with_cifg(int16_t* cell_state, const int16_t* forget_gate,
- const int16_t* cell_gate, int shift1, int shift2,
- int clip, int num_elms);
+ LstmTensors(TfLiteContext* context, TfLiteNode* node);
+ ~LstmTensors();
-void xa_nn_elm_mul_16x16_asym8s(int8_t* output, const int16_t* input_1,
- const int16_t* input_2, int32_t multiplier,
- int32_t shift, int32_t zero_point,
- int num_elms);
-#endif // defined(HIFI5)
+ // Verify the LSTM internal tensor properties (e.g., type checks)
+ // Input/output/states/fc weights tensors are required for kernel evaulation.
+ // The state tensors should be variables. Variants of the standard LSTM
+ // are not supported here, therefore their corresponding tensors should be
+ // invalid
+ TfLiteStatus ValidateTensorStatus(TfLiteContext* context) const;
-// Pamameters for integer LSTM.
-// Consider split this into two Integer Parameters if more fields are added.
-struct IntegerLstmParameter {
- int32_t effective_input_to_input_scale_a;
- int effective_input_to_input_scale_b;
- int32_t effective_recurrent_to_input_scale_a;
- int effective_recurrent_to_input_scale_b;
- int32_t effective_cell_to_input_scale_a;
- int effective_cell_to_input_scale_b;
- int32_t effective_input_to_forget_scale_a;
- int effective_input_to_forget_scale_b;
- int32_t effective_recurrent_to_forget_scale_a;
- int effective_recurrent_to_forget_scale_b;
- int32_t effective_cell_to_forget_scale_a;
- int effective_cell_to_forget_scale_b;
- int32_t effective_input_to_cell_scale_a;
- int effective_input_to_cell_scale_b;
- int32_t effective_recurrent_to_cell_scale_a;
- int effective_recurrent_to_cell_scale_b;
- int32_t effective_input_to_output_scale_a;
- int effective_input_to_output_scale_b;
- int32_t effective_recurrent_to_output_scale_a;
- int effective_recurrent_to_output_scale_b;
- int32_t effective_cell_to_output_scale_a;
- int effective_cell_to_output_scale_b;
- int32_t effective_proj_scale_a;
- int effective_proj_scale_b;
- int32_t effective_hidden_scale_a;
- int effective_hidden_scale_b;
- int32_t layer_norm_input_scale_a;
- int layer_norm_input_scale_b;
- int32_t layer_norm_forget_scale_a;
- int layer_norm_forget_scale_b;
- int32_t layer_norm_cell_scale_a;
- int layer_norm_cell_scale_b;
- int32_t layer_norm_output_scale_a;
- int layer_norm_output_scale_b;
- // Quantized clip value for cell and projection. Zero value means no clipping.
- int16_t quantized_cell_clip;
- int8_t quantized_proj_clip;
- int32_t hidden_zp;
- int32_t cell_scale;
+ // Internal tensors. see lstm_shared.h for tensor names
+ const TfLiteTensor* GetInternalTensor(const int tensor_index) const {
+ return internal_tensors_[tensor_index];
+ }
- int32_t input_variance_guard;
- int32_t forget_variance_guard;
- int32_t cell_variance_guard;
- int32_t output_variance_guard;
+ const TfLiteTensor* HiddenStateTensor() const {
+ return internal_tensors_[kLstmOutputStateTensor];
+ }
+ const TfLiteTensor* CellStateTensor() const {
+ return internal_tensors_[kLstmCellStateTensor];
+ }
+ const TfLiteTensor* OutputTensor() const { return output_tensor_; }
- // Pre-calculate bias + zero_point * weight.
- // Unabled to use temporary tensors since those are used in Prepare() and
- // scratch buffer is only allocated after Preapre().
- std::unique_ptr<int32_t[]> input_to_forget_effective_bias;
- std::unique_ptr<int32_t[]> recurrent_to_forget_effective_bias;
- std::unique_ptr<int32_t[]> input_to_cell_effective_bias;
- std::unique_ptr<int32_t[]> recurrent_to_cell_effective_bias;
- std::unique_ptr<int32_t[]> input_to_output_effective_bias;
- std::unique_ptr<int32_t[]> recurrent_to_output_effective_bias;
- std::unique_ptr<int32_t[]> input_to_input_effective_bias;
- std::unique_ptr<int32_t[]> recurrent_to_input_effective_bias;
- std::unique_ptr<int32_t[]> projection_effective_bias;
-
- // Scale and zero point for intermediate tensors.
- // Used only in the 8x8_8 case.
- int32_t intermediate_scale_a[8];
- int32_t intermediate_scale_b[8];
- int32_t intermediate_zp[12];
+ private:
+ // see lstm_shared.h for tensor names
+ MicroContext* micro_context_;
+ TfLiteTensor* internal_tensors_[24];
+ TfLiteTensor* output_tensor_;
};
-TfLiteStatus EvalFloat(const TfLiteEvalTensor* input,
- const TfLiteEvalTensor* input_to_input_weights,
- const TfLiteEvalTensor* input_to_forget_weights,
- const TfLiteEvalTensor* input_to_cell_weights,
- const TfLiteEvalTensor* input_to_output_weights,
- const TfLiteEvalTensor* recurrent_to_input_weights,
- const TfLiteEvalTensor* recurrent_to_forget_weights,
- const TfLiteEvalTensor* recurrent_to_cell_weights,
- const TfLiteEvalTensor* recurrent_to_output_weights,
- const TfLiteEvalTensor* cell_to_input_weights,
- const TfLiteEvalTensor* cell_to_forget_weights,
- const TfLiteEvalTensor* cell_to_output_weights,
- const TfLiteEvalTensor* input_layer_norm_coefficients,
- const TfLiteEvalTensor* forget_layer_norm_coefficients,
- const TfLiteEvalTensor* cell_layer_norm_coefficients,
- const TfLiteEvalTensor* output_layer_norm_coefficients,
- const TfLiteEvalTensor* aux_input,
- const TfLiteEvalTensor* aux_input_to_input_weights,
- const TfLiteEvalTensor* aux_input_to_forget_weights,
- const TfLiteEvalTensor* aux_input_to_cell_weights,
- const TfLiteEvalTensor* aux_input_to_output_weights,
- const TfLiteEvalTensor* input_gate_bias,
- const TfLiteEvalTensor* forget_gate_bias,
- const TfLiteEvalTensor* cell_gate_bias,
- const TfLiteEvalTensor* output_gate_bias,
- const TfLiteEvalTensor* projection_weights,
- const TfLiteEvalTensor* projection_bias,
- const TfLiteLSTMParams* params, bool forward_sequence,
- bool time_major, int output_offset,
- TfLiteEvalTensor* scratch_buffer,
- TfLiteEvalTensor* output_state,
- TfLiteEvalTensor* cell_state, TfLiteEvalTensor* output);
+// Deduce the size information (Batch (B), Time Steps (T), Input dimension (I),
+// State dimension (S)) that defines the LSTM using the input and hidden state
+// tensor
+LstmSizeInfo CreateLstmSizeInfo(
+ const bool time_major, const TfLiteIntArray* input_tensor_shape,
+ const TfLiteIntArray* hidden_state_tensor_shape);
-TfLiteStatus EvalInteger8x8_16(
- TfLiteContext* context, TfLiteNode* node, const TfLiteEvalTensor* input,
- const TfLiteEvalTensor* input_to_input_weights,
- const TfLiteEvalTensor* input_to_forget_weights,
- const TfLiteEvalTensor* input_to_cell_weights,
- const TfLiteEvalTensor* input_to_output_weights,
- const TfLiteEvalTensor* recurrent_to_input_weights,
- const TfLiteEvalTensor* recurrent_to_forget_weights,
- const TfLiteEvalTensor* recurrent_to_cell_weights,
- const TfLiteEvalTensor* recurrent_to_output_weights,
- const TfLiteEvalTensor* cell_to_input_weights,
- const TfLiteEvalTensor* cell_to_forget_weights,
- const TfLiteEvalTensor* cell_to_output_weights,
- const TfLiteEvalTensor* input_layer_norm_coefficients,
- const TfLiteEvalTensor* forget_layer_norm_coefficients,
- const TfLiteEvalTensor* cell_layer_norm_coefficients,
- const TfLiteEvalTensor* output_layer_norm_coefficients,
- const TfLiteEvalTensor* input_gate_bias,
- const TfLiteEvalTensor* forget_gate_bias,
- const TfLiteEvalTensor* cell_gate_bias,
- const TfLiteEvalTensor* output_gate_bias,
- const TfLiteEvalTensor* projection_weights,
- const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params,
- bool forward_sequence, bool time_major,
- const lstm_eval::IntegerLstmParameter* integer_lstm_param,
- TfLiteEvalTensor* output_state, TfLiteEvalTensor* cell_state,
- TfLiteEvalTensor* output, TfLiteEvalTensor* scratch0,
- TfLiteEvalTensor* scratch1, TfLiteEvalTensor* scratch2,
- TfLiteEvalTensor* scratch3, TfLiteEvalTensor* scratch4,
- TfLiteEvalTensor* scratch5);
+TfLiteStatus ValidateWeightTensorSize(TfLiteContext* context,
+ const TfLiteTensor* tensor, int dim1_size,
+ int dim2_size);
-TfLiteStatus EvalInteger8x8_8(
- const TfLiteEvalTensor* input,
- const TfLiteEvalTensor* input_to_input_weights,
- const TfLiteEvalTensor* input_to_forget_weights,
- const TfLiteEvalTensor* input_to_cell_weights,
- const TfLiteEvalTensor* input_to_output_weights,
- const TfLiteEvalTensor* recurrent_to_input_weights,
- const TfLiteEvalTensor* recurrent_to_forget_weights,
- const TfLiteEvalTensor* recurrent_to_cell_weights,
- const TfLiteEvalTensor* recurrent_to_output_weights,
- const TfLiteEvalTensor* cell_to_input_weights,
- const TfLiteEvalTensor* cell_to_forget_weights,
- const TfLiteEvalTensor* cell_to_output_weights,
- const TfLiteEvalTensor* input_layer_norm_coefficients,
- const TfLiteEvalTensor* forget_layer_norm_coefficients,
- const TfLiteEvalTensor* cell_layer_norm_coefficients,
- const TfLiteEvalTensor* output_layer_norm_coefficients,
- const TfLiteEvalTensor* input_gate_bias,
- const TfLiteEvalTensor* forget_gate_bias,
- const TfLiteEvalTensor* cell_gate_bias,
- const TfLiteEvalTensor* output_gate_bias,
- const TfLiteEvalTensor* projection_weights,
- const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params,
- TfLiteEvalTensor* output_state, TfLiteEvalTensor* cell_state,
- TfLiteEvalTensor* output,
- const lstm_eval::IntegerLstmParameter* integer_lstm_param,
- TfLiteEvalTensor* scratch0, TfLiteEvalTensor* scratch1,
- TfLiteEvalTensor* scratch2, TfLiteEvalTensor* scratch3,
- TfLiteEvalTensor* scratch4, TfLiteEvalTensor* scratch5,
- TfLiteEvalTensor* scratch6, TfLiteEvalTensor* scratch7);
+TfLiteStatus ValidateBiasTensorSize(TfLiteContext* context,
+ const TfLiteTensor* tensor, int size);
-} // namespace lstm_eval
-} // namespace micro
-} // namespace ops
+// Go through every tensors and make sure their shape match the kernel
+// configuration
+TfLiteStatus ValidateTensorSize(TfLiteContext* context,
+ const LstmTensors& tensors,
+ const LstmSizeInfo& size_info);
+
+// Wrapper function to create gate parameters for the four internal LSTM gates
+TfLiteStatus CreateGateParams(
+ TfLiteContext* context,
+ /*Input tensors*/
+ const TfLiteTensor* input, const TfLiteTensor* input_weight,
+ const TfLiteTensor* input_bias,
+ /*Hidden state tensors*/
+ const TfLiteTensor* hidden_state, const TfLiteTensor* hidden_state_weight,
+ const TfLiteTensor* hidden_state_bias,
+ /*Scale of the fc output (input to non-linear activation)*/
+ const float nonlinear_activation_input_scale, const TfLiteType cell_type,
+ const tflite::GateParameters& gate_params);
+
+// Create parameters for element wise multiplication that happens in a) cell
+// state update ; b) hidden state update
+// Note that all the output of gates are symmetrically quantized so only scales
+// are required for input. However, during the hidden state update phase, the
+// output is the updated hidden state, which is asymmetrically quantized. Thus
+// output may require zero point
+tflite::ArithmeticParams CreateInterGateMulParams(const float input1_scale,
+ const float input2_scale,
+ const float output_scale,
+ const TfLiteType output_type,
+ const int output_zp = 0);
+
+// Create the additional information about the cell state, which include:
+// cell_state_scale_power: used in integer nonlinear function (e.g., tanh)
+// quantized_cell_clip: quantized cell clip range
+CellStateInfo CreateLstmCellStateInfo(const float cell_state_scale,
+ const float cell_clip);
+
+CellStateInfo CreateLstmCellStateInfoFloat(const float cell_clip);
+tflite::FullyConnectedParams CreateFCParamsFloat();
+
+tflite::GateParameters CreateGateParamsFloat();
+
+tflite::ArithmeticParams CreateInterGateMulParamsFloat();
+
+TfLiteStatus PrepareGateParametersFloat(TfLiteContext* context,
+ const LstmTensors& lstm_tensors,
+ OpDataLSTM* op_data_lstm);
+
+TfLiteStatus PrepareGateParametersInteger(TfLiteContext* context,
+ const LstmTensors& lstm_tensors,
+ OpDataLSTM* op_data_lstm);
+
+LSTMKernelContents CreateLSTMKernelContent(TfLiteContext* context,
+ TfLiteNode* node);
+
+template <typename CellType>
+LSTMBuffers<CellType> CreateLSTMBuffers(TfLiteContext* context,
+ const int* buffer_indices) {
+ LSTMBuffers<CellType> buffers;
+ buffers.buffer0 = reinterpret_cast<CellType*>(
+ context->GetScratchBuffer(context, buffer_indices[0]));
+ buffers.buffer1 = reinterpret_cast<CellType*>(
+ context->GetScratchBuffer(context, buffer_indices[1]));
+ buffers.buffer2 = reinterpret_cast<CellType*>(
+ context->GetScratchBuffer(context, buffer_indices[2]));
+ buffers.buffer3 = reinterpret_cast<CellType*>(
+ context->GetScratchBuffer(context, buffer_indices[3]));
+ return buffers;
+}
+
+// Since LSTM includes multiple intermediate stages, introducing the internal
+// namespace to expose them for testing
+namespace lstm_internal {
+
+#if !(defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
+void Sigmoid(const RuntimeShape& data_shape, int16_t* data);
+
+void Sigmoid(const RuntimeShape& data_shape, float* data);
+
+void Tanh(int32_t cell_state_scale_power, const RuntimeShape& input_data_shape,
+ int16_t* input_data, const RuntimeShape& output_data_shape,
+ int16_t* output_data);
+
+void Tanh(int32_t cell_state_scale_power, const RuntimeShape& input_data_shape,
+ float* input_data, const RuntimeShape& output_data_shape,
+ float* output_data);
+
+void Mul(const RuntimeShape& shape, const ArithmeticParams& params,
+ const int16_t* input1_data, const int16_t* input2_data,
+ int8_t* output_data);
+
+void Mul(const RuntimeShape& shape, const ArithmeticParams& params,
+ const int16_t* input1_data, const int16_t* input2_data,
+ int16_t* output_data);
+
+void Mul(const RuntimeShape& shape, const ArithmeticParams& params,
+ const float* input1_data, const float* input2_data,
+ float* output_data);
+
+void FullyConnected(const FullyConnectedParams& params,
+ const RuntimeShape& input_shape, const int8_t* input_data,
+ const RuntimeShape& filter_shape, const int8_t* filter_data,
+ const RuntimeShape& bias_shape, const int32_t* bias_data,
+ const RuntimeShape& output_shape, int16_t* output_data);
+
+void FullyConnected(const FullyConnectedParams& params,
+ const RuntimeShape& input_shape, const int16_t* input_data,
+ const RuntimeShape& filter_shape, const int8_t* filter_data,
+ const RuntimeShape& bias_shape, const int64_t* bias_data,
+ const RuntimeShape& output_shape, int16_t* output_data);
+
+void FullyConnected(const FullyConnectedParams& params,
+ const RuntimeShape& input_shape, const float* input_data,
+ const RuntimeShape& filter_shape, const float* filter_data,
+ const RuntimeShape& bias_shape, const float* bias_data,
+ const RuntimeShape& output_shape, float* output_data);
+#else // #if !(defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
+void Sigmoid(int16_t* data, int32_t data_size);
+
+void Sigmoid(float* data, int32_t data_size);
+
+void Tanh(int32_t cell_state_scale_power, int16_t* input_data,
+ int16_t* output_data, int32_t data_size);
+
+void Tanh(int32_t cell_state_scale_power, float* input_data, float* output_data,
+ int32_t data_size);
+
+void Mul(const ArithmeticParams& params, const int16_t* input1_data,
+ const int16_t* input2_data, int8_t* output_data, int32_t data_size);
+
+void Mul(const ArithmeticParams& params, const int16_t* input1_data,
+ const int16_t* input2_data, int16_t* output_data, int32_t data_size);
+
+void Mul(const ArithmeticParams& params, const float* input1_data,
+ const float* input2_data, float* output_data, int32_t data_size);
+
+void FullyConnected(const FullyConnectedParams& params,
+ const int8_t* input_data, const int8_t* filter_data,
+ const int32_t* bias_data, int16_t* output_data,
+ const int num_batches, const int output_depth,
+ const int accum_depth);
+
+void FullyConnected(const FullyConnectedParams& params,
+ const int16_t* input_data, const int8_t* filter_data,
+ const int64_t* bias_data, int16_t* output_data,
+ const int num_batches, const int output_depth,
+ const int accum_depth);
+
+void FullyConnected(const FullyConnectedParams& params, const float* input_data,
+ const float* filter_data, const float* bias_data,
+ float* output_data, const int num_batches,
+ const int output_depth, const int accum_depth);
+#endif // #if !(defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
+
+void AddElementWise(const int16_t* input_1, const int16_t* input_2, int n_batch,
+ int n_input, int16_t* output);
+
+void AddElementWise(const float* input_1, const float* input_2, int n_batch,
+ int n_input, float* output);
+
+void Clipping(const int v_size, const CellStateInfo& cell_state_info,
+ int16_t* vector);
+
+void Clipping(const int v_size, const CellStateInfo& cell_state_info,
+ float* vector);
+
+// Manages the slice position (offset), slice length (sliced tensor shape),
+// and update rules for input/output/hidden state/cell state tensors at each
+// time step.
+class LstmStepManager {
+ public:
+ LstmStepManager() = delete;
+ // Does not take any ownership, and all pointers must refer to valid objects
+ // that outlive the one constructed.
+ explicit LstmStepManager(const LstmSizeInfo* size_info)
+ : size_info_(*size_info) {}
+
+ void UpdateTime();
+ void UpdateBatch();
+
+ void ResetTime() { current_time_ = 0; }
+ RuntimeShape InputShape() const;
+ RuntimeShape StateShape() const;
+
+ int InputOffset() const { return input_offset_; }
+ int OutputOffset() const { return output_offset_; }
+ int HiddenStateOffset() const { return hidden_state_offset_; }
+ int CellStateOffset() const { return cell_state_offset_; }
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+ int time_major() const { return size_info_.time_major; }
+
+ int batch_size() const { return size_info_.batch_size; }
+
+ int input_dimension() const { return size_info_.input_dimension; }
+
+ int state_dimension() const { return size_info_.state_dimension; }
+#endif
+
+ private:
+ int current_time_ = 0;
+ int current_batch_ = 0;
+ int input_offset_ = 0;
+ int output_offset_ = 0;
+ int hidden_state_offset_ = 0;
+ int cell_state_offset_ = 0;
+ // Sizeinfo is from LstmOpData, which reside in the memory arena
+ // (guarante to outlast LSTMStepManager, which reside in stack)
+ const LstmSizeInfo& size_info_;
+};
+
+// Calculates a single LSTM gate.
+// Implements the following formula:
+// gate = activate(FC(input) + FC(recurrent))
+// Activation is sigmoid except for the "cell" gate (configurable, usually tanh)
+#if !(defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
+template <typename ActivationType, typename WeightType, typename CellType,
+ typename BiasType>
+void CalculateLstmGate(
+ const LstmStepManager& step_info, const GateParameters& gate_params,
+ // Input FC
+ const TfLiteEvalTensor* input, const TfLiteEvalTensor* input_weight,
+ const TfLiteEvalTensor* input_bias,
+ // Recurrent FC
+ const TfLiteEvalTensor* recurrent, const TfLiteEvalTensor* recurrent_weight,
+ const TfLiteEvalTensor* recurrent_bias,
+ // Output
+ CellType* gate_output,
+ // Scratch arrays
+ CellType* fc_output_buffer, const TfLiteFusedActivation activation) {
+ const auto gate_output_shape = step_info.StateShape();
+ // Check offset validity to avoid memory overflow
+ TFLITE_DCHECK_LE(step_info.InputOffset() + step_info.InputShape().FlatSize(),
+ tflite::micro::GetTensorShape(input).FlatSize());
+ TFLITE_DCHECK_LE(
+ step_info.HiddenStateOffset() + step_info.StateShape().FlatSize(),
+ tflite::micro::GetTensorShape(recurrent).FlatSize());
+
+ // Input FC
+ FullyConnected(gate_params.input_fc_params, step_info.InputShape(),
+ tflite::micro::GetTensorData<ActivationType>(input) +
+ step_info.InputOffset(),
+ micro::GetTensorShape(input_weight),
+ tflite::micro::GetTensorData<WeightType>(input_weight),
+ tflite::micro::GetTensorShape(input_bias),
+ tflite::micro::GetOptionalTensorData<BiasType>(input_bias),
+ gate_output_shape, gate_output);
+
+ // Recurrent FC
+ FullyConnected(gate_params.recurrent_fc_params, step_info.StateShape(),
+ tflite::micro::GetTensorData<ActivationType>(recurrent) +
+ step_info.HiddenStateOffset(),
+ tflite::micro::GetTensorShape(recurrent_weight),
+ tflite::micro::GetTensorData<WeightType>(recurrent_weight),
+ tflite::micro::GetTensorShape(recurrent_bias),
+ tflite::micro::GetOptionalTensorData<BiasType>(recurrent_bias),
+ gate_output_shape, fc_output_buffer);
+
+ AddElementWise(gate_output, fc_output_buffer,
+ /*n_batch=*/gate_output_shape.DimsData()[0],
+ /*n_state=*/gate_output_shape.DimsData()[1], gate_output);
+ // Apply activation
+ switch (activation) {
+ case kTfLiteActSigmoid:
+ Sigmoid(gate_output_shape, gate_output);
+ break;
+ case kTfLiteActTanh: {
+ // Set the scale power to -12 to avoid shift
+ Tanh(/*cell_state_scale_power=*/-12, gate_output_shape, gate_output,
+ gate_output_shape, gate_output);
+ } break;
+ default:
+ // Only Sigmoid or Tanh is used.
+ TFLITE_ASSERT_FALSE;
+ }
+}
+
+// Update the cell state using the output from the forget gate, input gate, and
+// cell gate Formula: updated_cell_state = forget_gate_output*cell_state +
+// input_gate_output * cell_gate_output, where * denotes element wise
+// multiplication
+template <typename CellType>
+void UpdateLstmCell(const LstmStepManager& step_info,
+ TfLiteEvalTensor* cell_state,
+ // Gate outputs
+ CellType* forget_gate_output,
+ const CellType* input_gate_output,
+ const CellType* cell_gate_output,
+ // Mul parameters
+ const ArithmeticParams& forget_cell_mul_params,
+ const ArithmeticParams& input_mul_params,
+ const CellStateInfo& cell_state_info, CellType* buffer) {
+ // Check offset validity to avoid memory overflow
+ TFLITE_DCHECK_LE(
+ step_info.CellStateOffset() + step_info.StateShape().FlatSize(),
+ tflite::micro::GetTensorShape(cell_state).FlatSize());
+
+ auto cell_state_shape = step_info.StateShape();
+ // Forget Gate x Cell State
+ Mul(cell_state_shape, forget_cell_mul_params, forget_gate_output,
+ tflite::micro::GetTensorData<CellType>(cell_state) +
+ step_info.CellStateOffset(),
+ tflite::micro::GetTensorData<CellType>(cell_state) +
+ step_info.CellStateOffset());
+ // Input Gate x Cell Gate
+ Mul(cell_state_shape, input_mul_params, input_gate_output, cell_gate_output,
+ buffer);
+
+ // Update the cell state
+ AddElementWise(tflite::micro::GetTensorData<CellType>(cell_state) +
+ step_info.CellStateOffset(),
+ buffer,
+ /*n_batch=*/cell_state_shape.DimsData()[0],
+ /*n_state=*/cell_state_shape.DimsData()[1],
+ tflite::micro::GetTensorData<CellType>(cell_state) +
+ step_info.CellStateOffset());
+
+ if (cell_state_info.cell_clip > 0) {
+ Clipping(cell_state_shape.FlatSize(), cell_state_info,
+ tflite::micro::GetTensorData<CellType>(cell_state) +
+ step_info.CellStateOffset());
+ }
+}
+#else // #if !(defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
+template <typename ActivationType, typename WeightType, typename CellType,
+ typename BiasType>
+void CalculateLstmGate(
+ const LstmStepManager& step_info, const GateParameters& gate_params,
+ // Input FC
+ const TfLiteEvalTensor* input, const TfLiteEvalTensor* input_weight,
+ const TfLiteEvalTensor* input_bias,
+ // Recurrent FC
+ const TfLiteEvalTensor* recurrent, const TfLiteEvalTensor* recurrent_weight,
+ const TfLiteEvalTensor* recurrent_bias,
+ // Output
+ CellType* gate_output,
+ // Scratch arrays
+ CellType* fc_output_buffer, const TfLiteFusedActivation activation,
+ const int num_batches, const int input_dimension,
+ const int state_dimension) {
+ // RuntimeShape step_input_shape = step_info.InputShape();
+ // RuntimeShape input_shape = tflite::micro::GetTensorShape(input);
+ // RuntimeShape step_state_shape = step_info.StateShape();
+ // RuntimeShape recurrent_shape = tflite::micro::GetTensorShape(recurrent);
+
+ // Moved these to LstmStep function
+ // Check offset validity to avoid memory overflow
+ // TFLITE_DCHECK_LE(step_info.InputOffset() + step_input_shape.FlatSize(),
+ // input_shape.FlatSize());
+ // TFLITE_DCHECK_LE(
+ // step_info.HiddenStateOffset() + step_state_shape.FlatSize(),
+ // recurrent_shape.FlatSize());
+
+ // Input FC
+ FullyConnected(gate_params.input_fc_params,
+ tflite::micro::GetTensorData<ActivationType>(input) +
+ step_info.InputOffset(),
+ tflite::micro::GetTensorData<WeightType>(input_weight),
+ tflite::micro::GetOptionalTensorData<BiasType>(input_bias),
+ gate_output, num_batches, state_dimension, input_dimension);
+
+ // Recurrent FC
+ FullyConnected(gate_params.recurrent_fc_params,
+ tflite::micro::GetTensorData<ActivationType>(recurrent) +
+ step_info.HiddenStateOffset(),
+ tflite::micro::GetTensorData<WeightType>(recurrent_weight),
+ tflite::micro::GetOptionalTensorData<BiasType>(recurrent_bias),
+ fc_output_buffer, num_batches, state_dimension,
+ state_dimension);
+
+ AddElementWise(gate_output, fc_output_buffer,
+ /*n_batch=*/num_batches,
+ /*n_state=*/state_dimension, gate_output);
+ // Apply activation
+ switch (activation) {
+ case kTfLiteActSigmoid:
+ Sigmoid(gate_output, num_batches * state_dimension);
+ break;
+ case kTfLiteActTanh: {
+ // Set the scale power to -12 to avoid shift
+ Tanh(/*cell_state_scale_power=*/-12, gate_output, gate_output,
+ num_batches * state_dimension);
+ } break;
+ default:
+ // Only Sigmoid or Tanh is used.
+ TFLITE_ASSERT_FALSE;
+ }
+}
+
+// Update the cell state using the output from the forget gate, input gate, and
+// cell gate Formula: updated_cell_state = forget_gate_output*cell_state +
+// input_gate_output * cell_gate_output, where * denotes element wise
+// multiplication
+void UpdateLstmCell(const LstmStepManager& step_info,
+ TfLiteEvalTensor* cell_state,
+ // Gate outputs
+ int16_t* forget_gate_output,
+ const int16_t* input_gate_output,
+ const int16_t* cell_gate_output,
+ // Mul parameters
+ const ArithmeticParams& forget_cell_mul_params,
+ const ArithmeticParams& input_mul_params,
+ const CellStateInfo& cell_state_info, int16_t* buffer);
+
+void UpdateLstmCell(const LstmStepManager& step_info,
+ TfLiteEvalTensor* cell_state,
+ // Gate outputs
+ float* forget_gate_output, const float* input_gate_output,
+ const float* cell_gate_output,
+ // Mul parameters
+ const ArithmeticParams& forget_cell_mul_params,
+ const ArithmeticParams& input_mul_params,
+ const CellStateInfo& cell_state_info, float* buffer);
+#endif // #if !(defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
+
+// Update the hidden state of the LSTM kernel using the following formula:
+// updated_hidden_state = Tanh(updated_cell_state) * output_gate_output, * means
+// element wise multiplication
+template <typename CellType, typename ActivationType>
+void UpdateLstmHidden(const LstmStepManager& step_info,
+ TfLiteEvalTensor* cell_state,
+ TfLiteEvalTensor* hidden_state,
+ const CellType* output_gate_output,
+ const ArithmeticParams& mul_params,
+ int32_t cell_state_scale_power, CellType* buffer) {
+ // Check offset validity to avoid memory overflow
+ TFLITE_DCHECK_LE(
+ step_info.CellStateOffset() + step_info.StateShape().FlatSize(),
+ tflite::micro::GetTensorShape(cell_state).FlatSize());
+ TFLITE_DCHECK_LE(
+ step_info.HiddenStateOffset() + step_info.StateShape().FlatSize(),
+ tflite::micro::GetTensorShape(hidden_state).FlatSize());
+
+ auto cell_state_shape = step_info.StateShape();
+ CellType* cell_state_data =
+ tflite::micro::GetTensorData<CellType>(cell_state) +
+ step_info.CellStateOffset();
+ // Tanh(cell_state)
+#if !(defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
+ Tanh(cell_state_scale_power, cell_state_shape, cell_state_data,
+ cell_state_shape, buffer);
+ // Update the hidden state
+ Mul(cell_state_shape, mul_params, buffer, output_gate_output,
+ tflite::micro::GetTensorData<ActivationType>(hidden_state) +
+ step_info.HiddenStateOffset());
+#else
+ int32_t cell_state_size = cell_state_shape.FlatSize();
+ Tanh(cell_state_scale_power, cell_state_data, buffer, cell_state_size);
+ // Update the hidden state
+ Mul(mul_params, buffer, output_gate_output,
+ tflite::micro::GetTensorData<ActivationType>(hidden_state) +
+ step_info.HiddenStateOffset(),
+ cell_state_size);
+#endif
+}
+
+#if !(defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
+template <typename ActivationType, typename WeightType, typename CellType,
+ typename BiasType>
+void LstmStep(const LstmStepManager& step_info, const OpDataLSTM& op_data,
+ LSTMKernelContents& kernel_content,
+ const LSTMBuffers<CellType>& buffers) {
+ /*Step1: Calculate gate outputs to prepare cell state update*/
+ CellType* gate_internal_buffer = buffers.buffer3;
+ CellType* forget_gate_output = buffers.buffer0;
+ CalculateLstmGate<ActivationType, WeightType, CellType, BiasType>(
+ step_info, op_data.forget_gate_parameters,
+ // Input FC
+ kernel_content.GetInternalTensor(tflite::kLstmInputTensor),
+ kernel_content.GetInternalTensor(tflite::kLstmInputToForgetWeightsTensor),
+ kernel_content.GetInternalTensor(tflite::kLstmForgetGateBiasTensor),
+ // Recurrent FC
+ kernel_content.HiddenStateTensor(),
+ kernel_content.GetInternalTensor(
+ tflite::kLstmRecurrentToForgetWeightsTensor),
+ /*recurrent_bias*/ nullptr,
+ // Output
+ forget_gate_output,
+ // Scratch arrays
+ gate_internal_buffer, kTfLiteActSigmoid);
+
+ // Input Gate calculation;
+ CellType* input_gate_output = buffers.buffer1;
+ CalculateLstmGate<ActivationType, WeightType, CellType, BiasType>(
+ step_info, op_data.input_gate_parameters,
+ // Input FC
+ kernel_content.GetInternalTensor(tflite::kLstmInputTensor),
+ kernel_content.GetInternalTensor(tflite::kLstmInputToInputWeightsTensor),
+ kernel_content.GetInternalTensor(tflite::kLstmInputGateBiasTensor),
+ // Recurrent FC
+ kernel_content.HiddenStateTensor(),
+ kernel_content.GetInternalTensor(
+ tflite::kLstmRecurrentToInputWeightsTensor),
+ /*recurrent_bias*/ nullptr,
+ // Output
+ input_gate_output,
+ // Scratch arrays
+ gate_internal_buffer, kTfLiteActSigmoid);
+
+ // Cell Gate calculation
+ CellType* cell_gate_output = buffers.buffer2;
+ CalculateLstmGate<ActivationType, WeightType, CellType, BiasType>(
+ step_info, op_data.cell_gate_parameters,
+ // Input FC
+ kernel_content.GetInternalTensor(tflite::kLstmInputTensor),
+ kernel_content.GetInternalTensor(tflite::kLstmInputToCellWeightsTensor),
+ kernel_content.GetInternalTensor(tflite::kLstmCellGateBiasTensor),
+ // Recurrent FC
+ kernel_content.HiddenStateTensor(),
+ kernel_content.GetInternalTensor(
+ tflite::kLstmRecurrentToCellWeightsTensor),
+ /*recurrent_bias*/ nullptr,
+ // Output
+ cell_gate_output,
+ // Scratch arrays
+ gate_internal_buffer, op_data.cell_gate_nonlinear_type);
+
+ /*Step2: update the cell state */
+ const InterGateParameters& inter_gate_params = op_data.inter_gate_parameters;
+ CellType* updated_input_buffer = buffers.buffer1; // reuse buffer
+
+ UpdateLstmCell<CellType>(step_info, kernel_content.CellStateTensor(),
+ forget_gate_output, input_gate_output,
+ cell_gate_output,
+ inter_gate_params.forget_cell_mul_params,
+ inter_gate_params.input_mul_params,
+ op_data.cell_state_info, updated_input_buffer);
+
+ /*Step3: update the hidden state */
+ CellType* output_gate_output = buffers.buffer1; // reuse buffer
+ CalculateLstmGate<ActivationType, WeightType, CellType, BiasType>(
+ step_info, op_data.output_gate_parameters,
+ // Input FC
+ kernel_content.GetInternalTensor(tflite::kLstmInputTensor),
+ kernel_content.GetInternalTensor(tflite::kLstmInputToOutputWeightsTensor),
+ kernel_content.GetInternalTensor(tflite::kLstmOutputGateBiasTensor),
+ // Recurrent FC
+ kernel_content.HiddenStateTensor(),
+ kernel_content.GetInternalTensor(
+ tflite::kLstmRecurrentToOutputWeightsTensor),
+ /*recurrent_bias*/ nullptr,
+ // Output
+ output_gate_output,
+ // Scratch arrays
+ gate_internal_buffer, kTfLiteActSigmoid);
+
+ CellType* tanh_activated_cell_buffer = buffers.buffer0; // reuse buffer
+ tflite::lstm_internal::UpdateLstmHidden<CellType, ActivationType>(
+ step_info, kernel_content.CellStateTensor(),
+ kernel_content.HiddenStateTensor(), output_gate_output,
+ inter_gate_params.output_mul_params,
+ op_data.cell_state_info.cell_state_scale_power,
+ tanh_activated_cell_buffer);
+
+ /*Step4: copy the update the hidden state to output*/
+ // Check offset validity to avoid memory overflow
+ TFLITE_DCHECK_LE(
+ step_info.OutputOffset() + step_info.StateShape().FlatSize(),
+ tflite::micro::GetTensorShape(kernel_content.output_tensor).FlatSize());
+ // record the output (from the updated hidden state)
+ ActivationType* output_ptr = tflite::micro::GetTensorData<ActivationType>(
+ kernel_content.output_tensor);
+ const auto* hidden_state = kernel_content.HiddenStateTensor();
+ std::memcpy(output_ptr + step_info.OutputOffset(),
+ tflite::micro::GetTensorData<ActivationType>(hidden_state) +
+ step_info.HiddenStateOffset(),
+ step_info.StateShape().FlatSize() * sizeof(ActivationType));
+}
+#else // #if !(defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
+template <typename ActivationType, typename WeightType, typename CellType,
+ typename BiasType>
+void LstmStep(const LstmStepManager& step_info, const OpDataLSTM& op_data,
+ LSTMKernelContents& kernel_content,
+ const LSTMBuffers<CellType>& buffers) {
+ const TfLiteEvalTensor* input =
+ kernel_content.GetInternalTensor(tflite::kLstmInputTensor);
+ TfLiteEvalTensor* recurrent = kernel_content.HiddenStateTensor();
+
+ int time_major = step_info.time_major();
+ int num_batches = time_major == 0 ? 1 : step_info.batch_size();
+ int input_dimension = step_info.input_dimension();
+ int state_dimension = step_info.state_dimension();
+
+ // Check offset validity to avoid memory overflow
+ TFLITE_DCHECK_LE(step_info.InputOffset() + num_batches * input_dimension,
+ tflite::micro::GetTensorShape(input).FlatSize());
+ TFLITE_DCHECK_LE(
+ step_info.HiddenStateOffset() + num_batches * state_dimension,
+ tflite::micro::GetTensorShape(recurrent).FlatSize());
+
+ /*Step1: Calculate gate outputs to prepare cell state update*/
+ CellType* gate_internal_buffer = buffers.buffer3;
+ CellType* forget_gate_output = buffers.buffer0;
+ CalculateLstmGate<ActivationType, WeightType, CellType, BiasType>(
+ step_info, op_data.forget_gate_parameters,
+ // Input FC
+ input, // kernel_content.GetInternalTensor(tflite::kLstmInputTensor),
+ kernel_content.GetInternalTensor(tflite::kLstmInputToForgetWeightsTensor),
+ kernel_content.GetInternalTensor(tflite::kLstmForgetGateBiasTensor),
+ // Recurrent FC
+ recurrent, // kernel_content.HiddenStateTensor(),
+ kernel_content.GetInternalTensor(
+ tflite::kLstmRecurrentToForgetWeightsTensor),
+ /*recurrent_bias*/ nullptr,
+ // Output
+ forget_gate_output,
+ // Scratch arrays
+ gate_internal_buffer, kTfLiteActSigmoid, num_batches, input_dimension,
+ state_dimension);
+
+ // Input Gate calculation;
+ CellType* input_gate_output = buffers.buffer1;
+ CalculateLstmGate<ActivationType, WeightType, CellType, BiasType>(
+ step_info, op_data.input_gate_parameters,
+ // Input FC
+ input, // kernel_content.GetInternalTensor(tflite::kLstmInputTensor),
+ kernel_content.GetInternalTensor(tflite::kLstmInputToInputWeightsTensor),
+ kernel_content.GetInternalTensor(tflite::kLstmInputGateBiasTensor),
+ // Recurrent FC
+ recurrent, // kernel_content.HiddenStateTensor(),
+ kernel_content.GetInternalTensor(
+ tflite::kLstmRecurrentToInputWeightsTensor),
+ /*recurrent_bias*/ nullptr,
+ // Output
+ input_gate_output,
+ // Scratch arrays
+ gate_internal_buffer, kTfLiteActSigmoid, num_batches, input_dimension,
+ state_dimension);
+
+ // Cell Gate calculation
+ CellType* cell_gate_output = buffers.buffer2;
+ CalculateLstmGate<ActivationType, WeightType, CellType, BiasType>(
+ step_info, op_data.cell_gate_parameters,
+ // Input FC
+ input, // kernel_content.GetInternalTensor(tflite::kLstmInputTensor),
+ kernel_content.GetInternalTensor(tflite::kLstmInputToCellWeightsTensor),
+ kernel_content.GetInternalTensor(tflite::kLstmCellGateBiasTensor),
+ // Recurrent FC
+ recurrent, // kernel_content.HiddenStateTensor(),
+ kernel_content.GetInternalTensor(
+ tflite::kLstmRecurrentToCellWeightsTensor),
+ /*recurrent_bias*/ nullptr,
+ // Output
+ cell_gate_output,
+ // Scratch arrays
+ gate_internal_buffer, op_data.cell_gate_nonlinear_type, num_batches,
+ input_dimension, state_dimension);
+
+ /*Step2: update the cell state */
+ const InterGateParameters& inter_gate_params = op_data.inter_gate_parameters;
+ CellType* updated_input_buffer = buffers.buffer1; // reuse buffer
+
+ UpdateLstmCell(step_info, kernel_content.CellStateTensor(),
+ forget_gate_output, input_gate_output, cell_gate_output,
+ inter_gate_params.forget_cell_mul_params,
+ inter_gate_params.input_mul_params, op_data.cell_state_info,
+ updated_input_buffer);
+
+ /*Step3: update the hidden state */
+ CellType* output_gate_output = buffers.buffer1; // reuse buffer
+ CalculateLstmGate<ActivationType, WeightType, CellType, BiasType>(
+ step_info, op_data.output_gate_parameters,
+ // Input FC
+ input, // kernel_content.GetInternalTensor(tflite::kLstmInputTensor),
+ kernel_content.GetInternalTensor(tflite::kLstmInputToOutputWeightsTensor),
+ kernel_content.GetInternalTensor(tflite::kLstmOutputGateBiasTensor),
+ // Recurrent FC
+ recurrent, // kernel_content.HiddenStateTensor(),
+ kernel_content.GetInternalTensor(
+ tflite::kLstmRecurrentToOutputWeightsTensor),
+ /*recurrent_bias*/ nullptr,
+ // Output
+ output_gate_output,
+ // Scratch arrays
+ gate_internal_buffer, kTfLiteActSigmoid, num_batches, input_dimension,
+ state_dimension);
+
+ CellType* tanh_activated_cell_buffer = buffers.buffer0; // reuse buffer
+ tflite::lstm_internal::UpdateLstmHidden<CellType, ActivationType>(
+ step_info, kernel_content.CellStateTensor(), recurrent,
+ /* kernel_content.HiddenStateTensor(), */ output_gate_output,
+ inter_gate_params.output_mul_params,
+ op_data.cell_state_info.cell_state_scale_power,
+ tanh_activated_cell_buffer);
+
+ /*Step4: copy the update the hidden state to output*/
+ // Check offset validity to avoid memory overflow
+ TFLITE_DCHECK_LE(
+ step_info.OutputOffset() + step_info.StateShape().FlatSize(),
+ tflite::micro::GetTensorShape(kernel_content.output_tensor).FlatSize());
+ // record the output (from the updated hidden state)
+ ActivationType* output_ptr = tflite::micro::GetTensorData<ActivationType>(
+ kernel_content.output_tensor);
+ // const auto* hidden_state = kernel_content.HiddenStateTensor();
+ std::memcpy(output_ptr + step_info.OutputOffset(),
+ tflite::micro::GetTensorData<ActivationType>(recurrent) +
+ step_info.HiddenStateOffset(),
+ step_info.StateShape().FlatSize() * sizeof(ActivationType));
+}
+#endif // #if !(defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
+
+} // namespace lstm_internal
+
+// Evaulate the LSTM kernel with (potential) multi-steps and multi-batch input
+// Since
+template <typename ActivationType, typename WeightType, typename CellType,
+ typename BiasType>
+TfLiteStatus EvalLstm(const OpDataLSTM& op_data,
+ LSTMKernelContents& kernel_content,
+ const LSTMBuffers<CellType>& buffers) {
+ lstm_internal::LstmStepManager step_info(&op_data.size_info);
+ const auto& size_info = op_data.size_info;
+ // time is the first dimention, enable batch computation
+ if (size_info.time_major) {
+ for (int t = 0; t < size_info.time_steps; t++) {
+ lstm_internal::LstmStep<ActivationType, WeightType, CellType, BiasType>(
+ step_info, op_data, kernel_content, buffers);
+ // prepare for the next time step
+ step_info.UpdateTime();
+ }
+ } else {
+ // batch first, unable to size the input data. single batch inference
+ for (int b = 0; b < size_info.batch_size; b++) {
+ for (int t = 0; t < size_info.time_steps; t++) {
+ lstm_internal::LstmStep<ActivationType, WeightType, CellType, BiasType>(
+ step_info, op_data, kernel_content, buffers);
+ // prepare for the next time step
+ step_info.UpdateTime();
+ }
+ // prepare for the next batch
+ step_info.UpdateBatch();
+ step_info.ResetTime();
+ }
+ }
+ return kTfLiteOk;
+}
} // namespace tflite
-#endif // TENSORFLOW_LITE_KERNELS_LSTM_EVAL_H_
+
+#endif // TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_16ACT_H_
diff --git a/tensorflow/lite/micro/kernels/xtensa/lstm_eval_hifi.cc b/tensorflow/lite/micro/kernels/xtensa/lstm_eval_hifi.cc
index 2b49f26..f8b6fd8 100644
--- a/tensorflow/lite/micro/kernels/xtensa/lstm_eval_hifi.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/lstm_eval_hifi.cc
@@ -12,17 +12,65 @@
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+
+#include <xtensa/tie/xt_hifi2.h>
+
#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/micro/kernels/xtensa/lstm_eval.h"
#include "tensorflow/lite/micro/kernels/xtensa/xtensa.h"
namespace tflite {
-namespace ops {
-namespace micro {
-namespace lstm_eval {
#if defined(HIFI5)
+#if TFLITE_SINGLE_ROUNDING
+#define MPY_BY_QUANT_MULT_X2_OUT32(out, inp, multiplier, left_shift, \
+ right_shift) \
+ { \
+ ae_int64 out64_0, out64_1; \
+ ae_int64 INT64_ONE = AE_MOVINT64_FROMINT32X2(AE_MOVDA32X2(0, 1)); \
+ ae_int64 round_val = AE_SLAA64S(INT64_ONE, 30 - left_shift); \
+ AE_MUL32X2S_HH_LL(out64_0, out64_1, inp, AE_MOVDA32(multiplier)); \
+ out64_0 = AE_ADD64S(out64_0, round_val); \
+ out64_1 = AE_ADD64S(out64_1, round_val); \
+ out = AE_TRUNCA32X2F64S(out64_0, out64_1, 1 + left_shift); \
+ }
+
+#define MPY_BY_QUANT_MULT_X2X2_OUT32(out1, out2, inp1, inp2, multiplier, \
+ left_shift, right_shift) \
+ { \
+ ae_int64 out64_0, out64_1, out64_2, out64_3; \
+ ae_int64 INT64_ONE = AE_MOVINT64_FROMINT32X2(AE_MOVDA32X2(0, 1)); \
+ ae_int64 round_val = AE_SLAA64S(INT64_ONE, 30 - left_shift); \
+ AE_MUL32X2S_HH_LL(out64_0, out64_1, inp1, AE_MOVDA32(multiplier)); \
+ AE_MUL32X2S_HH_LL(out64_2, out64_3, inp2, AE_MOVDA32(multiplier)); \
+ out64_0 = AE_ADD64S(out64_0, round_val); \
+ out64_1 = AE_ADD64S(out64_1, round_val); \
+ out64_2 = AE_ADD64S(out64_2, round_val); \
+ out64_3 = AE_ADD64S(out64_3, round_val); \
+ out1 = AE_TRUNCA32X2F64S(out64_0, out64_1, 1 + left_shift); \
+ out2 = AE_TRUNCA32X2F64S(out64_2, out64_3, 1 + left_shift); \
+ }
+#else /* #if TFLITE_SINGLE_ROUNDING */
+#define MPY_BY_QUANT_MULT_X2_OUT32(out, inp, multiplier, left_shift, \
+ right_shift) \
+ out = AE_SLAA32(inp, left_shift); \
+ out = AE_MULFP32X2RAS(out, AE_MOVDA32(multiplier)); \
+ out = AE_SRAA32SYMS(out, right_shift);
+
+#define MPY_BY_QUANT_MULT_X2X2_OUT32(out1, out2, inp1, inp2, multiplier, \
+ left_shift, right_shift) \
+ { \
+ ae_int32x2 d_ls = AE_MOVDA32(1 << left_shift); \
+ AE_MUL2P32X4(out1, out2, inp1, inp2, d_ls, d_ls); \
+ AE_MULF2P32X4RAS(out1, out2, out1, out2, AE_MOVDA32(multiplier), \
+ AE_MOVDA32(multiplier)); \
+ out1 = AE_SRAA32SYMS(out1, right_shift); \
+ out2 = AE_SRAA32SYMS(out2, right_shift); \
+ }
+#endif /* #if TFLITE_SINGLE_ROUNDING */
+
void calc_cell_state_without_cifg(int16_t* cell_state,
const int16_t* forget_gate,
const int16_t* cell_gate,
@@ -124,7 +172,7 @@
AE_MUL16X4(d_mul_0, d_mul_1, d_cg_0, d_ig_0);
d_mul_0 = AE_SRAA32SYMS(d_mul_0, shift2);
- d_cg_0 = AE_SAT16X4(d_mul_0, d_mul_1);
+ d_cg_0 = AE_SAT16X4(d_mul_0, d_mul_0);
d_cs_w_0 = AE_ADD16S(d_cs_w_0, d_cg_0);
AE_MINMAX16(d_cs_w_0, d_min, d_max);
@@ -187,11 +235,11 @@
AE_MUL16X4(d_mul_0, d_mul_1, d_cs_r_0, d_fg_0);
d_mul_0 = AE_SRAA32SYMS(d_mul_0, shift1);
- d_cs_w_0 = AE_SAT16X4(d_mul_0, d_mul_1);
+ d_cs_w_0 = AE_SAT16X4(d_mul_0, d_mul_0);
AE_MUL16X4(d_mul_0, d_mul_1, d_cg_0, d_ig_0);
d_mul_0 = AE_SRAA32SYMS(d_mul_0, shift2);
- d_cg_0 = AE_SAT16X4(d_mul_0, d_mul_1);
+ d_cg_0 = AE_SAT16X4(d_mul_0, d_mul_0);
d_cs_w_0 = AE_ADD16S(d_cs_w_0, d_cg_0);
AE_MINMAX16(d_cs_w_0, d_min, d_max);
@@ -298,7 +346,7 @@
d_1mfg_0 = AE_SUB16S(d_one, d_fg_0);
AE_MUL16X4(d_mul_0, d_mul_1, d_cg_0, d_1mfg_0);
d_mul_0 = AE_SRAA32SYMS(d_mul_0, shift2);
- d_cg_0 = AE_SAT16X4(d_mul_0, d_mul_1);
+ d_cg_0 = AE_SAT16X4(d_mul_0, d_mul_0);
d_cs_w_0 = AE_ADD16S(d_cs_w_0, d_cg_0);
AE_MINMAX16(d_cs_w_0, d_min, d_max);
@@ -360,12 +408,12 @@
AE_MUL16X4(d_mul_0, d_mul_1, d_cs_r_0, d_fg_0);
d_mul_0 = AE_SRAA32SYMS(d_mul_0, shift1);
- d_cs_w_0 = AE_SAT16X4(d_mul_0, d_mul_1);
+ d_cs_w_0 = AE_SAT16X4(d_mul_0, d_mul_0);
d_1mfg_0 = AE_SUB16S(d_one, d_fg_0);
AE_MUL16X4(d_mul_0, d_mul_1, d_cg_0, d_1mfg_0);
d_mul_0 = AE_SRAA32SYMS(d_mul_0, shift2);
- d_cg_0 = AE_SAT16X4(d_mul_0, d_mul_1);
+ d_cg_0 = AE_SAT16X4(d_mul_0, d_mul_0);
d_cs_w_0 = AE_ADD16S(d_cs_w_0, d_cg_0);
AE_MINMAX16(d_cs_w_0, d_min, d_max);
@@ -387,7 +435,6 @@
ae_int16x4 data_a_0, data_a_1;
ae_int16x4 data_b_0, data_b_1;
ae_int32x2 data_ab_0, data_ab_1, data_ab_2, data_ab_3;
- ae_int32x2 d_multiplier, d_left_shift;
ae_int16x4 d_zp;
ae_int16x4 data_c_0, data_c_1;
ae_int8x8 data_c;
@@ -401,13 +448,15 @@
align_src_input_2 = AE_LA128_PP((ae_int16x8*)tmp_input_2);
align_dst_output = AE_ZALIGN64(); // zero alignment reg
- d_multiplier = AE_MOVDA32(multiplier);
d_zp = AE_MOVDA16(zero_point);
+#if TFLITE_SINGLE_ROUNDING
+ left_shift = shift;
+ (void)right_shift;
+#else /* #if TFLITE_SINGLE_ROUNDING */
left_shift = shift < 0 ? 0 : shift;
right_shift = shift > 0 ? 0 : -shift;
-
- d_left_shift = AE_MOVDA32(1 << left_shift);
+#endif /* #if TFLITE_SINGLE_ROUNDING */
#pragma concurrent
for (i = 0; i < (num_elms >> 3); i++) {
AE_LA16X4X2_IP(data_a_0, data_a_1, align_src_input_1, tmp_input_1);
@@ -415,18 +464,10 @@
AE_MUL16X4(data_ab_0, data_ab_1, data_a_0, data_b_0);
AE_MUL16X4(data_ab_2, data_ab_3, data_a_1, data_b_1);
- AE_MUL2P32X4(data_ab_0, data_ab_1, data_ab_0, data_ab_1, d_left_shift,
- d_left_shift);
- AE_MUL2P32X4(data_ab_2, data_ab_3, data_ab_2, data_ab_3, d_left_shift,
- d_left_shift);
- AE_MULF2P32X4RAS(data_ab_0, data_ab_1, data_ab_0, data_ab_1, d_multiplier,
- d_multiplier);
- AE_MULF2P32X4RAS(data_ab_2, data_ab_3, data_ab_2, data_ab_3, d_multiplier,
- d_multiplier);
- data_ab_0 = AE_SRAA32SYMS(data_ab_0, right_shift);
- data_ab_1 = AE_SRAA32SYMS(data_ab_1, right_shift);
- data_ab_2 = AE_SRAA32SYMS(data_ab_2, right_shift);
- data_ab_3 = AE_SRAA32SYMS(data_ab_3, right_shift);
+ MPY_BY_QUANT_MULT_X2X2_OUT32(data_ab_0, data_ab_1, data_ab_0, data_ab_1,
+ multiplier, left_shift, right_shift);
+ MPY_BY_QUANT_MULT_X2X2_OUT32(data_ab_2, data_ab_3, data_ab_2, data_ab_3,
+ multiplier, left_shift, right_shift);
data_c_0 = AE_SAT16X4(data_ab_0, data_ab_1);
data_c_1 = AE_SAT16X4(data_ab_2, data_ab_3);
data_c_0 = AE_SUB16S(data_c_0, d_zp);
@@ -445,18 +486,532 @@
AE_L16_IP(data_b_0, (ae_int16*)tmp_input_2, 2);
AE_MUL16X4(data_ab_0, data_ab_1, data_a_0, data_b_0);
- data_ab_0 = AE_MULP32X2(data_ab_0, d_left_shift);
- data_ab_0 = AE_MULFP32X2RAS(data_ab_0, d_multiplier);
- data_ab_0 = AE_SRAA32SYMS(data_ab_0, right_shift);
- data_c_0 = AE_SAT16X4(data_ab_0, data_ab_1);
+ MPY_BY_QUANT_MULT_X2_OUT32(data_ab_0, data_ab_0, multiplier, left_shift,
+ right_shift);
+ data_c_0 = AE_SAT16X4(data_ab_0, data_ab_0);
data_c_0 = AE_SUB16S(data_c_0, d_zp);
data_c = AE_SAT8X8X16(data_c_0, data_c_0);
AE_S8_0_IP(data_c, (ae_int8*)output, 1);
}
}
+#elif defined(HIFI3) || defined(HIFI4)
+#if TFLITE_SINGLE_ROUNDING
+#define MPY_BY_QUANT_MULT_X2_OUT32(out, inp, multiplier, l_shift, r_shift) \
+ { \
+ ae_int64 out64_0, out64_1; \
+ out64_0 = AE_MUL32_HH(inp, AE_MOVDA32(multiplier)); \
+ out64_1 = AE_MUL32_LL(inp, AE_MOVDA32(multiplier)); \
+ out64_0 = AE_SLAA64S(out64_0, 1 + l_shift); \
+ out64_1 = AE_SLAA64S(out64_1, 1 + l_shift); \
+ out = AE_ROUND32X2F64SASYM(out64_0, out64_1); \
+ }
+
+#define MPY_BY_QUANT_MULT_X2X2_OUT32(out1, out2, inp1, inp2, multiplier, \
+ l_shift, r_shift) \
+ { \
+ ae_int64 out64_0, out64_1, out64_2, out64_3; \
+ out64_0 = AE_MUL32_HH(inp1, AE_MOVDA32(multiplier)); \
+ out64_1 = AE_MUL32_LL(inp1, AE_MOVDA32(multiplier)); \
+ out64_2 = AE_MUL32_HH(inp2, AE_MOVDA32(multiplier)); \
+ out64_3 = AE_MUL32_LL(inp2, AE_MOVDA32(multiplier)); \
+ out64_0 = AE_SLAA64S(out64_0, 1 + l_shift); \
+ out64_1 = AE_SLAA64S(out64_1, 1 + l_shift); \
+ out64_2 = AE_SLAA64S(out64_2, 1 + l_shift); \
+ out64_3 = AE_SLAA64S(out64_3, 1 + l_shift); \
+ out1 = AE_ROUND32X2F64SASYM(out64_0, out64_1); \
+ out2 = AE_ROUND32X2F64SASYM(out64_2, out64_3); \
+ }
+#else /* #if TFLITE_SINGLE_ROUNDING */
+#define MPY_BY_QUANT_MULT_X2_OUT32(out, inp, multiplier, l_shift, r_shift) \
+ out = AE_SLAA32(inp, l_shift); \
+ out = AE_MULFP32X2RAS(out, AE_MOVDA32(multiplier)); \
+ out = AE_ROUND32X2F64SSYM(AE_SRAA64(AE_CVT64F32_H(out), r_shift), \
+ AE_SRAA64(AE_CVT64F32_L(out), r_shift));
+
+#define MPY_BY_QUANT_MULT_X2X2_OUT32(out1, out2, inp1, inp2, multiplier, \
+ l_shift, r_shift) \
+ { \
+ ae_int32x2 d_ls = AE_MOVDA32(1 << l_shift); \
+ out1 = AE_MULP32X2(inp1, d_ls); \
+ out2 = AE_MULP32X2(inp2, d_ls); \
+ out1 = AE_MULFP32X2RAS(out1, AE_MOVDA32(multiplier)); \
+ out2 = AE_MULFP32X2RAS(out2, AE_MOVDA32(multiplier)); \
+ out1 = AE_ROUND32X2F64SSYM(AE_SRAA64(AE_CVT64F32_H(out1), r_shift), \
+ AE_SRAA64(AE_CVT64F32_L(out1), r_shift)); \
+ out2 = AE_ROUND32X2F64SSYM(AE_SRAA64(AE_CVT64F32_H(out2), r_shift), \
+ AE_SRAA64(AE_CVT64F32_L(out2), r_shift)); \
+ }
+#endif /* #if TFLITE_SINGLE_ROUNDING */
+
+#ifndef AE_MULFP16X4RS
+static inline ae_f16x4 AE_MULFP16X4RS(ae_f16x4 d0, ae_f16x4 d1) {
+ ae_f16x4 output;
+ ae_f32x2 d0_32_0, d0_32_1, out32_0, out32_1;
+ ae_f16x4 one_d = AE_MOVDA16(1);
+ AE_MUL16X4(d0_32_0, d0_32_1, d0, one_d);
+ out32_0 = AE_MULFP32X16X2RS_H(d0_32_0, d1);
+ out32_1 = AE_MULFP32X16X2RS_L(d0_32_1, d1);
+ output = AE_SEL16_6420(AE_MOVF16X4_FROMF32X2(out32_0),
+ AE_MOVF16X4_FROMF32X2(out32_1));
+ return output;
+}
+#endif
+
+#ifndef AE_MINMAX16
+#define AE_MINMAX16(dinout, d_min, d_max) \
+ { \
+ xtbool4 b0 = AE_LT16(dinout, d_min); \
+ AE_MOVT16X4(dinout, d_min, b0); \
+ b0 = AE_LT16(d_max, dinout); \
+ AE_MOVT16X4(dinout, d_max, b0); \
+ }
+#endif
+
+#ifndef AE_SRAA32SYMS
+#define AE_SRAA32SYMS(inp, right_shift) \
+ AE_ROUND32X2F64SSYM(AE_SRAA64(AE_CVT64F32_H(inp), right_shift), \
+ AE_SRAA64(AE_CVT64F32_L(inp), right_shift))
+#endif
+
+void calc_cell_state_without_cifg(int16_t* cell_state,
+ const int16_t* forget_gate,
+ const int16_t* cell_gate,
+ const int16_t* input_gate, int shift1,
+ int shift2, int clip, int num_elms) {
+ const ae_int16x4 *p16x4_cs_r, *p16x4_fg_r;
+ const ae_int16x4 *p16x4_cg_r, *p16x4_ig_r;
+
+ ae_int16x4* p16x4_cs_w;
+
+ ae_valign align_cs_r, align_fg_r;
+ ae_valign align_cg_r, align_ig_r;
+ ae_valign align_cs_w;
+
+ ae_int16x4 d_cs_r_0, d_cs_r_1;
+ ae_int16x4 d_fg_0, d_fg_1;
+ ae_int16x4 d_cg_0, d_cg_1;
+ ae_int16x4 d_ig_0, d_ig_1;
+ ae_int16x4 d_cs_w_0, d_cs_w_1;
+ ae_int32x2 d_mul_0, d_mul_1, d_mul_2, d_mul_3;
+ ae_int32x2 d_mul_4, d_mul_5, d_mul_6, d_mul_7;
+
+ ae_int16x4 d_min, d_max;
+
+ int i = 0;
+ p16x4_cs_r = (const ae_int16x4*)cell_state;
+ p16x4_fg_r = (const ae_int16x4*)forget_gate;
+ p16x4_cg_r = (const ae_int16x4*)cell_gate;
+ p16x4_ig_r = (const ae_int16x4*)input_gate;
+
+ p16x4_cs_w = (ae_int16x4*)cell_state;
+
+ align_cs_r = AE_LA64_PP(p16x4_cs_r);
+ align_fg_r = AE_LA64_PP(p16x4_fg_r);
+ align_cg_r = AE_LA64_PP(p16x4_cg_r);
+ align_ig_r = AE_LA64_PP(p16x4_ig_r);
+
+ align_cs_w = AE_ZALIGN64();
+
+ if (clip > 0) {
+ d_min = AE_MOVDA16(-clip);
+ d_max = AE_MOVDA16(clip);
+ } else {
+ d_min = AE_MOVDA16(-32768);
+ d_max = AE_MOVDA16(32767);
+ }
+
+#pragma concurrent
+ if (shift1 == 15) {
+ for (i = 0; i < (num_elms >> 3); i++) {
+ AE_LA16X4_IP(d_cs_r_0, align_cs_r, p16x4_cs_r);
+ AE_LA16X4_IP(d_cs_r_1, align_cs_r, p16x4_cs_r);
+ AE_LA16X4_IP(d_fg_0, align_fg_r, p16x4_fg_r);
+ AE_LA16X4_IP(d_fg_1, align_fg_r, p16x4_fg_r);
+ AE_LA16X4_IP(d_cg_0, align_cg_r, p16x4_cg_r);
+ AE_LA16X4_IP(d_cg_1, align_cg_r, p16x4_cg_r);
+ AE_LA16X4_IP(d_ig_0, align_ig_r, p16x4_ig_r);
+ AE_LA16X4_IP(d_ig_1, align_ig_r, p16x4_ig_r);
+
+ d_cs_w_0 = AE_MULFP16X4RS(d_cs_r_0, d_fg_0);
+ d_cs_w_1 = AE_MULFP16X4RS(d_cs_r_1, d_fg_1);
+
+ AE_MUL16X4(d_mul_4, d_mul_5, d_cg_0, d_ig_0);
+ AE_MUL16X4(d_mul_6, d_mul_7, d_cg_1, d_ig_1);
+ d_mul_4 = AE_SRAA32SYMS(d_mul_4, shift2);
+ d_mul_5 = AE_SRAA32SYMS(d_mul_5, shift2);
+ d_mul_6 = AE_SRAA32SYMS(d_mul_6, shift2);
+ d_mul_7 = AE_SRAA32SYMS(d_mul_7, shift2);
+
+ d_cg_0 = AE_SAT16X4(d_mul_4, d_mul_5);
+ d_cg_1 = AE_SAT16X4(d_mul_6, d_mul_7);
+ d_cs_w_0 = AE_ADD16S(d_cs_w_0, d_cg_0);
+ d_cs_w_1 = AE_ADD16S(d_cs_w_1, d_cg_1);
+ AE_MINMAX16(d_cs_w_0, d_min, d_max);
+ AE_MINMAX16(d_cs_w_1, d_min, d_max);
+
+ AE_SA16X4_IP(d_cs_w_0, align_cs_w, p16x4_cs_w);
+ AE_SA16X4_IP(d_cs_w_1, align_cs_w, p16x4_cs_w);
+ }
+ AE_SA64POS_FP(align_cs_w, p16x4_cs_w); // finalize the stream
+
+ const ae_int16 *p16_cs_r, *p16_fg_r;
+ const ae_int16 *p16_cg_r, *p16_ig_r;
+
+ ae_int16* p16_cs_w;
+
+ p16_cs_r = (const ae_int16*)p16x4_cs_r;
+ p16_fg_r = (const ae_int16*)p16x4_fg_r;
+ p16_cg_r = (const ae_int16*)p16x4_cg_r;
+ p16_ig_r = (const ae_int16*)p16x4_ig_r;
+
+ p16_cs_w = (ae_int16*)p16x4_cs_w;
+ // residue iterations
+#pragma concurrent
+#pragma loop_count max = 7
+ for (i = 0; i < ((num_elms)&7); i++) {
+ d_cs_r_0 = p16_cs_r[i];
+ d_fg_0 = p16_fg_r[i];
+ d_cg_0 = p16_cg_r[i];
+ d_ig_0 = p16_ig_r[i];
+
+ d_cs_w_0 = AE_MULFP16X4RS(d_cs_r_0, d_fg_0);
+
+ AE_MUL16X4(d_mul_0, d_mul_1, d_cg_0, d_ig_0);
+ d_mul_0 = AE_SRAA32SYMS(d_mul_0, shift2);
+ d_cg_0 = AE_SAT16X4(d_mul_0, d_mul_0);
+
+ d_cs_w_0 = AE_ADD16S(d_cs_w_0, d_cg_0);
+ AE_MINMAX16(d_cs_w_0, d_min, d_max);
+ p16_cs_w[i] = d_cs_w_0;
+ }
+ } else {
+ for (i = 0; i < (num_elms >> 3); i++) {
+ AE_LA16X4_IP(d_cs_r_0, align_cs_r, p16x4_cs_r);
+ AE_LA16X4_IP(d_cs_r_1, align_cs_r, p16x4_cs_r);
+ AE_LA16X4_IP(d_fg_0, align_fg_r, p16x4_fg_r);
+ AE_LA16X4_IP(d_fg_1, align_fg_r, p16x4_fg_r);
+ AE_LA16X4_IP(d_cg_0, align_cg_r, p16x4_cg_r);
+ AE_LA16X4_IP(d_cg_1, align_cg_r, p16x4_cg_r);
+ AE_LA16X4_IP(d_ig_0, align_ig_r, p16x4_ig_r);
+ AE_LA16X4_IP(d_ig_1, align_ig_r, p16x4_ig_r);
+
+ AE_MUL16X4(d_mul_0, d_mul_1, d_cs_r_0, d_fg_0);
+ AE_MUL16X4(d_mul_2, d_mul_3, d_cs_r_1, d_fg_1);
+ d_mul_0 = AE_SRAA32SYMS(d_mul_0, shift1);
+ d_mul_1 = AE_SRAA32SYMS(d_mul_1, shift1);
+ d_mul_2 = AE_SRAA32SYMS(d_mul_2, shift1);
+ d_mul_3 = AE_SRAA32SYMS(d_mul_3, shift1);
+
+ d_cs_w_0 = AE_SAT16X4(d_mul_0, d_mul_1);
+ d_cs_w_1 = AE_SAT16X4(d_mul_2, d_mul_3);
+
+ AE_MUL16X4(d_mul_4, d_mul_5, d_cg_0, d_ig_0);
+ AE_MUL16X4(d_mul_6, d_mul_7, d_cg_1, d_ig_1);
+ d_mul_4 = AE_SRAA32SYMS(d_mul_4, shift2);
+ d_mul_5 = AE_SRAA32SYMS(d_mul_5, shift2);
+ d_mul_6 = AE_SRAA32SYMS(d_mul_6, shift2);
+ d_mul_7 = AE_SRAA32SYMS(d_mul_7, shift2);
+
+ d_cg_0 = AE_SAT16X4(d_mul_4, d_mul_5);
+ d_cg_1 = AE_SAT16X4(d_mul_6, d_mul_7);
+ d_cs_w_0 = AE_ADD16S(d_cs_w_0, d_cg_0);
+ d_cs_w_1 = AE_ADD16S(d_cs_w_1, d_cg_1);
+ AE_MINMAX16(d_cs_w_0, d_min, d_max);
+ AE_MINMAX16(d_cs_w_1, d_min, d_max);
+
+ AE_SA16X4_IP(d_cs_w_0, align_cs_w, p16x4_cs_w);
+ AE_SA16X4_IP(d_cs_w_1, align_cs_w, p16x4_cs_w);
+ }
+ AE_SA64POS_FP(align_cs_w, p16x4_cs_w); // finalize the stream
+
+ const ae_int16 *p16_cs_r, *p16_fg_r;
+ const ae_int16 *p16_cg_r, *p16_ig_r;
+
+ ae_int16* p16_cs_w;
+
+ p16_cs_r = (const ae_int16*)p16x4_cs_r;
+ p16_fg_r = (const ae_int16*)p16x4_fg_r;
+ p16_cg_r = (const ae_int16*)p16x4_cg_r;
+ p16_ig_r = (const ae_int16*)p16x4_ig_r;
+
+ p16_cs_w = (ae_int16*)p16x4_cs_w;
+ // residue iterations
+#pragma concurrent
+#pragma loop_count max = 7
+ for (i = 0; i < ((num_elms)&7); i++) {
+ d_cs_r_0 = p16_cs_r[i];
+ d_fg_0 = p16_fg_r[i];
+ d_cg_0 = p16_cg_r[i];
+ d_ig_0 = p16_ig_r[i];
+
+ AE_MUL16X4(d_mul_0, d_mul_1, d_cs_r_0, d_fg_0);
+ d_mul_0 = AE_SRAA32SYMS(d_mul_0, shift1);
+ d_cs_w_0 = AE_SAT16X4(d_mul_0, d_mul_0);
+
+ AE_MUL16X4(d_mul_0, d_mul_1, d_cg_0, d_ig_0);
+ d_mul_0 = AE_SRAA32SYMS(d_mul_0, shift2);
+ d_cg_0 = AE_SAT16X4(d_mul_0, d_mul_0);
+
+ d_cs_w_0 = AE_ADD16S(d_cs_w_0, d_cg_0);
+ AE_MINMAX16(d_cs_w_0, d_min, d_max);
+ p16_cs_w[i] = d_cs_w_0;
+ }
+ }
+}
+
+void calc_cell_state_with_cifg(int16_t* cell_state, const int16_t* forget_gate,
+ const int16_t* cell_gate, int shift1, int shift2,
+ int clip, int num_elms) {
+ const ae_int16x4 *p16x4_cs_r, *p16x4_fg_r;
+ const ae_int16x4* p16x4_cg_r;
+
+ ae_int16x4* p16x4_cs_w;
+
+ ae_valign align_cs_r, align_fg_r;
+ ae_valign align_cg_r;
+ ae_valign align_cs_w;
+
+ ae_int16x4 d_cs_r_0, d_cs_r_1;
+ ae_int16x4 d_fg_0, d_fg_1;
+ ae_int16x4 d_cg_0, d_cg_1;
+ ae_int16x4 d_1mfg_0, d_1mfg_1;
+ ae_int16x4 d_cs_w_0, d_cs_w_1;
+ ae_int32x2 d_mul_0, d_mul_1, d_mul_2, d_mul_3;
+ ae_int32x2 d_mul_4, d_mul_5, d_mul_6, d_mul_7;
+
+ ae_int16x4 d_min, d_max, d_one;
+
+ int i = 0;
+ p16x4_cs_r = (const ae_int16x4*)cell_state;
+ p16x4_fg_r = (const ae_int16x4*)forget_gate;
+ p16x4_cg_r = (const ae_int16x4*)cell_gate;
+
+ p16x4_cs_w = (ae_int16x4*)cell_state;
+
+ align_cs_r = AE_LA64_PP(p16x4_cs_r);
+ align_fg_r = AE_LA64_PP(p16x4_fg_r);
+ align_cg_r = AE_LA64_PP(p16x4_cg_r);
+
+ align_cs_w = AE_ZALIGN64();
+
+ if (clip > 0) {
+ d_min = AE_MOVDA16(-clip);
+ d_max = AE_MOVDA16(clip);
+ } else {
+ d_min = AE_MOVDA16(-32768);
+ d_max = AE_MOVDA16(32767);
+ }
+ d_one = AE_MOVDA16(32767);
+
+#pragma concurrent
+ if (shift1 == 15) {
+ for (i = 0; i < (num_elms >> 3); i++) {
+ AE_LA16X4_IP(d_cs_r_0, align_cs_r, p16x4_cs_r);
+ AE_LA16X4_IP(d_cs_r_1, align_cs_r, p16x4_cs_r);
+ AE_LA16X4_IP(d_fg_0, align_fg_r, p16x4_fg_r);
+ AE_LA16X4_IP(d_fg_1, align_fg_r, p16x4_fg_r);
+ AE_LA16X4_IP(d_cg_0, align_cg_r, p16x4_cg_r);
+ AE_LA16X4_IP(d_cg_1, align_cg_r, p16x4_cg_r);
+
+ d_cs_w_0 = AE_MULFP16X4RS(d_cs_r_0, d_fg_0);
+ d_cs_w_1 = AE_MULFP16X4RS(d_cs_r_1, d_fg_1);
+
+ d_1mfg_0 = AE_SUB16S(d_one, d_fg_0);
+ d_1mfg_1 = AE_SUB16S(d_one, d_fg_1);
+ AE_MUL16X4(d_mul_4, d_mul_5, d_cg_0, d_1mfg_0);
+ AE_MUL16X4(d_mul_6, d_mul_7, d_cg_1, d_1mfg_1);
+ d_mul_4 = AE_SRAA32SYMS(d_mul_4, shift2);
+ d_mul_5 = AE_SRAA32SYMS(d_mul_5, shift2);
+ d_mul_6 = AE_SRAA32SYMS(d_mul_6, shift2);
+ d_mul_7 = AE_SRAA32SYMS(d_mul_7, shift2);
+ d_cg_0 = AE_SAT16X4(d_mul_4, d_mul_5);
+ d_cg_1 = AE_SAT16X4(d_mul_6, d_mul_7);
+
+ d_cs_w_0 = AE_ADD16S(d_cs_w_0, d_cg_0);
+ d_cs_w_1 = AE_ADD16S(d_cs_w_1, d_cg_1);
+
+ AE_MINMAX16(d_cs_w_0, d_min, d_max);
+ AE_MINMAX16(d_cs_w_1, d_min, d_max);
+
+ AE_SA16X4_IP(d_cs_w_0, align_cs_w, p16x4_cs_w);
+ AE_SA16X4_IP(d_cs_w_1, align_cs_w, p16x4_cs_w);
+ }
+ AE_SA64POS_FP(align_cs_w, p16x4_cs_w); // finalize the stream
+
+ const ae_int16 *p16_cs_r, *p16_fg_r;
+ const ae_int16* p16_cg_r;
+
+ ae_int16* p16_cs_w;
+
+ p16_cs_r = (const ae_int16*)p16x4_cs_r;
+ p16_fg_r = (const ae_int16*)p16x4_fg_r;
+ p16_cg_r = (const ae_int16*)p16x4_cg_r;
+
+ p16_cs_w = (ae_int16*)p16x4_cs_w;
+ // residue iterations
+#pragma concurrent
+#pragma loop_count max = 7
+ for (i = 0; i < ((num_elms)&7); i++) {
+ d_cs_r_0 = p16_cs_r[i];
+ d_fg_0 = p16_fg_r[i];
+ d_cg_0 = p16_cg_r[i];
+
+ d_cs_w_0 = AE_MULFP16X4RS(d_cs_r_0, d_fg_0);
+
+ d_1mfg_0 = AE_SUB16S(d_one, d_fg_0);
+ AE_MUL16X4(d_mul_0, d_mul_1, d_cg_0, d_1mfg_0);
+ d_mul_0 = AE_SRAA32SYMS(d_mul_0, shift2);
+ d_cg_0 = AE_SAT16X4(d_mul_0, d_mul_0);
+
+ d_cs_w_0 = AE_ADD16S(d_cs_w_0, d_cg_0);
+ AE_MINMAX16(d_cs_w_0, d_min, d_max);
+ p16_cs_w[i] = d_cs_w_0;
+ }
+ } else {
+ for (i = 0; i < (num_elms >> 3); i++) {
+ AE_LA16X4_IP(d_cs_r_0, align_cs_r, p16x4_cs_r);
+ AE_LA16X4_IP(d_cs_r_1, align_cs_r, p16x4_cs_r);
+ AE_LA16X4_IP(d_fg_0, align_fg_r, p16x4_fg_r);
+ AE_LA16X4_IP(d_fg_1, align_fg_r, p16x4_fg_r);
+ AE_LA16X4_IP(d_cg_0, align_cg_r, p16x4_cg_r);
+ AE_LA16X4_IP(d_cg_1, align_cg_r, p16x4_cg_r);
+
+ AE_MUL16X4(d_mul_0, d_mul_1, d_cs_r_0, d_fg_0);
+ AE_MUL16X4(d_mul_2, d_mul_3, d_cs_r_1, d_fg_1);
+ d_mul_0 = AE_SRAA32SYMS(d_mul_0, shift1);
+ d_mul_1 = AE_SRAA32SYMS(d_mul_1, shift1);
+ d_mul_2 = AE_SRAA32SYMS(d_mul_2, shift1);
+ d_mul_3 = AE_SRAA32SYMS(d_mul_3, shift1);
+ d_cs_w_0 = AE_SAT16X4(d_mul_0, d_mul_1);
+ d_cs_w_1 = AE_SAT16X4(d_mul_2, d_mul_3);
+
+ d_1mfg_0 = AE_SUB16S(d_one, d_fg_0);
+ d_1mfg_1 = AE_SUB16S(d_one, d_fg_1);
+ AE_MUL16X4(d_mul_4, d_mul_5, d_cg_0, d_1mfg_0);
+ AE_MUL16X4(d_mul_6, d_mul_7, d_cg_1, d_1mfg_1);
+ d_mul_4 = AE_SRAA32SYMS(d_mul_4, shift2);
+ d_mul_5 = AE_SRAA32SYMS(d_mul_5, shift2);
+ d_mul_6 = AE_SRAA32SYMS(d_mul_6, shift2);
+ d_mul_7 = AE_SRAA32SYMS(d_mul_7, shift2);
+ d_cg_0 = AE_SAT16X4(d_mul_4, d_mul_5);
+ d_cg_1 = AE_SAT16X4(d_mul_6, d_mul_7);
+
+ d_cs_w_0 = AE_ADD16S(d_cs_w_0, d_cg_0);
+ d_cs_w_1 = AE_ADD16S(d_cs_w_1, d_cg_1);
+
+ AE_MINMAX16(d_cs_w_0, d_min, d_max);
+ AE_MINMAX16(d_cs_w_1, d_min, d_max);
+
+ AE_SA16X4_IP(d_cs_w_0, align_cs_w, p16x4_cs_w);
+ AE_SA16X4_IP(d_cs_w_1, align_cs_w, p16x4_cs_w);
+ }
+ AE_SA64POS_FP(align_cs_w, p16x4_cs_w); // finalize the stream
+
+ const ae_int16 *p16_cs_r, *p16_fg_r;
+ const ae_int16* p16_cg_r;
+
+ ae_int16* p16_cs_w;
+
+ p16_cs_r = (const ae_int16*)p16x4_cs_r;
+ p16_fg_r = (const ae_int16*)p16x4_fg_r;
+ p16_cg_r = (const ae_int16*)p16x4_cg_r;
+
+ p16_cs_w = (ae_int16*)p16x4_cs_w;
+ // residue iterations
+#pragma concurrent
+#pragma loop_count max = 7
+ for (i = 0; i < ((num_elms)&7); i++) {
+ d_cs_r_0 = p16_cs_r[i];
+ d_fg_0 = p16_fg_r[i];
+ d_cg_0 = p16_cg_r[i];
+
+ AE_MUL16X4(d_mul_0, d_mul_1, d_cs_r_0, d_fg_0);
+ d_mul_0 = AE_SRAA32SYMS(d_mul_0, shift1);
+ d_cs_w_0 = AE_SAT16X4(d_mul_0, d_mul_0);
+
+ d_1mfg_0 = AE_SUB16S(d_one, d_fg_0);
+ AE_MUL16X4(d_mul_0, d_mul_1, d_cg_0, d_1mfg_0);
+ d_mul_0 = AE_SRAA32SYMS(d_mul_0, shift2);
+ d_cg_0 = AE_SAT16X4(d_mul_0, d_mul_0);
+
+ d_cs_w_0 = AE_ADD16S(d_cs_w_0, d_cg_0);
+ AE_MINMAX16(d_cs_w_0, d_min, d_max);
+ p16_cs_w[i] = d_cs_w_0;
+ }
+ }
+}
+
+void xa_nn_elm_mul_16x16_asym8s(int8_t* output, const int16_t* input_1,
+ const int16_t* input_2, int32_t multiplier,
+ int32_t shift, int32_t zero_point,
+ int num_elms) {
+ ae_int16x4* tmp_input_1;
+ ae_int16x4* tmp_input_2;
+
+ ae_valign align_src_input_1, align_src_input_2;
+
+ ae_int16x4 data_a_0, data_b_0;
+ ae_int32x2 data_ab_0, data_ab_1;
+ ae_int16x4 d_zp;
+ ae_int16x4 data_c_0;
+ ae_int16x4 d_min8 = AE_MOVDA16(-128);
+ ae_int16x4 d_max8 = AE_MOVDA16(127);
+
+ int i = 0;
+ int left_shift, right_shift;
+ tmp_input_1 = (ae_int16x4*)(input_1);
+ tmp_input_2 = (ae_int16x4*)(input_2);
+
+ align_src_input_1 = AE_LA64_PP((ae_int16x4*)tmp_input_1);
+ align_src_input_2 = AE_LA64_PP((ae_int16x4*)tmp_input_2);
+
+ d_zp = AE_MOVDA16(zero_point);
+
+#if TFLITE_SINGLE_ROUNDING
+ left_shift = shift;
+ (void)right_shift;
+#else /* #if TFLITE_SINGLE_ROUNDING */
+ left_shift = shift < 0 ? 0 : shift;
+ right_shift = shift > 0 ? 0 : -shift;
+#endif /* #if TFLITE_SINGLE_ROUNDING */
+
+#pragma concurrent
+ for (i = 0; i < (num_elms >> 2); i++) {
+ AE_LA16X4_IP(data_a_0, align_src_input_1, tmp_input_1);
+ AE_LA16X4_IP(data_b_0, align_src_input_2, tmp_input_2);
+
+ AE_MUL16X4(data_ab_0, data_ab_1, data_a_0, data_b_0);
+ MPY_BY_QUANT_MULT_X2X2_OUT32(data_ab_0, data_ab_1, data_ab_0, data_ab_1,
+ multiplier, left_shift, right_shift);
+ data_c_0 = AE_SAT16X4(data_ab_0, data_ab_1);
+ data_c_0 = AE_SUB16S(data_c_0, d_zp);
+ AE_MINMAX16(data_c_0, d_min8, d_max8);
+
+ *output++ = AE_MOVAD16_3(data_c_0);
+ *output++ = AE_MOVAD16_2(data_c_0);
+ *output++ = AE_MOVAD16_1(data_c_0);
+ *output++ = AE_MOVAD16_0(data_c_0);
+ }
+
+ // residue iterations
+#pragma concurrent
+#pragma loop_count max = 3
+ for (int j = 0; j < ((num_elms)&3); j++) {
+ AE_L16_IP(data_a_0, (ae_int16*)tmp_input_1, 2);
+ AE_L16_IP(data_b_0, (ae_int16*)tmp_input_2, 2);
+
+ AE_MUL16X4(data_ab_0, data_ab_1, data_a_0, data_b_0);
+ MPY_BY_QUANT_MULT_X2_OUT32(data_ab_0, data_ab_0, multiplier, left_shift,
+ right_shift);
+ data_c_0 = AE_SAT16X4(data_ab_0, data_ab_0);
+ data_c_0 = AE_SUB16S(data_c_0, d_zp);
+ AE_MINMAX16(data_c_0, d_min8, d_max8);
+
+ *output++ = AE_MOVAD16_0(data_c_0);
+ }
+}
#endif // defined(HIFI5)
-} // namespace lstm_eval
-} // namespace micro
-} // namespace ops
} // namespace tflite
+
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
diff --git a/tensorflow/lite/micro/kernels/xtensa/lstm_shared.h b/tensorflow/lite/micro/kernels/xtensa/lstm_shared.h
deleted file mode 100644
index 4bcff1a..0000000
--- a/tensorflow/lite/micro/kernels/xtensa/lstm_shared.h
+++ /dev/null
@@ -1,78 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#ifndef TENSORFLOW_LITE_KERNELS_LSTM_SHARED_H_
-#define TENSORFLOW_LITE_KERNELS_LSTM_SHARED_H_
-
-namespace tflite {
-namespace ops {
-namespace micro {
-namespace lstm {
-// For full inputs kernel (24-inputs).
-// Please note the 20-input full kernel is deprecated and only kept
-// here for backward compatibility.
-namespace full {
-
-// Input Tensors of size {n_batch, n_input}
-constexpr int kInputTensor = 0;
-
-// Input weight tensors of size: {n_cell, n_input}
-constexpr int kInputToInputWeightsTensor = 1; // Optional
-constexpr int kInputToForgetWeightsTensor = 2;
-constexpr int kInputToCellWeightsTensor = 3;
-constexpr int kInputToOutputWeightsTensor = 4;
-
-// Recurrent weight tensors of size {n_cell, n_output}
-constexpr int kRecurrentToInputWeightsTensor = 5; // Optional
-constexpr int kRecurrentToForgetWeightsTensor = 6;
-constexpr int kRecurrentToCellWeightsTensor = 7;
-constexpr int kRecurrentToOutputWeightsTensor = 8;
-
-// Peephole weights tensors of size {n_cell}, representing a diagonal matrix.
-constexpr int kCellToInputWeightsTensor = 9; // Optional
-constexpr int kCellToForgetWeightsTensor = 10; // Optional
-constexpr int kCellToOutputWeightsTensor = 11; // Optional
-
-// Gates bias tensors of size {n_cell}
-constexpr int kInputGateBiasTensor = 12; // Optional
-constexpr int kForgetGateBiasTensor = 13;
-constexpr int kCellGateBiasTensor = 14;
-constexpr int kOutputGateBiasTensor = 15;
-
-// Projection weight tensor of size {n_output, n_cell}
-constexpr int kProjectionWeightsTensor = 16; // Optional
-// Projection bias tensor of size {n_output}
-constexpr int kProjectionBiasTensor = 17; // Optional
-
-// These state tensors are defined as variable tensors, and will be modified by
-// this op.
-constexpr int kOutputStateTensor = 18;
-constexpr int kCellStateTensor = 19;
-
-// Layer norm coefficient tensors of size {n_cell}, representing a diagonal
-// matrix.
-constexpr int kInputLayerNormCoefficientsTensor = 20; // Optional
-constexpr int kForgetLayerNormCoefficientsTensor = 21; // Optional
-constexpr int kCellLayerNormCoefficientsTensor = 22; // Optional
-constexpr int kOutputLayerNormCoefficientsTensor = 23; // Optional
-
-// Output tensors.
-constexpr int kOutputTensor = 0;
-} // namespace full
-
-} // namespace lstm
-} // namespace micro
-} // namespace ops
-} // namespace tflite
-#endif // TENSORFLOW_LITE_KERNELS_LSTM_SHARED_H_
diff --git a/tensorflow/lite/micro/kernels/xtensa/pad.cc b/tensorflow/lite/micro/kernels/xtensa/pad.cc
index bb00edb..d822c28 100644
--- a/tensorflow/lite/micro/kernels/xtensa/pad.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/pad.cc
@@ -215,7 +215,7 @@
constant_values == nullptr
? 0
: *tflite::micro::GetTensorData<int16_t>(constant_values);
-#if defined(HIFI4)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
/* NNLib currently only supports up to 4D input tensors */
if (tflite::micro::GetTensorShape(input).DimensionsCount() == 4) {
const TfLiteEvalTensor* paddings =
@@ -233,14 +233,14 @@
pad_value);
if (err != 0) return kTfLiteError;
} else {
-#endif // defined(HIFI4)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
reference_ops::Pad(data->params, tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<int16_t>(input),
&pad_value, tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int16_t>(output));
-#if defined(HIFI4)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
}
-#endif // defined(HIFI4)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
} break;
case kTfLiteInt32: {
int32_t pad_value =
diff --git a/tensorflow/lite/micro/kernels/xtensa/quantize.cc b/tensorflow/lite/micro/kernels/xtensa/quantize.cc
index e849108..06d4fbb 100644
--- a/tensorflow/lite/micro/kernels/xtensa/quantize.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/quantize.cc
@@ -29,7 +29,7 @@
namespace tflite {
namespace {
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
TfLiteStatus EvalXtensa(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
auto* op_data = static_cast<OpDataQuantizeReference*>(node->user_data);
@@ -75,12 +75,19 @@
case kTfLiteInt8: {
int size = ElementCount(*input->dims);
- reference_ops::Requantize(
- tflite::micro::GetTensorData<int8_t>(input), size,
- op_data->requantize_output_multiplier,
- op_data->requantize_output_shift, op_data->input_zero_point,
- op_data->quantization_params.zero_point,
- tflite::micro::GetTensorData<int8_t>(output));
+ int32_t zero_point = op_data->quantization_params.zero_point;
+ const int8_t* input_data_ptr;
+ int8_t* output_data_ptr;
+ input_data_ptr = tflite::micro::GetTensorData<int8_t>(input);
+ output_data_ptr = tflite::micro::GetTensorData<int8_t>(output);
+
+ TF_LITE_ENSURE_EQ(
+ context,
+ xa_nn_elm_requantize_asym8s_asym8s(
+ output_data_ptr, input_data_ptr, op_data->input_zero_point,
+ zero_point, op_data->requantize_output_shift,
+ op_data->requantize_output_multiplier, size),
+ 0);
break;
}
@@ -98,7 +105,6 @@
case kTfLiteInt32: {
int size = ElementCount(*input->dims);
int32_t zero_point = op_data->quantization_params.zero_point;
-#if defined(HIFI5)
const int8_t* input_data_ptr;
int32_t* output_data_ptr;
input_data_ptr = tflite::micro::GetTensorData<int8_t>(input);
@@ -111,13 +117,6 @@
zero_point, op_data->requantize_output_shift,
op_data->requantize_output_multiplier, size),
0);
-#else
- reference_ops::Requantize(
- tflite::micro::GetTensorData<int8_t>(input), size,
- op_data->requantize_output_multiplier,
- op_data->requantize_output_shift, op_data->input_zero_point,
- zero_point, tflite::micro::GetTensorData<int32_t>(output));
-#endif // defined(HIFI5)
break;
}
@@ -149,18 +148,20 @@
case kTfLiteInt16: {
int size = ElementCount(*input->dims);
- reference_ops::Requantize(
- tflite::micro::GetTensorData<int16_t>(input), size,
- op_data->requantize_output_multiplier,
- op_data->requantize_output_shift, op_data->input_zero_point,
- op_data->quantization_params.zero_point,
- tflite::micro::GetTensorData<int16_t>(output));
+ TF_LITE_ENSURE_EQ(context,
+ xa_nn_elm_requantize_asym16s_asym16s(
+ tflite::micro::GetTensorData<int16_t>(output),
+ tflite::micro::GetTensorData<int16_t>(input),
+ op_data->input_zero_point,
+ op_data->quantization_params.zero_point,
+ op_data->requantize_output_shift,
+ op_data->requantize_output_multiplier, size),
+ 0);
break;
}
case kTfLiteInt32: {
int size = ElementCount(*input->dims);
-#if defined(HIFI5)
TF_LITE_ENSURE_EQ(context,
xa_nn_elm_requantize_asym16s_asym32s(
tflite::micro::GetTensorData<int32_t>(output),
@@ -170,14 +171,6 @@
op_data->requantize_output_shift,
op_data->requantize_output_multiplier, size),
0);
-#else
- int32_t zero_point = op_data->quantization_params.zero_point;
- reference_ops::Requantize(
- tflite::micro::GetTensorData<int16_t>(input), size,
- op_data->requantize_output_multiplier,
- op_data->requantize_output_shift, op_data->input_zero_point,
- zero_point, tflite::micro::GetTensorData<int32_t>(output));
-#endif // defined(HIFI5)
break;
}
@@ -228,22 +221,56 @@
case kTfLiteFloat32: {
switch (output->type) {
case kTfLiteInt8: {
+#if HIFI_VFPU
+ int size = ElementCount(*input->dims);
+ int32_t zero_point = op_data->quantization_params.zero_point;
+ const float* input_data_ptr;
+ int8_t* output_data_ptr;
+ input_data_ptr = tflite::micro::GetTensorData<float>(input);
+ output_data_ptr = tflite::micro::GetTensorData<int8_t>(output);
+
+ TF_LITE_ENSURE_EQ(
+ context,
+ xa_nn_elm_quantize_f32_asym8s(
+ output_data_ptr, input_data_ptr,
+ static_cast<float>(op_data->quantization_params.scale),
+ zero_point, size),
+ 0);
+#else // #if HIFI_VFPU
reference_ops::AffineQuantize(
op_data->quantization_params,
tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<float>(input),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int8_t>(output));
+#endif // #if HIFI_VFPU
break;
}
case kTfLiteInt16: {
+#if HIFI_VFPU
+ int size = ElementCount(*input->dims);
+ int32_t zero_point = op_data->quantization_params.zero_point;
+ const float* input_data_ptr;
+ int16_t* output_data_ptr;
+ input_data_ptr = tflite::micro::GetTensorData<float>(input);
+ output_data_ptr = tflite::micro::GetTensorData<int16_t>(output);
+
+ TF_LITE_ENSURE_EQ(
+ context,
+ xa_nn_elm_quantize_f32_asym16s(
+ output_data_ptr, input_data_ptr,
+ static_cast<float>(op_data->quantization_params.scale),
+ zero_point, size),
+ 0);
+#else // #if HIFI_VFPU
reference_ops::AffineQuantize(
op_data->quantization_params,
tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<float>(input),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int16_t>(output));
+#endif // #if HIFI_VFPU
break;
}
@@ -267,7 +294,7 @@
return kTfLiteOk;
}
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
void* Init(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
@@ -301,11 +328,11 @@
}
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
return EvalXtensa(context, node);
#else
return EvalQuantizeReference(context, node);
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
}
} // namespace
diff --git a/tensorflow/lite/micro/kernels/xtensa/reduce_vision.cc b/tensorflow/lite/micro/kernels/xtensa/reduce_vision.cc
index c76525e..e539c29 100644
--- a/tensorflow/lite/micro/kernels/xtensa/reduce_vision.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/reduce_vision.cc
@@ -72,6 +72,7 @@
}
return true;
}
+
TfLiteStatus ReducePrepareVision(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
TFLITE_DCHECK(node->builtin_data != nullptr);
@@ -83,14 +84,14 @@
TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0);
TfLiteTensor* axis = micro_context->AllocateTempInputTensor(node, 1);
- uint32_t inputDims[4] = {1, 1, 1, 1};
- uint32_t outputDims[4] = {1, 1, 1, 1};
- uint32_t shouldReduceR[4] = {0, 0, 0, 0};
+ uint32_t input_dims[4] = {1, 1, 1, 1};
+ uint32_t output_dims[4] = {1, 1, 1, 1};
+ uint32_t should_reduce_r[4] = {0, 0, 0, 0};
int32_t resolved_axis[4] = {0, 0, 0, 0};
- OperandDims4D(inputDims, input);
- OperandDims4D(outputDims, output);
+ OperandDims4D(input_dims, input);
+ OperandDims4D(output_dims, output);
- uint32_t inputRank = NumDimensions(input);
+ const int input_rank = NumDimensions(input);
// Interpret an axis tensor with null dimensions as a scalar
int num_axis = static_cast<int>(ElementCount(*axis->dims));
// Resolve axis.
@@ -99,16 +100,22 @@
&num_resolved_axis)) {
return kTfLiteError;
}
- std::vector<bool> shouldReduce(inputRank);
- for (int32_t i = 0; i < num_axis; ++i) {
- int32_t axisD = resolved_axis[i];
- shouldReduce[axisD] = true;
+ // ResolveAxis should eliminate dupes and negative axis, so the number of axis
+ // should be no greater than the input rank.
+ TFLITE_DCHECK(num_resolved_axis <= input_rank);
+
+ bool should_reduce[4] = {false, false, false, false};
+
+ for (int32_t i = 0; i < num_resolved_axis; ++i) {
+ int32_t axis_d = resolved_axis[i];
+ should_reduce[axis_d] = true;
}
// reverse axes and align it to dimension 0 as OperandDims4D
- for (uint32_t axisI = 0; axisI < inputRank; ++axisI) {
- shouldReduceR[inputRank - 1 - axisI] = (uint32_t)shouldReduce[axisI];
+ for (int axis_i = 0; axis_i < input_rank; ++axis_i) {
+ should_reduce_r[input_rank - 1 - axis_i] =
+ static_cast<uint32_t>(should_reduce[axis_i]);
}
uint32_t context_size = 0;
@@ -123,8 +130,8 @@
data->context_size = context_size;
}
- status = xiReduceSetContext(data->p_context, data->context_size, inputDims,
- outputDims, shouldReduceR);
+ status = xiReduceSetContext(data->p_context, data->context_size, input_dims,
+ output_dims, should_reduce_r);
if (status) {
return kTfLiteError;
diff --git a/tensorflow/lite/micro/kernels/xtensa/softmax.cc b/tensorflow/lite/micro/kernels/xtensa/softmax.cc
index 76c380f..c248fc5 100644
--- a/tensorflow/lite/micro/kernels/xtensa/softmax.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/softmax.cc
@@ -31,7 +31,7 @@
namespace tflite {
namespace {
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
TfLiteStatus EvalHifiInt8(const XtensaSoftmaxOpData* op_data,
const TfLiteEvalTensor* input,
TfLiteEvalTensor* output, TfLiteContext* context) {
@@ -56,7 +56,7 @@
}
return kTfLiteOk;
}
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
@@ -68,7 +68,7 @@
TFLITE_DCHECK(node->user_data != nullptr);
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
XtensaSoftmaxOpData op_data =
*static_cast<XtensaSoftmaxOpData*>(node->user_data);
SoftmaxParams params = op_data.params;
@@ -77,7 +77,7 @@
#endif
if (input->type == kTfLiteInt8 && output->type == kTfLiteInt8) {
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
return EvalHifiInt8(static_cast<XtensaSoftmaxOpData*>(node->user_data),
input, output, context);
#elif defined(VISION_P6)
@@ -91,7 +91,7 @@
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int8_t>(output));
return kTfLiteOk;
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
}
if (input->type == kTfLiteInt16 && output->type == kTfLiteInt16) {
diff --git a/tensorflow/lite/micro/kernels/xtensa/softmax_int8_int16.cc b/tensorflow/lite/micro/kernels/xtensa/softmax_int8_int16.cc
index b23a9f7..d37a2f5 100644
--- a/tensorflow/lite/micro/kernels/xtensa/softmax_int8_int16.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/softmax_int8_int16.cc
@@ -30,7 +30,7 @@
namespace tflite {
namespace {
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
TfLiteStatus PrepareHifi(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_OK(context, SoftmaxPrepare(context, node));
@@ -86,13 +86,13 @@
}
return kTfLiteOk;
}
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
} // namespace
void* XtensaInitSoftmax(TfLiteContext* context, const char* buffer,
size_t length) {
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context,
sizeof(XtensaSoftmaxOpData));
@@ -105,11 +105,11 @@
sizeof(XtensaSoftmaxOpData));
#else
return SoftmaxInit(context, buffer, length);
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
}
TfLiteStatus XtensaPrepareSoftmax(TfLiteContext* context, TfLiteNode* node) {
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
return PrepareHifi(context, node);
#else
TF_LITE_ENSURE_OK(context, SoftmaxPrepare(context, node));
@@ -117,7 +117,7 @@
TF_LITE_ENSURE_OK(context, SoftmaxPrepareVision(context, node));
#endif
return kTfLiteOk;
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
}
TfLiteStatus XtensaEvalSoftmaxInt8Int16(TfLiteContext* context,
@@ -127,7 +127,7 @@
TFLITE_DCHECK(node->user_data != nullptr);
if (input->type == kTfLiteInt8 && output->type == kTfLiteInt16) {
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
return EvalHifi(static_cast<XtensaSoftmaxOpData*>(node->user_data), input,
output, context);
#else
@@ -138,7 +138,7 @@
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int16_t>(output));
return kTfLiteOk;
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
} else {
MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
input->type);
diff --git a/tensorflow/lite/micro/kernels/xtensa/strided_slice.cc b/tensorflow/lite/micro/kernels/xtensa/strided_slice.cc
index 0440cfc..8ebf724 100644
--- a/tensorflow/lite/micro/kernels/xtensa/strided_slice.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/strided_slice.cc
@@ -23,129 +23,14 @@
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/kernels/op_macros.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/strided_slice.h"
#include "tensorflow/lite/micro/kernels/xtensa/xtensa.h"
#include "tensorflow/lite/micro/micro_log.h"
namespace tflite {
namespace {
-constexpr int kInputTensor = 0;
-constexpr int kBeginTensor = 1;
-constexpr int kEndTensor = 2;
-constexpr int kStridesTensor = 3;
-constexpr int kOutputTensor = 0;
-
-struct StridedSliceContext {
- StridedSliceContext(TfLiteContext* context, TfLiteNode* node) {
- params = reinterpret_cast<TfLiteStridedSliceParams*>(node->builtin_data);
- micro_context = GetMicroContext(context);
- input = micro_context->AllocateTempInputTensor(node, kInputTensor);
- begin = micro_context->AllocateTempInputTensor(node, kBeginTensor);
- end = micro_context->AllocateTempInputTensor(node, kEndTensor);
- strides = micro_context->AllocateTempInputTensor(node, kStridesTensor);
- output = micro_context->AllocateTempOutputTensor(node, kOutputTensor);
- dims = NumDimensions(input);
- }
- ~StridedSliceContext() {
- micro_context->DeallocateTempTfLiteTensor(input);
- micro_context->DeallocateTempTfLiteTensor(begin);
- micro_context->DeallocateTempTfLiteTensor(end);
- micro_context->DeallocateTempTfLiteTensor(strides);
- micro_context->DeallocateTempTfLiteTensor(output);
- }
- const TfLiteStridedSliceParams* params;
- MicroContext* micro_context;
- TfLiteTensor* input;
- TfLiteTensor* begin;
- TfLiteTensor* end;
- TfLiteTensor* strides;
- TfLiteTensor* output;
- int dims;
-};
-
-// This Op only supports 1-4D cases and since we use the reference 4D
-// implementation, the 1-3D tensors are mapped to 4D.
-const int kMaxDim = 4;
-
-tflite::StridedSliceParams BuildStridedSliceParams(
- StridedSliceContext* op_context) {
- tflite::StridedSliceParams op_params;
- op_params.start_indices_count = op_context->dims;
- op_params.stop_indices_count = op_context->dims;
- op_params.strides_count = op_context->dims;
-
- for (int i = 0; i < op_context->dims; ++i) {
- op_params.start_indices[i] = GetTensorData<int32_t>(op_context->begin)[i];
- op_params.stop_indices[i] = GetTensorData<int32_t>(op_context->end)[i];
- op_params.strides[i] = GetTensorData<int32_t>(op_context->strides)[i];
- }
-
- op_params.begin_mask = op_context->params->begin_mask;
- op_params.ellipsis_mask = 0;
- op_params.end_mask = op_context->params->end_mask;
- op_params.new_axis_mask = 0;
- op_params.shrink_axis_mask = op_context->params->shrink_axis_mask;
- return op_params;
-}
-
-// Processes the indexing tensors (begin, end and strides) to resize the
-// output tensor. This function is callable from both Prepare() and Eval() as
-// long as the caller ensures the indexing tensors are present.
-TfLiteStatus CheckOutputSize(TfLiteContext* context,
- StridedSliceContext* op_context) {
- using ::tflite::strided_slice::StartForAxis;
- using ::tflite::strided_slice::StopForAxis;
- TfLiteIntArray* output_shape = op_context->output->dims;
- int shape_size = 0;
- auto op_params = BuildStridedSliceParams(op_context);
- auto input_shape = GetTensorShape(op_context->input);
- for (int idx = 0; idx < op_context->dims; ++idx) {
- int32_t stride = GetTensorData<int32_t>(op_context->strides)[idx];
- TF_LITE_ENSURE_MSG(context, stride != 0, "stride value has to be non-zero");
- int32_t begin = StartForAxis(op_params, input_shape, idx);
- int32_t end = StopForAxis(op_params, input_shape, idx, begin);
-
- // When shrinking an axis, the end position does not matter (and can be
- // incorrect when negative indexing is used, see Issue #19260). Always use
- // begin + 1 to generate a length 1 slice, since begin has
- // already been adjusted for negative indices by StartForAxis.
- const bool shrink_axis = op_context->params->shrink_axis_mask & (1 << idx);
- if (shrink_axis) {
- end = begin + 1;
- }
-
- // This is valid for both positive and negative strides
- int32_t dim_shape = std::ceil((end - begin) / static_cast<float>(stride));
- dim_shape = dim_shape < 0 ? 0 : dim_shape;
- if (!shrink_axis) {
- TF_LITE_ENSURE_EQ(context, output_shape->data[shape_size], dim_shape);
- shape_size++;
- }
- }
- TF_LITE_ENSURE_EQ(context, output_shape->size, shape_size);
- return kTfLiteOk;
-}
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
- TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
- return context->AllocatePersistentBuffer(context, sizeof(StridedSliceParams));
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
- TFLITE_DCHECK(node->user_data != nullptr);
- StridedSliceParams* op_params =
- static_cast<StridedSliceParams*>(node->user_data);
- TF_LITE_ENSURE_EQ(context, NumInputs(node), 4);
- TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
- StridedSliceContext op_context(context, node);
- TF_LITE_ENSURE_MSG(context, op_context.dims <= kMaxDim,
- "input dim should not exceed 4");
- auto params = BuildStridedSliceParams(&op_context);
- memcpy(op_params, ¶ms, sizeof(StridedSliceParams));
- return CheckOutputSize(context, &op_context);
-}
-
-#if defined(HIFI4)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
void StridedSlice_int16_hifi4opt(const tflite::StridedSliceParams& op_params,
const RuntimeShape& unextended_input_shape,
const int16_t* input_data,
@@ -192,7 +77,7 @@
input_shape.Dims(1), input_shape.Dims(2),
input_shape.Dims(3), input_shape.Dims(4));
}
-#endif // defined(HIFI4)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
@@ -200,9 +85,9 @@
*(static_cast<const StridedSliceParams*>(node->user_data));
const TfLiteEvalTensor* input =
- tflite::micro::GetEvalInput(context, node, kInputTensor);
+ tflite::micro::GetEvalInput(context, node, kStridedSliceInputTensor);
TfLiteEvalTensor* output =
- tflite::micro::GetEvalOutput(context, node, kOutputTensor);
+ tflite::micro::GetEvalOutput(context, node, kStridedSliceOutputTensor);
switch (output->type) {
case kTfLiteFloat32:
reference_ops::StridedSlice(op_params,
@@ -219,7 +104,7 @@
tflite::micro::GetTensorData<int8_t>(output));
break;
case kTfLiteInt16:
-#if defined(HIFI4)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
StridedSlice_int16_hifi4opt(
op_params, tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<int16_t>(input),
@@ -231,7 +116,7 @@
tflite::micro::GetTensorData<int16_t>(input),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int16_t>(output));
-#endif // defined(HIFI4)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
break;
case kTfLiteInt32:
reference_ops::StridedSlice(
@@ -257,7 +142,7 @@
} // namespace
TFLMRegistration Register_STRIDED_SLICE() {
- return tflite::micro::RegisterOp(Init, Prepare, Eval);
+ return tflite::micro::RegisterOp(StridedSliceInit, StridedSlicePrepare, Eval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/xtensa/sub.cc b/tensorflow/lite/micro/kernels/xtensa/sub.cc
index c4f0984..b8308c9 100644
--- a/tensorflow/lite/micro/kernels/xtensa/sub.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/sub.cc
@@ -83,15 +83,15 @@
&op_params);
// TODO(b/259724572): vision_p6 and hifi code path is getting very confusing.
// Let's separate them into two different files.
-#if !(defined(HIFI4))
+#if !(defined(HIFI3) || defined(HIFI4) || defined(HIFI5))
bool need_broadcast = reference_ops::ProcessBroadcastShapes(
tflite::micro::GetTensorShape(input1),
tflite::micro::GetTensorShape(input2), &op_params);
-#endif // !(defined(HIFI4))
+#endif // !(defined(HIFI3) || defined(HIFI4))
switch (output->type) {
case kTfLiteInt8: {
-#if defined(HIFI4)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
int err;
const RuntimeShape extended_input1_shape =
RuntimeShape::ExtendedShape(5, tflite::micro::GetTensorShape(input1));
@@ -105,7 +105,6 @@
// TODO(b/259724572): Refactor the following block of code.
int b;
int inp1_off = 0;
- int inp2_off = 0;
int out_off;
out_off =
output_dims[1] * output_dims[2] * output_dims[3] * output_dims[4];
@@ -113,10 +112,6 @@
inp1_off =
input1_dims[1] * input1_dims[2] * input1_dims[3] * input1_dims[4];
}
- if (input2_dims[0] > 1) {
- inp2_off =
- input2_dims[1] * input2_dims[2] * input2_dims[3] * input2_dims[4];
- }
for (b = 0; b < output_dims[0]; b++) {
err = xa_nn_elm_sub_broadcast_4D_asym8sxasym8s_asym8s(
@@ -133,7 +128,7 @@
TF_LITE_ENSURE(context, err == 0);
}
-#else // defined(HIFI4)
+#else // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
if (need_broadcast) {
tflite::reference_ops::BroadcastQuantSubSlow(
op_params, tflite::micro::GetTensorShape(input1),
@@ -151,11 +146,11 @@
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int8_t>(output));
}
-#endif // defined(HIFI4)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
break;
}
case kTfLiteInt16: {
-#if defined(HIFI4)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
int err;
const RuntimeShape extended_input1_shape =
RuntimeShape::ExtendedShape(5, tflite::micro::GetTensorShape(input1));
@@ -168,7 +163,6 @@
const int* output_dims = extended_output_shape.DimsData();
int b;
int inp1_off = 0;
- int inp2_off = 0;
int out_off;
out_off =
output_dims[1] * output_dims[2] * output_dims[3] * output_dims[4];
@@ -176,10 +170,6 @@
inp1_off =
input1_dims[1] * input1_dims[2] * input1_dims[3] * input1_dims[4];
}
- if (input2_dims[0] > 1) {
- inp2_off =
- input2_dims[1] * input2_dims[2] * input2_dims[3] * input2_dims[4];
- }
for (b = 0; b < output_dims[0]; b++) {
err = xa_nn_elm_sub_broadcast_4D_asym16sxasym16s_asym16s(
@@ -196,7 +186,7 @@
TF_LITE_ENSURE(context, err == 0);
}
-#else // defined(HIFI4)
+#else // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
if (need_broadcast) {
tflite::reference_ops::BroadcastQuantSubSlow(
op_params, tflite::micro::GetTensorShape(input1),
@@ -214,7 +204,7 @@
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int16_t>(output));
}
-#endif // defined(HIFI4)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
break;
}
default:
@@ -256,4 +246,4 @@
return tflite::micro::RegisterOp(SubInit, SubPrepare, SubEval);
}
-} // namespace tflite
\ No newline at end of file
+} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/xtensa/svdf.cc b/tensorflow/lite/micro/kernels/xtensa/svdf.cc
index c1dac3b..da34e09 100644
--- a/tensorflow/lite/micro/kernels/xtensa/svdf.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/svdf.cc
@@ -33,7 +33,7 @@
namespace tflite {
namespace {
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
TfLiteStatus EvalIntegerSvdfHifi(TfLiteContext* context, TfLiteNode* node,
const TfLiteEvalTensor* input_tensor,
@@ -63,7 +63,7 @@
#if defined(HIFI5)
memcpy(state_ptr, state_ptr + 1, num_bytes);
#else
- xa_nn_memmove_16(state_ptr, state_ptr + 1, num_bytes);
+ xa_nn_memmove_16(state_ptr, state_ptr + 1, (num_bytes >> 1));
#endif // defined(HIFI5)
// Note: no need to clear the latest activation, matmul is not accumulative.
@@ -108,7 +108,7 @@
}
return kTfLiteOk;
}
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
void* Init(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context != nullptr);
@@ -116,7 +116,7 @@
}
TfLiteStatus PrepareInt8(TfLiteContext* context, TfLiteNode* node) {
-#if defined(HIFIMINI) || defined(HIFI4) || defined(HIFI5)
+#if defined(HIFIMINI) || defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
TFLITE_DCHECK(node->builtin_data != nullptr);
const auto* params = static_cast<const TfLiteSVDFParams*>(node->builtin_data);
@@ -252,11 +252,12 @@
return kTfLiteOk;
#else
return PrepareSvdf(context, node);
-#endif // defined(HIFIMINI) || defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFIMINI) || defined(HIFI3) || defined(HIFI4) ||
+ // defined(HIFI5)
}
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-#if defined(HIFIMINI) || defined(HIFI4) || defined(HIFI5)
+#if defined(HIFIMINI) || defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
MicroContext* micro_context = GetMicroContext(context);
TfLiteTensor* input =
@@ -277,7 +278,8 @@
return status;
#else
return PrepareSvdf(context, node);
-#endif // defined(HIFIMINI) || defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFIMINI) || defined(HIFI3) || defined(HIFI4) ||
+ // defined(HIFI5)
}
TfLiteStatus EvalInt8(TfLiteContext* context, TfLiteNode* node) {
@@ -306,7 +308,7 @@
return EvalIntegerSvdfHifimini(context, node, input, weights_feature,
weights_time, bias, params, activation_state,
output, data);
-#elif defined(HIFI4) || defined(HIFI5)
+#elif defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
return EvalIntegerSvdfHifi(context, node, input, weights_feature,
weights_time, bias, params, activation_state,
output, data);
@@ -314,7 +316,7 @@
EvalInt16SvdfReference(context, node, input, weights_feature, weights_time,
bias, params, activation_state, output, data);
return kTfLiteOk;
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
}
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
diff --git a/tensorflow/lite/micro/kernels/xtensa/transpose_conv.cc b/tensorflow/lite/micro/kernels/xtensa/transpose_conv.cc
index 826e168..44a9f86 100644
--- a/tensorflow/lite/micro/kernels/xtensa/transpose_conv.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/transpose_conv.cc
@@ -183,19 +183,57 @@
// Quantized kernels use an int32 scratch buffer.
if (input->type == kTfLiteInt8) {
TFLITE_DCHECK(context->RequestScratchBufferInArena != nullptr);
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+ const int stride_width = params->stride_width;
+ const int stride_height = params->stride_height;
+
+ const int input_height = SizeOfDimension(input, 1);
+ const int input_width = SizeOfDimension(input, 2);
+ const int input_depth = SizeOfDimension(input, 3);
+ const int output_height = height;
+ const int output_width = width;
+ int32_t scratch_buffer_size = 0;
+ scratch_buffer_size = xa_nn_transpose_conv_getsize(
+ input_height, input_width, input_depth, filter_height, filter_width,
+ stride_width, stride_height, output_height, output_width, num_channels,
+ PREC_SYM8S, PREC_ASYM8S);
+ TFLITE_DCHECK(context->RequestScratchBufferInArena(
+ context, scratch_buffer_size,
+ &(data->scratch_buffer_index)) == kTfLiteOk);
+#else // #if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
TFLITE_DCHECK(context->RequestScratchBufferInArena(
context,
GetTensorShape(output).FlatSize() * sizeof(int32_t),
&(data->scratch_buffer_index)) == kTfLiteOk);
+#endif
}
// Quantized 16x8 kernels use an int64 scratch buffer.
if (input->type == kTfLiteInt16) {
TFLITE_DCHECK(context->RequestScratchBufferInArena != nullptr);
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+ const int stride_width = params->stride_width;
+ const int stride_height = params->stride_height;
+
+ const int input_height = SizeOfDimension(input, 1);
+ const int input_width = SizeOfDimension(input, 2);
+ const int input_depth = SizeOfDimension(input, 3);
+ const int output_height = height;
+ const int output_width = width;
+ int32_t scratch_buffer_size = 0;
+ scratch_buffer_size = xa_nn_transpose_conv_getsize(
+ input_height, input_width, input_depth, filter_height, filter_width,
+ stride_width, stride_height, output_height, output_width, num_channels,
+ PREC_SYM8S, PREC_SYM16S);
+ TFLITE_DCHECK(context->RequestScratchBufferInArena(
+ context, scratch_buffer_size,
+ &(data->scratch_buffer_index)) == kTfLiteOk);
+#else // #if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
TFLITE_DCHECK(context->RequestScratchBufferInArena(
context,
GetTensorShape(output).FlatSize() * sizeof(std::int64_t),
&(data->scratch_buffer_index)) == kTfLiteOk);
+#endif // #if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
}
// All per-channel quantized tensors need valid zero point and scale arrays.
@@ -282,6 +320,63 @@
case kTfLiteInt8: {
int32_t* scratch_buffer = static_cast<int32_t*>(
context->GetScratchBuffer(context, data.scratch_buffer_index));
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+ if (bias->type == kTfLiteInt32) {
+ const RuntimeShape& input_shape = tflite::micro::GetTensorShape(input);
+ const RuntimeShape& filter_shape =
+ tflite::micro::GetTensorShape(filter);
+ const RuntimeShape& output_shape =
+ tflite::micro::GetTensorShape(output);
+ const int stride_width = data.params.stride_width;
+ const int stride_height = data.params.stride_height;
+ const int pad_width = data.params.padding_values.width;
+ const int pad_height = data.params.padding_values.height;
+
+ const int batches = MatchingDim(input_shape, 0, output_shape, 0);
+ const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3);
+ const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3);
+
+ const int input_height = input_shape.Dims(1);
+ const int input_width = input_shape.Dims(2);
+ const int filter_height = filter_shape.Dims(1);
+ const int filter_width = filter_shape.Dims(2);
+ const int output_height = output_shape.Dims(1);
+ const int output_width = output_shape.Dims(2);
+ const int8_t* input_data = tflite::micro::GetTensorData<int8_t>(input);
+ const int8_t* filter_data =
+ tflite::micro::GetTensorData<int8_t>(filter);
+ const int32_t* bias_data = tflite::micro::GetTensorData<int32_t>(bias);
+ int8_t* output_data = tflite::micro::GetTensorData<int8_t>(output);
+
+ const int num_elements = output_shape.FlatSize();
+
+ for (int b = 0; b < batches; b++) {
+ xa_nn_transpose_conv_sym8sxasym8s(
+ &output_data[b * output_height * output_width * output_depth],
+ const_cast<WORD8*>(
+ &input_data[b * input_height * input_width * input_depth]),
+ const_cast<WORD8*>(filter_data), const_cast<WORD32*>(bias_data),
+ stride_width, stride_height, pad_width, pad_height, input_depth,
+ output_depth, input_height, input_width, filter_height,
+ filter_width, output_height, output_width, num_elements / batches,
+ data.params.input_offset, data.params.output_offset,
+ data.per_channel_output_shift, data.per_channel_output_multiplier,
+ scratch_buffer);
+ }
+ } else {
+ reference_integer_ops::TransposeConv(
+ data.params, data.per_channel_output_multiplier,
+ data.per_channel_output_shift, tflite::micro::GetTensorShape(input),
+ tflite::micro::GetTensorData<int8_t>(input),
+ tflite::micro::GetTensorShape(filter),
+ tflite::micro::GetTensorData<int8_t>(filter),
+ tflite::micro::GetTensorShape(bias),
+ tflite::micro::GetTensorData<int32_t>(bias),
+ tflite::micro::GetTensorShape(output),
+ tflite::micro::GetTensorData<int8_t>(output),
+ tflite::micro::GetTensorShape(nullptr), nullptr, scratch_buffer);
+ }
+#else
reference_integer_ops::TransposeConv(
data.params, data.per_channel_output_multiplier,
data.per_channel_output_shift, tflite::micro::GetTensorShape(input),
@@ -293,6 +388,7 @@
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int8_t>(output),
tflite::micro::GetTensorShape(nullptr), nullptr, scratch_buffer);
+#endif
break;
}
case kTfLiteInt16: {
@@ -319,7 +415,7 @@
tflite::micro::GetTensorData<int16_t>(output),
tflite::micro::GetTensorShape(nullptr), nullptr, scratch_buffer);
} else {
-#if defined(HIFI4)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
const RuntimeShape& input_shape = tflite::micro::GetTensorShape(input);
const RuntimeShape& filter_shape =
tflite::micro::GetTensorShape(filter);
@@ -359,9 +455,9 @@
output_depth, input_height, input_width, filter_height,
filter_width, output_height, output_width, num_elements / batches,
data.per_channel_output_shift, data.per_channel_output_multiplier,
- &scratch_buffer[b * output_height * output_width * output_depth]);
+ scratch_buffer);
}
-#else
+#else // #if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
reference_integer_ops::TransposeConv(
data.params, data.per_channel_output_multiplier,
data.per_channel_output_shift, tflite::micro::GetTensorShape(input),
@@ -373,7 +469,7 @@
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int16_t>(output),
tflite::micro::GetTensorShape(nullptr), nullptr, scratch_buffer);
-#endif // defined(HIFI4)
+#endif // #if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
}
break;
}
diff --git a/tensorflow/lite/micro/kernels/xtensa/unidirectional_sequence_lstm.cc b/tensorflow/lite/micro/kernels/xtensa/unidirectional_sequence_lstm.cc
index cbce1e1..0f6a02e 100644
--- a/tensorflow/lite/micro/kernels/xtensa/unidirectional_sequence_lstm.cc
+++ b/tensorflow/lite/micro/kernels/xtensa/unidirectional_sequence_lstm.cc
@@ -1,4 +1,4 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -13,1109 +13,156 @@
limitations under the License.
==============================================================================*/
-#include <math.h>
-#include <stdio.h>
+// Integer version of unidirectional sequence lstm. Only the standard LSTM
+// (defined in the keras LSTM layer, e.g., no peephole etc.) is supported here.
+// Currently used by the 16 bits activation case only
-#include <cstddef>
+#include <algorithm>
+#include <limits>
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/fully_connected.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/lstm_shared.h"
#include "tensorflow/lite/micro/kernels/xtensa/lstm_eval.h"
-#include "tensorflow/lite/micro/kernels/xtensa/lstm_shared.h"
-#include "tensorflow/lite/micro/micro_log.h"
-// TODO(b/230666079): Flatten the namespace to match the builtin kernel
-// implementation
namespace tflite {
-namespace ops {
-namespace micro {
-// namespace unidirectional_sequence_lstm {
+
namespace {
+/*Helper Functions*/
-struct OpData {
- // If the lstm is layer norm.
- bool use_layer_norm;
- // The scratch tensor index.
- int scratch_tensor_index;
- bool compute_row_sums = false;
+/*Kernel functions*/
- lstm_eval::IntegerLstmParameter integer_lstm_param;
-};
+void* UnidirectionalSequenceLstmInit(TfLiteContext* context, const char* buffer,
+ size_t length) {
+ TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
+ return context->AllocatePersistentBuffer(context, sizeof(OpDataLSTM));
+}
-TfLiteStatus PopulateQuantizedLstmParams8x8_16(
- TfLiteContext* context, TfLiteNode* node,
- lstm_eval::IntegerLstmParameter* integer_lstm_param) {
- // Calculate quantized clip for projection and cell.
- const auto* params =
+TfLiteStatus UnidirectionalSequenceLstmPrepare(TfLiteContext* context,
+ TfLiteNode* node) {
+ TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);
+ TF_LITE_ENSURE_EQ(context, node->inputs->size, 24);
+
+ TFLITE_DCHECK(node->builtin_data != nullptr);
+ TFLITE_DCHECK(node->user_data != nullptr);
+
+ OpDataLSTM* op_data = reinterpret_cast<OpDataLSTM*>(node->user_data);
+ const auto* builtin_data =
static_cast<TfLiteUnidirectionalSequenceLSTMParams*>(node->builtin_data);
- const float cell_clip = static_cast<float>(params->cell_clip);
- const float proj_clip = static_cast<float>(params->proj_clip);
+ // All TempTfLiteTensors will be deallocated through the destructor.
+ LstmTensors lstm_tensors(context, node);
+ TF_LITE_ENSURE_OK(context, lstm_tensors.ValidateTensorStatus(context));
- const TfLiteTensor* cell_state =
- GetVariableInput(context, node, micro::lstm::full::kCellStateTensor);
- TF_LITE_ENSURE(context, cell_state != nullptr);
- TfLiteTensor* output_tensor;
+ op_data->cell_gate_nonlinear_type = builtin_data->activation;
+ op_data->size_info =
+ CreateLstmSizeInfo(builtin_data->time_major,
+ lstm_tensors.GetInternalTensor(kLstmInputTensor)->dims,
+ lstm_tensors.HiddenStateTensor()->dims);
TF_LITE_ENSURE_OK(
- context, GetOutputSafe(context, node, micro::lstm::full::kOutputTensor,
- &output_tensor));
+ context, ValidateTensorSize(context, lstm_tensors, op_data->size_info));
- auto* cell_state_params =
- static_cast<TfLiteAffineQuantization*>(cell_state->quantization.params);
- auto* proj_params = static_cast<TfLiteAffineQuantization*>(
- output_tensor->quantization.params);
- if (cell_clip > static_cast<float>(0.0)) {
- integer_lstm_param->quantized_cell_clip = static_cast<int16_t>(std::min(
- std::max(cell_clip / cell_state_params->scale->data[0], -32768.0f),
- 32767.0f));
+ // Create cell state information and gate parameters (Fully Connected and Mul)
+ auto cell_state_type =
+ lstm_tensors.GetInternalTensor(kLstmCellStateTensor)->type;
+ if (cell_state_type == kTfLiteFloat32) {
+ op_data->cell_state_info =
+ CreateLstmCellStateInfoFloat(builtin_data->cell_clip);
+ TF_LITE_ENSURE_OK(
+ context, PrepareGateParametersFloat(context, lstm_tensors, op_data));
+ } else if (cell_state_type == kTfLiteInt16) {
+ op_data->cell_state_info = CreateLstmCellStateInfo(
+ lstm_tensors.CellStateTensor()->params.scale, builtin_data->cell_clip);
+ TF_LITE_ENSURE_OK(
+ context, PrepareGateParametersInteger(context, lstm_tensors, op_data));
} else {
- integer_lstm_param->quantized_cell_clip = 0;
+ MicroPrintf(
+ "Cell state type %s (%d) not supported. The quantized Unidirectional "
+ "Sequence LSTM Op only support int16 cell state",
+ TfLiteTypeGetName(cell_state_type), cell_state_type);
+ return kTfLiteError;
}
- if (proj_clip > static_cast<float>(0.0)) {
- integer_lstm_param->quantized_proj_clip = static_cast<int8_t>(std::min(
- std::max(proj_clip / proj_params->scale->data[0], -128.0f), 127.0f));
- } else {
- integer_lstm_param->quantized_proj_clip = 0;
+ // request buffers (four buffers)
+ for (size_t i = 0; i < 4; i++) {
+ TF_LITE_ENSURE_OK(context, context->RequestScratchBufferInArena(
+ context,
+ op_data->size_info.batch_size *
+ op_data->size_info.state_dimension *
+ TfLiteTypeGetSize(cell_state_type),
+ &(op_data->buffer_indices[i])));
}
+ return kTfLiteOk;
+}
- // Calculate effective scales.
- OpData* op_data = static_cast<OpData*>(node->user_data);
- const bool use_layer_norm = op_data->use_layer_norm;
+TfLiteStatus UnidirectionalSequenceLstmEval(TfLiteContext* context,
+ TfLiteNode* node) {
+ TFLITE_DCHECK(node->user_data != nullptr);
+ const OpDataLSTM& op_data = *reinterpret_cast<OpDataLSTM*>(node->user_data);
+ auto kernel_content = CreateLSTMKernelContent(context, node);
- const TfLiteTensor* input;
- TF_LITE_ENSURE_OK(
- context,
- GetInputSafe(context, node, micro::lstm::full::kInputTensor, &input));
+ const auto activation_type =
+ kernel_content.internal_tensors[kLstmInputTensor]->type;
+ const auto weight_type =
+ kernel_content.internal_tensors[kLstmInputToInputWeightsTensor]->type;
- const TfLiteTensor* input_to_input_weights = GetOptionalInputTensor(
- context, node, micro::lstm::full::kInputToInputWeightsTensor);
- const TfLiteTensor* input_to_forget_weights;
- TF_LITE_ENSURE_OK(context,
- GetInputSafe(context, node,
- micro::lstm::full::kInputToForgetWeightsTensor,
- &input_to_forget_weights));
- const TfLiteTensor* input_to_cell_weights;
- TF_LITE_ENSURE_OK(
- context,
- GetInputSafe(context, node, micro::lstm::full::kInputToCellWeightsTensor,
- &input_to_cell_weights));
- const TfLiteTensor* input_to_output_weights;
- TF_LITE_ENSURE_OK(context,
- GetInputSafe(context, node,
- micro::lstm::full::kInputToOutputWeightsTensor,
- &input_to_output_weights));
-
- const TfLiteTensor* recurrent_to_input_weights = GetOptionalInputTensor(
- context, node, micro::lstm::full::kRecurrentToInputWeightsTensor);
- const TfLiteTensor* recurrent_to_forget_weights;
- TF_LITE_ENSURE_OK(
- context, GetInputSafe(context, node,
- micro::lstm::full::kRecurrentToForgetWeightsTensor,
- &recurrent_to_forget_weights));
- const TfLiteTensor* recurrent_to_cell_weights;
- TF_LITE_ENSURE_OK(
- context, GetInputSafe(context, node,
- micro::lstm::full::kRecurrentToCellWeightsTensor,
- &recurrent_to_cell_weights));
- const TfLiteTensor* recurrent_to_output_weights;
- TF_LITE_ENSURE_OK(
- context, GetInputSafe(context, node,
- micro::lstm::full::kRecurrentToOutputWeightsTensor,
- &recurrent_to_output_weights));
-
- const TfLiteTensor* cell_to_input_weights = GetOptionalInputTensor(
- context, node, micro::lstm::full::kCellToInputWeightsTensor);
- const TfLiteTensor* cell_to_forget_weights = GetOptionalInputTensor(
- context, node, micro::lstm::full::kCellToForgetWeightsTensor);
- const TfLiteTensor* cell_to_output_weights = GetOptionalInputTensor(
- context, node, micro::lstm::full::kCellToOutputWeightsTensor);
-
- const TfLiteTensor* input_layer_norm_coefficients = GetOptionalInputTensor(
- context, node, micro::lstm::full::kInputLayerNormCoefficientsTensor);
- const TfLiteTensor* forget_layer_norm_coefficients = GetOptionalInputTensor(
- context, node, micro::lstm::full::kForgetLayerNormCoefficientsTensor);
- const TfLiteTensor* cell_layer_norm_coefficients = GetOptionalInputTensor(
- context, node, micro::lstm::full::kCellLayerNormCoefficientsTensor);
- const TfLiteTensor* output_layer_norm_coefficients = GetOptionalInputTensor(
- context, node, micro::lstm::full::kOutputLayerNormCoefficientsTensor);
-
- const TfLiteTensor* projection_weights = GetOptionalInputTensor(
- context, node, micro::lstm::full::kProjectionWeightsTensor);
-
- TfLiteTensor* output_state =
- GetVariableInput(context, node, micro::lstm::full::kOutputStateTensor);
- TF_LITE_ENSURE(context, output_state != nullptr);
-
- // Since we have already checked that weights are all there or none, we can
- // check the existence of only one to get the condition.
- const bool use_cifg = (input_to_input_weights == nullptr);
- const bool use_peephole = (cell_to_output_weights != nullptr);
- const bool use_projection = (projection_weights != nullptr);
-
- // Get intermediate scales and zero points.
- constexpr size_t kIntermediateCount = 5;
- float intermediate_scale[kIntermediateCount];
- int32_t intermediate_zp[kIntermediateCount];
- for (int i = 0; i < 4; ++i) {
- if (use_layer_norm) {
- TfLiteTensor* intermediate =
- context->GetTensor(context, node->intermediates->data[i]);
- auto* tmp_params = static_cast<TfLiteAffineQuantization*>(
- intermediate->quantization.params);
- intermediate_scale[i] = tmp_params->scale->data[0];
- intermediate_zp[i] = tmp_params->zero_point->data[0];
- } else {
- // Q3.12 for activation functions.
- intermediate_scale[i] = std::pow(2, -12);
- intermediate_zp[i] = 0;
+ switch (activation_type) {
+ case kTfLiteFloat32: {
+ LSTMBuffers<float> buffers =
+ CreateLSTMBuffers<float>(context, op_data.buffer_indices);
+ EvalLstm<float, float, float, float>(op_data, kernel_content, buffers);
+ break;
+ }
+ case kTfLiteInt8: {
+ switch (weight_type) {
+ case kTfLiteInt8: {
+ // 8(activation)x8(weight)->16(cell) LSTM with 32 bits bias
+ LSTMBuffers<int16_t> buffers =
+ CreateLSTMBuffers<int16_t>(context, op_data.buffer_indices);
+ EvalLstm<int8_t, int8_t, int16_t, int32_t>(op_data, kernel_content,
+ buffers);
+ break;
+ }
+ default: {
+ MicroPrintf("Filter type %s (%d) not supported.",
+ TfLiteTypeGetName(weight_type), activation_type);
+ return kTfLiteError;
+ }
+ }
+ break;
+ }
+ case kTfLiteInt16: {
+ switch (weight_type) {
+ case kTfLiteInt8: {
+ // 16(activation)x8(weight)->16(cell) LSTM with 64 bits bias
+ LSTMBuffers<int16_t> buffers =
+ CreateLSTMBuffers<int16_t>(context, op_data.buffer_indices);
+ EvalLstm<int16_t, int8_t, int16_t, int64_t>(op_data, kernel_content,
+ buffers);
+ break;
+ }
+ default: {
+ MicroPrintf("Filter type %s (%d) not supported.",
+ TfLiteTypeGetName(weight_type), weight_type);
+ return kTfLiteError;
+ }
+ }
+ break;
+ }
+ default: {
+ MicroPrintf("Input type %s (%d) not supported.",
+ TfLiteTypeGetName(activation_type), activation_type);
+ return kTfLiteError;
}
}
- // In the absence of projection, hidden becomes otuput and this intermediate
- // is ignored.
- TfLiteTensor* hidden =
- context->GetTensor(context, node->intermediates->data[4]);
- auto* hidden_params =
- static_cast<TfLiteAffineQuantization*>(hidden->quantization.params);
- intermediate_scale[4] = hidden_params->scale->data[0];
- intermediate_zp[4] = hidden_params->zero_point->data[0];
-
- // Scales.
- const float default_scale = 1.0;
- float input_scale = default_scale;
- float input_to_input_weight_scale = default_scale;
- float recurrent_to_input_weight_scale = default_scale;
- float cell_to_input_weight_scale = default_scale;
- float input_to_forget_weight_scale = default_scale;
- float recurrent_to_forget_weight_scale = default_scale;
- float cell_to_forget_weight_scale = default_scale;
- float input_to_cell_weight_scale = default_scale;
- float recurrent_to_cell_weight_scale = default_scale;
- float input_to_output_weight_scale = default_scale;
- float recurrent_to_output_weight_scale = default_scale;
- float cell_to_output_weight_scale = default_scale;
- float projection_weight_scale = default_scale;
- float layer_norm_input_scale = default_scale;
- float layer_norm_forget_scale = default_scale;
- float layer_norm_cell_scale = default_scale;
- float layer_norm_output_scale = default_scale;
- float output_state_scale = default_scale;
- int cell_scale = 1;
-
- // Effective scales.
- float effective_input_to_input_scale = default_scale;
- float effective_recurrent_to_input_scale = default_scale;
- float effective_cell_to_input_scale = default_scale;
- float effective_input_to_forget_scale = default_scale;
- float effective_recurrent_to_forget_scale = default_scale;
- float effective_cell_to_forget_scale = default_scale;
- float effective_input_to_cell_scale = default_scale;
- float effective_recurrent_to_cell_scale = default_scale;
- float effective_input_to_output_scale = default_scale;
- float effective_recurrent_to_output_scale = default_scale;
- float effective_cell_to_output_scale = default_scale;
- float effective_proj_scale = default_scale;
- float effective_hidden_scale = default_scale;
-
- // Populate scales.
- if (!use_cifg) {
- input_to_input_weight_scale = input_to_input_weights->params.scale;
- recurrent_to_input_weight_scale = recurrent_to_input_weights->params.scale;
- }
-
- if (use_peephole) {
- if (!use_cifg) {
- cell_to_input_weight_scale = cell_to_input_weights->params.scale;
- }
- cell_to_forget_weight_scale = cell_to_forget_weights->params.scale;
- cell_to_output_weight_scale = cell_to_output_weights->params.scale;
- }
-
- if (use_layer_norm) {
- if (!use_cifg) {
- layer_norm_input_scale = input_layer_norm_coefficients->params.scale;
- }
- layer_norm_forget_scale = forget_layer_norm_coefficients->params.scale;
- layer_norm_cell_scale = cell_layer_norm_coefficients->params.scale;
- layer_norm_output_scale = output_layer_norm_coefficients->params.scale;
- }
-
- if (use_projection) {
- projection_weight_scale = projection_weights->params.scale;
- }
- output_state_scale = output_state->params.scale;
-
- input_to_forget_weight_scale = input_to_forget_weights->params.scale;
- input_to_cell_weight_scale = input_to_cell_weights->params.scale;
- input_to_output_weight_scale = input_to_output_weights->params.scale;
- recurrent_to_forget_weight_scale = recurrent_to_forget_weights->params.scale;
- recurrent_to_cell_weight_scale = recurrent_to_cell_weights->params.scale;
- recurrent_to_output_weight_scale = recurrent_to_output_weights->params.scale;
-
- // Check cell state (already used above)
- TF_LITE_ENSURE(context, CheckedLog2(cell_state->params.scale, &cell_scale));
- // TF_LITE_ENSURE(context, cell_scale <= -9);
- integer_lstm_param->cell_scale = cell_scale;
- input_scale = input->params.scale;
-
- // Calculate effective scales.
- if (!use_cifg) {
- effective_input_to_input_scale =
- input_to_input_weight_scale * input_scale / intermediate_scale[0];
- effective_recurrent_to_input_scale = recurrent_to_input_weight_scale *
- output_state_scale /
- intermediate_scale[0];
- }
- effective_input_to_forget_scale =
- input_to_forget_weight_scale * input_scale / intermediate_scale[1];
- effective_recurrent_to_forget_scale = recurrent_to_forget_weight_scale *
- output_state_scale /
- intermediate_scale[1];
-
- effective_input_to_cell_scale =
- input_to_cell_weight_scale * input_scale / intermediate_scale[2];
- effective_recurrent_to_cell_scale = recurrent_to_cell_weight_scale *
- output_state_scale /
- intermediate_scale[2];
-
- effective_input_to_output_scale =
- input_to_output_weight_scale * input_scale / intermediate_scale[3];
- effective_recurrent_to_output_scale = recurrent_to_output_weight_scale *
- output_state_scale /
- intermediate_scale[3];
-
- effective_hidden_scale = std::pow((float)2, (float)-15) /
- intermediate_scale[4] *
- std::pow((float)2, (float)-15);
-
- effective_proj_scale =
- projection_weight_scale * intermediate_scale[4] / output_state_scale;
-
- if (use_peephole) {
- if (!use_cifg) {
- effective_cell_to_input_scale =
- std::pow((float)(2), (float)cell_scale) * // NOLINT
- (float)(cell_to_input_weight_scale) / intermediate_scale[0];
- }
- effective_cell_to_forget_scale =
- std::pow((float)2, (float)cell_scale) * // NOLINT
- (float)cell_to_forget_weight_scale / intermediate_scale[1];
- effective_cell_to_output_scale =
- std::pow((float)2, (float)cell_scale) * // NOLINT
- (float)cell_to_output_weight_scale / intermediate_scale[3];
- }
-
- // Decompose scales.
- QuantizeMultiplier(static_cast<double>(effective_input_to_input_scale),
- &integer_lstm_param->effective_input_to_input_scale_a,
- &integer_lstm_param->effective_input_to_input_scale_b);
- QuantizeMultiplier(static_cast<double>(effective_recurrent_to_input_scale),
- &integer_lstm_param->effective_recurrent_to_input_scale_a,
- &integer_lstm_param->effective_recurrent_to_input_scale_b);
- QuantizeMultiplier(static_cast<double>(effective_cell_to_input_scale),
- &integer_lstm_param->effective_cell_to_input_scale_a,
- &integer_lstm_param->effective_cell_to_input_scale_b);
- QuantizeMultiplier(static_cast<double>(effective_input_to_forget_scale),
- &integer_lstm_param->effective_input_to_forget_scale_a,
- &integer_lstm_param->effective_input_to_forget_scale_b);
- QuantizeMultiplier(
- static_cast<double>(effective_recurrent_to_forget_scale),
- &integer_lstm_param->effective_recurrent_to_forget_scale_a,
- &integer_lstm_param->effective_recurrent_to_forget_scale_b);
- QuantizeMultiplier(static_cast<double>(effective_cell_to_forget_scale),
- &integer_lstm_param->effective_cell_to_forget_scale_a,
- &integer_lstm_param->effective_cell_to_forget_scale_b);
- QuantizeMultiplier(static_cast<double>(effective_input_to_cell_scale),
- &integer_lstm_param->effective_input_to_cell_scale_a,
- &integer_lstm_param->effective_input_to_cell_scale_b);
- QuantizeMultiplier(static_cast<double>(effective_recurrent_to_cell_scale),
- &integer_lstm_param->effective_recurrent_to_cell_scale_a,
- &integer_lstm_param->effective_recurrent_to_cell_scale_b);
- QuantizeMultiplier(static_cast<double>(effective_input_to_output_scale),
- &integer_lstm_param->effective_input_to_output_scale_a,
- &integer_lstm_param->effective_input_to_output_scale_b);
- QuantizeMultiplier(
- static_cast<double>(effective_recurrent_to_output_scale),
- &integer_lstm_param->effective_recurrent_to_output_scale_a,
- &integer_lstm_param->effective_recurrent_to_output_scale_b);
- QuantizeMultiplier(static_cast<double>(effective_cell_to_output_scale),
- &integer_lstm_param->effective_cell_to_output_scale_a,
- &integer_lstm_param->effective_cell_to_output_scale_b);
- QuantizeMultiplier(static_cast<double>(effective_proj_scale),
- &integer_lstm_param->effective_proj_scale_a,
- &integer_lstm_param->effective_proj_scale_b);
- QuantizeMultiplier(static_cast<double>(effective_hidden_scale),
- &integer_lstm_param->effective_hidden_scale_a,
- &integer_lstm_param->effective_hidden_scale_b);
- QuantizeMultiplier(static_cast<double>(layer_norm_input_scale),
- &integer_lstm_param->layer_norm_input_scale_a,
- &integer_lstm_param->layer_norm_input_scale_b);
- QuantizeMultiplier(static_cast<double>(layer_norm_forget_scale),
- &integer_lstm_param->layer_norm_forget_scale_a,
- &integer_lstm_param->layer_norm_forget_scale_b);
- QuantizeMultiplier(static_cast<double>(layer_norm_cell_scale),
- &integer_lstm_param->layer_norm_cell_scale_a,
- &integer_lstm_param->layer_norm_cell_scale_b);
- QuantizeMultiplier(static_cast<double>(layer_norm_output_scale),
- &integer_lstm_param->layer_norm_output_scale_a,
- &integer_lstm_param->layer_norm_output_scale_b);
-
- integer_lstm_param->hidden_zp = intermediate_zp[4];
-
- // 10000 is used to make sure the kernel logic does not overflow.
- if (!use_cifg) {
- integer_lstm_param->input_variance_guard =
- std::max(static_cast<int32_t>(1),
- static_cast<int32_t>(10000 * layer_norm_input_scale));
- }
- integer_lstm_param->forget_variance_guard =
- std::max(static_cast<int32_t>(1),
- static_cast<int32_t>(10000 * layer_norm_forget_scale));
- integer_lstm_param->cell_variance_guard =
- std::max(static_cast<int32_t>(1),
- static_cast<int32_t>(10000 * layer_norm_cell_scale));
- integer_lstm_param->output_variance_guard =
- std::max(static_cast<int32_t>(1),
- static_cast<int32_t>(10000 * layer_norm_output_scale));
-
return kTfLiteOk;
}
} // namespace
-// Temporary tensors
-enum TemporaryTensor {
- kScratchBuffer = 0,
- kInputQuantized = 1,
- kOutputStateQuantized = 2,
- kCellStateQuantized = 3,
- kInputScalingFactors = 4,
- kOutputStateScalingFactors = 5,
- kProductScalingFactors = 6,
- kRecoveredCellWeights = 7,
- kAccumScratch = 8,
- kInputZeroPoints = 9,
- kOutputStateZeroPoints = 10,
- kRowSums = 11,
- kNumTemporaryTensors = 12,
-};
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
- OpData* op_data = reinterpret_cast<OpData*>(
- context->AllocatePersistentBuffer(context, sizeof(OpData)));
-
- return op_data;
-}
-
-// Check that input tensor dimensions matches with each other.
-TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context,
- TfLiteNode* node, int n_input,
- int n_output, int n_cell,
- bool use_layer_norm, bool is_integer) {
- const auto* params = reinterpret_cast<TfLiteLSTMParams*>(node->builtin_data);
-
- // Making sure clipping parameters have valid values.
- // == 0 means no clipping
- // > 0 means clipping
- TF_LITE_ENSURE(context, params->cell_clip >= 0);
- TF_LITE_ENSURE(context, params->proj_clip >= 0);
- const TfLiteEvalTensor* input_to_input_weights = tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kInputToInputWeightsTensor);
- if (input_to_input_weights != nullptr) {
- TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->size, 2);
- TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[0], n_cell);
- TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[1], n_input);
- }
- const TfLiteEvalTensor* input_to_forget_weights = tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kInputToForgetWeightsTensor);
-
- TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->size, 2);
- TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[0], n_cell);
- TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[1], n_input);
- const TfLiteEvalTensor* input_to_cell_weights = tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kInputToCellWeightsTensor);
-
- TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->size, 2);
- TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->data[0], n_cell);
- TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->data[1], n_input);
- const TfLiteEvalTensor* recurrent_to_input_weights =
- tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kRecurrentToInputWeightsTensor);
- if (recurrent_to_input_weights != nullptr) {
- TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->size, 2);
- TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->data[0],
- n_cell);
- TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->data[1],
- n_output);
- }
- const TfLiteEvalTensor* recurrent_to_forget_weights =
- tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kRecurrentToForgetWeightsTensor);
-
- TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->size, 2);
- TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[0],
- n_cell);
- TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[1],
- n_output);
- const TfLiteEvalTensor* recurrent_to_cell_weights =
- tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kRecurrentToCellWeightsTensor);
-
- TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->size, 2);
- TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[0], n_cell);
- TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[1],
- n_output);
-
- // We make sure the input-gate's parameters are either both present (regular
- // LSTM) or not at all (CIFG-LSTM).
- const bool cifg_weights_all_or_none =
- ((input_to_input_weights != nullptr) &&
- (recurrent_to_input_weights != nullptr)) ||
- ((input_to_input_weights == nullptr) &&
- (recurrent_to_input_weights == nullptr));
- TF_LITE_ENSURE(context, cifg_weights_all_or_none == true);
-
- const TfLiteTensor* cell_to_input_weights = GetOptionalInputTensor(
- context, node, micro::lstm::full::kCellToInputWeightsTensor);
- if (cell_to_input_weights != nullptr) {
- TF_LITE_ENSURE_EQ(context, cell_to_input_weights->dims->size, 1);
- TF_LITE_ENSURE_EQ(context, cell_to_input_weights->dims->data[0], n_cell);
- TF_LITE_ENSURE_TYPES_EQ(
- context, cell_to_input_weights->type,
- is_integer ? kTfLiteInt16 : input_to_forget_weights->type);
- }
-
- const TfLiteTensor* cell_to_forget_weights = GetOptionalInputTensor(
- context, node, lstm::full::kCellToForgetWeightsTensor);
- if (cell_to_forget_weights != nullptr) {
- TF_LITE_ENSURE_EQ(context, cell_to_forget_weights->dims->size, 1);
- TF_LITE_ENSURE_EQ(context, cell_to_forget_weights->dims->data[0], n_cell);
- TF_LITE_ENSURE_TYPES_EQ(
- context, cell_to_forget_weights->type,
- is_integer ? kTfLiteInt16 : input_to_forget_weights->type);
- }
-
- const TfLiteTensor* cell_to_output_weights = GetOptionalInputTensor(
- context, node, micro::lstm::full::kCellToOutputWeightsTensor);
- if (cell_to_output_weights != nullptr) {
- TF_LITE_ENSURE_EQ(context, cell_to_output_weights->dims->size, 1);
- TF_LITE_ENSURE_EQ(context, cell_to_output_weights->dims->data[0], n_cell);
- TF_LITE_ENSURE_TYPES_EQ(
- context, cell_to_output_weights->type,
- is_integer ? kTfLiteInt16 : input_to_forget_weights->type);
- }
-
- // Making sure the peephole weights are there all or none.
- const bool use_cifg = (input_to_input_weights == nullptr);
- const bool peephole_weights_all_or_none =
- ((cell_to_input_weights != nullptr || use_cifg) &&
- (cell_to_forget_weights != nullptr) &&
- (cell_to_output_weights != nullptr)) ||
- ((cell_to_input_weights == nullptr) &&
- (cell_to_forget_weights == nullptr) &&
- (cell_to_output_weights == nullptr));
- TF_LITE_ENSURE(context, peephole_weights_all_or_none == true);
- const TfLiteEvalTensor* input_gate_bias = tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kInputGateBiasTensor);
-
- if (use_cifg) {
- TF_LITE_ENSURE_EQ(context, input_gate_bias, nullptr);
- } else {
- TF_LITE_ENSURE_EQ(context, input_gate_bias->dims->size, 1);
- TF_LITE_ENSURE_EQ(context, input_gate_bias->dims->data[0], n_cell);
- if (is_integer) {
- TF_LITE_ENSURE_TYPES_EQ(context, input_gate_bias->type, kTfLiteInt32);
- } else {
- TF_LITE_ENSURE_TYPES_EQ(context, input_gate_bias->type, kTfLiteFloat32);
- }
- }
- const TfLiteEvalTensor* forget_gate_bias = tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kForgetGateBiasTensor);
-
- TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->size, 1);
- TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->data[0], n_cell);
- if (is_integer) {
- TF_LITE_ENSURE_TYPES_EQ(context, forget_gate_bias->type, kTfLiteInt32);
- } else {
- TF_LITE_ENSURE_TYPES_EQ(context, forget_gate_bias->type, kTfLiteFloat32);
- }
- const TfLiteEvalTensor* cell_gate_bias = tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kCellGateBiasTensor);
-
- TF_LITE_ENSURE_EQ(context, cell_gate_bias->dims->size, 1);
- TF_LITE_ENSURE_EQ(context, cell_gate_bias->dims->data[0], n_cell);
- if (is_integer) {
- TF_LITE_ENSURE_TYPES_EQ(context, cell_gate_bias->type, kTfLiteInt32);
- } else {
- TF_LITE_ENSURE_TYPES_EQ(context, cell_gate_bias->type, kTfLiteFloat32);
- }
- const TfLiteEvalTensor* output_gate_bias = tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kOutputGateBiasTensor);
- TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->size, 1);
- TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->data[0], n_cell);
- if (is_integer) {
- TF_LITE_ENSURE_TYPES_EQ(context, output_gate_bias->type, kTfLiteInt32);
- } else {
- TF_LITE_ENSURE_TYPES_EQ(context, output_gate_bias->type, kTfLiteFloat32);
- }
-
- const TfLiteTensor* projection_weights = GetOptionalInputTensor(
- context, node, micro::lstm::full::kProjectionWeightsTensor);
- if (projection_weights != nullptr) {
- TF_LITE_ENSURE_EQ(context, projection_weights->dims->size, 2);
- TF_LITE_ENSURE_EQ(context, projection_weights->dims->data[0], n_output);
- TF_LITE_ENSURE_EQ(context, projection_weights->dims->data[1], n_cell);
- }
-
- const TfLiteTensor* projection_bias = GetOptionalInputTensor(
- context, node, micro::lstm::full::kProjectionBiasTensor);
- if (projection_bias != nullptr) {
- TF_LITE_ENSURE_EQ(context, projection_bias->dims->size, 1);
- TF_LITE_ENSURE_EQ(context, projection_bias->dims->data[0], n_output);
- if (is_integer) {
- TF_LITE_ENSURE_TYPES_EQ(context, projection_bias->type, kTfLiteInt32);
- } else {
- TF_LITE_ENSURE_TYPES_EQ(context, projection_bias->type, kTfLiteFloat32);
- }
- }
-
- // Making sure the projection tensors are consistent:
- // 1) If projection weight is not present, then projection bias should not be
- // present.
- // 2) If projection weight is present, then projection bias is optional.
- const bool projecton_tensors_consistent =
- ((projection_weights != nullptr) || (projection_bias == nullptr));
- TF_LITE_ENSURE(context, projecton_tensors_consistent == true);
-
- if (use_layer_norm) {
- const TfLiteEvalTensor* input_layer_norm_coefficients =
- tflite::micro::GetEvalInput(
- context, node,
- micro::lstm::full::kInputLayerNormCoefficientsTensor);
- if (use_cifg) {
- TF_LITE_ENSURE_EQ(context, input_layer_norm_coefficients, nullptr);
- } else {
- TF_LITE_ENSURE(context, input_layer_norm_coefficients != nullptr);
- TF_LITE_ENSURE_EQ(context, input_layer_norm_coefficients->dims->size, 1);
- TF_LITE_ENSURE_EQ(context, input_layer_norm_coefficients->dims->data[0],
- n_cell);
- if (is_integer) {
- TF_LITE_ENSURE_TYPES_EQ(context, input_layer_norm_coefficients->type,
- kTfLiteInt16);
- } else {
- TF_LITE_ENSURE_TYPES_EQ(context, input_layer_norm_coefficients->type,
- kTfLiteFloat32);
- }
- }
- const TfLiteEvalTensor* forget_layer_norm_coefficients =
- tflite::micro::GetEvalInput(
- context, node,
- micro::lstm::full::kForgetLayerNormCoefficientsTensor);
- TF_LITE_ENSURE_EQ(context, forget_layer_norm_coefficients->dims->size, 1);
- TF_LITE_ENSURE_EQ(context, forget_layer_norm_coefficients->dims->data[0],
- n_cell);
- if (is_integer) {
- TF_LITE_ENSURE_TYPES_EQ(context, forget_layer_norm_coefficients->type,
- kTfLiteInt16);
- } else {
- TF_LITE_ENSURE_TYPES_EQ(context, forget_layer_norm_coefficients->type,
- kTfLiteFloat32);
- }
- const TfLiteEvalTensor* cell_layer_norm_coefficients =
- tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kCellLayerNormCoefficientsTensor);
- TF_LITE_ENSURE_EQ(context, cell_layer_norm_coefficients->dims->size, 1);
- TF_LITE_ENSURE_EQ(context, cell_layer_norm_coefficients->dims->data[0],
- n_cell);
- if (is_integer) {
- TF_LITE_ENSURE_TYPES_EQ(context, cell_layer_norm_coefficients->type,
- kTfLiteInt16);
- } else {
- TF_LITE_ENSURE_TYPES_EQ(context, cell_layer_norm_coefficients->type,
- kTfLiteFloat32);
- }
- const TfLiteEvalTensor* output_layer_norm_coefficients =
- tflite::micro::GetEvalInput(
- context, node,
- micro::lstm::full::kOutputLayerNormCoefficientsTensor);
-
- TF_LITE_ENSURE_EQ(context, output_layer_norm_coefficients->dims->size, 1);
- TF_LITE_ENSURE_EQ(context, output_layer_norm_coefficients->dims->data[0],
- n_cell);
- if (is_integer) {
- TF_LITE_ENSURE_TYPES_EQ(context, output_layer_norm_coefficients->type,
- kTfLiteInt16);
- } else {
- TF_LITE_ENSURE_TYPES_EQ(context, output_layer_norm_coefficients->type,
- kTfLiteFloat32);
- }
- }
-
- return kTfLiteOk;
-}
-
-TfLiteStatus PrecomputeZeroPointTimesWeightWithBias(
- TfLiteContext* context, int32_t zero_point,
- const TfLiteTensor* weight_tensor, const TfLiteTensor* bias_tensor,
- std::unique_ptr<int32_t[]>* output) {
- if (weight_tensor == nullptr) {
- return kTfLiteOk;
- }
-
- const RuntimeShape& weight_shape = GetTensorShape(weight_tensor);
- TF_LITE_ENSURE_EQ(context, weight_shape.DimensionsCount(), 2);
- const int row = weight_shape.Dims(0);
- const int col = weight_shape.Dims(1);
- output->reset(new int32_t[row]);
- if (bias_tensor == nullptr) {
- memset(output->get(), 0, row * sizeof(int32_t));
- } else {
- const int32_t* bias = GetTensorData<int32_t>(bias_tensor);
- memcpy(output->get(), bias, row * sizeof(int32_t));
- }
- if (zero_point != 0) {
- const int8_t* weight = GetTensorData<int8_t>(weight_tensor);
- tensor_utils::PortableMatrixScalarMultiplyAccumulate(
- weight, zero_point, row, col, output->get());
- }
- return kTfLiteOk;
-}
-
-TfLiteStatus PopulatePrecomputedZPTimesWeightsWithBias(TfLiteContext* context,
- OpData* op_data,
- TfLiteNode* node) {
- const TfLiteTensor* input;
- TF_LITE_ENSURE_OK(
- context,
- GetInputSafe(context, node, micro::lstm::full::kInputTensor, &input));
- const TfLiteTensor* output_state =
- GetVariableInput(context, node, micro::lstm::full::kOutputStateTensor);
- TF_LITE_ENSURE(context, output_state != nullptr);
-
- const int32_t input_zero_point = -input->params.zero_point;
- const int32_t output_state_zero_point = -output_state->params.zero_point;
-
- const TfLiteTensor* input_to_input_weights = GetOptionalInputTensor(
- context, node, micro::lstm::full::kInputToInputWeightsTensor);
- const TfLiteTensor* input_to_forget_weights;
- TF_LITE_ENSURE_OK(context,
- GetInputSafe(context, node,
- micro::lstm::full::kInputToForgetWeightsTensor,
- &input_to_forget_weights));
- const TfLiteTensor* input_to_cell_weights;
- TF_LITE_ENSURE_OK(
- context,
- GetInputSafe(context, node, micro::lstm::full::kInputToCellWeightsTensor,
- &input_to_cell_weights));
- const TfLiteTensor* input_to_output_weights;
- TF_LITE_ENSURE_OK(context,
- GetInputSafe(context, node,
- micro::lstm::full::kInputToOutputWeightsTensor,
- &input_to_output_weights));
-
- const TfLiteTensor* recurrent_to_input_weights = GetOptionalInputTensor(
- context, node, micro::lstm::full::kRecurrentToInputWeightsTensor);
- const TfLiteTensor* recurrent_to_forget_weights;
- TF_LITE_ENSURE_OK(
- context, GetInputSafe(context, node,
- micro::lstm::full::kRecurrentToForgetWeightsTensor,
- &recurrent_to_forget_weights));
- const TfLiteTensor* recurrent_to_cell_weights;
- TF_LITE_ENSURE_OK(
- context, GetInputSafe(context, node,
- micro::lstm::full::kRecurrentToCellWeightsTensor,
- &recurrent_to_cell_weights));
- const TfLiteTensor* recurrent_to_output_weights;
- TF_LITE_ENSURE_OK(
- context, GetInputSafe(context, node,
- micro::lstm::full::kRecurrentToOutputWeightsTensor,
- &recurrent_to_output_weights));
-
- const TfLiteTensor* projection_weights = GetOptionalInputTensor(
- context, node, lstm::full::kProjectionWeightsTensor);
- const TfLiteTensor* projection_bias = GetOptionalInputTensor(
- context, node, micro::lstm::full::kProjectionBiasTensor);
-
- lstm_eval::IntegerLstmParameter* integer_lstm_params =
- &op_data->integer_lstm_param;
-
- TfLiteTensor* intermediate =
- context->GetTensor(context, node->intermediates->data[4]);
- const auto* params =
- static_cast<TfLiteAffineQuantization*>(intermediate->quantization.params);
- const int32_t hidden_zp = params->zero_point->data[0];
-
- // Get bias and perform zero point calculation.
- // When there is layer normalization, the gate bias does not apply to matmul
- // directly:
- // y = ln(w * x + w * r + w * c) + b.
- const bool is_layer_norm = op_data->use_layer_norm;
-
- // Forget gate.
- const TfLiteTensor* forget_gate_bias =
- is_layer_norm
- ? nullptr
- : GetInput(context, node, micro::lstm::full::kForgetGateBiasTensor);
- TF_LITE_ENSURE_OK(
- context,
- PrecomputeZeroPointTimesWeightWithBias(
- context, input_zero_point, input_to_forget_weights, forget_gate_bias,
- &(integer_lstm_params->input_to_forget_effective_bias)));
-
- TF_LITE_ENSURE_OK(
- context,
- PrecomputeZeroPointTimesWeightWithBias(
- context, output_state_zero_point, recurrent_to_forget_weights,
- nullptr, &(integer_lstm_params->recurrent_to_forget_effective_bias)));
-
- // Modulation gate.
- const TfLiteTensor* cell_gate_bias =
- is_layer_norm
- ? nullptr
- : GetInput(context, node, micro::lstm::full::kCellGateBiasTensor);
- TF_LITE_ENSURE_OK(
- context,
- PrecomputeZeroPointTimesWeightWithBias(
- context, input_zero_point, input_to_cell_weights, cell_gate_bias,
- &(integer_lstm_params->input_to_cell_effective_bias)));
- TF_LITE_ENSURE_OK(
- context,
- PrecomputeZeroPointTimesWeightWithBias(
- context, output_state_zero_point, recurrent_to_cell_weights, nullptr,
- &(integer_lstm_params->recurrent_to_cell_effective_bias)));
-
- // Output gate.
- const TfLiteTensor* output_gate_bias =
- is_layer_norm
- ? nullptr
- : GetInput(context, node, micro::lstm::full::kOutputGateBiasTensor);
- TF_LITE_ENSURE_OK(
- context,
- PrecomputeZeroPointTimesWeightWithBias(
- context, input_zero_point, input_to_output_weights, output_gate_bias,
- &(integer_lstm_params->input_to_output_effective_bias)));
-
- TF_LITE_ENSURE_OK(
- context,
- PrecomputeZeroPointTimesWeightWithBias(
- context, output_state_zero_point, recurrent_to_output_weights,
- nullptr, &(integer_lstm_params->recurrent_to_output_effective_bias)));
-
- // Input gate. The calculation is only meaningful for non-cifg case.
- const TfLiteTensor* input_gate_bias =
- is_layer_norm
- ? nullptr
- : GetInput(context, node, micro::lstm::full::kInputGateBiasTensor);
- TF_LITE_ENSURE_OK(
- context,
- PrecomputeZeroPointTimesWeightWithBias(
- context, input_zero_point, input_to_input_weights, input_gate_bias,
- &(integer_lstm_params->input_to_input_effective_bias)));
- TF_LITE_ENSURE_OK(
- context,
- PrecomputeZeroPointTimesWeightWithBias(
- context, output_state_zero_point, recurrent_to_input_weights, nullptr,
- &(integer_lstm_params->recurrent_to_input_effective_bias)));
-
- // Projection bias. The calculation is only meaningful for with projection.
- TF_LITE_ENSURE_OK(context,
- PrecomputeZeroPointTimesWeightWithBias(
- context, hidden_zp, projection_weights, projection_bias,
- &(integer_lstm_params->projection_effective_bias)));
- return kTfLiteOk;
-}
-
-// Resize the output and state tensors based on the sizes of the input tensors.
-// Allocate a temporary scratch tensor. Also check that the sizes of the input
-// tensors match each other.
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
- OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
- // const int scratch_tensor_index = op_data->scratch_tensor_index;
-
- // Check we have all the inputs and outputs we need.
- bool use_layer_norm = false;
- if (node->inputs->size == 24) {
- const TfLiteTensor* forget_layer_norm_coefficients = GetOptionalInputTensor(
- context, node, micro::lstm::full::kForgetLayerNormCoefficientsTensor);
- if (forget_layer_norm_coefficients == nullptr) {
- use_layer_norm = false;
- } else {
- use_layer_norm = true;
- }
- } else if (node->inputs->size == 20) {
- // This is deprecated and is only kept here for backward compatibility.
- use_layer_norm = false;
- } else {
- MicroPrintf("The LSTM Full kernel expects 20 or 24 inputs. Got %d inputs",
- node->inputs->size);
- return kTfLiteError;
- }
- TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);
- op_data->use_layer_norm = use_layer_norm;
-
- // Inferring batch size, number of outputs and sequence length and
- // number of cells from the input tensors.
- const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kInputTensor);
- const bool is_integer = input->type == kTfLiteInt8;
- TF_LITE_ENSURE(context, input->dims->size > 1);
- const auto* params =
- reinterpret_cast<TfLiteUnidirectionalSequenceLSTMParams*>(
- node->builtin_data);
- const bool time_major = params->time_major;
- const int n_batch = time_major ? input->dims->data[1] : input->dims->data[0];
- const int n_input = input->dims->data[2];
- const TfLiteEvalTensor* input_to_output_weights = tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kInputToOutputWeightsTensor);
- const int n_cell = input_to_output_weights->dims->data[0];
- TF_LITE_ENSURE_EQ(context, input_to_output_weights->dims->size, 2);
- TF_LITE_ENSURE_EQ(context, input_to_output_weights->dims->data[1], n_input);
- const TfLiteEvalTensor* recurrent_to_output_weights =
- tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kRecurrentToOutputWeightsTensor);
-
- TF_LITE_ENSURE_EQ(context, recurrent_to_output_weights->dims->size, 2);
- TF_LITE_ENSURE_EQ(context, recurrent_to_output_weights->dims->data[0],
- n_cell);
- const int n_output = recurrent_to_output_weights->dims->data[1];
-
- // Check that input tensor dimensions matches with each other.
- TF_LITE_ENSURE_OK(
- context, CheckInputTensorDimensions(context, node, n_input, n_output,
- n_cell, use_layer_norm, is_integer));
- // Get the pointer to output, output_state and cell_state buffer tensors.
- // TfLiteEvalTensor* output =
- // tflite::micro::GetEvalOutput(context, node,
- // micro::lstm::full::kOutputTensor);
- TfLiteEvalTensor* output_state = tflite::micro::GetMutableEvalInput(
- context, node, micro::lstm::full::kOutputStateTensor);
- TFLITE_DCHECK(output_state != nullptr);
- TfLiteEvalTensor* cell_state = tflite::micro::GetMutableEvalInput(
- context, node, micro::lstm::full::kCellStateTensor);
- TFLITE_DCHECK(cell_state != nullptr);
- // Check the shape of input state tensors.
- // These tensor may be 1D or 2D. It's fine as long as the total size is
- // correct.
- TF_LITE_ENSURE_EQ(context, NumElements(output_state->dims),
- n_batch * n_output);
- TF_LITE_ENSURE_EQ(context, NumElements(cell_state->dims), n_batch * n_cell);
-
- if (is_integer) {
- const int num_intermediate_tensors = node->intermediates->size;
- TF_LITE_ENSURE(context, num_intermediate_tensors == 5);
- }
-
- if (is_integer) {
- // Integer UnidirectionalSequenceLSTM prepare function for 8x8->16.
- // This code path needs 5 intermediate tensors per Op.
- // Populate quantization parameters.
- PopulateQuantizedLstmParams8x8_16(context, node,
- &op_data->integer_lstm_param);
- // Allocate scratch buffer. Need 6 16bit buffer with size n_batch * n_cell
- // and 1 8bit buffer with size n_batch * n_cell. We also need 1 32 bit
- // buffer with size n_batch * n_cell.
- //
- // Handle cifg case as well, which might save one buffer.
-
- int scratch_idx = 0;
-
- context->RequestScratchBufferInArena(
- context, n_batch * n_cell * sizeof(int32_t), &(scratch_idx));
- op_data->scratch_tensor_index = scratch_idx;
-
- for (int scratch_index = 1; scratch_index < 6; ++scratch_index) {
- // node->temporaries->data[scratch_index] = op_data->scratch_tensor_index
- // + scratch_index;
- context->RequestScratchBufferInArena(
- context, n_batch * n_cell * sizeof(int32_t), &(scratch_idx));
- TFLITE_DCHECK(scratch_idx ==
- (op_data->scratch_tensor_index + scratch_index));
- }
-
- // Populate precomputed zp * weight.
- TF_LITE_ENSURE_OK(context, PopulatePrecomputedZPTimesWeightsWithBias(
- context, op_data, node));
- }
-
- return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
- const auto* params =
- reinterpret_cast<TfLiteUnidirectionalSequenceLSTMParams*>(
- node->builtin_data);
- const OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
- // const bool use_layer_norm = op_data->use_layer_norm;
- // const bool time_major = params->time_major;
-
- const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kInputTensor);
- const TfLiteEvalTensor* input_to_input_weights = tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kInputToInputWeightsTensor);
- const TfLiteEvalTensor* input_to_forget_weights = tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kInputToForgetWeightsTensor);
- const TfLiteEvalTensor* input_to_cell_weights = tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kInputToCellWeightsTensor);
- const TfLiteEvalTensor* input_to_output_weights = tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kInputToOutputWeightsTensor);
- const TfLiteEvalTensor* recurrent_to_input_weights =
- tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kRecurrentToInputWeightsTensor);
- const TfLiteEvalTensor* recurrent_to_forget_weights =
- tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kRecurrentToForgetWeightsTensor);
- const TfLiteEvalTensor* recurrent_to_cell_weights =
- tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kRecurrentToCellWeightsTensor);
- const TfLiteEvalTensor* recurrent_to_output_weights =
- tflite::micro::GetEvalInput(
- context, node, micro::lstm::full::kRecurrentToOutputWeightsTensor);
- const TfLiteEvalTensor* cell_to_input_weights = context->GetEvalTensor(
- context,
- node->inputs->data[micro::lstm::full::kCellToInputWeightsTensor]);
- const TfLiteEvalTensor* cell_to_forget_weights = context->GetEvalTensor(
- context,
- node->inputs->data[micro::lstm::full::kCellToForgetWeightsTensor]);
- const TfLiteEvalTensor* cell_to_output_weights = context->GetEvalTensor(
- context,
- node->inputs->data[micro::lstm::full::kCellToOutputWeightsTensor]);
- const TfLiteEvalTensor* input_gate_bias = context->GetEvalTensor(
- context, node->inputs->data[micro::lstm::full::kInputGateBiasTensor]);
-
- const TfLiteEvalTensor* forget_gate_bias = context->GetEvalTensor(
- context, node->inputs->data[micro::lstm::full::kForgetGateBiasTensor]);
- const TfLiteEvalTensor* cell_gate_bias = context->GetEvalTensor(
- context, node->inputs->data[micro::lstm::full::kCellGateBiasTensor]);
- const TfLiteEvalTensor* output_gate_bias = context->GetEvalTensor(
- context, node->inputs->data[micro::lstm::full::kOutputGateBiasTensor]);
-
- const TfLiteEvalTensor* projection_weights = context->GetEvalTensor(
- context, node->inputs->data[micro::lstm::full::kProjectionWeightsTensor]);
- const TfLiteEvalTensor* projection_bias = context->GetEvalTensor(
- context, node->inputs->data[micro::lstm::full::kProjectionBiasTensor]);
-
- TfLiteEvalTensor* output_state = context->GetEvalTensor(
- context, node->inputs->data[micro::lstm::full::kOutputStateTensor]);
- TFLITE_DCHECK(output_state != nullptr);
- TfLiteEvalTensor* cell_state = context->GetEvalTensor(
- context, node->inputs->data[micro::lstm::full::kCellStateTensor]);
- TFLITE_DCHECK(cell_state != nullptr);
- const TfLiteEvalTensor* input_layer_norm_coefficients =
- context->GetEvalTensor(
- context,
- node->inputs
- ->data[micro::lstm::full::kInputLayerNormCoefficientsTensor]);
-
- const TfLiteEvalTensor* forget_layer_norm_coefficients =
- context->GetEvalTensor(
- context,
- node->inputs
- ->data[micro::lstm::full::kForgetLayerNormCoefficientsTensor]);
- const TfLiteEvalTensor* cell_layer_norm_coefficients = context->GetEvalTensor(
- context,
- node->inputs->data[micro::lstm::full::kCellLayerNormCoefficientsTensor]);
-
- const TfLiteEvalTensor* output_layer_norm_coefficients =
- context->GetEvalTensor(
- context,
- node->inputs
- ->data[micro::lstm::full::kOutputLayerNormCoefficientsTensor]);
-
- TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(
- context, node, micro::lstm::full::kOutputTensor);
-
- // Copy out the LSTM specific params so they can be passed in the function.
- TfLiteLSTMParams lstm_params;
- lstm_params.activation = params->activation;
- lstm_params.cell_clip = params->cell_clip;
- lstm_params.proj_clip = params->proj_clip;
- lstm_params.asymmetric_quantize_inputs = params->asymmetric_quantize_inputs;
- switch (input_to_output_weights->type) {
- case kTfLiteInt8: {
- const bool is_hybrid = input->type == kTfLiteFloat32;
- if (is_hybrid) {
- MicroPrintf(" hybrid type is not supported.");
- return kTfLiteError;
-
- } else {
- TfLiteEvalTensor* scratch[6];
- // Allocate scratch buffer. Need 6 16bit buffer with size n_batch *
- // n_cell
- // and 1 8bit buffer with size n_batch * n_cell. We also need 1 32 bit
- // buffer with size n_batch * n_cell.
- //
- // Handle cifg case as well, which might save one buffer.
-
- const auto* tmp_params =
- reinterpret_cast<TfLiteUnidirectionalSequenceLSTMParams*>(
- node->builtin_data);
- const bool time_major = tmp_params->time_major;
- for (int scratch_index = 0; scratch_index < 6; ++scratch_index) {
- TFLITE_DCHECK(context != nullptr);
- TFLITE_DCHECK(context->GetScratchBuffer != nullptr);
- int32_t* scratch_tensor =
- static_cast<int32_t*>(context->GetScratchBuffer(
- context, op_data->scratch_tensor_index + scratch_index));
- scratch[scratch_index] = (TfLiteEvalTensor*)scratch_tensor;
- }
- /*
- TF_LITE_ENSURE_OK(context,
- GetScratchSafe(context, node, 0,
- &scratch0));
-
- TF_LITE_ENSURE_OK(context,
- GetScratchSafe(context, node, 1,
- &scratch1));
-
- TF_LITE_ENSURE_OK(context,
- GetScratchSafe(context, node, 2,
- &scratch2));
-
- TF_LITE_ENSURE_OK(context,
- GetScratchSafe(context, node, 3,
- &scratch3));
-
- TF_LITE_ENSURE_OK(context,
- GetScratchSafe(context, node, 4,
- &scratch4));
-
- TF_LITE_ENSURE_OK(context,
- GetScratchSafe(context, node, 5,
- &scratch5));
- */
- return lstm_eval::EvalInteger8x8_16(
- context, node, input, input_to_input_weights,
- input_to_forget_weights, input_to_cell_weights,
- input_to_output_weights, recurrent_to_input_weights,
- recurrent_to_forget_weights, recurrent_to_cell_weights,
- recurrent_to_output_weights, cell_to_input_weights,
- cell_to_forget_weights, cell_to_output_weights,
- input_layer_norm_coefficients, forget_layer_norm_coefficients,
- cell_layer_norm_coefficients, output_layer_norm_coefficients,
- input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias,
- projection_weights, projection_bias, &lstm_params,
- /*forward_sequence=*/true, time_major, &op_data->integer_lstm_param,
- output_state, cell_state, output, scratch[0], scratch[1],
- scratch[2], scratch[3], scratch[4], scratch[5]);
- }
- }
-
- default:
- MicroPrintf("Type %s is not currently supported.",
- TfLiteTypeGetName(input_to_output_weights->type));
- return kTfLiteError;
- }
- return kTfLiteOk;
-}
-//} // namespace unidirectional_sequence_lstm
-
-} // namespace micro
-} // namespace ops
-
TFLMRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM() {
- return tflite::micro::RegisterOp(ops::micro::Init, ops::micro::Prepare,
- ops::micro::Eval);
+ return tflite::micro::RegisterOp(UnidirectionalSequenceLstmInit,
+ UnidirectionalSequenceLstmPrepare,
+ UnidirectionalSequenceLstmEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/kernels/xtensa/xtensa.h b/tensorflow/lite/micro/kernels/xtensa/xtensa.h
index 47820d3..604736d 100644
--- a/tensorflow/lite/micro/kernels/xtensa/xtensa.h
+++ b/tensorflow/lite/micro/kernels/xtensa/xtensa.h
@@ -22,13 +22,13 @@
#include "tensorflow/lite/micro/kernels/xtensa/hifimini/fixedpoint_utils.h"
#endif // defined(HIFMINI)
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
#include "include/nnlib/xa_nnlib_api.h"
#include "include/nnlib/xa_nnlib_standards.h"
#define ALIGNED_SIZE(x, bytes) (((x) + (bytes - 1)) & (~(bytes - 1)))
#define ALIGN_PTR(x, bytes) ((((unsigned)(x)) + (bytes - 1)) & (~(bytes - 1)))
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
#if defined(VISION_P6)
#include "utils.h"
diff --git a/tensorflow/lite/micro/kernels/xtensa/xtensa_conv.h b/tensorflow/lite/micro/kernels/xtensa/xtensa_conv.h
index 355f022..f804a6d 100644
--- a/tensorflow/lite/micro/kernels/xtensa/xtensa_conv.h
+++ b/tensorflow/lite/micro/kernels/xtensa/xtensa_conv.h
@@ -25,9 +25,9 @@
struct XtensaConvOpData {
OpDataConv reference_op_data;
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
int scratch_tensor_index;
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
#if defined(VISION_P6)
int8_t* reorder_coefficient_bias; // buffers used to keep reordered coeff and
@@ -36,30 +36,30 @@
int8_t* per_channel_output_shift_int8;
uint8_t* p_context; // persistent lib context for this instance saved here
uint32_t context_size;
+ bool is_per_channel_quantized;
#endif // VISION_P6
};
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
TfLiteStatus ConvPrepareHifi(TfLiteContext* context, TfLiteNode* node);
-TfLiteStatus ConvEvalHifi(TfLiteContext* context, TfLiteNode* node,
- const TfLiteConvParams& params,
- const XtensaConvOpData& data,
- const TfLiteEvalTensor* input,
- const TfLiteEvalTensor* filter,
- const TfLiteEvalTensor* bias,
- TfLiteEvalTensor* output);
-#endif // defined(HIFI4) || defined(HIFI5)
+TfLiteStatus ConvEvalHifiInt8(TfLiteContext* context, TfLiteNode* node,
+ const TfLiteConvParams& params,
+ const XtensaConvOpData& data,
+ const TfLiteEvalTensor* input,
+ const TfLiteEvalTensor* filter,
+ const TfLiteEvalTensor* bias,
+ TfLiteEvalTensor* output);
-#if defined(HIFI4)
-TfLiteStatus ConvEvalHifi16(TfLiteContext* context, TfLiteNode* node,
- const TfLiteConvParams& params,
- const XtensaConvOpData& data,
- const TfLiteEvalTensor* input,
- const TfLiteEvalTensor* filter,
- const TfLiteEvalTensor* bias,
- TfLiteEvalTensor* output);
-#endif // defined(HIFI4)
+TfLiteStatus ConvEvalHifiInt16(TfLiteContext* context, TfLiteNode* node,
+ const TfLiteConvParams& params,
+ const XtensaConvOpData& data,
+ const TfLiteEvalTensor* input,
+ const TfLiteEvalTensor* filter,
+ const TfLiteEvalTensor* bias,
+ TfLiteEvalTensor* output);
+
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
#if defined(VISION_P6)
@@ -79,6 +79,9 @@
TfLiteStatus ConvReferenceEvalInt16(TfLiteContext* context, TfLiteNode* node);
+void* ConvInitXtensa(TfLiteContext* context, const char* buffer, size_t length);
+TfLiteStatus ConvPrepareXtensa(TfLiteContext* context, TfLiteNode* node);
+
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_CONV_H_
diff --git a/tensorflow/lite/micro/kernels/xtensa/xtensa_depthwise_conv.h b/tensorflow/lite/micro/kernels/xtensa/xtensa_depthwise_conv.h
index ca15719..7d0d765 100644
--- a/tensorflow/lite/micro/kernels/xtensa/xtensa_depthwise_conv.h
+++ b/tensorflow/lite/micro/kernels/xtensa/xtensa_depthwise_conv.h
@@ -25,9 +25,9 @@
struct XtensaDepthwiseConvOpData {
OpDataConv reference_op_data;
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
int scratch_tensor_index;
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
#if defined(VISION_P6)
int8_t* reorder_coefficient_bias; // buffers used to keep reordered coeff and
@@ -39,7 +39,7 @@
#endif // VISION_P6
};
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
TfLiteStatus DepthwiseConvPrepareHifi(TfLiteContext* context, TfLiteNode* node);
TfLiteStatus DepthwiseConvEvalHifi(TfLiteContext* context, TfLiteNode* node,
@@ -52,7 +52,7 @@
TfLiteStatus DepthwiseConvReferenceEvalInt8(TfLiteContext* context,
TfLiteNode* node);
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
#if defined(VISION_P6)
diff --git a/tensorflow/lite/micro/kernels/xtensa/xtensa_softmax.h b/tensorflow/lite/micro/kernels/xtensa/xtensa_softmax.h
index 7d0d461..d7e6a14 100644
--- a/tensorflow/lite/micro/kernels/xtensa/xtensa_softmax.h
+++ b/tensorflow/lite/micro/kernels/xtensa/xtensa_softmax.h
@@ -22,12 +22,12 @@
namespace tflite {
-#if defined(HIFI4) || defined(HIFI5)
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
struct XtensaSoftmaxOpData {
SoftmaxParams params;
int scratch_tensor_index;
};
-#endif // defined(HIFI4) || defined(HIFI5)
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
#if defined(VISION_P6)
struct XtensaSoftmaxOpData {
diff --git a/tensorflow/lite/micro/kernels/zeros_like.cc b/tensorflow/lite/micro/kernels/zeros_like.cc
index 597e50e..eb1f9c6 100644
--- a/tensorflow/lite/micro/kernels/zeros_like.cc
+++ b/tensorflow/lite/micro/kernels/zeros_like.cc
@@ -25,7 +25,7 @@
constexpr int kInputTensor = 0;
constexpr int kOutputTensor = 0;
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus ZerosLikePrepare(TfLiteContext* context, TfLiteNode* node) {
MicroContext* micro_context = GetMicroContext(context);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
@@ -50,7 +50,7 @@
}
}
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+TfLiteStatus ZerosLikeEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kInputTensor);
TfLiteEvalTensor* output =
@@ -82,7 +82,7 @@
} // namespace
TFLMRegistration Register_ZEROS_LIKE() {
- return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+ return tflite::micro::RegisterOp(nullptr, ZerosLikePrepare, ZerosLikeEval);
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/memory_arena_threshold_test.cc b/tensorflow/lite/micro/memory_arena_threshold_test.cc
index f6bb24f..3017f56 100644
--- a/tensorflow/lite/micro/memory_arena_threshold_test.cc
+++ b/tensorflow/lite/micro/memory_arena_threshold_test.cc
@@ -97,7 +97,7 @@
// Tail size contributed by the conv model excluding the
// RecordingMicroAllocator's overhead
// TODO(b/207157610): replace magic number that depends on OPs
-constexpr int kTestConvModelOnlyTailSize = 1744;
+constexpr int kTestConvModelOnlyTailSize = 1816;
constexpr int kTestConvModelPersistentTfLiteTensorDataSize = 128;
constexpr int kTestConvModelPersistentBufferDataSize = 728;
#else
@@ -108,7 +108,7 @@
// Tail size contributed by the conv model excluding the
// RecordingMicroAllocator's overhead
// TODO(b/207157610): replace magic number that depends on OPs
-constexpr int kTestConvModelOnlyTailSize = 2016;
+constexpr int kTestConvModelOnlyTailSize = 2088;
constexpr int kTestConvModelPersistentTfLiteTensorDataSize = 224;
constexpr int kTestConvModelPersistentBufferDataSize = 720;
#endif
diff --git a/tensorflow/lite/micro/memory_helpers.cc b/tensorflow/lite/micro/memory_helpers.cc
index 685f04b..94a6fe3 100644
--- a/tensorflow/lite/micro/memory_helpers.cc
+++ b/tensorflow/lite/micro/memory_helpers.cc
@@ -50,6 +50,9 @@
case kTfLiteFloat16:
*size = sizeof(int16_t);
break;
+ case kTfLiteBFloat16:
+ *size = sizeof(int16_t);
+ break;
case kTfLiteFloat32:
*size = sizeof(float);
break;
diff --git a/tensorflow/lite/micro/memory_helpers_test.cc b/tensorflow/lite/micro/memory_helpers_test.cc
index e44c586..9da2940 100644
--- a/tensorflow/lite/micro/memory_helpers_test.cc
+++ b/tensorflow/lite/micro/memory_helpers_test.cc
@@ -180,8 +180,8 @@
tflite::TfLiteTypeSizeOf(kTfLiteComplex128, &size));
TF_LITE_MICRO_EXPECT_EQ(sizeof(double) * 2, size);
- TF_LITE_MICRO_EXPECT_NE(
- kTfLiteOk, tflite::TfLiteTypeSizeOf(static_cast<TfLiteType>(-1), &size));
+ TF_LITE_MICRO_EXPECT_NE(kTfLiteOk,
+ tflite::TfLiteTypeSizeOf(kTfLiteNoType, &size));
}
TF_LITE_MICRO_TEST(TestBytesRequiredForTensor) {
diff --git a/tensorflow/lite/micro/memory_planner/greedy_memory_planner.h b/tensorflow/lite/micro/memory_planner/greedy_memory_planner.h
index ae3705d..b2cdb61 100644
--- a/tensorflow/lite/micro/memory_planner/greedy_memory_planner.h
+++ b/tensorflow/lite/micro/memory_planner/greedy_memory_planner.h
@@ -107,6 +107,11 @@
return per_buffer_size;
}
+ // Returns False because the GreedyMemoryPlanner doesn't preserves all tensors
+ // after invocation. Do to the fact that tensors that tensor data for tensors
+ // that aren't being used during a phase of invocation are overwritten.
+ bool preserves_all_tensors() const override { return false; }
+
private:
// Whether a buffer is active in a given time range.
bool DoesEntryOverlapInTime(const ListEntry* entry, const int first_time_used,
diff --git a/tensorflow/lite/micro/memory_planner/linear_memory_planner.cc b/tensorflow/lite/micro/memory_planner/linear_memory_planner.cc
index 5c6afb5..00d707a 100644
--- a/tensorflow/lite/micro/memory_planner/linear_memory_planner.cc
+++ b/tensorflow/lite/micro/memory_planner/linear_memory_planner.cc
@@ -19,6 +19,9 @@
namespace tflite {
+// C++11 requires defining a constexpr static class member in a .cc file
+constexpr int tflite::LinearMemoryPlanner::kMaxBufferCount;
+
LinearMemoryPlanner::LinearMemoryPlanner()
: current_buffer_count_(0), next_free_offset_(0) {}
LinearMemoryPlanner::~LinearMemoryPlanner() {}
diff --git a/tensorflow/lite/micro/memory_planner/linear_memory_planner.h b/tensorflow/lite/micro/memory_planner/linear_memory_planner.h
index d4938dd..9850569 100644
--- a/tensorflow/lite/micro/memory_planner/linear_memory_planner.h
+++ b/tensorflow/lite/micro/memory_planner/linear_memory_planner.h
@@ -35,6 +35,10 @@
int GetBufferCount() override;
TfLiteStatus GetOffsetForBuffer(int buffer_index, int* offset) override;
+ // Returns True because the LinearMemoryPlanner preserves all tensors after
+ // invocation.
+ bool preserves_all_tensors() const override { return true; }
+
private:
static constexpr int kMaxBufferCount = 1024;
size_t buffer_offsets_[kMaxBufferCount];
diff --git a/tensorflow/lite/micro/memory_planner/micro_memory_planner.h b/tensorflow/lite/micro/memory_planner/micro_memory_planner.h
index 0bfe693..035f467 100644
--- a/tensorflow/lite/micro/memory_planner/micro_memory_planner.h
+++ b/tensorflow/lite/micro/memory_planner/micro_memory_planner.h
@@ -81,6 +81,10 @@
return kTfLiteOk;
}
+ // Method will return True if the MicroMemoryPlanner preserves all tensors
+ // after invocation, and False if it doesn't.
+ virtual bool preserves_all_tensors() const = 0;
+
virtual void PrintMemoryPlan() {
// Default does nothing.
}
diff --git a/tensorflow/lite/micro/memory_planner/non_persistent_buffer_planner_shim.h b/tensorflow/lite/micro/memory_planner/non_persistent_buffer_planner_shim.h
index 8f9bb26..13a3fad 100644
--- a/tensorflow/lite/micro/memory_planner/non_persistent_buffer_planner_shim.h
+++ b/tensorflow/lite/micro/memory_planner/non_persistent_buffer_planner_shim.h
@@ -115,6 +115,10 @@
size_t GetMaximumMemorySize() override;
int GetBufferCount() override;
+ // Returns False because the NonPersistentMemoryPlannerShim doesn't preserves
+ // all tensors after invocation.
+ bool preserves_all_tensors() const override { return false; }
+
private:
const BufferPlan* buffer_plan_; // not owned, can't be null
diff --git a/tensorflow/lite/micro/micro_allocation_info.h b/tensorflow/lite/micro/micro_allocation_info.h
index 688d04e..c0c7bd5 100644
--- a/tensorflow/lite/micro/micro_allocation_info.h
+++ b/tensorflow/lite/micro/micro_allocation_info.h
@@ -129,7 +129,8 @@
const tflite::Model* model_ = nullptr;
INonPersistentBufferAllocator* non_persistent_allocator_ = nullptr;
- GraphAllocationInfo info_;
+ GraphAllocationInfo info_ =
+ {}; // Prevents problems caused by accessing uninitialized memory.
int allocation_scope_count_ = 0;
};
diff --git a/tensorflow/lite/micro/micro_allocator.cc b/tensorflow/lite/micro/micro_allocator.cc
index ba7cb66..930da75 100644
--- a/tensorflow/lite/micro/micro_allocator.cc
+++ b/tensorflow/lite/micro/micro_allocator.cc
@@ -28,13 +28,13 @@
#include "tensorflow/lite/micro/flatbuffer_utils.h"
#include "tensorflow/lite/micro/memory_helpers.h"
#include "tensorflow/lite/micro/memory_planner/greedy_memory_planner.h"
+#include "tensorflow/lite/micro/memory_planner/linear_memory_planner.h"
#include "tensorflow/lite/micro/memory_planner/micro_memory_planner.h"
#include "tensorflow/lite/micro/micro_allocation_info.h"
#include "tensorflow/lite/micro/micro_arena_constants.h"
#include "tensorflow/lite/micro/micro_log.h"
#include "tensorflow/lite/micro/tflite_bridge/flatbuffer_conversions_bridge.h"
#include "tensorflow/lite/schema/schema_generated.h"
-#include "tensorflow/lite/schema/schema_utils.h"
namespace tflite {
@@ -71,6 +71,29 @@
IPersistentBufferAllocator* persistent_allocator_;
};
+MicroMemoryPlanner* CreateMemoryPlanner(
+ MemoryPlannerType memory_planner_type,
+ IPersistentBufferAllocator* memory_allocator) {
+ MicroMemoryPlanner* memory_planner = nullptr;
+ uint8_t* memory_planner_buffer = nullptr;
+
+ switch (memory_planner_type) {
+ case MemoryPlannerType::kLinear: {
+ memory_planner_buffer = memory_allocator->AllocatePersistentBuffer(
+ sizeof(LinearMemoryPlanner), alignof(LinearMemoryPlanner));
+ memory_planner = new (memory_planner_buffer) LinearMemoryPlanner();
+ break;
+ }
+ case MemoryPlannerType::kGreedy: {
+ memory_planner_buffer = memory_allocator->AllocatePersistentBuffer(
+ sizeof(GreedyMemoryPlanner), alignof(GreedyMemoryPlanner));
+ memory_planner = new (memory_planner_buffer) GreedyMemoryPlanner();
+ break;
+ }
+ }
+ return memory_planner;
+}
+
TfLiteStatus CreatePlan(MicroMemoryPlanner* planner,
const AllocationInfo* allocation_info,
size_t allocation_info_size) {
@@ -374,8 +397,8 @@
return Create(memory_allocator, memory_planner);
}
-MicroAllocator* MicroAllocator::Create(uint8_t* tensor_arena,
- size_t arena_size) {
+MicroAllocator* MicroAllocator::Create(uint8_t* tensor_arena, size_t arena_size,
+ MemoryPlannerType memory_planner_type) {
uint8_t* aligned_arena =
AlignPointerUp(tensor_arena, MicroArenaBufferAlignment());
size_t aligned_arena_size = tensor_arena + arena_size - aligned_arena;
@@ -384,10 +407,8 @@
// By default create GreedyMemoryPlanner.
// If a different MemoryPlanner is needed, use the other api.
- uint8_t* memory_planner_buffer = memory_allocator->AllocatePersistentBuffer(
- sizeof(GreedyMemoryPlanner), alignof(GreedyMemoryPlanner));
- GreedyMemoryPlanner* memory_planner =
- new (memory_planner_buffer) GreedyMemoryPlanner();
+ MicroMemoryPlanner* memory_planner =
+ CreateMemoryPlanner(memory_planner_type, memory_allocator);
return Create(memory_allocator, memory_planner);
}
@@ -408,7 +429,8 @@
MicroAllocator* MicroAllocator::Create(uint8_t* persistent_tensor_arena,
size_t persistent_arena_size,
uint8_t* non_persistent_tensor_arena,
- size_t non_persistent_arena_size) {
+ size_t non_persistent_arena_size,
+ MemoryPlannerType memory_planner_type) {
TFLITE_DCHECK(persistent_tensor_arena != nullptr);
TFLITE_DCHECK(non_persistent_tensor_arena != nullptr);
TFLITE_DCHECK(persistent_tensor_arena != non_persistent_tensor_arena);
@@ -421,11 +443,22 @@
non_persistent_arena_size,
persistent_buffer_allocator);
- uint8_t* memory_planner_buffer =
- persistent_buffer_allocator->AllocatePersistentBuffer(
- sizeof(GreedyMemoryPlanner), alignof(GreedyMemoryPlanner));
- GreedyMemoryPlanner* memory_planner =
- new (memory_planner_buffer) GreedyMemoryPlanner();
+ // TODO(b/297821738): this should be changed to CreateMemoryPlanner if
+ // possible once it's figured out why it breaks the HifiMini Build
+ uint8_t* memory_planner_buffer = nullptr;
+ MicroMemoryPlanner* memory_planner = nullptr;
+
+ if (memory_planner_type == MemoryPlannerType::kGreedy) {
+ memory_planner_buffer =
+ persistent_buffer_allocator->AllocatePersistentBuffer(
+ sizeof(GreedyMemoryPlanner), alignof(GreedyMemoryPlanner));
+ memory_planner = new (memory_planner_buffer) GreedyMemoryPlanner();
+ } else if (memory_planner_type == MemoryPlannerType::kLinear) {
+ memory_planner_buffer =
+ persistent_buffer_allocator->AllocatePersistentBuffer(
+ sizeof(LinearMemoryPlanner), alignof(LinearMemoryPlanner));
+ memory_planner = new (memory_planner_buffer) LinearMemoryPlanner();
+ }
uint8_t* micro_allocator_buffer =
persistent_buffer_allocator->AllocatePersistentBuffer(
diff --git a/tensorflow/lite/micro/micro_allocator.h b/tensorflow/lite/micro/micro_allocator.h
index 3532577..0231722 100644
--- a/tensorflow/lite/micro/micro_allocator.h
+++ b/tensorflow/lite/micro/micro_allocator.h
@@ -66,6 +66,13 @@
} // namespace internal
+// Enum used to keep track of which MemoryPlanner is being used for
+// MicroAllocater::Create();
+enum class MemoryPlannerType {
+ kGreedy,
+ kLinear,
+};
+
struct NodeAndRegistration {
TfLiteNode node;
const TFLMRegistration* registration;
@@ -117,7 +124,9 @@
// Note: Please use alignas(16) to make sure tensor_arena is 16
// bytes aligned, otherwise some head room will be wasted.
// TODO(b/157615197): Cleanup constructor + factory usage.
- static MicroAllocator* Create(uint8_t* tensor_arena, size_t arena_size);
+ static MicroAllocator* Create(
+ uint8_t* tensor_arena, size_t arena_size,
+ MemoryPlannerType memory_planner_type = MemoryPlannerType::kGreedy);
// Creates a MicroAllocator instance from a given tensor arena and a given
// MemoryPlanner. This arena will be managed by the created instance. Note:
@@ -137,14 +146,20 @@
// SingleArenaBufferAllocator instance and the MemoryPlanner. This allocator
// instance will use the SingleArenaBufferAllocator instance to manage
// allocations internally.
- static MicroAllocator* Create(uint8_t* persistent_tensor_arena,
- size_t persistent_arena_size,
- uint8_t* non_persistent_tensor_arena,
- size_t non_persistent_arena_size);
+ static MicroAllocator* Create(
+ uint8_t* persistent_tensor_arena, size_t persistent_arena_size,
+ uint8_t* non_persistent_tensor_arena, size_t non_persistent_arena_size,
+ MemoryPlannerType memory_planner_type = MemoryPlannerType::kGreedy);
// Returns the fixed amount of memory overhead of MicroAllocator.
static size_t GetDefaultTailUsage(bool is_memory_planner_given);
+ // Returns True if the MicroAllocator uses a LinearMemoryPlanner(is compatible
+ // with the PerserveAllTensors flag / feature ) and False otherwise.
+ bool preserves_all_tensor() const {
+ return memory_planner_->preserves_all_tensors();
+ };
+
// Allocates internal resources required for model inference for each subgraph
// from the arena.
//
@@ -307,7 +322,9 @@
IPersistentBufferAllocator* persistent_buffer_allocator_;
// Allocator used to allocate persistent builtin data.
- TfLiteBridgeBuiltinDataAllocator* builtin_data_allocator_;
+ TfLiteBridgeBuiltinDataAllocator* builtin_data_allocator_ =
+ nullptr; // Initialized as nullptr to prevent any possible issues related
+ // to accessing uninitialized memory.
// Activation buffer memory planner.
MicroMemoryPlanner* memory_planner_;
diff --git a/tensorflow/lite/micro/micro_common.h b/tensorflow/lite/micro/micro_common.h
index dc0bc08..9ab427f 100644
--- a/tensorflow/lite/micro/micro_common.h
+++ b/tensorflow/lite/micro/micro_common.h
@@ -30,4 +30,9 @@
const char* custom_name;
};
+struct TFLMInferenceRegistration {
+ TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node);
+ void (*reset)(TfLiteContext* context, void* buffer);
+};
+
#endif // THIRD_PARTY_TFLITE_MICRO_TENSORFLOW_LITE_MICRO_MICRO_COMMON_H_
diff --git a/tensorflow/lite/micro/micro_context.cc b/tensorflow/lite/micro/micro_context.cc
index 352d2c0..680dee8 100644
--- a/tensorflow/lite/micro/micro_context.cc
+++ b/tensorflow/lite/micro/micro_context.cc
@@ -1,4 +1,4 @@
-/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -17,48 +17,16 @@
#include <cstdarg>
#include <cstddef>
-#include <cstdint>
#include "tensorflow/lite/kernels/internal/compatibility.h"
+#include "tensorflow/lite/micro/micro_common.h"
#include "tensorflow/lite/micro/micro_log.h"
+#include "tensorflow/lite/micro/micro_utils.h"
namespace tflite {
-MicroContext::MicroContext(MicroAllocator* allocator, const Model* model,
- MicroGraph* graph)
- : allocator_(*allocator),
- graph_(*graph),
- model_(model),
- state_(InterpreterState::kInit) {}
+namespace {
-MicroContext::~MicroContext() {}
-
-void* MicroContext::AllocatePersistentBuffer(size_t bytes) {
- TFLITE_DCHECK(state_ == InterpreterState::kPrepare ||
- state_ == InterpreterState::kInit);
- return allocator_.AllocatePersistentBuffer(bytes);
-}
-
-TfLiteStatus MicroContext::RequestScratchBufferInArena(size_t bytes,
- int* buffer_idx) {
- TFLITE_DCHECK(state_ == InterpreterState::kPrepare);
- return allocator_.RequestScratchBufferInArena(
- bytes, graph_.GetCurrentSubgraphIndex(), buffer_idx);
-}
-
-void* MicroContext::GetScratchBuffer(int buffer_idx) {
- TFLITE_DCHECK(state_ == InterpreterState::kInvoke);
- ScratchBufferHandle* handle = scratch_buffer_handles_ + buffer_idx;
- return handle->data;
-}
-
-TfLiteTensor* MicroContext::AllocateTempTfLiteTensor(int tensor_idx) {
- return allocator_.AllocateTempTfLiteTensor(model_, graph_.GetAllocations(),
- tensor_idx,
- graph_.GetCurrentSubgraphIndex());
-}
-
-int MicroContext::GetTensorIndex(int index, int max_size,
- const int* tensor_indices) {
+int GetTensorIndex(int index, int max_size, const int* tensor_indices) {
if (index >= 0 && index < max_size) {
const int tensor_index = tensor_indices[index];
if (tensor_index != kTfLiteOptionalTensor) {
@@ -68,6 +36,105 @@
return -1;
}
+#ifdef USE_TFLM_COMPRESSION
+
+struct DecompressionState {
+ DecompressionState() = delete;
+
+ DecompressionState(const uint8_t* compressed_indices,
+ const size_t count_indices,
+ const CompressionTensorData& comp_data,
+ const size_t num_channels)
+ : compressed_indices_(compressed_indices),
+ count_indices_(count_indices),
+ comp_data_(comp_data),
+ num_channels_(num_channels) {}
+
+ template <typename T>
+ T* DecompressToBuffer(void* buffer);
+
+ size_t GetNextTableIndex();
+ void UpdateBufferAndChannelIndex();
+
+ private:
+ const uint8_t* compressed_indices_;
+ const size_t count_indices_;
+ const CompressionTensorData& comp_data_;
+ const size_t num_channels_;
+ const size_t compressed_bit_width_ =
+ comp_data_.data.lut_data->compressed_bit_width;
+ size_t channel_ = 0;
+ size_t index_in_channel_ = 0;
+ const size_t elements_per_channel_ =
+ comp_data_.data.lut_data->use_alternate_axis
+ ? 1
+ : count_indices_ / num_channels_;
+ size_t buffer_index_ = 0;
+ size_t current_offset_ = 0;
+ size_t current_bits_remaining_ = 8;
+ uint8_t current_byte_ = compressed_indices_[0];
+};
+
+template <typename T>
+T* DecompressionState::DecompressToBuffer(void* buffer) {
+ while (buffer_index_ < count_indices_) {
+ const size_t table_index = GetNextTableIndex();
+ static_cast<T*>(buffer)[buffer_index_] =
+ static_cast<const T*>(comp_data_.data.lut_data->value_table)
+ [table_index +
+ (channel_ * comp_data_.data.lut_data->value_table_channel_stride)];
+ UpdateBufferAndChannelIndex();
+ }
+
+ return static_cast<T*>(buffer);
+}
+
+size_t DecompressionState::GetNextTableIndex() {
+ TFLITE_DCHECK(compressed_bit_width_ <= LookupTableData::kMaxBitWidth);
+ TFLITE_DCHECK(compressed_bit_width_ > 0);
+
+ size_t table_index_bits_to_fill = compressed_bit_width_;
+ size_t table_index = 0;
+
+ while (table_index_bits_to_fill > 0) {
+ if (current_bits_remaining_ == 0) {
+ current_offset_++;
+ current_byte_ = compressed_indices_[current_offset_];
+ current_bits_remaining_ = 8;
+ }
+
+ const uint8_t mask_bit_count =
+ std::min(table_index_bits_to_fill,
+ std::min(compressed_bit_width_, current_bits_remaining_));
+ const uint8_t current_byte_mask = (1 << mask_bit_count) - 1;
+ table_index <<= mask_bit_count;
+ table_index |=
+ (current_byte_ >> (current_bits_remaining_ - mask_bit_count)) &
+ current_byte_mask;
+
+ table_index_bits_to_fill -= mask_bit_count;
+ current_bits_remaining_ -= mask_bit_count;
+ }
+
+ return table_index;
+}
+
+void DecompressionState::UpdateBufferAndChannelIndex() {
+ buffer_index_++;
+ index_in_channel_++;
+ if (index_in_channel_ == elements_per_channel_) {
+ index_in_channel_ = 0;
+ channel_++;
+ if (channel_ == num_channels_) {
+ channel_ = 0;
+ }
+ }
+}
+
+#endif // USE_TFLM_COMPRESSION
+
+} // namespace
+
TfLiteTensor* MicroContext::AllocateTempInputTensor(const TfLiteNode* node,
int index) {
const int tensor_index =
@@ -98,46 +165,6 @@
return AllocateTempTfLiteTensor(tensor_index);
}
-void MicroContext::DeallocateTempTfLiteTensor(TfLiteTensor* tensor) {
- return allocator_.DeallocateTempTfLiteTensor(tensor);
-}
-
-uint8_t* MicroContext::AllocateTempBuffer(size_t size, size_t alignment) {
- TFLITE_DCHECK(state_ == InterpreterState::kPrepare);
- return allocator_.AllocateTempBuffer(size, alignment);
-}
-
-void MicroContext::DeallocateTempBuffer(uint8_t* buffer) {
- TFLITE_DCHECK(state_ == InterpreterState::kPrepare);
- allocator_.DeallocateTempBuffer(buffer);
-}
-
-TfLiteEvalTensor* MicroContext::GetEvalTensor(int tensor_idx) {
- return &graph_.GetAllocations()[graph_.GetCurrentSubgraphIndex()]
- .tensors[tensor_idx];
-}
-
-void MicroContext::SetScratchBufferHandles(
- ScratchBufferHandle* scratch_buffer_handles) {
- scratch_buffer_handles_ = scratch_buffer_handles;
-}
-
-TfLiteStatus MicroContext::set_external_context(
- void* external_context_payload) {
- TFLITE_DCHECK(state_ == InterpreterState::kPrepare ||
- state_ == InterpreterState::kInvoke);
- if (external_context_payload == nullptr ||
- external_context_payload_ != nullptr) {
- MicroPrintf(
- "Attempting to set external context to %x but it was %x already",
- external_context_payload, external_context_payload_);
- return kTfLiteError;
- }
-
- external_context_payload_ = external_context_payload;
- return kTfLiteOk;
-}
-
void MicroContextReportOpError(struct TfLiteContext* context,
const char* format, ...) {
va_list args;
@@ -146,12 +173,56 @@
va_end(args);
}
-void MicroContext::SetInterpreterState(MicroContext::InterpreterState state) {
- state_ = state;
+#ifdef USE_TFLM_COMPRESSION
+
+void* MicroContext::DecompressTensorToScratchBuffer(
+ const TfLiteEvalTensor& tensor,
+ const CompressionTensorData& compression_data, int scratch_buffer_handle) {
+ TFLITE_DCHECK(compression_data.scheme == CompressionScheme::kBinQuant);
+ TFLITE_DCHECK(scratch_buffer_handle != -1);
+ void* scratch_buffer = GetScratchBuffer(scratch_buffer_handle);
+ TFLITE_DCHECK(scratch_buffer != nullptr);
+ size_t count = ElementCount(*tensor.dims);
+ size_t num_channels = 1;
+
+ if (compression_data.data.lut_data->is_per_channel_quantized) {
+ const size_t channel_axis =
+ compression_data.data.lut_data->use_alternate_axis
+ ? tensor.dims->size - 1
+ : 0;
+ num_channels = tensor.dims->data[channel_axis];
+ }
+
+ DecompressionState ds(static_cast<uint8_t*>(tensor.data.data), count,
+ compression_data, num_channels);
+
+ switch (tensor.type) {
+ case kTfLiteBool: {
+ return ds.DecompressToBuffer<bool>(scratch_buffer);
+ } break;
+ case kTfLiteInt8: {
+ return ds.DecompressToBuffer<int8_t>(scratch_buffer);
+ } break;
+ case kTfLiteInt16: {
+ return ds.DecompressToBuffer<int16_t>(scratch_buffer);
+ } break;
+ case kTfLiteInt32: {
+ return ds.DecompressToBuffer<int32_t>(scratch_buffer);
+ } break;
+ case kTfLiteInt64: {
+ return ds.DecompressToBuffer<int64_t>(scratch_buffer);
+ } break;
+ case kTfLiteFloat32: {
+ return ds.DecompressToBuffer<float>(scratch_buffer);
+ } break;
+ default: {
+ MicroPrintf("Unsupported decompression tensor type %d", tensor.type);
+ } break;
+ }
+
+ return nullptr;
}
-MicroContext::InterpreterState MicroContext::GetInterpreterState() const {
- return state_;
-}
+#endif // USE_TFLM_COMPRESSION
} // namespace tflite
diff --git a/tensorflow/lite/micro/micro_context.h b/tensorflow/lite/micro/micro_context.h
index 73df9e6..33cad89 100644
--- a/tensorflow/lite/micro/micro_context.h
+++ b/tensorflow/lite/micro/micro_context.h
@@ -1,4 +1,4 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -17,9 +17,14 @@
#define TENSORFLOW_LITE_MICRO_MICRO_CONTEXT_H_
#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/micro/micro_allocator.h"
#include "tensorflow/lite/micro/micro_graph.h"
+#ifdef USE_TFLM_COMPRESSION
+
+#include "tensorflow/lite/micro/compression.h"
+
+#endif // USE_TFLM_COMPRESSION
+
namespace tflite {
// TODO(b/149795762): kTfLiteAbort cannot be part of the tflite TfLiteStatus.
const TfLiteStatus kTfLiteAbort = static_cast<TfLiteStatus>(15);
@@ -32,117 +37,95 @@
// micro_context-><TFLM kernel API>
class MicroContext {
public:
- // Enum that allows MicroContext to keep track of the stages different memory
- // planning APIs are available to kernels.
- enum class InterpreterState {
- kInit,
- kPrepare,
- kMemoryPlanning,
- kInvoke,
- };
-
- // Does not take any ownership, and all pointers must refer to valid objects
- // that outlive the one constructed.
- explicit MicroContext(MicroAllocator* allocator, const Model* model,
- MicroGraph* graph);
- virtual ~MicroContext();
+ virtual ~MicroContext() = default;
// Allocate persistent buffer which has the same life time as the interpreter.
// Returns nullptr on failure.
// The memory is allocated from the tail.
// This method is only available in Init or Prepare stage.
- // Virtual so that it can be faked for kernel tests.
- virtual void* AllocatePersistentBuffer(size_t bytes);
+ virtual void* AllocatePersistentBuffer(size_t bytes) = 0;
// Request a scratch buffer in the arena through static memory planning.
// This method is only available in Prepare stage and the buffer is allocated
// by the interpreter between Prepare and Eval stage. In Eval stage,
// GetScratchBuffer API can be used to fetch the address.
- // Virtual so that it can be faked for kernel tests.
virtual TfLiteStatus RequestScratchBufferInArena(size_t bytes,
- int* buffer_idx);
+ int* buffer_idx) = 0;
// Get the scratch buffer pointer.
// This method is only available in Eval stage.
- // Virtual so that it can be faked for kernel tests.
- virtual void* GetScratchBuffer(int buffer_idx);
+ virtual void* GetScratchBuffer(int buffer_idx) = 0;
// Returns a temporary TfLiteTensor struct for a given index.
- // Virtual so that it can be faked for kernel tests.
- virtual TfLiteTensor* AllocateTempTfLiteTensor(int tensor_idx);
+ virtual TfLiteTensor* AllocateTempTfLiteTensor(int tensor_idx) = 0;
// Returns a temporary TfLiteTensor struct for the specified input tensor of a
// given mode. This is the recommended API over the deprecated
// GetInput/GetInputSafe to get a temp input tensor. The returned tensor shall
// be freed via calling DeallocateTempTfLiteTensor.
- virtual TfLiteTensor* AllocateTempInputTensor(const TfLiteNode* node,
- int index);
+ TfLiteTensor* AllocateTempInputTensor(const TfLiteNode* node, int index);
// Returns a temporary TfLiteTensor struct for the specified output tensor of
// a given mode. This is the recommended API over the deprecated
// GetOutput/GetOutputSafe to get a temp output tensor. The returned tensor
// shall be freed via calling DeallocateTempTfLiteTensor.
- virtual TfLiteTensor* AllocateTempOutputTensor(const TfLiteNode* node,
- int index);
+ TfLiteTensor* AllocateTempOutputTensor(const TfLiteNode* node, int index);
// Returns a temporary TfLiteTensor struct for the specified intermediate
// tensor of a given mode. This is the recommended API over the deprecated
// GetIntermediates/GetIntermediatesSafe to get a temp intermediate tensor.
// The returned tensor shall be freed via calling DeallocateTempTfLiteTensor.
- virtual TfLiteTensor* AllocateTempIntermediateTensor(const TfLiteNode* node,
- int index);
+ TfLiteTensor* AllocateTempIntermediateTensor(const TfLiteNode* node,
+ int index);
// Deallocates a temp TfLiteTensor.
- // Virtual so that it can be faked for kernel tests.
- virtual void DeallocateTempTfLiteTensor(TfLiteTensor* tensor);
+ virtual void DeallocateTempTfLiteTensor(TfLiteTensor* tensor) = 0;
// Returns a pointer to a temporary buffer (from the arena).
// This API is only valid from the kernel's Prepare function and
// the buffer's lifetime is also that of the Prepare function.
- // Virtual so that it can be faked for kernel tests.
- virtual uint8_t* AllocateTempBuffer(size_t size, size_t alignment);
+ virtual uint8_t* AllocateTempBuffer(size_t size, size_t alignment) = 0;
// Signals that the temporary buffer is no longer needed.
- // Virtual so that it can be faked for kernel tests.
- virtual void DeallocateTempBuffer(uint8_t* buffer);
+ virtual void DeallocateTempBuffer(uint8_t* buffer) = 0;
// Returns a TfLiteEvalTensor struct for a given index.
- // Virtual so that it can be faked for kernel tests.
- virtual TfLiteEvalTensor* GetEvalTensor(int tensor_idx);
-
- // Sets the State of MemoryPlanning MicroContext
- void SetInterpreterState(MicroContext::InterpreterState state);
-
- // Sets the State of MemoryPlanning MicroContext
- MicroContext::InterpreterState GetInterpreterState() const;
+ virtual TfLiteEvalTensor* GetEvalTensor(int tensor_idx) = 0;
// Does not take ownership of the pointer and the pointer must refer to valid
// an object that outlive this class instance.
// This can only be called once to set one external context.
- TfLiteStatus set_external_context(void* external_context_payload);
+ virtual TfLiteStatus set_external_context(void* external_context_payload) = 0;
- void* external_context() { return external_context_payload_; }
+ virtual void* external_context() = 0;
- MicroGraph& graph() { return graph_; }
+ virtual MicroGraph& graph() = 0;
- // Sets the pointer to a list of ScratchBufferHandle instances.
- // Not API between TFLM and kernels. Primarily used by the framework for
- // housekeeping in MicroContext.
- void SetScratchBufferHandles(ScratchBufferHandle* scratch_buffer_handles);
+#ifdef USE_TFLM_COMPRESSION
+
+ // Available during Prepare & Eval. Returns false if tensor is not
+ // compressed.
+ virtual bool IsTensorCompressed(const TfLiteNode* node, int tensor_idx) = 0;
+
+ // Only available during Prepare. The kernel is responsible for storing the
+ // scratch buffer handle.
+ virtual int AllocateDecompressionScratchBuffer(const TfLiteNode* node,
+ int tensor_idx) = 0;
+
+ // Available during Prepare & Eval. Returns nullptr if tensor is not
+ // compressed.
+ virtual const CompressionTensorData* GetTensorCompressionData(
+ const TfLiteNode* node, int tensor_idx) = 0;
+
+ // Only available during Eval. Returns nullptr on failure, otherwise returns a
+ // pointer to the scratch buffer.
+ virtual void* DecompressTensorToScratchBuffer(
+ const TfLiteEvalTensor& tensor,
+ const CompressionTensorData& compression_data, int scratch_buffer_handle);
+
+#endif // USE_TFLM_COMPRESSION
private:
- // Return the tensor index as tensor_indices[index]. tensor_indices is of
- // max_size. Return -1 if index is not in the valid range of tensor_indices.
- int GetTensorIndex(int index, int max_size, const int* tensor_indices);
-
- MicroAllocator& allocator_;
- MicroGraph& graph_;
- const Model* model_;
- InterpreterState state_;
-
- ScratchBufferHandle* scratch_buffer_handles_ = nullptr;
- void* external_context_payload_ = nullptr;
-
TF_LITE_REMOVE_VIRTUAL_DELETE
};
diff --git a/tensorflow/lite/micro/micro_graph.h b/tensorflow/lite/micro/micro_graph.h
index ca8c40e..79b3649 100644
--- a/tensorflow/lite/micro/micro_graph.h
+++ b/tensorflow/lite/micro/micro_graph.h
@@ -16,90 +16,44 @@
#ifndef TENSORFLOW_LITE_MICRO_MICRO_GRAPH_H_
#define TENSORFLOW_LITE_MICRO_MICRO_GRAPH_H_
-#include "tensorflow/lite/micro/micro_allocator.h"
+#include "tensorflow/lite/micro/compatibility.h"
#include "tensorflow/lite/micro/micro_common.h"
#include "tensorflow/lite/micro/micro_resource_variable.h"
-#include "tensorflow/lite/schema/schema_generated.h"
namespace tflite {
-// Abstracts the details of interacting with the tflite::Model.
+// Abstracts the details of interacting with the graph from the kernels
//
-// Provides methods to access, initialize, prepare, invoke and free any
-// subgraph in the tflite::Graph.
+// Provides methods to invoke any subgraph in the tflite::Graph.
class MicroGraph {
public:
- // The lifetime of the context, model, allocator and resource_variables must
- // be at least as long as that of the graph object, since the this class may
- // need to access them at any time. If resource_variables is a nullptr,
- // GetResourceVariables will return a nullptr.
- MicroGraph(TfLiteContext* context, const Model* model,
- MicroAllocator* allocator,
- MicroResourceVariables* resource_variables);
- virtual ~MicroGraph();
-
- // Sets up builtin data and calls TFLMRegistration->Init for every
- // operator in every subgraph in the model.
- virtual TfLiteStatus InitSubgraphs();
-
- // Calls TFLMRegistration->Prepare for every operator in every subgraph
- // in the model.
- virtual TfLiteStatus PrepareSubgraphs();
-
- // Calls TFLMRegistration->Reset for every operator in every subgraph in
- // the model.
- virtual TfLiteStatus ResetSubgraphs();
-
- // Calls TFLMRegistration->Free for every operator in every subgraph in
- // the model.
- virtual TfLiteStatus FreeSubgraphs();
+ virtual ~MicroGraph() = default;
// Calls TFLMRegistration->Invoke for every operator in a single subgraph
// in the model.
- virtual TfLiteStatus InvokeSubgraph(int subgraph_idx);
-
- // Zeros out all variable tensors in all subgraphs in the model.
- virtual TfLiteStatus ResetVariableTensors();
+ virtual TfLiteStatus InvokeSubgraph(int subgraph_idx) = 0;
// Number of tensor inputs to a specified subgraph in the model.
- virtual size_t NumSubgraphInputs(int subgraph_idx);
+ virtual size_t NumSubgraphInputs(int subgraph_idx) = 0;
// Get the specified input tensor of a specified subgraph in the model.
- virtual TfLiteEvalTensor* GetSubgraphInput(int subgraph_idx, int input_idx);
+ virtual TfLiteEvalTensor* GetSubgraphInput(int subgraph_idx,
+ int input_idx) = 0;
// Number of tensor outputs from a specified subgraph in the model.
- virtual size_t NumSubgraphOutputs(int subgraph_idx);
+ virtual size_t NumSubgraphOutputs(int subgraph_idx) = 0;
// Get the specified output tensor of a specified subgraph in the model.
- virtual TfLiteEvalTensor* GetSubgraphOutput(int subgraph_idx, int output_idx);
+ virtual TfLiteEvalTensor* GetSubgraphOutput(int subgraph_idx,
+ int output_idx) = 0;
// Number of subgraphs in the model.
- virtual int NumSubgraphs();
-
- // Hook to pass in subgraph allocations tracked within the interpreter,
- // allowing MicroGraph to init / prepare / invoke subgraphs in the model.
- void SetSubgraphAllocations(SubgraphAllocations* subgraph_allocations);
-
- // Get the current subgraph index. Within an on operator, this is guaranteed
- // to be the subgraph of that operator.
- int GetCurrentSubgraphIndex() { return current_subgraph_index_; }
-
- // Gets the list of alloctions for each subgraph. This is the source of truth
- // for all per-subgraph allocation data.
- SubgraphAllocations* GetAllocations() { return subgraph_allocations_; }
+ virtual int NumSubgraphs() = 0;
// Get the resource variables for this TFLM graph.
- MicroResourceVariables* GetResourceVariables() { return resource_variables_; }
+ virtual MicroResourceVariables* GetResourceVariables() = 0;
private:
- TfLiteContext* context_;
- const Model* model_;
- MicroAllocator* allocator_;
- SubgraphAllocations* subgraph_allocations_ = nullptr;
- int current_subgraph_index_;
- MicroResourceVariables* resource_variables_;
- const flatbuffers::Vector<flatbuffers::Offset<SubGraph>>* subgraphs_;
-
TF_LITE_REMOVE_VIRTUAL_DELETE
};
diff --git a/tensorflow/lite/micro/micro_interpreter.cc b/tensorflow/lite/micro/micro_interpreter.cc
index c6917b4..7f4565e 100644
--- a/tensorflow/lite/micro/micro_interpreter.cc
+++ b/tensorflow/lite/micro/micro_interpreter.cc
@@ -24,7 +24,7 @@
#include "tensorflow/lite/micro/flatbuffer_utils.h"
#include "tensorflow/lite/micro/memory_helpers.h"
#include "tensorflow/lite/micro/micro_allocator.h"
-#include "tensorflow/lite/micro/micro_context.h"
+#include "tensorflow/lite/micro/micro_interpreter_context.h"
#include "tensorflow/lite/micro/micro_log.h"
#include "tensorflow/lite/micro/micro_op_resolver.h"
#include "tensorflow/lite/micro/micro_profiler_interface.h"
@@ -33,17 +33,28 @@
#include "tensorflow/lite/schema/schema_utils.h"
namespace tflite {
+namespace {
+MemoryPlannerType FlagToMemoryPlannerType(bool preserve_all_tensors) {
+ if (preserve_all_tensors) {
+ return MemoryPlannerType::kLinear;
+ } else {
+ return MemoryPlannerType::kGreedy;
+ }
+}
+} // namespace
MicroInterpreter::MicroInterpreter(const Model* model,
const MicroOpResolver& op_resolver,
uint8_t* tensor_arena,
size_t tensor_arena_size,
MicroResourceVariables* resource_variables,
- MicroProfilerInterface* profiler)
+ MicroProfilerInterface* profiler,
+ bool preserve_all_tensors)
: model_(model),
op_resolver_(op_resolver),
- allocator_(*MicroAllocator::Create(tensor_arena, tensor_arena_size)),
-
+ allocator_(*MicroAllocator::Create(
+ tensor_arena, tensor_arena_size,
+ FlagToMemoryPlannerType(preserve_all_tensors))),
graph_(&context_, model, &allocator_, resource_variables),
tensors_allocated_(false),
initialization_status_(kTfLiteError),
@@ -77,7 +88,8 @@
}
void MicroInterpreter::Init(MicroProfilerInterface* profiler) {
- micro_context_.SetInterpreterState(MicroContext::InterpreterState::kInit);
+ micro_context_.SetInterpreterState(
+ MicroInterpreterContext::InterpreterState::kInit);
context_.impl_ = static_cast<void*>(µ_context_);
context_.ReportError = MicroContextReportOpError;
context_.GetTensor = MicroContextGetTensor;
@@ -198,15 +210,17 @@
TF_LITE_ENSURE_STATUS(PrepareNodeAndRegistrationDataFromFlatbuffer());
- micro_context_.SetInterpreterState(MicroContext::InterpreterState::kInit);
+ micro_context_.SetInterpreterState(
+ MicroInterpreterContext::InterpreterState::kInit);
TF_LITE_ENSURE_STATUS(graph_.InitSubgraphs());
- micro_context_.SetInterpreterState(MicroContext::InterpreterState::kPrepare);
+ micro_context_.SetInterpreterState(
+ MicroInterpreterContext::InterpreterState::kPrepare);
TF_LITE_ENSURE_STATUS(graph_.PrepareSubgraphs());
micro_context_.SetInterpreterState(
- MicroContext::InterpreterState::kMemoryPlanning);
+ MicroInterpreterContext::InterpreterState::kMemoryPlanning);
TF_LITE_ENSURE_OK(&context_, allocator_.FinishModelAllocation(
model_, graph_.GetAllocations(),
@@ -261,7 +275,8 @@
TF_LITE_ENSURE_STATUS(Reset());
tensors_allocated_ = true;
- micro_context_.SetInterpreterState(MicroContext::InterpreterState::kInvoke);
+ micro_context_.SetInterpreterState(
+ MicroInterpreterContext::InterpreterState::kInvoke);
return kTfLiteOk;
}
@@ -305,6 +320,15 @@
return graph_.ResetVariableTensors();
}
+TfLiteEvalTensor* MicroInterpreter::GetTensor(int tensor_index,
+ int subgraph_index) {
+ if (!allocator_.preserves_all_tensor()) {
+ MicroPrintf("GetTensor requires all tensors to be preserved");
+ return nullptr;
+ }
+ return &graph_.GetAllocations()[subgraph_index].tensors[tensor_index];
+}
+
TfLiteStatus MicroInterpreter::SetMicroExternalContext(
void* external_context_payload) {
return micro_context_.set_external_context(external_context_payload);
diff --git a/tensorflow/lite/micro/micro_interpreter.h b/tensorflow/lite/micro/micro_interpreter.h
index a77b0e0..1c41996 100644
--- a/tensorflow/lite/micro/micro_interpreter.h
+++ b/tensorflow/lite/micro/micro_interpreter.h
@@ -24,8 +24,8 @@
#include "tensorflow/lite/core/api/error_reporter.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/micro/micro_allocator.h"
-#include "tensorflow/lite/micro/micro_context.h"
-#include "tensorflow/lite/micro/micro_graph.h"
+#include "tensorflow/lite/micro/micro_interpreter_context.h"
+#include "tensorflow/lite/micro/micro_interpreter_graph.h"
#include "tensorflow/lite/micro/micro_op_resolver.h"
#include "tensorflow/lite/micro/micro_profiler_interface.h"
#include "tensorflow/lite/portable_type_to_tflitetype.h"
@@ -50,7 +50,8 @@
MicroInterpreter(const Model* model, const MicroOpResolver& op_resolver,
uint8_t* tensor_arena, size_t tensor_arena_size,
MicroResourceVariables* resource_variables = nullptr,
- MicroProfilerInterface* profiler = nullptr);
+ MicroProfilerInterface* profiler = nullptr,
+ bool preserve_all_tensors = false);
// Create an interpreter instance using an existing MicroAllocator instance.
// This constructor should be used when creating an allocator that needs to
@@ -115,6 +116,9 @@
return nullptr;
}
+ // Returns a pointer to the tensor for the corresponding tensor_index
+ TfLiteEvalTensor* GetTensor(int tensor_index, int subgraph_index = 0);
+
// Reset the state to be what you would expect when the interpreter is first
// created. i.e. after Init and Prepare is called for the very first time.
TfLiteStatus Reset();
@@ -135,6 +139,13 @@
// arena_used_bytes() + 16.
size_t arena_used_bytes() const { return allocator_.used_bytes(); }
+ // Returns True if all Tensors are being preserves
+ // TODO(b/297106074) : revisit making C++ example or test for
+ // preserve_all_tesnors
+ bool preserve_all_tensors() const {
+ return allocator_.preserves_all_tensor();
+ }
+
protected:
const MicroAllocator& allocator() const { return allocator_; }
const TfLiteContext& context() const { return context_; }
@@ -151,7 +162,7 @@
const MicroOpResolver& op_resolver_;
TfLiteContext context_ = {};
MicroAllocator& allocator_;
- MicroGraph graph_;
+ MicroInterpreterGraph graph_;
bool tensors_allocated_;
TfLiteStatus initialization_status_;
@@ -163,7 +174,7 @@
TfLiteTensor** input_tensors_;
TfLiteTensor** output_tensors_;
- MicroContext micro_context_;
+ MicroInterpreterContext micro_context_;
};
} // namespace tflite
diff --git a/tensorflow/lite/micro/micro_interpreter_context.cc b/tensorflow/lite/micro/micro_interpreter_context.cc
new file mode 100644
index 0000000..0ba461f
--- /dev/null
+++ b/tensorflow/lite/micro/micro_interpreter_context.cc
@@ -0,0 +1,208 @@
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/lite/micro/micro_interpreter_context.h"
+
+#include <cstdint>
+
+#include "tensorflow/lite/kernels/internal/compatibility.h"
+#include "tensorflow/lite/micro/micro_utils.h"
+
+namespace tflite {
+
+namespace {
+
+#ifdef USE_TFLM_COMPRESSION
+
+int GetInputTensorIndex(const TfLiteNode* node, const int index) {
+ if (index >= 0 && index < node->inputs->size) {
+ const int tensor_index = node->inputs->data[index];
+ if (tensor_index != kTfLiteOptionalTensor) {
+ return tensor_index;
+ }
+ }
+ return -1;
+}
+
+#endif // USE_TFLM_COMPRESSION
+
+} // namespace
+
+MicroInterpreterContext::MicroInterpreterContext(MicroAllocator* allocator,
+ const Model* model,
+ MicroInterpreterGraph* graph)
+ : allocator_(*allocator),
+ graph_(*graph),
+ model_(model),
+ state_(InterpreterState::kInit) {}
+
+MicroInterpreterContext::~MicroInterpreterContext() {}
+
+void* MicroInterpreterContext::AllocatePersistentBuffer(size_t bytes) {
+ TFLITE_DCHECK(state_ == InterpreterState::kPrepare ||
+ state_ == InterpreterState::kInit);
+ return allocator_.AllocatePersistentBuffer(bytes);
+}
+
+TfLiteStatus MicroInterpreterContext::RequestScratchBufferInArena(
+ size_t bytes, int* buffer_idx) {
+ TFLITE_DCHECK(state_ == InterpreterState::kPrepare);
+ return allocator_.RequestScratchBufferInArena(
+ bytes, graph_.GetCurrentSubgraphIndex(), buffer_idx);
+}
+
+void* MicroInterpreterContext::GetScratchBuffer(int buffer_idx) {
+ TFLITE_DCHECK(state_ == InterpreterState::kInvoke);
+ ScratchBufferHandle* handle = scratch_buffer_handles_ + buffer_idx;
+ return handle->data;
+}
+
+TfLiteTensor* MicroInterpreterContext::AllocateTempTfLiteTensor(
+ int tensor_idx) {
+ return allocator_.AllocateTempTfLiteTensor(model_, graph_.GetAllocations(),
+ tensor_idx,
+ graph_.GetCurrentSubgraphIndex());
+}
+
+void MicroInterpreterContext::DeallocateTempTfLiteTensor(TfLiteTensor* tensor) {
+ return allocator_.DeallocateTempTfLiteTensor(tensor);
+}
+
+uint8_t* MicroInterpreterContext::AllocateTempBuffer(size_t size,
+ size_t alignment) {
+ TFLITE_DCHECK(state_ == InterpreterState::kPrepare);
+ return allocator_.AllocateTempBuffer(size, alignment);
+}
+
+void MicroInterpreterContext::DeallocateTempBuffer(uint8_t* buffer) {
+ TFLITE_DCHECK(state_ == InterpreterState::kPrepare);
+ allocator_.DeallocateTempBuffer(buffer);
+}
+
+TfLiteEvalTensor* MicroInterpreterContext::GetEvalTensor(int tensor_idx) {
+ return &graph_.GetAllocations()[graph_.GetCurrentSubgraphIndex()]
+ .tensors[tensor_idx];
+}
+
+void MicroInterpreterContext::SetScratchBufferHandles(
+ ScratchBufferHandle* scratch_buffer_handles) {
+ scratch_buffer_handles_ = scratch_buffer_handles;
+}
+
+TfLiteStatus MicroInterpreterContext::set_external_context(
+ void* external_context_payload) {
+ TFLITE_DCHECK(state_ == InterpreterState::kPrepare ||
+ state_ == InterpreterState::kInvoke);
+ if (external_context_payload == nullptr ||
+ external_context_payload_ != nullptr) {
+ MicroPrintf(
+ "Attempting to set external context to %x but it was %x already",
+ external_context_payload, external_context_payload_);
+ return kTfLiteError;
+ }
+
+ external_context_payload_ = external_context_payload;
+ return kTfLiteOk;
+}
+
+void MicroInterpreterContext::SetInterpreterState(InterpreterState state) {
+ state_ = state;
+}
+
+MicroInterpreterContext::InterpreterState
+MicroInterpreterContext::GetInterpreterState() const {
+ return state_;
+}
+
+#ifdef USE_TFLM_COMPRESSION
+
+// Available during Prepare & Eval. Returns false if tensor is not
+// compressed.
+bool MicroInterpreterContext::IsTensorCompressed(const TfLiteNode* node,
+ int tensor_idx) {
+ TFLITE_DCHECK(state_ == InterpreterState::kPrepare ||
+ state_ == InterpreterState::kInvoke);
+
+ const SubgraphAllocations* allocations =
+ &graph_.GetAllocations()[graph_.GetCurrentSubgraphIndex()];
+ if (allocations->compressed.tensors == nullptr) {
+ return false;
+ }
+ int index = GetInputTensorIndex(node, tensor_idx);
+ if (index == -1) {
+ return false;
+ }
+ return allocations->compressed.tensors[index] != nullptr;
+}
+
+// Only available during Prepare. The kernel is responsible for storing the
+// scratch buffer handle.
+int MicroInterpreterContext::AllocateDecompressionScratchBuffer(
+ const TfLiteNode* node, int tensor_idx) {
+ TFLITE_DCHECK(state_ == InterpreterState::kPrepare);
+
+ const SubgraphAllocations* allocations =
+ &graph_.GetAllocations()[graph_.GetCurrentSubgraphIndex()];
+ if (allocations->compressed.tensors == nullptr) {
+ return -1;
+ }
+ int index = GetInputTensorIndex(node, tensor_idx);
+ if (index == -1 || allocations->compressed.tensors[index] == nullptr) {
+ return -1;
+ }
+ const TfLiteEvalTensor* tensor = &allocations->tensors[index];
+ const size_t byte_count = EvalTensorBytes(tensor);
+ int scratch_index = -1;
+ TfLiteStatus result = RequestScratchBufferInArena(byte_count, &scratch_index);
+ if (result != kTfLiteOk) {
+ return -1;
+ }
+
+ return scratch_index;
+}
+
+// Available during Prepare & Eval. Returns nullptr if tensor is not
+// compressed.
+const CompressionTensorData* MicroInterpreterContext::GetTensorCompressionData(
+ const TfLiteNode* node, int tensor_idx) {
+ TFLITE_DCHECK(state_ == InterpreterState::kPrepare ||
+ state_ == InterpreterState::kInvoke);
+
+ const SubgraphAllocations* allocations =
+ &graph_.GetAllocations()[graph_.GetCurrentSubgraphIndex()];
+ if (allocations->compressed.tensors == nullptr) {
+ return nullptr;
+ }
+ int index = GetInputTensorIndex(node, tensor_idx);
+ if (index == -1) {
+ return nullptr;
+ }
+ return allocations->compressed.tensors[index];
+}
+
+// Only available during Eval. Returns nullptr on failure, otherwise returns a
+// pointer to the scratch buffer.
+void* MicroInterpreterContext::DecompressTensorToScratchBuffer(
+ const TfLiteEvalTensor& tensor,
+ const CompressionTensorData& compression_data, int scratch_buffer_handle) {
+ TFLITE_DCHECK(state_ == InterpreterState::kInvoke);
+
+ return MicroContext::DecompressTensorToScratchBuffer(tensor, compression_data,
+ scratch_buffer_handle);
+}
+
+#endif // USE_TFLM_COMPRESSION
+
+} // namespace tflite
diff --git a/tensorflow/lite/micro/micro_interpreter_context.h b/tensorflow/lite/micro/micro_interpreter_context.h
new file mode 100644
index 0000000..7b336aa
--- /dev/null
+++ b/tensorflow/lite/micro/micro_interpreter_context.h
@@ -0,0 +1,148 @@
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_CONTEXT_H_
+#define TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_CONTEXT_H_
+
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/micro/micro_allocator.h"
+#include "tensorflow/lite/micro/micro_context.h"
+#include "tensorflow/lite/micro/micro_interpreter_graph.h"
+#include "tensorflow/lite/micro/micro_log.h"
+
+namespace tflite {
+
+// A full implementation of the MicroContext, to be used by the
+// MicroInterpreter. Kernels should not depend on this directly. Instead they
+// should only depend on the MicroContext.
+class MicroInterpreterContext : public MicroContext {
+ public:
+ // Enum that allows MicroContext to keep track of the stages different memory
+ // planning APIs are available to kernels.
+ enum class InterpreterState {
+ kInit,
+ kPrepare,
+ kMemoryPlanning,
+ kInvoke,
+ };
+
+ // Does not take any ownership, and all pointers must refer to valid objects
+ // that outlive the one constructed.
+ MicroInterpreterContext(MicroAllocator* allocator, const Model* model,
+ MicroInterpreterGraph* graph);
+ virtual ~MicroInterpreterContext();
+
+ // Allocate persistent buffer which has the same life time as the interpreter.
+ // Returns nullptr on failure.
+ // The memory is allocated from the tail.
+ // This method is only available in Init or Prepare stage.
+ // Virtual so that it can be faked for kernel tests.
+ virtual void* AllocatePersistentBuffer(size_t bytes) override;
+
+ // Request a scratch buffer in the arena through static memory planning.
+ // This method is only available in Prepare stage and the buffer is allocated
+ // by the interpreter between Prepare and Eval stage. In Eval stage,
+ // GetScratchBuffer API can be used to fetch the address.
+ // Virtual so that it can be faked for kernel tests.
+ virtual TfLiteStatus RequestScratchBufferInArena(size_t bytes,
+ int* buffer_idx) override;
+
+ // Get the scratch buffer pointer.
+ // This method is only available in Eval stage.
+ // Virtual so that it can be faked for kernel tests.
+ virtual void* GetScratchBuffer(int buffer_idx) override;
+
+ // Returns a temporary TfLiteTensor struct for a given index.
+ // Virtual so that it can be faked for kernel tests.
+ virtual TfLiteTensor* AllocateTempTfLiteTensor(int tensor_idx) override;
+
+ // Deallocates a temp TfLiteTensor.
+ // Virtual so that it can be faked for kernel tests.
+ virtual void DeallocateTempTfLiteTensor(TfLiteTensor* tensor) override;
+
+ // Returns a pointer to a temporary buffer (from the arena).
+ // This API is only valid from the kernel's Prepare function and
+ // the buffer's lifetime is also that of the Prepare function.
+ // Virtual so that it can be faked for kernel tests.
+ virtual uint8_t* AllocateTempBuffer(size_t size, size_t alignment) override;
+
+ // Signals that the temporary buffer is no longer needed.
+ // Virtual so that it can be faked for kernel tests.
+ virtual void DeallocateTempBuffer(uint8_t* buffer) override;
+
+ // Returns a TfLiteEvalTensor struct for a given index.
+ // Virtual so that it can be faked for kernel tests.
+ virtual TfLiteEvalTensor* GetEvalTensor(int tensor_idx) override;
+
+ // Sets the State of MemoryPlanning MicroInterpreterContext
+ void SetInterpreterState(InterpreterState state);
+
+ // Sets the State of MemoryPlanning MicroInterpreterContext
+ InterpreterState GetInterpreterState() const;
+
+ // Does not take ownership of the pointer and the pointer must refer to valid
+ // an object that outlive this class instance.
+ // This can only be called once to set one external context.
+ TfLiteStatus set_external_context(void* external_context_payload) override;
+
+ void* external_context() override { return external_context_payload_; }
+
+ MicroGraph& graph() override { return graph_; }
+
+ // Sets the pointer to a list of ScratchBufferHandle instances.
+ // Not API between TFLM and kernels. Primarily used by the framework for
+ // housekeeping in MicroInterpreterContext.
+ void SetScratchBufferHandles(ScratchBufferHandle* scratch_buffer_handles);
+
+#ifdef USE_TFLM_COMPRESSION
+
+ // Available during Prepare & Eval. Returns false if tensor is not
+ // compressed.
+ bool IsTensorCompressed(const TfLiteNode* node, int tensor_idx) override;
+
+ // Only available during Prepare. The kernel is responsible for storing the
+ // scratch buffer handle.
+ int AllocateDecompressionScratchBuffer(const TfLiteNode* node,
+ int tensor_idx) override;
+
+ // Available during Prepare & Eval. Returns nullptr if tensor is not
+ // compressed.
+ const CompressionTensorData* GetTensorCompressionData(
+ const TfLiteNode* node, int tensor_idx) override;
+
+ // Only available during Eval. Returns nullptr on failure, otherwise returns a
+ // pointer to the scratch buffer.
+ void* DecompressTensorToScratchBuffer(
+ const TfLiteEvalTensor& tensor,
+ const CompressionTensorData& compression_data,
+ int scratch_buffer_handle) override;
+
+#endif // USE_TFLM_COMPRESSION
+
+ private:
+ MicroAllocator& allocator_;
+ MicroInterpreterGraph& graph_;
+ const Model* model_;
+ InterpreterState state_;
+
+ ScratchBufferHandle* scratch_buffer_handles_ = nullptr;
+ void* external_context_payload_ = nullptr;
+
+ TF_LITE_REMOVE_VIRTUAL_DELETE
+};
+
+} // namespace tflite
+
+#endif // TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_CONTEXT_H_
diff --git a/tensorflow/lite/micro/micro_context_test.cc b/tensorflow/lite/micro/micro_interpreter_context_test.cc
similarity index 78%
rename from tensorflow/lite/micro/micro_context_test.cc
rename to tensorflow/lite/micro/micro_interpreter_context_test.cc
index e01d387..3af123f 100644
--- a/tensorflow/lite/micro/micro_context_test.cc
+++ b/tensorflow/lite/micro/micro_interpreter_context_test.cc
@@ -12,12 +12,13 @@
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
-#include "tensorflow/lite/micro/micro_context.h"
+#include "tensorflow/lite/micro/micro_interpreter_context.h"
#include <cstdint>
#include "tensorflow/lite/micro/micro_allocator.h"
#include "tensorflow/lite/micro/micro_arena_constants.h"
+#include "tensorflow/lite/micro/micro_interpreter_graph.h"
#include "tensorflow/lite/micro/test_helpers.h"
#include "tensorflow/lite/micro/testing/micro_test.h"
@@ -26,23 +27,20 @@
namespace tflite {
namespace {
-tflite::MicroContext CreateMicroContext() {
+tflite::MicroInterpreterContext CreateMicroInterpreterContext() {
// Some targets do not support dynamic memory (i.e., no malloc or new), thus,
// the test need to place non-transient memories in static variables. This is
// safe because tests are guaranteed to run serially.
- constexpr size_t kMicroGraphPlacementBufferSize = 1024;
- alignas(4) static uint8_t
- micro_graph_placement_buffer[kMicroGraphPlacementBufferSize];
constexpr size_t kArenaSize = 1024;
static uint8_t tensor_arena[kArenaSize];
const tflite::Model* model = tflite::testing::GetSimpleMockModel();
MicroAllocator* micro_allocator =
MicroAllocator::Create(tensor_arena, kArenaSize);
- MicroGraph* micro_graph = new (micro_graph_placement_buffer)
- MicroGraph(nullptr, nullptr, nullptr, nullptr);
+ static MicroInterpreterGraph micro_graph(nullptr, nullptr, nullptr, nullptr);
- tflite::MicroContext micro_context(micro_allocator, model, micro_graph);
+ tflite::MicroInterpreterContext micro_context(micro_allocator, model,
+ µ_graph);
return micro_context;
}
@@ -58,9 +56,10 @@
// Ensures that a regular set and get pair works ok.
TF_LITE_MICRO_TEST(TestSetGetExternalContextSuccess) {
- tflite::MicroContext micro_context = tflite::CreateMicroContext();
+ tflite::MicroInterpreterContext micro_context =
+ tflite::CreateMicroInterpreterContext();
micro_context.SetInterpreterState(
- tflite::MicroContext::InterpreterState::kInvoke);
+ tflite::MicroInterpreterContext::InterpreterState::kInvoke);
tflite::TestExternalContextPayloadData payload;
TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk,
@@ -75,7 +74,8 @@
}
TF_LITE_MICRO_TEST(TestGetExternalContextWithoutSetShouldReturnNull) {
- tflite::MicroContext micro_context = tflite::CreateMicroContext();
+ tflite::MicroInterpreterContext micro_context =
+ tflite::CreateMicroInterpreterContext();
tflite::TestExternalContextPayloadData* returned_external_context =
reinterpret_cast<tflite::TestExternalContextPayloadData*>(
@@ -86,9 +86,10 @@
}
TF_LITE_MICRO_TEST(TestSetExternalContextCanOnlyBeCalledOnce) {
- tflite::MicroContext micro_context = tflite::CreateMicroContext();
+ tflite::MicroInterpreterContext micro_context =
+ tflite::CreateMicroInterpreterContext();
micro_context.SetInterpreterState(
- tflite::MicroContext::InterpreterState::kPrepare);
+ tflite::MicroInterpreterContext::InterpreterState::kPrepare);
tflite::TestExternalContextPayloadData payload;
TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk,
@@ -100,15 +101,17 @@
}
TF_LITE_MICRO_TEST(TestSetExternalContextToNullShouldFail) {
- tflite::MicroContext micro_context = tflite::CreateMicroContext();
+ tflite::MicroInterpreterContext micro_context =
+ tflite::CreateMicroInterpreterContext();
micro_context.SetInterpreterState(
- tflite::MicroContext::InterpreterState::kPrepare);
+ tflite::MicroInterpreterContext::InterpreterState::kPrepare);
TF_LITE_MICRO_EXPECT_EQ(kTfLiteError,
micro_context.set_external_context(nullptr));
}
TF_LITE_MICRO_TEST(TestGetTempInputTensor) {
- tflite::MicroContext micro_context = tflite::CreateMicroContext();
+ tflite::MicroInterpreterContext micro_context =
+ tflite::CreateMicroInterpreterContext();
TfLiteNode node;
int input_data[] = {2, 0, 1};
@@ -127,7 +130,8 @@
}
TF_LITE_MICRO_TEST(TestGetTempOutputTensor) {
- tflite::MicroContext micro_context = tflite::CreateMicroContext();
+ tflite::MicroInterpreterContext micro_context =
+ tflite::CreateMicroInterpreterContext();
TfLiteNode node;
int output_data[] = {1, 0};
@@ -143,16 +147,18 @@
}
TF_LITE_MICRO_TEST(TestAllocateTempBuffer) {
- tflite::MicroContext micro_context = tflite::CreateMicroContext();
+ tflite::MicroInterpreterContext micro_context =
+ tflite::CreateMicroInterpreterContext();
micro_context.SetInterpreterState(
- tflite::MicroContext::InterpreterState::kPrepare);
+ tflite::MicroInterpreterContext::InterpreterState::kPrepare);
uint8_t* buffer1 =
micro_context.AllocateTempBuffer(10, tflite::MicroArenaBufferAlignment());
TF_LITE_MICRO_EXPECT(buffer1 != nullptr);
}
TF_LITE_MICRO_TEST(TestGetTempIntermediateTensor) {
- tflite::MicroContext micro_context = tflite::CreateMicroContext();
+ tflite::MicroInterpreterContext micro_context =
+ tflite::CreateMicroInterpreterContext();
TfLiteNode node;
int intermediate_data[] = {1, 0};
diff --git a/tensorflow/lite/micro/micro_graph.cc b/tensorflow/lite/micro/micro_interpreter_graph.cc
similarity index 60%
rename from tensorflow/lite/micro/micro_graph.cc
rename to tensorflow/lite/micro/micro_interpreter_graph.cc
index 35c6c1f..7f096ae 100644
--- a/tensorflow/lite/micro/micro_graph.cc
+++ b/tensorflow/lite/micro/micro_interpreter_graph.cc
@@ -13,7 +13,7 @@
limitations under the License.
==============================================================================*/
-#include "tensorflow/lite/micro/micro_graph.h"
+#include "tensorflow/lite/micro/micro_interpreter_graph.h"
#include "flatbuffers/flatbuffers.h" // from @flatbuffers
#include "tensorflow/lite/c/common.h"
@@ -37,34 +37,39 @@
} // namespace
-MicroGraph::MicroGraph(TfLiteContext* context, const Model* model,
- MicroAllocator* allocator,
- MicroResourceVariables* resource_variables)
+MicroInterpreterGraph::MicroInterpreterGraph(
+ TfLiteContext* context, const Model* model, MicroAllocator* allocator,
+ MicroResourceVariables* resource_variables)
: context_(context),
model_(model),
allocator_(allocator),
current_subgraph_index_(0),
+ current_operator_index_(0),
resource_variables_(resource_variables) {
if (model != nullptr) {
subgraphs_ = model->subgraphs();
}
}
-MicroGraph::~MicroGraph() {}
+MicroInterpreterGraph::~MicroInterpreterGraph() {}
-TfLiteStatus MicroGraph::InitSubgraphs() {
+TfLiteStatus MicroInterpreterGraph::InitSubgraphs() {
int previous_subgraph_idx = current_subgraph_index_;
+ uint32_t previous_operator_idx = current_operator_index_;
for (size_t subgraph_idx = 0; subgraph_idx < subgraphs_->size();
subgraph_idx++) {
current_subgraph_index_ = subgraph_idx;
uint32_t operators_size = NumSubgraphOperators(model_, subgraph_idx);
- for (size_t i = 0; i < operators_size; ++i) {
- TfLiteNode* node =
- &(subgraph_allocations_[subgraph_idx].node_and_registrations[i].node);
- const TFLMRegistration* registration = subgraph_allocations_[subgraph_idx]
- .node_and_registrations[i]
- .registration;
+ for (current_operator_index_ = 0; current_operator_index_ < operators_size;
+ ++current_operator_index_) {
+ TfLiteNode* node = &(subgraph_allocations_[subgraph_idx]
+ .node_and_registrations[current_operator_index_]
+ .node);
+ const TFLMRegistration* registration =
+ subgraph_allocations_[subgraph_idx]
+ .node_and_registrations[current_operator_index_]
+ .registration;
size_t init_data_size;
const char* init_data;
if (registration->builtin_code == BuiltinOperator_CUSTOM) {
@@ -81,52 +86,62 @@
}
}
current_subgraph_index_ = previous_subgraph_idx;
+ current_operator_index_ = previous_operator_idx;
return kTfLiteOk;
}
-TfLiteStatus MicroGraph::PrepareSubgraphs() {
+TfLiteStatus MicroInterpreterGraph::PrepareSubgraphs() {
int previous_subgraph_idx = current_subgraph_index_;
-
+ uint32_t previous_operator_idx = current_operator_index_;
for (size_t subgraph_idx = 0; subgraph_idx < subgraphs_->size();
subgraph_idx++) {
current_subgraph_index_ = subgraph_idx;
uint32_t operators_size = NumSubgraphOperators(model_, subgraph_idx);
- for (size_t i = 0; i < operators_size; ++i) {
- TfLiteNode* node =
- &(subgraph_allocations_[subgraph_idx].node_and_registrations[i].node);
- const TFLMRegistration* registration = subgraph_allocations_[subgraph_idx]
- .node_and_registrations[i]
- .registration;
+ for (current_operator_index_ = 0; current_operator_index_ < operators_size;
+ ++current_operator_index_) {
+ TfLiteNode* node = &(subgraph_allocations_[subgraph_idx]
+ .node_and_registrations[current_operator_index_]
+ .node);
+ const TFLMRegistration* registration =
+ subgraph_allocations_[subgraph_idx]
+ .node_and_registrations[current_operator_index_]
+ .registration;
if (registration->prepare != nullptr) {
TfLiteStatus prepare_status = registration->prepare(context_, node);
if (prepare_status != kTfLiteOk) {
MicroPrintf("Node %s (number %df) failed to prepare with status %d",
- OpNameFromRegistration(registration), i, prepare_status);
+ OpNameFromRegistration(registration),
+ current_operator_index_, prepare_status);
return kTfLiteError;
}
}
- allocator_->FinishPrepareNodeAllocations(/*node_id=*/i);
+ allocator_->FinishPrepareNodeAllocations(
+ /*node_id=*/current_operator_index_);
}
}
current_subgraph_index_ = previous_subgraph_idx;
-
+ current_operator_index_ = previous_operator_idx;
return kTfLiteOk;
}
-TfLiteStatus MicroGraph::ResetSubgraphs() {
+TfLiteStatus MicroInterpreterGraph::ResetSubgraphs() {
int previous_subgraph_idx = current_subgraph_index_;
+ uint32_t previous_operator_idx = current_operator_index_;
for (size_t subgraph_idx = 0; subgraph_idx < subgraphs_->size();
subgraph_idx++) {
current_subgraph_index_ = subgraph_idx;
uint32_t operators_size = NumSubgraphOperators(model_, subgraph_idx);
- for (size_t i = 0; i < operators_size; ++i) {
- TfLiteNode* node =
- &(subgraph_allocations_[subgraph_idx].node_and_registrations[i].node);
- const TFLMRegistration* registration = subgraph_allocations_[subgraph_idx]
- .node_and_registrations[i]
- .registration;
+ for (current_operator_index_ = 0; current_operator_index_ < operators_size;
+ ++current_operator_index_) {
+ TfLiteNode* node = &(subgraph_allocations_[subgraph_idx]
+ .node_and_registrations[current_operator_index_]
+ .node);
+ const TFLMRegistration* registration =
+ subgraph_allocations_[subgraph_idx]
+ .node_and_registrations[current_operator_index_]
+ .registration;
// registration is allocated outside the interpreter, so double check to
// make sure it's not nullptr;
if (registration != nullptr && registration->reset != nullptr) {
@@ -135,23 +150,28 @@
}
}
current_subgraph_index_ = previous_subgraph_idx;
+ current_operator_index_ = previous_operator_idx;
return kTfLiteOk;
}
-TfLiteStatus MicroGraph::FreeSubgraphs() {
+TfLiteStatus MicroInterpreterGraph::FreeSubgraphs() {
int previous_subgraph_idx = current_subgraph_index_;
+ uint32_t previous_operator_idx = current_operator_index_;
for (size_t subgraph_idx = 0; subgraph_idx < subgraphs_->size();
subgraph_idx++) {
current_subgraph_index_ = subgraph_idx;
uint32_t operators_size = NumSubgraphOperators(model_, subgraph_idx);
- for (size_t i = 0; i < operators_size; ++i) {
- TfLiteNode* node =
- &(subgraph_allocations_[subgraph_idx].node_and_registrations[i].node);
- const TFLMRegistration* registration = subgraph_allocations_[subgraph_idx]
- .node_and_registrations[i]
- .registration;
+ for (current_operator_index_ = 0; current_operator_index_ < operators_size;
+ ++current_operator_index_) {
+ TfLiteNode* node = &(subgraph_allocations_[subgraph_idx]
+ .node_and_registrations[current_operator_index_]
+ .node);
+ const TFLMRegistration* registration =
+ subgraph_allocations_[subgraph_idx]
+ .node_and_registrations[current_operator_index_]
+ .registration;
// registration is allocated outside the interpreter, so double check to
// make sure it's not nullptr;
if (registration != nullptr && registration->free != nullptr) {
@@ -160,12 +180,14 @@
}
}
current_subgraph_index_ = previous_subgraph_idx;
+ current_operator_index_ = previous_operator_idx;
return kTfLiteOk;
}
-TfLiteStatus MicroGraph::InvokeSubgraph(int subgraph_idx) {
+TfLiteStatus MicroInterpreterGraph::InvokeSubgraph(int subgraph_idx) {
int previous_subgraph_idx = current_subgraph_index_;
+ uint32_t previous_operator_idx = current_operator_index_;
current_subgraph_index_ = subgraph_idx;
if (static_cast<size_t>(subgraph_idx) >= subgraphs_->size()) {
@@ -174,12 +196,15 @@
return kTfLiteError;
}
uint32_t operators_size = NumSubgraphOperators(model_, subgraph_idx);
- for (size_t i = 0; i < operators_size; ++i) {
- TfLiteNode* node =
- &(subgraph_allocations_[subgraph_idx].node_and_registrations[i].node);
- const TFLMRegistration* registration = subgraph_allocations_[subgraph_idx]
- .node_and_registrations[i]
- .registration;
+ for (current_operator_index_ = 0; current_operator_index_ < operators_size;
+ ++current_operator_index_) {
+ TfLiteNode* node = &(subgraph_allocations_[subgraph_idx]
+ .node_and_registrations[current_operator_index_]
+ .node);
+ const TFLMRegistration* registration =
+ subgraph_allocations_[subgraph_idx]
+ .node_and_registrations[current_operator_index_]
+ .registration;
// This ifdef is needed (even though ScopedMicroProfiler itself is a no-op with
// -DTF_LITE_STRIP_ERROR_STRINGS) because the function OpNameFromRegistration is
@@ -201,17 +226,19 @@
if (invoke_status == kTfLiteError) {
MicroPrintf("Node %s (number %d) failed to invoke with status %d",
- OpNameFromRegistration(registration), i, invoke_status);
+ OpNameFromRegistration(registration), current_operator_index_,
+ invoke_status);
return kTfLiteError;
} else if (invoke_status != kTfLiteOk) {
return invoke_status;
}
}
current_subgraph_index_ = previous_subgraph_idx;
+ current_operator_index_ = previous_operator_idx;
return kTfLiteOk;
}
-TfLiteStatus MicroGraph::ResetVariableTensors() {
+TfLiteStatus MicroInterpreterGraph::ResetVariableTensors() {
for (size_t subgraph_idx = 0; subgraph_idx < subgraphs_->size();
subgraph_idx++) {
const SubGraph* subgraph = (*subgraphs_)[subgraph_idx];
@@ -238,30 +265,34 @@
return kTfLiteOk;
}
-int MicroGraph::NumSubgraphs() { return model_->subgraphs()->size(); }
+int MicroInterpreterGraph::NumSubgraphs() {
+ return model_->subgraphs()->size();
+}
-void MicroGraph::SetSubgraphAllocations(
+void MicroInterpreterGraph::SetSubgraphAllocations(
SubgraphAllocations* subgraph_allocations) {
subgraph_allocations_ = subgraph_allocations;
}
-size_t MicroGraph::NumSubgraphInputs(int subgraph_idx) {
+size_t MicroInterpreterGraph::NumSubgraphInputs(int subgraph_idx) {
return model_->subgraphs()->Get(subgraph_idx)->inputs()->size();
}
-TfLiteEvalTensor* MicroGraph::GetSubgraphInput(int subgraph_idx,
- int input_idx) {
+TfLiteEvalTensor* MicroInterpreterGraph::GetSubgraphInput(int subgraph_idx,
+ int input_idx) {
int tensor_idx =
model_->subgraphs()->Get(subgraph_idx)->inputs()->Get(input_idx);
return &subgraph_allocations_[subgraph_idx].tensors[tensor_idx];
}
-size_t MicroGraph::NumSubgraphOutputs(int subgraph_idx) {
- return model_->subgraphs()->Get(subgraph_idx)->outputs()->size();
+size_t MicroInterpreterGraph::NumSubgraphOutputs(int subgraph_idx) {
+ return model_->subgraphs()->Get(subgraph_idx)->outputs() == nullptr
+ ? 0
+ : model_->subgraphs()->Get(subgraph_idx)->outputs()->size();
}
-TfLiteEvalTensor* MicroGraph::GetSubgraphOutput(int subgraph_idx,
- int output_idx) {
+TfLiteEvalTensor* MicroInterpreterGraph::GetSubgraphOutput(int subgraph_idx,
+ int output_idx) {
int tensor_idx =
model_->subgraphs()->Get(subgraph_idx)->outputs()->Get(output_idx);
return &subgraph_allocations_[subgraph_idx].tensors[tensor_idx];
diff --git a/tensorflow/lite/micro/micro_interpreter_graph.h b/tensorflow/lite/micro/micro_interpreter_graph.h
new file mode 100644
index 0000000..5fc3b4c
--- /dev/null
+++ b/tensorflow/lite/micro/micro_interpreter_graph.h
@@ -0,0 +1,118 @@
+/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_GRAPH_H_
+#define TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_GRAPH_H_
+
+#include "tensorflow/lite/micro/micro_allocator.h"
+#include "tensorflow/lite/micro/micro_common.h"
+#include "tensorflow/lite/micro/micro_graph.h"
+#include "tensorflow/lite/micro/micro_resource_variable.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+
+namespace tflite {
+
+// Abstracts the details of interacting with the tflite::Model.
+//
+// Provides methods to access, initialize, prepare, invoke and free any
+// subgraph in the tflite::Graph.
+class MicroInterpreterGraph : public MicroGraph {
+ public:
+ // The lifetime of the context, model, allocator and resource_variables must
+ // be at least as long as that of the graph object, since the this class may
+ // need to access them at any time. If resource_variables is a nullptr,
+ // GetResourceVariables will return a nullptr.
+ MicroInterpreterGraph(TfLiteContext* context, const Model* model,
+ MicroAllocator* allocator,
+ MicroResourceVariables* resource_variables);
+ virtual ~MicroInterpreterGraph();
+
+ // Sets up builtin data and calls TFLMRegistration->Init for every
+ // operator in every subgraph in the model.
+ virtual TfLiteStatus InitSubgraphs();
+
+ // Calls TFLMRegistration->Prepare for every operator in every subgraph
+ // in the model.
+ virtual TfLiteStatus PrepareSubgraphs();
+
+ // Calls TFLMRegistration->Reset for every operator in every subgraph in
+ // the model.
+ virtual TfLiteStatus ResetSubgraphs();
+
+ // Calls TFLMRegistration->Free for every operator in every subgraph in
+ // the model.
+ virtual TfLiteStatus FreeSubgraphs();
+
+ // Calls TFLMRegistration->Invoke for every operator in a single subgraph
+ // in the model.
+ virtual TfLiteStatus InvokeSubgraph(int subgraph_idx);
+
+ // Zeros out all variable tensors in all subgraphs in the model.
+ virtual TfLiteStatus ResetVariableTensors();
+
+ // Number of tensor inputs to a specified subgraph in the model.
+ virtual size_t NumSubgraphInputs(int subgraph_idx);
+
+ // Get the specified input tensor of a specified subgraph in the model.
+ virtual TfLiteEvalTensor* GetSubgraphInput(int subgraph_idx, int input_idx);
+
+ // Number of tensor outputs from a specified subgraph in the model.
+ virtual size_t NumSubgraphOutputs(int subgraph_idx);
+
+ // Get the specified output tensor of a specified subgraph in the model.
+ virtual TfLiteEvalTensor* GetSubgraphOutput(int subgraph_idx, int output_idx);
+
+ // Number of subgraphs in the model.
+ virtual int NumSubgraphs();
+
+ // Hook to pass in subgraph allocations tracked within the interpreter,
+ // allowing MicroInterpreterGraph to init / prepare / invoke subgraphs in the
+ // model.
+ void SetSubgraphAllocations(SubgraphAllocations* subgraph_allocations);
+
+ // Get the current subgraph index. Within an on operator, this is guaranteed
+ // to be the subgraph of that operator.
+ int GetCurrentSubgraphIndex() { return current_subgraph_index_; }
+
+ // Get the current operator index inside a subgraph.
+ // The couple GetCurrentSubgraphIndex GetCurrentSubgraphIndex creates a unique
+ // identifier of the operator inside the subgraph
+ int GetCurrentOperatorIndex() { return current_operator_index_; }
+
+ // Gets the list of allocations for each subgraph. This is the source of truth
+ // for all per-subgraph allocation data.
+ SubgraphAllocations* GetAllocations() { return subgraph_allocations_; }
+
+ // Get the resource variables for this TFLM graph.
+ MicroResourceVariables* GetResourceVariables() { return resource_variables_; }
+
+ private:
+ TfLiteContext* context_;
+ const Model* model_;
+ MicroAllocator* allocator_;
+ SubgraphAllocations* subgraph_allocations_ = nullptr;
+ int current_subgraph_index_;
+ uint32_t current_operator_index_;
+ MicroResourceVariables* resource_variables_;
+ const flatbuffers::Vector<flatbuffers::Offset<SubGraph>>* subgraphs_ =
+ nullptr; // Initialized as nullptr to prevent any possible issues
+ // related to accessing uninitialized memory.
+
+ TF_LITE_REMOVE_VIRTUAL_DELETE
+};
+
+} // namespace tflite
+
+#endif // TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_GRAPH_H_
diff --git a/tensorflow/lite/micro/micro_interpreter_test.cc b/tensorflow/lite/micro/micro_interpreter_test.cc
index 0ba31c4..e44de6b 100644
--- a/tensorflow/lite/micro/micro_interpreter_test.cc
+++ b/tensorflow/lite/micro/micro_interpreter_test.cc
@@ -548,4 +548,23 @@
TF_LITE_MICRO_EXPECT_EQ(interpreter2.Invoke(), kTfLiteOk);
}
+TF_LITE_MICRO_TEST(TestGetTensorFailsNoLinearMemoryPlanner) {
+ const tflite::Model* model = tflite::testing::GetModelWith256x256Tensor();
+ TF_LITE_MICRO_EXPECT(model != nullptr);
+
+ tflite::testing::TestingOpResolver op_resolver;
+ TF_LITE_MICRO_EXPECT_EQ(tflite::testing::GetTestingOpResolver(op_resolver),
+ kTfLiteOk);
+ tflite::MicroInterpreter interpreter(model, op_resolver, tflite::arena_buffer,
+ tflite::buffer_arena_size);
+ TF_LITE_MICRO_EXPECT_EQ(interpreter.AllocateTensors(), kTfLiteOk);
+
+ TF_LITE_MICRO_EXPECT_EQ(interpreter.Invoke(), kTfLiteOk);
+
+ // GetTensor Should return a null_ptr when a linear memory planner isn't used
+ // to initialize it. preserve_all_tensors() getter should also return false
+ TF_LITE_MICRO_EXPECT_EQ(interpreter.preserve_all_tensors(), false);
+ TF_LITE_MICRO_EXPECT(interpreter.GetTensor(0) == nullptr);
+}
+
TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/micro_log.cc b/tensorflow/lite/micro/micro_log.cc
index 45f7051..a08a07d 100644
--- a/tensorflow/lite/micro/micro_log.cc
+++ b/tensorflow/lite/micro/micro_log.cc
@@ -46,4 +46,17 @@
VMicroPrintf(format, args);
va_end(args);
}
+
+int MicroSnprintf(char* buffer, size_t buf_size, const char* format, ...) {
+ va_list args;
+ va_start(args, format);
+ int result = MicroVsnprintf(buffer, buf_size, format, args);
+ va_end(args);
+ return result;
+}
+
+int MicroVsnprintf(char* buffer, size_t buf_size, const char* format,
+ va_list vlist) {
+ return DebugVsnprintf(buffer, buf_size, format, vlist);
+}
#endif // !defined(TF_LITE_STRIP_ERROR_STRINGS)
diff --git a/tensorflow/lite/micro/micro_log.h b/tensorflow/lite/micro/micro_log.h
index 669e9e6..af3c24a 100644
--- a/tensorflow/lite/micro/micro_log.h
+++ b/tensorflow/lite/micro/micro_log.h
@@ -17,15 +17,21 @@
#if !defined(TF_LITE_STRIP_ERROR_STRINGS)
#include <cstdarg>
+#include <cstddef>
// These functions can be used independent of the MicroErrorReporter to get
// printf-like functionalitys and are common to all target platforms.
void MicroPrintf(const char* format, ...);
void VMicroPrintf(const char* format, va_list args);
+int MicroSnprintf(char* buffer, size_t buf_size, const char* format, ...);
+int MicroVsnprintf(char* buffer, size_t buf_size, const char* format,
+ va_list vlist);
#else
// We use a #define to ensure that the strings are completely stripped, to
// prevent an unnecessary increase in the binary size.
#define MicroPrintf(...) tflite::Unused(__VA_ARGS__)
#define VMicroPrintf(...) tflite::Unused(__VA_ARGS__)
+#define MicroSnprintf(...) tflite::Unused<int>(__VA_ARGS__)
+#define MicroVsnprintf(...) tflite::Unused<int>(__VA_ARGS__)
#endif
namespace tflite {
@@ -37,6 +43,12 @@
(void)(sizeof...(args));
}
+template <typename T, typename... Args>
+T Unused(Args&&... args) {
+ (void)(sizeof...(args));
+ return static_cast<T>(0);
+}
+
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_MICRO_LOG_H_
diff --git a/tensorflow/lite/micro/micro_log_test.cc b/tensorflow/lite/micro/micro_log_test.cc
index 97ac8be..7027e01 100644
--- a/tensorflow/lite/micro/micro_log_test.cc
+++ b/tensorflow/lite/micro/micro_log_test.cc
@@ -1,4 +1,4 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -15,18 +15,42 @@
#include "tensorflow/lite/micro/micro_log.h"
-#include "tensorflow/lite/micro/system_setup.h"
+#include <cstddef>
+#include <cstring>
-namespace tflite {
-inline void InitializeTest() { InitializeTarget(); }
-} // namespace tflite
+#include "tensorflow/lite/micro/testing/micro_test.h"
-int main(int argc, char** argv) {
- tflite::InitializeTest();
-#ifndef TF_LITE_STRIP_ERROR_STRINGS
- MicroPrintf("Number: %d", 42);
- MicroPrintf("Badly-formed format string %");
- MicroPrintf("Another % badly-formed %% format string");
- MicroPrintf("~~~%s~~~", "ALL TESTS PASSED");
+namespace {
+
+#if !defined(TF_LITE_STRIP_ERROR_STRINGS)
+constexpr int kMaxBufferSize = 128;
+const char* kFormat = "%2d%6.2f%#5x%5s";
+const char* kExpect = "42 42.42 0x42 \"42\"";
#endif // !defined(TF_LITE_STRIP_ERROR_STRINGS)
+
+} // namespace
+
+TF_LITE_MICRO_TESTS_BEGIN
+
+#if !defined(TF_LITE_STRIP_ERROR_STRINGS)
+
+TF_LITE_MICRO_TEST(MicroPrintfTest) {
+ MicroPrintf("Integer 42: %d", 42);
+ MicroPrintf("Float 42.42: %2.2f", 42.42);
+ MicroPrintf("String \"Hello World!\": %s", "\"Hello World!\"");
+ MicroPrintf("Badly-formed format string %");
+ MicroPrintf("Another %# badly-formed %% format string");
}
+
+TF_LITE_MICRO_TEST(MicroSnprintf) {
+ char buffer[kMaxBufferSize];
+ buffer[0] = '\0';
+ size_t result =
+ MicroSnprintf(buffer, kMaxBufferSize, kFormat, 42, 42.42, 0x42, "\"42\"");
+ TF_LITE_MICRO_EXPECT_EQ(result, strlen(buffer));
+ TF_LITE_MICRO_EXPECT_STRING_EQ(kExpect, buffer);
+}
+
+#endif // !defined(TF_LITE_STRIP_ERROR_STRINGS)
+
+TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/micro_mutable_op_resolver.h b/tensorflow/lite/micro/micro_mutable_op_resolver.h
index fe4f5fb..f5f6e38 100644
--- a/tensorflow/lite/micro/micro_mutable_op_resolver.h
+++ b/tensorflow/lite/micro/micro_mutable_op_resolver.h
@@ -29,9 +29,11 @@
#include "tensorflow/lite/micro/kernels/ethosu.h"
#include "tensorflow/lite/micro/kernels/fully_connected.h"
#include "tensorflow/lite/micro/kernels/micro_ops.h"
+#include "tensorflow/lite/micro/kernels/mul.h"
#include "tensorflow/lite/micro/kernels/pooling.h"
#include "tensorflow/lite/micro/kernels/reduce.h"
#include "tensorflow/lite/micro/kernels/softmax.h"
+#include "tensorflow/lite/micro/kernels/transpose_conv.h"
#include "tensorflow/lite/micro/micro_log.h"
#include "tensorflow/lite/micro/micro_op_resolver.h"
#include "tensorflow/lite/schema/schema_generated.h"
@@ -143,6 +145,11 @@
return AddBuiltin(BuiltinOperator_AVERAGE_POOL_2D, registration, ParsePool);
}
+ TfLiteStatus AddBatchMatMul() {
+ return AddBuiltin(BuiltinOperator_BATCH_MATMUL,
+ tflite::Register_BATCH_MATMUL(), ParseBatchMatMul);
+ }
+
TfLiteStatus AddBatchToSpaceNd() {
return AddBuiltin(BuiltinOperator_BATCH_TO_SPACE_ND,
Register_BATCH_TO_SPACE_ND(), ParseBatchToSpaceNd);
@@ -462,6 +469,11 @@
return AddBuiltin(BuiltinOperator_PADV2, Register_PADV2(), ParsePadV2);
}
+ TfLiteStatus AddPCAN() {
+ // TODO(b/286250473): change back name to "PCAN" and remove namespace
+ return AddCustom("SignalPCAN", tflite::tflm_signal::Register_PCAN());
+ }
+
TfLiteStatus AddPrelu() {
return AddBuiltin(BuiltinOperator_PRELU, tflite::Register_PRELU(),
ParsePrelu);
@@ -606,9 +618,10 @@
return AddBuiltin(BuiltinOperator_TANH, Register_TANH(), ParseTanh);
}
- TfLiteStatus AddTransposeConv() {
- return AddBuiltin(BuiltinOperator_TRANSPOSE_CONV,
- tflite::Register_TRANSPOSE_CONV(), ParseTransposeConv);
+ TfLiteStatus AddTransposeConv(
+ const TFLMRegistration& registration = Register_TRANSPOSE_CONV()) {
+ return AddBuiltin(BuiltinOperator_TRANSPOSE_CONV, registration,
+ ParseTransposeConv);
}
TfLiteStatus AddTranspose() {
diff --git a/tensorflow/lite/micro/micro_profiler.cc b/tensorflow/lite/micro/micro_profiler.cc
index e7743aa..ebead51 100644
--- a/tensorflow/lite/micro/micro_profiler.cc
+++ b/tensorflow/lite/micro/micro_profiler.cc
@@ -99,7 +99,7 @@
}
MicroPrintf("%s, %d", each_tag_entry.tag, each_tag_entry.ticks);
}
- MicroPrintf("total number of ticks, %d", total_ticks);
+ MicroPrintf("\"total number of ticks\", %d", total_ticks);
#endif
}
diff --git a/tensorflow/lite/micro/micro_profiler.h b/tensorflow/lite/micro/micro_profiler.h
index 1c39ea1..b52ebcb 100644
--- a/tensorflow/lite/micro/micro_profiler.h
+++ b/tensorflow/lite/micro/micro_profiler.h
@@ -40,7 +40,7 @@
// only once per event_handle.
//
// If EndEvent is called more than once for the same event_handle, the last
- // call will be used as the end of event marker.If EndEvent is called 0 times
+ // call will be used as the end of event marker. If EndEvent is called 0 times
// for a particular event_handle, the duration of that event will be 0 ticks.
virtual void EndEvent(uint32_t event_handle) override;
@@ -66,9 +66,9 @@
void LogTicksPerTagCsv();
private:
- // Maximum number of events that this class can keep track of. If we call
- // AddEvent more than kMaxEvents number of times, then the oldest event's
- // profiling information will be overwritten.
+ // Maximum number of events that this class can keep track of. The
+ // MicroProfiler will abort if AddEvent is called more than kMaxEvents number
+ // of times. Increase this number if you need more events.
static constexpr int kMaxEvents = 4096;
const char* tags_[kMaxEvents];
@@ -87,7 +87,7 @@
int FindExistingOrNextPosition(const char* tag_name);
- TF_LITE_REMOVE_VIRTUAL_DELETE;
+ TF_LITE_REMOVE_VIRTUAL_DELETE
};
#if defined(TF_LITE_STRIP_ERROR_STRINGS)
diff --git a/tensorflow/lite/micro/micro_time.h b/tensorflow/lite/micro/micro_time.h
index 7a8ab45..5e8ad11 100644
--- a/tensorflow/lite/micro/micro_time.h
+++ b/tensorflow/lite/micro/micro_time.h
@@ -27,8 +27,11 @@
uint32_t GetCurrentTimeTicks();
inline uint32_t TicksToMs(int32_t ticks) {
+ uint32_t _ticks_per_second = ticks_per_second();
+ _ticks_per_second =
+ _ticks_per_second > 0 ? _ticks_per_second : 1; // zero divide prevention
return static_cast<uint32_t>(1000.0f * static_cast<float>(ticks) /
- static_cast<float>(ticks_per_second()));
+ static_cast<float>(_ticks_per_second));
}
} // namespace tflite
diff --git a/tensorflow/lite/micro/mock_micro_graph.cc b/tensorflow/lite/micro/mock_micro_graph.cc
index 438a406..9c652fb 100644
--- a/tensorflow/lite/micro/mock_micro_graph.cc
+++ b/tensorflow/lite/micro/mock_micro_graph.cc
@@ -20,11 +20,7 @@
namespace tflite {
MockMicroGraph::MockMicroGraph(SingleArenaBufferAllocator* allocator)
- : MicroGraph(nullptr, nullptr, nullptr, nullptr),
- allocator_(allocator),
- init_count_(0),
- prepare_count_(0),
- free_count_(0) {
+ : allocator_(allocator), init_count_(0), prepare_count_(0), free_count_(0) {
memset(invoke_counts_, 0, sizeof(invoke_counts_));
mock_tensor_ =
reinterpret_cast<TfLiteEvalTensor*>(allocator_->AllocatePersistentBuffer(
@@ -45,8 +41,6 @@
return kTfLiteOk;
}
-TfLiteStatus MockMicroGraph::ResetVariableTensors() { return kTfLiteOk; }
-
size_t MockMicroGraph::NumSubgraphInputs(int subgraph_idx) { return 1; }
TfLiteEvalTensor* MockMicroGraph::GetSubgraphInput(int subgraph_idx,
@@ -63,4 +57,8 @@
int MockMicroGraph::NumSubgraphs() { return kMaxSubgraphs; }
+MicroResourceVariables* MockMicroGraph::GetResourceVariables() {
+ return nullptr;
+}
+
} // namespace tflite
diff --git a/tensorflow/lite/micro/mock_micro_graph.h b/tensorflow/lite/micro/mock_micro_graph.h
index 3ae7d7c..745e8f0 100644
--- a/tensorflow/lite/micro/mock_micro_graph.h
+++ b/tensorflow/lite/micro/mock_micro_graph.h
@@ -30,13 +30,13 @@
public:
explicit MockMicroGraph(SingleArenaBufferAllocator* allocator);
TfLiteStatus InvokeSubgraph(int subgraph_idx) override;
- TfLiteStatus ResetVariableTensors() override;
size_t NumSubgraphInputs(int subgraph_idx) override;
TfLiteEvalTensor* GetSubgraphInput(int subgraph_idx, int tensor_idx) override;
size_t NumSubgraphOutputs(int subgraph_idx) override;
TfLiteEvalTensor* GetSubgraphOutput(int subgraph_idx,
int tensor_idx) override;
int NumSubgraphs() override;
+ MicroResourceVariables* GetResourceVariables() override;
int get_init_count() const { return init_count_; }
int get_prepare_count() const { return prepare_count_; }
int get_free_count() const { return free_count_; }
diff --git a/tensorflow/lite/micro/models/person_detect_vela.tflite b/tensorflow/lite/micro/models/person_detect_vela.tflite
new file mode 100644
index 0000000..95cf476
--- /dev/null
+++ b/tensorflow/lite/micro/models/person_detect_vela.tflite
Binary files differ
diff --git a/tensorflow/lite/micro/python/interpreter/src/BUILD b/tensorflow/lite/micro/python/interpreter/src/BUILD
index 601e3db..f8be0ed 100644
--- a/tensorflow/lite/micro/python/interpreter/src/BUILD
+++ b/tensorflow/lite/micro/python/interpreter/src/BUILD
@@ -17,24 +17,6 @@
packages = tflm_python_op_resolver_friends(),
)
-# tflm_runtime is deprecated, please use //python/tflite_micro:runtime instead.
-# TODO(b/286456378): remove once all usage is changed to the runtime target.
-py_library(
- name = "tflm_runtime",
- srcs = ["tflm_runtime.py"],
- visibility = ["//visibility:public"],
- deps = ["//python/tflite_micro:runtime"],
-)
-
-# runtime is deprecated, please use //python/tflite_micro:runtime instead.
-# TODO(b/286456378): remove once all usage is changed to the runtime target.
-py_library(
- name = "runtime",
- srcs = ["runtime.py"],
- visibility = ["//visibility:public"],
- deps = ["//python/tflite_micro:runtime"],
-)
-
# TODO(b/286456378): remove once all internal usage is fixed.
cc_library(
name = "python_ops_resolver",
@@ -43,10 +25,7 @@
"python_ops_resolver.h",
],
copts = micro_copts(),
- visibility = [
- ":op_resolver_friends",
- "//tensorflow/lite/micro/integration_tests:__subpackages__",
- ],
+ visibility = [":op_resolver_friends"],
deps = [
"//python/tflite_micro:python_ops_resolver",
],
diff --git a/tensorflow/lite/micro/python/tflite_size/src/BUILD b/tensorflow/lite/micro/python/tflite_size/src/BUILD
index 66d9b50..b8f53a2 100644
--- a/tensorflow/lite/micro/python/tflite_size/src/BUILD
+++ b/tensorflow/lite/micro/python/tflite_size/src/BUILD
@@ -1,3 +1,4 @@
+load("@rules_python//python:defs.bzl", "py_binary", "py_library")
load("@pybind11_bazel//:build_defs.bzl", "pybind_extension", "pybind_library")
package(
diff --git a/tensorflow/lite/micro/python/tflite_size/tests/BUILD b/tensorflow/lite/micro/python/tflite_size/tests/BUILD
index 1b4c5b2..076a6ab 100644
--- a/tensorflow/lite/micro/python/tflite_size/tests/BUILD
+++ b/tensorflow/lite/micro/python/tflite_size/tests/BUILD
@@ -1,3 +1,4 @@
+load("@rules_python//python:defs.bzl", "py_test")
load("@tflm_pip_deps//:requirements.bzl", "requirement")
licenses(["notice"])
@@ -25,7 +26,7 @@
"noubsan",
],
deps = [
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
"//tensorflow/lite/micro/python/tflite_size/src:flatbuffer_size",
],
)
diff --git a/tensorflow/lite/micro/recording_micro_allocator.cc b/tensorflow/lite/micro/recording_micro_allocator.cc
index f41dba6..ee76196 100644
--- a/tensorflow/lite/micro/recording_micro_allocator.cc
+++ b/tensorflow/lite/micro/recording_micro_allocator.cc
@@ -78,9 +78,14 @@
return recorded_node_and_registration_array_data_;
case RecordedAllocationType::kOpData:
return recorded_op_data_;
+ // the function MicroPrintf was never reached outside the switch, because
+ // each case has a return. As the intention of the MicroPrintf is to be
+ // called when no matching case is found, a default case was added to
+ // contemplate an invalid allocation type
+ default:
+ MicroPrintf("Invalid allocation type supplied: %d", allocation_type);
+ return RecordedAllocation();
}
- MicroPrintf("Invalid allocation type supplied: %d", allocation_type);
- return RecordedAllocation();
}
const RecordingSingleArenaBufferAllocator*
diff --git a/tensorflow/lite/micro/riscv32_generic/debug_log.cc b/tensorflow/lite/micro/riscv32_generic/debug_log.cc
new file mode 100644
index 0000000..1d38fff
--- /dev/null
+++ b/tensorflow/lite/micro/riscv32_generic/debug_log.cc
@@ -0,0 +1,40 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/lite/micro/debug_log.h"
+
+#include <cstdio>
+
+#ifndef TF_LITE_STRIP_ERROR_STRINGS
+#include "eyalroz_printf/src/printf/printf.h"
+#endif
+
+extern "C" void DebugLog(const char* format, va_list args) {
+#ifndef TF_LITE_STRIP_ERROR_STRINGS
+ constexpr int kMaxLogLen = 256;
+ char log_buffer[kMaxLogLen];
+
+ vsnprintf_(log_buffer, kMaxLogLen, format, args);
+ std::fputs(log_buffer, stdout);
+#endif
+}
+
+#ifndef TF_LITE_STRIP_ERROR_STRINGS
+// Only called from MicroVsnprintf (micro_log.h)
+extern "C" int DebugVsnprintf(char* buffer, size_t buf_size, const char* format,
+ va_list vlist) {
+ return vsnprintf_(buffer, buf_size, format, vlist);
+}
+#endif
diff --git a/tensorflow/lite/micro/span.h b/tensorflow/lite/micro/span.h
new file mode 100644
index 0000000..9399f1d
--- /dev/null
+++ b/tensorflow/lite/micro/span.h
@@ -0,0 +1,69 @@
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#ifndef TENSORFLOW_LITE_MICRO_SPAN_H_
+#define TENSORFLOW_LITE_MICRO_SPAN_H_
+
+#include <array>
+#include <cstddef>
+
+namespace tflite {
+
+// A poor man's std::span, we should consider using the Pigweed span instead.
+template <typename T>
+class Span {
+ public:
+ constexpr Span(T* data, size_t size) noexcept : data_(data), size_(size) {}
+
+ template <size_t N>
+ constexpr Span(T (&data)[N]) noexcept : data_(data), size_(N) {}
+
+ template <size_t N>
+ constexpr Span(std::array<T, N>& array) noexcept
+ : data_(array.data()), size_(N) {}
+
+ constexpr T& operator[](size_t idx) const noexcept { return *(data_ + idx); }
+
+ constexpr T* data() const noexcept { return data_; }
+ constexpr size_t size() const noexcept { return size_; }
+
+ private:
+ T* data_;
+ size_t size_;
+};
+
+template <typename A, typename B>
+bool operator==(const Span<A>& a, const Span<B>& b) {
+ if (a.size() != b.size()) {
+ return false;
+ }
+
+ for (size_t i = 0; i < a.size(); ++i) {
+ if (a[i] != b[i]) {
+ return false;
+ }
+ }
+
+ return true;
+}
+
+template <typename A, typename B>
+bool operator!=(const Span<A>& a, const Span<B>& b) {
+ return !(a == b);
+}
+
+} // end namespace tflite
+
+#endif // TENSORFLOW_LITE_MICRO_SPAN_H_
diff --git a/tensorflow/lite/micro/span_test.cc b/tensorflow/lite/micro/span_test.cc
new file mode 100644
index 0000000..ef906c6
--- /dev/null
+++ b/tensorflow/lite/micro/span_test.cc
@@ -0,0 +1,59 @@
+// Copyright 2024 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "tensorflow/lite/micro/span.h"
+
+#include <array>
+
+#include "tensorflow/lite/micro/testing/micro_test.h"
+
+TF_LITE_MICRO_TESTS_BEGIN
+
+TF_LITE_MICRO_TEST(TestArrayInitialization) {
+ int a[]{1, 2, 3};
+ tflite::Span<int> s{a};
+ TF_LITE_MICRO_EXPECT(s.data() == a);
+ TF_LITE_MICRO_EXPECT(s.size() == sizeof(a) / sizeof(int));
+}
+
+TF_LITE_MICRO_TEST(TestStdArrayInitialization) {
+ std::array<char, 20> a;
+ tflite::Span<char> s{a};
+ TF_LITE_MICRO_EXPECT(s.data() == a.data());
+ TF_LITE_MICRO_EXPECT(s.size() == a.size());
+}
+
+TF_LITE_MICRO_TEST(TestEquality) {
+ constexpr int a[]{1, 2, 3};
+ constexpr int b[]{1, 2, 3};
+ constexpr int c[]{3, 2, 1};
+ tflite::Span<const int> s_a{a};
+ tflite::Span<const int> s_b{b};
+ tflite::Span<const int> s_c{c};
+ TF_LITE_MICRO_EXPECT_TRUE(s_a == s_b);
+ TF_LITE_MICRO_EXPECT_FALSE(s_a == s_c);
+}
+
+TF_LITE_MICRO_TEST(TestInequality) {
+ constexpr int a[]{1, 2, 3};
+ constexpr int b[]{1, 2, 3};
+ constexpr int c[]{3, 2, 1};
+ tflite::Span<const int> s_a{a};
+ tflite::Span<const int> s_b{b};
+ tflite::Span<const int> s_c{c};
+ TF_LITE_MICRO_EXPECT_FALSE(s_a != s_b);
+ TF_LITE_MICRO_EXPECT_TRUE(s_a != s_c);
+}
+
+TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/static_vector.h b/tensorflow/lite/micro/static_vector.h
new file mode 100644
index 0000000..8b9e063
--- /dev/null
+++ b/tensorflow/lite/micro/static_vector.h
@@ -0,0 +1,83 @@
+// Copyright 2024 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef TENSORFLOW_LITE_MICRO_STATIC_VECTOR_H_
+#define TENSORFLOW_LITE_MICRO_STATIC_VECTOR_H_
+
+#include <array>
+#include <cassert>
+#include <cstddef>
+
+#include "tensorflow/lite/kernels/op_macros.h" // for TF_LITE_ASSERT
+
+namespace tflite {
+
+template <typename T, std::size_t MaxSize>
+class StaticVector {
+ // A staticlly-allocated vector. Add to the interface as needed.
+
+ private:
+ std::array<T, MaxSize> array_;
+ std::size_t size_{0};
+
+ public:
+ using iterator = typename decltype(array_)::iterator;
+ using const_iterator = typename decltype(array_)::const_iterator;
+ using pointer = typename decltype(array_)::pointer;
+ using reference = typename decltype(array_)::reference;
+ using const_reference = typename decltype(array_)::const_reference;
+
+ StaticVector() {}
+
+ StaticVector(std::initializer_list<T> values) {
+ for (const T& v : values) {
+ push_back(v);
+ }
+ }
+
+ static constexpr std::size_t max_size() { return MaxSize; }
+ std::size_t size() const { return size_; }
+ bool full() const { return size() == max_size(); }
+ iterator begin() { return array_.begin(); }
+ const_iterator begin() const { return array_.begin(); }
+ iterator end() { return begin() + size(); }
+ const_iterator end() const { return begin() + size(); }
+ pointer data() { return array_.data(); }
+ reference operator[](int i) { return array_[i]; }
+ const_reference operator[](int i) const { return array_[i]; }
+ void clear() { size_ = 0; }
+
+ template <std::size_t N>
+ bool operator==(const StaticVector<T, N>& other) const {
+ return std::equal(begin(), end(), other.begin(), other.end());
+ }
+
+ template <std::size_t N>
+ bool operator!=(const StaticVector<T, N>& other) const {
+ return !(*this == other);
+ }
+
+ void push_back(const T& t) {
+ TF_LITE_ASSERT(!full());
+ *end() = t;
+ ++size_;
+ }
+};
+
+template <typename T, typename... U>
+StaticVector(T, U...) -> StaticVector<T, 1 + sizeof...(U)>;
+
+} // end namespace tflite
+
+#endif // TENSORFLOW_LITE_MICRO_STATIC_VECTOR_H_
diff --git a/tensorflow/lite/micro/static_vector_test.cc b/tensorflow/lite/micro/static_vector_test.cc
new file mode 100644
index 0000000..6d601bc
--- /dev/null
+++ b/tensorflow/lite/micro/static_vector_test.cc
@@ -0,0 +1,82 @@
+// Copyright 2024 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "tensorflow/lite/micro/static_vector.h"
+
+#include "tensorflow/lite/micro/testing/micro_test.h"
+
+using tflite::StaticVector;
+
+TF_LITE_MICRO_TESTS_BEGIN
+
+TF_LITE_MICRO_TEST(StaticVectorPushBack) {
+ StaticVector<int, 4> a;
+ TF_LITE_MICRO_EXPECT(a.max_size() == 4);
+ TF_LITE_MICRO_EXPECT(a.size() == 0);
+
+ a.push_back(1);
+ TF_LITE_MICRO_EXPECT(a.size() == 1);
+ TF_LITE_MICRO_EXPECT(a[0] == 1);
+
+ a.push_back(2);
+ TF_LITE_MICRO_EXPECT(a.size() == 2);
+ TF_LITE_MICRO_EXPECT(a[1] == 2);
+
+ a.push_back(3);
+ TF_LITE_MICRO_EXPECT(a.size() == 3);
+ TF_LITE_MICRO_EXPECT(a[2] == 3);
+}
+
+TF_LITE_MICRO_TEST(StaticVectorInitializationPartial) {
+ const StaticVector<int, 4> a{1, 2, 3};
+ TF_LITE_MICRO_EXPECT(a.max_size() == 4);
+ TF_LITE_MICRO_EXPECT(a.size() == 3);
+ TF_LITE_MICRO_EXPECT(a[0] == 1);
+ TF_LITE_MICRO_EXPECT(a[1] == 2);
+ TF_LITE_MICRO_EXPECT(a[2] == 3);
+}
+
+TF_LITE_MICRO_TEST(StaticVectorInitializationFull) {
+ const StaticVector b{1, 2, 3};
+ TF_LITE_MICRO_EXPECT(b.max_size() == 3);
+ TF_LITE_MICRO_EXPECT(b.size() == 3);
+}
+
+TF_LITE_MICRO_TEST(StaticVectorEquality) {
+ const StaticVector a{1, 2, 3};
+ const StaticVector b{1, 2, 3};
+ TF_LITE_MICRO_EXPECT(a == b);
+ TF_LITE_MICRO_EXPECT(!(a != b));
+}
+
+TF_LITE_MICRO_TEST(StaticVectorInequality) {
+ const StaticVector a{1, 2, 3};
+ const StaticVector b{3, 2, 1};
+ TF_LITE_MICRO_EXPECT(a != b);
+ TF_LITE_MICRO_EXPECT(!(a == b));
+}
+
+TF_LITE_MICRO_TEST(StaticVectorSizeInequality) {
+ const StaticVector a{1, 2};
+ const StaticVector b{1, 2, 3};
+ TF_LITE_MICRO_EXPECT(a != b);
+}
+
+TF_LITE_MICRO_TEST(StaticVectorPartialSizeInequality) {
+ const StaticVector<int, 3> a{1, 2};
+ const StaticVector<int, 3> b{1, 2, 3};
+ TF_LITE_MICRO_EXPECT(a != b);
+}
+
+TF_LITE_MICRO_TESTS_END
diff --git a/tensorflow/lite/micro/test_helpers.cc b/tensorflow/lite/micro/test_helpers.cc
index 15d2382..3f0f5ec 100644
--- a/tensorflow/lite/micro/test_helpers.cc
+++ b/tensorflow/lite/micro/test_helpers.cc
@@ -1876,8 +1876,8 @@
// Create a TfLiteIntArray from an array of ints. The first element in the
// supplied array must be the size of the array expressed as an int.
-TfLiteIntArray* IntArrayFromInts(int* int_array) {
- return reinterpret_cast<TfLiteIntArray*>(int_array);
+TfLiteIntArray* IntArrayFromInts(const int* int_array) {
+ return reinterpret_cast<TfLiteIntArray*>(const_cast<int*>(int_array));
}
// Create a TfLiteFloatArray from an array of floats. The first element in the
diff --git a/tensorflow/lite/micro/test_helpers.h b/tensorflow/lite/micro/test_helpers.h
index 578282e..6315b9f 100644
--- a/tensorflow/lite/micro/test_helpers.h
+++ b/tensorflow/lite/micro/test_helpers.h
@@ -1,4 +1,4 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -17,6 +17,7 @@
#define TENSORFLOW_LITE_MICRO_TEST_HELPERS_H_
#include <algorithm>
+#include <cmath>
#include <cstdint>
#include <limits>
#include <type_traits>
@@ -195,7 +196,7 @@
// Create a TfLiteIntArray from an array of ints. The first element in the
// supplied array must be the size of the array expressed as an int.
-TfLiteIntArray* IntArrayFromInts(int* int_array);
+TfLiteIntArray* IntArrayFromInts(const int* int_array);
// Create a TfLiteFloatArray from an array of floats. The first element in the
// supplied array must be the size of the array expressed as a float.
@@ -325,7 +326,7 @@
template <typename T>
inline int ZeroPointFromMinMax(const float min, const float max) {
return static_cast<int>(std::numeric_limits<T>::min()) +
- static_cast<int>(-min / ScaleFromMinMax<T>(min, max) + 0.5f);
+ static_cast<int>(roundf(-min / ScaleFromMinMax<T>(min, max)));
}
} // namespace testing
diff --git a/tensorflow/lite/micro/testing/BUILD b/tensorflow/lite/micro/testing/BUILD
index 58914bc..7a24699 100644
--- a/tensorflow/lite/micro/testing/BUILD
+++ b/tensorflow/lite/micro/testing/BUILD
@@ -1,3 +1,4 @@
+load("@rules_python//python:defs.bzl", "py_binary", "py_library")
load("@tflm_pip_deps//:requirements.bzl", "requirement")
load(
"//tensorflow:extra_rules.bzl",
@@ -32,7 +33,6 @@
],
visibility = [
":kernel_test_friends",
- ":microfrontend",
":tflite_micro",
],
deps = [
@@ -77,7 +77,7 @@
],
deps = [
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
],
)
@@ -93,6 +93,6 @@
deps = [
"@absl_py//absl:app",
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
],
)
diff --git a/tensorflow/lite/micro/testing/generate_test_models.py b/tensorflow/lite/micro/testing/generate_test_models.py
index 25902d0..2593653 100644
--- a/tensorflow/lite/micro/testing/generate_test_models.py
+++ b/tensorflow/lite/micro/testing/generate_test_models.py
@@ -71,6 +71,10 @@
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
converter.representative_dataset = representative_dataset_gen
+ # TODO(b/324385802): Disable per channel quantization in FC layers (currently
+ # default behaviour) since it's not yet supported in TFLM.
+ converter._experimental_disable_per_channel_quantization_for_dense_layers = ( # pylint: disable=protected-access
+ True)
tflite_model = converter.convert()
if write_to_file:
diff --git a/tensorflow/lite/micro/testing/micro_test.h b/tensorflow/lite/micro/testing/micro_test.h
index 2e119e1..1e17531 100644
--- a/tensorflow/lite/micro/testing/micro_test.h
+++ b/tensorflow/lite/micro/testing/micro_test.h
@@ -1,4 +1,4 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -259,8 +259,16 @@
MicroPrintf("FAIL: %s did not match %s", string1, string2, __FILE__, \
__LINE__); \
micro_test::did_test_fail = true; \
+ break; \
} \
} \
} while (false)
+#define TF_LITE_MICRO_CHECK_FAIL() \
+ do { \
+ if (micro_test::did_test_fail) { \
+ return kTfLiteError; \
+ } \
+ } while (false)
+
#endif // TENSORFLOW_LITE_MICRO_TESTING_MICRO_TEST_H_
diff --git a/tensorflow/lite/micro/testing/test_with_arm_corstone_300.sh b/tensorflow/lite/micro/testing/test_with_arm_corstone_300.sh
index 9b39ee4..27635ba 100755
--- a/tensorflow/lite/micro/testing/test_with_arm_corstone_300.sh
+++ b/tensorflow/lite/micro/testing/test_with_arm_corstone_300.sh
@@ -1,5 +1,5 @@
#!/bin/bash -e
-# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -36,7 +36,8 @@
FVP+="-C mps3_board.telnetterminal0.start_telnet=0 "
FVP+='-C mps3_board.uart0.out_file="-" '
FVP+='-C mps3_board.uart0.unbuffered_output=1 '
-FVP+='-C mps3_board.uart0.shutdown_on_eot=1'
+FVP+='-C mps3_board.uart0.shutdown_on_eot=1 '
+FVP+='--stat'
${FVP} ${BINARY_TO_TEST} | tee ${MICRO_LOG_FILENAME}
if [[ ${2} != "non_test_binary" ]]
diff --git a/tensorflow/lite/micro/tflite_bridge/BUILD b/tensorflow/lite/micro/tflite_bridge/BUILD
index 518015a..ea5efdf 100644
--- a/tensorflow/lite/micro/tflite_bridge/BUILD
+++ b/tensorflow/lite/micro/tflite_bridge/BUILD
@@ -38,9 +38,6 @@
"micro_error_reporter.h",
],
copts = micro_copts(),
- visibility = [
- "//tensorflow/lite/micro/tflite_bridge:__pkg__",
- ],
deps = [
"//tensorflow/lite/core/api:error_reporter",
"//tensorflow/lite/micro:micro_compatibility",
diff --git a/tensorflow/lite/micro/tools/BUILD b/tensorflow/lite/micro/tools/BUILD
index 60e3c3d..e3c6f0c 100644
--- a/tensorflow/lite/micro/tools/BUILD
+++ b/tensorflow/lite/micro/tools/BUILD
@@ -1,3 +1,5 @@
+load("@rules_python//python:defs.bzl", "py_binary", "py_library", "py_test")
+load("@flatbuffers//:build_defs.bzl", "flatbuffer_cc_library", "flatbuffer_py_library")
load("@tflm_pip_deps//:requirements.bzl", "requirement")
load("@pybind11_bazel//:build_defs.bzl", "pybind_extension")
load("//tensorflow:extra_rules.bzl", "tflm_application_friends")
@@ -21,8 +23,8 @@
name = "generate_cc_arrays_lib",
srcs = ["generate_cc_arrays.py"],
deps = [
- requirement("numpy"),
requirement("pillow"),
+ requirement("numpy"),
],
)
@@ -39,8 +41,8 @@
name = "generate_cc_arrays",
srcs = ["generate_cc_arrays.py"],
deps = [
- requirement("numpy"),
requirement("pillow"),
+ requirement("numpy"),
],
)
@@ -70,9 +72,9 @@
],
deps = [
":requantize_flatbuffer",
- "//python/tflite_micro:runtime",
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
+ "//python/tflite_micro:runtime",
],
)
@@ -131,11 +133,33 @@
],
deps = [
":model_transforms_utils",
- "//tensorflow/lite/micro/python/interpreter/src:runtime",
- "//tensorflow/lite/tools:flatbuffer_utils",
"@absl_py//absl/logging",
requirement("numpy"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
+ "//python/tflite_micro:runtime",
+ "//tensorflow/lite/tools:flatbuffer_utils",
+ ],
+)
+
+cc_binary(
+ name = "layer_by_layer_output_tool",
+ srcs = ["layer_by_layer.cc"],
+ deps = [
+ ":layer_by_layer_schema",
+ "//tensorflow/lite/c:c_api_types",
+ "//tensorflow/lite/c:common",
+ "//tensorflow/lite/kernels:op_macros",
+ "//tensorflow/lite/micro:micro_allocator",
+ "//tensorflow/lite/micro:micro_context",
+ "//tensorflow/lite/micro:micro_framework",
+ "//tensorflow/lite/micro:micro_log",
+ "//tensorflow/lite/micro:micro_resource_variable",
+ "//tensorflow/lite/micro:micro_utils",
+ "//tensorflow/lite/micro:op_resolvers",
+ "//tensorflow/lite/micro/kernels:kernel_util",
+ "//tensorflow/lite/micro/tools/benchmarking:op_resolver",
+ "//tensorflow/lite/schema:schema_fbs",
+ "@flatbuffers",
],
)
@@ -167,8 +191,34 @@
],
deps = [
":tflm_model_transforms_lib",
- "//tensorflow/lite/micro/examples/recipes:resource_variables_lib",
"@absl_py//absl/testing:parameterized",
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
+ "//tensorflow/lite/micro/examples/recipes:resource_variables_lib",
],
)
+
+py_binary(
+ name = "layer_by_layer_debugger",
+ srcs = ["layer_by_layer_debugger.py"],
+ python_version = "PY3",
+ srcs_version = "PY3",
+ deps = [
+ ":layer_by_layer_schema_py",
+ ":model_transforms_utils",
+ "@absl_py//absl:app",
+ "@absl_py//absl/flags",
+ requirement("tensorflow"),
+ "//python/tflite_micro:runtime",
+ "//tensorflow/lite/tools:flatbuffer_utils",
+ ],
+)
+
+flatbuffer_cc_library(
+ name = "layer_by_layer_schema",
+ srcs = ["layer_by_layer_schema.fbs"],
+)
+
+flatbuffer_py_library(
+ name = "layer_by_layer_schema_py",
+ srcs = ["layer_by_layer_schema.fbs"],
+)
diff --git a/tensorflow/lite/micro/tools/Makefile.inc b/tensorflow/lite/micro/tools/Makefile.inc
new file mode 100644
index 0000000..adbe73a
--- /dev/null
+++ b/tensorflow/lite/micro/tools/Makefile.inc
@@ -0,0 +1,12 @@
+MICROLITE_TOOL_ROOT_DIR := $(TENSORFLOW_ROOT)tensorflow/lite/micro/tools/
+
+LAYER_BY_LAYER_OUPUT_SRCS := \
+$(MICROLITE_TOOL_ROOT_DIR)/layer_by_layer.cc \
+
+LAYER_BY_LAYER_OUPUT_HDRS := \
+$(MICROLITE_TOOL_ROOT_DIR)benchmarking/op_resolver.h \
+
+ifneq ($(TARGET), bluepill cortex_m_corstone_300 riscv32_generic hexagon)
+ $(eval $(call microlite_test,layer_by_layer_output_tool,\
+ $(LAYER_BY_LAYER_OUPUT_SRCS),$(LAYER_BY_LAYER_OUPUT_HDRS),))
+endif
diff --git a/tensorflow/lite/micro/tools/benchmarking/BUILD b/tensorflow/lite/micro/tools/benchmarking/BUILD
index 9759546..6691ac3 100644
--- a/tensorflow/lite/micro/tools/benchmarking/BUILD
+++ b/tensorflow/lite/micro/tools/benchmarking/BUILD
@@ -1,6 +1,7 @@
cc_library(
name = "op_resolver",
hdrs = ["op_resolver.h"],
+ visibility = ["//tensorflow/lite/micro/tools:__subpackages__"],
deps = ["//tensorflow/lite/micro:op_resolvers"],
)
@@ -9,31 +10,24 @@
srcs = ["metrics.cc"],
hdrs = ["metrics.h"],
deps = [
- ":log_utils",
+ "//tensorflow/lite/kernels/internal:compatibility",
+ "//tensorflow/lite/micro:micro_log",
"//tensorflow/lite/micro:micro_profiler",
"//tensorflow/lite/micro:recording_allocators",
- "//tensorflow/lite/micro/arena_allocator:recording_simple_memory_allocator",
- ],
-)
-
-cc_library(
- name = "log_utils",
- srcs = ["log_utils.cc"],
- hdrs = ["log_utils.h"],
- deps = [
- "//tensorflow/lite/micro:micro_log",
],
)
cc_library(
name = "generic_benchmark_lib",
srcs = ["generic_model_benchmark.cc"],
+ hdrs = ["show_meta_data.h"],
+ defines = ["GENERIC_BENCHMARK_NO_META_DATA"],
deps = [
- ":log_utils",
":metrics",
":op_resolver",
"//tensorflow/lite/c:c_api_types",
"//tensorflow/lite/c:common",
+ "//tensorflow/lite/micro:micro_context",
"//tensorflow/lite/micro:micro_log",
"//tensorflow/lite/micro:micro_profiler",
"//tensorflow/lite/micro:op_resolvers",
diff --git a/tensorflow/lite/micro/tools/benchmarking/Makefile.inc b/tensorflow/lite/micro/tools/benchmarking/Makefile.inc
index 178d88b..396e701 100644
--- a/tensorflow/lite/micro/tools/benchmarking/Makefile.inc
+++ b/tensorflow/lite/micro/tools/benchmarking/Makefile.inc
@@ -1,18 +1,64 @@
MICROLITE_BENCHMARK_ROOT_DIR := $(TENSORFLOW_ROOT)tensorflow/lite/micro/tools/benchmarking
+ifneq ($(GENERIC_BENCHMARK_MODEL_PATH),)
+ GENERIC_BENCHMARK_MODEL_DIR := $(dir $(GENERIC_BENCHMARK_MODEL_PATH))
+ GENERIC_BENCHMARK_MODEL_NAME := $(notdir $(basename $(GENERIC_BENCHMARK_MODEL_PATH)))
+ CXXFLAGS += -DGENERIC_BENCHMARK_USING_BUILTIN_MODEL
+ CXXFLAGS += -DGENERIC_BENCHMARK_MODEL_HEADER_PATH=\"$(GENERIC_BENCHMARK_MODEL_DIR)$(GENERIC_BENCHMARK_MODEL_NAME)_model_data.h\"
+ CXXFLAGS += -DGENERIC_BENCHMARK_MODEL_NAME=$(GENERIC_BENCHMARK_MODEL_NAME)
+ifneq ($(GENERIC_BENCHMARK_ARENA_SIZE),)
+ CXXFLAGS += -DGENERIC_BENCHMARK_TENSOR_ARENA_SIZE=$(GENERIC_BENCHMARK_ARENA_SIZE)
+endif
+
+ # model path includes $(TENSORFLOW_ROOT) as part of the make invocation
+ GENERIC_BENCHMARK_GENERATOR_INPUTS := $(GENERIC_BENCHMARK_MODEL_PATH)
+
+ GENERIC_BENCHMARK_GENERATED_SRCS := \
+ $(GENERATED_SRCS_DIR)$(GENERIC_BENCHMARK_MODEL_DIR)$(GENERIC_BENCHMARK_MODEL_NAME)_model_data.cc
+
+ GENERIC_BENCHMARK_GENERATED_HDRS := \
+ $(GENERATED_SRCS_DIR)$(GENERIC_BENCHMARK_MODEL_DIR)$(GENERIC_BENCHMARK_MODEL_NAME)_model_data.h
+endif
+
GENERIC_BENCHMARK_SRCS := \
$(MICROLITE_BENCHMARK_ROOT_DIR)/generic_model_benchmark.cc \
-$(MICROLITE_BENCHMARK_ROOT_DIR)/log_utils.cc \
-$(MICROLITE_BENCHMARK_ROOT_DIR)/metrics.cc
+$(MICROLITE_BENCHMARK_ROOT_DIR)/metrics.cc \
+$(GENERATED_SRCS_DIR)$(MICROLITE_BENCHMARK_ROOT_DIR)/show_meta_data.cc
GENERIC_BENCHMARK_HDRS := \
$(MICROLITE_BENCHMARK_ROOT_DIR)/op_resolver.h \
-$(MICROLITE_BENCHMARK_ROOT_DIR)/log_utils.h \
-$(MICROLITE_BENCHMARK_ROOT_DIR)/metrics.h
+$(MICROLITE_BENCHMARK_ROOT_DIR)/metrics.h \
+$(MICROLITE_BENCHMARK_ROOT_DIR)/show_meta_data.h
+
+# always rebuild these to catch MODEL_PATH and ARENA_SIZE changes on command line
+.PHONY: $(GENERATED_SRCS_DIR)$(MICROLITE_BENCHMARK_ROOT_DIR)/show_meta_data.cc
+.PHONY: $(MICROLITE_BENCHMARK_ROOT_DIR)/generic_model_benchmark.cc
+
+$(GENERATED_SRCS_DIR)$(MICROLITE_BENCHMARK_ROOT_DIR)/show_meta_data.cc:
+ CC="$(CC)" \
+ CXX="$(CXX)" \
+ CC_FLAGS="$(CCFLAGS)" \
+ CXX_FLAGS="$(CXXFLAGS)" \
+ KERNEL_OPTIMIZATION="$(KERNEL_OPTIMIZATION_LEVEL)" \
+ CORE_OPTIMIZATION="$(CORE_OPTIMIZATION_LEVEL)" \
+ THIRD_PARTY_KERNEL_OPTIMIZATION="$(THIRD_PARTY_KERNEL_OPTIMIZATION_LEVEL)" \
+ TARGET=$(TARGET) \
+ TARGET_ARCH=$(TARGET_ARCH) \
+ TENSORFLOW_ROOT="$(TENSORFLOW_ROOT)" \
+ OPTIMIZED_KERNEL=$(OPTIMIZED_KERNEL_DIR) \
+ BUILD_TYPE=$(BUILD_TYPE) \
+ XTENSA_CORE=$(XTENSA_CORE) \
+ XTENSA_BASE=$(XTENSA_BASE) \
+ XTENSA_TOOLS_VERSION=$(XTENSA_TOOLS_VERSION) \
+ TEMPLATE_FILE="$(MICROLITE_BENCHMARK_ROOT_DIR)/show_meta_data.cc.template" \
+ GENERATED_FILE="$(GENERATED_SRCS_DIR)$(MICROLITE_BENCHMARK_ROOT_DIR)/show_meta_data.cc" \
+ MODEL_FILE="$(GENERIC_BENCHMARK_MODEL_PATH)" \
+ $(MICROLITE_BENCHMARK_ROOT_DIR)/collect_meta_data.sh
ifneq ($(TARGET),bluepill)
-ifneq ($(TARGET_ARCH), $(filter $(TARGET_ARCH), hifi5 hifimini))
+ifneq ($(TARGET_ARCH), $(filter $(TARGET_ARCH), hifimini))
$(eval $(call microlite_test,tflm_benchmark,\
- $(GENERIC_BENCHMARK_SRCS),$(GENERIC_BENCHMARK_HDRS),))
+ $(GENERIC_BENCHMARK_SRCS),$(GENERIC_BENCHMARK_HDRS),\
+ $(GENERIC_BENCHMARK_GENERATOR_INPUTS)))
endif
-endif
\ No newline at end of file
+endif
diff --git a/tensorflow/lite/micro/tools/benchmarking/README.md b/tensorflow/lite/micro/tools/benchmarking/README.md
new file mode 100644
index 0000000..c203820
--- /dev/null
+++ b/tensorflow/lite/micro/tools/benchmarking/README.md
@@ -0,0 +1,414 @@
+# Generic Benchmarking Tool build/run instructions
+This tool can be used to benchmark any TfLite format model. The tool can be
+compiled in one of two ways:
+1. Such that it takes command line arguments, allowing the path to the model
+file to be specified as a program argument
+2. With a model compiled into the tool, allowing use in any simulator or on
+any hardware platform
+
+Building the tool with the model compiled in uses two additional Makefile
+variables:
+* `GENERIC_BENCHMARK_MODEL_PATH`: the path to the TfLite format model file. This
+can be a relative or absolute path. This variable is required.
+* `GENERIC_BENCHMARK_ARENA_SIZE`: the size of the TFLM interpreter arena, in bytes.
+This variable is optional.
+
+## Tested, working targets
+* x86
+* cortex_m_qemu (no timing data)
+* Xtensa (p6, hifi3)
+* cortex_m_corstone_300
+
+## Tested, non-working targets
+* none currently
+
+## Build and run for x86
+Build for command line arguments:
+```
+make -f tensorflow/lite/micro/tools/make/Makefile tflm_benchmark -j$(nproc)
+```
+Run with command line arguments:
+```
+gen/linux_x86_64_default/bin/tflm_benchmark tensorflow/lite/micro/models/person_detect.tflite
+```
+
+Build and run with model compiled into tool:
+```
+make -f tensorflow/lite/micro/tools/make/Makefile BUILD_TYPE=default run_tflm_benchmark -j$(nproc) GENERIC_BENCHMARK_MODEL_PATH=tensorflow/lite/micro/models/person_detect.tflite GENERIC_BENCHMARK_ARENA_SIZE=`expr 150 \* 1024`
+```
+
+## Build and run for Xtensa
+Build and run with model compiled into tool:
+```
+make -f tensorflow/lite/micro/tools/make/Makefile TARGET=xtensa TARGET_ARCH=vision_p6 OPTIMIZED_KERNEL_DIR=xtensa XTENSA_CORE=P6_200528 BUILD_TYPE=default run_tflm_benchmark -j$(nproc) GENERIC_BENCHMARK_MODEL_PATH=/tmp/keyword_scrambled.tflite GENERIC_BENCHMARK_ARENA_SIZE=`expr 50 \* 1024`
+```
+
+## Build and run for Cortex-M using Corstone 300 simulator
+Build and run with model compiled into tool:
+```
+make -f tensorflow/lite/micro/tools/make/Makefile TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m4 OPTIMIZED_KERNEL_DIR=cmsis_nn BUILD_TYPE=default run_tflm_benchmark -j$(nproc) GENERIC_BENCHMARK_MODEL_PATH=tensorflow/lite/micro/models/person_detect.tflite GENERIC_BENCHMARK_ARENA_SIZE=`expr 150 \* 1024`
+```
+
+## Build and run using Bazel
+
+This is only for the x86 command line argument build, and does not contain meta-data:
+```
+bazel build tensorflow/lite/micro/tools/benchmarking:tflm_benchmark
+bazel-bin/tensorflow/lite/micro/tools/benchmarking/tflm_benchmark tensorflow/lite/micro/models/person_detect.tflite
+```
+
+## Example output with meta-data and built-in model layer information
+
+This sample output is for Cortex-M using Corstone 300:
+```
+Configured arena size = 153600
+
+--------------------
+Compiled on:
+
+Fri May 17 03:36:59 PM PDT 2024
+--------------------
+Git SHA: a4390a1d73edf5a8d3affa1da60e1eba88e0cb13
+
+Git status:
+
+On branch main
+Your branch is up to date with 'origin/main'.
+--------------------
+C compiler: tensorflow/lite/micro/tools/make/downloads/gcc_embedded/bin/arm-none-eabi-gcc
+Version:
+
+arm-none-eabi-gcc (Arm GNU Toolchain 13.2.rel1 (Build arm-13.7)) 13.2.1 20231009
+Copyright (C) 2023 Free Software Foundation, Inc.
+This is free software; see the source for copying conditions. There is NO
+warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+
+Flags:
+
+-Wimplicit-function-declaration -std=c11 -Werror -fno-unwind-tables -ffunction-sections
+-fdata-sections -fmessage-length=0 -DTF_LITE_STATIC_MEMORY -DTF_LITE_DISABLE_X86_NEON
+-DCMSIS_NN -DKERNELS_OPTIMIZED_FOR_SPEED -mcpu=cortex-m4+nofp -mfpu=auto
+-DTF_LITE_MCU_DEBUG_LOG -mthumb -mfloat-abi=soft -funsigned-char -mlittle-endian
+-fomit-frame-pointer -MD -DARMCM4
+
+C++ compiler: tensorflow/lite/micro/tools/make/downloads/gcc_embedded/bin/arm-none-eabi-g++
+Version:
+
+arm-none-eabi-g++ (Arm GNU Toolchain 13.2.rel1 (Build arm-13.7)) 13.2.1 20231009
+Copyright (C) 2023 Free Software Foundation, Inc.
+This is free software; see the source for copying conditions. There is NO
+warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+
+Flags:
+
+-std=c++11 -fno-rtti -fno-exceptions -fno-threadsafe-statics -Wnon-virtual-dtor -Werror
+-fno-unwind-tables -ffunction-sections -fdata-sections -fmessage-length=0
+-DTF_LITE_STATIC_MEMORY -DTF_LITE_DISABLE_X86_NEON -Wsign-compare -Wdouble-promotion
+-Wunused-variable -Wunused-function -Wswitch -Wvla -Wall -Wextra
+-Wmissing-field-initializers -Wstrict-aliasing -Wno-unused-parameter -DCMSIS_NN
+-DKERNELS_OPTIMIZED_FOR_SPEED -mcpu=cortex-m4+nofp -mfpu=auto -DTF_LITE_MCU_DEBUG_LOG
+-mthumb -mfloat-abi=soft -funsigned-char -mlittle-endian -fomit-frame-pointer -MD
+-DARMCM4 -DCMSIS_DEVICE_ARM_CORTEX_M_XX_HEADER_FILE="ARMCM4.h"
+-DGENERIC_BENCHMARK_USING_BUILTIN_MODEL
+-DGENERIC_BENCHMARK_MODEL_HEADER_PATH="tensorflow/lite/micro/models/person_detect_model_da
+ta.h" -DGENERIC_BENCHMARK_MODEL_NAME=person_detect
+-DGENERIC_BENCHMARK_TENSOR_ARENA_SIZE=153600
+
+Optimization: kernel= -O2 core= -Os third-party-kernel= -O2
+--------------------
+Target information:
+
+TARGET=cortex_m_corstone_300
+TARGET_ARCH=cortex-m4
+OPTIMIZATION=cmsis_nn
+BUILD_TYPE=default
+--------------------
+NN library download URLs:
+
+http://github.com/ARM-software/CMSIS-NN/archive/01dee38e6d6bfbbf202f0cd425bbea1731747d51.z
+ip
+
+NN library MD5 checksums:
+
+f20be93ededf42bb704c19f699a24313
+--------------------
+Model SHA1:
+
+bcafcaa99d2eaf089f0ca25d66f56a2177e93f76
+
+Model analysis:
+
+=== tensorflow/lite/micro/models/person_detect.tflite ===
+Your TFLite model has '1' subgraph(s). In the subgraph description below,
+T# represents the Tensor numbers. For example, in Subgraph#0, the DEPTHWISE_CONV_2D op
+takes
+tensor #88 and tensor #0 and tensor #33 as input and produces tensor #34 as output.
+Subgraph#0(T#88) -> [T#87]
+ Op#0 DEPTHWISE_CONV_2D(T#88, T#0, T#33[3774, -107, -84394, -13908, 20697, ...]) ->
+[T#34]
+ Op#1 DEPTHWISE_CONV_2D(T#34, T#9, T#52[31132, 28, 273, -2692, 7409, ...]) -> [T#51]
+ Op#2 CONV_2D(T#51, T#10, T#53[10064, 1130, -13056, -30284, -23349, ...]) -> [T#54]
+ Op#3 DEPTHWISE_CONV_2D(T#54, T#11, T#56[306, -158, 19181, -364, 6237, ...]) -> [T#55]
+ Op#4 CONV_2D(T#55, T#12, T#57[-7649, 12287, -4433, 5851, -188, ...]) -> [T#58]
+ Op#5 DEPTHWISE_CONV_2D(T#58, T#13, T#60[7297, -498, 263, -1975, 2260, ...]) -> [T#59]
+ Op#6 CONV_2D(T#59, T#14, T#61[-4742, -4160, 6985, 8647, 29773, ...]) -> [T#62]
+ Op#7 DEPTHWISE_CONV_2D(T#62, T#15, T#64[28588, 363, 27592, 22294, -4344, ...]) -> [T#63]
+ Op#8 CONV_2D(T#63, T#16, T#65[12683, 36581, 6206, 1236, 15834, ...]) -> [T#66]
+ Op#9 DEPTHWISE_CONV_2D(T#66, T#17, T#68[-6353, 9090, -30, -1019, -496, ...]) -> [T#67]
+ Op#10 CONV_2D(T#67, T#18, T#69[3895, -6563, -8843, -2066, -1372, ...]) -> [T#70]
+ Op#11 DEPTHWISE_CONV_2D(T#70, T#19, T#72[20437, -365, -2518, 20827, -904, ...]) ->
+[T#71]
+ Op#12 CONV_2D(T#71, T#20, T#73[-10120, 9768, 3524, 3796, 6896, ...]) -> [T#74]
+ Op#13 DEPTHWISE_CONV_2D(T#74, T#21, T#76[-3969, -1910, -2425, -114, 4456, ...]) ->
+[T#75]
+ Op#14 CONV_2D(T#75, T#22, T#77[-13202, 13929, -4357, 19492, 1971, ...]) -> [T#78]
+ Op#15 DEPTHWISE_CONV_2D(T#78, T#23, T#80[-6169, -10, -2788, 14420, -7457, ...]) ->
+[T#79]
+ Op#16 CONV_2D(T#79, T#24, T#81[155, -3073, 291, -902, -9942, ...]) -> [T#82]
+ Op#17 DEPTHWISE_CONV_2D(T#82, T#25, T#84[-2063, 10755, -12037, -6417, 2147, ...]) ->
+[T#83]
+ Op#18 CONV_2D(T#83, T#26, T#85[-1872, -7549, 13994, 3191, -614, ...]) -> [T#86]
+ Op#19 DEPTHWISE_CONV_2D(T#86, T#1, T#36[-6485, 294, 686, -6011, -5196, ...]) -> [T#35]
+ Op#20 CONV_2D(T#35, T#2, T#37[7116, 8066, 11755, 11674, 9983, ...]) -> [T#38]
+ Op#21 DEPTHWISE_CONV_2D(T#38, T#3, T#40[7735, 5235, 4334, -6485, 9397, ...]) -> [T#39]
+ Op#22 CONV_2D(T#39, T#4, T#41[2947, 10152, -7865, -554, -13760, ...]) -> [T#42]
+ Op#23 DEPTHWISE_CONV_2D(T#42, T#5, T#44[-4755, 7899, -488, -2954, 2990, ...]) -> [T#43]
+ Op#24 CONV_2D(T#43, T#6, T#45[-6269, -22458, 13332, -16368, 4435, ...]) -> [T#46]
+ Op#25 DEPTHWISE_CONV_2D(T#46, T#7, T#48[333, -4743, -310, -2471, 4804, ...]) -> [T#47]
+ Op#26 CONV_2D(T#47, T#8, T#49[6677, -3593, 3754, 26316, -4761, ...]) -> [T#50]
+ Op#27 AVERAGE_POOL_2D(T#50) -> [T#27]
+ Op#28 CONV_2D(T#27, T#30, T#29[16267, -17079]) -> [T#28]
+ Op#29 RESHAPE(T#28, T#32[1, 2]) -> [T#31]
+ Op#30 SOFTMAX(T#31) -> [T#87]
+Tensors of Subgraph#0
+ T#0(MobilenetV1/Conv2d_0/weights/read) shape:[1, 3, 3, 8], type:INT8 RO 72 bytes,
+buffer: 68, data:[., y, ., g, ., ...]
+ T#1(MobilenetV1/Conv2d_10_depthwise/depthwise_weights/read) shape:[1, 3, 3, 128],
+type:INT8 RO 1152 bytes, buffer: 72, data:[W, ., d, ., ., ...]
+ T#2(MobilenetV1/Conv2d_10_pointwise/weights/read) shape:[128, 1, 1, 128], type:INT8 RO
+16384 bytes, buffer: 14, data:[., .,
+, ., ., ...]
+ T#3(MobilenetV1/Conv2d_11_depthwise/depthwise_weights/read) shape:[1, 3, 3, 128],
+type:INT8 RO 1152 bytes, buffer: 13, data:[., `, ., :, ., ...]
+ T#4(MobilenetV1/Conv2d_11_pointwise/weights/read) shape:[128, 1, 1, 128], type:INT8 RO
+16384 bytes, buffer: 12, data:[., ., ., ., ., ...]
+ T#5(MobilenetV1/Conv2d_12_depthwise/depthwise_weights/read) shape:[1, 3, 3, 128],
+type:INT8 RO 1152 bytes, buffer: 10, data:[z, ., ., ?, ., ...]
+ T#6(MobilenetV1/Conv2d_12_pointwise/weights/read) shape:[256, 1, 1, 128], type:INT8 RO
+32768 bytes, buffer: 69, data:[/, ., ., ., #, ...]
+ T#7(MobilenetV1/Conv2d_13_depthwise/depthwise_weights/read) shape:[1, 3, 3, 256],
+type:INT8 RO 2304 bytes, buffer: 7, data:[., ., w, ., ., ...]
+ T#8(MobilenetV1/Conv2d_13_pointwise/weights/read) shape:[256, 1, 1, 256], type:INT8 RO
+65536 bytes, buffer: 5, data:[&, ., ., ., ., ...]
+ T#9(MobilenetV1/Conv2d_1_depthwise/depthwise_weights/read) shape:[1, 3, 3, 8],
+type:INT8 RO 72 bytes, buffer: 60, data:[., ., ., ., ., ...]
+ T#10(MobilenetV1/Conv2d_1_pointwise/weights/read) shape:[16, 1, 1, 8], type:INT8 RO 128
+bytes, buffer: 63, data:[., ., ., ., ., ...]
+ T#11(MobilenetV1/Conv2d_2_depthwise/depthwise_weights/read) shape:[1, 3, 3, 16],
+type:INT8 RO 144 bytes, buffer: 58, data:[O, *, ., !, ., ...]
+ T#12(MobilenetV1/Conv2d_2_pointwise/weights/read) shape:[32, 1, 1, 16], type:INT8 RO
+512 bytes, buffer: 61, data:[., 4, ., ., 8, ...]
+ T#13(MobilenetV1/Conv2d_3_depthwise/depthwise_weights/read) shape:[1, 3, 3, 32],
+type:INT8 RO 288 bytes, buffer: 35, data:[., 1, ;, M, ., ...]
+ T#14(MobilenetV1/Conv2d_3_pointwise/weights/read) shape:[32, 1, 1, 32], type:INT8 RO
+1024 bytes, buffer: 33, data:[., ., ., ., ., ...]
+ T#15(MobilenetV1/Conv2d_4_depthwise/depthwise_weights/read) shape:[1, 3, 3, 32],
+type:INT8 RO 288 bytes, buffer: 32, data:[., ;, ., ., ., ...]
+ T#16(MobilenetV1/Conv2d_4_pointwise/weights/read) shape:[64, 1, 1, 32], type:INT8 RO
+2048 bytes, buffer: 30, data:[., ., ., 5, ., ...]
+ T#17(MobilenetV1/Conv2d_5_depthwise/depthwise_weights/read) shape:[1, 3, 3, 64],
+type:INT8 RO 576 bytes, buffer: 77, data:[G, ., ., ., ., ...]
+ T#18(MobilenetV1/Conv2d_5_pointwise/weights/read) shape:[64, 1, 1, 64], type:INT8 RO
+4096 bytes, buffer: 28, data:[., 2, ., $, ., ...]
+ T#19(MobilenetV1/Conv2d_6_depthwise/depthwise_weights/read) shape:[1, 3, 3, 64],
+type:INT8 RO 576 bytes, buffer: 27, data:[., 1, z, ., U, ...]
+ T#20(MobilenetV1/Conv2d_6_pointwise/weights/read) shape:[128, 1, 1, 64], type:INT8 RO
+8192 bytes, buffer: 25, data:[5, ., ., ., V, ...]
+ T#21(MobilenetV1/Conv2d_7_depthwise/depthwise_weights/read) shape:[1, 3, 3, 128],
+type:INT8 RO 1152 bytes, buffer: 23, data:[., ., ., ., ., ...]
+ T#22(MobilenetV1/Conv2d_7_pointwise/weights/read) shape:[128, 1, 1, 128], type:INT8 RO
+16384 bytes, buffer: 21, data:[., ., ., ., ., ...]
+ T#23(MobilenetV1/Conv2d_8_depthwise/depthwise_weights/read) shape:[1, 3, 3, 128],
+type:INT8 RO 1152 bytes, buffer: 71, data:[., ., ., ., Q, ...]
+ T#24(MobilenetV1/Conv2d_8_pointwise/weights/read) shape:[128, 1, 1, 128], type:INT8 RO
+16384 bytes, buffer: 20, data:[@, ., 2, ., 8, ...]
+ T#25(MobilenetV1/Conv2d_9_depthwise/depthwise_weights/read) shape:[1, 3, 3, 128],
+type:INT8 RO 1152 bytes, buffer: 80, data:[^, ., ~, ., ., ...]
+ T#26(MobilenetV1/Conv2d_9_pointwise/weights/read) shape:[128, 1, 1, 128], type:INT8 RO
+16384 bytes, buffer: 16, data:[., ., , ., %, ...]
+ T#27(MobilenetV1/Logits/AvgPool_1a/AvgPool) shape:[1, 1, 1, 256], type:INT8
+ T#28(MobilenetV1/Logits/Conv2d_1c_1x1/BiasAdd) shape:[1, 1, 1, 2], type:INT8
+ T#29(MobilenetV1/Logits/Conv2d_1c_1x1/Conv2D_bias) shape:[2], type:INT32 RO 8 bytes,
+buffer: 2, data:[16267, -17079]
+ T#30(MobilenetV1/Logits/Conv2d_1c_1x1/weights/read) shape:[2, 1, 1, 256], type:INT8 RO
+512 bytes, buffer: 3, data:[., %, ., ., ., ...]
+ T#31(MobilenetV1/Logits/SpatialSqueeze) shape:[1, 2], type:INT8
+ T#32(MobilenetV1/Logits/SpatialSqueeze_shape) shape:[2], type:INT32 RO 8 bytes, buffer:
+1, data:[1, 2]
+ T#33(MobilenetV1/MobilenetV1/Conv2d_0/Conv2D_bias) shape:[8], type:INT32 RO 32 bytes,
+buffer: 82, data:[3774, -107, -84394, -13908, 20697, ...]
+ T#34(MobilenetV1/MobilenetV1/Conv2d_0/Relu6) shape:[1, 48, 48, 8], type:INT8
+ T#35(MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6) shape:[1, 6, 6, 128], type:INT8
+ T#36(MobilenetV1/MobilenetV1/Conv2d_10_depthwise/depthwise_bias) shape:[128],
+type:INT32 RO 512 bytes, buffer: 22, data:[-6485, 294, 686, -6011, -5196, ...]
+ T#37(MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Conv2D_bias) shape:[128], type:INT32
+RO 512 bytes, buffer: 70, data:[7116, 8066, 11755, 11674, 9983, ...]
+ T#38(MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6) shape:[1, 6, 6, 128], type:INT8
+ T#39(MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6) shape:[1, 6, 6, 128], type:INT8
+ T#40(MobilenetV1/MobilenetV1/Conv2d_11_depthwise/depthwise_bias) shape:[128],
+type:INT32 RO 512 bytes, buffer: 19, data:[7735, 5235, 4334, -6485, 9397, ...]
+ T#41(MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Conv2D_bias) shape:[128], type:INT32
+RO 512 bytes, buffer: 11, data:[2947, 10152, -7865, -554, -13760, ...]
+ T#42(MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6) shape:[1, 6, 6, 128], type:INT8
+ T#43(MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6) shape:[1, 3, 3, 128], type:INT8
+ T#44(MobilenetV1/MobilenetV1/Conv2d_12_depthwise/depthwise_bias) shape:[128],
+type:INT32 RO 512 bytes, buffer: 9, data:[-4755, 7899, -488, -2954, 2990, ...]
+ T#45(MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Conv2D_bias) shape:[256], type:INT32
+RO 1024 bytes, buffer: 8, data:[-6269, -22458, 13332, -16368, 4435, ...]
+ T#46(MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6) shape:[1, 3, 3, 256], type:INT8
+ T#47(MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6) shape:[1, 3, 3, 256], type:INT8
+ T#48(MobilenetV1/MobilenetV1/Conv2d_13_depthwise/depthwise_bias) shape:[256],
+type:INT32 RO 1024 bytes, buffer: 6, data:[333, -4743, -310, -2471, 4804, ...]
+ T#49(MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Conv2D_bias) shape:[256], type:INT32
+RO 1024 bytes, buffer: 4, data:[6677, -3593, 3754, 26316, -4761, ...]
+ T#50(MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6) shape:[1, 3, 3, 256], type:INT8
+ T#51(MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6) shape:[1, 48, 48, 8], type:INT8
+ T#52(MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise_bias) shape:[8], type:INT32
+RO 32 bytes, buffer: 56, data:[31132, 28, 273, -2692, 7409, ...]
+ T#53(MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Conv2D_bias) shape:[16], type:INT32 RO
+64 bytes, buffer: 36, data:[10064, 1130, -13056, -30284, -23349, ...]
+ T#54(MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6) shape:[1, 48, 48, 16], type:INT8
+ T#55(MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6) shape:[1, 24, 24, 16], type:INT8
+ T#56(MobilenetV1/MobilenetV1/Conv2d_2_depthwise/depthwise_bias) shape:[16], type:INT32
+RO 64 bytes, buffer: 48, data:[306, -158, 19181, -364, 6237, ...]
+ T#57(MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Conv2D_bias) shape:[32], type:INT32 RO
+128 bytes, buffer: 62, data:[-7649, 12287, -4433, 5851, -188, ...]
+ T#58(MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6) shape:[1, 24, 24, 32], type:INT8
+ T#59(MobilenetV1/MobilenetV1/Conv2d_3_depthwise/Relu6) shape:[1, 24, 24, 32], type:INT8
+ T#60(MobilenetV1/MobilenetV1/Conv2d_3_depthwise/depthwise_bias) shape:[32], type:INT32
+RO 128 bytes, buffer: 34, data:[7297, -498, 263, -1975, 2260, ...]
+ T#61(MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Conv2D_bias) shape:[32], type:INT32 RO
+128 bytes, buffer: 59, data:[-4742, -4160, 6985, 8647, 29773, ...]
+ T#62(MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6) shape:[1, 24, 24, 32], type:INT8
+ T#63(MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6) shape:[1, 12, 12, 32], type:INT8
+ T#64(MobilenetV1/MobilenetV1/Conv2d_4_depthwise/depthwise_bias) shape:[32], type:INT32
+RO 128 bytes, buffer: 31, data:[28588, 363, 27592, 22294, -4344, ...]
+ T#65(MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Conv2D_bias) shape:[64], type:INT32 RO
+256 bytes, buffer: 76, data:[12683, 36581, 6206, 1236, 15834, ...]
+ T#66(MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6) shape:[1, 12, 12, 64], type:INT8
+ T#67(MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6) shape:[1, 12, 12, 64], type:INT8
+ T#68(MobilenetV1/MobilenetV1/Conv2d_5_depthwise/depthwise_bias) shape:[64], type:INT32
+RO 256 bytes, buffer: 29, data:[-6353, 9090, -30, -1019, -496, ...]
+ T#69(MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Conv2D_bias) shape:[64], type:INT32 RO
+256 bytes, buffer: 84, data:[3895, -6563, -8843, -2066, -1372, ...]
+ T#70(MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6) shape:[1, 12, 12, 64], type:INT8
+ T#71(MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6) shape:[1, 6, 6, 64], type:INT8
+ T#72(MobilenetV1/MobilenetV1/Conv2d_6_depthwise/depthwise_bias) shape:[64], type:INT32
+RO 256 bytes, buffer: 26, data:[20437, -365, -2518, 20827, -904, ...]
+ T#73(MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Conv2D_bias) shape:[128], type:INT32 RO
+512 bytes, buffer: 24, data:[-10120, 9768, 3524, 3796, 6896, ...]
+ T#74(MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6) shape:[1, 6, 6, 128], type:INT8
+ T#75(MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6) shape:[1, 6, 6, 128], type:INT8
+ T#76(MobilenetV1/MobilenetV1/Conv2d_7_depthwise/depthwise_bias) shape:[128], type:INT32
+RO 512 bytes, buffer: 78, data:[-3969, -1910, -2425, -114, 4456, ...]
+ T#77(MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Conv2D_bias) shape:[128], type:INT32 RO
+512 bytes, buffer: 83, data:[-13202, 13929, -4357, 19492, 1971, ...]
+ T#78(MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6) shape:[1, 6, 6, 128], type:INT8
+ T#79(MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6) shape:[1, 6, 6, 128], type:INT8
+ T#80(MobilenetV1/MobilenetV1/Conv2d_8_depthwise/depthwise_bias) shape:[128], type:INT32
+RO 512 bytes, buffer: 55, data:[-6169, -10, -2788, 14420, -7457, ...]
+ T#81(MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Conv2D_bias) shape:[128], type:INT32 RO
+512 bytes, buffer: 18, data:[155, -3073, 291, -902, -9942, ...]
+ T#82(MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6) shape:[1, 6, 6, 128], type:INT8
+ T#83(MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6) shape:[1, 6, 6, 128], type:INT8
+ T#84(MobilenetV1/MobilenetV1/Conv2d_9_depthwise/depthwise_bias) shape:[128], type:INT32
+RO 512 bytes, buffer: 17, data:[-2063, 10755, -12037, -6417, 2147, ...]
+ T#85(MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Conv2D_bias) shape:[128], type:INT32 RO
+512 bytes, buffer: 15, data:[-1872, -7549, 13994, 3191, -614, ...]
+ T#86(MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6) shape:[1, 6, 6, 128], type:INT8
+ T#87(MobilenetV1/Predictions/Reshape_1) shape:[1, 2], type:INT8
+ T#88(input) shape:[1, 96, 96, 1], type:INT8
+---------------------------------------------------------------
+ Model size: 300568 bytes
+ Non-data buffer size: 81640 bytes (27.16 %)
+ Total data buffer size: 218928 bytes (72.84 %)
+ (Zero value buffers): 0 bytes (00.00 %)
+* Buffers of TFLite model are mostly used for constant tensors.
+ And zero value buffers are buffers filled with zeros.
+ Non-data buffers area are used to store operators, subgraphs and etc.
+ You can find more details from
+https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema.fbs
+--------------------
+TfliteGetModel took 4 ticks (0 ms).
+
+DEPTHWISE_CONV_2D took 224622 ticks (8 ms).
+DEPTHWISE_CONV_2D took 175917 ticks (7 ms).
+CONV_2D took 249560 ticks (9 ms).
+DEPTHWISE_CONV_2D took 84958 ticks (3 ms).
+CONV_2D took 145817 ticks (5 ms).
+DEPTHWISE_CONV_2D took 164915 ticks (6 ms).
+CONV_2D took 197283 ticks (7 ms).
+DEPTHWISE_CONV_2D took 41304 ticks (1 ms).
+CONV_2D took 99472 ticks (3 ms).
+DEPTHWISE_CONV_2D took 79969 ticks (3 ms).
+CONV_2D took 151505 ticks (6 ms).
+DEPTHWISE_CONV_2D took 20053 ticks (0 ms).
+CONV_2D took 78521 ticks (3 ms).
+DEPTHWISE_CONV_2D took 38127 ticks (1 ms).
+CONV_2D took 132862 ticks (5 ms).
+DEPTHWISE_CONV_2D took 38127 ticks (1 ms).
+CONV_2D took 132865 ticks (5 ms).
+DEPTHWISE_CONV_2D took 38127 ticks (1 ms).
+CONV_2D took 132859 ticks (5 ms).
+DEPTHWISE_CONV_2D took 38127 ticks (1 ms).
+CONV_2D took 132851 ticks (5 ms).
+DEPTHWISE_CONV_2D took 38127 ticks (1 ms).
+CONV_2D took 132853 ticks (5 ms).
+DEPTHWISE_CONV_2D took 9585 ticks (0 ms).
+CONV_2D took 78470 ticks (3 ms).
+DEPTHWISE_CONV_2D took 17473 ticks (0 ms).
+CONV_2D took 143615 ticks (5 ms).
+AVERAGE_POOL_2D took 2229 ticks (0 ms).
+CONV_2D took 386 ticks (0 ms).
+RESHAPE took 28 ticks (0 ms).
+SOFTMAX took 163 ticks (0 ms).
+
+"Unique Tag","Total ticks across all events with that tag."
+DEPTHWISE_CONV_2D, 1009431
+CONV_2D, 1808919
+AVERAGE_POOL_2D, 2229
+RESHAPE, 28
+SOFTMAX, 163
+"total number of ticks", 2820770
+
+[[ Table ]]: Arena
+ Arena Bytes % Arena
+ Total | 84436 | 100.00
+NonPersistent | 55296 | 65.49
+ Persistent | 29140 | 34.51
+
+[[ Table ]]: Allocations
+ Allocation Id Used Requested Count % Memory
+ Eval tensor data | 0 | 1068 | 1068 | 89 | 1.26
+ Persistent tensor data | 1 | 64 | 64 | 2 | 0.08
+Persistent quantization data | 2 | 40 | 40 | 4 | 0.05
+ Persistent buffer data | 3 | 25872 | 25704 | 90 | 30.64
+ Tensor variable buffer data | 4 | 0 | 0 | 0 | 0.00
+ Node and registration array | 5 | 992 | 992 | 31 | 1.17
+ Operation data | 6 | 0 | 0 | 0 | 0.00
+
+Application exit code: 0.
+
+Info: /OSCI/SystemC: Simulation stopped by user.
+[warning ][main@0][01 ns] Simulation stopped by user
+
+--- FVP_MPS3_Corstone_SSE_300 statistics: -------------------------------------
+Simulated time : 2.879993s
+User time : 2.027100s
+System time : 0.135914s
+Wall time : 2.663214s
+Performance index : 1.08
+cpu0 : 27.03 MIPS ( 71999848 Inst)
+Memory highwater mark : 0x11919000 bytes ( 0.275 GB )
+-------------------------------------------------------------------------------
+```
diff --git a/tensorflow/lite/micro/tools/benchmarking/analyze_model.py b/tensorflow/lite/micro/tools/benchmarking/analyze_model.py
new file mode 100644
index 0000000..f2ff013
--- /dev/null
+++ b/tensorflow/lite/micro/tools/benchmarking/analyze_model.py
@@ -0,0 +1,35 @@
+#!/usr/bin/env python3
+# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from absl import app
+from absl import flags
+
+import tensorflow as tf
+
+_MODEL_PATH = flags.DEFINE_string(
+ name='model_file',
+ default='',
+ help='path for the .tflite model file.',
+)
+
+
+def _main(_):
+ """outputs model analysis to stdout/stderr"""
+ tf.lite.experimental.Analyzer.analyze(model_path=_MODEL_PATH.value)
+
+
+if __name__ == '__main__':
+ app.run(_main)
diff --git a/tensorflow/lite/micro/tools/benchmarking/collect_meta_data.sh b/tensorflow/lite/micro/tools/benchmarking/collect_meta_data.sh
new file mode 100755
index 0000000..c60bdf3
--- /dev/null
+++ b/tensorflow/lite/micro/tools/benchmarking/collect_meta_data.sh
@@ -0,0 +1,177 @@
+#!/usr/bin/env bash
+# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Collect generic benchmark meta data and insert resulting strings into
+# the file designated by TEMPLATE_FILE.
+#
+# Takes no arguments.
+#
+# Uses the following environment variables:
+# TEMPLATE_FILE - path to the template source file
+# GENERATED_FILE - path to the generated source file with substituted strings
+# TENSORFLOW_ROOT - path to the root of the source tree
+# MODEL_FILE - path to the .tflite model file
+# CC - path to C compiler
+# CXX - path to C++ compiler
+# CC_FLAGS - C compiler flags
+# CXX_FLAGS - C++ compiler flags
+# KERNEL_OPTIMIZATION - kernel optimization flags
+# CORE_OPTIMIZATION - core optimization flags
+# THIRD_PARTY_KERNEL_OPTIMIZATION - third pary kernel optimization flags
+# TARGET - target platform (xtensa, cortex_m_corstone_300, etc.)
+# TARGET_ARCH - target architecture (hifi5, cortex-m0, etc.)
+# OPTIMIZED_KERNEL - optimized kernel (xtensa, cmsis_nn, etc.)
+# BUILD_TYPE - type of build (default, release, etc.)
+# XTENSA_CORE - Xtensa core specification
+# XTENSA_BASE - Xtensa base install directory
+# XTENSA_TOOLS_VERSION - Xtensa tooling version
+
+
+set -e
+
+source ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/ci_build/helper_functions.sh
+
+function substitute_strings() {
+ search="// %%%_$1_%%%"
+ lines=$(fold -w 90 -s <<< "$2")
+ SAVED_IFS=${IFS}
+ IFS=$'\n' lines_array=( ${lines} )
+ IFS=${SAVED_IFS}
+ replacement=()
+ for line in "${lines_array[@]}"; do
+ line=$(sed -e 's/"/\\"/g' <<< "${line}")
+ line=$(printf '"%s",\n ' "${line}")
+ replacement+=( "${line}" )
+ done
+
+ tempfile=$(mktemp)
+
+ SEARCH_PATTERN="$search" REPLACEMENT_PATTERN="${replacement[@]}" awk '
+ BEGIN {
+ search = ENVIRON["SEARCH_PATTERN"]
+ replacement = ENVIRON["REPLACEMENT_PATTERN"]
+ }
+ s = index($0,search) {
+ $0 = substr($0,1,s-1) replacement substr($0,s+length(search))
+ }
+ { print }
+ ' "${GENERATED_FILE}" > ${tempfile}
+ mv ${tempfile} "${GENERATED_FILE}"
+}
+
+mkdir -p $(dirname ${GENERATED_FILE})
+cp -p ${TEMPLATE_FILE} ${GENERATED_FILE}
+
+# model analysis and SHA1
+if [[ ${MODEL_FILE} ]]; then
+ python3 -m pip install absl-py tensorflow
+ result=$(python3 \
+ "${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/benchmarking/analyze_model.py" \
+ --model_file="${MODEL_FILE}" \
+ )
+ substitute_strings model_analysis_strings "${result}"
+
+ result=$(shasum -b "${MODEL_FILE}" | cut -f 1 -d ' ')
+ substitute_strings model_sha1_strings "${result}"
+fi
+
+# compile date
+result=$(date)
+substitute_strings compilation_date_strings "${result}"
+
+GIT_TENSORFLOW_ROOT="${TENSORFLOW_ROOT:-./}"
+set +e
+# Git repo commit information
+result=$(cd ${GIT_TENSORFLOW_ROOT} && git rev-parse --verify HEAD)
+if [[ $? != 0 ]]; then
+ result="<git commit information not available>"
+fi
+substitute_strings git_commit_strings "${result}"
+
+# Git repo status information
+result=$(cd ${GIT_TENSORFLOW_ROOT} && git status)
+if [[ $? != 0 ]]; then
+ result="<git status information not available>"
+fi
+substitute_strings git_status_strings "${result}"
+set -e
+
+# Compiler information
+result="${CC}"
+substitute_strings cc_name_strings "${result}"
+result=$("${CC}" --version)
+substitute_strings cc_version_strings "${result}"
+result="${CC_FLAGS}"
+substitute_strings cc_flags_strings "${result}"
+
+result="${CXX}"
+substitute_strings cxx_name_strings "${result}"
+result=$("${CXX}" --version)
+substitute_strings cxx_version_strings "${result}"
+result="${CXX_FLAGS}"
+substitute_strings cxx_flags_strings "${result}"
+
+result="kernel= ${KERNEL_OPTIMIZATION}"
+result+=" core= ${CORE_OPTIMIZATION}"
+result+=" third-party-kernel= ${THIRD_PARTY_KERNEL_OPTIMIZATION}"
+substitute_strings optimization_flag_strings "${result}"
+
+# Target information
+TARGET="${TARGET:-linux}"
+TARGET_ARCH="${TARGET_ARCH:-x86}"
+OPTIMIZED_KERNEL="${OPTIMIZED_KERNEL:-none}"
+BUILD_TYPE="${BUILD_TYPE:-default}"
+result=$(printf 'TARGET=%s\nTARGET_ARCH=%s\nOPTIMIZATION=%s\nBUILD_TYPE=%s\n' \
+ "${TARGET}" \
+ "${TARGET_ARCH}" \
+ "${OPTIMIZED_KERNEL}" \
+ "${BUILD_TYPE}" \
+)
+if [[ ${XTENSA_CORE} ]]; then
+ result+=$(printf '\nXTENSA_CORE=%s' "${XTENSA_CORE}")
+ result+=$(printf '\nXTENSA_BASE=%s' "${XTENSA_BASE}")
+ result+=$(printf '\nXTENSA_TOOLS_VERSION=%s' "${XTENSA_TOOLS_VERSION}")
+fi
+substitute_strings target_info_strings "${result}"
+
+download_scripts=()
+download_script_args=( "--no-downloads" )
+if [[ ${OPTIMIZED_KERNEL} == "cmsis_nn" ]]; then
+ download_scripts+=( "${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/ext_libs/cmsis_nn_download.sh" )
+ download_script_args+=( "${TENSORFLOW_ROOT}" )
+elif [[ ${OPTIMIZED_KERNEL} == "xtensa" ]]; then
+ download_script_args+=( "${TARGET_ARCH}" "${TENSORFLOW_ROOT}" )
+ if [[ ${TARGET_ARCH} =~ ^(vision_p6)$ ]]; then
+ download_scripts+=( "${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/ext_libs/xtensa_download.sh" )
+ elif [[ ${TARGET_ARCH} =~ ^(hifi3|hifi4|hifi5)$ ]]; then
+ download_scripts+=( "${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/ext_libs/xtensa_download.sh" )
+ download_scripts+=( "${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/ext_libs/xtensa_ndsp_download.sh" )
+ fi
+fi
+
+if [[ ${#download_scripts[@]} -gt 0 ]]; then
+ results_url=
+ results_md5=
+ for script in "${download_scripts[@]}"; do
+ results=$("${script}" "${download_script_args[@]}" 2>&1)
+ url=$(sed -rn 's/^LIBRARY_URL=(.*)$/\1/p' <<< "${results}")
+ results_url+=$(printf '\n%s' "${url}")
+ md5=$(sed -rn 's/^LIBRARY_MD5=(.*)$/\1/p' <<< "${results}")
+ results_md5+=$(printf '\n%s' "${md5}")
+ done
+ substitute_strings nn_library_url_strings "${results_url}"
+ substitute_strings nn_library_md5_strings "${results_md5}"
+fi
diff --git a/tensorflow/lite/micro/tools/benchmarking/generic_model_benchmark.cc b/tensorflow/lite/micro/tools/benchmarking/generic_model_benchmark.cc
index c1db8fa..f398963 100644
--- a/tensorflow/lite/micro/tools/benchmarking/generic_model_benchmark.cc
+++ b/tensorflow/lite/micro/tools/benchmarking/generic_model_benchmark.cc
@@ -1,4 +1,4 @@
-/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -17,11 +17,14 @@
#include <sys/stat.h>
#include <sys/types.h>
+#include <cstring>
#include <memory>
#include <random>
+#include <type_traits>
#include "tensorflow/lite/c/c_api_types.h"
#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/micro/micro_context.h"
#include "tensorflow/lite/micro/micro_log.h"
#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
#include "tensorflow/lite/micro/micro_op_resolver.h"
@@ -29,11 +32,30 @@
#include "tensorflow/lite/micro/recording_micro_allocator.h"
#include "tensorflow/lite/micro/recording_micro_interpreter.h"
#include "tensorflow/lite/micro/system_setup.h"
-#include "tensorflow/lite/micro/tools/benchmarking/log_utils.h"
#include "tensorflow/lite/micro/tools/benchmarking/metrics.h"
#include "tensorflow/lite/micro/tools/benchmarking/op_resolver.h"
+#include "tensorflow/lite/micro/tools/benchmarking/show_meta_data.h"
#include "tensorflow/lite/schema/schema_generated.h"
+#if defined(GENERIC_BENCHMARK_USING_BUILTIN_MODEL)
+#if !defined(GENERIC_BENCHMARK_MODEL_HEADER_PATH)
+#error "GENERIC_BENCHMARK_MODEL_HEADER_PATH missing from CXXFLAGS"
+#endif // !defined(GENERIC_BENCHMARK_MODEL_HEADER_PATH)
+#if !defined(GENERIC_BENCHMARK_MODEL_NAME)
+#error "GENERIC_BENCHMARK_MODEL_NAME missing from CXXFLAGS"
+#endif // !defined(GENERIC_BENCHMARK_MODEL_NAME)
+
+#include GENERIC_BENCHMARK_MODEL_HEADER_PATH
+
+#define __MODEL_DATA(x) g_##x##_model_data
+#define _MODEL_DATA(x) __MODEL_DATA(x)
+#define MODEL_DATA _MODEL_DATA(GENERIC_BENCHMARK_MODEL_NAME)
+#define __MODEL_SIZE(x) g_##x##_model_data_size
+#define _MODEL_SIZE(x) __MODEL_SIZE(x)
+#define MODEL_SIZE _MODEL_SIZE(GENERIC_BENCHMARK_MODEL_NAME)
+
+#endif // defind(GENERIC_BENCHMARK_USING_BUILTIN_MODEL)
+
/*
* Generic model benchmark. Evaluates runtime performance of a provided model
* with random inputs.
@@ -45,20 +67,20 @@
using Profiler = ::tflite::MicroProfiler;
-using TflmOpResolver = tflite::MicroMutableOpResolver<96>;
-
-constexpr int kTfLiteAbort = -9;
-
// Seed used for the random input. Input data shouldn't affect invocation timing
// so randomness isn't really needed.
constexpr uint32_t kRandomSeed = 0xFB;
-// Which format should be used to output debug information.
-constexpr PrettyPrintType kPrintType = PrettyPrintType::kTable;
+#if !defined(GENERIC_BENCHMARK_USING_BUILTIN_MODEL)
+constexpr size_t kTensorArenaSize = 3e6;
+constexpr size_t kModelSize = 2e6;
+#elif defined(GENERIC_BENCHMARK_TENSOR_ARENA_SIZE)
+constexpr size_t kTensorArenaSize = GENERIC_BENCHMARK_TENSOR_ARENA_SIZE;
+#else
+constexpr size_t kTensorArenaSize = 5e6 - MODEL_SIZE;
+#endif // !defined(GENERIC_BENCHMARK_USING_BUILTIN_MODEL)
-constexpr size_t kTensorArenaSize = 1024 * 1024;
constexpr int kNumResourceVariable = 100;
-constexpr size_t kModelSize = 511408;
void SetRandomInput(const uint32_t random_seed,
tflite::MicroInterpreter& interpreter) {
@@ -76,8 +98,14 @@
}
}
+#if !defined(GENERIC_BENCHMARK_USING_BUILTIN_MODEL)
+
+struct FileCloser {
+ void operator()(FILE* file) { fclose(file); }
+};
+
bool ReadFile(const char* file_name, void* buffer, size_t buffer_size) {
- std::unique_ptr<FILE, decltype(&fclose)> file(fopen(file_name, "rb"), fclose);
+ std::unique_ptr<FILE, FileCloser> file(fopen(file_name, "rb"));
const size_t bytes_read =
fread(buffer, sizeof(char), buffer_size, file.get());
@@ -86,7 +114,11 @@
return false;
}
if (!feof(file.get())) {
- MicroPrintf("Model buffer is too small for the model.\n");
+ // Note that http://b/297592546 can mean that this error message is
+ // confusing.
+ MicroPrintf(
+ "Model buffer (%d bytes) is too small for the model (%d bytes).\n",
+ buffer_size, bytes_read);
return false;
}
if (bytes_read == 0) {
@@ -96,17 +128,14 @@
return true;
}
+#endif // !defined(GENERIC_BENCHMARK_USING_BUILTIN_MODEL)
-int Benchmark(const char* model_file_name) {
+int Benchmark(const uint8_t* model_data, tflite::PrettyPrintType print_type) {
Profiler profiler;
alignas(16) static uint8_t tensor_arena[kTensorArenaSize];
- alignas(16) unsigned char model_file_content[kModelSize];
- if (!ReadFile(model_file_name, model_file_content, kModelSize)) {
- return -1;
- }
uint32_t event_handle = profiler.BeginEvent("TfliteGetModel");
- const tflite::Model* model = tflite::GetModel(model_file_content);
+ const tflite::Model* model = tflite::GetModel(model_data);
profiler.EndEvent(event_handle);
TflmOpResolver op_resolver;
@@ -150,11 +179,50 @@
}
}
- LogAllocatorEvents(*allocator, kPrintType);
+ LogAllocatorEvents(*allocator, print_type);
return 0;
}
} // namespace
} // namespace tflite
-int main(int argc, char** argv) { return tflite::Benchmark(argv[1]); }
+#if !defined(GENERIC_BENCHMARK_USING_BUILTIN_MODEL)
+void usage(const char* prog_name) {
+ MicroPrintf("usage: %s filename [--csv]", prog_name);
+}
+#endif // !defined(GENERIC_BENCHMARK_USING_BUILTIN_MODEL)
+
+int main(int argc, char** argv) {
+ // Which format should be used to output debug information.
+ tflite::PrettyPrintType print_type = tflite::PrettyPrintType::kTable;
+ tflite::InitializeTarget();
+
+#if !defined(GENERIC_BENCHMARK_USING_BUILTIN_MODEL)
+ if (argc < 2 || argc > 3) {
+ usage(argv[0]);
+ return -1;
+ }
+ const char* model_filename = argv[1];
+
+ if (argc == 3) {
+ if (std::strcmp(argv[2], "--csv") == 0) {
+ print_type = tflite::PrettyPrintType::kCsv;
+ } else {
+ usage(argv[0]);
+ return -1;
+ }
+ }
+
+ alignas(16) static uint8_t model_data[tflite::kModelSize];
+
+ if (!tflite::ReadFile(model_filename, model_data, tflite::kModelSize)) {
+ return -1;
+ }
+#else
+ const uint8_t* model_data = MODEL_DATA;
+#endif // !defined(GENERIC_BENCHMARK_USING_BUILTIN_MODEL)
+
+ MicroPrintf("\nConfigured arena size = %d\n", tflite::kTensorArenaSize);
+ tflite::GenericBenchmarkShowMetaData();
+ return tflite::Benchmark(model_data, print_type);
+}
diff --git a/tensorflow/lite/micro/tools/benchmarking/log_utils.cc b/tensorflow/lite/micro/tools/benchmarking/log_utils.cc
deleted file mode 100644
index 808e465..0000000
--- a/tensorflow/lite/micro/tools/benchmarking/log_utils.cc
+++ /dev/null
@@ -1,115 +0,0 @@
-/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/tools/benchmarking/log_utils.h"
-
-#include <inttypes.h>
-#include <stdio.h>
-#include <stdlib.h>
-
-namespace tflite {
-
-int GetLongestStringLength(const char strings[][kMaxStringLength],
- const int count) {
- int max_length = 0;
-
- for (int i = 0; i < count; ++i) {
- int size = strlen(strings[i]);
- if (size > max_length) {
- max_length = size;
- }
- }
-
- return max_length;
-}
-
-void FillColumnPadding(char* string, const int size, const int max_size,
- const int padding) {
- FillString(string, max_size - size + padding, kMaxStringLength);
-}
-
-void FillString(char* string, const int size, const int buffer_size,
- const char value) {
- if (buffer_size <= (static_cast<int>(strlen(string)))) {
- for (int i = 0; i < buffer_size; ++i) {
- string[i] = (i < size) ? value : 0;
- }
- }
-}
-
-void MicroStrcat(char* output, const char* input, const int size) {
- if (size < 0) {
- strcat(output, input); // NOLINT: strcat required due to no dynamic memory.
- } else {
- strncat(output, input, size);
- }
-}
-
-void MicroStrcpy(char* output, const char* input) {
- strcpy(output, input); // NOLINT: strcpy required due to no dynamic memory.
-}
-
-void FormatIntegerDivide(char* output, const int64_t numerator,
- const int64_t denominator, const int decimal_places) {
- int64_t multiplier = 1;
- for (int i = 0; i < decimal_places; ++i) {
- multiplier *= 10;
- }
-
- const int64_t total = numerator * multiplier / denominator;
- const int whole = static_cast<int>(total / multiplier);
- const int fractional = static_cast<int>(total % multiplier);
- sprintf(output, "%d.%d", whole, fractional); // NOLINT: sprintf is required.
-}
-
-void FormatAsPercentage(char* output, const int64_t numerator,
- const int64_t denominator, const int decimal_places) {
- FormatIntegerDivide(output, numerator * 100, denominator, decimal_places);
-}
-
-void PrettyPrintTableHeader(PrettyPrintType type, const char* table_name) {
- switch (type) {
- case PrettyPrintType::kCsv:
- MicroPrintf("[[ CSV ]]: %s", table_name);
- break;
- case PrettyPrintType::kTable:
- MicroPrintf("[[ TABLE ]]: %s", table_name);
- }
-}
-
-template <>
-void FormatNumber<int32_t>(char* output, int32_t value) {
- sprintf(output, "%" PRId32, value); // NOLINT: sprintf required.
-}
-
-template <>
-void FormatNumber<size_t>(char* output, size_t value) {
- sprintf(output, "%zu", value); // NOLINT: sprintf required.
-}
-
-template <>
-void FormatNumber<float>(char* output, float value) {
- constexpr int64_t kDenominator = 1000;
- FormatIntegerDivide(output, static_cast<int64_t>(value * kDenominator),
- kDenominator, 3);
-}
-
-template <>
-void FormatNumber<double>(char* output, double value) {
- constexpr int64_t kDenominator = 1000;
- FormatIntegerDivide(output, static_cast<int64_t>(value * kDenominator),
- kDenominator, 3);
-}
-} // namespace tflite
diff --git a/tensorflow/lite/micro/tools/benchmarking/log_utils.h b/tensorflow/lite/micro/tools/benchmarking/log_utils.h
deleted file mode 100644
index ee73f48..0000000
--- a/tensorflow/lite/micro/tools/benchmarking/log_utils.h
+++ /dev/null
@@ -1,273 +0,0 @@
-/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TFLM_BENCHMARK_INTERNAL_LOG_UTILS_H_
-#define TFLM_BENCHMARK_INTERNAL_LOG_UTILS_H_
-
-#include <algorithm>
-#include <cstdarg>
-#include <cstdint>
-#include <cstring>
-
-#include "tensorflow/lite/micro/micro_log.h"
-
-namespace tflite {
-
-// The maxmimum length of a string.
-static constexpr int kMaxStringLength = 32;
-
-// The maximum length of a table row, applies to the header as well.
-static constexpr int kMaxRowLength = 100;
-
-// The default padding between columns in a table.
-static constexpr int kDefaultColumnPadding = 4;
-
-// Defines how formatted data is printed to stdout.
-enum class PrettyPrintType {
- // Prints as a CSV file.
- kCsv,
- // Prints as a formatted table.
- kTable,
-};
-
-// Returns the length of the longest string in an array.
-// Args:
-// - strings: An array of strings.
-// - count: The number of strings in the array.
-int GetLongestStringLength(const char strings[][kMaxStringLength], int count);
-
-// Adds padding between two columns in a table.
-// ex) "hello" is being inserted into a column. The largest value in that column
-// is 10, and there's a global padding of 4 spaces. Therefore, 9 spaces (10
-// - 5 + 4) are added as padding.
-// Args:
-// - string: The input padding string.
-// - size: The size of the string that's being inserted into a column.
-// - max_size: The size of the largest string in the column.
-// - padding: The amount of padding to add to each column regardless of its
-// size.
-void FillColumnPadding(char* string, int size, int max_size,
- int padding = kDefaultColumnPadding);
-
-// Fills a string with a specified value.
-// Args:
-// - string: The input string. This is filled in with the specified value.
-// - size: The size of the string after being filled in. This must be less than
-// the allocated space for the string.
-// - buffer_size: The size of the string's buffer.
-// - value: The value to insert into the string. Defaults to a space.
-void FillString(char* string, int size, int buffer_size, char value = ' ');
-
-// Concatenates the input string onto the first.
-// Args:
-// - output: The destination string for where to append input.
-// - input: The input string to concatenate.
-// - size: The number of characters to concatenate from the first string. If
-// negative, the whole input string will be concatenated.
-void MicroStrcat(char* output, const char* input, int size = -1);
-
-// Copies the input string into the output.
-void MicroStrcpy(char* output, const char* input);
-
-// Formats a division operation to have a specified number of decimal places.
-// Args:
-// - output: The output string to be formatted.
-// - numerator: The numerator in the division operation.
-// - denominator: The denominator in the division operation.
-// - decimal places: The number of decimal places to print to.
-void FormatIntegerDivide(char* output, int64_t numerator, int64_t denominator,
- int decimal_places);
-
-// Formats a division operation as a percentage.
-// Args:
-// - output: The output string to be formatted.
-// - numerator: The numerator in the division operation.
-// - denominator: The denominator in the division operation.
-// - decimal places: The number of decimal places to print to.
-void FormatAsPercentage(char* output, int64_t numerator, int64_t denominator,
- int decimal_places);
-
-void PrettyPrintTableHeader(PrettyPrintType type, const char* table_name);
-
-// Formats a number as a string.
-// Args:
-// - output: The location of where to write the formatted number.
-// - value: The value to write to a string.
-template <typename T>
-void FormatNumber(char* output, T value);
-
-// Pretty prints a table to stdout.
-// Note: kMaxRows and kColumns should describe the allocated size of the table,
-// not the amount of data that is populated. It is required that all
-// columns are filled out, but not all rows.
-//
-// ex) PrintTable<3, 25>(headers, data, 4);
-// This will print a table with 3 columns and 4 rows. In this example, it
-// is required that data is defined as char[3][25][kMaxStringLength] to
-// properly print.
-//
-// op cycles cpu %
-// -------------------------
-// foo | 1000 | 10
-// bar | 2500 | 25
-// baz | 1000 | 10
-// lorem | 2000 | 20
-//
-// Args:
-// - headers: A 1D array of strings containing the headers of the table. This
-// must be equal in size to kColumns.
-// - data: A 2D array of string data organized in [columns, rows]. As stated
-// above, it is required that all columns are populated, but not all rows.
-// - rows: The number of populated rows in `data`.
-template <int kMaxRows, int kColumns>
-void PrintTable(const char headers[kColumns][kMaxStringLength],
- const char data[kColumns][kMaxRows][kMaxStringLength],
- const int rows) {
- // Get the maximum width for each column in the table.
- int max_column_width[kColumns];
- for (int i = 0; i < kColumns; ++i) {
- max_column_width[i] = std::max(GetLongestStringLength(data[i], rows),
- static_cast<int>(strlen(headers[i])));
- }
-
- // Add padding between each item in the header so it can be printed on one
- // line.
- char header_spaces[kColumns][kMaxStringLength];
- for (int i = 0; i < kColumns; ++i) {
- FillColumnPadding(header_spaces[i], strlen(headers[i]), max_column_width[i],
- kDefaultColumnPadding + 2);
- }
-
- // Print the header.
- char header[kMaxRowLength];
- memset(header, 0, kMaxRowLength);
- for (int i = 0; i < kColumns; ++i) {
- MicroStrcat(header, headers[i]);
- MicroStrcat(header, header_spaces[i]);
- }
- MicroPrintf("%s", header);
-
- // Print a separator to separate the header from the data.
- char separator[kMaxRowLength];
- FillString(separator, strlen(header) - 1, kMaxRowLength, '-');
- MicroPrintf("%s", separator);
-
- for (int i = 0; i < rows; ++i) {
- char spaces[kColumns][kMaxStringLength];
- for (int j = 0; j < kColumns; ++j) {
- FillColumnPadding(spaces[j], strlen(data[j][i]), max_column_width[j]);
- }
-
- char row[kMaxRowLength];
- memset(row, 0, kMaxRowLength);
-
- // Concatenate each column in a row with the format "[data][padding]| "
- for (int j = 0; j < kColumns; ++j) {
- MicroStrcat(row, data[j][i]);
- MicroStrcat(row, spaces[j]);
- MicroStrcat(row, "| ");
- }
-
- MicroPrintf("%s", row);
- }
-
- MicroPrintf(separator);
- MicroPrintf("");
-}
-
-// Pretty prints a csv to stdout.
-// Note: kMaxRows and kColumns should describe the allocated size of the table,
-// not the amount of data that is populated. It is required that all
-// columns are filled out, but not all rows.
-//
-// ex)
-// op,cycles,%cpu
-// foo,1000,10
-// bar,2500,25
-// baz,1000,10
-//
-// Args:
-// - headers: A 1D array of strings containing the headers of the table. This
-// must be equal in size to kColumns.
-// - data: A 2D array of string data organized in [columns, rows]. As stated
-// above, it is required that all columns are populated, but not all rows.
-// - rows: The number of populated rows in `data`.
-template <int kMaxRows, int kColumns>
-void PrintCsv(const char headers[kColumns][kMaxStringLength],
- const char data[kColumns][kMaxRows][kMaxStringLength],
- const int rows) {
- char header[kMaxRowLength];
- memset(header, 0, kMaxRowLength);
- for (int i = 0; i < kColumns; ++i) {
- MicroStrcat(header, headers[i]);
- if (i < kColumns - 1) {
- MicroStrcat(header, ",");
- }
- }
-
- MicroPrintf("%s", header);
-
- char row[kMaxRowLength];
- for (int i = 0; i < rows; ++i) {
- memset(row, 0, kMaxRowLength);
- for (int j = 0; j < kColumns; ++j) {
- MicroStrcat(row, data[j][i]);
- if (j < kColumns - 1) {
- MicroStrcat(row, ",");
- }
- }
-
- MicroPrintf("%s", row);
- }
-
- MicroPrintf(""); // Serves as a new line.
-}
-
-// Prints a 2D array of strings in a formatted manner along with a table name
-// that includes the table type.
-//
-// Note: kMaxRows and kColumns should describe the allocated size of the table,
-// not the amount of data that is populated. It is required that all
-// columns are filled out, but not all rows.
-//
-// ex) PrettyPrint::kCsv will print a csv with a [[ CSV ]]: table_name header.
-//
-// Args:
-// - headers: A 1D array of strings containing the headers of the table. This
-// must be equal in size to kColumns.
-// - data: A 2D array of string data organized in [columns, rows]. As stated
-// above, it is required that all columns are populated, but not all rows.
-// - rows: The number of populated rows in `data`.
-// - type: The format type that should be used to pretty print.
-// - table_name: The name of the table to be printed alongside the format type.
-template <int kMaxRows, int kColumns>
-void PrintFormattedData(const char headers[kColumns][kMaxStringLength],
- const char data[kColumns][kMaxRows][kMaxStringLength],
- const int rows, const PrettyPrintType type,
- const char* table_name) {
- PrettyPrintTableHeader(type, table_name);
- switch (type) {
- case PrettyPrintType::kCsv:
- PrintCsv<kMaxRows, kColumns>(headers, data, rows);
- break;
- case PrettyPrintType::kTable:
- PrintTable<kMaxRows, kColumns>(headers, data, rows);
- break;
- }
-}
-
-} // namespace tflite
-
-#endif // TFLM_BENCHMARK_INTERNAL_LOG_UTILS_H_
diff --git a/tensorflow/lite/micro/tools/benchmarking/metrics.cc b/tensorflow/lite/micro/tools/benchmarking/metrics.cc
index 115d879..3a4bf7e 100644
--- a/tensorflow/lite/micro/tools/benchmarking/metrics.cc
+++ b/tensorflow/lite/micro/tools/benchmarking/metrics.cc
@@ -15,44 +15,70 @@
#include "tensorflow/lite/micro/tools/benchmarking/metrics.h"
-#include <sys/types.h>
-
#include <cstddef>
+#include <cstring>
+#include <iterator>
+#include <type_traits>
+#include <utility>
-#include "tensorflow/lite/micro/tools/benchmarking/log_utils.h"
+#include "tensorflow/lite/kernels/internal/compatibility.h"
+#include "tensorflow/lite/micro/micro_log.h"
namespace tflite {
-void LogArenaAllocations(
+namespace {
+
+struct LogArenaRecord {
+ const char* title;
+ int allocations;
+ float percentage;
+};
+
+struct LogAllocationRecord {
+ const char* title;
+ int type;
+ int used_bytes;
+ int requested_bytes;
+ int count;
+ float percentage;
+};
+
+constexpr int kArenaRows = 3;
+constexpr int kArenaColumns = 3;
+
+constexpr int kAllocationTypes = 7;
+constexpr int kAllocationColumns = 6;
+
+constexpr int kMaxBufSize = 100;
+
+LogArenaRecord GetLogArenaRecord(
const tflite::RecordingSingleArenaBufferAllocator* allocator,
- const PrettyPrintType type) {
- constexpr int kArenaRows = 3;
- constexpr int kArenaCols = 3;
+ int row_index) {
+ TFLITE_DCHECK(row_index < kArenaRows);
const size_t total_bytes = allocator->GetUsedBytes();
+ const size_t allocations[] = {total_bytes,
+ allocator->GetNonPersistentUsedBytes(),
+ allocator->GetPersistentUsedBytes()};
+ static_assert(std::extent<decltype(allocations)>::value == kArenaRows,
+ "kArenaRows mismatch");
+ const char* titles[] = {"Total", "NonPersistent", "Persistent"};
+ static_assert(std::extent<decltype(titles)>::value == kArenaRows,
+ "kArenaRows mismatch");
- size_t allocations[kArenaRows] = {total_bytes,
- allocator->GetNonPersistentUsedBytes(),
- allocator->GetPersistentUsedBytes()};
- char titles[kArenaRows][kMaxStringLength] = {"Total", "Head", "Tail"};
- char headers[kArenaRows][kMaxStringLength] = {"Arena", "Bytes", "% Arena"};
+ LogArenaRecord record = {};
+ record.title = titles[row_index];
+ record.allocations = allocations[row_index];
+ record.percentage = record.allocations * 100.0f / total_bytes;
- char data[kArenaCols][kArenaRows][kMaxStringLength];
- for (int i = 0; i < kArenaRows; ++i) {
- MicroStrcpy(data[0][i], titles[i]);
- FormatNumber<int32_t>(data[1][i], allocations[i]);
- FormatAsPercentage(data[2][i], static_cast<int64_t>(allocations[i]),
- static_cast<int64_t>(total_bytes), 2);
- }
-
- PrintFormattedData<kArenaRows, kArenaCols>(headers, data, kArenaRows, type,
- "Arena");
+ return record;
}
-void LogAllocations(const tflite::RecordingMicroAllocator& allocator,
- const PrettyPrintType type) {
- constexpr int kAllocationTypes = 7;
- tflite::RecordedAllocationType types[kAllocationTypes] = {
+LogAllocationRecord GetLogAllocationRecord(
+ const tflite::RecordingMicroAllocator& allocator, int row_index) {
+ TFLITE_DCHECK(row_index < kAllocationTypes);
+
+ const tflite::RecordedAllocationType types[] = {
tflite::RecordedAllocationType::kTfLiteEvalTensorData,
tflite::RecordedAllocationType::kPersistentTfLiteTensorData,
tflite::RecordedAllocationType::kPersistentTfLiteTensorQuantizationData,
@@ -60,38 +86,227 @@
tflite::RecordedAllocationType::kTfLiteTensorVariableBufferData,
tflite::RecordedAllocationType::kNodeAndRegistrationArray,
tflite::RecordedAllocationType::kOpData};
-
- char titles[kAllocationTypes][kMaxStringLength] = {
- "Eval tensor data",
- "Persistent tensor data",
- "Persistent quantization data",
- "Persistent buffer data",
- "Tensor variable buffer data",
- "Node and registration array",
- "Operation data"};
-
- constexpr int kColumns = 6;
- const char headers[kColumns][kMaxStringLength] = {
- "Allocation", "Id", "Used", "Requested", "Count", "% Memory"};
-
+ static_assert(std::extent<decltype(types)>::value == kAllocationTypes,
+ "kAllocationTypes mismatch");
+ const char* titles[] = {"Eval tensor data",
+ "Persistent tensor data",
+ "Persistent quantization data",
+ "Persistent buffer data",
+ "Tensor variable buffer data",
+ "Node and registration array",
+ "Operation data"};
+ static_assert(std::extent<decltype(titles)>::value == kAllocationTypes,
+ "kAllocationTypes mismatch");
const size_t total_bytes =
allocator.GetSimpleMemoryAllocator()->GetUsedBytes();
+ tflite::RecordedAllocation allocation =
+ allocator.GetRecordedAllocation(types[row_index]);
- char data[kColumns][kAllocationTypes][kMaxStringLength];
- for (int i = 0; i < kAllocationTypes; ++i) {
- tflite::RecordedAllocation allocation =
- allocator.GetRecordedAllocation(types[i]);
- MicroStrcpy(data[0][i], titles[i]);
- FormatNumber<int32_t>(data[1][i], static_cast<int>(types[i]));
- FormatNumber<int32_t>(data[2][i], allocation.used_bytes);
- FormatNumber<int32_t>(data[3][i], allocation.requested_bytes);
- FormatNumber<int32_t>(data[4][i], allocation.count);
- FormatAsPercentage(data[5][i], static_cast<int64_t>(allocation.used_bytes),
- static_cast<int64_t>(total_bytes), 2);
+ LogAllocationRecord record = {};
+ record.title = titles[row_index];
+ record.type = static_cast<int>(types[row_index]);
+ record.used_bytes = allocation.used_bytes;
+ record.requested_bytes = allocation.requested_bytes;
+ record.count = allocation.count;
+ record.percentage = allocation.used_bytes * 100.0f / total_bytes;
+
+ return record;
+}
+
+template <int kColumns>
+void UpdateColumnWidths(int (&widths)[kColumns], const char* s[kColumns]) {
+ for (int i = 0; i < kColumns; i++) {
+ widths[i] = std::max(widths[i], static_cast<int>(std::strlen(s[i])));
+ }
+}
+
+void UpdateColumnWidths(int (&widths)[kArenaColumns],
+ const LogArenaRecord& record) {
+ char buf[kMaxBufSize];
+ int count;
+
+ count = MicroSnprintf(buf, kMaxBufSize, "%s", record.title);
+ widths[0] = std::max(widths[0], count);
+ count = MicroSnprintf(buf, kMaxBufSize, "%d", record.allocations);
+ widths[1] = std::max(widths[1], count);
+ count = MicroSnprintf(buf, kMaxBufSize, "%.2f",
+ static_cast<double>(record.percentage));
+ widths[2] = std::max(widths[2], count);
+}
+
+void UpdateColumnWidths(int (&widths)[kAllocationColumns],
+ const LogAllocationRecord& record) {
+ char buf[kMaxBufSize];
+ int count;
+
+ count = MicroSnprintf(buf, kMaxBufSize, "%s", record.title);
+ widths[0] = std::max(widths[0], count);
+ count = MicroSnprintf(buf, kMaxBufSize, "%d", record.type);
+ widths[1] = std::max(widths[1], count);
+ count = MicroSnprintf(buf, kMaxBufSize, "%d", record.used_bytes);
+ widths[2] = std::max(widths[2], count);
+ count = MicroSnprintf(buf, kMaxBufSize, "%d", record.requested_bytes);
+ widths[3] = std::max(widths[3], count);
+ count = MicroSnprintf(buf, kMaxBufSize, "%d", record.count);
+ widths[4] = std::max(widths[4], count);
+ count = MicroSnprintf(buf, kMaxBufSize, "%.2f",
+ static_cast<double>(record.percentage));
+ widths[5] = std::max(widths[5], count);
+}
+
+using BufferDatum = std::tuple<char*, char*>;
+
+template <typename T>
+BufferDatum AddTableColumnValue(const BufferDatum& buffer, const char* format,
+ int column_width, T value,
+ const char* separator = nullptr) {
+ char* p;
+ char* p_end;
+ std::tie(p, p_end) = buffer;
+ int count = MicroSnprintf(p, p_end - p, format, column_width, value);
+ p += count;
+ if (separator != nullptr && p < p_end) {
+ count = MicroSnprintf(p, p_end - p, separator);
+ p += count;
}
- PrintFormattedData<kAllocationTypes, kColumns>(
- headers, data, kAllocationTypes, type, "Allocations");
+ if (p > p_end) {
+ p = p_end;
+ }
+
+ return std::make_tuple(p, p_end);
+}
+
+} // namespace
+
+void LogArenaAllocations(
+ const tflite::RecordingSingleArenaBufferAllocator* allocator,
+ const PrettyPrintType type) {
+ const char* headers[] = {"Arena", "Bytes", "%% Arena"};
+ static_assert(std::extent<decltype(headers)>::value == kArenaColumns,
+ "kArenaColumns mismatch");
+ char buffer[kMaxBufSize];
+ BufferDatum buffer_datum =
+ std::make_tuple(std::begin(buffer), std::end(buffer));
+ int column_widths[kArenaColumns] = {};
+
+ const char* output_type;
+ const char* string_format;
+ if (type == PrettyPrintType::kCsv) {
+ output_type = "CSV";
+ string_format = "\"%*s\"";
+ } else {
+ output_type = "Table";
+ string_format = "%*s";
+
+ UpdateColumnWidths<kArenaColumns>(column_widths, headers);
+ for (int i = 0; i < kArenaRows; i++) {
+ LogArenaRecord record = GetLogArenaRecord(allocator, i);
+ UpdateColumnWidths(column_widths, record);
+ }
+ }
+
+ MicroPrintf("[[ %s ]]: Arena", output_type);
+
+ for (int i = 0; i < kArenaColumns; i++) {
+ // create header
+ const char* separator = nullptr;
+ if (i != kArenaColumns - 1) {
+ // separator for all but last column value
+ if (type == PrettyPrintType::kCsv) {
+ separator = ",";
+ } else {
+ separator = " ";
+ }
+ }
+ buffer_datum = AddTableColumnValue(buffer_datum, string_format,
+ column_widths[i], headers[i], separator);
+ }
+ MicroPrintf(buffer);
+
+ for (int i = 0; i < kArenaRows; ++i) {
+ // create rows
+ const char* separator = (type == PrettyPrintType::kCsv) ? "," : " | ";
+ buffer_datum = std::make_tuple(std::begin(buffer), std::end(buffer));
+ LogArenaRecord record = GetLogArenaRecord(allocator, i);
+ buffer_datum = AddTableColumnValue(
+ buffer_datum, string_format, column_widths[0], record.title, separator);
+ buffer_datum = AddTableColumnValue(buffer_datum, "%*d", column_widths[1],
+ record.allocations, separator);
+ buffer_datum = AddTableColumnValue(buffer_datum, "%*.2f", column_widths[2],
+ static_cast<double>(record.percentage));
+ MicroPrintf(buffer);
+ }
+
+ MicroPrintf(""); // output newline
+}
+
+void LogAllocations(const tflite::RecordingMicroAllocator& allocator,
+ const PrettyPrintType type) {
+ const char* headers[] = {"Allocation", "Id", "Used",
+ "Requested", "Count", "%% Memory"};
+ static_assert(std::extent<decltype(headers)>::value == kAllocationColumns,
+ "kAllocationColumns mismatch");
+ char buffer[kMaxBufSize];
+ BufferDatum buffer_datum =
+ std::make_tuple(std::begin(buffer), std::end(buffer));
+ int column_widths[kAllocationColumns] = {};
+
+ const char* output_type;
+ const char* string_format;
+ if (type == PrettyPrintType::kCsv) {
+ output_type = "CSV";
+ string_format = "\"%*s\"";
+ } else {
+ output_type = "Table";
+ string_format = "%*s";
+
+ UpdateColumnWidths<kAllocationColumns>(column_widths, headers);
+ for (int i = 0; i < kAllocationTypes; i++) {
+ LogAllocationRecord record = GetLogAllocationRecord(allocator, i);
+ UpdateColumnWidths(column_widths, record);
+ }
+ }
+
+ MicroPrintf("[[ %s ]]: Allocations", output_type);
+
+ for (int i = 0; i < kAllocationColumns; i++) {
+ // create header
+ const char* separator = nullptr;
+ if (i != kAllocationColumns - 1) {
+ // separator for all but last column value
+ if (type == PrettyPrintType::kCsv) {
+ separator = ",";
+ } else {
+ separator = " ";
+ }
+ }
+ buffer_datum = AddTableColumnValue(buffer_datum, string_format,
+ column_widths[i], headers[i], separator);
+ }
+ MicroPrintf(buffer);
+
+ for (int i = 0; i < kAllocationTypes; ++i) {
+ // create rows
+ const char* separator = (type == PrettyPrintType::kCsv) ? "," : " | ";
+ buffer_datum = std::make_tuple(std::begin(buffer), std::end(buffer));
+ LogAllocationRecord record = GetLogAllocationRecord(allocator, i);
+ buffer_datum = AddTableColumnValue(
+ buffer_datum, string_format, column_widths[0], record.title, separator);
+ buffer_datum = AddTableColumnValue(buffer_datum, "%*d", column_widths[1],
+ record.type, separator);
+ buffer_datum = AddTableColumnValue(buffer_datum, "%*d", column_widths[2],
+ record.used_bytes, separator);
+ buffer_datum = AddTableColumnValue(buffer_datum, "%*d", column_widths[3],
+ record.requested_bytes, separator);
+ buffer_datum = AddTableColumnValue(buffer_datum, "%*d", column_widths[4],
+ record.count, separator);
+ buffer_datum = AddTableColumnValue(buffer_datum, "%*.2f", column_widths[5],
+ static_cast<double>(record.percentage));
+ MicroPrintf(buffer);
+ }
+
+ MicroPrintf(""); // output newline
}
void LogAllocatorEvents(const tflite::RecordingMicroAllocator& allocator,
@@ -99,4 +314,5 @@
LogArenaAllocations(allocator.GetSimpleMemoryAllocator(), type);
LogAllocations(allocator, type);
}
+
} // namespace tflite
diff --git a/tensorflow/lite/micro/tools/benchmarking/metrics.h b/tensorflow/lite/micro/tools/benchmarking/metrics.h
index 5c0c880..996cde1 100644
--- a/tensorflow/lite/micro/tools/benchmarking/metrics.h
+++ b/tensorflow/lite/micro/tools/benchmarking/metrics.h
@@ -16,17 +16,19 @@
#ifndef TFLM_BENCHMARK_INTERNAL_METRICS_H_
#define TFLM_BENCHMARK_INTERNAL_METRICS_H_
-#include <stdio.h>
-
-#include <cmath>
-#include <cstdint>
-
#include "tensorflow/lite/micro/micro_profiler.h"
#include "tensorflow/lite/micro/recording_micro_allocator.h"
-#include "tensorflow/lite/micro/tools/benchmarking/log_utils.h"
namespace tflite {
+// Defines how formatted data is printed to stdout.
+enum class PrettyPrintType {
+ // Prints as a CSV file.
+ kCsv,
+ // Prints as a formatted table.
+ kTable,
+};
+
// Logs the allocation events. Prints out two tables, one for the arena
// allocations, and one for each type of TFLM allocation type.
// Args:
diff --git a/tensorflow/lite/micro/tools/benchmarking/op_resolver.h b/tensorflow/lite/micro/tools/benchmarking/op_resolver.h
index 447741b..9b98849 100644
--- a/tensorflow/lite/micro/tools/benchmarking/op_resolver.h
+++ b/tensorflow/lite/micro/tools/benchmarking/op_resolver.h
@@ -23,15 +23,17 @@
namespace tflite {
-inline TfLiteStatus CreateOpResolver(
- tflite::MicroMutableOpResolver<96>& op_resolver) {
- TF_LITE_ENSURE_STATUS(op_resolver.AddFullyConnected());
- TF_LITE_ENSURE_STATUS(op_resolver.AddAdd());
+using TflmOpResolver = MicroMutableOpResolver<113>;
+
+inline TfLiteStatus CreateOpResolver(TflmOpResolver& op_resolver) {
TF_LITE_ENSURE_STATUS(op_resolver.AddAbs());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddAdd());
TF_LITE_ENSURE_STATUS(op_resolver.AddAddN());
TF_LITE_ENSURE_STATUS(op_resolver.AddArgMax());
TF_LITE_ENSURE_STATUS(op_resolver.AddArgMin());
TF_LITE_ENSURE_STATUS(op_resolver.AddAssignVariable());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddAveragePool2D());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddBatchMatMul());
TF_LITE_ENSURE_STATUS(op_resolver.AddBatchToSpaceNd());
TF_LITE_ENSURE_STATUS(op_resolver.AddBroadcastArgs());
TF_LITE_ENSURE_STATUS(op_resolver.AddBroadcastTo());
@@ -40,44 +42,63 @@
TF_LITE_ENSURE_STATUS(op_resolver.AddCeil());
TF_LITE_ENSURE_STATUS(op_resolver.AddCircularBuffer());
TF_LITE_ENSURE_STATUS(op_resolver.AddConcatenation());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddConv2D());
TF_LITE_ENSURE_STATUS(op_resolver.AddCos());
TF_LITE_ENSURE_STATUS(op_resolver.AddCumSum());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddDelay());
TF_LITE_ENSURE_STATUS(op_resolver.AddDepthToSpace());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddDepthwiseConv2D());
TF_LITE_ENSURE_STATUS(op_resolver.AddDequantize());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddDetectionPostprocess());
TF_LITE_ENSURE_STATUS(op_resolver.AddDiv());
TF_LITE_ENSURE_STATUS(op_resolver.AddElu());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddEmbeddingLookup());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddEnergy());
TF_LITE_ENSURE_STATUS(op_resolver.AddEqual());
TF_LITE_ENSURE_STATUS(op_resolver.AddEthosU());
TF_LITE_ENSURE_STATUS(op_resolver.AddExp());
TF_LITE_ENSURE_STATUS(op_resolver.AddExpandDims());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddFftAutoScale());
TF_LITE_ENSURE_STATUS(op_resolver.AddFill());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddFilterBank());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddFilterBankLog());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddFilterBankSpectralSubtraction());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddFilterBankSquareRoot());
TF_LITE_ENSURE_STATUS(op_resolver.AddFloor());
TF_LITE_ENSURE_STATUS(op_resolver.AddFloorDiv());
TF_LITE_ENSURE_STATUS(op_resolver.AddFloorMod());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddFramer());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddFullyConnected());
TF_LITE_ENSURE_STATUS(op_resolver.AddGather());
TF_LITE_ENSURE_STATUS(op_resolver.AddGatherNd());
TF_LITE_ENSURE_STATUS(op_resolver.AddGreater());
TF_LITE_ENSURE_STATUS(op_resolver.AddGreaterEqual());
TF_LITE_ENSURE_STATUS(op_resolver.AddHardSwish());
TF_LITE_ENSURE_STATUS(op_resolver.AddIf());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddIrfft());
TF_LITE_ENSURE_STATUS(op_resolver.AddL2Normalization());
TF_LITE_ENSURE_STATUS(op_resolver.AddL2Pool2D());
TF_LITE_ENSURE_STATUS(op_resolver.AddLeakyRelu());
TF_LITE_ENSURE_STATUS(op_resolver.AddLess());
TF_LITE_ENSURE_STATUS(op_resolver.AddLessEqual());
TF_LITE_ENSURE_STATUS(op_resolver.AddLog());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddLogSoftmax());
TF_LITE_ENSURE_STATUS(op_resolver.AddLogicalAnd());
TF_LITE_ENSURE_STATUS(op_resolver.AddLogicalNot());
TF_LITE_ENSURE_STATUS(op_resolver.AddLogicalOr());
TF_LITE_ENSURE_STATUS(op_resolver.AddLogistic());
- TF_LITE_ENSURE_STATUS(op_resolver.AddLogSoftmax());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddMaxPool2D());
TF_LITE_ENSURE_STATUS(op_resolver.AddMaximum());
- TF_LITE_ENSURE_STATUS(op_resolver.AddMirrorPad());
TF_LITE_ENSURE_STATUS(op_resolver.AddMean());
TF_LITE_ENSURE_STATUS(op_resolver.AddMinimum());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddMirrorPad());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddMul());
TF_LITE_ENSURE_STATUS(op_resolver.AddNeg());
TF_LITE_ENSURE_STATUS(op_resolver.AddNotEqual());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddOverlapAdd());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddPCAN());
TF_LITE_ENSURE_STATUS(op_resolver.AddPack());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddPad());
TF_LITE_ENSURE_STATUS(op_resolver.AddPadV2());
TF_LITE_ENSURE_STATUS(op_resolver.AddPrelu());
TF_LITE_ENSURE_STATUS(op_resolver.AddQuantize());
@@ -88,6 +109,7 @@
TF_LITE_ENSURE_STATUS(op_resolver.AddReshape());
TF_LITE_ENSURE_STATUS(op_resolver.AddResizeBilinear());
TF_LITE_ENSURE_STATUS(op_resolver.AddResizeNearestNeighbor());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddRfft());
TF_LITE_ENSURE_STATUS(op_resolver.AddRound());
TF_LITE_ENSURE_STATUS(op_resolver.AddRsqrt());
TF_LITE_ENSURE_STATUS(op_resolver.AddSelectV2());
@@ -99,29 +121,27 @@
TF_LITE_ENSURE_STATUS(op_resolver.AddSpaceToDepth());
TF_LITE_ENSURE_STATUS(op_resolver.AddSplit());
TF_LITE_ENSURE_STATUS(op_resolver.AddSplitV());
- TF_LITE_ENSURE_STATUS(op_resolver.AddSqueeze());
TF_LITE_ENSURE_STATUS(op_resolver.AddSqrt());
TF_LITE_ENSURE_STATUS(op_resolver.AddSquare());
TF_LITE_ENSURE_STATUS(op_resolver.AddSquaredDifference());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddSqueeze());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddStacker());
TF_LITE_ENSURE_STATUS(op_resolver.AddStridedSlice());
TF_LITE_ENSURE_STATUS(op_resolver.AddSub());
TF_LITE_ENSURE_STATUS(op_resolver.AddSum());
TF_LITE_ENSURE_STATUS(op_resolver.AddSvdf());
TF_LITE_ENSURE_STATUS(op_resolver.AddTanh());
- TF_LITE_ENSURE_STATUS(op_resolver.AddTransposeConv());
TF_LITE_ENSURE_STATUS(op_resolver.AddTranspose());
- TF_LITE_ENSURE_STATUS(op_resolver.AddUnpack());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddTransposeConv());
TF_LITE_ENSURE_STATUS(op_resolver.AddUnidirectionalSequenceLSTM());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddUnpack());
TF_LITE_ENSURE_STATUS(op_resolver.AddVarHandle());
TF_LITE_ENSURE_STATUS(op_resolver.AddWhile());
+ TF_LITE_ENSURE_STATUS(op_resolver.AddWindow());
TF_LITE_ENSURE_STATUS(op_resolver.AddZerosLike());
- TF_LITE_ENSURE_STATUS(op_resolver.AddDepthwiseConv2D());
- TF_LITE_ENSURE_STATUS(op_resolver.AddConv2D());
- TF_LITE_ENSURE_STATUS(op_resolver.AddAveragePool2D());
- TF_LITE_ENSURE_STATUS(op_resolver.AddPad());
- TF_LITE_ENSURE_STATUS(op_resolver.AddMaxPool2D());
- TF_LITE_ENSURE_STATUS(op_resolver.AddMul());
+
return kTfLiteOk;
}
+
} // namespace tflite
#endif // TFLM_BENCHMARK_OP_RESOLVER_H_
diff --git a/tensorflow/lite/micro/tools/benchmarking/show_meta_data.cc.template b/tensorflow/lite/micro/tools/benchmarking/show_meta_data.cc.template
new file mode 100644
index 0000000..a2102a4
--- /dev/null
+++ b/tensorflow/lite/micro/tools/benchmarking/show_meta_data.cc.template
@@ -0,0 +1,177 @@
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <cstddef>
+#include <cstring>
+#include <type_traits>
+
+#include "tensorflow/lite/micro/micro_log.h"
+#include "tensorflow/lite/micro/tools/benchmarking/show_meta_data.h"
+
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+#include "NatureDSP_Signal_id.h"
+#include "xa_nnlib_standards.h"
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+
+namespace tflite {
+namespace {
+
+#if defined(GENERIC_BENCHMARK_USING_BUILTIN_MODEL)
+const char* model_analysis_strings[] = {
+ // %%%_model_analysis_strings_%%%
+};
+
+const char* model_sha1_strings[] = {
+ // %%%_model_sha1_strings_%%%
+};
+#endif // defind(GENERIC_BENCHMARK_USING_BUILTIN_MODEL)
+
+const char* compilation_date_strings[] = {
+ // %%%_compilation_date_strings_%%%
+};
+
+const char* git_commit_strings[] = {
+ // %%%_git_commit_strings_%%%
+};
+
+const char* git_status_strings[] = {
+ // %%%_git_status_strings_%%%
+};
+
+const char* cc_name_strings[] = {
+ // %%%_cc_name_strings_%%%
+};
+
+const char* cc_version_strings[] = {
+ // %%%_cc_version_strings_%%%
+};
+
+const char* cc_flags_strings[] = {
+ // %%%_cc_flags_strings_%%%
+};
+
+const char* cxx_name_strings[] = {
+ // %%%_cxx_name_strings_%%%
+};
+
+const char* cxx_version_strings[] = {
+ // %%%_cxx_version_strings_%%%
+};
+
+const char* cxx_flags_strings[] = {
+ // %%%_cxx_flags_strings_%%%
+};
+
+const char* optimization_flag_strings[] = {
+ // %%%_optimization_flag_strings_%%%
+};
+
+const char* target_info_strings[] = {
+ // %%%_target_info_strings_%%%
+};
+
+#if defined(CMSIS_NN) || defined(HIFI3) || defined(HIFI4) || defined(HIFI5) || \
+ defined(VISION_P6)
+const char* nn_library_url_strings[] = {
+ // %%%_nn_library_url_strings_%%%
+};
+
+const char* nn_library_md5_strings[] = {
+ // %%%_nn_library_md5_strings_%%%
+};
+#endif // defined(CMSIS_NN) || defined(HIFI3) || defined(HIFI4) ||
+ // defined(HIFI5) || defined(VISION_P6)
+
+void ShowStrings(const char* title, const char** str, const size_t count) {
+ MicroPrintf("%s%s", title, str[0]);
+ for (size_t i = 1; i < count; i++) {
+ MicroPrintf("%s", str[i]);
+ }
+}
+
+void ShowSeparator() { MicroPrintf("--------------------"); }
+
+} // namespace
+
+void GenericBenchmarkShowMetaData() {
+ ShowSeparator();
+ ShowStrings("Compiled on:\n\n", compilation_date_strings,
+ std::extent<decltype(compilation_date_strings)>::value);
+
+ ShowSeparator();
+ ShowStrings("Git SHA: ", git_commit_strings,
+ std::extent<decltype(git_commit_strings)>::value);
+ ShowStrings("\nGit status:\n\n", git_status_strings,
+ std::extent<decltype(git_status_strings)>::value);
+
+ ShowSeparator();
+ ShowStrings("C compiler: ", cc_name_strings,
+ std::extent<decltype(cc_name_strings)>::value);
+ ShowStrings("Version:\n\n", cc_version_strings,
+ std::extent<decltype(cc_version_strings)>::value);
+ ShowStrings("\nFlags:\n\n", cc_flags_strings,
+ std::extent<decltype(cc_flags_strings)>::value);
+ ShowStrings("\nC++ compiler: ", cxx_name_strings,
+ std::extent<decltype(cxx_name_strings)>::value);
+ ShowStrings("Version:\n\n", cxx_version_strings,
+ std::extent<decltype(cxx_version_strings)>::value);
+ ShowStrings("\nFlags:\n\n", cxx_flags_strings,
+ std::extent<decltype(cxx_flags_strings)>::value);
+ ShowStrings("\nOptimization: ", optimization_flag_strings,
+ std::extent<decltype(optimization_flag_strings)>::value);
+
+ ShowSeparator();
+ ShowStrings("Target information:\n\n", target_info_strings,
+ std::extent<decltype(target_info_strings)>::value);
+
+#if defined(CMSIS_NN) || defined(HIFI3) || defined(HIFI4) || defined(HIFI5) || \
+ defined(VISION_P6)
+ ShowSeparator();
+ ShowStrings("NN library download URLs:\n\n", nn_library_url_strings,
+ std::extent<decltype(nn_library_url_strings)>::value);
+ ShowStrings("\nNN library MD5 checksums:\n\n", nn_library_md5_strings,
+ std::extent<decltype(nn_library_md5_strings)>::value);
+#endif // defined(CMSIS_NN) || defined(HIFI3) || defined(HIFI4) ||
+ // defined(HIFI5) || defined(VISION_P6)
+
+#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+ ShowSeparator();
+
+ char version_buffer[30 + 1];
+ memset(version_buffer, 0, sizeof(version_buffer));
+ NatureDSP_Signal_get_library_version(version_buffer);
+ MicroPrintf("NatureDSP library version: %s", version_buffer);
+ memset(version_buffer, 0, sizeof(version_buffer));
+ NatureDSP_Signal_get_library_api_version(version_buffer);
+ MicroPrintf("NatureDSP API version: %s", version_buffer);
+
+ const char* nnlib_library_version = xa_nnlib_get_lib_version_string();
+ const char* nnlib_api_version = xa_nnlib_get_lib_api_version_string();
+ MicroPrintf("NNLIB library version: %s", nnlib_library_version);
+ MicroPrintf("NNLIB API version: %s", nnlib_api_version);
+#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5)
+
+#if defined(GENERIC_BENCHMARK_USING_BUILTIN_MODEL)
+ ShowSeparator();
+ ShowStrings("Model SHA1:\n\n", model_sha1_strings,
+ std::extent<decltype(model_sha1_strings)>::value);
+ ShowStrings("\nModel analysis:\n\n", model_analysis_strings,
+ std::extent<decltype(model_analysis_strings)>::value);
+#endif // defind(GENERIC_BENCHMARK_USING_BUILTIN_MODEL)
+
+ ShowSeparator();
+}
+
+} // namespace tflite
diff --git a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc b/tensorflow/lite/micro/tools/benchmarking/show_meta_data.h
similarity index 67%
copy from tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc
copy to tensorflow/lite/micro/tools/benchmarking/show_meta_data.h
index e2cf661..37cf616 100644
--- a/tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.cc
+++ b/tensorflow/lite/micro/tools/benchmarking/show_meta_data.h
@@ -1,4 +1,4 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -13,11 +13,12 @@
limitations under the License.
==============================================================================*/
-#include "tensorflow/lite/micro/examples/micro_speech/simple_features/simple_model_settings.h"
+namespace tflite {
-const char* kCategoryLabels[kCategoryCount] = {
- "silence",
- "unknown",
- "yes",
- "no",
-};
+#if !defined(GENERIC_BENCHMARK_NO_META_DATA)
+void GenericBenchmarkShowMetaData();
+#else
+inline void GenericBenchmarkShowMetaData() {}
+#endif // defined(GENERIC_BENCHMARK_NO_META_DATA)
+
+} // namespace tflite
diff --git a/tensorflow/lite/micro/tools/ci_build/test_code_style.sh b/tensorflow/lite/micro/tools/ci_build/test_code_style.sh
index 0d25c9f..d49f429 100755
--- a/tensorflow/lite/micro/tools/ci_build/test_code_style.sh
+++ b/tensorflow/lite/micro/tools/ci_build/test_code_style.sh
@@ -77,6 +77,8 @@
FIX_FORMAT_OPTIONS=""
fi
+EXCLUDE_SHARED_TFL_CODE=$(sed 's/^/-e /' ci/tflite_files.txt)
+
tensorflow/lite/micro/tools/make/downloads/pigweed/pw_presubmit/py/pw_presubmit/format_code.py \
${FIX_FORMAT_OPTIONS} \
-e "\.github" \
@@ -84,6 +86,8 @@
-e third_party/xtensa \
-e ci \
-e c/common.c \
+ -e codegen/preprocessor/preprocessor_schema_generated.h \
+ -e codegen/preprocessor/preprocessor_schema_py_generated.py \
-e core/api/error_reporter.cc \
-e kernels/internal/reference/integer_ops/ \
-e kernels/internal/reference/reference_ops.h \
@@ -93,8 +97,10 @@
-e experimental \
-e schema/schema_generated.h \
-e schema/schema_utils.h \
+ -e tensorflow/lite/micro/tools/layer_by_layer_schema_generated.h \
-e "\.inc" \
- -e "\.md"
+ -e "\.md" \
+ ${EXCLUDE_SHARED_TFL_CODE}
CODE_FORMAT_RESULT=$?
diff --git a/tensorflow/lite/micro/tools/ci_build/test_cortex_m_corstone_300.sh b/tensorflow/lite/micro/tools/ci_build/test_cortex_m_corstone_300.sh
index 516c181..ab136e5 100755
--- a/tensorflow/lite/micro/tools/ci_build/test_cortex_m_corstone_300.sh
+++ b/tensorflow/lite/micro/tools/ci_build/test_cortex_m_corstone_300.sh
@@ -1,5 +1,5 @@
#!/usr/bin/env bash
-# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -42,3 +42,14 @@
readable_run make -f tensorflow/lite/micro/tools/make/Makefile clean
readable_run make -j$(nproc) -f tensorflow/lite/micro/tools/make/Makefile CO_PROCESSOR=ethos_u OPTIMIZED_KERNEL_DIR=${OPTIMIZED_KERNEL_DIR} TARGET=${TARGET} TARGET_ARCH=${TARGET_ARCH} TOOLCHAIN=${TOOLCHAIN} build
readable_run make -f tensorflow/lite/micro/tools/make/Makefile CO_PROCESSOR=ethos_u OPTIMIZED_KERNEL_DIR=${OPTIMIZED_KERNEL_DIR} TARGET=${TARGET} TARGET_ARCH=${TARGET_ARCH} TOOLCHAIN=${TOOLCHAIN} test
+
+# Run generic benchmark.
+readable_run make -j$(nproc) -f tensorflow/lite/micro/tools/make/Makefile \
+ CO_PROCESSOR=ethos_u \
+ OPTIMIZED_KERNEL_DIR=${OPTIMIZED_KERNEL_DIR} \
+ TARGET=${TARGET} \
+ TARGET_ARCH=${TARGET_ARCH} \
+ TOOLCHAIN=${TOOLCHAIN} \
+ GENERIC_BENCHMARK_MODEL_PATH=tensorflow/lite/micro/models/person_detect_vela.tflite \
+ GENERIC_BENCHMARK_ARENA_SIZE=`expr 150 \* 1024` \
+ run_tflm_benchmark
diff --git a/tensorflow/lite/micro/tools/ci_build/test_x86_default.sh b/tensorflow/lite/micro/tools/ci_build/test_x86_default.sh
index 623238e..998827f 100755
--- a/tensorflow/lite/micro/tools/ci_build/test_x86_default.sh
+++ b/tensorflow/lite/micro/tools/ci_build/test_x86_default.sh
@@ -40,3 +40,10 @@
readable_run make -s -j8 -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile build TENSORFLOW_ROOT=${TENSORFLOW_ROOT} EXTERNAL_DIR=${EXTERNAL_DIR}
readable_run make -s -j8 -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile test TENSORFLOW_ROOT=${TENSORFLOW_ROOT} EXTERNAL_DIR=${EXTERNAL_DIR}
readable_run make -s -j8 -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile integration_tests TENSORFLOW_ROOT=${TENSORFLOW_ROOT} EXTERNAL_DIR=${EXTERNAL_DIR}
+
+# run generic benchmark
+readable_run make -j$(nproc) -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile \
+ TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
+ EXTERNAL_DIR=${EXTERNAL_DIR} \
+ GENERIC_BENCHMARK_MODEL_PATH=${TENSORFLOW_ROOT}tensorflow/lite/micro/models/person_detect.tflite \
+ run_tflm_benchmark
diff --git a/tensorflow/lite/micro/tools/ci_build/test_xtensa_fusion_f1.sh b/tensorflow/lite/micro/tools/ci_build/test_xtensa_fusion_f1.sh
index 8416792..2fd6bf8 100755
--- a/tensorflow/lite/micro/tools/ci_build/test_xtensa_fusion_f1.sh
+++ b/tensorflow/lite/micro/tools/ci_build/test_xtensa_fusion_f1.sh
@@ -35,7 +35,7 @@
if [[ ${1} == "INTERNAL" ]]; then
readable_run make -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile \
TARGET=xtensa \
- TARGET_ARCH=hifi4 \
+ TARGET_ARCH=hifi3 \
OPTIMIZED_KERNEL_DIR=xtensa \
XTENSA_CORE=F1_190305_swupgrade \
TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
@@ -44,7 +44,7 @@
readable_run make -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile \
TARGET=xtensa \
- TARGET_ARCH=hifi4 \
+ TARGET_ARCH=hifi3 \
OPTIMIZED_KERNEL_DIR=xtensa \
XTENSA_CORE=F1_190305_swupgrade \
TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
@@ -53,7 +53,7 @@
else
readable_run make -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile \
TARGET=xtensa \
- TARGET_ARCH=hifi4 \
+ TARGET_ARCH=hifi3 \
OPTIMIZED_KERNEL_DIR=xtensa \
XTENSA_CORE=F1_190305_swupgrade \
TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
@@ -62,10 +62,21 @@
readable_run make -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile \
TARGET=xtensa \
- TARGET_ARCH=hifi4 \
+ TARGET_ARCH=hifi3 \
OPTIMIZED_KERNEL_DIR=xtensa \
XTENSA_CORE=F1_190305_swupgrade \
TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
EXTERNAL_DIR=${EXTERNAL_DIR} \
test -j$(nproc)
+
+# run generic benchmark
+readable_run make -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile \
+ TARGET=xtensa \
+ TARGET_ARCH=hifi3 \
+ OPTIMIZED_KERNEL_DIR=xtensa \
+ XTENSA_CORE=F1_190305_swupgrade \
+ TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
+ EXTERNAL_DIR=${EXTERNAL_DIR} \
+ GENERIC_BENCHMARK_MODEL_PATH=${TENSORFLOW_ROOT}tensorflow/lite/micro/models/person_detect.tflite \
+ run_tflm_benchmark -j$(nproc)
fi
diff --git a/tensorflow/lite/micro/tools/ci_build/test_xtensa_hifi3z.sh b/tensorflow/lite/micro/tools/ci_build/test_xtensa_hifi3z.sh
index 1ddf0d9..ff3d600 100755
--- a/tensorflow/lite/micro/tools/ci_build/test_xtensa_hifi3z.sh
+++ b/tensorflow/lite/micro/tools/ci_build/test_xtensa_hifi3z.sh
@@ -35,7 +35,7 @@
if [[ ${1} == "INTERNAL" ]]; then
readable_run make -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile \
TARGET=xtensa \
- TARGET_ARCH=hifi4 \
+ TARGET_ARCH=hifi3 \
OPTIMIZED_KERNEL_DIR=xtensa \
XTENSA_CORE=HIFI_190304_swupgrade \
TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
@@ -44,7 +44,7 @@
readable_run make -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile \
TARGET=xtensa \
- TARGET_ARCH=hifi4 \
+ TARGET_ARCH=hifi3 \
OPTIMIZED_KERNEL_DIR=xtensa \
XTENSA_CORE=HIFI_190304_swupgrade \
TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
@@ -53,7 +53,7 @@
readable_run make -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile \
TARGET=xtensa \
- TARGET_ARCH=hifi4 \
+ TARGET_ARCH=hifi3 \
OPTIMIZED_KERNEL_DIR=xtensa \
XTENSA_CORE=HIFI_190304_swupgrade \
TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
@@ -62,7 +62,7 @@
readable_run make -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile \
TARGET=xtensa \
- TARGET_ARCH=hifi4 \
+ TARGET_ARCH=hifi3 \
OPTIMIZED_KERNEL_DIR=xtensa \
XTENSA_CORE=HIFI_190304_swupgrade \
TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
@@ -71,7 +71,7 @@
readable_run make -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile \
TARGET=xtensa \
- TARGET_ARCH=hifi4 \
+ TARGET_ARCH=hifi3 \
OPTIMIZED_KERNEL_DIR=xtensa \
XTENSA_CORE=HIFI_190304_swupgrade \
TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
@@ -80,7 +80,7 @@
else
readable_run make -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile \
TARGET=xtensa \
- TARGET_ARCH=hifi4 \
+ TARGET_ARCH=hifi3 \
OPTIMIZED_KERNEL_DIR=xtensa \
XTENSA_CORE=HIFI_190304_swupgrade \
TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
@@ -89,10 +89,21 @@
readable_run make -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile \
TARGET=xtensa \
- TARGET_ARCH=hifi4 \
+ TARGET_ARCH=hifi3 \
OPTIMIZED_KERNEL_DIR=xtensa \
XTENSA_CORE=HIFI_190304_swupgrade \
TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
EXTERNAL_DIR=${EXTERNAL_DIR} \
test -j$(nproc)
+
+ # run generic benchmark
+ readable_run make -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile \
+ TARGET=xtensa \
+ TARGET_ARCH=hifi3 \
+ OPTIMIZED_KERNEL_DIR=xtensa \
+ XTENSA_CORE=HIFI_190304_swupgrade \
+ TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
+ EXTERNAL_DIR=${EXTERNAL_DIR} \
+ GENERIC_BENCHMARK_MODEL_PATH=${TENSORFLOW_ROOT}tensorflow/lite/micro/models/person_detect.tflite \
+ run_tflm_benchmark -j$(nproc)
fi
diff --git a/tensorflow/lite/micro/tools/ci_build/test_xtensa_hifi5.sh b/tensorflow/lite/micro/tools/ci_build/test_xtensa_hifi5.sh
index 82a04a9..0ad29e8 100755
--- a/tensorflow/lite/micro/tools/ci_build/test_xtensa_hifi5.sh
+++ b/tensorflow/lite/micro/tools/ci_build/test_xtensa_hifi5.sh
@@ -47,3 +47,14 @@
TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
EXTERNAL_DIR=${EXTERNAL_DIR} \
test -j$(nproc)
+
+# run generic benchmark
+readable_run make -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile \
+ TARGET=xtensa \
+ TARGET_ARCH=hifi5 \
+ OPTIMIZED_KERNEL_DIR=xtensa \
+ XTENSA_CORE=PRD_H5_RDO_07_01_2022 \
+ TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
+ EXTERNAL_DIR=${EXTERNAL_DIR} \
+ GENERIC_BENCHMARK_MODEL_PATH=${TENSORFLOW_ROOT}tensorflow/lite/micro/models/person_detect.tflite \
+ run_tflm_benchmark -j$(nproc)
\ No newline at end of file
diff --git a/tensorflow/lite/micro/tools/ci_build/test_xtensa_vision_p6.sh b/tensorflow/lite/micro/tools/ci_build/test_xtensa_vision_p6.sh
index a2744b5..1c6de93 100755
--- a/tensorflow/lite/micro/tools/ci_build/test_xtensa_vision_p6.sh
+++ b/tensorflow/lite/micro/tools/ci_build/test_xtensa_vision_p6.sh
@@ -54,4 +54,15 @@
TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
EXTERNAL_DIR=${EXTERNAL_DIR} \
test -j$(nproc)
+
+ # run generic benchmark
+ readable_run make -f ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/Makefile \
+ TARGET=xtensa \
+ TARGET_ARCH=vision_p6 \
+ OPTIMIZED_KERNEL_DIR=xtensa \
+ XTENSA_CORE=P6_200528 \
+ TENSORFLOW_ROOT=${TENSORFLOW_ROOT} \
+ EXTERNAL_DIR=${EXTERNAL_DIR} \
+ GENERIC_BENCHMARK_MODEL_PATH=${TENSORFLOW_ROOT}tensorflow/lite/micro/models/person_detect.tflite \
+ run_tflm_benchmark -j$(nproc)
fi
diff --git a/tensorflow/lite/micro/tools/gen_micro_mutable_op_resolver/BUILD b/tensorflow/lite/micro/tools/gen_micro_mutable_op_resolver/BUILD
index 276e9c2..e590939 100644
--- a/tensorflow/lite/micro/tools/gen_micro_mutable_op_resolver/BUILD
+++ b/tensorflow/lite/micro/tools/gen_micro_mutable_op_resolver/BUILD
@@ -1,3 +1,4 @@
+load("@rules_python//python:defs.bzl", "py_binary")
load("@tflm_pip_deps//:requirements.bzl", "requirement")
package(
@@ -18,7 +19,6 @@
deps = [
"@absl_py//absl:app",
"@absl_py//absl/flags",
- requirement("tensorflow-cpu"),
requirement("mako"),
"//tensorflow/lite/python:schema_py",
"//tensorflow/lite/python:schema_util",
@@ -42,7 +42,7 @@
deps = [
"@absl_py//absl:app",
"@absl_py//absl/flags",
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
requirement("mako"),
"//tensorflow/lite/micro/tools:generate_test_for_model",
"//tensorflow/lite/python:schema_py",
diff --git a/tensorflow/lite/micro/tools/gen_micro_mutable_op_resolver/README.md b/tensorflow/lite/micro/tools/gen_micro_mutable_op_resolver/README.md
index 95a0c43..1837995 100644
--- a/tensorflow/lite/micro/tools/gen_micro_mutable_op_resolver/README.md
+++ b/tensorflow/lite/micro/tools/gen_micro_mutable_op_resolver/README.md
@@ -4,6 +4,8 @@
This generally requires manually finding out which operators are used in the model through the use of a visualization tool, which may be impractical in some cases.
This script will automatically generate a MicroMutableOpResolver with only the used operators for a given model or set of models.
+Note: Check ci/Dockerfile.micro for supported python version.
+
## How to run
bazel run tensorflow/lite/micro/tools/gen_micro_mutable_op_resolver:generate_micro_mutable_op_resolver_from_model -- \
diff --git a/tensorflow/lite/micro/tools/gen_micro_mutable_op_resolver/generate_micro_mutable_op_resolver_from_model.py b/tensorflow/lite/micro/tools/gen_micro_mutable_op_resolver/generate_micro_mutable_op_resolver_from_model.py
index de583da..d7e6140 100644
--- a/tensorflow/lite/micro/tools/gen_micro_mutable_op_resolver/generate_micro_mutable_op_resolver_from_model.py
+++ b/tensorflow/lite/micro/tools/gen_micro_mutable_op_resolver/generate_micro_mutable_op_resolver_from_model.py
@@ -23,7 +23,7 @@
from absl import flags
from mako import template
-from tflite_micro.tensorflow.lite.tools import visualize as visualize
+from tensorflow.lite.tools import visualize
TEMPLATE_DIR = os.path.join(os.path.dirname(__file__), 'templates')
TEMPLATE_DIR = os.path.abspath(TEMPLATE_DIR)
@@ -69,8 +69,9 @@
else:
formated_op_string += part.upper()
- # Edge case for AddUnidirectionalSequenceLSTM().
+ # Edge cases
formated_op_string = formated_op_string.replace('Lstm', 'LSTM')
+ formated_op_string = formated_op_string.replace('BatchMatmul', 'BatchMatMul')
return 'Add' + formated_op_string
diff --git a/tensorflow/lite/micro/tools/generate_cc_arrays.py b/tensorflow/lite/micro/tools/generate_cc_arrays.py
index 759db56..2a77b4d 100644
--- a/tensorflow/lite/micro/tools/generate_cc_arrays.py
+++ b/tensorflow/lite/micro/tools/generate_cc_arrays.py
@@ -35,8 +35,6 @@
out_cc_file.write('#include <cstdint>\n\n')
out_cc_file.write('#include "{}"\n\n'.format(
out_fname.split('genfiles/')[-1].replace('.cc', '.h')))
- out_cc_file.write('const unsigned int {}_size = {};\n'.format(
- array_name, str(size)))
out_cc_file.write('alignas(16) const {} {}[] = {{'.format(
array_type, array_name))
out_cc_file.write(array_contents)
@@ -45,8 +43,8 @@
elif out_fname.endswith('.h'):
out_hdr_file = open(out_fname, 'w')
out_hdr_file.write('#include <cstdint>\n\n')
- out_hdr_file.write(
- 'extern const unsigned int {}_size;\n'.format(array_name))
+ out_hdr_file.write('constexpr unsigned int {}_size = {};\n'.format(
+ array_name, str(size)))
out_hdr_file.write('extern const {} {}[];\n'.format(
array_type, array_name))
out_hdr_file.close()
diff --git a/tensorflow/lite/micro/tools/layer_by_layer.cc b/tensorflow/lite/micro/tools/layer_by_layer.cc
new file mode 100644
index 0000000..91d325e
--- /dev/null
+++ b/tensorflow/lite/micro/tools/layer_by_layer.cc
@@ -0,0 +1,332 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <stdio.h>
+#include <sys/stat.h>
+#include <sys/types.h>
+
+#include <cstddef>
+#include <cstdint>
+#include <cstdlib>
+#include <fstream>
+#include <ios>
+#include <memory>
+#include <random>
+#include <utility>
+
+#include "flatbuffers/flatbuffer_builder.h"
+#include "flatbuffers/util.h"
+#include "tensorflow/lite/c/c_api_types.h"
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/kernels/op_macros.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/micro_allocator.h"
+#include "tensorflow/lite/micro/micro_context.h"
+#include "tensorflow/lite/micro/micro_interpreter.h"
+#include "tensorflow/lite/micro/micro_log.h"
+#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
+#include "tensorflow/lite/micro/micro_resource_variable.h"
+#include "tensorflow/lite/micro/micro_utils.h"
+#include "tensorflow/lite/micro/tools/benchmarking/op_resolver.h"
+#include "tensorflow/lite/micro/tools/layer_by_layer_schema_generated.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+
+namespace tflite {
+
+namespace {
+
+// Seed used for the random input. Input data shouldn't affect invocation timing
+// so randomness isn't really needed.
+constexpr uint32_t kRandomSeed = 0xFB;
+
+constexpr size_t kTensorArenaSize = 3e6;
+constexpr int kNumResourceVariable = 100;
+
+bool SaveFile(const char* name, const char* buf, size_t len) {
+ std::ofstream ofs(name, std::ofstream::binary);
+ if (!ofs.is_open()) return false;
+ ofs.write(buf, len);
+ return !ofs.bad();
+}
+
+TfLiteStatus ConvertTensorType(TfLiteType type, TensorTypes& tensor_type) {
+ switch (type) {
+ case kTfLiteFloat16:
+ tensor_type = TensorTypes_FLOAT16;
+ return kTfLiteOk;
+ case kTfLiteBFloat16:
+ tensor_type = TensorTypes_BFLOAT16;
+ return kTfLiteOk;
+ case kTfLiteFloat32:
+ tensor_type = TensorTypes_FLOAT32;
+ return kTfLiteOk;
+ case kTfLiteFloat64:
+ tensor_type = TensorTypes_FLOAT64;
+ return kTfLiteOk;
+ case kTfLiteInt16:
+ tensor_type = TensorTypes_INT16;
+ return kTfLiteOk;
+ case kTfLiteUInt16:
+ tensor_type = TensorTypes_UINT16;
+ return kTfLiteOk;
+ case kTfLiteInt32:
+ tensor_type = TensorTypes_INT32;
+ return kTfLiteOk;
+ case kTfLiteUInt32:
+ tensor_type = TensorTypes_UINT32;
+ return kTfLiteOk;
+ case kTfLiteUInt8:
+ tensor_type = TensorTypes_UINT8;
+ return kTfLiteOk;
+ case kTfLiteInt8:
+ tensor_type = TensorTypes_INT8;
+ return kTfLiteOk;
+ case kTfLiteInt64:
+ tensor_type = TensorTypes_INT64;
+ return kTfLiteOk;
+ case kTfLiteUInt64:
+ tensor_type = TensorTypes_UINT64;
+ return kTfLiteOk;
+ case kTfLiteString:
+ tensor_type = TensorTypes_STRING;
+ return kTfLiteOk;
+ case kTfLiteBool:
+ tensor_type = TensorTypes_BOOL;
+ return kTfLiteOk;
+ case kTfLiteComplex64:
+ tensor_type = TensorTypes_COMPLEX64;
+ return kTfLiteOk;
+ case kTfLiteComplex128:
+ tensor_type = TensorTypes_COMPLEX128;
+ return kTfLiteOk;
+ case kTfLiteResource:
+ tensor_type = TensorTypes_RESOURCE;
+ return kTfLiteOk;
+ case kTfLiteVariant:
+ tensor_type = TensorTypes_VARIANT;
+ return kTfLiteOk;
+ case kTfLiteInt4:
+ tensor_type = TensorTypes_INT4;
+ return kTfLiteOk;
+ case kTfLiteNoType:
+ MicroPrintf("Unsupported data type %d in tensor\n", tensor_type);
+ return kTfLiteError;
+ }
+ return kTfLiteOk;
+}
+
+TfLiteStatus SetRandomInput(const uint32_t random_seed,
+ const ModelT& unpacked_model,
+ MicroInterpreter& interpreter,
+ ModelTestDataT& output_data) {
+ std::mt19937 eng(random_seed);
+ std::uniform_int_distribution<uint32_t> dist(0, 255);
+ for (size_t i = 0; i < interpreter.inputs_size(); ++i) {
+ TfLiteTensor* input = interpreter.input_tensor(i);
+ std::unique_ptr<TensorDataT> test_data(new TensorDataT());
+ test_data->input_index = i;
+ test_data->layer_number = -1;
+ test_data->tensor_index = -1;
+ test_data->num_bytes = input->bytes;
+ // make this share tensortype with tflite schema later
+ TF_LITE_ENSURE_STATUS(ConvertTensorType(input->type, test_data->dtype));
+ for (int x = 0; x < input->dims->size; ++x) {
+ test_data->shape.push_back(input->dims->data[x]);
+ }
+
+ // Pre-populate input tensor with random values.
+ uint8_t* input_values = GetTensorData<uint8_t>(input);
+ for (size_t j = 0; j < input->bytes; ++j) {
+ input_values[j] = dist(eng);
+ test_data->data.push_back(input_values[j]);
+ }
+ output_data.input_data.push_back(std::move(test_data));
+ }
+
+ // Get tensor indices for all model input_tensors
+ for (size_t i = 0; i < unpacked_model.subgraphs[0]->inputs.size(); ++i) {
+ output_data.input_data[i]->tensor_index =
+ unpacked_model.subgraphs[0]->inputs[i];
+ }
+ return kTfLiteOk;
+}
+
+std::unique_ptr<char[]> ReadModelFile(const char* model_file_name) {
+ std::ifstream model_file(model_file_name, std::ios::binary);
+ if (!model_file.is_open()) {
+ MicroPrintf("could not open model file \n ");
+ return nullptr;
+ }
+
+ model_file.seekg(0, std::ios::end);
+ size_t num_bytes = model_file.tellg();
+ model_file.seekg(0, std::ios::beg);
+ std::unique_ptr<char[]> model_data(new char[num_bytes]);
+ model_file.read(model_data.get(), num_bytes);
+
+ return model_data;
+}
+
+// Stores the Intermediate Tensor data for each layer into the unpacked
+// ModelTestDataT class which is packed into the flatbuffer !
+TfLiteStatus StoreLayerByLayerData(MicroInterpreter& interpreter,
+ const ModelT& tflite_model,
+ ModelTestDataT& output_data) {
+ for (size_t i = 0; i < tflite_model.subgraphs.size(); ++i) {
+ std::unique_ptr<SubgraphDataT> subgraph_data(new SubgraphDataT());
+ subgraph_data->subgraph_index = i;
+
+ for (size_t j = 0; j < tflite_model.subgraphs[i]->operators.size(); ++j) {
+ for (size_t k = 0;
+ k < tflite_model.subgraphs[i]->operators[j]->outputs.size(); ++k) {
+ subgraph_data->outputs.emplace_back(new TensorDataT());
+ std::unique_ptr<TensorDataT>& tensor_data =
+ subgraph_data->outputs.back();
+
+ // input_index
+ tensor_data->input_index = -1;
+
+ // tensor index
+ tensor_data->tensor_index =
+ tflite_model.subgraphs[i]->operators[j]->outputs[k];
+
+ TfLiteEvalTensor* layer_output_tensor =
+ interpreter.GetTensor(subgraph_data->outputs.back()->tensor_index,
+ subgraph_data->subgraph_index);
+
+ // dims
+ tensor_data->shape.assign(
+ layer_output_tensor->dims->data,
+ layer_output_tensor->dims->data + layer_output_tensor->dims->size);
+
+ // dtype
+ TF_LITE_ENSURE_STATUS(
+ ConvertTensorType(layer_output_tensor->type, tensor_data->dtype));
+ // num_bytes
+ tensor_data->num_bytes = EvalTensorBytes(layer_output_tensor);
+
+ uint8_t* tensor_values =
+ micro::GetTensorData<uint8_t>(layer_output_tensor);
+
+ // data
+ tensor_data->data.assign(
+ tensor_values,
+ tensor_values + EvalTensorBytes(layer_output_tensor));
+
+ // layer_number
+ tensor_data->layer_number = j;
+ }
+ }
+ output_data.subgraph_data.push_back(std::move(subgraph_data));
+ }
+
+ return kTfLiteOk;
+}
+
+bool WriteToFile(const char* output_file_name, ModelTestDataT& output_data) {
+ flatbuffers::DefaultAllocator allocator;
+ flatbuffers::FlatBufferBuilder fbb{2048, &allocator};
+ auto new_model = ModelTestData::Pack(fbb, &output_data);
+ fbb.Finish(new_model);
+ return SaveFile(output_file_name,
+ reinterpret_cast<char*>(fbb.GetBufferPointer()),
+ fbb.GetSize());
+}
+
+TfLiteStatus Invoke(const Model* model, ModelTestDataT& output_data) {
+ const tflite::ModelT unpacked_model = *model->UnPack();
+ alignas(16) static uint8_t tensor_arena[kTensorArenaSize];
+
+ TflmOpResolver op_resolver;
+ TF_LITE_ENSURE_STATUS(CreateOpResolver(op_resolver));
+
+ MicroAllocator* allocator = MicroAllocator::Create(
+ tensor_arena, kTensorArenaSize, MemoryPlannerType::kLinear);
+
+ MicroInterpreter interpreter(
+ model, op_resolver, allocator,
+ MicroResourceVariables::Create(allocator, kNumResourceVariable), nullptr);
+ TF_LITE_ENSURE_STATUS(interpreter.AllocateTensors());
+
+ TF_LITE_ASSERT(interpreter.preserve_all_tensors());
+
+ MicroPrintf(""); // null MicroPrintf serves as a newline.
+
+ // For streaming models, the interpreter will return kTfLiteAbort if the model
+ // does not yet have enough data to make an inference. As such, we need to
+ // invoke the interpreter multiple times until we either receive an error or
+ // kTfLiteOk. This loop also works for non-streaming models, as they'll just
+ // return kTfLiteOk after the first invocation.
+ uint32_t seed = kRandomSeed;
+ while (true) {
+ TF_LITE_ENSURE_STATUS(
+ SetRandomInput(seed++, unpacked_model, interpreter, output_data));
+ TfLiteStatus status = interpreter.Invoke();
+ if ((status != kTfLiteOk) && (static_cast<int>(status) != kTfLiteAbort)) {
+ MicroPrintf("Model interpreter invocation failed: %d\n", status);
+ return kTfLiteError;
+ }
+
+ if (status == kTfLiteOk) {
+ break;
+ }
+ }
+ TF_LITE_ENSURE_STATUS(
+ StoreLayerByLayerData(interpreter, unpacked_model, output_data));
+
+ return kTfLiteOk;
+}
+} // namespace
+} // namespace tflite
+
+/* Usage information:
+ This binary will write a debugging flatbuffer to the path provide in 2nd arg
+ using the tflite_model provided in the 1st arg :
+ `bazel run tensorflow/lite/micro/tools:layer_by_layer_output_tool -- \
+ </path/to/input_model.tflite>
+ </path/to/output.file_name>` */
+
+int main(int argc, char** argv) {
+ if (argc < 2) {
+ MicroPrintf("layer_by_layer: invalid usage!\n");
+ MicroPrintf(
+ "usage: layer_by_layer_output_tool </path/to/input_model.tflite> "
+ "</path/to/output.file_name>");
+ return EXIT_FAILURE;
+ }
+
+ const char* model_file_name = argv[1];
+ const char* output_file_name = argv[2];
+
+ const auto model_file_content = tflite::ReadModelFile(model_file_name);
+
+ if (!model_file_content) {
+ MicroPrintf("Could not read model from file: %s", model_file_name);
+ return EXIT_FAILURE;
+ }
+
+ const tflite::Model* model = tflite::GetModel(model_file_content.get());
+
+ ModelTestDataT output_data;
+
+ TF_LITE_ENSURE_STATUS(tflite::Invoke(model, output_data));
+
+ if (!tflite::WriteToFile(output_file_name, output_data)) {
+ MicroPrintf("Could not write to %s", output_file_name);
+ return EXIT_FAILURE;
+ }
+
+ return EXIT_SUCCESS;
+}
diff --git a/tensorflow/lite/micro/tools/layer_by_layer_debugger.py b/tensorflow/lite/micro/tools/layer_by_layer_debugger.py
new file mode 100644
index 0000000..8aa263b
--- /dev/null
+++ b/tensorflow/lite/micro/tools/layer_by_layer_debugger.py
@@ -0,0 +1,270 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Runs TFLM specific transformations to reduce model size on a .tflite model."""
+
+import sys
+import unittest
+
+from absl import app
+from absl import flags
+from absl import logging
+import numpy as np
+import tensorflow as tf
+
+from tflite_micro.tensorflow.lite.tools import flatbuffer_utils
+from tensorflow.python.platform import gfile
+from tflite_micro.python.tflite_micro import runtime
+from tflite_micro.tensorflow.lite.micro.tools import layer_by_layer_schema_py_generated as layer_schema_fb
+from tflite_micro.tensorflow.lite.micro.tools import model_transforms_utils
+
+np.set_printoptions(threshold=sys.maxsize)
+
+# Usage information:
+# This Python Tool/Script can first be used to compare TFLM vs Tflite outputs for
+# random Inputs by only providing a TfLite file
+
+# TfLite vs TFLM command:
+# `bazel run tensorflow/lite/micro/tools:layer_by_layer_debugger -- \
+# --input_tflite_file=</path/to/my_model.tflite>`
+
+# This Python Tool/Script can also be used to comapre TFLM vs Expected
+# Output/Flatbuffer provided by the C++ Tool/binary.
+
+# TFLM vs Expected Command:
+# `bazel run tensorflow/lite/micro/tools:layer_by_layer_debugger -- \
+# --input_tflite_file=</path/to/my_model.tflite> \
+# --dbg_file=</path/to/my_debug_flatbuffer_file>`
+
+# Optional Flags:
+# --print_dump
+# when this flags is set it will dump a part of the TFLM and Ouput
+# it's compared against for each layer
+
+# --rng
+# integer flag that only works in TfLite vs TFLM comparison(when only
+# a TfLite Model is Provided).It can be used to set the rng seed to a
+# differen value then it's default value of 42.
+
+_INPUT_TFLITE_FILE = flags.DEFINE_string(
+ "input_tflite_file",
+ None,
+ "Full path name to the input TFLite file.",
+ required=True)
+
+_RNG = flags.DEFINE_integer(
+ "rng",
+ 42,
+ "This flag defines rng seed used to generate random test data for the"
+ " provided model. This only occurs when no input/golden data are provided."
+ " It is defaulted to 42. ",
+)
+
+_DEBUG_FILE = flags.DEFINE_string(
+ "layer_by_layer_data_file",
+ None,
+ "Full path to the debug file , generated in C++",
+ required=False,
+)
+
+_PRINT_PREVIEW = flags.DEFINE_bool(
+ "print_dump",
+ False,
+ "When this flag is set to True, it prints a preview of elements of the TFLM"
+ " output and output it's being compared with.",
+ required=False,
+)
+
+
+def numpy_from_tensor_type(tensor_type_idx):
+ """Gives the equivalent numpy dtype based on TensorType class (schema) number."""
+ tensor_type_idx_to_numpy = {
+ layer_schema_fb.TensorTypes.FLOAT32:
+ np.float32,
+ layer_schema_fb.TensorTypes.FLOAT16:
+ np.float16,
+ layer_schema_fb.TensorTypes.INT32:
+ np.int32,
+ layer_schema_fb.TensorTypes.UINT8:
+ np.uint8,
+ layer_schema_fb.TensorTypes.INT64:
+ np.int64,
+ layer_schema_fb.TensorTypes.STRING:
+ np.bytes_,
+ layer_schema_fb.TensorTypes.BOOL:
+ np.bool_,
+ layer_schema_fb.TensorTypes.INT16:
+ np.int16,
+ layer_schema_fb.TensorTypes.COMPLEX64:
+ np.complex64,
+ layer_schema_fb.TensorTypes.INT8:
+ np.int8,
+ layer_schema_fb.TensorTypes.FLOAT64:
+ np.float64,
+ layer_schema_fb.TensorTypes.COMPLEX128:
+ np.complex128,
+ layer_schema_fb.TensorTypes.UINT64:
+ np.uint64,
+ layer_schema_fb.TensorTypes.RESOURCE:
+ "RESORCE",
+ layer_schema_fb.TensorTypes.VARIANT:
+ "VARIANT",
+ layer_schema_fb.TensorTypes.UINT32:
+ np.uint32,
+ layer_schema_fb.TensorTypes.UINT16:
+ np.uint16,
+ # INT4 is mapped to INT8, b/246806634
+ layer_schema_fb.TensorTypes.INT4:
+ np.int8,
+ }
+ return tensor_type_idx_to_numpy.get(tensor_type_idx)
+
+
+def GenerateRandomInputTfLiteComparison(tflm_interpreter, tflite_interpreter,
+ model, rng_value):
+ subgraph_info = layer_schema_fb.ModelTestDataT()
+ subgraph_info.subgraphData = []
+ rng_seed = np.random.default_rng(seed=rng_value)
+
+ for subgraph_index, subgraph in enumerate(model.subgraphs):
+ subgraph_data = layer_schema_fb.SubgraphDataT()
+ subgraph_data.subgraphIndex = subgraph_index
+ subgraph_data.outputs = []
+
+ for op_index, operator in enumerate(subgraph.operators):
+ for output in operator.outputs:
+ tensor_data = layer_schema_fb.TensorDataT()
+ tensor_data.layerNumber = op_index
+ tensor_data.tensorIndex = output
+ subgraph_data.outputs.append(tensor_data)
+ subgraph_info.subgraphData.append(subgraph_data)
+
+ for index, input_tensor_index in enumerate(model.subgraphs[0].inputs):
+ input_tensor = model.subgraphs[0].tensors[input_tensor_index]
+ random_data = model_transforms_utils.generate_random_input_data(
+ model, input_tensor, rng_seed)
+ tflm_interpreter.set_input(random_data, index)
+ tflite_interpreter.set_tensor(input_tensor_index, random_data)
+ return subgraph_info, tflm_interpreter, tflite_interpreter
+
+
+def ReadDebugFile():
+ with gfile.GFile(_DEBUG_FILE.value, "rb") as debug_file_handle:
+ debug_bytearray = bytearray(debug_file_handle.read())
+ flatbuffer_root_object = layer_schema_fb.ModelTestData.GetRootAs(
+ debug_bytearray, 0)
+ debug_obj = layer_schema_fb.ModelTestDataT.InitFromObj(
+ flatbuffer_root_object)
+ return debug_obj
+
+
+def SetDebugFileInterpreterInput(tflm_interpreter, tflite_interpreter,
+ debug_obj):
+ for inputs in debug_obj.inputData:
+ input_array = np.frombuffer(bytearray(inputs.data),
+ dtype=numpy_from_tensor_type(inputs.dtype))
+ input_array = np.reshape(input_array, inputs.shape)
+ tflm_interpreter.set_input(input_array, inputs.inputIndex)
+ tflite_interpreter.set_tensor(inputs.tensorIndex, input_array)
+
+ return tflm_interpreter, tflite_interpreter
+
+
+def main(_) -> None:
+ logging.info(
+ "\n--Running TFLM vs TfLite layer by layer debugger on: %s",
+ _INPUT_TFLITE_FILE.value,
+ )
+
+ model = flatbuffer_utils.read_model(_INPUT_TFLITE_FILE.value)
+
+ tflm_interpreter = runtime.Interpreter.from_file(
+ _INPUT_TFLITE_FILE.value,
+ intrepreter_config=runtime.InterpreterConfig.kPreserveAllTensors,
+ )
+
+ tflite_interpreter = tf.lite.Interpreter(
+ model_path=_INPUT_TFLITE_FILE.value,
+ experimental_preserve_all_tensors=True,
+ )
+
+ tflite_interpreter.allocate_tensors()
+
+ debug_obj = None
+
+ # Setting Inputs either randomly or using provided Debug File
+ if _DEBUG_FILE.value == None:
+ debug_obj, tflm_interpreter, tflite_interpreter = (
+ GenerateRandomInputTfLiteComparison(tflm_interpreter,
+ tflite_interpreter, model,
+ _RNG.value))
+ tflite_interpreter.invoke()
+ else:
+ debug_obj = ReadDebugFile()
+ tflm_interpreter, tflite_interpreter = SetDebugFileInterpreterInput(
+ tflm_interpreter, tflite_interpreter, debug_obj)
+
+ tflm_interpreter.invoke()
+ comparison = ""
+
+ for subgraph in debug_obj.subgraphData:
+ for output in subgraph.outputs:
+ tflm_ouput = tflm_interpreter.GetTensor(
+ output.tensorIndex, subgraph.subgraphIndex)["tensor_data"]
+
+ comparison_ouput = None
+
+ if _DEBUG_FILE.value == None:
+ tflite_output = tflite_interpreter.get_tensor(output.tensorIndex,
+ subgraph.subgraphIndex)
+ comparison_ouput = tflite_output
+ comparison = "TfLite"
+ else:
+ expected_output_data = np.frombuffer(bytearray(output.data),
+ dtype=numpy_from_tensor_type(
+ output.dtype))
+ expected_output_data = np.reshape(expected_output_data, output.shape)
+ comparison = "Expected Golden Data"
+ comparison_ouput = expected_output_data
+
+ error_message = (
+ "\n\nTFLM output does not match {comparison} output.\n Subgraph"
+ " number is {subgraph_index} \n Layer number is {layer_number} \n The"
+ " Tensor Index where this output does not match is {tensor_index}"
+ " \n\n\n".format(
+ comparison=comparison,
+ subgraph_index=subgraph.subgraphIndex,
+ layer_number=output.layerNumber,
+ tensor_index=output.tensorIndex,
+ ))
+ if _PRINT_PREVIEW.value:
+ print("layer number ", output.layerNumber)
+ print("tensor index ", output.tensorIndex, "\n\n")
+ print("TFLM output \n ", tflm_ouput[:10])
+ print(
+ "{comparison} output \n".format(comparison=comparison),
+ comparison_ouput[:_PRINT_PREVIEW.value],
+ )
+ print("--------------\n\n\n")
+ np.testing.assert_array_equal(tflm_ouput,
+ comparison_ouput,
+ err_msg=error_message,
+ verbose=True)
+ print(
+ "\n\nTFLM output matched {comparison} output for all Layers in the Model."
+ .format(comparison=comparison))
+
+
+if __name__ == "__main__":
+ app.run(main)
diff --git a/tensorflow/lite/micro/tools/layer_by_layer_schema.fbs b/tensorflow/lite/micro/tools/layer_by_layer_schema.fbs
new file mode 100644
index 0000000..4183c9c
--- /dev/null
+++ b/tensorflow/lite/micro/tools/layer_by_layer_schema.fbs
@@ -0,0 +1,62 @@
+// Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+enum TensorTypes : byte {
+ FLOAT32 = 0,
+ FLOAT16 = 1,
+ INT32 = 2,
+ UINT8 = 3,
+ INT64 = 4,
+ STRING = 5,
+ BOOL = 6,
+ INT16 = 7,
+ COMPLEX64 = 8,
+ INT8 = 9,
+ FLOAT64 = 10,
+ COMPLEX128 = 11,
+ UINT64 = 12,
+ // Experimental: Resource and variant types are experimental, that are subject
+ // to change. Do not implement custom kernels using resource & variant types
+ // now.
+ RESOURCE = 13,
+ VARIANT = 14,
+ UINT32 = 15,
+ UINT16 = 16,
+ INT4 = 17,
+ BFLOAT16 = 18,
+}
+
+table TensorData {
+ //input_index will be set to -1 for non-input_tensors
+ input_index:uint;
+ tensor_index: uint;
+ shape:[int];
+ dtype:TensorTypes;
+ data:[ubyte];
+ num_bytes:uint;
+ //layer_number will be set to -1 for input_tensors
+ layer_number:uint;
+}
+
+table SubgraphData {
+ outputs:[TensorData];
+ subgraph_index:uint;
+}
+
+table ModelTestData {
+ input_data:[TensorData];
+ subgraph_data:[SubgraphData];
+}
+
+root_type ModelTestData;
diff --git a/tensorflow/lite/micro/tools/layer_by_layer_schema_generated.h b/tensorflow/lite/micro/tools/layer_by_layer_schema_generated.h
new file mode 100644
index 0000000..67a2caa
--- /dev/null
+++ b/tensorflow/lite/micro/tools/layer_by_layer_schema_generated.h
@@ -0,0 +1,597 @@
+/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// automatically generated by the FlatBuffers compiler, do not modify
+
+
+#ifndef FLATBUFFERS_GENERATED_LAYERBYLAYERSCHEMA_H_
+#define FLATBUFFERS_GENERATED_LAYERBYLAYERSCHEMA_H_
+
+#include "flatbuffers/flatbuffers.h"
+
+// Ensure the included flatbuffers.h is the same version as when this file was
+// generated, otherwise it may not be compatible.
+static_assert(FLATBUFFERS_VERSION_MAJOR == 23 &&
+ FLATBUFFERS_VERSION_MINOR == 5 &&
+ FLATBUFFERS_VERSION_REVISION == 26,
+ "Non-compatible flatbuffers version included");
+
+struct TensorData;
+struct TensorDataBuilder;
+struct TensorDataT;
+
+struct SubgraphData;
+struct SubgraphDataBuilder;
+struct SubgraphDataT;
+
+struct ModelTestData;
+struct ModelTestDataBuilder;
+struct ModelTestDataT;
+
+enum TensorTypes : int8_t {
+ TensorTypes_FLOAT32 = 0,
+ TensorTypes_FLOAT16 = 1,
+ TensorTypes_INT32 = 2,
+ TensorTypes_UINT8 = 3,
+ TensorTypes_INT64 = 4,
+ TensorTypes_STRING = 5,
+ TensorTypes_BOOL = 6,
+ TensorTypes_INT16 = 7,
+ TensorTypes_COMPLEX64 = 8,
+ TensorTypes_INT8 = 9,
+ TensorTypes_FLOAT64 = 10,
+ TensorTypes_COMPLEX128 = 11,
+ TensorTypes_UINT64 = 12,
+ TensorTypes_RESOURCE = 13,
+ TensorTypes_VARIANT = 14,
+ TensorTypes_UINT32 = 15,
+ TensorTypes_UINT16 = 16,
+ TensorTypes_INT4 = 17,
+ TensorTypes_BFLOAT16 = 18,
+ TensorTypes_MIN = TensorTypes_FLOAT32,
+ TensorTypes_MAX = TensorTypes_BFLOAT16
+};
+
+inline const TensorTypes (&EnumValuesTensorTypes())[19] {
+ static const TensorTypes values[] = {
+ TensorTypes_FLOAT32,
+ TensorTypes_FLOAT16,
+ TensorTypes_INT32,
+ TensorTypes_UINT8,
+ TensorTypes_INT64,
+ TensorTypes_STRING,
+ TensorTypes_BOOL,
+ TensorTypes_INT16,
+ TensorTypes_COMPLEX64,
+ TensorTypes_INT8,
+ TensorTypes_FLOAT64,
+ TensorTypes_COMPLEX128,
+ TensorTypes_UINT64,
+ TensorTypes_RESOURCE,
+ TensorTypes_VARIANT,
+ TensorTypes_UINT32,
+ TensorTypes_UINT16,
+ TensorTypes_INT4,
+ TensorTypes_BFLOAT16
+ };
+ return values;
+}
+
+inline const char * const *EnumNamesTensorTypes() {
+ static const char * const names[20] = {
+ "FLOAT32",
+ "FLOAT16",
+ "INT32",
+ "UINT8",
+ "INT64",
+ "STRING",
+ "BOOL",
+ "INT16",
+ "COMPLEX64",
+ "INT8",
+ "FLOAT64",
+ "COMPLEX128",
+ "UINT64",
+ "RESOURCE",
+ "VARIANT",
+ "UINT32",
+ "UINT16",
+ "INT4",
+ "BFLOAT16",
+ nullptr
+ };
+ return names;
+}
+
+inline const char *EnumNameTensorTypes(TensorTypes e) {
+ if (::flatbuffers::IsOutRange(e, TensorTypes_FLOAT32, TensorTypes_BFLOAT16)) return "";
+ const size_t index = static_cast<size_t>(e);
+ return EnumNamesTensorTypes()[index];
+}
+
+struct TensorDataT : public ::flatbuffers::NativeTable {
+ typedef TensorData TableType;
+ uint32_t input_index = 0;
+ uint32_t tensor_index = 0;
+ std::vector<int32_t> shape{};
+ TensorTypes dtype = TensorTypes_FLOAT32;
+ std::vector<uint8_t> data{};
+ uint32_t num_bytes = 0;
+ uint32_t layer_number = 0;
+};
+
+struct TensorData FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef TensorDataT NativeTableType;
+ typedef TensorDataBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_INPUT_INDEX = 4,
+ VT_TENSOR_INDEX = 6,
+ VT_SHAPE = 8,
+ VT_DTYPE = 10,
+ VT_DATA = 12,
+ VT_NUM_BYTES = 14,
+ VT_LAYER_NUMBER = 16
+ };
+ uint32_t input_index() const {
+ return GetField<uint32_t>(VT_INPUT_INDEX, 0);
+ }
+ uint32_t tensor_index() const {
+ return GetField<uint32_t>(VT_TENSOR_INDEX, 0);
+ }
+ const ::flatbuffers::Vector<int32_t> *shape() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_SHAPE);
+ }
+ TensorTypes dtype() const {
+ return static_cast<TensorTypes>(GetField<int8_t>(VT_DTYPE, 0));
+ }
+ const ::flatbuffers::Vector<uint8_t> *data() const {
+ return GetPointer<const ::flatbuffers::Vector<uint8_t> *>(VT_DATA);
+ }
+ uint32_t num_bytes() const {
+ return GetField<uint32_t>(VT_NUM_BYTES, 0);
+ }
+ uint32_t layer_number() const {
+ return GetField<uint32_t>(VT_LAYER_NUMBER, 0);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyField<uint32_t>(verifier, VT_INPUT_INDEX, 4) &&
+ VerifyField<uint32_t>(verifier, VT_TENSOR_INDEX, 4) &&
+ VerifyOffset(verifier, VT_SHAPE) &&
+ verifier.VerifyVector(shape()) &&
+ VerifyField<int8_t>(verifier, VT_DTYPE, 1) &&
+ VerifyOffset(verifier, VT_DATA) &&
+ verifier.VerifyVector(data()) &&
+ VerifyField<uint32_t>(verifier, VT_NUM_BYTES, 4) &&
+ VerifyField<uint32_t>(verifier, VT_LAYER_NUMBER, 4) &&
+ verifier.EndTable();
+ }
+ TensorDataT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(TensorDataT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<TensorData> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const TensorDataT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct TensorDataBuilder {
+ typedef TensorData Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_input_index(uint32_t input_index) {
+ fbb_.AddElement<uint32_t>(TensorData::VT_INPUT_INDEX, input_index, 0);
+ }
+ void add_tensor_index(uint32_t tensor_index) {
+ fbb_.AddElement<uint32_t>(TensorData::VT_TENSOR_INDEX, tensor_index, 0);
+ }
+ void add_shape(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> shape) {
+ fbb_.AddOffset(TensorData::VT_SHAPE, shape);
+ }
+ void add_dtype(TensorTypes dtype) {
+ fbb_.AddElement<int8_t>(TensorData::VT_DTYPE, static_cast<int8_t>(dtype), 0);
+ }
+ void add_data(::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> data) {
+ fbb_.AddOffset(TensorData::VT_DATA, data);
+ }
+ void add_num_bytes(uint32_t num_bytes) {
+ fbb_.AddElement<uint32_t>(TensorData::VT_NUM_BYTES, num_bytes, 0);
+ }
+ void add_layer_number(uint32_t layer_number) {
+ fbb_.AddElement<uint32_t>(TensorData::VT_LAYER_NUMBER, layer_number, 0);
+ }
+ explicit TensorDataBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<TensorData> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<TensorData>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<TensorData> CreateTensorData(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ uint32_t input_index = 0,
+ uint32_t tensor_index = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> shape = 0,
+ TensorTypes dtype = TensorTypes_FLOAT32,
+ ::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> data = 0,
+ uint32_t num_bytes = 0,
+ uint32_t layer_number = 0) {
+ TensorDataBuilder builder_(_fbb);
+ builder_.add_layer_number(layer_number);
+ builder_.add_num_bytes(num_bytes);
+ builder_.add_data(data);
+ builder_.add_shape(shape);
+ builder_.add_tensor_index(tensor_index);
+ builder_.add_input_index(input_index);
+ builder_.add_dtype(dtype);
+ return builder_.Finish();
+}
+
+inline ::flatbuffers::Offset<TensorData> CreateTensorDataDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ uint32_t input_index = 0,
+ uint32_t tensor_index = 0,
+ const std::vector<int32_t> *shape = nullptr,
+ TensorTypes dtype = TensorTypes_FLOAT32,
+ const std::vector<uint8_t> *data = nullptr,
+ uint32_t num_bytes = 0,
+ uint32_t layer_number = 0) {
+ auto shape__ = shape ? _fbb.CreateVector<int32_t>(*shape) : 0;
+ auto data__ = data ? _fbb.CreateVector<uint8_t>(*data) : 0;
+ return CreateTensorData(
+ _fbb,
+ input_index,
+ tensor_index,
+ shape__,
+ dtype,
+ data__,
+ num_bytes,
+ layer_number);
+}
+
+::flatbuffers::Offset<TensorData> CreateTensorData(::flatbuffers::FlatBufferBuilder &_fbb, const TensorDataT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct SubgraphDataT : public ::flatbuffers::NativeTable {
+ typedef SubgraphData TableType;
+ std::vector<std::unique_ptr<TensorDataT>> outputs{};
+ uint32_t subgraph_index = 0;
+ SubgraphDataT() = default;
+ SubgraphDataT(const SubgraphDataT &o);
+ SubgraphDataT(SubgraphDataT&&) FLATBUFFERS_NOEXCEPT = default;
+ SubgraphDataT &operator=(SubgraphDataT o) FLATBUFFERS_NOEXCEPT;
+};
+
+struct SubgraphData FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef SubgraphDataT NativeTableType;
+ typedef SubgraphDataBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_OUTPUTS = 4,
+ VT_SUBGRAPH_INDEX = 6
+ };
+ const ::flatbuffers::Vector<::flatbuffers::Offset<TensorData>> *outputs() const {
+ return GetPointer<const ::flatbuffers::Vector<::flatbuffers::Offset<TensorData>> *>(VT_OUTPUTS);
+ }
+ uint32_t subgraph_index() const {
+ return GetField<uint32_t>(VT_SUBGRAPH_INDEX, 0);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_OUTPUTS) &&
+ verifier.VerifyVector(outputs()) &&
+ verifier.VerifyVectorOfTables(outputs()) &&
+ VerifyField<uint32_t>(verifier, VT_SUBGRAPH_INDEX, 4) &&
+ verifier.EndTable();
+ }
+ SubgraphDataT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SubgraphDataT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SubgraphData> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SubgraphDataT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct SubgraphDataBuilder {
+ typedef SubgraphData Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_outputs(::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<TensorData>>> outputs) {
+ fbb_.AddOffset(SubgraphData::VT_OUTPUTS, outputs);
+ }
+ void add_subgraph_index(uint32_t subgraph_index) {
+ fbb_.AddElement<uint32_t>(SubgraphData::VT_SUBGRAPH_INDEX, subgraph_index, 0);
+ }
+ explicit SubgraphDataBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<SubgraphData> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<SubgraphData>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<SubgraphData> CreateSubgraphData(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<TensorData>>> outputs = 0,
+ uint32_t subgraph_index = 0) {
+ SubgraphDataBuilder builder_(_fbb);
+ builder_.add_subgraph_index(subgraph_index);
+ builder_.add_outputs(outputs);
+ return builder_.Finish();
+}
+
+inline ::flatbuffers::Offset<SubgraphData> CreateSubgraphDataDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<::flatbuffers::Offset<TensorData>> *outputs = nullptr,
+ uint32_t subgraph_index = 0) {
+ auto outputs__ = outputs ? _fbb.CreateVector<::flatbuffers::Offset<TensorData>>(*outputs) : 0;
+ return CreateSubgraphData(
+ _fbb,
+ outputs__,
+ subgraph_index);
+}
+
+::flatbuffers::Offset<SubgraphData> CreateSubgraphData(::flatbuffers::FlatBufferBuilder &_fbb, const SubgraphDataT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct ModelTestDataT : public ::flatbuffers::NativeTable {
+ typedef ModelTestData TableType;
+ std::vector<std::unique_ptr<TensorDataT>> input_data{};
+ std::vector<std::unique_ptr<SubgraphDataT>> subgraph_data{};
+ ModelTestDataT() = default;
+ ModelTestDataT(const ModelTestDataT &o);
+ ModelTestDataT(ModelTestDataT&&) FLATBUFFERS_NOEXCEPT = default;
+ ModelTestDataT &operator=(ModelTestDataT o) FLATBUFFERS_NOEXCEPT;
+};
+
+struct ModelTestData FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef ModelTestDataT NativeTableType;
+ typedef ModelTestDataBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_INPUT_DATA = 4,
+ VT_SUBGRAPH_DATA = 6
+ };
+ const ::flatbuffers::Vector<::flatbuffers::Offset<TensorData>> *input_data() const {
+ return GetPointer<const ::flatbuffers::Vector<::flatbuffers::Offset<TensorData>> *>(VT_INPUT_DATA);
+ }
+ const ::flatbuffers::Vector<::flatbuffers::Offset<SubgraphData>> *subgraph_data() const {
+ return GetPointer<const ::flatbuffers::Vector<::flatbuffers::Offset<SubgraphData>> *>(VT_SUBGRAPH_DATA);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_INPUT_DATA) &&
+ verifier.VerifyVector(input_data()) &&
+ verifier.VerifyVectorOfTables(input_data()) &&
+ VerifyOffset(verifier, VT_SUBGRAPH_DATA) &&
+ verifier.VerifyVector(subgraph_data()) &&
+ verifier.VerifyVectorOfTables(subgraph_data()) &&
+ verifier.EndTable();
+ }
+ ModelTestDataT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ModelTestDataT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ModelTestData> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ModelTestDataT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct ModelTestDataBuilder {
+ typedef ModelTestData Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_input_data(::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<TensorData>>> input_data) {
+ fbb_.AddOffset(ModelTestData::VT_INPUT_DATA, input_data);
+ }
+ void add_subgraph_data(::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<SubgraphData>>> subgraph_data) {
+ fbb_.AddOffset(ModelTestData::VT_SUBGRAPH_DATA, subgraph_data);
+ }
+ explicit ModelTestDataBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<ModelTestData> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<ModelTestData>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<ModelTestData> CreateModelTestData(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<TensorData>>> input_data = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<SubgraphData>>> subgraph_data = 0) {
+ ModelTestDataBuilder builder_(_fbb);
+ builder_.add_subgraph_data(subgraph_data);
+ builder_.add_input_data(input_data);
+ return builder_.Finish();
+}
+
+inline ::flatbuffers::Offset<ModelTestData> CreateModelTestDataDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<::flatbuffers::Offset<TensorData>> *input_data = nullptr,
+ const std::vector<::flatbuffers::Offset<SubgraphData>> *subgraph_data = nullptr) {
+ auto input_data__ = input_data ? _fbb.CreateVector<::flatbuffers::Offset<TensorData>>(*input_data) : 0;
+ auto subgraph_data__ = subgraph_data ? _fbb.CreateVector<::flatbuffers::Offset<SubgraphData>>(*subgraph_data) : 0;
+ return CreateModelTestData(
+ _fbb,
+ input_data__,
+ subgraph_data__);
+}
+
+::flatbuffers::Offset<ModelTestData> CreateModelTestData(::flatbuffers::FlatBufferBuilder &_fbb, const ModelTestDataT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+inline TensorDataT *TensorData::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<TensorDataT>(new TensorDataT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void TensorData::UnPackTo(TensorDataT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = input_index(); _o->input_index = _e; }
+ { auto _e = tensor_index(); _o->tensor_index = _e; }
+ { auto _e = shape(); if (_e) { _o->shape.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape[_i] = _e->Get(_i); } } else { _o->shape.resize(0); } }
+ { auto _e = dtype(); _o->dtype = _e; }
+ { auto _e = data(); if (_e) { _o->data.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->data.begin()); } }
+ { auto _e = num_bytes(); _o->num_bytes = _e; }
+ { auto _e = layer_number(); _o->layer_number = _e; }
+}
+
+inline ::flatbuffers::Offset<TensorData> TensorData::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const TensorDataT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateTensorData(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<TensorData> CreateTensorData(::flatbuffers::FlatBufferBuilder &_fbb, const TensorDataT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const TensorDataT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _input_index = _o->input_index;
+ auto _tensor_index = _o->tensor_index;
+ auto _shape = _o->shape.size() ? _fbb.CreateVector(_o->shape) : 0;
+ auto _dtype = _o->dtype;
+ auto _data = _o->data.size() ? _fbb.CreateVector(_o->data) : 0;
+ auto _num_bytes = _o->num_bytes;
+ auto _layer_number = _o->layer_number;
+ return CreateTensorData(
+ _fbb,
+ _input_index,
+ _tensor_index,
+ _shape,
+ _dtype,
+ _data,
+ _num_bytes,
+ _layer_number);
+}
+
+inline SubgraphDataT::SubgraphDataT(const SubgraphDataT &o)
+ : subgraph_index(o.subgraph_index) {
+ outputs.reserve(o.outputs.size());
+ for (const auto &outputs_ : o.outputs) { outputs.emplace_back((outputs_) ? new TensorDataT(*outputs_) : nullptr); }
+}
+
+inline SubgraphDataT &SubgraphDataT::operator=(SubgraphDataT o) FLATBUFFERS_NOEXCEPT {
+ std::swap(outputs, o.outputs);
+ std::swap(subgraph_index, o.subgraph_index);
+ return *this;
+}
+
+inline SubgraphDataT *SubgraphData::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<SubgraphDataT>(new SubgraphDataT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void SubgraphData::UnPackTo(SubgraphDataT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->outputs[_i]) { _e->Get(_i)->UnPackTo(_o->outputs[_i].get(), _resolver); } else { _o->outputs[_i] = std::unique_ptr<TensorDataT>(_e->Get(_i)->UnPack(_resolver)); }; } } else { _o->outputs.resize(0); } }
+ { auto _e = subgraph_index(); _o->subgraph_index = _e; }
+}
+
+inline ::flatbuffers::Offset<SubgraphData> SubgraphData::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SubgraphDataT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateSubgraphData(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<SubgraphData> CreateSubgraphData(::flatbuffers::FlatBufferBuilder &_fbb, const SubgraphDataT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SubgraphDataT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _outputs = _o->outputs.size() ? _fbb.CreateVector<::flatbuffers::Offset<TensorData>> (_o->outputs.size(), [](size_t i, _VectorArgs *__va) { return CreateTensorData(*__va->__fbb, __va->__o->outputs[i].get(), __va->__rehasher); }, &_va ) : 0;
+ auto _subgraph_index = _o->subgraph_index;
+ return CreateSubgraphData(
+ _fbb,
+ _outputs,
+ _subgraph_index);
+}
+
+inline ModelTestDataT::ModelTestDataT(const ModelTestDataT &o) {
+ input_data.reserve(o.input_data.size());
+ for (const auto &input_data_ : o.input_data) { input_data.emplace_back((input_data_) ? new TensorDataT(*input_data_) : nullptr); }
+ subgraph_data.reserve(o.subgraph_data.size());
+ for (const auto &subgraph_data_ : o.subgraph_data) { subgraph_data.emplace_back((subgraph_data_) ? new SubgraphDataT(*subgraph_data_) : nullptr); }
+}
+
+inline ModelTestDataT &ModelTestDataT::operator=(ModelTestDataT o) FLATBUFFERS_NOEXCEPT {
+ std::swap(input_data, o.input_data);
+ std::swap(subgraph_data, o.subgraph_data);
+ return *this;
+}
+
+inline ModelTestDataT *ModelTestData::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<ModelTestDataT>(new ModelTestDataT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void ModelTestData::UnPackTo(ModelTestDataT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = input_data(); if (_e) { _o->input_data.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->input_data[_i]) { _e->Get(_i)->UnPackTo(_o->input_data[_i].get(), _resolver); } else { _o->input_data[_i] = std::unique_ptr<TensorDataT>(_e->Get(_i)->UnPack(_resolver)); }; } } else { _o->input_data.resize(0); } }
+ { auto _e = subgraph_data(); if (_e) { _o->subgraph_data.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->subgraph_data[_i]) { _e->Get(_i)->UnPackTo(_o->subgraph_data[_i].get(), _resolver); } else { _o->subgraph_data[_i] = std::unique_ptr<SubgraphDataT>(_e->Get(_i)->UnPack(_resolver)); }; } } else { _o->subgraph_data.resize(0); } }
+}
+
+inline ::flatbuffers::Offset<ModelTestData> ModelTestData::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ModelTestDataT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateModelTestData(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<ModelTestData> CreateModelTestData(::flatbuffers::FlatBufferBuilder &_fbb, const ModelTestDataT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ModelTestDataT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _input_data = _o->input_data.size() ? _fbb.CreateVector<::flatbuffers::Offset<TensorData>> (_o->input_data.size(), [](size_t i, _VectorArgs *__va) { return CreateTensorData(*__va->__fbb, __va->__o->input_data[i].get(), __va->__rehasher); }, &_va ) : 0;
+ auto _subgraph_data = _o->subgraph_data.size() ? _fbb.CreateVector<::flatbuffers::Offset<SubgraphData>> (_o->subgraph_data.size(), [](size_t i, _VectorArgs *__va) { return CreateSubgraphData(*__va->__fbb, __va->__o->subgraph_data[i].get(), __va->__rehasher); }, &_va ) : 0;
+ return CreateModelTestData(
+ _fbb,
+ _input_data,
+ _subgraph_data);
+}
+
+inline const ModelTestData *GetModelTestData(const void *buf) {
+ return ::flatbuffers::GetRoot<ModelTestData>(buf);
+}
+
+inline const ModelTestData *GetSizePrefixedModelTestData(const void *buf) {
+ return ::flatbuffers::GetSizePrefixedRoot<ModelTestData>(buf);
+}
+
+inline bool VerifyModelTestDataBuffer(
+ ::flatbuffers::Verifier &verifier) {
+ return verifier.VerifyBuffer<ModelTestData>(nullptr);
+}
+
+inline bool VerifySizePrefixedModelTestDataBuffer(
+ ::flatbuffers::Verifier &verifier) {
+ return verifier.VerifySizePrefixedBuffer<ModelTestData>(nullptr);
+}
+
+inline void FinishModelTestDataBuffer(
+ ::flatbuffers::FlatBufferBuilder &fbb,
+ ::flatbuffers::Offset<ModelTestData> root) {
+ fbb.Finish(root);
+}
+
+inline void FinishSizePrefixedModelTestDataBuffer(
+ ::flatbuffers::FlatBufferBuilder &fbb,
+ ::flatbuffers::Offset<ModelTestData> root) {
+ fbb.FinishSizePrefixed(root);
+}
+
+inline std::unique_ptr<ModelTestDataT> UnPackModelTestData(
+ const void *buf,
+ const ::flatbuffers::resolver_function_t *res = nullptr) {
+ return std::unique_ptr<ModelTestDataT>(GetModelTestData(buf)->UnPack(res));
+}
+
+inline std::unique_ptr<ModelTestDataT> UnPackSizePrefixedModelTestData(
+ const void *buf,
+ const ::flatbuffers::resolver_function_t *res = nullptr) {
+ return std::unique_ptr<ModelTestDataT>(GetSizePrefixedModelTestData(buf)->UnPack(res));
+}
+
+#endif // FLATBUFFERS_GENERATED_LAYERBYLAYERSCHEMA_H_
diff --git a/tensorflow/lite/micro/tools/make/Makefile b/tensorflow/lite/micro/tools/make/Makefile
index badd0ca..3bf2b54 100644
--- a/tensorflow/lite/micro/tools/make/Makefile
+++ b/tensorflow/lite/micro/tools/make/Makefile
@@ -1,4 +1,4 @@
-# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -60,9 +60,21 @@
# Specify which specialized kernel implementation should be pulled in.
OPTIMIZED_KERNEL_DIR :=
+# Optimize kernels for speed or memory. This is similar but not the same as KERNEL_OPTIMIZATION_LEVEL and
+# CORE_OPTIMIZATION_LEVEL, which specify compiler optimization level.
+# Instead this enables a kernel to provide multiple implementations that is configured at build time.
+# An example could be a kernel requiring a bigger scratch buffer for certain use cases.
+# The example kernel would have a smaller scratch buffer usage when building for size.
+# Vice versa it would use more scratch buffer when building for speed and would be more performant.
+# Note that this is optional. If having one implementation, nothing needs to be done.
+# OPTIMIZE_KERNELS_FOR has only two valid values, KERNELS_OPTIMIZED_FOR_SIZE and KERNELS_OPTIMIZED_FOR_SPEED where the
+# former is default.
+OPTIMIZE_KERNELS_FOR := KERNELS_OPTIMIZED_FOR_SPEED
+
# Override this variable from the command line in case the optimized kernels are
# in a different directory.
OPTIMIZED_KERNEL_DIR_PREFIX := $(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels
+OPTIMIZED_SIGNAL_KERNEL_DIR_PREFIX := $(TENSORFLOW_ROOT)signal/micro/kernels
# Specify which co-processor's kernel implementation should be pulled in.
# If the same kernel is implemented in both kernels/OPTIMIZED_KERNEL_DIR and
@@ -98,7 +110,7 @@
MICROLITE_LIBS := -lm
-# For the optimized_kernel_dir, and co-processor as specified on the
+# For the optimized_kernel_dir, co-processor and optimize_kernels_for as specified on the
# command line we add -D<tag> to the cflags to allow for #idefs in the code.
#
# We apply the following transformations (via the tr command):
@@ -112,6 +124,10 @@
ADDITIONAL_DEFINES += -D$(shell echo $(CO_PROCESSOR) | tr [a-z] [A-Z])
endif
+ifneq ($(OPTIMIZE_KERNELS_FOR),)
+ ADDITIONAL_DEFINES += -D$(shell echo $(OPTIMIZE_KERNELS_FOR) | tr [a-z] [A-Z])
+endif
+
ifeq ($(TOOLCHAIN), armclang)
CORE_OPTIMIZATION_LEVEL := -Oz
else
@@ -130,7 +146,6 @@
CC_WARNINGS := \
-Wsign-compare \
-Wdouble-promotion \
- -Wshadow \
-Wunused-variable \
-Wunused-function \
-Wswitch \
@@ -141,6 +156,14 @@
-Wstrict-aliasing \
-Wno-unused-parameter
+ifneq ($(TOOLCHAIN), gcc)
+ # GCC can be overly aggressive with shadow warnings, such as warning when a
+ # lambda has variable with the same name as a non-captured variable from the
+ # enclosing scope. As such, we don't enable shadow warnings on gcc.
+ # https://stackoverflow.com/q/66404751
+ CC_WARNINGS += -Wshadow
+endif
+
COMMON_FLAGS := \
-Werror \
-fno-unwind-tables \
@@ -159,7 +182,7 @@
endif
CXXFLAGS := \
- -std=c++11 \
+ -std=c++17 \
-fno-rtti \
-fno-exceptions \
-fno-threadsafe-statics \
@@ -168,7 +191,7 @@
CCFLAGS := \
-Wimplicit-function-declaration \
- -std=c11 \
+ -std=c17 \
$(COMMON_FLAGS)
ARFLAGS := -r
@@ -234,13 +257,6 @@
# https://github.com/tensorflow/tensorflow/issues/43076
CXXFLAGS := $(filter-out -DTF_LITE_STATIC_MEMORY, $(CXXFLAGS))
CCFLAGS := $(filter-out -DTF_LITE_STATIC_MEMORY, $(CCFLAGS))
-
- # We are using C++17 for the no_tf_lite_static_memory_build to make it close
- # to the TfLite bazel build.
- CXXFLAGS := $(filter-out -std=c++11, $(CXXFLAGS))
- CXXFLAGS += -std=c++17
- CCFLAGS := $(filter-out -std=c11, $(CCLAGS))
- CCFLAGS += -std=c17
endif
# This library is the main target for this makefile. It will contain a minimal
@@ -249,7 +265,15 @@
# Where compiled objects are stored.
BASE_GENDIR := gen
-GENDIR := $(BASE_GENDIR)/$(TARGET)_$(TARGET_ARCH)_$(BUILD_TYPE)/
+GENDIR := $(BASE_GENDIR)/$(TARGET)_$(TARGET_ARCH)_$(BUILD_TYPE)
+ifneq ($(OPTIMIZED_KERNEL_DIR),)
+ GENDIR := $(GENDIR)_$(OPTIMIZED_KERNEL_DIR)
+endif
+ifneq ($(CO_PROCESSOR),)
+ GENDIR := $(GENDIR)_$(CO_PROCESSOR)
+endif
+GENDIR := $(GENDIR)_$(TOOLCHAIN)/
+
CORE_OBJDIR := $(GENDIR)obj/core/
KERNEL_OBJDIR := $(GENDIR)obj/kernels/
THIRD_PARTY_KERNEL_OBJDIR := $(GENDIR)obj/third_party_kernels/
@@ -281,10 +305,14 @@
MICRO_LITE_GEN_MUTABLE_OP_RESOLVER_TEST += \
$(wildcard $(TENSORFLOW_ROOT)tensorflow/lite/micro/tools/gen_micro_mutable_op_resolver_test/person_detect/Makefile.inc)
+OLD_MICRO_LITE_BENCHMARKS := $(wildcard $(TENSORFLOW_ROOT)tensorflow/lite/micro/benchmarks/Makefile.inc)
MICRO_LITE_BENCHMARKS := $(wildcard $(TENSORFLOW_ROOT)tensorflow/lite/micro/tools/benchmarking/Makefile.inc)
+MICRO_LITE_LAYER_BY_LAYER_OUTPUT := $(wildcard $(TENSORFLOW_ROOT)tensorflow/lite/micro/tools/Makefile.inc)
+
# TODO(b/152645559): move all benchmarks to benchmarks directory.
MICROLITE_BENCHMARK_SRCS := \
+$(wildcard $(TENSORFLOW_ROOT)tensorflow/lite/micro/benchmarks/*benchmark.cc) \
$(wildcard $(TENSORFLOW_ROOT)tensorflow/lite/micro/tools/benchmarking/*benchmark.cc)
MICROLITE_TEST_SRCS := \
@@ -294,7 +322,7 @@
$(TENSORFLOW_ROOT)tensorflow/lite/micro/memory_helpers_test.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/micro_allocator_test.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/micro_allocation_info_test.cc \
-$(TENSORFLOW_ROOT)tensorflow/lite/micro/micro_context_test.cc \
+$(TENSORFLOW_ROOT)tensorflow/lite/micro/micro_interpreter_context_test.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/micro_log_test.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/micro_interpreter_test.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/micro_mutable_op_resolver_test.cc \
@@ -312,40 +340,6 @@
$(TENSORFLOW_ROOT)tensorflow/lite/micro/memory_planner/non_persistent_buffer_planner_shim_test.cc
MICROLITE_CC_KERNEL_SRCS := \
-$(TENSORFLOW_ROOT)signal/micro/kernels/delay.cc \
-$(TENSORFLOW_ROOT)signal/micro/kernels/energy.cc \
-$(TENSORFLOW_ROOT)signal/micro/kernels/fft_auto_scale.cc \
-$(TENSORFLOW_ROOT)signal/micro/kernels/filter_bank.cc \
-$(TENSORFLOW_ROOT)signal/micro/kernels/filter_bank_log.cc \
-$(TENSORFLOW_ROOT)signal/micro/kernels/filter_bank_square_root.cc \
-$(TENSORFLOW_ROOT)signal/micro/kernels/filter_bank_spectral_subtraction.cc \
-$(TENSORFLOW_ROOT)signal/micro/kernels/framer.cc \
-$(TENSORFLOW_ROOT)signal/micro/kernels/irfft.cc \
-$(TENSORFLOW_ROOT)signal/micro/kernels/rfft.cc \
-$(TENSORFLOW_ROOT)signal/micro/kernels/stacker.cc \
-$(TENSORFLOW_ROOT)signal/micro/kernels/overlap_add.cc \
-$(TENSORFLOW_ROOT)signal/micro/kernels/window.cc \
-$(TENSORFLOW_ROOT)signal/src/circular_buffer.cc \
-$(TENSORFLOW_ROOT)signal/src/energy.cc \
-$(TENSORFLOW_ROOT)signal/src/fft_auto_scale.cc \
-$(TENSORFLOW_ROOT)signal/src/filter_bank.cc \
-$(TENSORFLOW_ROOT)signal/src/filter_bank_log.cc \
-$(TENSORFLOW_ROOT)signal/src/filter_bank_square_root.cc \
-$(TENSORFLOW_ROOT)signal/src/filter_bank_spectral_subtraction.cc \
-$(TENSORFLOW_ROOT)signal/src/irfft_float.cc \
-$(TENSORFLOW_ROOT)signal/src/irfft_int16.cc \
-$(TENSORFLOW_ROOT)signal/src/irfft_int32.cc \
-$(TENSORFLOW_ROOT)signal/src/log.cc \
-$(TENSORFLOW_ROOT)signal/src/max_abs.cc \
-$(TENSORFLOW_ROOT)signal/src/msb_32.cc \
-$(TENSORFLOW_ROOT)signal/src/msb_64.cc \
-$(TENSORFLOW_ROOT)signal/src/overlap_add.cc \
-$(TENSORFLOW_ROOT)signal/src/rfft_float.cc \
-$(TENSORFLOW_ROOT)signal/src/rfft_int16.cc \
-$(TENSORFLOW_ROOT)signal/src/rfft_int32.cc \
-$(TENSORFLOW_ROOT)signal/src/square_root_32.cc \
-$(TENSORFLOW_ROOT)signal/src/square_root_64.cc \
-$(TENSORFLOW_ROOT)signal/src/window.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/activations.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/activations_common.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/add.cc \
@@ -353,6 +347,7 @@
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/add_n.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/arg_min_max.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/assign_variable.cc \
+$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/batch_matmul.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/batch_to_space_nd.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/broadcast_args.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/broadcast_to.cc \
@@ -437,6 +432,7 @@
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/squared_difference.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/squeeze.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/strided_slice.cc \
+$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/strided_slice_common.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/sub.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/sub_common.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/svdf.cc \
@@ -450,24 +446,64 @@
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/while.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/zeros_like.cc
+MICROLITE_CC_SIGNAL_KERNEL_SRCS := \
+$(TENSORFLOW_ROOT)signal/micro/kernels/delay.cc \
+$(TENSORFLOW_ROOT)signal/micro/kernels/energy.cc \
+$(TENSORFLOW_ROOT)signal/micro/kernels/fft_auto_scale_kernel.cc \
+$(TENSORFLOW_ROOT)signal/micro/kernels/fft_auto_scale_common.cc \
+$(TENSORFLOW_ROOT)signal/micro/kernels/filter_bank.cc \
+$(TENSORFLOW_ROOT)signal/micro/kernels/filter_bank_log.cc \
+$(TENSORFLOW_ROOT)signal/micro/kernels/filter_bank_square_root.cc \
+$(TENSORFLOW_ROOT)signal/micro/kernels/filter_bank_square_root_common.cc \
+$(TENSORFLOW_ROOT)signal/micro/kernels/filter_bank_spectral_subtraction.cc \
+$(TENSORFLOW_ROOT)signal/micro/kernels/framer.cc \
+$(TENSORFLOW_ROOT)signal/micro/kernels/irfft.cc \
+$(TENSORFLOW_ROOT)signal/micro/kernels/rfft.cc \
+$(TENSORFLOW_ROOT)signal/micro/kernels/stacker.cc \
+$(TENSORFLOW_ROOT)signal/micro/kernels/overlap_add.cc \
+$(TENSORFLOW_ROOT)signal/micro/kernels/pcan.cc \
+$(TENSORFLOW_ROOT)signal/micro/kernels/window.cc \
+$(TENSORFLOW_ROOT)signal/src/circular_buffer.cc \
+$(TENSORFLOW_ROOT)signal/src/energy.cc \
+$(TENSORFLOW_ROOT)signal/src/fft_auto_scale.cc \
+$(TENSORFLOW_ROOT)signal/src/filter_bank.cc \
+$(TENSORFLOW_ROOT)signal/src/filter_bank_log.cc \
+$(TENSORFLOW_ROOT)signal/src/filter_bank_square_root.cc \
+$(TENSORFLOW_ROOT)signal/src/filter_bank_spectral_subtraction.cc \
+$(TENSORFLOW_ROOT)signal/src/irfft_float.cc \
+$(TENSORFLOW_ROOT)signal/src/irfft_int16.cc \
+$(TENSORFLOW_ROOT)signal/src/irfft_int32.cc \
+$(TENSORFLOW_ROOT)signal/src/log.cc \
+$(TENSORFLOW_ROOT)signal/src/max_abs.cc \
+$(TENSORFLOW_ROOT)signal/src/msb_32.cc \
+$(TENSORFLOW_ROOT)signal/src/msb_64.cc \
+$(TENSORFLOW_ROOT)signal/src/overlap_add.cc \
+$(TENSORFLOW_ROOT)signal/src/pcan_argc_fixed.cc \
+$(TENSORFLOW_ROOT)signal/src/rfft_float.cc \
+$(TENSORFLOW_ROOT)signal/src/rfft_int16.cc \
+$(TENSORFLOW_ROOT)signal/src/rfft_int32.cc \
+$(TENSORFLOW_ROOT)signal/src/square_root_32.cc \
+$(TENSORFLOW_ROOT)signal/src/square_root_64.cc \
+$(TENSORFLOW_ROOT)signal/src/window.cc
+
MICROLITE_TEST_HDRS := \
$(wildcard $(TENSORFLOW_ROOT)tensorflow/lite/micro/testing/*.h)
# The explicitly specified list of sources and headers that are shared between
# TfLite and TFLM are in the ci/sync_from_upstream_tf.sh script.
TFL_CC_SRCS := \
-$(shell find $(TENSORFLOW_ROOT)tensorflow/lite -type d \( -path $(TENSORFLOW_ROOT)tensorflow/lite/experimental -o -path $(TENSORFLOW_ROOT)tensorflow/lite/micro \) -prune -false -o -name "*.cc" -o -name "*.c")
+$(shell find $(TENSORFLOW_ROOT)tensorflow -type d \( -path $(TENSORFLOW_ROOT)tensorflow/lite/experimental -o -path $(TENSORFLOW_ROOT)tensorflow/lite/micro \) -prune -false -o -name "*.cc" -o -name "*.c")
TFL_CC_HDRS := \
-$(shell find $(TENSORFLOW_ROOT)tensorflow/lite -type d \( -path $(TENSORFLOW_ROOT)tensorflow/lite/experimental -o -path $(TENSORFLOW_ROOT)tensorflow/lite/micro \) -prune -false -o -name "*.h")
+$(shell find $(TENSORFLOW_ROOT)tensorflow -type d \( -path $(TENSORFLOW_ROOT)tensorflow/lite/experimental -o -path $(TENSORFLOW_ROOT)tensorflow/lite/micro \) -prune -false -o -name "*.h")
ifneq ($(BUILD_TYPE), no_tf_lite_static_memory)
EXCLUDED_TFL_CC_SRCS := \
- $(TENSORFLOW_ROOT)tensorflow/lite/array.cc
+ $(TENSORFLOW_ROOT)tensorflow/lite/array.cc
TFL_CC_SRCS := $(filter-out $(EXCLUDED_TFL_CC_SRCS), $(TFL_CC_SRCS))
EXCLUDED_TFL_CC_HDRS := \
- $(TENSORFLOW_ROOT)tensorflow/lite/array.h
+ $(TENSORFLOW_ROOT)tensorflow/lite/array.h
TFL_CC_HDRS := $(filter-out $(EXCLUDED_TFL_CC_HDRS), $(TFL_CC_HDRS))
endif
@@ -501,20 +537,31 @@
$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/base.h \
$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/buffer.h \
$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/buffer_ref.h \
+$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/code_generator.h \
+$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/code_generators.h \
$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/default_allocator.h \
$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/detached_buffer.h \
+$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/file_manager.h \
$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/flatbuffer_builder.h \
$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/flatbuffers.h \
+$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/flex_flat_util.h \
$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/flexbuffers.h \
+$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/grpc.h \
+$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/hash.h \
+$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/idl.h \
+$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/minireflect.h \
+$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/reflection.h \
+$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/reflection_generated.h \
+$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/registry.h \
$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/stl_emulation.h \
$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/string.h \
$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/struct.h \
$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/table.h \
+$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/util.h \
$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/vector.h \
$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/vector_downward.h \
$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/verifier.h \
-$(DOWNLOADS_DIR)/flatbuffers/include/flatbuffers/util.h \
-$(DOWNLOADS_DIR)/flatbuffers/LICENSE.txt \
+$(DOWNLOADS_DIR)/flatbuffers/LICENSE \
$(DOWNLOADS_DIR)/gemmlowp/fixedpoint/fixedpoint.h \
$(DOWNLOADS_DIR)/gemmlowp/fixedpoint/fixedpoint_neon.h \
$(DOWNLOADS_DIR)/gemmlowp/fixedpoint/fixedpoint_sse.h \
@@ -583,8 +630,14 @@
include $(MAKEFILE_DIR)/targets/$(TARGET)_makefile.inc
endif
+# Validate valid options.
+ifeq (,$(filter $(OPTIMIZE_KERNELS_FOR),KERNELS_OPTIMIZED_FOR_SPEED KERNELS_OPTIMIZED_FOR_SIZE))
+ $(error Incorrect OPTIMIZE_KERNELS_FOR: $(OPTIMIZE_KERNELS_FOR))
+endif
+
ifneq ($(OPTIMIZED_KERNEL_DIR),)
PATH_TO_OPTIMIZED_KERNELS := $(OPTIMIZED_KERNEL_DIR_PREFIX)/$(OPTIMIZED_KERNEL_DIR)
+ PATH_TO_SIGNAL_OPTIMIZED_KERNELS := $(OPTIMIZED_SIGNAL_KERNEL_DIR_PREFIX)/$(OPTIMIZED_KERNEL_DIR)
# Check that OPTIMIZED_KERNEL_DIR is valid to avoid unexpected fallback to
# reference kernels. See http://b/183546742 for more context.
@@ -599,6 +652,22 @@
--base_files "$(MICROLITE_CC_KERNEL_SRCS)" \
--specialize_directory $(PATH_TO_OPTIMIZED_KERNELS))
+ ifneq ($(filter $(OPTIMIZED_KERNEL_DIR), xtensa),)
+ # Check that OPTIMIZED_KERNEL_DIR is valid to avoid unexpected fallback to
+ # reference kernels. See http://b/183546742 for more context.
+ RESULT := $(shell $(MAKEFILE_DIR)/check_optimized_kernel_dir.sh $(PATH_TO_SIGNAL_OPTIMIZED_KERNELS))
+ ifneq ($(RESULT), SUCCESS)
+ $(error Incorrect SIGNAL OPTIMIZED_KERNEL_DIR: $(RESULT))
+ endif
+
+ # Specialize for the optimized kernels
+ MICROLITE_CC_SIGNAL_KERNEL_SRCS := $(shell python3 $(MAKEFILE_DIR)/specialize_files.py \
+ --base_files "$(MICROLITE_CC_SIGNAL_KERNEL_SRCS)" \
+ --specialize_directory $(PATH_TO_SIGNAL_OPTIMIZED_KERNELS))
+ MICROLITE_CC_KERNEL_SRCS += $(wildcard $(PATH_TO_SIGNAL_OPTIMIZED_KERNELS)/*.S)
+ MICROLITE_CC_HDRS += $(wildcard $(PATH_TO_SIGNAL_OPTIMIZED_KERNELS)/*.h)
+ endif
+
# The first ifneq is needed to be compatible with make versions prior to 4.2
# which do not support .SHELLSTATUS. While make 4.2 was released in 2016,
# Ubuntu 18.04 only has version 4.1
@@ -614,6 +683,8 @@
MICROLITE_CC_HDRS += $(wildcard $(PATH_TO_OPTIMIZED_KERNELS)/*.h)
endif
+MICROLITE_CC_KERNEL_SRCS += $(MICROLITE_CC_SIGNAL_KERNEL_SRCS)
+
# If a co-processor is specified on the command line with
# CO_PROCESSOR=<co_processor> then we will include ext_libs/<co_processor>.inc
# and find additional kernel sources in kernels/<co_processor>/
@@ -682,7 +753,14 @@
# Load generated micro mutable op resolver test.
include ${MICRO_LITE_GEN_MUTABLE_OP_RESOLVER_TEST}
+# Load layer_by_layer_output_tool
+ifneq ($(TARGET), $(filter $(TARGET), \
+ bluepill cortex_m_corstone_300 riscv32_generic hexagon))
+include ${MICRO_LITE_LAYER_BY_LAYER_OUTPUT}
+endif
+
# Load the benchmarks.
+include $(OLD_MICRO_LITE_BENCHMARKS)
include $(MICRO_LITE_BENCHMARKS)
# Load custom kernel tests.
@@ -775,6 +853,7 @@
# These are microcontroller-specific rules for converting the ELF output
# of the linker into a binary image that can be loaded directly.
ifeq ($(TOOLCHAIN), armclang)
+ CXXFLAGS += -ffp-mode=full
FROMELF := ${TARGET_TOOLCHAIN_ROOT}$(TARGET_TOOLCHAIN_PREFIX)fromelf
$(BINDIR)%.bin: $(BINDIR)%
@mkdir -p $(dir $@)
@@ -850,6 +929,9 @@
# Just build the test targets
build: $(MICROLITE_BUILD_TARGETS)
+list_gendir:
+ @echo $(GENDIR)
+
list_library_sources:
@echo $(MICROLITE_CC_SRCS) $(MICROLITE_CC_KERNEL_SRCS)
diff --git a/tensorflow/lite/micro/tools/make/arm_gcc_download.sh b/tensorflow/lite/micro/tools/make/arm_gcc_download.sh
index 8e6d632..68d2ca1 100755
--- a/tensorflow/lite/micro/tools/make/arm_gcc_download.sh
+++ b/tensorflow/lite/micro/tools/make/arm_gcc_download.sh
@@ -1,5 +1,5 @@
#!/bin/bash
-# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -63,19 +63,26 @@
# host architechture
UNAME_M=`uname -m`
if [ "${UNAME_M}" == "x86_64" ]; then
- GCC_URL="https://developer.arm.com/-/media/Files/downloads/gnu-rm/10.3-2021.10/gcc-arm-none-eabi-10.3-2021.10-x86_64-linux.tar.bz2"
- EXPECTED_MD5="2383e4eb4ea23f248d33adc70dc3227e"
+ GCC_URL="https://developer.arm.com/-/media/Files/downloads/gnu/13.2.rel1/binrel/arm-gnu-toolchain-13.2.rel1-x86_64-arm-none-eabi.tar.xz"
+ EXPECTED_MD5="791754852f8c18ea04da7139f153a5b7"
elif [ "${UNAME_M}" == "aarch64" ]; then
- GCC_URL="https://developer.arm.com/-/media/Files/downloads/gnu-rm/10.3-2021.10/gcc-arm-none-eabi-10.3-2021.10-aarch64-linux.tar.bz2"
- EXPECTED_MD5="3fe3d8bb693bd0a6e4615b6569443d0d"
+ GCC_URL="https://developer.arm.com/-/media/Files/downloads/gnu/13.2.rel1/binrel/arm-gnu-toolchain-13.2.rel1-aarch64-arm-none-eabi.tar.xz"
+ EXPECTED_MD5="5a08122e6d4caf97c6ccd1d29e62599c"
fi
elif [ "${HOST_OS}" == "osx" ]; then
- GCC_URL="https://developer.arm.com/-/media/Files/downloads/gnu-rm/10.3-2021.10/gcc-arm-none-eabi-10.3-2021.10-mac.tar.bz2"
- EXPECTED_MD5="7f2a7b7b23797302a9d6182c6e482449"
+ # host architechture
+ UNAME_M=`uname -m`
+ if [ "${UNAME_M}" == "arm64" ]; then
+ GCC_URL="https://developer.arm.com/-/media/Files/downloads/gnu/13.2.rel1/binrel/arm-gnu-toolchain-13.2.rel1-darwin-arm64-arm-none-eabi.tar.xz"
+ EXPECTED_MD5="2c43e9d72206c1f81227b0a685df5ea6"
+ else
+ GCC_URL="https://developer.arm.com/-/media/Files/downloads/gnu/13.2.rel1/binrel/arm-gnu-toolchain-13.2.rel1-darwin-x86_64-arm-none-eabi.tar.xz"
+ EXPECTED_MD5="41d49840b0fc676d2ae35aab21a58693"
+ fi
elif [ "${HOST_OS}" == "windows" ]; then
- GCC_URL="https://developer.arm.com/-/media/Files/downloads/gnu-rm/10.3-2021.10/gcc-arm-none-eabi-10.3-2021.10-win32.zip"
- EXPECTED_MD5="2bc8f0c4c4659f8259c8176223eeafc1"
+ GCC_URL="https://developer.arm.com/-/media/Files/downloads/gnu/13.2.rel1/binrel/arm-gnu-toolchain-13.2.rel1-mingw-w64-i686-arm-none-eabi.zip"
+ EXPECTED_MD5="7fd677088038cdf82f33f149e2e943ee"
else
echo "OS type ${HOST_OS} not supported."
exit 1
@@ -92,7 +99,7 @@
unzip -q ${TEMPFILE} -d ${TEMPDIR} >&2
mv ${TEMPDIR}/*/* ${DOWNLOADED_GCC_PATH}
else
- tar -C ${DOWNLOADED_GCC_PATH} --strip-components=1 -xjf ${TEMPFILE} >&2
+ tar -C ${DOWNLOADED_GCC_PATH} --strip-components=1 -xJf ${TEMPFILE} >&2
fi
echo >&2 "Unpacked to directory: ${DOWNLOADED_GCC_PATH}"
fi
diff --git a/tensorflow/lite/micro/tools/make/bash_helpers.sh b/tensorflow/lite/micro/tools/make/bash_helpers.sh
index f29a641..e5446de 100755
--- a/tensorflow/lite/micro/tools/make/bash_helpers.sh
+++ b/tensorflow/lite/micro/tools/make/bash_helpers.sh
@@ -1,5 +1,5 @@
#!/bin/bash
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -14,6 +14,32 @@
# limitations under the License.
# ==============================================================================
+# Check the download path argument
+#
+# Parameter(s):
+# ${1} - path to the download directory or --no-downloads
+#
+# Outputs:
+# "yes" or "no"
+function check_should_download() {
+ if [[ ${1} == "--no-downloads" ]]; then
+ echo "no"
+ else
+ echo "yes"
+ fi
+}
+
+# Show the download URL and MD5 checksum
+#
+# Parameter(s):
+# ${1} - download URL
+# ${2} - download MD5 checksum
+#
+# Download scripts require informational output should be on stderr.
+function show_download_url_md5() {
+ echo >&2 "LIBRARY_URL=${1}"
+ echo >&2 "LIBRARY_MD5=${2}"
+}
# Compute the MD5 sum.
#
diff --git a/tensorflow/lite/micro/tools/make/corstone_300_download.sh b/tensorflow/lite/micro/tools/make/corstone_300_download.sh
index aa0a762..c485d3d 100755
--- a/tensorflow/lite/micro/tools/make/corstone_300_download.sh
+++ b/tensorflow/lite/micro/tools/make/corstone_300_download.sh
@@ -1,5 +1,5 @@
#!/bin/bash
-# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -17,6 +17,7 @@
# Called with following arguments:
# 1 - Path to the downloads folder which is typically
# tensorflow/lite/micro/tools/make/downloads
+# 2 - (optional) TENSORFLOW_ROOT: path to root of the TFLM tree (relative to directory from where the script is called).
#
# This script is called from the Makefile and uses the following convention to
# enable determination of sucess/failure:
@@ -31,11 +32,8 @@
set -e
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-ROOT_DIR=${SCRIPT_DIR}/../../../../..
-cd "${ROOT_DIR}"
-
-source tensorflow/lite/micro/tools/make/bash_helpers.sh
+TENSORFLOW_ROOT=${2}
+source ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/bash_helpers.sh
DOWNLOADS_DIR=${1}
if [ ! -d ${DOWNLOADS_DIR} ]; then
@@ -48,10 +46,19 @@
if [ -d ${DOWNLOADED_CORSTONE_PATH} ]; then
echo >&2 "${DOWNLOADED_CORSTONE_PATH} already exists, skipping the download."
else
- UNAME_S=`uname -s`
+ UNAME_S=$(uname -s)
+ UNAME_M=$(uname -m)
if [ ${UNAME_S} == Linux ]; then
- CORSTONE_URL=https://developer.arm.com/-/media/Arm%20Developer%20Community/Downloads/OSS/FVP/Corstone-300/FVP_Corstone_SSE-300_11.16_26.tgz
- EXPECTED_MD5=29d9208127b24a0d83356efb8343162d
+ if [ ${UNAME_M} == x86_64 ]; then
+ CORSTONE_URL=https://developer.arm.com/-/media/Arm%20Developer%20Community/Downloads/OSS/FVP/Corstone-300/FVP_Corstone_SSE-300_11.24_13_Linux64.tgz
+ EXPECTED_MD5=42500e49a4b9e8e0f633d1bad9b7c052
+ elif [ ${UNAME_M} == aarch64 ]; then
+ CORSTONE_URL=https://developer.arm.com/-/media/Arm%20Developer%20Community/Downloads/OSS/FVP/Corstone-300/FVP_Corstone_SSE-300_11.24_13_Linux64_armv8l.tgz
+ EXPECTED_MD5=89904e875c863235635e1570c4f6459e
+ else
+ echo "Cpu type ${UNAME_M} with OS type ${UNAME_S} not supported."
+ exit 1
+ fi
else
echo "OS type ${UNAME_S} not supported."
exit 1
diff --git a/tensorflow/lite/micro/tools/make/download_and_extract.sh b/tensorflow/lite/micro/tools/make/download_and_extract.sh
index 974dca1..dcd60da 100755
--- a/tensorflow/lite/micro/tools/make/download_and_extract.sh
+++ b/tensorflow/lite/micro/tools/make/download_and_extract.sh
@@ -1,5 +1,5 @@
#!/bin/bash
-# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -123,9 +123,9 @@
sleep 2
done
- # Check that the file was downloaded correctly using a checksum.
+ # Check that the file was downloaded correctly using a checksum. Put expected_md5 as "SKIP_MD5_CHECK" to skip this check.
DOWNLOADED_MD5=$(openssl dgst -md5 ${tempfile} | sed 's/.* //g')
- if [ ${expected_md5} != ${DOWNLOADED_MD5} ]; then
+ if [ ${expected_md5} != ${DOWNLOADED_MD5} ] && [ ${expected_md5} != "SKIP_MD5_CHECK" ]; then
echo "Checksum error for '${url}'. Expected ${expected_md5} but found ${DOWNLOADED_MD5}"
exit 1
fi
@@ -134,28 +134,30 @@
url=$(echo "${url}" | sed "s/\?.*//")
if [[ "${url}" == *gz ]]; then
- tar -C "${dir}" --strip-components=1 -xzf ${tempfile}
+ tar -C "${tempdir2}" -xzf ${tempfile}
elif [[ "${url}" == *tar.xz ]]; then
- tar -C "${dir}" --strip-components=1 -xf ${tempfile}
+ tar -C "${tempdir2}" -xf ${tempfile}
elif [[ "${url}" == *bz2 ]]; then
curl -Ls "${url}" > ${tempdir}/tarred.bz2
- tar -C "${dir}" --strip-components=1 -xjf ${tempfile}
+ tar -C "${tempdir2}" -xjf ${tempfile}
elif [[ "${url}" == *zip ]]; then
unzip ${tempfile} -d ${tempdir2} 2>&1 1>/dev/null
- # If the zip file contains nested directories, extract the files from the
- # inner directory.
- if [ $(find $tempdir2/* -maxdepth 0 | wc -l) = 1 ] && [ -d $tempdir2/* ]; then
- # unzip has no strip components, so unzip to a temp dir, and move the
- # files we want from the tempdir to destination.
- cp -R ${tempdir2}/*/* ${dir}/
- else
- cp -R ${tempdir2}/* ${dir}/
- fi
else
echo "Error unsupported archive type. Failed to extract tool after download."
exit 1
fi
- rm -rf ${tempdir2} ${tempdir}
+
+ # If the zip file contains nested directories, extract the files from the
+ # inner directory.
+ if [ $(find $tempdir2/* -maxdepth 0 | wc -l) = 1 ] && [ -d $tempdir2/* ]; then
+ # Unzip to a temp dir, and move the files we want from the tempdir to destination.
+ # We want this to be dependent on the folder structure of the zipped file, so --strip-components cannot be used.
+ cp -R ${tempdir2}/*/* ${dir}/
+ else
+ cp -R ${tempdir2}/* ${dir}/
+ fi
+
+ rm -rf ${tempdir} ${tempdir2}
# Delete any potential BUILD files, which would interfere with Bazel builds.
find "${dir}" -type f -name '*BUILD' -delete
diff --git a/tensorflow/lite/micro/tools/make/ethos_u_core_driver_download.sh b/tensorflow/lite/micro/tools/make/ethos_u_core_driver_download.sh
new file mode 100755
index 0000000..e6c96c1
--- /dev/null
+++ b/tensorflow/lite/micro/tools/make/ethos_u_core_driver_download.sh
@@ -0,0 +1,64 @@
+#!/bin/bash
+# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Called with following arguments:
+# 1 - Path to the downloads folder which is typically
+# tensorflow/lite/micro/tools/make/downloads
+#
+# This script is called from the Makefile and uses the following convention to
+# enable determination of sucess/failure:
+#
+# - If the script is successful, the only output on stdout should be SUCCESS.
+# The makefile checks for this particular string.
+#
+# - Any string on stdout that is not SUCCESS will be shown in the makefile as
+# the cause for the script to have failed.
+#
+# - Any other informational prints should be on stderr.
+
+set -e
+
+TENSORFLOW_ROOT=${2}
+source ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/bash_helpers.sh
+
+DOWNLOADS_DIR=${1}
+if [ ! -d ${DOWNLOADS_DIR} ]; then
+ echo "The top-level downloads directory: ${DOWNLOADS_DIR} does not exist."
+ exit 1
+fi
+
+DOWNLOADED_ETHOS_U_CORE_DRIVER_PATH=${DOWNLOADS_DIR}/ethos_u_core_driver
+
+if [ -d ${DOWNLOADED_ETHOS_U_CORE_DRIVER_PATH} ]; then
+ echo >&2 "${DOWNLOADED_ETHOS_U_CORE_DRIVER_PATH} already exists, skipping the download."
+else
+ UNAME_S=`uname -s`
+ if [ ${UNAME_S} != Linux ]; then
+ echo "OS type ${UNAME_S} not supported."
+ exit 1
+ fi
+
+ git clone "https://review.mlplatform.org/ml/ethos-u/ethos-u-core-driver" \
+ ${DOWNLOADED_ETHOS_U_CORE_DRIVER_PATH} >&2
+ pushd ${DOWNLOADED_ETHOS_U_CORE_DRIVER_PATH} > /dev/null
+ git -c advice.detachedHead=false checkout 9622608a5cc318c0933bcce720b59737d03bfb6f
+ rm -rf .git
+ create_git_repo ./
+ popd > /dev/null
+
+fi
+
+echo "SUCCESS"
diff --git a/tensorflow/lite/micro/tools/make/increase-stack-size-and-switch-DTCM-SRAM.patch b/tensorflow/lite/micro/tools/make/ethos_u_core_platform.patch
similarity index 72%
rename from tensorflow/lite/micro/tools/make/increase-stack-size-and-switch-DTCM-SRAM.patch
rename to tensorflow/lite/micro/tools/make/ethos_u_core_platform.patch
index 57c50c1..957c9c2 100644
--- a/tensorflow/lite/micro/tools/make/increase-stack-size-and-switch-DTCM-SRAM.patch
+++ b/tensorflow/lite/micro/tools/make/ethos_u_core_platform.patch
@@ -1,16 +1,16 @@
-From 470dee13bffc0adb9a778d56fab3028031f71e80 Mon Sep 17 00:00:00 2001
+From 70e504abb13fe56244250ac7ac58b1b5232481c7 Mon Sep 17 00:00:00 2001
From: TFLM <tflm@google.com>
-Date: Fri, 28 Oct 2022 11:01:15 +0200
+Date: Mon, 28 Aug 2023 16:07:22 +0000
Subject: [PATCH] TFLM patch
---
- targets/corstone-300/platform.ld | 8 +++-----
- targets/corstone-300/platform.scatter | 5 +++--
+ targets/corstone-300/platform.ld | 10 ++++------
+ targets/corstone-300/platform.scatter | 7 ++++---
targets/corstone-300/retarget.c | 16 ++++++++--------
- 3 files changed, 14 insertions(+), 15 deletions(-)
+ 3 files changed, 16 insertions(+), 17 deletions(-)
diff --git a/targets/corstone-300/platform.ld b/targets/corstone-300/platform.ld
-index ec58acc..21316a4 100644
+index ec58acc..51c93ca 100644
--- a/targets/corstone-300/platform.ld
+++ b/targets/corstone-300/platform.ld
@@ -75,7 +75,7 @@
@@ -22,6 +22,15 @@
__HEAP_SIZE = 0x00008000;
MEMORY
+@@ -83,7 +83,7 @@ MEMORY
+ ITCM (rx) : ORIGIN = 0x10000000, LENGTH = 0x00080000
+ BRAM (rw) : ORIGIN = 0x11000000, LENGTH = 0x00400000
+ DTCM (rw) : ORIGIN = 0x30000000, LENGTH = 0x00080000
+- SRAM (rw) : ORIGIN = 0x31000000, LENGTH = 0x00200000
++ SRAM (rw) : ORIGIN = 0x31000000, LENGTH = 0x02000000
+ DDR (rwx) : ORIGIN = 0x70000000, LENGTH = 0x60000000
+ }
+
@@ -150,9 +150,6 @@ SECTIONS
*(EXCLUDE_FILE(*crtend?.o *crtend.o) .dtors)
*(SORT(.dtors.*))
@@ -50,7 +59,7 @@
.heap (COPY) :
{
diff --git a/targets/corstone-300/platform.scatter b/targets/corstone-300/platform.scatter
-index fab12d1..be5c227 100644
+index fab12d1..2180aca 100644
--- a/targets/corstone-300/platform.scatter
+++ b/targets/corstone-300/platform.scatter
@@ -1,3 +1,4 @@
@@ -67,6 +76,15 @@
#endif
#ifndef HEAP_SIZE
+@@ -108,7 +109,7 @@
+ #define DTCM_SIZE 0x00080000
+
+ #define SRAM_START 0x31000000
+-#define SRAM_SIZE 0x00200000
++#define SRAM_SIZE 0x02000000
+
+ #define DDR_START 0x70000000
+ #define DDR_SIZE 0x02000000
@@ -136,7 +137,6 @@ APP_IMAGE LR_START LR_SIZE
; Make sure reset_handler ends up in root segment, when split across
; ITCM and DTCM
@@ -115,5 +133,5 @@
+}
+#endif
--
-2.17.1
+2.42.0.rc2.253.gd59a3bf2b4-goog
diff --git a/tensorflow/lite/micro/tools/make/ethos_u_core_platform_download.sh b/tensorflow/lite/micro/tools/make/ethos_u_core_platform_download.sh
index 76223db..3a1cd33 100755
--- a/tensorflow/lite/micro/tools/make/ethos_u_core_platform_download.sh
+++ b/tensorflow/lite/micro/tools/make/ethos_u_core_platform_download.sh
@@ -1,5 +1,5 @@
#!/bin/bash
-# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -31,11 +31,8 @@
set -e
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-ROOT_DIR=${SCRIPT_DIR}/../../../../..
-cd "${ROOT_DIR}"
-
-source tensorflow/lite/micro/tools/make/bash_helpers.sh
+TENSORFLOW_ROOT=${2}
+source ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/bash_helpers.sh
DOWNLOADS_DIR=${1}
if [ ! -d ${DOWNLOADS_DIR} ]; then
@@ -54,24 +51,22 @@
exit 1
fi
- git clone https://git.mlplatform.org/ml/ethos-u/ethos-u-core-platform.git ${DOWNLOADED_ETHOS_U_CORE_PLATFORM_PATH} >&2
- cd ${DOWNLOADED_ETHOS_U_CORE_PLATFORM_PATH}
+ git clone "https://review.mlplatform.org/ml/ethos-u/ethos-u-core-platform" ${DOWNLOADED_ETHOS_U_CORE_PLATFORM_PATH} >&2
+ pushd ${DOWNLOADED_ETHOS_U_CORE_PLATFORM_PATH} > /dev/null
git checkout e25a89dec1cf990f3168dbd6c565e3b0d51cb151 >&2
rm -rf .git
create_git_repo ./
-
- apply_patch_to_folder ./ ../../increase-stack-size-and-switch-DTCM-SRAM.patch "TFLM patch"
-
- cd "${ROOT_DIR}"
+ apply_patch_to_folder ./ ../../ethos_u_core_platform.patch "TFLM patch"
+ popd > /dev/null
LINKER_PATH=${DOWNLOADED_ETHOS_U_CORE_PLATFORM_PATH}/targets/corstone-300
# Run C preprocessor on linker file to get rid of ifdefs and make sure compiler is downloaded first.
COMPILER=${DOWNLOADS_DIR}/gcc_embedded/bin/arm-none-eabi-gcc
if [ ! -f ${COMPILER} ]; then
- RETURN_VALUE=`./tensorflow/lite/micro/tools/make/arm_gcc_download.sh ${DOWNLOADS_DIR}`
+ RETURN_VALUE=`${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/arm_gcc_download.sh ${DOWNLOADS_DIR} ${TENSORFLOW_ROOT}`
if [ "SUCCESS" != "${RETURN_VALUE}" ]; then
- echo "The script ./tensorflow/lite/micro/tools/make/arm_gcc_download.sh failed."
+ echo "The script ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/arm_gcc_download.sh failed."
exit 1
fi
fi
diff --git a/tensorflow/lite/micro/tools/make/ext_libs/cmsis_nn.inc b/tensorflow/lite/micro/tools/make/ext_libs/cmsis_nn.inc
index e9ae5fc..a78aa49 100644
--- a/tensorflow/lite/micro/tools/make/ext_libs/cmsis_nn.inc
+++ b/tensorflow/lite/micro/tools/make/ext_libs/cmsis_nn.inc
@@ -43,7 +43,7 @@
# the various intrinisics.
THIRD_PARTY_CC_HDRS += \
$(CMSIS_PATH)/LICENSE.txt \
- $(CMSIS_NN_PATH)/LICENSE.txt \
+ $(CMSIS_NN_PATH)/LICENSE \
$(wildcard $(CMSIS_PATH)/CMSIS/Core/Include/*.h)
# We add -I$(CMSIS_PATH) to enable the code in the TFLM repo (mostly in the
diff --git a/tensorflow/lite/micro/tools/make/ext_libs/cmsis_nn_download.sh b/tensorflow/lite/micro/tools/make/ext_libs/cmsis_nn_download.sh
index bc8e87b..fb0ad92 100755
--- a/tensorflow/lite/micro/tools/make/ext_libs/cmsis_nn_download.sh
+++ b/tensorflow/lite/micro/tools/make/ext_libs/cmsis_nn_download.sh
@@ -1,5 +1,5 @@
#!/bin/bash
-# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -36,21 +36,22 @@
source ${TENSORFLOW_ROOT}tensorflow/lite/micro/tools/make/bash_helpers.sh
DOWNLOADS_DIR=${1}
-if [ ! -d ${DOWNLOADS_DIR} ]; then
- echo "The top-level downloads directory: ${DOWNLOADS_DIR} does not exist."
- exit 1
-fi
-
DOWNLOADED_CMSIS_NN_PATH=${DOWNLOADS_DIR}/cmsis_nn
-if [ -d ${DOWNLOADED_CMSIS_NN_PATH} ]; then
+ZIP_PREFIX_NN="01dee38e6d6bfbbf202f0cd425bbea1731747d51"
+CMSIS_NN_URL="http://github.com/ARM-software/CMSIS-NN/archive/${ZIP_PREFIX_NN}.zip"
+CMSIS_NN_MD5="f20be93ededf42bb704c19f699a24313"
+
+should_download=$(check_should_download ${DOWNLOADS_DIR})
+
+if [[ ${should_download} == "no" ]]; then
+ show_download_url_md5 ${CMSIS_NN_URL} ${CMSIS_NN_MD5}
+elif [ ! -d ${DOWNLOADS_DIR} ]; then
+ echo "The top-level downloads directory: ${DOWNLOADS_DIR} does not exist."
+ exit 1
+elif [ -d ${DOWNLOADED_CMSIS_NN_PATH} ]; then
echo >&2 "${DOWNLOADED_CMSIS_NN_PATH} already exists, skipping the download."
else
-
- ZIP_PREFIX_NN="dc64e488f6655aa2792d2aceca316c896f78b4db"
- CMSIS_NN_URL="http://github.com/ARM-software/CMSIS-NN/archive/${ZIP_PREFIX_NN}.zip"
- CMSIS_NN_MD5="80f9cf0bcc10a4aefb6531ae53942044"
-
# wget is much faster than git clone of the entire repo. So we wget a specific
# version and can then apply a patch, as needed.
wget ${CMSIS_NN_URL} -O /tmp/${ZIP_PREFIX_NN}.zip >&2
diff --git a/tensorflow/lite/micro/tools/make/ext_libs/ethos_u.inc b/tensorflow/lite/micro/tools/make/ext_libs/ethos_u.inc
index c61aaff..73447cd 100644
--- a/tensorflow/lite/micro/tools/make/ext_libs/ethos_u.inc
+++ b/tensorflow/lite/micro/tools/make/ext_libs/ethos_u.inc
@@ -30,12 +30,22 @@
ETHOSU_DEFAULT_DOWNLOAD_DRIVER_PATH := $(MAKEFILE_DIR)/downloads/ethos_u_core_driver
ETHOSU_DRIVER_PATH := $(ETHOSU_DEFAULT_DOWNLOAD_DRIVER_PATH)
ifeq ($(ETHOSU_DRIVER_PATH), $(ETHOSU_DEFAULT_DOWNLOAD_DRIVER_PATH))
- $(call $(or $(shell $(DOWNLOAD_SCRIPT) $(ETHOSU_URL) $(ETHOSU_MD5) $(ETHOSU_DRIVER_PATH) >&2 && echo SUCCESS), $(error $(DOWNLOAD_SCRIPT) failed)))
+DOWNLOAD_RESULT := $(shell $(MAKEFILE_DIR)/ethos_u_core_driver_download.sh $(DOWNLOADS_DIR) $(TENSORFLOW_ROOT))
+ ifneq ($(DOWNLOAD_RESULT), SUCCESS)
+ $(error $(DOWNLOAD_SCRIPT) failed)
+ endif
endif
THIRD_PARTY_CC_HDRS += $(shell find $(ETHOSU_DRIVER_PATH)/include -name "*.h")
ifeq (,$(ETHOSU_DRIVER_LIBS))
- THIRD_PARTY_CC_SRCS += $(shell find $(ETHOSU_DRIVER_PATH)/src -name "*.c")
+ THIRD_PARTY_CC_SRCS += $(ETHOSU_DRIVER_PATH)/src/ethosu_pmu.c \
+ $(ETHOSU_DRIVER_PATH)/src/ethosu_driver.c
+ # Add the Ethos-U device-specific driver source.
+ ifeq ($(ETHOSU_ARCH), u85)
+ THIRD_PARTY_CC_SRCS += $(ETHOSU_DRIVER_PATH)/src/ethosu_device_u85.c
+ else
+ THIRD_PARTY_CC_SRCS += $(ETHOSU_DRIVER_PATH)/src/ethosu_device_u55_u65.c
+ endif
else
MICROLITE_LIBS += $(ETHOSU_DRIVER_LIBS)
endif
@@ -67,8 +77,8 @@
else
$(error "ETHOSU_ARCH=$(ETHOSU_ARCH) is not supported")
endif
-CCFLAGS += ${ETHOSU_FLAGS}
-CXXFLAGS += ${ETHOSU_FLAGS}
+CCFLAGS += ${ETHOSU_FLAGS} ${ETHOSU_EXTRA_FLAGS}
+CXXFLAGS += ${ETHOSU_FLAGS} ${ETHOSU_EXTRA_FLAGS}
# Convert downloaded person detect int8 model.
$(GENERATED_SRCS_DIR)tensorflow/lite/micro/models/person_detect_model_data_vela.cc:
diff --git a/tensorflow/lite/micro/tools/make/ext_libs/ndsplib-hifi3.patch b/tensorflow/lite/micro/tools/make/ext_libs/ndsplib-hifi3.patch
new file mode 100644
index 0000000..f216ce2
--- /dev/null
+++ b/tensorflow/lite/micro/tools/make/ext_libs/ndsplib-hifi3.patch
@@ -0,0 +1,140 @@
+diff --git a/library/include/NatureDSP_Signal_math.h b/library/include/NatureDSP_Signal_math.h
+index 2ffea63..adf08ab 100644
+--- a/library/include/NatureDSP_Signal_math.h
++++ b/library/include/NatureDSP_Signal_math.h
+@@ -38,7 +38,7 @@ extern "C" {
+ vec_log Logarithm
+ vec_antilog Antilogarithm
+ vec_sqrt Square Root
+- vec_rsqrt Reciprocal Square Root
++ vec_rsqrt Reciprocal Square Root
+ vec_sine,vec_cosine Sine/Cosine
+ vec_tan Tangent
+ vec_atan Arctangent
+@@ -384,9 +384,9 @@ int32_t scl_sqrt64x32(int64_t x);
+ part left by exponent value.
+
+ Mantissa accuracy is 1 LSB, so relative accuracy is:
+- vec_rsqrt16x16, scl_rsqrt16x16 6.2e-5
+- scl_rsqrt32x32 2.4e-7
+- vec_rsqrt32x32 9.2e-10
++ vec_rsqrt16x16, scl_rsqrt16x16 6.2e-5
++ scl_rsqrt32x32 2.4e-7
++ vec_rsqrt32x32 9.2e-10
+
+ Precision:
+ 16x16 16-bit inputs, 16-bit output. Accuracy: 2LSB
+@@ -406,11 +406,11 @@ int32_t scl_sqrt64x32(int64_t x);
+ ----------------
+ Returned packed value:
+ scl_rsqrt32x32():
+- bits 23Â…0 fractional part
+- bits 31Â…24 exponent
++ bits 23�0 fractional part
++ bits 31�24 exponent
+ scl_rsqrt16x16():
+- bits 15Â…0 fractional part
+- bits 31Â…16 exponent
++ bits 15�0 fractional part
++ bits 31�16 exponent
+
+ -------------------------------------------------------------------------*/
+ void vec_rsqrt32x32 ( int32_t * frac, int16_t * exp, const int32_t * x, int N);
+@@ -635,6 +635,7 @@ float32_t scl_atan2f (float32_t y, float32_t x);
+ return result, Q16.15
+ -------------------------------------------------------------------------*/
+ void vec_tanh32x32(int32_t * y, const int32_t * x, int N);
++void vec_tanhf (float32_t * y, const float32_t * x,int N);
+ int32_t scl_tanh32x32(int32_t x);
+
+ /*-------------------------------------------------------------------------
+@@ -659,7 +660,12 @@ int32_t scl_tanh32x32(int32_t x);
+ return result, Q16.15
+ -------------------------------------------------------------------------*/
+ void vec_sigmoid32x32(int32_t * y, const int32_t * x, int N);
++void vec_sigmoidf (float32_t * y, const float32_t * x, int N);
+ int32_t scl_sigmoid32x32(int32_t x);
++float32_t scl_sigmoidf(float32_t x);
++
++void vec_relu32x32 (int32_t * y, const int32_t * x, int32_t K, int N);
++void vec_reluf (float32_t * y, const float32_t * x, float32_t K, int N);
+
+ /*-------------------------------------------------------------------------
+ Softmax
+@@ -685,6 +691,7 @@ int32_t scl_sigmoid32x32(int32_t x);
+
+ -------------------------------------------------------------------------*/
+ void vec_softmax32x32(int32_t * y, const int32_t * x, int N);
++void vec_softmaxf (float32_t * y, const float32_t * x,int N);
+
+ /*-------------------------------------------------------------------------
+ Integer to float conversion
+diff --git a/library/include/NatureDSP_types.h b/library/include/NatureDSP_types.h
+index a38b334..6322852 100644
+--- a/library/include/NatureDSP_types.h
++++ b/library/include/NatureDSP_types.h
+@@ -332,7 +332,9 @@ typedef struct tagComplex32_t
+ #define NASSERT(x)
+ #define restrict
+ #elif defined (COMPILER_XTENSA)
++#if !defined restrict
+ #define restrict __restrict
++#endif
+ #define onchip
+ #define NASSERT(x) {(void)__builtin_expect((x)!=0,1);}
+ #else
+diff --git a/library/include_private/common.h b/library/include_private/common.h
+index 20206e4..74d4d42 100644
+--- a/library/include_private/common.h
++++ b/library/include_private/common.h
+@@ -157,6 +157,12 @@ __pragma (warning(pop))
+ __asm__(".type "#name", @object\n\t.global "#name"\n\t.align 4\n\t"#name":\n\t.long 0x49438B96,0x4D73F192\n\t");
+ #endif
+
++#if defined(COMPILER_XTENSA)
++#define DISCARD_FUN_FOR_NONVOID_RETURN(retval_type,name,arglist) \
++__attribute__ ((section ("/DISCARD/"))) \
++retval_type name arglist \
++{ return (retval_type) 0; }
++#endif
+
+ #ifdef __cplusplus
+ #define externC extern "C"
+diff --git a/library/include_private/fft_real_twiddles.h b/library/include_private/fft_real_twiddles.h
+old mode 100644
+new mode 100755
+index 10cd4b2..6f5c80b
+--- a/library/include_private/fft_real_twiddles.h
++++ b/library/include_private/fft_real_twiddles.h
+@@ -44,8 +44,12 @@
+ 7 128
+ */
+
++#if !defined MAX_RFFT_PWR
+ #define MAX_RFFT_PWR 13
++#endif
++#if !defined MAX_RFFT_LEN
+ #define MAX_RFFT_LEN (1<<MAX_RFFT_PWR)
++#endif
+
+ extern const int16_t twiddleSplit[MAX_RFFT_LEN/2];
+
+diff --git a/library/include_private/fft_real_twiddles_24x24.h b/library/include_private/fft_real_twiddles_24x24.h
+old mode 100644
+new mode 100755
+index 3017c61..257396e
+--- a/library/include_private/fft_real_twiddles_24x24.h
++++ b/library/include_private/fft_real_twiddles_24x24.h
+@@ -44,8 +44,12 @@
+ 7 128
+ */
+
++#if !defined MAX_RFFT_PWR
+ #define MAX_RFFT_PWR 13
++#endif
++#if !defined MAX_RFFT_LEN
+ #define MAX_RFFT_LEN (1<<MAX_RFFT_PWR)
++#endif
+
+ extern const int32_t twiddleSplit24x24[MAX_RFFT_LEN/2];
+
diff --git a/tensorflow/lite/micro/tools/make/ext_libs/ndsplib-hifi4.patch b/tensorflow/lite/micro/tools/make/ext_libs/ndsplib-hifi4.patch
new file mode 100644
index 0000000..38a1b3d
--- /dev/null
+++ b/tensorflow/lite/micro/tools/make/ext_libs/ndsplib-hifi4.patch
@@ -0,0 +1,42 @@
+diff --git a/library/fft/fft/fft_real24x24_hifi4.c b/library/fft/fft/fft_real24x24_hifi4.c
+index 82f48e2..bdce59e 100644
+--- a/library/fft/fft/fft_real24x24_hifi4.c
++++ b/library/fft/fft/fft_real24x24_hifi4.c
+@@ -78,7 +78,7 @@
+ x,y should not overlap
+ x,y aligned on a 8-bytes boundary
+ -------------------------------------------------------------------------*/
+-DISCARD_FUN( int,
++DISCARD_FUN_FOR_NONVOID_RETURN( int,
+ fft_real24x24,
+ (f24* y,
+ int32_t* x,
+diff --git a/library/include/NatureDSP_types.h b/library/include/NatureDSP_types.h
+index 0069361..c0f411f 100644
+--- a/library/include/NatureDSP_types.h
++++ b/library/include/NatureDSP_types.h
+@@ -332,7 +332,9 @@ typedef struct tagComplex32_t
+ #define NASSERT(x)
+ #define restrict
+ #elif defined (COMPILER_XTENSA)
++#if !defined restrict
+ #define restrict __restrict
++#endif
+ #define onchip
+ #define NASSERT(x) {(void)__builtin_expect((x)!=0,1);}
+ #else
+diff --git a/library/include_private/common.h b/library/include_private/common.h
+index d647af4..25d0ca8 100644
+--- a/library/include_private/common.h
++++ b/library/include_private/common.h
+@@ -187,6 +187,10 @@ F_UNDERSCORE STRINGIZE(name) ":\n" \
+ __attribute__ ((section ("/DISCARD/"))) \
+ retval_type name arglist \
+ { }
++#define DISCARD_FUN_FOR_NONVOID_RETURN(retval_type,name,arglist) \
++__attribute__ ((section ("/DISCARD/"))) \
++retval_type name arglist \
++{ return (retval_type) 0; }
+ #endif
+
+ #ifdef __cplusplus
diff --git a/tensorflow/lite/micro/tools/make/ext_libs/ndsplib-hifi5.patch b/tensorflow/lite/micro/tools/make/ext_libs/ndsplib-hifi5.patch
new file mode 100644
index 0000000..118845c
--- /dev/null
+++ b/tensorflow/lite/micro/tools/make/ext_libs/ndsplib-hifi5.patch
@@ -0,0 +1,31 @@
+diff --git a/library/include/NatureDSP_types.h b/library/include/NatureDSP_types.h
+index 82e16c7..ce7fb75 100644
+--- a/library/include/NatureDSP_types.h
++++ b/library/include/NatureDSP_types.h
+@@ -332,7 +332,9 @@ typedef struct tagComplex32_t
+ #define NASSERT(x)
+ #define restrict
+ #elif defined (COMPILER_XTENSA)
++#if !defined restrict
+ #define restrict __restrict
++#endif
+ #define onchip
+ #define NASSERT(x) {(void)__builtin_expect((x)!=0,1);}
+ #else
+diff --git a/library/include_private/common.h b/library/include_private/common.h
+index 2eaf70f..9df811c 100644
+--- a/library/include_private/common.h
++++ b/library/include_private/common.h
+@@ -172,6 +172,12 @@ __pragma (warning(pop))
+ #if defined(COMPILER_XTENSA) || defined(COMPILER_GNU)
+ #define DISCARD_FUN(retval_type,name,arglist) \
+ __asm__(".type "#name", @object\n\t.global "#name"\n\t.align 4\n\t"#name":\n\t.long 0x49438B96,0x4D73F192\n\t");
++
++#define DISCARD_FUN_FOR_NONVOID_RETURN(retval_type,name,arglist) \
++__attribute__ ((section ("/DISCARD/"))) \
++retval_type name arglist \
++{ return (retval_type) 0; }
++
+ #endif
+
+ /*------ LIST OF DEFINES DEPENDING ON ISA OPTIONS ------*/
diff --git a/tensorflow/lite/micro/tools/make/ext_libs/person_detection_int8_vela_convert.sh b/tensorflow/lite/micro/tools/make/ext_libs/person_detection_int8_vela_convert.sh
index da51d75..b3fe24e 100755
--- a/tensorflow/lite/micro/tools/make/ext_libs/person_detection_int8_vela_convert.sh
+++ b/tensorflow/lite/micro/tools/make/ext_libs/person_detection_int8_vela_convert.sh
@@ -1,5 +1,5 @@
#!/bin/bash
-# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -47,18 +47,30 @@
# See tensorflow/lite/micro/kernels/ethos_u/README.md for more info.
MODEL_DIR=${GENERATED_SRCS_DIR}tensorflow/lite/micro/models
CONVERTED_PERSON_MODEL_INT8=${MODEL_DIR}/person_detect_model_data_vela.cc
+PERSON_MODEL_HEADER=${MODEL_DIR}/person_detect_model_data.h
if [ ! -f ${CONVERTED_PERSON_MODEL_INT8} ]; then
+
+ # Install ethos-u-vela if not already installed.
+ set +e
+ pip show ethos-u-vela >&2
+ retVal=$?
+ set -e
+ if [ $retVal -ne 0 ]; then
+ TEMPFILE=$(mktemp -d)/
+ python3 -m venv $TEMPFILE
+ source $TEMPFILE/bin/activate
+ pip install numpy==1.26.4 >&2
+ pip install --prefer-binary ethos-u-vela >&2
+ fi
+
# Compile an optimized .tflite version for Ethos-U.
- TEMPFILE=$(mktemp -d)/
- python3 -m venv $TEMPFILE
- source $TEMPFILE/bin/activate
- python3 -m pip install --upgrade pip >&2
- pip install --upgrade cython >&2
- pip install --prefer-binary ethos-u-vela >&2
vela --accelerator-config=ethos-u55-256 ${DOWNLOADS_DIR}/../../../models/person_detect.tflite \
--output-dir ${MODEL_DIR} >&2
- deactivate
+
+ if [ $retVal -ne 0 ]; then
+ deactivate
+ fi
# Convert .tflite back to C array.
echo "// This file is generated by $0." > ${CONVERTED_PERSON_MODEL_INT8}
@@ -66,11 +78,12 @@
${CONVERTED_PERSON_MODEL_INT8}
echo -n "const " >> ${CONVERTED_PERSON_MODEL_INT8}
xxd -i ${MODEL_DIR}/person_detect_vela.tflite >> ${CONVERTED_PERSON_MODEL_INT8}
- sed -i 's/gen_cortex_m_corstone_300_cortex_m55_default_genfiles_tensorflow_lite_micro_models_person_detect_vela_tflite/g_person_detect_model_data/' \
+ sed -i 's/gen_cortex_m_corstone_300_cortex_m55_.*genfiles_tensorflow_lite_micro_models_person_detect_vela_tflite/g_person_detect_model_data/' \
${CONVERTED_PERSON_MODEL_INT8}
- sed -i 's/^const unsigned char g_person_detect_model_data/alignas\(16\) &/' ${CONVERTED_PERSON_MODEL_INT8}
- sed -i 's/g_person_detect_model_data_len/g_person_detect_model_data_size/' ${CONVERTED_PERSON_MODEL_INT8}
- sed -i 's/unsigned int/const unsigned int/' ${CONVERTED_PERSON_MODEL_INT8}
+ sed -i 's/^const unsigned char g_person_detect_model_data/alignas\(16\) &/' ${CONVERTED_PERSON_MODEL_INT8}
+ SIZE=$(sed -E -n -e 's/^.*g_person_detect_model_data_len = ([0-9]+);/\1/p' ${CONVERTED_PERSON_MODEL_INT8})
+ sed -i 's/^.*g_person_detect_model_data_len.*$//' ${CONVERTED_PERSON_MODEL_INT8}
+ sed -E -i "s/(^constexpr.*g_person_detect_model_data_size = )([0-9]+);/\1$SIZE;/" ${PERSON_MODEL_HEADER}
fi
echo "SUCCESS"
diff --git a/tensorflow/lite/micro/tools/make/ext_libs/xa_nnlib_hifi4.patch b/tensorflow/lite/micro/tools/make/ext_libs/xa_nnlib_hifi4.patch
index 227ee92..1bb15aa 100644
--- a/tensorflow/lite/micro/tools/make/ext_libs/xa_nnlib_hifi4.patch
+++ b/tensorflow/lite/micro/tools/make/ext_libs/xa_nnlib_hifi4.patch
@@ -1,207 +1,5 @@
-From 0a68f2ffa640d1b52314278cec838384722eb1d0 Mon Sep 17 00:00:00 2001
-From: William Huang <yushh@google.com>
-Date: Tue, 16 May 2023 09:18:55 +0000
-Subject: [PATCH] Optimize Xtensa transpose convolution for more kernel sizes
- and input channels.
-
-Previously, there were three code paths, in decreasing performance:
-
-1. Kernel size (H*W) multiple of 4, input channels multiple of 16
-2. Kernel size (H*W) multiple of 4, input channels multiple of 4
-3. Others (unoptimized case)
-
-This patch reduces them to the follow two cases:
-
-1. Input channels multiple of 4
-2. Others (unoptimized case)
-
-Original CL=cl/516144094
-
-BUG=227374718
-
-Signed-off-by: William Huang <yushh@google.com>
-
-Optimize Xtensa CONV2D circular buffer copy.
-
-In Xtensa's CONV2D kernel, data is shuffled around and padded so the 2D
-convolution turns into sequential vector products. Unfortunately, this
-process is somewhat slow, and the overhead is especially high for small
-vector lengths.
-
-This patch introduces the following:
-
-- Faster code path for no padding (since our models use VALID padding,
- i.e., no padding at all)
-- Manual loop if array is small and memcpy if array is large
-- Skip memset on padded channels as the corresponding kernels are
- already zero
-
-BUG=249796929
-
-Signed-off-by: William Huang <yushh@google.com>
-
-Add implementation for zero-copy CONV2D kernels.
-
-The previous `xa_nn_conv2d_std_sym8sxsym16s` implementation shuffles the
-input tensor into a circular buffer, flattening the dimensions, so that
-the 2D convolution turns into sequential vector products. However, this
-created significant overhead for layers where the resulting vector
-lengths are small.
-
-This patch implements an alternative zero-copy method that takes
-advantage of two facts:
-
-1. If `x_padding == 0`, the width dimension is automatically flattened
- with the channel dimension, and we need only `kernel_height`
- sequential vector products, even without the data shuffling
-2. Similar to the loop tiling done in
- `xa_nn_matXvec_sym8sxsym16s_sym16s_circ`, we can tile the `out_width`
- and `out_channels` dimensions, achieving the throughput of
- `_xa_nn_dot_product_2row_4vec_mat_vecs_4bytes_aligned` (i.e., 1.6
- MULAAAAQs/cycle), even when `out_height < 2`
-
-As a result, the patch significantly benefits layers where the kernel
-and output heights are small, leading to 25%+ cycle reductions in some
-use cases.
-
-Signed-off-by: William Huang <yushh@google.com>
----
- .../cnn/hifi4/xa_nn_conv2d_std_circ_buf.c | 84 +++++++-
- .../cnn/hifi4/xa_nn_conv2d_std_state.h | 15 ++
- .../cnn/hifi4/xa_nn_conv2d_std_sym8sxsym16s.c | 203 +++++++++++++++---
- .../hifi4/xa_nn_transpose_conv_sym8sxsym16s.c | 36 +---
- 4 files changed, 275 insertions(+), 63 deletions(-)
-
-diff --git a/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_circ_buf.c b/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_circ_buf.c
-index f8adba2..1a5f186 100644
---- a/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_circ_buf.c
-+++ b/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_circ_buf.c
-@@ -642,7 +642,8 @@ VOID conv2d_std_init_cir_buf(
- }
-
- // Add x_stride (but not more than kernel_width) x (input_height x input_channels) new planes to circular buffer
--VOID conv2d_std_update_cir_buf(
-+// Slow version of conv2d_std_update_cir_buf with fewer requirements
-+VOID conv2d_std_update_cir_buf_slow(
- WORD32 input_channels,
- WORD32 input_channels_pad,
- WORD32 input_bytewidth,
-@@ -742,6 +743,87 @@ VOID conv2d_std_update_cir_buf(
- *pp_inp = (VOID *)p_inp;
- }
-
-+// Add x_stride (but not more than kernel_width) x (input_height x input_channels) new planes to circular buffer
-+VOID conv2d_std_update_cir_buf(
-+ WORD32 input_channels,
-+ WORD32 input_channels_pad,
-+ WORD32 input_bytewidth,
-+ WORD32 input_width,
-+ WORD32 input_height,
-+ WORD32 y_padding,
-+ WORD32 y_b_pad,
-+ WORD32 x_padding,
-+ WORD32 kernel_width,
-+ WORD32 x_stride,
-+ VOID **pp_inp,
-+ WORD32 idx_beg_inp_width_pad,
-+ xa_nn_conv_state_t *p_state)
-+{
-+ if (y_padding != 0 || y_b_pad != 0 || x_padding != 0) {
-+ conv2d_std_update_cir_buf_slow(
-+ input_channels,
-+ input_channels_pad,
-+ input_bytewidth,
-+ input_width,
-+ input_height,
-+ y_padding,
-+ y_b_pad,
-+ x_padding,
-+ kernel_width,
-+ x_stride,
-+ pp_inp,
-+ idx_beg_inp_width_pad,
-+ p_state
-+ );
-+ return;
-+ }
-+
-+ WORD32 i,k;
-+ WORD8 *p_inp = (WORD8 *)*pp_inp;
-+ WORD32 planes_to_add = x_stride > kernel_width ? kernel_width : x_stride;
-+ WORD32 planes_to_keep = kernel_width - planes_to_add;
-+
-+ // Copy 'planes_to_add' planes of data to circular buffer
-+ AE_ADDCIRC16X4_XC((ae_int16x4 *)p_state->cir_buf.p_curr, planes_to_add * input_channels_pad * input_bytewidth);
-+ WORD8 *p_dst = (WORD8 *)p_state->cir_buf.p_curr;
-+ AE_ADDCIRC16X4_XC((ae_int16x4 *)p_dst, planes_to_keep * input_channels_pad * input_bytewidth);
-+
-+ WORD32 copy_inp_width = planes_to_add;
-+ WORD32 to_skip_inp_width = x_stride - planes_to_add; // Non-zero for x_stride > kernel_width
-+
-+ int size = input_channels * input_bytewidth;
-+ if (size <= 32) {
-+ for(i=0;i<input_height;i++)
-+ {
-+ for(k=0;k<copy_inp_width;k++)
-+ {
-+ for (int j = 0; j < size; ++j) {
-+ p_dst[j] = p_inp[j];
-+ }
-+ AE_ADDCIRC16X4_XC((ae_int16x4 *)p_dst, input_channels_pad * input_bytewidth);
-+ p_inp += input_channels * input_bytewidth;
-+ }
-+ AE_ADDCIRC16X4_XC((ae_int16x4 *)p_dst, planes_to_keep * input_channels_pad * input_bytewidth);
-+ p_inp += (input_width - copy_inp_width) * input_channels * input_bytewidth;
-+ }
-+ } else {
-+ for(i=0;i<input_height;i++)
-+ {
-+ for(k=0;k<copy_inp_width;k++)
-+ {
-+ memcpy(p_dst, p_inp, input_channels * input_bytewidth);
-+ AE_ADDCIRC16X4_XC((ae_int16x4 *)p_dst, input_channels_pad * input_bytewidth);
-+ p_inp += input_channels * input_bytewidth;
-+ }
-+ AE_ADDCIRC16X4_XC((ae_int16x4 *)p_dst, planes_to_keep * input_channels_pad * input_bytewidth);
-+ p_inp += (input_width - copy_inp_width) * input_channels * input_bytewidth;
-+ }
-+ }
-+ p_inp += (-input_height * input_width + copy_inp_width + to_skip_inp_width) * input_channels * input_bytewidth;
-+
-+ *pp_inp = (VOID *)p_inp;
-+}
-+
- VOID xa_nn_dilated_conv2d_std_load_cir_buf_asym8(
- WORD32 input_channels,
- WORD32 input_channels_pad,
-diff --git a/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_state.h b/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_state.h
-index a2ba355..8d33bad 100644
---- a/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_state.h
-+++ b/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_state.h
-@@ -214,6 +214,21 @@ VOID conv2d_std_init_cir_buf(
- VOID **pp_inp,
- xa_nn_conv_state_t *p_state);
-
-+VOID conv2d_std_update_cir_buf_slow(
-+ WORD32 input_channels,
-+ WORD32 input_channels_pad,
-+ WORD32 input_bytewidth,
-+ WORD32 input_width,
-+ WORD32 input_height,
-+ WORD32 y_padding,
-+ WORD32 y_b_pad,
-+ WORD32 x_padding,
-+ WORD32 kernel_width,
-+ WORD32 x_stride,
-+ VOID **pp_inp,
-+ WORD32 idx_beg_inp_width_pad,
-+ xa_nn_conv_state_t *p_state);
-+
- VOID conv2d_std_update_cir_buf(
- WORD32 input_channels,
- WORD32 input_channels_pad,
diff --git a/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_sym8sxsym16s.c b/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_sym8sxsym16s.c
-index 92721bc..6f868be 100644
+index b9905e9..990b713 100644
--- a/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_sym8sxsym16s.c
+++ b/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_sym8sxsym16s.c
@@ -49,6 +49,24 @@ static inline ae_int32x2 MultiplyByQuantizedMultiplier_ref(ae_int64 d_x,
@@ -229,78 +27,35 @@
static WORD32 conv_x_left_pad(
WORD32 x_padding,
WORD32 kernel_width,
-@@ -238,41 +256,166 @@ WORD32 xa_nn_conv2d_std_per_chan_sym8sxsym16s(
- WORD32 y_b_pad = kernel_height + (out_height - 1) * y_stride - (y_padding + input_height);
- y_b_pad = y_b_pad < 0 ? 0 : y_b_pad;
+@@ -129,6 +147,160 @@ static WORD32 conv_x_right_pad(
+ return out_width_over_x_r_pad;
+ }
-- conv2d_std_init_cir_buf(input_channels, input_channels_pad, input_bytewidth, input_width, input_height, y_padding, y_b_pad, x_padding_var, kernel_width, x_stride, (VOID**)&pp_inp, p_state);
-+ if (x_padding || (input_channels & 0x3) || (out_channels & 0x3) || (out_width & 0x1)) {
-+ conv2d_std_init_cir_buf(input_channels, input_channels_pad, input_bytewidth, input_width, input_height, y_padding, y_b_pad, x_padding_var, kernel_width, x_stride, (VOID**)&pp_inp, p_state);
-
-- // Index to padded input width
-- WORD32 idx_beg_inp_width_pad = kernel_width - x_stride;
-- idx_beg_inp_width_pad = idx_beg_inp_width_pad < 0 ? 0 : idx_beg_inp_width_pad;
-+ // Index to padded input width
-+ WORD32 idx_beg_inp_width_pad = kernel_width - x_stride;
-+ idx_beg_inp_width_pad = idx_beg_inp_width_pad < 0 ? 0 : idx_beg_inp_width_pad;
-
-
-- // Process Loop to compute one output plane [out_height x out_channels] per iteration
-- for(j=0;j<out_width-out_width_over_x_pad-out_width_over_x_r_pad;j++)
-- {
-- // Add x_stride x (input_height x input_channels) new planes to circular buffer
-- conv2d_std_update_cir_buf(input_channels, input_channels_pad, input_bytewidth, input_width, input_height, y_padding, y_b_pad, x_padding_var, kernel_width, x_stride, (VOID**)&pp_inp, idx_beg_inp_width_pad, p_state);
-+ // Process Loop to compute one output plane [out_height x out_channels] per iteration
-+ for(j=0;j<out_width-out_width_over_x_pad-out_width_over_x_r_pad;j++)
++static WORD32 xa_nn_conv2d_std_per_chan_sym8sxsym16s_no_circ_buf(
++ WORD16* __restrict__ p_out,
++ const WORD16* __restrict__ p_inp,
++ const WORD8* __restrict__ p_kernel,
++ const WORD64* __restrict__ p_bias,
++ WORD32 input_height,
++ WORD32 input_width,
++ WORD32 input_channels,
++ WORD32 kernel_height,
++ WORD32 kernel_width,
++ WORD32 out_channels,
++ WORD32 x_stride,
++ WORD32 y_stride,
++ WORD32 x_padding,
++ WORD32 y_padding,
++ WORD32 out_height,
++ WORD32 out_width,
++ WORD32 input_zero_bias,
++ WORD32 * p_out_multiplier,
++ WORD32 * p_out_shift,
++ WORD32 out_zero_bias,
++ WORD32 out_data_format
++ )
+ {
-+ // Add x_stride x (input_height x input_channels) new planes to circular buffer
-+ conv2d_std_update_cir_buf(input_channels, input_channels_pad, input_bytewidth, input_width, input_height, y_padding, y_b_pad, x_padding_var, kernel_width, x_stride, (VOID**)&pp_inp, idx_beg_inp_width_pad, p_state);
-
-- // Update index to input width padded
-- idx_beg_inp_width_pad += x_stride;
-+ // Update index to input width padded
-+ idx_beg_inp_width_pad += x_stride;
-
-- // Convolution using matXvec with matrix as circular buffer
-- xa_nn_matXvec_sym8sxsym16s_sym16s_circ
-- (p_out /* output */
-- ,p_state->cir_buf.p_curr/* matrix: rows x cols */
-- ,p_state->p_kernel_padded /* vec: cols */
-- ,p_bias /* bias */
-- ,out_height /* rows */
-- ,input_channels_pad * kernel_width * kernel_height /* cols */
-- ,input_channels_pad * kernel_width * y_stride/* row_offset */
-- ,out_channels /* vec_count */
-- ,input_channels_pad * kernel_width * kernel_height /* vec_stride */
-- ,out_channels_offset /* out_col_offset */
-- ,out_height_offset /* out_row_offset */
-- ,input_zero_bias
-- ,p_out_multiplier
-- ,p_out_shift
-- ,out_zero_bias
-- );
-- p_out += out_width_offset;
-+ // Convolution using matXvec with matrix as circular buffer
-+ xa_nn_matXvec_sym8sxsym16s_sym16s_circ
-+ (p_out /* output */
-+ ,p_state->cir_buf.p_curr/* matrix: rows x cols */
-+ ,p_state->p_kernel_padded /* vec: cols */
-+ ,p_bias /* bias */
-+ ,out_height /* rows */
-+ ,input_channels_pad * kernel_width * kernel_height /* cols */
-+ ,input_channels_pad * kernel_width * y_stride/* row_offset */
-+ ,out_channels /* vec_count */
-+ ,input_channels_pad * kernel_width * kernel_height /* vec_stride */
-+ ,out_channels_offset /* out_col_offset */
-+ ,out_height_offset /* out_row_offset */
-+ ,input_zero_bias
-+ ,p_out_multiplier
-+ ,p_out_shift
-+ ,out_zero_bias
-+ );
-+ p_out += out_width_offset;
-+ }
-+ } else {
++
+ const WORD16 *p_dst0_0 = p_out + 0;
+ const WORD16 *p_dst0_1 = p_out + 1;
+ const WORD16 *p_dst0_2 = p_out + 2;
@@ -310,8 +65,8 @@
+ const WORD16 *p_dst1_2 = p_out + out_channels + 2;
+ const WORD16 *p_dst1_3 = p_out + out_channels + 3;
+ int kernel_out_ch_offset = kernel_height * kernel_width * input_channels;
-+ int input_x_offset = input_channels * x_stride / 4;
-+ int p_inp_vec_stride = input_width * input_channels / 4;
++ int input_x_offset = (input_channels * x_stride) / 4;
++ int p_inp_vec_stride = (input_width * input_channels) / 4;
+ int p_kern_vec_stride = kernel_width * input_channels;
+ int vec_len = kernel_width * input_channels;
+ for (int out_y = 0; out_y < out_height; ++out_y) {
@@ -325,6 +80,7 @@
+ ae_int64 out1_1 = p_bias[out_ch + 1];
+ ae_int64 out1_2 = p_bias[out_ch + 2];
+ ae_int64 out1_3 = p_bias[out_ch + 3];
++
+ out0_0 = AE_SLAI64(out0_0, 8);
+ out0_1 = AE_SLAI64(out0_1, 8);
+ out0_2 = AE_SLAI64(out0_2, 8);
@@ -333,10 +89,11 @@
+ out1_1 = AE_SLAI64(out1_1, 8);
+ out1_2 = AE_SLAI64(out1_2, 8);
+ out1_3 = AE_SLAI64(out1_3, 8);
++
+ int in_x_o = out_x * x_stride;
+ int in_y_o = out_y * y_stride - y_padding;
+ int k_y_min = -in_y_o;
-+ int k_y_max = input_width - in_y_o;
++ int k_y_max = input_height - in_y_o;
+ k_y_min = (k_y_min < 0) ? 0 : k_y_min;
+ k_y_min = (k_y_min < kernel_height) ? k_y_min : kernel_height;
+ k_y_max = (k_y_max < 0) ? 0 : k_y_max;
@@ -382,6 +139,7 @@
+ AE_MULAAAAQ16(out1_3, d_inp1, d_kern3);
+ }
+ }
++
+ out0_0 = AE_SRAI64(out0_0, 8);
+ out0_1 = AE_SRAI64(out0_1, 8);
+ out0_2 = AE_SRAI64(out0_2, 8);
@@ -390,6 +148,7 @@
+ out1_1 = AE_SRAI64(out1_1, 8);
+ out1_2 = AE_SRAI64(out1_2, 8);
+ out1_3 = AE_SRAI64(out1_3, 8);
++
+ ae_int32x2 acc_vec0 = MultiplyByQuantizedMultiplier_x2_opt(
+ out0_0, out1_0, p_out_multiplier[out_ch + 0],
+ p_out_shift[out_ch + 0]);
@@ -423,70 +182,45 @@
+ p_dst1_3 += out_channels;
+ }
+ }
++ return 0;
++}
++
+ WORD32 xa_nn_conv2d_std_per_chan_sym8sxsym16s(
+ WORD16* __restrict__ p_out,
+ const WORD16* __restrict__ p_inp,
+@@ -180,6 +352,35 @@ WORD32 xa_nn_conv2d_std_per_chan_sym8sxsym16s(
+ XA_NNLIB_ARG_CHK_COND((p_out_shift[itr] < -31 || p_out_shift[itr] > 31), -1);
}
- return 0;
-diff --git a/algo/kernels/cnn/hifi4/xa_nn_transpose_conv_sym8sxsym16s.c b/algo/kernels/cnn/hifi4/xa_nn_transpose_conv_sym8sxsym16s.c
-index 7f31b75..a010d45 100644
---- a/algo/kernels/cnn/hifi4/xa_nn_transpose_conv_sym8sxsym16s.c
-+++ b/algo/kernels/cnn/hifi4/xa_nn_transpose_conv_sym8sxsym16s.c
-@@ -157,7 +157,7 @@ int xa_nn_transpose_conv_sym8sxsym16s(WORD16* output_data,
- */
- if(input_data && filter_data && output_data && scratch_buffer &&
- (((unsigned int)input_data&0x7)==0) && (((unsigned int)filter_data&0x3)==0) && (((unsigned int)output_data&0x7) == 0) &&
-- (((unsigned int)scratch_buffer&0x7) == 0) && ((input_depth&0xF)==0) && ((filter_height*filter_width&0x3)==0))
-+ (((unsigned int)scratch_buffer&0x7) == 0) && ((input_depth&0x3)==0))
- {
- {
- //tbd : batch = 1, need to handle other values and in_x_min/max= 0 .. need toc heck for other values
-@@ -180,7 +180,8 @@ int xa_nn_transpose_conv_sym8sxsym16s(WORD16* output_data,
- filt_y_max = (filt_y_max < filter_height) ? filt_y_max : filter_height;
- filt_y_max = (filt_y_max < 0) ? 0 : filt_y_max;
- pinp = (WORD16*)&input_data[in_y*input_width*input_depth+in_x*input_depth];
-- for (int in_channel = 0; in_channel < input_depth; in_channel+=16)
-+ int in_channel = 0;
-+ for (; in_channel + 15 < input_depth; in_channel+=16)
- {
- ae_int16x4 d_inp, d_inp1, d_inp2, d_inp3;
- AE_L16X4_IP(d_inp, (ae_int16x4*)pinp, sizeof(WORD64));
-@@ -235,36 +236,7 @@ int xa_nn_transpose_conv_sym8sxsym16s(WORD16* output_data,
- }
- }
- }
-- }
-- }
-- }
-- }
-- else if(input_data && filter_data && output_data && scratch_buffer &&
-- (((unsigned int)input_data&0x7)==0) && (((unsigned int)filter_data&0x3)==0) && (((unsigned int)output_data&0x7) == 0) &&
-- (((unsigned int)scratch_buffer&0x7) == 0) && ((input_depth&0x3)==0) && ((filter_height*filter_width&0x3)==0))
-- {
-- {
-- //tbd : batch = 1, need to handle other values and in_x_min/max= 0 .. need toc heck for other values
-- for (int in_y = 0; in_y < input_height; ++in_y)
-- {
-- for (int in_x = 0; in_x < input_width; ++in_x)
-- {
-- const int out_x_orig = in_x*stride_width - pad_width;
-- const int out_y_orig = in_y*stride_height - pad_height;
-- int filt_x_min = -out_x_orig;
-- int filt_x_max = output_width - out_x_orig;
-- int filt_y_min = -out_y_orig;
-- int filt_y_max = output_height - out_y_orig;
-- filt_x_min = (filt_x_min < filter_width) ? filt_x_min : filter_width;
-- filt_x_min = (filt_x_min < 0) ? 0 : filt_x_min;
-- filt_x_max = (filt_x_max < filter_width) ? filt_x_max : filter_width;
-- filt_x_max = (filt_x_max < 0) ? 0 : filt_x_max;
-- filt_y_min = (filt_y_min < filter_height) ? filt_y_min : filter_height;
-- filt_y_min = (filt_y_min < 0) ? 0 : filt_y_min;
-- filt_y_max = (filt_y_max < filter_height) ? filt_y_max : filter_height;
-- filt_y_max = (filt_y_max < 0) ? 0 : filt_y_max;
-- pinp = (WORD16*)&input_data[in_y*input_width*input_depth+in_x*input_depth];
-- for (int in_channel = 0; in_channel < input_depth; in_channel+=4)
-+ for (; in_channel + 3 < input_depth; in_channel+=4)
- {
- ae_int16x4 d_inp;
- AE_L16X4_IP(d_inp, (ae_int16x4*)pinp, sizeof(WORD64));
---
-2.41.0.162.gfafddb0af9-goog
-
++ if ( !(x_padding) && !(input_channels & 0x3) && !(out_channels & 0x3) && !(out_width & 0x1) && (out_data_format == 0) && ((out_width-1)*x_stride <=(input_width-kernel_width) ) )
++ {
++ int ret_val=0;
++ ret_val=xa_nn_conv2d_std_per_chan_sym8sxsym16s_no_circ_buf(p_out,
++ p_inp,
++ p_kernel,
++ p_bias,
++ input_height,
++ input_width,
++ input_channels,
++ kernel_height,
++ kernel_width,
++ out_channels,
++ x_stride,
++ y_stride,
++ x_padding,
++ y_padding,
++ out_height,
++ out_width,
++ input_zero_bias,
++ p_out_multiplier,
++ p_out_shift,
++ out_zero_bias,
++ out_data_format
++ );
++
++ return ret_val;
++ }
++
+ WORD32 j;
+ WORD32 input_bytewidth = 2;
+ VOID *pp_inp = (VOID *)p_inp;
diff --git a/tensorflow/lite/micro/tools/make/ext_libs/xa_nnlib_hifi5.patch b/tensorflow/lite/micro/tools/make/ext_libs/xa_nnlib_hifi5.patch
deleted file mode 100644
index 9d95c63..0000000
--- a/tensorflow/lite/micro/tools/make/ext_libs/xa_nnlib_hifi5.patch
+++ /dev/null
@@ -1,36 +0,0 @@
-diff --git a/algo/kernels/fc/hifi4/xa_nn_fully_connected.c b/algo/kernels/fc/hifi4/xa_nn_fully_connected.c
-index 26a2b73..61f0a64 100644
---- a/algo/kernels/fc/hifi4/xa_nn_fully_connected.c
-+++ b/algo/kernels/fc/hifi4/xa_nn_fully_connected.c
-@@ -298,7 +298,6 @@ WORD32 xa_nn_fully_connected_sym8sxasym8s_asym8s
- XA_NNLIB_ARG_CHK_PTR(p_out, -1);
- XA_NNLIB_ARG_CHK_PTR(p_weight, -1);
- XA_NNLIB_ARG_CHK_PTR(p_inp, -1);
-- XA_NNLIB_ARG_CHK_PTR(p_bias, -1);
- /* Pointer alignment checks */
- #if 0
- XA_NNLIB_ARG_CHK_ALIGN(p_out, ALIGNMENT, -1);
-@@ -310,7 +309,8 @@ WORD32 xa_nn_fully_connected_sym8sxasym8s_asym8s
- XA_NNLIB_ARG_CHK_ALIGN(p_out, sizeof(WORD8), -1);
- XA_NNLIB_ARG_CHK_ALIGN(p_weight, sizeof(WORD8), -1);
- XA_NNLIB_ARG_CHK_ALIGN(p_inp, sizeof(WORD8), -1);
-- XA_NNLIB_ARG_CHK_ALIGN(p_bias, sizeof(WORD32), -1);
-+ if (p_bias != NULL)
-+ XA_NNLIB_ARG_CHK_ALIGN(p_bias, sizeof(WORD32), -1);
- #endif
- /* Basic Parameter checks */
- XA_NNLIB_ARG_CHK_COND((out_depth <= 0), -1);
-diff --git a/algo/kernels/matXvec/hifi5/xa_nn_matXvec_sym8sxasym8s.c b/algo/kernels/matXvec/hifi5/xa_nn_matXvec_sym8sxasym8s.c
-index 5350cbe..a91e043 100644
---- a/algo/kernels/matXvec/hifi5/xa_nn_matXvec_sym8sxasym8s.c
-+++ b/algo/kernels/matXvec/hifi5/xa_nn_matXvec_sym8sxasym8s.c
-@@ -704,7 +704,8 @@ WORD32 xa_nn_matXvec_sym8sxasym8s_asym8s(
- XA_NNLIB_ARG_CHK_PTR(p_mat1, -1);
- XA_NNLIB_ARG_CHK_PTR(p_vec1, -1);
- /* Pointer alignment checks */
-- XA_NNLIB_ARG_CHK_ALIGN(p_bias, sizeof(WORD32), -1);
-+ if (p_bias != NULL)
-+ XA_NNLIB_ARG_CHK_ALIGN(p_bias, sizeof(WORD32), -1);
- /* Basic Parameter checks */
- XA_NNLIB_ARG_CHK_COND((rows <= 0), -1);
- XA_NNLIB_ARG_CHK_COND((cols1 <= 0), -1);
diff --git a/tensorflow/lite/micro/tools/make/ext_libs/xi_tflmlib_vision_p6.patch b/tensorflow/lite/micro/tools/make/ext_libs/xi_tflmlib_vision_p6.patch
new file mode 100644
index 0000000..d7df52f
--- /dev/null
+++ b/tensorflow/lite/micro/tools/make/ext_libs/xi_tflmlib_vision_p6.patch
@@ -0,0 +1,28 @@
+From ed92529a6be7a910462558edcf10070fbb0f2870 Mon Sep 17 00:00:00 2001
+From: Ryan Kuester <kuester@bdti.com>
+Date: Thu, 1 Aug 2024 12:48:12 -0500
+Subject: [PATCH] fix: use <climits> instead of <limits> to access INT_MAX and
+ friends
+
+For compatibility with the C++ library standard, use the header,
+<climits>, to access constants such as INT_MAX.
+---
+ runtime/include/cnnrt_xi.h | 2 +-
+ 1 file changed, 1 insertion(+), 1 deletion(-)
+
+diff --git a/runtime/include/cnnrt_xi.h b/runtime/include/cnnrt_xi.h
+index f3a911e..00c74b8 100644
+--- a/runtime/include/cnnrt_xi.h
++++ b/runtime/include/cnnrt_xi.h
+@@ -25,7 +25,7 @@
+ # define INCLUDE_XI_CNN
+ #endif
+
+-#include <limits>
++#include <climits>
+ #include "xi_api.h"
+ #include "xi_cnn_api.h"
+ #include "xi_tile_manager.h"
+--
+2.43.0
+
diff --git a/tensorflow/lite/micro/tools/make/ext_libs/xtensa.inc b/tensorflow/lite/micro/tools/make/ext_libs/xtensa.inc
index 3b28267..70e1880 100644
--- a/tensorflow/lite/micro/tools/make/ext_libs/xtensa.inc
+++ b/tensorflow/lite/micro/tools/make/ext_libs/xtensa.inc
@@ -1,9 +1,12 @@
+
# Explicitly add kernel sources specific to the Xtensa optimized
# implementations.
MICROLITE_CC_KERNEL_SRCS += \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/xtensa/add_vision.cc \
+ $(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/xtensa/conv_common_xtensa.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/xtensa/conv_hifi.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/xtensa/conv_int16_reference.cc \
+ $(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/xtensa/conv_int8_int16.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/xtensa/conv_int8_reference.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/xtensa/conv_vision.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/xtensa/depthwise_conv_hifi.cc \
@@ -25,12 +28,25 @@
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/xtensa/hifimini/svdf.cc \
$(TENSORFLOW_ROOT)tensorflow/lite/micro/kernels/xtensa/hifimini/fully_connected.cc
+ FFT_PATH := $(MAKEFILE_DIR)/downloads/hifi_fft
+ INCLUDES += -I$(FFT_PATH)/
+
+ THIRD_PARTY_KERNEL_CC_SRCS += \
+ $(shell find $(FFT_PATH)/hifi2_fft -name "*.c")
+ THIRD_PARTY_CC_HDRS += \
+ $(shell find $(FFT_PATH)/hifi2_fft -name "*.h")
+
else ifeq ($(TARGET_ARCH), hifi5)
DOWNLOAD_RESULT := $(shell $(MAKEFILE_DIR)/ext_libs/xtensa_download.sh ${DOWNLOADS_DIR} hifi5 $(TENSORFLOW_ROOT))
ifneq ($(DOWNLOAD_RESULT), SUCCESS)
$(error Something went wrong with the xtensa download: $(DOWNLOAD_RESULT))
endif
+ DOWNLOAD_RESULT := $(shell $(MAKEFILE_DIR)/ext_libs/xtensa_ndsp_download.sh ${DOWNLOADS_DIR} hifi5 $(TENSORFLOW_ROOT))
+ ifneq ($(DOWNLOAD_RESULT), SUCCESS)
+ $(error Something went wrong with the xtensa ndsp download: $(DOWNLOAD_RESULT))
+ endif
+
# TODO(b/161489252): -Wno-shadow is only needed for xannlib. But since we do
# not have separate cflags (or the concept of modular build targets) with the
# Makefile, -Wno-shadow will be used for everything.
@@ -43,10 +59,19 @@
CXXFLAGS += $(PLATFORM_FLAGS)
NNLIB_PATH := $(MAKEFILE_DIR)/downloads/xa_nnlib_hifi5
+ NDSPLIB_PATH := $(MAKEFILE_DIR)/downloads/ndsplib-hifi5
THIRD_PARTY_KERNEL_CC_SRCS += \
$(shell find $(NNLIB_PATH) -name "*.c")
+ # The NDSP library has a lot of file. Add as needed.
+ THIRD_PARTY_KERNEL_CC_SRCS += \
+ $(shell find $(NDSPLIB_PATH)/library/fft/fft -name "*.c") \
+ $(shell find $(NDSPLIB_PATH)/library/fft/fft_ie -name "*.c") \
+ $(shell find $(NDSPLIB_PATH)/library/fft/fft_ief -name "*.c") \
+ $(shell find $(NDSPLIB_PATH)/library/twiddles -name "*.c") \
+ $(shell find $(NDSPLIB_PATH)/library -name "version.c")
+
EXCLUDED_NNLIB_SRCS = \
$(NNLIB_PATH)/algo/layers/cnn/src/xa_nn_cnn_api.c \
$(NNLIB_PATH)/algo/layers/gru/src/xa_nn_gru_api.c \
@@ -55,7 +80,8 @@
THIRD_PARTY_KERNEL_CC_SRCS := $(filter-out $(EXCLUDED_NNLIB_SRCS), $(THIRD_PARTY_KERNEL_CC_SRCS))
THIRD_PARTY_CC_HDRS += \
- $(shell find $(NNLIB_PATH) -name "*.h")
+ $(shell find $(NNLIB_PATH) -name "*.h") \
+ $(shell find $(NDSPLIB_PATH) -name "*.h")
INCLUDES += \
-I$(NNLIB_PATH)/ \
@@ -63,15 +89,20 @@
-I$(NNLIB_PATH)/include/nnlib/ \
-I$(NNLIB_PATH)/include/ \
-I$(NNLIB_PATH)/algo/common/include/ \
- -I$(NNLIB_PATH)/algo/ndsp/hifi5/include/
-
-else ifeq ($(TARGET_ARCH), hifi4)
-
+ -I$(NDSPLIB_PATH)/library/include/ \
+ -I$(NDSPLIB_PATH)/library/include_private/
+else ifeq ($(TARGET_ARCH), $(filter $(TARGET_ARCH), hifi3 hifi4))
+ # NNLib hifi4 also supports hifi3
DOWNLOAD_RESULT := $(shell $(MAKEFILE_DIR)/ext_libs/xtensa_download.sh ${DOWNLOADS_DIR} hifi4 $(TENSORFLOW_ROOT))
ifneq ($(DOWNLOAD_RESULT), SUCCESS)
$(error Something went wrong with the xtensa download: $(DOWNLOAD_RESULT))
endif
+ DOWNLOAD_RESULT := $(shell $(MAKEFILE_DIR)/ext_libs/xtensa_ndsp_download.sh ${DOWNLOADS_DIR} $(TARGET_ARCH) $(TENSORFLOW_ROOT))
+ ifneq ($(DOWNLOAD_RESULT), SUCCESS)
+ $(error Something went wrong with the xtensa ndsp download: $(DOWNLOAD_RESULT))
+ endif
+
# TODO(b/161489252): -Wno-shadow is only needed for xannlib. But since we do
# not have separate cflags (or the concept of modular build targets) with the
# Makefile, -Wno-shadow will be used for everything.
@@ -83,20 +114,44 @@
CCFLAGS += $(PLATFORM_FLAGS)
CXXFLAGS += $(PLATFORM_FLAGS)
+ # NNLib for hifi4 also supports hifi3
NNLIB_PATH := $(MAKEFILE_DIR)/downloads/xa_nnlib_hifi4
+ NDSPLIB_PATH := $(MAKEFILE_DIR)/downloads/ndsplib-$(TARGET_ARCH)
THIRD_PARTY_KERNEL_CC_SRCS += \
$(shell find $(NNLIB_PATH) -name "*.c")
+ # The NDSP library has a lot of file. Add as needed.
+ THIRD_PARTY_KERNEL_CC_SRCS += \
+ $(shell find $(NDSPLIB_PATH)/library/fft/fft -name "*.c") \
+ $(shell find $(NDSPLIB_PATH)/library/fft/fft_ie -name "*.c") \
+ $(shell find $(NDSPLIB_PATH)/library/fft/fft_ief -name "*.c") \
+ $(shell find $(NDSPLIB_PATH)/library/twiddles -name "*.c") \
+ $(shell find $(NDSPLIB_PATH)/library -name "version.c")
+
EXCLUDED_NNLIB_SRCS = \
$(NNLIB_PATH)/algo/layers/cnn/src/xa_nn_cnn_api.c \
$(NNLIB_PATH)/algo/layers/gru/src/xa_nn_gru_api.c \
$(NNLIB_PATH)/algo/layers/lstm/src/xa_nn_lstm_api.c
+ ifeq ($(TARGET_ARCH), hifi3)
+ EXCLUDED_NNLIB_SRCS += \
+ $(NNLIB_PATH)/algo/ndsp/hifi4/src/pow2f_tbl.c \
+ $(NNLIB_PATH)/algo/ndsp/hifi4/src/scl_tanhf_hifi4.c \
+ $(NNLIB_PATH)/algo/ndsp/hifi4/src/vec_tanhf_hifi4.c \
+ $(NNLIB_PATH)/algo/ndsp/hifi4/src/tanhf_tbl.c
+ endif
+
+ ifeq ($(TARGET_ARCH), hifi4)
+ EXCLUDED_NNLIB_SRCS += \
+ $(NNLIB_PATH)/algo/kernels/activations/hifi4/xa_nn_activations_asym8_asym8.c
+ endif
+
THIRD_PARTY_KERNEL_CC_SRCS := $(filter-out $(EXCLUDED_NNLIB_SRCS), $(THIRD_PARTY_KERNEL_CC_SRCS))
THIRD_PARTY_CC_HDRS += \
- $(shell find $(NNLIB_PATH) -name "*.h")
+ $(shell find $(NNLIB_PATH) -name "*.h") \
+ $(shell find $(NDSPLIB_PATH) -name "*.h")
INCLUDES += \
-I$(NNLIB_PATH)/ \
@@ -104,7 +159,8 @@
-I$(NNLIB_PATH)/include/nnlib/ \
-I$(NNLIB_PATH)/include/ \
-I$(NNLIB_PATH)/algo/common/include/ \
- -I$(NNLIB_PATH)/algo/ndsp/hifi4/include/
+ -I$(NDSPLIB_PATH)/library/include/ \
+ -I$(NDSPLIB_PATH)/library/include_private/
else ifeq ($(TARGET_ARCH), vision_p6)
DOWNLOAD_RESULT := $(shell $(MAKEFILE_DIR)/ext_libs/xtensa_download.sh ${DOWNLOADS_DIR} vision_p6 $(TENSORFLOW_ROOT))
@@ -139,21 +195,3 @@
else
$(error Unsupported TARGET_ARCH=$(TARGET_ARCH))
endif
-
-FFT_PATH := $(MAKEFILE_DIR)/downloads/hifi_fft
-
-INCLUDES += -I$(FFT_PATH)/
-
-ifeq ($(TARGET_ARCH), $(filter $(TARGET_ARCH), hifi3 hifi4 hifi5))
-THIRD_PARTY_KERNEL_CC_SRCS += \
- $(shell find $(FFT_PATH)/hifi3_fft -name "*.c")
-
-THIRD_PARTY_CC_HDRS += \
- $(shell find $(FFT_PATH)/hifi3_fft -name "*.h")
-else ifeq ($(TARGET_ARCH), hifimini)
-THIRD_PARTY_KERNEL_CC_SRCS += \
- $(shell find $(FFT_PATH)/hifi2_fft -name "*.c")
-
-THIRD_PARTY_CC_HDRS += \
- $(shell find $(FFT_PATH)/hifi2_fft -name "*.h")
-endif
diff --git a/tensorflow/lite/micro/tools/make/ext_libs/xtensa_download.sh b/tensorflow/lite/micro/tools/make/ext_libs/xtensa_download.sh
index fb45123..9726980 100755
--- a/tensorflow/lite/micro/tools/make/ext_libs/xtensa_download.sh
+++ b/tensorflow/lite/micro/tools/make/ext_libs/xtensa_download.sh
@@ -1,5 +1,5 @@
#!/bin/bash
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -38,23 +38,26 @@
source ${3}tensorflow/lite/micro/tools/make/bash_helpers.sh
DOWNLOADS_DIR=${1}
-if [ ! -d ${DOWNLOADS_DIR} ]; then
- echo "The top-level downloads directory: ${DOWNLOADS_DIR} does not exist."
- exit 1
-fi
+PATCH=""
-if [[ ${2} == "hifi4" ]]; then
- LIBRARY_URL="http://github.com/foss-xtensa/nnlib-hifi4/raw/master/archive/xa_nnlib_hifi4_10_14_2022.zip"
+if [[ ${2} == "hifi3" ]]; then
+ LIBRARY_URL="http://github.com/foss-xtensa/nnlib-hifi4/raw/master/archive/xa_nnlib_hifi4_09_05_2023.zip"
LIBRARY_DIRNAME="xa_nnlib_hifi4"
- LIBRARY_MD5="2bf3c1c7fd5a23f157babc8e24fd2c55"
+ LIBRARY_MD5="2a54e056aef73a4fcffde4643998501a"
+elif [[ ${2} == "hifi4" ]]; then
+ LIBRARY_URL="http://github.com/foss-xtensa/nnlib-hifi4/raw/master/archive/xa_nnlib_hifi4_09_05_2023.zip"
+ LIBRARY_DIRNAME="xa_nnlib_hifi4"
+ LIBRARY_MD5="2a54e056aef73a4fcffde4643998501a"
+ PATCH="../../ext_libs/xa_nnlib_hifi4.patch"
elif [[ ${2} == "hifi5" ]]; then
- LIBRARY_URL="http://github.com/foss-xtensa/nnlib-hifi5/raw/master/archive/xa_nnlib_hifi5_12_19_2022.zip"
+ LIBRARY_URL="http://github.com/foss-xtensa/nnlib-hifi5/raw/master/archive/xa_nnlib_hifi5_09_05_2023.zip"
LIBRARY_DIRNAME="xa_nnlib_hifi5"
- LIBRARY_MD5="83306809191f42a064bde688b94e1eb1"
+ LIBRARY_MD5="1deb55ef200bf5dbedc70b99b02140c0"
elif [[ ${2} == "vision_p6" ]]; then
LIBRARY_URL="https://github.com/foss-xtensa/tflmlib_vision/raw/main/archive/xi_tflmlib_vision_p6_22_06_29.zip"
LIBRARY_DIRNAME="xi_tflmlib_vision_p6"
LIBRARY_MD5="fea3720d76fdb3a5a337ace7b6081b56"
+ PATCH="../../ext_libs/xi_tflmlib_vision_p6.patch"
else
echo "Attempting to download an unsupported xtensa variant: ${2}"
exit 1
@@ -62,35 +65,30 @@
LIBRARY_INSTALL_PATH=${DOWNLOADS_DIR}/${LIBRARY_DIRNAME}
-if [ -d ${LIBRARY_INSTALL_PATH} ]; then
+should_download=$(check_should_download ${DOWNLOADS_DIR})
+
+if [[ ${should_download} == "no" ]]; then
+ show_download_url_md5 ${LIBRARY_URL} ${LIBRARY_MD5}
+elif [ ! -d ${DOWNLOADS_DIR} ]; then
+ echo "The top-level downloads directory: ${DOWNLOADS_DIR} does not exist."
+ exit 1
+elif [ -d ${LIBRARY_INSTALL_PATH} ]; then
echo >&2 "${LIBRARY_INSTALL_PATH} already exists, skipping the download."
else
TEMPDIR="$(mktemp -d)"
TEMPFILE="${TEMPDIR}/${LIBRARY_DIRNAME}.zip"
wget ${LIBRARY_URL} -O "$TEMPFILE" >&2
- MD5=`md5sum "$TEMPFILE" | awk '{print $1}'`
+ check_md5 "${TEMPFILE}" ${LIBRARY_MD5}
- if [[ ${MD5} != ${LIBRARY_MD5} ]]
- then
- echo "Bad checksum. Expected: ${LIBRARY_MD5}, Got: ${MD5}"
- exit 1
- fi
+ unzip -qo "$TEMPFILE" -d ${DOWNLOADS_DIR} >&2
- # Check if another make process has already extracted the downloaded files.
- # If so, skip extracting and patching.
- if [ -d ${LIBRARY_INSTALL_PATH} ]; then
- echo >&2 "${LIBRARY_INSTALL_PATH} already exists, skipping the extraction."
- else
- unzip -qo "$TEMPFILE" -d ${DOWNLOADS_DIR} >&2
+ rm -rf "${TEMPDIR}"
- rm -rf "${TEMPDIR}"
-
- pushd "${LIBRARY_INSTALL_PATH}" > /dev/null
- chmod -R +w ./
- if [[ -f "../../ext_libs/xa_nnlib_${2}.patch" ]]; then
- create_git_repo ./
- apply_patch_to_folder ./ "../../ext_libs/xa_nnlib_${2}.patch" "TFLM patch"
- fi
+ pushd "${LIBRARY_INSTALL_PATH}" > /dev/null
+ chmod -R +w ./
+ if [ "${PATCH}" ]; then
+ create_git_repo ./
+ apply_patch_to_folder ./ ${PATCH} "TFLM patch"
fi
fi
diff --git a/tensorflow/lite/micro/tools/make/ext_libs/xtensa_ndsp_download.sh b/tensorflow/lite/micro/tools/make/ext_libs/xtensa_ndsp_download.sh
new file mode 100755
index 0000000..71fe1d1
--- /dev/null
+++ b/tensorflow/lite/micro/tools/make/ext_libs/xtensa_ndsp_download.sh
@@ -0,0 +1,100 @@
+#!/bin/bash
+# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+#
+# Downloads necessary to build with OPTIMIZED_KERNEL_DIR=xtensa.
+#
+# Called with four arguments:
+# 1 - Path to the downloads folder which is typically
+# ${TENSORFLOW_ROOT}/tensorflow/lite/micro/tools/make/downloads
+# 2 - Xtensa variant to download for (e.g. hifi4)
+# 3 - (optional) TENSORFLOW_ROOT: path to root of the TFLM tree (relative to directory from where the script is called).
+#
+# This script is called from the Makefile and uses the following convention to
+# enable determination of sucess/failure:
+#
+# - If the script is successful, the only output on stdout should be SUCCESS.
+# The makefile checks for this particular string.
+#
+# - Any string on stdout that is not SUCCESS will be shown in the makefile as
+# the cause for the script to have failed.
+#
+# - Any other informational prints should be on stderr.
+
+set -e
+
+source ${3}tensorflow/lite/micro/tools/make/bash_helpers.sh
+
+DOWNLOADS_DIR=${1}
+
+if [[ ${2} == "hifi3" ]]; then
+ COMMIT="d17bf205dc530a9e1a1d979249520f4401529db1"
+ LIBRARY_DIRNAME="ndsplib-hifi3"
+ LIBRARY_URL="https://github.com/foss-xtensa/${LIBRARY_DIRNAME}/archive/${COMMIT}.zip"
+ LIBRARY_MD5="5572b27361736c1f773474ebaf42c5d4"
+ CORE_NAME="HiFi3"
+elif [[ ${2} == "hifi4" ]]; then
+ COMMIT="aba2485ba12d9851fa398bcb5c18c05cc3731a17"
+ LIBRARY_DIRNAME="ndsplib-hifi4"
+ LIBRARY_URL="https://github.com/foss-xtensa/${LIBRARY_DIRNAME}/archive/${COMMIT}.zip"
+ LIBRARY_MD5="062b8f957c662b6ab834bbe284237b6c"
+ CORE_NAME="HiFi4"
+elif [[ ${2} == "hifi5" ]]; then
+ COMMIT="01c92ceb26cc0a598c6d83d17c3d88363bd8f7fc"
+ LIBRARY_DIRNAME="ndsplib-hifi5"
+ LIBRARY_URL="https://github.com/foss-xtensa/${LIBRARY_DIRNAME}/archive/${COMMIT}.zip"
+ LIBRARY_MD5="94b372d608781c13be2fb2d1a8fd3b58"
+ CORE_NAME="HiFi5"
+else
+ echo "Attempting to download an unsupported xtensa variant: ${2}"
+ exit 1
+fi
+
+LIBRARY_INSTALL_PATH=${DOWNLOADS_DIR}/${LIBRARY_DIRNAME}
+
+should_download=$(check_should_download ${DOWNLOADS_DIR})
+
+if [[ ${should_download} == "no" ]]; then
+ show_download_url_md5 ${LIBRARY_URL} ${LIBRARY_MD5}
+elif [ ! -d ${DOWNLOADS_DIR} ]; then
+ echo "The top-level downloads directory: ${DOWNLOADS_DIR} does not exist."
+ exit 1
+elif [ -d ${LIBRARY_INSTALL_PATH} ]; then
+ echo >&2 "${LIBRARY_INSTALL_PATH} already exists, skipping the download."
+else
+ TEMPDIR="$(mktemp -d)"
+ TEMPFILE="${TEMPDIR}/${LIBRARY_DIRNAME}.zip"
+ wget ${LIBRARY_URL} -O "$TEMPFILE" >&2
+ check_md5 "${TEMPFILE}" ${LIBRARY_MD5}
+
+ unzip -qo "$TEMPFILE" -d ${TEMPDIR} >&2
+ unzip -qo ${TEMPDIR}/${LIBRARY_DIRNAME}-${COMMIT}/NDSP_${CORE_NAME}/NDSP_${CORE_NAME}*.zip -d ${TEMPDIR}/${LIBRARY_DIRNAME}-${COMMIT}/NDSP_${CORE_NAME}/ >&2
+ find ${TEMPDIR}/${LIBRARY_DIRNAME}-${COMMIT}/NDSP_${CORE_NAME}/* -maxdepth 0 -type d -exec mv {} ${LIBRARY_INSTALL_PATH} \;
+ rm -rf "${TEMPDIR}"
+ # NDSP sources in GitHub currently uses DOS style newlines, which causes compiler errors.
+ find ${LIBRARY_INSTALL_PATH} -type f -exec sed -i.bak 's/\r$//g' {} \;
+
+ pushd "${LIBRARY_INSTALL_PATH}" > /dev/null
+ chmod -R +w ./
+ if [[ -f "../../ext_libs/ndsplib-${2}.patch" ]]; then
+ create_git_repo ./
+ apply_patch_to_folder ./ "../../ext_libs/ndsplib-${2}.patch" "TFLM patch"
+ fi
+ # Rename the strings in __renaming__.h to names that are traceable to TFLM.
+ # Note that renaming is disabled by default and must be enabled with -D__RENAMING__
+ sed -i 's/NatureDSP_/NatureDSP_TFLM_/' library/include_private/__renaming__.h
+fi
+
+echo "SUCCESS"
diff --git a/tensorflow/lite/micro/tools/make/flatbuffers.patch b/tensorflow/lite/micro/tools/make/flatbuffers.patch
index cb22cf0..2017775 100644
--- a/tensorflow/lite/micro/tools/make/flatbuffers.patch
+++ b/tensorflow/lite/micro/tools/make/flatbuffers.patch
@@ -1,5 +1,5 @@
diff --git a/include/flatbuffers/base.h b/include/flatbuffers/base.h
-index a5ac10d..371b6fd 100644
+index 5c4cae79..1a631641 100644
--- a/include/flatbuffers/base.h
+++ b/include/flatbuffers/base.h
@@ -1,6 +1,16 @@
@@ -20,10 +20,10 @@
// If activate should be declared and included first.
diff --git a/include/flatbuffers/default_allocator.h b/include/flatbuffers/default_allocator.h
-index 8b173af..975d938 100644
+index d4724122..975d9380 100644
--- a/include/flatbuffers/default_allocator.h
+++ b/include/flatbuffers/default_allocator.h
-@@ -39,26 +39,20 @@ class DefaultAllocator : public Allocator {
+@@ -39,24 +39,18 @@ class DefaultAllocator : public Allocator {
// This is to avoid having a statically or dynamically allocated default
// allocator, or having to move it between the classes that may own it.
inline uint8_t *Allocate(Allocator *allocator, size_t size) {
@@ -52,15 +52,11 @@
}
} // namespace flatbuffers
-
--#endif // FLATBUFFERS_DEFAULT_ALLOCATOR_H_
-\ No newline at end of file
-+#endif // FLATBUFFERS_DEFAULT_ALLOCATOR_H_
diff --git a/include/flatbuffers/flexbuffers.h b/include/flatbuffers/flexbuffers.h
-index 89f3f30..6e6d0b3 100644
+index 8e8cac14..52dae316 100644
--- a/include/flatbuffers/flexbuffers.h
+++ b/include/flatbuffers/flexbuffers.h
-@@ -496,9 +496,24 @@ class Reference {
+@@ -495,9 +495,24 @@ class Reference {
return static_cast<double>(ReadUInt64(Indirect(), byte_width_));
case FBT_NULL: return 0.0;
case FBT_STRING: {
@@ -86,10 +82,10 @@
case FBT_VECTOR: return static_cast<double>(AsVector().size());
case FBT_BOOL:
diff --git a/include/flatbuffers/util.h b/include/flatbuffers/util.h
-index 93a39de..1cd4e8f 100644
+index 1ccf3517..34a75193 100644
--- a/include/flatbuffers/util.h
+++ b/include/flatbuffers/util.h
-@@ -24,6 +24,12 @@
+@@ -23,6 +23,12 @@
#include "flatbuffers/base.h"
#include "flatbuffers/stl_emulation.h"
@@ -102,4 +98,3 @@
#ifndef FLATBUFFERS_PREFER_PRINTF
# include <iomanip>
# include <sstream>
-
\ No newline at end of file
diff --git a/tensorflow/lite/micro/tools/make/flatbuffers_download.sh b/tensorflow/lite/micro/tools/make/flatbuffers_download.sh
index af5e80f..52acccc 100755
--- a/tensorflow/lite/micro/tools/make/flatbuffers_download.sh
+++ b/tensorflow/lite/micro/tools/make/flatbuffers_download.sh
@@ -54,9 +54,9 @@
if [ -d ${DOWNLOADED_FLATBUFFERS_PATH} ]; then
echo >&2 "${DOWNLOADED_FLATBUFFERS_PATH} already exists, skipping the download."
else
- ZIP_PREFIX="a66de58af9565586832c276fbb4251fc416bf07f"
+ ZIP_PREFIX="v23.5.26"
FLATBUFFERS_URL="https://github.com/google/flatbuffers/archive/${ZIP_PREFIX}.zip"
- FLATBUFFERS_MD5="51a7a96747e1c33eb4aac6d52513a02f"
+ FLATBUFFERS_MD5="e87e8acd8e2d53653387ad78720316e2"
TEMPDIR="$(mktemp -d)"
TEMPFILE="${TEMPDIR}/${ZIP_PREFIX}.zip"
@@ -64,7 +64,7 @@
check_md5 "${TEMPFILE}" ${FLATBUFFERS_MD5}
unzip -qo "$TEMPFILE" -d "${TEMPDIR}" >&2
- mv "${TEMPDIR}/flatbuffers-${ZIP_PREFIX}" ${DOWNLOADED_FLATBUFFERS_PATH}
+ mv "${TEMPDIR}/flatbuffers-${ZIP_PREFIX#v}" ${DOWNLOADED_FLATBUFFERS_PATH}
rm -rf "${TEMPDIR}"
pushd ${DOWNLOADED_FLATBUFFERS_PATH} > /dev/null
diff --git a/tensorflow/lite/micro/tools/make/targets/arc/arc_common.inc b/tensorflow/lite/micro/tools/make/targets/arc/arc_common.inc
index abc45c3..8809279 100644
--- a/tensorflow/lite/micro/tools/make/targets/arc/arc_common.inc
+++ b/tensorflow/lite/micro/tools/make/targets/arc/arc_common.inc
@@ -64,9 +64,6 @@
PLATFORM_LDFLAGS += $(notdir $(LCF_FILE))
endif
- CXXFLAGS := $(filter-out -std=c++11,$(CXXFLAGS))
- CCFLAGS := $(filter-out -std=c11,$(CCFLAGS))
-
ldflags_to_remove = -Wl,--fatal-warnings -Wl,--gc-sections
LDFLAGS := $(filter-out $(ldflags_to_remove),$(LDFLAGS))
diff --git a/tensorflow/lite/micro/tools/make/targets/bluepill_makefile.inc b/tensorflow/lite/micro/tools/make/targets/bluepill_makefile.inc
index 532689c..ae3b0b0 100644
--- a/tensorflow/lite/micro/tools/make/targets/bluepill_makefile.inc
+++ b/tensorflow/lite/micro/tools/make/targets/bluepill_makefile.inc
@@ -58,6 +58,7 @@
LDFLAGS += \
-T $(MAKEFILE_DIR)/targets/bluepill/bluepill.lds \
+ -Wl,--no-warn-rwx-segment \
-Wl,-Map=gen/$(TARGET).map,--cref
# Additional include paths needed for the stm_32_bare_lib only.
@@ -87,8 +88,7 @@
MICROLITE_TEST_SRCS := $(filter-out $(EXCLUDED_TESTS), $(MICROLITE_TEST_SRCS))
-EXCLUDED_EXAMPLE_TESTS := \
- $(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/Makefile.inc
+EXCLUDED_EXAMPLE_TESTS :=
MICRO_LITE_EXAMPLE_TESTS := $(filter-out $(EXCLUDED_EXAMPLE_TESTS), $(MICRO_LITE_EXAMPLE_TESTS))
diff --git a/tensorflow/lite/micro/tools/make/targets/ceva_makefile.inc b/tensorflow/lite/micro/tools/make/targets/ceva_makefile.inc
index 5cc8ad1..d0ddc98 100755
--- a/tensorflow/lite/micro/tools/make/targets/ceva_makefile.inc
+++ b/tensorflow/lite/micro/tools/make/targets/ceva_makefile.inc
@@ -33,8 +33,8 @@
-D_LIBCPP_EXTERN_TEMPLATE_INLINE_VISIBILITY=""
-CXXFLAGS := -std=c++11 -DTF_LITE_STATIC_MEMORY
-CCFLAGS := -std=c11 -DTF_LITE_STATIC_MEMORY
+CXXFLAGS := -std=c++17 -DTF_LITE_STATIC_MEMORY
+CCFLAGS := -std=c17 -DTF_LITE_STATIC_MEMORY
ifeq ($(TARGET_ARCH), CEVA_BX1)
PLATFORM_FLAGS += \
diff --git a/tensorflow/lite/micro/tools/make/targets/cortex_a_generic_makefile.inc b/tensorflow/lite/micro/tools/make/targets/cortex_a_generic_makefile.inc
new file mode 100644
index 0000000..ad272cc
--- /dev/null
+++ b/tensorflow/lite/micro/tools/make/targets/cortex_a_generic_makefile.inc
@@ -0,0 +1,66 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+# Generic Makefile target for ARM Cortex A builds.
+
+FPU := neon
+FLOAT_ABI := softfp
+
+GCC_TARGET_ARCH :=
+GCC_TARGET_CPU :=
+
+ifeq ($(TARGET_ARCH), armv8.2-a)
+ GCC_TARGET_ARCH := armv8.2-a
+
+else ifeq ($(TARGET_ARCH), armv7-a)
+ GCC_TARGET_ARCH := armv7-a
+
+else ifeq ($(TARGET_ARCH), cortex-a32)
+ GCC_TARGET_CPU := cortex-a32
+
+else
+ $(error "TARGET_ARCH=$(TARGET_ARCH) is not supported")
+endif
+
+TARGET_TOOLCHAIN_PREFIX := arm-none-eabi-
+
+ifneq ($(GCC_TARGET_ARCH),)
+ FLAGS_GCC += -march=$(GCC_TARGET_ARCH)
+else ifneq ($(GCC_TARGET_CPU),)
+ FLAGS_GCC += -mcpu=$(GCC_TARGET_CPU)
+endif
+
+CXXFLAGS += $(FLAGS_GCC)
+CCFLAGS += $(FLAGS_GCC)
+
+PLATFORM_FLAGS = \
+ -DTF_LITE_MCU_DEBUG_LOG \
+ -mfloat-abi=$(FLOAT_ABI) \
+ -mfpu=$(FPU) \
+ -funsigned-char \
+ -mlittle-endian \
+ -Wno-type-limits \
+ -Wno-unused-private-field \
+ -fomit-frame-pointer \
+ -MD
+
+ifneq ($(PIC),)
+ PLATFORM_FLAGS += -fpic
+endif
+
+# Common + C/C++ flags
+CXXFLAGS += $(PLATFORM_FLAGS)
+CCFLAGS += $(PLATFORM_FLAGS)
+
diff --git a/tensorflow/lite/micro/tools/make/targets/cortex_m_corstone_300_makefile.inc b/tensorflow/lite/micro/tools/make/targets/cortex_m_corstone_300_makefile.inc
index 0ffe5a3..0c483ac 100644
--- a/tensorflow/lite/micro/tools/make/targets/cortex_m_corstone_300_makefile.inc
+++ b/tensorflow/lite/micro/tools/make/targets/cortex_m_corstone_300_makefile.inc
@@ -1,4 +1,4 @@
-# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -16,23 +16,29 @@
# ARM Cortex M makefile targeted for a FVP based on Arm Corstone-300 software.
# For more info see: tensorflow/lite/micro/cortex_m_corstone_300/README.md
-export PATH := $(MAKEFILE_DIR)/downloads/corstone300/models/Linux64_GCC-6.4:$(PATH)
-DOWNLOAD_RESULT := $(shell $(MAKEFILE_DIR)/corstone_300_download.sh ${MAKEFILE_DIR}/downloads)
+UNAME_M := $(shell uname -m)
+ifeq ($(UNAME_M), aarch64)
+ export PATH := $(DOWNLOADS_DIR)/corstone300/models/Linux64_armv8l_GCC-9.3:$(PATH)
+else
+ export PATH := $(DOWNLOADS_DIR)/corstone300/models/Linux64_GCC-9.3:$(PATH)
+endif
+
+DOWNLOAD_RESULT := $(shell $(MAKEFILE_DIR)/corstone_300_download.sh $(DOWNLOADS_DIR) $(TENSORFLOW_ROOT))
ifneq ($(DOWNLOAD_RESULT), SUCCESS)
$(error Something went wrong with the Arm Corstone-300 software download: $(DOWNLOAD_RESULT))
endif
-ETHOS_U_CORE_PLATFORM := ${PWD}/$(MAKEFILE_DIR)/downloads/ethos_u_core_platform/targets/corstone-300
-DOWNLOAD_RESULT := $(shell $(MAKEFILE_DIR)/ethos_u_core_platform_download.sh ${MAKEFILE_DIR}/downloads)
+ETHOS_U_CORE_PLATFORM := $(DOWNLOADS_DIR)/ethos_u_core_platform/targets/corstone-300
+DOWNLOAD_RESULT := $(shell $(MAKEFILE_DIR)/ethos_u_core_platform_download.sh $(DOWNLOADS_DIR) $(TENSORFLOW_ROOT))
ifneq ($(DOWNLOAD_RESULT), SUCCESS)
$(error Something went wrong with the Ethos-U Core Platform software download: $(DOWNLOAD_RESULT))
endif
# This target has dependencies to CMSIS-Device so just in case running without OPTIMIZED_KERNEL_DIR=cmsis_nn.
-CMSIS_DEFAULT_DOWNLOAD_PATH := $(MAKEFILE_DIR)/downloads/cmsis
+CMSIS_DEFAULT_DOWNLOAD_PATH := $(DOWNLOADS_DIR)/cmsis
CMSIS_PATH := $(CMSIS_DEFAULT_DOWNLOAD_PATH)
ifeq ($(CMSIS_PATH), $(CMSIS_DEFAULT_DOWNLOAD_PATH))
- DOWNLOAD_RESULT := $(shell $(MAKEFILE_DIR)/ext_libs/cmsis_download.sh ${MAKEFILE_DIR}/downloads)
+ DOWNLOAD_RESULT := $(shell $(MAKEFILE_DIR)/ext_libs/cmsis_download.sh $(DOWNLOADS_DIR) $(TENSORFLOW_ROOT))
ifneq ($(DOWNLOAD_RESULT), SUCCESS)
$(error Something went wrong with the CMSIS download: $(DOWNLOAD_RESULT))
endif
@@ -97,14 +103,13 @@
FLAGS_ARMC = \
--target=arm-arm-none-eabi \
-Wno-unused-private-field \
- -mcpu=$(MCPU_OPTION) \
- -ffp-mode=full
+ -mcpu=$(MCPU_OPTION)
# Pass comma separated linker options to armlink
ARMC6_LDFLAGS += -Wl,--strict,--summary_stderr,--info,summarysizes,--map
ARMC6_LDFLAGS += -Wl,--load_addr_map_info,--xref,--callgraph,--symbols
ARMC6_LDFLAGS += -Wl,--info,sizes,--info,totals,--info,unused,--info,veneers
- ARMC6_LDFLAGS += -Wl,--list=${TENSORFLOW_ROOT}gen/$(TARGET).map
+ ARMC6_LDFLAGS += -Wl,--list=gen/$(TARGET).map
ARMC6_LDFLAGS += -Wl,--entry=Reset_Handler --verbose
ARMC6_LDFLAGS += -Wl,--scatter=$(ETHOS_U_CORE_PLATFORM)/platform.scatter
@@ -130,11 +135,16 @@
# https://developer.arm.com/documentation/100891/0611/troubleshooting/general-troubleshooting-advice
MICROLITE_LIBS := $(filter-out -lm,$(MICROLITE_LIBS))
+ # This does not build with armclang and is anyway not used by this target.
+ EXCLUDED_TESTS := \
+ tensorflow/lite/micro/tools/benchmarking/Makefile.inc
+ MICRO_LITE_BENCHMARKS := $(filter-out $(EXCLUDED_TESTS), $(MICRO_LITE_BENCHMARKS))
+
else ifeq ($(TOOLCHAIN), gcc)
- TARGET_DEFAULT_TOOLCHAIN_ROOT := $(MAKEFILE_DIR)/downloads/gcc_embedded/bin/
+ TARGET_DEFAULT_TOOLCHAIN_ROOT := $(DOWNLOADS_DIR)/gcc_embedded/bin/
TARGET_TOOLCHAIN_ROOT := $(TARGET_DEFAULT_TOOLCHAIN_ROOT)
ifeq ($(TARGET_TOOLCHAIN_ROOT), $(TARGET_DEFAULT_TOOLCHAIN_ROOT))
- DOWNLOAD_RESULT := $(shell $(MAKEFILE_DIR)/arm_gcc_download.sh ${MAKEFILE_DIR}/downloads)
+ DOWNLOAD_RESULT := $(shell $(MAKEFILE_DIR)/arm_gcc_download.sh $(DOWNLOADS_DIR) $(TENSORFLOW_ROOT))
ifneq ($(DOWNLOAD_RESULT), SUCCESS)
$(error Something went wrong with the GCC download: $(DOWNLOAD_RESULT))
endif
@@ -148,7 +158,7 @@
LDFLAGS += \
--specs=nosys.specs \
-T $(ETHOS_U_CORE_PLATFORM)/platform_parsed.ld \
- -Wl,-Map=${TENSORFLOW_ROOT}gen/$(TARGET).map,--cref \
+ -Wl,-Map=gen/$(TARGET).map,--cref \
-Wl,--gc-sections \
--entry Reset_Handler
@@ -186,7 +196,7 @@
ETHOSU_ARCH=u55
endif
-CMSIS_DEFAULT_DOWNLOAD_PATH := $(MAKEFILE_DIR)/downloads/cmsis
+CMSIS_DEFAULT_DOWNLOAD_PATH := $(DOWNLOADS_DIR)/cmsis
CMSIS_PATH := $(CMSIS_DEFAULT_DOWNLOAD_PATH)
THIRD_PARTY_CC_SRCS += \
$(CMSIS_PATH)/Device/ARM/$(ARM_CPU)/Source/system_$(ARM_CPU).c \
@@ -197,8 +207,13 @@
# TODO(#274): Examine why some tests fail here.
EXCLUDED_TESTS := \
- tensorflow/lite/micro/memory_arena_threshold_test.cc \
- tensorflow/lite/micro/recording_micro_allocator_test.cc
+ $(TENSORFLOW_ROOT)tensorflow/lite/micro/memory_arena_threshold_test.cc \
+ $(TENSORFLOW_ROOT)tensorflow/lite/micro/recording_micro_allocator_test.cc
+ifeq ($(CO_PROCESSOR), ethos_u)
+# This does not work with Ethos-U enabled since then NPU PMU counters are used instead for the sake of the benchmark example.
+EXCLUDED_TESTS += \
+ $(TENSORFLOW_ROOT)tensorflow/lite/micro/micro_time_test.cc
+endif
MICROLITE_TEST_SRCS := $(filter-out $(EXCLUDED_TESTS), $(MICROLITE_TEST_SRCS))
-TEST_SCRIPT := tensorflow/lite/micro/testing/test_with_arm_corstone_300.sh
+TEST_SCRIPT := $(TENSORFLOW_ROOT)tensorflow/lite/micro/testing/test_with_arm_corstone_300.sh
diff --git a/tensorflow/lite/micro/tools/make/targets/cortex_m_generic_makefile.inc b/tensorflow/lite/micro/tools/make/targets/cortex_m_generic_makefile.inc
index 0e034ad..5aa5d25 100644
--- a/tensorflow/lite/micro/tools/make/targets/cortex_m_generic_makefile.inc
+++ b/tensorflow/lite/micro/tools/make/targets/cortex_m_generic_makefile.inc
@@ -1,4 +1,4 @@
-# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -16,6 +16,16 @@
# Generic Makefile target for ARM Cortex M builds.
# For more info see: tensorflow/lite/micro/cortex_m_generic/README.md
+# Needed in case running without OPTIMIZED_KERNEL_DIR=cmsis_nn.
+CMSIS_DEFAULT_DOWNLOAD_PATH := $(DOWNLOADS_DIR)/cmsis
+CMSIS_PATH := $(CMSIS_DEFAULT_DOWNLOAD_PATH)
+ifeq ($(CMSIS_PATH), $(CMSIS_DEFAULT_DOWNLOAD_PATH))
+ DOWNLOAD_RESULT := $(shell $(MAKEFILE_DIR)/ext_libs/cmsis_download.sh $(DOWNLOADS_DIR) $(TENSORFLOW_ROOT))
+ ifneq ($(DOWNLOAD_RESULT), SUCCESS)
+ $(error Something went wrong with the CMSIS download: $(DOWNLOAD_RESULT))
+ endif
+endif
+
FLOAT := soft
GCC_TARGET_ARCH := $(TARGET_ARCH)
@@ -92,8 +102,6 @@
CORE=M85
ARM_LDFLAGS := -Wl,--cpu=8.1-M.Main.mve.fp
FLOAT=hard
- # GCC does not yet support cortex-m85 option hence go with cortex-m55 for now.
- GCC_TARGET_ARCH := cortex-m55
else ifeq ($(TARGET_ARCH), project_generation)
# No flags needed here as project_generation does not build anything.
@@ -204,5 +212,3 @@
$(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/Makefile.inc
MICRO_LITE_EXAMPLE_TESTS := $(filter-out $(EXCLUDED_EXAMPLE_TESTS), $(MICRO_LITE_EXAMPLE_TESTS))
endif
-
-include $(MAKEFILE_DIR)/ext_libs/eyalroz_printf.inc
diff --git a/tensorflow/lite/micro/tools/make/targets/hexagon_makefile.inc b/tensorflow/lite/micro/tools/make/targets/hexagon_makefile.inc
index 2e7d18c..6fe7be7 100644
--- a/tensorflow/lite/micro/tools/make/targets/hexagon_makefile.inc
+++ b/tensorflow/lite/micro/tools/make/targets/hexagon_makefile.inc
@@ -80,6 +80,10 @@
-mcpu=$(HEXAGON_CPU_VER) \
-m$(HEXAGON_CPU_VER)
+ifeq ($(HEXAGON_PIC_BUILD), true)
+ PLATFORM_ARGS += -fPIC
+endif
+
# See http://b/183462077 for more details on why we need -G0 for an LPI build.
ifeq ($(HEXAGON_LPI_BUILD), true)
PLATFORM_ARGS += -G0
@@ -112,5 +116,10 @@
MICROLITE_TEST_SRCS := $(filter-out $(EXCLUDED_TESTS), $(MICROLITE_TEST_SRCS))
+# TODO(b/302404477): micro_speech_test example does not pass due to misprediction
+EXCLUDED_EXAMPLE_TESTS := \
+ $(TENSORFLOW_ROOT)tensorflow/lite/micro/examples/micro_speech/Makefile.inc
+MICRO_LITE_EXAMPLE_TESTS := $(filter-out $(EXCLUDED_EXAMPLE_TESTS), $(MICRO_LITE_EXAMPLE_TESTS))
+
TEST_SCRIPT := $(TENSORFLOW_ROOT)tensorflow/lite/micro/testing/test_hexagon_binary.sh
SIZE_SCRIPT := $(TENSORFLOW_ROOT)tensorflow/lite/micro/testing/size_hexagon_binary.sh
diff --git a/tensorflow/lite/micro/tools/make/targets/mips_makefile.inc b/tensorflow/lite/micro/tools/make/targets/mips_makefile.inc
new file mode 100644
index 0000000..cf0d38e
--- /dev/null
+++ b/tensorflow/lite/micro/tools/make/targets/mips_makefile.inc
@@ -0,0 +1,41 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+TARGET_ARCH := mips
+TARGET_TOOLCHAIN_PREFIX := mips-elf-
+
+
+# Allow additional flags on the command line for debugging.
+MIPS_EXTRA_CFLAGS :=
+
+export PATH := $(TARGET_TOOLCHAIN_ROOT):$(PATH)
+
+PLATFORM_FLAGS = \
+ -fno-builtin-printf \
+ -DTF_LITE_MCU_DEBUG_LOG \
+ -DTF_LITE_USE_GLOBAL_CMATH_FUNCTIONS \
+ -fno-delete-null-pointer-checks \
+ -fomit-frame-pointer
+
+CXXFLAGS += $(PLATFORM_FLAGS) \
+ -fpermissive \
+ -fno-use-cxa-atexit \
+ -DTF_LITE_USE_GLOBAL_MIN \
+ -DTF_LITE_USE_GLOBAL_MAX
+
+CCFLAGS += $(PLATFORM_FLAGS)
+
+CCFLAGS += $(MIPS_EXTRA_CFLAGS)
+CXXFLAGS += $(MIPS_EXTRA_CFLAGS)
diff --git a/tensorflow/lite/micro/tools/make/targets/riscv32_generic_makefile.inc b/tensorflow/lite/micro/tools/make/targets/riscv32_generic_makefile.inc
index ce5f0eb..453e9d0 100644
--- a/tensorflow/lite/micro/tools/make/targets/riscv32_generic_makefile.inc
+++ b/tensorflow/lite/micro/tools/make/targets/riscv32_generic_makefile.inc
@@ -2,6 +2,13 @@
TARGET_ARCH := riscv32
TARGET_TOOLCHAIN_PREFIX := riscv64-unknown-elf-
+RISCV_ARCH := rv32imc
+RISCV_ABI := ilp32
+RISCV_CODE_MODEL := medany
+
+# Allow additional flags on the command line for debugging.
+RISCV_EXTRA_CFLAGS :=
+
TARGET_DEFAULT_TOOLCHAIN_ROOT := $(DOWNLOADS_DIR)/riscv_toolchain/bin/
TARGET_TOOLCHAIN_ROOT := $(TARGET_DEFAULT_TOOLCHAIN_ROOT)
ifeq ($(TARGET_TOOLCHAIN_ROOT), $(TARGET_DEFAULT_TOOLCHAIN_ROOT))
@@ -11,9 +18,9 @@
export PATH := $(TARGET_TOOLCHAIN_ROOT):$(PATH)
PLATFORM_FLAGS = \
- -march=rv32imac \
- -mabi=ilp32 \
- -mcmodel=medany \
+ -march=$(RISCV_ARCH) \
+ -mabi=$(RISCV_ABI) \
+ -mcmodel=$(RISCV_CODE_MODEL) \
-mexplicit-relocs \
-fno-builtin-printf \
-DTF_LITE_MCU_DEBUG_LOG \
@@ -40,9 +47,13 @@
MICROLITE_TEST_SRCS := $(filter-out $(EXCLUDED_TESTS), $(MICROLITE_TEST_SRCS))
+CCFLAGS += $(RISCV_EXTRA_CFLAGS)
+CXXFLAGS += $(RISCV_EXTRA_CFLAGS)
+
# This disables the "linker relaxation" optimization, which produced incorrect code.
# TODO(b/279805615): Check whether this is fixed in newer versions of the toolchain.
LDFLAGS += -mno-relax
TEST_SCRIPT := $(TENSORFLOW_ROOT)tensorflow/lite/micro/testing/test_with_qemu.sh riscv32 rv32
SIZE_SCRIPT := ${TENSORFLOW_ROOT}tensorflow/lite/micro/testing/size_riscv32_binary.sh
+include $(MAKEFILE_DIR)/ext_libs/eyalroz_printf.inc
diff --git a/tensorflow/lite/micro/tools/make/targets/xtensa_makefile.inc b/tensorflow/lite/micro/tools/make/targets/xtensa_makefile.inc
index 8d970c7..92527ad 100644
--- a/tensorflow/lite/micro/tools/make/targets/xtensa_makefile.inc
+++ b/tensorflow/lite/micro/tools/make/targets/xtensa_makefile.inc
@@ -35,12 +35,11 @@
TARGET_ARCH_DEFINES := -D$(shell echo $(TARGET_ARCH) | tr [a-z] [A-Z])
PLATFORM_FLAGS = \
+ -stdlib=libc++ \
-DTF_LITE_MCU_DEBUG_LOG \
-DTF_LITE_USE_CTIME \
--xtensa-core=$(XTENSA_CORE) \
-mcoproc \
- -DMAX_RFFT_PWR=9 \
- -DMIN_RFFT_PWR=MAX_RFFT_PWR \
$(TARGET_ARCH_DEFINES) \
-mlongcalls
diff --git a/tensorflow/lite/micro/tools/make/third_party_downloads.inc b/tensorflow/lite/micro/tools/make/third_party_downloads.inc
index a8e63e1..902206d 100644
--- a/tensorflow/lite/micro/tools/make/third_party_downloads.inc
+++ b/tensorflow/lite/micro/tools/make/third_party_downloads.inc
@@ -39,5 +39,4 @@
EMBARC_MLI_PRE_COMPILED_URL := "https://github.com/foss-for-synopsys-dwc-arc-processors/embarc_mli/releases/download/Release_1.1/embARC_MLI_package.zip"
EMBARC_MLI_PRE_COMPILED_MD5 := "173990c2dde4efef6a2c95b92d1f0244"
-ETHOSU_URL := "https://git.mlplatform.org/ml/ethos-u/ethos-u-core-driver.git/snapshot/ethos-u-core-driver-24455eedb9e8939f8a28ca0101a6f2d171e1b2f9.tar.gz"
-ETHOSU_MD5 := "14b5712525d4af612d35217f0bc53fcc"
+# Skip md5sum-check since ethos-u-core-driver download link is non-deterministic, see https://github.com/google/gitiles/issues/84
diff --git a/tensorflow/lite/micro/tools/model_transforms_utils.py b/tensorflow/lite/micro/tools/model_transforms_utils.py
index c713f01..4a59d88 100644
--- a/tensorflow/lite/micro/tools/model_transforms_utils.py
+++ b/tensorflow/lite/micro/tools/model_transforms_utils.py
@@ -264,7 +264,7 @@
schema_fb.TensorType.INT32: np.int32,
schema_fb.TensorType.UINT8: np.uint8,
schema_fb.TensorType.INT64: np.int64,
- schema_fb.TensorType.STRING: np.string_,
+ schema_fb.TensorType.STRING: np.bytes_,
schema_fb.TensorType.BOOL: np.bool_,
schema_fb.TensorType.INT16: np.int16,
schema_fb.TensorType.COMPLEX64: np.complex64,
diff --git a/tensorflow/lite/micro/tools/project_generation/Makefile b/tensorflow/lite/micro/tools/project_generation/Makefile
index 7497f07..f73fbdb 100644
--- a/tensorflow/lite/micro/tools/project_generation/Makefile
+++ b/tensorflow/lite/micro/tools/project_generation/Makefile
@@ -72,14 +72,14 @@
endif
CXXFLAGS := \
- -std=c++11 \
+ -std=c++17 \
-fno-rtti \
-fno-exceptions \
-fno-threadsafe-statics \
$(COMMON_FLAGS)
CCFLAGS := \
- -std=c11 \
+ -std=c17 \
$(COMMON_FLAGS)
ARFLAGS := -r
@@ -90,6 +90,7 @@
LIB := $(GENDIR)/libtflm.a
TFLM_CC_SRCS := $(shell find $(TENSORFLOW_ROOT)tensorflow -name "*.cc" -o -name "*.c")
+TFLM_CC_SRCS += $(shell find $(TENSORFLOW_ROOT)signal -name "*.cc" -o -name "*.c")
OBJS := $(addprefix $(OBJDIR)/, $(patsubst %.c,%.o,$(patsubst %.cc,%.o,$(TFLM_CC_SRCS))))
# if the third party printf library is present, add the include paths
diff --git a/tensorflow/lite/micro/tools/requantize_flatbuffer_test.py b/tensorflow/lite/micro/tools/requantize_flatbuffer_test.py
index 4d80991..885af89 100644
--- a/tensorflow/lite/micro/tools/requantize_flatbuffer_test.py
+++ b/tensorflow/lite/micro/tools/requantize_flatbuffer_test.py
@@ -24,7 +24,7 @@
from tflite_micro.tensorflow.lite.tools import flatbuffer_utils
-#TODO(b/248061370): replace the keras model creation process with flatbuffer manipulation to speed up test
+# TODO(b/248061370): replace the keras model creation process with flatbuffer manipulation to speed up test
def create_simple_fc_model():
'''Create a simple model with two fully connected(fc) layers'''
model = tf.keras.models.Sequential([
@@ -60,6 +60,9 @@
EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8
]
converter.representative_dataset = representative_dataset_gen
+ # TODO(b/324385802): Support per-channel quantization for FullyConnected.
+ converter._experimental_disable_per_channel_quantization_for_dense_layers = True
+ converter._experimental_disable_per_channel = True
return converter.convert()
diff --git a/tensorflow/lite/micro/tools/requantize_flatbuffer_utils.py b/tensorflow/lite/micro/tools/requantize_flatbuffer_utils.py
index 5709ff2..c4a3b10 100644
--- a/tensorflow/lite/micro/tools/requantize_flatbuffer_utils.py
+++ b/tensorflow/lite/micro/tools/requantize_flatbuffer_utils.py
@@ -24,7 +24,7 @@
TensorType.INT32: np.int32,
TensorType.UINT8: np.uint8,
TensorType.INT64: np.int64,
- TensorType.STRING: np.string_,
+ TensorType.STRING: np.bytes_,
TensorType.BOOL: np.bool_,
TensorType.INT16: np.int16,
TensorType.COMPLEX64: np.complex64,
diff --git a/tensorflow/lite/micro/tools/tflm_model_transforms_lib.py b/tensorflow/lite/micro/tools/tflm_model_transforms_lib.py
index 60530ff..2bf90cf 100644
--- a/tensorflow/lite/micro/tools/tflm_model_transforms_lib.py
+++ b/tensorflow/lite/micro/tools/tflm_model_transforms_lib.py
@@ -29,7 +29,7 @@
from tflite_micro.tensorflow.lite.tools import flatbuffer_utils
from tflite_micro.tensorflow.lite.micro.tools import model_transforms_utils
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import runtime
+from tflite_micro.python.tflite_micro import runtime
def _save_and_align_flatbuffer(model, model_path):
diff --git a/tensorflow/lite/portable_type_to_tflitetype.h b/tensorflow/lite/portable_type_to_tflitetype.h
index b600585..03357db 100644
--- a/tensorflow/lite/portable_type_to_tflitetype.h
+++ b/tensorflow/lite/portable_type_to_tflitetype.h
@@ -52,6 +52,10 @@
return TFLITE_TYPE_ENUM; \
} \
template <> \
+ constexpr TfLiteType typeToTfLiteType<const CPP_TYPE>() { \
+ return TFLITE_TYPE_ENUM; \
+ } \
+ template <> \
struct TfLiteTypeToType<TFLITE_TYPE_ENUM> { \
using Type = CPP_TYPE; \
}
diff --git a/tensorflow/lite/python/BUILD b/tensorflow/lite/python/BUILD
index 3dc7232..7a7ce3c 100644
--- a/tensorflow/lite/python/BUILD
+++ b/tensorflow/lite/python/BUILD
@@ -10,7 +10,7 @@
flatbuffer_py_library(
name = "schema_py",
- srcs = ["//tensorflow/lite/schema:schema.fbs"],
+ srcs = ["//tensorflow/compiler/mlir/lite/schema:schema.fbs"],
)
py_library(
@@ -20,6 +20,6 @@
visibility = ["//:__subpackages__"],
deps = [
requirement("flatbuffers"),
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
],
)
diff --git a/tensorflow/lite/python/schema_py_generated.py b/tensorflow/lite/python/schema_py_generated.py
index 914340e..52999cb 100755
--- a/tensorflow/lite/python/schema_py_generated.py
+++ b/tensorflow/lite/python/schema_py_generated.py
@@ -7,1386 +7,64 @@
from flatbuffers.compat import import_numpy
np = import_numpy()
-class ATan2Options(object):
- __slots__ = ['_tab']
+class TensorType(object):
+ FLOAT32 = 0
+ FLOAT16 = 1
+ INT32 = 2
+ UINT8 = 3
+ INT64 = 4
+ STRING = 5
+ BOOL = 6
+ INT16 = 7
+ COMPLEX64 = 8
+ INT8 = 9
+ FLOAT64 = 10
+ COMPLEX128 = 11
+ UINT64 = 12
+ RESOURCE = 13
+ VARIANT = 14
+ UINT32 = 15
+ UINT16 = 16
+ INT4 = 17
+ BFLOAT16 = 18
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = ATan2Options()
- x.Init(buf, n + offset)
- return x
- @classmethod
- def GetRootAsATan2Options(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def ATan2OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # ATan2Options
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def ATan2OptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return ATan2OptionsStart(builder)
-def ATan2OptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return ATan2OptionsEnd(builder)
-
-class ATan2OptionsT(object):
-
- # ATan2OptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- atan2options = ATan2Options()
- atan2options.Init(buf, pos)
- return cls.InitFromObj(atan2options)
-
- @classmethod
- def InitFromObj(cls, atan2options):
- x = ATan2OptionsT()
- x._UnPack(atan2options)
- return x
-
- # ATan2OptionsT
- def _UnPack(self, atan2options):
- if atan2options is None:
- return
-
- # ATan2OptionsT
- def Pack(self, builder):
- ATan2OptionsStart(builder)
- atan2options = ATan2OptionsEnd(builder)
- return atan2options
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class AbsOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = AbsOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsAbsOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def AbsOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # AbsOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def AbsOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return AbsOptionsStart(builder)
-def AbsOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return AbsOptionsEnd(builder)
-
-class AbsOptionsT(object):
-
- # AbsOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- absOptions = AbsOptions()
- absOptions.Init(buf, pos)
- return cls.InitFromObj(absOptions)
-
- @classmethod
- def InitFromObj(cls, absOptions):
- x = AbsOptionsT()
- x._UnPack(absOptions)
- return x
-
- # AbsOptionsT
- def _UnPack(self, absOptions):
- if absOptions is None:
- return
-
- # AbsOptionsT
- def Pack(self, builder):
- AbsOptionsStart(builder)
- absOptions = AbsOptionsEnd(builder)
- return absOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-class ActivationFunctionType(object):
+class QuantizationDetails(object):
NONE = 0
- RELU = 1
- RELU_N1_TO_1 = 2
- RELU6 = 3
- TANH = 4
- SIGN_BIT = 5
-# automatically generated by the FlatBuffers compiler, do not modify
+ CustomQuantization = 1
-# namespace: tflite
+def QuantizationDetailsCreator(unionType, table):
+ from flatbuffers.table import Table
+ if not isinstance(table, Table):
+ return None
+ if unionType == QuantizationDetails().CustomQuantization:
+ return CustomQuantizationT.InitFromBuf(table.Bytes, table.Pos)
+ return None
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-class AddNOptions(object):
- __slots__ = ['_tab']
+class DimensionType(object):
+ DENSE = 0
+ SPARSE_CSR = 1
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = AddNOptions()
- x.Init(buf, n + offset)
- return x
- @classmethod
- def GetRootAsAddNOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def AddNOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+class SparseIndexVector(object):
+ NONE = 0
+ Int32Vector = 1
+ Uint16Vector = 2
+ Uint8Vector = 3
- # AddNOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def AddNOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return AddNOptionsStart(builder)
-def AddNOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return AddNOptionsEnd(builder)
-
-class AddNOptionsT(object):
-
- # AddNOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- addNoptions = AddNOptions()
- addNoptions.Init(buf, pos)
- return cls.InitFromObj(addNoptions)
-
- @classmethod
- def InitFromObj(cls, addNoptions):
- x = AddNOptionsT()
- x._UnPack(addNoptions)
- return x
-
- # AddNOptionsT
- def _UnPack(self, addNoptions):
- if addNoptions is None:
- return
-
- # AddNOptionsT
- def Pack(self, builder):
- AddNOptionsStart(builder)
- addNoptions = AddNOptionsEnd(builder)
- return addNoptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class AddOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = AddOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsAddOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def AddOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # AddOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # AddOptions
- def FusedActivationFunction(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # AddOptions
- def PotScaleInt16(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return True
-
-def AddOptionsStart(builder): builder.StartObject(2)
-def Start(builder):
- return AddOptionsStart(builder)
-def AddOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return AddOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def AddOptionsAddPotScaleInt16(builder, potScaleInt16): builder.PrependBoolSlot(1, potScaleInt16, 1)
-def AddPotScaleInt16(builder, potScaleInt16):
- return AddOptionsAddPotScaleInt16(builder, potScaleInt16)
-def AddOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return AddOptionsEnd(builder)
-
-class AddOptionsT(object):
-
- # AddOptionsT
- def __init__(self):
- self.fusedActivationFunction = 0 # type: int
- self.potScaleInt16 = True # type: bool
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- addOptions = AddOptions()
- addOptions.Init(buf, pos)
- return cls.InitFromObj(addOptions)
-
- @classmethod
- def InitFromObj(cls, addOptions):
- x = AddOptionsT()
- x._UnPack(addOptions)
- return x
-
- # AddOptionsT
- def _UnPack(self, addOptions):
- if addOptions is None:
- return
- self.fusedActivationFunction = addOptions.FusedActivationFunction()
- self.potScaleInt16 = addOptions.PotScaleInt16()
-
- # AddOptionsT
- def Pack(self, builder):
- AddOptionsStart(builder)
- AddOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
- AddOptionsAddPotScaleInt16(builder, self.potScaleInt16)
- addOptions = AddOptionsEnd(builder)
- return addOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class ArgMaxOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = ArgMaxOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsArgMaxOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def ArgMaxOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # ArgMaxOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # ArgMaxOptions
- def OutputType(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
-def ArgMaxOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return ArgMaxOptionsStart(builder)
-def ArgMaxOptionsAddOutputType(builder, outputType): builder.PrependInt8Slot(0, outputType, 0)
-def AddOutputType(builder, outputType):
- return ArgMaxOptionsAddOutputType(builder, outputType)
-def ArgMaxOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return ArgMaxOptionsEnd(builder)
-
-class ArgMaxOptionsT(object):
-
- # ArgMaxOptionsT
- def __init__(self):
- self.outputType = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- argMaxOptions = ArgMaxOptions()
- argMaxOptions.Init(buf, pos)
- return cls.InitFromObj(argMaxOptions)
-
- @classmethod
- def InitFromObj(cls, argMaxOptions):
- x = ArgMaxOptionsT()
- x._UnPack(argMaxOptions)
- return x
-
- # ArgMaxOptionsT
- def _UnPack(self, argMaxOptions):
- if argMaxOptions is None:
- return
- self.outputType = argMaxOptions.OutputType()
-
- # ArgMaxOptionsT
- def Pack(self, builder):
- ArgMaxOptionsStart(builder)
- ArgMaxOptionsAddOutputType(builder, self.outputType)
- argMaxOptions = ArgMaxOptionsEnd(builder)
- return argMaxOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class ArgMinOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = ArgMinOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsArgMinOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def ArgMinOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # ArgMinOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # ArgMinOptions
- def OutputType(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
-def ArgMinOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return ArgMinOptionsStart(builder)
-def ArgMinOptionsAddOutputType(builder, outputType): builder.PrependInt8Slot(0, outputType, 0)
-def AddOutputType(builder, outputType):
- return ArgMinOptionsAddOutputType(builder, outputType)
-def ArgMinOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return ArgMinOptionsEnd(builder)
-
-class ArgMinOptionsT(object):
-
- # ArgMinOptionsT
- def __init__(self):
- self.outputType = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- argMinOptions = ArgMinOptions()
- argMinOptions.Init(buf, pos)
- return cls.InitFromObj(argMinOptions)
-
- @classmethod
- def InitFromObj(cls, argMinOptions):
- x = ArgMinOptionsT()
- x._UnPack(argMinOptions)
- return x
-
- # ArgMinOptionsT
- def _UnPack(self, argMinOptions):
- if argMinOptions is None:
- return
- self.outputType = argMinOptions.OutputType()
-
- # ArgMinOptionsT
- def Pack(self, builder):
- ArgMinOptionsStart(builder)
- ArgMinOptionsAddOutputType(builder, self.outputType)
- argMinOptions = ArgMinOptionsEnd(builder)
- return argMinOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class AssignVariableOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = AssignVariableOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsAssignVariableOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def AssignVariableOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # AssignVariableOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def AssignVariableOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return AssignVariableOptionsStart(builder)
-def AssignVariableOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return AssignVariableOptionsEnd(builder)
-
-class AssignVariableOptionsT(object):
-
- # AssignVariableOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- assignVariableOptions = AssignVariableOptions()
- assignVariableOptions.Init(buf, pos)
- return cls.InitFromObj(assignVariableOptions)
-
- @classmethod
- def InitFromObj(cls, assignVariableOptions):
- x = AssignVariableOptionsT()
- x._UnPack(assignVariableOptions)
- return x
-
- # AssignVariableOptionsT
- def _UnPack(self, assignVariableOptions):
- if assignVariableOptions is None:
- return
-
- # AssignVariableOptionsT
- def Pack(self, builder):
- AssignVariableOptionsStart(builder)
- assignVariableOptions = AssignVariableOptionsEnd(builder)
- return assignVariableOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class BatchMatMulOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = BatchMatMulOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsBatchMatMulOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def BatchMatMulOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # BatchMatMulOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # BatchMatMulOptions
- def AdjX(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
- # BatchMatMulOptions
- def AdjY(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
- # BatchMatMulOptions
- def AsymmetricQuantizeInputs(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
-def BatchMatMulOptionsStart(builder): builder.StartObject(3)
-def Start(builder):
- return BatchMatMulOptionsStart(builder)
-def BatchMatMulOptionsAddAdjX(builder, adjX): builder.PrependBoolSlot(0, adjX, 0)
-def AddAdjX(builder, adjX):
- return BatchMatMulOptionsAddAdjX(builder, adjX)
-def BatchMatMulOptionsAddAdjY(builder, adjY): builder.PrependBoolSlot(1, adjY, 0)
-def AddAdjY(builder, adjY):
- return BatchMatMulOptionsAddAdjY(builder, adjY)
-def BatchMatMulOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs): builder.PrependBoolSlot(2, asymmetricQuantizeInputs, 0)
-def AddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
- return BatchMatMulOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs)
-def BatchMatMulOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return BatchMatMulOptionsEnd(builder)
-
-class BatchMatMulOptionsT(object):
-
- # BatchMatMulOptionsT
- def __init__(self):
- self.adjX = False # type: bool
- self.adjY = False # type: bool
- self.asymmetricQuantizeInputs = False # type: bool
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- batchMatMulOptions = BatchMatMulOptions()
- batchMatMulOptions.Init(buf, pos)
- return cls.InitFromObj(batchMatMulOptions)
-
- @classmethod
- def InitFromObj(cls, batchMatMulOptions):
- x = BatchMatMulOptionsT()
- x._UnPack(batchMatMulOptions)
- return x
-
- # BatchMatMulOptionsT
- def _UnPack(self, batchMatMulOptions):
- if batchMatMulOptions is None:
- return
- self.adjX = batchMatMulOptions.AdjX()
- self.adjY = batchMatMulOptions.AdjY()
- self.asymmetricQuantizeInputs = batchMatMulOptions.AsymmetricQuantizeInputs()
-
- # BatchMatMulOptionsT
- def Pack(self, builder):
- BatchMatMulOptionsStart(builder)
- BatchMatMulOptionsAddAdjX(builder, self.adjX)
- BatchMatMulOptionsAddAdjY(builder, self.adjY)
- BatchMatMulOptionsAddAsymmetricQuantizeInputs(builder, self.asymmetricQuantizeInputs)
- batchMatMulOptions = BatchMatMulOptionsEnd(builder)
- return batchMatMulOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class BatchToSpaceNDOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = BatchToSpaceNDOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsBatchToSpaceNDOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def BatchToSpaceNDOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # BatchToSpaceNDOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def BatchToSpaceNDOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return BatchToSpaceNDOptionsStart(builder)
-def BatchToSpaceNDOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return BatchToSpaceNDOptionsEnd(builder)
-
-class BatchToSpaceNDOptionsT(object):
-
- # BatchToSpaceNDOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- batchToSpaceNdoptions = BatchToSpaceNDOptions()
- batchToSpaceNdoptions.Init(buf, pos)
- return cls.InitFromObj(batchToSpaceNdoptions)
-
- @classmethod
- def InitFromObj(cls, batchToSpaceNdoptions):
- x = BatchToSpaceNDOptionsT()
- x._UnPack(batchToSpaceNdoptions)
- return x
-
- # BatchToSpaceNDOptionsT
- def _UnPack(self, batchToSpaceNdoptions):
- if batchToSpaceNdoptions is None:
- return
-
- # BatchToSpaceNDOptionsT
- def Pack(self, builder):
- BatchToSpaceNDOptionsStart(builder)
- batchToSpaceNdoptions = BatchToSpaceNDOptionsEnd(builder)
- return batchToSpaceNdoptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class BidirectionalSequenceLSTMOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = BidirectionalSequenceLSTMOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsBidirectionalSequenceLSTMOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def BidirectionalSequenceLSTMOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # BidirectionalSequenceLSTMOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # BidirectionalSequenceLSTMOptions
- def FusedActivationFunction(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # BidirectionalSequenceLSTMOptions
- def CellClip(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
- return 0.0
-
- # BidirectionalSequenceLSTMOptions
- def ProjClip(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
- return 0.0
-
- # BidirectionalSequenceLSTMOptions
- def MergeOutputs(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
- # BidirectionalSequenceLSTMOptions
- def TimeMajor(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return True
-
- # BidirectionalSequenceLSTMOptions
- def AsymmetricQuantizeInputs(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
-def BidirectionalSequenceLSTMOptionsStart(builder): builder.StartObject(6)
-def Start(builder):
- return BidirectionalSequenceLSTMOptionsStart(builder)
-def BidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return BidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def BidirectionalSequenceLSTMOptionsAddCellClip(builder, cellClip): builder.PrependFloat32Slot(1, cellClip, 0.0)
-def AddCellClip(builder, cellClip):
- return BidirectionalSequenceLSTMOptionsAddCellClip(builder, cellClip)
-def BidirectionalSequenceLSTMOptionsAddProjClip(builder, projClip): builder.PrependFloat32Slot(2, projClip, 0.0)
-def AddProjClip(builder, projClip):
- return BidirectionalSequenceLSTMOptionsAddProjClip(builder, projClip)
-def BidirectionalSequenceLSTMOptionsAddMergeOutputs(builder, mergeOutputs): builder.PrependBoolSlot(3, mergeOutputs, 0)
-def AddMergeOutputs(builder, mergeOutputs):
- return BidirectionalSequenceLSTMOptionsAddMergeOutputs(builder, mergeOutputs)
-def BidirectionalSequenceLSTMOptionsAddTimeMajor(builder, timeMajor): builder.PrependBoolSlot(4, timeMajor, 1)
-def AddTimeMajor(builder, timeMajor):
- return BidirectionalSequenceLSTMOptionsAddTimeMajor(builder, timeMajor)
-def BidirectionalSequenceLSTMOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs): builder.PrependBoolSlot(5, asymmetricQuantizeInputs, 0)
-def AddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
- return BidirectionalSequenceLSTMOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs)
-def BidirectionalSequenceLSTMOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return BidirectionalSequenceLSTMOptionsEnd(builder)
-
-class BidirectionalSequenceLSTMOptionsT(object):
-
- # BidirectionalSequenceLSTMOptionsT
- def __init__(self):
- self.fusedActivationFunction = 0 # type: int
- self.cellClip = 0.0 # type: float
- self.projClip = 0.0 # type: float
- self.mergeOutputs = False # type: bool
- self.timeMajor = True # type: bool
- self.asymmetricQuantizeInputs = False # type: bool
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- bidirectionalSequenceLstmoptions = BidirectionalSequenceLSTMOptions()
- bidirectionalSequenceLstmoptions.Init(buf, pos)
- return cls.InitFromObj(bidirectionalSequenceLstmoptions)
-
- @classmethod
- def InitFromObj(cls, bidirectionalSequenceLstmoptions):
- x = BidirectionalSequenceLSTMOptionsT()
- x._UnPack(bidirectionalSequenceLstmoptions)
- return x
-
- # BidirectionalSequenceLSTMOptionsT
- def _UnPack(self, bidirectionalSequenceLstmoptions):
- if bidirectionalSequenceLstmoptions is None:
- return
- self.fusedActivationFunction = bidirectionalSequenceLstmoptions.FusedActivationFunction()
- self.cellClip = bidirectionalSequenceLstmoptions.CellClip()
- self.projClip = bidirectionalSequenceLstmoptions.ProjClip()
- self.mergeOutputs = bidirectionalSequenceLstmoptions.MergeOutputs()
- self.timeMajor = bidirectionalSequenceLstmoptions.TimeMajor()
- self.asymmetricQuantizeInputs = bidirectionalSequenceLstmoptions.AsymmetricQuantizeInputs()
-
- # BidirectionalSequenceLSTMOptionsT
- def Pack(self, builder):
- BidirectionalSequenceLSTMOptionsStart(builder)
- BidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
- BidirectionalSequenceLSTMOptionsAddCellClip(builder, self.cellClip)
- BidirectionalSequenceLSTMOptionsAddProjClip(builder, self.projClip)
- BidirectionalSequenceLSTMOptionsAddMergeOutputs(builder, self.mergeOutputs)
- BidirectionalSequenceLSTMOptionsAddTimeMajor(builder, self.timeMajor)
- BidirectionalSequenceLSTMOptionsAddAsymmetricQuantizeInputs(builder, self.asymmetricQuantizeInputs)
- bidirectionalSequenceLstmoptions = BidirectionalSequenceLSTMOptionsEnd(builder)
- return bidirectionalSequenceLstmoptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class BidirectionalSequenceRNNOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = BidirectionalSequenceRNNOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsBidirectionalSequenceRNNOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def BidirectionalSequenceRNNOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # BidirectionalSequenceRNNOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # BidirectionalSequenceRNNOptions
- def TimeMajor(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
- # BidirectionalSequenceRNNOptions
- def FusedActivationFunction(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # BidirectionalSequenceRNNOptions
- def MergeOutputs(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
- # BidirectionalSequenceRNNOptions
- def AsymmetricQuantizeInputs(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
-def BidirectionalSequenceRNNOptionsStart(builder): builder.StartObject(4)
-def Start(builder):
- return BidirectionalSequenceRNNOptionsStart(builder)
-def BidirectionalSequenceRNNOptionsAddTimeMajor(builder, timeMajor): builder.PrependBoolSlot(0, timeMajor, 0)
-def AddTimeMajor(builder, timeMajor):
- return BidirectionalSequenceRNNOptionsAddTimeMajor(builder, timeMajor)
-def BidirectionalSequenceRNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(1, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return BidirectionalSequenceRNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def BidirectionalSequenceRNNOptionsAddMergeOutputs(builder, mergeOutputs): builder.PrependBoolSlot(2, mergeOutputs, 0)
-def AddMergeOutputs(builder, mergeOutputs):
- return BidirectionalSequenceRNNOptionsAddMergeOutputs(builder, mergeOutputs)
-def BidirectionalSequenceRNNOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs): builder.PrependBoolSlot(3, asymmetricQuantizeInputs, 0)
-def AddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
- return BidirectionalSequenceRNNOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs)
-def BidirectionalSequenceRNNOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return BidirectionalSequenceRNNOptionsEnd(builder)
-
-class BidirectionalSequenceRNNOptionsT(object):
-
- # BidirectionalSequenceRNNOptionsT
- def __init__(self):
- self.timeMajor = False # type: bool
- self.fusedActivationFunction = 0 # type: int
- self.mergeOutputs = False # type: bool
- self.asymmetricQuantizeInputs = False # type: bool
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- bidirectionalSequenceRnnoptions = BidirectionalSequenceRNNOptions()
- bidirectionalSequenceRnnoptions.Init(buf, pos)
- return cls.InitFromObj(bidirectionalSequenceRnnoptions)
-
- @classmethod
- def InitFromObj(cls, bidirectionalSequenceRnnoptions):
- x = BidirectionalSequenceRNNOptionsT()
- x._UnPack(bidirectionalSequenceRnnoptions)
- return x
-
- # BidirectionalSequenceRNNOptionsT
- def _UnPack(self, bidirectionalSequenceRnnoptions):
- if bidirectionalSequenceRnnoptions is None:
- return
- self.timeMajor = bidirectionalSequenceRnnoptions.TimeMajor()
- self.fusedActivationFunction = bidirectionalSequenceRnnoptions.FusedActivationFunction()
- self.mergeOutputs = bidirectionalSequenceRnnoptions.MergeOutputs()
- self.asymmetricQuantizeInputs = bidirectionalSequenceRnnoptions.AsymmetricQuantizeInputs()
-
- # BidirectionalSequenceRNNOptionsT
- def Pack(self, builder):
- BidirectionalSequenceRNNOptionsStart(builder)
- BidirectionalSequenceRNNOptionsAddTimeMajor(builder, self.timeMajor)
- BidirectionalSequenceRNNOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
- BidirectionalSequenceRNNOptionsAddMergeOutputs(builder, self.mergeOutputs)
- BidirectionalSequenceRNNOptionsAddAsymmetricQuantizeInputs(builder, self.asymmetricQuantizeInputs)
- bidirectionalSequenceRnnoptions = BidirectionalSequenceRNNOptionsEnd(builder)
- return bidirectionalSequenceRnnoptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class BitcastOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = BitcastOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsBitcastOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def BitcastOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # BitcastOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def BitcastOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return BitcastOptionsStart(builder)
-def BitcastOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return BitcastOptionsEnd(builder)
-
-class BitcastOptionsT(object):
-
- # BitcastOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- bitcastOptions = BitcastOptions()
- bitcastOptions.Init(buf, pos)
- return cls.InitFromObj(bitcastOptions)
-
- @classmethod
- def InitFromObj(cls, bitcastOptions):
- x = BitcastOptionsT()
- x._UnPack(bitcastOptions)
- return x
-
- # BitcastOptionsT
- def _UnPack(self, bitcastOptions):
- if bitcastOptions is None:
- return
-
- # BitcastOptionsT
- def Pack(self, builder):
- BitcastOptionsStart(builder)
- bitcastOptions = BitcastOptionsEnd(builder)
- return bitcastOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class BitwiseXorOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = BitwiseXorOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsBitwiseXorOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def BitwiseXorOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # BitwiseXorOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def BitwiseXorOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return BitwiseXorOptionsStart(builder)
-def BitwiseXorOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return BitwiseXorOptionsEnd(builder)
-
-class BitwiseXorOptionsT(object):
-
- # BitwiseXorOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- bitwiseXorOptions = BitwiseXorOptions()
- bitwiseXorOptions.Init(buf, pos)
- return cls.InitFromObj(bitwiseXorOptions)
-
- @classmethod
- def InitFromObj(cls, bitwiseXorOptions):
- x = BitwiseXorOptionsT()
- x._UnPack(bitwiseXorOptions)
- return x
-
- # BitwiseXorOptionsT
- def _UnPack(self, bitwiseXorOptions):
- if bitwiseXorOptions is None:
- return
-
- # BitwiseXorOptionsT
- def Pack(self, builder):
- BitwiseXorOptionsStart(builder)
- bitwiseXorOptions = BitwiseXorOptionsEnd(builder)
- return bitwiseXorOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class BroadcastToOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = BroadcastToOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsBroadcastToOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def BroadcastToOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # BroadcastToOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def BroadcastToOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return BroadcastToOptionsStart(builder)
-def BroadcastToOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return BroadcastToOptionsEnd(builder)
-
-class BroadcastToOptionsT(object):
-
- # BroadcastToOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- broadcastToOptions = BroadcastToOptions()
- broadcastToOptions.Init(buf, pos)
- return cls.InitFromObj(broadcastToOptions)
-
- @classmethod
- def InitFromObj(cls, broadcastToOptions):
- x = BroadcastToOptionsT()
- x._UnPack(broadcastToOptions)
- return x
-
- # BroadcastToOptionsT
- def _UnPack(self, broadcastToOptions):
- if broadcastToOptions is None:
- return
-
- # BroadcastToOptionsT
- def Pack(self, builder):
- BroadcastToOptionsStart(builder)
- broadcastToOptions = BroadcastToOptionsEnd(builder)
- return broadcastToOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class BucketizeOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = BucketizeOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsBucketizeOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def BucketizeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # BucketizeOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # BucketizeOptions
- def Boundaries(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- a = self._tab.Vector(o)
- return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
- return 0
-
- # BucketizeOptions
- def BoundariesAsNumpy(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o)
- return 0
-
- # BucketizeOptions
- def BoundariesLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # BucketizeOptions
- def BoundariesIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- return o == 0
-
-def BucketizeOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return BucketizeOptionsStart(builder)
-def BucketizeOptionsAddBoundaries(builder, boundaries): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(boundaries), 0)
-def AddBoundaries(builder, boundaries):
- return BucketizeOptionsAddBoundaries(builder, boundaries)
-def BucketizeOptionsStartBoundariesVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartBoundariesVector(builder, numElems):
- return BucketizeOptionsStartBoundariesVector(builder, numElems)
-def BucketizeOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return BucketizeOptionsEnd(builder)
-try:
- from typing import List
-except:
- pass
-
-class BucketizeOptionsT(object):
-
- # BucketizeOptionsT
- def __init__(self):
- self.boundaries = None # type: List[float]
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- bucketizeOptions = BucketizeOptions()
- bucketizeOptions.Init(buf, pos)
- return cls.InitFromObj(bucketizeOptions)
-
- @classmethod
- def InitFromObj(cls, bucketizeOptions):
- x = BucketizeOptionsT()
- x._UnPack(bucketizeOptions)
- return x
-
- # BucketizeOptionsT
- def _UnPack(self, bucketizeOptions):
- if bucketizeOptions is None:
- return
- if not bucketizeOptions.BoundariesIsNone():
- if np is None:
- self.boundaries = []
- for i in range(bucketizeOptions.BoundariesLength()):
- self.boundaries.append(bucketizeOptions.Boundaries(i))
- else:
- self.boundaries = bucketizeOptions.BoundariesAsNumpy()
-
- # BucketizeOptionsT
- def Pack(self, builder):
- if self.boundaries is not None:
- if np is not None and type(self.boundaries) is np.ndarray:
- boundaries = builder.CreateNumpyVector(self.boundaries)
- else:
- BucketizeOptionsStartBoundariesVector(builder, len(self.boundaries))
- for i in reversed(range(len(self.boundaries))):
- builder.PrependFloat32(self.boundaries[i])
- boundaries = builder.EndVector()
- BucketizeOptionsStart(builder)
- if self.boundaries is not None:
- BucketizeOptionsAddBoundaries(builder, boundaries)
- bucketizeOptions = BucketizeOptionsEnd(builder)
- return bucketizeOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class Buffer(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = Buffer()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsBuffer(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def BufferBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # Buffer
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # Buffer
- def Data(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- a = self._tab.Vector(o)
- return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1))
- return 0
-
- # Buffer
- def DataAsNumpy(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o)
- return 0
-
- # Buffer
- def DataLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # Buffer
- def DataIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- return o == 0
-
- # Buffer
- def Offset(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Uint64Flags, o + self._tab.Pos)
- return 0
-
- # Buffer
- def Size(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Uint64Flags, o + self._tab.Pos)
- return 0
-
-def BufferStart(builder): builder.StartObject(3)
-def Start(builder):
- return BufferStart(builder)
-def BufferAddData(builder, data): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(data), 0)
-def AddData(builder, data):
- return BufferAddData(builder, data)
-def BufferStartDataVector(builder, numElems): return builder.StartVector(1, numElems, 1)
-def StartDataVector(builder, numElems):
- return BufferStartDataVector(builder, numElems)
-def BufferAddOffset(builder, offset): builder.PrependUint64Slot(1, offset, 0)
-def AddOffset(builder, offset):
- return BufferAddOffset(builder, offset)
-def BufferAddSize(builder, size): builder.PrependUint64Slot(2, size, 0)
-def AddSize(builder, size):
- return BufferAddSize(builder, size)
-def BufferEnd(builder): return builder.EndObject()
-def End(builder):
- return BufferEnd(builder)
-try:
- from typing import List
-except:
- pass
-
-class BufferT(object):
-
- # BufferT
- def __init__(self):
- self.data = None # type: List[int]
- self.offset = 0 # type: int
- self.size = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- buffer = Buffer()
- buffer.Init(buf, pos)
- return cls.InitFromObj(buffer)
-
- @classmethod
- def InitFromObj(cls, buffer):
- x = BufferT()
- x._UnPack(buffer)
- return x
-
- # BufferT
- def _UnPack(self, buffer):
- if buffer is None:
- return
- if not buffer.DataIsNone():
- if np is None:
- self.data = []
- for i in range(buffer.DataLength()):
- self.data.append(buffer.Data(i))
- else:
- self.data = buffer.DataAsNumpy()
- self.offset = buffer.Offset()
- self.size = buffer.Size()
-
- # BufferT
- def Pack(self, builder):
- if self.data is not None:
- if np is not None and type(self.data) is np.ndarray:
- data = builder.CreateNumpyVector(self.data)
- else:
- BufferStartDataVector(builder, len(self.data))
- for i in reversed(range(len(self.data))):
- builder.PrependUint8(self.data[i])
- data = builder.EndVector()
- BufferStart(builder)
- if self.data is not None:
- BufferAddData(builder, data)
- BufferAddOffset(builder, self.offset)
- BufferAddSize(builder, self.size)
- buffer = BufferEnd(builder)
- return buffer
-# automatically generated by the FlatBuffers compiler, do not modify
+def SparseIndexVectorCreator(unionType, table):
+ from flatbuffers.table import Table
+ if not isinstance(table, Table):
+ return None
+ if unionType == SparseIndexVector().Int32Vector:
+ return Int32VectorT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == SparseIndexVector().Uint16Vector:
+ return Uint16VectorT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == SparseIndexVector().Uint8Vector:
+ return Uint8VectorT.InitFromBuf(table.Bytes, table.Pos)
+ return None
-# namespace: tflite
class BuiltinOperator(object):
ADD = 0
@@ -1551,9 +229,52 @@
BITCAST = 159
BITWISE_XOR = 160
RIGHT_SHIFT = 161
-# automatically generated by the FlatBuffers compiler, do not modify
+ STABLEHLO_LOGISTIC = 162
+ STABLEHLO_ADD = 163
+ STABLEHLO_DIVIDE = 164
+ STABLEHLO_MULTIPLY = 165
+ STABLEHLO_MAXIMUM = 166
+ STABLEHLO_RESHAPE = 167
+ STABLEHLO_CLAMP = 168
+ STABLEHLO_CONCATENATE = 169
+ STABLEHLO_BROADCAST_IN_DIM = 170
+ STABLEHLO_CONVOLUTION = 171
+ STABLEHLO_SLICE = 172
+ STABLEHLO_CUSTOM_CALL = 173
+ STABLEHLO_REDUCE = 174
+ STABLEHLO_ABS = 175
+ STABLEHLO_AND = 176
+ STABLEHLO_COSINE = 177
+ STABLEHLO_EXPONENTIAL = 178
+ STABLEHLO_FLOOR = 179
+ STABLEHLO_LOG = 180
+ STABLEHLO_MINIMUM = 181
+ STABLEHLO_NEGATE = 182
+ STABLEHLO_OR = 183
+ STABLEHLO_POWER = 184
+ STABLEHLO_REMAINDER = 185
+ STABLEHLO_RSQRT = 186
+ STABLEHLO_SELECT = 187
+ STABLEHLO_SUBTRACT = 188
+ STABLEHLO_TANH = 189
+ STABLEHLO_SCATTER = 190
+ STABLEHLO_COMPARE = 191
+ STABLEHLO_CONVERT = 192
+ STABLEHLO_DYNAMIC_SLICE = 193
+ STABLEHLO_DYNAMIC_UPDATE_SLICE = 194
+ STABLEHLO_PAD = 195
+ STABLEHLO_IOTA = 196
+ STABLEHLO_DOT_GENERAL = 197
+ STABLEHLO_REDUCE_WINDOW = 198
+ STABLEHLO_SORT = 199
+ STABLEHLO_WHILE = 200
+ STABLEHLO_GATHER = 201
+ STABLEHLO_TRANSPOSE = 202
+ DILATE = 203
+ STABLEHLO_RNG_BIT_GENERATOR = 204
+ REDUCE_WINDOW = 205
+ STABLEHLO_COMPOSITE = 206
-# namespace: tflite
class BuiltinOptions(object):
NONE = 0
@@ -1941,261 +662,5960 @@
if unionType == BuiltinOptions().RightShiftOptions:
return RightShiftOptionsT.InitFromBuf(table.Bytes, table.Pos)
return None
-# automatically generated by the FlatBuffers compiler, do not modify
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
+class BuiltinOptions2(object):
+ NONE = 0
+ StablehloConcatenateOptions = 1
+ StablehloBroadcastInDimOptions = 2
+ StablehloSliceOptions = 3
+ StablehloConvolutionOptions = 4
+ StablehloCustomCallOptions = 5
+ StablehloReduceOptions = 6
+ StablehloScatterOptions = 7
+ StablehloCompareOptions = 8
+ StablehloDynamicSliceOptions = 9
+ StablehloPadOptions = 10
+ StablehloIotaOptions = 11
+ StablehloDotGeneralOptions = 12
+ StablehloReduceWindowOptions = 13
+ StablehloSortOptions = 14
+ StablehloWhileOptions = 15
+ StablehloGatherOptions = 16
+ StablehloTransposeOptions = 17
+ DilateOptions = 18
+ StablehloRngBitGeneratorOptions = 19
+ ReduceWindowOptions = 20
+ StableHLOCompositeOptions = 21
-class CallOnceOptions(object):
- __slots__ = ['_tab']
+def BuiltinOptions2Creator(unionType, table):
+ from flatbuffers.table import Table
+ if not isinstance(table, Table):
+ return None
+ if unionType == BuiltinOptions2().StablehloConcatenateOptions:
+ return StablehloConcatenateOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StablehloBroadcastInDimOptions:
+ return StablehloBroadcastInDimOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StablehloSliceOptions:
+ return StablehloSliceOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StablehloConvolutionOptions:
+ return StablehloConvolutionOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StablehloCustomCallOptions:
+ return StablehloCustomCallOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StablehloReduceOptions:
+ return StablehloReduceOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StablehloScatterOptions:
+ return StablehloScatterOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StablehloCompareOptions:
+ return StablehloCompareOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StablehloDynamicSliceOptions:
+ return StablehloDynamicSliceOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StablehloPadOptions:
+ return StablehloPadOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StablehloIotaOptions:
+ return StablehloIotaOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StablehloDotGeneralOptions:
+ return StablehloDotGeneralOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StablehloReduceWindowOptions:
+ return StablehloReduceWindowOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StablehloSortOptions:
+ return StablehloSortOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StablehloWhileOptions:
+ return StablehloWhileOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StablehloGatherOptions:
+ return StablehloGatherOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StablehloTransposeOptions:
+ return StablehloTransposeOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().DilateOptions:
+ return DilateOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StablehloRngBitGeneratorOptions:
+ return StablehloRngBitGeneratorOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().ReduceWindowOptions:
+ return ReduceWindowOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ if unionType == BuiltinOptions2().StableHLOCompositeOptions:
+ return StableHLOCompositeOptionsT.InitFromBuf(table.Bytes, table.Pos)
+ return None
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = CallOnceOptions()
- x.Init(buf, n + offset)
- return x
- @classmethod
- def GetRootAsCallOnceOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def CallOnceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+class StablehloPrecisionConfig(object):
+ DEFAULT = 0
+ HIGH = 1
+ HIGHEST = 2
- # CallOnceOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
- # CallOnceOptions
- def InitSubgraphIndex(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
+class StablehloComparisonDirection(object):
+ STABLEHLO_COMPARISON_DIRECTION_EQ = 0
+ STABLEHLO_COMPARISON_DIRECTION_NE = 1
+ STABLEHLO_COMPARISON_DIRECTION_GE = 2
+ STABLEHLO_COMPARISON_DIRECTION_GT = 3
+ STABLEHLO_COMPARISON_DIRECTION_LE = 4
+ STABLEHLO_COMPARISON_DIRECTION_LT = 5
-def CallOnceOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return CallOnceOptionsStart(builder)
-def CallOnceOptionsAddInitSubgraphIndex(builder, initSubgraphIndex): builder.PrependInt32Slot(0, initSubgraphIndex, 0)
-def AddInitSubgraphIndex(builder, initSubgraphIndex):
- return CallOnceOptionsAddInitSubgraphIndex(builder, initSubgraphIndex)
-def CallOnceOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return CallOnceOptionsEnd(builder)
-class CallOnceOptionsT(object):
+class StablehloComparisonType(object):
+ STABLEHLO_COMPARISON_TYPE_NOTYPE = 0
+ STABLEHLO_COMPARISON_TYPE_FLOAT = 1
+ STABLEHLO_COMPARISON_TYPE_FLOAT_TOTAL_ORDER = 2
+ STABLEHLO_COMPARISON_TYPE_SIGNED = 3
+ STABLEHLO_COMPARISON_TYPE_UNSIGNED = 4
- # CallOnceOptionsT
- def __init__(self):
- self.initSubgraphIndex = 0 # type: int
- @classmethod
- def InitFromBuf(cls, buf, pos):
- callOnceOptions = CallOnceOptions()
- callOnceOptions.Init(buf, pos)
- return cls.InitFromObj(callOnceOptions)
+class RngAlgorithm(object):
+ DEFAULT = 0
+ PHILOX = 1
+ THREEFRY = 2
- @classmethod
- def InitFromObj(cls, callOnceOptions):
- x = CallOnceOptionsT()
- x._UnPack(callOnceOptions)
- return x
- # CallOnceOptionsT
- def _UnPack(self, callOnceOptions):
- if callOnceOptions is None:
- return
- self.initSubgraphIndex = callOnceOptions.InitSubgraphIndex()
+class Padding(object):
+ SAME = 0
+ VALID = 1
- # CallOnceOptionsT
- def Pack(self, builder):
- CallOnceOptionsStart(builder)
- CallOnceOptionsAddInitSubgraphIndex(builder, self.initSubgraphIndex)
- callOnceOptions = CallOnceOptionsEnd(builder)
- return callOnceOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-# namespace: tflite
+class ActivationFunctionType(object):
+ NONE = 0
+ RELU = 1
+ RELU_N1_TO_1 = 2
+ RELU6 = 3
+ TANH = 4
+ SIGN_BIT = 5
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-class CallOptions(object):
- __slots__ = ['_tab']
+class LSHProjectionType(object):
+ UNKNOWN = 0
+ SPARSE = 1
+ DENSE = 2
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = CallOptions()
- x.Init(buf, n + offset)
- return x
- @classmethod
- def GetRootAsCallOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def CallOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+class FullyConnectedOptionsWeightsFormat(object):
+ DEFAULT = 0
+ SHUFFLED4x16INT8 = 1
- # CallOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
- # CallOptions
- def Subgraph(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos)
- return 0
+class LSTMKernelType(object):
+ FULL = 0
+ BASIC = 1
-def CallOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return CallOptionsStart(builder)
-def CallOptionsAddSubgraph(builder, subgraph): builder.PrependUint32Slot(0, subgraph, 0)
-def AddSubgraph(builder, subgraph):
- return CallOptionsAddSubgraph(builder, subgraph)
-def CallOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return CallOptionsEnd(builder)
-
-class CallOptionsT(object):
-
- # CallOptionsT
- def __init__(self):
- self.subgraph = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- callOptions = CallOptions()
- callOptions.Init(buf, pos)
- return cls.InitFromObj(callOptions)
-
- @classmethod
- def InitFromObj(cls, callOptions):
- x = CallOptionsT()
- x._UnPack(callOptions)
- return x
-
- # CallOptionsT
- def _UnPack(self, callOptions):
- if callOptions is None:
- return
- self.subgraph = callOptions.Subgraph()
-
- # CallOptionsT
- def Pack(self, builder):
- CallOptionsStart(builder)
- CallOptionsAddSubgraph(builder, self.subgraph)
- callOptions = CallOptionsEnd(builder)
- return callOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class CastOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = CastOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsCastOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def CastOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # CastOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # CastOptions
- def InDataType(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # CastOptions
- def OutDataType(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
-def CastOptionsStart(builder): builder.StartObject(2)
-def Start(builder):
- return CastOptionsStart(builder)
-def CastOptionsAddInDataType(builder, inDataType): builder.PrependInt8Slot(0, inDataType, 0)
-def AddInDataType(builder, inDataType):
- return CastOptionsAddInDataType(builder, inDataType)
-def CastOptionsAddOutDataType(builder, outDataType): builder.PrependInt8Slot(1, outDataType, 0)
-def AddOutDataType(builder, outDataType):
- return CastOptionsAddOutDataType(builder, outDataType)
-def CastOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return CastOptionsEnd(builder)
-
-class CastOptionsT(object):
-
- # CastOptionsT
- def __init__(self):
- self.inDataType = 0 # type: int
- self.outDataType = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- castOptions = CastOptions()
- castOptions.Init(buf, pos)
- return cls.InitFromObj(castOptions)
-
- @classmethod
- def InitFromObj(cls, castOptions):
- x = CastOptionsT()
- x._UnPack(castOptions)
- return x
-
- # CastOptionsT
- def _UnPack(self, castOptions):
- if castOptions is None:
- return
- self.inDataType = castOptions.InDataType()
- self.outDataType = castOptions.OutDataType()
-
- # CastOptionsT
- def Pack(self, builder):
- CastOptionsStart(builder)
- CastOptionsAddInDataType(builder, self.inDataType)
- CastOptionsAddOutDataType(builder, self.outDataType)
- castOptions = CastOptionsEnd(builder)
- return castOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
class CombinerType(object):
SUM = 0
MEAN = 1
SQRTN = 2
-# automatically generated by the FlatBuffers compiler, do not modify
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
+class MirrorPadMode(object):
+ REFLECT = 0
+ SYMMETRIC = 1
+
+
+class ReduceWindowFunction(object):
+ UNSUPPORTED = 0
+ ADD = 1
+ MUL = 2
+ MINIMUM = 3
+ MAXIMUM = 4
+ ALL = 5
+ ANY = 6
+
+
+class CustomOptionsFormat(object):
+ FLEXBUFFERS = 0
+
+
+class CustomQuantization(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = CustomQuantization()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsCustomQuantization(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def CustomQuantizationBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # CustomQuantization
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # CustomQuantization
+ def Custom(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1))
+ return 0
+
+ # CustomQuantization
+ def CustomAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o)
+ return 0
+
+ # CustomQuantization
+ def CustomLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # CustomQuantization
+ def CustomIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+def CustomQuantizationStart(builder):
+ builder.StartObject(1)
+
+def CustomQuantizationAddCustom(builder, custom):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(custom), 0)
+
+def CustomQuantizationStartCustomVector(builder, numElems):
+ return builder.StartVector(1, numElems, 1)
+
+def CustomQuantizationEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class CustomQuantizationT(object):
+
+ # CustomQuantizationT
+ def __init__(self):
+ self.custom = None # type: List[int]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ customQuantization = CustomQuantization()
+ customQuantization.Init(buf, pos)
+ return cls.InitFromObj(customQuantization)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, customQuantization):
+ x = CustomQuantizationT()
+ x._UnPack(customQuantization)
+ return x
+
+ # CustomQuantizationT
+ def _UnPack(self, customQuantization):
+ if customQuantization is None:
+ return
+ if not customQuantization.CustomIsNone():
+ if np is None:
+ self.custom = []
+ for i in range(customQuantization.CustomLength()):
+ self.custom.append(customQuantization.Custom(i))
+ else:
+ self.custom = customQuantization.CustomAsNumpy()
+
+ # CustomQuantizationT
+ def Pack(self, builder):
+ if self.custom is not None:
+ if np is not None and type(self.custom) is np.ndarray:
+ custom = builder.CreateNumpyVector(self.custom)
+ else:
+ CustomQuantizationStartCustomVector(builder, len(self.custom))
+ for i in reversed(range(len(self.custom))):
+ builder.PrependUint8(self.custom[i])
+ custom = builder.EndVector()
+ CustomQuantizationStart(builder)
+ if self.custom is not None:
+ CustomQuantizationAddCustom(builder, custom)
+ customQuantization = CustomQuantizationEnd(builder)
+ return customQuantization
+
+
+class QuantizationParameters(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = QuantizationParameters()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsQuantizationParameters(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def QuantizationParametersBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # QuantizationParameters
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # QuantizationParameters
+ def Min(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+ return 0
+
+ # QuantizationParameters
+ def MinAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o)
+ return 0
+
+ # QuantizationParameters
+ def MinLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # QuantizationParameters
+ def MinIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+ # QuantizationParameters
+ def Max(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+ return 0
+
+ # QuantizationParameters
+ def MaxAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o)
+ return 0
+
+ # QuantizationParameters
+ def MaxLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # QuantizationParameters
+ def MaxIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ return o == 0
+
+ # QuantizationParameters
+ def Scale(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+ return 0
+
+ # QuantizationParameters
+ def ScaleAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o)
+ return 0
+
+ # QuantizationParameters
+ def ScaleLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # QuantizationParameters
+ def ScaleIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ return o == 0
+
+ # QuantizationParameters
+ def ZeroPoint(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # QuantizationParameters
+ def ZeroPointAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # QuantizationParameters
+ def ZeroPointLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # QuantizationParameters
+ def ZeroPointIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ return o == 0
+
+ # QuantizationParameters
+ def DetailsType(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos)
+ return 0
+
+ # QuantizationParameters
+ def Details(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ if o != 0:
+ from flatbuffers.table import Table
+ obj = Table(bytearray(), 0)
+ self._tab.Union(obj, o)
+ return obj
+ return None
+
+ # QuantizationParameters
+ def QuantizedDimension(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def QuantizationParametersStart(builder):
+ builder.StartObject(7)
+
+def QuantizationParametersAddMin(builder, min):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(min), 0)
+
+def QuantizationParametersStartMinVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def QuantizationParametersAddMax(builder, max):
+ builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(max), 0)
+
+def QuantizationParametersStartMaxVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def QuantizationParametersAddScale(builder, scale):
+ builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(scale), 0)
+
+def QuantizationParametersStartScaleVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def QuantizationParametersAddZeroPoint(builder, zeroPoint):
+ builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(zeroPoint), 0)
+
+def QuantizationParametersStartZeroPointVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def QuantizationParametersAddDetailsType(builder, detailsType):
+ builder.PrependUint8Slot(4, detailsType, 0)
+
+def QuantizationParametersAddDetails(builder, details):
+ builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(details), 0)
+
+def QuantizationParametersAddQuantizedDimension(builder, quantizedDimension):
+ builder.PrependInt32Slot(6, quantizedDimension, 0)
+
+def QuantizationParametersEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List, Union
+except:
+ pass
+
+class QuantizationParametersT(object):
+
+ # QuantizationParametersT
+ def __init__(self):
+ self.min = None # type: List[float]
+ self.max = None # type: List[float]
+ self.scale = None # type: List[float]
+ self.zeroPoint = None # type: List[int]
+ self.detailsType = 0 # type: int
+ self.details = None # type: Union[None, CustomQuantizationT]
+ self.quantizedDimension = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ quantizationParameters = QuantizationParameters()
+ quantizationParameters.Init(buf, pos)
+ return cls.InitFromObj(quantizationParameters)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, quantizationParameters):
+ x = QuantizationParametersT()
+ x._UnPack(quantizationParameters)
+ return x
+
+ # QuantizationParametersT
+ def _UnPack(self, quantizationParameters):
+ if quantizationParameters is None:
+ return
+ if not quantizationParameters.MinIsNone():
+ if np is None:
+ self.min = []
+ for i in range(quantizationParameters.MinLength()):
+ self.min.append(quantizationParameters.Min(i))
+ else:
+ self.min = quantizationParameters.MinAsNumpy()
+ if not quantizationParameters.MaxIsNone():
+ if np is None:
+ self.max = []
+ for i in range(quantizationParameters.MaxLength()):
+ self.max.append(quantizationParameters.Max(i))
+ else:
+ self.max = quantizationParameters.MaxAsNumpy()
+ if not quantizationParameters.ScaleIsNone():
+ if np is None:
+ self.scale = []
+ for i in range(quantizationParameters.ScaleLength()):
+ self.scale.append(quantizationParameters.Scale(i))
+ else:
+ self.scale = quantizationParameters.ScaleAsNumpy()
+ if not quantizationParameters.ZeroPointIsNone():
+ if np is None:
+ self.zeroPoint = []
+ for i in range(quantizationParameters.ZeroPointLength()):
+ self.zeroPoint.append(quantizationParameters.ZeroPoint(i))
+ else:
+ self.zeroPoint = quantizationParameters.ZeroPointAsNumpy()
+ self.detailsType = quantizationParameters.DetailsType()
+ self.details = QuantizationDetailsCreator(self.detailsType, quantizationParameters.Details())
+ self.quantizedDimension = quantizationParameters.QuantizedDimension()
+
+ # QuantizationParametersT
+ def Pack(self, builder):
+ if self.min is not None:
+ if np is not None and type(self.min) is np.ndarray:
+ min = builder.CreateNumpyVector(self.min)
+ else:
+ QuantizationParametersStartMinVector(builder, len(self.min))
+ for i in reversed(range(len(self.min))):
+ builder.PrependFloat32(self.min[i])
+ min = builder.EndVector()
+ if self.max is not None:
+ if np is not None and type(self.max) is np.ndarray:
+ max = builder.CreateNumpyVector(self.max)
+ else:
+ QuantizationParametersStartMaxVector(builder, len(self.max))
+ for i in reversed(range(len(self.max))):
+ builder.PrependFloat32(self.max[i])
+ max = builder.EndVector()
+ if self.scale is not None:
+ if np is not None and type(self.scale) is np.ndarray:
+ scale = builder.CreateNumpyVector(self.scale)
+ else:
+ QuantizationParametersStartScaleVector(builder, len(self.scale))
+ for i in reversed(range(len(self.scale))):
+ builder.PrependFloat32(self.scale[i])
+ scale = builder.EndVector()
+ if self.zeroPoint is not None:
+ if np is not None and type(self.zeroPoint) is np.ndarray:
+ zeroPoint = builder.CreateNumpyVector(self.zeroPoint)
+ else:
+ QuantizationParametersStartZeroPointVector(builder, len(self.zeroPoint))
+ for i in reversed(range(len(self.zeroPoint))):
+ builder.PrependInt64(self.zeroPoint[i])
+ zeroPoint = builder.EndVector()
+ if self.details is not None:
+ details = self.details.Pack(builder)
+ QuantizationParametersStart(builder)
+ if self.min is not None:
+ QuantizationParametersAddMin(builder, min)
+ if self.max is not None:
+ QuantizationParametersAddMax(builder, max)
+ if self.scale is not None:
+ QuantizationParametersAddScale(builder, scale)
+ if self.zeroPoint is not None:
+ QuantizationParametersAddZeroPoint(builder, zeroPoint)
+ QuantizationParametersAddDetailsType(builder, self.detailsType)
+ if self.details is not None:
+ QuantizationParametersAddDetails(builder, details)
+ QuantizationParametersAddQuantizedDimension(builder, self.quantizedDimension)
+ quantizationParameters = QuantizationParametersEnd(builder)
+ return quantizationParameters
+
+
+class Int32Vector(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = Int32Vector()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsInt32Vector(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def Int32VectorBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # Int32Vector
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # Int32Vector
+ def Values(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+ return 0
+
+ # Int32Vector
+ def ValuesAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
+ return 0
+
+ # Int32Vector
+ def ValuesLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # Int32Vector
+ def ValuesIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+def Int32VectorStart(builder):
+ builder.StartObject(1)
+
+def Int32VectorAddValues(builder, values):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(values), 0)
+
+def Int32VectorStartValuesVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def Int32VectorEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class Int32VectorT(object):
+
+ # Int32VectorT
+ def __init__(self):
+ self.values = None # type: List[int]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ int32Vector = Int32Vector()
+ int32Vector.Init(buf, pos)
+ return cls.InitFromObj(int32Vector)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, int32Vector):
+ x = Int32VectorT()
+ x._UnPack(int32Vector)
+ return x
+
+ # Int32VectorT
+ def _UnPack(self, int32Vector):
+ if int32Vector is None:
+ return
+ if not int32Vector.ValuesIsNone():
+ if np is None:
+ self.values = []
+ for i in range(int32Vector.ValuesLength()):
+ self.values.append(int32Vector.Values(i))
+ else:
+ self.values = int32Vector.ValuesAsNumpy()
+
+ # Int32VectorT
+ def Pack(self, builder):
+ if self.values is not None:
+ if np is not None and type(self.values) is np.ndarray:
+ values = builder.CreateNumpyVector(self.values)
+ else:
+ Int32VectorStartValuesVector(builder, len(self.values))
+ for i in reversed(range(len(self.values))):
+ builder.PrependInt32(self.values[i])
+ values = builder.EndVector()
+ Int32VectorStart(builder)
+ if self.values is not None:
+ Int32VectorAddValues(builder, values)
+ int32Vector = Int32VectorEnd(builder)
+ return int32Vector
+
+
+class Uint16Vector(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = Uint16Vector()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsUint16Vector(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def Uint16VectorBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # Uint16Vector
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # Uint16Vector
+ def Values(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Uint16Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 2))
+ return 0
+
+ # Uint16Vector
+ def ValuesAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint16Flags, o)
+ return 0
+
+ # Uint16Vector
+ def ValuesLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # Uint16Vector
+ def ValuesIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+def Uint16VectorStart(builder):
+ builder.StartObject(1)
+
+def Uint16VectorAddValues(builder, values):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(values), 0)
+
+def Uint16VectorStartValuesVector(builder, numElems):
+ return builder.StartVector(2, numElems, 2)
+
+def Uint16VectorEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class Uint16VectorT(object):
+
+ # Uint16VectorT
+ def __init__(self):
+ self.values = None # type: List[int]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ uint16Vector = Uint16Vector()
+ uint16Vector.Init(buf, pos)
+ return cls.InitFromObj(uint16Vector)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, uint16Vector):
+ x = Uint16VectorT()
+ x._UnPack(uint16Vector)
+ return x
+
+ # Uint16VectorT
+ def _UnPack(self, uint16Vector):
+ if uint16Vector is None:
+ return
+ if not uint16Vector.ValuesIsNone():
+ if np is None:
+ self.values = []
+ for i in range(uint16Vector.ValuesLength()):
+ self.values.append(uint16Vector.Values(i))
+ else:
+ self.values = uint16Vector.ValuesAsNumpy()
+
+ # Uint16VectorT
+ def Pack(self, builder):
+ if self.values is not None:
+ if np is not None and type(self.values) is np.ndarray:
+ values = builder.CreateNumpyVector(self.values)
+ else:
+ Uint16VectorStartValuesVector(builder, len(self.values))
+ for i in reversed(range(len(self.values))):
+ builder.PrependUint16(self.values[i])
+ values = builder.EndVector()
+ Uint16VectorStart(builder)
+ if self.values is not None:
+ Uint16VectorAddValues(builder, values)
+ uint16Vector = Uint16VectorEnd(builder)
+ return uint16Vector
+
+
+class Uint8Vector(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = Uint8Vector()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsUint8Vector(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def Uint8VectorBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # Uint8Vector
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # Uint8Vector
+ def Values(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1))
+ return 0
+
+ # Uint8Vector
+ def ValuesAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o)
+ return 0
+
+ # Uint8Vector
+ def ValuesLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # Uint8Vector
+ def ValuesIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+def Uint8VectorStart(builder):
+ builder.StartObject(1)
+
+def Uint8VectorAddValues(builder, values):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(values), 0)
+
+def Uint8VectorStartValuesVector(builder, numElems):
+ return builder.StartVector(1, numElems, 1)
+
+def Uint8VectorEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class Uint8VectorT(object):
+
+ # Uint8VectorT
+ def __init__(self):
+ self.values = None # type: List[int]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ uint8Vector = Uint8Vector()
+ uint8Vector.Init(buf, pos)
+ return cls.InitFromObj(uint8Vector)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, uint8Vector):
+ x = Uint8VectorT()
+ x._UnPack(uint8Vector)
+ return x
+
+ # Uint8VectorT
+ def _UnPack(self, uint8Vector):
+ if uint8Vector is None:
+ return
+ if not uint8Vector.ValuesIsNone():
+ if np is None:
+ self.values = []
+ for i in range(uint8Vector.ValuesLength()):
+ self.values.append(uint8Vector.Values(i))
+ else:
+ self.values = uint8Vector.ValuesAsNumpy()
+
+ # Uint8VectorT
+ def Pack(self, builder):
+ if self.values is not None:
+ if np is not None and type(self.values) is np.ndarray:
+ values = builder.CreateNumpyVector(self.values)
+ else:
+ Uint8VectorStartValuesVector(builder, len(self.values))
+ for i in reversed(range(len(self.values))):
+ builder.PrependUint8(self.values[i])
+ values = builder.EndVector()
+ Uint8VectorStart(builder)
+ if self.values is not None:
+ Uint8VectorAddValues(builder, values)
+ uint8Vector = Uint8VectorEnd(builder)
+ return uint8Vector
+
+
+class DimensionMetadata(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = DimensionMetadata()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsDimensionMetadata(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def DimensionMetadataBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # DimensionMetadata
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # DimensionMetadata
+ def Format(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # DimensionMetadata
+ def DenseSize(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # DimensionMetadata
+ def ArraySegmentsType(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos)
+ return 0
+
+ # DimensionMetadata
+ def ArraySegments(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ from flatbuffers.table import Table
+ obj = Table(bytearray(), 0)
+ self._tab.Union(obj, o)
+ return obj
+ return None
+
+ # DimensionMetadata
+ def ArrayIndicesType(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos)
+ return 0
+
+ # DimensionMetadata
+ def ArrayIndices(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ if o != 0:
+ from flatbuffers.table import Table
+ obj = Table(bytearray(), 0)
+ self._tab.Union(obj, o)
+ return obj
+ return None
+
+def DimensionMetadataStart(builder):
+ builder.StartObject(6)
+
+def DimensionMetadataAddFormat(builder, format):
+ builder.PrependInt8Slot(0, format, 0)
+
+def DimensionMetadataAddDenseSize(builder, denseSize):
+ builder.PrependInt32Slot(1, denseSize, 0)
+
+def DimensionMetadataAddArraySegmentsType(builder, arraySegmentsType):
+ builder.PrependUint8Slot(2, arraySegmentsType, 0)
+
+def DimensionMetadataAddArraySegments(builder, arraySegments):
+ builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(arraySegments), 0)
+
+def DimensionMetadataAddArrayIndicesType(builder, arrayIndicesType):
+ builder.PrependUint8Slot(4, arrayIndicesType, 0)
+
+def DimensionMetadataAddArrayIndices(builder, arrayIndices):
+ builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(arrayIndices), 0)
+
+def DimensionMetadataEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import Union
+except:
+ pass
+
+class DimensionMetadataT(object):
+
+ # DimensionMetadataT
+ def __init__(self):
+ self.format = 0 # type: int
+ self.denseSize = 0 # type: int
+ self.arraySegmentsType = 0 # type: int
+ self.arraySegments = None # type: Union[None, Int32VectorT, Uint16VectorT, Uint8VectorT]
+ self.arrayIndicesType = 0 # type: int
+ self.arrayIndices = None # type: Union[None, Int32VectorT, Uint16VectorT, Uint8VectorT]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ dimensionMetadata = DimensionMetadata()
+ dimensionMetadata.Init(buf, pos)
+ return cls.InitFromObj(dimensionMetadata)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, dimensionMetadata):
+ x = DimensionMetadataT()
+ x._UnPack(dimensionMetadata)
+ return x
+
+ # DimensionMetadataT
+ def _UnPack(self, dimensionMetadata):
+ if dimensionMetadata is None:
+ return
+ self.format = dimensionMetadata.Format()
+ self.denseSize = dimensionMetadata.DenseSize()
+ self.arraySegmentsType = dimensionMetadata.ArraySegmentsType()
+ self.arraySegments = SparseIndexVectorCreator(self.arraySegmentsType, dimensionMetadata.ArraySegments())
+ self.arrayIndicesType = dimensionMetadata.ArrayIndicesType()
+ self.arrayIndices = SparseIndexVectorCreator(self.arrayIndicesType, dimensionMetadata.ArrayIndices())
+
+ # DimensionMetadataT
+ def Pack(self, builder):
+ if self.arraySegments is not None:
+ arraySegments = self.arraySegments.Pack(builder)
+ if self.arrayIndices is not None:
+ arrayIndices = self.arrayIndices.Pack(builder)
+ DimensionMetadataStart(builder)
+ DimensionMetadataAddFormat(builder, self.format)
+ DimensionMetadataAddDenseSize(builder, self.denseSize)
+ DimensionMetadataAddArraySegmentsType(builder, self.arraySegmentsType)
+ if self.arraySegments is not None:
+ DimensionMetadataAddArraySegments(builder, arraySegments)
+ DimensionMetadataAddArrayIndicesType(builder, self.arrayIndicesType)
+ if self.arrayIndices is not None:
+ DimensionMetadataAddArrayIndices(builder, arrayIndices)
+ dimensionMetadata = DimensionMetadataEnd(builder)
+ return dimensionMetadata
+
+
+class SparsityParameters(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = SparsityParameters()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsSparsityParameters(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def SparsityParametersBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # SparsityParameters
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # SparsityParameters
+ def TraversalOrder(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+ return 0
+
+ # SparsityParameters
+ def TraversalOrderAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
+ return 0
+
+ # SparsityParameters
+ def TraversalOrderLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # SparsityParameters
+ def TraversalOrderIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+ # SparsityParameters
+ def BlockMap(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+ return 0
+
+ # SparsityParameters
+ def BlockMapAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
+ return 0
+
+ # SparsityParameters
+ def BlockMapLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # SparsityParameters
+ def BlockMapIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ return o == 0
+
+ # SparsityParameters
+ def DimMetadata(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ x = self._tab.Vector(o)
+ x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
+ x = self._tab.Indirect(x)
+ obj = DimensionMetadata()
+ obj.Init(self._tab.Bytes, x)
+ return obj
+ return None
+
+ # SparsityParameters
+ def DimMetadataLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # SparsityParameters
+ def DimMetadataIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ return o == 0
+
+def SparsityParametersStart(builder):
+ builder.StartObject(3)
+
+def SparsityParametersAddTraversalOrder(builder, traversalOrder):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(traversalOrder), 0)
+
+def SparsityParametersStartTraversalOrderVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def SparsityParametersAddBlockMap(builder, blockMap):
+ builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(blockMap), 0)
+
+def SparsityParametersStartBlockMapVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def SparsityParametersAddDimMetadata(builder, dimMetadata):
+ builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(dimMetadata), 0)
+
+def SparsityParametersStartDimMetadataVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def SparsityParametersEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class SparsityParametersT(object):
+
+ # SparsityParametersT
+ def __init__(self):
+ self.traversalOrder = None # type: List[int]
+ self.blockMap = None # type: List[int]
+ self.dimMetadata = None # type: List[DimensionMetadataT]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ sparsityParameters = SparsityParameters()
+ sparsityParameters.Init(buf, pos)
+ return cls.InitFromObj(sparsityParameters)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, sparsityParameters):
+ x = SparsityParametersT()
+ x._UnPack(sparsityParameters)
+ return x
+
+ # SparsityParametersT
+ def _UnPack(self, sparsityParameters):
+ if sparsityParameters is None:
+ return
+ if not sparsityParameters.TraversalOrderIsNone():
+ if np is None:
+ self.traversalOrder = []
+ for i in range(sparsityParameters.TraversalOrderLength()):
+ self.traversalOrder.append(sparsityParameters.TraversalOrder(i))
+ else:
+ self.traversalOrder = sparsityParameters.TraversalOrderAsNumpy()
+ if not sparsityParameters.BlockMapIsNone():
+ if np is None:
+ self.blockMap = []
+ for i in range(sparsityParameters.BlockMapLength()):
+ self.blockMap.append(sparsityParameters.BlockMap(i))
+ else:
+ self.blockMap = sparsityParameters.BlockMapAsNumpy()
+ if not sparsityParameters.DimMetadataIsNone():
+ self.dimMetadata = []
+ for i in range(sparsityParameters.DimMetadataLength()):
+ if sparsityParameters.DimMetadata(i) is None:
+ self.dimMetadata.append(None)
+ else:
+ dimensionMetadata_ = DimensionMetadataT.InitFromObj(sparsityParameters.DimMetadata(i))
+ self.dimMetadata.append(dimensionMetadata_)
+
+ # SparsityParametersT
+ def Pack(self, builder):
+ if self.traversalOrder is not None:
+ if np is not None and type(self.traversalOrder) is np.ndarray:
+ traversalOrder = builder.CreateNumpyVector(self.traversalOrder)
+ else:
+ SparsityParametersStartTraversalOrderVector(builder, len(self.traversalOrder))
+ for i in reversed(range(len(self.traversalOrder))):
+ builder.PrependInt32(self.traversalOrder[i])
+ traversalOrder = builder.EndVector()
+ if self.blockMap is not None:
+ if np is not None and type(self.blockMap) is np.ndarray:
+ blockMap = builder.CreateNumpyVector(self.blockMap)
+ else:
+ SparsityParametersStartBlockMapVector(builder, len(self.blockMap))
+ for i in reversed(range(len(self.blockMap))):
+ builder.PrependInt32(self.blockMap[i])
+ blockMap = builder.EndVector()
+ if self.dimMetadata is not None:
+ dimMetadatalist = []
+ for i in range(len(self.dimMetadata)):
+ dimMetadatalist.append(self.dimMetadata[i].Pack(builder))
+ SparsityParametersStartDimMetadataVector(builder, len(self.dimMetadata))
+ for i in reversed(range(len(self.dimMetadata))):
+ builder.PrependUOffsetTRelative(dimMetadatalist[i])
+ dimMetadata = builder.EndVector()
+ SparsityParametersStart(builder)
+ if self.traversalOrder is not None:
+ SparsityParametersAddTraversalOrder(builder, traversalOrder)
+ if self.blockMap is not None:
+ SparsityParametersAddBlockMap(builder, blockMap)
+ if self.dimMetadata is not None:
+ SparsityParametersAddDimMetadata(builder, dimMetadata)
+ sparsityParameters = SparsityParametersEnd(builder)
+ return sparsityParameters
+
+
+class VariantSubType(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = VariantSubType()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsVariantSubType(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def VariantSubTypeBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # VariantSubType
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # VariantSubType
+ def Shape(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+ return 0
+
+ # VariantSubType
+ def ShapeAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
+ return 0
+
+ # VariantSubType
+ def ShapeLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # VariantSubType
+ def ShapeIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+ # VariantSubType
+ def Type(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # VariantSubType
+ def HasRank(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+def VariantSubTypeStart(builder):
+ builder.StartObject(3)
+
+def VariantSubTypeAddShape(builder, shape):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(shape), 0)
+
+def VariantSubTypeStartShapeVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def VariantSubTypeAddType(builder, type):
+ builder.PrependInt8Slot(1, type, 0)
+
+def VariantSubTypeAddHasRank(builder, hasRank):
+ builder.PrependBoolSlot(2, hasRank, 0)
+
+def VariantSubTypeEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class VariantSubTypeT(object):
+
+ # VariantSubTypeT
+ def __init__(self):
+ self.shape = None # type: List[int]
+ self.type = 0 # type: int
+ self.hasRank = False # type: bool
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ variantSubType = VariantSubType()
+ variantSubType.Init(buf, pos)
+ return cls.InitFromObj(variantSubType)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, variantSubType):
+ x = VariantSubTypeT()
+ x._UnPack(variantSubType)
+ return x
+
+ # VariantSubTypeT
+ def _UnPack(self, variantSubType):
+ if variantSubType is None:
+ return
+ if not variantSubType.ShapeIsNone():
+ if np is None:
+ self.shape = []
+ for i in range(variantSubType.ShapeLength()):
+ self.shape.append(variantSubType.Shape(i))
+ else:
+ self.shape = variantSubType.ShapeAsNumpy()
+ self.type = variantSubType.Type()
+ self.hasRank = variantSubType.HasRank()
+
+ # VariantSubTypeT
+ def Pack(self, builder):
+ if self.shape is not None:
+ if np is not None and type(self.shape) is np.ndarray:
+ shape = builder.CreateNumpyVector(self.shape)
+ else:
+ VariantSubTypeStartShapeVector(builder, len(self.shape))
+ for i in reversed(range(len(self.shape))):
+ builder.PrependInt32(self.shape[i])
+ shape = builder.EndVector()
+ VariantSubTypeStart(builder)
+ if self.shape is not None:
+ VariantSubTypeAddShape(builder, shape)
+ VariantSubTypeAddType(builder, self.type)
+ VariantSubTypeAddHasRank(builder, self.hasRank)
+ variantSubType = VariantSubTypeEnd(builder)
+ return variantSubType
+
+
+class Tensor(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = Tensor()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsTensor(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def TensorBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # Tensor
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # Tensor
+ def Shape(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+ return 0
+
+ # Tensor
+ def ShapeAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
+ return 0
+
+ # Tensor
+ def ShapeLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # Tensor
+ def ShapeIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+ # Tensor
+ def Type(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # Tensor
+ def Buffer(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos)
+ return 0
+
+ # Tensor
+ def Name(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.String(o + self._tab.Pos)
+ return None
+
+ # Tensor
+ def Quantization(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ x = self._tab.Indirect(o + self._tab.Pos)
+ obj = QuantizationParameters()
+ obj.Init(self._tab.Bytes, x)
+ return obj
+ return None
+
+ # Tensor
+ def IsVariable(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+ # Tensor
+ def Sparsity(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
+ if o != 0:
+ x = self._tab.Indirect(o + self._tab.Pos)
+ obj = SparsityParameters()
+ obj.Init(self._tab.Bytes, x)
+ return obj
+ return None
+
+ # Tensor
+ def ShapeSignature(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+ return 0
+
+ # Tensor
+ def ShapeSignatureAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
+ return 0
+
+ # Tensor
+ def ShapeSignatureLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # Tensor
+ def ShapeSignatureIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
+ return o == 0
+
+ # Tensor
+ def HasRank(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(20))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+ # Tensor
+ def VariantTensors(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(22))
+ if o != 0:
+ x = self._tab.Vector(o)
+ x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
+ x = self._tab.Indirect(x)
+ obj = VariantSubType()
+ obj.Init(self._tab.Bytes, x)
+ return obj
+ return None
+
+ # Tensor
+ def VariantTensorsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(22))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # Tensor
+ def VariantTensorsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(22))
+ return o == 0
+
+def TensorStart(builder):
+ builder.StartObject(10)
+
+def TensorAddShape(builder, shape):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(shape), 0)
+
+def TensorStartShapeVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def TensorAddType(builder, type):
+ builder.PrependInt8Slot(1, type, 0)
+
+def TensorAddBuffer(builder, buffer):
+ builder.PrependUint32Slot(2, buffer, 0)
+
+def TensorAddName(builder, name):
+ builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0)
+
+def TensorAddQuantization(builder, quantization):
+ builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(quantization), 0)
+
+def TensorAddIsVariable(builder, isVariable):
+ builder.PrependBoolSlot(5, isVariable, 0)
+
+def TensorAddSparsity(builder, sparsity):
+ builder.PrependUOffsetTRelativeSlot(6, flatbuffers.number_types.UOffsetTFlags.py_type(sparsity), 0)
+
+def TensorAddShapeSignature(builder, shapeSignature):
+ builder.PrependUOffsetTRelativeSlot(7, flatbuffers.number_types.UOffsetTFlags.py_type(shapeSignature), 0)
+
+def TensorStartShapeSignatureVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def TensorAddHasRank(builder, hasRank):
+ builder.PrependBoolSlot(8, hasRank, 0)
+
+def TensorAddVariantTensors(builder, variantTensors):
+ builder.PrependUOffsetTRelativeSlot(9, flatbuffers.number_types.UOffsetTFlags.py_type(variantTensors), 0)
+
+def TensorStartVariantTensorsVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def TensorEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List, Optional
+except:
+ pass
+
+class TensorT(object):
+
+ # TensorT
+ def __init__(self):
+ self.shape = None # type: List[int]
+ self.type = 0 # type: int
+ self.buffer = 0 # type: int
+ self.name = None # type: str
+ self.quantization = None # type: Optional[QuantizationParametersT]
+ self.isVariable = False # type: bool
+ self.sparsity = None # type: Optional[SparsityParametersT]
+ self.shapeSignature = None # type: List[int]
+ self.hasRank = False # type: bool
+ self.variantTensors = None # type: List[VariantSubTypeT]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ tensor = Tensor()
+ tensor.Init(buf, pos)
+ return cls.InitFromObj(tensor)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, tensor):
+ x = TensorT()
+ x._UnPack(tensor)
+ return x
+
+ # TensorT
+ def _UnPack(self, tensor):
+ if tensor is None:
+ return
+ if not tensor.ShapeIsNone():
+ if np is None:
+ self.shape = []
+ for i in range(tensor.ShapeLength()):
+ self.shape.append(tensor.Shape(i))
+ else:
+ self.shape = tensor.ShapeAsNumpy()
+ self.type = tensor.Type()
+ self.buffer = tensor.Buffer()
+ self.name = tensor.Name()
+ if tensor.Quantization() is not None:
+ self.quantization = QuantizationParametersT.InitFromObj(tensor.Quantization())
+ self.isVariable = tensor.IsVariable()
+ if tensor.Sparsity() is not None:
+ self.sparsity = SparsityParametersT.InitFromObj(tensor.Sparsity())
+ if not tensor.ShapeSignatureIsNone():
+ if np is None:
+ self.shapeSignature = []
+ for i in range(tensor.ShapeSignatureLength()):
+ self.shapeSignature.append(tensor.ShapeSignature(i))
+ else:
+ self.shapeSignature = tensor.ShapeSignatureAsNumpy()
+ self.hasRank = tensor.HasRank()
+ if not tensor.VariantTensorsIsNone():
+ self.variantTensors = []
+ for i in range(tensor.VariantTensorsLength()):
+ if tensor.VariantTensors(i) is None:
+ self.variantTensors.append(None)
+ else:
+ variantSubType_ = VariantSubTypeT.InitFromObj(tensor.VariantTensors(i))
+ self.variantTensors.append(variantSubType_)
+
+ # TensorT
+ def Pack(self, builder):
+ if self.shape is not None:
+ if np is not None and type(self.shape) is np.ndarray:
+ shape = builder.CreateNumpyVector(self.shape)
+ else:
+ TensorStartShapeVector(builder, len(self.shape))
+ for i in reversed(range(len(self.shape))):
+ builder.PrependInt32(self.shape[i])
+ shape = builder.EndVector()
+ if self.name is not None:
+ name = builder.CreateString(self.name)
+ if self.quantization is not None:
+ quantization = self.quantization.Pack(builder)
+ if self.sparsity is not None:
+ sparsity = self.sparsity.Pack(builder)
+ if self.shapeSignature is not None:
+ if np is not None and type(self.shapeSignature) is np.ndarray:
+ shapeSignature = builder.CreateNumpyVector(self.shapeSignature)
+ else:
+ TensorStartShapeSignatureVector(builder, len(self.shapeSignature))
+ for i in reversed(range(len(self.shapeSignature))):
+ builder.PrependInt32(self.shapeSignature[i])
+ shapeSignature = builder.EndVector()
+ if self.variantTensors is not None:
+ variantTensorslist = []
+ for i in range(len(self.variantTensors)):
+ variantTensorslist.append(self.variantTensors[i].Pack(builder))
+ TensorStartVariantTensorsVector(builder, len(self.variantTensors))
+ for i in reversed(range(len(self.variantTensors))):
+ builder.PrependUOffsetTRelative(variantTensorslist[i])
+ variantTensors = builder.EndVector()
+ TensorStart(builder)
+ if self.shape is not None:
+ TensorAddShape(builder, shape)
+ TensorAddType(builder, self.type)
+ TensorAddBuffer(builder, self.buffer)
+ if self.name is not None:
+ TensorAddName(builder, name)
+ if self.quantization is not None:
+ TensorAddQuantization(builder, quantization)
+ TensorAddIsVariable(builder, self.isVariable)
+ if self.sparsity is not None:
+ TensorAddSparsity(builder, sparsity)
+ if self.shapeSignature is not None:
+ TensorAddShapeSignature(builder, shapeSignature)
+ TensorAddHasRank(builder, self.hasRank)
+ if self.variantTensors is not None:
+ TensorAddVariantTensors(builder, variantTensors)
+ tensor = TensorEnd(builder)
+ return tensor
+
+
+class StablehloGatherOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloGatherOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloGatherOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloGatherOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloGatherOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloGatherOptions
+ def OffsetDims(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloGatherOptions
+ def OffsetDimsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloGatherOptions
+ def OffsetDimsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloGatherOptions
+ def OffsetDimsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+ # StablehloGatherOptions
+ def CollapsedSliceDims(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloGatherOptions
+ def CollapsedSliceDimsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloGatherOptions
+ def CollapsedSliceDimsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloGatherOptions
+ def CollapsedSliceDimsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ return o == 0
+
+ # StablehloGatherOptions
+ def StartIndexMap(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloGatherOptions
+ def StartIndexMapAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloGatherOptions
+ def StartIndexMapLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloGatherOptions
+ def StartIndexMapIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ return o == 0
+
+ # StablehloGatherOptions
+ def IndexVectorDim(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, o + self._tab.Pos)
+ return 0
+
+ # StablehloGatherOptions
+ def SliceSizes(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloGatherOptions
+ def SliceSizesAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloGatherOptions
+ def SliceSizesLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloGatherOptions
+ def SliceSizesIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ return o == 0
+
+ # StablehloGatherOptions
+ def IndicesAreSorted(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+def StablehloGatherOptionsStart(builder):
+ builder.StartObject(6)
+
+def StablehloGatherOptionsAddOffsetDims(builder, offsetDims):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(offsetDims), 0)
+
+def StablehloGatherOptionsStartOffsetDimsVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloGatherOptionsAddCollapsedSliceDims(builder, collapsedSliceDims):
+ builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(collapsedSliceDims), 0)
+
+def StablehloGatherOptionsStartCollapsedSliceDimsVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloGatherOptionsAddStartIndexMap(builder, startIndexMap):
+ builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(startIndexMap), 0)
+
+def StablehloGatherOptionsStartStartIndexMapVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloGatherOptionsAddIndexVectorDim(builder, indexVectorDim):
+ builder.PrependInt64Slot(3, indexVectorDim, 0)
+
+def StablehloGatherOptionsAddSliceSizes(builder, sliceSizes):
+ builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(sliceSizes), 0)
+
+def StablehloGatherOptionsStartSliceSizesVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloGatherOptionsAddIndicesAreSorted(builder, indicesAreSorted):
+ builder.PrependBoolSlot(5, indicesAreSorted, 0)
+
+def StablehloGatherOptionsEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class StablehloGatherOptionsT(object):
+
+ # StablehloGatherOptionsT
+ def __init__(self):
+ self.offsetDims = None # type: List[int]
+ self.collapsedSliceDims = None # type: List[int]
+ self.startIndexMap = None # type: List[int]
+ self.indexVectorDim = 0 # type: int
+ self.sliceSizes = None # type: List[int]
+ self.indicesAreSorted = False # type: bool
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloGatherOptions = StablehloGatherOptions()
+ stablehloGatherOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloGatherOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloGatherOptions):
+ x = StablehloGatherOptionsT()
+ x._UnPack(stablehloGatherOptions)
+ return x
+
+ # StablehloGatherOptionsT
+ def _UnPack(self, stablehloGatherOptions):
+ if stablehloGatherOptions is None:
+ return
+ if not stablehloGatherOptions.OffsetDimsIsNone():
+ if np is None:
+ self.offsetDims = []
+ for i in range(stablehloGatherOptions.OffsetDimsLength()):
+ self.offsetDims.append(stablehloGatherOptions.OffsetDims(i))
+ else:
+ self.offsetDims = stablehloGatherOptions.OffsetDimsAsNumpy()
+ if not stablehloGatherOptions.CollapsedSliceDimsIsNone():
+ if np is None:
+ self.collapsedSliceDims = []
+ for i in range(stablehloGatherOptions.CollapsedSliceDimsLength()):
+ self.collapsedSliceDims.append(stablehloGatherOptions.CollapsedSliceDims(i))
+ else:
+ self.collapsedSliceDims = stablehloGatherOptions.CollapsedSliceDimsAsNumpy()
+ if not stablehloGatherOptions.StartIndexMapIsNone():
+ if np is None:
+ self.startIndexMap = []
+ for i in range(stablehloGatherOptions.StartIndexMapLength()):
+ self.startIndexMap.append(stablehloGatherOptions.StartIndexMap(i))
+ else:
+ self.startIndexMap = stablehloGatherOptions.StartIndexMapAsNumpy()
+ self.indexVectorDim = stablehloGatherOptions.IndexVectorDim()
+ if not stablehloGatherOptions.SliceSizesIsNone():
+ if np is None:
+ self.sliceSizes = []
+ for i in range(stablehloGatherOptions.SliceSizesLength()):
+ self.sliceSizes.append(stablehloGatherOptions.SliceSizes(i))
+ else:
+ self.sliceSizes = stablehloGatherOptions.SliceSizesAsNumpy()
+ self.indicesAreSorted = stablehloGatherOptions.IndicesAreSorted()
+
+ # StablehloGatherOptionsT
+ def Pack(self, builder):
+ if self.offsetDims is not None:
+ if np is not None and type(self.offsetDims) is np.ndarray:
+ offsetDims = builder.CreateNumpyVector(self.offsetDims)
+ else:
+ StablehloGatherOptionsStartOffsetDimsVector(builder, len(self.offsetDims))
+ for i in reversed(range(len(self.offsetDims))):
+ builder.PrependInt64(self.offsetDims[i])
+ offsetDims = builder.EndVector()
+ if self.collapsedSliceDims is not None:
+ if np is not None and type(self.collapsedSliceDims) is np.ndarray:
+ collapsedSliceDims = builder.CreateNumpyVector(self.collapsedSliceDims)
+ else:
+ StablehloGatherOptionsStartCollapsedSliceDimsVector(builder, len(self.collapsedSliceDims))
+ for i in reversed(range(len(self.collapsedSliceDims))):
+ builder.PrependInt64(self.collapsedSliceDims[i])
+ collapsedSliceDims = builder.EndVector()
+ if self.startIndexMap is not None:
+ if np is not None and type(self.startIndexMap) is np.ndarray:
+ startIndexMap = builder.CreateNumpyVector(self.startIndexMap)
+ else:
+ StablehloGatherOptionsStartStartIndexMapVector(builder, len(self.startIndexMap))
+ for i in reversed(range(len(self.startIndexMap))):
+ builder.PrependInt64(self.startIndexMap[i])
+ startIndexMap = builder.EndVector()
+ if self.sliceSizes is not None:
+ if np is not None and type(self.sliceSizes) is np.ndarray:
+ sliceSizes = builder.CreateNumpyVector(self.sliceSizes)
+ else:
+ StablehloGatherOptionsStartSliceSizesVector(builder, len(self.sliceSizes))
+ for i in reversed(range(len(self.sliceSizes))):
+ builder.PrependInt64(self.sliceSizes[i])
+ sliceSizes = builder.EndVector()
+ StablehloGatherOptionsStart(builder)
+ if self.offsetDims is not None:
+ StablehloGatherOptionsAddOffsetDims(builder, offsetDims)
+ if self.collapsedSliceDims is not None:
+ StablehloGatherOptionsAddCollapsedSliceDims(builder, collapsedSliceDims)
+ if self.startIndexMap is not None:
+ StablehloGatherOptionsAddStartIndexMap(builder, startIndexMap)
+ StablehloGatherOptionsAddIndexVectorDim(builder, self.indexVectorDim)
+ if self.sliceSizes is not None:
+ StablehloGatherOptionsAddSliceSizes(builder, sliceSizes)
+ StablehloGatherOptionsAddIndicesAreSorted(builder, self.indicesAreSorted)
+ stablehloGatherOptions = StablehloGatherOptionsEnd(builder)
+ return stablehloGatherOptions
+
+
+class StablehloTransposeOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloTransposeOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloTransposeOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloTransposeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloTransposeOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloTransposeOptions
+ def Permutation(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloTransposeOptions
+ def PermutationAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloTransposeOptions
+ def PermutationLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloTransposeOptions
+ def PermutationIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+def StablehloTransposeOptionsStart(builder):
+ builder.StartObject(1)
+
+def StablehloTransposeOptionsAddPermutation(builder, permutation):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(permutation), 0)
+
+def StablehloTransposeOptionsStartPermutationVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloTransposeOptionsEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class StablehloTransposeOptionsT(object):
+
+ # StablehloTransposeOptionsT
+ def __init__(self):
+ self.permutation = None # type: List[int]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloTransposeOptions = StablehloTransposeOptions()
+ stablehloTransposeOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloTransposeOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloTransposeOptions):
+ x = StablehloTransposeOptionsT()
+ x._UnPack(stablehloTransposeOptions)
+ return x
+
+ # StablehloTransposeOptionsT
+ def _UnPack(self, stablehloTransposeOptions):
+ if stablehloTransposeOptions is None:
+ return
+ if not stablehloTransposeOptions.PermutationIsNone():
+ if np is None:
+ self.permutation = []
+ for i in range(stablehloTransposeOptions.PermutationLength()):
+ self.permutation.append(stablehloTransposeOptions.Permutation(i))
+ else:
+ self.permutation = stablehloTransposeOptions.PermutationAsNumpy()
+
+ # StablehloTransposeOptionsT
+ def Pack(self, builder):
+ if self.permutation is not None:
+ if np is not None and type(self.permutation) is np.ndarray:
+ permutation = builder.CreateNumpyVector(self.permutation)
+ else:
+ StablehloTransposeOptionsStartPermutationVector(builder, len(self.permutation))
+ for i in reversed(range(len(self.permutation))):
+ builder.PrependInt64(self.permutation[i])
+ permutation = builder.EndVector()
+ StablehloTransposeOptionsStart(builder)
+ if self.permutation is not None:
+ StablehloTransposeOptionsAddPermutation(builder, permutation)
+ stablehloTransposeOptions = StablehloTransposeOptionsEnd(builder)
+ return stablehloTransposeOptions
+
+
+class StablehloDotGeneralOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloDotGeneralOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloDotGeneralOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloDotGeneralOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloDotGeneralOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloDotGeneralOptions
+ def LhsBatchingDimensions(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloDotGeneralOptions
+ def LhsBatchingDimensionsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloDotGeneralOptions
+ def LhsBatchingDimensionsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloDotGeneralOptions
+ def LhsBatchingDimensionsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+ # StablehloDotGeneralOptions
+ def RhsBatchingDimensions(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloDotGeneralOptions
+ def RhsBatchingDimensionsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloDotGeneralOptions
+ def RhsBatchingDimensionsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloDotGeneralOptions
+ def RhsBatchingDimensionsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ return o == 0
+
+ # StablehloDotGeneralOptions
+ def LhsContractingDimensions(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloDotGeneralOptions
+ def LhsContractingDimensionsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloDotGeneralOptions
+ def LhsContractingDimensionsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloDotGeneralOptions
+ def LhsContractingDimensionsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ return o == 0
+
+ # StablehloDotGeneralOptions
+ def RhsContractingDimensions(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloDotGeneralOptions
+ def RhsContractingDimensionsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloDotGeneralOptions
+ def RhsContractingDimensionsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloDotGeneralOptions
+ def RhsContractingDimensionsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ return o == 0
+
+ # StablehloDotGeneralOptions
+ def PrecisionConfig(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Uint32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+ return 0
+
+ # StablehloDotGeneralOptions
+ def PrecisionConfigAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint32Flags, o)
+ return 0
+
+ # StablehloDotGeneralOptions
+ def PrecisionConfigLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloDotGeneralOptions
+ def PrecisionConfigIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ return o == 0
+
+def StablehloDotGeneralOptionsStart(builder):
+ builder.StartObject(5)
+
+def StablehloDotGeneralOptionsAddLhsBatchingDimensions(builder, lhsBatchingDimensions):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(lhsBatchingDimensions), 0)
+
+def StablehloDotGeneralOptionsStartLhsBatchingDimensionsVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloDotGeneralOptionsAddRhsBatchingDimensions(builder, rhsBatchingDimensions):
+ builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(rhsBatchingDimensions), 0)
+
+def StablehloDotGeneralOptionsStartRhsBatchingDimensionsVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloDotGeneralOptionsAddLhsContractingDimensions(builder, lhsContractingDimensions):
+ builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(lhsContractingDimensions), 0)
+
+def StablehloDotGeneralOptionsStartLhsContractingDimensionsVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloDotGeneralOptionsAddRhsContractingDimensions(builder, rhsContractingDimensions):
+ builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(rhsContractingDimensions), 0)
+
+def StablehloDotGeneralOptionsStartRhsContractingDimensionsVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloDotGeneralOptionsAddPrecisionConfig(builder, precisionConfig):
+ builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(precisionConfig), 0)
+
+def StablehloDotGeneralOptionsStartPrecisionConfigVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def StablehloDotGeneralOptionsEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class StablehloDotGeneralOptionsT(object):
+
+ # StablehloDotGeneralOptionsT
+ def __init__(self):
+ self.lhsBatchingDimensions = None # type: List[int]
+ self.rhsBatchingDimensions = None # type: List[int]
+ self.lhsContractingDimensions = None # type: List[int]
+ self.rhsContractingDimensions = None # type: List[int]
+ self.precisionConfig = None # type: List[int]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloDotGeneralOptions = StablehloDotGeneralOptions()
+ stablehloDotGeneralOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloDotGeneralOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloDotGeneralOptions):
+ x = StablehloDotGeneralOptionsT()
+ x._UnPack(stablehloDotGeneralOptions)
+ return x
+
+ # StablehloDotGeneralOptionsT
+ def _UnPack(self, stablehloDotGeneralOptions):
+ if stablehloDotGeneralOptions is None:
+ return
+ if not stablehloDotGeneralOptions.LhsBatchingDimensionsIsNone():
+ if np is None:
+ self.lhsBatchingDimensions = []
+ for i in range(stablehloDotGeneralOptions.LhsBatchingDimensionsLength()):
+ self.lhsBatchingDimensions.append(stablehloDotGeneralOptions.LhsBatchingDimensions(i))
+ else:
+ self.lhsBatchingDimensions = stablehloDotGeneralOptions.LhsBatchingDimensionsAsNumpy()
+ if not stablehloDotGeneralOptions.RhsBatchingDimensionsIsNone():
+ if np is None:
+ self.rhsBatchingDimensions = []
+ for i in range(stablehloDotGeneralOptions.RhsBatchingDimensionsLength()):
+ self.rhsBatchingDimensions.append(stablehloDotGeneralOptions.RhsBatchingDimensions(i))
+ else:
+ self.rhsBatchingDimensions = stablehloDotGeneralOptions.RhsBatchingDimensionsAsNumpy()
+ if not stablehloDotGeneralOptions.LhsContractingDimensionsIsNone():
+ if np is None:
+ self.lhsContractingDimensions = []
+ for i in range(stablehloDotGeneralOptions.LhsContractingDimensionsLength()):
+ self.lhsContractingDimensions.append(stablehloDotGeneralOptions.LhsContractingDimensions(i))
+ else:
+ self.lhsContractingDimensions = stablehloDotGeneralOptions.LhsContractingDimensionsAsNumpy()
+ if not stablehloDotGeneralOptions.RhsContractingDimensionsIsNone():
+ if np is None:
+ self.rhsContractingDimensions = []
+ for i in range(stablehloDotGeneralOptions.RhsContractingDimensionsLength()):
+ self.rhsContractingDimensions.append(stablehloDotGeneralOptions.RhsContractingDimensions(i))
+ else:
+ self.rhsContractingDimensions = stablehloDotGeneralOptions.RhsContractingDimensionsAsNumpy()
+ if not stablehloDotGeneralOptions.PrecisionConfigIsNone():
+ if np is None:
+ self.precisionConfig = []
+ for i in range(stablehloDotGeneralOptions.PrecisionConfigLength()):
+ self.precisionConfig.append(stablehloDotGeneralOptions.PrecisionConfig(i))
+ else:
+ self.precisionConfig = stablehloDotGeneralOptions.PrecisionConfigAsNumpy()
+
+ # StablehloDotGeneralOptionsT
+ def Pack(self, builder):
+ if self.lhsBatchingDimensions is not None:
+ if np is not None and type(self.lhsBatchingDimensions) is np.ndarray:
+ lhsBatchingDimensions = builder.CreateNumpyVector(self.lhsBatchingDimensions)
+ else:
+ StablehloDotGeneralOptionsStartLhsBatchingDimensionsVector(builder, len(self.lhsBatchingDimensions))
+ for i in reversed(range(len(self.lhsBatchingDimensions))):
+ builder.PrependInt64(self.lhsBatchingDimensions[i])
+ lhsBatchingDimensions = builder.EndVector()
+ if self.rhsBatchingDimensions is not None:
+ if np is not None and type(self.rhsBatchingDimensions) is np.ndarray:
+ rhsBatchingDimensions = builder.CreateNumpyVector(self.rhsBatchingDimensions)
+ else:
+ StablehloDotGeneralOptionsStartRhsBatchingDimensionsVector(builder, len(self.rhsBatchingDimensions))
+ for i in reversed(range(len(self.rhsBatchingDimensions))):
+ builder.PrependInt64(self.rhsBatchingDimensions[i])
+ rhsBatchingDimensions = builder.EndVector()
+ if self.lhsContractingDimensions is not None:
+ if np is not None and type(self.lhsContractingDimensions) is np.ndarray:
+ lhsContractingDimensions = builder.CreateNumpyVector(self.lhsContractingDimensions)
+ else:
+ StablehloDotGeneralOptionsStartLhsContractingDimensionsVector(builder, len(self.lhsContractingDimensions))
+ for i in reversed(range(len(self.lhsContractingDimensions))):
+ builder.PrependInt64(self.lhsContractingDimensions[i])
+ lhsContractingDimensions = builder.EndVector()
+ if self.rhsContractingDimensions is not None:
+ if np is not None and type(self.rhsContractingDimensions) is np.ndarray:
+ rhsContractingDimensions = builder.CreateNumpyVector(self.rhsContractingDimensions)
+ else:
+ StablehloDotGeneralOptionsStartRhsContractingDimensionsVector(builder, len(self.rhsContractingDimensions))
+ for i in reversed(range(len(self.rhsContractingDimensions))):
+ builder.PrependInt64(self.rhsContractingDimensions[i])
+ rhsContractingDimensions = builder.EndVector()
+ if self.precisionConfig is not None:
+ if np is not None and type(self.precisionConfig) is np.ndarray:
+ precisionConfig = builder.CreateNumpyVector(self.precisionConfig)
+ else:
+ StablehloDotGeneralOptionsStartPrecisionConfigVector(builder, len(self.precisionConfig))
+ for i in reversed(range(len(self.precisionConfig))):
+ builder.PrependUint32(self.precisionConfig[i])
+ precisionConfig = builder.EndVector()
+ StablehloDotGeneralOptionsStart(builder)
+ if self.lhsBatchingDimensions is not None:
+ StablehloDotGeneralOptionsAddLhsBatchingDimensions(builder, lhsBatchingDimensions)
+ if self.rhsBatchingDimensions is not None:
+ StablehloDotGeneralOptionsAddRhsBatchingDimensions(builder, rhsBatchingDimensions)
+ if self.lhsContractingDimensions is not None:
+ StablehloDotGeneralOptionsAddLhsContractingDimensions(builder, lhsContractingDimensions)
+ if self.rhsContractingDimensions is not None:
+ StablehloDotGeneralOptionsAddRhsContractingDimensions(builder, rhsContractingDimensions)
+ if self.precisionConfig is not None:
+ StablehloDotGeneralOptionsAddPrecisionConfig(builder, precisionConfig)
+ stablehloDotGeneralOptions = StablehloDotGeneralOptionsEnd(builder)
+ return stablehloDotGeneralOptions
+
+
+class StablehloReduceWindowOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloReduceWindowOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloReduceWindowOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloReduceWindowOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloReduceWindowOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloReduceWindowOptions
+ def WindowDimensions(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloReduceWindowOptions
+ def WindowDimensionsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloReduceWindowOptions
+ def WindowDimensionsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloReduceWindowOptions
+ def WindowDimensionsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+ # StablehloReduceWindowOptions
+ def WindowStrides(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloReduceWindowOptions
+ def WindowStridesAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloReduceWindowOptions
+ def WindowStridesLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloReduceWindowOptions
+ def WindowStridesIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ return o == 0
+
+ # StablehloReduceWindowOptions
+ def BaseDilations(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloReduceWindowOptions
+ def BaseDilationsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloReduceWindowOptions
+ def BaseDilationsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloReduceWindowOptions
+ def BaseDilationsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ return o == 0
+
+ # StablehloReduceWindowOptions
+ def WindowDilations(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloReduceWindowOptions
+ def WindowDilationsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloReduceWindowOptions
+ def WindowDilationsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloReduceWindowOptions
+ def WindowDilationsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ return o == 0
+
+ # StablehloReduceWindowOptions
+ def Padding(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloReduceWindowOptions
+ def PaddingAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloReduceWindowOptions
+ def PaddingLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloReduceWindowOptions
+ def PaddingIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ return o == 0
+
+ # StablehloReduceWindowOptions
+ def BodySubgraphIndex(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def StablehloReduceWindowOptionsStart(builder):
+ builder.StartObject(6)
+
+def StablehloReduceWindowOptionsAddWindowDimensions(builder, windowDimensions):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(windowDimensions), 0)
+
+def StablehloReduceWindowOptionsStartWindowDimensionsVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloReduceWindowOptionsAddWindowStrides(builder, windowStrides):
+ builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(windowStrides), 0)
+
+def StablehloReduceWindowOptionsStartWindowStridesVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloReduceWindowOptionsAddBaseDilations(builder, baseDilations):
+ builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(baseDilations), 0)
+
+def StablehloReduceWindowOptionsStartBaseDilationsVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloReduceWindowOptionsAddWindowDilations(builder, windowDilations):
+ builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(windowDilations), 0)
+
+def StablehloReduceWindowOptionsStartWindowDilationsVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloReduceWindowOptionsAddPadding(builder, padding):
+ builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(padding), 0)
+
+def StablehloReduceWindowOptionsStartPaddingVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloReduceWindowOptionsAddBodySubgraphIndex(builder, bodySubgraphIndex):
+ builder.PrependInt32Slot(5, bodySubgraphIndex, 0)
+
+def StablehloReduceWindowOptionsEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class StablehloReduceWindowOptionsT(object):
+
+ # StablehloReduceWindowOptionsT
+ def __init__(self):
+ self.windowDimensions = None # type: List[int]
+ self.windowStrides = None # type: List[int]
+ self.baseDilations = None # type: List[int]
+ self.windowDilations = None # type: List[int]
+ self.padding = None # type: List[int]
+ self.bodySubgraphIndex = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloReduceWindowOptions = StablehloReduceWindowOptions()
+ stablehloReduceWindowOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloReduceWindowOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloReduceWindowOptions):
+ x = StablehloReduceWindowOptionsT()
+ x._UnPack(stablehloReduceWindowOptions)
+ return x
+
+ # StablehloReduceWindowOptionsT
+ def _UnPack(self, stablehloReduceWindowOptions):
+ if stablehloReduceWindowOptions is None:
+ return
+ if not stablehloReduceWindowOptions.WindowDimensionsIsNone():
+ if np is None:
+ self.windowDimensions = []
+ for i in range(stablehloReduceWindowOptions.WindowDimensionsLength()):
+ self.windowDimensions.append(stablehloReduceWindowOptions.WindowDimensions(i))
+ else:
+ self.windowDimensions = stablehloReduceWindowOptions.WindowDimensionsAsNumpy()
+ if not stablehloReduceWindowOptions.WindowStridesIsNone():
+ if np is None:
+ self.windowStrides = []
+ for i in range(stablehloReduceWindowOptions.WindowStridesLength()):
+ self.windowStrides.append(stablehloReduceWindowOptions.WindowStrides(i))
+ else:
+ self.windowStrides = stablehloReduceWindowOptions.WindowStridesAsNumpy()
+ if not stablehloReduceWindowOptions.BaseDilationsIsNone():
+ if np is None:
+ self.baseDilations = []
+ for i in range(stablehloReduceWindowOptions.BaseDilationsLength()):
+ self.baseDilations.append(stablehloReduceWindowOptions.BaseDilations(i))
+ else:
+ self.baseDilations = stablehloReduceWindowOptions.BaseDilationsAsNumpy()
+ if not stablehloReduceWindowOptions.WindowDilationsIsNone():
+ if np is None:
+ self.windowDilations = []
+ for i in range(stablehloReduceWindowOptions.WindowDilationsLength()):
+ self.windowDilations.append(stablehloReduceWindowOptions.WindowDilations(i))
+ else:
+ self.windowDilations = stablehloReduceWindowOptions.WindowDilationsAsNumpy()
+ if not stablehloReduceWindowOptions.PaddingIsNone():
+ if np is None:
+ self.padding = []
+ for i in range(stablehloReduceWindowOptions.PaddingLength()):
+ self.padding.append(stablehloReduceWindowOptions.Padding(i))
+ else:
+ self.padding = stablehloReduceWindowOptions.PaddingAsNumpy()
+ self.bodySubgraphIndex = stablehloReduceWindowOptions.BodySubgraphIndex()
+
+ # StablehloReduceWindowOptionsT
+ def Pack(self, builder):
+ if self.windowDimensions is not None:
+ if np is not None and type(self.windowDimensions) is np.ndarray:
+ windowDimensions = builder.CreateNumpyVector(self.windowDimensions)
+ else:
+ StablehloReduceWindowOptionsStartWindowDimensionsVector(builder, len(self.windowDimensions))
+ for i in reversed(range(len(self.windowDimensions))):
+ builder.PrependInt64(self.windowDimensions[i])
+ windowDimensions = builder.EndVector()
+ if self.windowStrides is not None:
+ if np is not None and type(self.windowStrides) is np.ndarray:
+ windowStrides = builder.CreateNumpyVector(self.windowStrides)
+ else:
+ StablehloReduceWindowOptionsStartWindowStridesVector(builder, len(self.windowStrides))
+ for i in reversed(range(len(self.windowStrides))):
+ builder.PrependInt64(self.windowStrides[i])
+ windowStrides = builder.EndVector()
+ if self.baseDilations is not None:
+ if np is not None and type(self.baseDilations) is np.ndarray:
+ baseDilations = builder.CreateNumpyVector(self.baseDilations)
+ else:
+ StablehloReduceWindowOptionsStartBaseDilationsVector(builder, len(self.baseDilations))
+ for i in reversed(range(len(self.baseDilations))):
+ builder.PrependInt64(self.baseDilations[i])
+ baseDilations = builder.EndVector()
+ if self.windowDilations is not None:
+ if np is not None and type(self.windowDilations) is np.ndarray:
+ windowDilations = builder.CreateNumpyVector(self.windowDilations)
+ else:
+ StablehloReduceWindowOptionsStartWindowDilationsVector(builder, len(self.windowDilations))
+ for i in reversed(range(len(self.windowDilations))):
+ builder.PrependInt64(self.windowDilations[i])
+ windowDilations = builder.EndVector()
+ if self.padding is not None:
+ if np is not None and type(self.padding) is np.ndarray:
+ padding = builder.CreateNumpyVector(self.padding)
+ else:
+ StablehloReduceWindowOptionsStartPaddingVector(builder, len(self.padding))
+ for i in reversed(range(len(self.padding))):
+ builder.PrependInt64(self.padding[i])
+ padding = builder.EndVector()
+ StablehloReduceWindowOptionsStart(builder)
+ if self.windowDimensions is not None:
+ StablehloReduceWindowOptionsAddWindowDimensions(builder, windowDimensions)
+ if self.windowStrides is not None:
+ StablehloReduceWindowOptionsAddWindowStrides(builder, windowStrides)
+ if self.baseDilations is not None:
+ StablehloReduceWindowOptionsAddBaseDilations(builder, baseDilations)
+ if self.windowDilations is not None:
+ StablehloReduceWindowOptionsAddWindowDilations(builder, windowDilations)
+ if self.padding is not None:
+ StablehloReduceWindowOptionsAddPadding(builder, padding)
+ StablehloReduceWindowOptionsAddBodySubgraphIndex(builder, self.bodySubgraphIndex)
+ stablehloReduceWindowOptions = StablehloReduceWindowOptionsEnd(builder)
+ return stablehloReduceWindowOptions
+
+
+class StablehloWhileOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloWhileOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloWhileOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloWhileOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloWhileOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloWhileOptions
+ def CondSubgraphIndex(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # StablehloWhileOptions
+ def BodySubgraphIndex(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def StablehloWhileOptionsStart(builder):
+ builder.StartObject(2)
+
+def StablehloWhileOptionsAddCondSubgraphIndex(builder, condSubgraphIndex):
+ builder.PrependInt32Slot(0, condSubgraphIndex, 0)
+
+def StablehloWhileOptionsAddBodySubgraphIndex(builder, bodySubgraphIndex):
+ builder.PrependInt32Slot(1, bodySubgraphIndex, 0)
+
+def StablehloWhileOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class StablehloWhileOptionsT(object):
+
+ # StablehloWhileOptionsT
+ def __init__(self):
+ self.condSubgraphIndex = 0 # type: int
+ self.bodySubgraphIndex = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloWhileOptions = StablehloWhileOptions()
+ stablehloWhileOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloWhileOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloWhileOptions):
+ x = StablehloWhileOptionsT()
+ x._UnPack(stablehloWhileOptions)
+ return x
+
+ # StablehloWhileOptionsT
+ def _UnPack(self, stablehloWhileOptions):
+ if stablehloWhileOptions is None:
+ return
+ self.condSubgraphIndex = stablehloWhileOptions.CondSubgraphIndex()
+ self.bodySubgraphIndex = stablehloWhileOptions.BodySubgraphIndex()
+
+ # StablehloWhileOptionsT
+ def Pack(self, builder):
+ StablehloWhileOptionsStart(builder)
+ StablehloWhileOptionsAddCondSubgraphIndex(builder, self.condSubgraphIndex)
+ StablehloWhileOptionsAddBodySubgraphIndex(builder, self.bodySubgraphIndex)
+ stablehloWhileOptions = StablehloWhileOptionsEnd(builder)
+ return stablehloWhileOptions
+
+
+class StablehloSortOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloSortOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloSortOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloSortOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloSortOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloSortOptions
+ def Dimension(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, o + self._tab.Pos)
+ return 0
+
+ # StablehloSortOptions
+ def IsStable(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+ # StablehloSortOptions
+ def ComparatorSubgraphIndex(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def StablehloSortOptionsStart(builder):
+ builder.StartObject(3)
+
+def StablehloSortOptionsAddDimension(builder, dimension):
+ builder.PrependInt64Slot(0, dimension, 0)
+
+def StablehloSortOptionsAddIsStable(builder, isStable):
+ builder.PrependBoolSlot(1, isStable, 0)
+
+def StablehloSortOptionsAddComparatorSubgraphIndex(builder, comparatorSubgraphIndex):
+ builder.PrependInt32Slot(2, comparatorSubgraphIndex, 0)
+
+def StablehloSortOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class StablehloSortOptionsT(object):
+
+ # StablehloSortOptionsT
+ def __init__(self):
+ self.dimension = 0 # type: int
+ self.isStable = False # type: bool
+ self.comparatorSubgraphIndex = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloSortOptions = StablehloSortOptions()
+ stablehloSortOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloSortOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloSortOptions):
+ x = StablehloSortOptionsT()
+ x._UnPack(stablehloSortOptions)
+ return x
+
+ # StablehloSortOptionsT
+ def _UnPack(self, stablehloSortOptions):
+ if stablehloSortOptions is None:
+ return
+ self.dimension = stablehloSortOptions.Dimension()
+ self.isStable = stablehloSortOptions.IsStable()
+ self.comparatorSubgraphIndex = stablehloSortOptions.ComparatorSubgraphIndex()
+
+ # StablehloSortOptionsT
+ def Pack(self, builder):
+ StablehloSortOptionsStart(builder)
+ StablehloSortOptionsAddDimension(builder, self.dimension)
+ StablehloSortOptionsAddIsStable(builder, self.isStable)
+ StablehloSortOptionsAddComparatorSubgraphIndex(builder, self.comparatorSubgraphIndex)
+ stablehloSortOptions = StablehloSortOptionsEnd(builder)
+ return stablehloSortOptions
+
+
+class StablehloConcatenateOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloConcatenateOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloConcatenateOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloConcatenateOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloConcatenateOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloConcatenateOptions
+ def Dimension(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, o + self._tab.Pos)
+ return 0
+
+def StablehloConcatenateOptionsStart(builder):
+ builder.StartObject(1)
+
+def StablehloConcatenateOptionsAddDimension(builder, dimension):
+ builder.PrependInt64Slot(0, dimension, 0)
+
+def StablehloConcatenateOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class StablehloConcatenateOptionsT(object):
+
+ # StablehloConcatenateOptionsT
+ def __init__(self):
+ self.dimension = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloConcatenateOptions = StablehloConcatenateOptions()
+ stablehloConcatenateOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloConcatenateOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloConcatenateOptions):
+ x = StablehloConcatenateOptionsT()
+ x._UnPack(stablehloConcatenateOptions)
+ return x
+
+ # StablehloConcatenateOptionsT
+ def _UnPack(self, stablehloConcatenateOptions):
+ if stablehloConcatenateOptions is None:
+ return
+ self.dimension = stablehloConcatenateOptions.Dimension()
+
+ # StablehloConcatenateOptionsT
+ def Pack(self, builder):
+ StablehloConcatenateOptionsStart(builder)
+ StablehloConcatenateOptionsAddDimension(builder, self.dimension)
+ stablehloConcatenateOptions = StablehloConcatenateOptionsEnd(builder)
+ return stablehloConcatenateOptions
+
+
+class StablehloBroadcastInDimOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloBroadcastInDimOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloBroadcastInDimOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloBroadcastInDimOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloBroadcastInDimOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloBroadcastInDimOptions
+ def BroadcastDimensions(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloBroadcastInDimOptions
+ def BroadcastDimensionsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloBroadcastInDimOptions
+ def BroadcastDimensionsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloBroadcastInDimOptions
+ def BroadcastDimensionsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+def StablehloBroadcastInDimOptionsStart(builder):
+ builder.StartObject(1)
+
+def StablehloBroadcastInDimOptionsAddBroadcastDimensions(builder, broadcastDimensions):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(broadcastDimensions), 0)
+
+def StablehloBroadcastInDimOptionsStartBroadcastDimensionsVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloBroadcastInDimOptionsEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class StablehloBroadcastInDimOptionsT(object):
+
+ # StablehloBroadcastInDimOptionsT
+ def __init__(self):
+ self.broadcastDimensions = None # type: List[int]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloBroadcastInDimOptions = StablehloBroadcastInDimOptions()
+ stablehloBroadcastInDimOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloBroadcastInDimOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloBroadcastInDimOptions):
+ x = StablehloBroadcastInDimOptionsT()
+ x._UnPack(stablehloBroadcastInDimOptions)
+ return x
+
+ # StablehloBroadcastInDimOptionsT
+ def _UnPack(self, stablehloBroadcastInDimOptions):
+ if stablehloBroadcastInDimOptions is None:
+ return
+ if not stablehloBroadcastInDimOptions.BroadcastDimensionsIsNone():
+ if np is None:
+ self.broadcastDimensions = []
+ for i in range(stablehloBroadcastInDimOptions.BroadcastDimensionsLength()):
+ self.broadcastDimensions.append(stablehloBroadcastInDimOptions.BroadcastDimensions(i))
+ else:
+ self.broadcastDimensions = stablehloBroadcastInDimOptions.BroadcastDimensionsAsNumpy()
+
+ # StablehloBroadcastInDimOptionsT
+ def Pack(self, builder):
+ if self.broadcastDimensions is not None:
+ if np is not None and type(self.broadcastDimensions) is np.ndarray:
+ broadcastDimensions = builder.CreateNumpyVector(self.broadcastDimensions)
+ else:
+ StablehloBroadcastInDimOptionsStartBroadcastDimensionsVector(builder, len(self.broadcastDimensions))
+ for i in reversed(range(len(self.broadcastDimensions))):
+ builder.PrependInt64(self.broadcastDimensions[i])
+ broadcastDimensions = builder.EndVector()
+ StablehloBroadcastInDimOptionsStart(builder)
+ if self.broadcastDimensions is not None:
+ StablehloBroadcastInDimOptionsAddBroadcastDimensions(builder, broadcastDimensions)
+ stablehloBroadcastInDimOptions = StablehloBroadcastInDimOptionsEnd(builder)
+ return stablehloBroadcastInDimOptions
+
+
+class StablehloCompareOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloCompareOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloCompareOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloCompareOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloCompareOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloCompareOptions
+ def ComparisonDirection(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos)
+ return 0
+
+ # StablehloCompareOptions
+ def CompareType(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos)
+ return 0
+
+def StablehloCompareOptionsStart(builder):
+ builder.StartObject(2)
+
+def StablehloCompareOptionsAddComparisonDirection(builder, comparisonDirection):
+ builder.PrependUint32Slot(0, comparisonDirection, 0)
+
+def StablehloCompareOptionsAddCompareType(builder, compareType):
+ builder.PrependUint32Slot(1, compareType, 0)
+
+def StablehloCompareOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class StablehloCompareOptionsT(object):
+
+ # StablehloCompareOptionsT
+ def __init__(self):
+ self.comparisonDirection = 0 # type: int
+ self.compareType = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloCompareOptions = StablehloCompareOptions()
+ stablehloCompareOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloCompareOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloCompareOptions):
+ x = StablehloCompareOptionsT()
+ x._UnPack(stablehloCompareOptions)
+ return x
+
+ # StablehloCompareOptionsT
+ def _UnPack(self, stablehloCompareOptions):
+ if stablehloCompareOptions is None:
+ return
+ self.comparisonDirection = stablehloCompareOptions.ComparisonDirection()
+ self.compareType = stablehloCompareOptions.CompareType()
+
+ # StablehloCompareOptionsT
+ def Pack(self, builder):
+ StablehloCompareOptionsStart(builder)
+ StablehloCompareOptionsAddComparisonDirection(builder, self.comparisonDirection)
+ StablehloCompareOptionsAddCompareType(builder, self.compareType)
+ stablehloCompareOptions = StablehloCompareOptionsEnd(builder)
+ return stablehloCompareOptions
+
+
+class StablehloDynamicSliceOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloDynamicSliceOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloDynamicSliceOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloDynamicSliceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloDynamicSliceOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloDynamicSliceOptions
+ def SliceSizes(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloDynamicSliceOptions
+ def SliceSizesAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloDynamicSliceOptions
+ def SliceSizesLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloDynamicSliceOptions
+ def SliceSizesIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+def StablehloDynamicSliceOptionsStart(builder):
+ builder.StartObject(1)
+
+def StablehloDynamicSliceOptionsAddSliceSizes(builder, sliceSizes):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(sliceSizes), 0)
+
+def StablehloDynamicSliceOptionsStartSliceSizesVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloDynamicSliceOptionsEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class StablehloDynamicSliceOptionsT(object):
+
+ # StablehloDynamicSliceOptionsT
+ def __init__(self):
+ self.sliceSizes = None # type: List[int]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloDynamicSliceOptions = StablehloDynamicSliceOptions()
+ stablehloDynamicSliceOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloDynamicSliceOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloDynamicSliceOptions):
+ x = StablehloDynamicSliceOptionsT()
+ x._UnPack(stablehloDynamicSliceOptions)
+ return x
+
+ # StablehloDynamicSliceOptionsT
+ def _UnPack(self, stablehloDynamicSliceOptions):
+ if stablehloDynamicSliceOptions is None:
+ return
+ if not stablehloDynamicSliceOptions.SliceSizesIsNone():
+ if np is None:
+ self.sliceSizes = []
+ for i in range(stablehloDynamicSliceOptions.SliceSizesLength()):
+ self.sliceSizes.append(stablehloDynamicSliceOptions.SliceSizes(i))
+ else:
+ self.sliceSizes = stablehloDynamicSliceOptions.SliceSizesAsNumpy()
+
+ # StablehloDynamicSliceOptionsT
+ def Pack(self, builder):
+ if self.sliceSizes is not None:
+ if np is not None and type(self.sliceSizes) is np.ndarray:
+ sliceSizes = builder.CreateNumpyVector(self.sliceSizes)
+ else:
+ StablehloDynamicSliceOptionsStartSliceSizesVector(builder, len(self.sliceSizes))
+ for i in reversed(range(len(self.sliceSizes))):
+ builder.PrependInt64(self.sliceSizes[i])
+ sliceSizes = builder.EndVector()
+ StablehloDynamicSliceOptionsStart(builder)
+ if self.sliceSizes is not None:
+ StablehloDynamicSliceOptionsAddSliceSizes(builder, sliceSizes)
+ stablehloDynamicSliceOptions = StablehloDynamicSliceOptionsEnd(builder)
+ return stablehloDynamicSliceOptions
+
+
+class StablehloPadOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloPadOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloPadOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloPadOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloPadOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloPadOptions
+ def EdgePaddingLow(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloPadOptions
+ def EdgePaddingLowAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloPadOptions
+ def EdgePaddingLowLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloPadOptions
+ def EdgePaddingLowIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+ # StablehloPadOptions
+ def EdgePaddingHigh(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloPadOptions
+ def EdgePaddingHighAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloPadOptions
+ def EdgePaddingHighLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloPadOptions
+ def EdgePaddingHighIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ return o == 0
+
+ # StablehloPadOptions
+ def InteriorPadding(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloPadOptions
+ def InteriorPaddingAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloPadOptions
+ def InteriorPaddingLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloPadOptions
+ def InteriorPaddingIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ return o == 0
+
+def StablehloPadOptionsStart(builder):
+ builder.StartObject(3)
+
+def StablehloPadOptionsAddEdgePaddingLow(builder, edgePaddingLow):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(edgePaddingLow), 0)
+
+def StablehloPadOptionsStartEdgePaddingLowVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloPadOptionsAddEdgePaddingHigh(builder, edgePaddingHigh):
+ builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(edgePaddingHigh), 0)
+
+def StablehloPadOptionsStartEdgePaddingHighVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloPadOptionsAddInteriorPadding(builder, interiorPadding):
+ builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(interiorPadding), 0)
+
+def StablehloPadOptionsStartInteriorPaddingVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloPadOptionsEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class StablehloPadOptionsT(object):
+
+ # StablehloPadOptionsT
+ def __init__(self):
+ self.edgePaddingLow = None # type: List[int]
+ self.edgePaddingHigh = None # type: List[int]
+ self.interiorPadding = None # type: List[int]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloPadOptions = StablehloPadOptions()
+ stablehloPadOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloPadOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloPadOptions):
+ x = StablehloPadOptionsT()
+ x._UnPack(stablehloPadOptions)
+ return x
+
+ # StablehloPadOptionsT
+ def _UnPack(self, stablehloPadOptions):
+ if stablehloPadOptions is None:
+ return
+ if not stablehloPadOptions.EdgePaddingLowIsNone():
+ if np is None:
+ self.edgePaddingLow = []
+ for i in range(stablehloPadOptions.EdgePaddingLowLength()):
+ self.edgePaddingLow.append(stablehloPadOptions.EdgePaddingLow(i))
+ else:
+ self.edgePaddingLow = stablehloPadOptions.EdgePaddingLowAsNumpy()
+ if not stablehloPadOptions.EdgePaddingHighIsNone():
+ if np is None:
+ self.edgePaddingHigh = []
+ for i in range(stablehloPadOptions.EdgePaddingHighLength()):
+ self.edgePaddingHigh.append(stablehloPadOptions.EdgePaddingHigh(i))
+ else:
+ self.edgePaddingHigh = stablehloPadOptions.EdgePaddingHighAsNumpy()
+ if not stablehloPadOptions.InteriorPaddingIsNone():
+ if np is None:
+ self.interiorPadding = []
+ for i in range(stablehloPadOptions.InteriorPaddingLength()):
+ self.interiorPadding.append(stablehloPadOptions.InteriorPadding(i))
+ else:
+ self.interiorPadding = stablehloPadOptions.InteriorPaddingAsNumpy()
+
+ # StablehloPadOptionsT
+ def Pack(self, builder):
+ if self.edgePaddingLow is not None:
+ if np is not None and type(self.edgePaddingLow) is np.ndarray:
+ edgePaddingLow = builder.CreateNumpyVector(self.edgePaddingLow)
+ else:
+ StablehloPadOptionsStartEdgePaddingLowVector(builder, len(self.edgePaddingLow))
+ for i in reversed(range(len(self.edgePaddingLow))):
+ builder.PrependInt64(self.edgePaddingLow[i])
+ edgePaddingLow = builder.EndVector()
+ if self.edgePaddingHigh is not None:
+ if np is not None and type(self.edgePaddingHigh) is np.ndarray:
+ edgePaddingHigh = builder.CreateNumpyVector(self.edgePaddingHigh)
+ else:
+ StablehloPadOptionsStartEdgePaddingHighVector(builder, len(self.edgePaddingHigh))
+ for i in reversed(range(len(self.edgePaddingHigh))):
+ builder.PrependInt64(self.edgePaddingHigh[i])
+ edgePaddingHigh = builder.EndVector()
+ if self.interiorPadding is not None:
+ if np is not None and type(self.interiorPadding) is np.ndarray:
+ interiorPadding = builder.CreateNumpyVector(self.interiorPadding)
+ else:
+ StablehloPadOptionsStartInteriorPaddingVector(builder, len(self.interiorPadding))
+ for i in reversed(range(len(self.interiorPadding))):
+ builder.PrependInt64(self.interiorPadding[i])
+ interiorPadding = builder.EndVector()
+ StablehloPadOptionsStart(builder)
+ if self.edgePaddingLow is not None:
+ StablehloPadOptionsAddEdgePaddingLow(builder, edgePaddingLow)
+ if self.edgePaddingHigh is not None:
+ StablehloPadOptionsAddEdgePaddingHigh(builder, edgePaddingHigh)
+ if self.interiorPadding is not None:
+ StablehloPadOptionsAddInteriorPadding(builder, interiorPadding)
+ stablehloPadOptions = StablehloPadOptionsEnd(builder)
+ return stablehloPadOptions
+
+
+class StablehloIotaOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloIotaOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloIotaOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloIotaOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloIotaOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloIotaOptions
+ def IotaDimension(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, o + self._tab.Pos)
+ return 0
+
+def StablehloIotaOptionsStart(builder):
+ builder.StartObject(1)
+
+def StablehloIotaOptionsAddIotaDimension(builder, iotaDimension):
+ builder.PrependInt64Slot(0, iotaDimension, 0)
+
+def StablehloIotaOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class StablehloIotaOptionsT(object):
+
+ # StablehloIotaOptionsT
+ def __init__(self):
+ self.iotaDimension = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloIotaOptions = StablehloIotaOptions()
+ stablehloIotaOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloIotaOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloIotaOptions):
+ x = StablehloIotaOptionsT()
+ x._UnPack(stablehloIotaOptions)
+ return x
+
+ # StablehloIotaOptionsT
+ def _UnPack(self, stablehloIotaOptions):
+ if stablehloIotaOptions is None:
+ return
+ self.iotaDimension = stablehloIotaOptions.IotaDimension()
+
+ # StablehloIotaOptionsT
+ def Pack(self, builder):
+ StablehloIotaOptionsStart(builder)
+ StablehloIotaOptionsAddIotaDimension(builder, self.iotaDimension)
+ stablehloIotaOptions = StablehloIotaOptionsEnd(builder)
+ return stablehloIotaOptions
+
+
+class StablehloCustomCallOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloCustomCallOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloCustomCallOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloCustomCallOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloCustomCallOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloCustomCallOptions
+ def CallTargetName(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.String(o + self._tab.Pos)
+ return None
+
+ # StablehloCustomCallOptions
+ def HasSideEffect(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+ # StablehloCustomCallOptions
+ def BackendConfig(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.String(o + self._tab.Pos)
+ return None
+
+ # StablehloCustomCallOptions
+ def ApiVersion(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # StablehloCustomCallOptions
+ def CalledComputations(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+ return 0
+
+ # StablehloCustomCallOptions
+ def CalledComputationsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
+ return 0
+
+ # StablehloCustomCallOptions
+ def CalledComputationsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloCustomCallOptions
+ def CalledComputationsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ return o == 0
+
+ # StablehloCustomCallOptions
+ def CustomAttributes(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1))
+ return 0
+
+ # StablehloCustomCallOptions
+ def CustomAttributesAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o)
+ return 0
+
+ # StablehloCustomCallOptions
+ def CustomAttributesLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloCustomCallOptions
+ def CustomAttributesIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ return o == 0
+
+def StablehloCustomCallOptionsStart(builder):
+ builder.StartObject(6)
+
+def StablehloCustomCallOptionsAddCallTargetName(builder, callTargetName):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(callTargetName), 0)
+
+def StablehloCustomCallOptionsAddHasSideEffect(builder, hasSideEffect):
+ builder.PrependBoolSlot(1, hasSideEffect, 0)
+
+def StablehloCustomCallOptionsAddBackendConfig(builder, backendConfig):
+ builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(backendConfig), 0)
+
+def StablehloCustomCallOptionsAddApiVersion(builder, apiVersion):
+ builder.PrependInt32Slot(3, apiVersion, 0)
+
+def StablehloCustomCallOptionsAddCalledComputations(builder, calledComputations):
+ builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(calledComputations), 0)
+
+def StablehloCustomCallOptionsStartCalledComputationsVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def StablehloCustomCallOptionsAddCustomAttributes(builder, customAttributes):
+ builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(customAttributes), 0)
+
+def StablehloCustomCallOptionsStartCustomAttributesVector(builder, numElems):
+ return builder.StartVector(1, numElems, 1)
+
+def StablehloCustomCallOptionsEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class StablehloCustomCallOptionsT(object):
+
+ # StablehloCustomCallOptionsT
+ def __init__(self):
+ self.callTargetName = None # type: str
+ self.hasSideEffect = False # type: bool
+ self.backendConfig = None # type: str
+ self.apiVersion = 0 # type: int
+ self.calledComputations = None # type: List[int]
+ self.customAttributes = None # type: List[int]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloCustomCallOptions = StablehloCustomCallOptions()
+ stablehloCustomCallOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloCustomCallOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloCustomCallOptions):
+ x = StablehloCustomCallOptionsT()
+ x._UnPack(stablehloCustomCallOptions)
+ return x
+
+ # StablehloCustomCallOptionsT
+ def _UnPack(self, stablehloCustomCallOptions):
+ if stablehloCustomCallOptions is None:
+ return
+ self.callTargetName = stablehloCustomCallOptions.CallTargetName()
+ self.hasSideEffect = stablehloCustomCallOptions.HasSideEffect()
+ self.backendConfig = stablehloCustomCallOptions.BackendConfig()
+ self.apiVersion = stablehloCustomCallOptions.ApiVersion()
+ if not stablehloCustomCallOptions.CalledComputationsIsNone():
+ if np is None:
+ self.calledComputations = []
+ for i in range(stablehloCustomCallOptions.CalledComputationsLength()):
+ self.calledComputations.append(stablehloCustomCallOptions.CalledComputations(i))
+ else:
+ self.calledComputations = stablehloCustomCallOptions.CalledComputationsAsNumpy()
+ if not stablehloCustomCallOptions.CustomAttributesIsNone():
+ if np is None:
+ self.customAttributes = []
+ for i in range(stablehloCustomCallOptions.CustomAttributesLength()):
+ self.customAttributes.append(stablehloCustomCallOptions.CustomAttributes(i))
+ else:
+ self.customAttributes = stablehloCustomCallOptions.CustomAttributesAsNumpy()
+
+ # StablehloCustomCallOptionsT
+ def Pack(self, builder):
+ if self.callTargetName is not None:
+ callTargetName = builder.CreateString(self.callTargetName)
+ if self.backendConfig is not None:
+ backendConfig = builder.CreateString(self.backendConfig)
+ if self.calledComputations is not None:
+ if np is not None and type(self.calledComputations) is np.ndarray:
+ calledComputations = builder.CreateNumpyVector(self.calledComputations)
+ else:
+ StablehloCustomCallOptionsStartCalledComputationsVector(builder, len(self.calledComputations))
+ for i in reversed(range(len(self.calledComputations))):
+ builder.PrependInt32(self.calledComputations[i])
+ calledComputations = builder.EndVector()
+ if self.customAttributes is not None:
+ if np is not None and type(self.customAttributes) is np.ndarray:
+ customAttributes = builder.CreateNumpyVector(self.customAttributes)
+ else:
+ StablehloCustomCallOptionsStartCustomAttributesVector(builder, len(self.customAttributes))
+ for i in reversed(range(len(self.customAttributes))):
+ builder.PrependUint8(self.customAttributes[i])
+ customAttributes = builder.EndVector()
+ StablehloCustomCallOptionsStart(builder)
+ if self.callTargetName is not None:
+ StablehloCustomCallOptionsAddCallTargetName(builder, callTargetName)
+ StablehloCustomCallOptionsAddHasSideEffect(builder, self.hasSideEffect)
+ if self.backendConfig is not None:
+ StablehloCustomCallOptionsAddBackendConfig(builder, backendConfig)
+ StablehloCustomCallOptionsAddApiVersion(builder, self.apiVersion)
+ if self.calledComputations is not None:
+ StablehloCustomCallOptionsAddCalledComputations(builder, calledComputations)
+ if self.customAttributes is not None:
+ StablehloCustomCallOptionsAddCustomAttributes(builder, customAttributes)
+ stablehloCustomCallOptions = StablehloCustomCallOptionsEnd(builder)
+ return stablehloCustomCallOptions
+
+
+class StablehloReduceOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloReduceOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloReduceOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloReduceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloReduceOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloReduceOptions
+ def Dimensions(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloReduceOptions
+ def DimensionsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloReduceOptions
+ def DimensionsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloReduceOptions
+ def DimensionsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+ # StablehloReduceOptions
+ def BodySubgraphIndex(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def StablehloReduceOptionsStart(builder):
+ builder.StartObject(2)
+
+def StablehloReduceOptionsAddDimensions(builder, dimensions):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(dimensions), 0)
+
+def StablehloReduceOptionsStartDimensionsVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloReduceOptionsAddBodySubgraphIndex(builder, bodySubgraphIndex):
+ builder.PrependInt32Slot(1, bodySubgraphIndex, 0)
+
+def StablehloReduceOptionsEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class StablehloReduceOptionsT(object):
+
+ # StablehloReduceOptionsT
+ def __init__(self):
+ self.dimensions = None # type: List[int]
+ self.bodySubgraphIndex = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloReduceOptions = StablehloReduceOptions()
+ stablehloReduceOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloReduceOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloReduceOptions):
+ x = StablehloReduceOptionsT()
+ x._UnPack(stablehloReduceOptions)
+ return x
+
+ # StablehloReduceOptionsT
+ def _UnPack(self, stablehloReduceOptions):
+ if stablehloReduceOptions is None:
+ return
+ if not stablehloReduceOptions.DimensionsIsNone():
+ if np is None:
+ self.dimensions = []
+ for i in range(stablehloReduceOptions.DimensionsLength()):
+ self.dimensions.append(stablehloReduceOptions.Dimensions(i))
+ else:
+ self.dimensions = stablehloReduceOptions.DimensionsAsNumpy()
+ self.bodySubgraphIndex = stablehloReduceOptions.BodySubgraphIndex()
+
+ # StablehloReduceOptionsT
+ def Pack(self, builder):
+ if self.dimensions is not None:
+ if np is not None and type(self.dimensions) is np.ndarray:
+ dimensions = builder.CreateNumpyVector(self.dimensions)
+ else:
+ StablehloReduceOptionsStartDimensionsVector(builder, len(self.dimensions))
+ for i in reversed(range(len(self.dimensions))):
+ builder.PrependInt64(self.dimensions[i])
+ dimensions = builder.EndVector()
+ StablehloReduceOptionsStart(builder)
+ if self.dimensions is not None:
+ StablehloReduceOptionsAddDimensions(builder, dimensions)
+ StablehloReduceOptionsAddBodySubgraphIndex(builder, self.bodySubgraphIndex)
+ stablehloReduceOptions = StablehloReduceOptionsEnd(builder)
+ return stablehloReduceOptions
+
+
+class StablehloSliceOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloSliceOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloSliceOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloSliceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloSliceOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloSliceOptions
+ def StartIndices(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloSliceOptions
+ def StartIndicesAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloSliceOptions
+ def StartIndicesLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloSliceOptions
+ def StartIndicesIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+ # StablehloSliceOptions
+ def LimitIndices(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloSliceOptions
+ def LimitIndicesAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloSliceOptions
+ def LimitIndicesLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloSliceOptions
+ def LimitIndicesIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ return o == 0
+
+ # StablehloSliceOptions
+ def Strides(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloSliceOptions
+ def StridesAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloSliceOptions
+ def StridesLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloSliceOptions
+ def StridesIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ return o == 0
+
+def StablehloSliceOptionsStart(builder):
+ builder.StartObject(3)
+
+def StablehloSliceOptionsAddStartIndices(builder, startIndices):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(startIndices), 0)
+
+def StablehloSliceOptionsStartStartIndicesVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloSliceOptionsAddLimitIndices(builder, limitIndices):
+ builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(limitIndices), 0)
+
+def StablehloSliceOptionsStartLimitIndicesVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloSliceOptionsAddStrides(builder, strides):
+ builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(strides), 0)
+
+def StablehloSliceOptionsStartStridesVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloSliceOptionsEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class StablehloSliceOptionsT(object):
+
+ # StablehloSliceOptionsT
+ def __init__(self):
+ self.startIndices = None # type: List[int]
+ self.limitIndices = None # type: List[int]
+ self.strides = None # type: List[int]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloSliceOptions = StablehloSliceOptions()
+ stablehloSliceOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloSliceOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloSliceOptions):
+ x = StablehloSliceOptionsT()
+ x._UnPack(stablehloSliceOptions)
+ return x
+
+ # StablehloSliceOptionsT
+ def _UnPack(self, stablehloSliceOptions):
+ if stablehloSliceOptions is None:
+ return
+ if not stablehloSliceOptions.StartIndicesIsNone():
+ if np is None:
+ self.startIndices = []
+ for i in range(stablehloSliceOptions.StartIndicesLength()):
+ self.startIndices.append(stablehloSliceOptions.StartIndices(i))
+ else:
+ self.startIndices = stablehloSliceOptions.StartIndicesAsNumpy()
+ if not stablehloSliceOptions.LimitIndicesIsNone():
+ if np is None:
+ self.limitIndices = []
+ for i in range(stablehloSliceOptions.LimitIndicesLength()):
+ self.limitIndices.append(stablehloSliceOptions.LimitIndices(i))
+ else:
+ self.limitIndices = stablehloSliceOptions.LimitIndicesAsNumpy()
+ if not stablehloSliceOptions.StridesIsNone():
+ if np is None:
+ self.strides = []
+ for i in range(stablehloSliceOptions.StridesLength()):
+ self.strides.append(stablehloSliceOptions.Strides(i))
+ else:
+ self.strides = stablehloSliceOptions.StridesAsNumpy()
+
+ # StablehloSliceOptionsT
+ def Pack(self, builder):
+ if self.startIndices is not None:
+ if np is not None and type(self.startIndices) is np.ndarray:
+ startIndices = builder.CreateNumpyVector(self.startIndices)
+ else:
+ StablehloSliceOptionsStartStartIndicesVector(builder, len(self.startIndices))
+ for i in reversed(range(len(self.startIndices))):
+ builder.PrependInt64(self.startIndices[i])
+ startIndices = builder.EndVector()
+ if self.limitIndices is not None:
+ if np is not None and type(self.limitIndices) is np.ndarray:
+ limitIndices = builder.CreateNumpyVector(self.limitIndices)
+ else:
+ StablehloSliceOptionsStartLimitIndicesVector(builder, len(self.limitIndices))
+ for i in reversed(range(len(self.limitIndices))):
+ builder.PrependInt64(self.limitIndices[i])
+ limitIndices = builder.EndVector()
+ if self.strides is not None:
+ if np is not None and type(self.strides) is np.ndarray:
+ strides = builder.CreateNumpyVector(self.strides)
+ else:
+ StablehloSliceOptionsStartStridesVector(builder, len(self.strides))
+ for i in reversed(range(len(self.strides))):
+ builder.PrependInt64(self.strides[i])
+ strides = builder.EndVector()
+ StablehloSliceOptionsStart(builder)
+ if self.startIndices is not None:
+ StablehloSliceOptionsAddStartIndices(builder, startIndices)
+ if self.limitIndices is not None:
+ StablehloSliceOptionsAddLimitIndices(builder, limitIndices)
+ if self.strides is not None:
+ StablehloSliceOptionsAddStrides(builder, strides)
+ stablehloSliceOptions = StablehloSliceOptionsEnd(builder)
+ return stablehloSliceOptions
+
+
+class StablehloConvolutionOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloConvolutionOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloConvolutionOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloConvolutionOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloConvolutionOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloConvolutionOptions
+ def WindowStrides(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloConvolutionOptions
+ def WindowStridesAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def WindowStridesLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def WindowStridesIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+ # StablehloConvolutionOptions
+ def Padding(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloConvolutionOptions
+ def PaddingAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def PaddingLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def PaddingIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ return o == 0
+
+ # StablehloConvolutionOptions
+ def LhsDilation(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloConvolutionOptions
+ def LhsDilationAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def LhsDilationLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def LhsDilationIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ return o == 0
+
+ # StablehloConvolutionOptions
+ def RhsDilation(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloConvolutionOptions
+ def RhsDilationAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def RhsDilationLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def RhsDilationIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ return o == 0
+
+ # StablehloConvolutionOptions
+ def WindowReversal(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.BoolFlags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1))
+ return 0
+
+ # StablehloConvolutionOptions
+ def WindowReversalAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.BoolFlags, o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def WindowReversalLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def WindowReversalIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ return o == 0
+
+ # StablehloConvolutionOptions
+ def InputBatchDimension(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, o + self._tab.Pos)
+ return 0
+
+ # StablehloConvolutionOptions
+ def InputFeatureDimension(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, o + self._tab.Pos)
+ return 0
+
+ # StablehloConvolutionOptions
+ def InputSpatialDimensions(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloConvolutionOptions
+ def InputSpatialDimensionsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def InputSpatialDimensionsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def InputSpatialDimensionsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
+ return o == 0
+
+ # StablehloConvolutionOptions
+ def KernelInputFeatureDimension(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(20))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, o + self._tab.Pos)
+ return 0
+
+ # StablehloConvolutionOptions
+ def KernelOutputFeatureDimension(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(22))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, o + self._tab.Pos)
+ return 0
+
+ # StablehloConvolutionOptions
+ def KernelSpatialDimensions(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(24))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloConvolutionOptions
+ def KernelSpatialDimensionsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(24))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def KernelSpatialDimensionsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(24))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def KernelSpatialDimensionsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(24))
+ return o == 0
+
+ # StablehloConvolutionOptions
+ def OutputBatchDimension(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(26))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, o + self._tab.Pos)
+ return 0
+
+ # StablehloConvolutionOptions
+ def OutputFeatureDimension(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(28))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, o + self._tab.Pos)
+ return 0
+
+ # StablehloConvolutionOptions
+ def OutputSpatialDimensions(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(30))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloConvolutionOptions
+ def OutputSpatialDimensionsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(30))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def OutputSpatialDimensionsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(30))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def OutputSpatialDimensionsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(30))
+ return o == 0
+
+ # StablehloConvolutionOptions
+ def FeatureGroupCount(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(32))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, o + self._tab.Pos)
+ return 0
+
+ # StablehloConvolutionOptions
+ def BatchGroupCount(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(34))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, o + self._tab.Pos)
+ return 0
+
+ # StablehloConvolutionOptions
+ def PrecisionConfig(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(36))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Uint32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+ return 0
+
+ # StablehloConvolutionOptions
+ def PrecisionConfigAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(36))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint32Flags, o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def PrecisionConfigLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(36))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloConvolutionOptions
+ def PrecisionConfigIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(36))
+ return o == 0
+
+def StablehloConvolutionOptionsStart(builder):
+ builder.StartObject(17)
+
+def StablehloConvolutionOptionsAddWindowStrides(builder, windowStrides):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(windowStrides), 0)
+
+def StablehloConvolutionOptionsStartWindowStridesVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloConvolutionOptionsAddPadding(builder, padding):
+ builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(padding), 0)
+
+def StablehloConvolutionOptionsStartPaddingVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloConvolutionOptionsAddLhsDilation(builder, lhsDilation):
+ builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(lhsDilation), 0)
+
+def StablehloConvolutionOptionsStartLhsDilationVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloConvolutionOptionsAddRhsDilation(builder, rhsDilation):
+ builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(rhsDilation), 0)
+
+def StablehloConvolutionOptionsStartRhsDilationVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloConvolutionOptionsAddWindowReversal(builder, windowReversal):
+ builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(windowReversal), 0)
+
+def StablehloConvolutionOptionsStartWindowReversalVector(builder, numElems):
+ return builder.StartVector(1, numElems, 1)
+
+def StablehloConvolutionOptionsAddInputBatchDimension(builder, inputBatchDimension):
+ builder.PrependInt64Slot(5, inputBatchDimension, 0)
+
+def StablehloConvolutionOptionsAddInputFeatureDimension(builder, inputFeatureDimension):
+ builder.PrependInt64Slot(6, inputFeatureDimension, 0)
+
+def StablehloConvolutionOptionsAddInputSpatialDimensions(builder, inputSpatialDimensions):
+ builder.PrependUOffsetTRelativeSlot(7, flatbuffers.number_types.UOffsetTFlags.py_type(inputSpatialDimensions), 0)
+
+def StablehloConvolutionOptionsStartInputSpatialDimensionsVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloConvolutionOptionsAddKernelInputFeatureDimension(builder, kernelInputFeatureDimension):
+ builder.PrependInt64Slot(8, kernelInputFeatureDimension, 0)
+
+def StablehloConvolutionOptionsAddKernelOutputFeatureDimension(builder, kernelOutputFeatureDimension):
+ builder.PrependInt64Slot(9, kernelOutputFeatureDimension, 0)
+
+def StablehloConvolutionOptionsAddKernelSpatialDimensions(builder, kernelSpatialDimensions):
+ builder.PrependUOffsetTRelativeSlot(10, flatbuffers.number_types.UOffsetTFlags.py_type(kernelSpatialDimensions), 0)
+
+def StablehloConvolutionOptionsStartKernelSpatialDimensionsVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloConvolutionOptionsAddOutputBatchDimension(builder, outputBatchDimension):
+ builder.PrependInt64Slot(11, outputBatchDimension, 0)
+
+def StablehloConvolutionOptionsAddOutputFeatureDimension(builder, outputFeatureDimension):
+ builder.PrependInt64Slot(12, outputFeatureDimension, 0)
+
+def StablehloConvolutionOptionsAddOutputSpatialDimensions(builder, outputSpatialDimensions):
+ builder.PrependUOffsetTRelativeSlot(13, flatbuffers.number_types.UOffsetTFlags.py_type(outputSpatialDimensions), 0)
+
+def StablehloConvolutionOptionsStartOutputSpatialDimensionsVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloConvolutionOptionsAddFeatureGroupCount(builder, featureGroupCount):
+ builder.PrependInt64Slot(14, featureGroupCount, 0)
+
+def StablehloConvolutionOptionsAddBatchGroupCount(builder, batchGroupCount):
+ builder.PrependInt64Slot(15, batchGroupCount, 0)
+
+def StablehloConvolutionOptionsAddPrecisionConfig(builder, precisionConfig):
+ builder.PrependUOffsetTRelativeSlot(16, flatbuffers.number_types.UOffsetTFlags.py_type(precisionConfig), 0)
+
+def StablehloConvolutionOptionsStartPrecisionConfigVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def StablehloConvolutionOptionsEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class StablehloConvolutionOptionsT(object):
+
+ # StablehloConvolutionOptionsT
+ def __init__(self):
+ self.windowStrides = None # type: List[int]
+ self.padding = None # type: List[int]
+ self.lhsDilation = None # type: List[int]
+ self.rhsDilation = None # type: List[int]
+ self.windowReversal = None # type: List[bool]
+ self.inputBatchDimension = 0 # type: int
+ self.inputFeatureDimension = 0 # type: int
+ self.inputSpatialDimensions = None # type: List[int]
+ self.kernelInputFeatureDimension = 0 # type: int
+ self.kernelOutputFeatureDimension = 0 # type: int
+ self.kernelSpatialDimensions = None # type: List[int]
+ self.outputBatchDimension = 0 # type: int
+ self.outputFeatureDimension = 0 # type: int
+ self.outputSpatialDimensions = None # type: List[int]
+ self.featureGroupCount = 0 # type: int
+ self.batchGroupCount = 0 # type: int
+ self.precisionConfig = None # type: List[int]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloConvolutionOptions = StablehloConvolutionOptions()
+ stablehloConvolutionOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloConvolutionOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloConvolutionOptions):
+ x = StablehloConvolutionOptionsT()
+ x._UnPack(stablehloConvolutionOptions)
+ return x
+
+ # StablehloConvolutionOptionsT
+ def _UnPack(self, stablehloConvolutionOptions):
+ if stablehloConvolutionOptions is None:
+ return
+ if not stablehloConvolutionOptions.WindowStridesIsNone():
+ if np is None:
+ self.windowStrides = []
+ for i in range(stablehloConvolutionOptions.WindowStridesLength()):
+ self.windowStrides.append(stablehloConvolutionOptions.WindowStrides(i))
+ else:
+ self.windowStrides = stablehloConvolutionOptions.WindowStridesAsNumpy()
+ if not stablehloConvolutionOptions.PaddingIsNone():
+ if np is None:
+ self.padding = []
+ for i in range(stablehloConvolutionOptions.PaddingLength()):
+ self.padding.append(stablehloConvolutionOptions.Padding(i))
+ else:
+ self.padding = stablehloConvolutionOptions.PaddingAsNumpy()
+ if not stablehloConvolutionOptions.LhsDilationIsNone():
+ if np is None:
+ self.lhsDilation = []
+ for i in range(stablehloConvolutionOptions.LhsDilationLength()):
+ self.lhsDilation.append(stablehloConvolutionOptions.LhsDilation(i))
+ else:
+ self.lhsDilation = stablehloConvolutionOptions.LhsDilationAsNumpy()
+ if not stablehloConvolutionOptions.RhsDilationIsNone():
+ if np is None:
+ self.rhsDilation = []
+ for i in range(stablehloConvolutionOptions.RhsDilationLength()):
+ self.rhsDilation.append(stablehloConvolutionOptions.RhsDilation(i))
+ else:
+ self.rhsDilation = stablehloConvolutionOptions.RhsDilationAsNumpy()
+ if not stablehloConvolutionOptions.WindowReversalIsNone():
+ if np is None:
+ self.windowReversal = []
+ for i in range(stablehloConvolutionOptions.WindowReversalLength()):
+ self.windowReversal.append(stablehloConvolutionOptions.WindowReversal(i))
+ else:
+ self.windowReversal = stablehloConvolutionOptions.WindowReversalAsNumpy()
+ self.inputBatchDimension = stablehloConvolutionOptions.InputBatchDimension()
+ self.inputFeatureDimension = stablehloConvolutionOptions.InputFeatureDimension()
+ if not stablehloConvolutionOptions.InputSpatialDimensionsIsNone():
+ if np is None:
+ self.inputSpatialDimensions = []
+ for i in range(stablehloConvolutionOptions.InputSpatialDimensionsLength()):
+ self.inputSpatialDimensions.append(stablehloConvolutionOptions.InputSpatialDimensions(i))
+ else:
+ self.inputSpatialDimensions = stablehloConvolutionOptions.InputSpatialDimensionsAsNumpy()
+ self.kernelInputFeatureDimension = stablehloConvolutionOptions.KernelInputFeatureDimension()
+ self.kernelOutputFeatureDimension = stablehloConvolutionOptions.KernelOutputFeatureDimension()
+ if not stablehloConvolutionOptions.KernelSpatialDimensionsIsNone():
+ if np is None:
+ self.kernelSpatialDimensions = []
+ for i in range(stablehloConvolutionOptions.KernelSpatialDimensionsLength()):
+ self.kernelSpatialDimensions.append(stablehloConvolutionOptions.KernelSpatialDimensions(i))
+ else:
+ self.kernelSpatialDimensions = stablehloConvolutionOptions.KernelSpatialDimensionsAsNumpy()
+ self.outputBatchDimension = stablehloConvolutionOptions.OutputBatchDimension()
+ self.outputFeatureDimension = stablehloConvolutionOptions.OutputFeatureDimension()
+ if not stablehloConvolutionOptions.OutputSpatialDimensionsIsNone():
+ if np is None:
+ self.outputSpatialDimensions = []
+ for i in range(stablehloConvolutionOptions.OutputSpatialDimensionsLength()):
+ self.outputSpatialDimensions.append(stablehloConvolutionOptions.OutputSpatialDimensions(i))
+ else:
+ self.outputSpatialDimensions = stablehloConvolutionOptions.OutputSpatialDimensionsAsNumpy()
+ self.featureGroupCount = stablehloConvolutionOptions.FeatureGroupCount()
+ self.batchGroupCount = stablehloConvolutionOptions.BatchGroupCount()
+ if not stablehloConvolutionOptions.PrecisionConfigIsNone():
+ if np is None:
+ self.precisionConfig = []
+ for i in range(stablehloConvolutionOptions.PrecisionConfigLength()):
+ self.precisionConfig.append(stablehloConvolutionOptions.PrecisionConfig(i))
+ else:
+ self.precisionConfig = stablehloConvolutionOptions.PrecisionConfigAsNumpy()
+
+ # StablehloConvolutionOptionsT
+ def Pack(self, builder):
+ if self.windowStrides is not None:
+ if np is not None and type(self.windowStrides) is np.ndarray:
+ windowStrides = builder.CreateNumpyVector(self.windowStrides)
+ else:
+ StablehloConvolutionOptionsStartWindowStridesVector(builder, len(self.windowStrides))
+ for i in reversed(range(len(self.windowStrides))):
+ builder.PrependInt64(self.windowStrides[i])
+ windowStrides = builder.EndVector()
+ if self.padding is not None:
+ if np is not None and type(self.padding) is np.ndarray:
+ padding = builder.CreateNumpyVector(self.padding)
+ else:
+ StablehloConvolutionOptionsStartPaddingVector(builder, len(self.padding))
+ for i in reversed(range(len(self.padding))):
+ builder.PrependInt64(self.padding[i])
+ padding = builder.EndVector()
+ if self.lhsDilation is not None:
+ if np is not None and type(self.lhsDilation) is np.ndarray:
+ lhsDilation = builder.CreateNumpyVector(self.lhsDilation)
+ else:
+ StablehloConvolutionOptionsStartLhsDilationVector(builder, len(self.lhsDilation))
+ for i in reversed(range(len(self.lhsDilation))):
+ builder.PrependInt64(self.lhsDilation[i])
+ lhsDilation = builder.EndVector()
+ if self.rhsDilation is not None:
+ if np is not None and type(self.rhsDilation) is np.ndarray:
+ rhsDilation = builder.CreateNumpyVector(self.rhsDilation)
+ else:
+ StablehloConvolutionOptionsStartRhsDilationVector(builder, len(self.rhsDilation))
+ for i in reversed(range(len(self.rhsDilation))):
+ builder.PrependInt64(self.rhsDilation[i])
+ rhsDilation = builder.EndVector()
+ if self.windowReversal is not None:
+ if np is not None and type(self.windowReversal) is np.ndarray:
+ windowReversal = builder.CreateNumpyVector(self.windowReversal)
+ else:
+ StablehloConvolutionOptionsStartWindowReversalVector(builder, len(self.windowReversal))
+ for i in reversed(range(len(self.windowReversal))):
+ builder.PrependBool(self.windowReversal[i])
+ windowReversal = builder.EndVector()
+ if self.inputSpatialDimensions is not None:
+ if np is not None and type(self.inputSpatialDimensions) is np.ndarray:
+ inputSpatialDimensions = builder.CreateNumpyVector(self.inputSpatialDimensions)
+ else:
+ StablehloConvolutionOptionsStartInputSpatialDimensionsVector(builder, len(self.inputSpatialDimensions))
+ for i in reversed(range(len(self.inputSpatialDimensions))):
+ builder.PrependInt64(self.inputSpatialDimensions[i])
+ inputSpatialDimensions = builder.EndVector()
+ if self.kernelSpatialDimensions is not None:
+ if np is not None and type(self.kernelSpatialDimensions) is np.ndarray:
+ kernelSpatialDimensions = builder.CreateNumpyVector(self.kernelSpatialDimensions)
+ else:
+ StablehloConvolutionOptionsStartKernelSpatialDimensionsVector(builder, len(self.kernelSpatialDimensions))
+ for i in reversed(range(len(self.kernelSpatialDimensions))):
+ builder.PrependInt64(self.kernelSpatialDimensions[i])
+ kernelSpatialDimensions = builder.EndVector()
+ if self.outputSpatialDimensions is not None:
+ if np is not None and type(self.outputSpatialDimensions) is np.ndarray:
+ outputSpatialDimensions = builder.CreateNumpyVector(self.outputSpatialDimensions)
+ else:
+ StablehloConvolutionOptionsStartOutputSpatialDimensionsVector(builder, len(self.outputSpatialDimensions))
+ for i in reversed(range(len(self.outputSpatialDimensions))):
+ builder.PrependInt64(self.outputSpatialDimensions[i])
+ outputSpatialDimensions = builder.EndVector()
+ if self.precisionConfig is not None:
+ if np is not None and type(self.precisionConfig) is np.ndarray:
+ precisionConfig = builder.CreateNumpyVector(self.precisionConfig)
+ else:
+ StablehloConvolutionOptionsStartPrecisionConfigVector(builder, len(self.precisionConfig))
+ for i in reversed(range(len(self.precisionConfig))):
+ builder.PrependUint32(self.precisionConfig[i])
+ precisionConfig = builder.EndVector()
+ StablehloConvolutionOptionsStart(builder)
+ if self.windowStrides is not None:
+ StablehloConvolutionOptionsAddWindowStrides(builder, windowStrides)
+ if self.padding is not None:
+ StablehloConvolutionOptionsAddPadding(builder, padding)
+ if self.lhsDilation is not None:
+ StablehloConvolutionOptionsAddLhsDilation(builder, lhsDilation)
+ if self.rhsDilation is not None:
+ StablehloConvolutionOptionsAddRhsDilation(builder, rhsDilation)
+ if self.windowReversal is not None:
+ StablehloConvolutionOptionsAddWindowReversal(builder, windowReversal)
+ StablehloConvolutionOptionsAddInputBatchDimension(builder, self.inputBatchDimension)
+ StablehloConvolutionOptionsAddInputFeatureDimension(builder, self.inputFeatureDimension)
+ if self.inputSpatialDimensions is not None:
+ StablehloConvolutionOptionsAddInputSpatialDimensions(builder, inputSpatialDimensions)
+ StablehloConvolutionOptionsAddKernelInputFeatureDimension(builder, self.kernelInputFeatureDimension)
+ StablehloConvolutionOptionsAddKernelOutputFeatureDimension(builder, self.kernelOutputFeatureDimension)
+ if self.kernelSpatialDimensions is not None:
+ StablehloConvolutionOptionsAddKernelSpatialDimensions(builder, kernelSpatialDimensions)
+ StablehloConvolutionOptionsAddOutputBatchDimension(builder, self.outputBatchDimension)
+ StablehloConvolutionOptionsAddOutputFeatureDimension(builder, self.outputFeatureDimension)
+ if self.outputSpatialDimensions is not None:
+ StablehloConvolutionOptionsAddOutputSpatialDimensions(builder, outputSpatialDimensions)
+ StablehloConvolutionOptionsAddFeatureGroupCount(builder, self.featureGroupCount)
+ StablehloConvolutionOptionsAddBatchGroupCount(builder, self.batchGroupCount)
+ if self.precisionConfig is not None:
+ StablehloConvolutionOptionsAddPrecisionConfig(builder, precisionConfig)
+ stablehloConvolutionOptions = StablehloConvolutionOptionsEnd(builder)
+ return stablehloConvolutionOptions
+
+
+class StablehloScatterOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloScatterOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloScatterOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloScatterOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloScatterOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloScatterOptions
+ def IndicesAreSorted(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+ # StablehloScatterOptions
+ def UpdateWindowDims(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloScatterOptions
+ def UpdateWindowDimsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloScatterOptions
+ def UpdateWindowDimsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloScatterOptions
+ def UpdateWindowDimsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ return o == 0
+
+ # StablehloScatterOptions
+ def InsertedWindowDims(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloScatterOptions
+ def InsertedWindowDimsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloScatterOptions
+ def InsertedWindowDimsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloScatterOptions
+ def InsertedWindowDimsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ return o == 0
+
+ # StablehloScatterOptions
+ def ScatterDimsToOperandDims(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
+ return 0
+
+ # StablehloScatterOptions
+ def ScatterDimsToOperandDimsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
+ return 0
+
+ # StablehloScatterOptions
+ def ScatterDimsToOperandDimsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StablehloScatterOptions
+ def ScatterDimsToOperandDimsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ return o == 0
+
+ # StablehloScatterOptions
+ def IndexVectorDim(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, o + self._tab.Pos)
+ return 0
+
+ # StablehloScatterOptions
+ def UniqueIndices(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+ # StablehloScatterOptions
+ def UpdateComputationSubgraphIndex(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def StablehloScatterOptionsStart(builder):
+ builder.StartObject(7)
+
+def StablehloScatterOptionsAddIndicesAreSorted(builder, indicesAreSorted):
+ builder.PrependBoolSlot(0, indicesAreSorted, 0)
+
+def StablehloScatterOptionsAddUpdateWindowDims(builder, updateWindowDims):
+ builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(updateWindowDims), 0)
+
+def StablehloScatterOptionsStartUpdateWindowDimsVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloScatterOptionsAddInsertedWindowDims(builder, insertedWindowDims):
+ builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(insertedWindowDims), 0)
+
+def StablehloScatterOptionsStartInsertedWindowDimsVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloScatterOptionsAddScatterDimsToOperandDims(builder, scatterDimsToOperandDims):
+ builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(scatterDimsToOperandDims), 0)
+
+def StablehloScatterOptionsStartScatterDimsToOperandDimsVector(builder, numElems):
+ return builder.StartVector(8, numElems, 8)
+
+def StablehloScatterOptionsAddIndexVectorDim(builder, indexVectorDim):
+ builder.PrependInt64Slot(4, indexVectorDim, 0)
+
+def StablehloScatterOptionsAddUniqueIndices(builder, uniqueIndices):
+ builder.PrependBoolSlot(5, uniqueIndices, 0)
+
+def StablehloScatterOptionsAddUpdateComputationSubgraphIndex(builder, updateComputationSubgraphIndex):
+ builder.PrependInt32Slot(6, updateComputationSubgraphIndex, 0)
+
+def StablehloScatterOptionsEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class StablehloScatterOptionsT(object):
+
+ # StablehloScatterOptionsT
+ def __init__(self):
+ self.indicesAreSorted = False # type: bool
+ self.updateWindowDims = None # type: List[int]
+ self.insertedWindowDims = None # type: List[int]
+ self.scatterDimsToOperandDims = None # type: List[int]
+ self.indexVectorDim = 0 # type: int
+ self.uniqueIndices = False # type: bool
+ self.updateComputationSubgraphIndex = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloScatterOptions = StablehloScatterOptions()
+ stablehloScatterOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloScatterOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloScatterOptions):
+ x = StablehloScatterOptionsT()
+ x._UnPack(stablehloScatterOptions)
+ return x
+
+ # StablehloScatterOptionsT
+ def _UnPack(self, stablehloScatterOptions):
+ if stablehloScatterOptions is None:
+ return
+ self.indicesAreSorted = stablehloScatterOptions.IndicesAreSorted()
+ if not stablehloScatterOptions.UpdateWindowDimsIsNone():
+ if np is None:
+ self.updateWindowDims = []
+ for i in range(stablehloScatterOptions.UpdateWindowDimsLength()):
+ self.updateWindowDims.append(stablehloScatterOptions.UpdateWindowDims(i))
+ else:
+ self.updateWindowDims = stablehloScatterOptions.UpdateWindowDimsAsNumpy()
+ if not stablehloScatterOptions.InsertedWindowDimsIsNone():
+ if np is None:
+ self.insertedWindowDims = []
+ for i in range(stablehloScatterOptions.InsertedWindowDimsLength()):
+ self.insertedWindowDims.append(stablehloScatterOptions.InsertedWindowDims(i))
+ else:
+ self.insertedWindowDims = stablehloScatterOptions.InsertedWindowDimsAsNumpy()
+ if not stablehloScatterOptions.ScatterDimsToOperandDimsIsNone():
+ if np is None:
+ self.scatterDimsToOperandDims = []
+ for i in range(stablehloScatterOptions.ScatterDimsToOperandDimsLength()):
+ self.scatterDimsToOperandDims.append(stablehloScatterOptions.ScatterDimsToOperandDims(i))
+ else:
+ self.scatterDimsToOperandDims = stablehloScatterOptions.ScatterDimsToOperandDimsAsNumpy()
+ self.indexVectorDim = stablehloScatterOptions.IndexVectorDim()
+ self.uniqueIndices = stablehloScatterOptions.UniqueIndices()
+ self.updateComputationSubgraphIndex = stablehloScatterOptions.UpdateComputationSubgraphIndex()
+
+ # StablehloScatterOptionsT
+ def Pack(self, builder):
+ if self.updateWindowDims is not None:
+ if np is not None and type(self.updateWindowDims) is np.ndarray:
+ updateWindowDims = builder.CreateNumpyVector(self.updateWindowDims)
+ else:
+ StablehloScatterOptionsStartUpdateWindowDimsVector(builder, len(self.updateWindowDims))
+ for i in reversed(range(len(self.updateWindowDims))):
+ builder.PrependInt64(self.updateWindowDims[i])
+ updateWindowDims = builder.EndVector()
+ if self.insertedWindowDims is not None:
+ if np is not None and type(self.insertedWindowDims) is np.ndarray:
+ insertedWindowDims = builder.CreateNumpyVector(self.insertedWindowDims)
+ else:
+ StablehloScatterOptionsStartInsertedWindowDimsVector(builder, len(self.insertedWindowDims))
+ for i in reversed(range(len(self.insertedWindowDims))):
+ builder.PrependInt64(self.insertedWindowDims[i])
+ insertedWindowDims = builder.EndVector()
+ if self.scatterDimsToOperandDims is not None:
+ if np is not None and type(self.scatterDimsToOperandDims) is np.ndarray:
+ scatterDimsToOperandDims = builder.CreateNumpyVector(self.scatterDimsToOperandDims)
+ else:
+ StablehloScatterOptionsStartScatterDimsToOperandDimsVector(builder, len(self.scatterDimsToOperandDims))
+ for i in reversed(range(len(self.scatterDimsToOperandDims))):
+ builder.PrependInt64(self.scatterDimsToOperandDims[i])
+ scatterDimsToOperandDims = builder.EndVector()
+ StablehloScatterOptionsStart(builder)
+ StablehloScatterOptionsAddIndicesAreSorted(builder, self.indicesAreSorted)
+ if self.updateWindowDims is not None:
+ StablehloScatterOptionsAddUpdateWindowDims(builder, updateWindowDims)
+ if self.insertedWindowDims is not None:
+ StablehloScatterOptionsAddInsertedWindowDims(builder, insertedWindowDims)
+ if self.scatterDimsToOperandDims is not None:
+ StablehloScatterOptionsAddScatterDimsToOperandDims(builder, scatterDimsToOperandDims)
+ StablehloScatterOptionsAddIndexVectorDim(builder, self.indexVectorDim)
+ StablehloScatterOptionsAddUniqueIndices(builder, self.uniqueIndices)
+ StablehloScatterOptionsAddUpdateComputationSubgraphIndex(builder, self.updateComputationSubgraphIndex)
+ stablehloScatterOptions = StablehloScatterOptionsEnd(builder)
+ return stablehloScatterOptions
+
+
+class StablehloRngBitGeneratorOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StablehloRngBitGeneratorOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStablehloRngBitGeneratorOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StablehloRngBitGeneratorOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StablehloRngBitGeneratorOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StablehloRngBitGeneratorOptions
+ def Algorithm(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+def StablehloRngBitGeneratorOptionsStart(builder):
+ builder.StartObject(1)
+
+def StablehloRngBitGeneratorOptionsAddAlgorithm(builder, algorithm):
+ builder.PrependInt8Slot(0, algorithm, 0)
+
+def StablehloRngBitGeneratorOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class StablehloRngBitGeneratorOptionsT(object):
+
+ # StablehloRngBitGeneratorOptionsT
+ def __init__(self):
+ self.algorithm = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stablehloRngBitGeneratorOptions = StablehloRngBitGeneratorOptions()
+ stablehloRngBitGeneratorOptions.Init(buf, pos)
+ return cls.InitFromObj(stablehloRngBitGeneratorOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stablehloRngBitGeneratorOptions):
+ x = StablehloRngBitGeneratorOptionsT()
+ x._UnPack(stablehloRngBitGeneratorOptions)
+ return x
+
+ # StablehloRngBitGeneratorOptionsT
+ def _UnPack(self, stablehloRngBitGeneratorOptions):
+ if stablehloRngBitGeneratorOptions is None:
+ return
+ self.algorithm = stablehloRngBitGeneratorOptions.Algorithm()
+
+ # StablehloRngBitGeneratorOptionsT
+ def Pack(self, builder):
+ StablehloRngBitGeneratorOptionsStart(builder)
+ StablehloRngBitGeneratorOptionsAddAlgorithm(builder, self.algorithm)
+ stablehloRngBitGeneratorOptions = StablehloRngBitGeneratorOptionsEnd(builder)
+ return stablehloRngBitGeneratorOptions
+
+
+class Conv2DOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = Conv2DOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsConv2DOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def Conv2DOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # Conv2DOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # Conv2DOptions
+ def Padding(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # Conv2DOptions
+ def StrideW(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # Conv2DOptions
+ def StrideH(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # Conv2DOptions
+ def FusedActivationFunction(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # Conv2DOptions
+ def DilationWFactor(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 1
+
+ # Conv2DOptions
+ def DilationHFactor(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 1
+
+ # Conv2DOptions
+ def QuantizedBiasType(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+def Conv2DOptionsStart(builder):
+ builder.StartObject(7)
+
+def Conv2DOptionsAddPadding(builder, padding):
+ builder.PrependInt8Slot(0, padding, 0)
+
+def Conv2DOptionsAddStrideW(builder, strideW):
+ builder.PrependInt32Slot(1, strideW, 0)
+
+def Conv2DOptionsAddStrideH(builder, strideH):
+ builder.PrependInt32Slot(2, strideH, 0)
+
+def Conv2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(3, fusedActivationFunction, 0)
+
+def Conv2DOptionsAddDilationWFactor(builder, dilationWFactor):
+ builder.PrependInt32Slot(4, dilationWFactor, 1)
+
+def Conv2DOptionsAddDilationHFactor(builder, dilationHFactor):
+ builder.PrependInt32Slot(5, dilationHFactor, 1)
+
+def Conv2DOptionsAddQuantizedBiasType(builder, quantizedBiasType):
+ builder.PrependInt8Slot(6, quantizedBiasType, 0)
+
+def Conv2DOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class Conv2DOptionsT(object):
+
+ # Conv2DOptionsT
+ def __init__(self):
+ self.padding = 0 # type: int
+ self.strideW = 0 # type: int
+ self.strideH = 0 # type: int
+ self.fusedActivationFunction = 0 # type: int
+ self.dilationWFactor = 1 # type: int
+ self.dilationHFactor = 1 # type: int
+ self.quantizedBiasType = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ conv2Doptions = Conv2DOptions()
+ conv2Doptions.Init(buf, pos)
+ return cls.InitFromObj(conv2Doptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, conv2Doptions):
+ x = Conv2DOptionsT()
+ x._UnPack(conv2Doptions)
+ return x
+
+ # Conv2DOptionsT
+ def _UnPack(self, conv2Doptions):
+ if conv2Doptions is None:
+ return
+ self.padding = conv2Doptions.Padding()
+ self.strideW = conv2Doptions.StrideW()
+ self.strideH = conv2Doptions.StrideH()
+ self.fusedActivationFunction = conv2Doptions.FusedActivationFunction()
+ self.dilationWFactor = conv2Doptions.DilationWFactor()
+ self.dilationHFactor = conv2Doptions.DilationHFactor()
+ self.quantizedBiasType = conv2Doptions.QuantizedBiasType()
+
+ # Conv2DOptionsT
+ def Pack(self, builder):
+ Conv2DOptionsStart(builder)
+ Conv2DOptionsAddPadding(builder, self.padding)
+ Conv2DOptionsAddStrideW(builder, self.strideW)
+ Conv2DOptionsAddStrideH(builder, self.strideH)
+ Conv2DOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
+ Conv2DOptionsAddDilationWFactor(builder, self.dilationWFactor)
+ Conv2DOptionsAddDilationHFactor(builder, self.dilationHFactor)
+ Conv2DOptionsAddQuantizedBiasType(builder, self.quantizedBiasType)
+ conv2Doptions = Conv2DOptionsEnd(builder)
+ return conv2Doptions
+
+
+class Conv3DOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = Conv3DOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsConv3DOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def Conv3DOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # Conv3DOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # Conv3DOptions
+ def Padding(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # Conv3DOptions
+ def StrideD(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # Conv3DOptions
+ def StrideW(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # Conv3DOptions
+ def StrideH(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # Conv3DOptions
+ def FusedActivationFunction(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # Conv3DOptions
+ def DilationDFactor(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 1
+
+ # Conv3DOptions
+ def DilationWFactor(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 1
+
+ # Conv3DOptions
+ def DilationHFactor(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 1
+
+def Conv3DOptionsStart(builder):
+ builder.StartObject(8)
+
+def Conv3DOptionsAddPadding(builder, padding):
+ builder.PrependInt8Slot(0, padding, 0)
+
+def Conv3DOptionsAddStrideD(builder, strideD):
+ builder.PrependInt32Slot(1, strideD, 0)
+
+def Conv3DOptionsAddStrideW(builder, strideW):
+ builder.PrependInt32Slot(2, strideW, 0)
+
+def Conv3DOptionsAddStrideH(builder, strideH):
+ builder.PrependInt32Slot(3, strideH, 0)
+
+def Conv3DOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(4, fusedActivationFunction, 0)
+
+def Conv3DOptionsAddDilationDFactor(builder, dilationDFactor):
+ builder.PrependInt32Slot(5, dilationDFactor, 1)
+
+def Conv3DOptionsAddDilationWFactor(builder, dilationWFactor):
+ builder.PrependInt32Slot(6, dilationWFactor, 1)
+
+def Conv3DOptionsAddDilationHFactor(builder, dilationHFactor):
+ builder.PrependInt32Slot(7, dilationHFactor, 1)
+
+def Conv3DOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class Conv3DOptionsT(object):
+
+ # Conv3DOptionsT
+ def __init__(self):
+ self.padding = 0 # type: int
+ self.strideD = 0 # type: int
+ self.strideW = 0 # type: int
+ self.strideH = 0 # type: int
+ self.fusedActivationFunction = 0 # type: int
+ self.dilationDFactor = 1 # type: int
+ self.dilationWFactor = 1 # type: int
+ self.dilationHFactor = 1 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ conv3Doptions = Conv3DOptions()
+ conv3Doptions.Init(buf, pos)
+ return cls.InitFromObj(conv3Doptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, conv3Doptions):
+ x = Conv3DOptionsT()
+ x._UnPack(conv3Doptions)
+ return x
+
+ # Conv3DOptionsT
+ def _UnPack(self, conv3Doptions):
+ if conv3Doptions is None:
+ return
+ self.padding = conv3Doptions.Padding()
+ self.strideD = conv3Doptions.StrideD()
+ self.strideW = conv3Doptions.StrideW()
+ self.strideH = conv3Doptions.StrideH()
+ self.fusedActivationFunction = conv3Doptions.FusedActivationFunction()
+ self.dilationDFactor = conv3Doptions.DilationDFactor()
+ self.dilationWFactor = conv3Doptions.DilationWFactor()
+ self.dilationHFactor = conv3Doptions.DilationHFactor()
+
+ # Conv3DOptionsT
+ def Pack(self, builder):
+ Conv3DOptionsStart(builder)
+ Conv3DOptionsAddPadding(builder, self.padding)
+ Conv3DOptionsAddStrideD(builder, self.strideD)
+ Conv3DOptionsAddStrideW(builder, self.strideW)
+ Conv3DOptionsAddStrideH(builder, self.strideH)
+ Conv3DOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
+ Conv3DOptionsAddDilationDFactor(builder, self.dilationDFactor)
+ Conv3DOptionsAddDilationWFactor(builder, self.dilationWFactor)
+ Conv3DOptionsAddDilationHFactor(builder, self.dilationHFactor)
+ conv3Doptions = Conv3DOptionsEnd(builder)
+ return conv3Doptions
+
+
+class Pool2DOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = Pool2DOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsPool2DOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def Pool2DOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # Pool2DOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # Pool2DOptions
+ def Padding(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # Pool2DOptions
+ def StrideW(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # Pool2DOptions
+ def StrideH(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # Pool2DOptions
+ def FilterWidth(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # Pool2DOptions
+ def FilterHeight(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # Pool2DOptions
+ def FusedActivationFunction(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+def Pool2DOptionsStart(builder):
+ builder.StartObject(6)
+
+def Pool2DOptionsAddPadding(builder, padding):
+ builder.PrependInt8Slot(0, padding, 0)
+
+def Pool2DOptionsAddStrideW(builder, strideW):
+ builder.PrependInt32Slot(1, strideW, 0)
+
+def Pool2DOptionsAddStrideH(builder, strideH):
+ builder.PrependInt32Slot(2, strideH, 0)
+
+def Pool2DOptionsAddFilterWidth(builder, filterWidth):
+ builder.PrependInt32Slot(3, filterWidth, 0)
+
+def Pool2DOptionsAddFilterHeight(builder, filterHeight):
+ builder.PrependInt32Slot(4, filterHeight, 0)
+
+def Pool2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(5, fusedActivationFunction, 0)
+
+def Pool2DOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class Pool2DOptionsT(object):
+
+ # Pool2DOptionsT
+ def __init__(self):
+ self.padding = 0 # type: int
+ self.strideW = 0 # type: int
+ self.strideH = 0 # type: int
+ self.filterWidth = 0 # type: int
+ self.filterHeight = 0 # type: int
+ self.fusedActivationFunction = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ pool2Doptions = Pool2DOptions()
+ pool2Doptions.Init(buf, pos)
+ return cls.InitFromObj(pool2Doptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, pool2Doptions):
+ x = Pool2DOptionsT()
+ x._UnPack(pool2Doptions)
+ return x
+
+ # Pool2DOptionsT
+ def _UnPack(self, pool2Doptions):
+ if pool2Doptions is None:
+ return
+ self.padding = pool2Doptions.Padding()
+ self.strideW = pool2Doptions.StrideW()
+ self.strideH = pool2Doptions.StrideH()
+ self.filterWidth = pool2Doptions.FilterWidth()
+ self.filterHeight = pool2Doptions.FilterHeight()
+ self.fusedActivationFunction = pool2Doptions.FusedActivationFunction()
+
+ # Pool2DOptionsT
+ def Pack(self, builder):
+ Pool2DOptionsStart(builder)
+ Pool2DOptionsAddPadding(builder, self.padding)
+ Pool2DOptionsAddStrideW(builder, self.strideW)
+ Pool2DOptionsAddStrideH(builder, self.strideH)
+ Pool2DOptionsAddFilterWidth(builder, self.filterWidth)
+ Pool2DOptionsAddFilterHeight(builder, self.filterHeight)
+ Pool2DOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
+ pool2Doptions = Pool2DOptionsEnd(builder)
+ return pool2Doptions
+
+
+class DepthwiseConv2DOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = DepthwiseConv2DOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsDepthwiseConv2DOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def DepthwiseConv2DOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # DepthwiseConv2DOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # DepthwiseConv2DOptions
+ def Padding(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # DepthwiseConv2DOptions
+ def StrideW(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # DepthwiseConv2DOptions
+ def StrideH(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # DepthwiseConv2DOptions
+ def DepthMultiplier(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # DepthwiseConv2DOptions
+ def FusedActivationFunction(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # DepthwiseConv2DOptions
+ def DilationWFactor(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 1
+
+ # DepthwiseConv2DOptions
+ def DilationHFactor(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 1
+
+def DepthwiseConv2DOptionsStart(builder):
+ builder.StartObject(7)
+
+def DepthwiseConv2DOptionsAddPadding(builder, padding):
+ builder.PrependInt8Slot(0, padding, 0)
+
+def DepthwiseConv2DOptionsAddStrideW(builder, strideW):
+ builder.PrependInt32Slot(1, strideW, 0)
+
+def DepthwiseConv2DOptionsAddStrideH(builder, strideH):
+ builder.PrependInt32Slot(2, strideH, 0)
+
+def DepthwiseConv2DOptionsAddDepthMultiplier(builder, depthMultiplier):
+ builder.PrependInt32Slot(3, depthMultiplier, 0)
+
+def DepthwiseConv2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(4, fusedActivationFunction, 0)
+
+def DepthwiseConv2DOptionsAddDilationWFactor(builder, dilationWFactor):
+ builder.PrependInt32Slot(5, dilationWFactor, 1)
+
+def DepthwiseConv2DOptionsAddDilationHFactor(builder, dilationHFactor):
+ builder.PrependInt32Slot(6, dilationHFactor, 1)
+
+def DepthwiseConv2DOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class DepthwiseConv2DOptionsT(object):
+
+ # DepthwiseConv2DOptionsT
+ def __init__(self):
+ self.padding = 0 # type: int
+ self.strideW = 0 # type: int
+ self.strideH = 0 # type: int
+ self.depthMultiplier = 0 # type: int
+ self.fusedActivationFunction = 0 # type: int
+ self.dilationWFactor = 1 # type: int
+ self.dilationHFactor = 1 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ depthwiseConv2Doptions = DepthwiseConv2DOptions()
+ depthwiseConv2Doptions.Init(buf, pos)
+ return cls.InitFromObj(depthwiseConv2Doptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, depthwiseConv2Doptions):
+ x = DepthwiseConv2DOptionsT()
+ x._UnPack(depthwiseConv2Doptions)
+ return x
+
+ # DepthwiseConv2DOptionsT
+ def _UnPack(self, depthwiseConv2Doptions):
+ if depthwiseConv2Doptions is None:
+ return
+ self.padding = depthwiseConv2Doptions.Padding()
+ self.strideW = depthwiseConv2Doptions.StrideW()
+ self.strideH = depthwiseConv2Doptions.StrideH()
+ self.depthMultiplier = depthwiseConv2Doptions.DepthMultiplier()
+ self.fusedActivationFunction = depthwiseConv2Doptions.FusedActivationFunction()
+ self.dilationWFactor = depthwiseConv2Doptions.DilationWFactor()
+ self.dilationHFactor = depthwiseConv2Doptions.DilationHFactor()
+
+ # DepthwiseConv2DOptionsT
+ def Pack(self, builder):
+ DepthwiseConv2DOptionsStart(builder)
+ DepthwiseConv2DOptionsAddPadding(builder, self.padding)
+ DepthwiseConv2DOptionsAddStrideW(builder, self.strideW)
+ DepthwiseConv2DOptionsAddStrideH(builder, self.strideH)
+ DepthwiseConv2DOptionsAddDepthMultiplier(builder, self.depthMultiplier)
+ DepthwiseConv2DOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
+ DepthwiseConv2DOptionsAddDilationWFactor(builder, self.dilationWFactor)
+ DepthwiseConv2DOptionsAddDilationHFactor(builder, self.dilationHFactor)
+ depthwiseConv2Doptions = DepthwiseConv2DOptionsEnd(builder)
+ return depthwiseConv2Doptions
+
class ConcatEmbeddingsOptions(object):
__slots__ = ['_tab']
@@ -2280,27 +6700,28 @@
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
return o == 0
-def ConcatEmbeddingsOptionsStart(builder): builder.StartObject(3)
-def Start(builder):
- return ConcatEmbeddingsOptionsStart(builder)
-def ConcatEmbeddingsOptionsAddNumChannels(builder, numChannels): builder.PrependInt32Slot(0, numChannels, 0)
-def AddNumChannels(builder, numChannels):
- return ConcatEmbeddingsOptionsAddNumChannels(builder, numChannels)
-def ConcatEmbeddingsOptionsAddNumColumnsPerChannel(builder, numColumnsPerChannel): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(numColumnsPerChannel), 0)
-def AddNumColumnsPerChannel(builder, numColumnsPerChannel):
- return ConcatEmbeddingsOptionsAddNumColumnsPerChannel(builder, numColumnsPerChannel)
-def ConcatEmbeddingsOptionsStartNumColumnsPerChannelVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartNumColumnsPerChannelVector(builder, numElems):
- return ConcatEmbeddingsOptionsStartNumColumnsPerChannelVector(builder, numElems)
-def ConcatEmbeddingsOptionsAddEmbeddingDimPerChannel(builder, embeddingDimPerChannel): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(embeddingDimPerChannel), 0)
-def AddEmbeddingDimPerChannel(builder, embeddingDimPerChannel):
- return ConcatEmbeddingsOptionsAddEmbeddingDimPerChannel(builder, embeddingDimPerChannel)
-def ConcatEmbeddingsOptionsStartEmbeddingDimPerChannelVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartEmbeddingDimPerChannelVector(builder, numElems):
- return ConcatEmbeddingsOptionsStartEmbeddingDimPerChannelVector(builder, numElems)
-def ConcatEmbeddingsOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return ConcatEmbeddingsOptionsEnd(builder)
+def ConcatEmbeddingsOptionsStart(builder):
+ builder.StartObject(3)
+
+def ConcatEmbeddingsOptionsAddNumChannels(builder, numChannels):
+ builder.PrependInt32Slot(0, numChannels, 0)
+
+def ConcatEmbeddingsOptionsAddNumColumnsPerChannel(builder, numColumnsPerChannel):
+ builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(numColumnsPerChannel), 0)
+
+def ConcatEmbeddingsOptionsStartNumColumnsPerChannelVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def ConcatEmbeddingsOptionsAddEmbeddingDimPerChannel(builder, embeddingDimPerChannel):
+ builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(embeddingDimPerChannel), 0)
+
+def ConcatEmbeddingsOptionsStartEmbeddingDimPerChannelVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def ConcatEmbeddingsOptionsEnd(builder):
+ return builder.EndObject()
+
+
try:
from typing import List
except:
@@ -2321,6 +6742,11 @@
return cls.InitFromObj(concatEmbeddingsOptions)
@classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
def InitFromObj(cls, concatEmbeddingsOptions):
x = ConcatEmbeddingsOptionsT()
x._UnPack(concatEmbeddingsOptions)
@@ -2372,1923 +6798,496 @@
ConcatEmbeddingsOptionsAddEmbeddingDimPerChannel(builder, embeddingDimPerChannel)
concatEmbeddingsOptions = ConcatEmbeddingsOptionsEnd(builder)
return concatEmbeddingsOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class ConcatenationOptions(object):
+class LSHProjectionOptions(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0):
n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = ConcatenationOptions()
+ x = LSHProjectionOptions()
x.Init(buf, n + offset)
return x
@classmethod
- def GetRootAsConcatenationOptions(cls, buf, offset=0):
+ def GetRootAsLSHProjectionOptions(cls, buf, offset=0):
"""This method is deprecated. Please switch to GetRootAs."""
return cls.GetRootAs(buf, offset)
@classmethod
- def ConcatenationOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ def LSHProjectionOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
- # ConcatenationOptions
+ # LSHProjectionOptions
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
- # ConcatenationOptions
- def Axis(self):
+ # LSHProjectionOptions
+ def Type(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+def LSHProjectionOptionsStart(builder):
+ builder.StartObject(1)
+
+def LSHProjectionOptionsAddType(builder, type):
+ builder.PrependInt8Slot(0, type, 0)
+
+def LSHProjectionOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class LSHProjectionOptionsT(object):
+
+ # LSHProjectionOptionsT
+ def __init__(self):
+ self.type = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ lshprojectionOptions = LSHProjectionOptions()
+ lshprojectionOptions.Init(buf, pos)
+ return cls.InitFromObj(lshprojectionOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, lshprojectionOptions):
+ x = LSHProjectionOptionsT()
+ x._UnPack(lshprojectionOptions)
+ return x
+
+ # LSHProjectionOptionsT
+ def _UnPack(self, lshprojectionOptions):
+ if lshprojectionOptions is None:
+ return
+ self.type = lshprojectionOptions.Type()
+
+ # LSHProjectionOptionsT
+ def Pack(self, builder):
+ LSHProjectionOptionsStart(builder)
+ LSHProjectionOptionsAddType(builder, self.type)
+ lshprojectionOptions = LSHProjectionOptionsEnd(builder)
+ return lshprojectionOptions
+
+
+class SVDFOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = SVDFOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsSVDFOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def SVDFOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # SVDFOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # SVDFOptions
+ def Rank(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
if o != 0:
return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
return 0
- # ConcatenationOptions
+ # SVDFOptions
def FusedActivationFunction(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
if o != 0:
return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
return 0
-def ConcatenationOptionsStart(builder): builder.StartObject(2)
-def Start(builder):
- return ConcatenationOptionsStart(builder)
-def ConcatenationOptionsAddAxis(builder, axis): builder.PrependInt32Slot(0, axis, 0)
-def AddAxis(builder, axis):
- return ConcatenationOptionsAddAxis(builder, axis)
-def ConcatenationOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(1, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return ConcatenationOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def ConcatenationOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return ConcatenationOptionsEnd(builder)
-
-class ConcatenationOptionsT(object):
-
- # ConcatenationOptionsT
- def __init__(self):
- self.axis = 0 # type: int
- self.fusedActivationFunction = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- concatenationOptions = ConcatenationOptions()
- concatenationOptions.Init(buf, pos)
- return cls.InitFromObj(concatenationOptions)
-
- @classmethod
- def InitFromObj(cls, concatenationOptions):
- x = ConcatenationOptionsT()
- x._UnPack(concatenationOptions)
- return x
-
- # ConcatenationOptionsT
- def _UnPack(self, concatenationOptions):
- if concatenationOptions is None:
- return
- self.axis = concatenationOptions.Axis()
- self.fusedActivationFunction = concatenationOptions.FusedActivationFunction()
-
- # ConcatenationOptionsT
- def Pack(self, builder):
- ConcatenationOptionsStart(builder)
- ConcatenationOptionsAddAxis(builder, self.axis)
- ConcatenationOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
- concatenationOptions = ConcatenationOptionsEnd(builder)
- return concatenationOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class Conv2DOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = Conv2DOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsConv2DOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def Conv2DOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # Conv2DOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # Conv2DOptions
- def Padding(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # Conv2DOptions
- def StrideW(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # Conv2DOptions
- def StrideH(self):
+ # SVDFOptions
+ def AsymmetricQuantizeInputs(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # Conv2DOptions
- def FusedActivationFunction(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # Conv2DOptions
- def DilationWFactor(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 1
-
- # Conv2DOptions
- def DilationHFactor(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 1
-
-def Conv2DOptionsStart(builder): builder.StartObject(6)
-def Start(builder):
- return Conv2DOptionsStart(builder)
-def Conv2DOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0)
-def AddPadding(builder, padding):
- return Conv2DOptionsAddPadding(builder, padding)
-def Conv2DOptionsAddStrideW(builder, strideW): builder.PrependInt32Slot(1, strideW, 0)
-def AddStrideW(builder, strideW):
- return Conv2DOptionsAddStrideW(builder, strideW)
-def Conv2DOptionsAddStrideH(builder, strideH): builder.PrependInt32Slot(2, strideH, 0)
-def AddStrideH(builder, strideH):
- return Conv2DOptionsAddStrideH(builder, strideH)
-def Conv2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(3, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return Conv2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def Conv2DOptionsAddDilationWFactor(builder, dilationWFactor): builder.PrependInt32Slot(4, dilationWFactor, 1)
-def AddDilationWFactor(builder, dilationWFactor):
- return Conv2DOptionsAddDilationWFactor(builder, dilationWFactor)
-def Conv2DOptionsAddDilationHFactor(builder, dilationHFactor): builder.PrependInt32Slot(5, dilationHFactor, 1)
-def AddDilationHFactor(builder, dilationHFactor):
- return Conv2DOptionsAddDilationHFactor(builder, dilationHFactor)
-def Conv2DOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return Conv2DOptionsEnd(builder)
-
-class Conv2DOptionsT(object):
-
- # Conv2DOptionsT
- def __init__(self):
- self.padding = 0 # type: int
- self.strideW = 0 # type: int
- self.strideH = 0 # type: int
- self.fusedActivationFunction = 0 # type: int
- self.dilationWFactor = 1 # type: int
- self.dilationHFactor = 1 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- conv2doptions = Conv2DOptions()
- conv2doptions.Init(buf, pos)
- return cls.InitFromObj(conv2doptions)
-
- @classmethod
- def InitFromObj(cls, conv2doptions):
- x = Conv2DOptionsT()
- x._UnPack(conv2doptions)
- return x
-
- # Conv2DOptionsT
- def _UnPack(self, conv2doptions):
- if conv2doptions is None:
- return
- self.padding = conv2doptions.Padding()
- self.strideW = conv2doptions.StrideW()
- self.strideH = conv2doptions.StrideH()
- self.fusedActivationFunction = conv2doptions.FusedActivationFunction()
- self.dilationWFactor = conv2doptions.DilationWFactor()
- self.dilationHFactor = conv2doptions.DilationHFactor()
-
- # Conv2DOptionsT
- def Pack(self, builder):
- Conv2DOptionsStart(builder)
- Conv2DOptionsAddPadding(builder, self.padding)
- Conv2DOptionsAddStrideW(builder, self.strideW)
- Conv2DOptionsAddStrideH(builder, self.strideH)
- Conv2DOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
- Conv2DOptionsAddDilationWFactor(builder, self.dilationWFactor)
- Conv2DOptionsAddDilationHFactor(builder, self.dilationHFactor)
- conv2doptions = Conv2DOptionsEnd(builder)
- return conv2doptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class Conv3DOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = Conv3DOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsConv3DOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def Conv3DOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # Conv3DOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # Conv3DOptions
- def Padding(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # Conv3DOptions
- def StrideD(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # Conv3DOptions
- def StrideW(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # Conv3DOptions
- def StrideH(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # Conv3DOptions
- def FusedActivationFunction(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # Conv3DOptions
- def DilationDFactor(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 1
-
- # Conv3DOptions
- def DilationWFactor(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 1
-
- # Conv3DOptions
- def DilationHFactor(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 1
-
-def Conv3DOptionsStart(builder): builder.StartObject(8)
-def Start(builder):
- return Conv3DOptionsStart(builder)
-def Conv3DOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0)
-def AddPadding(builder, padding):
- return Conv3DOptionsAddPadding(builder, padding)
-def Conv3DOptionsAddStrideD(builder, strideD): builder.PrependInt32Slot(1, strideD, 0)
-def AddStrideD(builder, strideD):
- return Conv3DOptionsAddStrideD(builder, strideD)
-def Conv3DOptionsAddStrideW(builder, strideW): builder.PrependInt32Slot(2, strideW, 0)
-def AddStrideW(builder, strideW):
- return Conv3DOptionsAddStrideW(builder, strideW)
-def Conv3DOptionsAddStrideH(builder, strideH): builder.PrependInt32Slot(3, strideH, 0)
-def AddStrideH(builder, strideH):
- return Conv3DOptionsAddStrideH(builder, strideH)
-def Conv3DOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(4, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return Conv3DOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def Conv3DOptionsAddDilationDFactor(builder, dilationDFactor): builder.PrependInt32Slot(5, dilationDFactor, 1)
-def AddDilationDFactor(builder, dilationDFactor):
- return Conv3DOptionsAddDilationDFactor(builder, dilationDFactor)
-def Conv3DOptionsAddDilationWFactor(builder, dilationWFactor): builder.PrependInt32Slot(6, dilationWFactor, 1)
-def AddDilationWFactor(builder, dilationWFactor):
- return Conv3DOptionsAddDilationWFactor(builder, dilationWFactor)
-def Conv3DOptionsAddDilationHFactor(builder, dilationHFactor): builder.PrependInt32Slot(7, dilationHFactor, 1)
-def AddDilationHFactor(builder, dilationHFactor):
- return Conv3DOptionsAddDilationHFactor(builder, dilationHFactor)
-def Conv3DOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return Conv3DOptionsEnd(builder)
-
-class Conv3DOptionsT(object):
-
- # Conv3DOptionsT
- def __init__(self):
- self.padding = 0 # type: int
- self.strideD = 0 # type: int
- self.strideW = 0 # type: int
- self.strideH = 0 # type: int
- self.fusedActivationFunction = 0 # type: int
- self.dilationDFactor = 1 # type: int
- self.dilationWFactor = 1 # type: int
- self.dilationHFactor = 1 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- conv3doptions = Conv3DOptions()
- conv3doptions.Init(buf, pos)
- return cls.InitFromObj(conv3doptions)
-
- @classmethod
- def InitFromObj(cls, conv3doptions):
- x = Conv3DOptionsT()
- x._UnPack(conv3doptions)
- return x
-
- # Conv3DOptionsT
- def _UnPack(self, conv3doptions):
- if conv3doptions is None:
- return
- self.padding = conv3doptions.Padding()
- self.strideD = conv3doptions.StrideD()
- self.strideW = conv3doptions.StrideW()
- self.strideH = conv3doptions.StrideH()
- self.fusedActivationFunction = conv3doptions.FusedActivationFunction()
- self.dilationDFactor = conv3doptions.DilationDFactor()
- self.dilationWFactor = conv3doptions.DilationWFactor()
- self.dilationHFactor = conv3doptions.DilationHFactor()
-
- # Conv3DOptionsT
- def Pack(self, builder):
- Conv3DOptionsStart(builder)
- Conv3DOptionsAddPadding(builder, self.padding)
- Conv3DOptionsAddStrideD(builder, self.strideD)
- Conv3DOptionsAddStrideW(builder, self.strideW)
- Conv3DOptionsAddStrideH(builder, self.strideH)
- Conv3DOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
- Conv3DOptionsAddDilationDFactor(builder, self.dilationDFactor)
- Conv3DOptionsAddDilationWFactor(builder, self.dilationWFactor)
- Conv3DOptionsAddDilationHFactor(builder, self.dilationHFactor)
- conv3doptions = Conv3DOptionsEnd(builder)
- return conv3doptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class CosOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = CosOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsCosOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def CosOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # CosOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def CosOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return CosOptionsStart(builder)
-def CosOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return CosOptionsEnd(builder)
-
-class CosOptionsT(object):
-
- # CosOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- cosOptions = CosOptions()
- cosOptions.Init(buf, pos)
- return cls.InitFromObj(cosOptions)
-
- @classmethod
- def InitFromObj(cls, cosOptions):
- x = CosOptionsT()
- x._UnPack(cosOptions)
- return x
-
- # CosOptionsT
- def _UnPack(self, cosOptions):
- if cosOptions is None:
- return
-
- # CosOptionsT
- def Pack(self, builder):
- CosOptionsStart(builder)
- cosOptions = CosOptionsEnd(builder)
- return cosOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class CumsumOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = CumsumOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsCumsumOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def CumsumOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # CumsumOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # CumsumOptions
- def Exclusive(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
return False
- # CumsumOptions
- def Reverse(self):
+def SVDFOptionsStart(builder):
+ builder.StartObject(3)
+
+def SVDFOptionsAddRank(builder, rank):
+ builder.PrependInt32Slot(0, rank, 0)
+
+def SVDFOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(1, fusedActivationFunction, 0)
+
+def SVDFOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
+ builder.PrependBoolSlot(2, asymmetricQuantizeInputs, 0)
+
+def SVDFOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class SVDFOptionsT(object):
+
+ # SVDFOptionsT
+ def __init__(self):
+ self.rank = 0 # type: int
+ self.fusedActivationFunction = 0 # type: int
+ self.asymmetricQuantizeInputs = False # type: bool
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ svdfoptions = SVDFOptions()
+ svdfoptions.Init(buf, pos)
+ return cls.InitFromObj(svdfoptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, svdfoptions):
+ x = SVDFOptionsT()
+ x._UnPack(svdfoptions)
+ return x
+
+ # SVDFOptionsT
+ def _UnPack(self, svdfoptions):
+ if svdfoptions is None:
+ return
+ self.rank = svdfoptions.Rank()
+ self.fusedActivationFunction = svdfoptions.FusedActivationFunction()
+ self.asymmetricQuantizeInputs = svdfoptions.AsymmetricQuantizeInputs()
+
+ # SVDFOptionsT
+ def Pack(self, builder):
+ SVDFOptionsStart(builder)
+ SVDFOptionsAddRank(builder, self.rank)
+ SVDFOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
+ SVDFOptionsAddAsymmetricQuantizeInputs(builder, self.asymmetricQuantizeInputs)
+ svdfoptions = SVDFOptionsEnd(builder)
+ return svdfoptions
+
+
+class RNNOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = RNNOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsRNNOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def RNNOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # RNNOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # RNNOptions
+ def FusedActivationFunction(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # RNNOptions
+ def AsymmetricQuantizeInputs(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
if o != 0:
return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
return False
-def CumsumOptionsStart(builder): builder.StartObject(2)
-def Start(builder):
- return CumsumOptionsStart(builder)
-def CumsumOptionsAddExclusive(builder, exclusive): builder.PrependBoolSlot(0, exclusive, 0)
-def AddExclusive(builder, exclusive):
- return CumsumOptionsAddExclusive(builder, exclusive)
-def CumsumOptionsAddReverse(builder, reverse): builder.PrependBoolSlot(1, reverse, 0)
-def AddReverse(builder, reverse):
- return CumsumOptionsAddReverse(builder, reverse)
-def CumsumOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return CumsumOptionsEnd(builder)
+def RNNOptionsStart(builder):
+ builder.StartObject(2)
-class CumsumOptionsT(object):
+def RNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(0, fusedActivationFunction, 0)
- # CumsumOptionsT
- def __init__(self):
- self.exclusive = False # type: bool
- self.reverse = False # type: bool
+def RNNOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
+ builder.PrependBoolSlot(1, asymmetricQuantizeInputs, 0)
- @classmethod
- def InitFromBuf(cls, buf, pos):
- cumsumOptions = CumsumOptions()
- cumsumOptions.Init(buf, pos)
- return cls.InitFromObj(cumsumOptions)
+def RNNOptionsEnd(builder):
+ return builder.EndObject()
- @classmethod
- def InitFromObj(cls, cumsumOptions):
- x = CumsumOptionsT()
- x._UnPack(cumsumOptions)
- return x
- # CumsumOptionsT
- def _UnPack(self, cumsumOptions):
- if cumsumOptions is None:
- return
- self.exclusive = cumsumOptions.Exclusive()
- self.reverse = cumsumOptions.Reverse()
- # CumsumOptionsT
- def Pack(self, builder):
- CumsumOptionsStart(builder)
- CumsumOptionsAddExclusive(builder, self.exclusive)
- CumsumOptionsAddReverse(builder, self.reverse)
- cumsumOptions = CumsumOptionsEnd(builder)
- return cumsumOptions
-# automatically generated by the FlatBuffers compiler, do not modify
+class RNNOptionsT(object):
-# namespace: tflite
-
-class CustomOptionsFormat(object):
- FLEXBUFFERS = 0
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class CustomQuantization(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = CustomQuantization()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsCustomQuantization(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def CustomQuantizationBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # CustomQuantization
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # CustomQuantization
- def Custom(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- a = self._tab.Vector(o)
- return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1))
- return 0
-
- # CustomQuantization
- def CustomAsNumpy(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o)
- return 0
-
- # CustomQuantization
- def CustomLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # CustomQuantization
- def CustomIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- return o == 0
-
-def CustomQuantizationStart(builder): builder.StartObject(1)
-def Start(builder):
- return CustomQuantizationStart(builder)
-def CustomQuantizationAddCustom(builder, custom): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(custom), 0)
-def AddCustom(builder, custom):
- return CustomQuantizationAddCustom(builder, custom)
-def CustomQuantizationStartCustomVector(builder, numElems): return builder.StartVector(1, numElems, 1)
-def StartCustomVector(builder, numElems):
- return CustomQuantizationStartCustomVector(builder, numElems)
-def CustomQuantizationEnd(builder): return builder.EndObject()
-def End(builder):
- return CustomQuantizationEnd(builder)
-try:
- from typing import List
-except:
- pass
-
-class CustomQuantizationT(object):
-
- # CustomQuantizationT
- def __init__(self):
- self.custom = None # type: List[int]
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- customQuantization = CustomQuantization()
- customQuantization.Init(buf, pos)
- return cls.InitFromObj(customQuantization)
-
- @classmethod
- def InitFromObj(cls, customQuantization):
- x = CustomQuantizationT()
- x._UnPack(customQuantization)
- return x
-
- # CustomQuantizationT
- def _UnPack(self, customQuantization):
- if customQuantization is None:
- return
- if not customQuantization.CustomIsNone():
- if np is None:
- self.custom = []
- for i in range(customQuantization.CustomLength()):
- self.custom.append(customQuantization.Custom(i))
- else:
- self.custom = customQuantization.CustomAsNumpy()
-
- # CustomQuantizationT
- def Pack(self, builder):
- if self.custom is not None:
- if np is not None and type(self.custom) is np.ndarray:
- custom = builder.CreateNumpyVector(self.custom)
- else:
- CustomQuantizationStartCustomVector(builder, len(self.custom))
- for i in reversed(range(len(self.custom))):
- builder.PrependUint8(self.custom[i])
- custom = builder.EndVector()
- CustomQuantizationStart(builder)
- if self.custom is not None:
- CustomQuantizationAddCustom(builder, custom)
- customQuantization = CustomQuantizationEnd(builder)
- return customQuantization
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class DensifyOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = DensifyOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsDensifyOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def DensifyOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # DensifyOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def DensifyOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return DensifyOptionsStart(builder)
-def DensifyOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return DensifyOptionsEnd(builder)
-
-class DensifyOptionsT(object):
-
- # DensifyOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- densifyOptions = DensifyOptions()
- densifyOptions.Init(buf, pos)
- return cls.InitFromObj(densifyOptions)
-
- @classmethod
- def InitFromObj(cls, densifyOptions):
- x = DensifyOptionsT()
- x._UnPack(densifyOptions)
- return x
-
- # DensifyOptionsT
- def _UnPack(self, densifyOptions):
- if densifyOptions is None:
- return
-
- # DensifyOptionsT
- def Pack(self, builder):
- DensifyOptionsStart(builder)
- densifyOptions = DensifyOptionsEnd(builder)
- return densifyOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class DepthToSpaceOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = DepthToSpaceOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsDepthToSpaceOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def DepthToSpaceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # DepthToSpaceOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # DepthToSpaceOptions
- def BlockSize(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
-def DepthToSpaceOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return DepthToSpaceOptionsStart(builder)
-def DepthToSpaceOptionsAddBlockSize(builder, blockSize): builder.PrependInt32Slot(0, blockSize, 0)
-def AddBlockSize(builder, blockSize):
- return DepthToSpaceOptionsAddBlockSize(builder, blockSize)
-def DepthToSpaceOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return DepthToSpaceOptionsEnd(builder)
-
-class DepthToSpaceOptionsT(object):
-
- # DepthToSpaceOptionsT
- def __init__(self):
- self.blockSize = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- depthToSpaceOptions = DepthToSpaceOptions()
- depthToSpaceOptions.Init(buf, pos)
- return cls.InitFromObj(depthToSpaceOptions)
-
- @classmethod
- def InitFromObj(cls, depthToSpaceOptions):
- x = DepthToSpaceOptionsT()
- x._UnPack(depthToSpaceOptions)
- return x
-
- # DepthToSpaceOptionsT
- def _UnPack(self, depthToSpaceOptions):
- if depthToSpaceOptions is None:
- return
- self.blockSize = depthToSpaceOptions.BlockSize()
-
- # DepthToSpaceOptionsT
- def Pack(self, builder):
- DepthToSpaceOptionsStart(builder)
- DepthToSpaceOptionsAddBlockSize(builder, self.blockSize)
- depthToSpaceOptions = DepthToSpaceOptionsEnd(builder)
- return depthToSpaceOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class DepthwiseConv2DOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = DepthwiseConv2DOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsDepthwiseConv2DOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def DepthwiseConv2DOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # DepthwiseConv2DOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # DepthwiseConv2DOptions
- def Padding(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # DepthwiseConv2DOptions
- def StrideW(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # DepthwiseConv2DOptions
- def StrideH(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # DepthwiseConv2DOptions
- def DepthMultiplier(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # DepthwiseConv2DOptions
- def FusedActivationFunction(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # DepthwiseConv2DOptions
- def DilationWFactor(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 1
-
- # DepthwiseConv2DOptions
- def DilationHFactor(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 1
-
-def DepthwiseConv2DOptionsStart(builder): builder.StartObject(7)
-def Start(builder):
- return DepthwiseConv2DOptionsStart(builder)
-def DepthwiseConv2DOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0)
-def AddPadding(builder, padding):
- return DepthwiseConv2DOptionsAddPadding(builder, padding)
-def DepthwiseConv2DOptionsAddStrideW(builder, strideW): builder.PrependInt32Slot(1, strideW, 0)
-def AddStrideW(builder, strideW):
- return DepthwiseConv2DOptionsAddStrideW(builder, strideW)
-def DepthwiseConv2DOptionsAddStrideH(builder, strideH): builder.PrependInt32Slot(2, strideH, 0)
-def AddStrideH(builder, strideH):
- return DepthwiseConv2DOptionsAddStrideH(builder, strideH)
-def DepthwiseConv2DOptionsAddDepthMultiplier(builder, depthMultiplier): builder.PrependInt32Slot(3, depthMultiplier, 0)
-def AddDepthMultiplier(builder, depthMultiplier):
- return DepthwiseConv2DOptionsAddDepthMultiplier(builder, depthMultiplier)
-def DepthwiseConv2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(4, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return DepthwiseConv2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def DepthwiseConv2DOptionsAddDilationWFactor(builder, dilationWFactor): builder.PrependInt32Slot(5, dilationWFactor, 1)
-def AddDilationWFactor(builder, dilationWFactor):
- return DepthwiseConv2DOptionsAddDilationWFactor(builder, dilationWFactor)
-def DepthwiseConv2DOptionsAddDilationHFactor(builder, dilationHFactor): builder.PrependInt32Slot(6, dilationHFactor, 1)
-def AddDilationHFactor(builder, dilationHFactor):
- return DepthwiseConv2DOptionsAddDilationHFactor(builder, dilationHFactor)
-def DepthwiseConv2DOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return DepthwiseConv2DOptionsEnd(builder)
-
-class DepthwiseConv2DOptionsT(object):
-
- # DepthwiseConv2DOptionsT
- def __init__(self):
- self.padding = 0 # type: int
- self.strideW = 0 # type: int
- self.strideH = 0 # type: int
- self.depthMultiplier = 0 # type: int
- self.fusedActivationFunction = 0 # type: int
- self.dilationWFactor = 1 # type: int
- self.dilationHFactor = 1 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- depthwiseConv2doptions = DepthwiseConv2DOptions()
- depthwiseConv2doptions.Init(buf, pos)
- return cls.InitFromObj(depthwiseConv2doptions)
-
- @classmethod
- def InitFromObj(cls, depthwiseConv2doptions):
- x = DepthwiseConv2DOptionsT()
- x._UnPack(depthwiseConv2doptions)
- return x
-
- # DepthwiseConv2DOptionsT
- def _UnPack(self, depthwiseConv2doptions):
- if depthwiseConv2doptions is None:
- return
- self.padding = depthwiseConv2doptions.Padding()
- self.strideW = depthwiseConv2doptions.StrideW()
- self.strideH = depthwiseConv2doptions.StrideH()
- self.depthMultiplier = depthwiseConv2doptions.DepthMultiplier()
- self.fusedActivationFunction = depthwiseConv2doptions.FusedActivationFunction()
- self.dilationWFactor = depthwiseConv2doptions.DilationWFactor()
- self.dilationHFactor = depthwiseConv2doptions.DilationHFactor()
-
- # DepthwiseConv2DOptionsT
- def Pack(self, builder):
- DepthwiseConv2DOptionsStart(builder)
- DepthwiseConv2DOptionsAddPadding(builder, self.padding)
- DepthwiseConv2DOptionsAddStrideW(builder, self.strideW)
- DepthwiseConv2DOptionsAddStrideH(builder, self.strideH)
- DepthwiseConv2DOptionsAddDepthMultiplier(builder, self.depthMultiplier)
- DepthwiseConv2DOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
- DepthwiseConv2DOptionsAddDilationWFactor(builder, self.dilationWFactor)
- DepthwiseConv2DOptionsAddDilationHFactor(builder, self.dilationHFactor)
- depthwiseConv2doptions = DepthwiseConv2DOptionsEnd(builder)
- return depthwiseConv2doptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class DequantizeOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = DequantizeOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsDequantizeOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def DequantizeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # DequantizeOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def DequantizeOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return DequantizeOptionsStart(builder)
-def DequantizeOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return DequantizeOptionsEnd(builder)
-
-class DequantizeOptionsT(object):
-
- # DequantizeOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- dequantizeOptions = DequantizeOptions()
- dequantizeOptions.Init(buf, pos)
- return cls.InitFromObj(dequantizeOptions)
-
- @classmethod
- def InitFromObj(cls, dequantizeOptions):
- x = DequantizeOptionsT()
- x._UnPack(dequantizeOptions)
- return x
-
- # DequantizeOptionsT
- def _UnPack(self, dequantizeOptions):
- if dequantizeOptions is None:
- return
-
- # DequantizeOptionsT
- def Pack(self, builder):
- DequantizeOptionsStart(builder)
- dequantizeOptions = DequantizeOptionsEnd(builder)
- return dequantizeOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class DimensionMetadata(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = DimensionMetadata()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsDimensionMetadata(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def DimensionMetadataBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # DimensionMetadata
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # DimensionMetadata
- def Format(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # DimensionMetadata
- def DenseSize(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # DimensionMetadata
- def ArraySegmentsType(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos)
- return 0
-
- # DimensionMetadata
- def ArraySegments(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
- if o != 0:
- from flatbuffers.table import Table
- obj = Table(bytearray(), 0)
- self._tab.Union(obj, o)
- return obj
- return None
-
- # DimensionMetadata
- def ArrayIndicesType(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos)
- return 0
-
- # DimensionMetadata
- def ArrayIndices(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
- if o != 0:
- from flatbuffers.table import Table
- obj = Table(bytearray(), 0)
- self._tab.Union(obj, o)
- return obj
- return None
-
-def DimensionMetadataStart(builder): builder.StartObject(6)
-def Start(builder):
- return DimensionMetadataStart(builder)
-def DimensionMetadataAddFormat(builder, format): builder.PrependInt8Slot(0, format, 0)
-def AddFormat(builder, format):
- return DimensionMetadataAddFormat(builder, format)
-def DimensionMetadataAddDenseSize(builder, denseSize): builder.PrependInt32Slot(1, denseSize, 0)
-def AddDenseSize(builder, denseSize):
- return DimensionMetadataAddDenseSize(builder, denseSize)
-def DimensionMetadataAddArraySegmentsType(builder, arraySegmentsType): builder.PrependUint8Slot(2, arraySegmentsType, 0)
-def AddArraySegmentsType(builder, arraySegmentsType):
- return DimensionMetadataAddArraySegmentsType(builder, arraySegmentsType)
-def DimensionMetadataAddArraySegments(builder, arraySegments): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(arraySegments), 0)
-def AddArraySegments(builder, arraySegments):
- return DimensionMetadataAddArraySegments(builder, arraySegments)
-def DimensionMetadataAddArrayIndicesType(builder, arrayIndicesType): builder.PrependUint8Slot(4, arrayIndicesType, 0)
-def AddArrayIndicesType(builder, arrayIndicesType):
- return DimensionMetadataAddArrayIndicesType(builder, arrayIndicesType)
-def DimensionMetadataAddArrayIndices(builder, arrayIndices): builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(arrayIndices), 0)
-def AddArrayIndices(builder, arrayIndices):
- return DimensionMetadataAddArrayIndices(builder, arrayIndices)
-def DimensionMetadataEnd(builder): return builder.EndObject()
-def End(builder):
- return DimensionMetadataEnd(builder)
-try:
- from typing import Union
-except:
- pass
-
-class DimensionMetadataT(object):
-
- # DimensionMetadataT
- def __init__(self):
- self.format = 0 # type: int
- self.denseSize = 0 # type: int
- self.arraySegmentsType = 0 # type: int
- self.arraySegments = None # type: Union[None, Int32VectorT, Uint16VectorT, Uint8VectorT]
- self.arrayIndicesType = 0 # type: int
- self.arrayIndices = None # type: Union[None, Int32VectorT, Uint16VectorT, Uint8VectorT]
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- dimensionMetadata = DimensionMetadata()
- dimensionMetadata.Init(buf, pos)
- return cls.InitFromObj(dimensionMetadata)
-
- @classmethod
- def InitFromObj(cls, dimensionMetadata):
- x = DimensionMetadataT()
- x._UnPack(dimensionMetadata)
- return x
-
- # DimensionMetadataT
- def _UnPack(self, dimensionMetadata):
- if dimensionMetadata is None:
- return
- self.format = dimensionMetadata.Format()
- self.denseSize = dimensionMetadata.DenseSize()
- self.arraySegmentsType = dimensionMetadata.ArraySegmentsType()
- self.arraySegments = SparseIndexVectorCreator(self.arraySegmentsType, dimensionMetadata.ArraySegments())
- self.arrayIndicesType = dimensionMetadata.ArrayIndicesType()
- self.arrayIndices = SparseIndexVectorCreator(self.arrayIndicesType, dimensionMetadata.ArrayIndices())
-
- # DimensionMetadataT
- def Pack(self, builder):
- if self.arraySegments is not None:
- arraySegments = self.arraySegments.Pack(builder)
- if self.arrayIndices is not None:
- arrayIndices = self.arrayIndices.Pack(builder)
- DimensionMetadataStart(builder)
- DimensionMetadataAddFormat(builder, self.format)
- DimensionMetadataAddDenseSize(builder, self.denseSize)
- DimensionMetadataAddArraySegmentsType(builder, self.arraySegmentsType)
- if self.arraySegments is not None:
- DimensionMetadataAddArraySegments(builder, arraySegments)
- DimensionMetadataAddArrayIndicesType(builder, self.arrayIndicesType)
- if self.arrayIndices is not None:
- DimensionMetadataAddArrayIndices(builder, arrayIndices)
- dimensionMetadata = DimensionMetadataEnd(builder)
- return dimensionMetadata
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-class DimensionType(object):
- DENSE = 0
- SPARSE_CSR = 1
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class DivOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = DivOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsDivOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def DivOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # DivOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # DivOptions
- def FusedActivationFunction(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
-def DivOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return DivOptionsStart(builder)
-def DivOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return DivOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def DivOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return DivOptionsEnd(builder)
-
-class DivOptionsT(object):
-
- # DivOptionsT
+ # RNNOptionsT
def __init__(self):
self.fusedActivationFunction = 0 # type: int
+ self.asymmetricQuantizeInputs = False # type: bool
@classmethod
def InitFromBuf(cls, buf, pos):
- divOptions = DivOptions()
- divOptions.Init(buf, pos)
- return cls.InitFromObj(divOptions)
+ rnnoptions = RNNOptions()
+ rnnoptions.Init(buf, pos)
+ return cls.InitFromObj(rnnoptions)
@classmethod
- def InitFromObj(cls, divOptions):
- x = DivOptionsT()
- x._UnPack(divOptions)
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, rnnoptions):
+ x = RNNOptionsT()
+ x._UnPack(rnnoptions)
return x
- # DivOptionsT
- def _UnPack(self, divOptions):
- if divOptions is None:
+ # RNNOptionsT
+ def _UnPack(self, rnnoptions):
+ if rnnoptions is None:
return
- self.fusedActivationFunction = divOptions.FusedActivationFunction()
+ self.fusedActivationFunction = rnnoptions.FusedActivationFunction()
+ self.asymmetricQuantizeInputs = rnnoptions.AsymmetricQuantizeInputs()
- # DivOptionsT
+ # RNNOptionsT
def Pack(self, builder):
- DivOptionsStart(builder)
- DivOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
- divOptions = DivOptionsEnd(builder)
- return divOptions
-# automatically generated by the FlatBuffers compiler, do not modify
+ RNNOptionsStart(builder)
+ RNNOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
+ RNNOptionsAddAsymmetricQuantizeInputs(builder, self.asymmetricQuantizeInputs)
+ rnnoptions = RNNOptionsEnd(builder)
+ return rnnoptions
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class DynamicUpdateSliceOptions(object):
+class SequenceRNNOptions(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0):
n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = DynamicUpdateSliceOptions()
+ x = SequenceRNNOptions()
x.Init(buf, n + offset)
return x
@classmethod
- def GetRootAsDynamicUpdateSliceOptions(cls, buf, offset=0):
+ def GetRootAsSequenceRNNOptions(cls, buf, offset=0):
"""This method is deprecated. Please switch to GetRootAs."""
return cls.GetRootAs(buf, offset)
@classmethod
- def DynamicUpdateSliceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ def SequenceRNNOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
- # DynamicUpdateSliceOptions
+ # SequenceRNNOptions
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
-def DynamicUpdateSliceOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return DynamicUpdateSliceOptionsStart(builder)
-def DynamicUpdateSliceOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return DynamicUpdateSliceOptionsEnd(builder)
-
-class DynamicUpdateSliceOptionsT(object):
-
- # DynamicUpdateSliceOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- dynamicUpdateSliceOptions = DynamicUpdateSliceOptions()
- dynamicUpdateSliceOptions.Init(buf, pos)
- return cls.InitFromObj(dynamicUpdateSliceOptions)
-
- @classmethod
- def InitFromObj(cls, dynamicUpdateSliceOptions):
- x = DynamicUpdateSliceOptionsT()
- x._UnPack(dynamicUpdateSliceOptions)
- return x
-
- # DynamicUpdateSliceOptionsT
- def _UnPack(self, dynamicUpdateSliceOptions):
- if dynamicUpdateSliceOptions is None:
- return
-
- # DynamicUpdateSliceOptionsT
- def Pack(self, builder):
- DynamicUpdateSliceOptionsStart(builder)
- dynamicUpdateSliceOptions = DynamicUpdateSliceOptionsEnd(builder)
- return dynamicUpdateSliceOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class EmbeddingLookupSparseOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = EmbeddingLookupSparseOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsEmbeddingLookupSparseOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def EmbeddingLookupSparseOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # EmbeddingLookupSparseOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # EmbeddingLookupSparseOptions
- def Combiner(self):
+ # SequenceRNNOptions
+ def TimeMajor(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+ # SequenceRNNOptions
+ def FusedActivationFunction(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
return 0
-def EmbeddingLookupSparseOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return EmbeddingLookupSparseOptionsStart(builder)
-def EmbeddingLookupSparseOptionsAddCombiner(builder, combiner): builder.PrependInt8Slot(0, combiner, 0)
-def AddCombiner(builder, combiner):
- return EmbeddingLookupSparseOptionsAddCombiner(builder, combiner)
-def EmbeddingLookupSparseOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return EmbeddingLookupSparseOptionsEnd(builder)
-
-class EmbeddingLookupSparseOptionsT(object):
-
- # EmbeddingLookupSparseOptionsT
- def __init__(self):
- self.combiner = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- embeddingLookupSparseOptions = EmbeddingLookupSparseOptions()
- embeddingLookupSparseOptions.Init(buf, pos)
- return cls.InitFromObj(embeddingLookupSparseOptions)
-
- @classmethod
- def InitFromObj(cls, embeddingLookupSparseOptions):
- x = EmbeddingLookupSparseOptionsT()
- x._UnPack(embeddingLookupSparseOptions)
- return x
-
- # EmbeddingLookupSparseOptionsT
- def _UnPack(self, embeddingLookupSparseOptions):
- if embeddingLookupSparseOptions is None:
- return
- self.combiner = embeddingLookupSparseOptions.Combiner()
-
- # EmbeddingLookupSparseOptionsT
- def Pack(self, builder):
- EmbeddingLookupSparseOptionsStart(builder)
- EmbeddingLookupSparseOptionsAddCombiner(builder, self.combiner)
- embeddingLookupSparseOptions = EmbeddingLookupSparseOptionsEnd(builder)
- return embeddingLookupSparseOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class EqualOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = EqualOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsEqualOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def EqualOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # EqualOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def EqualOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return EqualOptionsStart(builder)
-def EqualOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return EqualOptionsEnd(builder)
-
-class EqualOptionsT(object):
-
- # EqualOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- equalOptions = EqualOptions()
- equalOptions.Init(buf, pos)
- return cls.InitFromObj(equalOptions)
-
- @classmethod
- def InitFromObj(cls, equalOptions):
- x = EqualOptionsT()
- x._UnPack(equalOptions)
- return x
-
- # EqualOptionsT
- def _UnPack(self, equalOptions):
- if equalOptions is None:
- return
-
- # EqualOptionsT
- def Pack(self, builder):
- EqualOptionsStart(builder)
- equalOptions = EqualOptionsEnd(builder)
- return equalOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class ExpOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = ExpOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsExpOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def ExpOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # ExpOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def ExpOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return ExpOptionsStart(builder)
-def ExpOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return ExpOptionsEnd(builder)
-
-class ExpOptionsT(object):
-
- # ExpOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- expOptions = ExpOptions()
- expOptions.Init(buf, pos)
- return cls.InitFromObj(expOptions)
-
- @classmethod
- def InitFromObj(cls, expOptions):
- x = ExpOptionsT()
- x._UnPack(expOptions)
- return x
-
- # ExpOptionsT
- def _UnPack(self, expOptions):
- if expOptions is None:
- return
-
- # ExpOptionsT
- def Pack(self, builder):
- ExpOptionsStart(builder)
- expOptions = ExpOptionsEnd(builder)
- return expOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class ExpandDimsOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = ExpandDimsOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsExpandDimsOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def ExpandDimsOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # ExpandDimsOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def ExpandDimsOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return ExpandDimsOptionsStart(builder)
-def ExpandDimsOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return ExpandDimsOptionsEnd(builder)
-
-class ExpandDimsOptionsT(object):
-
- # ExpandDimsOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- expandDimsOptions = ExpandDimsOptions()
- expandDimsOptions.Init(buf, pos)
- return cls.InitFromObj(expandDimsOptions)
-
- @classmethod
- def InitFromObj(cls, expandDimsOptions):
- x = ExpandDimsOptionsT()
- x._UnPack(expandDimsOptions)
- return x
-
- # ExpandDimsOptionsT
- def _UnPack(self, expandDimsOptions):
- if expandDimsOptions is None:
- return
-
- # ExpandDimsOptionsT
- def Pack(self, builder):
- ExpandDimsOptionsStart(builder)
- expandDimsOptions = ExpandDimsOptionsEnd(builder)
- return expandDimsOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class FakeQuantOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = FakeQuantOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsFakeQuantOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def FakeQuantOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # FakeQuantOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # FakeQuantOptions
- def Min(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
- return 0.0
-
- # FakeQuantOptions
- def Max(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
- return 0.0
-
- # FakeQuantOptions
- def NumBits(self):
+ # SequenceRNNOptions
+ def AsymmetricQuantizeInputs(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+def SequenceRNNOptionsStart(builder):
+ builder.StartObject(3)
+
+def SequenceRNNOptionsAddTimeMajor(builder, timeMajor):
+ builder.PrependBoolSlot(0, timeMajor, 0)
+
+def SequenceRNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(1, fusedActivationFunction, 0)
+
+def SequenceRNNOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
+ builder.PrependBoolSlot(2, asymmetricQuantizeInputs, 0)
+
+def SequenceRNNOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class SequenceRNNOptionsT(object):
+
+ # SequenceRNNOptionsT
+ def __init__(self):
+ self.timeMajor = False # type: bool
+ self.fusedActivationFunction = 0 # type: int
+ self.asymmetricQuantizeInputs = False # type: bool
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ sequenceRnnoptions = SequenceRNNOptions()
+ sequenceRnnoptions.Init(buf, pos)
+ return cls.InitFromObj(sequenceRnnoptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, sequenceRnnoptions):
+ x = SequenceRNNOptionsT()
+ x._UnPack(sequenceRnnoptions)
+ return x
+
+ # SequenceRNNOptionsT
+ def _UnPack(self, sequenceRnnoptions):
+ if sequenceRnnoptions is None:
+ return
+ self.timeMajor = sequenceRnnoptions.TimeMajor()
+ self.fusedActivationFunction = sequenceRnnoptions.FusedActivationFunction()
+ self.asymmetricQuantizeInputs = sequenceRnnoptions.AsymmetricQuantizeInputs()
+
+ # SequenceRNNOptionsT
+ def Pack(self, builder):
+ SequenceRNNOptionsStart(builder)
+ SequenceRNNOptionsAddTimeMajor(builder, self.timeMajor)
+ SequenceRNNOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
+ SequenceRNNOptionsAddAsymmetricQuantizeInputs(builder, self.asymmetricQuantizeInputs)
+ sequenceRnnoptions = SequenceRNNOptionsEnd(builder)
+ return sequenceRnnoptions
+
+
+class BidirectionalSequenceRNNOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = BidirectionalSequenceRNNOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsBidirectionalSequenceRNNOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def BidirectionalSequenceRNNOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # BidirectionalSequenceRNNOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # BidirectionalSequenceRNNOptions
+ def TimeMajor(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+ # BidirectionalSequenceRNNOptions
+ def FusedActivationFunction(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
return 0
- # FakeQuantOptions
- def NarrowRange(self):
+ # BidirectionalSequenceRNNOptions
+ def MergeOutputs(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+ # BidirectionalSequenceRNNOptions
+ def AsymmetricQuantizeInputs(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
if o != 0:
return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
return False
-def FakeQuantOptionsStart(builder): builder.StartObject(4)
-def Start(builder):
- return FakeQuantOptionsStart(builder)
-def FakeQuantOptionsAddMin(builder, min): builder.PrependFloat32Slot(0, min, 0.0)
-def AddMin(builder, min):
- return FakeQuantOptionsAddMin(builder, min)
-def FakeQuantOptionsAddMax(builder, max): builder.PrependFloat32Slot(1, max, 0.0)
-def AddMax(builder, max):
- return FakeQuantOptionsAddMax(builder, max)
-def FakeQuantOptionsAddNumBits(builder, numBits): builder.PrependInt32Slot(2, numBits, 0)
-def AddNumBits(builder, numBits):
- return FakeQuantOptionsAddNumBits(builder, numBits)
-def FakeQuantOptionsAddNarrowRange(builder, narrowRange): builder.PrependBoolSlot(3, narrowRange, 0)
-def AddNarrowRange(builder, narrowRange):
- return FakeQuantOptionsAddNarrowRange(builder, narrowRange)
-def FakeQuantOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return FakeQuantOptionsEnd(builder)
+def BidirectionalSequenceRNNOptionsStart(builder):
+ builder.StartObject(4)
-class FakeQuantOptionsT(object):
+def BidirectionalSequenceRNNOptionsAddTimeMajor(builder, timeMajor):
+ builder.PrependBoolSlot(0, timeMajor, 0)
- # FakeQuantOptionsT
+def BidirectionalSequenceRNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(1, fusedActivationFunction, 0)
+
+def BidirectionalSequenceRNNOptionsAddMergeOutputs(builder, mergeOutputs):
+ builder.PrependBoolSlot(2, mergeOutputs, 0)
+
+def BidirectionalSequenceRNNOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
+ builder.PrependBoolSlot(3, asymmetricQuantizeInputs, 0)
+
+def BidirectionalSequenceRNNOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class BidirectionalSequenceRNNOptionsT(object):
+
+ # BidirectionalSequenceRNNOptionsT
def __init__(self):
- self.min = 0.0 # type: float
- self.max = 0.0 # type: float
- self.numBits = 0 # type: int
- self.narrowRange = False # type: bool
+ self.timeMajor = False # type: bool
+ self.fusedActivationFunction = 0 # type: int
+ self.mergeOutputs = False # type: bool
+ self.asymmetricQuantizeInputs = False # type: bool
@classmethod
def InitFromBuf(cls, buf, pos):
- fakeQuantOptions = FakeQuantOptions()
- fakeQuantOptions.Init(buf, pos)
- return cls.InitFromObj(fakeQuantOptions)
+ bidirectionalSequenceRnnoptions = BidirectionalSequenceRNNOptions()
+ bidirectionalSequenceRnnoptions.Init(buf, pos)
+ return cls.InitFromObj(bidirectionalSequenceRnnoptions)
@classmethod
- def InitFromObj(cls, fakeQuantOptions):
- x = FakeQuantOptionsT()
- x._UnPack(fakeQuantOptions)
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, bidirectionalSequenceRnnoptions):
+ x = BidirectionalSequenceRNNOptionsT()
+ x._UnPack(bidirectionalSequenceRnnoptions)
return x
- # FakeQuantOptionsT
- def _UnPack(self, fakeQuantOptions):
- if fakeQuantOptions is None:
+ # BidirectionalSequenceRNNOptionsT
+ def _UnPack(self, bidirectionalSequenceRnnoptions):
+ if bidirectionalSequenceRnnoptions is None:
return
- self.min = fakeQuantOptions.Min()
- self.max = fakeQuantOptions.Max()
- self.numBits = fakeQuantOptions.NumBits()
- self.narrowRange = fakeQuantOptions.NarrowRange()
+ self.timeMajor = bidirectionalSequenceRnnoptions.TimeMajor()
+ self.fusedActivationFunction = bidirectionalSequenceRnnoptions.FusedActivationFunction()
+ self.mergeOutputs = bidirectionalSequenceRnnoptions.MergeOutputs()
+ self.asymmetricQuantizeInputs = bidirectionalSequenceRnnoptions.AsymmetricQuantizeInputs()
- # FakeQuantOptionsT
+ # BidirectionalSequenceRNNOptionsT
def Pack(self, builder):
- FakeQuantOptionsStart(builder)
- FakeQuantOptionsAddMin(builder, self.min)
- FakeQuantOptionsAddMax(builder, self.max)
- FakeQuantOptionsAddNumBits(builder, self.numBits)
- FakeQuantOptionsAddNarrowRange(builder, self.narrowRange)
- fakeQuantOptions = FakeQuantOptionsEnd(builder)
- return fakeQuantOptions
-# automatically generated by the FlatBuffers compiler, do not modify
+ BidirectionalSequenceRNNOptionsStart(builder)
+ BidirectionalSequenceRNNOptionsAddTimeMajor(builder, self.timeMajor)
+ BidirectionalSequenceRNNOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
+ BidirectionalSequenceRNNOptionsAddMergeOutputs(builder, self.mergeOutputs)
+ BidirectionalSequenceRNNOptionsAddAsymmetricQuantizeInputs(builder, self.asymmetricQuantizeInputs)
+ bidirectionalSequenceRnnoptions = BidirectionalSequenceRNNOptionsEnd(builder)
+ return bidirectionalSequenceRnnoptions
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class FillOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = FillOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsFillOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def FillOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # FillOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def FillOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return FillOptionsStart(builder)
-def FillOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return FillOptionsEnd(builder)
-
-class FillOptionsT(object):
-
- # FillOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- fillOptions = FillOptions()
- fillOptions.Init(buf, pos)
- return cls.InitFromObj(fillOptions)
-
- @classmethod
- def InitFromObj(cls, fillOptions):
- x = FillOptionsT()
- x._UnPack(fillOptions)
- return x
-
- # FillOptionsT
- def _UnPack(self, fillOptions):
- if fillOptions is None:
- return
-
- # FillOptionsT
- def Pack(self, builder):
- FillOptionsStart(builder)
- fillOptions = FillOptionsEnd(builder)
- return fillOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class FloorDivOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = FloorDivOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsFloorDivOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def FloorDivOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # FloorDivOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def FloorDivOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return FloorDivOptionsStart(builder)
-def FloorDivOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return FloorDivOptionsEnd(builder)
-
-class FloorDivOptionsT(object):
-
- # FloorDivOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- floorDivOptions = FloorDivOptions()
- floorDivOptions.Init(buf, pos)
- return cls.InitFromObj(floorDivOptions)
-
- @classmethod
- def InitFromObj(cls, floorDivOptions):
- x = FloorDivOptionsT()
- x._UnPack(floorDivOptions)
- return x
-
- # FloorDivOptionsT
- def _UnPack(self, floorDivOptions):
- if floorDivOptions is None:
- return
-
- # FloorDivOptionsT
- def Pack(self, builder):
- FloorDivOptionsStart(builder)
- floorDivOptions = FloorDivOptionsEnd(builder)
- return floorDivOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class FloorModOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = FloorModOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsFloorModOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def FloorModOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # FloorModOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def FloorModOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return FloorModOptionsStart(builder)
-def FloorModOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return FloorModOptionsEnd(builder)
-
-class FloorModOptionsT(object):
-
- # FloorModOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- floorModOptions = FloorModOptions()
- floorModOptions.Init(buf, pos)
- return cls.InitFromObj(floorModOptions)
-
- @classmethod
- def InitFromObj(cls, floorModOptions):
- x = FloorModOptionsT()
- x._UnPack(floorModOptions)
- return x
-
- # FloorModOptionsT
- def _UnPack(self, floorModOptions):
- if floorModOptions is None:
- return
-
- # FloorModOptionsT
- def Pack(self, builder):
- FloorModOptionsStart(builder)
- floorModOptions = FloorModOptionsEnd(builder)
- return floorModOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
class FullyConnectedOptions(object):
__slots__ = ['_tab']
@@ -4340,24 +7339,35 @@
return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
return False
-def FullyConnectedOptionsStart(builder): builder.StartObject(4)
-def Start(builder):
- return FullyConnectedOptionsStart(builder)
-def FullyConnectedOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return FullyConnectedOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def FullyConnectedOptionsAddWeightsFormat(builder, weightsFormat): builder.PrependInt8Slot(1, weightsFormat, 0)
-def AddWeightsFormat(builder, weightsFormat):
- return FullyConnectedOptionsAddWeightsFormat(builder, weightsFormat)
-def FullyConnectedOptionsAddKeepNumDims(builder, keepNumDims): builder.PrependBoolSlot(2, keepNumDims, 0)
-def AddKeepNumDims(builder, keepNumDims):
- return FullyConnectedOptionsAddKeepNumDims(builder, keepNumDims)
-def FullyConnectedOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs): builder.PrependBoolSlot(3, asymmetricQuantizeInputs, 0)
-def AddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
- return FullyConnectedOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs)
-def FullyConnectedOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return FullyConnectedOptionsEnd(builder)
+ # FullyConnectedOptions
+ def QuantizedBiasType(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+def FullyConnectedOptionsStart(builder):
+ builder.StartObject(5)
+
+def FullyConnectedOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(0, fusedActivationFunction, 0)
+
+def FullyConnectedOptionsAddWeightsFormat(builder, weightsFormat):
+ builder.PrependInt8Slot(1, weightsFormat, 0)
+
+def FullyConnectedOptionsAddKeepNumDims(builder, keepNumDims):
+ builder.PrependBoolSlot(2, keepNumDims, 0)
+
+def FullyConnectedOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
+ builder.PrependBoolSlot(3, asymmetricQuantizeInputs, 0)
+
+def FullyConnectedOptionsAddQuantizedBiasType(builder, quantizedBiasType):
+ builder.PrependInt8Slot(4, quantizedBiasType, 0)
+
+def FullyConnectedOptionsEnd(builder):
+ return builder.EndObject()
+
+
class FullyConnectedOptionsT(object):
@@ -4367,6 +7377,7 @@
self.weightsFormat = 0 # type: int
self.keepNumDims = False # type: bool
self.asymmetricQuantizeInputs = False # type: bool
+ self.quantizedBiasType = 0 # type: int
@classmethod
def InitFromBuf(cls, buf, pos):
@@ -4375,6 +7386,11 @@
return cls.InitFromObj(fullyConnectedOptions)
@classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
def InitFromObj(cls, fullyConnectedOptions):
x = FullyConnectedOptionsT()
x._UnPack(fullyConnectedOptions)
@@ -4388,6 +7404,7 @@
self.weightsFormat = fullyConnectedOptions.WeightsFormat()
self.keepNumDims = fullyConnectedOptions.KeepNumDims()
self.asymmetricQuantizeInputs = fullyConnectedOptions.AsymmetricQuantizeInputs()
+ self.quantizedBiasType = fullyConnectedOptions.QuantizedBiasType()
# FullyConnectedOptionsT
def Pack(self, builder):
@@ -4396,943 +7413,344 @@
FullyConnectedOptionsAddWeightsFormat(builder, self.weightsFormat)
FullyConnectedOptionsAddKeepNumDims(builder, self.keepNumDims)
FullyConnectedOptionsAddAsymmetricQuantizeInputs(builder, self.asymmetricQuantizeInputs)
+ FullyConnectedOptionsAddQuantizedBiasType(builder, self.quantizedBiasType)
fullyConnectedOptions = FullyConnectedOptionsEnd(builder)
return fullyConnectedOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-# namespace: tflite
-class FullyConnectedOptionsWeightsFormat(object):
- DEFAULT = 0
- SHUFFLED4x16INT8 = 1
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class GatherNdOptions(object):
+class SoftmaxOptions(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0):
n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = GatherNdOptions()
+ x = SoftmaxOptions()
x.Init(buf, n + offset)
return x
@classmethod
- def GetRootAsGatherNdOptions(cls, buf, offset=0):
+ def GetRootAsSoftmaxOptions(cls, buf, offset=0):
"""This method is deprecated. Please switch to GetRootAs."""
return cls.GetRootAs(buf, offset)
@classmethod
- def GatherNdOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ def SoftmaxOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
- # GatherNdOptions
+ # SoftmaxOptions
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
-def GatherNdOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return GatherNdOptionsStart(builder)
-def GatherNdOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return GatherNdOptionsEnd(builder)
+ # SoftmaxOptions
+ def Beta(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
+ return 0.0
-class GatherNdOptionsT(object):
+def SoftmaxOptionsStart(builder):
+ builder.StartObject(1)
- # GatherNdOptionsT
+def SoftmaxOptionsAddBeta(builder, beta):
+ builder.PrependFloat32Slot(0, beta, 0.0)
+
+def SoftmaxOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class SoftmaxOptionsT(object):
+
+ # SoftmaxOptionsT
def __init__(self):
- pass
+ self.beta = 0.0 # type: float
@classmethod
def InitFromBuf(cls, buf, pos):
- gatherNdOptions = GatherNdOptions()
- gatherNdOptions.Init(buf, pos)
- return cls.InitFromObj(gatherNdOptions)
+ softmaxOptions = SoftmaxOptions()
+ softmaxOptions.Init(buf, pos)
+ return cls.InitFromObj(softmaxOptions)
@classmethod
- def InitFromObj(cls, gatherNdOptions):
- x = GatherNdOptionsT()
- x._UnPack(gatherNdOptions)
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, softmaxOptions):
+ x = SoftmaxOptionsT()
+ x._UnPack(softmaxOptions)
return x
- # GatherNdOptionsT
- def _UnPack(self, gatherNdOptions):
- if gatherNdOptions is None:
+ # SoftmaxOptionsT
+ def _UnPack(self, softmaxOptions):
+ if softmaxOptions is None:
return
+ self.beta = softmaxOptions.Beta()
- # GatherNdOptionsT
+ # SoftmaxOptionsT
def Pack(self, builder):
- GatherNdOptionsStart(builder)
- gatherNdOptions = GatherNdOptionsEnd(builder)
- return gatherNdOptions
-# automatically generated by the FlatBuffers compiler, do not modify
+ SoftmaxOptionsStart(builder)
+ SoftmaxOptionsAddBeta(builder, self.beta)
+ softmaxOptions = SoftmaxOptionsEnd(builder)
+ return softmaxOptions
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class GatherOptions(object):
+class ConcatenationOptions(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0):
n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = GatherOptions()
+ x = ConcatenationOptions()
x.Init(buf, n + offset)
return x
@classmethod
- def GetRootAsGatherOptions(cls, buf, offset=0):
+ def GetRootAsConcatenationOptions(cls, buf, offset=0):
"""This method is deprecated. Please switch to GetRootAs."""
return cls.GetRootAs(buf, offset)
@classmethod
- def GatherOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ def ConcatenationOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
- # GatherOptions
+ # ConcatenationOptions
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
- # GatherOptions
+ # ConcatenationOptions
def Axis(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
if o != 0:
return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
return 0
- # GatherOptions
- def BatchDims(self):
+ # ConcatenationOptions
+ def FusedActivationFunction(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
return 0
-def GatherOptionsStart(builder): builder.StartObject(2)
-def Start(builder):
- return GatherOptionsStart(builder)
-def GatherOptionsAddAxis(builder, axis): builder.PrependInt32Slot(0, axis, 0)
-def AddAxis(builder, axis):
- return GatherOptionsAddAxis(builder, axis)
-def GatherOptionsAddBatchDims(builder, batchDims): builder.PrependInt32Slot(1, batchDims, 0)
-def AddBatchDims(builder, batchDims):
- return GatherOptionsAddBatchDims(builder, batchDims)
-def GatherOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return GatherOptionsEnd(builder)
+def ConcatenationOptionsStart(builder):
+ builder.StartObject(2)
-class GatherOptionsT(object):
+def ConcatenationOptionsAddAxis(builder, axis):
+ builder.PrependInt32Slot(0, axis, 0)
- # GatherOptionsT
+def ConcatenationOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(1, fusedActivationFunction, 0)
+
+def ConcatenationOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class ConcatenationOptionsT(object):
+
+ # ConcatenationOptionsT
def __init__(self):
self.axis = 0 # type: int
- self.batchDims = 0 # type: int
+ self.fusedActivationFunction = 0 # type: int
@classmethod
def InitFromBuf(cls, buf, pos):
- gatherOptions = GatherOptions()
- gatherOptions.Init(buf, pos)
- return cls.InitFromObj(gatherOptions)
+ concatenationOptions = ConcatenationOptions()
+ concatenationOptions.Init(buf, pos)
+ return cls.InitFromObj(concatenationOptions)
@classmethod
- def InitFromObj(cls, gatherOptions):
- x = GatherOptionsT()
- x._UnPack(gatherOptions)
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, concatenationOptions):
+ x = ConcatenationOptionsT()
+ x._UnPack(concatenationOptions)
return x
- # GatherOptionsT
- def _UnPack(self, gatherOptions):
- if gatherOptions is None:
+ # ConcatenationOptionsT
+ def _UnPack(self, concatenationOptions):
+ if concatenationOptions is None:
return
- self.axis = gatherOptions.Axis()
- self.batchDims = gatherOptions.BatchDims()
+ self.axis = concatenationOptions.Axis()
+ self.fusedActivationFunction = concatenationOptions.FusedActivationFunction()
- # GatherOptionsT
+ # ConcatenationOptionsT
def Pack(self, builder):
- GatherOptionsStart(builder)
- GatherOptionsAddAxis(builder, self.axis)
- GatherOptionsAddBatchDims(builder, self.batchDims)
- gatherOptions = GatherOptionsEnd(builder)
- return gatherOptions
-# automatically generated by the FlatBuffers compiler, do not modify
+ ConcatenationOptionsStart(builder)
+ ConcatenationOptionsAddAxis(builder, self.axis)
+ ConcatenationOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
+ concatenationOptions = ConcatenationOptionsEnd(builder)
+ return concatenationOptions
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class GeluOptions(object):
+class AddOptions(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0):
n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = GeluOptions()
+ x = AddOptions()
x.Init(buf, n + offset)
return x
@classmethod
- def GetRootAsGeluOptions(cls, buf, offset=0):
+ def GetRootAsAddOptions(cls, buf, offset=0):
"""This method is deprecated. Please switch to GetRootAs."""
return cls.GetRootAs(buf, offset)
@classmethod
- def GeluOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ def AddOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
- # GeluOptions
+ # AddOptions
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
- # GeluOptions
- def Approximate(self):
+ # AddOptions
+ def FusedActivationFunction(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # AddOptions
+ def PotScaleInt16(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
+ return True
-def GeluOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return GeluOptionsStart(builder)
-def GeluOptionsAddApproximate(builder, approximate): builder.PrependBoolSlot(0, approximate, 0)
-def AddApproximate(builder, approximate):
- return GeluOptionsAddApproximate(builder, approximate)
-def GeluOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return GeluOptionsEnd(builder)
+def AddOptionsStart(builder):
+ builder.StartObject(2)
-class GeluOptionsT(object):
+def AddOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(0, fusedActivationFunction, 0)
- # GeluOptionsT
+def AddOptionsAddPotScaleInt16(builder, potScaleInt16):
+ builder.PrependBoolSlot(1, potScaleInt16, 1)
+
+def AddOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class AddOptionsT(object):
+
+ # AddOptionsT
def __init__(self):
- self.approximate = False # type: bool
+ self.fusedActivationFunction = 0 # type: int
+ self.potScaleInt16 = True # type: bool
@classmethod
def InitFromBuf(cls, buf, pos):
- geluOptions = GeluOptions()
- geluOptions.Init(buf, pos)
- return cls.InitFromObj(geluOptions)
+ addOptions = AddOptions()
+ addOptions.Init(buf, pos)
+ return cls.InitFromObj(addOptions)
@classmethod
- def InitFromObj(cls, geluOptions):
- x = GeluOptionsT()
- x._UnPack(geluOptions)
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, addOptions):
+ x = AddOptionsT()
+ x._UnPack(addOptions)
return x
- # GeluOptionsT
- def _UnPack(self, geluOptions):
- if geluOptions is None:
+ # AddOptionsT
+ def _UnPack(self, addOptions):
+ if addOptions is None:
return
- self.approximate = geluOptions.Approximate()
+ self.fusedActivationFunction = addOptions.FusedActivationFunction()
+ self.potScaleInt16 = addOptions.PotScaleInt16()
- # GeluOptionsT
+ # AddOptionsT
def Pack(self, builder):
- GeluOptionsStart(builder)
- GeluOptionsAddApproximate(builder, self.approximate)
- geluOptions = GeluOptionsEnd(builder)
- return geluOptions
-# automatically generated by the FlatBuffers compiler, do not modify
+ AddOptionsStart(builder)
+ AddOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
+ AddOptionsAddPotScaleInt16(builder, self.potScaleInt16)
+ addOptions = AddOptionsEnd(builder)
+ return addOptions
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class GreaterEqualOptions(object):
+class MulOptions(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0):
n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = GreaterEqualOptions()
+ x = MulOptions()
x.Init(buf, n + offset)
return x
@classmethod
- def GetRootAsGreaterEqualOptions(cls, buf, offset=0):
+ def GetRootAsMulOptions(cls, buf, offset=0):
"""This method is deprecated. Please switch to GetRootAs."""
return cls.GetRootAs(buf, offset)
@classmethod
- def GreaterEqualOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ def MulOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
- # GreaterEqualOptions
+ # MulOptions
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
-def GreaterEqualOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return GreaterEqualOptionsStart(builder)
-def GreaterEqualOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return GreaterEqualOptionsEnd(builder)
-
-class GreaterEqualOptionsT(object):
-
- # GreaterEqualOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- greaterEqualOptions = GreaterEqualOptions()
- greaterEqualOptions.Init(buf, pos)
- return cls.InitFromObj(greaterEqualOptions)
-
- @classmethod
- def InitFromObj(cls, greaterEqualOptions):
- x = GreaterEqualOptionsT()
- x._UnPack(greaterEqualOptions)
- return x
-
- # GreaterEqualOptionsT
- def _UnPack(self, greaterEqualOptions):
- if greaterEqualOptions is None:
- return
-
- # GreaterEqualOptionsT
- def Pack(self, builder):
- GreaterEqualOptionsStart(builder)
- greaterEqualOptions = GreaterEqualOptionsEnd(builder)
- return greaterEqualOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class GreaterOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = GreaterOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsGreaterOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def GreaterOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # GreaterOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def GreaterOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return GreaterOptionsStart(builder)
-def GreaterOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return GreaterOptionsEnd(builder)
-
-class GreaterOptionsT(object):
-
- # GreaterOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- greaterOptions = GreaterOptions()
- greaterOptions.Init(buf, pos)
- return cls.InitFromObj(greaterOptions)
-
- @classmethod
- def InitFromObj(cls, greaterOptions):
- x = GreaterOptionsT()
- x._UnPack(greaterOptions)
- return x
-
- # GreaterOptionsT
- def _UnPack(self, greaterOptions):
- if greaterOptions is None:
- return
-
- # GreaterOptionsT
- def Pack(self, builder):
- GreaterOptionsStart(builder)
- greaterOptions = GreaterOptionsEnd(builder)
- return greaterOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class HardSwishOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = HardSwishOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsHardSwishOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def HardSwishOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # HardSwishOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def HardSwishOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return HardSwishOptionsStart(builder)
-def HardSwishOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return HardSwishOptionsEnd(builder)
-
-class HardSwishOptionsT(object):
-
- # HardSwishOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- hardSwishOptions = HardSwishOptions()
- hardSwishOptions.Init(buf, pos)
- return cls.InitFromObj(hardSwishOptions)
-
- @classmethod
- def InitFromObj(cls, hardSwishOptions):
- x = HardSwishOptionsT()
- x._UnPack(hardSwishOptions)
- return x
-
- # HardSwishOptionsT
- def _UnPack(self, hardSwishOptions):
- if hardSwishOptions is None:
- return
-
- # HardSwishOptionsT
- def Pack(self, builder):
- HardSwishOptionsStart(builder)
- hardSwishOptions = HardSwishOptionsEnd(builder)
- return hardSwishOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class HashtableFindOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = HashtableFindOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsHashtableFindOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def HashtableFindOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # HashtableFindOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def HashtableFindOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return HashtableFindOptionsStart(builder)
-def HashtableFindOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return HashtableFindOptionsEnd(builder)
-
-class HashtableFindOptionsT(object):
-
- # HashtableFindOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- hashtableFindOptions = HashtableFindOptions()
- hashtableFindOptions.Init(buf, pos)
- return cls.InitFromObj(hashtableFindOptions)
-
- @classmethod
- def InitFromObj(cls, hashtableFindOptions):
- x = HashtableFindOptionsT()
- x._UnPack(hashtableFindOptions)
- return x
-
- # HashtableFindOptionsT
- def _UnPack(self, hashtableFindOptions):
- if hashtableFindOptions is None:
- return
-
- # HashtableFindOptionsT
- def Pack(self, builder):
- HashtableFindOptionsStart(builder)
- hashtableFindOptions = HashtableFindOptionsEnd(builder)
- return hashtableFindOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class HashtableImportOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = HashtableImportOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsHashtableImportOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def HashtableImportOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # HashtableImportOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def HashtableImportOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return HashtableImportOptionsStart(builder)
-def HashtableImportOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return HashtableImportOptionsEnd(builder)
-
-class HashtableImportOptionsT(object):
-
- # HashtableImportOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- hashtableImportOptions = HashtableImportOptions()
- hashtableImportOptions.Init(buf, pos)
- return cls.InitFromObj(hashtableImportOptions)
-
- @classmethod
- def InitFromObj(cls, hashtableImportOptions):
- x = HashtableImportOptionsT()
- x._UnPack(hashtableImportOptions)
- return x
-
- # HashtableImportOptionsT
- def _UnPack(self, hashtableImportOptions):
- if hashtableImportOptions is None:
- return
-
- # HashtableImportOptionsT
- def Pack(self, builder):
- HashtableImportOptionsStart(builder)
- hashtableImportOptions = HashtableImportOptionsEnd(builder)
- return hashtableImportOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class HashtableOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = HashtableOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsHashtableOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def HashtableOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # HashtableOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # HashtableOptions
- def TableId(self):
+ # MulOptions
+ def FusedActivationFunction(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # HashtableOptions
- def KeyDtype(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
return 0
- # HashtableOptions
- def ValueDtype(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
+def MulOptionsStart(builder):
+ builder.StartObject(1)
-def HashtableOptionsStart(builder): builder.StartObject(3)
-def Start(builder):
- return HashtableOptionsStart(builder)
-def HashtableOptionsAddTableId(builder, tableId): builder.PrependInt32Slot(0, tableId, 0)
-def AddTableId(builder, tableId):
- return HashtableOptionsAddTableId(builder, tableId)
-def HashtableOptionsAddKeyDtype(builder, keyDtype): builder.PrependInt8Slot(1, keyDtype, 0)
-def AddKeyDtype(builder, keyDtype):
- return HashtableOptionsAddKeyDtype(builder, keyDtype)
-def HashtableOptionsAddValueDtype(builder, valueDtype): builder.PrependInt8Slot(2, valueDtype, 0)
-def AddValueDtype(builder, valueDtype):
- return HashtableOptionsAddValueDtype(builder, valueDtype)
-def HashtableOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return HashtableOptionsEnd(builder)
+def MulOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(0, fusedActivationFunction, 0)
-class HashtableOptionsT(object):
+def MulOptionsEnd(builder):
+ return builder.EndObject()
- # HashtableOptionsT
+
+
+class MulOptionsT(object):
+
+ # MulOptionsT
def __init__(self):
- self.tableId = 0 # type: int
- self.keyDtype = 0 # type: int
- self.valueDtype = 0 # type: int
+ self.fusedActivationFunction = 0 # type: int
@classmethod
def InitFromBuf(cls, buf, pos):
- hashtableOptions = HashtableOptions()
- hashtableOptions.Init(buf, pos)
- return cls.InitFromObj(hashtableOptions)
+ mulOptions = MulOptions()
+ mulOptions.Init(buf, pos)
+ return cls.InitFromObj(mulOptions)
@classmethod
- def InitFromObj(cls, hashtableOptions):
- x = HashtableOptionsT()
- x._UnPack(hashtableOptions)
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, mulOptions):
+ x = MulOptionsT()
+ x._UnPack(mulOptions)
return x
- # HashtableOptionsT
- def _UnPack(self, hashtableOptions):
- if hashtableOptions is None:
+ # MulOptionsT
+ def _UnPack(self, mulOptions):
+ if mulOptions is None:
return
- self.tableId = hashtableOptions.TableId()
- self.keyDtype = hashtableOptions.KeyDtype()
- self.valueDtype = hashtableOptions.ValueDtype()
+ self.fusedActivationFunction = mulOptions.FusedActivationFunction()
- # HashtableOptionsT
+ # MulOptionsT
def Pack(self, builder):
- HashtableOptionsStart(builder)
- HashtableOptionsAddTableId(builder, self.tableId)
- HashtableOptionsAddKeyDtype(builder, self.keyDtype)
- HashtableOptionsAddValueDtype(builder, self.valueDtype)
- hashtableOptions = HashtableOptionsEnd(builder)
- return hashtableOptions
-# automatically generated by the FlatBuffers compiler, do not modify
+ MulOptionsStart(builder)
+ MulOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
+ mulOptions = MulOptionsEnd(builder)
+ return mulOptions
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class HashtableSizeOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = HashtableSizeOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsHashtableSizeOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def HashtableSizeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # HashtableSizeOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def HashtableSizeOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return HashtableSizeOptionsStart(builder)
-def HashtableSizeOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return HashtableSizeOptionsEnd(builder)
-
-class HashtableSizeOptionsT(object):
-
- # HashtableSizeOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- hashtableSizeOptions = HashtableSizeOptions()
- hashtableSizeOptions.Init(buf, pos)
- return cls.InitFromObj(hashtableSizeOptions)
-
- @classmethod
- def InitFromObj(cls, hashtableSizeOptions):
- x = HashtableSizeOptionsT()
- x._UnPack(hashtableSizeOptions)
- return x
-
- # HashtableSizeOptionsT
- def _UnPack(self, hashtableSizeOptions):
- if hashtableSizeOptions is None:
- return
-
- # HashtableSizeOptionsT
- def Pack(self, builder):
- HashtableSizeOptionsStart(builder)
- hashtableSizeOptions = HashtableSizeOptionsEnd(builder)
- return hashtableSizeOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class IfOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = IfOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsIfOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def IfOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # IfOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # IfOptions
- def ThenSubgraphIndex(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # IfOptions
- def ElseSubgraphIndex(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
-def IfOptionsStart(builder): builder.StartObject(2)
-def Start(builder):
- return IfOptionsStart(builder)
-def IfOptionsAddThenSubgraphIndex(builder, thenSubgraphIndex): builder.PrependInt32Slot(0, thenSubgraphIndex, 0)
-def AddThenSubgraphIndex(builder, thenSubgraphIndex):
- return IfOptionsAddThenSubgraphIndex(builder, thenSubgraphIndex)
-def IfOptionsAddElseSubgraphIndex(builder, elseSubgraphIndex): builder.PrependInt32Slot(1, elseSubgraphIndex, 0)
-def AddElseSubgraphIndex(builder, elseSubgraphIndex):
- return IfOptionsAddElseSubgraphIndex(builder, elseSubgraphIndex)
-def IfOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return IfOptionsEnd(builder)
-
-class IfOptionsT(object):
-
- # IfOptionsT
- def __init__(self):
- self.thenSubgraphIndex = 0 # type: int
- self.elseSubgraphIndex = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- ifOptions = IfOptions()
- ifOptions.Init(buf, pos)
- return cls.InitFromObj(ifOptions)
-
- @classmethod
- def InitFromObj(cls, ifOptions):
- x = IfOptionsT()
- x._UnPack(ifOptions)
- return x
-
- # IfOptionsT
- def _UnPack(self, ifOptions):
- if ifOptions is None:
- return
- self.thenSubgraphIndex = ifOptions.ThenSubgraphIndex()
- self.elseSubgraphIndex = ifOptions.ElseSubgraphIndex()
-
- # IfOptionsT
- def Pack(self, builder):
- IfOptionsStart(builder)
- IfOptionsAddThenSubgraphIndex(builder, self.thenSubgraphIndex)
- IfOptionsAddElseSubgraphIndex(builder, self.elseSubgraphIndex)
- ifOptions = IfOptionsEnd(builder)
- return ifOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class Int32Vector(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = Int32Vector()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsInt32Vector(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def Int32VectorBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # Int32Vector
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # Int32Vector
- def Values(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- a = self._tab.Vector(o)
- return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
- return 0
-
- # Int32Vector
- def ValuesAsNumpy(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
- return 0
-
- # Int32Vector
- def ValuesLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # Int32Vector
- def ValuesIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- return o == 0
-
-def Int32VectorStart(builder): builder.StartObject(1)
-def Start(builder):
- return Int32VectorStart(builder)
-def Int32VectorAddValues(builder, values): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(values), 0)
-def AddValues(builder, values):
- return Int32VectorAddValues(builder, values)
-def Int32VectorStartValuesVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartValuesVector(builder, numElems):
- return Int32VectorStartValuesVector(builder, numElems)
-def Int32VectorEnd(builder): return builder.EndObject()
-def End(builder):
- return Int32VectorEnd(builder)
-try:
- from typing import List
-except:
- pass
-
-class Int32VectorT(object):
-
- # Int32VectorT
- def __init__(self):
- self.values = None # type: List[int]
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- int32vector = Int32Vector()
- int32vector.Init(buf, pos)
- return cls.InitFromObj(int32vector)
-
- @classmethod
- def InitFromObj(cls, int32vector):
- x = Int32VectorT()
- x._UnPack(int32vector)
- return x
-
- # Int32VectorT
- def _UnPack(self, int32vector):
- if int32vector is None:
- return
- if not int32vector.ValuesIsNone():
- if np is None:
- self.values = []
- for i in range(int32vector.ValuesLength()):
- self.values.append(int32vector.Values(i))
- else:
- self.values = int32vector.ValuesAsNumpy()
-
- # Int32VectorT
- def Pack(self, builder):
- if self.values is not None:
- if np is not None and type(self.values) is np.ndarray:
- values = builder.CreateNumpyVector(self.values)
- else:
- Int32VectorStartValuesVector(builder, len(self.values))
- for i in reversed(range(len(self.values))):
- builder.PrependInt32(self.values[i])
- values = builder.EndVector()
- Int32VectorStart(builder)
- if self.values is not None:
- Int32VectorAddValues(builder, values)
- int32vector = Int32VectorEnd(builder)
- return int32vector
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
class L2NormOptions(object):
__slots__ = ['_tab']
@@ -5363,15 +7781,16 @@
return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
return 0
-def L2NormOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return L2NormOptionsStart(builder)
-def L2NormOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return L2NormOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def L2NormOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return L2NormOptionsEnd(builder)
+def L2NormOptionsStart(builder):
+ builder.StartObject(1)
+
+def L2NormOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(0, fusedActivationFunction, 0)
+
+def L2NormOptionsEnd(builder):
+ return builder.EndObject()
+
+
class L2NormOptionsT(object):
@@ -5381,125 +7800,150 @@
@classmethod
def InitFromBuf(cls, buf, pos):
- l2normOptions = L2NormOptions()
- l2normOptions.Init(buf, pos)
- return cls.InitFromObj(l2normOptions)
+ l2NormOptions = L2NormOptions()
+ l2NormOptions.Init(buf, pos)
+ return cls.InitFromObj(l2NormOptions)
@classmethod
- def InitFromObj(cls, l2normOptions):
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, l2NormOptions):
x = L2NormOptionsT()
- x._UnPack(l2normOptions)
+ x._UnPack(l2NormOptions)
return x
# L2NormOptionsT
- def _UnPack(self, l2normOptions):
- if l2normOptions is None:
+ def _UnPack(self, l2NormOptions):
+ if l2NormOptions is None:
return
- self.fusedActivationFunction = l2normOptions.FusedActivationFunction()
+ self.fusedActivationFunction = l2NormOptions.FusedActivationFunction()
# L2NormOptionsT
def Pack(self, builder):
L2NormOptionsStart(builder)
L2NormOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
- l2normOptions = L2NormOptionsEnd(builder)
- return l2normOptions
-# automatically generated by the FlatBuffers compiler, do not modify
+ l2NormOptions = L2NormOptionsEnd(builder)
+ return l2NormOptions
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class LSHProjectionOptions(object):
+class LocalResponseNormalizationOptions(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0):
n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = LSHProjectionOptions()
+ x = LocalResponseNormalizationOptions()
x.Init(buf, n + offset)
return x
@classmethod
- def GetRootAsLSHProjectionOptions(cls, buf, offset=0):
+ def GetRootAsLocalResponseNormalizationOptions(cls, buf, offset=0):
"""This method is deprecated. Please switch to GetRootAs."""
return cls.GetRootAs(buf, offset)
@classmethod
- def LSHProjectionOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ def LocalResponseNormalizationOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
- # LSHProjectionOptions
+ # LocalResponseNormalizationOptions
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
- # LSHProjectionOptions
- def Type(self):
+ # LocalResponseNormalizationOptions
+ def Radius(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
return 0
-def LSHProjectionOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return LSHProjectionOptionsStart(builder)
-def LSHProjectionOptionsAddType(builder, type): builder.PrependInt8Slot(0, type, 0)
-def AddType(builder, type):
- return LSHProjectionOptionsAddType(builder, type)
-def LSHProjectionOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return LSHProjectionOptionsEnd(builder)
+ # LocalResponseNormalizationOptions
+ def Bias(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
+ return 0.0
-class LSHProjectionOptionsT(object):
+ # LocalResponseNormalizationOptions
+ def Alpha(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
+ return 0.0
- # LSHProjectionOptionsT
+ # LocalResponseNormalizationOptions
+ def Beta(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
+ return 0.0
+
+def LocalResponseNormalizationOptionsStart(builder):
+ builder.StartObject(4)
+
+def LocalResponseNormalizationOptionsAddRadius(builder, radius):
+ builder.PrependInt32Slot(0, radius, 0)
+
+def LocalResponseNormalizationOptionsAddBias(builder, bias):
+ builder.PrependFloat32Slot(1, bias, 0.0)
+
+def LocalResponseNormalizationOptionsAddAlpha(builder, alpha):
+ builder.PrependFloat32Slot(2, alpha, 0.0)
+
+def LocalResponseNormalizationOptionsAddBeta(builder, beta):
+ builder.PrependFloat32Slot(3, beta, 0.0)
+
+def LocalResponseNormalizationOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class LocalResponseNormalizationOptionsT(object):
+
+ # LocalResponseNormalizationOptionsT
def __init__(self):
- self.type = 0 # type: int
+ self.radius = 0 # type: int
+ self.bias = 0.0 # type: float
+ self.alpha = 0.0 # type: float
+ self.beta = 0.0 # type: float
@classmethod
def InitFromBuf(cls, buf, pos):
- lshprojectionOptions = LSHProjectionOptions()
- lshprojectionOptions.Init(buf, pos)
- return cls.InitFromObj(lshprojectionOptions)
+ localResponseNormalizationOptions = LocalResponseNormalizationOptions()
+ localResponseNormalizationOptions.Init(buf, pos)
+ return cls.InitFromObj(localResponseNormalizationOptions)
@classmethod
- def InitFromObj(cls, lshprojectionOptions):
- x = LSHProjectionOptionsT()
- x._UnPack(lshprojectionOptions)
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, localResponseNormalizationOptions):
+ x = LocalResponseNormalizationOptionsT()
+ x._UnPack(localResponseNormalizationOptions)
return x
- # LSHProjectionOptionsT
- def _UnPack(self, lshprojectionOptions):
- if lshprojectionOptions is None:
+ # LocalResponseNormalizationOptionsT
+ def _UnPack(self, localResponseNormalizationOptions):
+ if localResponseNormalizationOptions is None:
return
- self.type = lshprojectionOptions.Type()
+ self.radius = localResponseNormalizationOptions.Radius()
+ self.bias = localResponseNormalizationOptions.Bias()
+ self.alpha = localResponseNormalizationOptions.Alpha()
+ self.beta = localResponseNormalizationOptions.Beta()
- # LSHProjectionOptionsT
+ # LocalResponseNormalizationOptionsT
def Pack(self, builder):
- LSHProjectionOptionsStart(builder)
- LSHProjectionOptionsAddType(builder, self.type)
- lshprojectionOptions = LSHProjectionOptionsEnd(builder)
- return lshprojectionOptions
-# automatically generated by the FlatBuffers compiler, do not modify
+ LocalResponseNormalizationOptionsStart(builder)
+ LocalResponseNormalizationOptionsAddRadius(builder, self.radius)
+ LocalResponseNormalizationOptionsAddBias(builder, self.bias)
+ LocalResponseNormalizationOptionsAddAlpha(builder, self.alpha)
+ LocalResponseNormalizationOptionsAddBeta(builder, self.beta)
+ localResponseNormalizationOptions = LocalResponseNormalizationOptionsEnd(builder)
+ return localResponseNormalizationOptions
-# namespace: tflite
-
-class LSHProjectionType(object):
- UNKNOWN = 0
- SPARSE = 1
- DENSE = 2
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-class LSTMKernelType(object):
- FULL = 0
- BASIC = 1
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
class LSTMOptions(object):
__slots__ = ['_tab']
@@ -5558,27 +8002,28 @@
return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
return False
-def LSTMOptionsStart(builder): builder.StartObject(5)
-def Start(builder):
- return LSTMOptionsStart(builder)
-def LSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return LSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def LSTMOptionsAddCellClip(builder, cellClip): builder.PrependFloat32Slot(1, cellClip, 0.0)
-def AddCellClip(builder, cellClip):
- return LSTMOptionsAddCellClip(builder, cellClip)
-def LSTMOptionsAddProjClip(builder, projClip): builder.PrependFloat32Slot(2, projClip, 0.0)
-def AddProjClip(builder, projClip):
- return LSTMOptionsAddProjClip(builder, projClip)
-def LSTMOptionsAddKernelType(builder, kernelType): builder.PrependInt8Slot(3, kernelType, 0)
-def AddKernelType(builder, kernelType):
- return LSTMOptionsAddKernelType(builder, kernelType)
-def LSTMOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs): builder.PrependBoolSlot(4, asymmetricQuantizeInputs, 0)
-def AddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
- return LSTMOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs)
-def LSTMOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return LSTMOptionsEnd(builder)
+def LSTMOptionsStart(builder):
+ builder.StartObject(5)
+
+def LSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(0, fusedActivationFunction, 0)
+
+def LSTMOptionsAddCellClip(builder, cellClip):
+ builder.PrependFloat32Slot(1, cellClip, 0.0)
+
+def LSTMOptionsAddProjClip(builder, projClip):
+ builder.PrependFloat32Slot(2, projClip, 0.0)
+
+def LSTMOptionsAddKernelType(builder, kernelType):
+ builder.PrependInt8Slot(3, kernelType, 0)
+
+def LSTMOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
+ builder.PrependBoolSlot(4, asymmetricQuantizeInputs, 0)
+
+def LSTMOptionsEnd(builder):
+ return builder.EndObject()
+
+
class LSTMOptionsT(object):
@@ -5597,6 +8042,11 @@
return cls.InitFromObj(lstmoptions)
@classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
def InitFromObj(cls, lstmoptions):
x = LSTMOptionsT()
x._UnPack(lstmoptions)
@@ -5622,331 +8072,2270 @@
LSTMOptionsAddAsymmetricQuantizeInputs(builder, self.asymmetricQuantizeInputs)
lstmoptions = LSTMOptionsEnd(builder)
return lstmoptions
-# automatically generated by the FlatBuffers compiler, do not modify
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class LeakyReluOptions(object):
+class UnidirectionalSequenceLSTMOptions(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0):
n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = LeakyReluOptions()
+ x = UnidirectionalSequenceLSTMOptions()
x.Init(buf, n + offset)
return x
@classmethod
- def GetRootAsLeakyReluOptions(cls, buf, offset=0):
+ def GetRootAsUnidirectionalSequenceLSTMOptions(cls, buf, offset=0):
"""This method is deprecated. Please switch to GetRootAs."""
return cls.GetRootAs(buf, offset)
@classmethod
- def LeakyReluOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ def UnidirectionalSequenceLSTMOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
- # LeakyReluOptions
+ # UnidirectionalSequenceLSTMOptions
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
- # LeakyReluOptions
- def Alpha(self):
+ # UnidirectionalSequenceLSTMOptions
+ def FusedActivationFunction(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
if o != 0:
- return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
- return 0.0
-
-def LeakyReluOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return LeakyReluOptionsStart(builder)
-def LeakyReluOptionsAddAlpha(builder, alpha): builder.PrependFloat32Slot(0, alpha, 0.0)
-def AddAlpha(builder, alpha):
- return LeakyReluOptionsAddAlpha(builder, alpha)
-def LeakyReluOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return LeakyReluOptionsEnd(builder)
-
-class LeakyReluOptionsT(object):
-
- # LeakyReluOptionsT
- def __init__(self):
- self.alpha = 0.0 # type: float
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- leakyReluOptions = LeakyReluOptions()
- leakyReluOptions.Init(buf, pos)
- return cls.InitFromObj(leakyReluOptions)
-
- @classmethod
- def InitFromObj(cls, leakyReluOptions):
- x = LeakyReluOptionsT()
- x._UnPack(leakyReluOptions)
- return x
-
- # LeakyReluOptionsT
- def _UnPack(self, leakyReluOptions):
- if leakyReluOptions is None:
- return
- self.alpha = leakyReluOptions.Alpha()
-
- # LeakyReluOptionsT
- def Pack(self, builder):
- LeakyReluOptionsStart(builder)
- LeakyReluOptionsAddAlpha(builder, self.alpha)
- leakyReluOptions = LeakyReluOptionsEnd(builder)
- return leakyReluOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class LessEqualOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = LessEqualOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsLessEqualOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def LessEqualOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # LessEqualOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def LessEqualOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return LessEqualOptionsStart(builder)
-def LessEqualOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return LessEqualOptionsEnd(builder)
-
-class LessEqualOptionsT(object):
-
- # LessEqualOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- lessEqualOptions = LessEqualOptions()
- lessEqualOptions.Init(buf, pos)
- return cls.InitFromObj(lessEqualOptions)
-
- @classmethod
- def InitFromObj(cls, lessEqualOptions):
- x = LessEqualOptionsT()
- x._UnPack(lessEqualOptions)
- return x
-
- # LessEqualOptionsT
- def _UnPack(self, lessEqualOptions):
- if lessEqualOptions is None:
- return
-
- # LessEqualOptionsT
- def Pack(self, builder):
- LessEqualOptionsStart(builder)
- lessEqualOptions = LessEqualOptionsEnd(builder)
- return lessEqualOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class LessOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = LessOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsLessOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def LessOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # LessOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def LessOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return LessOptionsStart(builder)
-def LessOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return LessOptionsEnd(builder)
-
-class LessOptionsT(object):
-
- # LessOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- lessOptions = LessOptions()
- lessOptions.Init(buf, pos)
- return cls.InitFromObj(lessOptions)
-
- @classmethod
- def InitFromObj(cls, lessOptions):
- x = LessOptionsT()
- x._UnPack(lessOptions)
- return x
-
- # LessOptionsT
- def _UnPack(self, lessOptions):
- if lessOptions is None:
- return
-
- # LessOptionsT
- def Pack(self, builder):
- LessOptionsStart(builder)
- lessOptions = LessOptionsEnd(builder)
- return lessOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class LocalResponseNormalizationOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = LocalResponseNormalizationOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsLocalResponseNormalizationOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def LocalResponseNormalizationOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # LocalResponseNormalizationOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # LocalResponseNormalizationOptions
- def Radius(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
return 0
- # LocalResponseNormalizationOptions
- def Bias(self):
+ # UnidirectionalSequenceLSTMOptions
+ def CellClip(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
if o != 0:
return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
return 0.0
- # LocalResponseNormalizationOptions
- def Alpha(self):
+ # UnidirectionalSequenceLSTMOptions
+ def ProjClip(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
if o != 0:
return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
return 0.0
- # LocalResponseNormalizationOptions
- def Beta(self):
+ # UnidirectionalSequenceLSTMOptions
+ def TimeMajor(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+ # UnidirectionalSequenceLSTMOptions
+ def AsymmetricQuantizeInputs(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+ # UnidirectionalSequenceLSTMOptions
+ def DiagonalRecurrentTensors(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+def UnidirectionalSequenceLSTMOptionsStart(builder):
+ builder.StartObject(6)
+
+def UnidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(0, fusedActivationFunction, 0)
+
+def UnidirectionalSequenceLSTMOptionsAddCellClip(builder, cellClip):
+ builder.PrependFloat32Slot(1, cellClip, 0.0)
+
+def UnidirectionalSequenceLSTMOptionsAddProjClip(builder, projClip):
+ builder.PrependFloat32Slot(2, projClip, 0.0)
+
+def UnidirectionalSequenceLSTMOptionsAddTimeMajor(builder, timeMajor):
+ builder.PrependBoolSlot(3, timeMajor, 0)
+
+def UnidirectionalSequenceLSTMOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
+ builder.PrependBoolSlot(4, asymmetricQuantizeInputs, 0)
+
+def UnidirectionalSequenceLSTMOptionsAddDiagonalRecurrentTensors(builder, diagonalRecurrentTensors):
+ builder.PrependBoolSlot(5, diagonalRecurrentTensors, 0)
+
+def UnidirectionalSequenceLSTMOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class UnidirectionalSequenceLSTMOptionsT(object):
+
+ # UnidirectionalSequenceLSTMOptionsT
+ def __init__(self):
+ self.fusedActivationFunction = 0 # type: int
+ self.cellClip = 0.0 # type: float
+ self.projClip = 0.0 # type: float
+ self.timeMajor = False # type: bool
+ self.asymmetricQuantizeInputs = False # type: bool
+ self.diagonalRecurrentTensors = False # type: bool
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ unidirectionalSequenceLstmoptions = UnidirectionalSequenceLSTMOptions()
+ unidirectionalSequenceLstmoptions.Init(buf, pos)
+ return cls.InitFromObj(unidirectionalSequenceLstmoptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, unidirectionalSequenceLstmoptions):
+ x = UnidirectionalSequenceLSTMOptionsT()
+ x._UnPack(unidirectionalSequenceLstmoptions)
+ return x
+
+ # UnidirectionalSequenceLSTMOptionsT
+ def _UnPack(self, unidirectionalSequenceLstmoptions):
+ if unidirectionalSequenceLstmoptions is None:
+ return
+ self.fusedActivationFunction = unidirectionalSequenceLstmoptions.FusedActivationFunction()
+ self.cellClip = unidirectionalSequenceLstmoptions.CellClip()
+ self.projClip = unidirectionalSequenceLstmoptions.ProjClip()
+ self.timeMajor = unidirectionalSequenceLstmoptions.TimeMajor()
+ self.asymmetricQuantizeInputs = unidirectionalSequenceLstmoptions.AsymmetricQuantizeInputs()
+ self.diagonalRecurrentTensors = unidirectionalSequenceLstmoptions.DiagonalRecurrentTensors()
+
+ # UnidirectionalSequenceLSTMOptionsT
+ def Pack(self, builder):
+ UnidirectionalSequenceLSTMOptionsStart(builder)
+ UnidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
+ UnidirectionalSequenceLSTMOptionsAddCellClip(builder, self.cellClip)
+ UnidirectionalSequenceLSTMOptionsAddProjClip(builder, self.projClip)
+ UnidirectionalSequenceLSTMOptionsAddTimeMajor(builder, self.timeMajor)
+ UnidirectionalSequenceLSTMOptionsAddAsymmetricQuantizeInputs(builder, self.asymmetricQuantizeInputs)
+ UnidirectionalSequenceLSTMOptionsAddDiagonalRecurrentTensors(builder, self.diagonalRecurrentTensors)
+ unidirectionalSequenceLstmoptions = UnidirectionalSequenceLSTMOptionsEnd(builder)
+ return unidirectionalSequenceLstmoptions
+
+
+class BidirectionalSequenceLSTMOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = BidirectionalSequenceLSTMOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsBidirectionalSequenceLSTMOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def BidirectionalSequenceLSTMOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # BidirectionalSequenceLSTMOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # BidirectionalSequenceLSTMOptions
+ def FusedActivationFunction(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # BidirectionalSequenceLSTMOptions
+ def CellClip(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
return 0.0
-def LocalResponseNormalizationOptionsStart(builder): builder.StartObject(4)
-def Start(builder):
- return LocalResponseNormalizationOptionsStart(builder)
-def LocalResponseNormalizationOptionsAddRadius(builder, radius): builder.PrependInt32Slot(0, radius, 0)
-def AddRadius(builder, radius):
- return LocalResponseNormalizationOptionsAddRadius(builder, radius)
-def LocalResponseNormalizationOptionsAddBias(builder, bias): builder.PrependFloat32Slot(1, bias, 0.0)
-def AddBias(builder, bias):
- return LocalResponseNormalizationOptionsAddBias(builder, bias)
-def LocalResponseNormalizationOptionsAddAlpha(builder, alpha): builder.PrependFloat32Slot(2, alpha, 0.0)
-def AddAlpha(builder, alpha):
- return LocalResponseNormalizationOptionsAddAlpha(builder, alpha)
-def LocalResponseNormalizationOptionsAddBeta(builder, beta): builder.PrependFloat32Slot(3, beta, 0.0)
-def AddBeta(builder, beta):
- return LocalResponseNormalizationOptionsAddBeta(builder, beta)
-def LocalResponseNormalizationOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return LocalResponseNormalizationOptionsEnd(builder)
+ # BidirectionalSequenceLSTMOptions
+ def ProjClip(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
+ return 0.0
-class LocalResponseNormalizationOptionsT(object):
+ # BidirectionalSequenceLSTMOptions
+ def MergeOutputs(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
- # LocalResponseNormalizationOptionsT
+ # BidirectionalSequenceLSTMOptions
+ def TimeMajor(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return True
+
+ # BidirectionalSequenceLSTMOptions
+ def AsymmetricQuantizeInputs(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+def BidirectionalSequenceLSTMOptionsStart(builder):
+ builder.StartObject(6)
+
+def BidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(0, fusedActivationFunction, 0)
+
+def BidirectionalSequenceLSTMOptionsAddCellClip(builder, cellClip):
+ builder.PrependFloat32Slot(1, cellClip, 0.0)
+
+def BidirectionalSequenceLSTMOptionsAddProjClip(builder, projClip):
+ builder.PrependFloat32Slot(2, projClip, 0.0)
+
+def BidirectionalSequenceLSTMOptionsAddMergeOutputs(builder, mergeOutputs):
+ builder.PrependBoolSlot(3, mergeOutputs, 0)
+
+def BidirectionalSequenceLSTMOptionsAddTimeMajor(builder, timeMajor):
+ builder.PrependBoolSlot(4, timeMajor, 1)
+
+def BidirectionalSequenceLSTMOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
+ builder.PrependBoolSlot(5, asymmetricQuantizeInputs, 0)
+
+def BidirectionalSequenceLSTMOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class BidirectionalSequenceLSTMOptionsT(object):
+
+ # BidirectionalSequenceLSTMOptionsT
def __init__(self):
- self.radius = 0 # type: int
- self.bias = 0.0 # type: float
- self.alpha = 0.0 # type: float
- self.beta = 0.0 # type: float
+ self.fusedActivationFunction = 0 # type: int
+ self.cellClip = 0.0 # type: float
+ self.projClip = 0.0 # type: float
+ self.mergeOutputs = False # type: bool
+ self.timeMajor = True # type: bool
+ self.asymmetricQuantizeInputs = False # type: bool
@classmethod
def InitFromBuf(cls, buf, pos):
- localResponseNormalizationOptions = LocalResponseNormalizationOptions()
- localResponseNormalizationOptions.Init(buf, pos)
- return cls.InitFromObj(localResponseNormalizationOptions)
+ bidirectionalSequenceLstmoptions = BidirectionalSequenceLSTMOptions()
+ bidirectionalSequenceLstmoptions.Init(buf, pos)
+ return cls.InitFromObj(bidirectionalSequenceLstmoptions)
@classmethod
- def InitFromObj(cls, localResponseNormalizationOptions):
- x = LocalResponseNormalizationOptionsT()
- x._UnPack(localResponseNormalizationOptions)
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, bidirectionalSequenceLstmoptions):
+ x = BidirectionalSequenceLSTMOptionsT()
+ x._UnPack(bidirectionalSequenceLstmoptions)
return x
- # LocalResponseNormalizationOptionsT
- def _UnPack(self, localResponseNormalizationOptions):
- if localResponseNormalizationOptions is None:
+ # BidirectionalSequenceLSTMOptionsT
+ def _UnPack(self, bidirectionalSequenceLstmoptions):
+ if bidirectionalSequenceLstmoptions is None:
return
- self.radius = localResponseNormalizationOptions.Radius()
- self.bias = localResponseNormalizationOptions.Bias()
- self.alpha = localResponseNormalizationOptions.Alpha()
- self.beta = localResponseNormalizationOptions.Beta()
+ self.fusedActivationFunction = bidirectionalSequenceLstmoptions.FusedActivationFunction()
+ self.cellClip = bidirectionalSequenceLstmoptions.CellClip()
+ self.projClip = bidirectionalSequenceLstmoptions.ProjClip()
+ self.mergeOutputs = bidirectionalSequenceLstmoptions.MergeOutputs()
+ self.timeMajor = bidirectionalSequenceLstmoptions.TimeMajor()
+ self.asymmetricQuantizeInputs = bidirectionalSequenceLstmoptions.AsymmetricQuantizeInputs()
- # LocalResponseNormalizationOptionsT
+ # BidirectionalSequenceLSTMOptionsT
def Pack(self, builder):
- LocalResponseNormalizationOptionsStart(builder)
- LocalResponseNormalizationOptionsAddRadius(builder, self.radius)
- LocalResponseNormalizationOptionsAddBias(builder, self.bias)
- LocalResponseNormalizationOptionsAddAlpha(builder, self.alpha)
- LocalResponseNormalizationOptionsAddBeta(builder, self.beta)
- localResponseNormalizationOptions = LocalResponseNormalizationOptionsEnd(builder)
- return localResponseNormalizationOptions
-# automatically generated by the FlatBuffers compiler, do not modify
+ BidirectionalSequenceLSTMOptionsStart(builder)
+ BidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
+ BidirectionalSequenceLSTMOptionsAddCellClip(builder, self.cellClip)
+ BidirectionalSequenceLSTMOptionsAddProjClip(builder, self.projClip)
+ BidirectionalSequenceLSTMOptionsAddMergeOutputs(builder, self.mergeOutputs)
+ BidirectionalSequenceLSTMOptionsAddTimeMajor(builder, self.timeMajor)
+ BidirectionalSequenceLSTMOptionsAddAsymmetricQuantizeInputs(builder, self.asymmetricQuantizeInputs)
+ bidirectionalSequenceLstmoptions = BidirectionalSequenceLSTMOptionsEnd(builder)
+ return bidirectionalSequenceLstmoptions
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
+class ResizeBilinearOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = ResizeBilinearOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsResizeBilinearOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def ResizeBilinearOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # ResizeBilinearOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # ResizeBilinearOptions
+ def AlignCorners(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+ # ResizeBilinearOptions
+ def HalfPixelCenters(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+def ResizeBilinearOptionsStart(builder):
+ builder.StartObject(4)
+
+def ResizeBilinearOptionsAddAlignCorners(builder, alignCorners):
+ builder.PrependBoolSlot(2, alignCorners, 0)
+
+def ResizeBilinearOptionsAddHalfPixelCenters(builder, halfPixelCenters):
+ builder.PrependBoolSlot(3, halfPixelCenters, 0)
+
+def ResizeBilinearOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class ResizeBilinearOptionsT(object):
+
+ # ResizeBilinearOptionsT
+ def __init__(self):
+ self.alignCorners = False # type: bool
+ self.halfPixelCenters = False # type: bool
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ resizeBilinearOptions = ResizeBilinearOptions()
+ resizeBilinearOptions.Init(buf, pos)
+ return cls.InitFromObj(resizeBilinearOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, resizeBilinearOptions):
+ x = ResizeBilinearOptionsT()
+ x._UnPack(resizeBilinearOptions)
+ return x
+
+ # ResizeBilinearOptionsT
+ def _UnPack(self, resizeBilinearOptions):
+ if resizeBilinearOptions is None:
+ return
+ self.alignCorners = resizeBilinearOptions.AlignCorners()
+ self.halfPixelCenters = resizeBilinearOptions.HalfPixelCenters()
+
+ # ResizeBilinearOptionsT
+ def Pack(self, builder):
+ ResizeBilinearOptionsStart(builder)
+ ResizeBilinearOptionsAddAlignCorners(builder, self.alignCorners)
+ ResizeBilinearOptionsAddHalfPixelCenters(builder, self.halfPixelCenters)
+ resizeBilinearOptions = ResizeBilinearOptionsEnd(builder)
+ return resizeBilinearOptions
+
+
+class ResizeNearestNeighborOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = ResizeNearestNeighborOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsResizeNearestNeighborOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def ResizeNearestNeighborOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # ResizeNearestNeighborOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # ResizeNearestNeighborOptions
+ def AlignCorners(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+ # ResizeNearestNeighborOptions
+ def HalfPixelCenters(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+def ResizeNearestNeighborOptionsStart(builder):
+ builder.StartObject(2)
+
+def ResizeNearestNeighborOptionsAddAlignCorners(builder, alignCorners):
+ builder.PrependBoolSlot(0, alignCorners, 0)
+
+def ResizeNearestNeighborOptionsAddHalfPixelCenters(builder, halfPixelCenters):
+ builder.PrependBoolSlot(1, halfPixelCenters, 0)
+
+def ResizeNearestNeighborOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class ResizeNearestNeighborOptionsT(object):
+
+ # ResizeNearestNeighborOptionsT
+ def __init__(self):
+ self.alignCorners = False # type: bool
+ self.halfPixelCenters = False # type: bool
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ resizeNearestNeighborOptions = ResizeNearestNeighborOptions()
+ resizeNearestNeighborOptions.Init(buf, pos)
+ return cls.InitFromObj(resizeNearestNeighborOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, resizeNearestNeighborOptions):
+ x = ResizeNearestNeighborOptionsT()
+ x._UnPack(resizeNearestNeighborOptions)
+ return x
+
+ # ResizeNearestNeighborOptionsT
+ def _UnPack(self, resizeNearestNeighborOptions):
+ if resizeNearestNeighborOptions is None:
+ return
+ self.alignCorners = resizeNearestNeighborOptions.AlignCorners()
+ self.halfPixelCenters = resizeNearestNeighborOptions.HalfPixelCenters()
+
+ # ResizeNearestNeighborOptionsT
+ def Pack(self, builder):
+ ResizeNearestNeighborOptionsStart(builder)
+ ResizeNearestNeighborOptionsAddAlignCorners(builder, self.alignCorners)
+ ResizeNearestNeighborOptionsAddHalfPixelCenters(builder, self.halfPixelCenters)
+ resizeNearestNeighborOptions = ResizeNearestNeighborOptionsEnd(builder)
+ return resizeNearestNeighborOptions
+
+
+class CallOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = CallOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsCallOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def CallOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # CallOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # CallOptions
+ def Subgraph(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos)
+ return 0
+
+def CallOptionsStart(builder):
+ builder.StartObject(1)
+
+def CallOptionsAddSubgraph(builder, subgraph):
+ builder.PrependUint32Slot(0, subgraph, 0)
+
+def CallOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class CallOptionsT(object):
+
+ # CallOptionsT
+ def __init__(self):
+ self.subgraph = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ callOptions = CallOptions()
+ callOptions.Init(buf, pos)
+ return cls.InitFromObj(callOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, callOptions):
+ x = CallOptionsT()
+ x._UnPack(callOptions)
+ return x
+
+ # CallOptionsT
+ def _UnPack(self, callOptions):
+ if callOptions is None:
+ return
+ self.subgraph = callOptions.Subgraph()
+
+ # CallOptionsT
+ def Pack(self, builder):
+ CallOptionsStart(builder)
+ CallOptionsAddSubgraph(builder, self.subgraph)
+ callOptions = CallOptionsEnd(builder)
+ return callOptions
+
+
+class PadOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = PadOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsPadOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def PadOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # PadOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def PadOptionsStart(builder):
+ builder.StartObject(0)
+
+def PadOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class PadOptionsT(object):
+
+ # PadOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ padOptions = PadOptions()
+ padOptions.Init(buf, pos)
+ return cls.InitFromObj(padOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, padOptions):
+ x = PadOptionsT()
+ x._UnPack(padOptions)
+ return x
+
+ # PadOptionsT
+ def _UnPack(self, padOptions):
+ if padOptions is None:
+ return
+
+ # PadOptionsT
+ def Pack(self, builder):
+ PadOptionsStart(builder)
+ padOptions = PadOptionsEnd(builder)
+ return padOptions
+
+
+class PadV2Options(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = PadV2Options()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsPadV2Options(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def PadV2OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # PadV2Options
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def PadV2OptionsStart(builder):
+ builder.StartObject(0)
+
+def PadV2OptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class PadV2OptionsT(object):
+
+ # PadV2OptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ padV2Options = PadV2Options()
+ padV2Options.Init(buf, pos)
+ return cls.InitFromObj(padV2Options)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, padV2Options):
+ x = PadV2OptionsT()
+ x._UnPack(padV2Options)
+ return x
+
+ # PadV2OptionsT
+ def _UnPack(self, padV2Options):
+ if padV2Options is None:
+ return
+
+ # PadV2OptionsT
+ def Pack(self, builder):
+ PadV2OptionsStart(builder)
+ padV2Options = PadV2OptionsEnd(builder)
+ return padV2Options
+
+
+class ReshapeOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = ReshapeOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsReshapeOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def ReshapeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # ReshapeOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # ReshapeOptions
+ def NewShape(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+ return 0
+
+ # ReshapeOptions
+ def NewShapeAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
+ return 0
+
+ # ReshapeOptions
+ def NewShapeLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # ReshapeOptions
+ def NewShapeIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+def ReshapeOptionsStart(builder):
+ builder.StartObject(1)
+
+def ReshapeOptionsAddNewShape(builder, newShape):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(newShape), 0)
+
+def ReshapeOptionsStartNewShapeVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def ReshapeOptionsEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class ReshapeOptionsT(object):
+
+ # ReshapeOptionsT
+ def __init__(self):
+ self.newShape = None # type: List[int]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ reshapeOptions = ReshapeOptions()
+ reshapeOptions.Init(buf, pos)
+ return cls.InitFromObj(reshapeOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, reshapeOptions):
+ x = ReshapeOptionsT()
+ x._UnPack(reshapeOptions)
+ return x
+
+ # ReshapeOptionsT
+ def _UnPack(self, reshapeOptions):
+ if reshapeOptions is None:
+ return
+ if not reshapeOptions.NewShapeIsNone():
+ if np is None:
+ self.newShape = []
+ for i in range(reshapeOptions.NewShapeLength()):
+ self.newShape.append(reshapeOptions.NewShape(i))
+ else:
+ self.newShape = reshapeOptions.NewShapeAsNumpy()
+
+ # ReshapeOptionsT
+ def Pack(self, builder):
+ if self.newShape is not None:
+ if np is not None and type(self.newShape) is np.ndarray:
+ newShape = builder.CreateNumpyVector(self.newShape)
+ else:
+ ReshapeOptionsStartNewShapeVector(builder, len(self.newShape))
+ for i in reversed(range(len(self.newShape))):
+ builder.PrependInt32(self.newShape[i])
+ newShape = builder.EndVector()
+ ReshapeOptionsStart(builder)
+ if self.newShape is not None:
+ ReshapeOptionsAddNewShape(builder, newShape)
+ reshapeOptions = ReshapeOptionsEnd(builder)
+ return reshapeOptions
+
+
+class SpaceToBatchNDOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = SpaceToBatchNDOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsSpaceToBatchNDOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def SpaceToBatchNDOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # SpaceToBatchNDOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def SpaceToBatchNDOptionsStart(builder):
+ builder.StartObject(0)
+
+def SpaceToBatchNDOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class SpaceToBatchNDOptionsT(object):
+
+ # SpaceToBatchNDOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ spaceToBatchNdoptions = SpaceToBatchNDOptions()
+ spaceToBatchNdoptions.Init(buf, pos)
+ return cls.InitFromObj(spaceToBatchNdoptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, spaceToBatchNdoptions):
+ x = SpaceToBatchNDOptionsT()
+ x._UnPack(spaceToBatchNdoptions)
+ return x
+
+ # SpaceToBatchNDOptionsT
+ def _UnPack(self, spaceToBatchNdoptions):
+ if spaceToBatchNdoptions is None:
+ return
+
+ # SpaceToBatchNDOptionsT
+ def Pack(self, builder):
+ SpaceToBatchNDOptionsStart(builder)
+ spaceToBatchNdoptions = SpaceToBatchNDOptionsEnd(builder)
+ return spaceToBatchNdoptions
+
+
+class BatchToSpaceNDOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = BatchToSpaceNDOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsBatchToSpaceNDOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def BatchToSpaceNDOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # BatchToSpaceNDOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def BatchToSpaceNDOptionsStart(builder):
+ builder.StartObject(0)
+
+def BatchToSpaceNDOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class BatchToSpaceNDOptionsT(object):
+
+ # BatchToSpaceNDOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ batchToSpaceNdoptions = BatchToSpaceNDOptions()
+ batchToSpaceNdoptions.Init(buf, pos)
+ return cls.InitFromObj(batchToSpaceNdoptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, batchToSpaceNdoptions):
+ x = BatchToSpaceNDOptionsT()
+ x._UnPack(batchToSpaceNdoptions)
+ return x
+
+ # BatchToSpaceNDOptionsT
+ def _UnPack(self, batchToSpaceNdoptions):
+ if batchToSpaceNdoptions is None:
+ return
+
+ # BatchToSpaceNDOptionsT
+ def Pack(self, builder):
+ BatchToSpaceNDOptionsStart(builder)
+ batchToSpaceNdoptions = BatchToSpaceNDOptionsEnd(builder)
+ return batchToSpaceNdoptions
+
+
+class SkipGramOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = SkipGramOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsSkipGramOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def SkipGramOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # SkipGramOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # SkipGramOptions
+ def NgramSize(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # SkipGramOptions
+ def MaxSkipSize(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # SkipGramOptions
+ def IncludeAllNgrams(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+def SkipGramOptionsStart(builder):
+ builder.StartObject(3)
+
+def SkipGramOptionsAddNgramSize(builder, ngramSize):
+ builder.PrependInt32Slot(0, ngramSize, 0)
+
+def SkipGramOptionsAddMaxSkipSize(builder, maxSkipSize):
+ builder.PrependInt32Slot(1, maxSkipSize, 0)
+
+def SkipGramOptionsAddIncludeAllNgrams(builder, includeAllNgrams):
+ builder.PrependBoolSlot(2, includeAllNgrams, 0)
+
+def SkipGramOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class SkipGramOptionsT(object):
+
+ # SkipGramOptionsT
+ def __init__(self):
+ self.ngramSize = 0 # type: int
+ self.maxSkipSize = 0 # type: int
+ self.includeAllNgrams = False # type: bool
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ skipGramOptions = SkipGramOptions()
+ skipGramOptions.Init(buf, pos)
+ return cls.InitFromObj(skipGramOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, skipGramOptions):
+ x = SkipGramOptionsT()
+ x._UnPack(skipGramOptions)
+ return x
+
+ # SkipGramOptionsT
+ def _UnPack(self, skipGramOptions):
+ if skipGramOptions is None:
+ return
+ self.ngramSize = skipGramOptions.NgramSize()
+ self.maxSkipSize = skipGramOptions.MaxSkipSize()
+ self.includeAllNgrams = skipGramOptions.IncludeAllNgrams()
+
+ # SkipGramOptionsT
+ def Pack(self, builder):
+ SkipGramOptionsStart(builder)
+ SkipGramOptionsAddNgramSize(builder, self.ngramSize)
+ SkipGramOptionsAddMaxSkipSize(builder, self.maxSkipSize)
+ SkipGramOptionsAddIncludeAllNgrams(builder, self.includeAllNgrams)
+ skipGramOptions = SkipGramOptionsEnd(builder)
+ return skipGramOptions
+
+
+class SpaceToDepthOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = SpaceToDepthOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsSpaceToDepthOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def SpaceToDepthOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # SpaceToDepthOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # SpaceToDepthOptions
+ def BlockSize(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def SpaceToDepthOptionsStart(builder):
+ builder.StartObject(1)
+
+def SpaceToDepthOptionsAddBlockSize(builder, blockSize):
+ builder.PrependInt32Slot(0, blockSize, 0)
+
+def SpaceToDepthOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class SpaceToDepthOptionsT(object):
+
+ # SpaceToDepthOptionsT
+ def __init__(self):
+ self.blockSize = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ spaceToDepthOptions = SpaceToDepthOptions()
+ spaceToDepthOptions.Init(buf, pos)
+ return cls.InitFromObj(spaceToDepthOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, spaceToDepthOptions):
+ x = SpaceToDepthOptionsT()
+ x._UnPack(spaceToDepthOptions)
+ return x
+
+ # SpaceToDepthOptionsT
+ def _UnPack(self, spaceToDepthOptions):
+ if spaceToDepthOptions is None:
+ return
+ self.blockSize = spaceToDepthOptions.BlockSize()
+
+ # SpaceToDepthOptionsT
+ def Pack(self, builder):
+ SpaceToDepthOptionsStart(builder)
+ SpaceToDepthOptionsAddBlockSize(builder, self.blockSize)
+ spaceToDepthOptions = SpaceToDepthOptionsEnd(builder)
+ return spaceToDepthOptions
+
+
+class DepthToSpaceOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = DepthToSpaceOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsDepthToSpaceOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def DepthToSpaceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # DepthToSpaceOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # DepthToSpaceOptions
+ def BlockSize(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def DepthToSpaceOptionsStart(builder):
+ builder.StartObject(1)
+
+def DepthToSpaceOptionsAddBlockSize(builder, blockSize):
+ builder.PrependInt32Slot(0, blockSize, 0)
+
+def DepthToSpaceOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class DepthToSpaceOptionsT(object):
+
+ # DepthToSpaceOptionsT
+ def __init__(self):
+ self.blockSize = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ depthToSpaceOptions = DepthToSpaceOptions()
+ depthToSpaceOptions.Init(buf, pos)
+ return cls.InitFromObj(depthToSpaceOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, depthToSpaceOptions):
+ x = DepthToSpaceOptionsT()
+ x._UnPack(depthToSpaceOptions)
+ return x
+
+ # DepthToSpaceOptionsT
+ def _UnPack(self, depthToSpaceOptions):
+ if depthToSpaceOptions is None:
+ return
+ self.blockSize = depthToSpaceOptions.BlockSize()
+
+ # DepthToSpaceOptionsT
+ def Pack(self, builder):
+ DepthToSpaceOptionsStart(builder)
+ DepthToSpaceOptionsAddBlockSize(builder, self.blockSize)
+ depthToSpaceOptions = DepthToSpaceOptionsEnd(builder)
+ return depthToSpaceOptions
+
+
+class SubOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = SubOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsSubOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def SubOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # SubOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # SubOptions
+ def FusedActivationFunction(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # SubOptions
+ def PotScaleInt16(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return True
+
+def SubOptionsStart(builder):
+ builder.StartObject(2)
+
+def SubOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(0, fusedActivationFunction, 0)
+
+def SubOptionsAddPotScaleInt16(builder, potScaleInt16):
+ builder.PrependBoolSlot(1, potScaleInt16, 1)
+
+def SubOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class SubOptionsT(object):
+
+ # SubOptionsT
+ def __init__(self):
+ self.fusedActivationFunction = 0 # type: int
+ self.potScaleInt16 = True # type: bool
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ subOptions = SubOptions()
+ subOptions.Init(buf, pos)
+ return cls.InitFromObj(subOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, subOptions):
+ x = SubOptionsT()
+ x._UnPack(subOptions)
+ return x
+
+ # SubOptionsT
+ def _UnPack(self, subOptions):
+ if subOptions is None:
+ return
+ self.fusedActivationFunction = subOptions.FusedActivationFunction()
+ self.potScaleInt16 = subOptions.PotScaleInt16()
+
+ # SubOptionsT
+ def Pack(self, builder):
+ SubOptionsStart(builder)
+ SubOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
+ SubOptionsAddPotScaleInt16(builder, self.potScaleInt16)
+ subOptions = SubOptionsEnd(builder)
+ return subOptions
+
+
+class DivOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = DivOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsDivOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def DivOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # DivOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # DivOptions
+ def FusedActivationFunction(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+def DivOptionsStart(builder):
+ builder.StartObject(1)
+
+def DivOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(0, fusedActivationFunction, 0)
+
+def DivOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class DivOptionsT(object):
+
+ # DivOptionsT
+ def __init__(self):
+ self.fusedActivationFunction = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ divOptions = DivOptions()
+ divOptions.Init(buf, pos)
+ return cls.InitFromObj(divOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, divOptions):
+ x = DivOptionsT()
+ x._UnPack(divOptions)
+ return x
+
+ # DivOptionsT
+ def _UnPack(self, divOptions):
+ if divOptions is None:
+ return
+ self.fusedActivationFunction = divOptions.FusedActivationFunction()
+
+ # DivOptionsT
+ def Pack(self, builder):
+ DivOptionsStart(builder)
+ DivOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
+ divOptions = DivOptionsEnd(builder)
+ return divOptions
+
+
+class TopKV2Options(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = TopKV2Options()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsTopKV2Options(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def TopKV2OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # TopKV2Options
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def TopKV2OptionsStart(builder):
+ builder.StartObject(0)
+
+def TopKV2OptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class TopKV2OptionsT(object):
+
+ # TopKV2OptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ topKv2Options = TopKV2Options()
+ topKv2Options.Init(buf, pos)
+ return cls.InitFromObj(topKv2Options)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, topKv2Options):
+ x = TopKV2OptionsT()
+ x._UnPack(topKv2Options)
+ return x
+
+ # TopKV2OptionsT
+ def _UnPack(self, topKv2Options):
+ if topKv2Options is None:
+ return
+
+ # TopKV2OptionsT
+ def Pack(self, builder):
+ TopKV2OptionsStart(builder)
+ topKv2Options = TopKV2OptionsEnd(builder)
+ return topKv2Options
+
+
+class EmbeddingLookupSparseOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = EmbeddingLookupSparseOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsEmbeddingLookupSparseOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def EmbeddingLookupSparseOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # EmbeddingLookupSparseOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # EmbeddingLookupSparseOptions
+ def Combiner(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+def EmbeddingLookupSparseOptionsStart(builder):
+ builder.StartObject(1)
+
+def EmbeddingLookupSparseOptionsAddCombiner(builder, combiner):
+ builder.PrependInt8Slot(0, combiner, 0)
+
+def EmbeddingLookupSparseOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class EmbeddingLookupSparseOptionsT(object):
+
+ # EmbeddingLookupSparseOptionsT
+ def __init__(self):
+ self.combiner = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ embeddingLookupSparseOptions = EmbeddingLookupSparseOptions()
+ embeddingLookupSparseOptions.Init(buf, pos)
+ return cls.InitFromObj(embeddingLookupSparseOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, embeddingLookupSparseOptions):
+ x = EmbeddingLookupSparseOptionsT()
+ x._UnPack(embeddingLookupSparseOptions)
+ return x
+
+ # EmbeddingLookupSparseOptionsT
+ def _UnPack(self, embeddingLookupSparseOptions):
+ if embeddingLookupSparseOptions is None:
+ return
+ self.combiner = embeddingLookupSparseOptions.Combiner()
+
+ # EmbeddingLookupSparseOptionsT
+ def Pack(self, builder):
+ EmbeddingLookupSparseOptionsStart(builder)
+ EmbeddingLookupSparseOptionsAddCombiner(builder, self.combiner)
+ embeddingLookupSparseOptions = EmbeddingLookupSparseOptionsEnd(builder)
+ return embeddingLookupSparseOptions
+
+
+class GatherOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = GatherOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsGatherOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def GatherOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # GatherOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # GatherOptions
+ def Axis(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # GatherOptions
+ def BatchDims(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def GatherOptionsStart(builder):
+ builder.StartObject(2)
+
+def GatherOptionsAddAxis(builder, axis):
+ builder.PrependInt32Slot(0, axis, 0)
+
+def GatherOptionsAddBatchDims(builder, batchDims):
+ builder.PrependInt32Slot(1, batchDims, 0)
+
+def GatherOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class GatherOptionsT(object):
+
+ # GatherOptionsT
+ def __init__(self):
+ self.axis = 0 # type: int
+ self.batchDims = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ gatherOptions = GatherOptions()
+ gatherOptions.Init(buf, pos)
+ return cls.InitFromObj(gatherOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, gatherOptions):
+ x = GatherOptionsT()
+ x._UnPack(gatherOptions)
+ return x
+
+ # GatherOptionsT
+ def _UnPack(self, gatherOptions):
+ if gatherOptions is None:
+ return
+ self.axis = gatherOptions.Axis()
+ self.batchDims = gatherOptions.BatchDims()
+
+ # GatherOptionsT
+ def Pack(self, builder):
+ GatherOptionsStart(builder)
+ GatherOptionsAddAxis(builder, self.axis)
+ GatherOptionsAddBatchDims(builder, self.batchDims)
+ gatherOptions = GatherOptionsEnd(builder)
+ return gatherOptions
+
+
+class TransposeOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = TransposeOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsTransposeOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def TransposeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # TransposeOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def TransposeOptionsStart(builder):
+ builder.StartObject(0)
+
+def TransposeOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class TransposeOptionsT(object):
+
+ # TransposeOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ transposeOptions = TransposeOptions()
+ transposeOptions.Init(buf, pos)
+ return cls.InitFromObj(transposeOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, transposeOptions):
+ x = TransposeOptionsT()
+ x._UnPack(transposeOptions)
+ return x
+
+ # TransposeOptionsT
+ def _UnPack(self, transposeOptions):
+ if transposeOptions is None:
+ return
+
+ # TransposeOptionsT
+ def Pack(self, builder):
+ TransposeOptionsStart(builder)
+ transposeOptions = TransposeOptionsEnd(builder)
+ return transposeOptions
+
+
+class ExpOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = ExpOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsExpOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def ExpOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # ExpOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def ExpOptionsStart(builder):
+ builder.StartObject(0)
+
+def ExpOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class ExpOptionsT(object):
+
+ # ExpOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ expOptions = ExpOptions()
+ expOptions.Init(buf, pos)
+ return cls.InitFromObj(expOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, expOptions):
+ x = ExpOptionsT()
+ x._UnPack(expOptions)
+ return x
+
+ # ExpOptionsT
+ def _UnPack(self, expOptions):
+ if expOptions is None:
+ return
+
+ # ExpOptionsT
+ def Pack(self, builder):
+ ExpOptionsStart(builder)
+ expOptions = ExpOptionsEnd(builder)
+ return expOptions
+
+
+class CosOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = CosOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsCosOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def CosOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # CosOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def CosOptionsStart(builder):
+ builder.StartObject(0)
+
+def CosOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class CosOptionsT(object):
+
+ # CosOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ cosOptions = CosOptions()
+ cosOptions.Init(buf, pos)
+ return cls.InitFromObj(cosOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, cosOptions):
+ x = CosOptionsT()
+ x._UnPack(cosOptions)
+ return x
+
+ # CosOptionsT
+ def _UnPack(self, cosOptions):
+ if cosOptions is None:
+ return
+
+ # CosOptionsT
+ def Pack(self, builder):
+ CosOptionsStart(builder)
+ cosOptions = CosOptionsEnd(builder)
+ return cosOptions
+
+
+class ReducerOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = ReducerOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsReducerOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def ReducerOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # ReducerOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # ReducerOptions
+ def KeepDims(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+def ReducerOptionsStart(builder):
+ builder.StartObject(1)
+
+def ReducerOptionsAddKeepDims(builder, keepDims):
+ builder.PrependBoolSlot(0, keepDims, 0)
+
+def ReducerOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class ReducerOptionsT(object):
+
+ # ReducerOptionsT
+ def __init__(self):
+ self.keepDims = False # type: bool
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ reducerOptions = ReducerOptions()
+ reducerOptions.Init(buf, pos)
+ return cls.InitFromObj(reducerOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, reducerOptions):
+ x = ReducerOptionsT()
+ x._UnPack(reducerOptions)
+ return x
+
+ # ReducerOptionsT
+ def _UnPack(self, reducerOptions):
+ if reducerOptions is None:
+ return
+ self.keepDims = reducerOptions.KeepDims()
+
+ # ReducerOptionsT
+ def Pack(self, builder):
+ ReducerOptionsStart(builder)
+ ReducerOptionsAddKeepDims(builder, self.keepDims)
+ reducerOptions = ReducerOptionsEnd(builder)
+ return reducerOptions
+
+
+class SqueezeOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = SqueezeOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsSqueezeOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def SqueezeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # SqueezeOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # SqueezeOptions
+ def SqueezeDims(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+ return 0
+
+ # SqueezeOptions
+ def SqueezeDimsAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
+ return 0
+
+ # SqueezeOptions
+ def SqueezeDimsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # SqueezeOptions
+ def SqueezeDimsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+def SqueezeOptionsStart(builder):
+ builder.StartObject(1)
+
+def SqueezeOptionsAddSqueezeDims(builder, squeezeDims):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(squeezeDims), 0)
+
+def SqueezeOptionsStartSqueezeDimsVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def SqueezeOptionsEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class SqueezeOptionsT(object):
+
+ # SqueezeOptionsT
+ def __init__(self):
+ self.squeezeDims = None # type: List[int]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ squeezeOptions = SqueezeOptions()
+ squeezeOptions.Init(buf, pos)
+ return cls.InitFromObj(squeezeOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, squeezeOptions):
+ x = SqueezeOptionsT()
+ x._UnPack(squeezeOptions)
+ return x
+
+ # SqueezeOptionsT
+ def _UnPack(self, squeezeOptions):
+ if squeezeOptions is None:
+ return
+ if not squeezeOptions.SqueezeDimsIsNone():
+ if np is None:
+ self.squeezeDims = []
+ for i in range(squeezeOptions.SqueezeDimsLength()):
+ self.squeezeDims.append(squeezeOptions.SqueezeDims(i))
+ else:
+ self.squeezeDims = squeezeOptions.SqueezeDimsAsNumpy()
+
+ # SqueezeOptionsT
+ def Pack(self, builder):
+ if self.squeezeDims is not None:
+ if np is not None and type(self.squeezeDims) is np.ndarray:
+ squeezeDims = builder.CreateNumpyVector(self.squeezeDims)
+ else:
+ SqueezeOptionsStartSqueezeDimsVector(builder, len(self.squeezeDims))
+ for i in reversed(range(len(self.squeezeDims))):
+ builder.PrependInt32(self.squeezeDims[i])
+ squeezeDims = builder.EndVector()
+ SqueezeOptionsStart(builder)
+ if self.squeezeDims is not None:
+ SqueezeOptionsAddSqueezeDims(builder, squeezeDims)
+ squeezeOptions = SqueezeOptionsEnd(builder)
+ return squeezeOptions
+
+
+class SplitOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = SplitOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsSplitOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def SplitOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # SplitOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # SplitOptions
+ def NumSplits(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def SplitOptionsStart(builder):
+ builder.StartObject(1)
+
+def SplitOptionsAddNumSplits(builder, numSplits):
+ builder.PrependInt32Slot(0, numSplits, 0)
+
+def SplitOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class SplitOptionsT(object):
+
+ # SplitOptionsT
+ def __init__(self):
+ self.numSplits = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ splitOptions = SplitOptions()
+ splitOptions.Init(buf, pos)
+ return cls.InitFromObj(splitOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, splitOptions):
+ x = SplitOptionsT()
+ x._UnPack(splitOptions)
+ return x
+
+ # SplitOptionsT
+ def _UnPack(self, splitOptions):
+ if splitOptions is None:
+ return
+ self.numSplits = splitOptions.NumSplits()
+
+ # SplitOptionsT
+ def Pack(self, builder):
+ SplitOptionsStart(builder)
+ SplitOptionsAddNumSplits(builder, self.numSplits)
+ splitOptions = SplitOptionsEnd(builder)
+ return splitOptions
+
+
+class SplitVOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = SplitVOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsSplitVOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def SplitVOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # SplitVOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # SplitVOptions
+ def NumSplits(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def SplitVOptionsStart(builder):
+ builder.StartObject(1)
+
+def SplitVOptionsAddNumSplits(builder, numSplits):
+ builder.PrependInt32Slot(0, numSplits, 0)
+
+def SplitVOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class SplitVOptionsT(object):
+
+ # SplitVOptionsT
+ def __init__(self):
+ self.numSplits = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ splitVoptions = SplitVOptions()
+ splitVoptions.Init(buf, pos)
+ return cls.InitFromObj(splitVoptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, splitVoptions):
+ x = SplitVOptionsT()
+ x._UnPack(splitVoptions)
+ return x
+
+ # SplitVOptionsT
+ def _UnPack(self, splitVoptions):
+ if splitVoptions is None:
+ return
+ self.numSplits = splitVoptions.NumSplits()
+
+ # SplitVOptionsT
+ def Pack(self, builder):
+ SplitVOptionsStart(builder)
+ SplitVOptionsAddNumSplits(builder, self.numSplits)
+ splitVoptions = SplitVOptionsEnd(builder)
+ return splitVoptions
+
+
+class StridedSliceOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StridedSliceOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStridedSliceOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StridedSliceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StridedSliceOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StridedSliceOptions
+ def BeginMask(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # StridedSliceOptions
+ def EndMask(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # StridedSliceOptions
+ def EllipsisMask(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # StridedSliceOptions
+ def NewAxisMask(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # StridedSliceOptions
+ def ShrinkAxisMask(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # StridedSliceOptions
+ def Offset(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+def StridedSliceOptionsStart(builder):
+ builder.StartObject(6)
+
+def StridedSliceOptionsAddBeginMask(builder, beginMask):
+ builder.PrependInt32Slot(0, beginMask, 0)
+
+def StridedSliceOptionsAddEndMask(builder, endMask):
+ builder.PrependInt32Slot(1, endMask, 0)
+
+def StridedSliceOptionsAddEllipsisMask(builder, ellipsisMask):
+ builder.PrependInt32Slot(2, ellipsisMask, 0)
+
+def StridedSliceOptionsAddNewAxisMask(builder, newAxisMask):
+ builder.PrependInt32Slot(3, newAxisMask, 0)
+
+def StridedSliceOptionsAddShrinkAxisMask(builder, shrinkAxisMask):
+ builder.PrependInt32Slot(4, shrinkAxisMask, 0)
+
+def StridedSliceOptionsAddOffset(builder, offset):
+ builder.PrependBoolSlot(5, offset, 0)
+
+def StridedSliceOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class StridedSliceOptionsT(object):
+
+ # StridedSliceOptionsT
+ def __init__(self):
+ self.beginMask = 0 # type: int
+ self.endMask = 0 # type: int
+ self.ellipsisMask = 0 # type: int
+ self.newAxisMask = 0 # type: int
+ self.shrinkAxisMask = 0 # type: int
+ self.offset = False # type: bool
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stridedSliceOptions = StridedSliceOptions()
+ stridedSliceOptions.Init(buf, pos)
+ return cls.InitFromObj(stridedSliceOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stridedSliceOptions):
+ x = StridedSliceOptionsT()
+ x._UnPack(stridedSliceOptions)
+ return x
+
+ # StridedSliceOptionsT
+ def _UnPack(self, stridedSliceOptions):
+ if stridedSliceOptions is None:
+ return
+ self.beginMask = stridedSliceOptions.BeginMask()
+ self.endMask = stridedSliceOptions.EndMask()
+ self.ellipsisMask = stridedSliceOptions.EllipsisMask()
+ self.newAxisMask = stridedSliceOptions.NewAxisMask()
+ self.shrinkAxisMask = stridedSliceOptions.ShrinkAxisMask()
+ self.offset = stridedSliceOptions.Offset()
+
+ # StridedSliceOptionsT
+ def Pack(self, builder):
+ StridedSliceOptionsStart(builder)
+ StridedSliceOptionsAddBeginMask(builder, self.beginMask)
+ StridedSliceOptionsAddEndMask(builder, self.endMask)
+ StridedSliceOptionsAddEllipsisMask(builder, self.ellipsisMask)
+ StridedSliceOptionsAddNewAxisMask(builder, self.newAxisMask)
+ StridedSliceOptionsAddShrinkAxisMask(builder, self.shrinkAxisMask)
+ StridedSliceOptionsAddOffset(builder, self.offset)
+ stridedSliceOptions = StridedSliceOptionsEnd(builder)
+ return stridedSliceOptions
+
class LogSoftmaxOptions(object):
__slots__ = ['_tab']
@@ -5970,12 +10359,13 @@
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
-def LogSoftmaxOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return LogSoftmaxOptionsStart(builder)
-def LogSoftmaxOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return LogSoftmaxOptionsEnd(builder)
+def LogSoftmaxOptionsStart(builder):
+ builder.StartObject(0)
+
+def LogSoftmaxOptionsEnd(builder):
+ return builder.EndObject()
+
+
class LogSoftmaxOptionsT(object):
@@ -5990,6 +10380,11 @@
return cls.InitFromObj(logSoftmaxOptions)
@classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
def InitFromObj(cls, logSoftmaxOptions):
x = LogSoftmaxOptionsT()
x._UnPack(logSoftmaxOptions)
@@ -6005,332 +10400,162 @@
LogSoftmaxOptionsStart(builder)
logSoftmaxOptions = LogSoftmaxOptionsEnd(builder)
return logSoftmaxOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class LogicalAndOptions(object):
+class CastOptions(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0):
n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = LogicalAndOptions()
+ x = CastOptions()
x.Init(buf, n + offset)
return x
@classmethod
- def GetRootAsLogicalAndOptions(cls, buf, offset=0):
+ def GetRootAsCastOptions(cls, buf, offset=0):
"""This method is deprecated. Please switch to GetRootAs."""
return cls.GetRootAs(buf, offset)
@classmethod
- def LogicalAndOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ def CastOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
- # LogicalAndOptions
+ # CastOptions
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
-def LogicalAndOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return LogicalAndOptionsStart(builder)
-def LogicalAndOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return LogicalAndOptionsEnd(builder)
+ # CastOptions
+ def InDataType(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
-class LogicalAndOptionsT(object):
+ # CastOptions
+ def OutDataType(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
- # LogicalAndOptionsT
+def CastOptionsStart(builder):
+ builder.StartObject(2)
+
+def CastOptionsAddInDataType(builder, inDataType):
+ builder.PrependInt8Slot(0, inDataType, 0)
+
+def CastOptionsAddOutDataType(builder, outDataType):
+ builder.PrependInt8Slot(1, outDataType, 0)
+
+def CastOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class CastOptionsT(object):
+
+ # CastOptionsT
def __init__(self):
- pass
+ self.inDataType = 0 # type: int
+ self.outDataType = 0 # type: int
@classmethod
def InitFromBuf(cls, buf, pos):
- logicalAndOptions = LogicalAndOptions()
- logicalAndOptions.Init(buf, pos)
- return cls.InitFromObj(logicalAndOptions)
+ castOptions = CastOptions()
+ castOptions.Init(buf, pos)
+ return cls.InitFromObj(castOptions)
@classmethod
- def InitFromObj(cls, logicalAndOptions):
- x = LogicalAndOptionsT()
- x._UnPack(logicalAndOptions)
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, castOptions):
+ x = CastOptionsT()
+ x._UnPack(castOptions)
return x
- # LogicalAndOptionsT
- def _UnPack(self, logicalAndOptions):
- if logicalAndOptions is None:
+ # CastOptionsT
+ def _UnPack(self, castOptions):
+ if castOptions is None:
return
+ self.inDataType = castOptions.InDataType()
+ self.outDataType = castOptions.OutDataType()
- # LogicalAndOptionsT
+ # CastOptionsT
def Pack(self, builder):
- LogicalAndOptionsStart(builder)
- logicalAndOptions = LogicalAndOptionsEnd(builder)
- return logicalAndOptions
-# automatically generated by the FlatBuffers compiler, do not modify
+ CastOptionsStart(builder)
+ CastOptionsAddInDataType(builder, self.inDataType)
+ CastOptionsAddOutDataType(builder, self.outDataType)
+ castOptions = CastOptionsEnd(builder)
+ return castOptions
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class LogicalNotOptions(object):
+class DequantizeOptions(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0):
n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = LogicalNotOptions()
+ x = DequantizeOptions()
x.Init(buf, n + offset)
return x
@classmethod
- def GetRootAsLogicalNotOptions(cls, buf, offset=0):
+ def GetRootAsDequantizeOptions(cls, buf, offset=0):
"""This method is deprecated. Please switch to GetRootAs."""
return cls.GetRootAs(buf, offset)
@classmethod
- def LogicalNotOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ def DequantizeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
- # LogicalNotOptions
+ # DequantizeOptions
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
-def LogicalNotOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return LogicalNotOptionsStart(builder)
-def LogicalNotOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return LogicalNotOptionsEnd(builder)
+def DequantizeOptionsStart(builder):
+ builder.StartObject(0)
-class LogicalNotOptionsT(object):
+def DequantizeOptionsEnd(builder):
+ return builder.EndObject()
- # LogicalNotOptionsT
+
+
+class DequantizeOptionsT(object):
+
+ # DequantizeOptionsT
def __init__(self):
pass
@classmethod
def InitFromBuf(cls, buf, pos):
- logicalNotOptions = LogicalNotOptions()
- logicalNotOptions.Init(buf, pos)
- return cls.InitFromObj(logicalNotOptions)
+ dequantizeOptions = DequantizeOptions()
+ dequantizeOptions.Init(buf, pos)
+ return cls.InitFromObj(dequantizeOptions)
@classmethod
- def InitFromObj(cls, logicalNotOptions):
- x = LogicalNotOptionsT()
- x._UnPack(logicalNotOptions)
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, dequantizeOptions):
+ x = DequantizeOptionsT()
+ x._UnPack(dequantizeOptions)
return x
- # LogicalNotOptionsT
- def _UnPack(self, logicalNotOptions):
- if logicalNotOptions is None:
+ # DequantizeOptionsT
+ def _UnPack(self, dequantizeOptions):
+ if dequantizeOptions is None:
return
- # LogicalNotOptionsT
+ # DequantizeOptionsT
def Pack(self, builder):
- LogicalNotOptionsStart(builder)
- logicalNotOptions = LogicalNotOptionsEnd(builder)
- return logicalNotOptions
-# automatically generated by the FlatBuffers compiler, do not modify
+ DequantizeOptionsStart(builder)
+ dequantizeOptions = DequantizeOptionsEnd(builder)
+ return dequantizeOptions
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class LogicalOrOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = LogicalOrOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsLogicalOrOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def LogicalOrOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # LogicalOrOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def LogicalOrOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return LogicalOrOptionsStart(builder)
-def LogicalOrOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return LogicalOrOptionsEnd(builder)
-
-class LogicalOrOptionsT(object):
-
- # LogicalOrOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- logicalOrOptions = LogicalOrOptions()
- logicalOrOptions.Init(buf, pos)
- return cls.InitFromObj(logicalOrOptions)
-
- @classmethod
- def InitFromObj(cls, logicalOrOptions):
- x = LogicalOrOptionsT()
- x._UnPack(logicalOrOptions)
- return x
-
- # LogicalOrOptionsT
- def _UnPack(self, logicalOrOptions):
- if logicalOrOptions is None:
- return
-
- # LogicalOrOptionsT
- def Pack(self, builder):
- LogicalOrOptionsStart(builder)
- logicalOrOptions = LogicalOrOptionsEnd(builder)
- return logicalOrOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class MatrixDiagOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = MatrixDiagOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsMatrixDiagOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def MatrixDiagOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # MatrixDiagOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def MatrixDiagOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return MatrixDiagOptionsStart(builder)
-def MatrixDiagOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return MatrixDiagOptionsEnd(builder)
-
-class MatrixDiagOptionsT(object):
-
- # MatrixDiagOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- matrixDiagOptions = MatrixDiagOptions()
- matrixDiagOptions.Init(buf, pos)
- return cls.InitFromObj(matrixDiagOptions)
-
- @classmethod
- def InitFromObj(cls, matrixDiagOptions):
- x = MatrixDiagOptionsT()
- x._UnPack(matrixDiagOptions)
- return x
-
- # MatrixDiagOptionsT
- def _UnPack(self, matrixDiagOptions):
- if matrixDiagOptions is None:
- return
-
- # MatrixDiagOptionsT
- def Pack(self, builder):
- MatrixDiagOptionsStart(builder)
- matrixDiagOptions = MatrixDiagOptionsEnd(builder)
- return matrixDiagOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class MatrixSetDiagOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = MatrixSetDiagOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsMatrixSetDiagOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def MatrixSetDiagOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # MatrixSetDiagOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def MatrixSetDiagOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return MatrixSetDiagOptionsStart(builder)
-def MatrixSetDiagOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return MatrixSetDiagOptionsEnd(builder)
-
-class MatrixSetDiagOptionsT(object):
-
- # MatrixSetDiagOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- matrixSetDiagOptions = MatrixSetDiagOptions()
- matrixSetDiagOptions.Init(buf, pos)
- return cls.InitFromObj(matrixSetDiagOptions)
-
- @classmethod
- def InitFromObj(cls, matrixSetDiagOptions):
- x = MatrixSetDiagOptionsT()
- x._UnPack(matrixSetDiagOptions)
- return x
-
- # MatrixSetDiagOptionsT
- def _UnPack(self, matrixSetDiagOptions):
- if matrixSetDiagOptions is None:
- return
-
- # MatrixSetDiagOptionsT
- def Pack(self, builder):
- MatrixSetDiagOptionsStart(builder)
- matrixSetDiagOptions = MatrixSetDiagOptionsEnd(builder)
- return matrixSetDiagOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
class MaximumMinimumOptions(object):
__slots__ = ['_tab']
@@ -6354,12 +10579,13 @@
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
-def MaximumMinimumOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return MaximumMinimumOptionsStart(builder)
-def MaximumMinimumOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return MaximumMinimumOptionsEnd(builder)
+def MaximumMinimumOptionsStart(builder):
+ builder.StartObject(0)
+
+def MaximumMinimumOptionsEnd(builder):
+ return builder.EndObject()
+
+
class MaximumMinimumOptionsT(object):
@@ -6374,6 +10600,11 @@
return cls.InitFromObj(maximumMinimumOptions)
@classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
def InitFromObj(cls, maximumMinimumOptions):
x = MaximumMinimumOptionsT()
x._UnPack(maximumMinimumOptions)
@@ -6389,655 +10620,486 @@
MaximumMinimumOptionsStart(builder)
maximumMinimumOptions = MaximumMinimumOptionsEnd(builder)
return maximumMinimumOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class Metadata(object):
+class TileOptions(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0):
n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = Metadata()
+ x = TileOptions()
x.Init(buf, n + offset)
return x
@classmethod
- def GetRootAsMetadata(cls, buf, offset=0):
+ def GetRootAsTileOptions(cls, buf, offset=0):
"""This method is deprecated. Please switch to GetRootAs."""
return cls.GetRootAs(buf, offset)
@classmethod
- def MetadataBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ def TileOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
- # Metadata
+ # TileOptions
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
- # Metadata
- def Name(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.String(o + self._tab.Pos)
- return None
+def TileOptionsStart(builder):
+ builder.StartObject(0)
- # Metadata
- def Buffer(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos)
- return 0
+def TileOptionsEnd(builder):
+ return builder.EndObject()
-def MetadataStart(builder): builder.StartObject(2)
-def Start(builder):
- return MetadataStart(builder)
-def MetadataAddName(builder, name): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0)
-def AddName(builder, name):
- return MetadataAddName(builder, name)
-def MetadataAddBuffer(builder, buffer): builder.PrependUint32Slot(1, buffer, 0)
-def AddBuffer(builder, buffer):
- return MetadataAddBuffer(builder, buffer)
-def MetadataEnd(builder): return builder.EndObject()
-def End(builder):
- return MetadataEnd(builder)
-class MetadataT(object):
- # MetadataT
+class TileOptionsT(object):
+
+ # TileOptionsT
def __init__(self):
- self.name = None # type: str
- self.buffer = 0 # type: int
+ pass
@classmethod
def InitFromBuf(cls, buf, pos):
- metadata = Metadata()
- metadata.Init(buf, pos)
- return cls.InitFromObj(metadata)
+ tileOptions = TileOptions()
+ tileOptions.Init(buf, pos)
+ return cls.InitFromObj(tileOptions)
@classmethod
- def InitFromObj(cls, metadata):
- x = MetadataT()
- x._UnPack(metadata)
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, tileOptions):
+ x = TileOptionsT()
+ x._UnPack(tileOptions)
return x
- # MetadataT
- def _UnPack(self, metadata):
- if metadata is None:
+ # TileOptionsT
+ def _UnPack(self, tileOptions):
+ if tileOptions is None:
return
- self.name = metadata.Name()
- self.buffer = metadata.Buffer()
- # MetadataT
+ # TileOptionsT
def Pack(self, builder):
- if self.name is not None:
- name = builder.CreateString(self.name)
- MetadataStart(builder)
- if self.name is not None:
- MetadataAddName(builder, name)
- MetadataAddBuffer(builder, self.buffer)
- metadata = MetadataEnd(builder)
- return metadata
-# automatically generated by the FlatBuffers compiler, do not modify
+ TileOptionsStart(builder)
+ tileOptions = TileOptionsEnd(builder)
+ return tileOptions
-# namespace: tflite
-class MirrorPadMode(object):
- REFLECT = 0
- SYMMETRIC = 1
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class MirrorPadOptions(object):
+class ArgMaxOptions(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0):
n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = MirrorPadOptions()
+ x = ArgMaxOptions()
x.Init(buf, n + offset)
return x
@classmethod
- def GetRootAsMirrorPadOptions(cls, buf, offset=0):
+ def GetRootAsArgMaxOptions(cls, buf, offset=0):
"""This method is deprecated. Please switch to GetRootAs."""
return cls.GetRootAs(buf, offset)
@classmethod
- def MirrorPadOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ def ArgMaxOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
- # MirrorPadOptions
+ # ArgMaxOptions
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
- # MirrorPadOptions
- def Mode(self):
+ # ArgMaxOptions
+ def OutputType(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
if o != 0:
return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
return 0
-def MirrorPadOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return MirrorPadOptionsStart(builder)
-def MirrorPadOptionsAddMode(builder, mode): builder.PrependInt8Slot(0, mode, 0)
-def AddMode(builder, mode):
- return MirrorPadOptionsAddMode(builder, mode)
-def MirrorPadOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return MirrorPadOptionsEnd(builder)
+def ArgMaxOptionsStart(builder):
+ builder.StartObject(1)
-class MirrorPadOptionsT(object):
+def ArgMaxOptionsAddOutputType(builder, outputType):
+ builder.PrependInt8Slot(0, outputType, 0)
- # MirrorPadOptionsT
+def ArgMaxOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class ArgMaxOptionsT(object):
+
+ # ArgMaxOptionsT
def __init__(self):
- self.mode = 0 # type: int
+ self.outputType = 0 # type: int
@classmethod
def InitFromBuf(cls, buf, pos):
- mirrorPadOptions = MirrorPadOptions()
- mirrorPadOptions.Init(buf, pos)
- return cls.InitFromObj(mirrorPadOptions)
+ argMaxOptions = ArgMaxOptions()
+ argMaxOptions.Init(buf, pos)
+ return cls.InitFromObj(argMaxOptions)
@classmethod
- def InitFromObj(cls, mirrorPadOptions):
- x = MirrorPadOptionsT()
- x._UnPack(mirrorPadOptions)
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, argMaxOptions):
+ x = ArgMaxOptionsT()
+ x._UnPack(argMaxOptions)
return x
- # MirrorPadOptionsT
- def _UnPack(self, mirrorPadOptions):
- if mirrorPadOptions is None:
+ # ArgMaxOptionsT
+ def _UnPack(self, argMaxOptions):
+ if argMaxOptions is None:
return
- self.mode = mirrorPadOptions.Mode()
+ self.outputType = argMaxOptions.OutputType()
- # MirrorPadOptionsT
+ # ArgMaxOptionsT
def Pack(self, builder):
- MirrorPadOptionsStart(builder)
- MirrorPadOptionsAddMode(builder, self.mode)
- mirrorPadOptions = MirrorPadOptionsEnd(builder)
- return mirrorPadOptions
-# automatically generated by the FlatBuffers compiler, do not modify
+ ArgMaxOptionsStart(builder)
+ ArgMaxOptionsAddOutputType(builder, self.outputType)
+ argMaxOptions = ArgMaxOptionsEnd(builder)
+ return argMaxOptions
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class Model(object):
+class ArgMinOptions(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0):
n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = Model()
+ x = ArgMinOptions()
x.Init(buf, n + offset)
return x
@classmethod
- def GetRootAsModel(cls, buf, offset=0):
+ def GetRootAsArgMinOptions(cls, buf, offset=0):
"""This method is deprecated. Please switch to GetRootAs."""
return cls.GetRootAs(buf, offset)
@classmethod
- def ModelBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ def ArgMinOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
- # Model
+ # ArgMinOptions
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
- # Model
- def Version(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos)
- return 0
-
- # Model
- def OperatorCodes(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- x = self._tab.Vector(o)
- x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
- x = self._tab.Indirect(x)
- obj = OperatorCode()
- obj.Init(self._tab.Bytes, x)
- return obj
- return None
-
- # Model
- def OperatorCodesLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # Model
- def OperatorCodesIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- return o == 0
-
- # Model
- def Subgraphs(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- x = self._tab.Vector(o)
- x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
- x = self._tab.Indirect(x)
- obj = SubGraph()
- obj.Init(self._tab.Bytes, x)
- return obj
- return None
-
- # Model
- def SubgraphsLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # Model
- def SubgraphsIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- return o == 0
-
- # Model
- def Description(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
- if o != 0:
- return self._tab.String(o + self._tab.Pos)
- return None
-
- # Model
- def Buffers(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
- if o != 0:
- x = self._tab.Vector(o)
- x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
- x = self._tab.Indirect(x)
- obj = Buffer()
- obj.Init(self._tab.Bytes, x)
- return obj
- return None
-
- # Model
- def BuffersLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # Model
- def BuffersIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
- return o == 0
-
- # Model
- def MetadataBuffer(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
- if o != 0:
- a = self._tab.Vector(o)
- return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
- return 0
-
- # Model
- def MetadataBufferAsNumpy(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
- if o != 0:
- return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
- return 0
-
- # Model
- def MetadataBufferLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # Model
- def MetadataBufferIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
- return o == 0
-
- # Model
- def Metadata(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
- if o != 0:
- x = self._tab.Vector(o)
- x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
- x = self._tab.Indirect(x)
- obj = Metadata()
- obj.Init(self._tab.Bytes, x)
- return obj
- return None
-
- # Model
- def MetadataLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # Model
- def MetadataIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
- return o == 0
-
- # Model
- def SignatureDefs(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
- if o != 0:
- x = self._tab.Vector(o)
- x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
- x = self._tab.Indirect(x)
- obj = SignatureDef()
- obj.Init(self._tab.Bytes, x)
- return obj
- return None
-
- # Model
- def SignatureDefsLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # Model
- def SignatureDefsIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
- return o == 0
-
-def ModelStart(builder): builder.StartObject(8)
-def Start(builder):
- return ModelStart(builder)
-def ModelAddVersion(builder, version): builder.PrependUint32Slot(0, version, 0)
-def AddVersion(builder, version):
- return ModelAddVersion(builder, version)
-def ModelAddOperatorCodes(builder, operatorCodes): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(operatorCodes), 0)
-def AddOperatorCodes(builder, operatorCodes):
- return ModelAddOperatorCodes(builder, operatorCodes)
-def ModelStartOperatorCodesVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartOperatorCodesVector(builder, numElems):
- return ModelStartOperatorCodesVector(builder, numElems)
-def ModelAddSubgraphs(builder, subgraphs): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(subgraphs), 0)
-def AddSubgraphs(builder, subgraphs):
- return ModelAddSubgraphs(builder, subgraphs)
-def ModelStartSubgraphsVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartSubgraphsVector(builder, numElems):
- return ModelStartSubgraphsVector(builder, numElems)
-def ModelAddDescription(builder, description): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(description), 0)
-def AddDescription(builder, description):
- return ModelAddDescription(builder, description)
-def ModelAddBuffers(builder, buffers): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(buffers), 0)
-def AddBuffers(builder, buffers):
- return ModelAddBuffers(builder, buffers)
-def ModelStartBuffersVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartBuffersVector(builder, numElems):
- return ModelStartBuffersVector(builder, numElems)
-def ModelAddMetadataBuffer(builder, metadataBuffer): builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(metadataBuffer), 0)
-def AddMetadataBuffer(builder, metadataBuffer):
- return ModelAddMetadataBuffer(builder, metadataBuffer)
-def ModelStartMetadataBufferVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartMetadataBufferVector(builder, numElems):
- return ModelStartMetadataBufferVector(builder, numElems)
-def ModelAddMetadata(builder, metadata): builder.PrependUOffsetTRelativeSlot(6, flatbuffers.number_types.UOffsetTFlags.py_type(metadata), 0)
-def AddMetadata(builder, metadata):
- return ModelAddMetadata(builder, metadata)
-def ModelStartMetadataVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartMetadataVector(builder, numElems):
- return ModelStartMetadataVector(builder, numElems)
-def ModelAddSignatureDefs(builder, signatureDefs): builder.PrependUOffsetTRelativeSlot(7, flatbuffers.number_types.UOffsetTFlags.py_type(signatureDefs), 0)
-def AddSignatureDefs(builder, signatureDefs):
- return ModelAddSignatureDefs(builder, signatureDefs)
-def ModelStartSignatureDefsVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartSignatureDefsVector(builder, numElems):
- return ModelStartSignatureDefsVector(builder, numElems)
-def ModelEnd(builder): return builder.EndObject()
-def End(builder):
- return ModelEnd(builder)
-try:
- from typing import List
-except:
- pass
-
-class ModelT(object):
-
- # ModelT
- def __init__(self):
- self.version = 0 # type: int
- self.operatorCodes = None # type: List[OperatorCodeT]
- self.subgraphs = None # type: List[SubGraphT]
- self.description = None # type: str
- self.buffers = None # type: List[BufferT]
- self.metadataBuffer = None # type: List[int]
- self.metadata = None # type: List[MetadataT]
- self.signatureDefs = None # type: List[SignatureDefT]
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- model = Model()
- model.Init(buf, pos)
- return cls.InitFromObj(model)
-
- @classmethod
- def InitFromObj(cls, model):
- x = ModelT()
- x._UnPack(model)
- return x
-
- # ModelT
- def _UnPack(self, model):
- if model is None:
- return
- self.version = model.Version()
- if not model.OperatorCodesIsNone():
- self.operatorCodes = []
- for i in range(model.OperatorCodesLength()):
- if model.OperatorCodes(i) is None:
- self.operatorCodes.append(None)
- else:
- operatorCode_ = OperatorCodeT.InitFromObj(model.OperatorCodes(i))
- self.operatorCodes.append(operatorCode_)
- if not model.SubgraphsIsNone():
- self.subgraphs = []
- for i in range(model.SubgraphsLength()):
- if model.Subgraphs(i) is None:
- self.subgraphs.append(None)
- else:
- subGraph_ = SubGraphT.InitFromObj(model.Subgraphs(i))
- self.subgraphs.append(subGraph_)
- self.description = model.Description()
- if not model.BuffersIsNone():
- self.buffers = []
- for i in range(model.BuffersLength()):
- if model.Buffers(i) is None:
- self.buffers.append(None)
- else:
- buffer_ = BufferT.InitFromObj(model.Buffers(i))
- self.buffers.append(buffer_)
- if not model.MetadataBufferIsNone():
- if np is None:
- self.metadataBuffer = []
- for i in range(model.MetadataBufferLength()):
- self.metadataBuffer.append(model.MetadataBuffer(i))
- else:
- self.metadataBuffer = model.MetadataBufferAsNumpy()
- if not model.MetadataIsNone():
- self.metadata = []
- for i in range(model.MetadataLength()):
- if model.Metadata(i) is None:
- self.metadata.append(None)
- else:
- metadata_ = MetadataT.InitFromObj(model.Metadata(i))
- self.metadata.append(metadata_)
- if not model.SignatureDefsIsNone():
- self.signatureDefs = []
- for i in range(model.SignatureDefsLength()):
- if model.SignatureDefs(i) is None:
- self.signatureDefs.append(None)
- else:
- signatureDef_ = SignatureDefT.InitFromObj(model.SignatureDefs(i))
- self.signatureDefs.append(signatureDef_)
-
- # ModelT
- def Pack(self, builder):
- if self.operatorCodes is not None:
- operatorCodeslist = []
- for i in range(len(self.operatorCodes)):
- operatorCodeslist.append(self.operatorCodes[i].Pack(builder))
- ModelStartOperatorCodesVector(builder, len(self.operatorCodes))
- for i in reversed(range(len(self.operatorCodes))):
- builder.PrependUOffsetTRelative(operatorCodeslist[i])
- operatorCodes = builder.EndVector()
- if self.subgraphs is not None:
- subgraphslist = []
- for i in range(len(self.subgraphs)):
- subgraphslist.append(self.subgraphs[i].Pack(builder))
- ModelStartSubgraphsVector(builder, len(self.subgraphs))
- for i in reversed(range(len(self.subgraphs))):
- builder.PrependUOffsetTRelative(subgraphslist[i])
- subgraphs = builder.EndVector()
- if self.description is not None:
- description = builder.CreateString(self.description)
- if self.buffers is not None:
- bufferslist = []
- for i in range(len(self.buffers)):
- bufferslist.append(self.buffers[i].Pack(builder))
- ModelStartBuffersVector(builder, len(self.buffers))
- for i in reversed(range(len(self.buffers))):
- builder.PrependUOffsetTRelative(bufferslist[i])
- buffers = builder.EndVector()
- if self.metadataBuffer is not None:
- if np is not None and type(self.metadataBuffer) is np.ndarray:
- metadataBuffer = builder.CreateNumpyVector(self.metadataBuffer)
- else:
- ModelStartMetadataBufferVector(builder, len(self.metadataBuffer))
- for i in reversed(range(len(self.metadataBuffer))):
- builder.PrependInt32(self.metadataBuffer[i])
- metadataBuffer = builder.EndVector()
- if self.metadata is not None:
- metadatalist = []
- for i in range(len(self.metadata)):
- metadatalist.append(self.metadata[i].Pack(builder))
- ModelStartMetadataVector(builder, len(self.metadata))
- for i in reversed(range(len(self.metadata))):
- builder.PrependUOffsetTRelative(metadatalist[i])
- metadata = builder.EndVector()
- if self.signatureDefs is not None:
- signatureDefslist = []
- for i in range(len(self.signatureDefs)):
- signatureDefslist.append(self.signatureDefs[i].Pack(builder))
- ModelStartSignatureDefsVector(builder, len(self.signatureDefs))
- for i in reversed(range(len(self.signatureDefs))):
- builder.PrependUOffsetTRelative(signatureDefslist[i])
- signatureDefs = builder.EndVector()
- ModelStart(builder)
- ModelAddVersion(builder, self.version)
- if self.operatorCodes is not None:
- ModelAddOperatorCodes(builder, operatorCodes)
- if self.subgraphs is not None:
- ModelAddSubgraphs(builder, subgraphs)
- if self.description is not None:
- ModelAddDescription(builder, description)
- if self.buffers is not None:
- ModelAddBuffers(builder, buffers)
- if self.metadataBuffer is not None:
- ModelAddMetadataBuffer(builder, metadataBuffer)
- if self.metadata is not None:
- ModelAddMetadata(builder, metadata)
- if self.signatureDefs is not None:
- ModelAddSignatureDefs(builder, signatureDefs)
- model = ModelEnd(builder)
- return model
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class MulOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = MulOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsMulOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def MulOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # MulOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # MulOptions
- def FusedActivationFunction(self):
+ # ArgMinOptions
+ def OutputType(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
if o != 0:
return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
return 0
-def MulOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return MulOptionsStart(builder)
-def MulOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return MulOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def MulOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return MulOptionsEnd(builder)
+def ArgMinOptionsStart(builder):
+ builder.StartObject(1)
-class MulOptionsT(object):
+def ArgMinOptionsAddOutputType(builder, outputType):
+ builder.PrependInt8Slot(0, outputType, 0)
- # MulOptionsT
+def ArgMinOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class ArgMinOptionsT(object):
+
+ # ArgMinOptionsT
def __init__(self):
- self.fusedActivationFunction = 0 # type: int
+ self.outputType = 0 # type: int
@classmethod
def InitFromBuf(cls, buf, pos):
- mulOptions = MulOptions()
- mulOptions.Init(buf, pos)
- return cls.InitFromObj(mulOptions)
+ argMinOptions = ArgMinOptions()
+ argMinOptions.Init(buf, pos)
+ return cls.InitFromObj(argMinOptions)
@classmethod
- def InitFromObj(cls, mulOptions):
- x = MulOptionsT()
- x._UnPack(mulOptions)
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, argMinOptions):
+ x = ArgMinOptionsT()
+ x._UnPack(argMinOptions)
return x
- # MulOptionsT
- def _UnPack(self, mulOptions):
- if mulOptions is None:
+ # ArgMinOptionsT
+ def _UnPack(self, argMinOptions):
+ if argMinOptions is None:
return
- self.fusedActivationFunction = mulOptions.FusedActivationFunction()
+ self.outputType = argMinOptions.OutputType()
- # MulOptionsT
+ # ArgMinOptionsT
def Pack(self, builder):
- MulOptionsStart(builder)
- MulOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
- mulOptions = MulOptionsEnd(builder)
- return mulOptions
-# automatically generated by the FlatBuffers compiler, do not modify
+ ArgMinOptionsStart(builder)
+ ArgMinOptionsAddOutputType(builder, self.outputType)
+ argMinOptions = ArgMinOptionsEnd(builder)
+ return argMinOptions
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
+class GreaterOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = GreaterOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsGreaterOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def GreaterOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # GreaterOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def GreaterOptionsStart(builder):
+ builder.StartObject(0)
+
+def GreaterOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class GreaterOptionsT(object):
+
+ # GreaterOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ greaterOptions = GreaterOptions()
+ greaterOptions.Init(buf, pos)
+ return cls.InitFromObj(greaterOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, greaterOptions):
+ x = GreaterOptionsT()
+ x._UnPack(greaterOptions)
+ return x
+
+ # GreaterOptionsT
+ def _UnPack(self, greaterOptions):
+ if greaterOptions is None:
+ return
+
+ # GreaterOptionsT
+ def Pack(self, builder):
+ GreaterOptionsStart(builder)
+ greaterOptions = GreaterOptionsEnd(builder)
+ return greaterOptions
+
+
+class GreaterEqualOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = GreaterEqualOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsGreaterEqualOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def GreaterEqualOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # GreaterEqualOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def GreaterEqualOptionsStart(builder):
+ builder.StartObject(0)
+
+def GreaterEqualOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class GreaterEqualOptionsT(object):
+
+ # GreaterEqualOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ greaterEqualOptions = GreaterEqualOptions()
+ greaterEqualOptions.Init(buf, pos)
+ return cls.InitFromObj(greaterEqualOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, greaterEqualOptions):
+ x = GreaterEqualOptionsT()
+ x._UnPack(greaterEqualOptions)
+ return x
+
+ # GreaterEqualOptionsT
+ def _UnPack(self, greaterEqualOptions):
+ if greaterEqualOptions is None:
+ return
+
+ # GreaterEqualOptionsT
+ def Pack(self, builder):
+ GreaterEqualOptionsStart(builder)
+ greaterEqualOptions = GreaterEqualOptionsEnd(builder)
+ return greaterEqualOptions
+
+
+class LessOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = LessOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsLessOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def LessOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # LessOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def LessOptionsStart(builder):
+ builder.StartObject(0)
+
+def LessOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class LessOptionsT(object):
+
+ # LessOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ lessOptions = LessOptions()
+ lessOptions.Init(buf, pos)
+ return cls.InitFromObj(lessOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, lessOptions):
+ x = LessOptionsT()
+ x._UnPack(lessOptions)
+ return x
+
+ # LessOptionsT
+ def _UnPack(self, lessOptions):
+ if lessOptions is None:
+ return
+
+ # LessOptionsT
+ def Pack(self, builder):
+ LessOptionsStart(builder)
+ lessOptions = LessOptionsEnd(builder)
+ return lessOptions
+
+
+class LessEqualOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = LessEqualOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsLessEqualOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def LessEqualOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # LessEqualOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def LessEqualOptionsStart(builder):
+ builder.StartObject(0)
+
+def LessEqualOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class LessEqualOptionsT(object):
+
+ # LessEqualOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ lessEqualOptions = LessEqualOptions()
+ lessEqualOptions.Init(buf, pos)
+ return cls.InitFromObj(lessEqualOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, lessEqualOptions):
+ x = LessEqualOptionsT()
+ x._UnPack(lessEqualOptions)
+ return x
+
+ # LessEqualOptionsT
+ def _UnPack(self, lessEqualOptions):
+ if lessEqualOptions is None:
+ return
+
+ # LessEqualOptionsT
+ def Pack(self, builder):
+ LessEqualOptionsStart(builder)
+ lessEqualOptions = LessEqualOptionsEnd(builder)
+ return lessEqualOptions
+
class NegOptions(object):
__slots__ = ['_tab']
@@ -7061,12 +11123,13 @@
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
-def NegOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return NegOptionsStart(builder)
-def NegOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return NegOptionsEnd(builder)
+def NegOptionsStart(builder):
+ builder.StartObject(0)
+
+def NegOptionsEnd(builder):
+ return builder.EndObject()
+
+
class NegOptionsT(object):
@@ -7081,6 +11144,11 @@
return cls.InitFromObj(negOptions)
@classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
def InitFromObj(cls, negOptions):
x = NegOptionsT()
x._UnPack(negOptions)
@@ -7096,140 +11164,473 @@
NegOptionsStart(builder)
negOptions = NegOptionsEnd(builder)
return negOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class NonMaxSuppressionV4Options(object):
+class SelectOptions(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0):
n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = NonMaxSuppressionV4Options()
+ x = SelectOptions()
x.Init(buf, n + offset)
return x
@classmethod
- def GetRootAsNonMaxSuppressionV4Options(cls, buf, offset=0):
+ def GetRootAsSelectOptions(cls, buf, offset=0):
"""This method is deprecated. Please switch to GetRootAs."""
return cls.GetRootAs(buf, offset)
@classmethod
- def NonMaxSuppressionV4OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ def SelectOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
- # NonMaxSuppressionV4Options
+ # SelectOptions
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
-def NonMaxSuppressionV4OptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return NonMaxSuppressionV4OptionsStart(builder)
-def NonMaxSuppressionV4OptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return NonMaxSuppressionV4OptionsEnd(builder)
+def SelectOptionsStart(builder):
+ builder.StartObject(0)
-class NonMaxSuppressionV4OptionsT(object):
+def SelectOptionsEnd(builder):
+ return builder.EndObject()
- # NonMaxSuppressionV4OptionsT
+
+
+class SelectOptionsT(object):
+
+ # SelectOptionsT
def __init__(self):
pass
@classmethod
def InitFromBuf(cls, buf, pos):
- nonMaxSuppressionV4options = NonMaxSuppressionV4Options()
- nonMaxSuppressionV4options.Init(buf, pos)
- return cls.InitFromObj(nonMaxSuppressionV4options)
+ selectOptions = SelectOptions()
+ selectOptions.Init(buf, pos)
+ return cls.InitFromObj(selectOptions)
@classmethod
- def InitFromObj(cls, nonMaxSuppressionV4options):
- x = NonMaxSuppressionV4OptionsT()
- x._UnPack(nonMaxSuppressionV4options)
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, selectOptions):
+ x = SelectOptionsT()
+ x._UnPack(selectOptions)
return x
- # NonMaxSuppressionV4OptionsT
- def _UnPack(self, nonMaxSuppressionV4options):
- if nonMaxSuppressionV4options is None:
+ # SelectOptionsT
+ def _UnPack(self, selectOptions):
+ if selectOptions is None:
return
- # NonMaxSuppressionV4OptionsT
+ # SelectOptionsT
def Pack(self, builder):
- NonMaxSuppressionV4OptionsStart(builder)
- nonMaxSuppressionV4options = NonMaxSuppressionV4OptionsEnd(builder)
- return nonMaxSuppressionV4options
-# automatically generated by the FlatBuffers compiler, do not modify
+ SelectOptionsStart(builder)
+ selectOptions = SelectOptionsEnd(builder)
+ return selectOptions
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class NonMaxSuppressionV5Options(object):
+class SliceOptions(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0):
n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = NonMaxSuppressionV5Options()
+ x = SliceOptions()
x.Init(buf, n + offset)
return x
@classmethod
- def GetRootAsNonMaxSuppressionV5Options(cls, buf, offset=0):
+ def GetRootAsSliceOptions(cls, buf, offset=0):
"""This method is deprecated. Please switch to GetRootAs."""
return cls.GetRootAs(buf, offset)
@classmethod
- def NonMaxSuppressionV5OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ def SliceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
- # NonMaxSuppressionV5Options
+ # SliceOptions
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
-def NonMaxSuppressionV5OptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return NonMaxSuppressionV5OptionsStart(builder)
-def NonMaxSuppressionV5OptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return NonMaxSuppressionV5OptionsEnd(builder)
+def SliceOptionsStart(builder):
+ builder.StartObject(0)
-class NonMaxSuppressionV5OptionsT(object):
+def SliceOptionsEnd(builder):
+ return builder.EndObject()
- # NonMaxSuppressionV5OptionsT
+
+
+class SliceOptionsT(object):
+
+ # SliceOptionsT
def __init__(self):
pass
@classmethod
def InitFromBuf(cls, buf, pos):
- nonMaxSuppressionV5options = NonMaxSuppressionV5Options()
- nonMaxSuppressionV5options.Init(buf, pos)
- return cls.InitFromObj(nonMaxSuppressionV5options)
+ sliceOptions = SliceOptions()
+ sliceOptions.Init(buf, pos)
+ return cls.InitFromObj(sliceOptions)
@classmethod
- def InitFromObj(cls, nonMaxSuppressionV5options):
- x = NonMaxSuppressionV5OptionsT()
- x._UnPack(nonMaxSuppressionV5options)
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, sliceOptions):
+ x = SliceOptionsT()
+ x._UnPack(sliceOptions)
return x
- # NonMaxSuppressionV5OptionsT
- def _UnPack(self, nonMaxSuppressionV5options):
- if nonMaxSuppressionV5options is None:
+ # SliceOptionsT
+ def _UnPack(self, sliceOptions):
+ if sliceOptions is None:
return
- # NonMaxSuppressionV5OptionsT
+ # SliceOptionsT
def Pack(self, builder):
- NonMaxSuppressionV5OptionsStart(builder)
- nonMaxSuppressionV5options = NonMaxSuppressionV5OptionsEnd(builder)
- return nonMaxSuppressionV5options
-# automatically generated by the FlatBuffers compiler, do not modify
+ SliceOptionsStart(builder)
+ sliceOptions = SliceOptionsEnd(builder)
+ return sliceOptions
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
+class TransposeConvOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = TransposeConvOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsTransposeConvOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def TransposeConvOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # TransposeConvOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # TransposeConvOptions
+ def Padding(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # TransposeConvOptions
+ def StrideW(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # TransposeConvOptions
+ def StrideH(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # TransposeConvOptions
+ def FusedActivationFunction(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # TransposeConvOptions
+ def QuantizedBiasType(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+def TransposeConvOptionsStart(builder):
+ builder.StartObject(5)
+
+def TransposeConvOptionsAddPadding(builder, padding):
+ builder.PrependInt8Slot(0, padding, 0)
+
+def TransposeConvOptionsAddStrideW(builder, strideW):
+ builder.PrependInt32Slot(1, strideW, 0)
+
+def TransposeConvOptionsAddStrideH(builder, strideH):
+ builder.PrependInt32Slot(2, strideH, 0)
+
+def TransposeConvOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
+ builder.PrependInt8Slot(3, fusedActivationFunction, 0)
+
+def TransposeConvOptionsAddQuantizedBiasType(builder, quantizedBiasType):
+ builder.PrependInt8Slot(4, quantizedBiasType, 0)
+
+def TransposeConvOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class TransposeConvOptionsT(object):
+
+ # TransposeConvOptionsT
+ def __init__(self):
+ self.padding = 0 # type: int
+ self.strideW = 0 # type: int
+ self.strideH = 0 # type: int
+ self.fusedActivationFunction = 0 # type: int
+ self.quantizedBiasType = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ transposeConvOptions = TransposeConvOptions()
+ transposeConvOptions.Init(buf, pos)
+ return cls.InitFromObj(transposeConvOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, transposeConvOptions):
+ x = TransposeConvOptionsT()
+ x._UnPack(transposeConvOptions)
+ return x
+
+ # TransposeConvOptionsT
+ def _UnPack(self, transposeConvOptions):
+ if transposeConvOptions is None:
+ return
+ self.padding = transposeConvOptions.Padding()
+ self.strideW = transposeConvOptions.StrideW()
+ self.strideH = transposeConvOptions.StrideH()
+ self.fusedActivationFunction = transposeConvOptions.FusedActivationFunction()
+ self.quantizedBiasType = transposeConvOptions.QuantizedBiasType()
+
+ # TransposeConvOptionsT
+ def Pack(self, builder):
+ TransposeConvOptionsStart(builder)
+ TransposeConvOptionsAddPadding(builder, self.padding)
+ TransposeConvOptionsAddStrideW(builder, self.strideW)
+ TransposeConvOptionsAddStrideH(builder, self.strideH)
+ TransposeConvOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
+ TransposeConvOptionsAddQuantizedBiasType(builder, self.quantizedBiasType)
+ transposeConvOptions = TransposeConvOptionsEnd(builder)
+ return transposeConvOptions
+
+
+class ExpandDimsOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = ExpandDimsOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsExpandDimsOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def ExpandDimsOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # ExpandDimsOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def ExpandDimsOptionsStart(builder):
+ builder.StartObject(0)
+
+def ExpandDimsOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class ExpandDimsOptionsT(object):
+
+ # ExpandDimsOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ expandDimsOptions = ExpandDimsOptions()
+ expandDimsOptions.Init(buf, pos)
+ return cls.InitFromObj(expandDimsOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, expandDimsOptions):
+ x = ExpandDimsOptionsT()
+ x._UnPack(expandDimsOptions)
+ return x
+
+ # ExpandDimsOptionsT
+ def _UnPack(self, expandDimsOptions):
+ if expandDimsOptions is None:
+ return
+
+ # ExpandDimsOptionsT
+ def Pack(self, builder):
+ ExpandDimsOptionsStart(builder)
+ expandDimsOptions = ExpandDimsOptionsEnd(builder)
+ return expandDimsOptions
+
+
+class SparseToDenseOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = SparseToDenseOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsSparseToDenseOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def SparseToDenseOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # SparseToDenseOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # SparseToDenseOptions
+ def ValidateIndices(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+def SparseToDenseOptionsStart(builder):
+ builder.StartObject(1)
+
+def SparseToDenseOptionsAddValidateIndices(builder, validateIndices):
+ builder.PrependBoolSlot(0, validateIndices, 0)
+
+def SparseToDenseOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class SparseToDenseOptionsT(object):
+
+ # SparseToDenseOptionsT
+ def __init__(self):
+ self.validateIndices = False # type: bool
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ sparseToDenseOptions = SparseToDenseOptions()
+ sparseToDenseOptions.Init(buf, pos)
+ return cls.InitFromObj(sparseToDenseOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, sparseToDenseOptions):
+ x = SparseToDenseOptionsT()
+ x._UnPack(sparseToDenseOptions)
+ return x
+
+ # SparseToDenseOptionsT
+ def _UnPack(self, sparseToDenseOptions):
+ if sparseToDenseOptions is None:
+ return
+ self.validateIndices = sparseToDenseOptions.ValidateIndices()
+
+ # SparseToDenseOptionsT
+ def Pack(self, builder):
+ SparseToDenseOptionsStart(builder)
+ SparseToDenseOptionsAddValidateIndices(builder, self.validateIndices)
+ sparseToDenseOptions = SparseToDenseOptionsEnd(builder)
+ return sparseToDenseOptions
+
+
+class EqualOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = EqualOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsEqualOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def EqualOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # EqualOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def EqualOptionsStart(builder):
+ builder.StartObject(0)
+
+def EqualOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class EqualOptionsT(object):
+
+ # EqualOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ equalOptions = EqualOptions()
+ equalOptions.Init(buf, pos)
+ return cls.InitFromObj(equalOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, equalOptions):
+ x = EqualOptionsT()
+ x._UnPack(equalOptions)
+ return x
+
+ # EqualOptionsT
+ def _UnPack(self, equalOptions):
+ if equalOptions is None:
+ return
+
+ # EqualOptionsT
+ def Pack(self, builder):
+ EqualOptionsStart(builder)
+ equalOptions = EqualOptionsEnd(builder)
+ return equalOptions
+
class NotEqualOptions(object):
__slots__ = ['_tab']
@@ -7253,12 +11654,13 @@
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
-def NotEqualOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return NotEqualOptionsStart(builder)
-def NotEqualOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return NotEqualOptionsEnd(builder)
+def NotEqualOptionsStart(builder):
+ builder.StartObject(0)
+
+def NotEqualOptionsEnd(builder):
+ return builder.EndObject()
+
+
class NotEqualOptionsT(object):
@@ -7273,6 +11675,11 @@
return cls.InitFromObj(notEqualOptions)
@classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
def InitFromObj(cls, notEqualOptions):
x = NotEqualOptionsT()
x._UnPack(notEqualOptions)
@@ -7288,12 +11695,485 @@
NotEqualOptionsStart(builder)
notEqualOptions = NotEqualOptionsEnd(builder)
return notEqualOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
+class ShapeOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = ShapeOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsShapeOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def ShapeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # ShapeOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # ShapeOptions
+ def OutType(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+def ShapeOptionsStart(builder):
+ builder.StartObject(1)
+
+def ShapeOptionsAddOutType(builder, outType):
+ builder.PrependInt8Slot(0, outType, 0)
+
+def ShapeOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class ShapeOptionsT(object):
+
+ # ShapeOptionsT
+ def __init__(self):
+ self.outType = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ shapeOptions = ShapeOptions()
+ shapeOptions.Init(buf, pos)
+ return cls.InitFromObj(shapeOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, shapeOptions):
+ x = ShapeOptionsT()
+ x._UnPack(shapeOptions)
+ return x
+
+ # ShapeOptionsT
+ def _UnPack(self, shapeOptions):
+ if shapeOptions is None:
+ return
+ self.outType = shapeOptions.OutType()
+
+ # ShapeOptionsT
+ def Pack(self, builder):
+ ShapeOptionsStart(builder)
+ ShapeOptionsAddOutType(builder, self.outType)
+ shapeOptions = ShapeOptionsEnd(builder)
+ return shapeOptions
+
+
+class RankOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = RankOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsRankOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def RankOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # RankOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def RankOptionsStart(builder):
+ builder.StartObject(0)
+
+def RankOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class RankOptionsT(object):
+
+ # RankOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ rankOptions = RankOptions()
+ rankOptions.Init(buf, pos)
+ return cls.InitFromObj(rankOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, rankOptions):
+ x = RankOptionsT()
+ x._UnPack(rankOptions)
+ return x
+
+ # RankOptionsT
+ def _UnPack(self, rankOptions):
+ if rankOptions is None:
+ return
+
+ # RankOptionsT
+ def Pack(self, builder):
+ RankOptionsStart(builder)
+ rankOptions = RankOptionsEnd(builder)
+ return rankOptions
+
+
+class PowOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = PowOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsPowOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def PowOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # PowOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def PowOptionsStart(builder):
+ builder.StartObject(0)
+
+def PowOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class PowOptionsT(object):
+
+ # PowOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ powOptions = PowOptions()
+ powOptions.Init(buf, pos)
+ return cls.InitFromObj(powOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, powOptions):
+ x = PowOptionsT()
+ x._UnPack(powOptions)
+ return x
+
+ # PowOptionsT
+ def _UnPack(self, powOptions):
+ if powOptions is None:
+ return
+
+ # PowOptionsT
+ def Pack(self, builder):
+ PowOptionsStart(builder)
+ powOptions = PowOptionsEnd(builder)
+ return powOptions
+
+
+class FakeQuantOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = FakeQuantOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsFakeQuantOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def FakeQuantOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # FakeQuantOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # FakeQuantOptions
+ def Min(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
+ return 0.0
+
+ # FakeQuantOptions
+ def Max(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
+ return 0.0
+
+ # FakeQuantOptions
+ def NumBits(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # FakeQuantOptions
+ def NarrowRange(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+def FakeQuantOptionsStart(builder):
+ builder.StartObject(4)
+
+def FakeQuantOptionsAddMin(builder, min):
+ builder.PrependFloat32Slot(0, min, 0.0)
+
+def FakeQuantOptionsAddMax(builder, max):
+ builder.PrependFloat32Slot(1, max, 0.0)
+
+def FakeQuantOptionsAddNumBits(builder, numBits):
+ builder.PrependInt32Slot(2, numBits, 0)
+
+def FakeQuantOptionsAddNarrowRange(builder, narrowRange):
+ builder.PrependBoolSlot(3, narrowRange, 0)
+
+def FakeQuantOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class FakeQuantOptionsT(object):
+
+ # FakeQuantOptionsT
+ def __init__(self):
+ self.min = 0.0 # type: float
+ self.max = 0.0 # type: float
+ self.numBits = 0 # type: int
+ self.narrowRange = False # type: bool
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ fakeQuantOptions = FakeQuantOptions()
+ fakeQuantOptions.Init(buf, pos)
+ return cls.InitFromObj(fakeQuantOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, fakeQuantOptions):
+ x = FakeQuantOptionsT()
+ x._UnPack(fakeQuantOptions)
+ return x
+
+ # FakeQuantOptionsT
+ def _UnPack(self, fakeQuantOptions):
+ if fakeQuantOptions is None:
+ return
+ self.min = fakeQuantOptions.Min()
+ self.max = fakeQuantOptions.Max()
+ self.numBits = fakeQuantOptions.NumBits()
+ self.narrowRange = fakeQuantOptions.NarrowRange()
+
+ # FakeQuantOptionsT
+ def Pack(self, builder):
+ FakeQuantOptionsStart(builder)
+ FakeQuantOptionsAddMin(builder, self.min)
+ FakeQuantOptionsAddMax(builder, self.max)
+ FakeQuantOptionsAddNumBits(builder, self.numBits)
+ FakeQuantOptionsAddNarrowRange(builder, self.narrowRange)
+ fakeQuantOptions = FakeQuantOptionsEnd(builder)
+ return fakeQuantOptions
+
+
+class PackOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = PackOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsPackOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def PackOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # PackOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # PackOptions
+ def ValuesCount(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # PackOptions
+ def Axis(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def PackOptionsStart(builder):
+ builder.StartObject(2)
+
+def PackOptionsAddValuesCount(builder, valuesCount):
+ builder.PrependInt32Slot(0, valuesCount, 0)
+
+def PackOptionsAddAxis(builder, axis):
+ builder.PrependInt32Slot(1, axis, 0)
+
+def PackOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class PackOptionsT(object):
+
+ # PackOptionsT
+ def __init__(self):
+ self.valuesCount = 0 # type: int
+ self.axis = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ packOptions = PackOptions()
+ packOptions.Init(buf, pos)
+ return cls.InitFromObj(packOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, packOptions):
+ x = PackOptionsT()
+ x._UnPack(packOptions)
+ return x
+
+ # PackOptionsT
+ def _UnPack(self, packOptions):
+ if packOptions is None:
+ return
+ self.valuesCount = packOptions.ValuesCount()
+ self.axis = packOptions.Axis()
+
+ # PackOptionsT
+ def Pack(self, builder):
+ PackOptionsStart(builder)
+ PackOptionsAddValuesCount(builder, self.valuesCount)
+ PackOptionsAddAxis(builder, self.axis)
+ packOptions = PackOptionsEnd(builder)
+ return packOptions
+
+
+class LogicalOrOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = LogicalOrOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsLogicalOrOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def LogicalOrOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # LogicalOrOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def LogicalOrOptionsStart(builder):
+ builder.StartObject(0)
+
+def LogicalOrOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class LogicalOrOptionsT(object):
+
+ # LogicalOrOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ logicalOrOptions = LogicalOrOptions()
+ logicalOrOptions.Init(buf, pos)
+ return cls.InitFromObj(logicalOrOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, logicalOrOptions):
+ x = LogicalOrOptionsT()
+ x._UnPack(logicalOrOptions)
+ return x
+
+ # LogicalOrOptionsT
+ def _UnPack(self, logicalOrOptions):
+ if logicalOrOptions is None:
+ return
+
+ # LogicalOrOptionsT
+ def Pack(self, builder):
+ LogicalOrOptionsStart(builder)
+ logicalOrOptions = LogicalOrOptionsEnd(builder)
+ return logicalOrOptions
+
class OneHotOptions(object):
__slots__ = ['_tab']
@@ -7324,15 +12204,16 @@
return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
return 0
-def OneHotOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return OneHotOptionsStart(builder)
-def OneHotOptionsAddAxis(builder, axis): builder.PrependInt32Slot(0, axis, 0)
-def AddAxis(builder, axis):
- return OneHotOptionsAddAxis(builder, axis)
-def OneHotOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return OneHotOptionsEnd(builder)
+def OneHotOptionsStart(builder):
+ builder.StartObject(1)
+
+def OneHotOptionsAddAxis(builder, axis):
+ builder.PrependInt32Slot(0, axis, 0)
+
+def OneHotOptionsEnd(builder):
+ return builder.EndObject()
+
+
class OneHotOptionsT(object):
@@ -7347,6 +12228,11 @@
return cls.InitFromObj(oneHotOptions)
@classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
def InitFromObj(cls, oneHotOptions):
x = OneHotOptionsT()
x._UnPack(oneHotOptions)
@@ -7364,12 +12250,4453 @@
OneHotOptionsAddAxis(builder, self.axis)
oneHotOptions = OneHotOptionsEnd(builder)
return oneHotOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
+class AbsOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = AbsOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsAbsOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def AbsOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # AbsOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def AbsOptionsStart(builder):
+ builder.StartObject(0)
+
+def AbsOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class AbsOptionsT(object):
+
+ # AbsOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ absOptions = AbsOptions()
+ absOptions.Init(buf, pos)
+ return cls.InitFromObj(absOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, absOptions):
+ x = AbsOptionsT()
+ x._UnPack(absOptions)
+ return x
+
+ # AbsOptionsT
+ def _UnPack(self, absOptions):
+ if absOptions is None:
+ return
+
+ # AbsOptionsT
+ def Pack(self, builder):
+ AbsOptionsStart(builder)
+ absOptions = AbsOptionsEnd(builder)
+ return absOptions
+
+
+class HardSwishOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = HardSwishOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsHardSwishOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def HardSwishOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # HardSwishOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def HardSwishOptionsStart(builder):
+ builder.StartObject(0)
+
+def HardSwishOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class HardSwishOptionsT(object):
+
+ # HardSwishOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ hardSwishOptions = HardSwishOptions()
+ hardSwishOptions.Init(buf, pos)
+ return cls.InitFromObj(hardSwishOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, hardSwishOptions):
+ x = HardSwishOptionsT()
+ x._UnPack(hardSwishOptions)
+ return x
+
+ # HardSwishOptionsT
+ def _UnPack(self, hardSwishOptions):
+ if hardSwishOptions is None:
+ return
+
+ # HardSwishOptionsT
+ def Pack(self, builder):
+ HardSwishOptionsStart(builder)
+ hardSwishOptions = HardSwishOptionsEnd(builder)
+ return hardSwishOptions
+
+
+class LogicalAndOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = LogicalAndOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsLogicalAndOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def LogicalAndOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # LogicalAndOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def LogicalAndOptionsStart(builder):
+ builder.StartObject(0)
+
+def LogicalAndOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class LogicalAndOptionsT(object):
+
+ # LogicalAndOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ logicalAndOptions = LogicalAndOptions()
+ logicalAndOptions.Init(buf, pos)
+ return cls.InitFromObj(logicalAndOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, logicalAndOptions):
+ x = LogicalAndOptionsT()
+ x._UnPack(logicalAndOptions)
+ return x
+
+ # LogicalAndOptionsT
+ def _UnPack(self, logicalAndOptions):
+ if logicalAndOptions is None:
+ return
+
+ # LogicalAndOptionsT
+ def Pack(self, builder):
+ LogicalAndOptionsStart(builder)
+ logicalAndOptions = LogicalAndOptionsEnd(builder)
+ return logicalAndOptions
+
+
+class LogicalNotOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = LogicalNotOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsLogicalNotOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def LogicalNotOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # LogicalNotOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def LogicalNotOptionsStart(builder):
+ builder.StartObject(0)
+
+def LogicalNotOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class LogicalNotOptionsT(object):
+
+ # LogicalNotOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ logicalNotOptions = LogicalNotOptions()
+ logicalNotOptions.Init(buf, pos)
+ return cls.InitFromObj(logicalNotOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, logicalNotOptions):
+ x = LogicalNotOptionsT()
+ x._UnPack(logicalNotOptions)
+ return x
+
+ # LogicalNotOptionsT
+ def _UnPack(self, logicalNotOptions):
+ if logicalNotOptions is None:
+ return
+
+ # LogicalNotOptionsT
+ def Pack(self, builder):
+ LogicalNotOptionsStart(builder)
+ logicalNotOptions = LogicalNotOptionsEnd(builder)
+ return logicalNotOptions
+
+
+class UnpackOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = UnpackOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsUnpackOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def UnpackOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # UnpackOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # UnpackOptions
+ def Num(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # UnpackOptions
+ def Axis(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def UnpackOptionsStart(builder):
+ builder.StartObject(2)
+
+def UnpackOptionsAddNum(builder, num):
+ builder.PrependInt32Slot(0, num, 0)
+
+def UnpackOptionsAddAxis(builder, axis):
+ builder.PrependInt32Slot(1, axis, 0)
+
+def UnpackOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class UnpackOptionsT(object):
+
+ # UnpackOptionsT
+ def __init__(self):
+ self.num = 0 # type: int
+ self.axis = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ unpackOptions = UnpackOptions()
+ unpackOptions.Init(buf, pos)
+ return cls.InitFromObj(unpackOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, unpackOptions):
+ x = UnpackOptionsT()
+ x._UnPack(unpackOptions)
+ return x
+
+ # UnpackOptionsT
+ def _UnPack(self, unpackOptions):
+ if unpackOptions is None:
+ return
+ self.num = unpackOptions.Num()
+ self.axis = unpackOptions.Axis()
+
+ # UnpackOptionsT
+ def Pack(self, builder):
+ UnpackOptionsStart(builder)
+ UnpackOptionsAddNum(builder, self.num)
+ UnpackOptionsAddAxis(builder, self.axis)
+ unpackOptions = UnpackOptionsEnd(builder)
+ return unpackOptions
+
+
+class FloorDivOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = FloorDivOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsFloorDivOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def FloorDivOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # FloorDivOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def FloorDivOptionsStart(builder):
+ builder.StartObject(0)
+
+def FloorDivOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class FloorDivOptionsT(object):
+
+ # FloorDivOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ floorDivOptions = FloorDivOptions()
+ floorDivOptions.Init(buf, pos)
+ return cls.InitFromObj(floorDivOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, floorDivOptions):
+ x = FloorDivOptionsT()
+ x._UnPack(floorDivOptions)
+ return x
+
+ # FloorDivOptionsT
+ def _UnPack(self, floorDivOptions):
+ if floorDivOptions is None:
+ return
+
+ # FloorDivOptionsT
+ def Pack(self, builder):
+ FloorDivOptionsStart(builder)
+ floorDivOptions = FloorDivOptionsEnd(builder)
+ return floorDivOptions
+
+
+class SquareOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = SquareOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsSquareOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def SquareOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # SquareOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def SquareOptionsStart(builder):
+ builder.StartObject(0)
+
+def SquareOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class SquareOptionsT(object):
+
+ # SquareOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ squareOptions = SquareOptions()
+ squareOptions.Init(buf, pos)
+ return cls.InitFromObj(squareOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, squareOptions):
+ x = SquareOptionsT()
+ x._UnPack(squareOptions)
+ return x
+
+ # SquareOptionsT
+ def _UnPack(self, squareOptions):
+ if squareOptions is None:
+ return
+
+ # SquareOptionsT
+ def Pack(self, builder):
+ SquareOptionsStart(builder)
+ squareOptions = SquareOptionsEnd(builder)
+ return squareOptions
+
+
+class ZerosLikeOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = ZerosLikeOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsZerosLikeOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def ZerosLikeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # ZerosLikeOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def ZerosLikeOptionsStart(builder):
+ builder.StartObject(0)
+
+def ZerosLikeOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class ZerosLikeOptionsT(object):
+
+ # ZerosLikeOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ zerosLikeOptions = ZerosLikeOptions()
+ zerosLikeOptions.Init(buf, pos)
+ return cls.InitFromObj(zerosLikeOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, zerosLikeOptions):
+ x = ZerosLikeOptionsT()
+ x._UnPack(zerosLikeOptions)
+ return x
+
+ # ZerosLikeOptionsT
+ def _UnPack(self, zerosLikeOptions):
+ if zerosLikeOptions is None:
+ return
+
+ # ZerosLikeOptionsT
+ def Pack(self, builder):
+ ZerosLikeOptionsStart(builder)
+ zerosLikeOptions = ZerosLikeOptionsEnd(builder)
+ return zerosLikeOptions
+
+
+class FillOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = FillOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsFillOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def FillOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # FillOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def FillOptionsStart(builder):
+ builder.StartObject(0)
+
+def FillOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class FillOptionsT(object):
+
+ # FillOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ fillOptions = FillOptions()
+ fillOptions.Init(buf, pos)
+ return cls.InitFromObj(fillOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, fillOptions):
+ x = FillOptionsT()
+ x._UnPack(fillOptions)
+ return x
+
+ # FillOptionsT
+ def _UnPack(self, fillOptions):
+ if fillOptions is None:
+ return
+
+ # FillOptionsT
+ def Pack(self, builder):
+ FillOptionsStart(builder)
+ fillOptions = FillOptionsEnd(builder)
+ return fillOptions
+
+
+class FloorModOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = FloorModOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsFloorModOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def FloorModOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # FloorModOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def FloorModOptionsStart(builder):
+ builder.StartObject(0)
+
+def FloorModOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class FloorModOptionsT(object):
+
+ # FloorModOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ floorModOptions = FloorModOptions()
+ floorModOptions.Init(buf, pos)
+ return cls.InitFromObj(floorModOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, floorModOptions):
+ x = FloorModOptionsT()
+ x._UnPack(floorModOptions)
+ return x
+
+ # FloorModOptionsT
+ def _UnPack(self, floorModOptions):
+ if floorModOptions is None:
+ return
+
+ # FloorModOptionsT
+ def Pack(self, builder):
+ FloorModOptionsStart(builder)
+ floorModOptions = FloorModOptionsEnd(builder)
+ return floorModOptions
+
+
+class RangeOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = RangeOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsRangeOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def RangeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # RangeOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def RangeOptionsStart(builder):
+ builder.StartObject(0)
+
+def RangeOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class RangeOptionsT(object):
+
+ # RangeOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ rangeOptions = RangeOptions()
+ rangeOptions.Init(buf, pos)
+ return cls.InitFromObj(rangeOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, rangeOptions):
+ x = RangeOptionsT()
+ x._UnPack(rangeOptions)
+ return x
+
+ # RangeOptionsT
+ def _UnPack(self, rangeOptions):
+ if rangeOptions is None:
+ return
+
+ # RangeOptionsT
+ def Pack(self, builder):
+ RangeOptionsStart(builder)
+ rangeOptions = RangeOptionsEnd(builder)
+ return rangeOptions
+
+
+class LeakyReluOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = LeakyReluOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsLeakyReluOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def LeakyReluOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # LeakyReluOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # LeakyReluOptions
+ def Alpha(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
+ return 0.0
+
+def LeakyReluOptionsStart(builder):
+ builder.StartObject(1)
+
+def LeakyReluOptionsAddAlpha(builder, alpha):
+ builder.PrependFloat32Slot(0, alpha, 0.0)
+
+def LeakyReluOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class LeakyReluOptionsT(object):
+
+ # LeakyReluOptionsT
+ def __init__(self):
+ self.alpha = 0.0 # type: float
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ leakyReluOptions = LeakyReluOptions()
+ leakyReluOptions.Init(buf, pos)
+ return cls.InitFromObj(leakyReluOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, leakyReluOptions):
+ x = LeakyReluOptionsT()
+ x._UnPack(leakyReluOptions)
+ return x
+
+ # LeakyReluOptionsT
+ def _UnPack(self, leakyReluOptions):
+ if leakyReluOptions is None:
+ return
+ self.alpha = leakyReluOptions.Alpha()
+
+ # LeakyReluOptionsT
+ def Pack(self, builder):
+ LeakyReluOptionsStart(builder)
+ LeakyReluOptionsAddAlpha(builder, self.alpha)
+ leakyReluOptions = LeakyReluOptionsEnd(builder)
+ return leakyReluOptions
+
+
+class SquaredDifferenceOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = SquaredDifferenceOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsSquaredDifferenceOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def SquaredDifferenceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # SquaredDifferenceOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def SquaredDifferenceOptionsStart(builder):
+ builder.StartObject(0)
+
+def SquaredDifferenceOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class SquaredDifferenceOptionsT(object):
+
+ # SquaredDifferenceOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ squaredDifferenceOptions = SquaredDifferenceOptions()
+ squaredDifferenceOptions.Init(buf, pos)
+ return cls.InitFromObj(squaredDifferenceOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, squaredDifferenceOptions):
+ x = SquaredDifferenceOptionsT()
+ x._UnPack(squaredDifferenceOptions)
+ return x
+
+ # SquaredDifferenceOptionsT
+ def _UnPack(self, squaredDifferenceOptions):
+ if squaredDifferenceOptions is None:
+ return
+
+ # SquaredDifferenceOptionsT
+ def Pack(self, builder):
+ SquaredDifferenceOptionsStart(builder)
+ squaredDifferenceOptions = SquaredDifferenceOptionsEnd(builder)
+ return squaredDifferenceOptions
+
+
+class MirrorPadOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = MirrorPadOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsMirrorPadOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def MirrorPadOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # MirrorPadOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # MirrorPadOptions
+ def Mode(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+def MirrorPadOptionsStart(builder):
+ builder.StartObject(1)
+
+def MirrorPadOptionsAddMode(builder, mode):
+ builder.PrependInt8Slot(0, mode, 0)
+
+def MirrorPadOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class MirrorPadOptionsT(object):
+
+ # MirrorPadOptionsT
+ def __init__(self):
+ self.mode = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ mirrorPadOptions = MirrorPadOptions()
+ mirrorPadOptions.Init(buf, pos)
+ return cls.InitFromObj(mirrorPadOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, mirrorPadOptions):
+ x = MirrorPadOptionsT()
+ x._UnPack(mirrorPadOptions)
+ return x
+
+ # MirrorPadOptionsT
+ def _UnPack(self, mirrorPadOptions):
+ if mirrorPadOptions is None:
+ return
+ self.mode = mirrorPadOptions.Mode()
+
+ # MirrorPadOptionsT
+ def Pack(self, builder):
+ MirrorPadOptionsStart(builder)
+ MirrorPadOptionsAddMode(builder, self.mode)
+ mirrorPadOptions = MirrorPadOptionsEnd(builder)
+ return mirrorPadOptions
+
+
+class UniqueOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = UniqueOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsUniqueOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def UniqueOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # UniqueOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # UniqueOptions
+ def IdxOutType(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 2
+
+def UniqueOptionsStart(builder):
+ builder.StartObject(1)
+
+def UniqueOptionsAddIdxOutType(builder, idxOutType):
+ builder.PrependInt8Slot(0, idxOutType, 2)
+
+def UniqueOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class UniqueOptionsT(object):
+
+ # UniqueOptionsT
+ def __init__(self):
+ self.idxOutType = 2 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ uniqueOptions = UniqueOptions()
+ uniqueOptions.Init(buf, pos)
+ return cls.InitFromObj(uniqueOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, uniqueOptions):
+ x = UniqueOptionsT()
+ x._UnPack(uniqueOptions)
+ return x
+
+ # UniqueOptionsT
+ def _UnPack(self, uniqueOptions):
+ if uniqueOptions is None:
+ return
+ self.idxOutType = uniqueOptions.IdxOutType()
+
+ # UniqueOptionsT
+ def Pack(self, builder):
+ UniqueOptionsStart(builder)
+ UniqueOptionsAddIdxOutType(builder, self.idxOutType)
+ uniqueOptions = UniqueOptionsEnd(builder)
+ return uniqueOptions
+
+
+class ReverseV2Options(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = ReverseV2Options()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsReverseV2Options(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def ReverseV2OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # ReverseV2Options
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def ReverseV2OptionsStart(builder):
+ builder.StartObject(0)
+
+def ReverseV2OptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class ReverseV2OptionsT(object):
+
+ # ReverseV2OptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ reverseV2Options = ReverseV2Options()
+ reverseV2Options.Init(buf, pos)
+ return cls.InitFromObj(reverseV2Options)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, reverseV2Options):
+ x = ReverseV2OptionsT()
+ x._UnPack(reverseV2Options)
+ return x
+
+ # ReverseV2OptionsT
+ def _UnPack(self, reverseV2Options):
+ if reverseV2Options is None:
+ return
+
+ # ReverseV2OptionsT
+ def Pack(self, builder):
+ ReverseV2OptionsStart(builder)
+ reverseV2Options = ReverseV2OptionsEnd(builder)
+ return reverseV2Options
+
+
+class AddNOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = AddNOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsAddNOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def AddNOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # AddNOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def AddNOptionsStart(builder):
+ builder.StartObject(0)
+
+def AddNOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class AddNOptionsT(object):
+
+ # AddNOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ addNoptions = AddNOptions()
+ addNoptions.Init(buf, pos)
+ return cls.InitFromObj(addNoptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, addNoptions):
+ x = AddNOptionsT()
+ x._UnPack(addNoptions)
+ return x
+
+ # AddNOptionsT
+ def _UnPack(self, addNoptions):
+ if addNoptions is None:
+ return
+
+ # AddNOptionsT
+ def Pack(self, builder):
+ AddNOptionsStart(builder)
+ addNoptions = AddNOptionsEnd(builder)
+ return addNoptions
+
+
+class GatherNdOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = GatherNdOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsGatherNdOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def GatherNdOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # GatherNdOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def GatherNdOptionsStart(builder):
+ builder.StartObject(0)
+
+def GatherNdOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class GatherNdOptionsT(object):
+
+ # GatherNdOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ gatherNdOptions = GatherNdOptions()
+ gatherNdOptions.Init(buf, pos)
+ return cls.InitFromObj(gatherNdOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, gatherNdOptions):
+ x = GatherNdOptionsT()
+ x._UnPack(gatherNdOptions)
+ return x
+
+ # GatherNdOptionsT
+ def _UnPack(self, gatherNdOptions):
+ if gatherNdOptions is None:
+ return
+
+ # GatherNdOptionsT
+ def Pack(self, builder):
+ GatherNdOptionsStart(builder)
+ gatherNdOptions = GatherNdOptionsEnd(builder)
+ return gatherNdOptions
+
+
+class WhereOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = WhereOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsWhereOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def WhereOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # WhereOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def WhereOptionsStart(builder):
+ builder.StartObject(0)
+
+def WhereOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class WhereOptionsT(object):
+
+ # WhereOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ whereOptions = WhereOptions()
+ whereOptions.Init(buf, pos)
+ return cls.InitFromObj(whereOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, whereOptions):
+ x = WhereOptionsT()
+ x._UnPack(whereOptions)
+ return x
+
+ # WhereOptionsT
+ def _UnPack(self, whereOptions):
+ if whereOptions is None:
+ return
+
+ # WhereOptionsT
+ def Pack(self, builder):
+ WhereOptionsStart(builder)
+ whereOptions = WhereOptionsEnd(builder)
+ return whereOptions
+
+
+class ReverseSequenceOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = ReverseSequenceOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsReverseSequenceOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def ReverseSequenceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # ReverseSequenceOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # ReverseSequenceOptions
+ def SeqDim(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # ReverseSequenceOptions
+ def BatchDim(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def ReverseSequenceOptionsStart(builder):
+ builder.StartObject(2)
+
+def ReverseSequenceOptionsAddSeqDim(builder, seqDim):
+ builder.PrependInt32Slot(0, seqDim, 0)
+
+def ReverseSequenceOptionsAddBatchDim(builder, batchDim):
+ builder.PrependInt32Slot(1, batchDim, 0)
+
+def ReverseSequenceOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class ReverseSequenceOptionsT(object):
+
+ # ReverseSequenceOptionsT
+ def __init__(self):
+ self.seqDim = 0 # type: int
+ self.batchDim = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ reverseSequenceOptions = ReverseSequenceOptions()
+ reverseSequenceOptions.Init(buf, pos)
+ return cls.InitFromObj(reverseSequenceOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, reverseSequenceOptions):
+ x = ReverseSequenceOptionsT()
+ x._UnPack(reverseSequenceOptions)
+ return x
+
+ # ReverseSequenceOptionsT
+ def _UnPack(self, reverseSequenceOptions):
+ if reverseSequenceOptions is None:
+ return
+ self.seqDim = reverseSequenceOptions.SeqDim()
+ self.batchDim = reverseSequenceOptions.BatchDim()
+
+ # ReverseSequenceOptionsT
+ def Pack(self, builder):
+ ReverseSequenceOptionsStart(builder)
+ ReverseSequenceOptionsAddSeqDim(builder, self.seqDim)
+ ReverseSequenceOptionsAddBatchDim(builder, self.batchDim)
+ reverseSequenceOptions = ReverseSequenceOptionsEnd(builder)
+ return reverseSequenceOptions
+
+
+class MatrixDiagOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = MatrixDiagOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsMatrixDiagOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def MatrixDiagOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # MatrixDiagOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def MatrixDiagOptionsStart(builder):
+ builder.StartObject(0)
+
+def MatrixDiagOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class MatrixDiagOptionsT(object):
+
+ # MatrixDiagOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ matrixDiagOptions = MatrixDiagOptions()
+ matrixDiagOptions.Init(buf, pos)
+ return cls.InitFromObj(matrixDiagOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, matrixDiagOptions):
+ x = MatrixDiagOptionsT()
+ x._UnPack(matrixDiagOptions)
+ return x
+
+ # MatrixDiagOptionsT
+ def _UnPack(self, matrixDiagOptions):
+ if matrixDiagOptions is None:
+ return
+
+ # MatrixDiagOptionsT
+ def Pack(self, builder):
+ MatrixDiagOptionsStart(builder)
+ matrixDiagOptions = MatrixDiagOptionsEnd(builder)
+ return matrixDiagOptions
+
+
+class QuantizeOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = QuantizeOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsQuantizeOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def QuantizeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # QuantizeOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def QuantizeOptionsStart(builder):
+ builder.StartObject(0)
+
+def QuantizeOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class QuantizeOptionsT(object):
+
+ # QuantizeOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ quantizeOptions = QuantizeOptions()
+ quantizeOptions.Init(buf, pos)
+ return cls.InitFromObj(quantizeOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, quantizeOptions):
+ x = QuantizeOptionsT()
+ x._UnPack(quantizeOptions)
+ return x
+
+ # QuantizeOptionsT
+ def _UnPack(self, quantizeOptions):
+ if quantizeOptions is None:
+ return
+
+ # QuantizeOptionsT
+ def Pack(self, builder):
+ QuantizeOptionsStart(builder)
+ quantizeOptions = QuantizeOptionsEnd(builder)
+ return quantizeOptions
+
+
+class MatrixSetDiagOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = MatrixSetDiagOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsMatrixSetDiagOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def MatrixSetDiagOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # MatrixSetDiagOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def MatrixSetDiagOptionsStart(builder):
+ builder.StartObject(0)
+
+def MatrixSetDiagOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class MatrixSetDiagOptionsT(object):
+
+ # MatrixSetDiagOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ matrixSetDiagOptions = MatrixSetDiagOptions()
+ matrixSetDiagOptions.Init(buf, pos)
+ return cls.InitFromObj(matrixSetDiagOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, matrixSetDiagOptions):
+ x = MatrixSetDiagOptionsT()
+ x._UnPack(matrixSetDiagOptions)
+ return x
+
+ # MatrixSetDiagOptionsT
+ def _UnPack(self, matrixSetDiagOptions):
+ if matrixSetDiagOptions is None:
+ return
+
+ # MatrixSetDiagOptionsT
+ def Pack(self, builder):
+ MatrixSetDiagOptionsStart(builder)
+ matrixSetDiagOptions = MatrixSetDiagOptionsEnd(builder)
+ return matrixSetDiagOptions
+
+
+class IfOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = IfOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsIfOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def IfOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # IfOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # IfOptions
+ def ThenSubgraphIndex(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # IfOptions
+ def ElseSubgraphIndex(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def IfOptionsStart(builder):
+ builder.StartObject(2)
+
+def IfOptionsAddThenSubgraphIndex(builder, thenSubgraphIndex):
+ builder.PrependInt32Slot(0, thenSubgraphIndex, 0)
+
+def IfOptionsAddElseSubgraphIndex(builder, elseSubgraphIndex):
+ builder.PrependInt32Slot(1, elseSubgraphIndex, 0)
+
+def IfOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class IfOptionsT(object):
+
+ # IfOptionsT
+ def __init__(self):
+ self.thenSubgraphIndex = 0 # type: int
+ self.elseSubgraphIndex = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ ifOptions = IfOptions()
+ ifOptions.Init(buf, pos)
+ return cls.InitFromObj(ifOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, ifOptions):
+ x = IfOptionsT()
+ x._UnPack(ifOptions)
+ return x
+
+ # IfOptionsT
+ def _UnPack(self, ifOptions):
+ if ifOptions is None:
+ return
+ self.thenSubgraphIndex = ifOptions.ThenSubgraphIndex()
+ self.elseSubgraphIndex = ifOptions.ElseSubgraphIndex()
+
+ # IfOptionsT
+ def Pack(self, builder):
+ IfOptionsStart(builder)
+ IfOptionsAddThenSubgraphIndex(builder, self.thenSubgraphIndex)
+ IfOptionsAddElseSubgraphIndex(builder, self.elseSubgraphIndex)
+ ifOptions = IfOptionsEnd(builder)
+ return ifOptions
+
+
+class CallOnceOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = CallOnceOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsCallOnceOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def CallOnceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # CallOnceOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # CallOnceOptions
+ def InitSubgraphIndex(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def CallOnceOptionsStart(builder):
+ builder.StartObject(1)
+
+def CallOnceOptionsAddInitSubgraphIndex(builder, initSubgraphIndex):
+ builder.PrependInt32Slot(0, initSubgraphIndex, 0)
+
+def CallOnceOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class CallOnceOptionsT(object):
+
+ # CallOnceOptionsT
+ def __init__(self):
+ self.initSubgraphIndex = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ callOnceOptions = CallOnceOptions()
+ callOnceOptions.Init(buf, pos)
+ return cls.InitFromObj(callOnceOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, callOnceOptions):
+ x = CallOnceOptionsT()
+ x._UnPack(callOnceOptions)
+ return x
+
+ # CallOnceOptionsT
+ def _UnPack(self, callOnceOptions):
+ if callOnceOptions is None:
+ return
+ self.initSubgraphIndex = callOnceOptions.InitSubgraphIndex()
+
+ # CallOnceOptionsT
+ def Pack(self, builder):
+ CallOnceOptionsStart(builder)
+ CallOnceOptionsAddInitSubgraphIndex(builder, self.initSubgraphIndex)
+ callOnceOptions = CallOnceOptionsEnd(builder)
+ return callOnceOptions
+
+
+class WhileOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = WhileOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsWhileOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def WhileOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # WhileOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # WhileOptions
+ def CondSubgraphIndex(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # WhileOptions
+ def BodySubgraphIndex(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def WhileOptionsStart(builder):
+ builder.StartObject(2)
+
+def WhileOptionsAddCondSubgraphIndex(builder, condSubgraphIndex):
+ builder.PrependInt32Slot(0, condSubgraphIndex, 0)
+
+def WhileOptionsAddBodySubgraphIndex(builder, bodySubgraphIndex):
+ builder.PrependInt32Slot(1, bodySubgraphIndex, 0)
+
+def WhileOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class WhileOptionsT(object):
+
+ # WhileOptionsT
+ def __init__(self):
+ self.condSubgraphIndex = 0 # type: int
+ self.bodySubgraphIndex = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ whileOptions = WhileOptions()
+ whileOptions.Init(buf, pos)
+ return cls.InitFromObj(whileOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, whileOptions):
+ x = WhileOptionsT()
+ x._UnPack(whileOptions)
+ return x
+
+ # WhileOptionsT
+ def _UnPack(self, whileOptions):
+ if whileOptions is None:
+ return
+ self.condSubgraphIndex = whileOptions.CondSubgraphIndex()
+ self.bodySubgraphIndex = whileOptions.BodySubgraphIndex()
+
+ # WhileOptionsT
+ def Pack(self, builder):
+ WhileOptionsStart(builder)
+ WhileOptionsAddCondSubgraphIndex(builder, self.condSubgraphIndex)
+ WhileOptionsAddBodySubgraphIndex(builder, self.bodySubgraphIndex)
+ whileOptions = WhileOptionsEnd(builder)
+ return whileOptions
+
+
+class NonMaxSuppressionV4Options(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = NonMaxSuppressionV4Options()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsNonMaxSuppressionV4Options(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def NonMaxSuppressionV4OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # NonMaxSuppressionV4Options
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def NonMaxSuppressionV4OptionsStart(builder):
+ builder.StartObject(0)
+
+def NonMaxSuppressionV4OptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class NonMaxSuppressionV4OptionsT(object):
+
+ # NonMaxSuppressionV4OptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ nonMaxSuppressionV4Options = NonMaxSuppressionV4Options()
+ nonMaxSuppressionV4Options.Init(buf, pos)
+ return cls.InitFromObj(nonMaxSuppressionV4Options)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, nonMaxSuppressionV4Options):
+ x = NonMaxSuppressionV4OptionsT()
+ x._UnPack(nonMaxSuppressionV4Options)
+ return x
+
+ # NonMaxSuppressionV4OptionsT
+ def _UnPack(self, nonMaxSuppressionV4Options):
+ if nonMaxSuppressionV4Options is None:
+ return
+
+ # NonMaxSuppressionV4OptionsT
+ def Pack(self, builder):
+ NonMaxSuppressionV4OptionsStart(builder)
+ nonMaxSuppressionV4Options = NonMaxSuppressionV4OptionsEnd(builder)
+ return nonMaxSuppressionV4Options
+
+
+class NonMaxSuppressionV5Options(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = NonMaxSuppressionV5Options()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsNonMaxSuppressionV5Options(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def NonMaxSuppressionV5OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # NonMaxSuppressionV5Options
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def NonMaxSuppressionV5OptionsStart(builder):
+ builder.StartObject(0)
+
+def NonMaxSuppressionV5OptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class NonMaxSuppressionV5OptionsT(object):
+
+ # NonMaxSuppressionV5OptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ nonMaxSuppressionV5Options = NonMaxSuppressionV5Options()
+ nonMaxSuppressionV5Options.Init(buf, pos)
+ return cls.InitFromObj(nonMaxSuppressionV5Options)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, nonMaxSuppressionV5Options):
+ x = NonMaxSuppressionV5OptionsT()
+ x._UnPack(nonMaxSuppressionV5Options)
+ return x
+
+ # NonMaxSuppressionV5OptionsT
+ def _UnPack(self, nonMaxSuppressionV5Options):
+ if nonMaxSuppressionV5Options is None:
+ return
+
+ # NonMaxSuppressionV5OptionsT
+ def Pack(self, builder):
+ NonMaxSuppressionV5OptionsStart(builder)
+ nonMaxSuppressionV5Options = NonMaxSuppressionV5OptionsEnd(builder)
+ return nonMaxSuppressionV5Options
+
+
+class ScatterNdOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = ScatterNdOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsScatterNdOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def ScatterNdOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # ScatterNdOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def ScatterNdOptionsStart(builder):
+ builder.StartObject(0)
+
+def ScatterNdOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class ScatterNdOptionsT(object):
+
+ # ScatterNdOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ scatterNdOptions = ScatterNdOptions()
+ scatterNdOptions.Init(buf, pos)
+ return cls.InitFromObj(scatterNdOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, scatterNdOptions):
+ x = ScatterNdOptionsT()
+ x._UnPack(scatterNdOptions)
+ return x
+
+ # ScatterNdOptionsT
+ def _UnPack(self, scatterNdOptions):
+ if scatterNdOptions is None:
+ return
+
+ # ScatterNdOptionsT
+ def Pack(self, builder):
+ ScatterNdOptionsStart(builder)
+ scatterNdOptions = ScatterNdOptionsEnd(builder)
+ return scatterNdOptions
+
+
+class SelectV2Options(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = SelectV2Options()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsSelectV2Options(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def SelectV2OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # SelectV2Options
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def SelectV2OptionsStart(builder):
+ builder.StartObject(0)
+
+def SelectV2OptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class SelectV2OptionsT(object):
+
+ # SelectV2OptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ selectV2Options = SelectV2Options()
+ selectV2Options.Init(buf, pos)
+ return cls.InitFromObj(selectV2Options)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, selectV2Options):
+ x = SelectV2OptionsT()
+ x._UnPack(selectV2Options)
+ return x
+
+ # SelectV2OptionsT
+ def _UnPack(self, selectV2Options):
+ if selectV2Options is None:
+ return
+
+ # SelectV2OptionsT
+ def Pack(self, builder):
+ SelectV2OptionsStart(builder)
+ selectV2Options = SelectV2OptionsEnd(builder)
+ return selectV2Options
+
+
+class DensifyOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = DensifyOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsDensifyOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def DensifyOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # DensifyOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def DensifyOptionsStart(builder):
+ builder.StartObject(0)
+
+def DensifyOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class DensifyOptionsT(object):
+
+ # DensifyOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ densifyOptions = DensifyOptions()
+ densifyOptions.Init(buf, pos)
+ return cls.InitFromObj(densifyOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, densifyOptions):
+ x = DensifyOptionsT()
+ x._UnPack(densifyOptions)
+ return x
+
+ # DensifyOptionsT
+ def _UnPack(self, densifyOptions):
+ if densifyOptions is None:
+ return
+
+ # DensifyOptionsT
+ def Pack(self, builder):
+ DensifyOptionsStart(builder)
+ densifyOptions = DensifyOptionsEnd(builder)
+ return densifyOptions
+
+
+class SegmentSumOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = SegmentSumOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsSegmentSumOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def SegmentSumOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # SegmentSumOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def SegmentSumOptionsStart(builder):
+ builder.StartObject(0)
+
+def SegmentSumOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class SegmentSumOptionsT(object):
+
+ # SegmentSumOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ segmentSumOptions = SegmentSumOptions()
+ segmentSumOptions.Init(buf, pos)
+ return cls.InitFromObj(segmentSumOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, segmentSumOptions):
+ x = SegmentSumOptionsT()
+ x._UnPack(segmentSumOptions)
+ return x
+
+ # SegmentSumOptionsT
+ def _UnPack(self, segmentSumOptions):
+ if segmentSumOptions is None:
+ return
+
+ # SegmentSumOptionsT
+ def Pack(self, builder):
+ SegmentSumOptionsStart(builder)
+ segmentSumOptions = SegmentSumOptionsEnd(builder)
+ return segmentSumOptions
+
+
+class BatchMatMulOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = BatchMatMulOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsBatchMatMulOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def BatchMatMulOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # BatchMatMulOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # BatchMatMulOptions
+ def AdjX(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+ # BatchMatMulOptions
+ def AdjY(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+ # BatchMatMulOptions
+ def AsymmetricQuantizeInputs(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+def BatchMatMulOptionsStart(builder):
+ builder.StartObject(3)
+
+def BatchMatMulOptionsAddAdjX(builder, adjX):
+ builder.PrependBoolSlot(0, adjX, 0)
+
+def BatchMatMulOptionsAddAdjY(builder, adjY):
+ builder.PrependBoolSlot(1, adjY, 0)
+
+def BatchMatMulOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
+ builder.PrependBoolSlot(2, asymmetricQuantizeInputs, 0)
+
+def BatchMatMulOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class BatchMatMulOptionsT(object):
+
+ # BatchMatMulOptionsT
+ def __init__(self):
+ self.adjX = False # type: bool
+ self.adjY = False # type: bool
+ self.asymmetricQuantizeInputs = False # type: bool
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ batchMatMulOptions = BatchMatMulOptions()
+ batchMatMulOptions.Init(buf, pos)
+ return cls.InitFromObj(batchMatMulOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, batchMatMulOptions):
+ x = BatchMatMulOptionsT()
+ x._UnPack(batchMatMulOptions)
+ return x
+
+ # BatchMatMulOptionsT
+ def _UnPack(self, batchMatMulOptions):
+ if batchMatMulOptions is None:
+ return
+ self.adjX = batchMatMulOptions.AdjX()
+ self.adjY = batchMatMulOptions.AdjY()
+ self.asymmetricQuantizeInputs = batchMatMulOptions.AsymmetricQuantizeInputs()
+
+ # BatchMatMulOptionsT
+ def Pack(self, builder):
+ BatchMatMulOptionsStart(builder)
+ BatchMatMulOptionsAddAdjX(builder, self.adjX)
+ BatchMatMulOptionsAddAdjY(builder, self.adjY)
+ BatchMatMulOptionsAddAsymmetricQuantizeInputs(builder, self.asymmetricQuantizeInputs)
+ batchMatMulOptions = BatchMatMulOptionsEnd(builder)
+ return batchMatMulOptions
+
+
+class CumsumOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = CumsumOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsCumsumOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def CumsumOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # CumsumOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # CumsumOptions
+ def Exclusive(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+ # CumsumOptions
+ def Reverse(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+def CumsumOptionsStart(builder):
+ builder.StartObject(2)
+
+def CumsumOptionsAddExclusive(builder, exclusive):
+ builder.PrependBoolSlot(0, exclusive, 0)
+
+def CumsumOptionsAddReverse(builder, reverse):
+ builder.PrependBoolSlot(1, reverse, 0)
+
+def CumsumOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class CumsumOptionsT(object):
+
+ # CumsumOptionsT
+ def __init__(self):
+ self.exclusive = False # type: bool
+ self.reverse = False # type: bool
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ cumsumOptions = CumsumOptions()
+ cumsumOptions.Init(buf, pos)
+ return cls.InitFromObj(cumsumOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, cumsumOptions):
+ x = CumsumOptionsT()
+ x._UnPack(cumsumOptions)
+ return x
+
+ # CumsumOptionsT
+ def _UnPack(self, cumsumOptions):
+ if cumsumOptions is None:
+ return
+ self.exclusive = cumsumOptions.Exclusive()
+ self.reverse = cumsumOptions.Reverse()
+
+ # CumsumOptionsT
+ def Pack(self, builder):
+ CumsumOptionsStart(builder)
+ CumsumOptionsAddExclusive(builder, self.exclusive)
+ CumsumOptionsAddReverse(builder, self.reverse)
+ cumsumOptions = CumsumOptionsEnd(builder)
+ return cumsumOptions
+
+
+class BroadcastToOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = BroadcastToOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsBroadcastToOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def BroadcastToOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # BroadcastToOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def BroadcastToOptionsStart(builder):
+ builder.StartObject(0)
+
+def BroadcastToOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class BroadcastToOptionsT(object):
+
+ # BroadcastToOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ broadcastToOptions = BroadcastToOptions()
+ broadcastToOptions.Init(buf, pos)
+ return cls.InitFromObj(broadcastToOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, broadcastToOptions):
+ x = BroadcastToOptionsT()
+ x._UnPack(broadcastToOptions)
+ return x
+
+ # BroadcastToOptionsT
+ def _UnPack(self, broadcastToOptions):
+ if broadcastToOptions is None:
+ return
+
+ # BroadcastToOptionsT
+ def Pack(self, builder):
+ BroadcastToOptionsStart(builder)
+ broadcastToOptions = BroadcastToOptionsEnd(builder)
+ return broadcastToOptions
+
+
+class Rfft2dOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = Rfft2dOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsRfft2dOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def Rfft2dOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # Rfft2dOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def Rfft2dOptionsStart(builder):
+ builder.StartObject(0)
+
+def Rfft2dOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class Rfft2dOptionsT(object):
+
+ # Rfft2dOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ rfft2dOptions = Rfft2dOptions()
+ rfft2dOptions.Init(buf, pos)
+ return cls.InitFromObj(rfft2dOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, rfft2dOptions):
+ x = Rfft2dOptionsT()
+ x._UnPack(rfft2dOptions)
+ return x
+
+ # Rfft2dOptionsT
+ def _UnPack(self, rfft2dOptions):
+ if rfft2dOptions is None:
+ return
+
+ # Rfft2dOptionsT
+ def Pack(self, builder):
+ Rfft2dOptionsStart(builder)
+ rfft2dOptions = Rfft2dOptionsEnd(builder)
+ return rfft2dOptions
+
+
+class HashtableOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = HashtableOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsHashtableOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def HashtableOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # HashtableOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # HashtableOptions
+ def TableId(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # HashtableOptions
+ def KeyDtype(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # HashtableOptions
+ def ValueDtype(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+def HashtableOptionsStart(builder):
+ builder.StartObject(3)
+
+def HashtableOptionsAddTableId(builder, tableId):
+ builder.PrependInt32Slot(0, tableId, 0)
+
+def HashtableOptionsAddKeyDtype(builder, keyDtype):
+ builder.PrependInt8Slot(1, keyDtype, 0)
+
+def HashtableOptionsAddValueDtype(builder, valueDtype):
+ builder.PrependInt8Slot(2, valueDtype, 0)
+
+def HashtableOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class HashtableOptionsT(object):
+
+ # HashtableOptionsT
+ def __init__(self):
+ self.tableId = 0 # type: int
+ self.keyDtype = 0 # type: int
+ self.valueDtype = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ hashtableOptions = HashtableOptions()
+ hashtableOptions.Init(buf, pos)
+ return cls.InitFromObj(hashtableOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, hashtableOptions):
+ x = HashtableOptionsT()
+ x._UnPack(hashtableOptions)
+ return x
+
+ # HashtableOptionsT
+ def _UnPack(self, hashtableOptions):
+ if hashtableOptions is None:
+ return
+ self.tableId = hashtableOptions.TableId()
+ self.keyDtype = hashtableOptions.KeyDtype()
+ self.valueDtype = hashtableOptions.ValueDtype()
+
+ # HashtableOptionsT
+ def Pack(self, builder):
+ HashtableOptionsStart(builder)
+ HashtableOptionsAddTableId(builder, self.tableId)
+ HashtableOptionsAddKeyDtype(builder, self.keyDtype)
+ HashtableOptionsAddValueDtype(builder, self.valueDtype)
+ hashtableOptions = HashtableOptionsEnd(builder)
+ return hashtableOptions
+
+
+class HashtableFindOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = HashtableFindOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsHashtableFindOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def HashtableFindOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # HashtableFindOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def HashtableFindOptionsStart(builder):
+ builder.StartObject(0)
+
+def HashtableFindOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class HashtableFindOptionsT(object):
+
+ # HashtableFindOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ hashtableFindOptions = HashtableFindOptions()
+ hashtableFindOptions.Init(buf, pos)
+ return cls.InitFromObj(hashtableFindOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, hashtableFindOptions):
+ x = HashtableFindOptionsT()
+ x._UnPack(hashtableFindOptions)
+ return x
+
+ # HashtableFindOptionsT
+ def _UnPack(self, hashtableFindOptions):
+ if hashtableFindOptions is None:
+ return
+
+ # HashtableFindOptionsT
+ def Pack(self, builder):
+ HashtableFindOptionsStart(builder)
+ hashtableFindOptions = HashtableFindOptionsEnd(builder)
+ return hashtableFindOptions
+
+
+class HashtableImportOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = HashtableImportOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsHashtableImportOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def HashtableImportOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # HashtableImportOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def HashtableImportOptionsStart(builder):
+ builder.StartObject(0)
+
+def HashtableImportOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class HashtableImportOptionsT(object):
+
+ # HashtableImportOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ hashtableImportOptions = HashtableImportOptions()
+ hashtableImportOptions.Init(buf, pos)
+ return cls.InitFromObj(hashtableImportOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, hashtableImportOptions):
+ x = HashtableImportOptionsT()
+ x._UnPack(hashtableImportOptions)
+ return x
+
+ # HashtableImportOptionsT
+ def _UnPack(self, hashtableImportOptions):
+ if hashtableImportOptions is None:
+ return
+
+ # HashtableImportOptionsT
+ def Pack(self, builder):
+ HashtableImportOptionsStart(builder)
+ hashtableImportOptions = HashtableImportOptionsEnd(builder)
+ return hashtableImportOptions
+
+
+class HashtableSizeOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = HashtableSizeOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsHashtableSizeOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def HashtableSizeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # HashtableSizeOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def HashtableSizeOptionsStart(builder):
+ builder.StartObject(0)
+
+def HashtableSizeOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class HashtableSizeOptionsT(object):
+
+ # HashtableSizeOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ hashtableSizeOptions = HashtableSizeOptions()
+ hashtableSizeOptions.Init(buf, pos)
+ return cls.InitFromObj(hashtableSizeOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, hashtableSizeOptions):
+ x = HashtableSizeOptionsT()
+ x._UnPack(hashtableSizeOptions)
+ return x
+
+ # HashtableSizeOptionsT
+ def _UnPack(self, hashtableSizeOptions):
+ if hashtableSizeOptions is None:
+ return
+
+ # HashtableSizeOptionsT
+ def Pack(self, builder):
+ HashtableSizeOptionsStart(builder)
+ hashtableSizeOptions = HashtableSizeOptionsEnd(builder)
+ return hashtableSizeOptions
+
+
+class VarHandleOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = VarHandleOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsVarHandleOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def VarHandleOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # VarHandleOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # VarHandleOptions
+ def Container(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.String(o + self._tab.Pos)
+ return None
+
+ # VarHandleOptions
+ def SharedName(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.String(o + self._tab.Pos)
+ return None
+
+def VarHandleOptionsStart(builder):
+ builder.StartObject(2)
+
+def VarHandleOptionsAddContainer(builder, container):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(container), 0)
+
+def VarHandleOptionsAddSharedName(builder, sharedName):
+ builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(sharedName), 0)
+
+def VarHandleOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class VarHandleOptionsT(object):
+
+ # VarHandleOptionsT
+ def __init__(self):
+ self.container = None # type: str
+ self.sharedName = None # type: str
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ varHandleOptions = VarHandleOptions()
+ varHandleOptions.Init(buf, pos)
+ return cls.InitFromObj(varHandleOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, varHandleOptions):
+ x = VarHandleOptionsT()
+ x._UnPack(varHandleOptions)
+ return x
+
+ # VarHandleOptionsT
+ def _UnPack(self, varHandleOptions):
+ if varHandleOptions is None:
+ return
+ self.container = varHandleOptions.Container()
+ self.sharedName = varHandleOptions.SharedName()
+
+ # VarHandleOptionsT
+ def Pack(self, builder):
+ if self.container is not None:
+ container = builder.CreateString(self.container)
+ if self.sharedName is not None:
+ sharedName = builder.CreateString(self.sharedName)
+ VarHandleOptionsStart(builder)
+ if self.container is not None:
+ VarHandleOptionsAddContainer(builder, container)
+ if self.sharedName is not None:
+ VarHandleOptionsAddSharedName(builder, sharedName)
+ varHandleOptions = VarHandleOptionsEnd(builder)
+ return varHandleOptions
+
+
+class ReadVariableOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = ReadVariableOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsReadVariableOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def ReadVariableOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # ReadVariableOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def ReadVariableOptionsStart(builder):
+ builder.StartObject(0)
+
+def ReadVariableOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class ReadVariableOptionsT(object):
+
+ # ReadVariableOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ readVariableOptions = ReadVariableOptions()
+ readVariableOptions.Init(buf, pos)
+ return cls.InitFromObj(readVariableOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, readVariableOptions):
+ x = ReadVariableOptionsT()
+ x._UnPack(readVariableOptions)
+ return x
+
+ # ReadVariableOptionsT
+ def _UnPack(self, readVariableOptions):
+ if readVariableOptions is None:
+ return
+
+ # ReadVariableOptionsT
+ def Pack(self, builder):
+ ReadVariableOptionsStart(builder)
+ readVariableOptions = ReadVariableOptionsEnd(builder)
+ return readVariableOptions
+
+
+class AssignVariableOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = AssignVariableOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsAssignVariableOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def AssignVariableOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # AssignVariableOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def AssignVariableOptionsStart(builder):
+ builder.StartObject(0)
+
+def AssignVariableOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class AssignVariableOptionsT(object):
+
+ # AssignVariableOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ assignVariableOptions = AssignVariableOptions()
+ assignVariableOptions.Init(buf, pos)
+ return cls.InitFromObj(assignVariableOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, assignVariableOptions):
+ x = AssignVariableOptionsT()
+ x._UnPack(assignVariableOptions)
+ return x
+
+ # AssignVariableOptionsT
+ def _UnPack(self, assignVariableOptions):
+ if assignVariableOptions is None:
+ return
+
+ # AssignVariableOptionsT
+ def Pack(self, builder):
+ AssignVariableOptionsStart(builder)
+ assignVariableOptions = AssignVariableOptionsEnd(builder)
+ return assignVariableOptions
+
+
+class RandomOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = RandomOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsRandomOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def RandomOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # RandomOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # RandomOptions
+ def Seed(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, o + self._tab.Pos)
+ return 0
+
+ # RandomOptions
+ def Seed2(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int64Flags, o + self._tab.Pos)
+ return 0
+
+def RandomOptionsStart(builder):
+ builder.StartObject(2)
+
+def RandomOptionsAddSeed(builder, seed):
+ builder.PrependInt64Slot(0, seed, 0)
+
+def RandomOptionsAddSeed2(builder, seed2):
+ builder.PrependInt64Slot(1, seed2, 0)
+
+def RandomOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class RandomOptionsT(object):
+
+ # RandomOptionsT
+ def __init__(self):
+ self.seed = 0 # type: int
+ self.seed2 = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ randomOptions = RandomOptions()
+ randomOptions.Init(buf, pos)
+ return cls.InitFromObj(randomOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, randomOptions):
+ x = RandomOptionsT()
+ x._UnPack(randomOptions)
+ return x
+
+ # RandomOptionsT
+ def _UnPack(self, randomOptions):
+ if randomOptions is None:
+ return
+ self.seed = randomOptions.Seed()
+ self.seed2 = randomOptions.Seed2()
+
+ # RandomOptionsT
+ def Pack(self, builder):
+ RandomOptionsStart(builder)
+ RandomOptionsAddSeed(builder, self.seed)
+ RandomOptionsAddSeed2(builder, self.seed2)
+ randomOptions = RandomOptionsEnd(builder)
+ return randomOptions
+
+
+class BucketizeOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = BucketizeOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsBucketizeOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def BucketizeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # BucketizeOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # BucketizeOptions
+ def Boundaries(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+ return 0
+
+ # BucketizeOptions
+ def BoundariesAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o)
+ return 0
+
+ # BucketizeOptions
+ def BoundariesLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # BucketizeOptions
+ def BoundariesIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ return o == 0
+
+def BucketizeOptionsStart(builder):
+ builder.StartObject(1)
+
+def BucketizeOptionsAddBoundaries(builder, boundaries):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(boundaries), 0)
+
+def BucketizeOptionsStartBoundariesVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def BucketizeOptionsEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class BucketizeOptionsT(object):
+
+ # BucketizeOptionsT
+ def __init__(self):
+ self.boundaries = None # type: List[float]
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ bucketizeOptions = BucketizeOptions()
+ bucketizeOptions.Init(buf, pos)
+ return cls.InitFromObj(bucketizeOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, bucketizeOptions):
+ x = BucketizeOptionsT()
+ x._UnPack(bucketizeOptions)
+ return x
+
+ # BucketizeOptionsT
+ def _UnPack(self, bucketizeOptions):
+ if bucketizeOptions is None:
+ return
+ if not bucketizeOptions.BoundariesIsNone():
+ if np is None:
+ self.boundaries = []
+ for i in range(bucketizeOptions.BoundariesLength()):
+ self.boundaries.append(bucketizeOptions.Boundaries(i))
+ else:
+ self.boundaries = bucketizeOptions.BoundariesAsNumpy()
+
+ # BucketizeOptionsT
+ def Pack(self, builder):
+ if self.boundaries is not None:
+ if np is not None and type(self.boundaries) is np.ndarray:
+ boundaries = builder.CreateNumpyVector(self.boundaries)
+ else:
+ BucketizeOptionsStartBoundariesVector(builder, len(self.boundaries))
+ for i in reversed(range(len(self.boundaries))):
+ builder.PrependFloat32(self.boundaries[i])
+ boundaries = builder.EndVector()
+ BucketizeOptionsStart(builder)
+ if self.boundaries is not None:
+ BucketizeOptionsAddBoundaries(builder, boundaries)
+ bucketizeOptions = BucketizeOptionsEnd(builder)
+ return bucketizeOptions
+
+
+class GeluOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = GeluOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsGeluOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def GeluOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # GeluOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # GeluOptions
+ def Approximate(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+ return False
+
+def GeluOptionsStart(builder):
+ builder.StartObject(1)
+
+def GeluOptionsAddApproximate(builder, approximate):
+ builder.PrependBoolSlot(0, approximate, 0)
+
+def GeluOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class GeluOptionsT(object):
+
+ # GeluOptionsT
+ def __init__(self):
+ self.approximate = False # type: bool
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ geluOptions = GeluOptions()
+ geluOptions.Init(buf, pos)
+ return cls.InitFromObj(geluOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, geluOptions):
+ x = GeluOptionsT()
+ x._UnPack(geluOptions)
+ return x
+
+ # GeluOptionsT
+ def _UnPack(self, geluOptions):
+ if geluOptions is None:
+ return
+ self.approximate = geluOptions.Approximate()
+
+ # GeluOptionsT
+ def Pack(self, builder):
+ GeluOptionsStart(builder)
+ GeluOptionsAddApproximate(builder, self.approximate)
+ geluOptions = GeluOptionsEnd(builder)
+ return geluOptions
+
+
+class DynamicUpdateSliceOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = DynamicUpdateSliceOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsDynamicUpdateSliceOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def DynamicUpdateSliceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # DynamicUpdateSliceOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def DynamicUpdateSliceOptionsStart(builder):
+ builder.StartObject(0)
+
+def DynamicUpdateSliceOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class DynamicUpdateSliceOptionsT(object):
+
+ # DynamicUpdateSliceOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ dynamicUpdateSliceOptions = DynamicUpdateSliceOptions()
+ dynamicUpdateSliceOptions.Init(buf, pos)
+ return cls.InitFromObj(dynamicUpdateSliceOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, dynamicUpdateSliceOptions):
+ x = DynamicUpdateSliceOptionsT()
+ x._UnPack(dynamicUpdateSliceOptions)
+ return x
+
+ # DynamicUpdateSliceOptionsT
+ def _UnPack(self, dynamicUpdateSliceOptions):
+ if dynamicUpdateSliceOptions is None:
+ return
+
+ # DynamicUpdateSliceOptionsT
+ def Pack(self, builder):
+ DynamicUpdateSliceOptionsStart(builder)
+ dynamicUpdateSliceOptions = DynamicUpdateSliceOptionsEnd(builder)
+ return dynamicUpdateSliceOptions
+
+
+class UnsortedSegmentProdOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = UnsortedSegmentProdOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsUnsortedSegmentProdOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def UnsortedSegmentProdOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # UnsortedSegmentProdOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def UnsortedSegmentProdOptionsStart(builder):
+ builder.StartObject(0)
+
+def UnsortedSegmentProdOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class UnsortedSegmentProdOptionsT(object):
+
+ # UnsortedSegmentProdOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ unsortedSegmentProdOptions = UnsortedSegmentProdOptions()
+ unsortedSegmentProdOptions.Init(buf, pos)
+ return cls.InitFromObj(unsortedSegmentProdOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, unsortedSegmentProdOptions):
+ x = UnsortedSegmentProdOptionsT()
+ x._UnPack(unsortedSegmentProdOptions)
+ return x
+
+ # UnsortedSegmentProdOptionsT
+ def _UnPack(self, unsortedSegmentProdOptions):
+ if unsortedSegmentProdOptions is None:
+ return
+
+ # UnsortedSegmentProdOptionsT
+ def Pack(self, builder):
+ UnsortedSegmentProdOptionsStart(builder)
+ unsortedSegmentProdOptions = UnsortedSegmentProdOptionsEnd(builder)
+ return unsortedSegmentProdOptions
+
+
+class UnsortedSegmentMaxOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = UnsortedSegmentMaxOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsUnsortedSegmentMaxOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def UnsortedSegmentMaxOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # UnsortedSegmentMaxOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def UnsortedSegmentMaxOptionsStart(builder):
+ builder.StartObject(0)
+
+def UnsortedSegmentMaxOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class UnsortedSegmentMaxOptionsT(object):
+
+ # UnsortedSegmentMaxOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ unsortedSegmentMaxOptions = UnsortedSegmentMaxOptions()
+ unsortedSegmentMaxOptions.Init(buf, pos)
+ return cls.InitFromObj(unsortedSegmentMaxOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, unsortedSegmentMaxOptions):
+ x = UnsortedSegmentMaxOptionsT()
+ x._UnPack(unsortedSegmentMaxOptions)
+ return x
+
+ # UnsortedSegmentMaxOptionsT
+ def _UnPack(self, unsortedSegmentMaxOptions):
+ if unsortedSegmentMaxOptions is None:
+ return
+
+ # UnsortedSegmentMaxOptionsT
+ def Pack(self, builder):
+ UnsortedSegmentMaxOptionsStart(builder)
+ unsortedSegmentMaxOptions = UnsortedSegmentMaxOptionsEnd(builder)
+ return unsortedSegmentMaxOptions
+
+
+class UnsortedSegmentSumOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = UnsortedSegmentSumOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsUnsortedSegmentSumOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def UnsortedSegmentSumOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # UnsortedSegmentSumOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def UnsortedSegmentSumOptionsStart(builder):
+ builder.StartObject(0)
+
+def UnsortedSegmentSumOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class UnsortedSegmentSumOptionsT(object):
+
+ # UnsortedSegmentSumOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ unsortedSegmentSumOptions = UnsortedSegmentSumOptions()
+ unsortedSegmentSumOptions.Init(buf, pos)
+ return cls.InitFromObj(unsortedSegmentSumOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, unsortedSegmentSumOptions):
+ x = UnsortedSegmentSumOptionsT()
+ x._UnPack(unsortedSegmentSumOptions)
+ return x
+
+ # UnsortedSegmentSumOptionsT
+ def _UnPack(self, unsortedSegmentSumOptions):
+ if unsortedSegmentSumOptions is None:
+ return
+
+ # UnsortedSegmentSumOptionsT
+ def Pack(self, builder):
+ UnsortedSegmentSumOptionsStart(builder)
+ unsortedSegmentSumOptions = UnsortedSegmentSumOptionsEnd(builder)
+ return unsortedSegmentSumOptions
+
+
+class ATan2Options(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = ATan2Options()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsATan2Options(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def ATan2OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # ATan2Options
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def ATan2OptionsStart(builder):
+ builder.StartObject(0)
+
+def ATan2OptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class ATan2OptionsT(object):
+
+ # ATan2OptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ atan2Options = ATan2Options()
+ atan2Options.Init(buf, pos)
+ return cls.InitFromObj(atan2Options)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, atan2Options):
+ x = ATan2OptionsT()
+ x._UnPack(atan2Options)
+ return x
+
+ # ATan2OptionsT
+ def _UnPack(self, atan2Options):
+ if atan2Options is None:
+ return
+
+ # ATan2OptionsT
+ def Pack(self, builder):
+ ATan2OptionsStart(builder)
+ atan2Options = ATan2OptionsEnd(builder)
+ return atan2Options
+
+
+class UnsortedSegmentMinOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = UnsortedSegmentMinOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsUnsortedSegmentMinOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def UnsortedSegmentMinOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # UnsortedSegmentMinOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def UnsortedSegmentMinOptionsStart(builder):
+ builder.StartObject(0)
+
+def UnsortedSegmentMinOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class UnsortedSegmentMinOptionsT(object):
+
+ # UnsortedSegmentMinOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ unsortedSegmentMinOptions = UnsortedSegmentMinOptions()
+ unsortedSegmentMinOptions.Init(buf, pos)
+ return cls.InitFromObj(unsortedSegmentMinOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, unsortedSegmentMinOptions):
+ x = UnsortedSegmentMinOptionsT()
+ x._UnPack(unsortedSegmentMinOptions)
+ return x
+
+ # UnsortedSegmentMinOptionsT
+ def _UnPack(self, unsortedSegmentMinOptions):
+ if unsortedSegmentMinOptions is None:
+ return
+
+ # UnsortedSegmentMinOptionsT
+ def Pack(self, builder):
+ UnsortedSegmentMinOptionsStart(builder)
+ unsortedSegmentMinOptions = UnsortedSegmentMinOptionsEnd(builder)
+ return unsortedSegmentMinOptions
+
+
+class SignOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = SignOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsSignOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def SignOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # SignOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def SignOptionsStart(builder):
+ builder.StartObject(0)
+
+def SignOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class SignOptionsT(object):
+
+ # SignOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ signOptions = SignOptions()
+ signOptions.Init(buf, pos)
+ return cls.InitFromObj(signOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, signOptions):
+ x = SignOptionsT()
+ x._UnPack(signOptions)
+ return x
+
+ # SignOptionsT
+ def _UnPack(self, signOptions):
+ if signOptions is None:
+ return
+
+ # SignOptionsT
+ def Pack(self, builder):
+ SignOptionsStart(builder)
+ signOptions = SignOptionsEnd(builder)
+ return signOptions
+
+
+class BitcastOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = BitcastOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsBitcastOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def BitcastOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # BitcastOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def BitcastOptionsStart(builder):
+ builder.StartObject(0)
+
+def BitcastOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class BitcastOptionsT(object):
+
+ # BitcastOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ bitcastOptions = BitcastOptions()
+ bitcastOptions.Init(buf, pos)
+ return cls.InitFromObj(bitcastOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, bitcastOptions):
+ x = BitcastOptionsT()
+ x._UnPack(bitcastOptions)
+ return x
+
+ # BitcastOptionsT
+ def _UnPack(self, bitcastOptions):
+ if bitcastOptions is None:
+ return
+
+ # BitcastOptionsT
+ def Pack(self, builder):
+ BitcastOptionsStart(builder)
+ bitcastOptions = BitcastOptionsEnd(builder)
+ return bitcastOptions
+
+
+class BitwiseXorOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = BitwiseXorOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsBitwiseXorOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def BitwiseXorOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # BitwiseXorOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def BitwiseXorOptionsStart(builder):
+ builder.StartObject(0)
+
+def BitwiseXorOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class BitwiseXorOptionsT(object):
+
+ # BitwiseXorOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ bitwiseXorOptions = BitwiseXorOptions()
+ bitwiseXorOptions.Init(buf, pos)
+ return cls.InitFromObj(bitwiseXorOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, bitwiseXorOptions):
+ x = BitwiseXorOptionsT()
+ x._UnPack(bitwiseXorOptions)
+ return x
+
+ # BitwiseXorOptionsT
+ def _UnPack(self, bitwiseXorOptions):
+ if bitwiseXorOptions is None:
+ return
+
+ # BitwiseXorOptionsT
+ def Pack(self, builder):
+ BitwiseXorOptionsStart(builder)
+ bitwiseXorOptions = BitwiseXorOptionsEnd(builder)
+ return bitwiseXorOptions
+
+
+class RightShiftOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = RightShiftOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsRightShiftOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def RightShiftOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # RightShiftOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def RightShiftOptionsStart(builder):
+ builder.StartObject(0)
+
+def RightShiftOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class RightShiftOptionsT(object):
+
+ # RightShiftOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ rightShiftOptions = RightShiftOptions()
+ rightShiftOptions.Init(buf, pos)
+ return cls.InitFromObj(rightShiftOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, rightShiftOptions):
+ x = RightShiftOptionsT()
+ x._UnPack(rightShiftOptions)
+ return x
+
+ # RightShiftOptionsT
+ def _UnPack(self, rightShiftOptions):
+ if rightShiftOptions is None:
+ return
+
+ # RightShiftOptionsT
+ def Pack(self, builder):
+ RightShiftOptionsStart(builder)
+ rightShiftOptions = RightShiftOptionsEnd(builder)
+ return rightShiftOptions
+
+
+class DilateOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = DilateOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsDilateOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def DilateOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # DilateOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+def DilateOptionsStart(builder):
+ builder.StartObject(0)
+
+def DilateOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class DilateOptionsT(object):
+
+ # DilateOptionsT
+ def __init__(self):
+ pass
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ dilateOptions = DilateOptions()
+ dilateOptions.Init(buf, pos)
+ return cls.InitFromObj(dilateOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, dilateOptions):
+ x = DilateOptionsT()
+ x._UnPack(dilateOptions)
+ return x
+
+ # DilateOptionsT
+ def _UnPack(self, dilateOptions):
+ if dilateOptions is None:
+ return
+
+ # DilateOptionsT
+ def Pack(self, builder):
+ DilateOptionsStart(builder)
+ dilateOptions = DilateOptionsEnd(builder)
+ return dilateOptions
+
+
+class ReduceWindowOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = ReduceWindowOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsReduceWindowOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def ReduceWindowOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # ReduceWindowOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # ReduceWindowOptions
+ def ReduceFunction(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def ReduceWindowOptionsStart(builder):
+ builder.StartObject(1)
+
+def ReduceWindowOptionsAddReduceFunction(builder, reduceFunction):
+ builder.PrependInt32Slot(0, reduceFunction, 0)
+
+def ReduceWindowOptionsEnd(builder):
+ return builder.EndObject()
+
+
+
+class ReduceWindowOptionsT(object):
+
+ # ReduceWindowOptionsT
+ def __init__(self):
+ self.reduceFunction = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ reduceWindowOptions = ReduceWindowOptions()
+ reduceWindowOptions.Init(buf, pos)
+ return cls.InitFromObj(reduceWindowOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, reduceWindowOptions):
+ x = ReduceWindowOptionsT()
+ x._UnPack(reduceWindowOptions)
+ return x
+
+ # ReduceWindowOptionsT
+ def _UnPack(self, reduceWindowOptions):
+ if reduceWindowOptions is None:
+ return
+ self.reduceFunction = reduceWindowOptions.ReduceFunction()
+
+ # ReduceWindowOptionsT
+ def Pack(self, builder):
+ ReduceWindowOptionsStart(builder)
+ ReduceWindowOptionsAddReduceFunction(builder, self.reduceFunction)
+ reduceWindowOptions = ReduceWindowOptionsEnd(builder)
+ return reduceWindowOptions
+
+
+class OperatorCode(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = OperatorCode()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsOperatorCode(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def OperatorCodeBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # OperatorCode
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # OperatorCode
+ def DeprecatedBuiltinCode(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # OperatorCode
+ def CustomCode(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.String(o + self._tab.Pos)
+ return None
+
+ # OperatorCode
+ def Version(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 1
+
+ # OperatorCode
+ def BuiltinCode(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def OperatorCodeStart(builder):
+ builder.StartObject(4)
+
+def OperatorCodeAddDeprecatedBuiltinCode(builder, deprecatedBuiltinCode):
+ builder.PrependInt8Slot(0, deprecatedBuiltinCode, 0)
+
+def OperatorCodeAddCustomCode(builder, customCode):
+ builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(customCode), 0)
+
+def OperatorCodeAddVersion(builder, version):
+ builder.PrependInt32Slot(2, version, 1)
+
+def OperatorCodeAddBuiltinCode(builder, builtinCode):
+ builder.PrependInt32Slot(3, builtinCode, 0)
+
+def OperatorCodeEnd(builder):
+ return builder.EndObject()
+
+
+
+class OperatorCodeT(object):
+
+ # OperatorCodeT
+ def __init__(self):
+ self.deprecatedBuiltinCode = 0 # type: int
+ self.customCode = None # type: str
+ self.version = 1 # type: int
+ self.builtinCode = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ operatorCode = OperatorCode()
+ operatorCode.Init(buf, pos)
+ return cls.InitFromObj(operatorCode)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, operatorCode):
+ x = OperatorCodeT()
+ x._UnPack(operatorCode)
+ return x
+
+ # OperatorCodeT
+ def _UnPack(self, operatorCode):
+ if operatorCode is None:
+ return
+ self.deprecatedBuiltinCode = operatorCode.DeprecatedBuiltinCode()
+ self.customCode = operatorCode.CustomCode()
+ self.version = operatorCode.Version()
+ self.builtinCode = operatorCode.BuiltinCode()
+
+ # OperatorCodeT
+ def Pack(self, builder):
+ if self.customCode is not None:
+ customCode = builder.CreateString(self.customCode)
+ OperatorCodeStart(builder)
+ OperatorCodeAddDeprecatedBuiltinCode(builder, self.deprecatedBuiltinCode)
+ if self.customCode is not None:
+ OperatorCodeAddCustomCode(builder, customCode)
+ OperatorCodeAddVersion(builder, self.version)
+ OperatorCodeAddBuiltinCode(builder, self.builtinCode)
+ operatorCode = OperatorCodeEnd(builder)
+ return operatorCode
+
+
+class StableHLOCompositeOptions(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = StableHLOCompositeOptions()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsStableHLOCompositeOptions(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def StableHLOCompositeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # StableHLOCompositeOptions
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # StableHLOCompositeOptions
+ def Name(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.String(o + self._tab.Pos)
+ return None
+
+ # StableHLOCompositeOptions
+ def DecompositionSubgraphIndex(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+ # StableHLOCompositeOptions
+ def CompositeAttributes(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ a = self._tab.Vector(o)
+ return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1))
+ return 0
+
+ # StableHLOCompositeOptions
+ def CompositeAttributesAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o)
+ return 0
+
+ # StableHLOCompositeOptions
+ def CompositeAttributesLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # StableHLOCompositeOptions
+ def CompositeAttributesIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ return o == 0
+
+ # StableHLOCompositeOptions
+ def CompositeAttributesFormat(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return 0
+
+ # StableHLOCompositeOptions
+ def Version(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
+ return 0
+
+def StableHLOCompositeOptionsStart(builder):
+ builder.StartObject(5)
+
+def StableHLOCompositeOptionsAddName(builder, name):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0)
+
+def StableHLOCompositeOptionsAddDecompositionSubgraphIndex(builder, decompositionSubgraphIndex):
+ builder.PrependInt32Slot(1, decompositionSubgraphIndex, 0)
+
+def StableHLOCompositeOptionsAddCompositeAttributes(builder, compositeAttributes):
+ builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(compositeAttributes), 0)
+
+def StableHLOCompositeOptionsStartCompositeAttributesVector(builder, numElems):
+ return builder.StartVector(1, numElems, 1)
+
+def StableHLOCompositeOptionsAddCompositeAttributesFormat(builder, compositeAttributesFormat):
+ builder.PrependInt8Slot(3, compositeAttributesFormat, 0)
+
+def StableHLOCompositeOptionsAddVersion(builder, version):
+ builder.PrependInt32Slot(4, version, 0)
+
+def StableHLOCompositeOptionsEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class StableHLOCompositeOptionsT(object):
+
+ # StableHLOCompositeOptionsT
+ def __init__(self):
+ self.name = None # type: str
+ self.decompositionSubgraphIndex = 0 # type: int
+ self.compositeAttributes = None # type: List[int]
+ self.compositeAttributesFormat = 0 # type: int
+ self.version = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ stableHlocompositeOptions = StableHLOCompositeOptions()
+ stableHlocompositeOptions.Init(buf, pos)
+ return cls.InitFromObj(stableHlocompositeOptions)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, stableHlocompositeOptions):
+ x = StableHLOCompositeOptionsT()
+ x._UnPack(stableHlocompositeOptions)
+ return x
+
+ # StableHLOCompositeOptionsT
+ def _UnPack(self, stableHlocompositeOptions):
+ if stableHlocompositeOptions is None:
+ return
+ self.name = stableHlocompositeOptions.Name()
+ self.decompositionSubgraphIndex = stableHlocompositeOptions.DecompositionSubgraphIndex()
+ if not stableHlocompositeOptions.CompositeAttributesIsNone():
+ if np is None:
+ self.compositeAttributes = []
+ for i in range(stableHlocompositeOptions.CompositeAttributesLength()):
+ self.compositeAttributes.append(stableHlocompositeOptions.CompositeAttributes(i))
+ else:
+ self.compositeAttributes = stableHlocompositeOptions.CompositeAttributesAsNumpy()
+ self.compositeAttributesFormat = stableHlocompositeOptions.CompositeAttributesFormat()
+ self.version = stableHlocompositeOptions.Version()
+
+ # StableHLOCompositeOptionsT
+ def Pack(self, builder):
+ if self.name is not None:
+ name = builder.CreateString(self.name)
+ if self.compositeAttributes is not None:
+ if np is not None and type(self.compositeAttributes) is np.ndarray:
+ compositeAttributes = builder.CreateNumpyVector(self.compositeAttributes)
+ else:
+ StableHLOCompositeOptionsStartCompositeAttributesVector(builder, len(self.compositeAttributes))
+ for i in reversed(range(len(self.compositeAttributes))):
+ builder.PrependUint8(self.compositeAttributes[i])
+ compositeAttributes = builder.EndVector()
+ StableHLOCompositeOptionsStart(builder)
+ if self.name is not None:
+ StableHLOCompositeOptionsAddName(builder, name)
+ StableHLOCompositeOptionsAddDecompositionSubgraphIndex(builder, self.decompositionSubgraphIndex)
+ if self.compositeAttributes is not None:
+ StableHLOCompositeOptionsAddCompositeAttributes(builder, compositeAttributes)
+ StableHLOCompositeOptionsAddCompositeAttributesFormat(builder, self.compositeAttributesFormat)
+ StableHLOCompositeOptionsAddVersion(builder, self.version)
+ stableHlocompositeOptions = StableHLOCompositeOptionsEnd(builder)
+ return stableHlocompositeOptions
+
class Operator(object):
__slots__ = ['_tab']
@@ -7573,60 +16900,84 @@
return self._tab.Get(flatbuffers.number_types.Uint64Flags, o + self._tab.Pos)
return 0
-def OperatorStart(builder): builder.StartObject(11)
-def Start(builder):
- return OperatorStart(builder)
-def OperatorAddOpcodeIndex(builder, opcodeIndex): builder.PrependUint32Slot(0, opcodeIndex, 0)
-def AddOpcodeIndex(builder, opcodeIndex):
- return OperatorAddOpcodeIndex(builder, opcodeIndex)
-def OperatorAddInputs(builder, inputs): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(inputs), 0)
-def AddInputs(builder, inputs):
- return OperatorAddInputs(builder, inputs)
-def OperatorStartInputsVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartInputsVector(builder, numElems):
- return OperatorStartInputsVector(builder, numElems)
-def OperatorAddOutputs(builder, outputs): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(outputs), 0)
-def AddOutputs(builder, outputs):
- return OperatorAddOutputs(builder, outputs)
-def OperatorStartOutputsVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartOutputsVector(builder, numElems):
- return OperatorStartOutputsVector(builder, numElems)
-def OperatorAddBuiltinOptionsType(builder, builtinOptionsType): builder.PrependUint8Slot(3, builtinOptionsType, 0)
-def AddBuiltinOptionsType(builder, builtinOptionsType):
- return OperatorAddBuiltinOptionsType(builder, builtinOptionsType)
-def OperatorAddBuiltinOptions(builder, builtinOptions): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(builtinOptions), 0)
-def AddBuiltinOptions(builder, builtinOptions):
- return OperatorAddBuiltinOptions(builder, builtinOptions)
-def OperatorAddCustomOptions(builder, customOptions): builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(customOptions), 0)
-def AddCustomOptions(builder, customOptions):
- return OperatorAddCustomOptions(builder, customOptions)
-def OperatorStartCustomOptionsVector(builder, numElems): return builder.StartVector(1, numElems, 1)
-def StartCustomOptionsVector(builder, numElems):
- return OperatorStartCustomOptionsVector(builder, numElems)
-def OperatorAddCustomOptionsFormat(builder, customOptionsFormat): builder.PrependInt8Slot(6, customOptionsFormat, 0)
-def AddCustomOptionsFormat(builder, customOptionsFormat):
- return OperatorAddCustomOptionsFormat(builder, customOptionsFormat)
-def OperatorAddMutatingVariableInputs(builder, mutatingVariableInputs): builder.PrependUOffsetTRelativeSlot(7, flatbuffers.number_types.UOffsetTFlags.py_type(mutatingVariableInputs), 0)
-def AddMutatingVariableInputs(builder, mutatingVariableInputs):
- return OperatorAddMutatingVariableInputs(builder, mutatingVariableInputs)
-def OperatorStartMutatingVariableInputsVector(builder, numElems): return builder.StartVector(1, numElems, 1)
-def StartMutatingVariableInputsVector(builder, numElems):
- return OperatorStartMutatingVariableInputsVector(builder, numElems)
-def OperatorAddIntermediates(builder, intermediates): builder.PrependUOffsetTRelativeSlot(8, flatbuffers.number_types.UOffsetTFlags.py_type(intermediates), 0)
-def AddIntermediates(builder, intermediates):
- return OperatorAddIntermediates(builder, intermediates)
-def OperatorStartIntermediatesVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartIntermediatesVector(builder, numElems):
- return OperatorStartIntermediatesVector(builder, numElems)
-def OperatorAddLargeCustomOptionsOffset(builder, largeCustomOptionsOffset): builder.PrependUint64Slot(9, largeCustomOptionsOffset, 0)
-def AddLargeCustomOptionsOffset(builder, largeCustomOptionsOffset):
- return OperatorAddLargeCustomOptionsOffset(builder, largeCustomOptionsOffset)
-def OperatorAddLargeCustomOptionsSize(builder, largeCustomOptionsSize): builder.PrependUint64Slot(10, largeCustomOptionsSize, 0)
-def AddLargeCustomOptionsSize(builder, largeCustomOptionsSize):
- return OperatorAddLargeCustomOptionsSize(builder, largeCustomOptionsSize)
-def OperatorEnd(builder): return builder.EndObject()
-def End(builder):
- return OperatorEnd(builder)
+ # Operator
+ def BuiltinOptions2Type(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(26))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos)
+ return 0
+
+ # Operator
+ def BuiltinOptions2(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(28))
+ if o != 0:
+ from flatbuffers.table import Table
+ obj = Table(bytearray(), 0)
+ self._tab.Union(obj, o)
+ return obj
+ return None
+
+def OperatorStart(builder):
+ builder.StartObject(13)
+
+def OperatorAddOpcodeIndex(builder, opcodeIndex):
+ builder.PrependUint32Slot(0, opcodeIndex, 0)
+
+def OperatorAddInputs(builder, inputs):
+ builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(inputs), 0)
+
+def OperatorStartInputsVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def OperatorAddOutputs(builder, outputs):
+ builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(outputs), 0)
+
+def OperatorStartOutputsVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def OperatorAddBuiltinOptionsType(builder, builtinOptionsType):
+ builder.PrependUint8Slot(3, builtinOptionsType, 0)
+
+def OperatorAddBuiltinOptions(builder, builtinOptions):
+ builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(builtinOptions), 0)
+
+def OperatorAddCustomOptions(builder, customOptions):
+ builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(customOptions), 0)
+
+def OperatorStartCustomOptionsVector(builder, numElems):
+ return builder.StartVector(1, numElems, 1)
+
+def OperatorAddCustomOptionsFormat(builder, customOptionsFormat):
+ builder.PrependInt8Slot(6, customOptionsFormat, 0)
+
+def OperatorAddMutatingVariableInputs(builder, mutatingVariableInputs):
+ builder.PrependUOffsetTRelativeSlot(7, flatbuffers.number_types.UOffsetTFlags.py_type(mutatingVariableInputs), 0)
+
+def OperatorStartMutatingVariableInputsVector(builder, numElems):
+ return builder.StartVector(1, numElems, 1)
+
+def OperatorAddIntermediates(builder, intermediates):
+ builder.PrependUOffsetTRelativeSlot(8, flatbuffers.number_types.UOffsetTFlags.py_type(intermediates), 0)
+
+def OperatorStartIntermediatesVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def OperatorAddLargeCustomOptionsOffset(builder, largeCustomOptionsOffset):
+ builder.PrependUint64Slot(9, largeCustomOptionsOffset, 0)
+
+def OperatorAddLargeCustomOptionsSize(builder, largeCustomOptionsSize):
+ builder.PrependUint64Slot(10, largeCustomOptionsSize, 0)
+
+def OperatorAddBuiltinOptions2Type(builder, builtinOptions2Type):
+ builder.PrependUint8Slot(11, builtinOptions2Type, 0)
+
+def OperatorAddBuiltinOptions2(builder, builtinOptions2):
+ builder.PrependUOffsetTRelativeSlot(12, flatbuffers.number_types.UOffsetTFlags.py_type(builtinOptions2), 0)
+
+def OperatorEnd(builder):
+ return builder.EndObject()
+
+
try:
from typing import List, Union
except:
@@ -7647,6 +16998,8 @@
self.intermediates = None # type: List[int]
self.largeCustomOptionsOffset = 0 # type: int
self.largeCustomOptionsSize = 0 # type: int
+ self.builtinOptions2Type = 0 # type: int
+ self.builtinOptions2 = None # type: Union[None, StablehloConcatenateOptionsT, StablehloBroadcastInDimOptionsT, StablehloSliceOptionsT, StablehloConvolutionOptionsT, StablehloCustomCallOptionsT, StablehloReduceOptionsT, StablehloScatterOptionsT, StablehloCompareOptionsT, StablehloDynamicSliceOptionsT, StablehloPadOptionsT, StablehloIotaOptionsT, StablehloDotGeneralOptionsT, StablehloReduceWindowOptionsT, StablehloSortOptionsT, StablehloWhileOptionsT, StablehloGatherOptionsT, StablehloTransposeOptionsT, DilateOptionsT, StablehloRngBitGeneratorOptionsT, ReduceWindowOptionsT, StableHLOCompositeOptionsT]
@classmethod
def InitFromBuf(cls, buf, pos):
@@ -7655,6 +17008,11 @@
return cls.InitFromObj(operator)
@classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
def InitFromObj(cls, operator):
x = OperatorT()
x._UnPack(operator)
@@ -7705,6 +17063,8 @@
self.intermediates = operator.IntermediatesAsNumpy()
self.largeCustomOptionsOffset = operator.LargeCustomOptionsOffset()
self.largeCustomOptionsSize = operator.LargeCustomOptionsSize()
+ self.builtinOptions2Type = operator.BuiltinOptions2Type()
+ self.builtinOptions2 = BuiltinOptions2Creator(self.builtinOptions2Type, operator.BuiltinOptions2())
# OperatorT
def Pack(self, builder):
@@ -7750,6 +17110,8 @@
for i in reversed(range(len(self.intermediates))):
builder.PrependInt32(self.intermediates[i])
intermediates = builder.EndVector()
+ if self.builtinOptions2 is not None:
+ builtinOptions2 = self.builtinOptions2.Pack(builder)
OperatorStart(builder)
OperatorAddOpcodeIndex(builder, self.opcodeIndex)
if self.inputs is not None:
@@ -7768,4009 +17130,12 @@
OperatorAddIntermediates(builder, intermediates)
OperatorAddLargeCustomOptionsOffset(builder, self.largeCustomOptionsOffset)
OperatorAddLargeCustomOptionsSize(builder, self.largeCustomOptionsSize)
+ OperatorAddBuiltinOptions2Type(builder, self.builtinOptions2Type)
+ if self.builtinOptions2 is not None:
+ OperatorAddBuiltinOptions2(builder, builtinOptions2)
operator = OperatorEnd(builder)
return operator
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class OperatorCode(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = OperatorCode()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsOperatorCode(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def OperatorCodeBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # OperatorCode
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # OperatorCode
- def DeprecatedBuiltinCode(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # OperatorCode
- def CustomCode(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.String(o + self._tab.Pos)
- return None
-
- # OperatorCode
- def Version(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 1
-
- # OperatorCode
- def BuiltinCode(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
-def OperatorCodeStart(builder): builder.StartObject(4)
-def Start(builder):
- return OperatorCodeStart(builder)
-def OperatorCodeAddDeprecatedBuiltinCode(builder, deprecatedBuiltinCode): builder.PrependInt8Slot(0, deprecatedBuiltinCode, 0)
-def AddDeprecatedBuiltinCode(builder, deprecatedBuiltinCode):
- return OperatorCodeAddDeprecatedBuiltinCode(builder, deprecatedBuiltinCode)
-def OperatorCodeAddCustomCode(builder, customCode): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(customCode), 0)
-def AddCustomCode(builder, customCode):
- return OperatorCodeAddCustomCode(builder, customCode)
-def OperatorCodeAddVersion(builder, version): builder.PrependInt32Slot(2, version, 1)
-def AddVersion(builder, version):
- return OperatorCodeAddVersion(builder, version)
-def OperatorCodeAddBuiltinCode(builder, builtinCode): builder.PrependInt32Slot(3, builtinCode, 0)
-def AddBuiltinCode(builder, builtinCode):
- return OperatorCodeAddBuiltinCode(builder, builtinCode)
-def OperatorCodeEnd(builder): return builder.EndObject()
-def End(builder):
- return OperatorCodeEnd(builder)
-
-class OperatorCodeT(object):
-
- # OperatorCodeT
- def __init__(self):
- self.deprecatedBuiltinCode = 0 # type: int
- self.customCode = None # type: str
- self.version = 1 # type: int
- self.builtinCode = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- operatorCode = OperatorCode()
- operatorCode.Init(buf, pos)
- return cls.InitFromObj(operatorCode)
-
- @classmethod
- def InitFromObj(cls, operatorCode):
- x = OperatorCodeT()
- x._UnPack(operatorCode)
- return x
-
- # OperatorCodeT
- def _UnPack(self, operatorCode):
- if operatorCode is None:
- return
- self.deprecatedBuiltinCode = operatorCode.DeprecatedBuiltinCode()
- self.customCode = operatorCode.CustomCode()
- self.version = operatorCode.Version()
- self.builtinCode = operatorCode.BuiltinCode()
-
- # OperatorCodeT
- def Pack(self, builder):
- if self.customCode is not None:
- customCode = builder.CreateString(self.customCode)
- OperatorCodeStart(builder)
- OperatorCodeAddDeprecatedBuiltinCode(builder, self.deprecatedBuiltinCode)
- if self.customCode is not None:
- OperatorCodeAddCustomCode(builder, customCode)
- OperatorCodeAddVersion(builder, self.version)
- OperatorCodeAddBuiltinCode(builder, self.builtinCode)
- operatorCode = OperatorCodeEnd(builder)
- return operatorCode
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class PackOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = PackOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsPackOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def PackOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # PackOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # PackOptions
- def ValuesCount(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # PackOptions
- def Axis(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
-def PackOptionsStart(builder): builder.StartObject(2)
-def Start(builder):
- return PackOptionsStart(builder)
-def PackOptionsAddValuesCount(builder, valuesCount): builder.PrependInt32Slot(0, valuesCount, 0)
-def AddValuesCount(builder, valuesCount):
- return PackOptionsAddValuesCount(builder, valuesCount)
-def PackOptionsAddAxis(builder, axis): builder.PrependInt32Slot(1, axis, 0)
-def AddAxis(builder, axis):
- return PackOptionsAddAxis(builder, axis)
-def PackOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return PackOptionsEnd(builder)
-
-class PackOptionsT(object):
-
- # PackOptionsT
- def __init__(self):
- self.valuesCount = 0 # type: int
- self.axis = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- packOptions = PackOptions()
- packOptions.Init(buf, pos)
- return cls.InitFromObj(packOptions)
-
- @classmethod
- def InitFromObj(cls, packOptions):
- x = PackOptionsT()
- x._UnPack(packOptions)
- return x
-
- # PackOptionsT
- def _UnPack(self, packOptions):
- if packOptions is None:
- return
- self.valuesCount = packOptions.ValuesCount()
- self.axis = packOptions.Axis()
-
- # PackOptionsT
- def Pack(self, builder):
- PackOptionsStart(builder)
- PackOptionsAddValuesCount(builder, self.valuesCount)
- PackOptionsAddAxis(builder, self.axis)
- packOptions = PackOptionsEnd(builder)
- return packOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class PadOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = PadOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsPadOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def PadOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # PadOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def PadOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return PadOptionsStart(builder)
-def PadOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return PadOptionsEnd(builder)
-
-class PadOptionsT(object):
-
- # PadOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- padOptions = PadOptions()
- padOptions.Init(buf, pos)
- return cls.InitFromObj(padOptions)
-
- @classmethod
- def InitFromObj(cls, padOptions):
- x = PadOptionsT()
- x._UnPack(padOptions)
- return x
-
- # PadOptionsT
- def _UnPack(self, padOptions):
- if padOptions is None:
- return
-
- # PadOptionsT
- def Pack(self, builder):
- PadOptionsStart(builder)
- padOptions = PadOptionsEnd(builder)
- return padOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class PadV2Options(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = PadV2Options()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsPadV2Options(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def PadV2OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # PadV2Options
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def PadV2OptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return PadV2OptionsStart(builder)
-def PadV2OptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return PadV2OptionsEnd(builder)
-
-class PadV2OptionsT(object):
-
- # PadV2OptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- padV2options = PadV2Options()
- padV2options.Init(buf, pos)
- return cls.InitFromObj(padV2options)
-
- @classmethod
- def InitFromObj(cls, padV2options):
- x = PadV2OptionsT()
- x._UnPack(padV2options)
- return x
-
- # PadV2OptionsT
- def _UnPack(self, padV2options):
- if padV2options is None:
- return
-
- # PadV2OptionsT
- def Pack(self, builder):
- PadV2OptionsStart(builder)
- padV2options = PadV2OptionsEnd(builder)
- return padV2options
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-class Padding(object):
- SAME = 0
- VALID = 1
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class Pool2DOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = Pool2DOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsPool2DOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def Pool2DOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # Pool2DOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # Pool2DOptions
- def Padding(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # Pool2DOptions
- def StrideW(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # Pool2DOptions
- def StrideH(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # Pool2DOptions
- def FilterWidth(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # Pool2DOptions
- def FilterHeight(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # Pool2DOptions
- def FusedActivationFunction(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
-def Pool2DOptionsStart(builder): builder.StartObject(6)
-def Start(builder):
- return Pool2DOptionsStart(builder)
-def Pool2DOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0)
-def AddPadding(builder, padding):
- return Pool2DOptionsAddPadding(builder, padding)
-def Pool2DOptionsAddStrideW(builder, strideW): builder.PrependInt32Slot(1, strideW, 0)
-def AddStrideW(builder, strideW):
- return Pool2DOptionsAddStrideW(builder, strideW)
-def Pool2DOptionsAddStrideH(builder, strideH): builder.PrependInt32Slot(2, strideH, 0)
-def AddStrideH(builder, strideH):
- return Pool2DOptionsAddStrideH(builder, strideH)
-def Pool2DOptionsAddFilterWidth(builder, filterWidth): builder.PrependInt32Slot(3, filterWidth, 0)
-def AddFilterWidth(builder, filterWidth):
- return Pool2DOptionsAddFilterWidth(builder, filterWidth)
-def Pool2DOptionsAddFilterHeight(builder, filterHeight): builder.PrependInt32Slot(4, filterHeight, 0)
-def AddFilterHeight(builder, filterHeight):
- return Pool2DOptionsAddFilterHeight(builder, filterHeight)
-def Pool2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(5, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return Pool2DOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def Pool2DOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return Pool2DOptionsEnd(builder)
-
-class Pool2DOptionsT(object):
-
- # Pool2DOptionsT
- def __init__(self):
- self.padding = 0 # type: int
- self.strideW = 0 # type: int
- self.strideH = 0 # type: int
- self.filterWidth = 0 # type: int
- self.filterHeight = 0 # type: int
- self.fusedActivationFunction = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- pool2doptions = Pool2DOptions()
- pool2doptions.Init(buf, pos)
- return cls.InitFromObj(pool2doptions)
-
- @classmethod
- def InitFromObj(cls, pool2doptions):
- x = Pool2DOptionsT()
- x._UnPack(pool2doptions)
- return x
-
- # Pool2DOptionsT
- def _UnPack(self, pool2doptions):
- if pool2doptions is None:
- return
- self.padding = pool2doptions.Padding()
- self.strideW = pool2doptions.StrideW()
- self.strideH = pool2doptions.StrideH()
- self.filterWidth = pool2doptions.FilterWidth()
- self.filterHeight = pool2doptions.FilterHeight()
- self.fusedActivationFunction = pool2doptions.FusedActivationFunction()
-
- # Pool2DOptionsT
- def Pack(self, builder):
- Pool2DOptionsStart(builder)
- Pool2DOptionsAddPadding(builder, self.padding)
- Pool2DOptionsAddStrideW(builder, self.strideW)
- Pool2DOptionsAddStrideH(builder, self.strideH)
- Pool2DOptionsAddFilterWidth(builder, self.filterWidth)
- Pool2DOptionsAddFilterHeight(builder, self.filterHeight)
- Pool2DOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
- pool2doptions = Pool2DOptionsEnd(builder)
- return pool2doptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class PowOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = PowOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsPowOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def PowOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # PowOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def PowOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return PowOptionsStart(builder)
-def PowOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return PowOptionsEnd(builder)
-
-class PowOptionsT(object):
-
- # PowOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- powOptions = PowOptions()
- powOptions.Init(buf, pos)
- return cls.InitFromObj(powOptions)
-
- @classmethod
- def InitFromObj(cls, powOptions):
- x = PowOptionsT()
- x._UnPack(powOptions)
- return x
-
- # PowOptionsT
- def _UnPack(self, powOptions):
- if powOptions is None:
- return
-
- # PowOptionsT
- def Pack(self, builder):
- PowOptionsStart(builder)
- powOptions = PowOptionsEnd(builder)
- return powOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-class QuantizationDetails(object):
- NONE = 0
- CustomQuantization = 1
-
-def QuantizationDetailsCreator(unionType, table):
- from flatbuffers.table import Table
- if not isinstance(table, Table):
- return None
- if unionType == QuantizationDetails().CustomQuantization:
- return CustomQuantizationT.InitFromBuf(table.Bytes, table.Pos)
- return None
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class QuantizationParameters(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = QuantizationParameters()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsQuantizationParameters(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def QuantizationParametersBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # QuantizationParameters
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # QuantizationParameters
- def Min(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- a = self._tab.Vector(o)
- return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
- return 0
-
- # QuantizationParameters
- def MinAsNumpy(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o)
- return 0
-
- # QuantizationParameters
- def MinLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # QuantizationParameters
- def MinIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- return o == 0
-
- # QuantizationParameters
- def Max(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- a = self._tab.Vector(o)
- return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
- return 0
-
- # QuantizationParameters
- def MaxAsNumpy(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o)
- return 0
-
- # QuantizationParameters
- def MaxLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # QuantizationParameters
- def MaxIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- return o == 0
-
- # QuantizationParameters
- def Scale(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- a = self._tab.Vector(o)
- return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
- return 0
-
- # QuantizationParameters
- def ScaleAsNumpy(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o)
- return 0
-
- # QuantizationParameters
- def ScaleLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # QuantizationParameters
- def ScaleIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- return o == 0
-
- # QuantizationParameters
- def ZeroPoint(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
- if o != 0:
- a = self._tab.Vector(o)
- return self._tab.Get(flatbuffers.number_types.Int64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
- return 0
-
- # QuantizationParameters
- def ZeroPointAsNumpy(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
- if o != 0:
- return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int64Flags, o)
- return 0
-
- # QuantizationParameters
- def ZeroPointLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # QuantizationParameters
- def ZeroPointIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
- return o == 0
-
- # QuantizationParameters
- def DetailsType(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos)
- return 0
-
- # QuantizationParameters
- def Details(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
- if o != 0:
- from flatbuffers.table import Table
- obj = Table(bytearray(), 0)
- self._tab.Union(obj, o)
- return obj
- return None
-
- # QuantizationParameters
- def QuantizedDimension(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
-def QuantizationParametersStart(builder): builder.StartObject(7)
-def Start(builder):
- return QuantizationParametersStart(builder)
-def QuantizationParametersAddMin(builder, min): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(min), 0)
-def AddMin(builder, min):
- return QuantizationParametersAddMin(builder, min)
-def QuantizationParametersStartMinVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartMinVector(builder, numElems):
- return QuantizationParametersStartMinVector(builder, numElems)
-def QuantizationParametersAddMax(builder, max): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(max), 0)
-def AddMax(builder, max):
- return QuantizationParametersAddMax(builder, max)
-def QuantizationParametersStartMaxVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartMaxVector(builder, numElems):
- return QuantizationParametersStartMaxVector(builder, numElems)
-def QuantizationParametersAddScale(builder, scale): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(scale), 0)
-def AddScale(builder, scale):
- return QuantizationParametersAddScale(builder, scale)
-def QuantizationParametersStartScaleVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartScaleVector(builder, numElems):
- return QuantizationParametersStartScaleVector(builder, numElems)
-def QuantizationParametersAddZeroPoint(builder, zeroPoint): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(zeroPoint), 0)
-def AddZeroPoint(builder, zeroPoint):
- return QuantizationParametersAddZeroPoint(builder, zeroPoint)
-def QuantizationParametersStartZeroPointVector(builder, numElems): return builder.StartVector(8, numElems, 8)
-def StartZeroPointVector(builder, numElems):
- return QuantizationParametersStartZeroPointVector(builder, numElems)
-def QuantizationParametersAddDetailsType(builder, detailsType): builder.PrependUint8Slot(4, detailsType, 0)
-def AddDetailsType(builder, detailsType):
- return QuantizationParametersAddDetailsType(builder, detailsType)
-def QuantizationParametersAddDetails(builder, details): builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(details), 0)
-def AddDetails(builder, details):
- return QuantizationParametersAddDetails(builder, details)
-def QuantizationParametersAddQuantizedDimension(builder, quantizedDimension): builder.PrependInt32Slot(6, quantizedDimension, 0)
-def AddQuantizedDimension(builder, quantizedDimension):
- return QuantizationParametersAddQuantizedDimension(builder, quantizedDimension)
-def QuantizationParametersEnd(builder): return builder.EndObject()
-def End(builder):
- return QuantizationParametersEnd(builder)
-try:
- from typing import List, Union
-except:
- pass
-
-class QuantizationParametersT(object):
-
- # QuantizationParametersT
- def __init__(self):
- self.min = None # type: List[float]
- self.max = None # type: List[float]
- self.scale = None # type: List[float]
- self.zeroPoint = None # type: List[int]
- self.detailsType = 0 # type: int
- self.details = None # type: Union[None, CustomQuantizationT]
- self.quantizedDimension = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- quantizationParameters = QuantizationParameters()
- quantizationParameters.Init(buf, pos)
- return cls.InitFromObj(quantizationParameters)
-
- @classmethod
- def InitFromObj(cls, quantizationParameters):
- x = QuantizationParametersT()
- x._UnPack(quantizationParameters)
- return x
-
- # QuantizationParametersT
- def _UnPack(self, quantizationParameters):
- if quantizationParameters is None:
- return
- if not quantizationParameters.MinIsNone():
- if np is None:
- self.min = []
- for i in range(quantizationParameters.MinLength()):
- self.min.append(quantizationParameters.Min(i))
- else:
- self.min = quantizationParameters.MinAsNumpy()
- if not quantizationParameters.MaxIsNone():
- if np is None:
- self.max = []
- for i in range(quantizationParameters.MaxLength()):
- self.max.append(quantizationParameters.Max(i))
- else:
- self.max = quantizationParameters.MaxAsNumpy()
- if not quantizationParameters.ScaleIsNone():
- if np is None:
- self.scale = []
- for i in range(quantizationParameters.ScaleLength()):
- self.scale.append(quantizationParameters.Scale(i))
- else:
- self.scale = quantizationParameters.ScaleAsNumpy()
- if not quantizationParameters.ZeroPointIsNone():
- if np is None:
- self.zeroPoint = []
- for i in range(quantizationParameters.ZeroPointLength()):
- self.zeroPoint.append(quantizationParameters.ZeroPoint(i))
- else:
- self.zeroPoint = quantizationParameters.ZeroPointAsNumpy()
- self.detailsType = quantizationParameters.DetailsType()
- self.details = QuantizationDetailsCreator(self.detailsType, quantizationParameters.Details())
- self.quantizedDimension = quantizationParameters.QuantizedDimension()
-
- # QuantizationParametersT
- def Pack(self, builder):
- if self.min is not None:
- if np is not None and type(self.min) is np.ndarray:
- min = builder.CreateNumpyVector(self.min)
- else:
- QuantizationParametersStartMinVector(builder, len(self.min))
- for i in reversed(range(len(self.min))):
- builder.PrependFloat32(self.min[i])
- min = builder.EndVector()
- if self.max is not None:
- if np is not None and type(self.max) is np.ndarray:
- max = builder.CreateNumpyVector(self.max)
- else:
- QuantizationParametersStartMaxVector(builder, len(self.max))
- for i in reversed(range(len(self.max))):
- builder.PrependFloat32(self.max[i])
- max = builder.EndVector()
- if self.scale is not None:
- if np is not None and type(self.scale) is np.ndarray:
- scale = builder.CreateNumpyVector(self.scale)
- else:
- QuantizationParametersStartScaleVector(builder, len(self.scale))
- for i in reversed(range(len(self.scale))):
- builder.PrependFloat32(self.scale[i])
- scale = builder.EndVector()
- if self.zeroPoint is not None:
- if np is not None and type(self.zeroPoint) is np.ndarray:
- zeroPoint = builder.CreateNumpyVector(self.zeroPoint)
- else:
- QuantizationParametersStartZeroPointVector(builder, len(self.zeroPoint))
- for i in reversed(range(len(self.zeroPoint))):
- builder.PrependInt64(self.zeroPoint[i])
- zeroPoint = builder.EndVector()
- if self.details is not None:
- details = self.details.Pack(builder)
- QuantizationParametersStart(builder)
- if self.min is not None:
- QuantizationParametersAddMin(builder, min)
- if self.max is not None:
- QuantizationParametersAddMax(builder, max)
- if self.scale is not None:
- QuantizationParametersAddScale(builder, scale)
- if self.zeroPoint is not None:
- QuantizationParametersAddZeroPoint(builder, zeroPoint)
- QuantizationParametersAddDetailsType(builder, self.detailsType)
- if self.details is not None:
- QuantizationParametersAddDetails(builder, details)
- QuantizationParametersAddQuantizedDimension(builder, self.quantizedDimension)
- quantizationParameters = QuantizationParametersEnd(builder)
- return quantizationParameters
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class QuantizeOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = QuantizeOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsQuantizeOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def QuantizeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # QuantizeOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def QuantizeOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return QuantizeOptionsStart(builder)
-def QuantizeOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return QuantizeOptionsEnd(builder)
-
-class QuantizeOptionsT(object):
-
- # QuantizeOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- quantizeOptions = QuantizeOptions()
- quantizeOptions.Init(buf, pos)
- return cls.InitFromObj(quantizeOptions)
-
- @classmethod
- def InitFromObj(cls, quantizeOptions):
- x = QuantizeOptionsT()
- x._UnPack(quantizeOptions)
- return x
-
- # QuantizeOptionsT
- def _UnPack(self, quantizeOptions):
- if quantizeOptions is None:
- return
-
- # QuantizeOptionsT
- def Pack(self, builder):
- QuantizeOptionsStart(builder)
- quantizeOptions = QuantizeOptionsEnd(builder)
- return quantizeOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class RNNOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = RNNOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsRNNOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def RNNOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # RNNOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # RNNOptions
- def FusedActivationFunction(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # RNNOptions
- def AsymmetricQuantizeInputs(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
-def RNNOptionsStart(builder): builder.StartObject(2)
-def Start(builder):
- return RNNOptionsStart(builder)
-def RNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return RNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def RNNOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs): builder.PrependBoolSlot(1, asymmetricQuantizeInputs, 0)
-def AddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
- return RNNOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs)
-def RNNOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return RNNOptionsEnd(builder)
-
-class RNNOptionsT(object):
-
- # RNNOptionsT
- def __init__(self):
- self.fusedActivationFunction = 0 # type: int
- self.asymmetricQuantizeInputs = False # type: bool
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- rnnoptions = RNNOptions()
- rnnoptions.Init(buf, pos)
- return cls.InitFromObj(rnnoptions)
-
- @classmethod
- def InitFromObj(cls, rnnoptions):
- x = RNNOptionsT()
- x._UnPack(rnnoptions)
- return x
-
- # RNNOptionsT
- def _UnPack(self, rnnoptions):
- if rnnoptions is None:
- return
- self.fusedActivationFunction = rnnoptions.FusedActivationFunction()
- self.asymmetricQuantizeInputs = rnnoptions.AsymmetricQuantizeInputs()
-
- # RNNOptionsT
- def Pack(self, builder):
- RNNOptionsStart(builder)
- RNNOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
- RNNOptionsAddAsymmetricQuantizeInputs(builder, self.asymmetricQuantizeInputs)
- rnnoptions = RNNOptionsEnd(builder)
- return rnnoptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class RandomOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = RandomOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsRandomOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def RandomOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # RandomOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # RandomOptions
- def Seed(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int64Flags, o + self._tab.Pos)
- return 0
-
- # RandomOptions
- def Seed2(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int64Flags, o + self._tab.Pos)
- return 0
-
-def RandomOptionsStart(builder): builder.StartObject(2)
-def Start(builder):
- return RandomOptionsStart(builder)
-def RandomOptionsAddSeed(builder, seed): builder.PrependInt64Slot(0, seed, 0)
-def AddSeed(builder, seed):
- return RandomOptionsAddSeed(builder, seed)
-def RandomOptionsAddSeed2(builder, seed2): builder.PrependInt64Slot(1, seed2, 0)
-def AddSeed2(builder, seed2):
- return RandomOptionsAddSeed2(builder, seed2)
-def RandomOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return RandomOptionsEnd(builder)
-
-class RandomOptionsT(object):
-
- # RandomOptionsT
- def __init__(self):
- self.seed = 0 # type: int
- self.seed2 = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- randomOptions = RandomOptions()
- randomOptions.Init(buf, pos)
- return cls.InitFromObj(randomOptions)
-
- @classmethod
- def InitFromObj(cls, randomOptions):
- x = RandomOptionsT()
- x._UnPack(randomOptions)
- return x
-
- # RandomOptionsT
- def _UnPack(self, randomOptions):
- if randomOptions is None:
- return
- self.seed = randomOptions.Seed()
- self.seed2 = randomOptions.Seed2()
-
- # RandomOptionsT
- def Pack(self, builder):
- RandomOptionsStart(builder)
- RandomOptionsAddSeed(builder, self.seed)
- RandomOptionsAddSeed2(builder, self.seed2)
- randomOptions = RandomOptionsEnd(builder)
- return randomOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class RangeOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = RangeOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsRangeOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def RangeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # RangeOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def RangeOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return RangeOptionsStart(builder)
-def RangeOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return RangeOptionsEnd(builder)
-
-class RangeOptionsT(object):
-
- # RangeOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- rangeOptions = RangeOptions()
- rangeOptions.Init(buf, pos)
- return cls.InitFromObj(rangeOptions)
-
- @classmethod
- def InitFromObj(cls, rangeOptions):
- x = RangeOptionsT()
- x._UnPack(rangeOptions)
- return x
-
- # RangeOptionsT
- def _UnPack(self, rangeOptions):
- if rangeOptions is None:
- return
-
- # RangeOptionsT
- def Pack(self, builder):
- RangeOptionsStart(builder)
- rangeOptions = RangeOptionsEnd(builder)
- return rangeOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class RankOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = RankOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsRankOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def RankOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # RankOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def RankOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return RankOptionsStart(builder)
-def RankOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return RankOptionsEnd(builder)
-
-class RankOptionsT(object):
-
- # RankOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- rankOptions = RankOptions()
- rankOptions.Init(buf, pos)
- return cls.InitFromObj(rankOptions)
-
- @classmethod
- def InitFromObj(cls, rankOptions):
- x = RankOptionsT()
- x._UnPack(rankOptions)
- return x
-
- # RankOptionsT
- def _UnPack(self, rankOptions):
- if rankOptions is None:
- return
-
- # RankOptionsT
- def Pack(self, builder):
- RankOptionsStart(builder)
- rankOptions = RankOptionsEnd(builder)
- return rankOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class ReadVariableOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = ReadVariableOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsReadVariableOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def ReadVariableOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # ReadVariableOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def ReadVariableOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return ReadVariableOptionsStart(builder)
-def ReadVariableOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return ReadVariableOptionsEnd(builder)
-
-class ReadVariableOptionsT(object):
-
- # ReadVariableOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- readVariableOptions = ReadVariableOptions()
- readVariableOptions.Init(buf, pos)
- return cls.InitFromObj(readVariableOptions)
-
- @classmethod
- def InitFromObj(cls, readVariableOptions):
- x = ReadVariableOptionsT()
- x._UnPack(readVariableOptions)
- return x
-
- # ReadVariableOptionsT
- def _UnPack(self, readVariableOptions):
- if readVariableOptions is None:
- return
-
- # ReadVariableOptionsT
- def Pack(self, builder):
- ReadVariableOptionsStart(builder)
- readVariableOptions = ReadVariableOptionsEnd(builder)
- return readVariableOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class ReducerOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = ReducerOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsReducerOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def ReducerOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # ReducerOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # ReducerOptions
- def KeepDims(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
-def ReducerOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return ReducerOptionsStart(builder)
-def ReducerOptionsAddKeepDims(builder, keepDims): builder.PrependBoolSlot(0, keepDims, 0)
-def AddKeepDims(builder, keepDims):
- return ReducerOptionsAddKeepDims(builder, keepDims)
-def ReducerOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return ReducerOptionsEnd(builder)
-
-class ReducerOptionsT(object):
-
- # ReducerOptionsT
- def __init__(self):
- self.keepDims = False # type: bool
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- reducerOptions = ReducerOptions()
- reducerOptions.Init(buf, pos)
- return cls.InitFromObj(reducerOptions)
-
- @classmethod
- def InitFromObj(cls, reducerOptions):
- x = ReducerOptionsT()
- x._UnPack(reducerOptions)
- return x
-
- # ReducerOptionsT
- def _UnPack(self, reducerOptions):
- if reducerOptions is None:
- return
- self.keepDims = reducerOptions.KeepDims()
-
- # ReducerOptionsT
- def Pack(self, builder):
- ReducerOptionsStart(builder)
- ReducerOptionsAddKeepDims(builder, self.keepDims)
- reducerOptions = ReducerOptionsEnd(builder)
- return reducerOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class ReshapeOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = ReshapeOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsReshapeOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def ReshapeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # ReshapeOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # ReshapeOptions
- def NewShape(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- a = self._tab.Vector(o)
- return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
- return 0
-
- # ReshapeOptions
- def NewShapeAsNumpy(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
- return 0
-
- # ReshapeOptions
- def NewShapeLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # ReshapeOptions
- def NewShapeIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- return o == 0
-
-def ReshapeOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return ReshapeOptionsStart(builder)
-def ReshapeOptionsAddNewShape(builder, newShape): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(newShape), 0)
-def AddNewShape(builder, newShape):
- return ReshapeOptionsAddNewShape(builder, newShape)
-def ReshapeOptionsStartNewShapeVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartNewShapeVector(builder, numElems):
- return ReshapeOptionsStartNewShapeVector(builder, numElems)
-def ReshapeOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return ReshapeOptionsEnd(builder)
-try:
- from typing import List
-except:
- pass
-
-class ReshapeOptionsT(object):
-
- # ReshapeOptionsT
- def __init__(self):
- self.newShape = None # type: List[int]
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- reshapeOptions = ReshapeOptions()
- reshapeOptions.Init(buf, pos)
- return cls.InitFromObj(reshapeOptions)
-
- @classmethod
- def InitFromObj(cls, reshapeOptions):
- x = ReshapeOptionsT()
- x._UnPack(reshapeOptions)
- return x
-
- # ReshapeOptionsT
- def _UnPack(self, reshapeOptions):
- if reshapeOptions is None:
- return
- if not reshapeOptions.NewShapeIsNone():
- if np is None:
- self.newShape = []
- for i in range(reshapeOptions.NewShapeLength()):
- self.newShape.append(reshapeOptions.NewShape(i))
- else:
- self.newShape = reshapeOptions.NewShapeAsNumpy()
-
- # ReshapeOptionsT
- def Pack(self, builder):
- if self.newShape is not None:
- if np is not None and type(self.newShape) is np.ndarray:
- newShape = builder.CreateNumpyVector(self.newShape)
- else:
- ReshapeOptionsStartNewShapeVector(builder, len(self.newShape))
- for i in reversed(range(len(self.newShape))):
- builder.PrependInt32(self.newShape[i])
- newShape = builder.EndVector()
- ReshapeOptionsStart(builder)
- if self.newShape is not None:
- ReshapeOptionsAddNewShape(builder, newShape)
- reshapeOptions = ReshapeOptionsEnd(builder)
- return reshapeOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class ResizeBilinearOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = ResizeBilinearOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsResizeBilinearOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def ResizeBilinearOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # ResizeBilinearOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # ResizeBilinearOptions
- def AlignCorners(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
- # ResizeBilinearOptions
- def HalfPixelCenters(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
-def ResizeBilinearOptionsStart(builder): builder.StartObject(4)
-def Start(builder):
- return ResizeBilinearOptionsStart(builder)
-def ResizeBilinearOptionsAddAlignCorners(builder, alignCorners): builder.PrependBoolSlot(2, alignCorners, 0)
-def AddAlignCorners(builder, alignCorners):
- return ResizeBilinearOptionsAddAlignCorners(builder, alignCorners)
-def ResizeBilinearOptionsAddHalfPixelCenters(builder, halfPixelCenters): builder.PrependBoolSlot(3, halfPixelCenters, 0)
-def AddHalfPixelCenters(builder, halfPixelCenters):
- return ResizeBilinearOptionsAddHalfPixelCenters(builder, halfPixelCenters)
-def ResizeBilinearOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return ResizeBilinearOptionsEnd(builder)
-
-class ResizeBilinearOptionsT(object):
-
- # ResizeBilinearOptionsT
- def __init__(self):
- self.alignCorners = False # type: bool
- self.halfPixelCenters = False # type: bool
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- resizeBilinearOptions = ResizeBilinearOptions()
- resizeBilinearOptions.Init(buf, pos)
- return cls.InitFromObj(resizeBilinearOptions)
-
- @classmethod
- def InitFromObj(cls, resizeBilinearOptions):
- x = ResizeBilinearOptionsT()
- x._UnPack(resizeBilinearOptions)
- return x
-
- # ResizeBilinearOptionsT
- def _UnPack(self, resizeBilinearOptions):
- if resizeBilinearOptions is None:
- return
- self.alignCorners = resizeBilinearOptions.AlignCorners()
- self.halfPixelCenters = resizeBilinearOptions.HalfPixelCenters()
-
- # ResizeBilinearOptionsT
- def Pack(self, builder):
- ResizeBilinearOptionsStart(builder)
- ResizeBilinearOptionsAddAlignCorners(builder, self.alignCorners)
- ResizeBilinearOptionsAddHalfPixelCenters(builder, self.halfPixelCenters)
- resizeBilinearOptions = ResizeBilinearOptionsEnd(builder)
- return resizeBilinearOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class ResizeNearestNeighborOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = ResizeNearestNeighborOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsResizeNearestNeighborOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def ResizeNearestNeighborOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # ResizeNearestNeighborOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # ResizeNearestNeighborOptions
- def AlignCorners(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
- # ResizeNearestNeighborOptions
- def HalfPixelCenters(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
-def ResizeNearestNeighborOptionsStart(builder): builder.StartObject(2)
-def Start(builder):
- return ResizeNearestNeighborOptionsStart(builder)
-def ResizeNearestNeighborOptionsAddAlignCorners(builder, alignCorners): builder.PrependBoolSlot(0, alignCorners, 0)
-def AddAlignCorners(builder, alignCorners):
- return ResizeNearestNeighborOptionsAddAlignCorners(builder, alignCorners)
-def ResizeNearestNeighborOptionsAddHalfPixelCenters(builder, halfPixelCenters): builder.PrependBoolSlot(1, halfPixelCenters, 0)
-def AddHalfPixelCenters(builder, halfPixelCenters):
- return ResizeNearestNeighborOptionsAddHalfPixelCenters(builder, halfPixelCenters)
-def ResizeNearestNeighborOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return ResizeNearestNeighborOptionsEnd(builder)
-
-class ResizeNearestNeighborOptionsT(object):
-
- # ResizeNearestNeighborOptionsT
- def __init__(self):
- self.alignCorners = False # type: bool
- self.halfPixelCenters = False # type: bool
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- resizeNearestNeighborOptions = ResizeNearestNeighborOptions()
- resizeNearestNeighborOptions.Init(buf, pos)
- return cls.InitFromObj(resizeNearestNeighborOptions)
-
- @classmethod
- def InitFromObj(cls, resizeNearestNeighborOptions):
- x = ResizeNearestNeighborOptionsT()
- x._UnPack(resizeNearestNeighborOptions)
- return x
-
- # ResizeNearestNeighborOptionsT
- def _UnPack(self, resizeNearestNeighborOptions):
- if resizeNearestNeighborOptions is None:
- return
- self.alignCorners = resizeNearestNeighborOptions.AlignCorners()
- self.halfPixelCenters = resizeNearestNeighborOptions.HalfPixelCenters()
-
- # ResizeNearestNeighborOptionsT
- def Pack(self, builder):
- ResizeNearestNeighborOptionsStart(builder)
- ResizeNearestNeighborOptionsAddAlignCorners(builder, self.alignCorners)
- ResizeNearestNeighborOptionsAddHalfPixelCenters(builder, self.halfPixelCenters)
- resizeNearestNeighborOptions = ResizeNearestNeighborOptionsEnd(builder)
- return resizeNearestNeighborOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class ReverseSequenceOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = ReverseSequenceOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsReverseSequenceOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def ReverseSequenceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # ReverseSequenceOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # ReverseSequenceOptions
- def SeqDim(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # ReverseSequenceOptions
- def BatchDim(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
-def ReverseSequenceOptionsStart(builder): builder.StartObject(2)
-def Start(builder):
- return ReverseSequenceOptionsStart(builder)
-def ReverseSequenceOptionsAddSeqDim(builder, seqDim): builder.PrependInt32Slot(0, seqDim, 0)
-def AddSeqDim(builder, seqDim):
- return ReverseSequenceOptionsAddSeqDim(builder, seqDim)
-def ReverseSequenceOptionsAddBatchDim(builder, batchDim): builder.PrependInt32Slot(1, batchDim, 0)
-def AddBatchDim(builder, batchDim):
- return ReverseSequenceOptionsAddBatchDim(builder, batchDim)
-def ReverseSequenceOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return ReverseSequenceOptionsEnd(builder)
-
-class ReverseSequenceOptionsT(object):
-
- # ReverseSequenceOptionsT
- def __init__(self):
- self.seqDim = 0 # type: int
- self.batchDim = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- reverseSequenceOptions = ReverseSequenceOptions()
- reverseSequenceOptions.Init(buf, pos)
- return cls.InitFromObj(reverseSequenceOptions)
-
- @classmethod
- def InitFromObj(cls, reverseSequenceOptions):
- x = ReverseSequenceOptionsT()
- x._UnPack(reverseSequenceOptions)
- return x
-
- # ReverseSequenceOptionsT
- def _UnPack(self, reverseSequenceOptions):
- if reverseSequenceOptions is None:
- return
- self.seqDim = reverseSequenceOptions.SeqDim()
- self.batchDim = reverseSequenceOptions.BatchDim()
-
- # ReverseSequenceOptionsT
- def Pack(self, builder):
- ReverseSequenceOptionsStart(builder)
- ReverseSequenceOptionsAddSeqDim(builder, self.seqDim)
- ReverseSequenceOptionsAddBatchDim(builder, self.batchDim)
- reverseSequenceOptions = ReverseSequenceOptionsEnd(builder)
- return reverseSequenceOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class ReverseV2Options(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = ReverseV2Options()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsReverseV2Options(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def ReverseV2OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # ReverseV2Options
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def ReverseV2OptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return ReverseV2OptionsStart(builder)
-def ReverseV2OptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return ReverseV2OptionsEnd(builder)
-
-class ReverseV2OptionsT(object):
-
- # ReverseV2OptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- reverseV2options = ReverseV2Options()
- reverseV2options.Init(buf, pos)
- return cls.InitFromObj(reverseV2options)
-
- @classmethod
- def InitFromObj(cls, reverseV2options):
- x = ReverseV2OptionsT()
- x._UnPack(reverseV2options)
- return x
-
- # ReverseV2OptionsT
- def _UnPack(self, reverseV2options):
- if reverseV2options is None:
- return
-
- # ReverseV2OptionsT
- def Pack(self, builder):
- ReverseV2OptionsStart(builder)
- reverseV2options = ReverseV2OptionsEnd(builder)
- return reverseV2options
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class Rfft2dOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = Rfft2dOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsRfft2dOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def Rfft2dOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # Rfft2dOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def Rfft2dOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return Rfft2dOptionsStart(builder)
-def Rfft2dOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return Rfft2dOptionsEnd(builder)
-
-class Rfft2dOptionsT(object):
-
- # Rfft2dOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- rfft2dOptions = Rfft2dOptions()
- rfft2dOptions.Init(buf, pos)
- return cls.InitFromObj(rfft2dOptions)
-
- @classmethod
- def InitFromObj(cls, rfft2dOptions):
- x = Rfft2dOptionsT()
- x._UnPack(rfft2dOptions)
- return x
-
- # Rfft2dOptionsT
- def _UnPack(self, rfft2dOptions):
- if rfft2dOptions is None:
- return
-
- # Rfft2dOptionsT
- def Pack(self, builder):
- Rfft2dOptionsStart(builder)
- rfft2dOptions = Rfft2dOptionsEnd(builder)
- return rfft2dOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class RightShiftOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = RightShiftOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsRightShiftOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def RightShiftOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # RightShiftOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def RightShiftOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return RightShiftOptionsStart(builder)
-def RightShiftOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return RightShiftOptionsEnd(builder)
-
-class RightShiftOptionsT(object):
-
- # RightShiftOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- rightShiftOptions = RightShiftOptions()
- rightShiftOptions.Init(buf, pos)
- return cls.InitFromObj(rightShiftOptions)
-
- @classmethod
- def InitFromObj(cls, rightShiftOptions):
- x = RightShiftOptionsT()
- x._UnPack(rightShiftOptions)
- return x
-
- # RightShiftOptionsT
- def _UnPack(self, rightShiftOptions):
- if rightShiftOptions is None:
- return
-
- # RightShiftOptionsT
- def Pack(self, builder):
- RightShiftOptionsStart(builder)
- rightShiftOptions = RightShiftOptionsEnd(builder)
- return rightShiftOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SVDFOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SVDFOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSVDFOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SVDFOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SVDFOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # SVDFOptions
- def Rank(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # SVDFOptions
- def FusedActivationFunction(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # SVDFOptions
- def AsymmetricQuantizeInputs(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
-def SVDFOptionsStart(builder): builder.StartObject(3)
-def Start(builder):
- return SVDFOptionsStart(builder)
-def SVDFOptionsAddRank(builder, rank): builder.PrependInt32Slot(0, rank, 0)
-def AddRank(builder, rank):
- return SVDFOptionsAddRank(builder, rank)
-def SVDFOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(1, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return SVDFOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def SVDFOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs): builder.PrependBoolSlot(2, asymmetricQuantizeInputs, 0)
-def AddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
- return SVDFOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs)
-def SVDFOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SVDFOptionsEnd(builder)
-
-class SVDFOptionsT(object):
-
- # SVDFOptionsT
- def __init__(self):
- self.rank = 0 # type: int
- self.fusedActivationFunction = 0 # type: int
- self.asymmetricQuantizeInputs = False # type: bool
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- svdfoptions = SVDFOptions()
- svdfoptions.Init(buf, pos)
- return cls.InitFromObj(svdfoptions)
-
- @classmethod
- def InitFromObj(cls, svdfoptions):
- x = SVDFOptionsT()
- x._UnPack(svdfoptions)
- return x
-
- # SVDFOptionsT
- def _UnPack(self, svdfoptions):
- if svdfoptions is None:
- return
- self.rank = svdfoptions.Rank()
- self.fusedActivationFunction = svdfoptions.FusedActivationFunction()
- self.asymmetricQuantizeInputs = svdfoptions.AsymmetricQuantizeInputs()
-
- # SVDFOptionsT
- def Pack(self, builder):
- SVDFOptionsStart(builder)
- SVDFOptionsAddRank(builder, self.rank)
- SVDFOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
- SVDFOptionsAddAsymmetricQuantizeInputs(builder, self.asymmetricQuantizeInputs)
- svdfoptions = SVDFOptionsEnd(builder)
- return svdfoptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class ScatterNdOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = ScatterNdOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsScatterNdOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def ScatterNdOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # ScatterNdOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def ScatterNdOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return ScatterNdOptionsStart(builder)
-def ScatterNdOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return ScatterNdOptionsEnd(builder)
-
-class ScatterNdOptionsT(object):
-
- # ScatterNdOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- scatterNdOptions = ScatterNdOptions()
- scatterNdOptions.Init(buf, pos)
- return cls.InitFromObj(scatterNdOptions)
-
- @classmethod
- def InitFromObj(cls, scatterNdOptions):
- x = ScatterNdOptionsT()
- x._UnPack(scatterNdOptions)
- return x
-
- # ScatterNdOptionsT
- def _UnPack(self, scatterNdOptions):
- if scatterNdOptions is None:
- return
-
- # ScatterNdOptionsT
- def Pack(self, builder):
- ScatterNdOptionsStart(builder)
- scatterNdOptions = ScatterNdOptionsEnd(builder)
- return scatterNdOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SegmentSumOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SegmentSumOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSegmentSumOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SegmentSumOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SegmentSumOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def SegmentSumOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return SegmentSumOptionsStart(builder)
-def SegmentSumOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SegmentSumOptionsEnd(builder)
-
-class SegmentSumOptionsT(object):
-
- # SegmentSumOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- segmentSumOptions = SegmentSumOptions()
- segmentSumOptions.Init(buf, pos)
- return cls.InitFromObj(segmentSumOptions)
-
- @classmethod
- def InitFromObj(cls, segmentSumOptions):
- x = SegmentSumOptionsT()
- x._UnPack(segmentSumOptions)
- return x
-
- # SegmentSumOptionsT
- def _UnPack(self, segmentSumOptions):
- if segmentSumOptions is None:
- return
-
- # SegmentSumOptionsT
- def Pack(self, builder):
- SegmentSumOptionsStart(builder)
- segmentSumOptions = SegmentSumOptionsEnd(builder)
- return segmentSumOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SelectOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SelectOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSelectOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SelectOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SelectOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def SelectOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return SelectOptionsStart(builder)
-def SelectOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SelectOptionsEnd(builder)
-
-class SelectOptionsT(object):
-
- # SelectOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- selectOptions = SelectOptions()
- selectOptions.Init(buf, pos)
- return cls.InitFromObj(selectOptions)
-
- @classmethod
- def InitFromObj(cls, selectOptions):
- x = SelectOptionsT()
- x._UnPack(selectOptions)
- return x
-
- # SelectOptionsT
- def _UnPack(self, selectOptions):
- if selectOptions is None:
- return
-
- # SelectOptionsT
- def Pack(self, builder):
- SelectOptionsStart(builder)
- selectOptions = SelectOptionsEnd(builder)
- return selectOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SelectV2Options(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SelectV2Options()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSelectV2Options(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SelectV2OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SelectV2Options
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def SelectV2OptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return SelectV2OptionsStart(builder)
-def SelectV2OptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SelectV2OptionsEnd(builder)
-
-class SelectV2OptionsT(object):
-
- # SelectV2OptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- selectV2options = SelectV2Options()
- selectV2options.Init(buf, pos)
- return cls.InitFromObj(selectV2options)
-
- @classmethod
- def InitFromObj(cls, selectV2options):
- x = SelectV2OptionsT()
- x._UnPack(selectV2options)
- return x
-
- # SelectV2OptionsT
- def _UnPack(self, selectV2options):
- if selectV2options is None:
- return
-
- # SelectV2OptionsT
- def Pack(self, builder):
- SelectV2OptionsStart(builder)
- selectV2options = SelectV2OptionsEnd(builder)
- return selectV2options
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SequenceRNNOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SequenceRNNOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSequenceRNNOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SequenceRNNOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SequenceRNNOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # SequenceRNNOptions
- def TimeMajor(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
- # SequenceRNNOptions
- def FusedActivationFunction(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # SequenceRNNOptions
- def AsymmetricQuantizeInputs(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
-def SequenceRNNOptionsStart(builder): builder.StartObject(3)
-def Start(builder):
- return SequenceRNNOptionsStart(builder)
-def SequenceRNNOptionsAddTimeMajor(builder, timeMajor): builder.PrependBoolSlot(0, timeMajor, 0)
-def AddTimeMajor(builder, timeMajor):
- return SequenceRNNOptionsAddTimeMajor(builder, timeMajor)
-def SequenceRNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(1, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return SequenceRNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def SequenceRNNOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs): builder.PrependBoolSlot(2, asymmetricQuantizeInputs, 0)
-def AddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
- return SequenceRNNOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs)
-def SequenceRNNOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SequenceRNNOptionsEnd(builder)
-
-class SequenceRNNOptionsT(object):
-
- # SequenceRNNOptionsT
- def __init__(self):
- self.timeMajor = False # type: bool
- self.fusedActivationFunction = 0 # type: int
- self.asymmetricQuantizeInputs = False # type: bool
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- sequenceRnnoptions = SequenceRNNOptions()
- sequenceRnnoptions.Init(buf, pos)
- return cls.InitFromObj(sequenceRnnoptions)
-
- @classmethod
- def InitFromObj(cls, sequenceRnnoptions):
- x = SequenceRNNOptionsT()
- x._UnPack(sequenceRnnoptions)
- return x
-
- # SequenceRNNOptionsT
- def _UnPack(self, sequenceRnnoptions):
- if sequenceRnnoptions is None:
- return
- self.timeMajor = sequenceRnnoptions.TimeMajor()
- self.fusedActivationFunction = sequenceRnnoptions.FusedActivationFunction()
- self.asymmetricQuantizeInputs = sequenceRnnoptions.AsymmetricQuantizeInputs()
-
- # SequenceRNNOptionsT
- def Pack(self, builder):
- SequenceRNNOptionsStart(builder)
- SequenceRNNOptionsAddTimeMajor(builder, self.timeMajor)
- SequenceRNNOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
- SequenceRNNOptionsAddAsymmetricQuantizeInputs(builder, self.asymmetricQuantizeInputs)
- sequenceRnnoptions = SequenceRNNOptionsEnd(builder)
- return sequenceRnnoptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class ShapeOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = ShapeOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsShapeOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def ShapeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # ShapeOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # ShapeOptions
- def OutType(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
-def ShapeOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return ShapeOptionsStart(builder)
-def ShapeOptionsAddOutType(builder, outType): builder.PrependInt8Slot(0, outType, 0)
-def AddOutType(builder, outType):
- return ShapeOptionsAddOutType(builder, outType)
-def ShapeOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return ShapeOptionsEnd(builder)
-
-class ShapeOptionsT(object):
-
- # ShapeOptionsT
- def __init__(self):
- self.outType = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- shapeOptions = ShapeOptions()
- shapeOptions.Init(buf, pos)
- return cls.InitFromObj(shapeOptions)
-
- @classmethod
- def InitFromObj(cls, shapeOptions):
- x = ShapeOptionsT()
- x._UnPack(shapeOptions)
- return x
-
- # ShapeOptionsT
- def _UnPack(self, shapeOptions):
- if shapeOptions is None:
- return
- self.outType = shapeOptions.OutType()
-
- # ShapeOptionsT
- def Pack(self, builder):
- ShapeOptionsStart(builder)
- ShapeOptionsAddOutType(builder, self.outType)
- shapeOptions = ShapeOptionsEnd(builder)
- return shapeOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SignOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SignOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSignOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SignOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SignOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def SignOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return SignOptionsStart(builder)
-def SignOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SignOptionsEnd(builder)
-
-class SignOptionsT(object):
-
- # SignOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- signOptions = SignOptions()
- signOptions.Init(buf, pos)
- return cls.InitFromObj(signOptions)
-
- @classmethod
- def InitFromObj(cls, signOptions):
- x = SignOptionsT()
- x._UnPack(signOptions)
- return x
-
- # SignOptionsT
- def _UnPack(self, signOptions):
- if signOptions is None:
- return
-
- # SignOptionsT
- def Pack(self, builder):
- SignOptionsStart(builder)
- signOptions = SignOptionsEnd(builder)
- return signOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SignatureDef(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SignatureDef()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSignatureDef(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SignatureDefBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SignatureDef
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # SignatureDef
- def Inputs(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- x = self._tab.Vector(o)
- x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
- x = self._tab.Indirect(x)
- obj = TensorMap()
- obj.Init(self._tab.Bytes, x)
- return obj
- return None
-
- # SignatureDef
- def InputsLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # SignatureDef
- def InputsIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- return o == 0
-
- # SignatureDef
- def Outputs(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- x = self._tab.Vector(o)
- x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
- x = self._tab.Indirect(x)
- obj = TensorMap()
- obj.Init(self._tab.Bytes, x)
- return obj
- return None
-
- # SignatureDef
- def OutputsLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # SignatureDef
- def OutputsIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- return o == 0
-
- # SignatureDef
- def SignatureKey(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return self._tab.String(o + self._tab.Pos)
- return None
-
- # SignatureDef
- def SubgraphIndex(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos)
- return 0
-
-def SignatureDefStart(builder): builder.StartObject(5)
-def Start(builder):
- return SignatureDefStart(builder)
-def SignatureDefAddInputs(builder, inputs): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(inputs), 0)
-def AddInputs(builder, inputs):
- return SignatureDefAddInputs(builder, inputs)
-def SignatureDefStartInputsVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartInputsVector(builder, numElems):
- return SignatureDefStartInputsVector(builder, numElems)
-def SignatureDefAddOutputs(builder, outputs): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(outputs), 0)
-def AddOutputs(builder, outputs):
- return SignatureDefAddOutputs(builder, outputs)
-def SignatureDefStartOutputsVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartOutputsVector(builder, numElems):
- return SignatureDefStartOutputsVector(builder, numElems)
-def SignatureDefAddSignatureKey(builder, signatureKey): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(signatureKey), 0)
-def AddSignatureKey(builder, signatureKey):
- return SignatureDefAddSignatureKey(builder, signatureKey)
-def SignatureDefAddSubgraphIndex(builder, subgraphIndex): builder.PrependUint32Slot(4, subgraphIndex, 0)
-def AddSubgraphIndex(builder, subgraphIndex):
- return SignatureDefAddSubgraphIndex(builder, subgraphIndex)
-def SignatureDefEnd(builder): return builder.EndObject()
-def End(builder):
- return SignatureDefEnd(builder)
-try:
- from typing import List
-except:
- pass
-
-class SignatureDefT(object):
-
- # SignatureDefT
- def __init__(self):
- self.inputs = None # type: List[TensorMapT]
- self.outputs = None # type: List[TensorMapT]
- self.signatureKey = None # type: str
- self.subgraphIndex = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- signatureDef = SignatureDef()
- signatureDef.Init(buf, pos)
- return cls.InitFromObj(signatureDef)
-
- @classmethod
- def InitFromObj(cls, signatureDef):
- x = SignatureDefT()
- x._UnPack(signatureDef)
- return x
-
- # SignatureDefT
- def _UnPack(self, signatureDef):
- if signatureDef is None:
- return
- if not signatureDef.InputsIsNone():
- self.inputs = []
- for i in range(signatureDef.InputsLength()):
- if signatureDef.Inputs(i) is None:
- self.inputs.append(None)
- else:
- tensorMap_ = TensorMapT.InitFromObj(signatureDef.Inputs(i))
- self.inputs.append(tensorMap_)
- if not signatureDef.OutputsIsNone():
- self.outputs = []
- for i in range(signatureDef.OutputsLength()):
- if signatureDef.Outputs(i) is None:
- self.outputs.append(None)
- else:
- tensorMap_ = TensorMapT.InitFromObj(signatureDef.Outputs(i))
- self.outputs.append(tensorMap_)
- self.signatureKey = signatureDef.SignatureKey()
- self.subgraphIndex = signatureDef.SubgraphIndex()
-
- # SignatureDefT
- def Pack(self, builder):
- if self.inputs is not None:
- inputslist = []
- for i in range(len(self.inputs)):
- inputslist.append(self.inputs[i].Pack(builder))
- SignatureDefStartInputsVector(builder, len(self.inputs))
- for i in reversed(range(len(self.inputs))):
- builder.PrependUOffsetTRelative(inputslist[i])
- inputs = builder.EndVector()
- if self.outputs is not None:
- outputslist = []
- for i in range(len(self.outputs)):
- outputslist.append(self.outputs[i].Pack(builder))
- SignatureDefStartOutputsVector(builder, len(self.outputs))
- for i in reversed(range(len(self.outputs))):
- builder.PrependUOffsetTRelative(outputslist[i])
- outputs = builder.EndVector()
- if self.signatureKey is not None:
- signatureKey = builder.CreateString(self.signatureKey)
- SignatureDefStart(builder)
- if self.inputs is not None:
- SignatureDefAddInputs(builder, inputs)
- if self.outputs is not None:
- SignatureDefAddOutputs(builder, outputs)
- if self.signatureKey is not None:
- SignatureDefAddSignatureKey(builder, signatureKey)
- SignatureDefAddSubgraphIndex(builder, self.subgraphIndex)
- signatureDef = SignatureDefEnd(builder)
- return signatureDef
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SkipGramOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SkipGramOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSkipGramOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SkipGramOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SkipGramOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # SkipGramOptions
- def NgramSize(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # SkipGramOptions
- def MaxSkipSize(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # SkipGramOptions
- def IncludeAllNgrams(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
-def SkipGramOptionsStart(builder): builder.StartObject(3)
-def Start(builder):
- return SkipGramOptionsStart(builder)
-def SkipGramOptionsAddNgramSize(builder, ngramSize): builder.PrependInt32Slot(0, ngramSize, 0)
-def AddNgramSize(builder, ngramSize):
- return SkipGramOptionsAddNgramSize(builder, ngramSize)
-def SkipGramOptionsAddMaxSkipSize(builder, maxSkipSize): builder.PrependInt32Slot(1, maxSkipSize, 0)
-def AddMaxSkipSize(builder, maxSkipSize):
- return SkipGramOptionsAddMaxSkipSize(builder, maxSkipSize)
-def SkipGramOptionsAddIncludeAllNgrams(builder, includeAllNgrams): builder.PrependBoolSlot(2, includeAllNgrams, 0)
-def AddIncludeAllNgrams(builder, includeAllNgrams):
- return SkipGramOptionsAddIncludeAllNgrams(builder, includeAllNgrams)
-def SkipGramOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SkipGramOptionsEnd(builder)
-
-class SkipGramOptionsT(object):
-
- # SkipGramOptionsT
- def __init__(self):
- self.ngramSize = 0 # type: int
- self.maxSkipSize = 0 # type: int
- self.includeAllNgrams = False # type: bool
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- skipGramOptions = SkipGramOptions()
- skipGramOptions.Init(buf, pos)
- return cls.InitFromObj(skipGramOptions)
-
- @classmethod
- def InitFromObj(cls, skipGramOptions):
- x = SkipGramOptionsT()
- x._UnPack(skipGramOptions)
- return x
-
- # SkipGramOptionsT
- def _UnPack(self, skipGramOptions):
- if skipGramOptions is None:
- return
- self.ngramSize = skipGramOptions.NgramSize()
- self.maxSkipSize = skipGramOptions.MaxSkipSize()
- self.includeAllNgrams = skipGramOptions.IncludeAllNgrams()
-
- # SkipGramOptionsT
- def Pack(self, builder):
- SkipGramOptionsStart(builder)
- SkipGramOptionsAddNgramSize(builder, self.ngramSize)
- SkipGramOptionsAddMaxSkipSize(builder, self.maxSkipSize)
- SkipGramOptionsAddIncludeAllNgrams(builder, self.includeAllNgrams)
- skipGramOptions = SkipGramOptionsEnd(builder)
- return skipGramOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SliceOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SliceOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSliceOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SliceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SliceOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def SliceOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return SliceOptionsStart(builder)
-def SliceOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SliceOptionsEnd(builder)
-
-class SliceOptionsT(object):
-
- # SliceOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- sliceOptions = SliceOptions()
- sliceOptions.Init(buf, pos)
- return cls.InitFromObj(sliceOptions)
-
- @classmethod
- def InitFromObj(cls, sliceOptions):
- x = SliceOptionsT()
- x._UnPack(sliceOptions)
- return x
-
- # SliceOptionsT
- def _UnPack(self, sliceOptions):
- if sliceOptions is None:
- return
-
- # SliceOptionsT
- def Pack(self, builder):
- SliceOptionsStart(builder)
- sliceOptions = SliceOptionsEnd(builder)
- return sliceOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SoftmaxOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SoftmaxOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSoftmaxOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SoftmaxOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SoftmaxOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # SoftmaxOptions
- def Beta(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
- return 0.0
-
-def SoftmaxOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return SoftmaxOptionsStart(builder)
-def SoftmaxOptionsAddBeta(builder, beta): builder.PrependFloat32Slot(0, beta, 0.0)
-def AddBeta(builder, beta):
- return SoftmaxOptionsAddBeta(builder, beta)
-def SoftmaxOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SoftmaxOptionsEnd(builder)
-
-class SoftmaxOptionsT(object):
-
- # SoftmaxOptionsT
- def __init__(self):
- self.beta = 0.0 # type: float
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- softmaxOptions = SoftmaxOptions()
- softmaxOptions.Init(buf, pos)
- return cls.InitFromObj(softmaxOptions)
-
- @classmethod
- def InitFromObj(cls, softmaxOptions):
- x = SoftmaxOptionsT()
- x._UnPack(softmaxOptions)
- return x
-
- # SoftmaxOptionsT
- def _UnPack(self, softmaxOptions):
- if softmaxOptions is None:
- return
- self.beta = softmaxOptions.Beta()
-
- # SoftmaxOptionsT
- def Pack(self, builder):
- SoftmaxOptionsStart(builder)
- SoftmaxOptionsAddBeta(builder, self.beta)
- softmaxOptions = SoftmaxOptionsEnd(builder)
- return softmaxOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SpaceToBatchNDOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SpaceToBatchNDOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSpaceToBatchNDOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SpaceToBatchNDOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SpaceToBatchNDOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def SpaceToBatchNDOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return SpaceToBatchNDOptionsStart(builder)
-def SpaceToBatchNDOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SpaceToBatchNDOptionsEnd(builder)
-
-class SpaceToBatchNDOptionsT(object):
-
- # SpaceToBatchNDOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- spaceToBatchNdoptions = SpaceToBatchNDOptions()
- spaceToBatchNdoptions.Init(buf, pos)
- return cls.InitFromObj(spaceToBatchNdoptions)
-
- @classmethod
- def InitFromObj(cls, spaceToBatchNdoptions):
- x = SpaceToBatchNDOptionsT()
- x._UnPack(spaceToBatchNdoptions)
- return x
-
- # SpaceToBatchNDOptionsT
- def _UnPack(self, spaceToBatchNdoptions):
- if spaceToBatchNdoptions is None:
- return
-
- # SpaceToBatchNDOptionsT
- def Pack(self, builder):
- SpaceToBatchNDOptionsStart(builder)
- spaceToBatchNdoptions = SpaceToBatchNDOptionsEnd(builder)
- return spaceToBatchNdoptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SpaceToDepthOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SpaceToDepthOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSpaceToDepthOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SpaceToDepthOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SpaceToDepthOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # SpaceToDepthOptions
- def BlockSize(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
-def SpaceToDepthOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return SpaceToDepthOptionsStart(builder)
-def SpaceToDepthOptionsAddBlockSize(builder, blockSize): builder.PrependInt32Slot(0, blockSize, 0)
-def AddBlockSize(builder, blockSize):
- return SpaceToDepthOptionsAddBlockSize(builder, blockSize)
-def SpaceToDepthOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SpaceToDepthOptionsEnd(builder)
-
-class SpaceToDepthOptionsT(object):
-
- # SpaceToDepthOptionsT
- def __init__(self):
- self.blockSize = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- spaceToDepthOptions = SpaceToDepthOptions()
- spaceToDepthOptions.Init(buf, pos)
- return cls.InitFromObj(spaceToDepthOptions)
-
- @classmethod
- def InitFromObj(cls, spaceToDepthOptions):
- x = SpaceToDepthOptionsT()
- x._UnPack(spaceToDepthOptions)
- return x
-
- # SpaceToDepthOptionsT
- def _UnPack(self, spaceToDepthOptions):
- if spaceToDepthOptions is None:
- return
- self.blockSize = spaceToDepthOptions.BlockSize()
-
- # SpaceToDepthOptionsT
- def Pack(self, builder):
- SpaceToDepthOptionsStart(builder)
- SpaceToDepthOptionsAddBlockSize(builder, self.blockSize)
- spaceToDepthOptions = SpaceToDepthOptionsEnd(builder)
- return spaceToDepthOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-class SparseIndexVector(object):
- NONE = 0
- Int32Vector = 1
- Uint16Vector = 2
- Uint8Vector = 3
-
-def SparseIndexVectorCreator(unionType, table):
- from flatbuffers.table import Table
- if not isinstance(table, Table):
- return None
- if unionType == SparseIndexVector().Int32Vector:
- return Int32VectorT.InitFromBuf(table.Bytes, table.Pos)
- if unionType == SparseIndexVector().Uint16Vector:
- return Uint16VectorT.InitFromBuf(table.Bytes, table.Pos)
- if unionType == SparseIndexVector().Uint8Vector:
- return Uint8VectorT.InitFromBuf(table.Bytes, table.Pos)
- return None
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SparseToDenseOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SparseToDenseOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSparseToDenseOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SparseToDenseOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SparseToDenseOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # SparseToDenseOptions
- def ValidateIndices(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
-def SparseToDenseOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return SparseToDenseOptionsStart(builder)
-def SparseToDenseOptionsAddValidateIndices(builder, validateIndices): builder.PrependBoolSlot(0, validateIndices, 0)
-def AddValidateIndices(builder, validateIndices):
- return SparseToDenseOptionsAddValidateIndices(builder, validateIndices)
-def SparseToDenseOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SparseToDenseOptionsEnd(builder)
-
-class SparseToDenseOptionsT(object):
-
- # SparseToDenseOptionsT
- def __init__(self):
- self.validateIndices = False # type: bool
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- sparseToDenseOptions = SparseToDenseOptions()
- sparseToDenseOptions.Init(buf, pos)
- return cls.InitFromObj(sparseToDenseOptions)
-
- @classmethod
- def InitFromObj(cls, sparseToDenseOptions):
- x = SparseToDenseOptionsT()
- x._UnPack(sparseToDenseOptions)
- return x
-
- # SparseToDenseOptionsT
- def _UnPack(self, sparseToDenseOptions):
- if sparseToDenseOptions is None:
- return
- self.validateIndices = sparseToDenseOptions.ValidateIndices()
-
- # SparseToDenseOptionsT
- def Pack(self, builder):
- SparseToDenseOptionsStart(builder)
- SparseToDenseOptionsAddValidateIndices(builder, self.validateIndices)
- sparseToDenseOptions = SparseToDenseOptionsEnd(builder)
- return sparseToDenseOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SparsityParameters(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SparsityParameters()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSparsityParameters(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SparsityParametersBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SparsityParameters
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # SparsityParameters
- def TraversalOrder(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- a = self._tab.Vector(o)
- return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
- return 0
-
- # SparsityParameters
- def TraversalOrderAsNumpy(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
- return 0
-
- # SparsityParameters
- def TraversalOrderLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # SparsityParameters
- def TraversalOrderIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- return o == 0
-
- # SparsityParameters
- def BlockMap(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- a = self._tab.Vector(o)
- return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
- return 0
-
- # SparsityParameters
- def BlockMapAsNumpy(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
- return 0
-
- # SparsityParameters
- def BlockMapLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # SparsityParameters
- def BlockMapIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- return o == 0
-
- # SparsityParameters
- def DimMetadata(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- x = self._tab.Vector(o)
- x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
- x = self._tab.Indirect(x)
- obj = DimensionMetadata()
- obj.Init(self._tab.Bytes, x)
- return obj
- return None
-
- # SparsityParameters
- def DimMetadataLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # SparsityParameters
- def DimMetadataIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- return o == 0
-
-def SparsityParametersStart(builder): builder.StartObject(3)
-def Start(builder):
- return SparsityParametersStart(builder)
-def SparsityParametersAddTraversalOrder(builder, traversalOrder): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(traversalOrder), 0)
-def AddTraversalOrder(builder, traversalOrder):
- return SparsityParametersAddTraversalOrder(builder, traversalOrder)
-def SparsityParametersStartTraversalOrderVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartTraversalOrderVector(builder, numElems):
- return SparsityParametersStartTraversalOrderVector(builder, numElems)
-def SparsityParametersAddBlockMap(builder, blockMap): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(blockMap), 0)
-def AddBlockMap(builder, blockMap):
- return SparsityParametersAddBlockMap(builder, blockMap)
-def SparsityParametersStartBlockMapVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartBlockMapVector(builder, numElems):
- return SparsityParametersStartBlockMapVector(builder, numElems)
-def SparsityParametersAddDimMetadata(builder, dimMetadata): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(dimMetadata), 0)
-def AddDimMetadata(builder, dimMetadata):
- return SparsityParametersAddDimMetadata(builder, dimMetadata)
-def SparsityParametersStartDimMetadataVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartDimMetadataVector(builder, numElems):
- return SparsityParametersStartDimMetadataVector(builder, numElems)
-def SparsityParametersEnd(builder): return builder.EndObject()
-def End(builder):
- return SparsityParametersEnd(builder)
-try:
- from typing import List
-except:
- pass
-
-class SparsityParametersT(object):
-
- # SparsityParametersT
- def __init__(self):
- self.traversalOrder = None # type: List[int]
- self.blockMap = None # type: List[int]
- self.dimMetadata = None # type: List[DimensionMetadataT]
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- sparsityParameters = SparsityParameters()
- sparsityParameters.Init(buf, pos)
- return cls.InitFromObj(sparsityParameters)
-
- @classmethod
- def InitFromObj(cls, sparsityParameters):
- x = SparsityParametersT()
- x._UnPack(sparsityParameters)
- return x
-
- # SparsityParametersT
- def _UnPack(self, sparsityParameters):
- if sparsityParameters is None:
- return
- if not sparsityParameters.TraversalOrderIsNone():
- if np is None:
- self.traversalOrder = []
- for i in range(sparsityParameters.TraversalOrderLength()):
- self.traversalOrder.append(sparsityParameters.TraversalOrder(i))
- else:
- self.traversalOrder = sparsityParameters.TraversalOrderAsNumpy()
- if not sparsityParameters.BlockMapIsNone():
- if np is None:
- self.blockMap = []
- for i in range(sparsityParameters.BlockMapLength()):
- self.blockMap.append(sparsityParameters.BlockMap(i))
- else:
- self.blockMap = sparsityParameters.BlockMapAsNumpy()
- if not sparsityParameters.DimMetadataIsNone():
- self.dimMetadata = []
- for i in range(sparsityParameters.DimMetadataLength()):
- if sparsityParameters.DimMetadata(i) is None:
- self.dimMetadata.append(None)
- else:
- dimensionMetadata_ = DimensionMetadataT.InitFromObj(sparsityParameters.DimMetadata(i))
- self.dimMetadata.append(dimensionMetadata_)
-
- # SparsityParametersT
- def Pack(self, builder):
- if self.traversalOrder is not None:
- if np is not None and type(self.traversalOrder) is np.ndarray:
- traversalOrder = builder.CreateNumpyVector(self.traversalOrder)
- else:
- SparsityParametersStartTraversalOrderVector(builder, len(self.traversalOrder))
- for i in reversed(range(len(self.traversalOrder))):
- builder.PrependInt32(self.traversalOrder[i])
- traversalOrder = builder.EndVector()
- if self.blockMap is not None:
- if np is not None and type(self.blockMap) is np.ndarray:
- blockMap = builder.CreateNumpyVector(self.blockMap)
- else:
- SparsityParametersStartBlockMapVector(builder, len(self.blockMap))
- for i in reversed(range(len(self.blockMap))):
- builder.PrependInt32(self.blockMap[i])
- blockMap = builder.EndVector()
- if self.dimMetadata is not None:
- dimMetadatalist = []
- for i in range(len(self.dimMetadata)):
- dimMetadatalist.append(self.dimMetadata[i].Pack(builder))
- SparsityParametersStartDimMetadataVector(builder, len(self.dimMetadata))
- for i in reversed(range(len(self.dimMetadata))):
- builder.PrependUOffsetTRelative(dimMetadatalist[i])
- dimMetadata = builder.EndVector()
- SparsityParametersStart(builder)
- if self.traversalOrder is not None:
- SparsityParametersAddTraversalOrder(builder, traversalOrder)
- if self.blockMap is not None:
- SparsityParametersAddBlockMap(builder, blockMap)
- if self.dimMetadata is not None:
- SparsityParametersAddDimMetadata(builder, dimMetadata)
- sparsityParameters = SparsityParametersEnd(builder)
- return sparsityParameters
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SplitOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SplitOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSplitOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SplitOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SplitOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # SplitOptions
- def NumSplits(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
-def SplitOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return SplitOptionsStart(builder)
-def SplitOptionsAddNumSplits(builder, numSplits): builder.PrependInt32Slot(0, numSplits, 0)
-def AddNumSplits(builder, numSplits):
- return SplitOptionsAddNumSplits(builder, numSplits)
-def SplitOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SplitOptionsEnd(builder)
-
-class SplitOptionsT(object):
-
- # SplitOptionsT
- def __init__(self):
- self.numSplits = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- splitOptions = SplitOptions()
- splitOptions.Init(buf, pos)
- return cls.InitFromObj(splitOptions)
-
- @classmethod
- def InitFromObj(cls, splitOptions):
- x = SplitOptionsT()
- x._UnPack(splitOptions)
- return x
-
- # SplitOptionsT
- def _UnPack(self, splitOptions):
- if splitOptions is None:
- return
- self.numSplits = splitOptions.NumSplits()
-
- # SplitOptionsT
- def Pack(self, builder):
- SplitOptionsStart(builder)
- SplitOptionsAddNumSplits(builder, self.numSplits)
- splitOptions = SplitOptionsEnd(builder)
- return splitOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SplitVOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SplitVOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSplitVOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SplitVOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SplitVOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # SplitVOptions
- def NumSplits(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
-def SplitVOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return SplitVOptionsStart(builder)
-def SplitVOptionsAddNumSplits(builder, numSplits): builder.PrependInt32Slot(0, numSplits, 0)
-def AddNumSplits(builder, numSplits):
- return SplitVOptionsAddNumSplits(builder, numSplits)
-def SplitVOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SplitVOptionsEnd(builder)
-
-class SplitVOptionsT(object):
-
- # SplitVOptionsT
- def __init__(self):
- self.numSplits = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- splitVoptions = SplitVOptions()
- splitVoptions.Init(buf, pos)
- return cls.InitFromObj(splitVoptions)
-
- @classmethod
- def InitFromObj(cls, splitVoptions):
- x = SplitVOptionsT()
- x._UnPack(splitVoptions)
- return x
-
- # SplitVOptionsT
- def _UnPack(self, splitVoptions):
- if splitVoptions is None:
- return
- self.numSplits = splitVoptions.NumSplits()
-
- # SplitVOptionsT
- def Pack(self, builder):
- SplitVOptionsStart(builder)
- SplitVOptionsAddNumSplits(builder, self.numSplits)
- splitVoptions = SplitVOptionsEnd(builder)
- return splitVoptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SquareOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SquareOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSquareOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SquareOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SquareOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def SquareOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return SquareOptionsStart(builder)
-def SquareOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SquareOptionsEnd(builder)
-
-class SquareOptionsT(object):
-
- # SquareOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- squareOptions = SquareOptions()
- squareOptions.Init(buf, pos)
- return cls.InitFromObj(squareOptions)
-
- @classmethod
- def InitFromObj(cls, squareOptions):
- x = SquareOptionsT()
- x._UnPack(squareOptions)
- return x
-
- # SquareOptionsT
- def _UnPack(self, squareOptions):
- if squareOptions is None:
- return
-
- # SquareOptionsT
- def Pack(self, builder):
- SquareOptionsStart(builder)
- squareOptions = SquareOptionsEnd(builder)
- return squareOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SquaredDifferenceOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SquaredDifferenceOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSquaredDifferenceOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SquaredDifferenceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SquaredDifferenceOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def SquaredDifferenceOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return SquaredDifferenceOptionsStart(builder)
-def SquaredDifferenceOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SquaredDifferenceOptionsEnd(builder)
-
-class SquaredDifferenceOptionsT(object):
-
- # SquaredDifferenceOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- squaredDifferenceOptions = SquaredDifferenceOptions()
- squaredDifferenceOptions.Init(buf, pos)
- return cls.InitFromObj(squaredDifferenceOptions)
-
- @classmethod
- def InitFromObj(cls, squaredDifferenceOptions):
- x = SquaredDifferenceOptionsT()
- x._UnPack(squaredDifferenceOptions)
- return x
-
- # SquaredDifferenceOptionsT
- def _UnPack(self, squaredDifferenceOptions):
- if squaredDifferenceOptions is None:
- return
-
- # SquaredDifferenceOptionsT
- def Pack(self, builder):
- SquaredDifferenceOptionsStart(builder)
- squaredDifferenceOptions = SquaredDifferenceOptionsEnd(builder)
- return squaredDifferenceOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SqueezeOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SqueezeOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsSqueezeOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def SqueezeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # SqueezeOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # SqueezeOptions
- def SqueezeDims(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- a = self._tab.Vector(o)
- return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
- return 0
-
- # SqueezeOptions
- def SqueezeDimsAsNumpy(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
- return 0
-
- # SqueezeOptions
- def SqueezeDimsLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
-
- # SqueezeOptions
- def SqueezeDimsIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- return o == 0
-
-def SqueezeOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return SqueezeOptionsStart(builder)
-def SqueezeOptionsAddSqueezeDims(builder, squeezeDims): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(squeezeDims), 0)
-def AddSqueezeDims(builder, squeezeDims):
- return SqueezeOptionsAddSqueezeDims(builder, squeezeDims)
-def SqueezeOptionsStartSqueezeDimsVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartSqueezeDimsVector(builder, numElems):
- return SqueezeOptionsStartSqueezeDimsVector(builder, numElems)
-def SqueezeOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SqueezeOptionsEnd(builder)
-try:
- from typing import List
-except:
- pass
-
-class SqueezeOptionsT(object):
-
- # SqueezeOptionsT
- def __init__(self):
- self.squeezeDims = None # type: List[int]
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- squeezeOptions = SqueezeOptions()
- squeezeOptions.Init(buf, pos)
- return cls.InitFromObj(squeezeOptions)
-
- @classmethod
- def InitFromObj(cls, squeezeOptions):
- x = SqueezeOptionsT()
- x._UnPack(squeezeOptions)
- return x
-
- # SqueezeOptionsT
- def _UnPack(self, squeezeOptions):
- if squeezeOptions is None:
- return
- if not squeezeOptions.SqueezeDimsIsNone():
- if np is None:
- self.squeezeDims = []
- for i in range(squeezeOptions.SqueezeDimsLength()):
- self.squeezeDims.append(squeezeOptions.SqueezeDims(i))
- else:
- self.squeezeDims = squeezeOptions.SqueezeDimsAsNumpy()
-
- # SqueezeOptionsT
- def Pack(self, builder):
- if self.squeezeDims is not None:
- if np is not None and type(self.squeezeDims) is np.ndarray:
- squeezeDims = builder.CreateNumpyVector(self.squeezeDims)
- else:
- SqueezeOptionsStartSqueezeDimsVector(builder, len(self.squeezeDims))
- for i in reversed(range(len(self.squeezeDims))):
- builder.PrependInt32(self.squeezeDims[i])
- squeezeDims = builder.EndVector()
- SqueezeOptionsStart(builder)
- if self.squeezeDims is not None:
- SqueezeOptionsAddSqueezeDims(builder, squeezeDims)
- squeezeOptions = SqueezeOptionsEnd(builder)
- return squeezeOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class StridedSliceOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = StridedSliceOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsStridedSliceOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def StridedSliceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # StridedSliceOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # StridedSliceOptions
- def BeginMask(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # StridedSliceOptions
- def EndMask(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # StridedSliceOptions
- def EllipsisMask(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # StridedSliceOptions
- def NewAxisMask(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # StridedSliceOptions
- def ShrinkAxisMask(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # StridedSliceOptions
- def Offset(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
-def StridedSliceOptionsStart(builder): builder.StartObject(6)
-def Start(builder):
- return StridedSliceOptionsStart(builder)
-def StridedSliceOptionsAddBeginMask(builder, beginMask): builder.PrependInt32Slot(0, beginMask, 0)
-def AddBeginMask(builder, beginMask):
- return StridedSliceOptionsAddBeginMask(builder, beginMask)
-def StridedSliceOptionsAddEndMask(builder, endMask): builder.PrependInt32Slot(1, endMask, 0)
-def AddEndMask(builder, endMask):
- return StridedSliceOptionsAddEndMask(builder, endMask)
-def StridedSliceOptionsAddEllipsisMask(builder, ellipsisMask): builder.PrependInt32Slot(2, ellipsisMask, 0)
-def AddEllipsisMask(builder, ellipsisMask):
- return StridedSliceOptionsAddEllipsisMask(builder, ellipsisMask)
-def StridedSliceOptionsAddNewAxisMask(builder, newAxisMask): builder.PrependInt32Slot(3, newAxisMask, 0)
-def AddNewAxisMask(builder, newAxisMask):
- return StridedSliceOptionsAddNewAxisMask(builder, newAxisMask)
-def StridedSliceOptionsAddShrinkAxisMask(builder, shrinkAxisMask): builder.PrependInt32Slot(4, shrinkAxisMask, 0)
-def AddShrinkAxisMask(builder, shrinkAxisMask):
- return StridedSliceOptionsAddShrinkAxisMask(builder, shrinkAxisMask)
-def StridedSliceOptionsAddOffset(builder, offset): builder.PrependBoolSlot(5, offset, 0)
-def AddOffset(builder, offset):
- return StridedSliceOptionsAddOffset(builder, offset)
-def StridedSliceOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return StridedSliceOptionsEnd(builder)
-
-class StridedSliceOptionsT(object):
-
- # StridedSliceOptionsT
- def __init__(self):
- self.beginMask = 0 # type: int
- self.endMask = 0 # type: int
- self.ellipsisMask = 0 # type: int
- self.newAxisMask = 0 # type: int
- self.shrinkAxisMask = 0 # type: int
- self.offset = False # type: bool
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- stridedSliceOptions = StridedSliceOptions()
- stridedSliceOptions.Init(buf, pos)
- return cls.InitFromObj(stridedSliceOptions)
-
- @classmethod
- def InitFromObj(cls, stridedSliceOptions):
- x = StridedSliceOptionsT()
- x._UnPack(stridedSliceOptions)
- return x
-
- # StridedSliceOptionsT
- def _UnPack(self, stridedSliceOptions):
- if stridedSliceOptions is None:
- return
- self.beginMask = stridedSliceOptions.BeginMask()
- self.endMask = stridedSliceOptions.EndMask()
- self.ellipsisMask = stridedSliceOptions.EllipsisMask()
- self.newAxisMask = stridedSliceOptions.NewAxisMask()
- self.shrinkAxisMask = stridedSliceOptions.ShrinkAxisMask()
- self.offset = stridedSliceOptions.Offset()
-
- # StridedSliceOptionsT
- def Pack(self, builder):
- StridedSliceOptionsStart(builder)
- StridedSliceOptionsAddBeginMask(builder, self.beginMask)
- StridedSliceOptionsAddEndMask(builder, self.endMask)
- StridedSliceOptionsAddEllipsisMask(builder, self.ellipsisMask)
- StridedSliceOptionsAddNewAxisMask(builder, self.newAxisMask)
- StridedSliceOptionsAddShrinkAxisMask(builder, self.shrinkAxisMask)
- StridedSliceOptionsAddOffset(builder, self.offset)
- stridedSliceOptions = StridedSliceOptionsEnd(builder)
- return stridedSliceOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
class SubGraph(object):
__slots__ = ['_tab']
@@ -11903,39 +17268,40 @@
return self._tab.String(o + self._tab.Pos)
return None
-def SubGraphStart(builder): builder.StartObject(5)
-def Start(builder):
- return SubGraphStart(builder)
-def SubGraphAddTensors(builder, tensors): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(tensors), 0)
-def AddTensors(builder, tensors):
- return SubGraphAddTensors(builder, tensors)
-def SubGraphStartTensorsVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartTensorsVector(builder, numElems):
- return SubGraphStartTensorsVector(builder, numElems)
-def SubGraphAddInputs(builder, inputs): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(inputs), 0)
-def AddInputs(builder, inputs):
- return SubGraphAddInputs(builder, inputs)
-def SubGraphStartInputsVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartInputsVector(builder, numElems):
- return SubGraphStartInputsVector(builder, numElems)
-def SubGraphAddOutputs(builder, outputs): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(outputs), 0)
-def AddOutputs(builder, outputs):
- return SubGraphAddOutputs(builder, outputs)
-def SubGraphStartOutputsVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartOutputsVector(builder, numElems):
- return SubGraphStartOutputsVector(builder, numElems)
-def SubGraphAddOperators(builder, operators): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(operators), 0)
-def AddOperators(builder, operators):
- return SubGraphAddOperators(builder, operators)
-def SubGraphStartOperatorsVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartOperatorsVector(builder, numElems):
- return SubGraphStartOperatorsVector(builder, numElems)
-def SubGraphAddName(builder, name): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0)
-def AddName(builder, name):
- return SubGraphAddName(builder, name)
-def SubGraphEnd(builder): return builder.EndObject()
-def End(builder):
- return SubGraphEnd(builder)
+def SubGraphStart(builder):
+ builder.StartObject(5)
+
+def SubGraphAddTensors(builder, tensors):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(tensors), 0)
+
+def SubGraphStartTensorsVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def SubGraphAddInputs(builder, inputs):
+ builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(inputs), 0)
+
+def SubGraphStartInputsVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def SubGraphAddOutputs(builder, outputs):
+ builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(outputs), 0)
+
+def SubGraphStartOutputsVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def SubGraphAddOperators(builder, operators):
+ builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(operators), 0)
+
+def SubGraphStartOperatorsVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def SubGraphAddName(builder, name):
+ builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0)
+
+def SubGraphEnd(builder):
+ return builder.EndObject()
+
+
try:
from typing import List
except:
@@ -11958,6 +17324,11 @@
return cls.InitFromObj(subGraph)
@classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
def InitFromObj(cls, subGraph):
x = SubGraphT()
x._UnPack(subGraph)
@@ -12048,427 +17419,245 @@
SubGraphAddName(builder, name)
subGraph = SubGraphEnd(builder)
return subGraph
-# automatically generated by the FlatBuffers compiler, do not modify
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class SubOptions(object):
+class Buffer(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0):
n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = SubOptions()
+ x = Buffer()
x.Init(buf, n + offset)
return x
@classmethod
- def GetRootAsSubOptions(cls, buf, offset=0):
+ def GetRootAsBuffer(cls, buf, offset=0):
"""This method is deprecated. Please switch to GetRootAs."""
return cls.GetRootAs(buf, offset)
@classmethod
- def SubOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ def BufferBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
- # SubOptions
+ # Buffer
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
- # SubOptions
- def FusedActivationFunction(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # SubOptions
- def PotScaleInt16(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return True
-
-def SubOptionsStart(builder): builder.StartObject(2)
-def Start(builder):
- return SubOptionsStart(builder)
-def SubOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return SubOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def SubOptionsAddPotScaleInt16(builder, potScaleInt16): builder.PrependBoolSlot(1, potScaleInt16, 1)
-def AddPotScaleInt16(builder, potScaleInt16):
- return SubOptionsAddPotScaleInt16(builder, potScaleInt16)
-def SubOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return SubOptionsEnd(builder)
-
-class SubOptionsT(object):
-
- # SubOptionsT
- def __init__(self):
- self.fusedActivationFunction = 0 # type: int
- self.potScaleInt16 = True # type: bool
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- subOptions = SubOptions()
- subOptions.Init(buf, pos)
- return cls.InitFromObj(subOptions)
-
- @classmethod
- def InitFromObj(cls, subOptions):
- x = SubOptionsT()
- x._UnPack(subOptions)
- return x
-
- # SubOptionsT
- def _UnPack(self, subOptions):
- if subOptions is None:
- return
- self.fusedActivationFunction = subOptions.FusedActivationFunction()
- self.potScaleInt16 = subOptions.PotScaleInt16()
-
- # SubOptionsT
- def Pack(self, builder):
- SubOptionsStart(builder)
- SubOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
- SubOptionsAddPotScaleInt16(builder, self.potScaleInt16)
- subOptions = SubOptionsEnd(builder)
- return subOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class Tensor(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = Tensor()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsTensor(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def TensorBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # Tensor
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # Tensor
- def Shape(self, j):
+ # Buffer
+ def Data(self, j):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
if o != 0:
a = self._tab.Vector(o)
- return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+ return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1))
return 0
- # Tensor
- def ShapeAsNumpy(self):
+ # Buffer
+ def DataAsNumpy(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
if o != 0:
- return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
+ return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o)
return 0
- # Tensor
- def ShapeLength(self):
+ # Buffer
+ def DataLength(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
if o != 0:
return self._tab.VectorLen(o)
return 0
- # Tensor
- def ShapeIsNone(self):
+ # Buffer
+ def DataIsNone(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
return o == 0
- # Tensor
- def Type(self):
+ # Buffer
+ def Offset(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ return self._tab.Get(flatbuffers.number_types.Uint64Flags, o + self._tab.Pos)
return 0
- # Tensor
- def Buffer(self):
+ # Buffer
+ def Size(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
if o != 0:
- return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos)
+ return self._tab.Get(flatbuffers.number_types.Uint64Flags, o + self._tab.Pos)
return 0
- # Tensor
+def BufferStart(builder):
+ builder.StartObject(3)
+
+def BufferAddData(builder, data):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(data), 0)
+
+def BufferStartDataVector(builder, numElems):
+ return builder.StartVector(1, numElems, 1)
+
+def BufferAddOffset(builder, offset):
+ builder.PrependUint64Slot(1, offset, 0)
+
+def BufferAddSize(builder, size):
+ builder.PrependUint64Slot(2, size, 0)
+
+def BufferEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class BufferT(object):
+
+ # BufferT
+ def __init__(self):
+ self.data = None # type: List[int]
+ self.offset = 0 # type: int
+ self.size = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ buffer = Buffer()
+ buffer.Init(buf, pos)
+ return cls.InitFromObj(buffer)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, buffer):
+ x = BufferT()
+ x._UnPack(buffer)
+ return x
+
+ # BufferT
+ def _UnPack(self, buffer):
+ if buffer is None:
+ return
+ if not buffer.DataIsNone():
+ if np is None:
+ self.data = []
+ for i in range(buffer.DataLength()):
+ self.data.append(buffer.Data(i))
+ else:
+ self.data = buffer.DataAsNumpy()
+ self.offset = buffer.Offset()
+ self.size = buffer.Size()
+
+ # BufferT
+ def Pack(self, builder):
+ if self.data is not None:
+ if np is not None and type(self.data) is np.ndarray:
+ data = builder.CreateNumpyVector(self.data)
+ else:
+ BufferStartDataVector(builder, len(self.data))
+ for i in reversed(range(len(self.data))):
+ builder.PrependUint8(self.data[i])
+ data = builder.EndVector()
+ BufferStart(builder)
+ if self.data is not None:
+ BufferAddData(builder, data)
+ BufferAddOffset(builder, self.offset)
+ BufferAddSize(builder, self.size)
+ buffer = BufferEnd(builder)
+ return buffer
+
+
+class Metadata(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = Metadata()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsMetadata(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def MetadataBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # Metadata
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # Metadata
def Name(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
if o != 0:
return self._tab.String(o + self._tab.Pos)
return None
- # Tensor
- def Quantization(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ # Metadata
+ def Buffer(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
if o != 0:
- x = self._tab.Indirect(o + self._tab.Pos)
- obj = QuantizationParameters()
- obj.Init(self._tab.Bytes, x)
- return obj
- return None
-
- # Tensor
- def IsVariable(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
- # Tensor
- def Sparsity(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
- if o != 0:
- x = self._tab.Indirect(o + self._tab.Pos)
- obj = SparsityParameters()
- obj.Init(self._tab.Bytes, x)
- return obj
- return None
-
- # Tensor
- def ShapeSignature(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
- if o != 0:
- a = self._tab.Vector(o)
- return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+ return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos)
return 0
- # Tensor
- def ShapeSignatureAsNumpy(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
- if o != 0:
- return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
- return 0
+def MetadataStart(builder):
+ builder.StartObject(2)
- # Tensor
- def ShapeSignatureLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
+def MetadataAddName(builder, name):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0)
- # Tensor
- def ShapeSignatureIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
- return o == 0
+def MetadataAddBuffer(builder, buffer):
+ builder.PrependUint32Slot(1, buffer, 0)
- # Tensor
- def HasRank(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(20))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
+def MetadataEnd(builder):
+ return builder.EndObject()
- # Tensor
- def VariantTensors(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(22))
- if o != 0:
- x = self._tab.Vector(o)
- x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
- x = self._tab.Indirect(x)
- obj = VariantSubType()
- obj.Init(self._tab.Bytes, x)
- return obj
- return None
- # Tensor
- def VariantTensorsLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(22))
- if o != 0:
- return self._tab.VectorLen(o)
- return 0
- # Tensor
- def VariantTensorsIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(22))
- return o == 0
+class MetadataT(object):
-def TensorStart(builder): builder.StartObject(10)
-def Start(builder):
- return TensorStart(builder)
-def TensorAddShape(builder, shape): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(shape), 0)
-def AddShape(builder, shape):
- return TensorAddShape(builder, shape)
-def TensorStartShapeVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartShapeVector(builder, numElems):
- return TensorStartShapeVector(builder, numElems)
-def TensorAddType(builder, type): builder.PrependInt8Slot(1, type, 0)
-def AddType(builder, type):
- return TensorAddType(builder, type)
-def TensorAddBuffer(builder, buffer): builder.PrependUint32Slot(2, buffer, 0)
-def AddBuffer(builder, buffer):
- return TensorAddBuffer(builder, buffer)
-def TensorAddName(builder, name): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0)
-def AddName(builder, name):
- return TensorAddName(builder, name)
-def TensorAddQuantization(builder, quantization): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(quantization), 0)
-def AddQuantization(builder, quantization):
- return TensorAddQuantization(builder, quantization)
-def TensorAddIsVariable(builder, isVariable): builder.PrependBoolSlot(5, isVariable, 0)
-def AddIsVariable(builder, isVariable):
- return TensorAddIsVariable(builder, isVariable)
-def TensorAddSparsity(builder, sparsity): builder.PrependUOffsetTRelativeSlot(6, flatbuffers.number_types.UOffsetTFlags.py_type(sparsity), 0)
-def AddSparsity(builder, sparsity):
- return TensorAddSparsity(builder, sparsity)
-def TensorAddShapeSignature(builder, shapeSignature): builder.PrependUOffsetTRelativeSlot(7, flatbuffers.number_types.UOffsetTFlags.py_type(shapeSignature), 0)
-def AddShapeSignature(builder, shapeSignature):
- return TensorAddShapeSignature(builder, shapeSignature)
-def TensorStartShapeSignatureVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartShapeSignatureVector(builder, numElems):
- return TensorStartShapeSignatureVector(builder, numElems)
-def TensorAddHasRank(builder, hasRank): builder.PrependBoolSlot(8, hasRank, 0)
-def AddHasRank(builder, hasRank):
- return TensorAddHasRank(builder, hasRank)
-def TensorAddVariantTensors(builder, variantTensors): builder.PrependUOffsetTRelativeSlot(9, flatbuffers.number_types.UOffsetTFlags.py_type(variantTensors), 0)
-def AddVariantTensors(builder, variantTensors):
- return TensorAddVariantTensors(builder, variantTensors)
-def TensorStartVariantTensorsVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartVariantTensorsVector(builder, numElems):
- return TensorStartVariantTensorsVector(builder, numElems)
-def TensorEnd(builder): return builder.EndObject()
-def End(builder):
- return TensorEnd(builder)
-try:
- from typing import List, Optional
-except:
- pass
-
-class TensorT(object):
-
- # TensorT
+ # MetadataT
def __init__(self):
- self.shape = None # type: List[int]
- self.type = 0 # type: int
- self.buffer = 0 # type: int
self.name = None # type: str
- self.quantization = None # type: Optional[QuantizationParametersT]
- self.isVariable = False # type: bool
- self.sparsity = None # type: Optional[SparsityParametersT]
- self.shapeSignature = None # type: List[int]
- self.hasRank = False # type: bool
- self.variantTensors = None # type: List[VariantSubTypeT]
+ self.buffer = 0 # type: int
@classmethod
def InitFromBuf(cls, buf, pos):
- tensor = Tensor()
- tensor.Init(buf, pos)
- return cls.InitFromObj(tensor)
+ metadata = Metadata()
+ metadata.Init(buf, pos)
+ return cls.InitFromObj(metadata)
@classmethod
- def InitFromObj(cls, tensor):
- x = TensorT()
- x._UnPack(tensor)
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, metadata):
+ x = MetadataT()
+ x._UnPack(metadata)
return x
- # TensorT
- def _UnPack(self, tensor):
- if tensor is None:
+ # MetadataT
+ def _UnPack(self, metadata):
+ if metadata is None:
return
- if not tensor.ShapeIsNone():
- if np is None:
- self.shape = []
- for i in range(tensor.ShapeLength()):
- self.shape.append(tensor.Shape(i))
- else:
- self.shape = tensor.ShapeAsNumpy()
- self.type = tensor.Type()
- self.buffer = tensor.Buffer()
- self.name = tensor.Name()
- if tensor.Quantization() is not None:
- self.quantization = QuantizationParametersT.InitFromObj(tensor.Quantization())
- self.isVariable = tensor.IsVariable()
- if tensor.Sparsity() is not None:
- self.sparsity = SparsityParametersT.InitFromObj(tensor.Sparsity())
- if not tensor.ShapeSignatureIsNone():
- if np is None:
- self.shapeSignature = []
- for i in range(tensor.ShapeSignatureLength()):
- self.shapeSignature.append(tensor.ShapeSignature(i))
- else:
- self.shapeSignature = tensor.ShapeSignatureAsNumpy()
- self.hasRank = tensor.HasRank()
- if not tensor.VariantTensorsIsNone():
- self.variantTensors = []
- for i in range(tensor.VariantTensorsLength()):
- if tensor.VariantTensors(i) is None:
- self.variantTensors.append(None)
- else:
- variantSubType_ = VariantSubTypeT.InitFromObj(tensor.VariantTensors(i))
- self.variantTensors.append(variantSubType_)
+ self.name = metadata.Name()
+ self.buffer = metadata.Buffer()
- # TensorT
+ # MetadataT
def Pack(self, builder):
- if self.shape is not None:
- if np is not None and type(self.shape) is np.ndarray:
- shape = builder.CreateNumpyVector(self.shape)
- else:
- TensorStartShapeVector(builder, len(self.shape))
- for i in reversed(range(len(self.shape))):
- builder.PrependInt32(self.shape[i])
- shape = builder.EndVector()
if self.name is not None:
name = builder.CreateString(self.name)
- if self.quantization is not None:
- quantization = self.quantization.Pack(builder)
- if self.sparsity is not None:
- sparsity = self.sparsity.Pack(builder)
- if self.shapeSignature is not None:
- if np is not None and type(self.shapeSignature) is np.ndarray:
- shapeSignature = builder.CreateNumpyVector(self.shapeSignature)
- else:
- TensorStartShapeSignatureVector(builder, len(self.shapeSignature))
- for i in reversed(range(len(self.shapeSignature))):
- builder.PrependInt32(self.shapeSignature[i])
- shapeSignature = builder.EndVector()
- if self.variantTensors is not None:
- variantTensorslist = []
- for i in range(len(self.variantTensors)):
- variantTensorslist.append(self.variantTensors[i].Pack(builder))
- TensorStartVariantTensorsVector(builder, len(self.variantTensors))
- for i in reversed(range(len(self.variantTensors))):
- builder.PrependUOffsetTRelative(variantTensorslist[i])
- variantTensors = builder.EndVector()
- TensorStart(builder)
- if self.shape is not None:
- TensorAddShape(builder, shape)
- TensorAddType(builder, self.type)
- TensorAddBuffer(builder, self.buffer)
+ MetadataStart(builder)
if self.name is not None:
- TensorAddName(builder, name)
- if self.quantization is not None:
- TensorAddQuantization(builder, quantization)
- TensorAddIsVariable(builder, self.isVariable)
- if self.sparsity is not None:
- TensorAddSparsity(builder, sparsity)
- if self.shapeSignature is not None:
- TensorAddShapeSignature(builder, shapeSignature)
- TensorAddHasRank(builder, self.hasRank)
- if self.variantTensors is not None:
- TensorAddVariantTensors(builder, variantTensors)
- tensor = TensorEnd(builder)
- return tensor
-# automatically generated by the FlatBuffers compiler, do not modify
+ MetadataAddName(builder, name)
+ MetadataAddBuffer(builder, self.buffer)
+ metadata = MetadataEnd(builder)
+ return metadata
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
class TensorMap(object):
__slots__ = ['_tab']
@@ -12506,18 +17695,19 @@
return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos)
return 0
-def TensorMapStart(builder): builder.StartObject(2)
-def Start(builder):
- return TensorMapStart(builder)
-def TensorMapAddName(builder, name): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0)
-def AddName(builder, name):
- return TensorMapAddName(builder, name)
-def TensorMapAddTensorIndex(builder, tensorIndex): builder.PrependUint32Slot(1, tensorIndex, 0)
-def AddTensorIndex(builder, tensorIndex):
- return TensorMapAddTensorIndex(builder, tensorIndex)
-def TensorMapEnd(builder): return builder.EndObject()
-def End(builder):
- return TensorMapEnd(builder)
+def TensorMapStart(builder):
+ builder.StartObject(2)
+
+def TensorMapAddName(builder, name):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0)
+
+def TensorMapAddTensorIndex(builder, tensorIndex):
+ builder.PrependUint32Slot(1, tensorIndex, 0)
+
+def TensorMapEnd(builder):
+ return builder.EndObject()
+
+
class TensorMapT(object):
@@ -12533,6 +17723,11 @@
return cls.InitFromObj(tensorMap)
@classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
def InitFromObj(cls, tensorMap):
x = TensorMapT()
x._UnPack(tensorMap)
@@ -12555,1587 +17750,593 @@
TensorMapAddTensorIndex(builder, self.tensorIndex)
tensorMap = TensorMapEnd(builder)
return tensorMap
-# automatically generated by the FlatBuffers compiler, do not modify
-# namespace: tflite
-class TensorType(object):
- FLOAT32 = 0
- FLOAT16 = 1
- INT32 = 2
- UINT8 = 3
- INT64 = 4
- STRING = 5
- BOOL = 6
- INT16 = 7
- COMPLEX64 = 8
- INT8 = 9
- FLOAT64 = 10
- COMPLEX128 = 11
- UINT64 = 12
- RESOURCE = 13
- VARIANT = 14
- UINT32 = 15
- UINT16 = 16
- INT4 = 17
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class TileOptions(object):
+class SignatureDef(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0):
n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = TileOptions()
+ x = SignatureDef()
x.Init(buf, n + offset)
return x
@classmethod
- def GetRootAsTileOptions(cls, buf, offset=0):
+ def GetRootAsSignatureDef(cls, buf, offset=0):
"""This method is deprecated. Please switch to GetRootAs."""
return cls.GetRootAs(buf, offset)
@classmethod
- def TileOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ def SignatureDefBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
- # TileOptions
+ # SignatureDef
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
-def TileOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return TileOptionsStart(builder)
-def TileOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return TileOptionsEnd(builder)
-
-class TileOptionsT(object):
-
- # TileOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- tileOptions = TileOptions()
- tileOptions.Init(buf, pos)
- return cls.InitFromObj(tileOptions)
-
- @classmethod
- def InitFromObj(cls, tileOptions):
- x = TileOptionsT()
- x._UnPack(tileOptions)
- return x
-
- # TileOptionsT
- def _UnPack(self, tileOptions):
- if tileOptions is None:
- return
-
- # TileOptionsT
- def Pack(self, builder):
- TileOptionsStart(builder)
- tileOptions = TileOptionsEnd(builder)
- return tileOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class TopKV2Options(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = TopKV2Options()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsTopKV2Options(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def TopKV2OptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # TopKV2Options
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def TopKV2OptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return TopKV2OptionsStart(builder)
-def TopKV2OptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return TopKV2OptionsEnd(builder)
-
-class TopKV2OptionsT(object):
-
- # TopKV2OptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- topKv2options = TopKV2Options()
- topKv2options.Init(buf, pos)
- return cls.InitFromObj(topKv2options)
-
- @classmethod
- def InitFromObj(cls, topKv2options):
- x = TopKV2OptionsT()
- x._UnPack(topKv2options)
- return x
-
- # TopKV2OptionsT
- def _UnPack(self, topKv2options):
- if topKv2options is None:
- return
-
- # TopKV2OptionsT
- def Pack(self, builder):
- TopKV2OptionsStart(builder)
- topKv2options = TopKV2OptionsEnd(builder)
- return topKv2options
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class TransposeConvOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = TransposeConvOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsTransposeConvOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def TransposeConvOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # TransposeConvOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # TransposeConvOptions
- def Padding(self):
+ # SignatureDef
+ def Inputs(self, j):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
+ x = self._tab.Vector(o)
+ x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
+ x = self._tab.Indirect(x)
+ obj = TensorMap()
+ obj.Init(self._tab.Bytes, x)
+ return obj
+ return None
- # TransposeConvOptions
- def StrideW(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # TransposeConvOptions
- def StrideH(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # TransposeConvOptions
- def FusedActivationFunction(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
-def TransposeConvOptionsStart(builder): builder.StartObject(4)
-def Start(builder):
- return TransposeConvOptionsStart(builder)
-def TransposeConvOptionsAddPadding(builder, padding): builder.PrependInt8Slot(0, padding, 0)
-def AddPadding(builder, padding):
- return TransposeConvOptionsAddPadding(builder, padding)
-def TransposeConvOptionsAddStrideW(builder, strideW): builder.PrependInt32Slot(1, strideW, 0)
-def AddStrideW(builder, strideW):
- return TransposeConvOptionsAddStrideW(builder, strideW)
-def TransposeConvOptionsAddStrideH(builder, strideH): builder.PrependInt32Slot(2, strideH, 0)
-def AddStrideH(builder, strideH):
- return TransposeConvOptionsAddStrideH(builder, strideH)
-def TransposeConvOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(3, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return TransposeConvOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def TransposeConvOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return TransposeConvOptionsEnd(builder)
-
-class TransposeConvOptionsT(object):
-
- # TransposeConvOptionsT
- def __init__(self):
- self.padding = 0 # type: int
- self.strideW = 0 # type: int
- self.strideH = 0 # type: int
- self.fusedActivationFunction = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- transposeConvOptions = TransposeConvOptions()
- transposeConvOptions.Init(buf, pos)
- return cls.InitFromObj(transposeConvOptions)
-
- @classmethod
- def InitFromObj(cls, transposeConvOptions):
- x = TransposeConvOptionsT()
- x._UnPack(transposeConvOptions)
- return x
-
- # TransposeConvOptionsT
- def _UnPack(self, transposeConvOptions):
- if transposeConvOptions is None:
- return
- self.padding = transposeConvOptions.Padding()
- self.strideW = transposeConvOptions.StrideW()
- self.strideH = transposeConvOptions.StrideH()
- self.fusedActivationFunction = transposeConvOptions.FusedActivationFunction()
-
- # TransposeConvOptionsT
- def Pack(self, builder):
- TransposeConvOptionsStart(builder)
- TransposeConvOptionsAddPadding(builder, self.padding)
- TransposeConvOptionsAddStrideW(builder, self.strideW)
- TransposeConvOptionsAddStrideH(builder, self.strideH)
- TransposeConvOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
- transposeConvOptions = TransposeConvOptionsEnd(builder)
- return transposeConvOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class TransposeOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = TransposeOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsTransposeOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def TransposeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # TransposeOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def TransposeOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return TransposeOptionsStart(builder)
-def TransposeOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return TransposeOptionsEnd(builder)
-
-class TransposeOptionsT(object):
-
- # TransposeOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- transposeOptions = TransposeOptions()
- transposeOptions.Init(buf, pos)
- return cls.InitFromObj(transposeOptions)
-
- @classmethod
- def InitFromObj(cls, transposeOptions):
- x = TransposeOptionsT()
- x._UnPack(transposeOptions)
- return x
-
- # TransposeOptionsT
- def _UnPack(self, transposeOptions):
- if transposeOptions is None:
- return
-
- # TransposeOptionsT
- def Pack(self, builder):
- TransposeOptionsStart(builder)
- transposeOptions = TransposeOptionsEnd(builder)
- return transposeOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class Uint16Vector(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = Uint16Vector()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsUint16Vector(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def Uint16VectorBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # Uint16Vector
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # Uint16Vector
- def Values(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- a = self._tab.Vector(o)
- return self._tab.Get(flatbuffers.number_types.Uint16Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 2))
- return 0
-
- # Uint16Vector
- def ValuesAsNumpy(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint16Flags, o)
- return 0
-
- # Uint16Vector
- def ValuesLength(self):
+ # SignatureDef
+ def InputsLength(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
if o != 0:
return self._tab.VectorLen(o)
return 0
- # Uint16Vector
- def ValuesIsNone(self):
+ # SignatureDef
+ def InputsIsNone(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
return o == 0
-def Uint16VectorStart(builder): builder.StartObject(1)
-def Start(builder):
- return Uint16VectorStart(builder)
-def Uint16VectorAddValues(builder, values): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(values), 0)
-def AddValues(builder, values):
- return Uint16VectorAddValues(builder, values)
-def Uint16VectorStartValuesVector(builder, numElems): return builder.StartVector(2, numElems, 2)
-def StartValuesVector(builder, numElems):
- return Uint16VectorStartValuesVector(builder, numElems)
-def Uint16VectorEnd(builder): return builder.EndObject()
-def End(builder):
- return Uint16VectorEnd(builder)
-try:
- from typing import List
-except:
- pass
-
-class Uint16VectorT(object):
-
- # Uint16VectorT
- def __init__(self):
- self.values = None # type: List[int]
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- uint16vector = Uint16Vector()
- uint16vector.Init(buf, pos)
- return cls.InitFromObj(uint16vector)
-
- @classmethod
- def InitFromObj(cls, uint16vector):
- x = Uint16VectorT()
- x._UnPack(uint16vector)
- return x
-
- # Uint16VectorT
- def _UnPack(self, uint16vector):
- if uint16vector is None:
- return
- if not uint16vector.ValuesIsNone():
- if np is None:
- self.values = []
- for i in range(uint16vector.ValuesLength()):
- self.values.append(uint16vector.Values(i))
- else:
- self.values = uint16vector.ValuesAsNumpy()
-
- # Uint16VectorT
- def Pack(self, builder):
- if self.values is not None:
- if np is not None and type(self.values) is np.ndarray:
- values = builder.CreateNumpyVector(self.values)
- else:
- Uint16VectorStartValuesVector(builder, len(self.values))
- for i in reversed(range(len(self.values))):
- builder.PrependUint16(self.values[i])
- values = builder.EndVector()
- Uint16VectorStart(builder)
- if self.values is not None:
- Uint16VectorAddValues(builder, values)
- uint16vector = Uint16VectorEnd(builder)
- return uint16vector
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class Uint8Vector(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = Uint8Vector()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsUint8Vector(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def Uint8VectorBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # Uint8Vector
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # Uint8Vector
- def Values(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ # SignatureDef
+ def Outputs(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
if o != 0:
- a = self._tab.Vector(o)
- return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1))
- return 0
+ x = self._tab.Vector(o)
+ x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
+ x = self._tab.Indirect(x)
+ obj = TensorMap()
+ obj.Init(self._tab.Bytes, x)
+ return obj
+ return None
- # Uint8Vector
- def ValuesAsNumpy(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o)
- return 0
-
- # Uint8Vector
- def ValuesLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ # SignatureDef
+ def OutputsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
if o != 0:
return self._tab.VectorLen(o)
return 0
- # Uint8Vector
- def ValuesIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ # SignatureDef
+ def OutputsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
return o == 0
-def Uint8VectorStart(builder): builder.StartObject(1)
-def Start(builder):
- return Uint8VectorStart(builder)
-def Uint8VectorAddValues(builder, values): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(values), 0)
-def AddValues(builder, values):
- return Uint8VectorAddValues(builder, values)
-def Uint8VectorStartValuesVector(builder, numElems): return builder.StartVector(1, numElems, 1)
-def StartValuesVector(builder, numElems):
- return Uint8VectorStartValuesVector(builder, numElems)
-def Uint8VectorEnd(builder): return builder.EndObject()
-def End(builder):
- return Uint8VectorEnd(builder)
-try:
- from typing import List
-except:
- pass
-
-class Uint8VectorT(object):
-
- # Uint8VectorT
- def __init__(self):
- self.values = None # type: List[int]
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- uint8vector = Uint8Vector()
- uint8vector.Init(buf, pos)
- return cls.InitFromObj(uint8vector)
-
- @classmethod
- def InitFromObj(cls, uint8vector):
- x = Uint8VectorT()
- x._UnPack(uint8vector)
- return x
-
- # Uint8VectorT
- def _UnPack(self, uint8vector):
- if uint8vector is None:
- return
- if not uint8vector.ValuesIsNone():
- if np is None:
- self.values = []
- for i in range(uint8vector.ValuesLength()):
- self.values.append(uint8vector.Values(i))
- else:
- self.values = uint8vector.ValuesAsNumpy()
-
- # Uint8VectorT
- def Pack(self, builder):
- if self.values is not None:
- if np is not None and type(self.values) is np.ndarray:
- values = builder.CreateNumpyVector(self.values)
- else:
- Uint8VectorStartValuesVector(builder, len(self.values))
- for i in reversed(range(len(self.values))):
- builder.PrependUint8(self.values[i])
- values = builder.EndVector()
- Uint8VectorStart(builder)
- if self.values is not None:
- Uint8VectorAddValues(builder, values)
- uint8vector = Uint8VectorEnd(builder)
- return uint8vector
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class UnidirectionalSequenceLSTMOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = UnidirectionalSequenceLSTMOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsUnidirectionalSequenceLSTMOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def UnidirectionalSequenceLSTMOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # UnidirectionalSequenceLSTMOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # UnidirectionalSequenceLSTMOptions
- def FusedActivationFunction(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 0
-
- # UnidirectionalSequenceLSTMOptions
- def CellClip(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
- return 0.0
-
- # UnidirectionalSequenceLSTMOptions
- def ProjClip(self):
+ # SignatureDef
+ def SignatureKey(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
if o != 0:
- return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
- return 0.0
+ return self._tab.String(o + self._tab.Pos)
+ return None
- # UnidirectionalSequenceLSTMOptions
- def TimeMajor(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
- # UnidirectionalSequenceLSTMOptions
- def AsymmetricQuantizeInputs(self):
+ # SignatureDef
+ def SubgraphIndex(self):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
+ return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos)
+ return 0
- # UnidirectionalSequenceLSTMOptions
- def DiagonalRecurrentTensors(self):
+def SignatureDefStart(builder):
+ builder.StartObject(5)
+
+def SignatureDefAddInputs(builder, inputs):
+ builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(inputs), 0)
+
+def SignatureDefStartInputsVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def SignatureDefAddOutputs(builder, outputs):
+ builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(outputs), 0)
+
+def SignatureDefStartOutputsVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def SignatureDefAddSignatureKey(builder, signatureKey):
+ builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(signatureKey), 0)
+
+def SignatureDefAddSubgraphIndex(builder, subgraphIndex):
+ builder.PrependUint32Slot(4, subgraphIndex, 0)
+
+def SignatureDefEnd(builder):
+ return builder.EndObject()
+
+
+try:
+ from typing import List
+except:
+ pass
+
+class SignatureDefT(object):
+
+ # SignatureDefT
+ def __init__(self):
+ self.inputs = None # type: List[TensorMapT]
+ self.outputs = None # type: List[TensorMapT]
+ self.signatureKey = None # type: str
+ self.subgraphIndex = 0 # type: int
+
+ @classmethod
+ def InitFromBuf(cls, buf, pos):
+ signatureDef = SignatureDef()
+ signatureDef.Init(buf, pos)
+ return cls.InitFromObj(signatureDef)
+
+ @classmethod
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, signatureDef):
+ x = SignatureDefT()
+ x._UnPack(signatureDef)
+ return x
+
+ # SignatureDefT
+ def _UnPack(self, signatureDef):
+ if signatureDef is None:
+ return
+ if not signatureDef.InputsIsNone():
+ self.inputs = []
+ for i in range(signatureDef.InputsLength()):
+ if signatureDef.Inputs(i) is None:
+ self.inputs.append(None)
+ else:
+ tensorMap_ = TensorMapT.InitFromObj(signatureDef.Inputs(i))
+ self.inputs.append(tensorMap_)
+ if not signatureDef.OutputsIsNone():
+ self.outputs = []
+ for i in range(signatureDef.OutputsLength()):
+ if signatureDef.Outputs(i) is None:
+ self.outputs.append(None)
+ else:
+ tensorMap_ = TensorMapT.InitFromObj(signatureDef.Outputs(i))
+ self.outputs.append(tensorMap_)
+ self.signatureKey = signatureDef.SignatureKey()
+ self.subgraphIndex = signatureDef.SubgraphIndex()
+
+ # SignatureDefT
+ def Pack(self, builder):
+ if self.inputs is not None:
+ inputslist = []
+ for i in range(len(self.inputs)):
+ inputslist.append(self.inputs[i].Pack(builder))
+ SignatureDefStartInputsVector(builder, len(self.inputs))
+ for i in reversed(range(len(self.inputs))):
+ builder.PrependUOffsetTRelative(inputslist[i])
+ inputs = builder.EndVector()
+ if self.outputs is not None:
+ outputslist = []
+ for i in range(len(self.outputs)):
+ outputslist.append(self.outputs[i].Pack(builder))
+ SignatureDefStartOutputsVector(builder, len(self.outputs))
+ for i in reversed(range(len(self.outputs))):
+ builder.PrependUOffsetTRelative(outputslist[i])
+ outputs = builder.EndVector()
+ if self.signatureKey is not None:
+ signatureKey = builder.CreateString(self.signatureKey)
+ SignatureDefStart(builder)
+ if self.inputs is not None:
+ SignatureDefAddInputs(builder, inputs)
+ if self.outputs is not None:
+ SignatureDefAddOutputs(builder, outputs)
+ if self.signatureKey is not None:
+ SignatureDefAddSignatureKey(builder, signatureKey)
+ SignatureDefAddSubgraphIndex(builder, self.subgraphIndex)
+ signatureDef = SignatureDefEnd(builder)
+ return signatureDef
+
+
+class Model(object):
+ __slots__ = ['_tab']
+
+ @classmethod
+ def GetRootAs(cls, buf, offset=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+ x = Model()
+ x.Init(buf, n + offset)
+ return x
+
+ @classmethod
+ def GetRootAsModel(cls, buf, offset=0):
+ """This method is deprecated. Please switch to GetRootAs."""
+ return cls.GetRootAs(buf, offset)
+ @classmethod
+ def ModelBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+ return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+ # Model
+ def Init(self, buf, pos):
+ self._tab = flatbuffers.table.Table(buf, pos)
+
+ # Model
+ def Version(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ if o != 0:
+ return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos)
+ return 0
+
+ # Model
+ def OperatorCodes(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ x = self._tab.Vector(o)
+ x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
+ x = self._tab.Indirect(x)
+ obj = OperatorCode()
+ obj.Init(self._tab.Bytes, x)
+ return obj
+ return None
+
+ # Model
+ def OperatorCodesLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # Model
+ def OperatorCodesIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ return o == 0
+
+ # Model
+ def Subgraphs(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ x = self._tab.Vector(o)
+ x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
+ x = self._tab.Indirect(x)
+ obj = SubGraph()
+ obj.Init(self._tab.Bytes, x)
+ return obj
+ return None
+
+ # Model
+ def SubgraphsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # Model
+ def SubgraphsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
+ return o == 0
+
+ # Model
+ def Description(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
+ if o != 0:
+ return self._tab.String(o + self._tab.Pos)
+ return None
+
+ # Model
+ def Buffers(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ x = self._tab.Vector(o)
+ x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
+ x = self._tab.Indirect(x)
+ obj = Buffer()
+ obj.Init(self._tab.Bytes, x)
+ return obj
+ return None
+
+ # Model
+ def BuffersLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # Model
+ def BuffersIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
+ return o == 0
+
+ # Model
+ def MetadataBuffer(self, j):
o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
-
-def UnidirectionalSequenceLSTMOptionsStart(builder): builder.StartObject(6)
-def Start(builder):
- return UnidirectionalSequenceLSTMOptionsStart(builder)
-def UnidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0)
-def AddFusedActivationFunction(builder, fusedActivationFunction):
- return UnidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction)
-def UnidirectionalSequenceLSTMOptionsAddCellClip(builder, cellClip): builder.PrependFloat32Slot(1, cellClip, 0.0)
-def AddCellClip(builder, cellClip):
- return UnidirectionalSequenceLSTMOptionsAddCellClip(builder, cellClip)
-def UnidirectionalSequenceLSTMOptionsAddProjClip(builder, projClip): builder.PrependFloat32Slot(2, projClip, 0.0)
-def AddProjClip(builder, projClip):
- return UnidirectionalSequenceLSTMOptionsAddProjClip(builder, projClip)
-def UnidirectionalSequenceLSTMOptionsAddTimeMajor(builder, timeMajor): builder.PrependBoolSlot(3, timeMajor, 0)
-def AddTimeMajor(builder, timeMajor):
- return UnidirectionalSequenceLSTMOptionsAddTimeMajor(builder, timeMajor)
-def UnidirectionalSequenceLSTMOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs): builder.PrependBoolSlot(4, asymmetricQuantizeInputs, 0)
-def AddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
- return UnidirectionalSequenceLSTMOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs)
-def UnidirectionalSequenceLSTMOptionsAddDiagonalRecurrentTensors(builder, diagonalRecurrentTensors): builder.PrependBoolSlot(5, diagonalRecurrentTensors, 0)
-def AddDiagonalRecurrentTensors(builder, diagonalRecurrentTensors):
- return UnidirectionalSequenceLSTMOptionsAddDiagonalRecurrentTensors(builder, diagonalRecurrentTensors)
-def UnidirectionalSequenceLSTMOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return UnidirectionalSequenceLSTMOptionsEnd(builder)
-
-class UnidirectionalSequenceLSTMOptionsT(object):
-
- # UnidirectionalSequenceLSTMOptionsT
- def __init__(self):
- self.fusedActivationFunction = 0 # type: int
- self.cellClip = 0.0 # type: float
- self.projClip = 0.0 # type: float
- self.timeMajor = False # type: bool
- self.asymmetricQuantizeInputs = False # type: bool
- self.diagonalRecurrentTensors = False # type: bool
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- unidirectionalSequenceLstmoptions = UnidirectionalSequenceLSTMOptions()
- unidirectionalSequenceLstmoptions.Init(buf, pos)
- return cls.InitFromObj(unidirectionalSequenceLstmoptions)
-
- @classmethod
- def InitFromObj(cls, unidirectionalSequenceLstmoptions):
- x = UnidirectionalSequenceLSTMOptionsT()
- x._UnPack(unidirectionalSequenceLstmoptions)
- return x
-
- # UnidirectionalSequenceLSTMOptionsT
- def _UnPack(self, unidirectionalSequenceLstmoptions):
- if unidirectionalSequenceLstmoptions is None:
- return
- self.fusedActivationFunction = unidirectionalSequenceLstmoptions.FusedActivationFunction()
- self.cellClip = unidirectionalSequenceLstmoptions.CellClip()
- self.projClip = unidirectionalSequenceLstmoptions.ProjClip()
- self.timeMajor = unidirectionalSequenceLstmoptions.TimeMajor()
- self.asymmetricQuantizeInputs = unidirectionalSequenceLstmoptions.AsymmetricQuantizeInputs()
- self.diagonalRecurrentTensors = unidirectionalSequenceLstmoptions.DiagonalRecurrentTensors()
-
- # UnidirectionalSequenceLSTMOptionsT
- def Pack(self, builder):
- UnidirectionalSequenceLSTMOptionsStart(builder)
- UnidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, self.fusedActivationFunction)
- UnidirectionalSequenceLSTMOptionsAddCellClip(builder, self.cellClip)
- UnidirectionalSequenceLSTMOptionsAddProjClip(builder, self.projClip)
- UnidirectionalSequenceLSTMOptionsAddTimeMajor(builder, self.timeMajor)
- UnidirectionalSequenceLSTMOptionsAddAsymmetricQuantizeInputs(builder, self.asymmetricQuantizeInputs)
- UnidirectionalSequenceLSTMOptionsAddDiagonalRecurrentTensors(builder, self.diagonalRecurrentTensors)
- unidirectionalSequenceLstmoptions = UnidirectionalSequenceLSTMOptionsEnd(builder)
- return unidirectionalSequenceLstmoptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class UniqueOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = UniqueOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsUniqueOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def UniqueOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # UniqueOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # UniqueOptions
- def IdxOutType(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
- return 2
-
-def UniqueOptionsStart(builder): builder.StartObject(1)
-def Start(builder):
- return UniqueOptionsStart(builder)
-def UniqueOptionsAddIdxOutType(builder, idxOutType): builder.PrependInt8Slot(0, idxOutType, 2)
-def AddIdxOutType(builder, idxOutType):
- return UniqueOptionsAddIdxOutType(builder, idxOutType)
-def UniqueOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return UniqueOptionsEnd(builder)
-
-class UniqueOptionsT(object):
-
- # UniqueOptionsT
- def __init__(self):
- self.idxOutType = 2 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- uniqueOptions = UniqueOptions()
- uniqueOptions.Init(buf, pos)
- return cls.InitFromObj(uniqueOptions)
-
- @classmethod
- def InitFromObj(cls, uniqueOptions):
- x = UniqueOptionsT()
- x._UnPack(uniqueOptions)
- return x
-
- # UniqueOptionsT
- def _UnPack(self, uniqueOptions):
- if uniqueOptions is None:
- return
- self.idxOutType = uniqueOptions.IdxOutType()
-
- # UniqueOptionsT
- def Pack(self, builder):
- UniqueOptionsStart(builder)
- UniqueOptionsAddIdxOutType(builder, self.idxOutType)
- uniqueOptions = UniqueOptionsEnd(builder)
- return uniqueOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class UnpackOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = UnpackOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsUnpackOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def UnpackOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # UnpackOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # UnpackOptions
- def Num(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # UnpackOptions
- def Axis(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
-def UnpackOptionsStart(builder): builder.StartObject(2)
-def Start(builder):
- return UnpackOptionsStart(builder)
-def UnpackOptionsAddNum(builder, num): builder.PrependInt32Slot(0, num, 0)
-def AddNum(builder, num):
- return UnpackOptionsAddNum(builder, num)
-def UnpackOptionsAddAxis(builder, axis): builder.PrependInt32Slot(1, axis, 0)
-def AddAxis(builder, axis):
- return UnpackOptionsAddAxis(builder, axis)
-def UnpackOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return UnpackOptionsEnd(builder)
-
-class UnpackOptionsT(object):
-
- # UnpackOptionsT
- def __init__(self):
- self.num = 0 # type: int
- self.axis = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- unpackOptions = UnpackOptions()
- unpackOptions.Init(buf, pos)
- return cls.InitFromObj(unpackOptions)
-
- @classmethod
- def InitFromObj(cls, unpackOptions):
- x = UnpackOptionsT()
- x._UnPack(unpackOptions)
- return x
-
- # UnpackOptionsT
- def _UnPack(self, unpackOptions):
- if unpackOptions is None:
- return
- self.num = unpackOptions.Num()
- self.axis = unpackOptions.Axis()
-
- # UnpackOptionsT
- def Pack(self, builder):
- UnpackOptionsStart(builder)
- UnpackOptionsAddNum(builder, self.num)
- UnpackOptionsAddAxis(builder, self.axis)
- unpackOptions = UnpackOptionsEnd(builder)
- return unpackOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class UnsortedSegmentMaxOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = UnsortedSegmentMaxOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsUnsortedSegmentMaxOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def UnsortedSegmentMaxOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # UnsortedSegmentMaxOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def UnsortedSegmentMaxOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return UnsortedSegmentMaxOptionsStart(builder)
-def UnsortedSegmentMaxOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return UnsortedSegmentMaxOptionsEnd(builder)
-
-class UnsortedSegmentMaxOptionsT(object):
-
- # UnsortedSegmentMaxOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- unsortedSegmentMaxOptions = UnsortedSegmentMaxOptions()
- unsortedSegmentMaxOptions.Init(buf, pos)
- return cls.InitFromObj(unsortedSegmentMaxOptions)
-
- @classmethod
- def InitFromObj(cls, unsortedSegmentMaxOptions):
- x = UnsortedSegmentMaxOptionsT()
- x._UnPack(unsortedSegmentMaxOptions)
- return x
-
- # UnsortedSegmentMaxOptionsT
- def _UnPack(self, unsortedSegmentMaxOptions):
- if unsortedSegmentMaxOptions is None:
- return
-
- # UnsortedSegmentMaxOptionsT
- def Pack(self, builder):
- UnsortedSegmentMaxOptionsStart(builder)
- unsortedSegmentMaxOptions = UnsortedSegmentMaxOptionsEnd(builder)
- return unsortedSegmentMaxOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class UnsortedSegmentMinOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = UnsortedSegmentMinOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsUnsortedSegmentMinOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def UnsortedSegmentMinOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # UnsortedSegmentMinOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def UnsortedSegmentMinOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return UnsortedSegmentMinOptionsStart(builder)
-def UnsortedSegmentMinOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return UnsortedSegmentMinOptionsEnd(builder)
-
-class UnsortedSegmentMinOptionsT(object):
-
- # UnsortedSegmentMinOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- unsortedSegmentMinOptions = UnsortedSegmentMinOptions()
- unsortedSegmentMinOptions.Init(buf, pos)
- return cls.InitFromObj(unsortedSegmentMinOptions)
-
- @classmethod
- def InitFromObj(cls, unsortedSegmentMinOptions):
- x = UnsortedSegmentMinOptionsT()
- x._UnPack(unsortedSegmentMinOptions)
- return x
-
- # UnsortedSegmentMinOptionsT
- def _UnPack(self, unsortedSegmentMinOptions):
- if unsortedSegmentMinOptions is None:
- return
-
- # UnsortedSegmentMinOptionsT
- def Pack(self, builder):
- UnsortedSegmentMinOptionsStart(builder)
- unsortedSegmentMinOptions = UnsortedSegmentMinOptionsEnd(builder)
- return unsortedSegmentMinOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class UnsortedSegmentProdOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = UnsortedSegmentProdOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsUnsortedSegmentProdOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def UnsortedSegmentProdOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # UnsortedSegmentProdOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def UnsortedSegmentProdOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return UnsortedSegmentProdOptionsStart(builder)
-def UnsortedSegmentProdOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return UnsortedSegmentProdOptionsEnd(builder)
-
-class UnsortedSegmentProdOptionsT(object):
-
- # UnsortedSegmentProdOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- unsortedSegmentProdOptions = UnsortedSegmentProdOptions()
- unsortedSegmentProdOptions.Init(buf, pos)
- return cls.InitFromObj(unsortedSegmentProdOptions)
-
- @classmethod
- def InitFromObj(cls, unsortedSegmentProdOptions):
- x = UnsortedSegmentProdOptionsT()
- x._UnPack(unsortedSegmentProdOptions)
- return x
-
- # UnsortedSegmentProdOptionsT
- def _UnPack(self, unsortedSegmentProdOptions):
- if unsortedSegmentProdOptions is None:
- return
-
- # UnsortedSegmentProdOptionsT
- def Pack(self, builder):
- UnsortedSegmentProdOptionsStart(builder)
- unsortedSegmentProdOptions = UnsortedSegmentProdOptionsEnd(builder)
- return unsortedSegmentProdOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class UnsortedSegmentSumOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = UnsortedSegmentSumOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsUnsortedSegmentSumOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def UnsortedSegmentSumOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # UnsortedSegmentSumOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def UnsortedSegmentSumOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return UnsortedSegmentSumOptionsStart(builder)
-def UnsortedSegmentSumOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return UnsortedSegmentSumOptionsEnd(builder)
-
-class UnsortedSegmentSumOptionsT(object):
-
- # UnsortedSegmentSumOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- unsortedSegmentSumOptions = UnsortedSegmentSumOptions()
- unsortedSegmentSumOptions.Init(buf, pos)
- return cls.InitFromObj(unsortedSegmentSumOptions)
-
- @classmethod
- def InitFromObj(cls, unsortedSegmentSumOptions):
- x = UnsortedSegmentSumOptionsT()
- x._UnPack(unsortedSegmentSumOptions)
- return x
-
- # UnsortedSegmentSumOptionsT
- def _UnPack(self, unsortedSegmentSumOptions):
- if unsortedSegmentSumOptions is None:
- return
-
- # UnsortedSegmentSumOptionsT
- def Pack(self, builder):
- UnsortedSegmentSumOptionsStart(builder)
- unsortedSegmentSumOptions = UnsortedSegmentSumOptionsEnd(builder)
- return unsortedSegmentSumOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class VarHandleOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = VarHandleOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsVarHandleOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def VarHandleOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # VarHandleOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # VarHandleOptions
- def Container(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.String(o + self._tab.Pos)
- return None
-
- # VarHandleOptions
- def SharedName(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.String(o + self._tab.Pos)
- return None
-
-def VarHandleOptionsStart(builder): builder.StartObject(2)
-def Start(builder):
- return VarHandleOptionsStart(builder)
-def VarHandleOptionsAddContainer(builder, container): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(container), 0)
-def AddContainer(builder, container):
- return VarHandleOptionsAddContainer(builder, container)
-def VarHandleOptionsAddSharedName(builder, sharedName): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(sharedName), 0)
-def AddSharedName(builder, sharedName):
- return VarHandleOptionsAddSharedName(builder, sharedName)
-def VarHandleOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return VarHandleOptionsEnd(builder)
-
-class VarHandleOptionsT(object):
-
- # VarHandleOptionsT
- def __init__(self):
- self.container = None # type: str
- self.sharedName = None # type: str
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- varHandleOptions = VarHandleOptions()
- varHandleOptions.Init(buf, pos)
- return cls.InitFromObj(varHandleOptions)
-
- @classmethod
- def InitFromObj(cls, varHandleOptions):
- x = VarHandleOptionsT()
- x._UnPack(varHandleOptions)
- return x
-
- # VarHandleOptionsT
- def _UnPack(self, varHandleOptions):
- if varHandleOptions is None:
- return
- self.container = varHandleOptions.Container()
- self.sharedName = varHandleOptions.SharedName()
-
- # VarHandleOptionsT
- def Pack(self, builder):
- if self.container is not None:
- container = builder.CreateString(self.container)
- if self.sharedName is not None:
- sharedName = builder.CreateString(self.sharedName)
- VarHandleOptionsStart(builder)
- if self.container is not None:
- VarHandleOptionsAddContainer(builder, container)
- if self.sharedName is not None:
- VarHandleOptionsAddSharedName(builder, sharedName)
- varHandleOptions = VarHandleOptionsEnd(builder)
- return varHandleOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class VariantSubType(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = VariantSubType()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsVariantSubType(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def VariantSubTypeBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # VariantSubType
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # VariantSubType
- def Shape(self, j):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
a = self._tab.Vector(o)
return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
return 0
- # VariantSubType
- def ShapeAsNumpy(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ # Model
+ def MetadataBufferAsNumpy(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
if o != 0:
return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
return 0
- # VariantSubType
- def ShapeLength(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ # Model
+ def MetadataBufferLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
if o != 0:
return self._tab.VectorLen(o)
return 0
- # VariantSubType
- def ShapeIsNone(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+ # Model
+ def MetadataBufferIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
return o == 0
- # VariantSubType
- def Type(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
+ # Model
+ def Metadata(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
+ x = self._tab.Vector(o)
+ x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
+ x = self._tab.Indirect(x)
+ obj = Metadata()
+ obj.Init(self._tab.Bytes, x)
+ return obj
+ return None
+
+ # Model
+ def MetadataLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
+ if o != 0:
+ return self._tab.VectorLen(o)
return 0
- # VariantSubType
- def HasRank(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
- if o != 0:
- return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
- return False
+ # Model
+ def MetadataIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
+ return o == 0
-def VariantSubTypeStart(builder): builder.StartObject(3)
-def Start(builder):
- return VariantSubTypeStart(builder)
-def VariantSubTypeAddShape(builder, shape): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(shape), 0)
-def AddShape(builder, shape):
- return VariantSubTypeAddShape(builder, shape)
-def VariantSubTypeStartShapeVector(builder, numElems): return builder.StartVector(4, numElems, 4)
-def StartShapeVector(builder, numElems):
- return VariantSubTypeStartShapeVector(builder, numElems)
-def VariantSubTypeAddType(builder, type): builder.PrependInt8Slot(1, type, 0)
-def AddType(builder, type):
- return VariantSubTypeAddType(builder, type)
-def VariantSubTypeAddHasRank(builder, hasRank): builder.PrependBoolSlot(2, hasRank, 0)
-def AddHasRank(builder, hasRank):
- return VariantSubTypeAddHasRank(builder, hasRank)
-def VariantSubTypeEnd(builder): return builder.EndObject()
-def End(builder):
- return VariantSubTypeEnd(builder)
+ # Model
+ def SignatureDefs(self, j):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
+ if o != 0:
+ x = self._tab.Vector(o)
+ x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
+ x = self._tab.Indirect(x)
+ obj = SignatureDef()
+ obj.Init(self._tab.Bytes, x)
+ return obj
+ return None
+
+ # Model
+ def SignatureDefsLength(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
+ if o != 0:
+ return self._tab.VectorLen(o)
+ return 0
+
+ # Model
+ def SignatureDefsIsNone(self):
+ o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
+ return o == 0
+
+def ModelStart(builder):
+ builder.StartObject(8)
+
+def ModelAddVersion(builder, version):
+ builder.PrependUint32Slot(0, version, 0)
+
+def ModelAddOperatorCodes(builder, operatorCodes):
+ builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(operatorCodes), 0)
+
+def ModelStartOperatorCodesVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def ModelAddSubgraphs(builder, subgraphs):
+ builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(subgraphs), 0)
+
+def ModelStartSubgraphsVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def ModelAddDescription(builder, description):
+ builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(description), 0)
+
+def ModelAddBuffers(builder, buffers):
+ builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(buffers), 0)
+
+def ModelStartBuffersVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def ModelAddMetadataBuffer(builder, metadataBuffer):
+ builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(metadataBuffer), 0)
+
+def ModelStartMetadataBufferVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def ModelAddMetadata(builder, metadata):
+ builder.PrependUOffsetTRelativeSlot(6, flatbuffers.number_types.UOffsetTFlags.py_type(metadata), 0)
+
+def ModelStartMetadataVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def ModelAddSignatureDefs(builder, signatureDefs):
+ builder.PrependUOffsetTRelativeSlot(7, flatbuffers.number_types.UOffsetTFlags.py_type(signatureDefs), 0)
+
+def ModelStartSignatureDefsVector(builder, numElems):
+ return builder.StartVector(4, numElems, 4)
+
+def ModelEnd(builder):
+ return builder.EndObject()
+
+
try:
from typing import List
except:
pass
-class VariantSubTypeT(object):
+class ModelT(object):
- # VariantSubTypeT
+ # ModelT
def __init__(self):
- self.shape = None # type: List[int]
- self.type = 0 # type: int
- self.hasRank = False # type: bool
+ self.version = 0 # type: int
+ self.operatorCodes = None # type: List[OperatorCodeT]
+ self.subgraphs = None # type: List[SubGraphT]
+ self.description = None # type: str
+ self.buffers = None # type: List[BufferT]
+ self.metadataBuffer = None # type: List[int]
+ self.metadata = None # type: List[MetadataT]
+ self.signatureDefs = None # type: List[SignatureDefT]
@classmethod
def InitFromBuf(cls, buf, pos):
- variantSubType = VariantSubType()
- variantSubType.Init(buf, pos)
- return cls.InitFromObj(variantSubType)
+ model = Model()
+ model.Init(buf, pos)
+ return cls.InitFromObj(model)
@classmethod
- def InitFromObj(cls, variantSubType):
- x = VariantSubTypeT()
- x._UnPack(variantSubType)
+ def InitFromPackedBuf(cls, buf, pos=0):
+ n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, pos)
+ return cls.InitFromBuf(buf, pos+n)
+
+ @classmethod
+ def InitFromObj(cls, model):
+ x = ModelT()
+ x._UnPack(model)
return x
- # VariantSubTypeT
- def _UnPack(self, variantSubType):
- if variantSubType is None:
+ # ModelT
+ def _UnPack(self, model):
+ if model is None:
return
- if not variantSubType.ShapeIsNone():
+ self.version = model.Version()
+ if not model.OperatorCodesIsNone():
+ self.operatorCodes = []
+ for i in range(model.OperatorCodesLength()):
+ if model.OperatorCodes(i) is None:
+ self.operatorCodes.append(None)
+ else:
+ operatorCode_ = OperatorCodeT.InitFromObj(model.OperatorCodes(i))
+ self.operatorCodes.append(operatorCode_)
+ if not model.SubgraphsIsNone():
+ self.subgraphs = []
+ for i in range(model.SubgraphsLength()):
+ if model.Subgraphs(i) is None:
+ self.subgraphs.append(None)
+ else:
+ subGraph_ = SubGraphT.InitFromObj(model.Subgraphs(i))
+ self.subgraphs.append(subGraph_)
+ self.description = model.Description()
+ if not model.BuffersIsNone():
+ self.buffers = []
+ for i in range(model.BuffersLength()):
+ if model.Buffers(i) is None:
+ self.buffers.append(None)
+ else:
+ buffer_ = BufferT.InitFromObj(model.Buffers(i))
+ self.buffers.append(buffer_)
+ if not model.MetadataBufferIsNone():
if np is None:
- self.shape = []
- for i in range(variantSubType.ShapeLength()):
- self.shape.append(variantSubType.Shape(i))
+ self.metadataBuffer = []
+ for i in range(model.MetadataBufferLength()):
+ self.metadataBuffer.append(model.MetadataBuffer(i))
else:
- self.shape = variantSubType.ShapeAsNumpy()
- self.type = variantSubType.Type()
- self.hasRank = variantSubType.HasRank()
+ self.metadataBuffer = model.MetadataBufferAsNumpy()
+ if not model.MetadataIsNone():
+ self.metadata = []
+ for i in range(model.MetadataLength()):
+ if model.Metadata(i) is None:
+ self.metadata.append(None)
+ else:
+ metadata_ = MetadataT.InitFromObj(model.Metadata(i))
+ self.metadata.append(metadata_)
+ if not model.SignatureDefsIsNone():
+ self.signatureDefs = []
+ for i in range(model.SignatureDefsLength()):
+ if model.SignatureDefs(i) is None:
+ self.signatureDefs.append(None)
+ else:
+ signatureDef_ = SignatureDefT.InitFromObj(model.SignatureDefs(i))
+ self.signatureDefs.append(signatureDef_)
- # VariantSubTypeT
+ # ModelT
def Pack(self, builder):
- if self.shape is not None:
- if np is not None and type(self.shape) is np.ndarray:
- shape = builder.CreateNumpyVector(self.shape)
+ if self.operatorCodes is not None:
+ operatorCodeslist = []
+ for i in range(len(self.operatorCodes)):
+ operatorCodeslist.append(self.operatorCodes[i].Pack(builder))
+ ModelStartOperatorCodesVector(builder, len(self.operatorCodes))
+ for i in reversed(range(len(self.operatorCodes))):
+ builder.PrependUOffsetTRelative(operatorCodeslist[i])
+ operatorCodes = builder.EndVector()
+ if self.subgraphs is not None:
+ subgraphslist = []
+ for i in range(len(self.subgraphs)):
+ subgraphslist.append(self.subgraphs[i].Pack(builder))
+ ModelStartSubgraphsVector(builder, len(self.subgraphs))
+ for i in reversed(range(len(self.subgraphs))):
+ builder.PrependUOffsetTRelative(subgraphslist[i])
+ subgraphs = builder.EndVector()
+ if self.description is not None:
+ description = builder.CreateString(self.description)
+ if self.buffers is not None:
+ bufferslist = []
+ for i in range(len(self.buffers)):
+ bufferslist.append(self.buffers[i].Pack(builder))
+ ModelStartBuffersVector(builder, len(self.buffers))
+ for i in reversed(range(len(self.buffers))):
+ builder.PrependUOffsetTRelative(bufferslist[i])
+ buffers = builder.EndVector()
+ if self.metadataBuffer is not None:
+ if np is not None and type(self.metadataBuffer) is np.ndarray:
+ metadataBuffer = builder.CreateNumpyVector(self.metadataBuffer)
else:
- VariantSubTypeStartShapeVector(builder, len(self.shape))
- for i in reversed(range(len(self.shape))):
- builder.PrependInt32(self.shape[i])
- shape = builder.EndVector()
- VariantSubTypeStart(builder)
- if self.shape is not None:
- VariantSubTypeAddShape(builder, shape)
- VariantSubTypeAddType(builder, self.type)
- VariantSubTypeAddHasRank(builder, self.hasRank)
- variantSubType = VariantSubTypeEnd(builder)
- return variantSubType
-# automatically generated by the FlatBuffers compiler, do not modify
+ ModelStartMetadataBufferVector(builder, len(self.metadataBuffer))
+ for i in reversed(range(len(self.metadataBuffer))):
+ builder.PrependInt32(self.metadataBuffer[i])
+ metadataBuffer = builder.EndVector()
+ if self.metadata is not None:
+ metadatalist = []
+ for i in range(len(self.metadata)):
+ metadatalist.append(self.metadata[i].Pack(builder))
+ ModelStartMetadataVector(builder, len(self.metadata))
+ for i in reversed(range(len(self.metadata))):
+ builder.PrependUOffsetTRelative(metadatalist[i])
+ metadata = builder.EndVector()
+ if self.signatureDefs is not None:
+ signatureDefslist = []
+ for i in range(len(self.signatureDefs)):
+ signatureDefslist.append(self.signatureDefs[i].Pack(builder))
+ ModelStartSignatureDefsVector(builder, len(self.signatureDefs))
+ for i in reversed(range(len(self.signatureDefs))):
+ builder.PrependUOffsetTRelative(signatureDefslist[i])
+ signatureDefs = builder.EndVector()
+ ModelStart(builder)
+ ModelAddVersion(builder, self.version)
+ if self.operatorCodes is not None:
+ ModelAddOperatorCodes(builder, operatorCodes)
+ if self.subgraphs is not None:
+ ModelAddSubgraphs(builder, subgraphs)
+ if self.description is not None:
+ ModelAddDescription(builder, description)
+ if self.buffers is not None:
+ ModelAddBuffers(builder, buffers)
+ if self.metadataBuffer is not None:
+ ModelAddMetadataBuffer(builder, metadataBuffer)
+ if self.metadata is not None:
+ ModelAddMetadata(builder, metadata)
+ if self.signatureDefs is not None:
+ ModelAddSignatureDefs(builder, signatureDefs)
+ model = ModelEnd(builder)
+ return model
-# namespace: tflite
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class WhereOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = WhereOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsWhereOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def WhereOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # WhereOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def WhereOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return WhereOptionsStart(builder)
-def WhereOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return WhereOptionsEnd(builder)
-
-class WhereOptionsT(object):
-
- # WhereOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- whereOptions = WhereOptions()
- whereOptions.Init(buf, pos)
- return cls.InitFromObj(whereOptions)
-
- @classmethod
- def InitFromObj(cls, whereOptions):
- x = WhereOptionsT()
- x._UnPack(whereOptions)
- return x
-
- # WhereOptionsT
- def _UnPack(self, whereOptions):
- if whereOptions is None:
- return
-
- # WhereOptionsT
- def Pack(self, builder):
- WhereOptionsStart(builder)
- whereOptions = WhereOptionsEnd(builder)
- return whereOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class WhileOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = WhileOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsWhileOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def WhileOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # WhileOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
- # WhileOptions
- def CondSubgraphIndex(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
- # WhileOptions
- def BodySubgraphIndex(self):
- o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
- if o != 0:
- return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
- return 0
-
-def WhileOptionsStart(builder): builder.StartObject(2)
-def Start(builder):
- return WhileOptionsStart(builder)
-def WhileOptionsAddCondSubgraphIndex(builder, condSubgraphIndex): builder.PrependInt32Slot(0, condSubgraphIndex, 0)
-def AddCondSubgraphIndex(builder, condSubgraphIndex):
- return WhileOptionsAddCondSubgraphIndex(builder, condSubgraphIndex)
-def WhileOptionsAddBodySubgraphIndex(builder, bodySubgraphIndex): builder.PrependInt32Slot(1, bodySubgraphIndex, 0)
-def AddBodySubgraphIndex(builder, bodySubgraphIndex):
- return WhileOptionsAddBodySubgraphIndex(builder, bodySubgraphIndex)
-def WhileOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return WhileOptionsEnd(builder)
-
-class WhileOptionsT(object):
-
- # WhileOptionsT
- def __init__(self):
- self.condSubgraphIndex = 0 # type: int
- self.bodySubgraphIndex = 0 # type: int
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- whileOptions = WhileOptions()
- whileOptions.Init(buf, pos)
- return cls.InitFromObj(whileOptions)
-
- @classmethod
- def InitFromObj(cls, whileOptions):
- x = WhileOptionsT()
- x._UnPack(whileOptions)
- return x
-
- # WhileOptionsT
- def _UnPack(self, whileOptions):
- if whileOptions is None:
- return
- self.condSubgraphIndex = whileOptions.CondSubgraphIndex()
- self.bodySubgraphIndex = whileOptions.BodySubgraphIndex()
-
- # WhileOptionsT
- def Pack(self, builder):
- WhileOptionsStart(builder)
- WhileOptionsAddCondSubgraphIndex(builder, self.condSubgraphIndex)
- WhileOptionsAddBodySubgraphIndex(builder, self.bodySubgraphIndex)
- whileOptions = WhileOptionsEnd(builder)
- return whileOptions
-# automatically generated by the FlatBuffers compiler, do not modify
-
-# namespace: tflite
-
-from flatbuffers.compat import import_numpy
-np = import_numpy()
-
-class ZerosLikeOptions(object):
- __slots__ = ['_tab']
-
- @classmethod
- def GetRootAs(cls, buf, offset=0):
- n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
- x = ZerosLikeOptions()
- x.Init(buf, n + offset)
- return x
-
- @classmethod
- def GetRootAsZerosLikeOptions(cls, buf, offset=0):
- """This method is deprecated. Please switch to GetRootAs."""
- return cls.GetRootAs(buf, offset)
- @classmethod
- def ZerosLikeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
- return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
-
- # ZerosLikeOptions
- def Init(self, buf, pos):
- self._tab = flatbuffers.table.Table(buf, pos)
-
-def ZerosLikeOptionsStart(builder): builder.StartObject(0)
-def Start(builder):
- return ZerosLikeOptionsStart(builder)
-def ZerosLikeOptionsEnd(builder): return builder.EndObject()
-def End(builder):
- return ZerosLikeOptionsEnd(builder)
-
-class ZerosLikeOptionsT(object):
-
- # ZerosLikeOptionsT
- def __init__(self):
- pass
-
- @classmethod
- def InitFromBuf(cls, buf, pos):
- zerosLikeOptions = ZerosLikeOptions()
- zerosLikeOptions.Init(buf, pos)
- return cls.InitFromObj(zerosLikeOptions)
-
- @classmethod
- def InitFromObj(cls, zerosLikeOptions):
- x = ZerosLikeOptionsT()
- x._UnPack(zerosLikeOptions)
- return x
-
- # ZerosLikeOptionsT
- def _UnPack(self, zerosLikeOptions):
- if zerosLikeOptions is None:
- return
-
- # ZerosLikeOptionsT
- def Pack(self, builder):
- ZerosLikeOptionsStart(builder)
- zerosLikeOptions = ZerosLikeOptionsEnd(builder)
- return zerosLikeOptions
diff --git a/tensorflow/lite/schema/BUILD b/tensorflow/lite/schema/BUILD
index e87375a..199c105 100644
--- a/tensorflow/lite/schema/BUILD
+++ b/tensorflow/lite/schema/BUILD
@@ -11,13 +11,13 @@
# bazel build schema_fbs_srcs.
flatbuffer_cc_library(
name = "schema_fbs",
- srcs = ["schema.fbs"],
+ srcs = ["//tensorflow/compiler/mlir/lite/schema:schema.fbs"],
)
# Generic schema for inference on device (but with reflections makes bigger).
flatbuffer_cc_library(
name = "schema_fbs_with_reflection",
- srcs = ["schema.fbs"],
+ srcs = ["//tensorflow/compiler/mlir/lite/schema:schema.fbs"],
flatc_args = [
"--reflect-types",
"--reflect-names",
@@ -29,11 +29,8 @@
cc_library(
name = "schema_utils",
- srcs = ["schema_utils.cc"],
hdrs = ["schema_utils.h"],
deps = [
- ":schema_fbs",
- "//tensorflow/lite/kernels/internal:compatibility",
- "@flatbuffers//:runtime_cc",
+ "//tensorflow/compiler/mlir/lite/schema:schema_utils",
],
)
diff --git a/tensorflow/lite/schema/schema_generated.h b/tensorflow/lite/schema/schema_generated.h
index c9d92f8..d728617 100755
--- a/tensorflow/lite/schema/schema_generated.h
+++ b/tensorflow/lite/schema/schema_generated.h
@@ -8,9 +8,9 @@
// Ensure the included flatbuffers.h is the same version as when this file was
// generated, otherwise it may not be compatible.
-static_assert(FLATBUFFERS_VERSION_MAJOR == 2 &&
- FLATBUFFERS_VERSION_MINOR == 0 &&
- FLATBUFFERS_VERSION_REVISION == 6,
+static_assert(FLATBUFFERS_VERSION_MAJOR == 23 &&
+ FLATBUFFERS_VERSION_MINOR == 5 &&
+ FLATBUFFERS_VERSION_REVISION == 26,
"Non-compatible flatbuffers version included");
namespace tflite {
@@ -51,6 +51,78 @@
struct TensorBuilder;
struct TensorT;
+struct StablehloGatherOptions;
+struct StablehloGatherOptionsBuilder;
+struct StablehloGatherOptionsT;
+
+struct StablehloTransposeOptions;
+struct StablehloTransposeOptionsBuilder;
+struct StablehloTransposeOptionsT;
+
+struct StablehloDotGeneralOptions;
+struct StablehloDotGeneralOptionsBuilder;
+struct StablehloDotGeneralOptionsT;
+
+struct StablehloReduceWindowOptions;
+struct StablehloReduceWindowOptionsBuilder;
+struct StablehloReduceWindowOptionsT;
+
+struct StablehloWhileOptions;
+struct StablehloWhileOptionsBuilder;
+struct StablehloWhileOptionsT;
+
+struct StablehloSortOptions;
+struct StablehloSortOptionsBuilder;
+struct StablehloSortOptionsT;
+
+struct StablehloConcatenateOptions;
+struct StablehloConcatenateOptionsBuilder;
+struct StablehloConcatenateOptionsT;
+
+struct StablehloBroadcastInDimOptions;
+struct StablehloBroadcastInDimOptionsBuilder;
+struct StablehloBroadcastInDimOptionsT;
+
+struct StablehloCompareOptions;
+struct StablehloCompareOptionsBuilder;
+struct StablehloCompareOptionsT;
+
+struct StablehloDynamicSliceOptions;
+struct StablehloDynamicSliceOptionsBuilder;
+struct StablehloDynamicSliceOptionsT;
+
+struct StablehloPadOptions;
+struct StablehloPadOptionsBuilder;
+struct StablehloPadOptionsT;
+
+struct StablehloIotaOptions;
+struct StablehloIotaOptionsBuilder;
+struct StablehloIotaOptionsT;
+
+struct StablehloCustomCallOptions;
+struct StablehloCustomCallOptionsBuilder;
+struct StablehloCustomCallOptionsT;
+
+struct StablehloReduceOptions;
+struct StablehloReduceOptionsBuilder;
+struct StablehloReduceOptionsT;
+
+struct StablehloSliceOptions;
+struct StablehloSliceOptionsBuilder;
+struct StablehloSliceOptionsT;
+
+struct StablehloConvolutionOptions;
+struct StablehloConvolutionOptionsBuilder;
+struct StablehloConvolutionOptionsT;
+
+struct StablehloScatterOptions;
+struct StablehloScatterOptionsBuilder;
+struct StablehloScatterOptionsT;
+
+struct StablehloRngBitGeneratorOptions;
+struct StablehloRngBitGeneratorOptionsBuilder;
+struct StablehloRngBitGeneratorOptionsT;
+
struct Conv2DOptions;
struct Conv2DOptionsBuilder;
struct Conv2DOptionsT;
@@ -555,10 +627,22 @@
struct RightShiftOptionsBuilder;
struct RightShiftOptionsT;
+struct DilateOptions;
+struct DilateOptionsBuilder;
+struct DilateOptionsT;
+
+struct ReduceWindowOptions;
+struct ReduceWindowOptionsBuilder;
+struct ReduceWindowOptionsT;
+
struct OperatorCode;
struct OperatorCodeBuilder;
struct OperatorCodeT;
+struct StableHLOCompositeOptions;
+struct StableHLOCompositeOptionsBuilder;
+struct StableHLOCompositeOptionsT;
+
struct Operator;
struct OperatorBuilder;
struct OperatorT;
@@ -606,11 +690,12 @@
TensorType_UINT32 = 15,
TensorType_UINT16 = 16,
TensorType_INT4 = 17,
+ TensorType_BFLOAT16 = 18,
TensorType_MIN = TensorType_FLOAT32,
- TensorType_MAX = TensorType_INT4
+ TensorType_MAX = TensorType_BFLOAT16
};
-inline const TensorType (&EnumValuesTensorType())[18] {
+inline const TensorType (&EnumValuesTensorType())[19] {
static const TensorType values[] = {
TensorType_FLOAT32,
TensorType_FLOAT16,
@@ -629,13 +714,14 @@
TensorType_VARIANT,
TensorType_UINT32,
TensorType_UINT16,
- TensorType_INT4
+ TensorType_INT4,
+ TensorType_BFLOAT16
};
return values;
}
inline const char * const *EnumNamesTensorType() {
- static const char * const names[19] = {
+ static const char * const names[20] = {
"FLOAT32",
"FLOAT16",
"INT32",
@@ -654,13 +740,14 @@
"UINT32",
"UINT16",
"INT4",
+ "BFLOAT16",
nullptr
};
return names;
}
inline const char *EnumNameTensorType(TensorType e) {
- if (flatbuffers::IsOutRange(e, TensorType_FLOAT32, TensorType_INT4)) return "";
+ if (::flatbuffers::IsOutRange(e, TensorType_FLOAT32, TensorType_BFLOAT16)) return "";
const size_t index = static_cast<size_t>(e);
return EnumNamesTensorType()[index];
}
@@ -690,7 +777,7 @@
}
inline const char *EnumNameQuantizationDetails(QuantizationDetails e) {
- if (flatbuffers::IsOutRange(e, QuantizationDetails_NONE, QuantizationDetails_CustomQuantization)) return "";
+ if (::flatbuffers::IsOutRange(e, QuantizationDetails_NONE, QuantizationDetails_CustomQuantization)) return "";
const size_t index = static_cast<size_t>(e);
return EnumNamesQuantizationDetails()[index];
}
@@ -738,8 +825,8 @@
}
}
- static void *UnPack(const void *obj, QuantizationDetails type, const flatbuffers::resolver_function_t *resolver);
- flatbuffers::Offset<void> Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const;
+ static void *UnPack(const void *obj, QuantizationDetails type, const ::flatbuffers::resolver_function_t *resolver);
+ ::flatbuffers::Offset<void> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr) const;
tflite::CustomQuantizationT *AsCustomQuantization() {
return type == QuantizationDetails_CustomQuantization ?
@@ -751,8 +838,8 @@
}
};
-bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj, QuantizationDetails type);
-bool VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector<flatbuffers::Offset<void>> *values, const flatbuffers::Vector<uint8_t> *types);
+bool VerifyQuantizationDetails(::flatbuffers::Verifier &verifier, const void *obj, QuantizationDetails type);
+bool VerifyQuantizationDetailsVector(::flatbuffers::Verifier &verifier, const ::flatbuffers::Vector<::flatbuffers::Offset<void>> *values, const ::flatbuffers::Vector<uint8_t> *types);
enum DimensionType : int8_t {
DimensionType_DENSE = 0,
@@ -779,7 +866,7 @@
}
inline const char *EnumNameDimensionType(DimensionType e) {
- if (flatbuffers::IsOutRange(e, DimensionType_DENSE, DimensionType_SPARSE_CSR)) return "";
+ if (::flatbuffers::IsOutRange(e, DimensionType_DENSE, DimensionType_SPARSE_CSR)) return "";
const size_t index = static_cast<size_t>(e);
return EnumNamesDimensionType()[index];
}
@@ -815,7 +902,7 @@
}
inline const char *EnumNameSparseIndexVector(SparseIndexVector e) {
- if (flatbuffers::IsOutRange(e, SparseIndexVector_NONE, SparseIndexVector_Uint8Vector)) return "";
+ if (::flatbuffers::IsOutRange(e, SparseIndexVector_NONE, SparseIndexVector_Uint8Vector)) return "";
const size_t index = static_cast<size_t>(e);
return EnumNamesSparseIndexVector()[index];
}
@@ -879,8 +966,8 @@
}
}
- static void *UnPack(const void *obj, SparseIndexVector type, const flatbuffers::resolver_function_t *resolver);
- flatbuffers::Offset<void> Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const;
+ static void *UnPack(const void *obj, SparseIndexVector type, const ::flatbuffers::resolver_function_t *resolver);
+ ::flatbuffers::Offset<void> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr) const;
tflite::Int32VectorT *AsInt32Vector() {
return type == SparseIndexVector_Int32Vector ?
@@ -908,8 +995,8 @@
}
};
-bool VerifySparseIndexVector(flatbuffers::Verifier &verifier, const void *obj, SparseIndexVector type);
-bool VerifySparseIndexVectorVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector<flatbuffers::Offset<void>> *values, const flatbuffers::Vector<uint8_t> *types);
+bool VerifySparseIndexVector(::flatbuffers::Verifier &verifier, const void *obj, SparseIndexVector type);
+bool VerifySparseIndexVectorVector(::flatbuffers::Verifier &verifier, const ::flatbuffers::Vector<::flatbuffers::Offset<void>> *values, const ::flatbuffers::Vector<uint8_t> *types);
enum BuiltinOperator : int32_t {
BuiltinOperator_ADD = 0,
@@ -1074,11 +1161,56 @@
BuiltinOperator_BITCAST = 159,
BuiltinOperator_BITWISE_XOR = 160,
BuiltinOperator_RIGHT_SHIFT = 161,
+ BuiltinOperator_STABLEHLO_LOGISTIC = 162,
+ BuiltinOperator_STABLEHLO_ADD = 163,
+ BuiltinOperator_STABLEHLO_DIVIDE = 164,
+ BuiltinOperator_STABLEHLO_MULTIPLY = 165,
+ BuiltinOperator_STABLEHLO_MAXIMUM = 166,
+ BuiltinOperator_STABLEHLO_RESHAPE = 167,
+ BuiltinOperator_STABLEHLO_CLAMP = 168,
+ BuiltinOperator_STABLEHLO_CONCATENATE = 169,
+ BuiltinOperator_STABLEHLO_BROADCAST_IN_DIM = 170,
+ BuiltinOperator_STABLEHLO_CONVOLUTION = 171,
+ BuiltinOperator_STABLEHLO_SLICE = 172,
+ BuiltinOperator_STABLEHLO_CUSTOM_CALL = 173,
+ BuiltinOperator_STABLEHLO_REDUCE = 174,
+ BuiltinOperator_STABLEHLO_ABS = 175,
+ BuiltinOperator_STABLEHLO_AND = 176,
+ BuiltinOperator_STABLEHLO_COSINE = 177,
+ BuiltinOperator_STABLEHLO_EXPONENTIAL = 178,
+ BuiltinOperator_STABLEHLO_FLOOR = 179,
+ BuiltinOperator_STABLEHLO_LOG = 180,
+ BuiltinOperator_STABLEHLO_MINIMUM = 181,
+ BuiltinOperator_STABLEHLO_NEGATE = 182,
+ BuiltinOperator_STABLEHLO_OR = 183,
+ BuiltinOperator_STABLEHLO_POWER = 184,
+ BuiltinOperator_STABLEHLO_REMAINDER = 185,
+ BuiltinOperator_STABLEHLO_RSQRT = 186,
+ BuiltinOperator_STABLEHLO_SELECT = 187,
+ BuiltinOperator_STABLEHLO_SUBTRACT = 188,
+ BuiltinOperator_STABLEHLO_TANH = 189,
+ BuiltinOperator_STABLEHLO_SCATTER = 190,
+ BuiltinOperator_STABLEHLO_COMPARE = 191,
+ BuiltinOperator_STABLEHLO_CONVERT = 192,
+ BuiltinOperator_STABLEHLO_DYNAMIC_SLICE = 193,
+ BuiltinOperator_STABLEHLO_DYNAMIC_UPDATE_SLICE = 194,
+ BuiltinOperator_STABLEHLO_PAD = 195,
+ BuiltinOperator_STABLEHLO_IOTA = 196,
+ BuiltinOperator_STABLEHLO_DOT_GENERAL = 197,
+ BuiltinOperator_STABLEHLO_REDUCE_WINDOW = 198,
+ BuiltinOperator_STABLEHLO_SORT = 199,
+ BuiltinOperator_STABLEHLO_WHILE = 200,
+ BuiltinOperator_STABLEHLO_GATHER = 201,
+ BuiltinOperator_STABLEHLO_TRANSPOSE = 202,
+ BuiltinOperator_DILATE = 203,
+ BuiltinOperator_STABLEHLO_RNG_BIT_GENERATOR = 204,
+ BuiltinOperator_REDUCE_WINDOW = 205,
+ BuiltinOperator_STABLEHLO_COMPOSITE = 206,
BuiltinOperator_MIN = BuiltinOperator_ADD,
- BuiltinOperator_MAX = BuiltinOperator_RIGHT_SHIFT
+ BuiltinOperator_MAX = BuiltinOperator_STABLEHLO_COMPOSITE
};
-inline const BuiltinOperator (&EnumValuesBuiltinOperator())[162] {
+inline const BuiltinOperator (&EnumValuesBuiltinOperator())[207] {
static const BuiltinOperator values[] = {
BuiltinOperator_ADD,
BuiltinOperator_AVERAGE_POOL_2D,
@@ -1241,13 +1373,58 @@
BuiltinOperator_SIGN,
BuiltinOperator_BITCAST,
BuiltinOperator_BITWISE_XOR,
- BuiltinOperator_RIGHT_SHIFT
+ BuiltinOperator_RIGHT_SHIFT,
+ BuiltinOperator_STABLEHLO_LOGISTIC,
+ BuiltinOperator_STABLEHLO_ADD,
+ BuiltinOperator_STABLEHLO_DIVIDE,
+ BuiltinOperator_STABLEHLO_MULTIPLY,
+ BuiltinOperator_STABLEHLO_MAXIMUM,
+ BuiltinOperator_STABLEHLO_RESHAPE,
+ BuiltinOperator_STABLEHLO_CLAMP,
+ BuiltinOperator_STABLEHLO_CONCATENATE,
+ BuiltinOperator_STABLEHLO_BROADCAST_IN_DIM,
+ BuiltinOperator_STABLEHLO_CONVOLUTION,
+ BuiltinOperator_STABLEHLO_SLICE,
+ BuiltinOperator_STABLEHLO_CUSTOM_CALL,
+ BuiltinOperator_STABLEHLO_REDUCE,
+ BuiltinOperator_STABLEHLO_ABS,
+ BuiltinOperator_STABLEHLO_AND,
+ BuiltinOperator_STABLEHLO_COSINE,
+ BuiltinOperator_STABLEHLO_EXPONENTIAL,
+ BuiltinOperator_STABLEHLO_FLOOR,
+ BuiltinOperator_STABLEHLO_LOG,
+ BuiltinOperator_STABLEHLO_MINIMUM,
+ BuiltinOperator_STABLEHLO_NEGATE,
+ BuiltinOperator_STABLEHLO_OR,
+ BuiltinOperator_STABLEHLO_POWER,
+ BuiltinOperator_STABLEHLO_REMAINDER,
+ BuiltinOperator_STABLEHLO_RSQRT,
+ BuiltinOperator_STABLEHLO_SELECT,
+ BuiltinOperator_STABLEHLO_SUBTRACT,
+ BuiltinOperator_STABLEHLO_TANH,
+ BuiltinOperator_STABLEHLO_SCATTER,
+ BuiltinOperator_STABLEHLO_COMPARE,
+ BuiltinOperator_STABLEHLO_CONVERT,
+ BuiltinOperator_STABLEHLO_DYNAMIC_SLICE,
+ BuiltinOperator_STABLEHLO_DYNAMIC_UPDATE_SLICE,
+ BuiltinOperator_STABLEHLO_PAD,
+ BuiltinOperator_STABLEHLO_IOTA,
+ BuiltinOperator_STABLEHLO_DOT_GENERAL,
+ BuiltinOperator_STABLEHLO_REDUCE_WINDOW,
+ BuiltinOperator_STABLEHLO_SORT,
+ BuiltinOperator_STABLEHLO_WHILE,
+ BuiltinOperator_STABLEHLO_GATHER,
+ BuiltinOperator_STABLEHLO_TRANSPOSE,
+ BuiltinOperator_DILATE,
+ BuiltinOperator_STABLEHLO_RNG_BIT_GENERATOR,
+ BuiltinOperator_REDUCE_WINDOW,
+ BuiltinOperator_STABLEHLO_COMPOSITE
};
return values;
}
inline const char * const *EnumNamesBuiltinOperator() {
- static const char * const names[163] = {
+ static const char * const names[208] = {
"ADD",
"AVERAGE_POOL_2D",
"CONCATENATION",
@@ -1410,13 +1587,58 @@
"BITCAST",
"BITWISE_XOR",
"RIGHT_SHIFT",
+ "STABLEHLO_LOGISTIC",
+ "STABLEHLO_ADD",
+ "STABLEHLO_DIVIDE",
+ "STABLEHLO_MULTIPLY",
+ "STABLEHLO_MAXIMUM",
+ "STABLEHLO_RESHAPE",
+ "STABLEHLO_CLAMP",
+ "STABLEHLO_CONCATENATE",
+ "STABLEHLO_BROADCAST_IN_DIM",
+ "STABLEHLO_CONVOLUTION",
+ "STABLEHLO_SLICE",
+ "STABLEHLO_CUSTOM_CALL",
+ "STABLEHLO_REDUCE",
+ "STABLEHLO_ABS",
+ "STABLEHLO_AND",
+ "STABLEHLO_COSINE",
+ "STABLEHLO_EXPONENTIAL",
+ "STABLEHLO_FLOOR",
+ "STABLEHLO_LOG",
+ "STABLEHLO_MINIMUM",
+ "STABLEHLO_NEGATE",
+ "STABLEHLO_OR",
+ "STABLEHLO_POWER",
+ "STABLEHLO_REMAINDER",
+ "STABLEHLO_RSQRT",
+ "STABLEHLO_SELECT",
+ "STABLEHLO_SUBTRACT",
+ "STABLEHLO_TANH",
+ "STABLEHLO_SCATTER",
+ "STABLEHLO_COMPARE",
+ "STABLEHLO_CONVERT",
+ "STABLEHLO_DYNAMIC_SLICE",
+ "STABLEHLO_DYNAMIC_UPDATE_SLICE",
+ "STABLEHLO_PAD",
+ "STABLEHLO_IOTA",
+ "STABLEHLO_DOT_GENERAL",
+ "STABLEHLO_REDUCE_WINDOW",
+ "STABLEHLO_SORT",
+ "STABLEHLO_WHILE",
+ "STABLEHLO_GATHER",
+ "STABLEHLO_TRANSPOSE",
+ "DILATE",
+ "STABLEHLO_RNG_BIT_GENERATOR",
+ "REDUCE_WINDOW",
+ "STABLEHLO_COMPOSITE",
nullptr
};
return names;
}
inline const char *EnumNameBuiltinOperator(BuiltinOperator e) {
- if (flatbuffers::IsOutRange(e, BuiltinOperator_ADD, BuiltinOperator_RIGHT_SHIFT)) return "";
+ if (::flatbuffers::IsOutRange(e, BuiltinOperator_ADD, BuiltinOperator_STABLEHLO_COMPOSITE)) return "";
const size_t index = static_cast<size_t>(e);
return EnumNamesBuiltinOperator()[index];
}
@@ -1821,7 +2043,7 @@
}
inline const char *EnumNameBuiltinOptions(BuiltinOptions e) {
- if (flatbuffers::IsOutRange(e, BuiltinOptions_NONE, BuiltinOptions_RightShiftOptions)) return "";
+ if (::flatbuffers::IsOutRange(e, BuiltinOptions_NONE, BuiltinOptions_RightShiftOptions)) return "";
const size_t index = static_cast<size_t>(e);
return EnumNamesBuiltinOptions()[index];
}
@@ -2869,8 +3091,8 @@
}
}
- static void *UnPack(const void *obj, BuiltinOptions type, const flatbuffers::resolver_function_t *resolver);
- flatbuffers::Offset<void> Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const;
+ static void *UnPack(const void *obj, BuiltinOptions type, const ::flatbuffers::resolver_function_t *resolver);
+ ::flatbuffers::Offset<void> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr) const;
tflite::Conv2DOptionsT *AsConv2DOptions() {
return type == BuiltinOptions_Conv2DOptions ?
@@ -3882,8 +4104,624 @@
}
};
-bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type);
-bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector<flatbuffers::Offset<void>> *values, const flatbuffers::Vector<uint8_t> *types);
+bool VerifyBuiltinOptions(::flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type);
+bool VerifyBuiltinOptionsVector(::flatbuffers::Verifier &verifier, const ::flatbuffers::Vector<::flatbuffers::Offset<void>> *values, const ::flatbuffers::Vector<uint8_t> *types);
+
+enum BuiltinOptions2 : uint8_t {
+ BuiltinOptions2_NONE = 0,
+ BuiltinOptions2_StablehloConcatenateOptions = 1,
+ BuiltinOptions2_StablehloBroadcastInDimOptions = 2,
+ BuiltinOptions2_StablehloSliceOptions = 3,
+ BuiltinOptions2_StablehloConvolutionOptions = 4,
+ BuiltinOptions2_StablehloCustomCallOptions = 5,
+ BuiltinOptions2_StablehloReduceOptions = 6,
+ BuiltinOptions2_StablehloScatterOptions = 7,
+ BuiltinOptions2_StablehloCompareOptions = 8,
+ BuiltinOptions2_StablehloDynamicSliceOptions = 9,
+ BuiltinOptions2_StablehloPadOptions = 10,
+ BuiltinOptions2_StablehloIotaOptions = 11,
+ BuiltinOptions2_StablehloDotGeneralOptions = 12,
+ BuiltinOptions2_StablehloReduceWindowOptions = 13,
+ BuiltinOptions2_StablehloSortOptions = 14,
+ BuiltinOptions2_StablehloWhileOptions = 15,
+ BuiltinOptions2_StablehloGatherOptions = 16,
+ BuiltinOptions2_StablehloTransposeOptions = 17,
+ BuiltinOptions2_DilateOptions = 18,
+ BuiltinOptions2_StablehloRngBitGeneratorOptions = 19,
+ BuiltinOptions2_ReduceWindowOptions = 20,
+ BuiltinOptions2_StableHLOCompositeOptions = 21,
+ BuiltinOptions2_MIN = BuiltinOptions2_NONE,
+ BuiltinOptions2_MAX = BuiltinOptions2_StableHLOCompositeOptions
+};
+
+inline const BuiltinOptions2 (&EnumValuesBuiltinOptions2())[22] {
+ static const BuiltinOptions2 values[] = {
+ BuiltinOptions2_NONE,
+ BuiltinOptions2_StablehloConcatenateOptions,
+ BuiltinOptions2_StablehloBroadcastInDimOptions,
+ BuiltinOptions2_StablehloSliceOptions,
+ BuiltinOptions2_StablehloConvolutionOptions,
+ BuiltinOptions2_StablehloCustomCallOptions,
+ BuiltinOptions2_StablehloReduceOptions,
+ BuiltinOptions2_StablehloScatterOptions,
+ BuiltinOptions2_StablehloCompareOptions,
+ BuiltinOptions2_StablehloDynamicSliceOptions,
+ BuiltinOptions2_StablehloPadOptions,
+ BuiltinOptions2_StablehloIotaOptions,
+ BuiltinOptions2_StablehloDotGeneralOptions,
+ BuiltinOptions2_StablehloReduceWindowOptions,
+ BuiltinOptions2_StablehloSortOptions,
+ BuiltinOptions2_StablehloWhileOptions,
+ BuiltinOptions2_StablehloGatherOptions,
+ BuiltinOptions2_StablehloTransposeOptions,
+ BuiltinOptions2_DilateOptions,
+ BuiltinOptions2_StablehloRngBitGeneratorOptions,
+ BuiltinOptions2_ReduceWindowOptions,
+ BuiltinOptions2_StableHLOCompositeOptions
+ };
+ return values;
+}
+
+inline const char * const *EnumNamesBuiltinOptions2() {
+ static const char * const names[23] = {
+ "NONE",
+ "StablehloConcatenateOptions",
+ "StablehloBroadcastInDimOptions",
+ "StablehloSliceOptions",
+ "StablehloConvolutionOptions",
+ "StablehloCustomCallOptions",
+ "StablehloReduceOptions",
+ "StablehloScatterOptions",
+ "StablehloCompareOptions",
+ "StablehloDynamicSliceOptions",
+ "StablehloPadOptions",
+ "StablehloIotaOptions",
+ "StablehloDotGeneralOptions",
+ "StablehloReduceWindowOptions",
+ "StablehloSortOptions",
+ "StablehloWhileOptions",
+ "StablehloGatherOptions",
+ "StablehloTransposeOptions",
+ "DilateOptions",
+ "StablehloRngBitGeneratorOptions",
+ "ReduceWindowOptions",
+ "StableHLOCompositeOptions",
+ nullptr
+ };
+ return names;
+}
+
+inline const char *EnumNameBuiltinOptions2(BuiltinOptions2 e) {
+ if (::flatbuffers::IsOutRange(e, BuiltinOptions2_NONE, BuiltinOptions2_StableHLOCompositeOptions)) return "";
+ const size_t index = static_cast<size_t>(e);
+ return EnumNamesBuiltinOptions2()[index];
+}
+
+template<typename T> struct BuiltinOptions2Traits {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_NONE;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloConcatenateOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloConcatenateOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloBroadcastInDimOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloBroadcastInDimOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloSliceOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloSliceOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloConvolutionOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloConvolutionOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloCustomCallOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloCustomCallOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloReduceOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloReduceOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloScatterOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloScatterOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloCompareOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloCompareOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloDynamicSliceOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloDynamicSliceOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloPadOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloPadOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloIotaOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloIotaOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloDotGeneralOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloDotGeneralOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloReduceWindowOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloReduceWindowOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloSortOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloSortOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloWhileOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloWhileOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloGatherOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloGatherOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloTransposeOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloTransposeOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::DilateOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_DilateOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StablehloRngBitGeneratorOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloRngBitGeneratorOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::ReduceWindowOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_ReduceWindowOptions;
+};
+
+template<> struct BuiltinOptions2Traits<tflite::StableHLOCompositeOptions> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StableHLOCompositeOptions;
+};
+
+template<typename T> struct BuiltinOptions2UnionTraits {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_NONE;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloConcatenateOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloConcatenateOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloBroadcastInDimOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloBroadcastInDimOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloSliceOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloSliceOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloConvolutionOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloConvolutionOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloCustomCallOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloCustomCallOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloReduceOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloReduceOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloScatterOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloScatterOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloCompareOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloCompareOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloDynamicSliceOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloDynamicSliceOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloPadOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloPadOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloIotaOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloIotaOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloDotGeneralOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloDotGeneralOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloReduceWindowOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloReduceWindowOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloSortOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloSortOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloWhileOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloWhileOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloGatherOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloGatherOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloTransposeOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloTransposeOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::DilateOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_DilateOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StablehloRngBitGeneratorOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloRngBitGeneratorOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::ReduceWindowOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_ReduceWindowOptions;
+};
+
+template<> struct BuiltinOptions2UnionTraits<tflite::StableHLOCompositeOptionsT> {
+ static const BuiltinOptions2 enum_value = BuiltinOptions2_StableHLOCompositeOptions;
+};
+
+struct BuiltinOptions2Union {
+ BuiltinOptions2 type;
+ void *value;
+
+ BuiltinOptions2Union() : type(BuiltinOptions2_NONE), value(nullptr) {}
+ BuiltinOptions2Union(BuiltinOptions2Union&& u) FLATBUFFERS_NOEXCEPT :
+ type(BuiltinOptions2_NONE), value(nullptr)
+ { std::swap(type, u.type); std::swap(value, u.value); }
+ BuiltinOptions2Union(const BuiltinOptions2Union &);
+ BuiltinOptions2Union &operator=(const BuiltinOptions2Union &u)
+ { BuiltinOptions2Union t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; }
+ BuiltinOptions2Union &operator=(BuiltinOptions2Union &&u) FLATBUFFERS_NOEXCEPT
+ { std::swap(type, u.type); std::swap(value, u.value); return *this; }
+ ~BuiltinOptions2Union() { Reset(); }
+
+ void Reset();
+
+ template <typename T>
+ void Set(T&& val) {
+ typedef typename std::remove_reference<T>::type RT;
+ Reset();
+ type = BuiltinOptions2UnionTraits<RT>::enum_value;
+ if (type != BuiltinOptions2_NONE) {
+ value = new RT(std::forward<T>(val));
+ }
+ }
+
+ static void *UnPack(const void *obj, BuiltinOptions2 type, const ::flatbuffers::resolver_function_t *resolver);
+ ::flatbuffers::Offset<void> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr) const;
+
+ tflite::StablehloConcatenateOptionsT *AsStablehloConcatenateOptions() {
+ return type == BuiltinOptions2_StablehloConcatenateOptions ?
+ reinterpret_cast<tflite::StablehloConcatenateOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloConcatenateOptionsT *AsStablehloConcatenateOptions() const {
+ return type == BuiltinOptions2_StablehloConcatenateOptions ?
+ reinterpret_cast<const tflite::StablehloConcatenateOptionsT *>(value) : nullptr;
+ }
+ tflite::StablehloBroadcastInDimOptionsT *AsStablehloBroadcastInDimOptions() {
+ return type == BuiltinOptions2_StablehloBroadcastInDimOptions ?
+ reinterpret_cast<tflite::StablehloBroadcastInDimOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloBroadcastInDimOptionsT *AsStablehloBroadcastInDimOptions() const {
+ return type == BuiltinOptions2_StablehloBroadcastInDimOptions ?
+ reinterpret_cast<const tflite::StablehloBroadcastInDimOptionsT *>(value) : nullptr;
+ }
+ tflite::StablehloSliceOptionsT *AsStablehloSliceOptions() {
+ return type == BuiltinOptions2_StablehloSliceOptions ?
+ reinterpret_cast<tflite::StablehloSliceOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloSliceOptionsT *AsStablehloSliceOptions() const {
+ return type == BuiltinOptions2_StablehloSliceOptions ?
+ reinterpret_cast<const tflite::StablehloSliceOptionsT *>(value) : nullptr;
+ }
+ tflite::StablehloConvolutionOptionsT *AsStablehloConvolutionOptions() {
+ return type == BuiltinOptions2_StablehloConvolutionOptions ?
+ reinterpret_cast<tflite::StablehloConvolutionOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloConvolutionOptionsT *AsStablehloConvolutionOptions() const {
+ return type == BuiltinOptions2_StablehloConvolutionOptions ?
+ reinterpret_cast<const tflite::StablehloConvolutionOptionsT *>(value) : nullptr;
+ }
+ tflite::StablehloCustomCallOptionsT *AsStablehloCustomCallOptions() {
+ return type == BuiltinOptions2_StablehloCustomCallOptions ?
+ reinterpret_cast<tflite::StablehloCustomCallOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloCustomCallOptionsT *AsStablehloCustomCallOptions() const {
+ return type == BuiltinOptions2_StablehloCustomCallOptions ?
+ reinterpret_cast<const tflite::StablehloCustomCallOptionsT *>(value) : nullptr;
+ }
+ tflite::StablehloReduceOptionsT *AsStablehloReduceOptions() {
+ return type == BuiltinOptions2_StablehloReduceOptions ?
+ reinterpret_cast<tflite::StablehloReduceOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloReduceOptionsT *AsStablehloReduceOptions() const {
+ return type == BuiltinOptions2_StablehloReduceOptions ?
+ reinterpret_cast<const tflite::StablehloReduceOptionsT *>(value) : nullptr;
+ }
+ tflite::StablehloScatterOptionsT *AsStablehloScatterOptions() {
+ return type == BuiltinOptions2_StablehloScatterOptions ?
+ reinterpret_cast<tflite::StablehloScatterOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloScatterOptionsT *AsStablehloScatterOptions() const {
+ return type == BuiltinOptions2_StablehloScatterOptions ?
+ reinterpret_cast<const tflite::StablehloScatterOptionsT *>(value) : nullptr;
+ }
+ tflite::StablehloCompareOptionsT *AsStablehloCompareOptions() {
+ return type == BuiltinOptions2_StablehloCompareOptions ?
+ reinterpret_cast<tflite::StablehloCompareOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloCompareOptionsT *AsStablehloCompareOptions() const {
+ return type == BuiltinOptions2_StablehloCompareOptions ?
+ reinterpret_cast<const tflite::StablehloCompareOptionsT *>(value) : nullptr;
+ }
+ tflite::StablehloDynamicSliceOptionsT *AsStablehloDynamicSliceOptions() {
+ return type == BuiltinOptions2_StablehloDynamicSliceOptions ?
+ reinterpret_cast<tflite::StablehloDynamicSliceOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloDynamicSliceOptionsT *AsStablehloDynamicSliceOptions() const {
+ return type == BuiltinOptions2_StablehloDynamicSliceOptions ?
+ reinterpret_cast<const tflite::StablehloDynamicSliceOptionsT *>(value) : nullptr;
+ }
+ tflite::StablehloPadOptionsT *AsStablehloPadOptions() {
+ return type == BuiltinOptions2_StablehloPadOptions ?
+ reinterpret_cast<tflite::StablehloPadOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloPadOptionsT *AsStablehloPadOptions() const {
+ return type == BuiltinOptions2_StablehloPadOptions ?
+ reinterpret_cast<const tflite::StablehloPadOptionsT *>(value) : nullptr;
+ }
+ tflite::StablehloIotaOptionsT *AsStablehloIotaOptions() {
+ return type == BuiltinOptions2_StablehloIotaOptions ?
+ reinterpret_cast<tflite::StablehloIotaOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloIotaOptionsT *AsStablehloIotaOptions() const {
+ return type == BuiltinOptions2_StablehloIotaOptions ?
+ reinterpret_cast<const tflite::StablehloIotaOptionsT *>(value) : nullptr;
+ }
+ tflite::StablehloDotGeneralOptionsT *AsStablehloDotGeneralOptions() {
+ return type == BuiltinOptions2_StablehloDotGeneralOptions ?
+ reinterpret_cast<tflite::StablehloDotGeneralOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloDotGeneralOptionsT *AsStablehloDotGeneralOptions() const {
+ return type == BuiltinOptions2_StablehloDotGeneralOptions ?
+ reinterpret_cast<const tflite::StablehloDotGeneralOptionsT *>(value) : nullptr;
+ }
+ tflite::StablehloReduceWindowOptionsT *AsStablehloReduceWindowOptions() {
+ return type == BuiltinOptions2_StablehloReduceWindowOptions ?
+ reinterpret_cast<tflite::StablehloReduceWindowOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloReduceWindowOptionsT *AsStablehloReduceWindowOptions() const {
+ return type == BuiltinOptions2_StablehloReduceWindowOptions ?
+ reinterpret_cast<const tflite::StablehloReduceWindowOptionsT *>(value) : nullptr;
+ }
+ tflite::StablehloSortOptionsT *AsStablehloSortOptions() {
+ return type == BuiltinOptions2_StablehloSortOptions ?
+ reinterpret_cast<tflite::StablehloSortOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloSortOptionsT *AsStablehloSortOptions() const {
+ return type == BuiltinOptions2_StablehloSortOptions ?
+ reinterpret_cast<const tflite::StablehloSortOptionsT *>(value) : nullptr;
+ }
+ tflite::StablehloWhileOptionsT *AsStablehloWhileOptions() {
+ return type == BuiltinOptions2_StablehloWhileOptions ?
+ reinterpret_cast<tflite::StablehloWhileOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloWhileOptionsT *AsStablehloWhileOptions() const {
+ return type == BuiltinOptions2_StablehloWhileOptions ?
+ reinterpret_cast<const tflite::StablehloWhileOptionsT *>(value) : nullptr;
+ }
+ tflite::StablehloGatherOptionsT *AsStablehloGatherOptions() {
+ return type == BuiltinOptions2_StablehloGatherOptions ?
+ reinterpret_cast<tflite::StablehloGatherOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloGatherOptionsT *AsStablehloGatherOptions() const {
+ return type == BuiltinOptions2_StablehloGatherOptions ?
+ reinterpret_cast<const tflite::StablehloGatherOptionsT *>(value) : nullptr;
+ }
+ tflite::StablehloTransposeOptionsT *AsStablehloTransposeOptions() {
+ return type == BuiltinOptions2_StablehloTransposeOptions ?
+ reinterpret_cast<tflite::StablehloTransposeOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloTransposeOptionsT *AsStablehloTransposeOptions() const {
+ return type == BuiltinOptions2_StablehloTransposeOptions ?
+ reinterpret_cast<const tflite::StablehloTransposeOptionsT *>(value) : nullptr;
+ }
+ tflite::DilateOptionsT *AsDilateOptions() {
+ return type == BuiltinOptions2_DilateOptions ?
+ reinterpret_cast<tflite::DilateOptionsT *>(value) : nullptr;
+ }
+ const tflite::DilateOptionsT *AsDilateOptions() const {
+ return type == BuiltinOptions2_DilateOptions ?
+ reinterpret_cast<const tflite::DilateOptionsT *>(value) : nullptr;
+ }
+ tflite::StablehloRngBitGeneratorOptionsT *AsStablehloRngBitGeneratorOptions() {
+ return type == BuiltinOptions2_StablehloRngBitGeneratorOptions ?
+ reinterpret_cast<tflite::StablehloRngBitGeneratorOptionsT *>(value) : nullptr;
+ }
+ const tflite::StablehloRngBitGeneratorOptionsT *AsStablehloRngBitGeneratorOptions() const {
+ return type == BuiltinOptions2_StablehloRngBitGeneratorOptions ?
+ reinterpret_cast<const tflite::StablehloRngBitGeneratorOptionsT *>(value) : nullptr;
+ }
+ tflite::ReduceWindowOptionsT *AsReduceWindowOptions() {
+ return type == BuiltinOptions2_ReduceWindowOptions ?
+ reinterpret_cast<tflite::ReduceWindowOptionsT *>(value) : nullptr;
+ }
+ const tflite::ReduceWindowOptionsT *AsReduceWindowOptions() const {
+ return type == BuiltinOptions2_ReduceWindowOptions ?
+ reinterpret_cast<const tflite::ReduceWindowOptionsT *>(value) : nullptr;
+ }
+ tflite::StableHLOCompositeOptionsT *AsStableHLOCompositeOptions() {
+ return type == BuiltinOptions2_StableHLOCompositeOptions ?
+ reinterpret_cast<tflite::StableHLOCompositeOptionsT *>(value) : nullptr;
+ }
+ const tflite::StableHLOCompositeOptionsT *AsStableHLOCompositeOptions() const {
+ return type == BuiltinOptions2_StableHLOCompositeOptions ?
+ reinterpret_cast<const tflite::StableHLOCompositeOptionsT *>(value) : nullptr;
+ }
+};
+
+bool VerifyBuiltinOptions2(::flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions2 type);
+bool VerifyBuiltinOptions2Vector(::flatbuffers::Verifier &verifier, const ::flatbuffers::Vector<::flatbuffers::Offset<void>> *values, const ::flatbuffers::Vector<uint8_t> *types);
+
+enum StablehloPrecisionConfig : uint32_t {
+ StablehloPrecisionConfig_DEFAULT = 0,
+ StablehloPrecisionConfig_HIGH = 1,
+ StablehloPrecisionConfig_HIGHEST = 2,
+ StablehloPrecisionConfig_MIN = StablehloPrecisionConfig_DEFAULT,
+ StablehloPrecisionConfig_MAX = StablehloPrecisionConfig_HIGHEST
+};
+
+inline const StablehloPrecisionConfig (&EnumValuesStablehloPrecisionConfig())[3] {
+ static const StablehloPrecisionConfig values[] = {
+ StablehloPrecisionConfig_DEFAULT,
+ StablehloPrecisionConfig_HIGH,
+ StablehloPrecisionConfig_HIGHEST
+ };
+ return values;
+}
+
+inline const char * const *EnumNamesStablehloPrecisionConfig() {
+ static const char * const names[4] = {
+ "DEFAULT",
+ "HIGH",
+ "HIGHEST",
+ nullptr
+ };
+ return names;
+}
+
+inline const char *EnumNameStablehloPrecisionConfig(StablehloPrecisionConfig e) {
+ if (::flatbuffers::IsOutRange(e, StablehloPrecisionConfig_DEFAULT, StablehloPrecisionConfig_HIGHEST)) return "";
+ const size_t index = static_cast<size_t>(e);
+ return EnumNamesStablehloPrecisionConfig()[index];
+}
+
+enum StablehloComparisonDirection : uint32_t {
+ StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ = 0,
+ StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_NE = 1,
+ StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_GE = 2,
+ StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_GT = 3,
+ StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LE = 4,
+ StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LT = 5,
+ StablehloComparisonDirection_MIN = StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ,
+ StablehloComparisonDirection_MAX = StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LT
+};
+
+inline const StablehloComparisonDirection (&EnumValuesStablehloComparisonDirection())[6] {
+ static const StablehloComparisonDirection values[] = {
+ StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ,
+ StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_NE,
+ StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_GE,
+ StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_GT,
+ StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LE,
+ StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LT
+ };
+ return values;
+}
+
+inline const char * const *EnumNamesStablehloComparisonDirection() {
+ static const char * const names[7] = {
+ "STABLEHLO_COMPARISON_DIRECTION_EQ",
+ "STABLEHLO_COMPARISON_DIRECTION_NE",
+ "STABLEHLO_COMPARISON_DIRECTION_GE",
+ "STABLEHLO_COMPARISON_DIRECTION_GT",
+ "STABLEHLO_COMPARISON_DIRECTION_LE",
+ "STABLEHLO_COMPARISON_DIRECTION_LT",
+ nullptr
+ };
+ return names;
+}
+
+inline const char *EnumNameStablehloComparisonDirection(StablehloComparisonDirection e) {
+ if (::flatbuffers::IsOutRange(e, StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ, StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LT)) return "";
+ const size_t index = static_cast<size_t>(e);
+ return EnumNamesStablehloComparisonDirection()[index];
+}
+
+enum StablehloComparisonType : uint32_t {
+ StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE = 0,
+ StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_FLOAT = 1,
+ StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_FLOAT_TOTAL_ORDER = 2,
+ StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_SIGNED = 3,
+ StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_UNSIGNED = 4,
+ StablehloComparisonType_MIN = StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE,
+ StablehloComparisonType_MAX = StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_UNSIGNED
+};
+
+inline const StablehloComparisonType (&EnumValuesStablehloComparisonType())[5] {
+ static const StablehloComparisonType values[] = {
+ StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE,
+ StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_FLOAT,
+ StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_FLOAT_TOTAL_ORDER,
+ StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_SIGNED,
+ StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_UNSIGNED
+ };
+ return values;
+}
+
+inline const char * const *EnumNamesStablehloComparisonType() {
+ static const char * const names[6] = {
+ "STABLEHLO_COMPARISON_TYPE_NOTYPE",
+ "STABLEHLO_COMPARISON_TYPE_FLOAT",
+ "STABLEHLO_COMPARISON_TYPE_FLOAT_TOTAL_ORDER",
+ "STABLEHLO_COMPARISON_TYPE_SIGNED",
+ "STABLEHLO_COMPARISON_TYPE_UNSIGNED",
+ nullptr
+ };
+ return names;
+}
+
+inline const char *EnumNameStablehloComparisonType(StablehloComparisonType e) {
+ if (::flatbuffers::IsOutRange(e, StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE, StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_UNSIGNED)) return "";
+ const size_t index = static_cast<size_t>(e);
+ return EnumNamesStablehloComparisonType()[index];
+}
+
+enum RngAlgorithm : int8_t {
+ RngAlgorithm_DEFAULT = 0,
+ RngAlgorithm_PHILOX = 1,
+ RngAlgorithm_THREEFRY = 2,
+ RngAlgorithm_MIN = RngAlgorithm_DEFAULT,
+ RngAlgorithm_MAX = RngAlgorithm_THREEFRY
+};
+
+inline const RngAlgorithm (&EnumValuesRngAlgorithm())[3] {
+ static const RngAlgorithm values[] = {
+ RngAlgorithm_DEFAULT,
+ RngAlgorithm_PHILOX,
+ RngAlgorithm_THREEFRY
+ };
+ return values;
+}
+
+inline const char * const *EnumNamesRngAlgorithm() {
+ static const char * const names[4] = {
+ "DEFAULT",
+ "PHILOX",
+ "THREEFRY",
+ nullptr
+ };
+ return names;
+}
+
+inline const char *EnumNameRngAlgorithm(RngAlgorithm e) {
+ if (::flatbuffers::IsOutRange(e, RngAlgorithm_DEFAULT, RngAlgorithm_THREEFRY)) return "";
+ const size_t index = static_cast<size_t>(e);
+ return EnumNamesRngAlgorithm()[index];
+}
enum Padding : int8_t {
Padding_SAME = 0,
@@ -3910,7 +4748,7 @@
}
inline const char *EnumNamePadding(Padding e) {
- if (flatbuffers::IsOutRange(e, Padding_SAME, Padding_VALID)) return "";
+ if (::flatbuffers::IsOutRange(e, Padding_SAME, Padding_VALID)) return "";
const size_t index = static_cast<size_t>(e);
return EnumNamesPadding()[index];
}
@@ -3952,7 +4790,7 @@
}
inline const char *EnumNameActivationFunctionType(ActivationFunctionType e) {
- if (flatbuffers::IsOutRange(e, ActivationFunctionType_NONE, ActivationFunctionType_SIGN_BIT)) return "";
+ if (::flatbuffers::IsOutRange(e, ActivationFunctionType_NONE, ActivationFunctionType_SIGN_BIT)) return "";
const size_t index = static_cast<size_t>(e);
return EnumNamesActivationFunctionType()[index];
}
@@ -3985,7 +4823,7 @@
}
inline const char *EnumNameLSHProjectionType(LSHProjectionType e) {
- if (flatbuffers::IsOutRange(e, LSHProjectionType_UNKNOWN, LSHProjectionType_DENSE)) return "";
+ if (::flatbuffers::IsOutRange(e, LSHProjectionType_UNKNOWN, LSHProjectionType_DENSE)) return "";
const size_t index = static_cast<size_t>(e);
return EnumNamesLSHProjectionType()[index];
}
@@ -4015,7 +4853,7 @@
}
inline const char *EnumNameFullyConnectedOptionsWeightsFormat(FullyConnectedOptionsWeightsFormat e) {
- if (flatbuffers::IsOutRange(e, FullyConnectedOptionsWeightsFormat_DEFAULT, FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8)) return "";
+ if (::flatbuffers::IsOutRange(e, FullyConnectedOptionsWeightsFormat_DEFAULT, FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8)) return "";
const size_t index = static_cast<size_t>(e);
return EnumNamesFullyConnectedOptionsWeightsFormat()[index];
}
@@ -4045,7 +4883,7 @@
}
inline const char *EnumNameLSTMKernelType(LSTMKernelType e) {
- if (flatbuffers::IsOutRange(e, LSTMKernelType_FULL, LSTMKernelType_BASIC)) return "";
+ if (::flatbuffers::IsOutRange(e, LSTMKernelType_FULL, LSTMKernelType_BASIC)) return "";
const size_t index = static_cast<size_t>(e);
return EnumNamesLSTMKernelType()[index];
}
@@ -4078,7 +4916,7 @@
}
inline const char *EnumNameCombinerType(CombinerType e) {
- if (flatbuffers::IsOutRange(e, CombinerType_SUM, CombinerType_SQRTN)) return "";
+ if (::flatbuffers::IsOutRange(e, CombinerType_SUM, CombinerType_SQRTN)) return "";
const size_t index = static_cast<size_t>(e);
return EnumNamesCombinerType()[index];
}
@@ -4108,11 +4946,56 @@
}
inline const char *EnumNameMirrorPadMode(MirrorPadMode e) {
- if (flatbuffers::IsOutRange(e, MirrorPadMode_REFLECT, MirrorPadMode_SYMMETRIC)) return "";
+ if (::flatbuffers::IsOutRange(e, MirrorPadMode_REFLECT, MirrorPadMode_SYMMETRIC)) return "";
const size_t index = static_cast<size_t>(e);
return EnumNamesMirrorPadMode()[index];
}
+enum ReduceWindowFunction : int32_t {
+ ReduceWindowFunction_UNSUPPORTED = 0,
+ ReduceWindowFunction_ADD = 1,
+ ReduceWindowFunction_MUL = 2,
+ ReduceWindowFunction_MINIMUM = 3,
+ ReduceWindowFunction_MAXIMUM = 4,
+ ReduceWindowFunction_ALL = 5,
+ ReduceWindowFunction_ANY = 6,
+ ReduceWindowFunction_MIN = ReduceWindowFunction_UNSUPPORTED,
+ ReduceWindowFunction_MAX = ReduceWindowFunction_ANY
+};
+
+inline const ReduceWindowFunction (&EnumValuesReduceWindowFunction())[7] {
+ static const ReduceWindowFunction values[] = {
+ ReduceWindowFunction_UNSUPPORTED,
+ ReduceWindowFunction_ADD,
+ ReduceWindowFunction_MUL,
+ ReduceWindowFunction_MINIMUM,
+ ReduceWindowFunction_MAXIMUM,
+ ReduceWindowFunction_ALL,
+ ReduceWindowFunction_ANY
+ };
+ return values;
+}
+
+inline const char * const *EnumNamesReduceWindowFunction() {
+ static const char * const names[8] = {
+ "UNSUPPORTED",
+ "ADD",
+ "MUL",
+ "MINIMUM",
+ "MAXIMUM",
+ "ALL",
+ "ANY",
+ nullptr
+ };
+ return names;
+}
+
+inline const char *EnumNameReduceWindowFunction(ReduceWindowFunction e) {
+ if (::flatbuffers::IsOutRange(e, ReduceWindowFunction_UNSUPPORTED, ReduceWindowFunction_ANY)) return "";
+ const size_t index = static_cast<size_t>(e);
+ return EnumNamesReduceWindowFunction()[index];
+}
+
enum CustomOptionsFormat : int8_t {
CustomOptionsFormat_FLEXBUFFERS = 0,
CustomOptionsFormat_MIN = CustomOptionsFormat_FLEXBUFFERS,
@@ -4135,64 +5018,64 @@
}
inline const char *EnumNameCustomOptionsFormat(CustomOptionsFormat e) {
- if (flatbuffers::IsOutRange(e, CustomOptionsFormat_FLEXBUFFERS, CustomOptionsFormat_FLEXBUFFERS)) return "";
+ if (::flatbuffers::IsOutRange(e, CustomOptionsFormat_FLEXBUFFERS, CustomOptionsFormat_FLEXBUFFERS)) return "";
const size_t index = static_cast<size_t>(e);
return EnumNamesCustomOptionsFormat()[index];
}
-struct CustomQuantizationT : public flatbuffers::NativeTable {
+struct CustomQuantizationT : public ::flatbuffers::NativeTable {
typedef CustomQuantization TableType;
std::vector<uint8_t> custom{};
};
-struct CustomQuantization FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct CustomQuantization FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef CustomQuantizationT NativeTableType;
typedef CustomQuantizationBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
VT_CUSTOM = 4
};
- const flatbuffers::Vector<uint8_t> *custom() const {
- return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_CUSTOM);
+ const ::flatbuffers::Vector<uint8_t> *custom() const {
+ return GetPointer<const ::flatbuffers::Vector<uint8_t> *>(VT_CUSTOM);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyOffset(verifier, VT_CUSTOM) &&
verifier.VerifyVector(custom()) &&
verifier.EndTable();
}
- CustomQuantizationT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(CustomQuantizationT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<CustomQuantization> Pack(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ CustomQuantizationT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(CustomQuantizationT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<CustomQuantization> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct CustomQuantizationBuilder {
typedef CustomQuantization Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- void add_custom(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom) {
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_custom(::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> custom) {
fbb_.AddOffset(CustomQuantization::VT_CUSTOM, custom);
}
- explicit CustomQuantizationBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit CustomQuantizationBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<CustomQuantization> Finish() {
+ ::flatbuffers::Offset<CustomQuantization> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<CustomQuantization>(end);
+ auto o = ::flatbuffers::Offset<CustomQuantization>(end);
return o;
}
};
-inline flatbuffers::Offset<CustomQuantization> CreateCustomQuantization(
- flatbuffers::FlatBufferBuilder &_fbb,
- flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom = 0) {
+inline ::flatbuffers::Offset<CustomQuantization> CreateCustomQuantization(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> custom = 0) {
CustomQuantizationBuilder builder_(_fbb);
builder_.add_custom(custom);
return builder_.Finish();
}
-inline flatbuffers::Offset<CustomQuantization> CreateCustomQuantizationDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<CustomQuantization> CreateCustomQuantizationDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
const std::vector<uint8_t> *custom = nullptr) {
if (custom) { _fbb.ForceVectorAlignment(custom->size(), sizeof(uint8_t), 16); }
auto custom__ = custom ? _fbb.CreateVector<uint8_t>(*custom) : 0;
@@ -4201,9 +5084,9 @@
custom__);
}
-flatbuffers::Offset<CustomQuantization> CreateCustomQuantization(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<CustomQuantization> CreateCustomQuantization(::flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct QuantizationParametersT : public flatbuffers::NativeTable {
+struct QuantizationParametersT : public ::flatbuffers::NativeTable {
typedef QuantizationParameters TableType;
std::vector<float> min{};
std::vector<float> max{};
@@ -4213,7 +5096,7 @@
int32_t quantized_dimension = 0;
};
-struct QuantizationParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct QuantizationParameters FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef QuantizationParametersT NativeTableType;
typedef QuantizationParametersBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -4225,17 +5108,17 @@
VT_DETAILS = 14,
VT_QUANTIZED_DIMENSION = 16
};
- const flatbuffers::Vector<float> *min() const {
- return GetPointer<const flatbuffers::Vector<float> *>(VT_MIN);
+ const ::flatbuffers::Vector<float> *min() const {
+ return GetPointer<const ::flatbuffers::Vector<float> *>(VT_MIN);
}
- const flatbuffers::Vector<float> *max() const {
- return GetPointer<const flatbuffers::Vector<float> *>(VT_MAX);
+ const ::flatbuffers::Vector<float> *max() const {
+ return GetPointer<const ::flatbuffers::Vector<float> *>(VT_MAX);
}
- const flatbuffers::Vector<float> *scale() const {
- return GetPointer<const flatbuffers::Vector<float> *>(VT_SCALE);
+ const ::flatbuffers::Vector<float> *scale() const {
+ return GetPointer<const ::flatbuffers::Vector<float> *>(VT_SCALE);
}
- const flatbuffers::Vector<int64_t> *zero_point() const {
- return GetPointer<const flatbuffers::Vector<int64_t> *>(VT_ZERO_POINT);
+ const ::flatbuffers::Vector<int64_t> *zero_point() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_ZERO_POINT);
}
tflite::QuantizationDetails details_type() const {
return static_cast<tflite::QuantizationDetails>(GetField<uint8_t>(VT_DETAILS_TYPE, 0));
@@ -4250,7 +5133,7 @@
int32_t quantized_dimension() const {
return GetField<int32_t>(VT_QUANTIZED_DIMENSION, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyOffset(verifier, VT_MIN) &&
verifier.VerifyVector(min()) &&
@@ -4266,9 +5149,9 @@
VerifyField<int32_t>(verifier, VT_QUANTIZED_DIMENSION, 4) &&
verifier.EndTable();
}
- QuantizationParametersT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(QuantizationParametersT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<QuantizationParameters> Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ QuantizationParametersT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(QuantizationParametersT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<QuantizationParameters> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
template<> inline const tflite::CustomQuantization *QuantizationParameters::details_as<tflite::CustomQuantization>() const {
@@ -4277,48 +5160,48 @@
struct QuantizationParametersBuilder {
typedef QuantizationParameters Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- void add_min(flatbuffers::Offset<flatbuffers::Vector<float>> min) {
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_min(::flatbuffers::Offset<::flatbuffers::Vector<float>> min) {
fbb_.AddOffset(QuantizationParameters::VT_MIN, min);
}
- void add_max(flatbuffers::Offset<flatbuffers::Vector<float>> max) {
+ void add_max(::flatbuffers::Offset<::flatbuffers::Vector<float>> max) {
fbb_.AddOffset(QuantizationParameters::VT_MAX, max);
}
- void add_scale(flatbuffers::Offset<flatbuffers::Vector<float>> scale) {
+ void add_scale(::flatbuffers::Offset<::flatbuffers::Vector<float>> scale) {
fbb_.AddOffset(QuantizationParameters::VT_SCALE, scale);
}
- void add_zero_point(flatbuffers::Offset<flatbuffers::Vector<int64_t>> zero_point) {
+ void add_zero_point(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> zero_point) {
fbb_.AddOffset(QuantizationParameters::VT_ZERO_POINT, zero_point);
}
void add_details_type(tflite::QuantizationDetails details_type) {
fbb_.AddElement<uint8_t>(QuantizationParameters::VT_DETAILS_TYPE, static_cast<uint8_t>(details_type), 0);
}
- void add_details(flatbuffers::Offset<void> details) {
+ void add_details(::flatbuffers::Offset<void> details) {
fbb_.AddOffset(QuantizationParameters::VT_DETAILS, details);
}
void add_quantized_dimension(int32_t quantized_dimension) {
fbb_.AddElement<int32_t>(QuantizationParameters::VT_QUANTIZED_DIMENSION, quantized_dimension, 0);
}
- explicit QuantizationParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit QuantizationParametersBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<QuantizationParameters> Finish() {
+ ::flatbuffers::Offset<QuantizationParameters> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<QuantizationParameters>(end);
+ auto o = ::flatbuffers::Offset<QuantizationParameters>(end);
return o;
}
};
-inline flatbuffers::Offset<QuantizationParameters> CreateQuantizationParameters(
- flatbuffers::FlatBufferBuilder &_fbb,
- flatbuffers::Offset<flatbuffers::Vector<float>> min = 0,
- flatbuffers::Offset<flatbuffers::Vector<float>> max = 0,
- flatbuffers::Offset<flatbuffers::Vector<float>> scale = 0,
- flatbuffers::Offset<flatbuffers::Vector<int64_t>> zero_point = 0,
+inline ::flatbuffers::Offset<QuantizationParameters> CreateQuantizationParameters(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<float>> min = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<float>> max = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<float>> scale = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> zero_point = 0,
tflite::QuantizationDetails details_type = tflite::QuantizationDetails_NONE,
- flatbuffers::Offset<void> details = 0,
+ ::flatbuffers::Offset<void> details = 0,
int32_t quantized_dimension = 0) {
QuantizationParametersBuilder builder_(_fbb);
builder_.add_quantized_dimension(quantized_dimension);
@@ -4331,14 +5214,14 @@
return builder_.Finish();
}
-inline flatbuffers::Offset<QuantizationParameters> CreateQuantizationParametersDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<QuantizationParameters> CreateQuantizationParametersDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
const std::vector<float> *min = nullptr,
const std::vector<float> *max = nullptr,
const std::vector<float> *scale = nullptr,
const std::vector<int64_t> *zero_point = nullptr,
tflite::QuantizationDetails details_type = tflite::QuantizationDetails_NONE,
- flatbuffers::Offset<void> details = 0,
+ ::flatbuffers::Offset<void> details = 0,
int32_t quantized_dimension = 0) {
auto min__ = min ? _fbb.CreateVector<float>(*min) : 0;
auto max__ = max ? _fbb.CreateVector<float>(*max) : 0;
@@ -4355,61 +5238,61 @@
quantized_dimension);
}
-flatbuffers::Offset<QuantizationParameters> CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<QuantizationParameters> CreateQuantizationParameters(::flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct Int32VectorT : public flatbuffers::NativeTable {
+struct Int32VectorT : public ::flatbuffers::NativeTable {
typedef Int32Vector TableType;
std::vector<int32_t> values{};
};
-struct Int32Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct Int32Vector FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef Int32VectorT NativeTableType;
typedef Int32VectorBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
VT_VALUES = 4
};
- const flatbuffers::Vector<int32_t> *values() const {
- return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_VALUES);
+ const ::flatbuffers::Vector<int32_t> *values() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_VALUES);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyOffset(verifier, VT_VALUES) &&
verifier.VerifyVector(values()) &&
verifier.EndTable();
}
- Int32VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(Int32VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<Int32Vector> Pack(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ Int32VectorT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(Int32VectorT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<Int32Vector> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct Int32VectorBuilder {
typedef Int32Vector Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- void add_values(flatbuffers::Offset<flatbuffers::Vector<int32_t>> values) {
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_values(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> values) {
fbb_.AddOffset(Int32Vector::VT_VALUES, values);
}
- explicit Int32VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit Int32VectorBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<Int32Vector> Finish() {
+ ::flatbuffers::Offset<Int32Vector> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<Int32Vector>(end);
+ auto o = ::flatbuffers::Offset<Int32Vector>(end);
return o;
}
};
-inline flatbuffers::Offset<Int32Vector> CreateInt32Vector(
- flatbuffers::FlatBufferBuilder &_fbb,
- flatbuffers::Offset<flatbuffers::Vector<int32_t>> values = 0) {
+inline ::flatbuffers::Offset<Int32Vector> CreateInt32Vector(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> values = 0) {
Int32VectorBuilder builder_(_fbb);
builder_.add_values(values);
return builder_.Finish();
}
-inline flatbuffers::Offset<Int32Vector> CreateInt32VectorDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<Int32Vector> CreateInt32VectorDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
const std::vector<int32_t> *values = nullptr) {
auto values__ = values ? _fbb.CreateVector<int32_t>(*values) : 0;
return tflite::CreateInt32Vector(
@@ -4417,61 +5300,61 @@
values__);
}
-flatbuffers::Offset<Int32Vector> CreateInt32Vector(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<Int32Vector> CreateInt32Vector(::flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct Uint16VectorT : public flatbuffers::NativeTable {
+struct Uint16VectorT : public ::flatbuffers::NativeTable {
typedef Uint16Vector TableType;
std::vector<uint16_t> values{};
};
-struct Uint16Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct Uint16Vector FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef Uint16VectorT NativeTableType;
typedef Uint16VectorBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
VT_VALUES = 4
};
- const flatbuffers::Vector<uint16_t> *values() const {
- return GetPointer<const flatbuffers::Vector<uint16_t> *>(VT_VALUES);
+ const ::flatbuffers::Vector<uint16_t> *values() const {
+ return GetPointer<const ::flatbuffers::Vector<uint16_t> *>(VT_VALUES);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyOffset(verifier, VT_VALUES) &&
verifier.VerifyVector(values()) &&
verifier.EndTable();
}
- Uint16VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(Uint16VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<Uint16Vector> Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ Uint16VectorT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(Uint16VectorT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<Uint16Vector> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct Uint16VectorBuilder {
typedef Uint16Vector Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- void add_values(flatbuffers::Offset<flatbuffers::Vector<uint16_t>> values) {
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_values(::flatbuffers::Offset<::flatbuffers::Vector<uint16_t>> values) {
fbb_.AddOffset(Uint16Vector::VT_VALUES, values);
}
- explicit Uint16VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit Uint16VectorBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<Uint16Vector> Finish() {
+ ::flatbuffers::Offset<Uint16Vector> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<Uint16Vector>(end);
+ auto o = ::flatbuffers::Offset<Uint16Vector>(end);
return o;
}
};
-inline flatbuffers::Offset<Uint16Vector> CreateUint16Vector(
- flatbuffers::FlatBufferBuilder &_fbb,
- flatbuffers::Offset<flatbuffers::Vector<uint16_t>> values = 0) {
+inline ::flatbuffers::Offset<Uint16Vector> CreateUint16Vector(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<uint16_t>> values = 0) {
Uint16VectorBuilder builder_(_fbb);
builder_.add_values(values);
return builder_.Finish();
}
-inline flatbuffers::Offset<Uint16Vector> CreateUint16VectorDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<Uint16Vector> CreateUint16VectorDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
const std::vector<uint16_t> *values = nullptr) {
if (values) { _fbb.ForceVectorAlignment(values->size(), sizeof(uint16_t), 4); }
auto values__ = values ? _fbb.CreateVector<uint16_t>(*values) : 0;
@@ -4480,61 +5363,61 @@
values__);
}
-flatbuffers::Offset<Uint16Vector> CreateUint16Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<Uint16Vector> CreateUint16Vector(::flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct Uint8VectorT : public flatbuffers::NativeTable {
+struct Uint8VectorT : public ::flatbuffers::NativeTable {
typedef Uint8Vector TableType;
std::vector<uint8_t> values{};
};
-struct Uint8Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct Uint8Vector FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef Uint8VectorT NativeTableType;
typedef Uint8VectorBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
VT_VALUES = 4
};
- const flatbuffers::Vector<uint8_t> *values() const {
- return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_VALUES);
+ const ::flatbuffers::Vector<uint8_t> *values() const {
+ return GetPointer<const ::flatbuffers::Vector<uint8_t> *>(VT_VALUES);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyOffset(verifier, VT_VALUES) &&
verifier.VerifyVector(values()) &&
verifier.EndTable();
}
- Uint8VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(Uint8VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<Uint8Vector> Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ Uint8VectorT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(Uint8VectorT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<Uint8Vector> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct Uint8VectorBuilder {
typedef Uint8Vector Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- void add_values(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> values) {
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_values(::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> values) {
fbb_.AddOffset(Uint8Vector::VT_VALUES, values);
}
- explicit Uint8VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit Uint8VectorBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<Uint8Vector> Finish() {
+ ::flatbuffers::Offset<Uint8Vector> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<Uint8Vector>(end);
+ auto o = ::flatbuffers::Offset<Uint8Vector>(end);
return o;
}
};
-inline flatbuffers::Offset<Uint8Vector> CreateUint8Vector(
- flatbuffers::FlatBufferBuilder &_fbb,
- flatbuffers::Offset<flatbuffers::Vector<uint8_t>> values = 0) {
+inline ::flatbuffers::Offset<Uint8Vector> CreateUint8Vector(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> values = 0) {
Uint8VectorBuilder builder_(_fbb);
builder_.add_values(values);
return builder_.Finish();
}
-inline flatbuffers::Offset<Uint8Vector> CreateUint8VectorDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<Uint8Vector> CreateUint8VectorDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
const std::vector<uint8_t> *values = nullptr) {
if (values) { _fbb.ForceVectorAlignment(values->size(), sizeof(uint8_t), 4); }
auto values__ = values ? _fbb.CreateVector<uint8_t>(*values) : 0;
@@ -4543,9 +5426,9 @@
values__);
}
-flatbuffers::Offset<Uint8Vector> CreateUint8Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<Uint8Vector> CreateUint8Vector(::flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct DimensionMetadataT : public flatbuffers::NativeTable {
+struct DimensionMetadataT : public ::flatbuffers::NativeTable {
typedef DimensionMetadata TableType;
tflite::DimensionType format = tflite::DimensionType_DENSE;
int32_t dense_size = 0;
@@ -4553,7 +5436,7 @@
tflite::SparseIndexVectorUnion array_indices{};
};
-struct DimensionMetadata FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct DimensionMetadata FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef DimensionMetadataT NativeTableType;
typedef DimensionMetadataBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -4602,7 +5485,7 @@
const tflite::Uint8Vector *array_indices_as_Uint8Vector() const {
return array_indices_type() == tflite::SparseIndexVector_Uint8Vector ? static_cast<const tflite::Uint8Vector *>(array_indices()) : nullptr;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_FORMAT, 1) &&
VerifyField<int32_t>(verifier, VT_DENSE_SIZE, 4) &&
@@ -4614,9 +5497,9 @@
VerifySparseIndexVector(verifier, array_indices(), array_indices_type()) &&
verifier.EndTable();
}
- DimensionMetadataT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(DimensionMetadataT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<DimensionMetadata> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ DimensionMetadataT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(DimensionMetadataT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<DimensionMetadata> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
template<> inline const tflite::Int32Vector *DimensionMetadata::array_segments_as<tflite::Int32Vector>() const {
@@ -4645,8 +5528,8 @@
struct DimensionMetadataBuilder {
typedef DimensionMetadata Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_format(tflite::DimensionType format) {
fbb_.AddElement<int8_t>(DimensionMetadata::VT_FORMAT, static_cast<int8_t>(format), 0);
}
@@ -4656,34 +5539,34 @@
void add_array_segments_type(tflite::SparseIndexVector array_segments_type) {
fbb_.AddElement<uint8_t>(DimensionMetadata::VT_ARRAY_SEGMENTS_TYPE, static_cast<uint8_t>(array_segments_type), 0);
}
- void add_array_segments(flatbuffers::Offset<void> array_segments) {
+ void add_array_segments(::flatbuffers::Offset<void> array_segments) {
fbb_.AddOffset(DimensionMetadata::VT_ARRAY_SEGMENTS, array_segments);
}
void add_array_indices_type(tflite::SparseIndexVector array_indices_type) {
fbb_.AddElement<uint8_t>(DimensionMetadata::VT_ARRAY_INDICES_TYPE, static_cast<uint8_t>(array_indices_type), 0);
}
- void add_array_indices(flatbuffers::Offset<void> array_indices) {
+ void add_array_indices(::flatbuffers::Offset<void> array_indices) {
fbb_.AddOffset(DimensionMetadata::VT_ARRAY_INDICES, array_indices);
}
- explicit DimensionMetadataBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit DimensionMetadataBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<DimensionMetadata> Finish() {
+ ::flatbuffers::Offset<DimensionMetadata> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<DimensionMetadata>(end);
+ auto o = ::flatbuffers::Offset<DimensionMetadata>(end);
return o;
}
};
-inline flatbuffers::Offset<DimensionMetadata> CreateDimensionMetadata(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<DimensionMetadata> CreateDimensionMetadata(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::DimensionType format = tflite::DimensionType_DENSE,
int32_t dense_size = 0,
tflite::SparseIndexVector array_segments_type = tflite::SparseIndexVector_NONE,
- flatbuffers::Offset<void> array_segments = 0,
+ ::flatbuffers::Offset<void> array_segments = 0,
tflite::SparseIndexVector array_indices_type = tflite::SparseIndexVector_NONE,
- flatbuffers::Offset<void> array_indices = 0) {
+ ::flatbuffers::Offset<void> array_indices = 0) {
DimensionMetadataBuilder builder_(_fbb);
builder_.add_array_indices(array_indices);
builder_.add_array_segments(array_segments);
@@ -4694,9 +5577,9 @@
return builder_.Finish();
}
-flatbuffers::Offset<DimensionMetadata> CreateDimensionMetadata(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<DimensionMetadata> CreateDimensionMetadata(::flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SparsityParametersT : public flatbuffers::NativeTable {
+struct SparsityParametersT : public ::flatbuffers::NativeTable {
typedef SparsityParameters TableType;
std::vector<int32_t> traversal_order{};
std::vector<int32_t> block_map{};
@@ -4707,7 +5590,7 @@
SparsityParametersT &operator=(SparsityParametersT o) FLATBUFFERS_NOEXCEPT;
};
-struct SparsityParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SparsityParameters FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SparsityParametersT NativeTableType;
typedef SparsityParametersBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -4715,16 +5598,16 @@
VT_BLOCK_MAP = 6,
VT_DIM_METADATA = 8
};
- const flatbuffers::Vector<int32_t> *traversal_order() const {
- return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_TRAVERSAL_ORDER);
+ const ::flatbuffers::Vector<int32_t> *traversal_order() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_TRAVERSAL_ORDER);
}
- const flatbuffers::Vector<int32_t> *block_map() const {
- return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_BLOCK_MAP);
+ const ::flatbuffers::Vector<int32_t> *block_map() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_BLOCK_MAP);
}
- const flatbuffers::Vector<flatbuffers::Offset<tflite::DimensionMetadata>> *dim_metadata() const {
- return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::DimensionMetadata>> *>(VT_DIM_METADATA);
+ const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::DimensionMetadata>> *dim_metadata() const {
+ return GetPointer<const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::DimensionMetadata>> *>(VT_DIM_METADATA);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyOffset(verifier, VT_TRAVERSAL_ORDER) &&
verifier.VerifyVector(traversal_order()) &&
@@ -4735,40 +5618,40 @@
verifier.VerifyVectorOfTables(dim_metadata()) &&
verifier.EndTable();
}
- SparsityParametersT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SparsityParametersT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SparsityParameters> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SparsityParametersT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SparsityParametersT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SparsityParameters> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SparsityParametersBuilder {
typedef SparsityParameters Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- void add_traversal_order(flatbuffers::Offset<flatbuffers::Vector<int32_t>> traversal_order) {
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_traversal_order(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> traversal_order) {
fbb_.AddOffset(SparsityParameters::VT_TRAVERSAL_ORDER, traversal_order);
}
- void add_block_map(flatbuffers::Offset<flatbuffers::Vector<int32_t>> block_map) {
+ void add_block_map(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> block_map) {
fbb_.AddOffset(SparsityParameters::VT_BLOCK_MAP, block_map);
}
- void add_dim_metadata(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::DimensionMetadata>>> dim_metadata) {
+ void add_dim_metadata(::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::DimensionMetadata>>> dim_metadata) {
fbb_.AddOffset(SparsityParameters::VT_DIM_METADATA, dim_metadata);
}
- explicit SparsityParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit SparsityParametersBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SparsityParameters> Finish() {
+ ::flatbuffers::Offset<SparsityParameters> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SparsityParameters>(end);
+ auto o = ::flatbuffers::Offset<SparsityParameters>(end);
return o;
}
};
-inline flatbuffers::Offset<SparsityParameters> CreateSparsityParameters(
- flatbuffers::FlatBufferBuilder &_fbb,
- flatbuffers::Offset<flatbuffers::Vector<int32_t>> traversal_order = 0,
- flatbuffers::Offset<flatbuffers::Vector<int32_t>> block_map = 0,
- flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::DimensionMetadata>>> dim_metadata = 0) {
+inline ::flatbuffers::Offset<SparsityParameters> CreateSparsityParameters(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> traversal_order = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> block_map = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::DimensionMetadata>>> dim_metadata = 0) {
SparsityParametersBuilder builder_(_fbb);
builder_.add_dim_metadata(dim_metadata);
builder_.add_block_map(block_map);
@@ -4776,14 +5659,14 @@
return builder_.Finish();
}
-inline flatbuffers::Offset<SparsityParameters> CreateSparsityParametersDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<SparsityParameters> CreateSparsityParametersDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
const std::vector<int32_t> *traversal_order = nullptr,
const std::vector<int32_t> *block_map = nullptr,
- const std::vector<flatbuffers::Offset<tflite::DimensionMetadata>> *dim_metadata = nullptr) {
+ const std::vector<::flatbuffers::Offset<tflite::DimensionMetadata>> *dim_metadata = nullptr) {
auto traversal_order__ = traversal_order ? _fbb.CreateVector<int32_t>(*traversal_order) : 0;
auto block_map__ = block_map ? _fbb.CreateVector<int32_t>(*block_map) : 0;
- auto dim_metadata__ = dim_metadata ? _fbb.CreateVector<flatbuffers::Offset<tflite::DimensionMetadata>>(*dim_metadata) : 0;
+ auto dim_metadata__ = dim_metadata ? _fbb.CreateVector<::flatbuffers::Offset<tflite::DimensionMetadata>>(*dim_metadata) : 0;
return tflite::CreateSparsityParameters(
_fbb,
traversal_order__,
@@ -4791,16 +5674,16 @@
dim_metadata__);
}
-flatbuffers::Offset<SparsityParameters> CreateSparsityParameters(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SparsityParameters> CreateSparsityParameters(::flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct VariantSubTypeT : public flatbuffers::NativeTable {
+struct VariantSubTypeT : public ::flatbuffers::NativeTable {
typedef VariantSubType TableType;
std::vector<int32_t> shape{};
tflite::TensorType type = tflite::TensorType_FLOAT32;
bool has_rank = false;
};
-struct VariantSubType FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct VariantSubType FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef VariantSubTypeT NativeTableType;
typedef VariantSubTypeBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -4808,8 +5691,8 @@
VT_TYPE = 6,
VT_HAS_RANK = 8
};
- const flatbuffers::Vector<int32_t> *shape() const {
- return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SHAPE);
+ const ::flatbuffers::Vector<int32_t> *shape() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_SHAPE);
}
tflite::TensorType type() const {
return static_cast<tflite::TensorType>(GetField<int8_t>(VT_TYPE, 0));
@@ -4817,7 +5700,7 @@
bool has_rank() const {
return GetField<uint8_t>(VT_HAS_RANK, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyOffset(verifier, VT_SHAPE) &&
verifier.VerifyVector(shape()) &&
@@ -4825,16 +5708,16 @@
VerifyField<uint8_t>(verifier, VT_HAS_RANK, 1) &&
verifier.EndTable();
}
- VariantSubTypeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(VariantSubTypeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<VariantSubType> Pack(flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ VariantSubTypeT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(VariantSubTypeT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<VariantSubType> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct VariantSubTypeBuilder {
typedef VariantSubType Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- void add_shape(flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape) {
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_shape(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> shape) {
fbb_.AddOffset(VariantSubType::VT_SHAPE, shape);
}
void add_type(tflite::TensorType type) {
@@ -4843,20 +5726,20 @@
void add_has_rank(bool has_rank) {
fbb_.AddElement<uint8_t>(VariantSubType::VT_HAS_RANK, static_cast<uint8_t>(has_rank), 0);
}
- explicit VariantSubTypeBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit VariantSubTypeBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<VariantSubType> Finish() {
+ ::flatbuffers::Offset<VariantSubType> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<VariantSubType>(end);
+ auto o = ::flatbuffers::Offset<VariantSubType>(end);
return o;
}
};
-inline flatbuffers::Offset<VariantSubType> CreateVariantSubType(
- flatbuffers::FlatBufferBuilder &_fbb,
- flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape = 0,
+inline ::flatbuffers::Offset<VariantSubType> CreateVariantSubType(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> shape = 0,
tflite::TensorType type = tflite::TensorType_FLOAT32,
bool has_rank = false) {
VariantSubTypeBuilder builder_(_fbb);
@@ -4866,8 +5749,8 @@
return builder_.Finish();
}
-inline flatbuffers::Offset<VariantSubType> CreateVariantSubTypeDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<VariantSubType> CreateVariantSubTypeDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
const std::vector<int32_t> *shape = nullptr,
tflite::TensorType type = tflite::TensorType_FLOAT32,
bool has_rank = false) {
@@ -4879,9 +5762,9 @@
has_rank);
}
-flatbuffers::Offset<VariantSubType> CreateVariantSubType(flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<VariantSubType> CreateVariantSubType(::flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct TensorT : public flatbuffers::NativeTable {
+struct TensorT : public ::flatbuffers::NativeTable {
typedef Tensor TableType;
std::vector<int32_t> shape{};
tflite::TensorType type = tflite::TensorType_FLOAT32;
@@ -4899,7 +5782,7 @@
TensorT &operator=(TensorT o) FLATBUFFERS_NOEXCEPT;
};
-struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct Tensor FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef TensorT NativeTableType;
typedef TensorBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -4914,8 +5797,8 @@
VT_HAS_RANK = 20,
VT_VARIANT_TENSORS = 22
};
- const flatbuffers::Vector<int32_t> *shape() const {
- return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SHAPE);
+ const ::flatbuffers::Vector<int32_t> *shape() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_SHAPE);
}
tflite::TensorType type() const {
return static_cast<tflite::TensorType>(GetField<int8_t>(VT_TYPE, 0));
@@ -4923,8 +5806,8 @@
uint32_t buffer() const {
return GetField<uint32_t>(VT_BUFFER, 0);
}
- const flatbuffers::String *name() const {
- return GetPointer<const flatbuffers::String *>(VT_NAME);
+ const ::flatbuffers::String *name() const {
+ return GetPointer<const ::flatbuffers::String *>(VT_NAME);
}
const tflite::QuantizationParameters *quantization() const {
return GetPointer<const tflite::QuantizationParameters *>(VT_QUANTIZATION);
@@ -4935,16 +5818,16 @@
const tflite::SparsityParameters *sparsity() const {
return GetPointer<const tflite::SparsityParameters *>(VT_SPARSITY);
}
- const flatbuffers::Vector<int32_t> *shape_signature() const {
- return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SHAPE_SIGNATURE);
+ const ::flatbuffers::Vector<int32_t> *shape_signature() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_SHAPE_SIGNATURE);
}
bool has_rank() const {
return GetField<uint8_t>(VT_HAS_RANK, 0) != 0;
}
- const flatbuffers::Vector<flatbuffers::Offset<tflite::VariantSubType>> *variant_tensors() const {
- return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::VariantSubType>> *>(VT_VARIANT_TENSORS);
+ const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::VariantSubType>> *variant_tensors() const {
+ return GetPointer<const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::VariantSubType>> *>(VT_VARIANT_TENSORS);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyOffset(verifier, VT_SHAPE) &&
verifier.VerifyVector(shape()) &&
@@ -4965,16 +5848,16 @@
verifier.VerifyVectorOfTables(variant_tensors()) &&
verifier.EndTable();
}
- TensorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<Tensor> Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ TensorT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(TensorT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<Tensor> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct TensorBuilder {
typedef Tensor Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- void add_shape(flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape) {
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_shape(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> shape) {
fbb_.AddOffset(Tensor::VT_SHAPE, shape);
}
void add_type(tflite::TensorType type) {
@@ -4983,50 +5866,50 @@
void add_buffer(uint32_t buffer) {
fbb_.AddElement<uint32_t>(Tensor::VT_BUFFER, buffer, 0);
}
- void add_name(flatbuffers::Offset<flatbuffers::String> name) {
+ void add_name(::flatbuffers::Offset<::flatbuffers::String> name) {
fbb_.AddOffset(Tensor::VT_NAME, name);
}
- void add_quantization(flatbuffers::Offset<tflite::QuantizationParameters> quantization) {
+ void add_quantization(::flatbuffers::Offset<tflite::QuantizationParameters> quantization) {
fbb_.AddOffset(Tensor::VT_QUANTIZATION, quantization);
}
void add_is_variable(bool is_variable) {
fbb_.AddElement<uint8_t>(Tensor::VT_IS_VARIABLE, static_cast<uint8_t>(is_variable), 0);
}
- void add_sparsity(flatbuffers::Offset<tflite::SparsityParameters> sparsity) {
+ void add_sparsity(::flatbuffers::Offset<tflite::SparsityParameters> sparsity) {
fbb_.AddOffset(Tensor::VT_SPARSITY, sparsity);
}
- void add_shape_signature(flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape_signature) {
+ void add_shape_signature(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> shape_signature) {
fbb_.AddOffset(Tensor::VT_SHAPE_SIGNATURE, shape_signature);
}
void add_has_rank(bool has_rank) {
fbb_.AddElement<uint8_t>(Tensor::VT_HAS_RANK, static_cast<uint8_t>(has_rank), 0);
}
- void add_variant_tensors(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::VariantSubType>>> variant_tensors) {
+ void add_variant_tensors(::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::VariantSubType>>> variant_tensors) {
fbb_.AddOffset(Tensor::VT_VARIANT_TENSORS, variant_tensors);
}
- explicit TensorBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit TensorBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<Tensor> Finish() {
+ ::flatbuffers::Offset<Tensor> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<Tensor>(end);
+ auto o = ::flatbuffers::Offset<Tensor>(end);
return o;
}
};
-inline flatbuffers::Offset<Tensor> CreateTensor(
- flatbuffers::FlatBufferBuilder &_fbb,
- flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape = 0,
+inline ::flatbuffers::Offset<Tensor> CreateTensor(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> shape = 0,
tflite::TensorType type = tflite::TensorType_FLOAT32,
uint32_t buffer = 0,
- flatbuffers::Offset<flatbuffers::String> name = 0,
- flatbuffers::Offset<tflite::QuantizationParameters> quantization = 0,
+ ::flatbuffers::Offset<::flatbuffers::String> name = 0,
+ ::flatbuffers::Offset<tflite::QuantizationParameters> quantization = 0,
bool is_variable = false,
- flatbuffers::Offset<tflite::SparsityParameters> sparsity = 0,
- flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape_signature = 0,
+ ::flatbuffers::Offset<tflite::SparsityParameters> sparsity = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> shape_signature = 0,
bool has_rank = false,
- flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::VariantSubType>>> variant_tensors = 0) {
+ ::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::VariantSubType>>> variant_tensors = 0) {
TensorBuilder builder_(_fbb);
builder_.add_variant_tensors(variant_tensors);
builder_.add_shape_signature(shape_signature);
@@ -5041,22 +5924,22 @@
return builder_.Finish();
}
-inline flatbuffers::Offset<Tensor> CreateTensorDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<Tensor> CreateTensorDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
const std::vector<int32_t> *shape = nullptr,
tflite::TensorType type = tflite::TensorType_FLOAT32,
uint32_t buffer = 0,
const char *name = nullptr,
- flatbuffers::Offset<tflite::QuantizationParameters> quantization = 0,
+ ::flatbuffers::Offset<tflite::QuantizationParameters> quantization = 0,
bool is_variable = false,
- flatbuffers::Offset<tflite::SparsityParameters> sparsity = 0,
+ ::flatbuffers::Offset<tflite::SparsityParameters> sparsity = 0,
const std::vector<int32_t> *shape_signature = nullptr,
bool has_rank = false,
- const std::vector<flatbuffers::Offset<tflite::VariantSubType>> *variant_tensors = nullptr) {
+ const std::vector<::flatbuffers::Offset<tflite::VariantSubType>> *variant_tensors = nullptr) {
auto shape__ = shape ? _fbb.CreateVector<int32_t>(*shape) : 0;
auto name__ = name ? _fbb.CreateString(name) : 0;
auto shape_signature__ = shape_signature ? _fbb.CreateVector<int32_t>(*shape_signature) : 0;
- auto variant_tensors__ = variant_tensors ? _fbb.CreateVector<flatbuffers::Offset<tflite::VariantSubType>>(*variant_tensors) : 0;
+ auto variant_tensors__ = variant_tensors ? _fbb.CreateVector<::flatbuffers::Offset<tflite::VariantSubType>>(*variant_tensors) : 0;
return tflite::CreateTensor(
_fbb,
shape__,
@@ -5071,9 +5954,1763 @@
variant_tensors__);
}
-flatbuffers::Offset<Tensor> CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<Tensor> CreateTensor(::flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct Conv2DOptionsT : public flatbuffers::NativeTable {
+struct StablehloGatherOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloGatherOptions TableType;
+ std::vector<int64_t> offset_dims{};
+ std::vector<int64_t> collapsed_slice_dims{};
+ std::vector<int64_t> start_index_map{};
+ int64_t index_vector_dim = 0;
+ std::vector<int64_t> slice_sizes{};
+ bool indices_are_sorted = false;
+};
+
+struct StablehloGatherOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloGatherOptionsT NativeTableType;
+ typedef StablehloGatherOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_OFFSET_DIMS = 4,
+ VT_COLLAPSED_SLICE_DIMS = 6,
+ VT_START_INDEX_MAP = 8,
+ VT_INDEX_VECTOR_DIM = 10,
+ VT_SLICE_SIZES = 12,
+ VT_INDICES_ARE_SORTED = 14
+ };
+ const ::flatbuffers::Vector<int64_t> *offset_dims() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_OFFSET_DIMS);
+ }
+ const ::flatbuffers::Vector<int64_t> *collapsed_slice_dims() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_COLLAPSED_SLICE_DIMS);
+ }
+ const ::flatbuffers::Vector<int64_t> *start_index_map() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_START_INDEX_MAP);
+ }
+ int64_t index_vector_dim() const {
+ return GetField<int64_t>(VT_INDEX_VECTOR_DIM, 0);
+ }
+ const ::flatbuffers::Vector<int64_t> *slice_sizes() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_SLICE_SIZES);
+ }
+ bool indices_are_sorted() const {
+ return GetField<uint8_t>(VT_INDICES_ARE_SORTED, 0) != 0;
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_OFFSET_DIMS) &&
+ verifier.VerifyVector(offset_dims()) &&
+ VerifyOffset(verifier, VT_COLLAPSED_SLICE_DIMS) &&
+ verifier.VerifyVector(collapsed_slice_dims()) &&
+ VerifyOffset(verifier, VT_START_INDEX_MAP) &&
+ verifier.VerifyVector(start_index_map()) &&
+ VerifyField<int64_t>(verifier, VT_INDEX_VECTOR_DIM, 8) &&
+ VerifyOffset(verifier, VT_SLICE_SIZES) &&
+ verifier.VerifyVector(slice_sizes()) &&
+ VerifyField<uint8_t>(verifier, VT_INDICES_ARE_SORTED, 1) &&
+ verifier.EndTable();
+ }
+ StablehloGatherOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloGatherOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloGatherOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloGatherOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloGatherOptionsBuilder {
+ typedef StablehloGatherOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_offset_dims(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> offset_dims) {
+ fbb_.AddOffset(StablehloGatherOptions::VT_OFFSET_DIMS, offset_dims);
+ }
+ void add_collapsed_slice_dims(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> collapsed_slice_dims) {
+ fbb_.AddOffset(StablehloGatherOptions::VT_COLLAPSED_SLICE_DIMS, collapsed_slice_dims);
+ }
+ void add_start_index_map(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> start_index_map) {
+ fbb_.AddOffset(StablehloGatherOptions::VT_START_INDEX_MAP, start_index_map);
+ }
+ void add_index_vector_dim(int64_t index_vector_dim) {
+ fbb_.AddElement<int64_t>(StablehloGatherOptions::VT_INDEX_VECTOR_DIM, index_vector_dim, 0);
+ }
+ void add_slice_sizes(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> slice_sizes) {
+ fbb_.AddOffset(StablehloGatherOptions::VT_SLICE_SIZES, slice_sizes);
+ }
+ void add_indices_are_sorted(bool indices_are_sorted) {
+ fbb_.AddElement<uint8_t>(StablehloGatherOptions::VT_INDICES_ARE_SORTED, static_cast<uint8_t>(indices_are_sorted), 0);
+ }
+ explicit StablehloGatherOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloGatherOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloGatherOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloGatherOptions> CreateStablehloGatherOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> offset_dims = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> collapsed_slice_dims = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> start_index_map = 0,
+ int64_t index_vector_dim = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> slice_sizes = 0,
+ bool indices_are_sorted = false) {
+ StablehloGatherOptionsBuilder builder_(_fbb);
+ builder_.add_index_vector_dim(index_vector_dim);
+ builder_.add_slice_sizes(slice_sizes);
+ builder_.add_start_index_map(start_index_map);
+ builder_.add_collapsed_slice_dims(collapsed_slice_dims);
+ builder_.add_offset_dims(offset_dims);
+ builder_.add_indices_are_sorted(indices_are_sorted);
+ return builder_.Finish();
+}
+
+inline ::flatbuffers::Offset<StablehloGatherOptions> CreateStablehloGatherOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<int64_t> *offset_dims = nullptr,
+ const std::vector<int64_t> *collapsed_slice_dims = nullptr,
+ const std::vector<int64_t> *start_index_map = nullptr,
+ int64_t index_vector_dim = 0,
+ const std::vector<int64_t> *slice_sizes = nullptr,
+ bool indices_are_sorted = false) {
+ auto offset_dims__ = offset_dims ? _fbb.CreateVector<int64_t>(*offset_dims) : 0;
+ auto collapsed_slice_dims__ = collapsed_slice_dims ? _fbb.CreateVector<int64_t>(*collapsed_slice_dims) : 0;
+ auto start_index_map__ = start_index_map ? _fbb.CreateVector<int64_t>(*start_index_map) : 0;
+ auto slice_sizes__ = slice_sizes ? _fbb.CreateVector<int64_t>(*slice_sizes) : 0;
+ return tflite::CreateStablehloGatherOptions(
+ _fbb,
+ offset_dims__,
+ collapsed_slice_dims__,
+ start_index_map__,
+ index_vector_dim,
+ slice_sizes__,
+ indices_are_sorted);
+}
+
+::flatbuffers::Offset<StablehloGatherOptions> CreateStablehloGatherOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloGatherOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct StablehloTransposeOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloTransposeOptions TableType;
+ std::vector<int64_t> permutation{};
+};
+
+struct StablehloTransposeOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloTransposeOptionsT NativeTableType;
+ typedef StablehloTransposeOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_PERMUTATION = 4
+ };
+ const ::flatbuffers::Vector<int64_t> *permutation() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_PERMUTATION);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_PERMUTATION) &&
+ verifier.VerifyVector(permutation()) &&
+ verifier.EndTable();
+ }
+ StablehloTransposeOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloTransposeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloTransposeOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloTransposeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloTransposeOptionsBuilder {
+ typedef StablehloTransposeOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_permutation(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> permutation) {
+ fbb_.AddOffset(StablehloTransposeOptions::VT_PERMUTATION, permutation);
+ }
+ explicit StablehloTransposeOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloTransposeOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloTransposeOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloTransposeOptions> CreateStablehloTransposeOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> permutation = 0) {
+ StablehloTransposeOptionsBuilder builder_(_fbb);
+ builder_.add_permutation(permutation);
+ return builder_.Finish();
+}
+
+inline ::flatbuffers::Offset<StablehloTransposeOptions> CreateStablehloTransposeOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<int64_t> *permutation = nullptr) {
+ auto permutation__ = permutation ? _fbb.CreateVector<int64_t>(*permutation) : 0;
+ return tflite::CreateStablehloTransposeOptions(
+ _fbb,
+ permutation__);
+}
+
+::flatbuffers::Offset<StablehloTransposeOptions> CreateStablehloTransposeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloTransposeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct StablehloDotGeneralOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloDotGeneralOptions TableType;
+ std::vector<int64_t> lhs_batching_dimensions{};
+ std::vector<int64_t> rhs_batching_dimensions{};
+ std::vector<int64_t> lhs_contracting_dimensions{};
+ std::vector<int64_t> rhs_contracting_dimensions{};
+ std::vector<tflite::StablehloPrecisionConfig> precision_config{};
+};
+
+struct StablehloDotGeneralOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloDotGeneralOptionsT NativeTableType;
+ typedef StablehloDotGeneralOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_LHS_BATCHING_DIMENSIONS = 4,
+ VT_RHS_BATCHING_DIMENSIONS = 6,
+ VT_LHS_CONTRACTING_DIMENSIONS = 8,
+ VT_RHS_CONTRACTING_DIMENSIONS = 10,
+ VT_PRECISION_CONFIG = 12
+ };
+ const ::flatbuffers::Vector<int64_t> *lhs_batching_dimensions() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_LHS_BATCHING_DIMENSIONS);
+ }
+ const ::flatbuffers::Vector<int64_t> *rhs_batching_dimensions() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_RHS_BATCHING_DIMENSIONS);
+ }
+ const ::flatbuffers::Vector<int64_t> *lhs_contracting_dimensions() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_LHS_CONTRACTING_DIMENSIONS);
+ }
+ const ::flatbuffers::Vector<int64_t> *rhs_contracting_dimensions() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_RHS_CONTRACTING_DIMENSIONS);
+ }
+ const ::flatbuffers::Vector<uint32_t> *precision_config() const {
+ return GetPointer<const ::flatbuffers::Vector<uint32_t> *>(VT_PRECISION_CONFIG);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_LHS_BATCHING_DIMENSIONS) &&
+ verifier.VerifyVector(lhs_batching_dimensions()) &&
+ VerifyOffset(verifier, VT_RHS_BATCHING_DIMENSIONS) &&
+ verifier.VerifyVector(rhs_batching_dimensions()) &&
+ VerifyOffset(verifier, VT_LHS_CONTRACTING_DIMENSIONS) &&
+ verifier.VerifyVector(lhs_contracting_dimensions()) &&
+ VerifyOffset(verifier, VT_RHS_CONTRACTING_DIMENSIONS) &&
+ verifier.VerifyVector(rhs_contracting_dimensions()) &&
+ VerifyOffset(verifier, VT_PRECISION_CONFIG) &&
+ verifier.VerifyVector(precision_config()) &&
+ verifier.EndTable();
+ }
+ StablehloDotGeneralOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloDotGeneralOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloDotGeneralOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloDotGeneralOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloDotGeneralOptionsBuilder {
+ typedef StablehloDotGeneralOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_lhs_batching_dimensions(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> lhs_batching_dimensions) {
+ fbb_.AddOffset(StablehloDotGeneralOptions::VT_LHS_BATCHING_DIMENSIONS, lhs_batching_dimensions);
+ }
+ void add_rhs_batching_dimensions(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> rhs_batching_dimensions) {
+ fbb_.AddOffset(StablehloDotGeneralOptions::VT_RHS_BATCHING_DIMENSIONS, rhs_batching_dimensions);
+ }
+ void add_lhs_contracting_dimensions(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> lhs_contracting_dimensions) {
+ fbb_.AddOffset(StablehloDotGeneralOptions::VT_LHS_CONTRACTING_DIMENSIONS, lhs_contracting_dimensions);
+ }
+ void add_rhs_contracting_dimensions(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> rhs_contracting_dimensions) {
+ fbb_.AddOffset(StablehloDotGeneralOptions::VT_RHS_CONTRACTING_DIMENSIONS, rhs_contracting_dimensions);
+ }
+ void add_precision_config(::flatbuffers::Offset<::flatbuffers::Vector<uint32_t>> precision_config) {
+ fbb_.AddOffset(StablehloDotGeneralOptions::VT_PRECISION_CONFIG, precision_config);
+ }
+ explicit StablehloDotGeneralOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloDotGeneralOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloDotGeneralOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloDotGeneralOptions> CreateStablehloDotGeneralOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> lhs_batching_dimensions = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> rhs_batching_dimensions = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> lhs_contracting_dimensions = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> rhs_contracting_dimensions = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<uint32_t>> precision_config = 0) {
+ StablehloDotGeneralOptionsBuilder builder_(_fbb);
+ builder_.add_precision_config(precision_config);
+ builder_.add_rhs_contracting_dimensions(rhs_contracting_dimensions);
+ builder_.add_lhs_contracting_dimensions(lhs_contracting_dimensions);
+ builder_.add_rhs_batching_dimensions(rhs_batching_dimensions);
+ builder_.add_lhs_batching_dimensions(lhs_batching_dimensions);
+ return builder_.Finish();
+}
+
+inline ::flatbuffers::Offset<StablehloDotGeneralOptions> CreateStablehloDotGeneralOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<int64_t> *lhs_batching_dimensions = nullptr,
+ const std::vector<int64_t> *rhs_batching_dimensions = nullptr,
+ const std::vector<int64_t> *lhs_contracting_dimensions = nullptr,
+ const std::vector<int64_t> *rhs_contracting_dimensions = nullptr,
+ const std::vector<uint32_t> *precision_config = nullptr) {
+ auto lhs_batching_dimensions__ = lhs_batching_dimensions ? _fbb.CreateVector<int64_t>(*lhs_batching_dimensions) : 0;
+ auto rhs_batching_dimensions__ = rhs_batching_dimensions ? _fbb.CreateVector<int64_t>(*rhs_batching_dimensions) : 0;
+ auto lhs_contracting_dimensions__ = lhs_contracting_dimensions ? _fbb.CreateVector<int64_t>(*lhs_contracting_dimensions) : 0;
+ auto rhs_contracting_dimensions__ = rhs_contracting_dimensions ? _fbb.CreateVector<int64_t>(*rhs_contracting_dimensions) : 0;
+ auto precision_config__ = precision_config ? _fbb.CreateVector<uint32_t>(*precision_config) : 0;
+ return tflite::CreateStablehloDotGeneralOptions(
+ _fbb,
+ lhs_batching_dimensions__,
+ rhs_batching_dimensions__,
+ lhs_contracting_dimensions__,
+ rhs_contracting_dimensions__,
+ precision_config__);
+}
+
+::flatbuffers::Offset<StablehloDotGeneralOptions> CreateStablehloDotGeneralOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloDotGeneralOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct StablehloReduceWindowOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloReduceWindowOptions TableType;
+ std::vector<int64_t> window_dimensions{};
+ std::vector<int64_t> window_strides{};
+ std::vector<int64_t> base_dilations{};
+ std::vector<int64_t> window_dilations{};
+ std::vector<int64_t> padding{};
+ int32_t body_subgraph_index = 0;
+};
+
+struct StablehloReduceWindowOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloReduceWindowOptionsT NativeTableType;
+ typedef StablehloReduceWindowOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_WINDOW_DIMENSIONS = 4,
+ VT_WINDOW_STRIDES = 6,
+ VT_BASE_DILATIONS = 8,
+ VT_WINDOW_DILATIONS = 10,
+ VT_PADDING = 12,
+ VT_BODY_SUBGRAPH_INDEX = 14
+ };
+ const ::flatbuffers::Vector<int64_t> *window_dimensions() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_WINDOW_DIMENSIONS);
+ }
+ const ::flatbuffers::Vector<int64_t> *window_strides() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_WINDOW_STRIDES);
+ }
+ const ::flatbuffers::Vector<int64_t> *base_dilations() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_BASE_DILATIONS);
+ }
+ const ::flatbuffers::Vector<int64_t> *window_dilations() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_WINDOW_DILATIONS);
+ }
+ const ::flatbuffers::Vector<int64_t> *padding() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_PADDING);
+ }
+ int32_t body_subgraph_index() const {
+ return GetField<int32_t>(VT_BODY_SUBGRAPH_INDEX, 0);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_WINDOW_DIMENSIONS) &&
+ verifier.VerifyVector(window_dimensions()) &&
+ VerifyOffset(verifier, VT_WINDOW_STRIDES) &&
+ verifier.VerifyVector(window_strides()) &&
+ VerifyOffset(verifier, VT_BASE_DILATIONS) &&
+ verifier.VerifyVector(base_dilations()) &&
+ VerifyOffset(verifier, VT_WINDOW_DILATIONS) &&
+ verifier.VerifyVector(window_dilations()) &&
+ VerifyOffset(verifier, VT_PADDING) &&
+ verifier.VerifyVector(padding()) &&
+ VerifyField<int32_t>(verifier, VT_BODY_SUBGRAPH_INDEX, 4) &&
+ verifier.EndTable();
+ }
+ StablehloReduceWindowOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloReduceWindowOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloReduceWindowOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceWindowOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloReduceWindowOptionsBuilder {
+ typedef StablehloReduceWindowOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_window_dimensions(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> window_dimensions) {
+ fbb_.AddOffset(StablehloReduceWindowOptions::VT_WINDOW_DIMENSIONS, window_dimensions);
+ }
+ void add_window_strides(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> window_strides) {
+ fbb_.AddOffset(StablehloReduceWindowOptions::VT_WINDOW_STRIDES, window_strides);
+ }
+ void add_base_dilations(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> base_dilations) {
+ fbb_.AddOffset(StablehloReduceWindowOptions::VT_BASE_DILATIONS, base_dilations);
+ }
+ void add_window_dilations(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> window_dilations) {
+ fbb_.AddOffset(StablehloReduceWindowOptions::VT_WINDOW_DILATIONS, window_dilations);
+ }
+ void add_padding(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> padding) {
+ fbb_.AddOffset(StablehloReduceWindowOptions::VT_PADDING, padding);
+ }
+ void add_body_subgraph_index(int32_t body_subgraph_index) {
+ fbb_.AddElement<int32_t>(StablehloReduceWindowOptions::VT_BODY_SUBGRAPH_INDEX, body_subgraph_index, 0);
+ }
+ explicit StablehloReduceWindowOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloReduceWindowOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloReduceWindowOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloReduceWindowOptions> CreateStablehloReduceWindowOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> window_dimensions = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> window_strides = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> base_dilations = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> window_dilations = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> padding = 0,
+ int32_t body_subgraph_index = 0) {
+ StablehloReduceWindowOptionsBuilder builder_(_fbb);
+ builder_.add_body_subgraph_index(body_subgraph_index);
+ builder_.add_padding(padding);
+ builder_.add_window_dilations(window_dilations);
+ builder_.add_base_dilations(base_dilations);
+ builder_.add_window_strides(window_strides);
+ builder_.add_window_dimensions(window_dimensions);
+ return builder_.Finish();
+}
+
+inline ::flatbuffers::Offset<StablehloReduceWindowOptions> CreateStablehloReduceWindowOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<int64_t> *window_dimensions = nullptr,
+ const std::vector<int64_t> *window_strides = nullptr,
+ const std::vector<int64_t> *base_dilations = nullptr,
+ const std::vector<int64_t> *window_dilations = nullptr,
+ const std::vector<int64_t> *padding = nullptr,
+ int32_t body_subgraph_index = 0) {
+ auto window_dimensions__ = window_dimensions ? _fbb.CreateVector<int64_t>(*window_dimensions) : 0;
+ auto window_strides__ = window_strides ? _fbb.CreateVector<int64_t>(*window_strides) : 0;
+ auto base_dilations__ = base_dilations ? _fbb.CreateVector<int64_t>(*base_dilations) : 0;
+ auto window_dilations__ = window_dilations ? _fbb.CreateVector<int64_t>(*window_dilations) : 0;
+ auto padding__ = padding ? _fbb.CreateVector<int64_t>(*padding) : 0;
+ return tflite::CreateStablehloReduceWindowOptions(
+ _fbb,
+ window_dimensions__,
+ window_strides__,
+ base_dilations__,
+ window_dilations__,
+ padding__,
+ body_subgraph_index);
+}
+
+::flatbuffers::Offset<StablehloReduceWindowOptions> CreateStablehloReduceWindowOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceWindowOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct StablehloWhileOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloWhileOptions TableType;
+ int32_t cond_subgraph_index = 0;
+ int32_t body_subgraph_index = 0;
+};
+
+struct StablehloWhileOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloWhileOptionsT NativeTableType;
+ typedef StablehloWhileOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_COND_SUBGRAPH_INDEX = 4,
+ VT_BODY_SUBGRAPH_INDEX = 6
+ };
+ int32_t cond_subgraph_index() const {
+ return GetField<int32_t>(VT_COND_SUBGRAPH_INDEX, 0);
+ }
+ int32_t body_subgraph_index() const {
+ return GetField<int32_t>(VT_BODY_SUBGRAPH_INDEX, 0);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyField<int32_t>(verifier, VT_COND_SUBGRAPH_INDEX, 4) &&
+ VerifyField<int32_t>(verifier, VT_BODY_SUBGRAPH_INDEX, 4) &&
+ verifier.EndTable();
+ }
+ StablehloWhileOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloWhileOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloWhileOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloWhileOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloWhileOptionsBuilder {
+ typedef StablehloWhileOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_cond_subgraph_index(int32_t cond_subgraph_index) {
+ fbb_.AddElement<int32_t>(StablehloWhileOptions::VT_COND_SUBGRAPH_INDEX, cond_subgraph_index, 0);
+ }
+ void add_body_subgraph_index(int32_t body_subgraph_index) {
+ fbb_.AddElement<int32_t>(StablehloWhileOptions::VT_BODY_SUBGRAPH_INDEX, body_subgraph_index, 0);
+ }
+ explicit StablehloWhileOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloWhileOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloWhileOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloWhileOptions> CreateStablehloWhileOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ int32_t cond_subgraph_index = 0,
+ int32_t body_subgraph_index = 0) {
+ StablehloWhileOptionsBuilder builder_(_fbb);
+ builder_.add_body_subgraph_index(body_subgraph_index);
+ builder_.add_cond_subgraph_index(cond_subgraph_index);
+ return builder_.Finish();
+}
+
+::flatbuffers::Offset<StablehloWhileOptions> CreateStablehloWhileOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloWhileOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct StablehloSortOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloSortOptions TableType;
+ int64_t dimension = 0;
+ bool is_stable = false;
+ int32_t comparator_subgraph_index = 0;
+};
+
+struct StablehloSortOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloSortOptionsT NativeTableType;
+ typedef StablehloSortOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_DIMENSION = 4,
+ VT_IS_STABLE = 6,
+ VT_COMPARATOR_SUBGRAPH_INDEX = 8
+ };
+ int64_t dimension() const {
+ return GetField<int64_t>(VT_DIMENSION, 0);
+ }
+ bool is_stable() const {
+ return GetField<uint8_t>(VT_IS_STABLE, 0) != 0;
+ }
+ int32_t comparator_subgraph_index() const {
+ return GetField<int32_t>(VT_COMPARATOR_SUBGRAPH_INDEX, 0);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyField<int64_t>(verifier, VT_DIMENSION, 8) &&
+ VerifyField<uint8_t>(verifier, VT_IS_STABLE, 1) &&
+ VerifyField<int32_t>(verifier, VT_COMPARATOR_SUBGRAPH_INDEX, 4) &&
+ verifier.EndTable();
+ }
+ StablehloSortOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloSortOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloSortOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloSortOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloSortOptionsBuilder {
+ typedef StablehloSortOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_dimension(int64_t dimension) {
+ fbb_.AddElement<int64_t>(StablehloSortOptions::VT_DIMENSION, dimension, 0);
+ }
+ void add_is_stable(bool is_stable) {
+ fbb_.AddElement<uint8_t>(StablehloSortOptions::VT_IS_STABLE, static_cast<uint8_t>(is_stable), 0);
+ }
+ void add_comparator_subgraph_index(int32_t comparator_subgraph_index) {
+ fbb_.AddElement<int32_t>(StablehloSortOptions::VT_COMPARATOR_SUBGRAPH_INDEX, comparator_subgraph_index, 0);
+ }
+ explicit StablehloSortOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloSortOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloSortOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloSortOptions> CreateStablehloSortOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ int64_t dimension = 0,
+ bool is_stable = false,
+ int32_t comparator_subgraph_index = 0) {
+ StablehloSortOptionsBuilder builder_(_fbb);
+ builder_.add_dimension(dimension);
+ builder_.add_comparator_subgraph_index(comparator_subgraph_index);
+ builder_.add_is_stable(is_stable);
+ return builder_.Finish();
+}
+
+::flatbuffers::Offset<StablehloSortOptions> CreateStablehloSortOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloSortOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct StablehloConcatenateOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloConcatenateOptions TableType;
+ int64_t dimension = 0;
+};
+
+struct StablehloConcatenateOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloConcatenateOptionsT NativeTableType;
+ typedef StablehloConcatenateOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_DIMENSION = 4
+ };
+ int64_t dimension() const {
+ return GetField<int64_t>(VT_DIMENSION, 0);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyField<int64_t>(verifier, VT_DIMENSION, 8) &&
+ verifier.EndTable();
+ }
+ StablehloConcatenateOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloConcatenateOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloConcatenateOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloConcatenateOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloConcatenateOptionsBuilder {
+ typedef StablehloConcatenateOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_dimension(int64_t dimension) {
+ fbb_.AddElement<int64_t>(StablehloConcatenateOptions::VT_DIMENSION, dimension, 0);
+ }
+ explicit StablehloConcatenateOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloConcatenateOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloConcatenateOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloConcatenateOptions> CreateStablehloConcatenateOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ int64_t dimension = 0) {
+ StablehloConcatenateOptionsBuilder builder_(_fbb);
+ builder_.add_dimension(dimension);
+ return builder_.Finish();
+}
+
+::flatbuffers::Offset<StablehloConcatenateOptions> CreateStablehloConcatenateOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloConcatenateOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct StablehloBroadcastInDimOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloBroadcastInDimOptions TableType;
+ std::vector<int64_t> broadcast_dimensions{};
+};
+
+struct StablehloBroadcastInDimOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloBroadcastInDimOptionsT NativeTableType;
+ typedef StablehloBroadcastInDimOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_BROADCAST_DIMENSIONS = 4
+ };
+ const ::flatbuffers::Vector<int64_t> *broadcast_dimensions() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_BROADCAST_DIMENSIONS);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_BROADCAST_DIMENSIONS) &&
+ verifier.VerifyVector(broadcast_dimensions()) &&
+ verifier.EndTable();
+ }
+ StablehloBroadcastInDimOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloBroadcastInDimOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloBroadcastInDimOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloBroadcastInDimOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloBroadcastInDimOptionsBuilder {
+ typedef StablehloBroadcastInDimOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_broadcast_dimensions(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> broadcast_dimensions) {
+ fbb_.AddOffset(StablehloBroadcastInDimOptions::VT_BROADCAST_DIMENSIONS, broadcast_dimensions);
+ }
+ explicit StablehloBroadcastInDimOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloBroadcastInDimOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloBroadcastInDimOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloBroadcastInDimOptions> CreateStablehloBroadcastInDimOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> broadcast_dimensions = 0) {
+ StablehloBroadcastInDimOptionsBuilder builder_(_fbb);
+ builder_.add_broadcast_dimensions(broadcast_dimensions);
+ return builder_.Finish();
+}
+
+inline ::flatbuffers::Offset<StablehloBroadcastInDimOptions> CreateStablehloBroadcastInDimOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<int64_t> *broadcast_dimensions = nullptr) {
+ auto broadcast_dimensions__ = broadcast_dimensions ? _fbb.CreateVector<int64_t>(*broadcast_dimensions) : 0;
+ return tflite::CreateStablehloBroadcastInDimOptions(
+ _fbb,
+ broadcast_dimensions__);
+}
+
+::flatbuffers::Offset<StablehloBroadcastInDimOptions> CreateStablehloBroadcastInDimOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloBroadcastInDimOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct StablehloCompareOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloCompareOptions TableType;
+ tflite::StablehloComparisonDirection comparison_direction = tflite::StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ;
+ tflite::StablehloComparisonType compare_type = tflite::StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE;
+};
+
+struct StablehloCompareOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloCompareOptionsT NativeTableType;
+ typedef StablehloCompareOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_COMPARISON_DIRECTION = 4,
+ VT_COMPARE_TYPE = 6
+ };
+ tflite::StablehloComparisonDirection comparison_direction() const {
+ return static_cast<tflite::StablehloComparisonDirection>(GetField<uint32_t>(VT_COMPARISON_DIRECTION, 0));
+ }
+ tflite::StablehloComparisonType compare_type() const {
+ return static_cast<tflite::StablehloComparisonType>(GetField<uint32_t>(VT_COMPARE_TYPE, 0));
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyField<uint32_t>(verifier, VT_COMPARISON_DIRECTION, 4) &&
+ VerifyField<uint32_t>(verifier, VT_COMPARE_TYPE, 4) &&
+ verifier.EndTable();
+ }
+ StablehloCompareOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloCompareOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloCompareOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloCompareOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloCompareOptionsBuilder {
+ typedef StablehloCompareOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_comparison_direction(tflite::StablehloComparisonDirection comparison_direction) {
+ fbb_.AddElement<uint32_t>(StablehloCompareOptions::VT_COMPARISON_DIRECTION, static_cast<uint32_t>(comparison_direction), 0);
+ }
+ void add_compare_type(tflite::StablehloComparisonType compare_type) {
+ fbb_.AddElement<uint32_t>(StablehloCompareOptions::VT_COMPARE_TYPE, static_cast<uint32_t>(compare_type), 0);
+ }
+ explicit StablehloCompareOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloCompareOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloCompareOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloCompareOptions> CreateStablehloCompareOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ tflite::StablehloComparisonDirection comparison_direction = tflite::StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ,
+ tflite::StablehloComparisonType compare_type = tflite::StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE) {
+ StablehloCompareOptionsBuilder builder_(_fbb);
+ builder_.add_compare_type(compare_type);
+ builder_.add_comparison_direction(comparison_direction);
+ return builder_.Finish();
+}
+
+::flatbuffers::Offset<StablehloCompareOptions> CreateStablehloCompareOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloCompareOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct StablehloDynamicSliceOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloDynamicSliceOptions TableType;
+ std::vector<int64_t> slice_sizes{};
+};
+
+struct StablehloDynamicSliceOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloDynamicSliceOptionsT NativeTableType;
+ typedef StablehloDynamicSliceOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_SLICE_SIZES = 4
+ };
+ const ::flatbuffers::Vector<int64_t> *slice_sizes() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_SLICE_SIZES);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_SLICE_SIZES) &&
+ verifier.VerifyVector(slice_sizes()) &&
+ verifier.EndTable();
+ }
+ StablehloDynamicSliceOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloDynamicSliceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloDynamicSliceOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloDynamicSliceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloDynamicSliceOptionsBuilder {
+ typedef StablehloDynamicSliceOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_slice_sizes(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> slice_sizes) {
+ fbb_.AddOffset(StablehloDynamicSliceOptions::VT_SLICE_SIZES, slice_sizes);
+ }
+ explicit StablehloDynamicSliceOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloDynamicSliceOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloDynamicSliceOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloDynamicSliceOptions> CreateStablehloDynamicSliceOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> slice_sizes = 0) {
+ StablehloDynamicSliceOptionsBuilder builder_(_fbb);
+ builder_.add_slice_sizes(slice_sizes);
+ return builder_.Finish();
+}
+
+inline ::flatbuffers::Offset<StablehloDynamicSliceOptions> CreateStablehloDynamicSliceOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<int64_t> *slice_sizes = nullptr) {
+ auto slice_sizes__ = slice_sizes ? _fbb.CreateVector<int64_t>(*slice_sizes) : 0;
+ return tflite::CreateStablehloDynamicSliceOptions(
+ _fbb,
+ slice_sizes__);
+}
+
+::flatbuffers::Offset<StablehloDynamicSliceOptions> CreateStablehloDynamicSliceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloDynamicSliceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct StablehloPadOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloPadOptions TableType;
+ std::vector<int64_t> edge_padding_low{};
+ std::vector<int64_t> edge_padding_high{};
+ std::vector<int64_t> interior_padding{};
+};
+
+struct StablehloPadOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloPadOptionsT NativeTableType;
+ typedef StablehloPadOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_EDGE_PADDING_LOW = 4,
+ VT_EDGE_PADDING_HIGH = 6,
+ VT_INTERIOR_PADDING = 8
+ };
+ const ::flatbuffers::Vector<int64_t> *edge_padding_low() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_EDGE_PADDING_LOW);
+ }
+ const ::flatbuffers::Vector<int64_t> *edge_padding_high() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_EDGE_PADDING_HIGH);
+ }
+ const ::flatbuffers::Vector<int64_t> *interior_padding() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_INTERIOR_PADDING);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_EDGE_PADDING_LOW) &&
+ verifier.VerifyVector(edge_padding_low()) &&
+ VerifyOffset(verifier, VT_EDGE_PADDING_HIGH) &&
+ verifier.VerifyVector(edge_padding_high()) &&
+ VerifyOffset(verifier, VT_INTERIOR_PADDING) &&
+ verifier.VerifyVector(interior_padding()) &&
+ verifier.EndTable();
+ }
+ StablehloPadOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloPadOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloPadOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloPadOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloPadOptionsBuilder {
+ typedef StablehloPadOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_edge_padding_low(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> edge_padding_low) {
+ fbb_.AddOffset(StablehloPadOptions::VT_EDGE_PADDING_LOW, edge_padding_low);
+ }
+ void add_edge_padding_high(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> edge_padding_high) {
+ fbb_.AddOffset(StablehloPadOptions::VT_EDGE_PADDING_HIGH, edge_padding_high);
+ }
+ void add_interior_padding(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> interior_padding) {
+ fbb_.AddOffset(StablehloPadOptions::VT_INTERIOR_PADDING, interior_padding);
+ }
+ explicit StablehloPadOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloPadOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloPadOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloPadOptions> CreateStablehloPadOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> edge_padding_low = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> edge_padding_high = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> interior_padding = 0) {
+ StablehloPadOptionsBuilder builder_(_fbb);
+ builder_.add_interior_padding(interior_padding);
+ builder_.add_edge_padding_high(edge_padding_high);
+ builder_.add_edge_padding_low(edge_padding_low);
+ return builder_.Finish();
+}
+
+inline ::flatbuffers::Offset<StablehloPadOptions> CreateStablehloPadOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<int64_t> *edge_padding_low = nullptr,
+ const std::vector<int64_t> *edge_padding_high = nullptr,
+ const std::vector<int64_t> *interior_padding = nullptr) {
+ auto edge_padding_low__ = edge_padding_low ? _fbb.CreateVector<int64_t>(*edge_padding_low) : 0;
+ auto edge_padding_high__ = edge_padding_high ? _fbb.CreateVector<int64_t>(*edge_padding_high) : 0;
+ auto interior_padding__ = interior_padding ? _fbb.CreateVector<int64_t>(*interior_padding) : 0;
+ return tflite::CreateStablehloPadOptions(
+ _fbb,
+ edge_padding_low__,
+ edge_padding_high__,
+ interior_padding__);
+}
+
+::flatbuffers::Offset<StablehloPadOptions> CreateStablehloPadOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloPadOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct StablehloIotaOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloIotaOptions TableType;
+ int64_t iota_dimension = 0;
+};
+
+struct StablehloIotaOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloIotaOptionsT NativeTableType;
+ typedef StablehloIotaOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_IOTA_DIMENSION = 4
+ };
+ int64_t iota_dimension() const {
+ return GetField<int64_t>(VT_IOTA_DIMENSION, 0);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyField<int64_t>(verifier, VT_IOTA_DIMENSION, 8) &&
+ verifier.EndTable();
+ }
+ StablehloIotaOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloIotaOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloIotaOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloIotaOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloIotaOptionsBuilder {
+ typedef StablehloIotaOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_iota_dimension(int64_t iota_dimension) {
+ fbb_.AddElement<int64_t>(StablehloIotaOptions::VT_IOTA_DIMENSION, iota_dimension, 0);
+ }
+ explicit StablehloIotaOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloIotaOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloIotaOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloIotaOptions> CreateStablehloIotaOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ int64_t iota_dimension = 0) {
+ StablehloIotaOptionsBuilder builder_(_fbb);
+ builder_.add_iota_dimension(iota_dimension);
+ return builder_.Finish();
+}
+
+::flatbuffers::Offset<StablehloIotaOptions> CreateStablehloIotaOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloIotaOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct StablehloCustomCallOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloCustomCallOptions TableType;
+ std::string call_target_name{};
+ bool has_side_effect = false;
+ std::string backend_config{};
+ int32_t api_version = 0;
+ std::vector<int32_t> called_computations{};
+ std::vector<uint8_t> custom_attributes{};
+};
+
+struct StablehloCustomCallOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloCustomCallOptionsT NativeTableType;
+ typedef StablehloCustomCallOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_CALL_TARGET_NAME = 4,
+ VT_HAS_SIDE_EFFECT = 6,
+ VT_BACKEND_CONFIG = 8,
+ VT_API_VERSION = 10,
+ VT_CALLED_COMPUTATIONS = 12,
+ VT_CUSTOM_ATTRIBUTES = 14
+ };
+ const ::flatbuffers::String *call_target_name() const {
+ return GetPointer<const ::flatbuffers::String *>(VT_CALL_TARGET_NAME);
+ }
+ bool has_side_effect() const {
+ return GetField<uint8_t>(VT_HAS_SIDE_EFFECT, 0) != 0;
+ }
+ const ::flatbuffers::String *backend_config() const {
+ return GetPointer<const ::flatbuffers::String *>(VT_BACKEND_CONFIG);
+ }
+ int32_t api_version() const {
+ return GetField<int32_t>(VT_API_VERSION, 0);
+ }
+ const ::flatbuffers::Vector<int32_t> *called_computations() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_CALLED_COMPUTATIONS);
+ }
+ const ::flatbuffers::Vector<uint8_t> *custom_attributes() const {
+ return GetPointer<const ::flatbuffers::Vector<uint8_t> *>(VT_CUSTOM_ATTRIBUTES);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_CALL_TARGET_NAME) &&
+ verifier.VerifyString(call_target_name()) &&
+ VerifyField<uint8_t>(verifier, VT_HAS_SIDE_EFFECT, 1) &&
+ VerifyOffset(verifier, VT_BACKEND_CONFIG) &&
+ verifier.VerifyString(backend_config()) &&
+ VerifyField<int32_t>(verifier, VT_API_VERSION, 4) &&
+ VerifyOffset(verifier, VT_CALLED_COMPUTATIONS) &&
+ verifier.VerifyVector(called_computations()) &&
+ VerifyOffset(verifier, VT_CUSTOM_ATTRIBUTES) &&
+ verifier.VerifyVector(custom_attributes()) &&
+ verifier.EndTable();
+ }
+ StablehloCustomCallOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloCustomCallOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloCustomCallOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloCustomCallOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloCustomCallOptionsBuilder {
+ typedef StablehloCustomCallOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_call_target_name(::flatbuffers::Offset<::flatbuffers::String> call_target_name) {
+ fbb_.AddOffset(StablehloCustomCallOptions::VT_CALL_TARGET_NAME, call_target_name);
+ }
+ void add_has_side_effect(bool has_side_effect) {
+ fbb_.AddElement<uint8_t>(StablehloCustomCallOptions::VT_HAS_SIDE_EFFECT, static_cast<uint8_t>(has_side_effect), 0);
+ }
+ void add_backend_config(::flatbuffers::Offset<::flatbuffers::String> backend_config) {
+ fbb_.AddOffset(StablehloCustomCallOptions::VT_BACKEND_CONFIG, backend_config);
+ }
+ void add_api_version(int32_t api_version) {
+ fbb_.AddElement<int32_t>(StablehloCustomCallOptions::VT_API_VERSION, api_version, 0);
+ }
+ void add_called_computations(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> called_computations) {
+ fbb_.AddOffset(StablehloCustomCallOptions::VT_CALLED_COMPUTATIONS, called_computations);
+ }
+ void add_custom_attributes(::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> custom_attributes) {
+ fbb_.AddOffset(StablehloCustomCallOptions::VT_CUSTOM_ATTRIBUTES, custom_attributes);
+ }
+ explicit StablehloCustomCallOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloCustomCallOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloCustomCallOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloCustomCallOptions> CreateStablehloCustomCallOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::String> call_target_name = 0,
+ bool has_side_effect = false,
+ ::flatbuffers::Offset<::flatbuffers::String> backend_config = 0,
+ int32_t api_version = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> called_computations = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> custom_attributes = 0) {
+ StablehloCustomCallOptionsBuilder builder_(_fbb);
+ builder_.add_custom_attributes(custom_attributes);
+ builder_.add_called_computations(called_computations);
+ builder_.add_api_version(api_version);
+ builder_.add_backend_config(backend_config);
+ builder_.add_call_target_name(call_target_name);
+ builder_.add_has_side_effect(has_side_effect);
+ return builder_.Finish();
+}
+
+inline ::flatbuffers::Offset<StablehloCustomCallOptions> CreateStablehloCustomCallOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ const char *call_target_name = nullptr,
+ bool has_side_effect = false,
+ const char *backend_config = nullptr,
+ int32_t api_version = 0,
+ const std::vector<int32_t> *called_computations = nullptr,
+ const std::vector<uint8_t> *custom_attributes = nullptr) {
+ auto call_target_name__ = call_target_name ? _fbb.CreateString(call_target_name) : 0;
+ auto backend_config__ = backend_config ? _fbb.CreateString(backend_config) : 0;
+ auto called_computations__ = called_computations ? _fbb.CreateVector<int32_t>(*called_computations) : 0;
+ auto custom_attributes__ = custom_attributes ? _fbb.CreateVector<uint8_t>(*custom_attributes) : 0;
+ return tflite::CreateStablehloCustomCallOptions(
+ _fbb,
+ call_target_name__,
+ has_side_effect,
+ backend_config__,
+ api_version,
+ called_computations__,
+ custom_attributes__);
+}
+
+::flatbuffers::Offset<StablehloCustomCallOptions> CreateStablehloCustomCallOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloCustomCallOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct StablehloReduceOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloReduceOptions TableType;
+ std::vector<int64_t> dimensions{};
+ int32_t body_subgraph_index = 0;
+};
+
+struct StablehloReduceOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloReduceOptionsT NativeTableType;
+ typedef StablehloReduceOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_DIMENSIONS = 4,
+ VT_BODY_SUBGRAPH_INDEX = 6
+ };
+ const ::flatbuffers::Vector<int64_t> *dimensions() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_DIMENSIONS);
+ }
+ int32_t body_subgraph_index() const {
+ return GetField<int32_t>(VT_BODY_SUBGRAPH_INDEX, 0);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_DIMENSIONS) &&
+ verifier.VerifyVector(dimensions()) &&
+ VerifyField<int32_t>(verifier, VT_BODY_SUBGRAPH_INDEX, 4) &&
+ verifier.EndTable();
+ }
+ StablehloReduceOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloReduceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloReduceOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloReduceOptionsBuilder {
+ typedef StablehloReduceOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_dimensions(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> dimensions) {
+ fbb_.AddOffset(StablehloReduceOptions::VT_DIMENSIONS, dimensions);
+ }
+ void add_body_subgraph_index(int32_t body_subgraph_index) {
+ fbb_.AddElement<int32_t>(StablehloReduceOptions::VT_BODY_SUBGRAPH_INDEX, body_subgraph_index, 0);
+ }
+ explicit StablehloReduceOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloReduceOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloReduceOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloReduceOptions> CreateStablehloReduceOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> dimensions = 0,
+ int32_t body_subgraph_index = 0) {
+ StablehloReduceOptionsBuilder builder_(_fbb);
+ builder_.add_body_subgraph_index(body_subgraph_index);
+ builder_.add_dimensions(dimensions);
+ return builder_.Finish();
+}
+
+inline ::flatbuffers::Offset<StablehloReduceOptions> CreateStablehloReduceOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<int64_t> *dimensions = nullptr,
+ int32_t body_subgraph_index = 0) {
+ auto dimensions__ = dimensions ? _fbb.CreateVector<int64_t>(*dimensions) : 0;
+ return tflite::CreateStablehloReduceOptions(
+ _fbb,
+ dimensions__,
+ body_subgraph_index);
+}
+
+::flatbuffers::Offset<StablehloReduceOptions> CreateStablehloReduceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct StablehloSliceOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloSliceOptions TableType;
+ std::vector<int64_t> start_indices{};
+ std::vector<int64_t> limit_indices{};
+ std::vector<int64_t> strides{};
+};
+
+struct StablehloSliceOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloSliceOptionsT NativeTableType;
+ typedef StablehloSliceOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_START_INDICES = 4,
+ VT_LIMIT_INDICES = 6,
+ VT_STRIDES = 8
+ };
+ const ::flatbuffers::Vector<int64_t> *start_indices() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_START_INDICES);
+ }
+ const ::flatbuffers::Vector<int64_t> *limit_indices() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_LIMIT_INDICES);
+ }
+ const ::flatbuffers::Vector<int64_t> *strides() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_STRIDES);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_START_INDICES) &&
+ verifier.VerifyVector(start_indices()) &&
+ VerifyOffset(verifier, VT_LIMIT_INDICES) &&
+ verifier.VerifyVector(limit_indices()) &&
+ VerifyOffset(verifier, VT_STRIDES) &&
+ verifier.VerifyVector(strides()) &&
+ verifier.EndTable();
+ }
+ StablehloSliceOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloSliceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloSliceOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloSliceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloSliceOptionsBuilder {
+ typedef StablehloSliceOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_start_indices(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> start_indices) {
+ fbb_.AddOffset(StablehloSliceOptions::VT_START_INDICES, start_indices);
+ }
+ void add_limit_indices(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> limit_indices) {
+ fbb_.AddOffset(StablehloSliceOptions::VT_LIMIT_INDICES, limit_indices);
+ }
+ void add_strides(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> strides) {
+ fbb_.AddOffset(StablehloSliceOptions::VT_STRIDES, strides);
+ }
+ explicit StablehloSliceOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloSliceOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloSliceOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloSliceOptions> CreateStablehloSliceOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> start_indices = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> limit_indices = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> strides = 0) {
+ StablehloSliceOptionsBuilder builder_(_fbb);
+ builder_.add_strides(strides);
+ builder_.add_limit_indices(limit_indices);
+ builder_.add_start_indices(start_indices);
+ return builder_.Finish();
+}
+
+inline ::flatbuffers::Offset<StablehloSliceOptions> CreateStablehloSliceOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<int64_t> *start_indices = nullptr,
+ const std::vector<int64_t> *limit_indices = nullptr,
+ const std::vector<int64_t> *strides = nullptr) {
+ auto start_indices__ = start_indices ? _fbb.CreateVector<int64_t>(*start_indices) : 0;
+ auto limit_indices__ = limit_indices ? _fbb.CreateVector<int64_t>(*limit_indices) : 0;
+ auto strides__ = strides ? _fbb.CreateVector<int64_t>(*strides) : 0;
+ return tflite::CreateStablehloSliceOptions(
+ _fbb,
+ start_indices__,
+ limit_indices__,
+ strides__);
+}
+
+::flatbuffers::Offset<StablehloSliceOptions> CreateStablehloSliceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloSliceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct StablehloConvolutionOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloConvolutionOptions TableType;
+ std::vector<int64_t> window_strides{};
+ std::vector<int64_t> padding{};
+ std::vector<int64_t> lhs_dilation{};
+ std::vector<int64_t> rhs_dilation{};
+ std::vector<bool> window_reversal{};
+ int64_t input_batch_dimension = 0;
+ int64_t input_feature_dimension = 0;
+ std::vector<int64_t> input_spatial_dimensions{};
+ int64_t kernel_input_feature_dimension = 0;
+ int64_t kernel_output_feature_dimension = 0;
+ std::vector<int64_t> kernel_spatial_dimensions{};
+ int64_t output_batch_dimension = 0;
+ int64_t output_feature_dimension = 0;
+ std::vector<int64_t> output_spatial_dimensions{};
+ int64_t feature_group_count = 0;
+ int64_t batch_group_count = 0;
+ std::vector<tflite::StablehloPrecisionConfig> precision_config{};
+};
+
+struct StablehloConvolutionOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloConvolutionOptionsT NativeTableType;
+ typedef StablehloConvolutionOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_WINDOW_STRIDES = 4,
+ VT_PADDING = 6,
+ VT_LHS_DILATION = 8,
+ VT_RHS_DILATION = 10,
+ VT_WINDOW_REVERSAL = 12,
+ VT_INPUT_BATCH_DIMENSION = 14,
+ VT_INPUT_FEATURE_DIMENSION = 16,
+ VT_INPUT_SPATIAL_DIMENSIONS = 18,
+ VT_KERNEL_INPUT_FEATURE_DIMENSION = 20,
+ VT_KERNEL_OUTPUT_FEATURE_DIMENSION = 22,
+ VT_KERNEL_SPATIAL_DIMENSIONS = 24,
+ VT_OUTPUT_BATCH_DIMENSION = 26,
+ VT_OUTPUT_FEATURE_DIMENSION = 28,
+ VT_OUTPUT_SPATIAL_DIMENSIONS = 30,
+ VT_FEATURE_GROUP_COUNT = 32,
+ VT_BATCH_GROUP_COUNT = 34,
+ VT_PRECISION_CONFIG = 36
+ };
+ const ::flatbuffers::Vector<int64_t> *window_strides() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_WINDOW_STRIDES);
+ }
+ const ::flatbuffers::Vector<int64_t> *padding() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_PADDING);
+ }
+ const ::flatbuffers::Vector<int64_t> *lhs_dilation() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_LHS_DILATION);
+ }
+ const ::flatbuffers::Vector<int64_t> *rhs_dilation() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_RHS_DILATION);
+ }
+ const ::flatbuffers::Vector<uint8_t> *window_reversal() const {
+ return GetPointer<const ::flatbuffers::Vector<uint8_t> *>(VT_WINDOW_REVERSAL);
+ }
+ int64_t input_batch_dimension() const {
+ return GetField<int64_t>(VT_INPUT_BATCH_DIMENSION, 0);
+ }
+ int64_t input_feature_dimension() const {
+ return GetField<int64_t>(VT_INPUT_FEATURE_DIMENSION, 0);
+ }
+ const ::flatbuffers::Vector<int64_t> *input_spatial_dimensions() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_INPUT_SPATIAL_DIMENSIONS);
+ }
+ int64_t kernel_input_feature_dimension() const {
+ return GetField<int64_t>(VT_KERNEL_INPUT_FEATURE_DIMENSION, 0);
+ }
+ int64_t kernel_output_feature_dimension() const {
+ return GetField<int64_t>(VT_KERNEL_OUTPUT_FEATURE_DIMENSION, 0);
+ }
+ const ::flatbuffers::Vector<int64_t> *kernel_spatial_dimensions() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_KERNEL_SPATIAL_DIMENSIONS);
+ }
+ int64_t output_batch_dimension() const {
+ return GetField<int64_t>(VT_OUTPUT_BATCH_DIMENSION, 0);
+ }
+ int64_t output_feature_dimension() const {
+ return GetField<int64_t>(VT_OUTPUT_FEATURE_DIMENSION, 0);
+ }
+ const ::flatbuffers::Vector<int64_t> *output_spatial_dimensions() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_OUTPUT_SPATIAL_DIMENSIONS);
+ }
+ int64_t feature_group_count() const {
+ return GetField<int64_t>(VT_FEATURE_GROUP_COUNT, 0);
+ }
+ int64_t batch_group_count() const {
+ return GetField<int64_t>(VT_BATCH_GROUP_COUNT, 0);
+ }
+ const ::flatbuffers::Vector<uint32_t> *precision_config() const {
+ return GetPointer<const ::flatbuffers::Vector<uint32_t> *>(VT_PRECISION_CONFIG);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_WINDOW_STRIDES) &&
+ verifier.VerifyVector(window_strides()) &&
+ VerifyOffset(verifier, VT_PADDING) &&
+ verifier.VerifyVector(padding()) &&
+ VerifyOffset(verifier, VT_LHS_DILATION) &&
+ verifier.VerifyVector(lhs_dilation()) &&
+ VerifyOffset(verifier, VT_RHS_DILATION) &&
+ verifier.VerifyVector(rhs_dilation()) &&
+ VerifyOffset(verifier, VT_WINDOW_REVERSAL) &&
+ verifier.VerifyVector(window_reversal()) &&
+ VerifyField<int64_t>(verifier, VT_INPUT_BATCH_DIMENSION, 8) &&
+ VerifyField<int64_t>(verifier, VT_INPUT_FEATURE_DIMENSION, 8) &&
+ VerifyOffset(verifier, VT_INPUT_SPATIAL_DIMENSIONS) &&
+ verifier.VerifyVector(input_spatial_dimensions()) &&
+ VerifyField<int64_t>(verifier, VT_KERNEL_INPUT_FEATURE_DIMENSION, 8) &&
+ VerifyField<int64_t>(verifier, VT_KERNEL_OUTPUT_FEATURE_DIMENSION, 8) &&
+ VerifyOffset(verifier, VT_KERNEL_SPATIAL_DIMENSIONS) &&
+ verifier.VerifyVector(kernel_spatial_dimensions()) &&
+ VerifyField<int64_t>(verifier, VT_OUTPUT_BATCH_DIMENSION, 8) &&
+ VerifyField<int64_t>(verifier, VT_OUTPUT_FEATURE_DIMENSION, 8) &&
+ VerifyOffset(verifier, VT_OUTPUT_SPATIAL_DIMENSIONS) &&
+ verifier.VerifyVector(output_spatial_dimensions()) &&
+ VerifyField<int64_t>(verifier, VT_FEATURE_GROUP_COUNT, 8) &&
+ VerifyField<int64_t>(verifier, VT_BATCH_GROUP_COUNT, 8) &&
+ VerifyOffset(verifier, VT_PRECISION_CONFIG) &&
+ verifier.VerifyVector(precision_config()) &&
+ verifier.EndTable();
+ }
+ StablehloConvolutionOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloConvolutionOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloConvolutionOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloConvolutionOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloConvolutionOptionsBuilder {
+ typedef StablehloConvolutionOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_window_strides(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> window_strides) {
+ fbb_.AddOffset(StablehloConvolutionOptions::VT_WINDOW_STRIDES, window_strides);
+ }
+ void add_padding(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> padding) {
+ fbb_.AddOffset(StablehloConvolutionOptions::VT_PADDING, padding);
+ }
+ void add_lhs_dilation(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> lhs_dilation) {
+ fbb_.AddOffset(StablehloConvolutionOptions::VT_LHS_DILATION, lhs_dilation);
+ }
+ void add_rhs_dilation(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> rhs_dilation) {
+ fbb_.AddOffset(StablehloConvolutionOptions::VT_RHS_DILATION, rhs_dilation);
+ }
+ void add_window_reversal(::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> window_reversal) {
+ fbb_.AddOffset(StablehloConvolutionOptions::VT_WINDOW_REVERSAL, window_reversal);
+ }
+ void add_input_batch_dimension(int64_t input_batch_dimension) {
+ fbb_.AddElement<int64_t>(StablehloConvolutionOptions::VT_INPUT_BATCH_DIMENSION, input_batch_dimension, 0);
+ }
+ void add_input_feature_dimension(int64_t input_feature_dimension) {
+ fbb_.AddElement<int64_t>(StablehloConvolutionOptions::VT_INPUT_FEATURE_DIMENSION, input_feature_dimension, 0);
+ }
+ void add_input_spatial_dimensions(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> input_spatial_dimensions) {
+ fbb_.AddOffset(StablehloConvolutionOptions::VT_INPUT_SPATIAL_DIMENSIONS, input_spatial_dimensions);
+ }
+ void add_kernel_input_feature_dimension(int64_t kernel_input_feature_dimension) {
+ fbb_.AddElement<int64_t>(StablehloConvolutionOptions::VT_KERNEL_INPUT_FEATURE_DIMENSION, kernel_input_feature_dimension, 0);
+ }
+ void add_kernel_output_feature_dimension(int64_t kernel_output_feature_dimension) {
+ fbb_.AddElement<int64_t>(StablehloConvolutionOptions::VT_KERNEL_OUTPUT_FEATURE_DIMENSION, kernel_output_feature_dimension, 0);
+ }
+ void add_kernel_spatial_dimensions(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> kernel_spatial_dimensions) {
+ fbb_.AddOffset(StablehloConvolutionOptions::VT_KERNEL_SPATIAL_DIMENSIONS, kernel_spatial_dimensions);
+ }
+ void add_output_batch_dimension(int64_t output_batch_dimension) {
+ fbb_.AddElement<int64_t>(StablehloConvolutionOptions::VT_OUTPUT_BATCH_DIMENSION, output_batch_dimension, 0);
+ }
+ void add_output_feature_dimension(int64_t output_feature_dimension) {
+ fbb_.AddElement<int64_t>(StablehloConvolutionOptions::VT_OUTPUT_FEATURE_DIMENSION, output_feature_dimension, 0);
+ }
+ void add_output_spatial_dimensions(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> output_spatial_dimensions) {
+ fbb_.AddOffset(StablehloConvolutionOptions::VT_OUTPUT_SPATIAL_DIMENSIONS, output_spatial_dimensions);
+ }
+ void add_feature_group_count(int64_t feature_group_count) {
+ fbb_.AddElement<int64_t>(StablehloConvolutionOptions::VT_FEATURE_GROUP_COUNT, feature_group_count, 0);
+ }
+ void add_batch_group_count(int64_t batch_group_count) {
+ fbb_.AddElement<int64_t>(StablehloConvolutionOptions::VT_BATCH_GROUP_COUNT, batch_group_count, 0);
+ }
+ void add_precision_config(::flatbuffers::Offset<::flatbuffers::Vector<uint32_t>> precision_config) {
+ fbb_.AddOffset(StablehloConvolutionOptions::VT_PRECISION_CONFIG, precision_config);
+ }
+ explicit StablehloConvolutionOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloConvolutionOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloConvolutionOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloConvolutionOptions> CreateStablehloConvolutionOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> window_strides = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> padding = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> lhs_dilation = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> rhs_dilation = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> window_reversal = 0,
+ int64_t input_batch_dimension = 0,
+ int64_t input_feature_dimension = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> input_spatial_dimensions = 0,
+ int64_t kernel_input_feature_dimension = 0,
+ int64_t kernel_output_feature_dimension = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> kernel_spatial_dimensions = 0,
+ int64_t output_batch_dimension = 0,
+ int64_t output_feature_dimension = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> output_spatial_dimensions = 0,
+ int64_t feature_group_count = 0,
+ int64_t batch_group_count = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<uint32_t>> precision_config = 0) {
+ StablehloConvolutionOptionsBuilder builder_(_fbb);
+ builder_.add_batch_group_count(batch_group_count);
+ builder_.add_feature_group_count(feature_group_count);
+ builder_.add_output_feature_dimension(output_feature_dimension);
+ builder_.add_output_batch_dimension(output_batch_dimension);
+ builder_.add_kernel_output_feature_dimension(kernel_output_feature_dimension);
+ builder_.add_kernel_input_feature_dimension(kernel_input_feature_dimension);
+ builder_.add_input_feature_dimension(input_feature_dimension);
+ builder_.add_input_batch_dimension(input_batch_dimension);
+ builder_.add_precision_config(precision_config);
+ builder_.add_output_spatial_dimensions(output_spatial_dimensions);
+ builder_.add_kernel_spatial_dimensions(kernel_spatial_dimensions);
+ builder_.add_input_spatial_dimensions(input_spatial_dimensions);
+ builder_.add_window_reversal(window_reversal);
+ builder_.add_rhs_dilation(rhs_dilation);
+ builder_.add_lhs_dilation(lhs_dilation);
+ builder_.add_padding(padding);
+ builder_.add_window_strides(window_strides);
+ return builder_.Finish();
+}
+
+inline ::flatbuffers::Offset<StablehloConvolutionOptions> CreateStablehloConvolutionOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<int64_t> *window_strides = nullptr,
+ const std::vector<int64_t> *padding = nullptr,
+ const std::vector<int64_t> *lhs_dilation = nullptr,
+ const std::vector<int64_t> *rhs_dilation = nullptr,
+ const std::vector<uint8_t> *window_reversal = nullptr,
+ int64_t input_batch_dimension = 0,
+ int64_t input_feature_dimension = 0,
+ const std::vector<int64_t> *input_spatial_dimensions = nullptr,
+ int64_t kernel_input_feature_dimension = 0,
+ int64_t kernel_output_feature_dimension = 0,
+ const std::vector<int64_t> *kernel_spatial_dimensions = nullptr,
+ int64_t output_batch_dimension = 0,
+ int64_t output_feature_dimension = 0,
+ const std::vector<int64_t> *output_spatial_dimensions = nullptr,
+ int64_t feature_group_count = 0,
+ int64_t batch_group_count = 0,
+ const std::vector<uint32_t> *precision_config = nullptr) {
+ auto window_strides__ = window_strides ? _fbb.CreateVector<int64_t>(*window_strides) : 0;
+ auto padding__ = padding ? _fbb.CreateVector<int64_t>(*padding) : 0;
+ auto lhs_dilation__ = lhs_dilation ? _fbb.CreateVector<int64_t>(*lhs_dilation) : 0;
+ auto rhs_dilation__ = rhs_dilation ? _fbb.CreateVector<int64_t>(*rhs_dilation) : 0;
+ auto window_reversal__ = window_reversal ? _fbb.CreateVector<uint8_t>(*window_reversal) : 0;
+ auto input_spatial_dimensions__ = input_spatial_dimensions ? _fbb.CreateVector<int64_t>(*input_spatial_dimensions) : 0;
+ auto kernel_spatial_dimensions__ = kernel_spatial_dimensions ? _fbb.CreateVector<int64_t>(*kernel_spatial_dimensions) : 0;
+ auto output_spatial_dimensions__ = output_spatial_dimensions ? _fbb.CreateVector<int64_t>(*output_spatial_dimensions) : 0;
+ auto precision_config__ = precision_config ? _fbb.CreateVector<uint32_t>(*precision_config) : 0;
+ return tflite::CreateStablehloConvolutionOptions(
+ _fbb,
+ window_strides__,
+ padding__,
+ lhs_dilation__,
+ rhs_dilation__,
+ window_reversal__,
+ input_batch_dimension,
+ input_feature_dimension,
+ input_spatial_dimensions__,
+ kernel_input_feature_dimension,
+ kernel_output_feature_dimension,
+ kernel_spatial_dimensions__,
+ output_batch_dimension,
+ output_feature_dimension,
+ output_spatial_dimensions__,
+ feature_group_count,
+ batch_group_count,
+ precision_config__);
+}
+
+::flatbuffers::Offset<StablehloConvolutionOptions> CreateStablehloConvolutionOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloConvolutionOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct StablehloScatterOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloScatterOptions TableType;
+ bool indices_are_sorted = false;
+ std::vector<int64_t> update_window_dims{};
+ std::vector<int64_t> inserted_window_dims{};
+ std::vector<int64_t> scatter_dims_to_operand_dims{};
+ int64_t index_vector_dim = 0;
+ bool unique_indices = false;
+ int32_t update_computation_subgraph_index = 0;
+};
+
+struct StablehloScatterOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloScatterOptionsT NativeTableType;
+ typedef StablehloScatterOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_INDICES_ARE_SORTED = 4,
+ VT_UPDATE_WINDOW_DIMS = 6,
+ VT_INSERTED_WINDOW_DIMS = 8,
+ VT_SCATTER_DIMS_TO_OPERAND_DIMS = 10,
+ VT_INDEX_VECTOR_DIM = 12,
+ VT_UNIQUE_INDICES = 14,
+ VT_UPDATE_COMPUTATION_SUBGRAPH_INDEX = 16
+ };
+ bool indices_are_sorted() const {
+ return GetField<uint8_t>(VT_INDICES_ARE_SORTED, 0) != 0;
+ }
+ const ::flatbuffers::Vector<int64_t> *update_window_dims() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_UPDATE_WINDOW_DIMS);
+ }
+ const ::flatbuffers::Vector<int64_t> *inserted_window_dims() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_INSERTED_WINDOW_DIMS);
+ }
+ const ::flatbuffers::Vector<int64_t> *scatter_dims_to_operand_dims() const {
+ return GetPointer<const ::flatbuffers::Vector<int64_t> *>(VT_SCATTER_DIMS_TO_OPERAND_DIMS);
+ }
+ int64_t index_vector_dim() const {
+ return GetField<int64_t>(VT_INDEX_VECTOR_DIM, 0);
+ }
+ bool unique_indices() const {
+ return GetField<uint8_t>(VT_UNIQUE_INDICES, 0) != 0;
+ }
+ int32_t update_computation_subgraph_index() const {
+ return GetField<int32_t>(VT_UPDATE_COMPUTATION_SUBGRAPH_INDEX, 0);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyField<uint8_t>(verifier, VT_INDICES_ARE_SORTED, 1) &&
+ VerifyOffset(verifier, VT_UPDATE_WINDOW_DIMS) &&
+ verifier.VerifyVector(update_window_dims()) &&
+ VerifyOffset(verifier, VT_INSERTED_WINDOW_DIMS) &&
+ verifier.VerifyVector(inserted_window_dims()) &&
+ VerifyOffset(verifier, VT_SCATTER_DIMS_TO_OPERAND_DIMS) &&
+ verifier.VerifyVector(scatter_dims_to_operand_dims()) &&
+ VerifyField<int64_t>(verifier, VT_INDEX_VECTOR_DIM, 8) &&
+ VerifyField<uint8_t>(verifier, VT_UNIQUE_INDICES, 1) &&
+ VerifyField<int32_t>(verifier, VT_UPDATE_COMPUTATION_SUBGRAPH_INDEX, 4) &&
+ verifier.EndTable();
+ }
+ StablehloScatterOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloScatterOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloScatterOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloScatterOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloScatterOptionsBuilder {
+ typedef StablehloScatterOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_indices_are_sorted(bool indices_are_sorted) {
+ fbb_.AddElement<uint8_t>(StablehloScatterOptions::VT_INDICES_ARE_SORTED, static_cast<uint8_t>(indices_are_sorted), 0);
+ }
+ void add_update_window_dims(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> update_window_dims) {
+ fbb_.AddOffset(StablehloScatterOptions::VT_UPDATE_WINDOW_DIMS, update_window_dims);
+ }
+ void add_inserted_window_dims(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> inserted_window_dims) {
+ fbb_.AddOffset(StablehloScatterOptions::VT_INSERTED_WINDOW_DIMS, inserted_window_dims);
+ }
+ void add_scatter_dims_to_operand_dims(::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> scatter_dims_to_operand_dims) {
+ fbb_.AddOffset(StablehloScatterOptions::VT_SCATTER_DIMS_TO_OPERAND_DIMS, scatter_dims_to_operand_dims);
+ }
+ void add_index_vector_dim(int64_t index_vector_dim) {
+ fbb_.AddElement<int64_t>(StablehloScatterOptions::VT_INDEX_VECTOR_DIM, index_vector_dim, 0);
+ }
+ void add_unique_indices(bool unique_indices) {
+ fbb_.AddElement<uint8_t>(StablehloScatterOptions::VT_UNIQUE_INDICES, static_cast<uint8_t>(unique_indices), 0);
+ }
+ void add_update_computation_subgraph_index(int32_t update_computation_subgraph_index) {
+ fbb_.AddElement<int32_t>(StablehloScatterOptions::VT_UPDATE_COMPUTATION_SUBGRAPH_INDEX, update_computation_subgraph_index, 0);
+ }
+ explicit StablehloScatterOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloScatterOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloScatterOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloScatterOptions> CreateStablehloScatterOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ bool indices_are_sorted = false,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> update_window_dims = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> inserted_window_dims = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int64_t>> scatter_dims_to_operand_dims = 0,
+ int64_t index_vector_dim = 0,
+ bool unique_indices = false,
+ int32_t update_computation_subgraph_index = 0) {
+ StablehloScatterOptionsBuilder builder_(_fbb);
+ builder_.add_index_vector_dim(index_vector_dim);
+ builder_.add_update_computation_subgraph_index(update_computation_subgraph_index);
+ builder_.add_scatter_dims_to_operand_dims(scatter_dims_to_operand_dims);
+ builder_.add_inserted_window_dims(inserted_window_dims);
+ builder_.add_update_window_dims(update_window_dims);
+ builder_.add_unique_indices(unique_indices);
+ builder_.add_indices_are_sorted(indices_are_sorted);
+ return builder_.Finish();
+}
+
+inline ::flatbuffers::Offset<StablehloScatterOptions> CreateStablehloScatterOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ bool indices_are_sorted = false,
+ const std::vector<int64_t> *update_window_dims = nullptr,
+ const std::vector<int64_t> *inserted_window_dims = nullptr,
+ const std::vector<int64_t> *scatter_dims_to_operand_dims = nullptr,
+ int64_t index_vector_dim = 0,
+ bool unique_indices = false,
+ int32_t update_computation_subgraph_index = 0) {
+ auto update_window_dims__ = update_window_dims ? _fbb.CreateVector<int64_t>(*update_window_dims) : 0;
+ auto inserted_window_dims__ = inserted_window_dims ? _fbb.CreateVector<int64_t>(*inserted_window_dims) : 0;
+ auto scatter_dims_to_operand_dims__ = scatter_dims_to_operand_dims ? _fbb.CreateVector<int64_t>(*scatter_dims_to_operand_dims) : 0;
+ return tflite::CreateStablehloScatterOptions(
+ _fbb,
+ indices_are_sorted,
+ update_window_dims__,
+ inserted_window_dims__,
+ scatter_dims_to_operand_dims__,
+ index_vector_dim,
+ unique_indices,
+ update_computation_subgraph_index);
+}
+
+::flatbuffers::Offset<StablehloScatterOptions> CreateStablehloScatterOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloScatterOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct StablehloRngBitGeneratorOptionsT : public ::flatbuffers::NativeTable {
+ typedef StablehloRngBitGeneratorOptions TableType;
+ tflite::RngAlgorithm algorithm = tflite::RngAlgorithm_DEFAULT;
+};
+
+struct StablehloRngBitGeneratorOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StablehloRngBitGeneratorOptionsT NativeTableType;
+ typedef StablehloRngBitGeneratorOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_ALGORITHM = 4
+ };
+ tflite::RngAlgorithm algorithm() const {
+ return static_cast<tflite::RngAlgorithm>(GetField<int8_t>(VT_ALGORITHM, 0));
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyField<int8_t>(verifier, VT_ALGORITHM, 1) &&
+ verifier.EndTable();
+ }
+ StablehloRngBitGeneratorOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StablehloRngBitGeneratorOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StablehloRngBitGeneratorOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloRngBitGeneratorOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StablehloRngBitGeneratorOptionsBuilder {
+ typedef StablehloRngBitGeneratorOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_algorithm(tflite::RngAlgorithm algorithm) {
+ fbb_.AddElement<int8_t>(StablehloRngBitGeneratorOptions::VT_ALGORITHM, static_cast<int8_t>(algorithm), 0);
+ }
+ explicit StablehloRngBitGeneratorOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StablehloRngBitGeneratorOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StablehloRngBitGeneratorOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StablehloRngBitGeneratorOptions> CreateStablehloRngBitGeneratorOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ tflite::RngAlgorithm algorithm = tflite::RngAlgorithm_DEFAULT) {
+ StablehloRngBitGeneratorOptionsBuilder builder_(_fbb);
+ builder_.add_algorithm(algorithm);
+ return builder_.Finish();
+}
+
+::flatbuffers::Offset<StablehloRngBitGeneratorOptions> CreateStablehloRngBitGeneratorOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloRngBitGeneratorOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct Conv2DOptionsT : public ::flatbuffers::NativeTable {
typedef Conv2DOptions TableType;
tflite::Padding padding = tflite::Padding_SAME;
int32_t stride_w = 0;
@@ -5081,9 +7718,10 @@
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
int32_t dilation_w_factor = 1;
int32_t dilation_h_factor = 1;
+ tflite::TensorType quantized_bias_type = tflite::TensorType_FLOAT32;
};
-struct Conv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct Conv2DOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef Conv2DOptionsT NativeTableType;
typedef Conv2DOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -5092,7 +7730,8 @@
VT_STRIDE_H = 8,
VT_FUSED_ACTIVATION_FUNCTION = 10,
VT_DILATION_W_FACTOR = 12,
- VT_DILATION_H_FACTOR = 14
+ VT_DILATION_H_FACTOR = 14,
+ VT_QUANTIZED_BIAS_TYPE = 16
};
tflite::Padding padding() const {
return static_cast<tflite::Padding>(GetField<int8_t>(VT_PADDING, 0));
@@ -5112,7 +7751,10 @@
int32_t dilation_h_factor() const {
return GetField<int32_t>(VT_DILATION_H_FACTOR, 1);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ tflite::TensorType quantized_bias_type() const {
+ return static_cast<tflite::TensorType>(GetField<int8_t>(VT_QUANTIZED_BIAS_TYPE, 0));
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_PADDING, 1) &&
VerifyField<int32_t>(verifier, VT_STRIDE_W, 4) &&
@@ -5120,17 +7762,18 @@
VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
VerifyField<int32_t>(verifier, VT_DILATION_W_FACTOR, 4) &&
VerifyField<int32_t>(verifier, VT_DILATION_H_FACTOR, 4) &&
+ VerifyField<int8_t>(verifier, VT_QUANTIZED_BIAS_TYPE, 1) &&
verifier.EndTable();
}
- Conv2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(Conv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<Conv2DOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ Conv2DOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(Conv2DOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<Conv2DOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct Conv2DOptionsBuilder {
typedef Conv2DOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_padding(tflite::Padding padding) {
fbb_.AddElement<int8_t>(Conv2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
}
@@ -5149,38 +7792,43 @@
void add_dilation_h_factor(int32_t dilation_h_factor) {
fbb_.AddElement<int32_t>(Conv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1);
}
- explicit Conv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ void add_quantized_bias_type(tflite::TensorType quantized_bias_type) {
+ fbb_.AddElement<int8_t>(Conv2DOptions::VT_QUANTIZED_BIAS_TYPE, static_cast<int8_t>(quantized_bias_type), 0);
+ }
+ explicit Conv2DOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<Conv2DOptions> Finish() {
+ ::flatbuffers::Offset<Conv2DOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<Conv2DOptions>(end);
+ auto o = ::flatbuffers::Offset<Conv2DOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<Conv2DOptions> CreateConv2DOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<Conv2DOptions> CreateConv2DOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::Padding padding = tflite::Padding_SAME,
int32_t stride_w = 0,
int32_t stride_h = 0,
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
int32_t dilation_w_factor = 1,
- int32_t dilation_h_factor = 1) {
+ int32_t dilation_h_factor = 1,
+ tflite::TensorType quantized_bias_type = tflite::TensorType_FLOAT32) {
Conv2DOptionsBuilder builder_(_fbb);
builder_.add_dilation_h_factor(dilation_h_factor);
builder_.add_dilation_w_factor(dilation_w_factor);
builder_.add_stride_h(stride_h);
builder_.add_stride_w(stride_w);
+ builder_.add_quantized_bias_type(quantized_bias_type);
builder_.add_fused_activation_function(fused_activation_function);
builder_.add_padding(padding);
return builder_.Finish();
}
-flatbuffers::Offset<Conv2DOptions> CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<Conv2DOptions> CreateConv2DOptions(::flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct Conv3DOptionsT : public flatbuffers::NativeTable {
+struct Conv3DOptionsT : public ::flatbuffers::NativeTable {
typedef Conv3DOptions TableType;
tflite::Padding padding = tflite::Padding_SAME;
int32_t stride_d = 0;
@@ -5192,7 +7840,7 @@
int32_t dilation_h_factor = 1;
};
-struct Conv3DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct Conv3DOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef Conv3DOptionsT NativeTableType;
typedef Conv3DOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -5229,7 +7877,7 @@
int32_t dilation_h_factor() const {
return GetField<int32_t>(VT_DILATION_H_FACTOR, 1);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_PADDING, 1) &&
VerifyField<int32_t>(verifier, VT_STRIDE_D, 4) &&
@@ -5241,15 +7889,15 @@
VerifyField<int32_t>(verifier, VT_DILATION_H_FACTOR, 4) &&
verifier.EndTable();
}
- Conv3DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(Conv3DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<Conv3DOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ Conv3DOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(Conv3DOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<Conv3DOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct Conv3DOptionsBuilder {
typedef Conv3DOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_padding(tflite::Padding padding) {
fbb_.AddElement<int8_t>(Conv3DOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
}
@@ -5274,19 +7922,19 @@
void add_dilation_h_factor(int32_t dilation_h_factor) {
fbb_.AddElement<int32_t>(Conv3DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1);
}
- explicit Conv3DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit Conv3DOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<Conv3DOptions> Finish() {
+ ::flatbuffers::Offset<Conv3DOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<Conv3DOptions>(end);
+ auto o = ::flatbuffers::Offset<Conv3DOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<Conv3DOptions> CreateConv3DOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<Conv3DOptions> CreateConv3DOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::Padding padding = tflite::Padding_SAME,
int32_t stride_d = 0,
int32_t stride_w = 0,
@@ -5307,9 +7955,9 @@
return builder_.Finish();
}
-flatbuffers::Offset<Conv3DOptions> CreateConv3DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<Conv3DOptions> CreateConv3DOptions(::flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct Pool2DOptionsT : public flatbuffers::NativeTable {
+struct Pool2DOptionsT : public ::flatbuffers::NativeTable {
typedef Pool2DOptions TableType;
tflite::Padding padding = tflite::Padding_SAME;
int32_t stride_w = 0;
@@ -5319,7 +7967,7 @@
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
};
-struct Pool2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct Pool2DOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef Pool2DOptionsT NativeTableType;
typedef Pool2DOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -5348,7 +7996,7 @@
tflite::ActivationFunctionType fused_activation_function() const {
return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_PADDING, 1) &&
VerifyField<int32_t>(verifier, VT_STRIDE_W, 4) &&
@@ -5358,15 +8006,15 @@
VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
verifier.EndTable();
}
- Pool2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(Pool2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<Pool2DOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ Pool2DOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(Pool2DOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<Pool2DOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct Pool2DOptionsBuilder {
typedef Pool2DOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_padding(tflite::Padding padding) {
fbb_.AddElement<int8_t>(Pool2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
}
@@ -5385,19 +8033,19 @@
void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
fbb_.AddElement<int8_t>(Pool2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
}
- explicit Pool2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit Pool2DOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<Pool2DOptions> Finish() {
+ ::flatbuffers::Offset<Pool2DOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<Pool2DOptions>(end);
+ auto o = ::flatbuffers::Offset<Pool2DOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<Pool2DOptions> CreatePool2DOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<Pool2DOptions> CreatePool2DOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::Padding padding = tflite::Padding_SAME,
int32_t stride_w = 0,
int32_t stride_h = 0,
@@ -5414,9 +8062,9 @@
return builder_.Finish();
}
-flatbuffers::Offset<Pool2DOptions> CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<Pool2DOptions> CreatePool2DOptions(::flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct DepthwiseConv2DOptionsT : public flatbuffers::NativeTable {
+struct DepthwiseConv2DOptionsT : public ::flatbuffers::NativeTable {
typedef DepthwiseConv2DOptions TableType;
tflite::Padding padding = tflite::Padding_SAME;
int32_t stride_w = 0;
@@ -5427,7 +8075,7 @@
int32_t dilation_h_factor = 1;
};
-struct DepthwiseConv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct DepthwiseConv2DOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef DepthwiseConv2DOptionsT NativeTableType;
typedef DepthwiseConv2DOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -5460,7 +8108,7 @@
int32_t dilation_h_factor() const {
return GetField<int32_t>(VT_DILATION_H_FACTOR, 1);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_PADDING, 1) &&
VerifyField<int32_t>(verifier, VT_STRIDE_W, 4) &&
@@ -5471,15 +8119,15 @@
VerifyField<int32_t>(verifier, VT_DILATION_H_FACTOR, 4) &&
verifier.EndTable();
}
- DepthwiseConv2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(DepthwiseConv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<DepthwiseConv2DOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ DepthwiseConv2DOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(DepthwiseConv2DOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<DepthwiseConv2DOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct DepthwiseConv2DOptionsBuilder {
typedef DepthwiseConv2DOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_padding(tflite::Padding padding) {
fbb_.AddElement<int8_t>(DepthwiseConv2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
}
@@ -5501,19 +8149,19 @@
void add_dilation_h_factor(int32_t dilation_h_factor) {
fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1);
}
- explicit DepthwiseConv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit DepthwiseConv2DOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<DepthwiseConv2DOptions> Finish() {
+ ::flatbuffers::Offset<DepthwiseConv2DOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<DepthwiseConv2DOptions>(end);
+ auto o = ::flatbuffers::Offset<DepthwiseConv2DOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<DepthwiseConv2DOptions> CreateDepthwiseConv2DOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<DepthwiseConv2DOptions> CreateDepthwiseConv2DOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::Padding padding = tflite::Padding_SAME,
int32_t stride_w = 0,
int32_t stride_h = 0,
@@ -5532,16 +8180,16 @@
return builder_.Finish();
}
-flatbuffers::Offset<DepthwiseConv2DOptions> CreateDepthwiseConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<DepthwiseConv2DOptions> CreateDepthwiseConv2DOptions(::flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ConcatEmbeddingsOptionsT : public flatbuffers::NativeTable {
+struct ConcatEmbeddingsOptionsT : public ::flatbuffers::NativeTable {
typedef ConcatEmbeddingsOptions TableType;
int32_t num_channels = 0;
std::vector<int32_t> num_columns_per_channel{};
std::vector<int32_t> embedding_dim_per_channel{};
};
-struct ConcatEmbeddingsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct ConcatEmbeddingsOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ConcatEmbeddingsOptionsT NativeTableType;
typedef ConcatEmbeddingsOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -5552,13 +8200,13 @@
int32_t num_channels() const {
return GetField<int32_t>(VT_NUM_CHANNELS, 0);
}
- const flatbuffers::Vector<int32_t> *num_columns_per_channel() const {
- return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_NUM_COLUMNS_PER_CHANNEL);
+ const ::flatbuffers::Vector<int32_t> *num_columns_per_channel() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_NUM_COLUMNS_PER_CHANNEL);
}
- const flatbuffers::Vector<int32_t> *embedding_dim_per_channel() const {
- return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_EMBEDDING_DIM_PER_CHANNEL);
+ const ::flatbuffers::Vector<int32_t> *embedding_dim_per_channel() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_EMBEDDING_DIM_PER_CHANNEL);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_NUM_CHANNELS, 4) &&
VerifyOffset(verifier, VT_NUM_COLUMNS_PER_CHANNEL) &&
@@ -5567,40 +8215,40 @@
verifier.VerifyVector(embedding_dim_per_channel()) &&
verifier.EndTable();
}
- ConcatEmbeddingsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ConcatEmbeddingsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<ConcatEmbeddingsOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ConcatEmbeddingsOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ConcatEmbeddingsOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ConcatEmbeddingsOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ConcatEmbeddingsOptionsBuilder {
typedef ConcatEmbeddingsOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_num_channels(int32_t num_channels) {
fbb_.AddElement<int32_t>(ConcatEmbeddingsOptions::VT_NUM_CHANNELS, num_channels, 0);
}
- void add_num_columns_per_channel(flatbuffers::Offset<flatbuffers::Vector<int32_t>> num_columns_per_channel) {
+ void add_num_columns_per_channel(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> num_columns_per_channel) {
fbb_.AddOffset(ConcatEmbeddingsOptions::VT_NUM_COLUMNS_PER_CHANNEL, num_columns_per_channel);
}
- void add_embedding_dim_per_channel(flatbuffers::Offset<flatbuffers::Vector<int32_t>> embedding_dim_per_channel) {
+ void add_embedding_dim_per_channel(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> embedding_dim_per_channel) {
fbb_.AddOffset(ConcatEmbeddingsOptions::VT_EMBEDDING_DIM_PER_CHANNEL, embedding_dim_per_channel);
}
- explicit ConcatEmbeddingsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit ConcatEmbeddingsOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<ConcatEmbeddingsOptions> Finish() {
+ ::flatbuffers::Offset<ConcatEmbeddingsOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<ConcatEmbeddingsOptions>(end);
+ auto o = ::flatbuffers::Offset<ConcatEmbeddingsOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<ConcatEmbeddingsOptions> CreateConcatEmbeddingsOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<ConcatEmbeddingsOptions> CreateConcatEmbeddingsOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t num_channels = 0,
- flatbuffers::Offset<flatbuffers::Vector<int32_t>> num_columns_per_channel = 0,
- flatbuffers::Offset<flatbuffers::Vector<int32_t>> embedding_dim_per_channel = 0) {
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> num_columns_per_channel = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> embedding_dim_per_channel = 0) {
ConcatEmbeddingsOptionsBuilder builder_(_fbb);
builder_.add_embedding_dim_per_channel(embedding_dim_per_channel);
builder_.add_num_columns_per_channel(num_columns_per_channel);
@@ -5608,8 +8256,8 @@
return builder_.Finish();
}
-inline flatbuffers::Offset<ConcatEmbeddingsOptions> CreateConcatEmbeddingsOptionsDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<ConcatEmbeddingsOptions> CreateConcatEmbeddingsOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t num_channels = 0,
const std::vector<int32_t> *num_columns_per_channel = nullptr,
const std::vector<int32_t> *embedding_dim_per_channel = nullptr) {
@@ -5622,14 +8270,14 @@
embedding_dim_per_channel__);
}
-flatbuffers::Offset<ConcatEmbeddingsOptions> CreateConcatEmbeddingsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<ConcatEmbeddingsOptions> CreateConcatEmbeddingsOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct LSHProjectionOptionsT : public flatbuffers::NativeTable {
+struct LSHProjectionOptionsT : public ::flatbuffers::NativeTable {
typedef LSHProjectionOptions TableType;
tflite::LSHProjectionType type = tflite::LSHProjectionType_UNKNOWN;
};
-struct LSHProjectionOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct LSHProjectionOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef LSHProjectionOptionsT NativeTableType;
typedef LSHProjectionOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -5638,52 +8286,52 @@
tflite::LSHProjectionType type() const {
return static_cast<tflite::LSHProjectionType>(GetField<int8_t>(VT_TYPE, 0));
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_TYPE, 1) &&
verifier.EndTable();
}
- LSHProjectionOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(LSHProjectionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<LSHProjectionOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ LSHProjectionOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(LSHProjectionOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<LSHProjectionOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct LSHProjectionOptionsBuilder {
typedef LSHProjectionOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_type(tflite::LSHProjectionType type) {
fbb_.AddElement<int8_t>(LSHProjectionOptions::VT_TYPE, static_cast<int8_t>(type), 0);
}
- explicit LSHProjectionOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit LSHProjectionOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<LSHProjectionOptions> Finish() {
+ ::flatbuffers::Offset<LSHProjectionOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<LSHProjectionOptions>(end);
+ auto o = ::flatbuffers::Offset<LSHProjectionOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<LSHProjectionOptions> CreateLSHProjectionOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<LSHProjectionOptions> CreateLSHProjectionOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::LSHProjectionType type = tflite::LSHProjectionType_UNKNOWN) {
LSHProjectionOptionsBuilder builder_(_fbb);
builder_.add_type(type);
return builder_.Finish();
}
-flatbuffers::Offset<LSHProjectionOptions> CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<LSHProjectionOptions> CreateLSHProjectionOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SVDFOptionsT : public flatbuffers::NativeTable {
+struct SVDFOptionsT : public ::flatbuffers::NativeTable {
typedef SVDFOptions TableType;
int32_t rank = 0;
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
bool asymmetric_quantize_inputs = false;
};
-struct SVDFOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SVDFOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SVDFOptionsT NativeTableType;
typedef SVDFOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -5700,22 +8348,22 @@
bool asymmetric_quantize_inputs() const {
return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_RANK, 4) &&
VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) &&
verifier.EndTable();
}
- SVDFOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SVDFOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SVDFOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SVDFOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SVDFOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SVDFOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SVDFOptionsBuilder {
typedef SVDFOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_rank(int32_t rank) {
fbb_.AddElement<int32_t>(SVDFOptions::VT_RANK, rank, 0);
}
@@ -5725,19 +8373,19 @@
void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) {
fbb_.AddElement<uint8_t>(SVDFOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast<uint8_t>(asymmetric_quantize_inputs), 0);
}
- explicit SVDFOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit SVDFOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SVDFOptions> Finish() {
+ ::flatbuffers::Offset<SVDFOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SVDFOptions>(end);
+ auto o = ::flatbuffers::Offset<SVDFOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<SVDFOptions> CreateSVDFOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<SVDFOptions> CreateSVDFOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t rank = 0,
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
bool asymmetric_quantize_inputs = false) {
@@ -5748,15 +8396,15 @@
return builder_.Finish();
}
-flatbuffers::Offset<SVDFOptions> CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SVDFOptions> CreateSVDFOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct RNNOptionsT : public flatbuffers::NativeTable {
+struct RNNOptionsT : public ::flatbuffers::NativeTable {
typedef RNNOptions TableType;
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
bool asymmetric_quantize_inputs = false;
};
-struct RNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct RNNOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef RNNOptionsT NativeTableType;
typedef RNNOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -5769,40 +8417,40 @@
bool asymmetric_quantize_inputs() const {
return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) &&
verifier.EndTable();
}
- RNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(RNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<RNNOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ RNNOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(RNNOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<RNNOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct RNNOptionsBuilder {
typedef RNNOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
fbb_.AddElement<int8_t>(RNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
}
void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) {
fbb_.AddElement<uint8_t>(RNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast<uint8_t>(asymmetric_quantize_inputs), 0);
}
- explicit RNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit RNNOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<RNNOptions> Finish() {
+ ::flatbuffers::Offset<RNNOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<RNNOptions>(end);
+ auto o = ::flatbuffers::Offset<RNNOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<RNNOptions> CreateRNNOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<RNNOptions> CreateRNNOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
bool asymmetric_quantize_inputs = false) {
RNNOptionsBuilder builder_(_fbb);
@@ -5811,16 +8459,16 @@
return builder_.Finish();
}
-flatbuffers::Offset<RNNOptions> CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<RNNOptions> CreateRNNOptions(::flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SequenceRNNOptionsT : public flatbuffers::NativeTable {
+struct SequenceRNNOptionsT : public ::flatbuffers::NativeTable {
typedef SequenceRNNOptions TableType;
bool time_major = false;
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
bool asymmetric_quantize_inputs = false;
};
-struct SequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SequenceRNNOptionsT NativeTableType;
typedef SequenceRNNOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -5837,22 +8485,22 @@
bool asymmetric_quantize_inputs() const {
return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<uint8_t>(verifier, VT_TIME_MAJOR, 1) &&
VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) &&
verifier.EndTable();
}
- SequenceRNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SequenceRNNOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SequenceRNNOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SequenceRNNOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SequenceRNNOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SequenceRNNOptionsBuilder {
typedef SequenceRNNOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_time_major(bool time_major) {
fbb_.AddElement<uint8_t>(SequenceRNNOptions::VT_TIME_MAJOR, static_cast<uint8_t>(time_major), 0);
}
@@ -5862,19 +8510,19 @@
void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) {
fbb_.AddElement<uint8_t>(SequenceRNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast<uint8_t>(asymmetric_quantize_inputs), 0);
}
- explicit SequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit SequenceRNNOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SequenceRNNOptions> Finish() {
+ ::flatbuffers::Offset<SequenceRNNOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SequenceRNNOptions>(end);
+ auto o = ::flatbuffers::Offset<SequenceRNNOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<SequenceRNNOptions> CreateSequenceRNNOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<SequenceRNNOptions> CreateSequenceRNNOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
bool time_major = false,
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
bool asymmetric_quantize_inputs = false) {
@@ -5885,9 +8533,9 @@
return builder_.Finish();
}
-flatbuffers::Offset<SequenceRNNOptions> CreateSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SequenceRNNOptions> CreateSequenceRNNOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct BidirectionalSequenceRNNOptionsT : public flatbuffers::NativeTable {
+struct BidirectionalSequenceRNNOptionsT : public ::flatbuffers::NativeTable {
typedef BidirectionalSequenceRNNOptions TableType;
bool time_major = false;
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
@@ -5895,7 +8543,7 @@
bool asymmetric_quantize_inputs = false;
};
-struct BidirectionalSequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct BidirectionalSequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef BidirectionalSequenceRNNOptionsT NativeTableType;
typedef BidirectionalSequenceRNNOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -5916,7 +8564,7 @@
bool asymmetric_quantize_inputs() const {
return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<uint8_t>(verifier, VT_TIME_MAJOR, 1) &&
VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
@@ -5924,15 +8572,15 @@
VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) &&
verifier.EndTable();
}
- BidirectionalSequenceRNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<BidirectionalSequenceRNNOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ BidirectionalSequenceRNNOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(BidirectionalSequenceRNNOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<BidirectionalSequenceRNNOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct BidirectionalSequenceRNNOptionsBuilder {
typedef BidirectionalSequenceRNNOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_time_major(bool time_major) {
fbb_.AddElement<uint8_t>(BidirectionalSequenceRNNOptions::VT_TIME_MAJOR, static_cast<uint8_t>(time_major), 0);
}
@@ -5945,19 +8593,19 @@
void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) {
fbb_.AddElement<uint8_t>(BidirectionalSequenceRNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast<uint8_t>(asymmetric_quantize_inputs), 0);
}
- explicit BidirectionalSequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit BidirectionalSequenceRNNOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<BidirectionalSequenceRNNOptions> Finish() {
+ ::flatbuffers::Offset<BidirectionalSequenceRNNOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<BidirectionalSequenceRNNOptions>(end);
+ auto o = ::flatbuffers::Offset<BidirectionalSequenceRNNOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<BidirectionalSequenceRNNOptions> CreateBidirectionalSequenceRNNOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<BidirectionalSequenceRNNOptions> CreateBidirectionalSequenceRNNOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
bool time_major = false,
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
bool merge_outputs = false,
@@ -5970,24 +8618,26 @@
return builder_.Finish();
}
-flatbuffers::Offset<BidirectionalSequenceRNNOptions> CreateBidirectionalSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<BidirectionalSequenceRNNOptions> CreateBidirectionalSequenceRNNOptions(::flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct FullyConnectedOptionsT : public flatbuffers::NativeTable {
+struct FullyConnectedOptionsT : public ::flatbuffers::NativeTable {
typedef FullyConnectedOptions TableType;
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
tflite::FullyConnectedOptionsWeightsFormat weights_format = tflite::FullyConnectedOptionsWeightsFormat_DEFAULT;
bool keep_num_dims = false;
bool asymmetric_quantize_inputs = false;
+ tflite::TensorType quantized_bias_type = tflite::TensorType_FLOAT32;
};
-struct FullyConnectedOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct FullyConnectedOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef FullyConnectedOptionsT NativeTableType;
typedef FullyConnectedOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
VT_FUSED_ACTIVATION_FUNCTION = 4,
VT_WEIGHTS_FORMAT = 6,
VT_KEEP_NUM_DIMS = 8,
- VT_ASYMMETRIC_QUANTIZE_INPUTS = 10
+ VT_ASYMMETRIC_QUANTIZE_INPUTS = 10,
+ VT_QUANTIZED_BIAS_TYPE = 12
};
tflite::ActivationFunctionType fused_activation_function() const {
return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
@@ -6001,23 +8651,27 @@
bool asymmetric_quantize_inputs() const {
return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ tflite::TensorType quantized_bias_type() const {
+ return static_cast<tflite::TensorType>(GetField<int8_t>(VT_QUANTIZED_BIAS_TYPE, 0));
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
VerifyField<int8_t>(verifier, VT_WEIGHTS_FORMAT, 1) &&
VerifyField<uint8_t>(verifier, VT_KEEP_NUM_DIMS, 1) &&
VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) &&
+ VerifyField<int8_t>(verifier, VT_QUANTIZED_BIAS_TYPE, 1) &&
verifier.EndTable();
}
- FullyConnectedOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(FullyConnectedOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<FullyConnectedOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ FullyConnectedOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(FullyConnectedOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<FullyConnectedOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct FullyConnectedOptionsBuilder {
typedef FullyConnectedOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
fbb_.AddElement<int8_t>(FullyConnectedOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
}
@@ -6030,24 +8684,29 @@
void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) {
fbb_.AddElement<uint8_t>(FullyConnectedOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast<uint8_t>(asymmetric_quantize_inputs), 0);
}
- explicit FullyConnectedOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ void add_quantized_bias_type(tflite::TensorType quantized_bias_type) {
+ fbb_.AddElement<int8_t>(FullyConnectedOptions::VT_QUANTIZED_BIAS_TYPE, static_cast<int8_t>(quantized_bias_type), 0);
+ }
+ explicit FullyConnectedOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<FullyConnectedOptions> Finish() {
+ ::flatbuffers::Offset<FullyConnectedOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<FullyConnectedOptions>(end);
+ auto o = ::flatbuffers::Offset<FullyConnectedOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<FullyConnectedOptions> CreateFullyConnectedOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<FullyConnectedOptions> CreateFullyConnectedOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
tflite::FullyConnectedOptionsWeightsFormat weights_format = tflite::FullyConnectedOptionsWeightsFormat_DEFAULT,
bool keep_num_dims = false,
- bool asymmetric_quantize_inputs = false) {
+ bool asymmetric_quantize_inputs = false,
+ tflite::TensorType quantized_bias_type = tflite::TensorType_FLOAT32) {
FullyConnectedOptionsBuilder builder_(_fbb);
+ builder_.add_quantized_bias_type(quantized_bias_type);
builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs);
builder_.add_keep_num_dims(keep_num_dims);
builder_.add_weights_format(weights_format);
@@ -6055,14 +8714,14 @@
return builder_.Finish();
}
-flatbuffers::Offset<FullyConnectedOptions> CreateFullyConnectedOptions(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<FullyConnectedOptions> CreateFullyConnectedOptions(::flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SoftmaxOptionsT : public flatbuffers::NativeTable {
+struct SoftmaxOptionsT : public ::flatbuffers::NativeTable {
typedef SoftmaxOptions TableType;
float beta = 0.0f;
};
-struct SoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SoftmaxOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SoftmaxOptionsT NativeTableType;
typedef SoftmaxOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -6071,51 +8730,51 @@
float beta() const {
return GetField<float>(VT_BETA, 0.0f);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<float>(verifier, VT_BETA, 4) &&
verifier.EndTable();
}
- SoftmaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SoftmaxOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SoftmaxOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SoftmaxOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SoftmaxOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SoftmaxOptionsBuilder {
typedef SoftmaxOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_beta(float beta) {
fbb_.AddElement<float>(SoftmaxOptions::VT_BETA, beta, 0.0f);
}
- explicit SoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit SoftmaxOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SoftmaxOptions> Finish() {
+ ::flatbuffers::Offset<SoftmaxOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SoftmaxOptions>(end);
+ auto o = ::flatbuffers::Offset<SoftmaxOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<SoftmaxOptions> CreateSoftmaxOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<SoftmaxOptions> CreateSoftmaxOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
float beta = 0.0f) {
SoftmaxOptionsBuilder builder_(_fbb);
builder_.add_beta(beta);
return builder_.Finish();
}
-flatbuffers::Offset<SoftmaxOptions> CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SoftmaxOptions> CreateSoftmaxOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ConcatenationOptionsT : public flatbuffers::NativeTable {
+struct ConcatenationOptionsT : public ::flatbuffers::NativeTable {
typedef ConcatenationOptions TableType;
int32_t axis = 0;
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
};
-struct ConcatenationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct ConcatenationOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ConcatenationOptionsT NativeTableType;
typedef ConcatenationOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -6128,40 +8787,40 @@
tflite::ActivationFunctionType fused_activation_function() const {
return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_AXIS, 4) &&
VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
verifier.EndTable();
}
- ConcatenationOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ConcatenationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<ConcatenationOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ConcatenationOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ConcatenationOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ConcatenationOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ConcatenationOptionsBuilder {
typedef ConcatenationOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_axis(int32_t axis) {
fbb_.AddElement<int32_t>(ConcatenationOptions::VT_AXIS, axis, 0);
}
void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
fbb_.AddElement<int8_t>(ConcatenationOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
}
- explicit ConcatenationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit ConcatenationOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<ConcatenationOptions> Finish() {
+ ::flatbuffers::Offset<ConcatenationOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<ConcatenationOptions>(end);
+ auto o = ::flatbuffers::Offset<ConcatenationOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<ConcatenationOptions> CreateConcatenationOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<ConcatenationOptions> CreateConcatenationOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t axis = 0,
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) {
ConcatenationOptionsBuilder builder_(_fbb);
@@ -6170,15 +8829,15 @@
return builder_.Finish();
}
-flatbuffers::Offset<ConcatenationOptions> CreateConcatenationOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<ConcatenationOptions> CreateConcatenationOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct AddOptionsT : public flatbuffers::NativeTable {
+struct AddOptionsT : public ::flatbuffers::NativeTable {
typedef AddOptions TableType;
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
bool pot_scale_int16 = true;
};
-struct AddOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct AddOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef AddOptionsT NativeTableType;
typedef AddOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -6191,40 +8850,40 @@
bool pot_scale_int16() const {
return GetField<uint8_t>(VT_POT_SCALE_INT16, 1) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
VerifyField<uint8_t>(verifier, VT_POT_SCALE_INT16, 1) &&
verifier.EndTable();
}
- AddOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(AddOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<AddOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ AddOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(AddOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<AddOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct AddOptionsBuilder {
typedef AddOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
fbb_.AddElement<int8_t>(AddOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
}
void add_pot_scale_int16(bool pot_scale_int16) {
fbb_.AddElement<uint8_t>(AddOptions::VT_POT_SCALE_INT16, static_cast<uint8_t>(pot_scale_int16), 1);
}
- explicit AddOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit AddOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<AddOptions> Finish() {
+ ::flatbuffers::Offset<AddOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<AddOptions>(end);
+ auto o = ::flatbuffers::Offset<AddOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<AddOptions> CreateAddOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<AddOptions> CreateAddOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
bool pot_scale_int16 = true) {
AddOptionsBuilder builder_(_fbb);
@@ -6233,14 +8892,14 @@
return builder_.Finish();
}
-flatbuffers::Offset<AddOptions> CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<AddOptions> CreateAddOptions(::flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct MulOptionsT : public flatbuffers::NativeTable {
+struct MulOptionsT : public ::flatbuffers::NativeTable {
typedef MulOptions TableType;
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
};
-struct MulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct MulOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef MulOptionsT NativeTableType;
typedef MulOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -6249,50 +8908,50 @@
tflite::ActivationFunctionType fused_activation_function() const {
return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
verifier.EndTable();
}
- MulOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(MulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<MulOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ MulOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(MulOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<MulOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct MulOptionsBuilder {
typedef MulOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
fbb_.AddElement<int8_t>(MulOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
}
- explicit MulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit MulOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<MulOptions> Finish() {
+ ::flatbuffers::Offset<MulOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<MulOptions>(end);
+ auto o = ::flatbuffers::Offset<MulOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<MulOptions> CreateMulOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<MulOptions> CreateMulOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) {
MulOptionsBuilder builder_(_fbb);
builder_.add_fused_activation_function(fused_activation_function);
return builder_.Finish();
}
-flatbuffers::Offset<MulOptions> CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<MulOptions> CreateMulOptions(::flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct L2NormOptionsT : public flatbuffers::NativeTable {
+struct L2NormOptionsT : public ::flatbuffers::NativeTable {
typedef L2NormOptions TableType;
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
};
-struct L2NormOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct L2NormOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef L2NormOptionsT NativeTableType;
typedef L2NormOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -6301,45 +8960,45 @@
tflite::ActivationFunctionType fused_activation_function() const {
return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
verifier.EndTable();
}
- L2NormOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(L2NormOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<L2NormOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ L2NormOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(L2NormOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<L2NormOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct L2NormOptionsBuilder {
typedef L2NormOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
fbb_.AddElement<int8_t>(L2NormOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
}
- explicit L2NormOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit L2NormOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<L2NormOptions> Finish() {
+ ::flatbuffers::Offset<L2NormOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<L2NormOptions>(end);
+ auto o = ::flatbuffers::Offset<L2NormOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<L2NormOptions> CreateL2NormOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<L2NormOptions> CreateL2NormOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) {
L2NormOptionsBuilder builder_(_fbb);
builder_.add_fused_activation_function(fused_activation_function);
return builder_.Finish();
}
-flatbuffers::Offset<L2NormOptions> CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<L2NormOptions> CreateL2NormOptions(::flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct LocalResponseNormalizationOptionsT : public flatbuffers::NativeTable {
+struct LocalResponseNormalizationOptionsT : public ::flatbuffers::NativeTable {
typedef LocalResponseNormalizationOptions TableType;
int32_t radius = 0;
float bias = 0.0f;
@@ -6347,7 +9006,7 @@
float beta = 0.0f;
};
-struct LocalResponseNormalizationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct LocalResponseNormalizationOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef LocalResponseNormalizationOptionsT NativeTableType;
typedef LocalResponseNormalizationOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -6368,7 +9027,7 @@
float beta() const {
return GetField<float>(VT_BETA, 0.0f);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_RADIUS, 4) &&
VerifyField<float>(verifier, VT_BIAS, 4) &&
@@ -6376,15 +9035,15 @@
VerifyField<float>(verifier, VT_BETA, 4) &&
verifier.EndTable();
}
- LocalResponseNormalizationOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(LocalResponseNormalizationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<LocalResponseNormalizationOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ LocalResponseNormalizationOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(LocalResponseNormalizationOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<LocalResponseNormalizationOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct LocalResponseNormalizationOptionsBuilder {
typedef LocalResponseNormalizationOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_radius(int32_t radius) {
fbb_.AddElement<int32_t>(LocalResponseNormalizationOptions::VT_RADIUS, radius, 0);
}
@@ -6397,19 +9056,19 @@
void add_beta(float beta) {
fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_BETA, beta, 0.0f);
}
- explicit LocalResponseNormalizationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit LocalResponseNormalizationOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<LocalResponseNormalizationOptions> Finish() {
+ ::flatbuffers::Offset<LocalResponseNormalizationOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<LocalResponseNormalizationOptions>(end);
+ auto o = ::flatbuffers::Offset<LocalResponseNormalizationOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<LocalResponseNormalizationOptions> CreateLocalResponseNormalizationOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<LocalResponseNormalizationOptions> CreateLocalResponseNormalizationOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t radius = 0,
float bias = 0.0f,
float alpha = 0.0f,
@@ -6422,9 +9081,9 @@
return builder_.Finish();
}
-flatbuffers::Offset<LocalResponseNormalizationOptions> CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<LocalResponseNormalizationOptions> CreateLocalResponseNormalizationOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct LSTMOptionsT : public flatbuffers::NativeTable {
+struct LSTMOptionsT : public ::flatbuffers::NativeTable {
typedef LSTMOptions TableType;
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
float cell_clip = 0.0f;
@@ -6433,7 +9092,7 @@
bool asymmetric_quantize_inputs = false;
};
-struct LSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct LSTMOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef LSTMOptionsT NativeTableType;
typedef LSTMOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -6458,7 +9117,7 @@
bool asymmetric_quantize_inputs() const {
return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
VerifyField<float>(verifier, VT_CELL_CLIP, 4) &&
@@ -6467,15 +9126,15 @@
VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) &&
verifier.EndTable();
}
- LSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(LSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<LSTMOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ LSTMOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(LSTMOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<LSTMOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct LSTMOptionsBuilder {
typedef LSTMOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
fbb_.AddElement<int8_t>(LSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
}
@@ -6491,19 +9150,19 @@
void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) {
fbb_.AddElement<uint8_t>(LSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast<uint8_t>(asymmetric_quantize_inputs), 0);
}
- explicit LSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit LSTMOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<LSTMOptions> Finish() {
+ ::flatbuffers::Offset<LSTMOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<LSTMOptions>(end);
+ auto o = ::flatbuffers::Offset<LSTMOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<LSTMOptions> CreateLSTMOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<LSTMOptions> CreateLSTMOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
float cell_clip = 0.0f,
float proj_clip = 0.0f,
@@ -6518,9 +9177,9 @@
return builder_.Finish();
}
-flatbuffers::Offset<LSTMOptions> CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<LSTMOptions> CreateLSTMOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct UnidirectionalSequenceLSTMOptionsT : public flatbuffers::NativeTable {
+struct UnidirectionalSequenceLSTMOptionsT : public ::flatbuffers::NativeTable {
typedef UnidirectionalSequenceLSTMOptions TableType;
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
float cell_clip = 0.0f;
@@ -6530,7 +9189,7 @@
bool diagonal_recurrent_tensors = false;
};
-struct UnidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct UnidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef UnidirectionalSequenceLSTMOptionsT NativeTableType;
typedef UnidirectionalSequenceLSTMOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -6559,7 +9218,7 @@
bool diagonal_recurrent_tensors() const {
return GetField<uint8_t>(VT_DIAGONAL_RECURRENT_TENSORS, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
VerifyField<float>(verifier, VT_CELL_CLIP, 4) &&
@@ -6569,15 +9228,15 @@
VerifyField<uint8_t>(verifier, VT_DIAGONAL_RECURRENT_TENSORS, 1) &&
verifier.EndTable();
}
- UnidirectionalSequenceLSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ UnidirectionalSequenceLSTMOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(UnidirectionalSequenceLSTMOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct UnidirectionalSequenceLSTMOptionsBuilder {
typedef UnidirectionalSequenceLSTMOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
fbb_.AddElement<int8_t>(UnidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
}
@@ -6596,19 +9255,19 @@
void add_diagonal_recurrent_tensors(bool diagonal_recurrent_tensors) {
fbb_.AddElement<uint8_t>(UnidirectionalSequenceLSTMOptions::VT_DIAGONAL_RECURRENT_TENSORS, static_cast<uint8_t>(diagonal_recurrent_tensors), 0);
}
- explicit UnidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit UnidirectionalSequenceLSTMOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> Finish() {
+ ::flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<UnidirectionalSequenceLSTMOptions>(end);
+ auto o = ::flatbuffers::Offset<UnidirectionalSequenceLSTMOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> CreateUnidirectionalSequenceLSTMOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> CreateUnidirectionalSequenceLSTMOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
float cell_clip = 0.0f,
float proj_clip = 0.0f,
@@ -6625,9 +9284,9 @@
return builder_.Finish();
}
-flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> CreateUnidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> CreateUnidirectionalSequenceLSTMOptions(::flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct BidirectionalSequenceLSTMOptionsT : public flatbuffers::NativeTable {
+struct BidirectionalSequenceLSTMOptionsT : public ::flatbuffers::NativeTable {
typedef BidirectionalSequenceLSTMOptions TableType;
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
float cell_clip = 0.0f;
@@ -6637,7 +9296,7 @@
bool asymmetric_quantize_inputs = false;
};
-struct BidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct BidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef BidirectionalSequenceLSTMOptionsT NativeTableType;
typedef BidirectionalSequenceLSTMOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -6666,7 +9325,7 @@
bool asymmetric_quantize_inputs() const {
return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
VerifyField<float>(verifier, VT_CELL_CLIP, 4) &&
@@ -6676,15 +9335,15 @@
VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) &&
verifier.EndTable();
}
- BidirectionalSequenceLSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<BidirectionalSequenceLSTMOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ BidirectionalSequenceLSTMOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(BidirectionalSequenceLSTMOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<BidirectionalSequenceLSTMOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct BidirectionalSequenceLSTMOptionsBuilder {
typedef BidirectionalSequenceLSTMOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
fbb_.AddElement<int8_t>(BidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
}
@@ -6703,19 +9362,19 @@
void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) {
fbb_.AddElement<uint8_t>(BidirectionalSequenceLSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast<uint8_t>(asymmetric_quantize_inputs), 0);
}
- explicit BidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit BidirectionalSequenceLSTMOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<BidirectionalSequenceLSTMOptions> Finish() {
+ ::flatbuffers::Offset<BidirectionalSequenceLSTMOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<BidirectionalSequenceLSTMOptions>(end);
+ auto o = ::flatbuffers::Offset<BidirectionalSequenceLSTMOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<BidirectionalSequenceLSTMOptions> CreateBidirectionalSequenceLSTMOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<BidirectionalSequenceLSTMOptions> CreateBidirectionalSequenceLSTMOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
float cell_clip = 0.0f,
float proj_clip = 0.0f,
@@ -6732,15 +9391,15 @@
return builder_.Finish();
}
-flatbuffers::Offset<BidirectionalSequenceLSTMOptions> CreateBidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<BidirectionalSequenceLSTMOptions> CreateBidirectionalSequenceLSTMOptions(::flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ResizeBilinearOptionsT : public flatbuffers::NativeTable {
+struct ResizeBilinearOptionsT : public ::flatbuffers::NativeTable {
typedef ResizeBilinearOptions TableType;
bool align_corners = false;
bool half_pixel_centers = false;
};
-struct ResizeBilinearOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct ResizeBilinearOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ResizeBilinearOptionsT NativeTableType;
typedef ResizeBilinearOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -6753,40 +9412,40 @@
bool half_pixel_centers() const {
return GetField<uint8_t>(VT_HALF_PIXEL_CENTERS, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<uint8_t>(verifier, VT_ALIGN_CORNERS, 1) &&
VerifyField<uint8_t>(verifier, VT_HALF_PIXEL_CENTERS, 1) &&
verifier.EndTable();
}
- ResizeBilinearOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ResizeBilinearOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<ResizeBilinearOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ResizeBilinearOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ResizeBilinearOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ResizeBilinearOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ResizeBilinearOptionsBuilder {
typedef ResizeBilinearOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_align_corners(bool align_corners) {
fbb_.AddElement<uint8_t>(ResizeBilinearOptions::VT_ALIGN_CORNERS, static_cast<uint8_t>(align_corners), 0);
}
void add_half_pixel_centers(bool half_pixel_centers) {
fbb_.AddElement<uint8_t>(ResizeBilinearOptions::VT_HALF_PIXEL_CENTERS, static_cast<uint8_t>(half_pixel_centers), 0);
}
- explicit ResizeBilinearOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit ResizeBilinearOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<ResizeBilinearOptions> Finish() {
+ ::flatbuffers::Offset<ResizeBilinearOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<ResizeBilinearOptions>(end);
+ auto o = ::flatbuffers::Offset<ResizeBilinearOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<ResizeBilinearOptions> CreateResizeBilinearOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<ResizeBilinearOptions> CreateResizeBilinearOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
bool align_corners = false,
bool half_pixel_centers = false) {
ResizeBilinearOptionsBuilder builder_(_fbb);
@@ -6795,15 +9454,15 @@
return builder_.Finish();
}
-flatbuffers::Offset<ResizeBilinearOptions> CreateResizeBilinearOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<ResizeBilinearOptions> CreateResizeBilinearOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ResizeNearestNeighborOptionsT : public flatbuffers::NativeTable {
+struct ResizeNearestNeighborOptionsT : public ::flatbuffers::NativeTable {
typedef ResizeNearestNeighborOptions TableType;
bool align_corners = false;
bool half_pixel_centers = false;
};
-struct ResizeNearestNeighborOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct ResizeNearestNeighborOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ResizeNearestNeighborOptionsT NativeTableType;
typedef ResizeNearestNeighborOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -6816,40 +9475,40 @@
bool half_pixel_centers() const {
return GetField<uint8_t>(VT_HALF_PIXEL_CENTERS, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<uint8_t>(verifier, VT_ALIGN_CORNERS, 1) &&
VerifyField<uint8_t>(verifier, VT_HALF_PIXEL_CENTERS, 1) &&
verifier.EndTable();
}
- ResizeNearestNeighborOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ResizeNearestNeighborOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<ResizeNearestNeighborOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ResizeNearestNeighborOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ResizeNearestNeighborOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ResizeNearestNeighborOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ResizeNearestNeighborOptionsBuilder {
typedef ResizeNearestNeighborOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_align_corners(bool align_corners) {
fbb_.AddElement<uint8_t>(ResizeNearestNeighborOptions::VT_ALIGN_CORNERS, static_cast<uint8_t>(align_corners), 0);
}
void add_half_pixel_centers(bool half_pixel_centers) {
fbb_.AddElement<uint8_t>(ResizeNearestNeighborOptions::VT_HALF_PIXEL_CENTERS, static_cast<uint8_t>(half_pixel_centers), 0);
}
- explicit ResizeNearestNeighborOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit ResizeNearestNeighborOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<ResizeNearestNeighborOptions> Finish() {
+ ::flatbuffers::Offset<ResizeNearestNeighborOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<ResizeNearestNeighborOptions>(end);
+ auto o = ::flatbuffers::Offset<ResizeNearestNeighborOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<ResizeNearestNeighborOptions> CreateResizeNearestNeighborOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<ResizeNearestNeighborOptions> CreateResizeNearestNeighborOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
bool align_corners = false,
bool half_pixel_centers = false) {
ResizeNearestNeighborOptionsBuilder builder_(_fbb);
@@ -6858,14 +9517,14 @@
return builder_.Finish();
}
-flatbuffers::Offset<ResizeNearestNeighborOptions> CreateResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<ResizeNearestNeighborOptions> CreateResizeNearestNeighborOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct CallOptionsT : public flatbuffers::NativeTable {
+struct CallOptionsT : public ::flatbuffers::NativeTable {
typedef CallOptions TableType;
uint32_t subgraph = 0;
};
-struct CallOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct CallOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef CallOptionsT NativeTableType;
typedef CallOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -6874,175 +9533,175 @@
uint32_t subgraph() const {
return GetField<uint32_t>(VT_SUBGRAPH, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<uint32_t>(verifier, VT_SUBGRAPH, 4) &&
verifier.EndTable();
}
- CallOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(CallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<CallOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ CallOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(CallOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<CallOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct CallOptionsBuilder {
typedef CallOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_subgraph(uint32_t subgraph) {
fbb_.AddElement<uint32_t>(CallOptions::VT_SUBGRAPH, subgraph, 0);
}
- explicit CallOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit CallOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<CallOptions> Finish() {
+ ::flatbuffers::Offset<CallOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<CallOptions>(end);
+ auto o = ::flatbuffers::Offset<CallOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<CallOptions> CreateCallOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<CallOptions> CreateCallOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
uint32_t subgraph = 0) {
CallOptionsBuilder builder_(_fbb);
builder_.add_subgraph(subgraph);
return builder_.Finish();
}
-flatbuffers::Offset<CallOptions> CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<CallOptions> CreateCallOptions(::flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct PadOptionsT : public flatbuffers::NativeTable {
+struct PadOptionsT : public ::flatbuffers::NativeTable {
typedef PadOptions TableType;
};
-struct PadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct PadOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef PadOptionsT NativeTableType;
typedef PadOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- PadOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(PadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<PadOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ PadOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(PadOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<PadOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct PadOptionsBuilder {
typedef PadOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit PadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit PadOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<PadOptions> Finish() {
+ ::flatbuffers::Offset<PadOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<PadOptions>(end);
+ auto o = ::flatbuffers::Offset<PadOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<PadOptions> CreatePadOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<PadOptions> CreatePadOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
PadOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<PadOptions> CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<PadOptions> CreatePadOptions(::flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct PadV2OptionsT : public flatbuffers::NativeTable {
+struct PadV2OptionsT : public ::flatbuffers::NativeTable {
typedef PadV2Options TableType;
};
-struct PadV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct PadV2Options FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef PadV2OptionsT NativeTableType;
typedef PadV2OptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- PadV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(PadV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<PadV2Options> Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ PadV2OptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(PadV2OptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<PadV2Options> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct PadV2OptionsBuilder {
typedef PadV2Options Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit PadV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit PadV2OptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<PadV2Options> Finish() {
+ ::flatbuffers::Offset<PadV2Options> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<PadV2Options>(end);
+ auto o = ::flatbuffers::Offset<PadV2Options>(end);
return o;
}
};
-inline flatbuffers::Offset<PadV2Options> CreatePadV2Options(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<PadV2Options> CreatePadV2Options(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
PadV2OptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<PadV2Options> CreatePadV2Options(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<PadV2Options> CreatePadV2Options(::flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ReshapeOptionsT : public flatbuffers::NativeTable {
+struct ReshapeOptionsT : public ::flatbuffers::NativeTable {
typedef ReshapeOptions TableType;
std::vector<int32_t> new_shape{};
};
-struct ReshapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct ReshapeOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ReshapeOptionsT NativeTableType;
typedef ReshapeOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
VT_NEW_SHAPE = 4
};
- const flatbuffers::Vector<int32_t> *new_shape() const {
- return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_NEW_SHAPE);
+ const ::flatbuffers::Vector<int32_t> *new_shape() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_NEW_SHAPE);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyOffset(verifier, VT_NEW_SHAPE) &&
verifier.VerifyVector(new_shape()) &&
verifier.EndTable();
}
- ReshapeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ReshapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<ReshapeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ReshapeOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ReshapeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ReshapeOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ReshapeOptionsBuilder {
typedef ReshapeOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- void add_new_shape(flatbuffers::Offset<flatbuffers::Vector<int32_t>> new_shape) {
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_new_shape(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> new_shape) {
fbb_.AddOffset(ReshapeOptions::VT_NEW_SHAPE, new_shape);
}
- explicit ReshapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit ReshapeOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<ReshapeOptions> Finish() {
+ ::flatbuffers::Offset<ReshapeOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<ReshapeOptions>(end);
+ auto o = ::flatbuffers::Offset<ReshapeOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<ReshapeOptions> CreateReshapeOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
- flatbuffers::Offset<flatbuffers::Vector<int32_t>> new_shape = 0) {
+inline ::flatbuffers::Offset<ReshapeOptions> CreateReshapeOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> new_shape = 0) {
ReshapeOptionsBuilder builder_(_fbb);
builder_.add_new_shape(new_shape);
return builder_.Finish();
}
-inline flatbuffers::Offset<ReshapeOptions> CreateReshapeOptionsDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<ReshapeOptions> CreateReshapeOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
const std::vector<int32_t> *new_shape = nullptr) {
auto new_shape__ = new_shape ? _fbb.CreateVector<int32_t>(*new_shape) : 0;
return tflite::CreateReshapeOptions(
@@ -7050,94 +9709,94 @@
new_shape__);
}
-flatbuffers::Offset<ReshapeOptions> CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<ReshapeOptions> CreateReshapeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SpaceToBatchNDOptionsT : public flatbuffers::NativeTable {
+struct SpaceToBatchNDOptionsT : public ::flatbuffers::NativeTable {
typedef SpaceToBatchNDOptions TableType;
};
-struct SpaceToBatchNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SpaceToBatchNDOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SpaceToBatchNDOptionsT NativeTableType;
typedef SpaceToBatchNDOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- SpaceToBatchNDOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SpaceToBatchNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SpaceToBatchNDOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SpaceToBatchNDOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SpaceToBatchNDOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SpaceToBatchNDOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SpaceToBatchNDOptionsBuilder {
typedef SpaceToBatchNDOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit SpaceToBatchNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit SpaceToBatchNDOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SpaceToBatchNDOptions> Finish() {
+ ::flatbuffers::Offset<SpaceToBatchNDOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SpaceToBatchNDOptions>(end);
+ auto o = ::flatbuffers::Offset<SpaceToBatchNDOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<SpaceToBatchNDOptions> CreateSpaceToBatchNDOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<SpaceToBatchNDOptions> CreateSpaceToBatchNDOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
SpaceToBatchNDOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<SpaceToBatchNDOptions> CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SpaceToBatchNDOptions> CreateSpaceToBatchNDOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct BatchToSpaceNDOptionsT : public flatbuffers::NativeTable {
+struct BatchToSpaceNDOptionsT : public ::flatbuffers::NativeTable {
typedef BatchToSpaceNDOptions TableType;
};
-struct BatchToSpaceNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct BatchToSpaceNDOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef BatchToSpaceNDOptionsT NativeTableType;
typedef BatchToSpaceNDOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- BatchToSpaceNDOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(BatchToSpaceNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<BatchToSpaceNDOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ BatchToSpaceNDOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(BatchToSpaceNDOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<BatchToSpaceNDOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct BatchToSpaceNDOptionsBuilder {
typedef BatchToSpaceNDOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit BatchToSpaceNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit BatchToSpaceNDOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<BatchToSpaceNDOptions> Finish() {
+ ::flatbuffers::Offset<BatchToSpaceNDOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<BatchToSpaceNDOptions>(end);
+ auto o = ::flatbuffers::Offset<BatchToSpaceNDOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<BatchToSpaceNDOptions> CreateBatchToSpaceNDOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<BatchToSpaceNDOptions> CreateBatchToSpaceNDOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
BatchToSpaceNDOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<BatchToSpaceNDOptions> CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<BatchToSpaceNDOptions> CreateBatchToSpaceNDOptions(::flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SkipGramOptionsT : public flatbuffers::NativeTable {
+struct SkipGramOptionsT : public ::flatbuffers::NativeTable {
typedef SkipGramOptions TableType;
int32_t ngram_size = 0;
int32_t max_skip_size = 0;
bool include_all_ngrams = false;
};
-struct SkipGramOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SkipGramOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SkipGramOptionsT NativeTableType;
typedef SkipGramOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -7154,22 +9813,22 @@
bool include_all_ngrams() const {
return GetField<uint8_t>(VT_INCLUDE_ALL_NGRAMS, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_NGRAM_SIZE, 4) &&
VerifyField<int32_t>(verifier, VT_MAX_SKIP_SIZE, 4) &&
VerifyField<uint8_t>(verifier, VT_INCLUDE_ALL_NGRAMS, 1) &&
verifier.EndTable();
}
- SkipGramOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SkipGramOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SkipGramOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SkipGramOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SkipGramOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SkipGramOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SkipGramOptionsBuilder {
typedef SkipGramOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_ngram_size(int32_t ngram_size) {
fbb_.AddElement<int32_t>(SkipGramOptions::VT_NGRAM_SIZE, ngram_size, 0);
}
@@ -7179,19 +9838,19 @@
void add_include_all_ngrams(bool include_all_ngrams) {
fbb_.AddElement<uint8_t>(SkipGramOptions::VT_INCLUDE_ALL_NGRAMS, static_cast<uint8_t>(include_all_ngrams), 0);
}
- explicit SkipGramOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit SkipGramOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SkipGramOptions> Finish() {
+ ::flatbuffers::Offset<SkipGramOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SkipGramOptions>(end);
+ auto o = ::flatbuffers::Offset<SkipGramOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<SkipGramOptions> CreateSkipGramOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<SkipGramOptions> CreateSkipGramOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t ngram_size = 0,
int32_t max_skip_size = 0,
bool include_all_ngrams = false) {
@@ -7202,14 +9861,14 @@
return builder_.Finish();
}
-flatbuffers::Offset<SkipGramOptions> CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SkipGramOptions> CreateSkipGramOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SpaceToDepthOptionsT : public flatbuffers::NativeTable {
+struct SpaceToDepthOptionsT : public ::flatbuffers::NativeTable {
typedef SpaceToDepthOptions TableType;
int32_t block_size = 0;
};
-struct SpaceToDepthOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SpaceToDepthOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SpaceToDepthOptionsT NativeTableType;
typedef SpaceToDepthOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -7218,50 +9877,50 @@
int32_t block_size() const {
return GetField<int32_t>(VT_BLOCK_SIZE, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_BLOCK_SIZE, 4) &&
verifier.EndTable();
}
- SpaceToDepthOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SpaceToDepthOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SpaceToDepthOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SpaceToDepthOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SpaceToDepthOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SpaceToDepthOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SpaceToDepthOptionsBuilder {
typedef SpaceToDepthOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_block_size(int32_t block_size) {
fbb_.AddElement<int32_t>(SpaceToDepthOptions::VT_BLOCK_SIZE, block_size, 0);
}
- explicit SpaceToDepthOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit SpaceToDepthOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SpaceToDepthOptions> Finish() {
+ ::flatbuffers::Offset<SpaceToDepthOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SpaceToDepthOptions>(end);
+ auto o = ::flatbuffers::Offset<SpaceToDepthOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<SpaceToDepthOptions> CreateSpaceToDepthOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<SpaceToDepthOptions> CreateSpaceToDepthOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t block_size = 0) {
SpaceToDepthOptionsBuilder builder_(_fbb);
builder_.add_block_size(block_size);
return builder_.Finish();
}
-flatbuffers::Offset<SpaceToDepthOptions> CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SpaceToDepthOptions> CreateSpaceToDepthOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct DepthToSpaceOptionsT : public flatbuffers::NativeTable {
+struct DepthToSpaceOptionsT : public ::flatbuffers::NativeTable {
typedef DepthToSpaceOptions TableType;
int32_t block_size = 0;
};
-struct DepthToSpaceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct DepthToSpaceOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef DepthToSpaceOptionsT NativeTableType;
typedef DepthToSpaceOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -7270,51 +9929,51 @@
int32_t block_size() const {
return GetField<int32_t>(VT_BLOCK_SIZE, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_BLOCK_SIZE, 4) &&
verifier.EndTable();
}
- DepthToSpaceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(DepthToSpaceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<DepthToSpaceOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ DepthToSpaceOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(DepthToSpaceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<DepthToSpaceOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct DepthToSpaceOptionsBuilder {
typedef DepthToSpaceOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_block_size(int32_t block_size) {
fbb_.AddElement<int32_t>(DepthToSpaceOptions::VT_BLOCK_SIZE, block_size, 0);
}
- explicit DepthToSpaceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit DepthToSpaceOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<DepthToSpaceOptions> Finish() {
+ ::flatbuffers::Offset<DepthToSpaceOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<DepthToSpaceOptions>(end);
+ auto o = ::flatbuffers::Offset<DepthToSpaceOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<DepthToSpaceOptions> CreateDepthToSpaceOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<DepthToSpaceOptions> CreateDepthToSpaceOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t block_size = 0) {
DepthToSpaceOptionsBuilder builder_(_fbb);
builder_.add_block_size(block_size);
return builder_.Finish();
}
-flatbuffers::Offset<DepthToSpaceOptions> CreateDepthToSpaceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<DepthToSpaceOptions> CreateDepthToSpaceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SubOptionsT : public flatbuffers::NativeTable {
+struct SubOptionsT : public ::flatbuffers::NativeTable {
typedef SubOptions TableType;
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
bool pot_scale_int16 = true;
};
-struct SubOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SubOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SubOptionsT NativeTableType;
typedef SubOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -7327,40 +9986,40 @@
bool pot_scale_int16() const {
return GetField<uint8_t>(VT_POT_SCALE_INT16, 1) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
VerifyField<uint8_t>(verifier, VT_POT_SCALE_INT16, 1) &&
verifier.EndTable();
}
- SubOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SubOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SubOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SubOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SubOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SubOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SubOptionsBuilder {
typedef SubOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
fbb_.AddElement<int8_t>(SubOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
}
void add_pot_scale_int16(bool pot_scale_int16) {
fbb_.AddElement<uint8_t>(SubOptions::VT_POT_SCALE_INT16, static_cast<uint8_t>(pot_scale_int16), 1);
}
- explicit SubOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit SubOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SubOptions> Finish() {
+ ::flatbuffers::Offset<SubOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SubOptions>(end);
+ auto o = ::flatbuffers::Offset<SubOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<SubOptions> CreateSubOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<SubOptions> CreateSubOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
bool pot_scale_int16 = true) {
SubOptionsBuilder builder_(_fbb);
@@ -7369,14 +10028,14 @@
return builder_.Finish();
}
-flatbuffers::Offset<SubOptions> CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SubOptions> CreateSubOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct DivOptionsT : public flatbuffers::NativeTable {
+struct DivOptionsT : public ::flatbuffers::NativeTable {
typedef DivOptions TableType;
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
};
-struct DivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct DivOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef DivOptionsT NativeTableType;
typedef DivOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -7385,89 +10044,89 @@
tflite::ActivationFunctionType fused_activation_function() const {
return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
verifier.EndTable();
}
- DivOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(DivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<DivOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ DivOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(DivOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<DivOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct DivOptionsBuilder {
typedef DivOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
fbb_.AddElement<int8_t>(DivOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
}
- explicit DivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit DivOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<DivOptions> Finish() {
+ ::flatbuffers::Offset<DivOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<DivOptions>(end);
+ auto o = ::flatbuffers::Offset<DivOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<DivOptions> CreateDivOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<DivOptions> CreateDivOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) {
DivOptionsBuilder builder_(_fbb);
builder_.add_fused_activation_function(fused_activation_function);
return builder_.Finish();
}
-flatbuffers::Offset<DivOptions> CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<DivOptions> CreateDivOptions(::flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct TopKV2OptionsT : public flatbuffers::NativeTable {
+struct TopKV2OptionsT : public ::flatbuffers::NativeTable {
typedef TopKV2Options TableType;
};
-struct TopKV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct TopKV2Options FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef TopKV2OptionsT NativeTableType;
typedef TopKV2OptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- TopKV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(TopKV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<TopKV2Options> Pack(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ TopKV2OptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(TopKV2OptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<TopKV2Options> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct TopKV2OptionsBuilder {
typedef TopKV2Options Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit TopKV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit TopKV2OptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<TopKV2Options> Finish() {
+ ::flatbuffers::Offset<TopKV2Options> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<TopKV2Options>(end);
+ auto o = ::flatbuffers::Offset<TopKV2Options>(end);
return o;
}
};
-inline flatbuffers::Offset<TopKV2Options> CreateTopKV2Options(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<TopKV2Options> CreateTopKV2Options(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
TopKV2OptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<TopKV2Options> CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<TopKV2Options> CreateTopKV2Options(::flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct EmbeddingLookupSparseOptionsT : public flatbuffers::NativeTable {
+struct EmbeddingLookupSparseOptionsT : public ::flatbuffers::NativeTable {
typedef EmbeddingLookupSparseOptions TableType;
tflite::CombinerType combiner = tflite::CombinerType_SUM;
};
-struct EmbeddingLookupSparseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct EmbeddingLookupSparseOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef EmbeddingLookupSparseOptionsT NativeTableType;
typedef EmbeddingLookupSparseOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -7476,51 +10135,51 @@
tflite::CombinerType combiner() const {
return static_cast<tflite::CombinerType>(GetField<int8_t>(VT_COMBINER, 0));
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_COMBINER, 1) &&
verifier.EndTable();
}
- EmbeddingLookupSparseOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(EmbeddingLookupSparseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<EmbeddingLookupSparseOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ EmbeddingLookupSparseOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(EmbeddingLookupSparseOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<EmbeddingLookupSparseOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct EmbeddingLookupSparseOptionsBuilder {
typedef EmbeddingLookupSparseOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_combiner(tflite::CombinerType combiner) {
fbb_.AddElement<int8_t>(EmbeddingLookupSparseOptions::VT_COMBINER, static_cast<int8_t>(combiner), 0);
}
- explicit EmbeddingLookupSparseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit EmbeddingLookupSparseOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<EmbeddingLookupSparseOptions> Finish() {
+ ::flatbuffers::Offset<EmbeddingLookupSparseOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<EmbeddingLookupSparseOptions>(end);
+ auto o = ::flatbuffers::Offset<EmbeddingLookupSparseOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<EmbeddingLookupSparseOptions> CreateEmbeddingLookupSparseOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<EmbeddingLookupSparseOptions> CreateEmbeddingLookupSparseOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::CombinerType combiner = tflite::CombinerType_SUM) {
EmbeddingLookupSparseOptionsBuilder builder_(_fbb);
builder_.add_combiner(combiner);
return builder_.Finish();
}
-flatbuffers::Offset<EmbeddingLookupSparseOptions> CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<EmbeddingLookupSparseOptions> CreateEmbeddingLookupSparseOptions(::flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct GatherOptionsT : public flatbuffers::NativeTable {
+struct GatherOptionsT : public ::flatbuffers::NativeTable {
typedef GatherOptions TableType;
int32_t axis = 0;
int32_t batch_dims = 0;
};
-struct GatherOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct GatherOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef GatherOptionsT NativeTableType;
typedef GatherOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -7533,40 +10192,40 @@
int32_t batch_dims() const {
return GetField<int32_t>(VT_BATCH_DIMS, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_AXIS, 4) &&
VerifyField<int32_t>(verifier, VT_BATCH_DIMS, 4) &&
verifier.EndTable();
}
- GatherOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(GatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<GatherOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ GatherOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(GatherOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<GatherOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct GatherOptionsBuilder {
typedef GatherOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_axis(int32_t axis) {
fbb_.AddElement<int32_t>(GatherOptions::VT_AXIS, axis, 0);
}
void add_batch_dims(int32_t batch_dims) {
fbb_.AddElement<int32_t>(GatherOptions::VT_BATCH_DIMS, batch_dims, 0);
}
- explicit GatherOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit GatherOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<GatherOptions> Finish() {
+ ::flatbuffers::Offset<GatherOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<GatherOptions>(end);
+ auto o = ::flatbuffers::Offset<GatherOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<GatherOptions> CreateGatherOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<GatherOptions> CreateGatherOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t axis = 0,
int32_t batch_dims = 0) {
GatherOptionsBuilder builder_(_fbb);
@@ -7575,131 +10234,131 @@
return builder_.Finish();
}
-flatbuffers::Offset<GatherOptions> CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<GatherOptions> CreateGatherOptions(::flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct TransposeOptionsT : public flatbuffers::NativeTable {
+struct TransposeOptionsT : public ::flatbuffers::NativeTable {
typedef TransposeOptions TableType;
};
-struct TransposeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct TransposeOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef TransposeOptionsT NativeTableType;
typedef TransposeOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- TransposeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(TransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<TransposeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ TransposeOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(TransposeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<TransposeOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct TransposeOptionsBuilder {
typedef TransposeOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit TransposeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit TransposeOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<TransposeOptions> Finish() {
+ ::flatbuffers::Offset<TransposeOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<TransposeOptions>(end);
+ auto o = ::flatbuffers::Offset<TransposeOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<TransposeOptions> CreateTransposeOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<TransposeOptions> CreateTransposeOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
TransposeOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<TransposeOptions> CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<TransposeOptions> CreateTransposeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ExpOptionsT : public flatbuffers::NativeTable {
+struct ExpOptionsT : public ::flatbuffers::NativeTable {
typedef ExpOptions TableType;
};
-struct ExpOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct ExpOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ExpOptionsT NativeTableType;
typedef ExpOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- ExpOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ExpOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<ExpOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ExpOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ExpOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ExpOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ExpOptionsBuilder {
typedef ExpOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit ExpOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit ExpOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<ExpOptions> Finish() {
+ ::flatbuffers::Offset<ExpOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<ExpOptions>(end);
+ auto o = ::flatbuffers::Offset<ExpOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<ExpOptions> CreateExpOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<ExpOptions> CreateExpOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
ExpOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<ExpOptions> CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<ExpOptions> CreateExpOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct CosOptionsT : public flatbuffers::NativeTable {
+struct CosOptionsT : public ::flatbuffers::NativeTable {
typedef CosOptions TableType;
};
-struct CosOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct CosOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef CosOptionsT NativeTableType;
typedef CosOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- CosOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(CosOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<CosOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ CosOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(CosOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<CosOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct CosOptionsBuilder {
typedef CosOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit CosOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit CosOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<CosOptions> Finish() {
+ ::flatbuffers::Offset<CosOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<CosOptions>(end);
+ auto o = ::flatbuffers::Offset<CosOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<CosOptions> CreateCosOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<CosOptions> CreateCosOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
CosOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<CosOptions> CreateCosOptions(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<CosOptions> CreateCosOptions(::flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ReducerOptionsT : public flatbuffers::NativeTable {
+struct ReducerOptionsT : public ::flatbuffers::NativeTable {
typedef ReducerOptions TableType;
bool keep_dims = false;
};
-struct ReducerOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct ReducerOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ReducerOptionsT NativeTableType;
typedef ReducerOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -7708,97 +10367,97 @@
bool keep_dims() const {
return GetField<uint8_t>(VT_KEEP_DIMS, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<uint8_t>(verifier, VT_KEEP_DIMS, 1) &&
verifier.EndTable();
}
- ReducerOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ReducerOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<ReducerOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ReducerOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ReducerOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ReducerOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ReducerOptionsBuilder {
typedef ReducerOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_keep_dims(bool keep_dims) {
fbb_.AddElement<uint8_t>(ReducerOptions::VT_KEEP_DIMS, static_cast<uint8_t>(keep_dims), 0);
}
- explicit ReducerOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit ReducerOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<ReducerOptions> Finish() {
+ ::flatbuffers::Offset<ReducerOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<ReducerOptions>(end);
+ auto o = ::flatbuffers::Offset<ReducerOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<ReducerOptions> CreateReducerOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<ReducerOptions> CreateReducerOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
bool keep_dims = false) {
ReducerOptionsBuilder builder_(_fbb);
builder_.add_keep_dims(keep_dims);
return builder_.Finish();
}
-flatbuffers::Offset<ReducerOptions> CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<ReducerOptions> CreateReducerOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SqueezeOptionsT : public flatbuffers::NativeTable {
+struct SqueezeOptionsT : public ::flatbuffers::NativeTable {
typedef SqueezeOptions TableType;
std::vector<int32_t> squeeze_dims{};
};
-struct SqueezeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SqueezeOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SqueezeOptionsT NativeTableType;
typedef SqueezeOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
VT_SQUEEZE_DIMS = 4
};
- const flatbuffers::Vector<int32_t> *squeeze_dims() const {
- return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SQUEEZE_DIMS);
+ const ::flatbuffers::Vector<int32_t> *squeeze_dims() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_SQUEEZE_DIMS);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyOffset(verifier, VT_SQUEEZE_DIMS) &&
verifier.VerifyVector(squeeze_dims()) &&
verifier.EndTable();
}
- SqueezeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SqueezeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SqueezeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SqueezeOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SqueezeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SqueezeOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SqueezeOptionsBuilder {
typedef SqueezeOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- void add_squeeze_dims(flatbuffers::Offset<flatbuffers::Vector<int32_t>> squeeze_dims) {
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_squeeze_dims(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> squeeze_dims) {
fbb_.AddOffset(SqueezeOptions::VT_SQUEEZE_DIMS, squeeze_dims);
}
- explicit SqueezeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit SqueezeOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SqueezeOptions> Finish() {
+ ::flatbuffers::Offset<SqueezeOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SqueezeOptions>(end);
+ auto o = ::flatbuffers::Offset<SqueezeOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<SqueezeOptions> CreateSqueezeOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
- flatbuffers::Offset<flatbuffers::Vector<int32_t>> squeeze_dims = 0) {
+inline ::flatbuffers::Offset<SqueezeOptions> CreateSqueezeOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> squeeze_dims = 0) {
SqueezeOptionsBuilder builder_(_fbb);
builder_.add_squeeze_dims(squeeze_dims);
return builder_.Finish();
}
-inline flatbuffers::Offset<SqueezeOptions> CreateSqueezeOptionsDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<SqueezeOptions> CreateSqueezeOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
const std::vector<int32_t> *squeeze_dims = nullptr) {
auto squeeze_dims__ = squeeze_dims ? _fbb.CreateVector<int32_t>(*squeeze_dims) : 0;
return tflite::CreateSqueezeOptions(
@@ -7806,14 +10465,14 @@
squeeze_dims__);
}
-flatbuffers::Offset<SqueezeOptions> CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SqueezeOptions> CreateSqueezeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SplitOptionsT : public flatbuffers::NativeTable {
+struct SplitOptionsT : public ::flatbuffers::NativeTable {
typedef SplitOptions TableType;
int32_t num_splits = 0;
};
-struct SplitOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SplitOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SplitOptionsT NativeTableType;
typedef SplitOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -7822,50 +10481,50 @@
int32_t num_splits() const {
return GetField<int32_t>(VT_NUM_SPLITS, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_NUM_SPLITS, 4) &&
verifier.EndTable();
}
- SplitOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SplitOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SplitOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SplitOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SplitOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SplitOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SplitOptionsBuilder {
typedef SplitOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_num_splits(int32_t num_splits) {
fbb_.AddElement<int32_t>(SplitOptions::VT_NUM_SPLITS, num_splits, 0);
}
- explicit SplitOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit SplitOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SplitOptions> Finish() {
+ ::flatbuffers::Offset<SplitOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SplitOptions>(end);
+ auto o = ::flatbuffers::Offset<SplitOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<SplitOptions> CreateSplitOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<SplitOptions> CreateSplitOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t num_splits = 0) {
SplitOptionsBuilder builder_(_fbb);
builder_.add_num_splits(num_splits);
return builder_.Finish();
}
-flatbuffers::Offset<SplitOptions> CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SplitOptions> CreateSplitOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SplitVOptionsT : public flatbuffers::NativeTable {
+struct SplitVOptionsT : public ::flatbuffers::NativeTable {
typedef SplitVOptions TableType;
int32_t num_splits = 0;
};
-struct SplitVOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SplitVOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SplitVOptionsT NativeTableType;
typedef SplitVOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -7874,45 +10533,45 @@
int32_t num_splits() const {
return GetField<int32_t>(VT_NUM_SPLITS, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_NUM_SPLITS, 4) &&
verifier.EndTable();
}
- SplitVOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SplitVOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SplitVOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SplitVOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SplitVOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SplitVOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SplitVOptionsBuilder {
typedef SplitVOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_num_splits(int32_t num_splits) {
fbb_.AddElement<int32_t>(SplitVOptions::VT_NUM_SPLITS, num_splits, 0);
}
- explicit SplitVOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit SplitVOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SplitVOptions> Finish() {
+ ::flatbuffers::Offset<SplitVOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SplitVOptions>(end);
+ auto o = ::flatbuffers::Offset<SplitVOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<SplitVOptions> CreateSplitVOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<SplitVOptions> CreateSplitVOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t num_splits = 0) {
SplitVOptionsBuilder builder_(_fbb);
builder_.add_num_splits(num_splits);
return builder_.Finish();
}
-flatbuffers::Offset<SplitVOptions> CreateSplitVOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SplitVOptions> CreateSplitVOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct StridedSliceOptionsT : public flatbuffers::NativeTable {
+struct StridedSliceOptionsT : public ::flatbuffers::NativeTable {
typedef StridedSliceOptions TableType;
int32_t begin_mask = 0;
int32_t end_mask = 0;
@@ -7922,7 +10581,7 @@
bool offset = false;
};
-struct StridedSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct StridedSliceOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef StridedSliceOptionsT NativeTableType;
typedef StridedSliceOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -7951,7 +10610,7 @@
bool offset() const {
return GetField<uint8_t>(VT_OFFSET, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_BEGIN_MASK, 4) &&
VerifyField<int32_t>(verifier, VT_END_MASK, 4) &&
@@ -7961,15 +10620,15 @@
VerifyField<uint8_t>(verifier, VT_OFFSET, 1) &&
verifier.EndTable();
}
- StridedSliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(StridedSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<StridedSliceOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ StridedSliceOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StridedSliceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StridedSliceOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct StridedSliceOptionsBuilder {
typedef StridedSliceOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_begin_mask(int32_t begin_mask) {
fbb_.AddElement<int32_t>(StridedSliceOptions::VT_BEGIN_MASK, begin_mask, 0);
}
@@ -7988,19 +10647,19 @@
void add_offset(bool offset) {
fbb_.AddElement<uint8_t>(StridedSliceOptions::VT_OFFSET, static_cast<uint8_t>(offset), 0);
}
- explicit StridedSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit StridedSliceOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<StridedSliceOptions> Finish() {
+ ::flatbuffers::Offset<StridedSliceOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<StridedSliceOptions>(end);
+ auto o = ::flatbuffers::Offset<StridedSliceOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<StridedSliceOptions> CreateStridedSliceOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<StridedSliceOptions> CreateStridedSliceOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t begin_mask = 0,
int32_t end_mask = 0,
int32_t ellipsis_mask = 0,
@@ -8017,54 +10676,54 @@
return builder_.Finish();
}
-flatbuffers::Offset<StridedSliceOptions> CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<StridedSliceOptions> CreateStridedSliceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct LogSoftmaxOptionsT : public flatbuffers::NativeTable {
+struct LogSoftmaxOptionsT : public ::flatbuffers::NativeTable {
typedef LogSoftmaxOptions TableType;
};
-struct LogSoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct LogSoftmaxOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef LogSoftmaxOptionsT NativeTableType;
typedef LogSoftmaxOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- LogSoftmaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(LogSoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<LogSoftmaxOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ LogSoftmaxOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(LogSoftmaxOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<LogSoftmaxOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct LogSoftmaxOptionsBuilder {
typedef LogSoftmaxOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit LogSoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit LogSoftmaxOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<LogSoftmaxOptions> Finish() {
+ ::flatbuffers::Offset<LogSoftmaxOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<LogSoftmaxOptions>(end);
+ auto o = ::flatbuffers::Offset<LogSoftmaxOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<LogSoftmaxOptions> CreateLogSoftmaxOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<LogSoftmaxOptions> CreateLogSoftmaxOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
LogSoftmaxOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<LogSoftmaxOptions> CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<LogSoftmaxOptions> CreateLogSoftmaxOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct CastOptionsT : public flatbuffers::NativeTable {
+struct CastOptionsT : public ::flatbuffers::NativeTable {
typedef CastOptions TableType;
tflite::TensorType in_data_type = tflite::TensorType_FLOAT32;
tflite::TensorType out_data_type = tflite::TensorType_FLOAT32;
};
-struct CastOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct CastOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef CastOptionsT NativeTableType;
typedef CastOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -8077,40 +10736,40 @@
tflite::TensorType out_data_type() const {
return static_cast<tflite::TensorType>(GetField<int8_t>(VT_OUT_DATA_TYPE, 0));
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_IN_DATA_TYPE, 1) &&
VerifyField<int8_t>(verifier, VT_OUT_DATA_TYPE, 1) &&
verifier.EndTable();
}
- CastOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(CastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<CastOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ CastOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(CastOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<CastOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct CastOptionsBuilder {
typedef CastOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_in_data_type(tflite::TensorType in_data_type) {
fbb_.AddElement<int8_t>(CastOptions::VT_IN_DATA_TYPE, static_cast<int8_t>(in_data_type), 0);
}
void add_out_data_type(tflite::TensorType out_data_type) {
fbb_.AddElement<int8_t>(CastOptions::VT_OUT_DATA_TYPE, static_cast<int8_t>(out_data_type), 0);
}
- explicit CastOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit CastOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<CastOptions> Finish() {
+ ::flatbuffers::Offset<CastOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<CastOptions>(end);
+ auto o = ::flatbuffers::Offset<CastOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<CastOptions> CreateCastOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<CastOptions> CreateCastOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::TensorType in_data_type = tflite::TensorType_FLOAT32,
tflite::TensorType out_data_type = tflite::TensorType_FLOAT32) {
CastOptionsBuilder builder_(_fbb);
@@ -8119,131 +10778,131 @@
return builder_.Finish();
}
-flatbuffers::Offset<CastOptions> CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<CastOptions> CreateCastOptions(::flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct DequantizeOptionsT : public flatbuffers::NativeTable {
+struct DequantizeOptionsT : public ::flatbuffers::NativeTable {
typedef DequantizeOptions TableType;
};
-struct DequantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct DequantizeOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef DequantizeOptionsT NativeTableType;
typedef DequantizeOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- DequantizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(DequantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<DequantizeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ DequantizeOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(DequantizeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<DequantizeOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct DequantizeOptionsBuilder {
typedef DequantizeOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit DequantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit DequantizeOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<DequantizeOptions> Finish() {
+ ::flatbuffers::Offset<DequantizeOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<DequantizeOptions>(end);
+ auto o = ::flatbuffers::Offset<DequantizeOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<DequantizeOptions> CreateDequantizeOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<DequantizeOptions> CreateDequantizeOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
DequantizeOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<DequantizeOptions> CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<DequantizeOptions> CreateDequantizeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct MaximumMinimumOptionsT : public flatbuffers::NativeTable {
+struct MaximumMinimumOptionsT : public ::flatbuffers::NativeTable {
typedef MaximumMinimumOptions TableType;
};
-struct MaximumMinimumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct MaximumMinimumOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef MaximumMinimumOptionsT NativeTableType;
typedef MaximumMinimumOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- MaximumMinimumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(MaximumMinimumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<MaximumMinimumOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ MaximumMinimumOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(MaximumMinimumOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<MaximumMinimumOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct MaximumMinimumOptionsBuilder {
typedef MaximumMinimumOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit MaximumMinimumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit MaximumMinimumOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<MaximumMinimumOptions> Finish() {
+ ::flatbuffers::Offset<MaximumMinimumOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<MaximumMinimumOptions>(end);
+ auto o = ::flatbuffers::Offset<MaximumMinimumOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<MaximumMinimumOptions> CreateMaximumMinimumOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<MaximumMinimumOptions> CreateMaximumMinimumOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
MaximumMinimumOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<MaximumMinimumOptions> CreateMaximumMinimumOptions(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<MaximumMinimumOptions> CreateMaximumMinimumOptions(::flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct TileOptionsT : public flatbuffers::NativeTable {
+struct TileOptionsT : public ::flatbuffers::NativeTable {
typedef TileOptions TableType;
};
-struct TileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct TileOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef TileOptionsT NativeTableType;
typedef TileOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- TileOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(TileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<TileOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ TileOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(TileOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<TileOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct TileOptionsBuilder {
typedef TileOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit TileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit TileOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<TileOptions> Finish() {
+ ::flatbuffers::Offset<TileOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<TileOptions>(end);
+ auto o = ::flatbuffers::Offset<TileOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<TileOptions> CreateTileOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<TileOptions> CreateTileOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
TileOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<TileOptions> CreateTileOptions(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<TileOptions> CreateTileOptions(::flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ArgMaxOptionsT : public flatbuffers::NativeTable {
+struct ArgMaxOptionsT : public ::flatbuffers::NativeTable {
typedef ArgMaxOptions TableType;
tflite::TensorType output_type = tflite::TensorType_FLOAT32;
};
-struct ArgMaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct ArgMaxOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ArgMaxOptionsT NativeTableType;
typedef ArgMaxOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -8252,50 +10911,50 @@
tflite::TensorType output_type() const {
return static_cast<tflite::TensorType>(GetField<int8_t>(VT_OUTPUT_TYPE, 0));
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_OUTPUT_TYPE, 1) &&
verifier.EndTable();
}
- ArgMaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ArgMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<ArgMaxOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ArgMaxOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ArgMaxOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ArgMaxOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ArgMaxOptionsBuilder {
typedef ArgMaxOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_output_type(tflite::TensorType output_type) {
fbb_.AddElement<int8_t>(ArgMaxOptions::VT_OUTPUT_TYPE, static_cast<int8_t>(output_type), 0);
}
- explicit ArgMaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit ArgMaxOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<ArgMaxOptions> Finish() {
+ ::flatbuffers::Offset<ArgMaxOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<ArgMaxOptions>(end);
+ auto o = ::flatbuffers::Offset<ArgMaxOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<ArgMaxOptions> CreateArgMaxOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<ArgMaxOptions> CreateArgMaxOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::TensorType output_type = tflite::TensorType_FLOAT32) {
ArgMaxOptionsBuilder builder_(_fbb);
builder_.add_output_type(output_type);
return builder_.Finish();
}
-flatbuffers::Offset<ArgMaxOptions> CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<ArgMaxOptions> CreateArgMaxOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ArgMinOptionsT : public flatbuffers::NativeTable {
+struct ArgMinOptionsT : public ::flatbuffers::NativeTable {
typedef ArgMinOptions TableType;
tflite::TensorType output_type = tflite::TensorType_FLOAT32;
};
-struct ArgMinOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct ArgMinOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ArgMinOptionsT NativeTableType;
typedef ArgMinOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -8304,333 +10963,335 @@
tflite::TensorType output_type() const {
return static_cast<tflite::TensorType>(GetField<int8_t>(VT_OUTPUT_TYPE, 0));
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_OUTPUT_TYPE, 1) &&
verifier.EndTable();
}
- ArgMinOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ArgMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<ArgMinOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ArgMinOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ArgMinOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ArgMinOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ArgMinOptionsBuilder {
typedef ArgMinOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_output_type(tflite::TensorType output_type) {
fbb_.AddElement<int8_t>(ArgMinOptions::VT_OUTPUT_TYPE, static_cast<int8_t>(output_type), 0);
}
- explicit ArgMinOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit ArgMinOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<ArgMinOptions> Finish() {
+ ::flatbuffers::Offset<ArgMinOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<ArgMinOptions>(end);
+ auto o = ::flatbuffers::Offset<ArgMinOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<ArgMinOptions> CreateArgMinOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<ArgMinOptions> CreateArgMinOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::TensorType output_type = tflite::TensorType_FLOAT32) {
ArgMinOptionsBuilder builder_(_fbb);
builder_.add_output_type(output_type);
return builder_.Finish();
}
-flatbuffers::Offset<ArgMinOptions> CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<ArgMinOptions> CreateArgMinOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct GreaterOptionsT : public flatbuffers::NativeTable {
+struct GreaterOptionsT : public ::flatbuffers::NativeTable {
typedef GreaterOptions TableType;
};
-struct GreaterOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct GreaterOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef GreaterOptionsT NativeTableType;
typedef GreaterOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- GreaterOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(GreaterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<GreaterOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ GreaterOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(GreaterOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<GreaterOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct GreaterOptionsBuilder {
typedef GreaterOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit GreaterOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit GreaterOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<GreaterOptions> Finish() {
+ ::flatbuffers::Offset<GreaterOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<GreaterOptions>(end);
+ auto o = ::flatbuffers::Offset<GreaterOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<GreaterOptions> CreateGreaterOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<GreaterOptions> CreateGreaterOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
GreaterOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<GreaterOptions> CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<GreaterOptions> CreateGreaterOptions(::flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct GreaterEqualOptionsT : public flatbuffers::NativeTable {
+struct GreaterEqualOptionsT : public ::flatbuffers::NativeTable {
typedef GreaterEqualOptions TableType;
};
-struct GreaterEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct GreaterEqualOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef GreaterEqualOptionsT NativeTableType;
typedef GreaterEqualOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- GreaterEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(GreaterEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<GreaterEqualOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ GreaterEqualOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(GreaterEqualOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<GreaterEqualOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct GreaterEqualOptionsBuilder {
typedef GreaterEqualOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit GreaterEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit GreaterEqualOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<GreaterEqualOptions> Finish() {
+ ::flatbuffers::Offset<GreaterEqualOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<GreaterEqualOptions>(end);
+ auto o = ::flatbuffers::Offset<GreaterEqualOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<GreaterEqualOptions> CreateGreaterEqualOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<GreaterEqualOptions> CreateGreaterEqualOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
GreaterEqualOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<GreaterEqualOptions> CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<GreaterEqualOptions> CreateGreaterEqualOptions(::flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct LessOptionsT : public flatbuffers::NativeTable {
+struct LessOptionsT : public ::flatbuffers::NativeTable {
typedef LessOptions TableType;
};
-struct LessOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct LessOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef LessOptionsT NativeTableType;
typedef LessOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- LessOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(LessOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<LessOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ LessOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(LessOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<LessOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct LessOptionsBuilder {
typedef LessOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit LessOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit LessOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<LessOptions> Finish() {
+ ::flatbuffers::Offset<LessOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<LessOptions>(end);
+ auto o = ::flatbuffers::Offset<LessOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<LessOptions> CreateLessOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<LessOptions> CreateLessOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
LessOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<LessOptions> CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<LessOptions> CreateLessOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct LessEqualOptionsT : public flatbuffers::NativeTable {
+struct LessEqualOptionsT : public ::flatbuffers::NativeTable {
typedef LessEqualOptions TableType;
};
-struct LessEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct LessEqualOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef LessEqualOptionsT NativeTableType;
typedef LessEqualOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- LessEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(LessEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<LessEqualOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ LessEqualOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(LessEqualOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<LessEqualOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct LessEqualOptionsBuilder {
typedef LessEqualOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit LessEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit LessEqualOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<LessEqualOptions> Finish() {
+ ::flatbuffers::Offset<LessEqualOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<LessEqualOptions>(end);
+ auto o = ::flatbuffers::Offset<LessEqualOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<LessEqualOptions> CreateLessEqualOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<LessEqualOptions> CreateLessEqualOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
LessEqualOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<LessEqualOptions> CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<LessEqualOptions> CreateLessEqualOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct NegOptionsT : public flatbuffers::NativeTable {
+struct NegOptionsT : public ::flatbuffers::NativeTable {
typedef NegOptions TableType;
};
-struct NegOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct NegOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef NegOptionsT NativeTableType;
typedef NegOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- NegOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(NegOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<NegOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ NegOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(NegOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<NegOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct NegOptionsBuilder {
typedef NegOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit NegOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit NegOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<NegOptions> Finish() {
+ ::flatbuffers::Offset<NegOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<NegOptions>(end);
+ auto o = ::flatbuffers::Offset<NegOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<NegOptions> CreateNegOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<NegOptions> CreateNegOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
NegOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<NegOptions> CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<NegOptions> CreateNegOptions(::flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SelectOptionsT : public flatbuffers::NativeTable {
+struct SelectOptionsT : public ::flatbuffers::NativeTable {
typedef SelectOptions TableType;
};
-struct SelectOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SelectOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SelectOptionsT NativeTableType;
typedef SelectOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- SelectOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SelectOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SelectOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SelectOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SelectOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SelectOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SelectOptionsBuilder {
typedef SelectOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit SelectOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit SelectOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SelectOptions> Finish() {
+ ::flatbuffers::Offset<SelectOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SelectOptions>(end);
+ auto o = ::flatbuffers::Offset<SelectOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<SelectOptions> CreateSelectOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<SelectOptions> CreateSelectOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
SelectOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<SelectOptions> CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SelectOptions> CreateSelectOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SliceOptionsT : public flatbuffers::NativeTable {
+struct SliceOptionsT : public ::flatbuffers::NativeTable {
typedef SliceOptions TableType;
};
-struct SliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SliceOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SliceOptionsT NativeTableType;
typedef SliceOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- SliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SliceOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SliceOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SliceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SliceOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SliceOptionsBuilder {
typedef SliceOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit SliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit SliceOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SliceOptions> Finish() {
+ ::flatbuffers::Offset<SliceOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SliceOptions>(end);
+ auto o = ::flatbuffers::Offset<SliceOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<SliceOptions> CreateSliceOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<SliceOptions> CreateSliceOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
SliceOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<SliceOptions> CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SliceOptions> CreateSliceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct TransposeConvOptionsT : public flatbuffers::NativeTable {
+struct TransposeConvOptionsT : public ::flatbuffers::NativeTable {
typedef TransposeConvOptions TableType;
tflite::Padding padding = tflite::Padding_SAME;
int32_t stride_w = 0;
int32_t stride_h = 0;
tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
+ tflite::TensorType quantized_bias_type = tflite::TensorType_FLOAT32;
};
-struct TransposeConvOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct TransposeConvOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef TransposeConvOptionsT NativeTableType;
typedef TransposeConvOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
VT_PADDING = 4,
VT_STRIDE_W = 6,
VT_STRIDE_H = 8,
- VT_FUSED_ACTIVATION_FUNCTION = 10
+ VT_FUSED_ACTIVATION_FUNCTION = 10,
+ VT_QUANTIZED_BIAS_TYPE = 12
};
tflite::Padding padding() const {
return static_cast<tflite::Padding>(GetField<int8_t>(VT_PADDING, 0));
@@ -8644,23 +11305,27 @@
tflite::ActivationFunctionType fused_activation_function() const {
return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ tflite::TensorType quantized_bias_type() const {
+ return static_cast<tflite::TensorType>(GetField<int8_t>(VT_QUANTIZED_BIAS_TYPE, 0));
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_PADDING, 1) &&
VerifyField<int32_t>(verifier, VT_STRIDE_W, 4) &&
VerifyField<int32_t>(verifier, VT_STRIDE_H, 4) &&
VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
+ VerifyField<int8_t>(verifier, VT_QUANTIZED_BIAS_TYPE, 1) &&
verifier.EndTable();
}
- TransposeConvOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(TransposeConvOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<TransposeConvOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ TransposeConvOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(TransposeConvOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<TransposeConvOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct TransposeConvOptionsBuilder {
typedef TransposeConvOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_padding(tflite::Padding padding) {
fbb_.AddElement<int8_t>(TransposeConvOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
}
@@ -8673,78 +11338,83 @@
void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
fbb_.AddElement<int8_t>(TransposeConvOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
}
- explicit TransposeConvOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ void add_quantized_bias_type(tflite::TensorType quantized_bias_type) {
+ fbb_.AddElement<int8_t>(TransposeConvOptions::VT_QUANTIZED_BIAS_TYPE, static_cast<int8_t>(quantized_bias_type), 0);
+ }
+ explicit TransposeConvOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<TransposeConvOptions> Finish() {
+ ::flatbuffers::Offset<TransposeConvOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<TransposeConvOptions>(end);
+ auto o = ::flatbuffers::Offset<TransposeConvOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<TransposeConvOptions> CreateTransposeConvOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<TransposeConvOptions> CreateTransposeConvOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::Padding padding = tflite::Padding_SAME,
int32_t stride_w = 0,
int32_t stride_h = 0,
- tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) {
+ tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
+ tflite::TensorType quantized_bias_type = tflite::TensorType_FLOAT32) {
TransposeConvOptionsBuilder builder_(_fbb);
builder_.add_stride_h(stride_h);
builder_.add_stride_w(stride_w);
+ builder_.add_quantized_bias_type(quantized_bias_type);
builder_.add_fused_activation_function(fused_activation_function);
builder_.add_padding(padding);
return builder_.Finish();
}
-flatbuffers::Offset<TransposeConvOptions> CreateTransposeConvOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<TransposeConvOptions> CreateTransposeConvOptions(::flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ExpandDimsOptionsT : public flatbuffers::NativeTable {
+struct ExpandDimsOptionsT : public ::flatbuffers::NativeTable {
typedef ExpandDimsOptions TableType;
};
-struct ExpandDimsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct ExpandDimsOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ExpandDimsOptionsT NativeTableType;
typedef ExpandDimsOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- ExpandDimsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ExpandDimsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<ExpandDimsOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ExpandDimsOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ExpandDimsOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ExpandDimsOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ExpandDimsOptionsBuilder {
typedef ExpandDimsOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit ExpandDimsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit ExpandDimsOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<ExpandDimsOptions> Finish() {
+ ::flatbuffers::Offset<ExpandDimsOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<ExpandDimsOptions>(end);
+ auto o = ::flatbuffers::Offset<ExpandDimsOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<ExpandDimsOptions> CreateExpandDimsOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<ExpandDimsOptions> CreateExpandDimsOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
ExpandDimsOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<ExpandDimsOptions> CreateExpandDimsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<ExpandDimsOptions> CreateExpandDimsOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SparseToDenseOptionsT : public flatbuffers::NativeTable {
+struct SparseToDenseOptionsT : public ::flatbuffers::NativeTable {
typedef SparseToDenseOptions TableType;
bool validate_indices = false;
};
-struct SparseToDenseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SparseToDenseOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SparseToDenseOptionsT NativeTableType;
typedef SparseToDenseOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -8753,128 +11423,128 @@
bool validate_indices() const {
return GetField<uint8_t>(VT_VALIDATE_INDICES, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<uint8_t>(verifier, VT_VALIDATE_INDICES, 1) &&
verifier.EndTable();
}
- SparseToDenseOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SparseToDenseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SparseToDenseOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SparseToDenseOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SparseToDenseOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SparseToDenseOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SparseToDenseOptionsBuilder {
typedef SparseToDenseOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_validate_indices(bool validate_indices) {
fbb_.AddElement<uint8_t>(SparseToDenseOptions::VT_VALIDATE_INDICES, static_cast<uint8_t>(validate_indices), 0);
}
- explicit SparseToDenseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit SparseToDenseOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SparseToDenseOptions> Finish() {
+ ::flatbuffers::Offset<SparseToDenseOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SparseToDenseOptions>(end);
+ auto o = ::flatbuffers::Offset<SparseToDenseOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<SparseToDenseOptions> CreateSparseToDenseOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<SparseToDenseOptions> CreateSparseToDenseOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
bool validate_indices = false) {
SparseToDenseOptionsBuilder builder_(_fbb);
builder_.add_validate_indices(validate_indices);
return builder_.Finish();
}
-flatbuffers::Offset<SparseToDenseOptions> CreateSparseToDenseOptions(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SparseToDenseOptions> CreateSparseToDenseOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct EqualOptionsT : public flatbuffers::NativeTable {
+struct EqualOptionsT : public ::flatbuffers::NativeTable {
typedef EqualOptions TableType;
};
-struct EqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct EqualOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef EqualOptionsT NativeTableType;
typedef EqualOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- EqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(EqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<EqualOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ EqualOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(EqualOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<EqualOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct EqualOptionsBuilder {
typedef EqualOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit EqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit EqualOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<EqualOptions> Finish() {
+ ::flatbuffers::Offset<EqualOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<EqualOptions>(end);
+ auto o = ::flatbuffers::Offset<EqualOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<EqualOptions> CreateEqualOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<EqualOptions> CreateEqualOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
EqualOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<EqualOptions> CreateEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<EqualOptions> CreateEqualOptions(::flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct NotEqualOptionsT : public flatbuffers::NativeTable {
+struct NotEqualOptionsT : public ::flatbuffers::NativeTable {
typedef NotEqualOptions TableType;
};
-struct NotEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct NotEqualOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef NotEqualOptionsT NativeTableType;
typedef NotEqualOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- NotEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(NotEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<NotEqualOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ NotEqualOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(NotEqualOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<NotEqualOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct NotEqualOptionsBuilder {
typedef NotEqualOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit NotEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit NotEqualOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<NotEqualOptions> Finish() {
+ ::flatbuffers::Offset<NotEqualOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<NotEqualOptions>(end);
+ auto o = ::flatbuffers::Offset<NotEqualOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<NotEqualOptions> CreateNotEqualOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<NotEqualOptions> CreateNotEqualOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
NotEqualOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<NotEqualOptions> CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<NotEqualOptions> CreateNotEqualOptions(::flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ShapeOptionsT : public flatbuffers::NativeTable {
+struct ShapeOptionsT : public ::flatbuffers::NativeTable {
typedef ShapeOptions TableType;
tflite::TensorType out_type = tflite::TensorType_FLOAT32;
};
-struct ShapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct ShapeOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ShapeOptionsT NativeTableType;
typedef ShapeOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -8883,123 +11553,123 @@
tflite::TensorType out_type() const {
return static_cast<tflite::TensorType>(GetField<int8_t>(VT_OUT_TYPE, 0));
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_OUT_TYPE, 1) &&
verifier.EndTable();
}
- ShapeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ShapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<ShapeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ShapeOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ShapeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ShapeOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ShapeOptionsBuilder {
typedef ShapeOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_out_type(tflite::TensorType out_type) {
fbb_.AddElement<int8_t>(ShapeOptions::VT_OUT_TYPE, static_cast<int8_t>(out_type), 0);
}
- explicit ShapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit ShapeOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<ShapeOptions> Finish() {
+ ::flatbuffers::Offset<ShapeOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<ShapeOptions>(end);
+ auto o = ::flatbuffers::Offset<ShapeOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<ShapeOptions> CreateShapeOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<ShapeOptions> CreateShapeOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::TensorType out_type = tflite::TensorType_FLOAT32) {
ShapeOptionsBuilder builder_(_fbb);
builder_.add_out_type(out_type);
return builder_.Finish();
}
-flatbuffers::Offset<ShapeOptions> CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<ShapeOptions> CreateShapeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct RankOptionsT : public flatbuffers::NativeTable {
+struct RankOptionsT : public ::flatbuffers::NativeTable {
typedef RankOptions TableType;
};
-struct RankOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct RankOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef RankOptionsT NativeTableType;
typedef RankOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- RankOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(RankOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<RankOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ RankOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(RankOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<RankOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct RankOptionsBuilder {
typedef RankOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit RankOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit RankOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<RankOptions> Finish() {
+ ::flatbuffers::Offset<RankOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<RankOptions>(end);
+ auto o = ::flatbuffers::Offset<RankOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<RankOptions> CreateRankOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<RankOptions> CreateRankOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
RankOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<RankOptions> CreateRankOptions(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<RankOptions> CreateRankOptions(::flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct PowOptionsT : public flatbuffers::NativeTable {
+struct PowOptionsT : public ::flatbuffers::NativeTable {
typedef PowOptions TableType;
};
-struct PowOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct PowOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef PowOptionsT NativeTableType;
typedef PowOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- PowOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(PowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<PowOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ PowOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(PowOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<PowOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct PowOptionsBuilder {
typedef PowOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit PowOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit PowOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<PowOptions> Finish() {
+ ::flatbuffers::Offset<PowOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<PowOptions>(end);
+ auto o = ::flatbuffers::Offset<PowOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<PowOptions> CreatePowOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<PowOptions> CreatePowOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
PowOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<PowOptions> CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<PowOptions> CreatePowOptions(::flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct FakeQuantOptionsT : public flatbuffers::NativeTable {
+struct FakeQuantOptionsT : public ::flatbuffers::NativeTable {
typedef FakeQuantOptions TableType;
float min = 0.0f;
float max = 0.0f;
@@ -9007,7 +11677,7 @@
bool narrow_range = false;
};
-struct FakeQuantOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct FakeQuantOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef FakeQuantOptionsT NativeTableType;
typedef FakeQuantOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -9028,7 +11698,7 @@
bool narrow_range() const {
return GetField<uint8_t>(VT_NARROW_RANGE, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<float>(verifier, VT_MIN, 4) &&
VerifyField<float>(verifier, VT_MAX, 4) &&
@@ -9036,15 +11706,15 @@
VerifyField<uint8_t>(verifier, VT_NARROW_RANGE, 1) &&
verifier.EndTable();
}
- FakeQuantOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(FakeQuantOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<FakeQuantOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ FakeQuantOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(FakeQuantOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<FakeQuantOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct FakeQuantOptionsBuilder {
typedef FakeQuantOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_min(float min) {
fbb_.AddElement<float>(FakeQuantOptions::VT_MIN, min, 0.0f);
}
@@ -9057,19 +11727,19 @@
void add_narrow_range(bool narrow_range) {
fbb_.AddElement<uint8_t>(FakeQuantOptions::VT_NARROW_RANGE, static_cast<uint8_t>(narrow_range), 0);
}
- explicit FakeQuantOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit FakeQuantOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<FakeQuantOptions> Finish() {
+ ::flatbuffers::Offset<FakeQuantOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<FakeQuantOptions>(end);
+ auto o = ::flatbuffers::Offset<FakeQuantOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<FakeQuantOptions> CreateFakeQuantOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<FakeQuantOptions> CreateFakeQuantOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
float min = 0.0f,
float max = 0.0f,
int32_t num_bits = 0,
@@ -9082,15 +11752,15 @@
return builder_.Finish();
}
-flatbuffers::Offset<FakeQuantOptions> CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<FakeQuantOptions> CreateFakeQuantOptions(::flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct PackOptionsT : public flatbuffers::NativeTable {
+struct PackOptionsT : public ::flatbuffers::NativeTable {
typedef PackOptions TableType;
int32_t values_count = 0;
int32_t axis = 0;
};
-struct PackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct PackOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef PackOptionsT NativeTableType;
typedef PackOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -9103,40 +11773,40 @@
int32_t axis() const {
return GetField<int32_t>(VT_AXIS, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_VALUES_COUNT, 4) &&
VerifyField<int32_t>(verifier, VT_AXIS, 4) &&
verifier.EndTable();
}
- PackOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(PackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<PackOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ PackOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(PackOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<PackOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct PackOptionsBuilder {
typedef PackOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_values_count(int32_t values_count) {
fbb_.AddElement<int32_t>(PackOptions::VT_VALUES_COUNT, values_count, 0);
}
void add_axis(int32_t axis) {
fbb_.AddElement<int32_t>(PackOptions::VT_AXIS, axis, 0);
}
- explicit PackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit PackOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<PackOptions> Finish() {
+ ::flatbuffers::Offset<PackOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<PackOptions>(end);
+ auto o = ::flatbuffers::Offset<PackOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<PackOptions> CreatePackOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<PackOptions> CreatePackOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t values_count = 0,
int32_t axis = 0) {
PackOptionsBuilder builder_(_fbb);
@@ -9145,53 +11815,53 @@
return builder_.Finish();
}
-flatbuffers::Offset<PackOptions> CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<PackOptions> CreatePackOptions(::flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct LogicalOrOptionsT : public flatbuffers::NativeTable {
+struct LogicalOrOptionsT : public ::flatbuffers::NativeTable {
typedef LogicalOrOptions TableType;
};
-struct LogicalOrOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct LogicalOrOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef LogicalOrOptionsT NativeTableType;
typedef LogicalOrOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- LogicalOrOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(LogicalOrOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<LogicalOrOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ LogicalOrOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(LogicalOrOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<LogicalOrOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct LogicalOrOptionsBuilder {
typedef LogicalOrOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit LogicalOrOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit LogicalOrOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<LogicalOrOptions> Finish() {
+ ::flatbuffers::Offset<LogicalOrOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<LogicalOrOptions>(end);
+ auto o = ::flatbuffers::Offset<LogicalOrOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<LogicalOrOptions> CreateLogicalOrOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<LogicalOrOptions> CreateLogicalOrOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
LogicalOrOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<LogicalOrOptions> CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<LogicalOrOptions> CreateLogicalOrOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct OneHotOptionsT : public flatbuffers::NativeTable {
+struct OneHotOptionsT : public ::flatbuffers::NativeTable {
typedef OneHotOptions TableType;
int32_t axis = 0;
};
-struct OneHotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct OneHotOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef OneHotOptionsT NativeTableType;
typedef OneHotOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -9200,207 +11870,207 @@
int32_t axis() const {
return GetField<int32_t>(VT_AXIS, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_AXIS, 4) &&
verifier.EndTable();
}
- OneHotOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(OneHotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<OneHotOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ OneHotOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(OneHotOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<OneHotOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct OneHotOptionsBuilder {
typedef OneHotOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_axis(int32_t axis) {
fbb_.AddElement<int32_t>(OneHotOptions::VT_AXIS, axis, 0);
}
- explicit OneHotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit OneHotOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<OneHotOptions> Finish() {
+ ::flatbuffers::Offset<OneHotOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<OneHotOptions>(end);
+ auto o = ::flatbuffers::Offset<OneHotOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<OneHotOptions> CreateOneHotOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<OneHotOptions> CreateOneHotOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t axis = 0) {
OneHotOptionsBuilder builder_(_fbb);
builder_.add_axis(axis);
return builder_.Finish();
}
-flatbuffers::Offset<OneHotOptions> CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<OneHotOptions> CreateOneHotOptions(::flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct AbsOptionsT : public flatbuffers::NativeTable {
+struct AbsOptionsT : public ::flatbuffers::NativeTable {
typedef AbsOptions TableType;
};
-struct AbsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct AbsOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef AbsOptionsT NativeTableType;
typedef AbsOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- AbsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(AbsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<AbsOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ AbsOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(AbsOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<AbsOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct AbsOptionsBuilder {
typedef AbsOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit AbsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit AbsOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<AbsOptions> Finish() {
+ ::flatbuffers::Offset<AbsOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<AbsOptions>(end);
+ auto o = ::flatbuffers::Offset<AbsOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<AbsOptions> CreateAbsOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<AbsOptions> CreateAbsOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
AbsOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<AbsOptions> CreateAbsOptions(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<AbsOptions> CreateAbsOptions(::flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct HardSwishOptionsT : public flatbuffers::NativeTable {
+struct HardSwishOptionsT : public ::flatbuffers::NativeTable {
typedef HardSwishOptions TableType;
};
-struct HardSwishOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct HardSwishOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef HardSwishOptionsT NativeTableType;
typedef HardSwishOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- HardSwishOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(HardSwishOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<HardSwishOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ HardSwishOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(HardSwishOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<HardSwishOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct HardSwishOptionsBuilder {
typedef HardSwishOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit HardSwishOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit HardSwishOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<HardSwishOptions> Finish() {
+ ::flatbuffers::Offset<HardSwishOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<HardSwishOptions>(end);
+ auto o = ::flatbuffers::Offset<HardSwishOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<HardSwishOptions> CreateHardSwishOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<HardSwishOptions> CreateHardSwishOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
HardSwishOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<HardSwishOptions> CreateHardSwishOptions(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<HardSwishOptions> CreateHardSwishOptions(::flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct LogicalAndOptionsT : public flatbuffers::NativeTable {
+struct LogicalAndOptionsT : public ::flatbuffers::NativeTable {
typedef LogicalAndOptions TableType;
};
-struct LogicalAndOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct LogicalAndOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef LogicalAndOptionsT NativeTableType;
typedef LogicalAndOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- LogicalAndOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(LogicalAndOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<LogicalAndOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ LogicalAndOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(LogicalAndOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<LogicalAndOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct LogicalAndOptionsBuilder {
typedef LogicalAndOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit LogicalAndOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit LogicalAndOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<LogicalAndOptions> Finish() {
+ ::flatbuffers::Offset<LogicalAndOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<LogicalAndOptions>(end);
+ auto o = ::flatbuffers::Offset<LogicalAndOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<LogicalAndOptions> CreateLogicalAndOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<LogicalAndOptions> CreateLogicalAndOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
LogicalAndOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<LogicalAndOptions> CreateLogicalAndOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<LogicalAndOptions> CreateLogicalAndOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct LogicalNotOptionsT : public flatbuffers::NativeTable {
+struct LogicalNotOptionsT : public ::flatbuffers::NativeTable {
typedef LogicalNotOptions TableType;
};
-struct LogicalNotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct LogicalNotOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef LogicalNotOptionsT NativeTableType;
typedef LogicalNotOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- LogicalNotOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(LogicalNotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<LogicalNotOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ LogicalNotOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(LogicalNotOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<LogicalNotOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct LogicalNotOptionsBuilder {
typedef LogicalNotOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit LogicalNotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit LogicalNotOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<LogicalNotOptions> Finish() {
+ ::flatbuffers::Offset<LogicalNotOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<LogicalNotOptions>(end);
+ auto o = ::flatbuffers::Offset<LogicalNotOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<LogicalNotOptions> CreateLogicalNotOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<LogicalNotOptions> CreateLogicalNotOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
LogicalNotOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<LogicalNotOptions> CreateLogicalNotOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<LogicalNotOptions> CreateLogicalNotOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct UnpackOptionsT : public flatbuffers::NativeTable {
+struct UnpackOptionsT : public ::flatbuffers::NativeTable {
typedef UnpackOptions TableType;
int32_t num = 0;
int32_t axis = 0;
};
-struct UnpackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct UnpackOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef UnpackOptionsT NativeTableType;
typedef UnpackOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -9413,40 +12083,40 @@
int32_t axis() const {
return GetField<int32_t>(VT_AXIS, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_NUM, 4) &&
VerifyField<int32_t>(verifier, VT_AXIS, 4) &&
verifier.EndTable();
}
- UnpackOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(UnpackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<UnpackOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ UnpackOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(UnpackOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<UnpackOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct UnpackOptionsBuilder {
typedef UnpackOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_num(int32_t num) {
fbb_.AddElement<int32_t>(UnpackOptions::VT_NUM, num, 0);
}
void add_axis(int32_t axis) {
fbb_.AddElement<int32_t>(UnpackOptions::VT_AXIS, axis, 0);
}
- explicit UnpackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit UnpackOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<UnpackOptions> Finish() {
+ ::flatbuffers::Offset<UnpackOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<UnpackOptions>(end);
+ auto o = ::flatbuffers::Offset<UnpackOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<UnpackOptions> CreateUnpackOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<UnpackOptions> CreateUnpackOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t num = 0,
int32_t axis = 0) {
UnpackOptionsBuilder builder_(_fbb);
@@ -9455,248 +12125,248 @@
return builder_.Finish();
}
-flatbuffers::Offset<UnpackOptions> CreateUnpackOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<UnpackOptions> CreateUnpackOptions(::flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct FloorDivOptionsT : public flatbuffers::NativeTable {
+struct FloorDivOptionsT : public ::flatbuffers::NativeTable {
typedef FloorDivOptions TableType;
};
-struct FloorDivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct FloorDivOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef FloorDivOptionsT NativeTableType;
typedef FloorDivOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- FloorDivOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(FloorDivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<FloorDivOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ FloorDivOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(FloorDivOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<FloorDivOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct FloorDivOptionsBuilder {
typedef FloorDivOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit FloorDivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit FloorDivOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<FloorDivOptions> Finish() {
+ ::flatbuffers::Offset<FloorDivOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<FloorDivOptions>(end);
+ auto o = ::flatbuffers::Offset<FloorDivOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<FloorDivOptions> CreateFloorDivOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<FloorDivOptions> CreateFloorDivOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
FloorDivOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<FloorDivOptions> CreateFloorDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<FloorDivOptions> CreateFloorDivOptions(::flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SquareOptionsT : public flatbuffers::NativeTable {
+struct SquareOptionsT : public ::flatbuffers::NativeTable {
typedef SquareOptions TableType;
};
-struct SquareOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SquareOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SquareOptionsT NativeTableType;
typedef SquareOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- SquareOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SquareOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SquareOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SquareOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SquareOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SquareOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SquareOptionsBuilder {
typedef SquareOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit SquareOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit SquareOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SquareOptions> Finish() {
+ ::flatbuffers::Offset<SquareOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SquareOptions>(end);
+ auto o = ::flatbuffers::Offset<SquareOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<SquareOptions> CreateSquareOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<SquareOptions> CreateSquareOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
SquareOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<SquareOptions> CreateSquareOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SquareOptions> CreateSquareOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ZerosLikeOptionsT : public flatbuffers::NativeTable {
+struct ZerosLikeOptionsT : public ::flatbuffers::NativeTable {
typedef ZerosLikeOptions TableType;
};
-struct ZerosLikeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct ZerosLikeOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ZerosLikeOptionsT NativeTableType;
typedef ZerosLikeOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- ZerosLikeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ZerosLikeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<ZerosLikeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ZerosLikeOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ZerosLikeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ZerosLikeOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ZerosLikeOptionsBuilder {
typedef ZerosLikeOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit ZerosLikeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit ZerosLikeOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<ZerosLikeOptions> Finish() {
+ ::flatbuffers::Offset<ZerosLikeOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<ZerosLikeOptions>(end);
+ auto o = ::flatbuffers::Offset<ZerosLikeOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<ZerosLikeOptions> CreateZerosLikeOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<ZerosLikeOptions> CreateZerosLikeOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
ZerosLikeOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<ZerosLikeOptions> CreateZerosLikeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<ZerosLikeOptions> CreateZerosLikeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct FillOptionsT : public flatbuffers::NativeTable {
+struct FillOptionsT : public ::flatbuffers::NativeTable {
typedef FillOptions TableType;
};
-struct FillOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct FillOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef FillOptionsT NativeTableType;
typedef FillOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- FillOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(FillOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<FillOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ FillOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(FillOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<FillOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct FillOptionsBuilder {
typedef FillOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit FillOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit FillOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<FillOptions> Finish() {
+ ::flatbuffers::Offset<FillOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<FillOptions>(end);
+ auto o = ::flatbuffers::Offset<FillOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<FillOptions> CreateFillOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<FillOptions> CreateFillOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
FillOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<FillOptions> CreateFillOptions(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<FillOptions> CreateFillOptions(::flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct FloorModOptionsT : public flatbuffers::NativeTable {
+struct FloorModOptionsT : public ::flatbuffers::NativeTable {
typedef FloorModOptions TableType;
};
-struct FloorModOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct FloorModOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef FloorModOptionsT NativeTableType;
typedef FloorModOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- FloorModOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(FloorModOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<FloorModOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ FloorModOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(FloorModOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<FloorModOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct FloorModOptionsBuilder {
typedef FloorModOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit FloorModOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit FloorModOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<FloorModOptions> Finish() {
+ ::flatbuffers::Offset<FloorModOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<FloorModOptions>(end);
+ auto o = ::flatbuffers::Offset<FloorModOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<FloorModOptions> CreateFloorModOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<FloorModOptions> CreateFloorModOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
FloorModOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<FloorModOptions> CreateFloorModOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<FloorModOptions> CreateFloorModOptions(::flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct RangeOptionsT : public flatbuffers::NativeTable {
+struct RangeOptionsT : public ::flatbuffers::NativeTable {
typedef RangeOptions TableType;
};
-struct RangeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct RangeOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef RangeOptionsT NativeTableType;
typedef RangeOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- RangeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(RangeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<RangeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ RangeOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(RangeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<RangeOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct RangeOptionsBuilder {
typedef RangeOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit RangeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit RangeOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<RangeOptions> Finish() {
+ ::flatbuffers::Offset<RangeOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<RangeOptions>(end);
+ auto o = ::flatbuffers::Offset<RangeOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<RangeOptions> CreateRangeOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<RangeOptions> CreateRangeOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
RangeOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<RangeOptions> CreateRangeOptions(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<RangeOptions> CreateRangeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct LeakyReluOptionsT : public flatbuffers::NativeTable {
+struct LeakyReluOptionsT : public ::flatbuffers::NativeTable {
typedef LeakyReluOptions TableType;
float alpha = 0.0f;
};
-struct LeakyReluOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct LeakyReluOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef LeakyReluOptionsT NativeTableType;
typedef LeakyReluOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -9705,89 +12375,89 @@
float alpha() const {
return GetField<float>(VT_ALPHA, 0.0f);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<float>(verifier, VT_ALPHA, 4) &&
verifier.EndTable();
}
- LeakyReluOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(LeakyReluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<LeakyReluOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ LeakyReluOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(LeakyReluOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<LeakyReluOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct LeakyReluOptionsBuilder {
typedef LeakyReluOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_alpha(float alpha) {
fbb_.AddElement<float>(LeakyReluOptions::VT_ALPHA, alpha, 0.0f);
}
- explicit LeakyReluOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit LeakyReluOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<LeakyReluOptions> Finish() {
+ ::flatbuffers::Offset<LeakyReluOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<LeakyReluOptions>(end);
+ auto o = ::flatbuffers::Offset<LeakyReluOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<LeakyReluOptions> CreateLeakyReluOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<LeakyReluOptions> CreateLeakyReluOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
float alpha = 0.0f) {
LeakyReluOptionsBuilder builder_(_fbb);
builder_.add_alpha(alpha);
return builder_.Finish();
}
-flatbuffers::Offset<LeakyReluOptions> CreateLeakyReluOptions(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<LeakyReluOptions> CreateLeakyReluOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SquaredDifferenceOptionsT : public flatbuffers::NativeTable {
+struct SquaredDifferenceOptionsT : public ::flatbuffers::NativeTable {
typedef SquaredDifferenceOptions TableType;
};
-struct SquaredDifferenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SquaredDifferenceOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SquaredDifferenceOptionsT NativeTableType;
typedef SquaredDifferenceOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- SquaredDifferenceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SquaredDifferenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SquaredDifferenceOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SquaredDifferenceOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SquaredDifferenceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SquaredDifferenceOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SquaredDifferenceOptionsBuilder {
typedef SquaredDifferenceOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit SquaredDifferenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit SquaredDifferenceOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SquaredDifferenceOptions> Finish() {
+ ::flatbuffers::Offset<SquaredDifferenceOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SquaredDifferenceOptions>(end);
+ auto o = ::flatbuffers::Offset<SquaredDifferenceOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<SquaredDifferenceOptions> CreateSquaredDifferenceOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<SquaredDifferenceOptions> CreateSquaredDifferenceOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
SquaredDifferenceOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<SquaredDifferenceOptions> CreateSquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SquaredDifferenceOptions> CreateSquaredDifferenceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct MirrorPadOptionsT : public flatbuffers::NativeTable {
+struct MirrorPadOptionsT : public ::flatbuffers::NativeTable {
typedef MirrorPadOptions TableType;
tflite::MirrorPadMode mode = tflite::MirrorPadMode_REFLECT;
};
-struct MirrorPadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct MirrorPadOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef MirrorPadOptionsT NativeTableType;
typedef MirrorPadOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -9796,50 +12466,50 @@
tflite::MirrorPadMode mode() const {
return static_cast<tflite::MirrorPadMode>(GetField<int8_t>(VT_MODE, 0));
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_MODE, 1) &&
verifier.EndTable();
}
- MirrorPadOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(MirrorPadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<MirrorPadOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ MirrorPadOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(MirrorPadOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<MirrorPadOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct MirrorPadOptionsBuilder {
typedef MirrorPadOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_mode(tflite::MirrorPadMode mode) {
fbb_.AddElement<int8_t>(MirrorPadOptions::VT_MODE, static_cast<int8_t>(mode), 0);
}
- explicit MirrorPadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit MirrorPadOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<MirrorPadOptions> Finish() {
+ ::flatbuffers::Offset<MirrorPadOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<MirrorPadOptions>(end);
+ auto o = ::flatbuffers::Offset<MirrorPadOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<MirrorPadOptions> CreateMirrorPadOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<MirrorPadOptions> CreateMirrorPadOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::MirrorPadMode mode = tflite::MirrorPadMode_REFLECT) {
MirrorPadOptionsBuilder builder_(_fbb);
builder_.add_mode(mode);
return builder_.Finish();
}
-flatbuffers::Offset<MirrorPadOptions> CreateMirrorPadOptions(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<MirrorPadOptions> CreateMirrorPadOptions(::flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct UniqueOptionsT : public flatbuffers::NativeTable {
+struct UniqueOptionsT : public ::flatbuffers::NativeTable {
typedef UniqueOptions TableType;
tflite::TensorType idx_out_type = tflite::TensorType_INT32;
};
-struct UniqueOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct UniqueOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef UniqueOptionsT NativeTableType;
typedef UniqueOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -9848,207 +12518,207 @@
tflite::TensorType idx_out_type() const {
return static_cast<tflite::TensorType>(GetField<int8_t>(VT_IDX_OUT_TYPE, 2));
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_IDX_OUT_TYPE, 1) &&
verifier.EndTable();
}
- UniqueOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(UniqueOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<UniqueOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ UniqueOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(UniqueOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<UniqueOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct UniqueOptionsBuilder {
typedef UniqueOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_idx_out_type(tflite::TensorType idx_out_type) {
fbb_.AddElement<int8_t>(UniqueOptions::VT_IDX_OUT_TYPE, static_cast<int8_t>(idx_out_type), 2);
}
- explicit UniqueOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit UniqueOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<UniqueOptions> Finish() {
+ ::flatbuffers::Offset<UniqueOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<UniqueOptions>(end);
+ auto o = ::flatbuffers::Offset<UniqueOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<UniqueOptions> CreateUniqueOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<UniqueOptions> CreateUniqueOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
tflite::TensorType idx_out_type = tflite::TensorType_INT32) {
UniqueOptionsBuilder builder_(_fbb);
builder_.add_idx_out_type(idx_out_type);
return builder_.Finish();
}
-flatbuffers::Offset<UniqueOptions> CreateUniqueOptions(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<UniqueOptions> CreateUniqueOptions(::flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ReverseV2OptionsT : public flatbuffers::NativeTable {
+struct ReverseV2OptionsT : public ::flatbuffers::NativeTable {
typedef ReverseV2Options TableType;
};
-struct ReverseV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct ReverseV2Options FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ReverseV2OptionsT NativeTableType;
typedef ReverseV2OptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- ReverseV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ReverseV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<ReverseV2Options> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ReverseV2OptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ReverseV2OptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ReverseV2Options> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ReverseV2OptionsBuilder {
typedef ReverseV2Options Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit ReverseV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit ReverseV2OptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<ReverseV2Options> Finish() {
+ ::flatbuffers::Offset<ReverseV2Options> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<ReverseV2Options>(end);
+ auto o = ::flatbuffers::Offset<ReverseV2Options>(end);
return o;
}
};
-inline flatbuffers::Offset<ReverseV2Options> CreateReverseV2Options(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<ReverseV2Options> CreateReverseV2Options(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
ReverseV2OptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<ReverseV2Options> CreateReverseV2Options(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<ReverseV2Options> CreateReverseV2Options(::flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct AddNOptionsT : public flatbuffers::NativeTable {
+struct AddNOptionsT : public ::flatbuffers::NativeTable {
typedef AddNOptions TableType;
};
-struct AddNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct AddNOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef AddNOptionsT NativeTableType;
typedef AddNOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- AddNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(AddNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<AddNOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ AddNOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(AddNOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<AddNOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct AddNOptionsBuilder {
typedef AddNOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit AddNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit AddNOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<AddNOptions> Finish() {
+ ::flatbuffers::Offset<AddNOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<AddNOptions>(end);
+ auto o = ::flatbuffers::Offset<AddNOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<AddNOptions> CreateAddNOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<AddNOptions> CreateAddNOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
AddNOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<AddNOptions> CreateAddNOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<AddNOptions> CreateAddNOptions(::flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct GatherNdOptionsT : public flatbuffers::NativeTable {
+struct GatherNdOptionsT : public ::flatbuffers::NativeTable {
typedef GatherNdOptions TableType;
};
-struct GatherNdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct GatherNdOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef GatherNdOptionsT NativeTableType;
typedef GatherNdOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- GatherNdOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(GatherNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<GatherNdOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ GatherNdOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(GatherNdOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<GatherNdOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct GatherNdOptionsBuilder {
typedef GatherNdOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit GatherNdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit GatherNdOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<GatherNdOptions> Finish() {
+ ::flatbuffers::Offset<GatherNdOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<GatherNdOptions>(end);
+ auto o = ::flatbuffers::Offset<GatherNdOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<GatherNdOptions> CreateGatherNdOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<GatherNdOptions> CreateGatherNdOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
GatherNdOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<GatherNdOptions> CreateGatherNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<GatherNdOptions> CreateGatherNdOptions(::flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct WhereOptionsT : public flatbuffers::NativeTable {
+struct WhereOptionsT : public ::flatbuffers::NativeTable {
typedef WhereOptions TableType;
};
-struct WhereOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct WhereOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef WhereOptionsT NativeTableType;
typedef WhereOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- WhereOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(WhereOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<WhereOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ WhereOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(WhereOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<WhereOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct WhereOptionsBuilder {
typedef WhereOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit WhereOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit WhereOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<WhereOptions> Finish() {
+ ::flatbuffers::Offset<WhereOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<WhereOptions>(end);
+ auto o = ::flatbuffers::Offset<WhereOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<WhereOptions> CreateWhereOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<WhereOptions> CreateWhereOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
WhereOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<WhereOptions> CreateWhereOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<WhereOptions> CreateWhereOptions(::flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ReverseSequenceOptionsT : public flatbuffers::NativeTable {
+struct ReverseSequenceOptionsT : public ::flatbuffers::NativeTable {
typedef ReverseSequenceOptions TableType;
int32_t seq_dim = 0;
int32_t batch_dim = 0;
};
-struct ReverseSequenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct ReverseSequenceOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ReverseSequenceOptionsT NativeTableType;
typedef ReverseSequenceOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -10061,40 +12731,40 @@
int32_t batch_dim() const {
return GetField<int32_t>(VT_BATCH_DIM, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_SEQ_DIM, 4) &&
VerifyField<int32_t>(verifier, VT_BATCH_DIM, 4) &&
verifier.EndTable();
}
- ReverseSequenceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ReverseSequenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<ReverseSequenceOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ReverseSequenceOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ReverseSequenceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ReverseSequenceOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ReverseSequenceOptionsBuilder {
typedef ReverseSequenceOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_seq_dim(int32_t seq_dim) {
fbb_.AddElement<int32_t>(ReverseSequenceOptions::VT_SEQ_DIM, seq_dim, 0);
}
void add_batch_dim(int32_t batch_dim) {
fbb_.AddElement<int32_t>(ReverseSequenceOptions::VT_BATCH_DIM, batch_dim, 0);
}
- explicit ReverseSequenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit ReverseSequenceOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<ReverseSequenceOptions> Finish() {
+ ::flatbuffers::Offset<ReverseSequenceOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<ReverseSequenceOptions>(end);
+ auto o = ::flatbuffers::Offset<ReverseSequenceOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<ReverseSequenceOptions> CreateReverseSequenceOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<ReverseSequenceOptions> CreateReverseSequenceOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t seq_dim = 0,
int32_t batch_dim = 0) {
ReverseSequenceOptionsBuilder builder_(_fbb);
@@ -10103,132 +12773,132 @@
return builder_.Finish();
}
-flatbuffers::Offset<ReverseSequenceOptions> CreateReverseSequenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<ReverseSequenceOptions> CreateReverseSequenceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct MatrixDiagOptionsT : public flatbuffers::NativeTable {
+struct MatrixDiagOptionsT : public ::flatbuffers::NativeTable {
typedef MatrixDiagOptions TableType;
};
-struct MatrixDiagOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct MatrixDiagOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef MatrixDiagOptionsT NativeTableType;
typedef MatrixDiagOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- MatrixDiagOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(MatrixDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<MatrixDiagOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ MatrixDiagOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(MatrixDiagOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<MatrixDiagOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct MatrixDiagOptionsBuilder {
typedef MatrixDiagOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit MatrixDiagOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit MatrixDiagOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<MatrixDiagOptions> Finish() {
+ ::flatbuffers::Offset<MatrixDiagOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<MatrixDiagOptions>(end);
+ auto o = ::flatbuffers::Offset<MatrixDiagOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<MatrixDiagOptions> CreateMatrixDiagOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<MatrixDiagOptions> CreateMatrixDiagOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
MatrixDiagOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<MatrixDiagOptions> CreateMatrixDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<MatrixDiagOptions> CreateMatrixDiagOptions(::flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct QuantizeOptionsT : public flatbuffers::NativeTable {
+struct QuantizeOptionsT : public ::flatbuffers::NativeTable {
typedef QuantizeOptions TableType;
};
-struct QuantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct QuantizeOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef QuantizeOptionsT NativeTableType;
typedef QuantizeOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- QuantizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(QuantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<QuantizeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ QuantizeOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(QuantizeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<QuantizeOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct QuantizeOptionsBuilder {
typedef QuantizeOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit QuantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit QuantizeOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<QuantizeOptions> Finish() {
+ ::flatbuffers::Offset<QuantizeOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<QuantizeOptions>(end);
+ auto o = ::flatbuffers::Offset<QuantizeOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<QuantizeOptions> CreateQuantizeOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<QuantizeOptions> CreateQuantizeOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
QuantizeOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<QuantizeOptions> CreateQuantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<QuantizeOptions> CreateQuantizeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct MatrixSetDiagOptionsT : public flatbuffers::NativeTable {
+struct MatrixSetDiagOptionsT : public ::flatbuffers::NativeTable {
typedef MatrixSetDiagOptions TableType;
};
-struct MatrixSetDiagOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct MatrixSetDiagOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef MatrixSetDiagOptionsT NativeTableType;
typedef MatrixSetDiagOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- MatrixSetDiagOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(MatrixSetDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<MatrixSetDiagOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ MatrixSetDiagOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(MatrixSetDiagOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<MatrixSetDiagOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct MatrixSetDiagOptionsBuilder {
typedef MatrixSetDiagOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit MatrixSetDiagOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit MatrixSetDiagOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<MatrixSetDiagOptions> Finish() {
+ ::flatbuffers::Offset<MatrixSetDiagOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<MatrixSetDiagOptions>(end);
+ auto o = ::flatbuffers::Offset<MatrixSetDiagOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<MatrixSetDiagOptions> CreateMatrixSetDiagOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<MatrixSetDiagOptions> CreateMatrixSetDiagOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
MatrixSetDiagOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<MatrixSetDiagOptions> CreateMatrixSetDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<MatrixSetDiagOptions> CreateMatrixSetDiagOptions(::flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct IfOptionsT : public flatbuffers::NativeTable {
+struct IfOptionsT : public ::flatbuffers::NativeTable {
typedef IfOptions TableType;
int32_t then_subgraph_index = 0;
int32_t else_subgraph_index = 0;
};
-struct IfOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct IfOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef IfOptionsT NativeTableType;
typedef IfOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -10241,40 +12911,40 @@
int32_t else_subgraph_index() const {
return GetField<int32_t>(VT_ELSE_SUBGRAPH_INDEX, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_THEN_SUBGRAPH_INDEX, 4) &&
VerifyField<int32_t>(verifier, VT_ELSE_SUBGRAPH_INDEX, 4) &&
verifier.EndTable();
}
- IfOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(IfOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<IfOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ IfOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(IfOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<IfOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct IfOptionsBuilder {
typedef IfOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_then_subgraph_index(int32_t then_subgraph_index) {
fbb_.AddElement<int32_t>(IfOptions::VT_THEN_SUBGRAPH_INDEX, then_subgraph_index, 0);
}
void add_else_subgraph_index(int32_t else_subgraph_index) {
fbb_.AddElement<int32_t>(IfOptions::VT_ELSE_SUBGRAPH_INDEX, else_subgraph_index, 0);
}
- explicit IfOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit IfOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<IfOptions> Finish() {
+ ::flatbuffers::Offset<IfOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<IfOptions>(end);
+ auto o = ::flatbuffers::Offset<IfOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<IfOptions> CreateIfOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<IfOptions> CreateIfOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t then_subgraph_index = 0,
int32_t else_subgraph_index = 0) {
IfOptionsBuilder builder_(_fbb);
@@ -10283,14 +12953,14 @@
return builder_.Finish();
}
-flatbuffers::Offset<IfOptions> CreateIfOptions(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<IfOptions> CreateIfOptions(::flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct CallOnceOptionsT : public flatbuffers::NativeTable {
+struct CallOnceOptionsT : public ::flatbuffers::NativeTable {
typedef CallOnceOptions TableType;
int32_t init_subgraph_index = 0;
};
-struct CallOnceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct CallOnceOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef CallOnceOptionsT NativeTableType;
typedef CallOnceOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -10299,51 +12969,51 @@
int32_t init_subgraph_index() const {
return GetField<int32_t>(VT_INIT_SUBGRAPH_INDEX, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_INIT_SUBGRAPH_INDEX, 4) &&
verifier.EndTable();
}
- CallOnceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(CallOnceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<CallOnceOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ CallOnceOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(CallOnceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<CallOnceOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct CallOnceOptionsBuilder {
typedef CallOnceOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_init_subgraph_index(int32_t init_subgraph_index) {
fbb_.AddElement<int32_t>(CallOnceOptions::VT_INIT_SUBGRAPH_INDEX, init_subgraph_index, 0);
}
- explicit CallOnceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit CallOnceOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<CallOnceOptions> Finish() {
+ ::flatbuffers::Offset<CallOnceOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<CallOnceOptions>(end);
+ auto o = ::flatbuffers::Offset<CallOnceOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<CallOnceOptions> CreateCallOnceOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<CallOnceOptions> CreateCallOnceOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t init_subgraph_index = 0) {
CallOnceOptionsBuilder builder_(_fbb);
builder_.add_init_subgraph_index(init_subgraph_index);
return builder_.Finish();
}
-flatbuffers::Offset<CallOnceOptions> CreateCallOnceOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<CallOnceOptions> CreateCallOnceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct WhileOptionsT : public flatbuffers::NativeTable {
+struct WhileOptionsT : public ::flatbuffers::NativeTable {
typedef WhileOptions TableType;
int32_t cond_subgraph_index = 0;
int32_t body_subgraph_index = 0;
};
-struct WhileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct WhileOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef WhileOptionsT NativeTableType;
typedef WhileOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -10356,40 +13026,40 @@
int32_t body_subgraph_index() const {
return GetField<int32_t>(VT_BODY_SUBGRAPH_INDEX, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_COND_SUBGRAPH_INDEX, 4) &&
VerifyField<int32_t>(verifier, VT_BODY_SUBGRAPH_INDEX, 4) &&
verifier.EndTable();
}
- WhileOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(WhileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<WhileOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ WhileOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(WhileOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<WhileOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct WhileOptionsBuilder {
typedef WhileOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_cond_subgraph_index(int32_t cond_subgraph_index) {
fbb_.AddElement<int32_t>(WhileOptions::VT_COND_SUBGRAPH_INDEX, cond_subgraph_index, 0);
}
void add_body_subgraph_index(int32_t body_subgraph_index) {
fbb_.AddElement<int32_t>(WhileOptions::VT_BODY_SUBGRAPH_INDEX, body_subgraph_index, 0);
}
- explicit WhileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit WhileOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<WhileOptions> Finish() {
+ ::flatbuffers::Offset<WhileOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<WhileOptions>(end);
+ auto o = ::flatbuffers::Offset<WhileOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<WhileOptions> CreateWhileOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<WhileOptions> CreateWhileOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t cond_subgraph_index = 0,
int32_t body_subgraph_index = 0) {
WhileOptionsBuilder builder_(_fbb);
@@ -10398,250 +13068,250 @@
return builder_.Finish();
}
-flatbuffers::Offset<WhileOptions> CreateWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<WhileOptions> CreateWhileOptions(::flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct NonMaxSuppressionV4OptionsT : public flatbuffers::NativeTable {
+struct NonMaxSuppressionV4OptionsT : public ::flatbuffers::NativeTable {
typedef NonMaxSuppressionV4Options TableType;
};
-struct NonMaxSuppressionV4Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct NonMaxSuppressionV4Options FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef NonMaxSuppressionV4OptionsT NativeTableType;
typedef NonMaxSuppressionV4OptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- NonMaxSuppressionV4OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(NonMaxSuppressionV4OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<NonMaxSuppressionV4Options> Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ NonMaxSuppressionV4OptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(NonMaxSuppressionV4OptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<NonMaxSuppressionV4Options> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct NonMaxSuppressionV4OptionsBuilder {
typedef NonMaxSuppressionV4Options Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit NonMaxSuppressionV4OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit NonMaxSuppressionV4OptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<NonMaxSuppressionV4Options> Finish() {
+ ::flatbuffers::Offset<NonMaxSuppressionV4Options> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<NonMaxSuppressionV4Options>(end);
+ auto o = ::flatbuffers::Offset<NonMaxSuppressionV4Options>(end);
return o;
}
};
-inline flatbuffers::Offset<NonMaxSuppressionV4Options> CreateNonMaxSuppressionV4Options(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<NonMaxSuppressionV4Options> CreateNonMaxSuppressionV4Options(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
NonMaxSuppressionV4OptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<NonMaxSuppressionV4Options> CreateNonMaxSuppressionV4Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<NonMaxSuppressionV4Options> CreateNonMaxSuppressionV4Options(::flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct NonMaxSuppressionV5OptionsT : public flatbuffers::NativeTable {
+struct NonMaxSuppressionV5OptionsT : public ::flatbuffers::NativeTable {
typedef NonMaxSuppressionV5Options TableType;
};
-struct NonMaxSuppressionV5Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct NonMaxSuppressionV5Options FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef NonMaxSuppressionV5OptionsT NativeTableType;
typedef NonMaxSuppressionV5OptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- NonMaxSuppressionV5OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(NonMaxSuppressionV5OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<NonMaxSuppressionV5Options> Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ NonMaxSuppressionV5OptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(NonMaxSuppressionV5OptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<NonMaxSuppressionV5Options> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct NonMaxSuppressionV5OptionsBuilder {
typedef NonMaxSuppressionV5Options Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit NonMaxSuppressionV5OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit NonMaxSuppressionV5OptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<NonMaxSuppressionV5Options> Finish() {
+ ::flatbuffers::Offset<NonMaxSuppressionV5Options> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<NonMaxSuppressionV5Options>(end);
+ auto o = ::flatbuffers::Offset<NonMaxSuppressionV5Options>(end);
return o;
}
};
-inline flatbuffers::Offset<NonMaxSuppressionV5Options> CreateNonMaxSuppressionV5Options(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<NonMaxSuppressionV5Options> CreateNonMaxSuppressionV5Options(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
NonMaxSuppressionV5OptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<NonMaxSuppressionV5Options> CreateNonMaxSuppressionV5Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<NonMaxSuppressionV5Options> CreateNonMaxSuppressionV5Options(::flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ScatterNdOptionsT : public flatbuffers::NativeTable {
+struct ScatterNdOptionsT : public ::flatbuffers::NativeTable {
typedef ScatterNdOptions TableType;
};
-struct ScatterNdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct ScatterNdOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ScatterNdOptionsT NativeTableType;
typedef ScatterNdOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- ScatterNdOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ScatterNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<ScatterNdOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ScatterNdOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ScatterNdOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ScatterNdOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ScatterNdOptionsBuilder {
typedef ScatterNdOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit ScatterNdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit ScatterNdOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<ScatterNdOptions> Finish() {
+ ::flatbuffers::Offset<ScatterNdOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<ScatterNdOptions>(end);
+ auto o = ::flatbuffers::Offset<ScatterNdOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<ScatterNdOptions> CreateScatterNdOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<ScatterNdOptions> CreateScatterNdOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
ScatterNdOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<ScatterNdOptions> CreateScatterNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<ScatterNdOptions> CreateScatterNdOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SelectV2OptionsT : public flatbuffers::NativeTable {
+struct SelectV2OptionsT : public ::flatbuffers::NativeTable {
typedef SelectV2Options TableType;
};
-struct SelectV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SelectV2Options FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SelectV2OptionsT NativeTableType;
typedef SelectV2OptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- SelectV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SelectV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SelectV2Options> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SelectV2OptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SelectV2OptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SelectV2Options> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SelectV2OptionsBuilder {
typedef SelectV2Options Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit SelectV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit SelectV2OptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SelectV2Options> Finish() {
+ ::flatbuffers::Offset<SelectV2Options> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SelectV2Options>(end);
+ auto o = ::flatbuffers::Offset<SelectV2Options>(end);
return o;
}
};
-inline flatbuffers::Offset<SelectV2Options> CreateSelectV2Options(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<SelectV2Options> CreateSelectV2Options(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
SelectV2OptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<SelectV2Options> CreateSelectV2Options(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SelectV2Options> CreateSelectV2Options(::flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct DensifyOptionsT : public flatbuffers::NativeTable {
+struct DensifyOptionsT : public ::flatbuffers::NativeTable {
typedef DensifyOptions TableType;
};
-struct DensifyOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct DensifyOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef DensifyOptionsT NativeTableType;
typedef DensifyOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- DensifyOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(DensifyOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<DensifyOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ DensifyOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(DensifyOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<DensifyOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct DensifyOptionsBuilder {
typedef DensifyOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit DensifyOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit DensifyOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<DensifyOptions> Finish() {
+ ::flatbuffers::Offset<DensifyOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<DensifyOptions>(end);
+ auto o = ::flatbuffers::Offset<DensifyOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<DensifyOptions> CreateDensifyOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<DensifyOptions> CreateDensifyOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
DensifyOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<DensifyOptions> CreateDensifyOptions(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<DensifyOptions> CreateDensifyOptions(::flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SegmentSumOptionsT : public flatbuffers::NativeTable {
+struct SegmentSumOptionsT : public ::flatbuffers::NativeTable {
typedef SegmentSumOptions TableType;
};
-struct SegmentSumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SegmentSumOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SegmentSumOptionsT NativeTableType;
typedef SegmentSumOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- SegmentSumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SegmentSumOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SegmentSumOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SegmentSumOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SegmentSumOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SegmentSumOptionsBuilder {
typedef SegmentSumOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit SegmentSumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit SegmentSumOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SegmentSumOptions> Finish() {
+ ::flatbuffers::Offset<SegmentSumOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SegmentSumOptions>(end);
+ auto o = ::flatbuffers::Offset<SegmentSumOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<SegmentSumOptions> CreateSegmentSumOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<SegmentSumOptions> CreateSegmentSumOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
SegmentSumOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<SegmentSumOptions> CreateSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SegmentSumOptions> CreateSegmentSumOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct BatchMatMulOptionsT : public flatbuffers::NativeTable {
+struct BatchMatMulOptionsT : public ::flatbuffers::NativeTable {
typedef BatchMatMulOptions TableType;
bool adj_x = false;
bool adj_y = false;
bool asymmetric_quantize_inputs = false;
};
-struct BatchMatMulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct BatchMatMulOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef BatchMatMulOptionsT NativeTableType;
typedef BatchMatMulOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -10658,22 +13328,22 @@
bool asymmetric_quantize_inputs() const {
return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<uint8_t>(verifier, VT_ADJ_X, 1) &&
VerifyField<uint8_t>(verifier, VT_ADJ_Y, 1) &&
VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) &&
verifier.EndTable();
}
- BatchMatMulOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(BatchMatMulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<BatchMatMulOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ BatchMatMulOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(BatchMatMulOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<BatchMatMulOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct BatchMatMulOptionsBuilder {
typedef BatchMatMulOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_adj_x(bool adj_x) {
fbb_.AddElement<uint8_t>(BatchMatMulOptions::VT_ADJ_X, static_cast<uint8_t>(adj_x), 0);
}
@@ -10683,19 +13353,19 @@
void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) {
fbb_.AddElement<uint8_t>(BatchMatMulOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast<uint8_t>(asymmetric_quantize_inputs), 0);
}
- explicit BatchMatMulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit BatchMatMulOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<BatchMatMulOptions> Finish() {
+ ::flatbuffers::Offset<BatchMatMulOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<BatchMatMulOptions>(end);
+ auto o = ::flatbuffers::Offset<BatchMatMulOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<BatchMatMulOptions> CreateBatchMatMulOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<BatchMatMulOptions> CreateBatchMatMulOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
bool adj_x = false,
bool adj_y = false,
bool asymmetric_quantize_inputs = false) {
@@ -10706,15 +13376,15 @@
return builder_.Finish();
}
-flatbuffers::Offset<BatchMatMulOptions> CreateBatchMatMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<BatchMatMulOptions> CreateBatchMatMulOptions(::flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct CumsumOptionsT : public flatbuffers::NativeTable {
+struct CumsumOptionsT : public ::flatbuffers::NativeTable {
typedef CumsumOptions TableType;
bool exclusive = false;
bool reverse = false;
};
-struct CumsumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct CumsumOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef CumsumOptionsT NativeTableType;
typedef CumsumOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -10727,40 +13397,40 @@
bool reverse() const {
return GetField<uint8_t>(VT_REVERSE, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<uint8_t>(verifier, VT_EXCLUSIVE, 1) &&
VerifyField<uint8_t>(verifier, VT_REVERSE, 1) &&
verifier.EndTable();
}
- CumsumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(CumsumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<CumsumOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ CumsumOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(CumsumOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<CumsumOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct CumsumOptionsBuilder {
typedef CumsumOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_exclusive(bool exclusive) {
fbb_.AddElement<uint8_t>(CumsumOptions::VT_EXCLUSIVE, static_cast<uint8_t>(exclusive), 0);
}
void add_reverse(bool reverse) {
fbb_.AddElement<uint8_t>(CumsumOptions::VT_REVERSE, static_cast<uint8_t>(reverse), 0);
}
- explicit CumsumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit CumsumOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<CumsumOptions> Finish() {
+ ::flatbuffers::Offset<CumsumOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<CumsumOptions>(end);
+ auto o = ::flatbuffers::Offset<CumsumOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<CumsumOptions> CreateCumsumOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<CumsumOptions> CreateCumsumOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
bool exclusive = false,
bool reverse = false) {
CumsumOptionsBuilder builder_(_fbb);
@@ -10769,94 +13439,94 @@
return builder_.Finish();
}
-flatbuffers::Offset<CumsumOptions> CreateCumsumOptions(flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<CumsumOptions> CreateCumsumOptions(::flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct BroadcastToOptionsT : public flatbuffers::NativeTable {
+struct BroadcastToOptionsT : public ::flatbuffers::NativeTable {
typedef BroadcastToOptions TableType;
};
-struct BroadcastToOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct BroadcastToOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef BroadcastToOptionsT NativeTableType;
typedef BroadcastToOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- BroadcastToOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(BroadcastToOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<BroadcastToOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ BroadcastToOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(BroadcastToOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<BroadcastToOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct BroadcastToOptionsBuilder {
typedef BroadcastToOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit BroadcastToOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit BroadcastToOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<BroadcastToOptions> Finish() {
+ ::flatbuffers::Offset<BroadcastToOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<BroadcastToOptions>(end);
+ auto o = ::flatbuffers::Offset<BroadcastToOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<BroadcastToOptions> CreateBroadcastToOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<BroadcastToOptions> CreateBroadcastToOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
BroadcastToOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<BroadcastToOptions> CreateBroadcastToOptions(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<BroadcastToOptions> CreateBroadcastToOptions(::flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct Rfft2dOptionsT : public flatbuffers::NativeTable {
+struct Rfft2dOptionsT : public ::flatbuffers::NativeTable {
typedef Rfft2dOptions TableType;
};
-struct Rfft2dOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct Rfft2dOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef Rfft2dOptionsT NativeTableType;
typedef Rfft2dOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- Rfft2dOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(Rfft2dOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<Rfft2dOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ Rfft2dOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(Rfft2dOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<Rfft2dOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct Rfft2dOptionsBuilder {
typedef Rfft2dOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit Rfft2dOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit Rfft2dOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<Rfft2dOptions> Finish() {
+ ::flatbuffers::Offset<Rfft2dOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<Rfft2dOptions>(end);
+ auto o = ::flatbuffers::Offset<Rfft2dOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<Rfft2dOptions> CreateRfft2dOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<Rfft2dOptions> CreateRfft2dOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
Rfft2dOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<Rfft2dOptions> CreateRfft2dOptions(flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<Rfft2dOptions> CreateRfft2dOptions(::flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct HashtableOptionsT : public flatbuffers::NativeTable {
+struct HashtableOptionsT : public ::flatbuffers::NativeTable {
typedef HashtableOptions TableType;
int32_t table_id = 0;
tflite::TensorType key_dtype = tflite::TensorType_FLOAT32;
tflite::TensorType value_dtype = tflite::TensorType_FLOAT32;
};
-struct HashtableOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct HashtableOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef HashtableOptionsT NativeTableType;
typedef HashtableOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -10873,22 +13543,22 @@
tflite::TensorType value_dtype() const {
return static_cast<tflite::TensorType>(GetField<int8_t>(VT_VALUE_DTYPE, 0));
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int32_t>(verifier, VT_TABLE_ID, 4) &&
VerifyField<int8_t>(verifier, VT_KEY_DTYPE, 1) &&
VerifyField<int8_t>(verifier, VT_VALUE_DTYPE, 1) &&
verifier.EndTable();
}
- HashtableOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(HashtableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<HashtableOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ HashtableOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(HashtableOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<HashtableOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct HashtableOptionsBuilder {
typedef HashtableOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_table_id(int32_t table_id) {
fbb_.AddElement<int32_t>(HashtableOptions::VT_TABLE_ID, table_id, 0);
}
@@ -10898,19 +13568,19 @@
void add_value_dtype(tflite::TensorType value_dtype) {
fbb_.AddElement<int8_t>(HashtableOptions::VT_VALUE_DTYPE, static_cast<int8_t>(value_dtype), 0);
}
- explicit HashtableOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit HashtableOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<HashtableOptions> Finish() {
+ ::flatbuffers::Offset<HashtableOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<HashtableOptions>(end);
+ auto o = ::flatbuffers::Offset<HashtableOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<HashtableOptions> CreateHashtableOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<HashtableOptions> CreateHashtableOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int32_t table_id = 0,
tflite::TensorType key_dtype = tflite::TensorType_FLOAT32,
tflite::TensorType value_dtype = tflite::TensorType_FLOAT32) {
@@ -10921,145 +13591,145 @@
return builder_.Finish();
}
-flatbuffers::Offset<HashtableOptions> CreateHashtableOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<HashtableOptions> CreateHashtableOptions(::flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct HashtableFindOptionsT : public flatbuffers::NativeTable {
+struct HashtableFindOptionsT : public ::flatbuffers::NativeTable {
typedef HashtableFindOptions TableType;
};
-struct HashtableFindOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct HashtableFindOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef HashtableFindOptionsT NativeTableType;
typedef HashtableFindOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- HashtableFindOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(HashtableFindOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<HashtableFindOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ HashtableFindOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(HashtableFindOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<HashtableFindOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct HashtableFindOptionsBuilder {
typedef HashtableFindOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit HashtableFindOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit HashtableFindOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<HashtableFindOptions> Finish() {
+ ::flatbuffers::Offset<HashtableFindOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<HashtableFindOptions>(end);
+ auto o = ::flatbuffers::Offset<HashtableFindOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<HashtableFindOptions> CreateHashtableFindOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<HashtableFindOptions> CreateHashtableFindOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
HashtableFindOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<HashtableFindOptions> CreateHashtableFindOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<HashtableFindOptions> CreateHashtableFindOptions(::flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct HashtableImportOptionsT : public flatbuffers::NativeTable {
+struct HashtableImportOptionsT : public ::flatbuffers::NativeTable {
typedef HashtableImportOptions TableType;
};
-struct HashtableImportOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct HashtableImportOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef HashtableImportOptionsT NativeTableType;
typedef HashtableImportOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- HashtableImportOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(HashtableImportOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<HashtableImportOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ HashtableImportOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(HashtableImportOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<HashtableImportOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct HashtableImportOptionsBuilder {
typedef HashtableImportOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit HashtableImportOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit HashtableImportOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<HashtableImportOptions> Finish() {
+ ::flatbuffers::Offset<HashtableImportOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<HashtableImportOptions>(end);
+ auto o = ::flatbuffers::Offset<HashtableImportOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<HashtableImportOptions> CreateHashtableImportOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<HashtableImportOptions> CreateHashtableImportOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
HashtableImportOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<HashtableImportOptions> CreateHashtableImportOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<HashtableImportOptions> CreateHashtableImportOptions(::flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct HashtableSizeOptionsT : public flatbuffers::NativeTable {
+struct HashtableSizeOptionsT : public ::flatbuffers::NativeTable {
typedef HashtableSizeOptions TableType;
};
-struct HashtableSizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct HashtableSizeOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef HashtableSizeOptionsT NativeTableType;
typedef HashtableSizeOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- HashtableSizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(HashtableSizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<HashtableSizeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ HashtableSizeOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(HashtableSizeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<HashtableSizeOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct HashtableSizeOptionsBuilder {
typedef HashtableSizeOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit HashtableSizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit HashtableSizeOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<HashtableSizeOptions> Finish() {
+ ::flatbuffers::Offset<HashtableSizeOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<HashtableSizeOptions>(end);
+ auto o = ::flatbuffers::Offset<HashtableSizeOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<HashtableSizeOptions> CreateHashtableSizeOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<HashtableSizeOptions> CreateHashtableSizeOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
HashtableSizeOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<HashtableSizeOptions> CreateHashtableSizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<HashtableSizeOptions> CreateHashtableSizeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct VarHandleOptionsT : public flatbuffers::NativeTable {
+struct VarHandleOptionsT : public ::flatbuffers::NativeTable {
typedef VarHandleOptions TableType;
std::string container{};
std::string shared_name{};
};
-struct VarHandleOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct VarHandleOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef VarHandleOptionsT NativeTableType;
typedef VarHandleOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
VT_CONTAINER = 4,
VT_SHARED_NAME = 6
};
- const flatbuffers::String *container() const {
- return GetPointer<const flatbuffers::String *>(VT_CONTAINER);
+ const ::flatbuffers::String *container() const {
+ return GetPointer<const ::flatbuffers::String *>(VT_CONTAINER);
}
- const flatbuffers::String *shared_name() const {
- return GetPointer<const flatbuffers::String *>(VT_SHARED_NAME);
+ const ::flatbuffers::String *shared_name() const {
+ return GetPointer<const ::flatbuffers::String *>(VT_SHARED_NAME);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyOffset(verifier, VT_CONTAINER) &&
verifier.VerifyString(container()) &&
@@ -11067,44 +13737,44 @@
verifier.VerifyString(shared_name()) &&
verifier.EndTable();
}
- VarHandleOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(VarHandleOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<VarHandleOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ VarHandleOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(VarHandleOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<VarHandleOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct VarHandleOptionsBuilder {
typedef VarHandleOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- void add_container(flatbuffers::Offset<flatbuffers::String> container) {
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_container(::flatbuffers::Offset<::flatbuffers::String> container) {
fbb_.AddOffset(VarHandleOptions::VT_CONTAINER, container);
}
- void add_shared_name(flatbuffers::Offset<flatbuffers::String> shared_name) {
+ void add_shared_name(::flatbuffers::Offset<::flatbuffers::String> shared_name) {
fbb_.AddOffset(VarHandleOptions::VT_SHARED_NAME, shared_name);
}
- explicit VarHandleOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit VarHandleOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<VarHandleOptions> Finish() {
+ ::flatbuffers::Offset<VarHandleOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<VarHandleOptions>(end);
+ auto o = ::flatbuffers::Offset<VarHandleOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<VarHandleOptions> CreateVarHandleOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
- flatbuffers::Offset<flatbuffers::String> container = 0,
- flatbuffers::Offset<flatbuffers::String> shared_name = 0) {
+inline ::flatbuffers::Offset<VarHandleOptions> CreateVarHandleOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::String> container = 0,
+ ::flatbuffers::Offset<::flatbuffers::String> shared_name = 0) {
VarHandleOptionsBuilder builder_(_fbb);
builder_.add_shared_name(shared_name);
builder_.add_container(container);
return builder_.Finish();
}
-inline flatbuffers::Offset<VarHandleOptions> CreateVarHandleOptionsDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<VarHandleOptions> CreateVarHandleOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
const char *container = nullptr,
const char *shared_name = nullptr) {
auto container__ = container ? _fbb.CreateString(container) : 0;
@@ -11115,93 +13785,93 @@
shared_name__);
}
-flatbuffers::Offset<VarHandleOptions> CreateVarHandleOptions(flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<VarHandleOptions> CreateVarHandleOptions(::flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ReadVariableOptionsT : public flatbuffers::NativeTable {
+struct ReadVariableOptionsT : public ::flatbuffers::NativeTable {
typedef ReadVariableOptions TableType;
};
-struct ReadVariableOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct ReadVariableOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ReadVariableOptionsT NativeTableType;
typedef ReadVariableOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- ReadVariableOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ReadVariableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<ReadVariableOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ReadVariableOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ReadVariableOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ReadVariableOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ReadVariableOptionsBuilder {
typedef ReadVariableOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit ReadVariableOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit ReadVariableOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<ReadVariableOptions> Finish() {
+ ::flatbuffers::Offset<ReadVariableOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<ReadVariableOptions>(end);
+ auto o = ::flatbuffers::Offset<ReadVariableOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<ReadVariableOptions> CreateReadVariableOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<ReadVariableOptions> CreateReadVariableOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
ReadVariableOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<ReadVariableOptions> CreateReadVariableOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<ReadVariableOptions> CreateReadVariableOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct AssignVariableOptionsT : public flatbuffers::NativeTable {
+struct AssignVariableOptionsT : public ::flatbuffers::NativeTable {
typedef AssignVariableOptions TableType;
};
-struct AssignVariableOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct AssignVariableOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef AssignVariableOptionsT NativeTableType;
typedef AssignVariableOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- AssignVariableOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(AssignVariableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<AssignVariableOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ AssignVariableOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(AssignVariableOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<AssignVariableOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct AssignVariableOptionsBuilder {
typedef AssignVariableOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit AssignVariableOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit AssignVariableOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<AssignVariableOptions> Finish() {
+ ::flatbuffers::Offset<AssignVariableOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<AssignVariableOptions>(end);
+ auto o = ::flatbuffers::Offset<AssignVariableOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<AssignVariableOptions> CreateAssignVariableOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<AssignVariableOptions> CreateAssignVariableOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
AssignVariableOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<AssignVariableOptions> CreateAssignVariableOptions(flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<AssignVariableOptions> CreateAssignVariableOptions(::flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct RandomOptionsT : public flatbuffers::NativeTable {
+struct RandomOptionsT : public ::flatbuffers::NativeTable {
typedef RandomOptions TableType;
int64_t seed = 0;
int64_t seed2 = 0;
};
-struct RandomOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct RandomOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef RandomOptionsT NativeTableType;
typedef RandomOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -11214,40 +13884,40 @@
int64_t seed2() const {
return GetField<int64_t>(VT_SEED2, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int64_t>(verifier, VT_SEED, 8) &&
VerifyField<int64_t>(verifier, VT_SEED2, 8) &&
verifier.EndTable();
}
- RandomOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(RandomOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<RandomOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ RandomOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(RandomOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<RandomOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct RandomOptionsBuilder {
typedef RandomOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_seed(int64_t seed) {
fbb_.AddElement<int64_t>(RandomOptions::VT_SEED, seed, 0);
}
void add_seed2(int64_t seed2) {
fbb_.AddElement<int64_t>(RandomOptions::VT_SEED2, seed2, 0);
}
- explicit RandomOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit RandomOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<RandomOptions> Finish() {
+ ::flatbuffers::Offset<RandomOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<RandomOptions>(end);
+ auto o = ::flatbuffers::Offset<RandomOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<RandomOptions> CreateRandomOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<RandomOptions> CreateRandomOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int64_t seed = 0,
int64_t seed2 = 0) {
RandomOptionsBuilder builder_(_fbb);
@@ -11256,61 +13926,61 @@
return builder_.Finish();
}
-flatbuffers::Offset<RandomOptions> CreateRandomOptions(flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<RandomOptions> CreateRandomOptions(::flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct BucketizeOptionsT : public flatbuffers::NativeTable {
+struct BucketizeOptionsT : public ::flatbuffers::NativeTable {
typedef BucketizeOptions TableType;
std::vector<float> boundaries{};
};
-struct BucketizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct BucketizeOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef BucketizeOptionsT NativeTableType;
typedef BucketizeOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
VT_BOUNDARIES = 4
};
- const flatbuffers::Vector<float> *boundaries() const {
- return GetPointer<const flatbuffers::Vector<float> *>(VT_BOUNDARIES);
+ const ::flatbuffers::Vector<float> *boundaries() const {
+ return GetPointer<const ::flatbuffers::Vector<float> *>(VT_BOUNDARIES);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyOffset(verifier, VT_BOUNDARIES) &&
verifier.VerifyVector(boundaries()) &&
verifier.EndTable();
}
- BucketizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(BucketizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<BucketizeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ BucketizeOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(BucketizeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<BucketizeOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct BucketizeOptionsBuilder {
typedef BucketizeOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- void add_boundaries(flatbuffers::Offset<flatbuffers::Vector<float>> boundaries) {
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_boundaries(::flatbuffers::Offset<::flatbuffers::Vector<float>> boundaries) {
fbb_.AddOffset(BucketizeOptions::VT_BOUNDARIES, boundaries);
}
- explicit BucketizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit BucketizeOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<BucketizeOptions> Finish() {
+ ::flatbuffers::Offset<BucketizeOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<BucketizeOptions>(end);
+ auto o = ::flatbuffers::Offset<BucketizeOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<BucketizeOptions> CreateBucketizeOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
- flatbuffers::Offset<flatbuffers::Vector<float>> boundaries = 0) {
+inline ::flatbuffers::Offset<BucketizeOptions> CreateBucketizeOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<float>> boundaries = 0) {
BucketizeOptionsBuilder builder_(_fbb);
builder_.add_boundaries(boundaries);
return builder_.Finish();
}
-inline flatbuffers::Offset<BucketizeOptions> CreateBucketizeOptionsDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<BucketizeOptions> CreateBucketizeOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
const std::vector<float> *boundaries = nullptr) {
auto boundaries__ = boundaries ? _fbb.CreateVector<float>(*boundaries) : 0;
return tflite::CreateBucketizeOptions(
@@ -11318,14 +13988,14 @@
boundaries__);
}
-flatbuffers::Offset<BucketizeOptions> CreateBucketizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<BucketizeOptions> CreateBucketizeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct GeluOptionsT : public flatbuffers::NativeTable {
+struct GeluOptionsT : public ::flatbuffers::NativeTable {
typedef GeluOptions TableType;
bool approximate = false;
};
-struct GeluOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct GeluOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef GeluOptionsT NativeTableType;
typedef GeluOptionsBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -11334,435 +14004,526 @@
bool approximate() const {
return GetField<uint8_t>(VT_APPROXIMATE, 0) != 0;
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<uint8_t>(verifier, VT_APPROXIMATE, 1) &&
verifier.EndTable();
}
- GeluOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(GeluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<GeluOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ GeluOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(GeluOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<GeluOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct GeluOptionsBuilder {
typedef GeluOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_approximate(bool approximate) {
fbb_.AddElement<uint8_t>(GeluOptions::VT_APPROXIMATE, static_cast<uint8_t>(approximate), 0);
}
- explicit GeluOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit GeluOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<GeluOptions> Finish() {
+ ::flatbuffers::Offset<GeluOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<GeluOptions>(end);
+ auto o = ::flatbuffers::Offset<GeluOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<GeluOptions> CreateGeluOptions(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<GeluOptions> CreateGeluOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
bool approximate = false) {
GeluOptionsBuilder builder_(_fbb);
builder_.add_approximate(approximate);
return builder_.Finish();
}
-flatbuffers::Offset<GeluOptions> CreateGeluOptions(flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<GeluOptions> CreateGeluOptions(::flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct DynamicUpdateSliceOptionsT : public flatbuffers::NativeTable {
+struct DynamicUpdateSliceOptionsT : public ::flatbuffers::NativeTable {
typedef DynamicUpdateSliceOptions TableType;
};
-struct DynamicUpdateSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct DynamicUpdateSliceOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef DynamicUpdateSliceOptionsT NativeTableType;
typedef DynamicUpdateSliceOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- DynamicUpdateSliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(DynamicUpdateSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<DynamicUpdateSliceOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ DynamicUpdateSliceOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(DynamicUpdateSliceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<DynamicUpdateSliceOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct DynamicUpdateSliceOptionsBuilder {
typedef DynamicUpdateSliceOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit DynamicUpdateSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit DynamicUpdateSliceOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<DynamicUpdateSliceOptions> Finish() {
+ ::flatbuffers::Offset<DynamicUpdateSliceOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<DynamicUpdateSliceOptions>(end);
+ auto o = ::flatbuffers::Offset<DynamicUpdateSliceOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<DynamicUpdateSliceOptions> CreateDynamicUpdateSliceOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<DynamicUpdateSliceOptions> CreateDynamicUpdateSliceOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
DynamicUpdateSliceOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<DynamicUpdateSliceOptions> CreateDynamicUpdateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<DynamicUpdateSliceOptions> CreateDynamicUpdateSliceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct UnsortedSegmentProdOptionsT : public flatbuffers::NativeTable {
+struct UnsortedSegmentProdOptionsT : public ::flatbuffers::NativeTable {
typedef UnsortedSegmentProdOptions TableType;
};
-struct UnsortedSegmentProdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct UnsortedSegmentProdOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef UnsortedSegmentProdOptionsT NativeTableType;
typedef UnsortedSegmentProdOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- UnsortedSegmentProdOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(UnsortedSegmentProdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<UnsortedSegmentProdOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ UnsortedSegmentProdOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(UnsortedSegmentProdOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<UnsortedSegmentProdOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct UnsortedSegmentProdOptionsBuilder {
typedef UnsortedSegmentProdOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit UnsortedSegmentProdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit UnsortedSegmentProdOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<UnsortedSegmentProdOptions> Finish() {
+ ::flatbuffers::Offset<UnsortedSegmentProdOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<UnsortedSegmentProdOptions>(end);
+ auto o = ::flatbuffers::Offset<UnsortedSegmentProdOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<UnsortedSegmentProdOptions> CreateUnsortedSegmentProdOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<UnsortedSegmentProdOptions> CreateUnsortedSegmentProdOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
UnsortedSegmentProdOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<UnsortedSegmentProdOptions> CreateUnsortedSegmentProdOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<UnsortedSegmentProdOptions> CreateUnsortedSegmentProdOptions(::flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct UnsortedSegmentMaxOptionsT : public flatbuffers::NativeTable {
+struct UnsortedSegmentMaxOptionsT : public ::flatbuffers::NativeTable {
typedef UnsortedSegmentMaxOptions TableType;
};
-struct UnsortedSegmentMaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct UnsortedSegmentMaxOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef UnsortedSegmentMaxOptionsT NativeTableType;
typedef UnsortedSegmentMaxOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- UnsortedSegmentMaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(UnsortedSegmentMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<UnsortedSegmentMaxOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ UnsortedSegmentMaxOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(UnsortedSegmentMaxOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<UnsortedSegmentMaxOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct UnsortedSegmentMaxOptionsBuilder {
typedef UnsortedSegmentMaxOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit UnsortedSegmentMaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit UnsortedSegmentMaxOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<UnsortedSegmentMaxOptions> Finish() {
+ ::flatbuffers::Offset<UnsortedSegmentMaxOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<UnsortedSegmentMaxOptions>(end);
+ auto o = ::flatbuffers::Offset<UnsortedSegmentMaxOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<UnsortedSegmentMaxOptions> CreateUnsortedSegmentMaxOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<UnsortedSegmentMaxOptions> CreateUnsortedSegmentMaxOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
UnsortedSegmentMaxOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<UnsortedSegmentMaxOptions> CreateUnsortedSegmentMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<UnsortedSegmentMaxOptions> CreateUnsortedSegmentMaxOptions(::flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct UnsortedSegmentSumOptionsT : public flatbuffers::NativeTable {
+struct UnsortedSegmentSumOptionsT : public ::flatbuffers::NativeTable {
typedef UnsortedSegmentSumOptions TableType;
};
-struct UnsortedSegmentSumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct UnsortedSegmentSumOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef UnsortedSegmentSumOptionsT NativeTableType;
typedef UnsortedSegmentSumOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- UnsortedSegmentSumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(UnsortedSegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<UnsortedSegmentSumOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ UnsortedSegmentSumOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(UnsortedSegmentSumOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<UnsortedSegmentSumOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct UnsortedSegmentSumOptionsBuilder {
typedef UnsortedSegmentSumOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit UnsortedSegmentSumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit UnsortedSegmentSumOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<UnsortedSegmentSumOptions> Finish() {
+ ::flatbuffers::Offset<UnsortedSegmentSumOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<UnsortedSegmentSumOptions>(end);
+ auto o = ::flatbuffers::Offset<UnsortedSegmentSumOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<UnsortedSegmentSumOptions> CreateUnsortedSegmentSumOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<UnsortedSegmentSumOptions> CreateUnsortedSegmentSumOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
UnsortedSegmentSumOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<UnsortedSegmentSumOptions> CreateUnsortedSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<UnsortedSegmentSumOptions> CreateUnsortedSegmentSumOptions(::flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ATan2OptionsT : public flatbuffers::NativeTable {
+struct ATan2OptionsT : public ::flatbuffers::NativeTable {
typedef ATan2Options TableType;
};
-struct ATan2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct ATan2Options FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ATan2OptionsT NativeTableType;
typedef ATan2OptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- ATan2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ATan2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<ATan2Options> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ATan2OptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ATan2OptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ATan2Options> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ATan2OptionsBuilder {
typedef ATan2Options Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit ATan2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit ATan2OptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<ATan2Options> Finish() {
+ ::flatbuffers::Offset<ATan2Options> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<ATan2Options>(end);
+ auto o = ::flatbuffers::Offset<ATan2Options>(end);
return o;
}
};
-inline flatbuffers::Offset<ATan2Options> CreateATan2Options(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<ATan2Options> CreateATan2Options(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
ATan2OptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<ATan2Options> CreateATan2Options(flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<ATan2Options> CreateATan2Options(::flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct UnsortedSegmentMinOptionsT : public flatbuffers::NativeTable {
+struct UnsortedSegmentMinOptionsT : public ::flatbuffers::NativeTable {
typedef UnsortedSegmentMinOptions TableType;
};
-struct UnsortedSegmentMinOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct UnsortedSegmentMinOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef UnsortedSegmentMinOptionsT NativeTableType;
typedef UnsortedSegmentMinOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- UnsortedSegmentMinOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(UnsortedSegmentMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<UnsortedSegmentMinOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ UnsortedSegmentMinOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(UnsortedSegmentMinOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<UnsortedSegmentMinOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct UnsortedSegmentMinOptionsBuilder {
typedef UnsortedSegmentMinOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit UnsortedSegmentMinOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit UnsortedSegmentMinOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<UnsortedSegmentMinOptions> Finish() {
+ ::flatbuffers::Offset<UnsortedSegmentMinOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<UnsortedSegmentMinOptions>(end);
+ auto o = ::flatbuffers::Offset<UnsortedSegmentMinOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<UnsortedSegmentMinOptions> CreateUnsortedSegmentMinOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<UnsortedSegmentMinOptions> CreateUnsortedSegmentMinOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
UnsortedSegmentMinOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<UnsortedSegmentMinOptions> CreateUnsortedSegmentMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<UnsortedSegmentMinOptions> CreateUnsortedSegmentMinOptions(::flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SignOptionsT : public flatbuffers::NativeTable {
+struct SignOptionsT : public ::flatbuffers::NativeTable {
typedef SignOptions TableType;
};
-struct SignOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SignOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SignOptionsT NativeTableType;
typedef SignOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- SignOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SignOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SignOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SignOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SignOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SignOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SignOptionsBuilder {
typedef SignOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit SignOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit SignOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SignOptions> Finish() {
+ ::flatbuffers::Offset<SignOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SignOptions>(end);
+ auto o = ::flatbuffers::Offset<SignOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<SignOptions> CreateSignOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<SignOptions> CreateSignOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
SignOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<SignOptions> CreateSignOptions(flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SignOptions> CreateSignOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct BitcastOptionsT : public flatbuffers::NativeTable {
+struct BitcastOptionsT : public ::flatbuffers::NativeTable {
typedef BitcastOptions TableType;
};
-struct BitcastOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct BitcastOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef BitcastOptionsT NativeTableType;
typedef BitcastOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- BitcastOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(BitcastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<BitcastOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BitcastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ BitcastOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(BitcastOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<BitcastOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BitcastOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct BitcastOptionsBuilder {
typedef BitcastOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit BitcastOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit BitcastOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<BitcastOptions> Finish() {
+ ::flatbuffers::Offset<BitcastOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<BitcastOptions>(end);
+ auto o = ::flatbuffers::Offset<BitcastOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<BitcastOptions> CreateBitcastOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<BitcastOptions> CreateBitcastOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
BitcastOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<BitcastOptions> CreateBitcastOptions(flatbuffers::FlatBufferBuilder &_fbb, const BitcastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<BitcastOptions> CreateBitcastOptions(::flatbuffers::FlatBufferBuilder &_fbb, const BitcastOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct BitwiseXorOptionsT : public flatbuffers::NativeTable {
+struct BitwiseXorOptionsT : public ::flatbuffers::NativeTable {
typedef BitwiseXorOptions TableType;
};
-struct BitwiseXorOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct BitwiseXorOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef BitwiseXorOptionsT NativeTableType;
typedef BitwiseXorOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- BitwiseXorOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(BitwiseXorOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<BitwiseXorOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BitwiseXorOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ BitwiseXorOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(BitwiseXorOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<BitwiseXorOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BitwiseXorOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct BitwiseXorOptionsBuilder {
typedef BitwiseXorOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit BitwiseXorOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit BitwiseXorOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<BitwiseXorOptions> Finish() {
+ ::flatbuffers::Offset<BitwiseXorOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<BitwiseXorOptions>(end);
+ auto o = ::flatbuffers::Offset<BitwiseXorOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<BitwiseXorOptions> CreateBitwiseXorOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<BitwiseXorOptions> CreateBitwiseXorOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
BitwiseXorOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<BitwiseXorOptions> CreateBitwiseXorOptions(flatbuffers::FlatBufferBuilder &_fbb, const BitwiseXorOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<BitwiseXorOptions> CreateBitwiseXorOptions(::flatbuffers::FlatBufferBuilder &_fbb, const BitwiseXorOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct RightShiftOptionsT : public flatbuffers::NativeTable {
+struct RightShiftOptionsT : public ::flatbuffers::NativeTable {
typedef RightShiftOptions TableType;
};
-struct RightShiftOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct RightShiftOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef RightShiftOptionsT NativeTableType;
typedef RightShiftOptionsBuilder Builder;
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
verifier.EndTable();
}
- RightShiftOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(RightShiftOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<RightShiftOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const RightShiftOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ RightShiftOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(RightShiftOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<RightShiftOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const RightShiftOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct RightShiftOptionsBuilder {
typedef RightShiftOptions Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- explicit RightShiftOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit RightShiftOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<RightShiftOptions> Finish() {
+ ::flatbuffers::Offset<RightShiftOptions> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<RightShiftOptions>(end);
+ auto o = ::flatbuffers::Offset<RightShiftOptions>(end);
return o;
}
};
-inline flatbuffers::Offset<RightShiftOptions> CreateRightShiftOptions(
- flatbuffers::FlatBufferBuilder &_fbb) {
+inline ::flatbuffers::Offset<RightShiftOptions> CreateRightShiftOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
RightShiftOptionsBuilder builder_(_fbb);
return builder_.Finish();
}
-flatbuffers::Offset<RightShiftOptions> CreateRightShiftOptions(flatbuffers::FlatBufferBuilder &_fbb, const RightShiftOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<RightShiftOptions> CreateRightShiftOptions(::flatbuffers::FlatBufferBuilder &_fbb, const RightShiftOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct OperatorCodeT : public flatbuffers::NativeTable {
+struct DilateOptionsT : public ::flatbuffers::NativeTable {
+ typedef DilateOptions TableType;
+};
+
+struct DilateOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef DilateOptionsT NativeTableType;
+ typedef DilateOptionsBuilder Builder;
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ verifier.EndTable();
+ }
+ DilateOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(DilateOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<DilateOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const DilateOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct DilateOptionsBuilder {
+ typedef DilateOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ explicit DilateOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<DilateOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<DilateOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<DilateOptions> CreateDilateOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb) {
+ DilateOptionsBuilder builder_(_fbb);
+ return builder_.Finish();
+}
+
+::flatbuffers::Offset<DilateOptions> CreateDilateOptions(::flatbuffers::FlatBufferBuilder &_fbb, const DilateOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct ReduceWindowOptionsT : public ::flatbuffers::NativeTable {
+ typedef ReduceWindowOptions TableType;
+ tflite::ReduceWindowFunction reduce_function = tflite::ReduceWindowFunction_UNSUPPORTED;
+};
+
+struct ReduceWindowOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef ReduceWindowOptionsT NativeTableType;
+ typedef ReduceWindowOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_REDUCE_FUNCTION = 4
+ };
+ tflite::ReduceWindowFunction reduce_function() const {
+ return static_cast<tflite::ReduceWindowFunction>(GetField<int32_t>(VT_REDUCE_FUNCTION, 0));
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyField<int32_t>(verifier, VT_REDUCE_FUNCTION, 4) &&
+ verifier.EndTable();
+ }
+ ReduceWindowOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ReduceWindowOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<ReduceWindowOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ReduceWindowOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct ReduceWindowOptionsBuilder {
+ typedef ReduceWindowOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_reduce_function(tflite::ReduceWindowFunction reduce_function) {
+ fbb_.AddElement<int32_t>(ReduceWindowOptions::VT_REDUCE_FUNCTION, static_cast<int32_t>(reduce_function), 0);
+ }
+ explicit ReduceWindowOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<ReduceWindowOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<ReduceWindowOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<ReduceWindowOptions> CreateReduceWindowOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ tflite::ReduceWindowFunction reduce_function = tflite::ReduceWindowFunction_UNSUPPORTED) {
+ ReduceWindowOptionsBuilder builder_(_fbb);
+ builder_.add_reduce_function(reduce_function);
+ return builder_.Finish();
+}
+
+::flatbuffers::Offset<ReduceWindowOptions> CreateReduceWindowOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ReduceWindowOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct OperatorCodeT : public ::flatbuffers::NativeTable {
typedef OperatorCode TableType;
int8_t deprecated_builtin_code = 0;
std::string custom_code{};
@@ -11770,7 +14531,7 @@
tflite::BuiltinOperator builtin_code = tflite::BuiltinOperator_ADD;
};
-struct OperatorCode FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct OperatorCode FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef OperatorCodeT NativeTableType;
typedef OperatorCodeBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -11782,8 +14543,8 @@
int8_t deprecated_builtin_code() const {
return GetField<int8_t>(VT_DEPRECATED_BUILTIN_CODE, 0);
}
- const flatbuffers::String *custom_code() const {
- return GetPointer<const flatbuffers::String *>(VT_CUSTOM_CODE);
+ const ::flatbuffers::String *custom_code() const {
+ return GetPointer<const ::flatbuffers::String *>(VT_CUSTOM_CODE);
}
int32_t version() const {
return GetField<int32_t>(VT_VERSION, 1);
@@ -11791,7 +14552,7 @@
tflite::BuiltinOperator builtin_code() const {
return static_cast<tflite::BuiltinOperator>(GetField<int32_t>(VT_BUILTIN_CODE, 0));
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<int8_t>(verifier, VT_DEPRECATED_BUILTIN_CODE, 1) &&
VerifyOffset(verifier, VT_CUSTOM_CODE) &&
@@ -11800,19 +14561,19 @@
VerifyField<int32_t>(verifier, VT_BUILTIN_CODE, 4) &&
verifier.EndTable();
}
- OperatorCodeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(OperatorCodeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<OperatorCode> Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ OperatorCodeT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(OperatorCodeT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<OperatorCode> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct OperatorCodeBuilder {
typedef OperatorCode Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_deprecated_builtin_code(int8_t deprecated_builtin_code) {
fbb_.AddElement<int8_t>(OperatorCode::VT_DEPRECATED_BUILTIN_CODE, deprecated_builtin_code, 0);
}
- void add_custom_code(flatbuffers::Offset<flatbuffers::String> custom_code) {
+ void add_custom_code(::flatbuffers::Offset<::flatbuffers::String> custom_code) {
fbb_.AddOffset(OperatorCode::VT_CUSTOM_CODE, custom_code);
}
void add_version(int32_t version) {
@@ -11821,21 +14582,21 @@
void add_builtin_code(tflite::BuiltinOperator builtin_code) {
fbb_.AddElement<int32_t>(OperatorCode::VT_BUILTIN_CODE, static_cast<int32_t>(builtin_code), 0);
}
- explicit OperatorCodeBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit OperatorCodeBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<OperatorCode> Finish() {
+ ::flatbuffers::Offset<OperatorCode> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<OperatorCode>(end);
+ auto o = ::flatbuffers::Offset<OperatorCode>(end);
return o;
}
};
-inline flatbuffers::Offset<OperatorCode> CreateOperatorCode(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<OperatorCode> CreateOperatorCode(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int8_t deprecated_builtin_code = 0,
- flatbuffers::Offset<flatbuffers::String> custom_code = 0,
+ ::flatbuffers::Offset<::flatbuffers::String> custom_code = 0,
int32_t version = 1,
tflite::BuiltinOperator builtin_code = tflite::BuiltinOperator_ADD) {
OperatorCodeBuilder builder_(_fbb);
@@ -11846,8 +14607,8 @@
return builder_.Finish();
}
-inline flatbuffers::Offset<OperatorCode> CreateOperatorCodeDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<OperatorCode> CreateOperatorCodeDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
int8_t deprecated_builtin_code = 0,
const char *custom_code = nullptr,
int32_t version = 1,
@@ -11861,9 +14622,125 @@
builtin_code);
}
-flatbuffers::Offset<OperatorCode> CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<OperatorCode> CreateOperatorCode(::flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct OperatorT : public flatbuffers::NativeTable {
+struct StableHLOCompositeOptionsT : public ::flatbuffers::NativeTable {
+ typedef StableHLOCompositeOptions TableType;
+ std::string name{};
+ int32_t decomposition_subgraph_index = 0;
+ std::vector<uint8_t> composite_attributes{};
+ tflite::CustomOptionsFormat composite_attributes_format = tflite::CustomOptionsFormat_FLEXBUFFERS;
+ int32_t version = 0;
+};
+
+struct StableHLOCompositeOptions FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
+ typedef StableHLOCompositeOptionsT NativeTableType;
+ typedef StableHLOCompositeOptionsBuilder Builder;
+ enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
+ VT_NAME = 4,
+ VT_DECOMPOSITION_SUBGRAPH_INDEX = 6,
+ VT_COMPOSITE_ATTRIBUTES = 8,
+ VT_COMPOSITE_ATTRIBUTES_FORMAT = 10,
+ VT_VERSION = 12
+ };
+ const ::flatbuffers::String *name() const {
+ return GetPointer<const ::flatbuffers::String *>(VT_NAME);
+ }
+ int32_t decomposition_subgraph_index() const {
+ return GetField<int32_t>(VT_DECOMPOSITION_SUBGRAPH_INDEX, 0);
+ }
+ const ::flatbuffers::Vector<uint8_t> *composite_attributes() const {
+ return GetPointer<const ::flatbuffers::Vector<uint8_t> *>(VT_COMPOSITE_ATTRIBUTES);
+ }
+ tflite::CustomOptionsFormat composite_attributes_format() const {
+ return static_cast<tflite::CustomOptionsFormat>(GetField<int8_t>(VT_COMPOSITE_ATTRIBUTES_FORMAT, 0));
+ }
+ int32_t version() const {
+ return GetField<int32_t>(VT_VERSION, 0);
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_NAME) &&
+ verifier.VerifyString(name()) &&
+ VerifyField<int32_t>(verifier, VT_DECOMPOSITION_SUBGRAPH_INDEX, 4) &&
+ VerifyOffset(verifier, VT_COMPOSITE_ATTRIBUTES) &&
+ verifier.VerifyVector(composite_attributes()) &&
+ VerifyField<int8_t>(verifier, VT_COMPOSITE_ATTRIBUTES_FORMAT, 1) &&
+ VerifyField<int32_t>(verifier, VT_VERSION, 4) &&
+ verifier.EndTable();
+ }
+ StableHLOCompositeOptionsT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(StableHLOCompositeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<StableHLOCompositeOptions> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StableHLOCompositeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StableHLOCompositeOptionsBuilder {
+ typedef StableHLOCompositeOptions Table;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_name(::flatbuffers::Offset<::flatbuffers::String> name) {
+ fbb_.AddOffset(StableHLOCompositeOptions::VT_NAME, name);
+ }
+ void add_decomposition_subgraph_index(int32_t decomposition_subgraph_index) {
+ fbb_.AddElement<int32_t>(StableHLOCompositeOptions::VT_DECOMPOSITION_SUBGRAPH_INDEX, decomposition_subgraph_index, 0);
+ }
+ void add_composite_attributes(::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> composite_attributes) {
+ fbb_.AddOffset(StableHLOCompositeOptions::VT_COMPOSITE_ATTRIBUTES, composite_attributes);
+ }
+ void add_composite_attributes_format(tflite::CustomOptionsFormat composite_attributes_format) {
+ fbb_.AddElement<int8_t>(StableHLOCompositeOptions::VT_COMPOSITE_ATTRIBUTES_FORMAT, static_cast<int8_t>(composite_attributes_format), 0);
+ }
+ void add_version(int32_t version) {
+ fbb_.AddElement<int32_t>(StableHLOCompositeOptions::VT_VERSION, version, 0);
+ }
+ explicit StableHLOCompositeOptionsBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ::flatbuffers::Offset<StableHLOCompositeOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = ::flatbuffers::Offset<StableHLOCompositeOptions>(end);
+ return o;
+ }
+};
+
+inline ::flatbuffers::Offset<StableHLOCompositeOptions> CreateStableHLOCompositeOptions(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::String> name = 0,
+ int32_t decomposition_subgraph_index = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> composite_attributes = 0,
+ tflite::CustomOptionsFormat composite_attributes_format = tflite::CustomOptionsFormat_FLEXBUFFERS,
+ int32_t version = 0) {
+ StableHLOCompositeOptionsBuilder builder_(_fbb);
+ builder_.add_version(version);
+ builder_.add_composite_attributes(composite_attributes);
+ builder_.add_decomposition_subgraph_index(decomposition_subgraph_index);
+ builder_.add_name(name);
+ builder_.add_composite_attributes_format(composite_attributes_format);
+ return builder_.Finish();
+}
+
+inline ::flatbuffers::Offset<StableHLOCompositeOptions> CreateStableHLOCompositeOptionsDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ const char *name = nullptr,
+ int32_t decomposition_subgraph_index = 0,
+ const std::vector<uint8_t> *composite_attributes = nullptr,
+ tflite::CustomOptionsFormat composite_attributes_format = tflite::CustomOptionsFormat_FLEXBUFFERS,
+ int32_t version = 0) {
+ auto name__ = name ? _fbb.CreateString(name) : 0;
+ auto composite_attributes__ = composite_attributes ? _fbb.CreateVector<uint8_t>(*composite_attributes) : 0;
+ return tflite::CreateStableHLOCompositeOptions(
+ _fbb,
+ name__,
+ decomposition_subgraph_index,
+ composite_attributes__,
+ composite_attributes_format,
+ version);
+}
+
+::flatbuffers::Offset<StableHLOCompositeOptions> CreateStableHLOCompositeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StableHLOCompositeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct OperatorT : public ::flatbuffers::NativeTable {
typedef Operator TableType;
uint32_t opcode_index = 0;
std::vector<int32_t> inputs{};
@@ -11875,9 +14752,10 @@
std::vector<int32_t> intermediates{};
uint64_t large_custom_options_offset = 0;
uint64_t large_custom_options_size = 0;
+ tflite::BuiltinOptions2Union builtin_options_2{};
};
-struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct Operator FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef OperatorT NativeTableType;
typedef OperatorBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -11891,16 +14769,18 @@
VT_MUTATING_VARIABLE_INPUTS = 18,
VT_INTERMEDIATES = 20,
VT_LARGE_CUSTOM_OPTIONS_OFFSET = 22,
- VT_LARGE_CUSTOM_OPTIONS_SIZE = 24
+ VT_LARGE_CUSTOM_OPTIONS_SIZE = 24,
+ VT_BUILTIN_OPTIONS_2_TYPE = 26,
+ VT_BUILTIN_OPTIONS_2 = 28
};
uint32_t opcode_index() const {
return GetField<uint32_t>(VT_OPCODE_INDEX, 0);
}
- const flatbuffers::Vector<int32_t> *inputs() const {
- return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_INPUTS);
+ const ::flatbuffers::Vector<int32_t> *inputs() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_INPUTS);
}
- const flatbuffers::Vector<int32_t> *outputs() const {
- return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_OUTPUTS);
+ const ::flatbuffers::Vector<int32_t> *outputs() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_OUTPUTS);
}
tflite::BuiltinOptions builtin_options_type() const {
return static_cast<tflite::BuiltinOptions>(GetField<uint8_t>(VT_BUILTIN_OPTIONS_TYPE, 0));
@@ -12287,17 +15167,17 @@
const tflite::RightShiftOptions *builtin_options_as_RightShiftOptions() const {
return builtin_options_type() == tflite::BuiltinOptions_RightShiftOptions ? static_cast<const tflite::RightShiftOptions *>(builtin_options()) : nullptr;
}
- const flatbuffers::Vector<uint8_t> *custom_options() const {
- return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_CUSTOM_OPTIONS);
+ const ::flatbuffers::Vector<uint8_t> *custom_options() const {
+ return GetPointer<const ::flatbuffers::Vector<uint8_t> *>(VT_CUSTOM_OPTIONS);
}
tflite::CustomOptionsFormat custom_options_format() const {
return static_cast<tflite::CustomOptionsFormat>(GetField<int8_t>(VT_CUSTOM_OPTIONS_FORMAT, 0));
}
- const flatbuffers::Vector<uint8_t> *mutating_variable_inputs() const {
- return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_MUTATING_VARIABLE_INPUTS);
+ const ::flatbuffers::Vector<uint8_t> *mutating_variable_inputs() const {
+ return GetPointer<const ::flatbuffers::Vector<uint8_t> *>(VT_MUTATING_VARIABLE_INPUTS);
}
- const flatbuffers::Vector<int32_t> *intermediates() const {
- return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_INTERMEDIATES);
+ const ::flatbuffers::Vector<int32_t> *intermediates() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_INTERMEDIATES);
}
uint64_t large_custom_options_offset() const {
return GetField<uint64_t>(VT_LARGE_CUSTOM_OPTIONS_OFFSET, 0);
@@ -12305,7 +15185,77 @@
uint64_t large_custom_options_size() const {
return GetField<uint64_t>(VT_LARGE_CUSTOM_OPTIONS_SIZE, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ tflite::BuiltinOptions2 builtin_options_2_type() const {
+ return static_cast<tflite::BuiltinOptions2>(GetField<uint8_t>(VT_BUILTIN_OPTIONS_2_TYPE, 0));
+ }
+ const void *builtin_options_2() const {
+ return GetPointer<const void *>(VT_BUILTIN_OPTIONS_2);
+ }
+ template<typename T> const T *builtin_options_2_as() const;
+ const tflite::StablehloConcatenateOptions *builtin_options_2_as_StablehloConcatenateOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloConcatenateOptions ? static_cast<const tflite::StablehloConcatenateOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StablehloBroadcastInDimOptions *builtin_options_2_as_StablehloBroadcastInDimOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloBroadcastInDimOptions ? static_cast<const tflite::StablehloBroadcastInDimOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StablehloSliceOptions *builtin_options_2_as_StablehloSliceOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloSliceOptions ? static_cast<const tflite::StablehloSliceOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StablehloConvolutionOptions *builtin_options_2_as_StablehloConvolutionOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloConvolutionOptions ? static_cast<const tflite::StablehloConvolutionOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StablehloCustomCallOptions *builtin_options_2_as_StablehloCustomCallOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloCustomCallOptions ? static_cast<const tflite::StablehloCustomCallOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StablehloReduceOptions *builtin_options_2_as_StablehloReduceOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloReduceOptions ? static_cast<const tflite::StablehloReduceOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StablehloScatterOptions *builtin_options_2_as_StablehloScatterOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloScatterOptions ? static_cast<const tflite::StablehloScatterOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StablehloCompareOptions *builtin_options_2_as_StablehloCompareOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloCompareOptions ? static_cast<const tflite::StablehloCompareOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StablehloDynamicSliceOptions *builtin_options_2_as_StablehloDynamicSliceOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloDynamicSliceOptions ? static_cast<const tflite::StablehloDynamicSliceOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StablehloPadOptions *builtin_options_2_as_StablehloPadOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloPadOptions ? static_cast<const tflite::StablehloPadOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StablehloIotaOptions *builtin_options_2_as_StablehloIotaOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloIotaOptions ? static_cast<const tflite::StablehloIotaOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StablehloDotGeneralOptions *builtin_options_2_as_StablehloDotGeneralOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloDotGeneralOptions ? static_cast<const tflite::StablehloDotGeneralOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StablehloReduceWindowOptions *builtin_options_2_as_StablehloReduceWindowOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloReduceWindowOptions ? static_cast<const tflite::StablehloReduceWindowOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StablehloSortOptions *builtin_options_2_as_StablehloSortOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloSortOptions ? static_cast<const tflite::StablehloSortOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StablehloWhileOptions *builtin_options_2_as_StablehloWhileOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloWhileOptions ? static_cast<const tflite::StablehloWhileOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StablehloGatherOptions *builtin_options_2_as_StablehloGatherOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloGatherOptions ? static_cast<const tflite::StablehloGatherOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StablehloTransposeOptions *builtin_options_2_as_StablehloTransposeOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloTransposeOptions ? static_cast<const tflite::StablehloTransposeOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::DilateOptions *builtin_options_2_as_DilateOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_DilateOptions ? static_cast<const tflite::DilateOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StablehloRngBitGeneratorOptions *builtin_options_2_as_StablehloRngBitGeneratorOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StablehloRngBitGeneratorOptions ? static_cast<const tflite::StablehloRngBitGeneratorOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::ReduceWindowOptions *builtin_options_2_as_ReduceWindowOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_ReduceWindowOptions ? static_cast<const tflite::ReduceWindowOptions *>(builtin_options_2()) : nullptr;
+ }
+ const tflite::StableHLOCompositeOptions *builtin_options_2_as_StableHLOCompositeOptions() const {
+ return builtin_options_2_type() == tflite::BuiltinOptions2_StableHLOCompositeOptions ? static_cast<const tflite::StableHLOCompositeOptions *>(builtin_options_2()) : nullptr;
+ }
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<uint32_t>(verifier, VT_OPCODE_INDEX, 4) &&
VerifyOffset(verifier, VT_INPUTS) &&
@@ -12324,11 +15274,14 @@
verifier.VerifyVector(intermediates()) &&
VerifyField<uint64_t>(verifier, VT_LARGE_CUSTOM_OPTIONS_OFFSET, 8) &&
VerifyField<uint64_t>(verifier, VT_LARGE_CUSTOM_OPTIONS_SIZE, 8) &&
+ VerifyField<uint8_t>(verifier, VT_BUILTIN_OPTIONS_2_TYPE, 1) &&
+ VerifyOffset(verifier, VT_BUILTIN_OPTIONS_2) &&
+ VerifyBuiltinOptions2(verifier, builtin_options_2(), builtin_options_2_type()) &&
verifier.EndTable();
}
- OperatorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(OperatorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<Operator> Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ OperatorT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(OperatorT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<Operator> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
template<> inline const tflite::Conv2DOptions *Operator::builtin_options_as<tflite::Conv2DOptions>() const {
@@ -12835,35 +15788,119 @@
return builtin_options_as_RightShiftOptions();
}
+template<> inline const tflite::StablehloConcatenateOptions *Operator::builtin_options_2_as<tflite::StablehloConcatenateOptions>() const {
+ return builtin_options_2_as_StablehloConcatenateOptions();
+}
+
+template<> inline const tflite::StablehloBroadcastInDimOptions *Operator::builtin_options_2_as<tflite::StablehloBroadcastInDimOptions>() const {
+ return builtin_options_2_as_StablehloBroadcastInDimOptions();
+}
+
+template<> inline const tflite::StablehloSliceOptions *Operator::builtin_options_2_as<tflite::StablehloSliceOptions>() const {
+ return builtin_options_2_as_StablehloSliceOptions();
+}
+
+template<> inline const tflite::StablehloConvolutionOptions *Operator::builtin_options_2_as<tflite::StablehloConvolutionOptions>() const {
+ return builtin_options_2_as_StablehloConvolutionOptions();
+}
+
+template<> inline const tflite::StablehloCustomCallOptions *Operator::builtin_options_2_as<tflite::StablehloCustomCallOptions>() const {
+ return builtin_options_2_as_StablehloCustomCallOptions();
+}
+
+template<> inline const tflite::StablehloReduceOptions *Operator::builtin_options_2_as<tflite::StablehloReduceOptions>() const {
+ return builtin_options_2_as_StablehloReduceOptions();
+}
+
+template<> inline const tflite::StablehloScatterOptions *Operator::builtin_options_2_as<tflite::StablehloScatterOptions>() const {
+ return builtin_options_2_as_StablehloScatterOptions();
+}
+
+template<> inline const tflite::StablehloCompareOptions *Operator::builtin_options_2_as<tflite::StablehloCompareOptions>() const {
+ return builtin_options_2_as_StablehloCompareOptions();
+}
+
+template<> inline const tflite::StablehloDynamicSliceOptions *Operator::builtin_options_2_as<tflite::StablehloDynamicSliceOptions>() const {
+ return builtin_options_2_as_StablehloDynamicSliceOptions();
+}
+
+template<> inline const tflite::StablehloPadOptions *Operator::builtin_options_2_as<tflite::StablehloPadOptions>() const {
+ return builtin_options_2_as_StablehloPadOptions();
+}
+
+template<> inline const tflite::StablehloIotaOptions *Operator::builtin_options_2_as<tflite::StablehloIotaOptions>() const {
+ return builtin_options_2_as_StablehloIotaOptions();
+}
+
+template<> inline const tflite::StablehloDotGeneralOptions *Operator::builtin_options_2_as<tflite::StablehloDotGeneralOptions>() const {
+ return builtin_options_2_as_StablehloDotGeneralOptions();
+}
+
+template<> inline const tflite::StablehloReduceWindowOptions *Operator::builtin_options_2_as<tflite::StablehloReduceWindowOptions>() const {
+ return builtin_options_2_as_StablehloReduceWindowOptions();
+}
+
+template<> inline const tflite::StablehloSortOptions *Operator::builtin_options_2_as<tflite::StablehloSortOptions>() const {
+ return builtin_options_2_as_StablehloSortOptions();
+}
+
+template<> inline const tflite::StablehloWhileOptions *Operator::builtin_options_2_as<tflite::StablehloWhileOptions>() const {
+ return builtin_options_2_as_StablehloWhileOptions();
+}
+
+template<> inline const tflite::StablehloGatherOptions *Operator::builtin_options_2_as<tflite::StablehloGatherOptions>() const {
+ return builtin_options_2_as_StablehloGatherOptions();
+}
+
+template<> inline const tflite::StablehloTransposeOptions *Operator::builtin_options_2_as<tflite::StablehloTransposeOptions>() const {
+ return builtin_options_2_as_StablehloTransposeOptions();
+}
+
+template<> inline const tflite::DilateOptions *Operator::builtin_options_2_as<tflite::DilateOptions>() const {
+ return builtin_options_2_as_DilateOptions();
+}
+
+template<> inline const tflite::StablehloRngBitGeneratorOptions *Operator::builtin_options_2_as<tflite::StablehloRngBitGeneratorOptions>() const {
+ return builtin_options_2_as_StablehloRngBitGeneratorOptions();
+}
+
+template<> inline const tflite::ReduceWindowOptions *Operator::builtin_options_2_as<tflite::ReduceWindowOptions>() const {
+ return builtin_options_2_as_ReduceWindowOptions();
+}
+
+template<> inline const tflite::StableHLOCompositeOptions *Operator::builtin_options_2_as<tflite::StableHLOCompositeOptions>() const {
+ return builtin_options_2_as_StableHLOCompositeOptions();
+}
+
struct OperatorBuilder {
typedef Operator Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_opcode_index(uint32_t opcode_index) {
fbb_.AddElement<uint32_t>(Operator::VT_OPCODE_INDEX, opcode_index, 0);
}
- void add_inputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs) {
+ void add_inputs(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> inputs) {
fbb_.AddOffset(Operator::VT_INPUTS, inputs);
}
- void add_outputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs) {
+ void add_outputs(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> outputs) {
fbb_.AddOffset(Operator::VT_OUTPUTS, outputs);
}
void add_builtin_options_type(tflite::BuiltinOptions builtin_options_type) {
fbb_.AddElement<uint8_t>(Operator::VT_BUILTIN_OPTIONS_TYPE, static_cast<uint8_t>(builtin_options_type), 0);
}
- void add_builtin_options(flatbuffers::Offset<void> builtin_options) {
+ void add_builtin_options(::flatbuffers::Offset<void> builtin_options) {
fbb_.AddOffset(Operator::VT_BUILTIN_OPTIONS, builtin_options);
}
- void add_custom_options(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom_options) {
+ void add_custom_options(::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> custom_options) {
fbb_.AddOffset(Operator::VT_CUSTOM_OPTIONS, custom_options);
}
void add_custom_options_format(tflite::CustomOptionsFormat custom_options_format) {
fbb_.AddElement<int8_t>(Operator::VT_CUSTOM_OPTIONS_FORMAT, static_cast<int8_t>(custom_options_format), 0);
}
- void add_mutating_variable_inputs(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> mutating_variable_inputs) {
+ void add_mutating_variable_inputs(::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> mutating_variable_inputs) {
fbb_.AddOffset(Operator::VT_MUTATING_VARIABLE_INPUTS, mutating_variable_inputs);
}
- void add_intermediates(flatbuffers::Offset<flatbuffers::Vector<int32_t>> intermediates) {
+ void add_intermediates(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> intermediates) {
fbb_.AddOffset(Operator::VT_INTERMEDIATES, intermediates);
}
void add_large_custom_options_offset(uint64_t large_custom_options_offset) {
@@ -12872,33 +15909,42 @@
void add_large_custom_options_size(uint64_t large_custom_options_size) {
fbb_.AddElement<uint64_t>(Operator::VT_LARGE_CUSTOM_OPTIONS_SIZE, large_custom_options_size, 0);
}
- explicit OperatorBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ void add_builtin_options_2_type(tflite::BuiltinOptions2 builtin_options_2_type) {
+ fbb_.AddElement<uint8_t>(Operator::VT_BUILTIN_OPTIONS_2_TYPE, static_cast<uint8_t>(builtin_options_2_type), 0);
+ }
+ void add_builtin_options_2(::flatbuffers::Offset<void> builtin_options_2) {
+ fbb_.AddOffset(Operator::VT_BUILTIN_OPTIONS_2, builtin_options_2);
+ }
+ explicit OperatorBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<Operator> Finish() {
+ ::flatbuffers::Offset<Operator> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<Operator>(end);
+ auto o = ::flatbuffers::Offset<Operator>(end);
return o;
}
};
-inline flatbuffers::Offset<Operator> CreateOperator(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<Operator> CreateOperator(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
uint32_t opcode_index = 0,
- flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs = 0,
- flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> inputs = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> outputs = 0,
tflite::BuiltinOptions builtin_options_type = tflite::BuiltinOptions_NONE,
- flatbuffers::Offset<void> builtin_options = 0,
- flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom_options = 0,
+ ::flatbuffers::Offset<void> builtin_options = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> custom_options = 0,
tflite::CustomOptionsFormat custom_options_format = tflite::CustomOptionsFormat_FLEXBUFFERS,
- flatbuffers::Offset<flatbuffers::Vector<uint8_t>> mutating_variable_inputs = 0,
- flatbuffers::Offset<flatbuffers::Vector<int32_t>> intermediates = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> mutating_variable_inputs = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> intermediates = 0,
uint64_t large_custom_options_offset = 0,
- uint64_t large_custom_options_size = 0) {
+ uint64_t large_custom_options_size = 0,
+ tflite::BuiltinOptions2 builtin_options_2_type = tflite::BuiltinOptions2_NONE,
+ ::flatbuffers::Offset<void> builtin_options_2 = 0) {
OperatorBuilder builder_(_fbb);
builder_.add_large_custom_options_size(large_custom_options_size);
builder_.add_large_custom_options_offset(large_custom_options_offset);
+ builder_.add_builtin_options_2(builtin_options_2);
builder_.add_intermediates(intermediates);
builder_.add_mutating_variable_inputs(mutating_variable_inputs);
builder_.add_custom_options(custom_options);
@@ -12906,24 +15952,27 @@
builder_.add_outputs(outputs);
builder_.add_inputs(inputs);
builder_.add_opcode_index(opcode_index);
+ builder_.add_builtin_options_2_type(builtin_options_2_type);
builder_.add_custom_options_format(custom_options_format);
builder_.add_builtin_options_type(builtin_options_type);
return builder_.Finish();
}
-inline flatbuffers::Offset<Operator> CreateOperatorDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<Operator> CreateOperatorDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
uint32_t opcode_index = 0,
const std::vector<int32_t> *inputs = nullptr,
const std::vector<int32_t> *outputs = nullptr,
tflite::BuiltinOptions builtin_options_type = tflite::BuiltinOptions_NONE,
- flatbuffers::Offset<void> builtin_options = 0,
+ ::flatbuffers::Offset<void> builtin_options = 0,
const std::vector<uint8_t> *custom_options = nullptr,
tflite::CustomOptionsFormat custom_options_format = tflite::CustomOptionsFormat_FLEXBUFFERS,
const std::vector<uint8_t> *mutating_variable_inputs = nullptr,
const std::vector<int32_t> *intermediates = nullptr,
uint64_t large_custom_options_offset = 0,
- uint64_t large_custom_options_size = 0) {
+ uint64_t large_custom_options_size = 0,
+ tflite::BuiltinOptions2 builtin_options_2_type = tflite::BuiltinOptions2_NONE,
+ ::flatbuffers::Offset<void> builtin_options_2 = 0) {
auto inputs__ = inputs ? _fbb.CreateVector<int32_t>(*inputs) : 0;
auto outputs__ = outputs ? _fbb.CreateVector<int32_t>(*outputs) : 0;
auto custom_options__ = custom_options ? _fbb.CreateVector<uint8_t>(*custom_options) : 0;
@@ -12941,12 +15990,14 @@
mutating_variable_inputs__,
intermediates__,
large_custom_options_offset,
- large_custom_options_size);
+ large_custom_options_size,
+ builtin_options_2_type,
+ builtin_options_2);
}
-flatbuffers::Offset<Operator> CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<Operator> CreateOperator(::flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SubGraphT : public flatbuffers::NativeTable {
+struct SubGraphT : public ::flatbuffers::NativeTable {
typedef SubGraph TableType;
std::vector<std::unique_ptr<tflite::TensorT>> tensors{};
std::vector<int32_t> inputs{};
@@ -12959,7 +16010,7 @@
SubGraphT &operator=(SubGraphT o) FLATBUFFERS_NOEXCEPT;
};
-struct SubGraph FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SubGraph FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SubGraphT NativeTableType;
typedef SubGraphBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -12969,22 +16020,22 @@
VT_OPERATORS = 10,
VT_NAME = 12
};
- const flatbuffers::Vector<flatbuffers::Offset<tflite::Tensor>> *tensors() const {
- return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::Tensor>> *>(VT_TENSORS);
+ const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::Tensor>> *tensors() const {
+ return GetPointer<const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::Tensor>> *>(VT_TENSORS);
}
- const flatbuffers::Vector<int32_t> *inputs() const {
- return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_INPUTS);
+ const ::flatbuffers::Vector<int32_t> *inputs() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_INPUTS);
}
- const flatbuffers::Vector<int32_t> *outputs() const {
- return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_OUTPUTS);
+ const ::flatbuffers::Vector<int32_t> *outputs() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_OUTPUTS);
}
- const flatbuffers::Vector<flatbuffers::Offset<tflite::Operator>> *operators() const {
- return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::Operator>> *>(VT_OPERATORS);
+ const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::Operator>> *operators() const {
+ return GetPointer<const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::Operator>> *>(VT_OPERATORS);
}
- const flatbuffers::String *name() const {
- return GetPointer<const flatbuffers::String *>(VT_NAME);
+ const ::flatbuffers::String *name() const {
+ return GetPointer<const ::flatbuffers::String *>(VT_NAME);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyOffset(verifier, VT_TENSORS) &&
verifier.VerifyVector(tensors()) &&
@@ -13000,48 +16051,48 @@
verifier.VerifyString(name()) &&
verifier.EndTable();
}
- SubGraphT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SubGraphT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SubGraph> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SubGraphT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SubGraphT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SubGraph> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SubGraphBuilder {
typedef SubGraph Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- void add_tensors(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::Tensor>>> tensors) {
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_tensors(::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::Tensor>>> tensors) {
fbb_.AddOffset(SubGraph::VT_TENSORS, tensors);
}
- void add_inputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs) {
+ void add_inputs(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> inputs) {
fbb_.AddOffset(SubGraph::VT_INPUTS, inputs);
}
- void add_outputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs) {
+ void add_outputs(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> outputs) {
fbb_.AddOffset(SubGraph::VT_OUTPUTS, outputs);
}
- void add_operators(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::Operator>>> operators) {
+ void add_operators(::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::Operator>>> operators) {
fbb_.AddOffset(SubGraph::VT_OPERATORS, operators);
}
- void add_name(flatbuffers::Offset<flatbuffers::String> name) {
+ void add_name(::flatbuffers::Offset<::flatbuffers::String> name) {
fbb_.AddOffset(SubGraph::VT_NAME, name);
}
- explicit SubGraphBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit SubGraphBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SubGraph> Finish() {
+ ::flatbuffers::Offset<SubGraph> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SubGraph>(end);
+ auto o = ::flatbuffers::Offset<SubGraph>(end);
return o;
}
};
-inline flatbuffers::Offset<SubGraph> CreateSubGraph(
- flatbuffers::FlatBufferBuilder &_fbb,
- flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::Tensor>>> tensors = 0,
- flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs = 0,
- flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs = 0,
- flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::Operator>>> operators = 0,
- flatbuffers::Offset<flatbuffers::String> name = 0) {
+inline ::flatbuffers::Offset<SubGraph> CreateSubGraph(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::Tensor>>> tensors = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> inputs = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> outputs = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::Operator>>> operators = 0,
+ ::flatbuffers::Offset<::flatbuffers::String> name = 0) {
SubGraphBuilder builder_(_fbb);
builder_.add_name(name);
builder_.add_operators(operators);
@@ -13051,17 +16102,17 @@
return builder_.Finish();
}
-inline flatbuffers::Offset<SubGraph> CreateSubGraphDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
- const std::vector<flatbuffers::Offset<tflite::Tensor>> *tensors = nullptr,
+inline ::flatbuffers::Offset<SubGraph> CreateSubGraphDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<::flatbuffers::Offset<tflite::Tensor>> *tensors = nullptr,
const std::vector<int32_t> *inputs = nullptr,
const std::vector<int32_t> *outputs = nullptr,
- const std::vector<flatbuffers::Offset<tflite::Operator>> *operators = nullptr,
+ const std::vector<::flatbuffers::Offset<tflite::Operator>> *operators = nullptr,
const char *name = nullptr) {
- auto tensors__ = tensors ? _fbb.CreateVector<flatbuffers::Offset<tflite::Tensor>>(*tensors) : 0;
+ auto tensors__ = tensors ? _fbb.CreateVector<::flatbuffers::Offset<tflite::Tensor>>(*tensors) : 0;
auto inputs__ = inputs ? _fbb.CreateVector<int32_t>(*inputs) : 0;
auto outputs__ = outputs ? _fbb.CreateVector<int32_t>(*outputs) : 0;
- auto operators__ = operators ? _fbb.CreateVector<flatbuffers::Offset<tflite::Operator>>(*operators) : 0;
+ auto operators__ = operators ? _fbb.CreateVector<::flatbuffers::Offset<tflite::Operator>>(*operators) : 0;
auto name__ = name ? _fbb.CreateString(name) : 0;
return tflite::CreateSubGraph(
_fbb,
@@ -13072,16 +16123,16 @@
name__);
}
-flatbuffers::Offset<SubGraph> CreateSubGraph(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SubGraph> CreateSubGraph(::flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct BufferT : public flatbuffers::NativeTable {
+struct BufferT : public ::flatbuffers::NativeTable {
typedef Buffer TableType;
std::vector<uint8_t> data{};
uint64_t offset = 0;
uint64_t size = 0;
};
-struct Buffer FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct Buffer FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef BufferT NativeTableType;
typedef BufferBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -13089,8 +16140,8 @@
VT_OFFSET = 6,
VT_SIZE = 8
};
- const flatbuffers::Vector<uint8_t> *data() const {
- return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_DATA);
+ const ::flatbuffers::Vector<uint8_t> *data() const {
+ return GetPointer<const ::flatbuffers::Vector<uint8_t> *>(VT_DATA);
}
uint64_t offset() const {
return GetField<uint64_t>(VT_OFFSET, 0);
@@ -13098,7 +16149,7 @@
uint64_t size() const {
return GetField<uint64_t>(VT_SIZE, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyOffset(verifier, VT_DATA) &&
verifier.VerifyVector(data()) &&
@@ -13106,16 +16157,16 @@
VerifyField<uint64_t>(verifier, VT_SIZE, 8) &&
verifier.EndTable();
}
- BufferT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(BufferT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<Buffer> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BufferT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ BufferT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(BufferT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<Buffer> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BufferT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct BufferBuilder {
typedef Buffer Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- void add_data(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> data) {
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_data(::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> data) {
fbb_.AddOffset(Buffer::VT_DATA, data);
}
void add_offset(uint64_t offset) {
@@ -13124,20 +16175,20 @@
void add_size(uint64_t size) {
fbb_.AddElement<uint64_t>(Buffer::VT_SIZE, size, 0);
}
- explicit BufferBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit BufferBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<Buffer> Finish() {
+ ::flatbuffers::Offset<Buffer> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<Buffer>(end);
+ auto o = ::flatbuffers::Offset<Buffer>(end);
return o;
}
};
-inline flatbuffers::Offset<Buffer> CreateBuffer(
- flatbuffers::FlatBufferBuilder &_fbb,
- flatbuffers::Offset<flatbuffers::Vector<uint8_t>> data = 0,
+inline ::flatbuffers::Offset<Buffer> CreateBuffer(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<uint8_t>> data = 0,
uint64_t offset = 0,
uint64_t size = 0) {
BufferBuilder builder_(_fbb);
@@ -13147,8 +16198,8 @@
return builder_.Finish();
}
-inline flatbuffers::Offset<Buffer> CreateBufferDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<Buffer> CreateBufferDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
const std::vector<uint8_t> *data = nullptr,
uint64_t offset = 0,
uint64_t size = 0) {
@@ -13161,63 +16212,63 @@
size);
}
-flatbuffers::Offset<Buffer> CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<Buffer> CreateBuffer(::flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct MetadataT : public flatbuffers::NativeTable {
+struct MetadataT : public ::flatbuffers::NativeTable {
typedef Metadata TableType;
std::string name{};
uint32_t buffer = 0;
};
-struct Metadata FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct Metadata FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef MetadataT NativeTableType;
typedef MetadataBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
VT_NAME = 4,
VT_BUFFER = 6
};
- const flatbuffers::String *name() const {
- return GetPointer<const flatbuffers::String *>(VT_NAME);
+ const ::flatbuffers::String *name() const {
+ return GetPointer<const ::flatbuffers::String *>(VT_NAME);
}
uint32_t buffer() const {
return GetField<uint32_t>(VT_BUFFER, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyOffset(verifier, VT_NAME) &&
verifier.VerifyString(name()) &&
VerifyField<uint32_t>(verifier, VT_BUFFER, 4) &&
verifier.EndTable();
}
- MetadataT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(MetadataT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<Metadata> Pack(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ MetadataT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(MetadataT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<Metadata> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const MetadataT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct MetadataBuilder {
typedef Metadata Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- void add_name(flatbuffers::Offset<flatbuffers::String> name) {
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_name(::flatbuffers::Offset<::flatbuffers::String> name) {
fbb_.AddOffset(Metadata::VT_NAME, name);
}
void add_buffer(uint32_t buffer) {
fbb_.AddElement<uint32_t>(Metadata::VT_BUFFER, buffer, 0);
}
- explicit MetadataBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit MetadataBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<Metadata> Finish() {
+ ::flatbuffers::Offset<Metadata> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<Metadata>(end);
+ auto o = ::flatbuffers::Offset<Metadata>(end);
return o;
}
};
-inline flatbuffers::Offset<Metadata> CreateMetadata(
- flatbuffers::FlatBufferBuilder &_fbb,
- flatbuffers::Offset<flatbuffers::String> name = 0,
+inline ::flatbuffers::Offset<Metadata> CreateMetadata(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::String> name = 0,
uint32_t buffer = 0) {
MetadataBuilder builder_(_fbb);
builder_.add_buffer(buffer);
@@ -13225,8 +16276,8 @@
return builder_.Finish();
}
-inline flatbuffers::Offset<Metadata> CreateMetadataDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<Metadata> CreateMetadataDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
const char *name = nullptr,
uint32_t buffer = 0) {
auto name__ = name ? _fbb.CreateString(name) : 0;
@@ -13236,63 +16287,63 @@
buffer);
}
-flatbuffers::Offset<Metadata> CreateMetadata(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<Metadata> CreateMetadata(::flatbuffers::FlatBufferBuilder &_fbb, const MetadataT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct TensorMapT : public flatbuffers::NativeTable {
+struct TensorMapT : public ::flatbuffers::NativeTable {
typedef TensorMap TableType;
std::string name{};
uint32_t tensor_index = 0;
};
-struct TensorMap FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct TensorMap FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef TensorMapT NativeTableType;
typedef TensorMapBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
VT_NAME = 4,
VT_TENSOR_INDEX = 6
};
- const flatbuffers::String *name() const {
- return GetPointer<const flatbuffers::String *>(VT_NAME);
+ const ::flatbuffers::String *name() const {
+ return GetPointer<const ::flatbuffers::String *>(VT_NAME);
}
uint32_t tensor_index() const {
return GetField<uint32_t>(VT_TENSOR_INDEX, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyOffset(verifier, VT_NAME) &&
verifier.VerifyString(name()) &&
VerifyField<uint32_t>(verifier, VT_TENSOR_INDEX, 4) &&
verifier.EndTable();
}
- TensorMapT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(TensorMapT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<TensorMap> Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ TensorMapT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(TensorMapT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<TensorMap> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct TensorMapBuilder {
typedef TensorMap Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- void add_name(flatbuffers::Offset<flatbuffers::String> name) {
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_name(::flatbuffers::Offset<::flatbuffers::String> name) {
fbb_.AddOffset(TensorMap::VT_NAME, name);
}
void add_tensor_index(uint32_t tensor_index) {
fbb_.AddElement<uint32_t>(TensorMap::VT_TENSOR_INDEX, tensor_index, 0);
}
- explicit TensorMapBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit TensorMapBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<TensorMap> Finish() {
+ ::flatbuffers::Offset<TensorMap> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<TensorMap>(end);
+ auto o = ::flatbuffers::Offset<TensorMap>(end);
return o;
}
};
-inline flatbuffers::Offset<TensorMap> CreateTensorMap(
- flatbuffers::FlatBufferBuilder &_fbb,
- flatbuffers::Offset<flatbuffers::String> name = 0,
+inline ::flatbuffers::Offset<TensorMap> CreateTensorMap(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::String> name = 0,
uint32_t tensor_index = 0) {
TensorMapBuilder builder_(_fbb);
builder_.add_tensor_index(tensor_index);
@@ -13300,8 +16351,8 @@
return builder_.Finish();
}
-inline flatbuffers::Offset<TensorMap> CreateTensorMapDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<TensorMap> CreateTensorMapDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
const char *name = nullptr,
uint32_t tensor_index = 0) {
auto name__ = name ? _fbb.CreateString(name) : 0;
@@ -13311,9 +16362,9 @@
tensor_index);
}
-flatbuffers::Offset<TensorMap> CreateTensorMap(flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<TensorMap> CreateTensorMap(::flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct SignatureDefT : public flatbuffers::NativeTable {
+struct SignatureDefT : public ::flatbuffers::NativeTable {
typedef SignatureDef TableType;
std::vector<std::unique_ptr<tflite::TensorMapT>> inputs{};
std::vector<std::unique_ptr<tflite::TensorMapT>> outputs{};
@@ -13325,7 +16376,7 @@
SignatureDefT &operator=(SignatureDefT o) FLATBUFFERS_NOEXCEPT;
};
-struct SignatureDef FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct SignatureDef FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef SignatureDefT NativeTableType;
typedef SignatureDefBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -13334,19 +16385,19 @@
VT_SIGNATURE_KEY = 8,
VT_SUBGRAPH_INDEX = 12
};
- const flatbuffers::Vector<flatbuffers::Offset<tflite::TensorMap>> *inputs() const {
- return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::TensorMap>> *>(VT_INPUTS);
+ const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::TensorMap>> *inputs() const {
+ return GetPointer<const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::TensorMap>> *>(VT_INPUTS);
}
- const flatbuffers::Vector<flatbuffers::Offset<tflite::TensorMap>> *outputs() const {
- return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::TensorMap>> *>(VT_OUTPUTS);
+ const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::TensorMap>> *outputs() const {
+ return GetPointer<const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::TensorMap>> *>(VT_OUTPUTS);
}
- const flatbuffers::String *signature_key() const {
- return GetPointer<const flatbuffers::String *>(VT_SIGNATURE_KEY);
+ const ::flatbuffers::String *signature_key() const {
+ return GetPointer<const ::flatbuffers::String *>(VT_SIGNATURE_KEY);
}
uint32_t subgraph_index() const {
return GetField<uint32_t>(VT_SUBGRAPH_INDEX, 0);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyOffset(verifier, VT_INPUTS) &&
verifier.VerifyVector(inputs()) &&
@@ -13359,43 +16410,43 @@
VerifyField<uint32_t>(verifier, VT_SUBGRAPH_INDEX, 4) &&
verifier.EndTable();
}
- SignatureDefT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(SignatureDefT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<SignatureDef> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ SignatureDefT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SignatureDefT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<SignatureDef> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct SignatureDefBuilder {
typedef SignatureDef Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
- void add_inputs(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::TensorMap>>> inputs) {
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
+ void add_inputs(::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::TensorMap>>> inputs) {
fbb_.AddOffset(SignatureDef::VT_INPUTS, inputs);
}
- void add_outputs(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::TensorMap>>> outputs) {
+ void add_outputs(::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::TensorMap>>> outputs) {
fbb_.AddOffset(SignatureDef::VT_OUTPUTS, outputs);
}
- void add_signature_key(flatbuffers::Offset<flatbuffers::String> signature_key) {
+ void add_signature_key(::flatbuffers::Offset<::flatbuffers::String> signature_key) {
fbb_.AddOffset(SignatureDef::VT_SIGNATURE_KEY, signature_key);
}
void add_subgraph_index(uint32_t subgraph_index) {
fbb_.AddElement<uint32_t>(SignatureDef::VT_SUBGRAPH_INDEX, subgraph_index, 0);
}
- explicit SignatureDefBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit SignatureDefBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<SignatureDef> Finish() {
+ ::flatbuffers::Offset<SignatureDef> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<SignatureDef>(end);
+ auto o = ::flatbuffers::Offset<SignatureDef>(end);
return o;
}
};
-inline flatbuffers::Offset<SignatureDef> CreateSignatureDef(
- flatbuffers::FlatBufferBuilder &_fbb,
- flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::TensorMap>>> inputs = 0,
- flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::TensorMap>>> outputs = 0,
- flatbuffers::Offset<flatbuffers::String> signature_key = 0,
+inline ::flatbuffers::Offset<SignatureDef> CreateSignatureDef(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ ::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::TensorMap>>> inputs = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::TensorMap>>> outputs = 0,
+ ::flatbuffers::Offset<::flatbuffers::String> signature_key = 0,
uint32_t subgraph_index = 0) {
SignatureDefBuilder builder_(_fbb);
builder_.add_subgraph_index(subgraph_index);
@@ -13405,14 +16456,14 @@
return builder_.Finish();
}
-inline flatbuffers::Offset<SignatureDef> CreateSignatureDefDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
- const std::vector<flatbuffers::Offset<tflite::TensorMap>> *inputs = nullptr,
- const std::vector<flatbuffers::Offset<tflite::TensorMap>> *outputs = nullptr,
+inline ::flatbuffers::Offset<SignatureDef> CreateSignatureDefDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<::flatbuffers::Offset<tflite::TensorMap>> *inputs = nullptr,
+ const std::vector<::flatbuffers::Offset<tflite::TensorMap>> *outputs = nullptr,
const char *signature_key = nullptr,
uint32_t subgraph_index = 0) {
- auto inputs__ = inputs ? _fbb.CreateVector<flatbuffers::Offset<tflite::TensorMap>>(*inputs) : 0;
- auto outputs__ = outputs ? _fbb.CreateVector<flatbuffers::Offset<tflite::TensorMap>>(*outputs) : 0;
+ auto inputs__ = inputs ? _fbb.CreateVector<::flatbuffers::Offset<tflite::TensorMap>>(*inputs) : 0;
+ auto outputs__ = outputs ? _fbb.CreateVector<::flatbuffers::Offset<tflite::TensorMap>>(*outputs) : 0;
auto signature_key__ = signature_key ? _fbb.CreateString(signature_key) : 0;
return tflite::CreateSignatureDef(
_fbb,
@@ -13422,9 +16473,9 @@
subgraph_index);
}
-flatbuffers::Offset<SignatureDef> CreateSignatureDef(flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<SignatureDef> CreateSignatureDef(::flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-struct ModelT : public flatbuffers::NativeTable {
+struct ModelT : public ::flatbuffers::NativeTable {
typedef Model TableType;
uint32_t version = 0;
std::vector<std::unique_ptr<tflite::OperatorCodeT>> operator_codes{};
@@ -13440,7 +16491,7 @@
ModelT &operator=(ModelT o) FLATBUFFERS_NOEXCEPT;
};
-struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+struct Model FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
typedef ModelT NativeTableType;
typedef ModelBuilder Builder;
enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
@@ -13456,28 +16507,28 @@
uint32_t version() const {
return GetField<uint32_t>(VT_VERSION, 0);
}
- const flatbuffers::Vector<flatbuffers::Offset<tflite::OperatorCode>> *operator_codes() const {
- return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::OperatorCode>> *>(VT_OPERATOR_CODES);
+ const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::OperatorCode>> *operator_codes() const {
+ return GetPointer<const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::OperatorCode>> *>(VT_OPERATOR_CODES);
}
- const flatbuffers::Vector<flatbuffers::Offset<tflite::SubGraph>> *subgraphs() const {
- return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::SubGraph>> *>(VT_SUBGRAPHS);
+ const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::SubGraph>> *subgraphs() const {
+ return GetPointer<const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::SubGraph>> *>(VT_SUBGRAPHS);
}
- const flatbuffers::String *description() const {
- return GetPointer<const flatbuffers::String *>(VT_DESCRIPTION);
+ const ::flatbuffers::String *description() const {
+ return GetPointer<const ::flatbuffers::String *>(VT_DESCRIPTION);
}
- const flatbuffers::Vector<flatbuffers::Offset<tflite::Buffer>> *buffers() const {
- return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::Buffer>> *>(VT_BUFFERS);
+ const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::Buffer>> *buffers() const {
+ return GetPointer<const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::Buffer>> *>(VT_BUFFERS);
}
- const flatbuffers::Vector<int32_t> *metadata_buffer() const {
- return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_METADATA_BUFFER);
+ const ::flatbuffers::Vector<int32_t> *metadata_buffer() const {
+ return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_METADATA_BUFFER);
}
- const flatbuffers::Vector<flatbuffers::Offset<tflite::Metadata>> *metadata() const {
- return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::Metadata>> *>(VT_METADATA);
+ const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::Metadata>> *metadata() const {
+ return GetPointer<const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::Metadata>> *>(VT_METADATA);
}
- const flatbuffers::Vector<flatbuffers::Offset<tflite::SignatureDef>> *signature_defs() const {
- return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::SignatureDef>> *>(VT_SIGNATURE_DEFS);
+ const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::SignatureDef>> *signature_defs() const {
+ return GetPointer<const ::flatbuffers::Vector<::flatbuffers::Offset<tflite::SignatureDef>> *>(VT_SIGNATURE_DEFS);
}
- bool Verify(flatbuffers::Verifier &verifier) const {
+ bool Verify(::flatbuffers::Verifier &verifier) const {
return VerifyTableStart(verifier) &&
VerifyField<uint32_t>(verifier, VT_VERSION, 4) &&
VerifyOffset(verifier, VT_OPERATOR_CODES) &&
@@ -13501,60 +16552,60 @@
verifier.VerifyVectorOfTables(signature_defs()) &&
verifier.EndTable();
}
- ModelT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- void UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
- static flatbuffers::Offset<Model> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+ ModelT *UnPack(const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ModelT *_o, const ::flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static ::flatbuffers::Offset<Model> Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
};
struct ModelBuilder {
typedef Model Table;
- flatbuffers::FlatBufferBuilder &fbb_;
- flatbuffers::uoffset_t start_;
+ ::flatbuffers::FlatBufferBuilder &fbb_;
+ ::flatbuffers::uoffset_t start_;
void add_version(uint32_t version) {
fbb_.AddElement<uint32_t>(Model::VT_VERSION, version, 0);
}
- void add_operator_codes(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::OperatorCode>>> operator_codes) {
+ void add_operator_codes(::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::OperatorCode>>> operator_codes) {
fbb_.AddOffset(Model::VT_OPERATOR_CODES, operator_codes);
}
- void add_subgraphs(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::SubGraph>>> subgraphs) {
+ void add_subgraphs(::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::SubGraph>>> subgraphs) {
fbb_.AddOffset(Model::VT_SUBGRAPHS, subgraphs);
}
- void add_description(flatbuffers::Offset<flatbuffers::String> description) {
+ void add_description(::flatbuffers::Offset<::flatbuffers::String> description) {
fbb_.AddOffset(Model::VT_DESCRIPTION, description);
}
- void add_buffers(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::Buffer>>> buffers) {
+ void add_buffers(::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::Buffer>>> buffers) {
fbb_.AddOffset(Model::VT_BUFFERS, buffers);
}
- void add_metadata_buffer(flatbuffers::Offset<flatbuffers::Vector<int32_t>> metadata_buffer) {
+ void add_metadata_buffer(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> metadata_buffer) {
fbb_.AddOffset(Model::VT_METADATA_BUFFER, metadata_buffer);
}
- void add_metadata(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::Metadata>>> metadata) {
+ void add_metadata(::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::Metadata>>> metadata) {
fbb_.AddOffset(Model::VT_METADATA, metadata);
}
- void add_signature_defs(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::SignatureDef>>> signature_defs) {
+ void add_signature_defs(::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::SignatureDef>>> signature_defs) {
fbb_.AddOffset(Model::VT_SIGNATURE_DEFS, signature_defs);
}
- explicit ModelBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ explicit ModelBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
: fbb_(_fbb) {
start_ = fbb_.StartTable();
}
- flatbuffers::Offset<Model> Finish() {
+ ::flatbuffers::Offset<Model> Finish() {
const auto end = fbb_.EndTable(start_);
- auto o = flatbuffers::Offset<Model>(end);
+ auto o = ::flatbuffers::Offset<Model>(end);
return o;
}
};
-inline flatbuffers::Offset<Model> CreateModel(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<Model> CreateModel(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
uint32_t version = 0,
- flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::OperatorCode>>> operator_codes = 0,
- flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::SubGraph>>> subgraphs = 0,
- flatbuffers::Offset<flatbuffers::String> description = 0,
- flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::Buffer>>> buffers = 0,
- flatbuffers::Offset<flatbuffers::Vector<int32_t>> metadata_buffer = 0,
- flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::Metadata>>> metadata = 0,
- flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::SignatureDef>>> signature_defs = 0) {
+ ::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::OperatorCode>>> operator_codes = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::SubGraph>>> subgraphs = 0,
+ ::flatbuffers::Offset<::flatbuffers::String> description = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::Buffer>>> buffers = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> metadata_buffer = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::Metadata>>> metadata = 0,
+ ::flatbuffers::Offset<::flatbuffers::Vector<::flatbuffers::Offset<tflite::SignatureDef>>> signature_defs = 0) {
ModelBuilder builder_(_fbb);
builder_.add_signature_defs(signature_defs);
builder_.add_metadata(metadata);
@@ -13567,23 +16618,23 @@
return builder_.Finish();
}
-inline flatbuffers::Offset<Model> CreateModelDirect(
- flatbuffers::FlatBufferBuilder &_fbb,
+inline ::flatbuffers::Offset<Model> CreateModelDirect(
+ ::flatbuffers::FlatBufferBuilder &_fbb,
uint32_t version = 0,
- const std::vector<flatbuffers::Offset<tflite::OperatorCode>> *operator_codes = nullptr,
- const std::vector<flatbuffers::Offset<tflite::SubGraph>> *subgraphs = nullptr,
+ const std::vector<::flatbuffers::Offset<tflite::OperatorCode>> *operator_codes = nullptr,
+ const std::vector<::flatbuffers::Offset<tflite::SubGraph>> *subgraphs = nullptr,
const char *description = nullptr,
- const std::vector<flatbuffers::Offset<tflite::Buffer>> *buffers = nullptr,
+ const std::vector<::flatbuffers::Offset<tflite::Buffer>> *buffers = nullptr,
const std::vector<int32_t> *metadata_buffer = nullptr,
- const std::vector<flatbuffers::Offset<tflite::Metadata>> *metadata = nullptr,
- const std::vector<flatbuffers::Offset<tflite::SignatureDef>> *signature_defs = nullptr) {
- auto operator_codes__ = operator_codes ? _fbb.CreateVector<flatbuffers::Offset<tflite::OperatorCode>>(*operator_codes) : 0;
- auto subgraphs__ = subgraphs ? _fbb.CreateVector<flatbuffers::Offset<tflite::SubGraph>>(*subgraphs) : 0;
+ const std::vector<::flatbuffers::Offset<tflite::Metadata>> *metadata = nullptr,
+ const std::vector<::flatbuffers::Offset<tflite::SignatureDef>> *signature_defs = nullptr) {
+ auto operator_codes__ = operator_codes ? _fbb.CreateVector<::flatbuffers::Offset<tflite::OperatorCode>>(*operator_codes) : 0;
+ auto subgraphs__ = subgraphs ? _fbb.CreateVector<::flatbuffers::Offset<tflite::SubGraph>>(*subgraphs) : 0;
auto description__ = description ? _fbb.CreateString(description) : 0;
- auto buffers__ = buffers ? _fbb.CreateVector<flatbuffers::Offset<tflite::Buffer>>(*buffers) : 0;
+ auto buffers__ = buffers ? _fbb.CreateVector<::flatbuffers::Offset<tflite::Buffer>>(*buffers) : 0;
auto metadata_buffer__ = metadata_buffer ? _fbb.CreateVector<int32_t>(*metadata_buffer) : 0;
- auto metadata__ = metadata ? _fbb.CreateVector<flatbuffers::Offset<tflite::Metadata>>(*metadata) : 0;
- auto signature_defs__ = signature_defs ? _fbb.CreateVector<flatbuffers::Offset<tflite::SignatureDef>>(*signature_defs) : 0;
+ auto metadata__ = metadata ? _fbb.CreateVector<::flatbuffers::Offset<tflite::Metadata>>(*metadata) : 0;
+ auto signature_defs__ = signature_defs ? _fbb.CreateVector<::flatbuffers::Offset<tflite::SignatureDef>>(*signature_defs) : 0;
return tflite::CreateModel(
_fbb,
version,
@@ -13596,28 +16647,28 @@
signature_defs__);
}
-flatbuffers::Offset<Model> CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+::flatbuffers::Offset<Model> CreateModel(::flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const ::flatbuffers::rehasher_function_t *_rehasher = nullptr);
-inline CustomQuantizationT *CustomQuantization::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline CustomQuantizationT *CustomQuantization::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<CustomQuantizationT>(new CustomQuantizationT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void CustomQuantization::UnPackTo(CustomQuantizationT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void CustomQuantization::UnPackTo(CustomQuantizationT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = custom(); if (_e) { _o->custom.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->custom.begin()); } }
}
-inline flatbuffers::Offset<CustomQuantization> CustomQuantization::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<CustomQuantization> CustomQuantization::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateCustomQuantization(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<CustomQuantization> CreateCustomQuantization(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<CustomQuantization> CreateCustomQuantization(::flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CustomQuantizationT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const CustomQuantizationT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
_fbb.ForceVectorAlignment(_o->custom.size(), sizeof(uint8_t), 16);
auto _custom = _o->custom.size() ? _fbb.CreateVector(_o->custom) : 0;
return tflite::CreateCustomQuantization(
@@ -13625,32 +16676,32 @@
_custom);
}
-inline QuantizationParametersT *QuantizationParameters::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline QuantizationParametersT *QuantizationParameters::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<QuantizationParametersT>(new QuantizationParametersT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void QuantizationParameters::UnPackTo(QuantizationParametersT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void QuantizationParameters::UnPackTo(QuantizationParametersT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
- { auto _e = min(); if (_e) { _o->min.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->min[_i] = _e->Get(_i); } } }
- { auto _e = max(); if (_e) { _o->max.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->max[_i] = _e->Get(_i); } } }
- { auto _e = scale(); if (_e) { _o->scale.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->scale[_i] = _e->Get(_i); } } }
- { auto _e = zero_point(); if (_e) { _o->zero_point.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->zero_point[_i] = _e->Get(_i); } } }
+ { auto _e = min(); if (_e) { _o->min.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->min[_i] = _e->Get(_i); } } else { _o->min.resize(0); } }
+ { auto _e = max(); if (_e) { _o->max.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->max[_i] = _e->Get(_i); } } else { _o->max.resize(0); } }
+ { auto _e = scale(); if (_e) { _o->scale.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->scale[_i] = _e->Get(_i); } } else { _o->scale.resize(0); } }
+ { auto _e = zero_point(); if (_e) { _o->zero_point.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->zero_point[_i] = _e->Get(_i); } } else { _o->zero_point.resize(0); } }
{ auto _e = details_type(); _o->details.type = _e; }
{ auto _e = details(); if (_e) _o->details.value = tflite::QuantizationDetailsUnion::UnPack(_e, details_type(), _resolver); }
{ auto _e = quantized_dimension(); _o->quantized_dimension = _e; }
}
-inline flatbuffers::Offset<QuantizationParameters> QuantizationParameters::Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<QuantizationParameters> QuantizationParameters::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateQuantizationParameters(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<QuantizationParameters> CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<QuantizationParameters> CreateQuantizationParameters(::flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const QuantizationParametersT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const QuantizationParametersT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _min = _o->min.size() ? _fbb.CreateVector(_o->min) : 0;
auto _max = _o->max.size() ? _fbb.CreateVector(_o->max) : 0;
auto _scale = _o->scale.size() ? _fbb.CreateVector(_o->scale) : 0;
@@ -13669,52 +16720,52 @@
_quantized_dimension);
}
-inline Int32VectorT *Int32Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline Int32VectorT *Int32Vector::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<Int32VectorT>(new Int32VectorT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void Int32Vector::UnPackTo(Int32VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void Int32Vector::UnPackTo(Int32VectorT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
- { auto _e = values(); if (_e) { _o->values.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->values[_i] = _e->Get(_i); } } }
+ { auto _e = values(); if (_e) { _o->values.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->values[_i] = _e->Get(_i); } } else { _o->values.resize(0); } }
}
-inline flatbuffers::Offset<Int32Vector> Int32Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Int32Vector> Int32Vector::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateInt32Vector(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<Int32Vector> CreateInt32Vector(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Int32Vector> CreateInt32Vector(::flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Int32VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const Int32VectorT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0;
return tflite::CreateInt32Vector(
_fbb,
_values);
}
-inline Uint16VectorT *Uint16Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline Uint16VectorT *Uint16Vector::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<Uint16VectorT>(new Uint16VectorT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void Uint16Vector::UnPackTo(Uint16VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void Uint16Vector::UnPackTo(Uint16VectorT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
- { auto _e = values(); if (_e) { _o->values.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->values[_i] = _e->Get(_i); } } }
+ { auto _e = values(); if (_e) { _o->values.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->values[_i] = _e->Get(_i); } } else { _o->values.resize(0); } }
}
-inline flatbuffers::Offset<Uint16Vector> Uint16Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Uint16Vector> Uint16Vector::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateUint16Vector(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<Uint16Vector> CreateUint16Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Uint16Vector> CreateUint16Vector(::flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Uint16VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const Uint16VectorT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
_fbb.ForceVectorAlignment(_o->values.size(), sizeof(uint16_t), 4);
auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0;
return tflite::CreateUint16Vector(
@@ -13722,26 +16773,26 @@
_values);
}
-inline Uint8VectorT *Uint8Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline Uint8VectorT *Uint8Vector::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<Uint8VectorT>(new Uint8VectorT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void Uint8Vector::UnPackTo(Uint8VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void Uint8Vector::UnPackTo(Uint8VectorT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = values(); if (_e) { _o->values.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->values.begin()); } }
}
-inline flatbuffers::Offset<Uint8Vector> Uint8Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Uint8Vector> Uint8Vector::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateUint8Vector(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<Uint8Vector> CreateUint8Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Uint8Vector> CreateUint8Vector(::flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Uint8VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const Uint8VectorT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
_fbb.ForceVectorAlignment(_o->values.size(), sizeof(uint8_t), 4);
auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0;
return tflite::CreateUint8Vector(
@@ -13749,13 +16800,13 @@
_values);
}
-inline DimensionMetadataT *DimensionMetadata::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline DimensionMetadataT *DimensionMetadata::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<DimensionMetadataT>(new DimensionMetadataT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void DimensionMetadata::UnPackTo(DimensionMetadataT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void DimensionMetadata::UnPackTo(DimensionMetadataT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = format(); _o->format = _e; }
@@ -13766,14 +16817,14 @@
{ auto _e = array_indices(); if (_e) _o->array_indices.value = tflite::SparseIndexVectorUnion::UnPack(_e, array_indices_type(), _resolver); }
}
-inline flatbuffers::Offset<DimensionMetadata> DimensionMetadata::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<DimensionMetadata> DimensionMetadata::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateDimensionMetadata(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<DimensionMetadata> CreateDimensionMetadata(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<DimensionMetadata> CreateDimensionMetadata(::flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DimensionMetadataT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const DimensionMetadataT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _format = _o->format;
auto _dense_size = _o->dense_size;
auto _array_segments_type = _o->array_segments.type;
@@ -13804,31 +16855,31 @@
return *this;
}
-inline SparsityParametersT *SparsityParameters::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SparsityParametersT *SparsityParameters::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SparsityParametersT>(new SparsityParametersT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SparsityParameters::UnPackTo(SparsityParametersT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SparsityParameters::UnPackTo(SparsityParametersT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
- { auto _e = traversal_order(); if (_e) { _o->traversal_order.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->traversal_order[_i] = _e->Get(_i); } } }
- { auto _e = block_map(); if (_e) { _o->block_map.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->block_map[_i] = _e->Get(_i); } } }
- { auto _e = dim_metadata(); if (_e) { _o->dim_metadata.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->dim_metadata[_i]) { _e->Get(_i)->UnPackTo(_o->dim_metadata[_i].get(), _resolver); } else { _o->dim_metadata[_i] = std::unique_ptr<tflite::DimensionMetadataT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
+ { auto _e = traversal_order(); if (_e) { _o->traversal_order.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->traversal_order[_i] = _e->Get(_i); } } else { _o->traversal_order.resize(0); } }
+ { auto _e = block_map(); if (_e) { _o->block_map.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->block_map[_i] = _e->Get(_i); } } else { _o->block_map.resize(0); } }
+ { auto _e = dim_metadata(); if (_e) { _o->dim_metadata.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->dim_metadata[_i]) { _e->Get(_i)->UnPackTo(_o->dim_metadata[_i].get(), _resolver); } else { _o->dim_metadata[_i] = std::unique_ptr<tflite::DimensionMetadataT>(_e->Get(_i)->UnPack(_resolver)); }; } } else { _o->dim_metadata.resize(0); } }
}
-inline flatbuffers::Offset<SparsityParameters> SparsityParameters::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SparsityParameters> SparsityParameters::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSparsityParameters(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SparsityParameters> CreateSparsityParameters(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SparsityParameters> CreateSparsityParameters(::flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SparsityParametersT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SparsityParametersT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _traversal_order = _o->traversal_order.size() ? _fbb.CreateVector(_o->traversal_order) : 0;
auto _block_map = _o->block_map.size() ? _fbb.CreateVector(_o->block_map) : 0;
- auto _dim_metadata = _o->dim_metadata.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::DimensionMetadata>> (_o->dim_metadata.size(), [](size_t i, _VectorArgs *__va) { return CreateDimensionMetadata(*__va->__fbb, __va->__o->dim_metadata[i].get(), __va->__rehasher); }, &_va ) : 0;
+ auto _dim_metadata = _o->dim_metadata.size() ? _fbb.CreateVector<::flatbuffers::Offset<tflite::DimensionMetadata>> (_o->dim_metadata.size(), [](size_t i, _VectorArgs *__va) { return CreateDimensionMetadata(*__va->__fbb, __va->__o->dim_metadata[i].get(), __va->__rehasher); }, &_va ) : 0;
return tflite::CreateSparsityParameters(
_fbb,
_traversal_order,
@@ -13836,28 +16887,28 @@
_dim_metadata);
}
-inline VariantSubTypeT *VariantSubType::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline VariantSubTypeT *VariantSubType::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<VariantSubTypeT>(new VariantSubTypeT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void VariantSubType::UnPackTo(VariantSubTypeT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void VariantSubType::UnPackTo(VariantSubTypeT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
- { auto _e = shape(); if (_e) { _o->shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape[_i] = _e->Get(_i); } } }
+ { auto _e = shape(); if (_e) { _o->shape.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape[_i] = _e->Get(_i); } } else { _o->shape.resize(0); } }
{ auto _e = type(); _o->type = _e; }
{ auto _e = has_rank(); _o->has_rank = _e; }
}
-inline flatbuffers::Offset<VariantSubType> VariantSubType::Pack(flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<VariantSubType> VariantSubType::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateVariantSubType(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<VariantSubType> CreateVariantSubType(flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<VariantSubType> CreateVariantSubType(::flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const VariantSubTypeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const VariantSubTypeT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _shape = _o->shape.size() ? _fbb.CreateVector(_o->shape) : 0;
auto _type = _o->type;
auto _has_rank = _o->has_rank;
@@ -13896,35 +16947,35 @@
return *this;
}
-inline TensorT *Tensor::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline TensorT *Tensor::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<TensorT>(new TensorT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void Tensor::UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void Tensor::UnPackTo(TensorT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
- { auto _e = shape(); if (_e) { _o->shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape[_i] = _e->Get(_i); } } }
+ { auto _e = shape(); if (_e) { _o->shape.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape[_i] = _e->Get(_i); } } else { _o->shape.resize(0); } }
{ auto _e = type(); _o->type = _e; }
{ auto _e = buffer(); _o->buffer = _e; }
{ auto _e = name(); if (_e) _o->name = _e->str(); }
- { auto _e = quantization(); if (_e) { if(_o->quantization) { _e->UnPackTo(_o->quantization.get(), _resolver); } else { _o->quantization = std::unique_ptr<tflite::QuantizationParametersT>(_e->UnPack(_resolver)); } } }
+ { auto _e = quantization(); if (_e) { if(_o->quantization) { _e->UnPackTo(_o->quantization.get(), _resolver); } else { _o->quantization = std::unique_ptr<tflite::QuantizationParametersT>(_e->UnPack(_resolver)); } } else if (_o->quantization) { _o->quantization.reset(); } }
{ auto _e = is_variable(); _o->is_variable = _e; }
- { auto _e = sparsity(); if (_e) { if(_o->sparsity) { _e->UnPackTo(_o->sparsity.get(), _resolver); } else { _o->sparsity = std::unique_ptr<tflite::SparsityParametersT>(_e->UnPack(_resolver)); } } }
- { auto _e = shape_signature(); if (_e) { _o->shape_signature.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape_signature[_i] = _e->Get(_i); } } }
+ { auto _e = sparsity(); if (_e) { if(_o->sparsity) { _e->UnPackTo(_o->sparsity.get(), _resolver); } else { _o->sparsity = std::unique_ptr<tflite::SparsityParametersT>(_e->UnPack(_resolver)); } } else if (_o->sparsity) { _o->sparsity.reset(); } }
+ { auto _e = shape_signature(); if (_e) { _o->shape_signature.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape_signature[_i] = _e->Get(_i); } } else { _o->shape_signature.resize(0); } }
{ auto _e = has_rank(); _o->has_rank = _e; }
- { auto _e = variant_tensors(); if (_e) { _o->variant_tensors.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->variant_tensors[_i]) { _e->Get(_i)->UnPackTo(_o->variant_tensors[_i].get(), _resolver); } else { _o->variant_tensors[_i] = std::unique_ptr<tflite::VariantSubTypeT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
+ { auto _e = variant_tensors(); if (_e) { _o->variant_tensors.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->variant_tensors[_i]) { _e->Get(_i)->UnPackTo(_o->variant_tensors[_i].get(), _resolver); } else { _o->variant_tensors[_i] = std::unique_ptr<tflite::VariantSubTypeT>(_e->Get(_i)->UnPack(_resolver)); }; } } else { _o->variant_tensors.resize(0); } }
}
-inline flatbuffers::Offset<Tensor> Tensor::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Tensor> Tensor::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateTensor(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<Tensor> CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Tensor> CreateTensor(::flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TensorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const TensorT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _shape = _o->shape.size() ? _fbb.CreateVector(_o->shape) : 0;
auto _type = _o->type;
auto _buffer = _o->buffer;
@@ -13934,7 +16985,7 @@
auto _sparsity = _o->sparsity ? CreateSparsityParameters(_fbb, _o->sparsity.get(), _rehasher) : 0;
auto _shape_signature = _o->shape_signature.size() ? _fbb.CreateVector(_o->shape_signature) : 0;
auto _has_rank = _o->has_rank;
- auto _variant_tensors = _o->variant_tensors.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::VariantSubType>> (_o->variant_tensors.size(), [](size_t i, _VectorArgs *__va) { return CreateVariantSubType(*__va->__fbb, __va->__o->variant_tensors[i].get(), __va->__rehasher); }, &_va ) : 0;
+ auto _variant_tensors = _o->variant_tensors.size() ? _fbb.CreateVector<::flatbuffers::Offset<tflite::VariantSubType>> (_o->variant_tensors.size(), [](size_t i, _VectorArgs *__va) { return CreateVariantSubType(*__va->__fbb, __va->__o->variant_tensors[i].get(), __va->__rehasher); }, &_va ) : 0;
return tflite::CreateTensor(
_fbb,
_shape,
@@ -13949,13 +17000,631 @@
_variant_tensors);
}
-inline Conv2DOptionsT *Conv2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline StablehloGatherOptionsT *StablehloGatherOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloGatherOptionsT>(new StablehloGatherOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloGatherOptions::UnPackTo(StablehloGatherOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = offset_dims(); if (_e) { _o->offset_dims.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->offset_dims[_i] = _e->Get(_i); } } else { _o->offset_dims.resize(0); } }
+ { auto _e = collapsed_slice_dims(); if (_e) { _o->collapsed_slice_dims.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->collapsed_slice_dims[_i] = _e->Get(_i); } } else { _o->collapsed_slice_dims.resize(0); } }
+ { auto _e = start_index_map(); if (_e) { _o->start_index_map.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->start_index_map[_i] = _e->Get(_i); } } else { _o->start_index_map.resize(0); } }
+ { auto _e = index_vector_dim(); _o->index_vector_dim = _e; }
+ { auto _e = slice_sizes(); if (_e) { _o->slice_sizes.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->slice_sizes[_i] = _e->Get(_i); } } else { _o->slice_sizes.resize(0); } }
+ { auto _e = indices_are_sorted(); _o->indices_are_sorted = _e; }
+}
+
+inline ::flatbuffers::Offset<StablehloGatherOptions> StablehloGatherOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloGatherOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloGatherOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloGatherOptions> CreateStablehloGatherOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloGatherOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloGatherOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _offset_dims = _o->offset_dims.size() ? _fbb.CreateVector(_o->offset_dims) : 0;
+ auto _collapsed_slice_dims = _o->collapsed_slice_dims.size() ? _fbb.CreateVector(_o->collapsed_slice_dims) : 0;
+ auto _start_index_map = _o->start_index_map.size() ? _fbb.CreateVector(_o->start_index_map) : 0;
+ auto _index_vector_dim = _o->index_vector_dim;
+ auto _slice_sizes = _o->slice_sizes.size() ? _fbb.CreateVector(_o->slice_sizes) : 0;
+ auto _indices_are_sorted = _o->indices_are_sorted;
+ return tflite::CreateStablehloGatherOptions(
+ _fbb,
+ _offset_dims,
+ _collapsed_slice_dims,
+ _start_index_map,
+ _index_vector_dim,
+ _slice_sizes,
+ _indices_are_sorted);
+}
+
+inline StablehloTransposeOptionsT *StablehloTransposeOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloTransposeOptionsT>(new StablehloTransposeOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloTransposeOptions::UnPackTo(StablehloTransposeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = permutation(); if (_e) { _o->permutation.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->permutation[_i] = _e->Get(_i); } } else { _o->permutation.resize(0); } }
+}
+
+inline ::flatbuffers::Offset<StablehloTransposeOptions> StablehloTransposeOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloTransposeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloTransposeOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloTransposeOptions> CreateStablehloTransposeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloTransposeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloTransposeOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _permutation = _o->permutation.size() ? _fbb.CreateVector(_o->permutation) : 0;
+ return tflite::CreateStablehloTransposeOptions(
+ _fbb,
+ _permutation);
+}
+
+inline StablehloDotGeneralOptionsT *StablehloDotGeneralOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloDotGeneralOptionsT>(new StablehloDotGeneralOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloDotGeneralOptions::UnPackTo(StablehloDotGeneralOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = lhs_batching_dimensions(); if (_e) { _o->lhs_batching_dimensions.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->lhs_batching_dimensions[_i] = _e->Get(_i); } } else { _o->lhs_batching_dimensions.resize(0); } }
+ { auto _e = rhs_batching_dimensions(); if (_e) { _o->rhs_batching_dimensions.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->rhs_batching_dimensions[_i] = _e->Get(_i); } } else { _o->rhs_batching_dimensions.resize(0); } }
+ { auto _e = lhs_contracting_dimensions(); if (_e) { _o->lhs_contracting_dimensions.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->lhs_contracting_dimensions[_i] = _e->Get(_i); } } else { _o->lhs_contracting_dimensions.resize(0); } }
+ { auto _e = rhs_contracting_dimensions(); if (_e) { _o->rhs_contracting_dimensions.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->rhs_contracting_dimensions[_i] = _e->Get(_i); } } else { _o->rhs_contracting_dimensions.resize(0); } }
+ { auto _e = precision_config(); if (_e) { _o->precision_config.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->precision_config[_i] = static_cast<tflite::StablehloPrecisionConfig>(_e->Get(_i)); } } else { _o->precision_config.resize(0); } }
+}
+
+inline ::flatbuffers::Offset<StablehloDotGeneralOptions> StablehloDotGeneralOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloDotGeneralOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloDotGeneralOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloDotGeneralOptions> CreateStablehloDotGeneralOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloDotGeneralOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloDotGeneralOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _lhs_batching_dimensions = _o->lhs_batching_dimensions.size() ? _fbb.CreateVector(_o->lhs_batching_dimensions) : 0;
+ auto _rhs_batching_dimensions = _o->rhs_batching_dimensions.size() ? _fbb.CreateVector(_o->rhs_batching_dimensions) : 0;
+ auto _lhs_contracting_dimensions = _o->lhs_contracting_dimensions.size() ? _fbb.CreateVector(_o->lhs_contracting_dimensions) : 0;
+ auto _rhs_contracting_dimensions = _o->rhs_contracting_dimensions.size() ? _fbb.CreateVector(_o->rhs_contracting_dimensions) : 0;
+ auto _precision_config = _o->precision_config.size() ? _fbb.CreateVectorScalarCast<uint32_t>(::flatbuffers::data(_o->precision_config), _o->precision_config.size()) : 0;
+ return tflite::CreateStablehloDotGeneralOptions(
+ _fbb,
+ _lhs_batching_dimensions,
+ _rhs_batching_dimensions,
+ _lhs_contracting_dimensions,
+ _rhs_contracting_dimensions,
+ _precision_config);
+}
+
+inline StablehloReduceWindowOptionsT *StablehloReduceWindowOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloReduceWindowOptionsT>(new StablehloReduceWindowOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloReduceWindowOptions::UnPackTo(StablehloReduceWindowOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = window_dimensions(); if (_e) { _o->window_dimensions.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->window_dimensions[_i] = _e->Get(_i); } } else { _o->window_dimensions.resize(0); } }
+ { auto _e = window_strides(); if (_e) { _o->window_strides.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->window_strides[_i] = _e->Get(_i); } } else { _o->window_strides.resize(0); } }
+ { auto _e = base_dilations(); if (_e) { _o->base_dilations.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->base_dilations[_i] = _e->Get(_i); } } else { _o->base_dilations.resize(0); } }
+ { auto _e = window_dilations(); if (_e) { _o->window_dilations.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->window_dilations[_i] = _e->Get(_i); } } else { _o->window_dilations.resize(0); } }
+ { auto _e = padding(); if (_e) { _o->padding.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->padding[_i] = _e->Get(_i); } } else { _o->padding.resize(0); } }
+ { auto _e = body_subgraph_index(); _o->body_subgraph_index = _e; }
+}
+
+inline ::flatbuffers::Offset<StablehloReduceWindowOptions> StablehloReduceWindowOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceWindowOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloReduceWindowOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloReduceWindowOptions> CreateStablehloReduceWindowOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceWindowOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloReduceWindowOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _window_dimensions = _o->window_dimensions.size() ? _fbb.CreateVector(_o->window_dimensions) : 0;
+ auto _window_strides = _o->window_strides.size() ? _fbb.CreateVector(_o->window_strides) : 0;
+ auto _base_dilations = _o->base_dilations.size() ? _fbb.CreateVector(_o->base_dilations) : 0;
+ auto _window_dilations = _o->window_dilations.size() ? _fbb.CreateVector(_o->window_dilations) : 0;
+ auto _padding = _o->padding.size() ? _fbb.CreateVector(_o->padding) : 0;
+ auto _body_subgraph_index = _o->body_subgraph_index;
+ return tflite::CreateStablehloReduceWindowOptions(
+ _fbb,
+ _window_dimensions,
+ _window_strides,
+ _base_dilations,
+ _window_dilations,
+ _padding,
+ _body_subgraph_index);
+}
+
+inline StablehloWhileOptionsT *StablehloWhileOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloWhileOptionsT>(new StablehloWhileOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloWhileOptions::UnPackTo(StablehloWhileOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = cond_subgraph_index(); _o->cond_subgraph_index = _e; }
+ { auto _e = body_subgraph_index(); _o->body_subgraph_index = _e; }
+}
+
+inline ::flatbuffers::Offset<StablehloWhileOptions> StablehloWhileOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloWhileOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloWhileOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloWhileOptions> CreateStablehloWhileOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloWhileOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloWhileOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _cond_subgraph_index = _o->cond_subgraph_index;
+ auto _body_subgraph_index = _o->body_subgraph_index;
+ return tflite::CreateStablehloWhileOptions(
+ _fbb,
+ _cond_subgraph_index,
+ _body_subgraph_index);
+}
+
+inline StablehloSortOptionsT *StablehloSortOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloSortOptionsT>(new StablehloSortOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloSortOptions::UnPackTo(StablehloSortOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = dimension(); _o->dimension = _e; }
+ { auto _e = is_stable(); _o->is_stable = _e; }
+ { auto _e = comparator_subgraph_index(); _o->comparator_subgraph_index = _e; }
+}
+
+inline ::flatbuffers::Offset<StablehloSortOptions> StablehloSortOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloSortOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloSortOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloSortOptions> CreateStablehloSortOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloSortOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloSortOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _dimension = _o->dimension;
+ auto _is_stable = _o->is_stable;
+ auto _comparator_subgraph_index = _o->comparator_subgraph_index;
+ return tflite::CreateStablehloSortOptions(
+ _fbb,
+ _dimension,
+ _is_stable,
+ _comparator_subgraph_index);
+}
+
+inline StablehloConcatenateOptionsT *StablehloConcatenateOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloConcatenateOptionsT>(new StablehloConcatenateOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloConcatenateOptions::UnPackTo(StablehloConcatenateOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = dimension(); _o->dimension = _e; }
+}
+
+inline ::flatbuffers::Offset<StablehloConcatenateOptions> StablehloConcatenateOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloConcatenateOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloConcatenateOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloConcatenateOptions> CreateStablehloConcatenateOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloConcatenateOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloConcatenateOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _dimension = _o->dimension;
+ return tflite::CreateStablehloConcatenateOptions(
+ _fbb,
+ _dimension);
+}
+
+inline StablehloBroadcastInDimOptionsT *StablehloBroadcastInDimOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloBroadcastInDimOptionsT>(new StablehloBroadcastInDimOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloBroadcastInDimOptions::UnPackTo(StablehloBroadcastInDimOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = broadcast_dimensions(); if (_e) { _o->broadcast_dimensions.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->broadcast_dimensions[_i] = _e->Get(_i); } } else { _o->broadcast_dimensions.resize(0); } }
+}
+
+inline ::flatbuffers::Offset<StablehloBroadcastInDimOptions> StablehloBroadcastInDimOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloBroadcastInDimOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloBroadcastInDimOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloBroadcastInDimOptions> CreateStablehloBroadcastInDimOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloBroadcastInDimOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloBroadcastInDimOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _broadcast_dimensions = _o->broadcast_dimensions.size() ? _fbb.CreateVector(_o->broadcast_dimensions) : 0;
+ return tflite::CreateStablehloBroadcastInDimOptions(
+ _fbb,
+ _broadcast_dimensions);
+}
+
+inline StablehloCompareOptionsT *StablehloCompareOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloCompareOptionsT>(new StablehloCompareOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloCompareOptions::UnPackTo(StablehloCompareOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = comparison_direction(); _o->comparison_direction = _e; }
+ { auto _e = compare_type(); _o->compare_type = _e; }
+}
+
+inline ::flatbuffers::Offset<StablehloCompareOptions> StablehloCompareOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloCompareOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloCompareOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloCompareOptions> CreateStablehloCompareOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloCompareOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloCompareOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _comparison_direction = _o->comparison_direction;
+ auto _compare_type = _o->compare_type;
+ return tflite::CreateStablehloCompareOptions(
+ _fbb,
+ _comparison_direction,
+ _compare_type);
+}
+
+inline StablehloDynamicSliceOptionsT *StablehloDynamicSliceOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloDynamicSliceOptionsT>(new StablehloDynamicSliceOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloDynamicSliceOptions::UnPackTo(StablehloDynamicSliceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = slice_sizes(); if (_e) { _o->slice_sizes.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->slice_sizes[_i] = _e->Get(_i); } } else { _o->slice_sizes.resize(0); } }
+}
+
+inline ::flatbuffers::Offset<StablehloDynamicSliceOptions> StablehloDynamicSliceOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloDynamicSliceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloDynamicSliceOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloDynamicSliceOptions> CreateStablehloDynamicSliceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloDynamicSliceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloDynamicSliceOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _slice_sizes = _o->slice_sizes.size() ? _fbb.CreateVector(_o->slice_sizes) : 0;
+ return tflite::CreateStablehloDynamicSliceOptions(
+ _fbb,
+ _slice_sizes);
+}
+
+inline StablehloPadOptionsT *StablehloPadOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloPadOptionsT>(new StablehloPadOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloPadOptions::UnPackTo(StablehloPadOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = edge_padding_low(); if (_e) { _o->edge_padding_low.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->edge_padding_low[_i] = _e->Get(_i); } } else { _o->edge_padding_low.resize(0); } }
+ { auto _e = edge_padding_high(); if (_e) { _o->edge_padding_high.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->edge_padding_high[_i] = _e->Get(_i); } } else { _o->edge_padding_high.resize(0); } }
+ { auto _e = interior_padding(); if (_e) { _o->interior_padding.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->interior_padding[_i] = _e->Get(_i); } } else { _o->interior_padding.resize(0); } }
+}
+
+inline ::flatbuffers::Offset<StablehloPadOptions> StablehloPadOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloPadOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloPadOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloPadOptions> CreateStablehloPadOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloPadOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloPadOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _edge_padding_low = _o->edge_padding_low.size() ? _fbb.CreateVector(_o->edge_padding_low) : 0;
+ auto _edge_padding_high = _o->edge_padding_high.size() ? _fbb.CreateVector(_o->edge_padding_high) : 0;
+ auto _interior_padding = _o->interior_padding.size() ? _fbb.CreateVector(_o->interior_padding) : 0;
+ return tflite::CreateStablehloPadOptions(
+ _fbb,
+ _edge_padding_low,
+ _edge_padding_high,
+ _interior_padding);
+}
+
+inline StablehloIotaOptionsT *StablehloIotaOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloIotaOptionsT>(new StablehloIotaOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloIotaOptions::UnPackTo(StablehloIotaOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = iota_dimension(); _o->iota_dimension = _e; }
+}
+
+inline ::flatbuffers::Offset<StablehloIotaOptions> StablehloIotaOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloIotaOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloIotaOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloIotaOptions> CreateStablehloIotaOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloIotaOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloIotaOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _iota_dimension = _o->iota_dimension;
+ return tflite::CreateStablehloIotaOptions(
+ _fbb,
+ _iota_dimension);
+}
+
+inline StablehloCustomCallOptionsT *StablehloCustomCallOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloCustomCallOptionsT>(new StablehloCustomCallOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloCustomCallOptions::UnPackTo(StablehloCustomCallOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = call_target_name(); if (_e) _o->call_target_name = _e->str(); }
+ { auto _e = has_side_effect(); _o->has_side_effect = _e; }
+ { auto _e = backend_config(); if (_e) _o->backend_config = _e->str(); }
+ { auto _e = api_version(); _o->api_version = _e; }
+ { auto _e = called_computations(); if (_e) { _o->called_computations.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->called_computations[_i] = _e->Get(_i); } } else { _o->called_computations.resize(0); } }
+ { auto _e = custom_attributes(); if (_e) { _o->custom_attributes.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->custom_attributes.begin()); } }
+}
+
+inline ::flatbuffers::Offset<StablehloCustomCallOptions> StablehloCustomCallOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloCustomCallOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloCustomCallOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloCustomCallOptions> CreateStablehloCustomCallOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloCustomCallOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloCustomCallOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _call_target_name = _o->call_target_name.empty() ? 0 : _fbb.CreateString(_o->call_target_name);
+ auto _has_side_effect = _o->has_side_effect;
+ auto _backend_config = _o->backend_config.empty() ? 0 : _fbb.CreateString(_o->backend_config);
+ auto _api_version = _o->api_version;
+ auto _called_computations = _o->called_computations.size() ? _fbb.CreateVector(_o->called_computations) : 0;
+ auto _custom_attributes = _o->custom_attributes.size() ? _fbb.CreateVector(_o->custom_attributes) : 0;
+ return tflite::CreateStablehloCustomCallOptions(
+ _fbb,
+ _call_target_name,
+ _has_side_effect,
+ _backend_config,
+ _api_version,
+ _called_computations,
+ _custom_attributes);
+}
+
+inline StablehloReduceOptionsT *StablehloReduceOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloReduceOptionsT>(new StablehloReduceOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloReduceOptions::UnPackTo(StablehloReduceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = dimensions(); if (_e) { _o->dimensions.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->dimensions[_i] = _e->Get(_i); } } else { _o->dimensions.resize(0); } }
+ { auto _e = body_subgraph_index(); _o->body_subgraph_index = _e; }
+}
+
+inline ::flatbuffers::Offset<StablehloReduceOptions> StablehloReduceOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloReduceOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloReduceOptions> CreateStablehloReduceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloReduceOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _dimensions = _o->dimensions.size() ? _fbb.CreateVector(_o->dimensions) : 0;
+ auto _body_subgraph_index = _o->body_subgraph_index;
+ return tflite::CreateStablehloReduceOptions(
+ _fbb,
+ _dimensions,
+ _body_subgraph_index);
+}
+
+inline StablehloSliceOptionsT *StablehloSliceOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloSliceOptionsT>(new StablehloSliceOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloSliceOptions::UnPackTo(StablehloSliceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = start_indices(); if (_e) { _o->start_indices.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->start_indices[_i] = _e->Get(_i); } } else { _o->start_indices.resize(0); } }
+ { auto _e = limit_indices(); if (_e) { _o->limit_indices.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->limit_indices[_i] = _e->Get(_i); } } else { _o->limit_indices.resize(0); } }
+ { auto _e = strides(); if (_e) { _o->strides.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->strides[_i] = _e->Get(_i); } } else { _o->strides.resize(0); } }
+}
+
+inline ::flatbuffers::Offset<StablehloSliceOptions> StablehloSliceOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloSliceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloSliceOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloSliceOptions> CreateStablehloSliceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloSliceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloSliceOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _start_indices = _o->start_indices.size() ? _fbb.CreateVector(_o->start_indices) : 0;
+ auto _limit_indices = _o->limit_indices.size() ? _fbb.CreateVector(_o->limit_indices) : 0;
+ auto _strides = _o->strides.size() ? _fbb.CreateVector(_o->strides) : 0;
+ return tflite::CreateStablehloSliceOptions(
+ _fbb,
+ _start_indices,
+ _limit_indices,
+ _strides);
+}
+
+inline StablehloConvolutionOptionsT *StablehloConvolutionOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloConvolutionOptionsT>(new StablehloConvolutionOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloConvolutionOptions::UnPackTo(StablehloConvolutionOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = window_strides(); if (_e) { _o->window_strides.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->window_strides[_i] = _e->Get(_i); } } else { _o->window_strides.resize(0); } }
+ { auto _e = padding(); if (_e) { _o->padding.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->padding[_i] = _e->Get(_i); } } else { _o->padding.resize(0); } }
+ { auto _e = lhs_dilation(); if (_e) { _o->lhs_dilation.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->lhs_dilation[_i] = _e->Get(_i); } } else { _o->lhs_dilation.resize(0); } }
+ { auto _e = rhs_dilation(); if (_e) { _o->rhs_dilation.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->rhs_dilation[_i] = _e->Get(_i); } } else { _o->rhs_dilation.resize(0); } }
+ { auto _e = window_reversal(); if (_e) { _o->window_reversal.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->window_reversal[_i] = _e->Get(_i) != 0; } } else { _o->window_reversal.resize(0); } }
+ { auto _e = input_batch_dimension(); _o->input_batch_dimension = _e; }
+ { auto _e = input_feature_dimension(); _o->input_feature_dimension = _e; }
+ { auto _e = input_spatial_dimensions(); if (_e) { _o->input_spatial_dimensions.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->input_spatial_dimensions[_i] = _e->Get(_i); } } else { _o->input_spatial_dimensions.resize(0); } }
+ { auto _e = kernel_input_feature_dimension(); _o->kernel_input_feature_dimension = _e; }
+ { auto _e = kernel_output_feature_dimension(); _o->kernel_output_feature_dimension = _e; }
+ { auto _e = kernel_spatial_dimensions(); if (_e) { _o->kernel_spatial_dimensions.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->kernel_spatial_dimensions[_i] = _e->Get(_i); } } else { _o->kernel_spatial_dimensions.resize(0); } }
+ { auto _e = output_batch_dimension(); _o->output_batch_dimension = _e; }
+ { auto _e = output_feature_dimension(); _o->output_feature_dimension = _e; }
+ { auto _e = output_spatial_dimensions(); if (_e) { _o->output_spatial_dimensions.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->output_spatial_dimensions[_i] = _e->Get(_i); } } else { _o->output_spatial_dimensions.resize(0); } }
+ { auto _e = feature_group_count(); _o->feature_group_count = _e; }
+ { auto _e = batch_group_count(); _o->batch_group_count = _e; }
+ { auto _e = precision_config(); if (_e) { _o->precision_config.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->precision_config[_i] = static_cast<tflite::StablehloPrecisionConfig>(_e->Get(_i)); } } else { _o->precision_config.resize(0); } }
+}
+
+inline ::flatbuffers::Offset<StablehloConvolutionOptions> StablehloConvolutionOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloConvolutionOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloConvolutionOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloConvolutionOptions> CreateStablehloConvolutionOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloConvolutionOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloConvolutionOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _window_strides = _o->window_strides.size() ? _fbb.CreateVector(_o->window_strides) : 0;
+ auto _padding = _o->padding.size() ? _fbb.CreateVector(_o->padding) : 0;
+ auto _lhs_dilation = _o->lhs_dilation.size() ? _fbb.CreateVector(_o->lhs_dilation) : 0;
+ auto _rhs_dilation = _o->rhs_dilation.size() ? _fbb.CreateVector(_o->rhs_dilation) : 0;
+ auto _window_reversal = _o->window_reversal.size() ? _fbb.CreateVector(_o->window_reversal) : 0;
+ auto _input_batch_dimension = _o->input_batch_dimension;
+ auto _input_feature_dimension = _o->input_feature_dimension;
+ auto _input_spatial_dimensions = _o->input_spatial_dimensions.size() ? _fbb.CreateVector(_o->input_spatial_dimensions) : 0;
+ auto _kernel_input_feature_dimension = _o->kernel_input_feature_dimension;
+ auto _kernel_output_feature_dimension = _o->kernel_output_feature_dimension;
+ auto _kernel_spatial_dimensions = _o->kernel_spatial_dimensions.size() ? _fbb.CreateVector(_o->kernel_spatial_dimensions) : 0;
+ auto _output_batch_dimension = _o->output_batch_dimension;
+ auto _output_feature_dimension = _o->output_feature_dimension;
+ auto _output_spatial_dimensions = _o->output_spatial_dimensions.size() ? _fbb.CreateVector(_o->output_spatial_dimensions) : 0;
+ auto _feature_group_count = _o->feature_group_count;
+ auto _batch_group_count = _o->batch_group_count;
+ auto _precision_config = _o->precision_config.size() ? _fbb.CreateVectorScalarCast<uint32_t>(::flatbuffers::data(_o->precision_config), _o->precision_config.size()) : 0;
+ return tflite::CreateStablehloConvolutionOptions(
+ _fbb,
+ _window_strides,
+ _padding,
+ _lhs_dilation,
+ _rhs_dilation,
+ _window_reversal,
+ _input_batch_dimension,
+ _input_feature_dimension,
+ _input_spatial_dimensions,
+ _kernel_input_feature_dimension,
+ _kernel_output_feature_dimension,
+ _kernel_spatial_dimensions,
+ _output_batch_dimension,
+ _output_feature_dimension,
+ _output_spatial_dimensions,
+ _feature_group_count,
+ _batch_group_count,
+ _precision_config);
+}
+
+inline StablehloScatterOptionsT *StablehloScatterOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloScatterOptionsT>(new StablehloScatterOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloScatterOptions::UnPackTo(StablehloScatterOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = indices_are_sorted(); _o->indices_are_sorted = _e; }
+ { auto _e = update_window_dims(); if (_e) { _o->update_window_dims.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->update_window_dims[_i] = _e->Get(_i); } } else { _o->update_window_dims.resize(0); } }
+ { auto _e = inserted_window_dims(); if (_e) { _o->inserted_window_dims.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inserted_window_dims[_i] = _e->Get(_i); } } else { _o->inserted_window_dims.resize(0); } }
+ { auto _e = scatter_dims_to_operand_dims(); if (_e) { _o->scatter_dims_to_operand_dims.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->scatter_dims_to_operand_dims[_i] = _e->Get(_i); } } else { _o->scatter_dims_to_operand_dims.resize(0); } }
+ { auto _e = index_vector_dim(); _o->index_vector_dim = _e; }
+ { auto _e = unique_indices(); _o->unique_indices = _e; }
+ { auto _e = update_computation_subgraph_index(); _o->update_computation_subgraph_index = _e; }
+}
+
+inline ::flatbuffers::Offset<StablehloScatterOptions> StablehloScatterOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloScatterOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloScatterOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloScatterOptions> CreateStablehloScatterOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloScatterOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloScatterOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _indices_are_sorted = _o->indices_are_sorted;
+ auto _update_window_dims = _o->update_window_dims.size() ? _fbb.CreateVector(_o->update_window_dims) : 0;
+ auto _inserted_window_dims = _o->inserted_window_dims.size() ? _fbb.CreateVector(_o->inserted_window_dims) : 0;
+ auto _scatter_dims_to_operand_dims = _o->scatter_dims_to_operand_dims.size() ? _fbb.CreateVector(_o->scatter_dims_to_operand_dims) : 0;
+ auto _index_vector_dim = _o->index_vector_dim;
+ auto _unique_indices = _o->unique_indices;
+ auto _update_computation_subgraph_index = _o->update_computation_subgraph_index;
+ return tflite::CreateStablehloScatterOptions(
+ _fbb,
+ _indices_are_sorted,
+ _update_window_dims,
+ _inserted_window_dims,
+ _scatter_dims_to_operand_dims,
+ _index_vector_dim,
+ _unique_indices,
+ _update_computation_subgraph_index);
+}
+
+inline StablehloRngBitGeneratorOptionsT *StablehloRngBitGeneratorOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StablehloRngBitGeneratorOptionsT>(new StablehloRngBitGeneratorOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StablehloRngBitGeneratorOptions::UnPackTo(StablehloRngBitGeneratorOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = algorithm(); _o->algorithm = _e; }
+}
+
+inline ::flatbuffers::Offset<StablehloRngBitGeneratorOptions> StablehloRngBitGeneratorOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloRngBitGeneratorOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStablehloRngBitGeneratorOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StablehloRngBitGeneratorOptions> CreateStablehloRngBitGeneratorOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StablehloRngBitGeneratorOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StablehloRngBitGeneratorOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _algorithm = _o->algorithm;
+ return tflite::CreateStablehloRngBitGeneratorOptions(
+ _fbb,
+ _algorithm);
+}
+
+inline Conv2DOptionsT *Conv2DOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<Conv2DOptionsT>(new Conv2DOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void Conv2DOptions::UnPackTo(Conv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void Conv2DOptions::UnPackTo(Conv2DOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = padding(); _o->padding = _e; }
@@ -13964,22 +17633,24 @@
{ auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
{ auto _e = dilation_w_factor(); _o->dilation_w_factor = _e; }
{ auto _e = dilation_h_factor(); _o->dilation_h_factor = _e; }
+ { auto _e = quantized_bias_type(); _o->quantized_bias_type = _e; }
}
-inline flatbuffers::Offset<Conv2DOptions> Conv2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Conv2DOptions> Conv2DOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateConv2DOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<Conv2DOptions> CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Conv2DOptions> CreateConv2DOptions(::flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Conv2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const Conv2DOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _padding = _o->padding;
auto _stride_w = _o->stride_w;
auto _stride_h = _o->stride_h;
auto _fused_activation_function = _o->fused_activation_function;
auto _dilation_w_factor = _o->dilation_w_factor;
auto _dilation_h_factor = _o->dilation_h_factor;
+ auto _quantized_bias_type = _o->quantized_bias_type;
return tflite::CreateConv2DOptions(
_fbb,
_padding,
@@ -13987,16 +17658,17 @@
_stride_h,
_fused_activation_function,
_dilation_w_factor,
- _dilation_h_factor);
+ _dilation_h_factor,
+ _quantized_bias_type);
}
-inline Conv3DOptionsT *Conv3DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline Conv3DOptionsT *Conv3DOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<Conv3DOptionsT>(new Conv3DOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void Conv3DOptions::UnPackTo(Conv3DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void Conv3DOptions::UnPackTo(Conv3DOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = padding(); _o->padding = _e; }
@@ -14009,14 +17681,14 @@
{ auto _e = dilation_h_factor(); _o->dilation_h_factor = _e; }
}
-inline flatbuffers::Offset<Conv3DOptions> Conv3DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Conv3DOptions> Conv3DOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateConv3DOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<Conv3DOptions> CreateConv3DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Conv3DOptions> CreateConv3DOptions(::flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Conv3DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const Conv3DOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _padding = _o->padding;
auto _stride_d = _o->stride_d;
auto _stride_w = _o->stride_w;
@@ -14037,13 +17709,13 @@
_dilation_h_factor);
}
-inline Pool2DOptionsT *Pool2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline Pool2DOptionsT *Pool2DOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<Pool2DOptionsT>(new Pool2DOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void Pool2DOptions::UnPackTo(Pool2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void Pool2DOptions::UnPackTo(Pool2DOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = padding(); _o->padding = _e; }
@@ -14054,14 +17726,14 @@
{ auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
}
-inline flatbuffers::Offset<Pool2DOptions> Pool2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Pool2DOptions> Pool2DOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreatePool2DOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<Pool2DOptions> CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Pool2DOptions> CreatePool2DOptions(::flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Pool2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const Pool2DOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _padding = _o->padding;
auto _stride_w = _o->stride_w;
auto _stride_h = _o->stride_h;
@@ -14078,13 +17750,13 @@
_fused_activation_function);
}
-inline DepthwiseConv2DOptionsT *DepthwiseConv2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline DepthwiseConv2DOptionsT *DepthwiseConv2DOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<DepthwiseConv2DOptionsT>(new DepthwiseConv2DOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void DepthwiseConv2DOptions::UnPackTo(DepthwiseConv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void DepthwiseConv2DOptions::UnPackTo(DepthwiseConv2DOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = padding(); _o->padding = _e; }
@@ -14096,14 +17768,14 @@
{ auto _e = dilation_h_factor(); _o->dilation_h_factor = _e; }
}
-inline flatbuffers::Offset<DepthwiseConv2DOptions> DepthwiseConv2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<DepthwiseConv2DOptions> DepthwiseConv2DOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateDepthwiseConv2DOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<DepthwiseConv2DOptions> CreateDepthwiseConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<DepthwiseConv2DOptions> CreateDepthwiseConv2DOptions(::flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DepthwiseConv2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const DepthwiseConv2DOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _padding = _o->padding;
auto _stride_w = _o->stride_w;
auto _stride_h = _o->stride_h;
@@ -14122,28 +17794,28 @@
_dilation_h_factor);
}
-inline ConcatEmbeddingsOptionsT *ConcatEmbeddingsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ConcatEmbeddingsOptionsT *ConcatEmbeddingsOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ConcatEmbeddingsOptionsT>(new ConcatEmbeddingsOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void ConcatEmbeddingsOptions::UnPackTo(ConcatEmbeddingsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void ConcatEmbeddingsOptions::UnPackTo(ConcatEmbeddingsOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = num_channels(); _o->num_channels = _e; }
- { auto _e = num_columns_per_channel(); if (_e) { _o->num_columns_per_channel.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->num_columns_per_channel[_i] = _e->Get(_i); } } }
- { auto _e = embedding_dim_per_channel(); if (_e) { _o->embedding_dim_per_channel.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->embedding_dim_per_channel[_i] = _e->Get(_i); } } }
+ { auto _e = num_columns_per_channel(); if (_e) { _o->num_columns_per_channel.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->num_columns_per_channel[_i] = _e->Get(_i); } } else { _o->num_columns_per_channel.resize(0); } }
+ { auto _e = embedding_dim_per_channel(); if (_e) { _o->embedding_dim_per_channel.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->embedding_dim_per_channel[_i] = _e->Get(_i); } } else { _o->embedding_dim_per_channel.resize(0); } }
}
-inline flatbuffers::Offset<ConcatEmbeddingsOptions> ConcatEmbeddingsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ConcatEmbeddingsOptions> ConcatEmbeddingsOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateConcatEmbeddingsOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<ConcatEmbeddingsOptions> CreateConcatEmbeddingsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ConcatEmbeddingsOptions> CreateConcatEmbeddingsOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ConcatEmbeddingsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ConcatEmbeddingsOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _num_channels = _o->num_channels;
auto _num_columns_per_channel = _o->num_columns_per_channel.size() ? _fbb.CreateVector(_o->num_columns_per_channel) : 0;
auto _embedding_dim_per_channel = _o->embedding_dim_per_channel.size() ? _fbb.CreateVector(_o->embedding_dim_per_channel) : 0;
@@ -14154,39 +17826,39 @@
_embedding_dim_per_channel);
}
-inline LSHProjectionOptionsT *LSHProjectionOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline LSHProjectionOptionsT *LSHProjectionOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<LSHProjectionOptionsT>(new LSHProjectionOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void LSHProjectionOptions::UnPackTo(LSHProjectionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void LSHProjectionOptions::UnPackTo(LSHProjectionOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = type(); _o->type = _e; }
}
-inline flatbuffers::Offset<LSHProjectionOptions> LSHProjectionOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LSHProjectionOptions> LSHProjectionOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateLSHProjectionOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<LSHProjectionOptions> CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LSHProjectionOptions> CreateLSHProjectionOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LSHProjectionOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const LSHProjectionOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _type = _o->type;
return tflite::CreateLSHProjectionOptions(
_fbb,
_type);
}
-inline SVDFOptionsT *SVDFOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SVDFOptionsT *SVDFOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SVDFOptionsT>(new SVDFOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SVDFOptions::UnPackTo(SVDFOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SVDFOptions::UnPackTo(SVDFOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = rank(); _o->rank = _e; }
@@ -14194,14 +17866,14 @@
{ auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; }
}
-inline flatbuffers::Offset<SVDFOptions> SVDFOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SVDFOptions> SVDFOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSVDFOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SVDFOptions> CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SVDFOptions> CreateSVDFOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SVDFOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SVDFOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _rank = _o->rank;
auto _fused_activation_function = _o->fused_activation_function;
auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs;
@@ -14212,27 +17884,27 @@
_asymmetric_quantize_inputs);
}
-inline RNNOptionsT *RNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline RNNOptionsT *RNNOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<RNNOptionsT>(new RNNOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void RNNOptions::UnPackTo(RNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void RNNOptions::UnPackTo(RNNOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
{ auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; }
}
-inline flatbuffers::Offset<RNNOptions> RNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<RNNOptions> RNNOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateRNNOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<RNNOptions> CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<RNNOptions> CreateRNNOptions(::flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const RNNOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _fused_activation_function = _o->fused_activation_function;
auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs;
return tflite::CreateRNNOptions(
@@ -14241,13 +17913,13 @@
_asymmetric_quantize_inputs);
}
-inline SequenceRNNOptionsT *SequenceRNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SequenceRNNOptionsT *SequenceRNNOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SequenceRNNOptionsT>(new SequenceRNNOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SequenceRNNOptions::UnPackTo(SequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SequenceRNNOptions::UnPackTo(SequenceRNNOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = time_major(); _o->time_major = _e; }
@@ -14255,14 +17927,14 @@
{ auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; }
}
-inline flatbuffers::Offset<SequenceRNNOptions> SequenceRNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SequenceRNNOptions> SequenceRNNOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSequenceRNNOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SequenceRNNOptions> CreateSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SequenceRNNOptions> CreateSequenceRNNOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SequenceRNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SequenceRNNOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _time_major = _o->time_major;
auto _fused_activation_function = _o->fused_activation_function;
auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs;
@@ -14273,13 +17945,13 @@
_asymmetric_quantize_inputs);
}
-inline BidirectionalSequenceRNNOptionsT *BidirectionalSequenceRNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline BidirectionalSequenceRNNOptionsT *BidirectionalSequenceRNNOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<BidirectionalSequenceRNNOptionsT>(new BidirectionalSequenceRNNOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void BidirectionalSequenceRNNOptions::UnPackTo(BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void BidirectionalSequenceRNNOptions::UnPackTo(BidirectionalSequenceRNNOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = time_major(); _o->time_major = _e; }
@@ -14288,14 +17960,14 @@
{ auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; }
}
-inline flatbuffers::Offset<BidirectionalSequenceRNNOptions> BidirectionalSequenceRNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<BidirectionalSequenceRNNOptions> BidirectionalSequenceRNNOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateBidirectionalSequenceRNNOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<BidirectionalSequenceRNNOptions> CreateBidirectionalSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<BidirectionalSequenceRNNOptions> CreateBidirectionalSequenceRNNOptions(::flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BidirectionalSequenceRNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const BidirectionalSequenceRNNOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _time_major = _o->time_major;
auto _fused_activation_function = _o->fused_activation_function;
auto _merge_outputs = _o->merge_outputs;
@@ -14308,88 +17980,91 @@
_asymmetric_quantize_inputs);
}
-inline FullyConnectedOptionsT *FullyConnectedOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline FullyConnectedOptionsT *FullyConnectedOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<FullyConnectedOptionsT>(new FullyConnectedOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void FullyConnectedOptions::UnPackTo(FullyConnectedOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void FullyConnectedOptions::UnPackTo(FullyConnectedOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
{ auto _e = weights_format(); _o->weights_format = _e; }
{ auto _e = keep_num_dims(); _o->keep_num_dims = _e; }
{ auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; }
+ { auto _e = quantized_bias_type(); _o->quantized_bias_type = _e; }
}
-inline flatbuffers::Offset<FullyConnectedOptions> FullyConnectedOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<FullyConnectedOptions> FullyConnectedOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateFullyConnectedOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<FullyConnectedOptions> CreateFullyConnectedOptions(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<FullyConnectedOptions> CreateFullyConnectedOptions(::flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FullyConnectedOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const FullyConnectedOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _fused_activation_function = _o->fused_activation_function;
auto _weights_format = _o->weights_format;
auto _keep_num_dims = _o->keep_num_dims;
auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs;
+ auto _quantized_bias_type = _o->quantized_bias_type;
return tflite::CreateFullyConnectedOptions(
_fbb,
_fused_activation_function,
_weights_format,
_keep_num_dims,
- _asymmetric_quantize_inputs);
+ _asymmetric_quantize_inputs,
+ _quantized_bias_type);
}
-inline SoftmaxOptionsT *SoftmaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SoftmaxOptionsT *SoftmaxOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SoftmaxOptionsT>(new SoftmaxOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SoftmaxOptions::UnPackTo(SoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SoftmaxOptions::UnPackTo(SoftmaxOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = beta(); _o->beta = _e; }
}
-inline flatbuffers::Offset<SoftmaxOptions> SoftmaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SoftmaxOptions> SoftmaxOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSoftmaxOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SoftmaxOptions> CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SoftmaxOptions> CreateSoftmaxOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SoftmaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SoftmaxOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _beta = _o->beta;
return tflite::CreateSoftmaxOptions(
_fbb,
_beta);
}
-inline ConcatenationOptionsT *ConcatenationOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ConcatenationOptionsT *ConcatenationOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ConcatenationOptionsT>(new ConcatenationOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void ConcatenationOptions::UnPackTo(ConcatenationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void ConcatenationOptions::UnPackTo(ConcatenationOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = axis(); _o->axis = _e; }
{ auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
}
-inline flatbuffers::Offset<ConcatenationOptions> ConcatenationOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ConcatenationOptions> ConcatenationOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateConcatenationOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<ConcatenationOptions> CreateConcatenationOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ConcatenationOptions> CreateConcatenationOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ConcatenationOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ConcatenationOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _axis = _o->axis;
auto _fused_activation_function = _o->fused_activation_function;
return tflite::CreateConcatenationOptions(
@@ -14398,27 +18073,27 @@
_fused_activation_function);
}
-inline AddOptionsT *AddOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline AddOptionsT *AddOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<AddOptionsT>(new AddOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void AddOptions::UnPackTo(AddOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void AddOptions::UnPackTo(AddOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
{ auto _e = pot_scale_int16(); _o->pot_scale_int16 = _e; }
}
-inline flatbuffers::Offset<AddOptions> AddOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<AddOptions> AddOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateAddOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<AddOptions> CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<AddOptions> CreateAddOptions(::flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AddOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const AddOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _fused_activation_function = _o->fused_activation_function;
auto _pot_scale_int16 = _o->pot_scale_int16;
return tflite::CreateAddOptions(
@@ -14427,65 +18102,65 @@
_pot_scale_int16);
}
-inline MulOptionsT *MulOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline MulOptionsT *MulOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<MulOptionsT>(new MulOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void MulOptions::UnPackTo(MulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void MulOptions::UnPackTo(MulOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
}
-inline flatbuffers::Offset<MulOptions> MulOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<MulOptions> MulOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateMulOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<MulOptions> CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<MulOptions> CreateMulOptions(::flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MulOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const MulOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _fused_activation_function = _o->fused_activation_function;
return tflite::CreateMulOptions(
_fbb,
_fused_activation_function);
}
-inline L2NormOptionsT *L2NormOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline L2NormOptionsT *L2NormOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<L2NormOptionsT>(new L2NormOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void L2NormOptions::UnPackTo(L2NormOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void L2NormOptions::UnPackTo(L2NormOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
}
-inline flatbuffers::Offset<L2NormOptions> L2NormOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<L2NormOptions> L2NormOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateL2NormOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<L2NormOptions> CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<L2NormOptions> CreateL2NormOptions(::flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const L2NormOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const L2NormOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _fused_activation_function = _o->fused_activation_function;
return tflite::CreateL2NormOptions(
_fbb,
_fused_activation_function);
}
-inline LocalResponseNormalizationOptionsT *LocalResponseNormalizationOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline LocalResponseNormalizationOptionsT *LocalResponseNormalizationOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<LocalResponseNormalizationOptionsT>(new LocalResponseNormalizationOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void LocalResponseNormalizationOptions::UnPackTo(LocalResponseNormalizationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void LocalResponseNormalizationOptions::UnPackTo(LocalResponseNormalizationOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = radius(); _o->radius = _e; }
@@ -14494,14 +18169,14 @@
{ auto _e = beta(); _o->beta = _e; }
}
-inline flatbuffers::Offset<LocalResponseNormalizationOptions> LocalResponseNormalizationOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LocalResponseNormalizationOptions> LocalResponseNormalizationOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateLocalResponseNormalizationOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<LocalResponseNormalizationOptions> CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LocalResponseNormalizationOptions> CreateLocalResponseNormalizationOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LocalResponseNormalizationOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const LocalResponseNormalizationOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _radius = _o->radius;
auto _bias = _o->bias;
auto _alpha = _o->alpha;
@@ -14514,13 +18189,13 @@
_beta);
}
-inline LSTMOptionsT *LSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline LSTMOptionsT *LSTMOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<LSTMOptionsT>(new LSTMOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void LSTMOptions::UnPackTo(LSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void LSTMOptions::UnPackTo(LSTMOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
@@ -14530,14 +18205,14 @@
{ auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; }
}
-inline flatbuffers::Offset<LSTMOptions> LSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LSTMOptions> LSTMOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateLSTMOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<LSTMOptions> CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LSTMOptions> CreateLSTMOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const LSTMOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _fused_activation_function = _o->fused_activation_function;
auto _cell_clip = _o->cell_clip;
auto _proj_clip = _o->proj_clip;
@@ -14552,13 +18227,13 @@
_asymmetric_quantize_inputs);
}
-inline UnidirectionalSequenceLSTMOptionsT *UnidirectionalSequenceLSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline UnidirectionalSequenceLSTMOptionsT *UnidirectionalSequenceLSTMOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<UnidirectionalSequenceLSTMOptionsT>(new UnidirectionalSequenceLSTMOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void UnidirectionalSequenceLSTMOptions::UnPackTo(UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void UnidirectionalSequenceLSTMOptions::UnPackTo(UnidirectionalSequenceLSTMOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
@@ -14569,14 +18244,14 @@
{ auto _e = diagonal_recurrent_tensors(); _o->diagonal_recurrent_tensors = _e; }
}
-inline flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> UnidirectionalSequenceLSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> UnidirectionalSequenceLSTMOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateUnidirectionalSequenceLSTMOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> CreateUnidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> CreateUnidirectionalSequenceLSTMOptions(::flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnidirectionalSequenceLSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const UnidirectionalSequenceLSTMOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _fused_activation_function = _o->fused_activation_function;
auto _cell_clip = _o->cell_clip;
auto _proj_clip = _o->proj_clip;
@@ -14593,13 +18268,13 @@
_diagonal_recurrent_tensors);
}
-inline BidirectionalSequenceLSTMOptionsT *BidirectionalSequenceLSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline BidirectionalSequenceLSTMOptionsT *BidirectionalSequenceLSTMOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<BidirectionalSequenceLSTMOptionsT>(new BidirectionalSequenceLSTMOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void BidirectionalSequenceLSTMOptions::UnPackTo(BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void BidirectionalSequenceLSTMOptions::UnPackTo(BidirectionalSequenceLSTMOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
@@ -14610,14 +18285,14 @@
{ auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; }
}
-inline flatbuffers::Offset<BidirectionalSequenceLSTMOptions> BidirectionalSequenceLSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<BidirectionalSequenceLSTMOptions> BidirectionalSequenceLSTMOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateBidirectionalSequenceLSTMOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<BidirectionalSequenceLSTMOptions> CreateBidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<BidirectionalSequenceLSTMOptions> CreateBidirectionalSequenceLSTMOptions(::flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BidirectionalSequenceLSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const BidirectionalSequenceLSTMOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _fused_activation_function = _o->fused_activation_function;
auto _cell_clip = _o->cell_clip;
auto _proj_clip = _o->proj_clip;
@@ -14634,27 +18309,27 @@
_asymmetric_quantize_inputs);
}
-inline ResizeBilinearOptionsT *ResizeBilinearOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ResizeBilinearOptionsT *ResizeBilinearOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ResizeBilinearOptionsT>(new ResizeBilinearOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void ResizeBilinearOptions::UnPackTo(ResizeBilinearOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void ResizeBilinearOptions::UnPackTo(ResizeBilinearOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = align_corners(); _o->align_corners = _e; }
{ auto _e = half_pixel_centers(); _o->half_pixel_centers = _e; }
}
-inline flatbuffers::Offset<ResizeBilinearOptions> ResizeBilinearOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ResizeBilinearOptions> ResizeBilinearOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateResizeBilinearOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<ResizeBilinearOptions> CreateResizeBilinearOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ResizeBilinearOptions> CreateResizeBilinearOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ResizeBilinearOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ResizeBilinearOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _align_corners = _o->align_corners;
auto _half_pixel_centers = _o->half_pixel_centers;
return tflite::CreateResizeBilinearOptions(
@@ -14663,27 +18338,27 @@
_half_pixel_centers);
}
-inline ResizeNearestNeighborOptionsT *ResizeNearestNeighborOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ResizeNearestNeighborOptionsT *ResizeNearestNeighborOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ResizeNearestNeighborOptionsT>(new ResizeNearestNeighborOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void ResizeNearestNeighborOptions::UnPackTo(ResizeNearestNeighborOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void ResizeNearestNeighborOptions::UnPackTo(ResizeNearestNeighborOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = align_corners(); _o->align_corners = _e; }
{ auto _e = half_pixel_centers(); _o->half_pixel_centers = _e; }
}
-inline flatbuffers::Offset<ResizeNearestNeighborOptions> ResizeNearestNeighborOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ResizeNearestNeighborOptions> ResizeNearestNeighborOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateResizeNearestNeighborOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<ResizeNearestNeighborOptions> CreateResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ResizeNearestNeighborOptions> CreateResizeNearestNeighborOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ResizeNearestNeighborOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ResizeNearestNeighborOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _align_corners = _o->align_corners;
auto _half_pixel_centers = _o->half_pixel_centers;
return tflite::CreateResizeNearestNeighborOptions(
@@ -14692,157 +18367,157 @@
_half_pixel_centers);
}
-inline CallOptionsT *CallOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline CallOptionsT *CallOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<CallOptionsT>(new CallOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void CallOptions::UnPackTo(CallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void CallOptions::UnPackTo(CallOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = subgraph(); _o->subgraph = _e; }
}
-inline flatbuffers::Offset<CallOptions> CallOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<CallOptions> CallOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateCallOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<CallOptions> CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<CallOptions> CreateCallOptions(::flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CallOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const CallOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _subgraph = _o->subgraph;
return tflite::CreateCallOptions(
_fbb,
_subgraph);
}
-inline PadOptionsT *PadOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline PadOptionsT *PadOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<PadOptionsT>(new PadOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void PadOptions::UnPackTo(PadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void PadOptions::UnPackTo(PadOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<PadOptions> PadOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<PadOptions> PadOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreatePadOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<PadOptions> CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<PadOptions> CreatePadOptions(::flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PadOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const PadOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreatePadOptions(
_fbb);
}
-inline PadV2OptionsT *PadV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline PadV2OptionsT *PadV2Options::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<PadV2OptionsT>(new PadV2OptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void PadV2Options::UnPackTo(PadV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void PadV2Options::UnPackTo(PadV2OptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<PadV2Options> PadV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<PadV2Options> PadV2Options::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreatePadV2Options(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<PadV2Options> CreatePadV2Options(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<PadV2Options> CreatePadV2Options(::flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PadV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const PadV2OptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreatePadV2Options(
_fbb);
}
-inline ReshapeOptionsT *ReshapeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ReshapeOptionsT *ReshapeOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ReshapeOptionsT>(new ReshapeOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void ReshapeOptions::UnPackTo(ReshapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void ReshapeOptions::UnPackTo(ReshapeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
- { auto _e = new_shape(); if (_e) { _o->new_shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->new_shape[_i] = _e->Get(_i); } } }
+ { auto _e = new_shape(); if (_e) { _o->new_shape.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->new_shape[_i] = _e->Get(_i); } } else { _o->new_shape.resize(0); } }
}
-inline flatbuffers::Offset<ReshapeOptions> ReshapeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ReshapeOptions> ReshapeOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateReshapeOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<ReshapeOptions> CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ReshapeOptions> CreateReshapeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReshapeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ReshapeOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _new_shape = _o->new_shape.size() ? _fbb.CreateVector(_o->new_shape) : 0;
return tflite::CreateReshapeOptions(
_fbb,
_new_shape);
}
-inline SpaceToBatchNDOptionsT *SpaceToBatchNDOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SpaceToBatchNDOptionsT *SpaceToBatchNDOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SpaceToBatchNDOptionsT>(new SpaceToBatchNDOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SpaceToBatchNDOptions::UnPackTo(SpaceToBatchNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SpaceToBatchNDOptions::UnPackTo(SpaceToBatchNDOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<SpaceToBatchNDOptions> SpaceToBatchNDOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SpaceToBatchNDOptions> SpaceToBatchNDOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSpaceToBatchNDOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SpaceToBatchNDOptions> CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SpaceToBatchNDOptions> CreateSpaceToBatchNDOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SpaceToBatchNDOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SpaceToBatchNDOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateSpaceToBatchNDOptions(
_fbb);
}
-inline BatchToSpaceNDOptionsT *BatchToSpaceNDOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline BatchToSpaceNDOptionsT *BatchToSpaceNDOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<BatchToSpaceNDOptionsT>(new BatchToSpaceNDOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void BatchToSpaceNDOptions::UnPackTo(BatchToSpaceNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void BatchToSpaceNDOptions::UnPackTo(BatchToSpaceNDOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<BatchToSpaceNDOptions> BatchToSpaceNDOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<BatchToSpaceNDOptions> BatchToSpaceNDOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateBatchToSpaceNDOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<BatchToSpaceNDOptions> CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<BatchToSpaceNDOptions> CreateBatchToSpaceNDOptions(::flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BatchToSpaceNDOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const BatchToSpaceNDOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateBatchToSpaceNDOptions(
_fbb);
}
-inline SkipGramOptionsT *SkipGramOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SkipGramOptionsT *SkipGramOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SkipGramOptionsT>(new SkipGramOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SkipGramOptions::UnPackTo(SkipGramOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SkipGramOptions::UnPackTo(SkipGramOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = ngram_size(); _o->ngram_size = _e; }
@@ -14850,14 +18525,14 @@
{ auto _e = include_all_ngrams(); _o->include_all_ngrams = _e; }
}
-inline flatbuffers::Offset<SkipGramOptions> SkipGramOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SkipGramOptions> SkipGramOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSkipGramOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SkipGramOptions> CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SkipGramOptions> CreateSkipGramOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SkipGramOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SkipGramOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _ngram_size = _o->ngram_size;
auto _max_skip_size = _o->max_skip_size;
auto _include_all_ngrams = _o->include_all_ngrams;
@@ -14868,79 +18543,79 @@
_include_all_ngrams);
}
-inline SpaceToDepthOptionsT *SpaceToDepthOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SpaceToDepthOptionsT *SpaceToDepthOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SpaceToDepthOptionsT>(new SpaceToDepthOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SpaceToDepthOptions::UnPackTo(SpaceToDepthOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SpaceToDepthOptions::UnPackTo(SpaceToDepthOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = block_size(); _o->block_size = _e; }
}
-inline flatbuffers::Offset<SpaceToDepthOptions> SpaceToDepthOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SpaceToDepthOptions> SpaceToDepthOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSpaceToDepthOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SpaceToDepthOptions> CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SpaceToDepthOptions> CreateSpaceToDepthOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SpaceToDepthOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SpaceToDepthOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _block_size = _o->block_size;
return tflite::CreateSpaceToDepthOptions(
_fbb,
_block_size);
}
-inline DepthToSpaceOptionsT *DepthToSpaceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline DepthToSpaceOptionsT *DepthToSpaceOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<DepthToSpaceOptionsT>(new DepthToSpaceOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void DepthToSpaceOptions::UnPackTo(DepthToSpaceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void DepthToSpaceOptions::UnPackTo(DepthToSpaceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = block_size(); _o->block_size = _e; }
}
-inline flatbuffers::Offset<DepthToSpaceOptions> DepthToSpaceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<DepthToSpaceOptions> DepthToSpaceOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateDepthToSpaceOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<DepthToSpaceOptions> CreateDepthToSpaceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<DepthToSpaceOptions> CreateDepthToSpaceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DepthToSpaceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const DepthToSpaceOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _block_size = _o->block_size;
return tflite::CreateDepthToSpaceOptions(
_fbb,
_block_size);
}
-inline SubOptionsT *SubOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SubOptionsT *SubOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SubOptionsT>(new SubOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SubOptions::UnPackTo(SubOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SubOptions::UnPackTo(SubOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
{ auto _e = pot_scale_int16(); _o->pot_scale_int16 = _e; }
}
-inline flatbuffers::Offset<SubOptions> SubOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SubOptions> SubOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSubOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SubOptions> CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SubOptions> CreateSubOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SubOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SubOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _fused_activation_function = _o->fused_activation_function;
auto _pot_scale_int16 = _o->pot_scale_int16;
return tflite::CreateSubOptions(
@@ -14949,102 +18624,102 @@
_pot_scale_int16);
}
-inline DivOptionsT *DivOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline DivOptionsT *DivOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<DivOptionsT>(new DivOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void DivOptions::UnPackTo(DivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void DivOptions::UnPackTo(DivOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
}
-inline flatbuffers::Offset<DivOptions> DivOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<DivOptions> DivOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateDivOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<DivOptions> CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<DivOptions> CreateDivOptions(::flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DivOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const DivOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _fused_activation_function = _o->fused_activation_function;
return tflite::CreateDivOptions(
_fbb,
_fused_activation_function);
}
-inline TopKV2OptionsT *TopKV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline TopKV2OptionsT *TopKV2Options::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<TopKV2OptionsT>(new TopKV2OptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void TopKV2Options::UnPackTo(TopKV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void TopKV2Options::UnPackTo(TopKV2OptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<TopKV2Options> TopKV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<TopKV2Options> TopKV2Options::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateTopKV2Options(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<TopKV2Options> CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<TopKV2Options> CreateTopKV2Options(::flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TopKV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const TopKV2OptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateTopKV2Options(
_fbb);
}
-inline EmbeddingLookupSparseOptionsT *EmbeddingLookupSparseOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline EmbeddingLookupSparseOptionsT *EmbeddingLookupSparseOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<EmbeddingLookupSparseOptionsT>(new EmbeddingLookupSparseOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void EmbeddingLookupSparseOptions::UnPackTo(EmbeddingLookupSparseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void EmbeddingLookupSparseOptions::UnPackTo(EmbeddingLookupSparseOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = combiner(); _o->combiner = _e; }
}
-inline flatbuffers::Offset<EmbeddingLookupSparseOptions> EmbeddingLookupSparseOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<EmbeddingLookupSparseOptions> EmbeddingLookupSparseOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateEmbeddingLookupSparseOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<EmbeddingLookupSparseOptions> CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<EmbeddingLookupSparseOptions> CreateEmbeddingLookupSparseOptions(::flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const EmbeddingLookupSparseOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const EmbeddingLookupSparseOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _combiner = _o->combiner;
return tflite::CreateEmbeddingLookupSparseOptions(
_fbb,
_combiner);
}
-inline GatherOptionsT *GatherOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline GatherOptionsT *GatherOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<GatherOptionsT>(new GatherOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void GatherOptions::UnPackTo(GatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void GatherOptions::UnPackTo(GatherOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = axis(); _o->axis = _e; }
{ auto _e = batch_dims(); _o->batch_dims = _e; }
}
-inline flatbuffers::Offset<GatherOptions> GatherOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<GatherOptions> GatherOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateGatherOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<GatherOptions> CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<GatherOptions> CreateGatherOptions(::flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GatherOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const GatherOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _axis = _o->axis;
auto _batch_dims = _o->batch_dims;
return tflite::CreateGatherOptions(
@@ -15053,186 +18728,186 @@
_batch_dims);
}
-inline TransposeOptionsT *TransposeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline TransposeOptionsT *TransposeOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<TransposeOptionsT>(new TransposeOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void TransposeOptions::UnPackTo(TransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void TransposeOptions::UnPackTo(TransposeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<TransposeOptions> TransposeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<TransposeOptions> TransposeOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateTransposeOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<TransposeOptions> CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<TransposeOptions> CreateTransposeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TransposeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const TransposeOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateTransposeOptions(
_fbb);
}
-inline ExpOptionsT *ExpOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ExpOptionsT *ExpOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ExpOptionsT>(new ExpOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void ExpOptions::UnPackTo(ExpOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void ExpOptions::UnPackTo(ExpOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<ExpOptions> ExpOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ExpOptions> ExpOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateExpOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<ExpOptions> CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ExpOptions> CreateExpOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ExpOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ExpOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateExpOptions(
_fbb);
}
-inline CosOptionsT *CosOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline CosOptionsT *CosOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<CosOptionsT>(new CosOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void CosOptions::UnPackTo(CosOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void CosOptions::UnPackTo(CosOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<CosOptions> CosOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<CosOptions> CosOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateCosOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<CosOptions> CreateCosOptions(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<CosOptions> CreateCosOptions(::flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CosOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const CosOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateCosOptions(
_fbb);
}
-inline ReducerOptionsT *ReducerOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ReducerOptionsT *ReducerOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ReducerOptionsT>(new ReducerOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void ReducerOptions::UnPackTo(ReducerOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void ReducerOptions::UnPackTo(ReducerOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = keep_dims(); _o->keep_dims = _e; }
}
-inline flatbuffers::Offset<ReducerOptions> ReducerOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ReducerOptions> ReducerOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateReducerOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<ReducerOptions> CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ReducerOptions> CreateReducerOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReducerOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ReducerOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _keep_dims = _o->keep_dims;
return tflite::CreateReducerOptions(
_fbb,
_keep_dims);
}
-inline SqueezeOptionsT *SqueezeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SqueezeOptionsT *SqueezeOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SqueezeOptionsT>(new SqueezeOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SqueezeOptions::UnPackTo(SqueezeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SqueezeOptions::UnPackTo(SqueezeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
- { auto _e = squeeze_dims(); if (_e) { _o->squeeze_dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->squeeze_dims[_i] = _e->Get(_i); } } }
+ { auto _e = squeeze_dims(); if (_e) { _o->squeeze_dims.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->squeeze_dims[_i] = _e->Get(_i); } } else { _o->squeeze_dims.resize(0); } }
}
-inline flatbuffers::Offset<SqueezeOptions> SqueezeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SqueezeOptions> SqueezeOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSqueezeOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SqueezeOptions> CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SqueezeOptions> CreateSqueezeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SqueezeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SqueezeOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _squeeze_dims = _o->squeeze_dims.size() ? _fbb.CreateVector(_o->squeeze_dims) : 0;
return tflite::CreateSqueezeOptions(
_fbb,
_squeeze_dims);
}
-inline SplitOptionsT *SplitOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SplitOptionsT *SplitOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SplitOptionsT>(new SplitOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SplitOptions::UnPackTo(SplitOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SplitOptions::UnPackTo(SplitOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = num_splits(); _o->num_splits = _e; }
}
-inline flatbuffers::Offset<SplitOptions> SplitOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SplitOptions> SplitOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSplitOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SplitOptions> CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SplitOptions> CreateSplitOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SplitOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SplitOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _num_splits = _o->num_splits;
return tflite::CreateSplitOptions(
_fbb,
_num_splits);
}
-inline SplitVOptionsT *SplitVOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SplitVOptionsT *SplitVOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SplitVOptionsT>(new SplitVOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SplitVOptions::UnPackTo(SplitVOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SplitVOptions::UnPackTo(SplitVOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = num_splits(); _o->num_splits = _e; }
}
-inline flatbuffers::Offset<SplitVOptions> SplitVOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SplitVOptions> SplitVOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSplitVOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SplitVOptions> CreateSplitVOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SplitVOptions> CreateSplitVOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SplitVOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SplitVOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _num_splits = _o->num_splits;
return tflite::CreateSplitVOptions(
_fbb,
_num_splits);
}
-inline StridedSliceOptionsT *StridedSliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline StridedSliceOptionsT *StridedSliceOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<StridedSliceOptionsT>(new StridedSliceOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void StridedSliceOptions::UnPackTo(StridedSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void StridedSliceOptions::UnPackTo(StridedSliceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = begin_mask(); _o->begin_mask = _e; }
@@ -15243,14 +18918,14 @@
{ auto _e = offset(); _o->offset = _e; }
}
-inline flatbuffers::Offset<StridedSliceOptions> StridedSliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<StridedSliceOptions> StridedSliceOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateStridedSliceOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<StridedSliceOptions> CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<StridedSliceOptions> CreateStridedSliceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StridedSliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StridedSliceOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _begin_mask = _o->begin_mask;
auto _end_mask = _o->end_mask;
auto _ellipsis_mask = _o->ellipsis_mask;
@@ -15267,50 +18942,50 @@
_offset);
}
-inline LogSoftmaxOptionsT *LogSoftmaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline LogSoftmaxOptionsT *LogSoftmaxOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<LogSoftmaxOptionsT>(new LogSoftmaxOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void LogSoftmaxOptions::UnPackTo(LogSoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void LogSoftmaxOptions::UnPackTo(LogSoftmaxOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<LogSoftmaxOptions> LogSoftmaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LogSoftmaxOptions> LogSoftmaxOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateLogSoftmaxOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<LogSoftmaxOptions> CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LogSoftmaxOptions> CreateLogSoftmaxOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogSoftmaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const LogSoftmaxOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateLogSoftmaxOptions(
_fbb);
}
-inline CastOptionsT *CastOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline CastOptionsT *CastOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<CastOptionsT>(new CastOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void CastOptions::UnPackTo(CastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void CastOptions::UnPackTo(CastOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = in_data_type(); _o->in_data_type = _e; }
{ auto _e = out_data_type(); _o->out_data_type = _e; }
}
-inline flatbuffers::Offset<CastOptions> CastOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<CastOptions> CastOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateCastOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<CastOptions> CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<CastOptions> CreateCastOptions(::flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CastOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const CastOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _in_data_type = _o->in_data_type;
auto _out_data_type = _o->out_data_type;
return tflite::CreateCastOptions(
@@ -15319,497 +18994,500 @@
_out_data_type);
}
-inline DequantizeOptionsT *DequantizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline DequantizeOptionsT *DequantizeOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<DequantizeOptionsT>(new DequantizeOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void DequantizeOptions::UnPackTo(DequantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void DequantizeOptions::UnPackTo(DequantizeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<DequantizeOptions> DequantizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<DequantizeOptions> DequantizeOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateDequantizeOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<DequantizeOptions> CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<DequantizeOptions> CreateDequantizeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DequantizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const DequantizeOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateDequantizeOptions(
_fbb);
}
-inline MaximumMinimumOptionsT *MaximumMinimumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline MaximumMinimumOptionsT *MaximumMinimumOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<MaximumMinimumOptionsT>(new MaximumMinimumOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void MaximumMinimumOptions::UnPackTo(MaximumMinimumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void MaximumMinimumOptions::UnPackTo(MaximumMinimumOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<MaximumMinimumOptions> MaximumMinimumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<MaximumMinimumOptions> MaximumMinimumOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateMaximumMinimumOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<MaximumMinimumOptions> CreateMaximumMinimumOptions(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<MaximumMinimumOptions> CreateMaximumMinimumOptions(::flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MaximumMinimumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const MaximumMinimumOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateMaximumMinimumOptions(
_fbb);
}
-inline TileOptionsT *TileOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline TileOptionsT *TileOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<TileOptionsT>(new TileOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void TileOptions::UnPackTo(TileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void TileOptions::UnPackTo(TileOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<TileOptions> TileOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<TileOptions> TileOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateTileOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<TileOptions> CreateTileOptions(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<TileOptions> CreateTileOptions(::flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TileOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const TileOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateTileOptions(
_fbb);
}
-inline ArgMaxOptionsT *ArgMaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ArgMaxOptionsT *ArgMaxOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ArgMaxOptionsT>(new ArgMaxOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void ArgMaxOptions::UnPackTo(ArgMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void ArgMaxOptions::UnPackTo(ArgMaxOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = output_type(); _o->output_type = _e; }
}
-inline flatbuffers::Offset<ArgMaxOptions> ArgMaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ArgMaxOptions> ArgMaxOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateArgMaxOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<ArgMaxOptions> CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ArgMaxOptions> CreateArgMaxOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ArgMaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ArgMaxOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _output_type = _o->output_type;
return tflite::CreateArgMaxOptions(
_fbb,
_output_type);
}
-inline ArgMinOptionsT *ArgMinOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ArgMinOptionsT *ArgMinOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ArgMinOptionsT>(new ArgMinOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void ArgMinOptions::UnPackTo(ArgMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void ArgMinOptions::UnPackTo(ArgMinOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = output_type(); _o->output_type = _e; }
}
-inline flatbuffers::Offset<ArgMinOptions> ArgMinOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ArgMinOptions> ArgMinOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateArgMinOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<ArgMinOptions> CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ArgMinOptions> CreateArgMinOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ArgMinOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ArgMinOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _output_type = _o->output_type;
return tflite::CreateArgMinOptions(
_fbb,
_output_type);
}
-inline GreaterOptionsT *GreaterOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline GreaterOptionsT *GreaterOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<GreaterOptionsT>(new GreaterOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void GreaterOptions::UnPackTo(GreaterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void GreaterOptions::UnPackTo(GreaterOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<GreaterOptions> GreaterOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<GreaterOptions> GreaterOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateGreaterOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<GreaterOptions> CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<GreaterOptions> CreateGreaterOptions(::flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GreaterOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const GreaterOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateGreaterOptions(
_fbb);
}
-inline GreaterEqualOptionsT *GreaterEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline GreaterEqualOptionsT *GreaterEqualOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<GreaterEqualOptionsT>(new GreaterEqualOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void GreaterEqualOptions::UnPackTo(GreaterEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void GreaterEqualOptions::UnPackTo(GreaterEqualOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<GreaterEqualOptions> GreaterEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<GreaterEqualOptions> GreaterEqualOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateGreaterEqualOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<GreaterEqualOptions> CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<GreaterEqualOptions> CreateGreaterEqualOptions(::flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GreaterEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const GreaterEqualOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateGreaterEqualOptions(
_fbb);
}
-inline LessOptionsT *LessOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline LessOptionsT *LessOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<LessOptionsT>(new LessOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void LessOptions::UnPackTo(LessOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void LessOptions::UnPackTo(LessOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<LessOptions> LessOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LessOptions> LessOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateLessOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<LessOptions> CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LessOptions> CreateLessOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LessOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const LessOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateLessOptions(
_fbb);
}
-inline LessEqualOptionsT *LessEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline LessEqualOptionsT *LessEqualOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<LessEqualOptionsT>(new LessEqualOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void LessEqualOptions::UnPackTo(LessEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void LessEqualOptions::UnPackTo(LessEqualOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<LessEqualOptions> LessEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LessEqualOptions> LessEqualOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateLessEqualOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<LessEqualOptions> CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LessEqualOptions> CreateLessEqualOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LessEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const LessEqualOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateLessEqualOptions(
_fbb);
}
-inline NegOptionsT *NegOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline NegOptionsT *NegOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<NegOptionsT>(new NegOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void NegOptions::UnPackTo(NegOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void NegOptions::UnPackTo(NegOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<NegOptions> NegOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<NegOptions> NegOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateNegOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<NegOptions> CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<NegOptions> CreateNegOptions(::flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NegOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const NegOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateNegOptions(
_fbb);
}
-inline SelectOptionsT *SelectOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SelectOptionsT *SelectOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SelectOptionsT>(new SelectOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SelectOptions::UnPackTo(SelectOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SelectOptions::UnPackTo(SelectOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<SelectOptions> SelectOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SelectOptions> SelectOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSelectOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SelectOptions> CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SelectOptions> CreateSelectOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SelectOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SelectOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateSelectOptions(
_fbb);
}
-inline SliceOptionsT *SliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SliceOptionsT *SliceOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SliceOptionsT>(new SliceOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SliceOptions::UnPackTo(SliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SliceOptions::UnPackTo(SliceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<SliceOptions> SliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SliceOptions> SliceOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSliceOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SliceOptions> CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SliceOptions> CreateSliceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SliceOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateSliceOptions(
_fbb);
}
-inline TransposeConvOptionsT *TransposeConvOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline TransposeConvOptionsT *TransposeConvOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<TransposeConvOptionsT>(new TransposeConvOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void TransposeConvOptions::UnPackTo(TransposeConvOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void TransposeConvOptions::UnPackTo(TransposeConvOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = padding(); _o->padding = _e; }
{ auto _e = stride_w(); _o->stride_w = _e; }
{ auto _e = stride_h(); _o->stride_h = _e; }
{ auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
+ { auto _e = quantized_bias_type(); _o->quantized_bias_type = _e; }
}
-inline flatbuffers::Offset<TransposeConvOptions> TransposeConvOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<TransposeConvOptions> TransposeConvOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateTransposeConvOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<TransposeConvOptions> CreateTransposeConvOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<TransposeConvOptions> CreateTransposeConvOptions(::flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TransposeConvOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const TransposeConvOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _padding = _o->padding;
auto _stride_w = _o->stride_w;
auto _stride_h = _o->stride_h;
auto _fused_activation_function = _o->fused_activation_function;
+ auto _quantized_bias_type = _o->quantized_bias_type;
return tflite::CreateTransposeConvOptions(
_fbb,
_padding,
_stride_w,
_stride_h,
- _fused_activation_function);
+ _fused_activation_function,
+ _quantized_bias_type);
}
-inline ExpandDimsOptionsT *ExpandDimsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ExpandDimsOptionsT *ExpandDimsOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ExpandDimsOptionsT>(new ExpandDimsOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void ExpandDimsOptions::UnPackTo(ExpandDimsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void ExpandDimsOptions::UnPackTo(ExpandDimsOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<ExpandDimsOptions> ExpandDimsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ExpandDimsOptions> ExpandDimsOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateExpandDimsOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<ExpandDimsOptions> CreateExpandDimsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ExpandDimsOptions> CreateExpandDimsOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ExpandDimsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ExpandDimsOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateExpandDimsOptions(
_fbb);
}
-inline SparseToDenseOptionsT *SparseToDenseOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SparseToDenseOptionsT *SparseToDenseOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SparseToDenseOptionsT>(new SparseToDenseOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SparseToDenseOptions::UnPackTo(SparseToDenseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SparseToDenseOptions::UnPackTo(SparseToDenseOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = validate_indices(); _o->validate_indices = _e; }
}
-inline flatbuffers::Offset<SparseToDenseOptions> SparseToDenseOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SparseToDenseOptions> SparseToDenseOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSparseToDenseOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SparseToDenseOptions> CreateSparseToDenseOptions(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SparseToDenseOptions> CreateSparseToDenseOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SparseToDenseOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SparseToDenseOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _validate_indices = _o->validate_indices;
return tflite::CreateSparseToDenseOptions(
_fbb,
_validate_indices);
}
-inline EqualOptionsT *EqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline EqualOptionsT *EqualOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<EqualOptionsT>(new EqualOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void EqualOptions::UnPackTo(EqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void EqualOptions::UnPackTo(EqualOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<EqualOptions> EqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<EqualOptions> EqualOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateEqualOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<EqualOptions> CreateEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<EqualOptions> CreateEqualOptions(::flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const EqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const EqualOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateEqualOptions(
_fbb);
}
-inline NotEqualOptionsT *NotEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline NotEqualOptionsT *NotEqualOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<NotEqualOptionsT>(new NotEqualOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void NotEqualOptions::UnPackTo(NotEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void NotEqualOptions::UnPackTo(NotEqualOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<NotEqualOptions> NotEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<NotEqualOptions> NotEqualOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateNotEqualOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<NotEqualOptions> CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<NotEqualOptions> CreateNotEqualOptions(::flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NotEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const NotEqualOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateNotEqualOptions(
_fbb);
}
-inline ShapeOptionsT *ShapeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ShapeOptionsT *ShapeOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ShapeOptionsT>(new ShapeOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void ShapeOptions::UnPackTo(ShapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void ShapeOptions::UnPackTo(ShapeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = out_type(); _o->out_type = _e; }
}
-inline flatbuffers::Offset<ShapeOptions> ShapeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ShapeOptions> ShapeOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateShapeOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<ShapeOptions> CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ShapeOptions> CreateShapeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ShapeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ShapeOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _out_type = _o->out_type;
return tflite::CreateShapeOptions(
_fbb,
_out_type);
}
-inline RankOptionsT *RankOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline RankOptionsT *RankOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<RankOptionsT>(new RankOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void RankOptions::UnPackTo(RankOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void RankOptions::UnPackTo(RankOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<RankOptions> RankOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<RankOptions> RankOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateRankOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<RankOptions> CreateRankOptions(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<RankOptions> CreateRankOptions(::flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RankOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const RankOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateRankOptions(
_fbb);
}
-inline PowOptionsT *PowOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline PowOptionsT *PowOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<PowOptionsT>(new PowOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void PowOptions::UnPackTo(PowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void PowOptions::UnPackTo(PowOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<PowOptions> PowOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<PowOptions> PowOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreatePowOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<PowOptions> CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<PowOptions> CreatePowOptions(::flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PowOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const PowOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreatePowOptions(
_fbb);
}
-inline FakeQuantOptionsT *FakeQuantOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline FakeQuantOptionsT *FakeQuantOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<FakeQuantOptionsT>(new FakeQuantOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void FakeQuantOptions::UnPackTo(FakeQuantOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void FakeQuantOptions::UnPackTo(FakeQuantOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = min(); _o->min = _e; }
@@ -15818,14 +19496,14 @@
{ auto _e = narrow_range(); _o->narrow_range = _e; }
}
-inline flatbuffers::Offset<FakeQuantOptions> FakeQuantOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<FakeQuantOptions> FakeQuantOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateFakeQuantOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<FakeQuantOptions> CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<FakeQuantOptions> CreateFakeQuantOptions(::flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FakeQuantOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const FakeQuantOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _min = _o->min;
auto _max = _o->max;
auto _num_bits = _o->num_bits;
@@ -15838,27 +19516,27 @@
_narrow_range);
}
-inline PackOptionsT *PackOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline PackOptionsT *PackOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<PackOptionsT>(new PackOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void PackOptions::UnPackTo(PackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void PackOptions::UnPackTo(PackOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = values_count(); _o->values_count = _e; }
{ auto _e = axis(); _o->axis = _e; }
}
-inline flatbuffers::Offset<PackOptions> PackOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<PackOptions> PackOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreatePackOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<PackOptions> CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<PackOptions> CreatePackOptions(::flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PackOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const PackOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _values_count = _o->values_count;
auto _axis = _o->axis;
return tflite::CreatePackOptions(
@@ -15867,168 +19545,168 @@
_axis);
}
-inline LogicalOrOptionsT *LogicalOrOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline LogicalOrOptionsT *LogicalOrOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<LogicalOrOptionsT>(new LogicalOrOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void LogicalOrOptions::UnPackTo(LogicalOrOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void LogicalOrOptions::UnPackTo(LogicalOrOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<LogicalOrOptions> LogicalOrOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LogicalOrOptions> LogicalOrOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateLogicalOrOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<LogicalOrOptions> CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LogicalOrOptions> CreateLogicalOrOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalOrOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const LogicalOrOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateLogicalOrOptions(
_fbb);
}
-inline OneHotOptionsT *OneHotOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline OneHotOptionsT *OneHotOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<OneHotOptionsT>(new OneHotOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void OneHotOptions::UnPackTo(OneHotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void OneHotOptions::UnPackTo(OneHotOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = axis(); _o->axis = _e; }
}
-inline flatbuffers::Offset<OneHotOptions> OneHotOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<OneHotOptions> OneHotOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateOneHotOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<OneHotOptions> CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<OneHotOptions> CreateOneHotOptions(::flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OneHotOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const OneHotOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _axis = _o->axis;
return tflite::CreateOneHotOptions(
_fbb,
_axis);
}
-inline AbsOptionsT *AbsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline AbsOptionsT *AbsOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<AbsOptionsT>(new AbsOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void AbsOptions::UnPackTo(AbsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void AbsOptions::UnPackTo(AbsOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<AbsOptions> AbsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<AbsOptions> AbsOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateAbsOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<AbsOptions> CreateAbsOptions(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<AbsOptions> CreateAbsOptions(::flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AbsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const AbsOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateAbsOptions(
_fbb);
}
-inline HardSwishOptionsT *HardSwishOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline HardSwishOptionsT *HardSwishOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<HardSwishOptionsT>(new HardSwishOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void HardSwishOptions::UnPackTo(HardSwishOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void HardSwishOptions::UnPackTo(HardSwishOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<HardSwishOptions> HardSwishOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<HardSwishOptions> HardSwishOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateHardSwishOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<HardSwishOptions> CreateHardSwishOptions(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<HardSwishOptions> CreateHardSwishOptions(::flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HardSwishOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const HardSwishOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateHardSwishOptions(
_fbb);
}
-inline LogicalAndOptionsT *LogicalAndOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline LogicalAndOptionsT *LogicalAndOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<LogicalAndOptionsT>(new LogicalAndOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void LogicalAndOptions::UnPackTo(LogicalAndOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void LogicalAndOptions::UnPackTo(LogicalAndOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<LogicalAndOptions> LogicalAndOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LogicalAndOptions> LogicalAndOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateLogicalAndOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<LogicalAndOptions> CreateLogicalAndOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LogicalAndOptions> CreateLogicalAndOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalAndOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const LogicalAndOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateLogicalAndOptions(
_fbb);
}
-inline LogicalNotOptionsT *LogicalNotOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline LogicalNotOptionsT *LogicalNotOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<LogicalNotOptionsT>(new LogicalNotOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void LogicalNotOptions::UnPackTo(LogicalNotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void LogicalNotOptions::UnPackTo(LogicalNotOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<LogicalNotOptions> LogicalNotOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LogicalNotOptions> LogicalNotOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateLogicalNotOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<LogicalNotOptions> CreateLogicalNotOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LogicalNotOptions> CreateLogicalNotOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalNotOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const LogicalNotOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateLogicalNotOptions(
_fbb);
}
-inline UnpackOptionsT *UnpackOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline UnpackOptionsT *UnpackOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<UnpackOptionsT>(new UnpackOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void UnpackOptions::UnPackTo(UnpackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void UnpackOptions::UnPackTo(UnpackOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = num(); _o->num = _e; }
{ auto _e = axis(); _o->axis = _e; }
}
-inline flatbuffers::Offset<UnpackOptions> UnpackOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<UnpackOptions> UnpackOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateUnpackOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<UnpackOptions> CreateUnpackOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<UnpackOptions> CreateUnpackOptions(::flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnpackOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const UnpackOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _num = _o->num;
auto _axis = _o->axis;
return tflite::CreateUnpackOptions(
@@ -16037,358 +19715,358 @@
_axis);
}
-inline FloorDivOptionsT *FloorDivOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline FloorDivOptionsT *FloorDivOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<FloorDivOptionsT>(new FloorDivOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void FloorDivOptions::UnPackTo(FloorDivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void FloorDivOptions::UnPackTo(FloorDivOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<FloorDivOptions> FloorDivOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<FloorDivOptions> FloorDivOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateFloorDivOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<FloorDivOptions> CreateFloorDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<FloorDivOptions> CreateFloorDivOptions(::flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FloorDivOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const FloorDivOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateFloorDivOptions(
_fbb);
}
-inline SquareOptionsT *SquareOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SquareOptionsT *SquareOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SquareOptionsT>(new SquareOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SquareOptions::UnPackTo(SquareOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SquareOptions::UnPackTo(SquareOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<SquareOptions> SquareOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SquareOptions> SquareOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSquareOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SquareOptions> CreateSquareOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SquareOptions> CreateSquareOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SquareOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SquareOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateSquareOptions(
_fbb);
}
-inline ZerosLikeOptionsT *ZerosLikeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ZerosLikeOptionsT *ZerosLikeOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ZerosLikeOptionsT>(new ZerosLikeOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void ZerosLikeOptions::UnPackTo(ZerosLikeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void ZerosLikeOptions::UnPackTo(ZerosLikeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<ZerosLikeOptions> ZerosLikeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ZerosLikeOptions> ZerosLikeOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateZerosLikeOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<ZerosLikeOptions> CreateZerosLikeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ZerosLikeOptions> CreateZerosLikeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ZerosLikeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ZerosLikeOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateZerosLikeOptions(
_fbb);
}
-inline FillOptionsT *FillOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline FillOptionsT *FillOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<FillOptionsT>(new FillOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void FillOptions::UnPackTo(FillOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void FillOptions::UnPackTo(FillOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<FillOptions> FillOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<FillOptions> FillOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateFillOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<FillOptions> CreateFillOptions(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<FillOptions> CreateFillOptions(::flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FillOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const FillOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateFillOptions(
_fbb);
}
-inline FloorModOptionsT *FloorModOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline FloorModOptionsT *FloorModOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<FloorModOptionsT>(new FloorModOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void FloorModOptions::UnPackTo(FloorModOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void FloorModOptions::UnPackTo(FloorModOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<FloorModOptions> FloorModOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<FloorModOptions> FloorModOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateFloorModOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<FloorModOptions> CreateFloorModOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<FloorModOptions> CreateFloorModOptions(::flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FloorModOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const FloorModOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateFloorModOptions(
_fbb);
}
-inline RangeOptionsT *RangeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline RangeOptionsT *RangeOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<RangeOptionsT>(new RangeOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void RangeOptions::UnPackTo(RangeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void RangeOptions::UnPackTo(RangeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<RangeOptions> RangeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<RangeOptions> RangeOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateRangeOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<RangeOptions> CreateRangeOptions(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<RangeOptions> CreateRangeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RangeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const RangeOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateRangeOptions(
_fbb);
}
-inline LeakyReluOptionsT *LeakyReluOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline LeakyReluOptionsT *LeakyReluOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<LeakyReluOptionsT>(new LeakyReluOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void LeakyReluOptions::UnPackTo(LeakyReluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void LeakyReluOptions::UnPackTo(LeakyReluOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = alpha(); _o->alpha = _e; }
}
-inline flatbuffers::Offset<LeakyReluOptions> LeakyReluOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LeakyReluOptions> LeakyReluOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateLeakyReluOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<LeakyReluOptions> CreateLeakyReluOptions(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<LeakyReluOptions> CreateLeakyReluOptions(::flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LeakyReluOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const LeakyReluOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _alpha = _o->alpha;
return tflite::CreateLeakyReluOptions(
_fbb,
_alpha);
}
-inline SquaredDifferenceOptionsT *SquaredDifferenceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SquaredDifferenceOptionsT *SquaredDifferenceOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SquaredDifferenceOptionsT>(new SquaredDifferenceOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SquaredDifferenceOptions::UnPackTo(SquaredDifferenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SquaredDifferenceOptions::UnPackTo(SquaredDifferenceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<SquaredDifferenceOptions> SquaredDifferenceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SquaredDifferenceOptions> SquaredDifferenceOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSquaredDifferenceOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SquaredDifferenceOptions> CreateSquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SquaredDifferenceOptions> CreateSquaredDifferenceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SquaredDifferenceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SquaredDifferenceOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateSquaredDifferenceOptions(
_fbb);
}
-inline MirrorPadOptionsT *MirrorPadOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline MirrorPadOptionsT *MirrorPadOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<MirrorPadOptionsT>(new MirrorPadOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void MirrorPadOptions::UnPackTo(MirrorPadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void MirrorPadOptions::UnPackTo(MirrorPadOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = mode(); _o->mode = _e; }
}
-inline flatbuffers::Offset<MirrorPadOptions> MirrorPadOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<MirrorPadOptions> MirrorPadOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateMirrorPadOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<MirrorPadOptions> CreateMirrorPadOptions(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<MirrorPadOptions> CreateMirrorPadOptions(::flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MirrorPadOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const MirrorPadOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _mode = _o->mode;
return tflite::CreateMirrorPadOptions(
_fbb,
_mode);
}
-inline UniqueOptionsT *UniqueOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline UniqueOptionsT *UniqueOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<UniqueOptionsT>(new UniqueOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void UniqueOptions::UnPackTo(UniqueOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void UniqueOptions::UnPackTo(UniqueOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = idx_out_type(); _o->idx_out_type = _e; }
}
-inline flatbuffers::Offset<UniqueOptions> UniqueOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<UniqueOptions> UniqueOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateUniqueOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<UniqueOptions> CreateUniqueOptions(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<UniqueOptions> CreateUniqueOptions(::flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UniqueOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const UniqueOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _idx_out_type = _o->idx_out_type;
return tflite::CreateUniqueOptions(
_fbb,
_idx_out_type);
}
-inline ReverseV2OptionsT *ReverseV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ReverseV2OptionsT *ReverseV2Options::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ReverseV2OptionsT>(new ReverseV2OptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void ReverseV2Options::UnPackTo(ReverseV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void ReverseV2Options::UnPackTo(ReverseV2OptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<ReverseV2Options> ReverseV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ReverseV2Options> ReverseV2Options::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateReverseV2Options(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<ReverseV2Options> CreateReverseV2Options(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ReverseV2Options> CreateReverseV2Options(::flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReverseV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ReverseV2OptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateReverseV2Options(
_fbb);
}
-inline AddNOptionsT *AddNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline AddNOptionsT *AddNOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<AddNOptionsT>(new AddNOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void AddNOptions::UnPackTo(AddNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void AddNOptions::UnPackTo(AddNOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<AddNOptions> AddNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<AddNOptions> AddNOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateAddNOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<AddNOptions> CreateAddNOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<AddNOptions> CreateAddNOptions(::flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AddNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const AddNOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateAddNOptions(
_fbb);
}
-inline GatherNdOptionsT *GatherNdOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline GatherNdOptionsT *GatherNdOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<GatherNdOptionsT>(new GatherNdOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void GatherNdOptions::UnPackTo(GatherNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void GatherNdOptions::UnPackTo(GatherNdOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<GatherNdOptions> GatherNdOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<GatherNdOptions> GatherNdOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateGatherNdOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<GatherNdOptions> CreateGatherNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<GatherNdOptions> CreateGatherNdOptions(::flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GatherNdOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const GatherNdOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateGatherNdOptions(
_fbb);
}
-inline WhereOptionsT *WhereOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline WhereOptionsT *WhereOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<WhereOptionsT>(new WhereOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void WhereOptions::UnPackTo(WhereOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void WhereOptions::UnPackTo(WhereOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<WhereOptions> WhereOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<WhereOptions> WhereOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateWhereOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<WhereOptions> CreateWhereOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<WhereOptions> CreateWhereOptions(::flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const WhereOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const WhereOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateWhereOptions(
_fbb);
}
-inline ReverseSequenceOptionsT *ReverseSequenceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ReverseSequenceOptionsT *ReverseSequenceOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ReverseSequenceOptionsT>(new ReverseSequenceOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void ReverseSequenceOptions::UnPackTo(ReverseSequenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void ReverseSequenceOptions::UnPackTo(ReverseSequenceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = seq_dim(); _o->seq_dim = _e; }
{ auto _e = batch_dim(); _o->batch_dim = _e; }
}
-inline flatbuffers::Offset<ReverseSequenceOptions> ReverseSequenceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ReverseSequenceOptions> ReverseSequenceOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateReverseSequenceOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<ReverseSequenceOptions> CreateReverseSequenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ReverseSequenceOptions> CreateReverseSequenceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReverseSequenceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ReverseSequenceOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _seq_dim = _o->seq_dim;
auto _batch_dim = _o->batch_dim;
return tflite::CreateReverseSequenceOptions(
@@ -16397,96 +20075,96 @@
_batch_dim);
}
-inline MatrixDiagOptionsT *MatrixDiagOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline MatrixDiagOptionsT *MatrixDiagOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<MatrixDiagOptionsT>(new MatrixDiagOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void MatrixDiagOptions::UnPackTo(MatrixDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void MatrixDiagOptions::UnPackTo(MatrixDiagOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<MatrixDiagOptions> MatrixDiagOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<MatrixDiagOptions> MatrixDiagOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateMatrixDiagOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<MatrixDiagOptions> CreateMatrixDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<MatrixDiagOptions> CreateMatrixDiagOptions(::flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MatrixDiagOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const MatrixDiagOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateMatrixDiagOptions(
_fbb);
}
-inline QuantizeOptionsT *QuantizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline QuantizeOptionsT *QuantizeOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<QuantizeOptionsT>(new QuantizeOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void QuantizeOptions::UnPackTo(QuantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void QuantizeOptions::UnPackTo(QuantizeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<QuantizeOptions> QuantizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<QuantizeOptions> QuantizeOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateQuantizeOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<QuantizeOptions> CreateQuantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<QuantizeOptions> CreateQuantizeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const QuantizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const QuantizeOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateQuantizeOptions(
_fbb);
}
-inline MatrixSetDiagOptionsT *MatrixSetDiagOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline MatrixSetDiagOptionsT *MatrixSetDiagOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<MatrixSetDiagOptionsT>(new MatrixSetDiagOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void MatrixSetDiagOptions::UnPackTo(MatrixSetDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void MatrixSetDiagOptions::UnPackTo(MatrixSetDiagOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<MatrixSetDiagOptions> MatrixSetDiagOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<MatrixSetDiagOptions> MatrixSetDiagOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateMatrixSetDiagOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<MatrixSetDiagOptions> CreateMatrixSetDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<MatrixSetDiagOptions> CreateMatrixSetDiagOptions(::flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MatrixSetDiagOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const MatrixSetDiagOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateMatrixSetDiagOptions(
_fbb);
}
-inline IfOptionsT *IfOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline IfOptionsT *IfOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<IfOptionsT>(new IfOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void IfOptions::UnPackTo(IfOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void IfOptions::UnPackTo(IfOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = then_subgraph_index(); _o->then_subgraph_index = _e; }
{ auto _e = else_subgraph_index(); _o->else_subgraph_index = _e; }
}
-inline flatbuffers::Offset<IfOptions> IfOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<IfOptions> IfOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateIfOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<IfOptions> CreateIfOptions(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<IfOptions> CreateIfOptions(::flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const IfOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const IfOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _then_subgraph_index = _o->then_subgraph_index;
auto _else_subgraph_index = _o->else_subgraph_index;
return tflite::CreateIfOptions(
@@ -16495,53 +20173,53 @@
_else_subgraph_index);
}
-inline CallOnceOptionsT *CallOnceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline CallOnceOptionsT *CallOnceOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<CallOnceOptionsT>(new CallOnceOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void CallOnceOptions::UnPackTo(CallOnceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void CallOnceOptions::UnPackTo(CallOnceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = init_subgraph_index(); _o->init_subgraph_index = _e; }
}
-inline flatbuffers::Offset<CallOnceOptions> CallOnceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<CallOnceOptions> CallOnceOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateCallOnceOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<CallOnceOptions> CreateCallOnceOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<CallOnceOptions> CreateCallOnceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CallOnceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const CallOnceOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _init_subgraph_index = _o->init_subgraph_index;
return tflite::CreateCallOnceOptions(
_fbb,
_init_subgraph_index);
}
-inline WhileOptionsT *WhileOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline WhileOptionsT *WhileOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<WhileOptionsT>(new WhileOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void WhileOptions::UnPackTo(WhileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void WhileOptions::UnPackTo(WhileOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = cond_subgraph_index(); _o->cond_subgraph_index = _e; }
{ auto _e = body_subgraph_index(); _o->body_subgraph_index = _e; }
}
-inline flatbuffers::Offset<WhileOptions> WhileOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<WhileOptions> WhileOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateWhileOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<WhileOptions> CreateWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<WhileOptions> CreateWhileOptions(::flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const WhileOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const WhileOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _cond_subgraph_index = _o->cond_subgraph_index;
auto _body_subgraph_index = _o->body_subgraph_index;
return tflite::CreateWhileOptions(
@@ -16550,151 +20228,151 @@
_body_subgraph_index);
}
-inline NonMaxSuppressionV4OptionsT *NonMaxSuppressionV4Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline NonMaxSuppressionV4OptionsT *NonMaxSuppressionV4Options::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<NonMaxSuppressionV4OptionsT>(new NonMaxSuppressionV4OptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void NonMaxSuppressionV4Options::UnPackTo(NonMaxSuppressionV4OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void NonMaxSuppressionV4Options::UnPackTo(NonMaxSuppressionV4OptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<NonMaxSuppressionV4Options> NonMaxSuppressionV4Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<NonMaxSuppressionV4Options> NonMaxSuppressionV4Options::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateNonMaxSuppressionV4Options(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<NonMaxSuppressionV4Options> CreateNonMaxSuppressionV4Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<NonMaxSuppressionV4Options> CreateNonMaxSuppressionV4Options(::flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NonMaxSuppressionV4OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const NonMaxSuppressionV4OptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateNonMaxSuppressionV4Options(
_fbb);
}
-inline NonMaxSuppressionV5OptionsT *NonMaxSuppressionV5Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline NonMaxSuppressionV5OptionsT *NonMaxSuppressionV5Options::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<NonMaxSuppressionV5OptionsT>(new NonMaxSuppressionV5OptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void NonMaxSuppressionV5Options::UnPackTo(NonMaxSuppressionV5OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void NonMaxSuppressionV5Options::UnPackTo(NonMaxSuppressionV5OptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<NonMaxSuppressionV5Options> NonMaxSuppressionV5Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<NonMaxSuppressionV5Options> NonMaxSuppressionV5Options::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateNonMaxSuppressionV5Options(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<NonMaxSuppressionV5Options> CreateNonMaxSuppressionV5Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<NonMaxSuppressionV5Options> CreateNonMaxSuppressionV5Options(::flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NonMaxSuppressionV5OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const NonMaxSuppressionV5OptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateNonMaxSuppressionV5Options(
_fbb);
}
-inline ScatterNdOptionsT *ScatterNdOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ScatterNdOptionsT *ScatterNdOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ScatterNdOptionsT>(new ScatterNdOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void ScatterNdOptions::UnPackTo(ScatterNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void ScatterNdOptions::UnPackTo(ScatterNdOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<ScatterNdOptions> ScatterNdOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ScatterNdOptions> ScatterNdOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateScatterNdOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<ScatterNdOptions> CreateScatterNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ScatterNdOptions> CreateScatterNdOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ScatterNdOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ScatterNdOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateScatterNdOptions(
_fbb);
}
-inline SelectV2OptionsT *SelectV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SelectV2OptionsT *SelectV2Options::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SelectV2OptionsT>(new SelectV2OptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SelectV2Options::UnPackTo(SelectV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SelectV2Options::UnPackTo(SelectV2OptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<SelectV2Options> SelectV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SelectV2Options> SelectV2Options::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSelectV2Options(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SelectV2Options> CreateSelectV2Options(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SelectV2Options> CreateSelectV2Options(::flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SelectV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SelectV2OptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateSelectV2Options(
_fbb);
}
-inline DensifyOptionsT *DensifyOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline DensifyOptionsT *DensifyOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<DensifyOptionsT>(new DensifyOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void DensifyOptions::UnPackTo(DensifyOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void DensifyOptions::UnPackTo(DensifyOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<DensifyOptions> DensifyOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<DensifyOptions> DensifyOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateDensifyOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<DensifyOptions> CreateDensifyOptions(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<DensifyOptions> CreateDensifyOptions(::flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DensifyOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const DensifyOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateDensifyOptions(
_fbb);
}
-inline SegmentSumOptionsT *SegmentSumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SegmentSumOptionsT *SegmentSumOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SegmentSumOptionsT>(new SegmentSumOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SegmentSumOptions::UnPackTo(SegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SegmentSumOptions::UnPackTo(SegmentSumOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<SegmentSumOptions> SegmentSumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SegmentSumOptions> SegmentSumOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSegmentSumOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SegmentSumOptions> CreateSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SegmentSumOptions> CreateSegmentSumOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SegmentSumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SegmentSumOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateSegmentSumOptions(
_fbb);
}
-inline BatchMatMulOptionsT *BatchMatMulOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline BatchMatMulOptionsT *BatchMatMulOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<BatchMatMulOptionsT>(new BatchMatMulOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void BatchMatMulOptions::UnPackTo(BatchMatMulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void BatchMatMulOptions::UnPackTo(BatchMatMulOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = adj_x(); _o->adj_x = _e; }
@@ -16702,14 +20380,14 @@
{ auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; }
}
-inline flatbuffers::Offset<BatchMatMulOptions> BatchMatMulOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<BatchMatMulOptions> BatchMatMulOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateBatchMatMulOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<BatchMatMulOptions> CreateBatchMatMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<BatchMatMulOptions> CreateBatchMatMulOptions(::flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BatchMatMulOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const BatchMatMulOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _adj_x = _o->adj_x;
auto _adj_y = _o->adj_y;
auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs;
@@ -16720,27 +20398,27 @@
_asymmetric_quantize_inputs);
}
-inline CumsumOptionsT *CumsumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline CumsumOptionsT *CumsumOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<CumsumOptionsT>(new CumsumOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void CumsumOptions::UnPackTo(CumsumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void CumsumOptions::UnPackTo(CumsumOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = exclusive(); _o->exclusive = _e; }
{ auto _e = reverse(); _o->reverse = _e; }
}
-inline flatbuffers::Offset<CumsumOptions> CumsumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<CumsumOptions> CumsumOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateCumsumOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<CumsumOptions> CreateCumsumOptions(flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<CumsumOptions> CreateCumsumOptions(::flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CumsumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const CumsumOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _exclusive = _o->exclusive;
auto _reverse = _o->reverse;
return tflite::CreateCumsumOptions(
@@ -16749,59 +20427,59 @@
_reverse);
}
-inline BroadcastToOptionsT *BroadcastToOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline BroadcastToOptionsT *BroadcastToOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<BroadcastToOptionsT>(new BroadcastToOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void BroadcastToOptions::UnPackTo(BroadcastToOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void BroadcastToOptions::UnPackTo(BroadcastToOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<BroadcastToOptions> BroadcastToOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<BroadcastToOptions> BroadcastToOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateBroadcastToOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<BroadcastToOptions> CreateBroadcastToOptions(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<BroadcastToOptions> CreateBroadcastToOptions(::flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BroadcastToOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const BroadcastToOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateBroadcastToOptions(
_fbb);
}
-inline Rfft2dOptionsT *Rfft2dOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline Rfft2dOptionsT *Rfft2dOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<Rfft2dOptionsT>(new Rfft2dOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void Rfft2dOptions::UnPackTo(Rfft2dOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void Rfft2dOptions::UnPackTo(Rfft2dOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<Rfft2dOptions> Rfft2dOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Rfft2dOptions> Rfft2dOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateRfft2dOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<Rfft2dOptions> CreateRfft2dOptions(flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Rfft2dOptions> CreateRfft2dOptions(::flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Rfft2dOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const Rfft2dOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateRfft2dOptions(
_fbb);
}
-inline HashtableOptionsT *HashtableOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline HashtableOptionsT *HashtableOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<HashtableOptionsT>(new HashtableOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void HashtableOptions::UnPackTo(HashtableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void HashtableOptions::UnPackTo(HashtableOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = table_id(); _o->table_id = _e; }
@@ -16809,14 +20487,14 @@
{ auto _e = value_dtype(); _o->value_dtype = _e; }
}
-inline flatbuffers::Offset<HashtableOptions> HashtableOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<HashtableOptions> HashtableOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateHashtableOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<HashtableOptions> CreateHashtableOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<HashtableOptions> CreateHashtableOptions(::flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HashtableOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const HashtableOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _table_id = _o->table_id;
auto _key_dtype = _o->key_dtype;
auto _value_dtype = _o->value_dtype;
@@ -16827,96 +20505,96 @@
_value_dtype);
}
-inline HashtableFindOptionsT *HashtableFindOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline HashtableFindOptionsT *HashtableFindOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<HashtableFindOptionsT>(new HashtableFindOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void HashtableFindOptions::UnPackTo(HashtableFindOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void HashtableFindOptions::UnPackTo(HashtableFindOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<HashtableFindOptions> HashtableFindOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<HashtableFindOptions> HashtableFindOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateHashtableFindOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<HashtableFindOptions> CreateHashtableFindOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<HashtableFindOptions> CreateHashtableFindOptions(::flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HashtableFindOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const HashtableFindOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateHashtableFindOptions(
_fbb);
}
-inline HashtableImportOptionsT *HashtableImportOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline HashtableImportOptionsT *HashtableImportOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<HashtableImportOptionsT>(new HashtableImportOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void HashtableImportOptions::UnPackTo(HashtableImportOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void HashtableImportOptions::UnPackTo(HashtableImportOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<HashtableImportOptions> HashtableImportOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<HashtableImportOptions> HashtableImportOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateHashtableImportOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<HashtableImportOptions> CreateHashtableImportOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<HashtableImportOptions> CreateHashtableImportOptions(::flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HashtableImportOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const HashtableImportOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateHashtableImportOptions(
_fbb);
}
-inline HashtableSizeOptionsT *HashtableSizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline HashtableSizeOptionsT *HashtableSizeOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<HashtableSizeOptionsT>(new HashtableSizeOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void HashtableSizeOptions::UnPackTo(HashtableSizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void HashtableSizeOptions::UnPackTo(HashtableSizeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<HashtableSizeOptions> HashtableSizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<HashtableSizeOptions> HashtableSizeOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateHashtableSizeOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<HashtableSizeOptions> CreateHashtableSizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<HashtableSizeOptions> CreateHashtableSizeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HashtableSizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const HashtableSizeOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateHashtableSizeOptions(
_fbb);
}
-inline VarHandleOptionsT *VarHandleOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline VarHandleOptionsT *VarHandleOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<VarHandleOptionsT>(new VarHandleOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void VarHandleOptions::UnPackTo(VarHandleOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void VarHandleOptions::UnPackTo(VarHandleOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = container(); if (_e) _o->container = _e->str(); }
{ auto _e = shared_name(); if (_e) _o->shared_name = _e->str(); }
}
-inline flatbuffers::Offset<VarHandleOptions> VarHandleOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<VarHandleOptions> VarHandleOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateVarHandleOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<VarHandleOptions> CreateVarHandleOptions(flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<VarHandleOptions> CreateVarHandleOptions(::flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const VarHandleOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const VarHandleOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _container = _o->container.empty() ? 0 : _fbb.CreateString(_o->container);
auto _shared_name = _o->shared_name.empty() ? 0 : _fbb.CreateString(_o->shared_name);
return tflite::CreateVarHandleOptions(
@@ -16925,73 +20603,73 @@
_shared_name);
}
-inline ReadVariableOptionsT *ReadVariableOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ReadVariableOptionsT *ReadVariableOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ReadVariableOptionsT>(new ReadVariableOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void ReadVariableOptions::UnPackTo(ReadVariableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void ReadVariableOptions::UnPackTo(ReadVariableOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<ReadVariableOptions> ReadVariableOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ReadVariableOptions> ReadVariableOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateReadVariableOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<ReadVariableOptions> CreateReadVariableOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ReadVariableOptions> CreateReadVariableOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReadVariableOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ReadVariableOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateReadVariableOptions(
_fbb);
}
-inline AssignVariableOptionsT *AssignVariableOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline AssignVariableOptionsT *AssignVariableOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<AssignVariableOptionsT>(new AssignVariableOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void AssignVariableOptions::UnPackTo(AssignVariableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void AssignVariableOptions::UnPackTo(AssignVariableOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<AssignVariableOptions> AssignVariableOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<AssignVariableOptions> AssignVariableOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateAssignVariableOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<AssignVariableOptions> CreateAssignVariableOptions(flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<AssignVariableOptions> CreateAssignVariableOptions(::flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AssignVariableOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const AssignVariableOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateAssignVariableOptions(
_fbb);
}
-inline RandomOptionsT *RandomOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline RandomOptionsT *RandomOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<RandomOptionsT>(new RandomOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void RandomOptions::UnPackTo(RandomOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void RandomOptions::UnPackTo(RandomOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = seed(); _o->seed = _e; }
{ auto _e = seed2(); _o->seed2 = _e; }
}
-inline flatbuffers::Offset<RandomOptions> RandomOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<RandomOptions> RandomOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateRandomOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<RandomOptions> CreateRandomOptions(flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<RandomOptions> CreateRandomOptions(::flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RandomOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const RandomOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _seed = _o->seed;
auto _seed2 = _o->seed2;
return tflite::CreateRandomOptions(
@@ -17000,295 +20678,344 @@
_seed2);
}
-inline BucketizeOptionsT *BucketizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline BucketizeOptionsT *BucketizeOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<BucketizeOptionsT>(new BucketizeOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void BucketizeOptions::UnPackTo(BucketizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void BucketizeOptions::UnPackTo(BucketizeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
- { auto _e = boundaries(); if (_e) { _o->boundaries.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->boundaries[_i] = _e->Get(_i); } } }
+ { auto _e = boundaries(); if (_e) { _o->boundaries.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->boundaries[_i] = _e->Get(_i); } } else { _o->boundaries.resize(0); } }
}
-inline flatbuffers::Offset<BucketizeOptions> BucketizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<BucketizeOptions> BucketizeOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateBucketizeOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<BucketizeOptions> CreateBucketizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<BucketizeOptions> CreateBucketizeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BucketizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const BucketizeOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _boundaries = _o->boundaries.size() ? _fbb.CreateVector(_o->boundaries) : 0;
return tflite::CreateBucketizeOptions(
_fbb,
_boundaries);
}
-inline GeluOptionsT *GeluOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline GeluOptionsT *GeluOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<GeluOptionsT>(new GeluOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void GeluOptions::UnPackTo(GeluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void GeluOptions::UnPackTo(GeluOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = approximate(); _o->approximate = _e; }
}
-inline flatbuffers::Offset<GeluOptions> GeluOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<GeluOptions> GeluOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateGeluOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<GeluOptions> CreateGeluOptions(flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<GeluOptions> CreateGeluOptions(::flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GeluOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const GeluOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _approximate = _o->approximate;
return tflite::CreateGeluOptions(
_fbb,
_approximate);
}
-inline DynamicUpdateSliceOptionsT *DynamicUpdateSliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline DynamicUpdateSliceOptionsT *DynamicUpdateSliceOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<DynamicUpdateSliceOptionsT>(new DynamicUpdateSliceOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void DynamicUpdateSliceOptions::UnPackTo(DynamicUpdateSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void DynamicUpdateSliceOptions::UnPackTo(DynamicUpdateSliceOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<DynamicUpdateSliceOptions> DynamicUpdateSliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<DynamicUpdateSliceOptions> DynamicUpdateSliceOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateDynamicUpdateSliceOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<DynamicUpdateSliceOptions> CreateDynamicUpdateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<DynamicUpdateSliceOptions> CreateDynamicUpdateSliceOptions(::flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DynamicUpdateSliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const DynamicUpdateSliceOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateDynamicUpdateSliceOptions(
_fbb);
}
-inline UnsortedSegmentProdOptionsT *UnsortedSegmentProdOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline UnsortedSegmentProdOptionsT *UnsortedSegmentProdOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<UnsortedSegmentProdOptionsT>(new UnsortedSegmentProdOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void UnsortedSegmentProdOptions::UnPackTo(UnsortedSegmentProdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void UnsortedSegmentProdOptions::UnPackTo(UnsortedSegmentProdOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<UnsortedSegmentProdOptions> UnsortedSegmentProdOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<UnsortedSegmentProdOptions> UnsortedSegmentProdOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateUnsortedSegmentProdOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<UnsortedSegmentProdOptions> CreateUnsortedSegmentProdOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<UnsortedSegmentProdOptions> CreateUnsortedSegmentProdOptions(::flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentProdOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentProdOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateUnsortedSegmentProdOptions(
_fbb);
}
-inline UnsortedSegmentMaxOptionsT *UnsortedSegmentMaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline UnsortedSegmentMaxOptionsT *UnsortedSegmentMaxOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<UnsortedSegmentMaxOptionsT>(new UnsortedSegmentMaxOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void UnsortedSegmentMaxOptions::UnPackTo(UnsortedSegmentMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void UnsortedSegmentMaxOptions::UnPackTo(UnsortedSegmentMaxOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<UnsortedSegmentMaxOptions> UnsortedSegmentMaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<UnsortedSegmentMaxOptions> UnsortedSegmentMaxOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateUnsortedSegmentMaxOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<UnsortedSegmentMaxOptions> CreateUnsortedSegmentMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<UnsortedSegmentMaxOptions> CreateUnsortedSegmentMaxOptions(::flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentMaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentMaxOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateUnsortedSegmentMaxOptions(
_fbb);
}
-inline UnsortedSegmentSumOptionsT *UnsortedSegmentSumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline UnsortedSegmentSumOptionsT *UnsortedSegmentSumOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<UnsortedSegmentSumOptionsT>(new UnsortedSegmentSumOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void UnsortedSegmentSumOptions::UnPackTo(UnsortedSegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void UnsortedSegmentSumOptions::UnPackTo(UnsortedSegmentSumOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<UnsortedSegmentSumOptions> UnsortedSegmentSumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<UnsortedSegmentSumOptions> UnsortedSegmentSumOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateUnsortedSegmentSumOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<UnsortedSegmentSumOptions> CreateUnsortedSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<UnsortedSegmentSumOptions> CreateUnsortedSegmentSumOptions(::flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentSumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentSumOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateUnsortedSegmentSumOptions(
_fbb);
}
-inline ATan2OptionsT *ATan2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ATan2OptionsT *ATan2Options::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ATan2OptionsT>(new ATan2OptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void ATan2Options::UnPackTo(ATan2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void ATan2Options::UnPackTo(ATan2OptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<ATan2Options> ATan2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ATan2Options> ATan2Options::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateATan2Options(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<ATan2Options> CreateATan2Options(flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<ATan2Options> CreateATan2Options(::flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ATan2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ATan2OptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateATan2Options(
_fbb);
}
-inline UnsortedSegmentMinOptionsT *UnsortedSegmentMinOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline UnsortedSegmentMinOptionsT *UnsortedSegmentMinOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<UnsortedSegmentMinOptionsT>(new UnsortedSegmentMinOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void UnsortedSegmentMinOptions::UnPackTo(UnsortedSegmentMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void UnsortedSegmentMinOptions::UnPackTo(UnsortedSegmentMinOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<UnsortedSegmentMinOptions> UnsortedSegmentMinOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<UnsortedSegmentMinOptions> UnsortedSegmentMinOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateUnsortedSegmentMinOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<UnsortedSegmentMinOptions> CreateUnsortedSegmentMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<UnsortedSegmentMinOptions> CreateUnsortedSegmentMinOptions(::flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentMinOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentMinOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateUnsortedSegmentMinOptions(
_fbb);
}
-inline SignOptionsT *SignOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SignOptionsT *SignOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SignOptionsT>(new SignOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SignOptions::UnPackTo(SignOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SignOptions::UnPackTo(SignOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<SignOptions> SignOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SignOptions> SignOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSignOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SignOptions> CreateSignOptions(flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SignOptions> CreateSignOptions(::flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SignOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SignOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateSignOptions(
_fbb);
}
-inline BitcastOptionsT *BitcastOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline BitcastOptionsT *BitcastOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<BitcastOptionsT>(new BitcastOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void BitcastOptions::UnPackTo(BitcastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void BitcastOptions::UnPackTo(BitcastOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<BitcastOptions> BitcastOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BitcastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<BitcastOptions> BitcastOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BitcastOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateBitcastOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<BitcastOptions> CreateBitcastOptions(flatbuffers::FlatBufferBuilder &_fbb, const BitcastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<BitcastOptions> CreateBitcastOptions(::flatbuffers::FlatBufferBuilder &_fbb, const BitcastOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BitcastOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const BitcastOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateBitcastOptions(
_fbb);
}
-inline BitwiseXorOptionsT *BitwiseXorOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline BitwiseXorOptionsT *BitwiseXorOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<BitwiseXorOptionsT>(new BitwiseXorOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void BitwiseXorOptions::UnPackTo(BitwiseXorOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void BitwiseXorOptions::UnPackTo(BitwiseXorOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<BitwiseXorOptions> BitwiseXorOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BitwiseXorOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<BitwiseXorOptions> BitwiseXorOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BitwiseXorOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateBitwiseXorOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<BitwiseXorOptions> CreateBitwiseXorOptions(flatbuffers::FlatBufferBuilder &_fbb, const BitwiseXorOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<BitwiseXorOptions> CreateBitwiseXorOptions(::flatbuffers::FlatBufferBuilder &_fbb, const BitwiseXorOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BitwiseXorOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const BitwiseXorOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateBitwiseXorOptions(
_fbb);
}
-inline RightShiftOptionsT *RightShiftOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline RightShiftOptionsT *RightShiftOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<RightShiftOptionsT>(new RightShiftOptionsT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void RightShiftOptions::UnPackTo(RightShiftOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void RightShiftOptions::UnPackTo(RightShiftOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
}
-inline flatbuffers::Offset<RightShiftOptions> RightShiftOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RightShiftOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<RightShiftOptions> RightShiftOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const RightShiftOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateRightShiftOptions(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<RightShiftOptions> CreateRightShiftOptions(flatbuffers::FlatBufferBuilder &_fbb, const RightShiftOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<RightShiftOptions> CreateRightShiftOptions(::flatbuffers::FlatBufferBuilder &_fbb, const RightShiftOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RightShiftOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const RightShiftOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
return tflite::CreateRightShiftOptions(
_fbb);
}
-inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline DilateOptionsT *DilateOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<DilateOptionsT>(new DilateOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void DilateOptions::UnPackTo(DilateOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+}
+
+inline ::flatbuffers::Offset<DilateOptions> DilateOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const DilateOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateDilateOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<DilateOptions> CreateDilateOptions(::flatbuffers::FlatBufferBuilder &_fbb, const DilateOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const DilateOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ return tflite::CreateDilateOptions(
+ _fbb);
+}
+
+inline ReduceWindowOptionsT *ReduceWindowOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<ReduceWindowOptionsT>(new ReduceWindowOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void ReduceWindowOptions::UnPackTo(ReduceWindowOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = reduce_function(); _o->reduce_function = _e; }
+}
+
+inline ::flatbuffers::Offset<ReduceWindowOptions> ReduceWindowOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ReduceWindowOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateReduceWindowOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<ReduceWindowOptions> CreateReduceWindowOptions(::flatbuffers::FlatBufferBuilder &_fbb, const ReduceWindowOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ReduceWindowOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _reduce_function = _o->reduce_function;
+ return tflite::CreateReduceWindowOptions(
+ _fbb,
+ _reduce_function);
+}
+
+inline OperatorCodeT *OperatorCode::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<OperatorCodeT>(new OperatorCodeT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void OperatorCode::UnPackTo(OperatorCodeT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void OperatorCode::UnPackTo(OperatorCodeT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = deprecated_builtin_code(); _o->deprecated_builtin_code = _e; }
@@ -17297,14 +21024,14 @@
{ auto _e = builtin_code(); _o->builtin_code = _e; }
}
-inline flatbuffers::Offset<OperatorCode> OperatorCode::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<OperatorCode> OperatorCode::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateOperatorCode(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<OperatorCode> CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<OperatorCode> CreateOperatorCode(::flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OperatorCodeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const OperatorCodeT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _deprecated_builtin_code = _o->deprecated_builtin_code;
auto _custom_code = _o->custom_code.empty() ? 0 : _fbb.CreateString(_o->custom_code);
auto _version = _o->version;
@@ -17317,36 +21044,76 @@
_builtin_code);
}
-inline OperatorT *Operator::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline StableHLOCompositeOptionsT *StableHLOCompositeOptions::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = std::unique_ptr<StableHLOCompositeOptionsT>(new StableHLOCompositeOptionsT());
+ UnPackTo(_o.get(), _resolver);
+ return _o.release();
+}
+
+inline void StableHLOCompositeOptions::UnPackTo(StableHLOCompositeOptionsT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = name(); if (_e) _o->name = _e->str(); }
+ { auto _e = decomposition_subgraph_index(); _o->decomposition_subgraph_index = _e; }
+ { auto _e = composite_attributes(); if (_e) { _o->composite_attributes.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->composite_attributes.begin()); } }
+ { auto _e = composite_attributes_format(); _o->composite_attributes_format = _e; }
+ { auto _e = version(); _o->version = _e; }
+}
+
+inline ::flatbuffers::Offset<StableHLOCompositeOptions> StableHLOCompositeOptions::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const StableHLOCompositeOptionsT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateStableHLOCompositeOptions(_fbb, _o, _rehasher);
+}
+
+inline ::flatbuffers::Offset<StableHLOCompositeOptions> CreateStableHLOCompositeOptions(::flatbuffers::FlatBufferBuilder &_fbb, const StableHLOCompositeOptionsT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const StableHLOCompositeOptionsT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name);
+ auto _decomposition_subgraph_index = _o->decomposition_subgraph_index;
+ auto _composite_attributes = _o->composite_attributes.size() ? _fbb.CreateVector(_o->composite_attributes) : 0;
+ auto _composite_attributes_format = _o->composite_attributes_format;
+ auto _version = _o->version;
+ return tflite::CreateStableHLOCompositeOptions(
+ _fbb,
+ _name,
+ _decomposition_subgraph_index,
+ _composite_attributes,
+ _composite_attributes_format,
+ _version);
+}
+
+inline OperatorT *Operator::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<OperatorT>(new OperatorT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void Operator::UnPackTo(OperatorT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void Operator::UnPackTo(OperatorT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = opcode_index(); _o->opcode_index = _e; }
- { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputs[_i] = _e->Get(_i); } } }
- { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputs[_i] = _e->Get(_i); } } }
+ { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputs[_i] = _e->Get(_i); } } else { _o->inputs.resize(0); } }
+ { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputs[_i] = _e->Get(_i); } } else { _o->outputs.resize(0); } }
{ auto _e = builtin_options_type(); _o->builtin_options.type = _e; }
{ auto _e = builtin_options(); if (_e) _o->builtin_options.value = tflite::BuiltinOptionsUnion::UnPack(_e, builtin_options_type(), _resolver); }
{ auto _e = custom_options(); if (_e) { _o->custom_options.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->custom_options.begin()); } }
{ auto _e = custom_options_format(); _o->custom_options_format = _e; }
- { auto _e = mutating_variable_inputs(); if (_e) { _o->mutating_variable_inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->mutating_variable_inputs[_i] = _e->Get(_i) != 0; } } }
- { auto _e = intermediates(); if (_e) { _o->intermediates.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->intermediates[_i] = _e->Get(_i); } } }
+ { auto _e = mutating_variable_inputs(); if (_e) { _o->mutating_variable_inputs.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->mutating_variable_inputs[_i] = _e->Get(_i) != 0; } } else { _o->mutating_variable_inputs.resize(0); } }
+ { auto _e = intermediates(); if (_e) { _o->intermediates.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->intermediates[_i] = _e->Get(_i); } } else { _o->intermediates.resize(0); } }
{ auto _e = large_custom_options_offset(); _o->large_custom_options_offset = _e; }
{ auto _e = large_custom_options_size(); _o->large_custom_options_size = _e; }
+ { auto _e = builtin_options_2_type(); _o->builtin_options_2.type = _e; }
+ { auto _e = builtin_options_2(); if (_e) _o->builtin_options_2.value = tflite::BuiltinOptions2Union::UnPack(_e, builtin_options_2_type(), _resolver); }
}
-inline flatbuffers::Offset<Operator> Operator::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Operator> Operator::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateOperator(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<Operator> CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Operator> CreateOperator(::flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OperatorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const OperatorT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _opcode_index = _o->opcode_index;
auto _inputs = _o->inputs.size() ? _fbb.CreateVector(_o->inputs) : 0;
auto _outputs = _o->outputs.size() ? _fbb.CreateVector(_o->outputs) : 0;
@@ -17358,6 +21125,8 @@
auto _intermediates = _o->intermediates.size() ? _fbb.CreateVector(_o->intermediates) : 0;
auto _large_custom_options_offset = _o->large_custom_options_offset;
auto _large_custom_options_size = _o->large_custom_options_size;
+ auto _builtin_options_2_type = _o->builtin_options_2.type;
+ auto _builtin_options_2 = _o->builtin_options_2.Pack(_fbb);
return tflite::CreateOperator(
_fbb,
_opcode_index,
@@ -17370,7 +21139,9 @@
_mutating_variable_inputs,
_intermediates,
_large_custom_options_offset,
- _large_custom_options_size);
+ _large_custom_options_size,
+ _builtin_options_2_type,
+ _builtin_options_2);
}
inline SubGraphT::SubGraphT(const SubGraphT &o)
@@ -17392,34 +21163,34 @@
return *this;
}
-inline SubGraphT *SubGraph::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SubGraphT *SubGraph::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SubGraphT>(new SubGraphT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SubGraph::UnPackTo(SubGraphT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SubGraph::UnPackTo(SubGraphT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
- { auto _e = tensors(); if (_e) { _o->tensors.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->tensors[_i]) { _e->Get(_i)->UnPackTo(_o->tensors[_i].get(), _resolver); } else { _o->tensors[_i] = std::unique_ptr<tflite::TensorT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
- { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputs[_i] = _e->Get(_i); } } }
- { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputs[_i] = _e->Get(_i); } } }
- { auto _e = operators(); if (_e) { _o->operators.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->operators[_i]) { _e->Get(_i)->UnPackTo(_o->operators[_i].get(), _resolver); } else { _o->operators[_i] = std::unique_ptr<tflite::OperatorT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
+ { auto _e = tensors(); if (_e) { _o->tensors.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->tensors[_i]) { _e->Get(_i)->UnPackTo(_o->tensors[_i].get(), _resolver); } else { _o->tensors[_i] = std::unique_ptr<tflite::TensorT>(_e->Get(_i)->UnPack(_resolver)); }; } } else { _o->tensors.resize(0); } }
+ { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputs[_i] = _e->Get(_i); } } else { _o->inputs.resize(0); } }
+ { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputs[_i] = _e->Get(_i); } } else { _o->outputs.resize(0); } }
+ { auto _e = operators(); if (_e) { _o->operators.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->operators[_i]) { _e->Get(_i)->UnPackTo(_o->operators[_i].get(), _resolver); } else { _o->operators[_i] = std::unique_ptr<tflite::OperatorT>(_e->Get(_i)->UnPack(_resolver)); }; } } else { _o->operators.resize(0); } }
{ auto _e = name(); if (_e) _o->name = _e->str(); }
}
-inline flatbuffers::Offset<SubGraph> SubGraph::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SubGraph> SubGraph::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSubGraph(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SubGraph> CreateSubGraph(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SubGraph> CreateSubGraph(::flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SubGraphT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
- auto _tensors = _o->tensors.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::Tensor>> (_o->tensors.size(), [](size_t i, _VectorArgs *__va) { return CreateTensor(*__va->__fbb, __va->__o->tensors[i].get(), __va->__rehasher); }, &_va ) : 0;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SubGraphT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _tensors = _o->tensors.size() ? _fbb.CreateVector<::flatbuffers::Offset<tflite::Tensor>> (_o->tensors.size(), [](size_t i, _VectorArgs *__va) { return CreateTensor(*__va->__fbb, __va->__o->tensors[i].get(), __va->__rehasher); }, &_va ) : 0;
auto _inputs = _o->inputs.size() ? _fbb.CreateVector(_o->inputs) : 0;
auto _outputs = _o->outputs.size() ? _fbb.CreateVector(_o->outputs) : 0;
- auto _operators = _o->operators.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::Operator>> (_o->operators.size(), [](size_t i, _VectorArgs *__va) { return CreateOperator(*__va->__fbb, __va->__o->operators[i].get(), __va->__rehasher); }, &_va ) : 0;
+ auto _operators = _o->operators.size() ? _fbb.CreateVector<::flatbuffers::Offset<tflite::Operator>> (_o->operators.size(), [](size_t i, _VectorArgs *__va) { return CreateOperator(*__va->__fbb, __va->__o->operators[i].get(), __va->__rehasher); }, &_va ) : 0;
auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name);
return tflite::CreateSubGraph(
_fbb,
@@ -17430,13 +21201,13 @@
_name);
}
-inline BufferT *Buffer::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline BufferT *Buffer::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<BufferT>(new BufferT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void Buffer::UnPackTo(BufferT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void Buffer::UnPackTo(BufferT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = data(); if (_e) { _o->data.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->data.begin()); } }
@@ -17444,14 +21215,14 @@
{ auto _e = size(); _o->size = _e; }
}
-inline flatbuffers::Offset<Buffer> Buffer::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BufferT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Buffer> Buffer::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const BufferT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateBuffer(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<Buffer> CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Buffer> CreateBuffer(::flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BufferT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const BufferT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
_fbb.ForceVectorAlignment(_o->data.size(), sizeof(uint8_t), 16);
auto _data = _o->data.size() ? _fbb.CreateVector(_o->data) : 0;
auto _offset = _o->offset;
@@ -17463,27 +21234,27 @@
_size);
}
-inline MetadataT *Metadata::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline MetadataT *Metadata::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<MetadataT>(new MetadataT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void Metadata::UnPackTo(MetadataT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void Metadata::UnPackTo(MetadataT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = name(); if (_e) _o->name = _e->str(); }
{ auto _e = buffer(); _o->buffer = _e; }
}
-inline flatbuffers::Offset<Metadata> Metadata::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Metadata> Metadata::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const MetadataT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateMetadata(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<Metadata> CreateMetadata(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Metadata> CreateMetadata(::flatbuffers::FlatBufferBuilder &_fbb, const MetadataT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MetadataT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const MetadataT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name);
auto _buffer = _o->buffer;
return tflite::CreateMetadata(
@@ -17492,27 +21263,27 @@
_buffer);
}
-inline TensorMapT *TensorMap::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline TensorMapT *TensorMap::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<TensorMapT>(new TensorMapT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void TensorMap::UnPackTo(TensorMapT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void TensorMap::UnPackTo(TensorMapT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = name(); if (_e) _o->name = _e->str(); }
{ auto _e = tensor_index(); _o->tensor_index = _e; }
}
-inline flatbuffers::Offset<TensorMap> TensorMap::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<TensorMap> TensorMap::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateTensorMap(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<TensorMap> CreateTensorMap(flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<TensorMap> CreateTensorMap(::flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TensorMapT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const TensorMapT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name);
auto _tensor_index = _o->tensor_index;
return tflite::CreateTensorMap(
@@ -17538,31 +21309,31 @@
return *this;
}
-inline SignatureDefT *SignatureDef::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline SignatureDefT *SignatureDef::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<SignatureDefT>(new SignatureDefT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void SignatureDef::UnPackTo(SignatureDefT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void SignatureDef::UnPackTo(SignatureDefT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
- { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->inputs[_i]) { _e->Get(_i)->UnPackTo(_o->inputs[_i].get(), _resolver); } else { _o->inputs[_i] = std::unique_ptr<tflite::TensorMapT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
- { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->outputs[_i]) { _e->Get(_i)->UnPackTo(_o->outputs[_i].get(), _resolver); } else { _o->outputs[_i] = std::unique_ptr<tflite::TensorMapT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
+ { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->inputs[_i]) { _e->Get(_i)->UnPackTo(_o->inputs[_i].get(), _resolver); } else { _o->inputs[_i] = std::unique_ptr<tflite::TensorMapT>(_e->Get(_i)->UnPack(_resolver)); }; } } else { _o->inputs.resize(0); } }
+ { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->outputs[_i]) { _e->Get(_i)->UnPackTo(_o->outputs[_i].get(), _resolver); } else { _o->outputs[_i] = std::unique_ptr<tflite::TensorMapT>(_e->Get(_i)->UnPack(_resolver)); }; } } else { _o->outputs.resize(0); } }
{ auto _e = signature_key(); if (_e) _o->signature_key = _e->str(); }
{ auto _e = subgraph_index(); _o->subgraph_index = _e; }
}
-inline flatbuffers::Offset<SignatureDef> SignatureDef::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SignatureDef> SignatureDef::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateSignatureDef(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<SignatureDef> CreateSignatureDef(flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<SignatureDef> CreateSignatureDef(::flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SignatureDefT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
- auto _inputs = _o->inputs.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::TensorMap>> (_o->inputs.size(), [](size_t i, _VectorArgs *__va) { return CreateTensorMap(*__va->__fbb, __va->__o->inputs[i].get(), __va->__rehasher); }, &_va ) : 0;
- auto _outputs = _o->outputs.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::TensorMap>> (_o->outputs.size(), [](size_t i, _VectorArgs *__va) { return CreateTensorMap(*__va->__fbb, __va->__o->outputs[i].get(), __va->__rehasher); }, &_va ) : 0;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const SignatureDefT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _inputs = _o->inputs.size() ? _fbb.CreateVector<::flatbuffers::Offset<tflite::TensorMap>> (_o->inputs.size(), [](size_t i, _VectorArgs *__va) { return CreateTensorMap(*__va->__fbb, __va->__o->inputs[i].get(), __va->__rehasher); }, &_va ) : 0;
+ auto _outputs = _o->outputs.size() ? _fbb.CreateVector<::flatbuffers::Offset<tflite::TensorMap>> (_o->outputs.size(), [](size_t i, _VectorArgs *__va) { return CreateTensorMap(*__va->__fbb, __va->__o->outputs[i].get(), __va->__rehasher); }, &_va ) : 0;
auto _signature_key = _o->signature_key.empty() ? 0 : _fbb.CreateString(_o->signature_key);
auto _subgraph_index = _o->subgraph_index;
return tflite::CreateSignatureDef(
@@ -17601,41 +21372,41 @@
return *this;
}
-inline ModelT *Model::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+inline ModelT *Model::UnPack(const ::flatbuffers::resolver_function_t *_resolver) const {
auto _o = std::unique_ptr<ModelT>(new ModelT());
UnPackTo(_o.get(), _resolver);
return _o.release();
}
-inline void Model::UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+inline void Model::UnPackTo(ModelT *_o, const ::flatbuffers::resolver_function_t *_resolver) const {
(void)_o;
(void)_resolver;
{ auto _e = version(); _o->version = _e; }
- { auto _e = operator_codes(); if (_e) { _o->operator_codes.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->operator_codes[_i]) { _e->Get(_i)->UnPackTo(_o->operator_codes[_i].get(), _resolver); } else { _o->operator_codes[_i] = std::unique_ptr<tflite::OperatorCodeT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
- { auto _e = subgraphs(); if (_e) { _o->subgraphs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->subgraphs[_i]) { _e->Get(_i)->UnPackTo(_o->subgraphs[_i].get(), _resolver); } else { _o->subgraphs[_i] = std::unique_ptr<tflite::SubGraphT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
+ { auto _e = operator_codes(); if (_e) { _o->operator_codes.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->operator_codes[_i]) { _e->Get(_i)->UnPackTo(_o->operator_codes[_i].get(), _resolver); } else { _o->operator_codes[_i] = std::unique_ptr<tflite::OperatorCodeT>(_e->Get(_i)->UnPack(_resolver)); }; } } else { _o->operator_codes.resize(0); } }
+ { auto _e = subgraphs(); if (_e) { _o->subgraphs.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->subgraphs[_i]) { _e->Get(_i)->UnPackTo(_o->subgraphs[_i].get(), _resolver); } else { _o->subgraphs[_i] = std::unique_ptr<tflite::SubGraphT>(_e->Get(_i)->UnPack(_resolver)); }; } } else { _o->subgraphs.resize(0); } }
{ auto _e = description(); if (_e) _o->description = _e->str(); }
- { auto _e = buffers(); if (_e) { _o->buffers.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->buffers[_i]) { _e->Get(_i)->UnPackTo(_o->buffers[_i].get(), _resolver); } else { _o->buffers[_i] = std::unique_ptr<tflite::BufferT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
- { auto _e = metadata_buffer(); if (_e) { _o->metadata_buffer.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->metadata_buffer[_i] = _e->Get(_i); } } }
- { auto _e = metadata(); if (_e) { _o->metadata.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->metadata[_i]) { _e->Get(_i)->UnPackTo(_o->metadata[_i].get(), _resolver); } else { _o->metadata[_i] = std::unique_ptr<tflite::MetadataT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
- { auto _e = signature_defs(); if (_e) { _o->signature_defs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->signature_defs[_i]) { _e->Get(_i)->UnPackTo(_o->signature_defs[_i].get(), _resolver); } else { _o->signature_defs[_i] = std::unique_ptr<tflite::SignatureDefT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
+ { auto _e = buffers(); if (_e) { _o->buffers.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->buffers[_i]) { _e->Get(_i)->UnPackTo(_o->buffers[_i].get(), _resolver); } else { _o->buffers[_i] = std::unique_ptr<tflite::BufferT>(_e->Get(_i)->UnPack(_resolver)); }; } } else { _o->buffers.resize(0); } }
+ { auto _e = metadata_buffer(); if (_e) { _o->metadata_buffer.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->metadata_buffer[_i] = _e->Get(_i); } } else { _o->metadata_buffer.resize(0); } }
+ { auto _e = metadata(); if (_e) { _o->metadata.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->metadata[_i]) { _e->Get(_i)->UnPackTo(_o->metadata[_i].get(), _resolver); } else { _o->metadata[_i] = std::unique_ptr<tflite::MetadataT>(_e->Get(_i)->UnPack(_resolver)); }; } } else { _o->metadata.resize(0); } }
+ { auto _e = signature_defs(); if (_e) { _o->signature_defs.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->signature_defs[_i]) { _e->Get(_i)->UnPackTo(_o->signature_defs[_i].get(), _resolver); } else { _o->signature_defs[_i] = std::unique_ptr<tflite::SignatureDefT>(_e->Get(_i)->UnPack(_resolver)); }; } } else { _o->signature_defs.resize(0); } }
}
-inline flatbuffers::Offset<Model> Model::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Model> Model::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const ::flatbuffers::rehasher_function_t *_rehasher) {
return CreateModel(_fbb, _o, _rehasher);
}
-inline flatbuffers::Offset<Model> CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+inline ::flatbuffers::Offset<Model> CreateModel(::flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const ::flatbuffers::rehasher_function_t *_rehasher) {
(void)_rehasher;
(void)_o;
- struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ModelT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ struct _VectorArgs { ::flatbuffers::FlatBufferBuilder *__fbb; const ModelT* __o; const ::flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
auto _version = _o->version;
- auto _operator_codes = _o->operator_codes.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::OperatorCode>> (_o->operator_codes.size(), [](size_t i, _VectorArgs *__va) { return CreateOperatorCode(*__va->__fbb, __va->__o->operator_codes[i].get(), __va->__rehasher); }, &_va ) : 0;
- auto _subgraphs = _o->subgraphs.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::SubGraph>> (_o->subgraphs.size(), [](size_t i, _VectorArgs *__va) { return CreateSubGraph(*__va->__fbb, __va->__o->subgraphs[i].get(), __va->__rehasher); }, &_va ) : 0;
+ auto _operator_codes = _o->operator_codes.size() ? _fbb.CreateVector<::flatbuffers::Offset<tflite::OperatorCode>> (_o->operator_codes.size(), [](size_t i, _VectorArgs *__va) { return CreateOperatorCode(*__va->__fbb, __va->__o->operator_codes[i].get(), __va->__rehasher); }, &_va ) : 0;
+ auto _subgraphs = _o->subgraphs.size() ? _fbb.CreateVector<::flatbuffers::Offset<tflite::SubGraph>> (_o->subgraphs.size(), [](size_t i, _VectorArgs *__va) { return CreateSubGraph(*__va->__fbb, __va->__o->subgraphs[i].get(), __va->__rehasher); }, &_va ) : 0;
auto _description = _o->description.empty() ? 0 : _fbb.CreateString(_o->description);
- auto _buffers = _o->buffers.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::Buffer>> (_o->buffers.size(), [](size_t i, _VectorArgs *__va) { return CreateBuffer(*__va->__fbb, __va->__o->buffers[i].get(), __va->__rehasher); }, &_va ) : 0;
+ auto _buffers = _o->buffers.size() ? _fbb.CreateVector<::flatbuffers::Offset<tflite::Buffer>> (_o->buffers.size(), [](size_t i, _VectorArgs *__va) { return CreateBuffer(*__va->__fbb, __va->__o->buffers[i].get(), __va->__rehasher); }, &_va ) : 0;
auto _metadata_buffer = _o->metadata_buffer.size() ? _fbb.CreateVector(_o->metadata_buffer) : 0;
- auto _metadata = _o->metadata.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::Metadata>> (_o->metadata.size(), [](size_t i, _VectorArgs *__va) { return CreateMetadata(*__va->__fbb, __va->__o->metadata[i].get(), __va->__rehasher); }, &_va ) : 0;
- auto _signature_defs = _o->signature_defs.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::SignatureDef>> (_o->signature_defs.size(), [](size_t i, _VectorArgs *__va) { return CreateSignatureDef(*__va->__fbb, __va->__o->signature_defs[i].get(), __va->__rehasher); }, &_va ) : 0;
+ auto _metadata = _o->metadata.size() ? _fbb.CreateVector<::flatbuffers::Offset<tflite::Metadata>> (_o->metadata.size(), [](size_t i, _VectorArgs *__va) { return CreateMetadata(*__va->__fbb, __va->__o->metadata[i].get(), __va->__rehasher); }, &_va ) : 0;
+ auto _signature_defs = _o->signature_defs.size() ? _fbb.CreateVector<::flatbuffers::Offset<tflite::SignatureDef>> (_o->signature_defs.size(), [](size_t i, _VectorArgs *__va) { return CreateSignatureDef(*__va->__fbb, __va->__o->signature_defs[i].get(), __va->__rehasher); }, &_va ) : 0;
return tflite::CreateModel(
_fbb,
_version,
@@ -17648,7 +21419,7 @@
_signature_defs);
}
-inline bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj, QuantizationDetails type) {
+inline bool VerifyQuantizationDetails(::flatbuffers::Verifier &verifier, const void *obj, QuantizationDetails type) {
switch (type) {
case QuantizationDetails_NONE: {
return true;
@@ -17661,10 +21432,10 @@
}
}
-inline bool VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector<flatbuffers::Offset<void>> *values, const flatbuffers::Vector<uint8_t> *types) {
+inline bool VerifyQuantizationDetailsVector(::flatbuffers::Verifier &verifier, const ::flatbuffers::Vector<::flatbuffers::Offset<void>> *values, const ::flatbuffers::Vector<uint8_t> *types) {
if (!values || !types) return !values && !types;
if (values->size() != types->size()) return false;
- for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) {
+ for (::flatbuffers::uoffset_t i = 0; i < values->size(); ++i) {
if (!VerifyQuantizationDetails(
verifier, values->Get(i), types->GetEnum<QuantizationDetails>(i))) {
return false;
@@ -17673,7 +21444,7 @@
return true;
}
-inline void *QuantizationDetailsUnion::UnPack(const void *obj, QuantizationDetails type, const flatbuffers::resolver_function_t *resolver) {
+inline void *QuantizationDetailsUnion::UnPack(const void *obj, QuantizationDetails type, const ::flatbuffers::resolver_function_t *resolver) {
(void)resolver;
switch (type) {
case QuantizationDetails_CustomQuantization: {
@@ -17684,7 +21455,7 @@
}
}
-inline flatbuffers::Offset<void> QuantizationDetailsUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const {
+inline ::flatbuffers::Offset<void> QuantizationDetailsUnion::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ::flatbuffers::rehasher_function_t *_rehasher) const {
(void)_rehasher;
switch (type) {
case QuantizationDetails_CustomQuantization: {
@@ -17719,7 +21490,7 @@
type = QuantizationDetails_NONE;
}
-inline bool VerifySparseIndexVector(flatbuffers::Verifier &verifier, const void *obj, SparseIndexVector type) {
+inline bool VerifySparseIndexVector(::flatbuffers::Verifier &verifier, const void *obj, SparseIndexVector type) {
switch (type) {
case SparseIndexVector_NONE: {
return true;
@@ -17740,10 +21511,10 @@
}
}
-inline bool VerifySparseIndexVectorVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector<flatbuffers::Offset<void>> *values, const flatbuffers::Vector<uint8_t> *types) {
+inline bool VerifySparseIndexVectorVector(::flatbuffers::Verifier &verifier, const ::flatbuffers::Vector<::flatbuffers::Offset<void>> *values, const ::flatbuffers::Vector<uint8_t> *types) {
if (!values || !types) return !values && !types;
if (values->size() != types->size()) return false;
- for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) {
+ for (::flatbuffers::uoffset_t i = 0; i < values->size(); ++i) {
if (!VerifySparseIndexVector(
verifier, values->Get(i), types->GetEnum<SparseIndexVector>(i))) {
return false;
@@ -17752,7 +21523,7 @@
return true;
}
-inline void *SparseIndexVectorUnion::UnPack(const void *obj, SparseIndexVector type, const flatbuffers::resolver_function_t *resolver) {
+inline void *SparseIndexVectorUnion::UnPack(const void *obj, SparseIndexVector type, const ::flatbuffers::resolver_function_t *resolver) {
(void)resolver;
switch (type) {
case SparseIndexVector_Int32Vector: {
@@ -17771,7 +21542,7 @@
}
}
-inline flatbuffers::Offset<void> SparseIndexVectorUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const {
+inline ::flatbuffers::Offset<void> SparseIndexVectorUnion::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ::flatbuffers::rehasher_function_t *_rehasher) const {
(void)_rehasher;
switch (type) {
case SparseIndexVector_Int32Vector: {
@@ -17832,7 +21603,7 @@
type = SparseIndexVector_NONE;
}
-inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type) {
+inline bool VerifyBuiltinOptions(::flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type) {
switch (type) {
case BuiltinOptions_NONE: {
return true;
@@ -18345,10 +22116,10 @@
}
}
-inline bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector<flatbuffers::Offset<void>> *values, const flatbuffers::Vector<uint8_t> *types) {
+inline bool VerifyBuiltinOptionsVector(::flatbuffers::Verifier &verifier, const ::flatbuffers::Vector<::flatbuffers::Offset<void>> *values, const ::flatbuffers::Vector<uint8_t> *types) {
if (!values || !types) return !values && !types;
if (values->size() != types->size()) return false;
- for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) {
+ for (::flatbuffers::uoffset_t i = 0; i < values->size(); ++i) {
if (!VerifyBuiltinOptions(
verifier, values->Get(i), types->GetEnum<BuiltinOptions>(i))) {
return false;
@@ -18357,7 +22128,7 @@
return true;
}
-inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, const flatbuffers::resolver_function_t *resolver) {
+inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, const ::flatbuffers::resolver_function_t *resolver) {
(void)resolver;
switch (type) {
case BuiltinOptions_Conv2DOptions: {
@@ -18868,7 +22639,7 @@
}
}
-inline flatbuffers::Offset<void> BuiltinOptionsUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const {
+inline ::flatbuffers::Offset<void> BuiltinOptionsUnion::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ::flatbuffers::rehasher_function_t *_rehasher) const {
(void)_rehasher;
switch (type) {
case BuiltinOptions_Conv2DOptions: {
@@ -20528,12 +24299,503 @@
type = BuiltinOptions_NONE;
}
+inline bool VerifyBuiltinOptions2(::flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions2 type) {
+ switch (type) {
+ case BuiltinOptions2_NONE: {
+ return true;
+ }
+ case BuiltinOptions2_StablehloConcatenateOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloConcatenateOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StablehloBroadcastInDimOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloBroadcastInDimOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StablehloSliceOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloSliceOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StablehloConvolutionOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloConvolutionOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StablehloCustomCallOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloCustomCallOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StablehloReduceOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloReduceOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StablehloScatterOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloScatterOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StablehloCompareOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloCompareOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StablehloDynamicSliceOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloDynamicSliceOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StablehloPadOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloPadOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StablehloIotaOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloIotaOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StablehloDotGeneralOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloDotGeneralOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StablehloReduceWindowOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloReduceWindowOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StablehloSortOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloSortOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StablehloWhileOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloWhileOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StablehloGatherOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloGatherOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StablehloTransposeOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloTransposeOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_DilateOptions: {
+ auto ptr = reinterpret_cast<const tflite::DilateOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StablehloRngBitGeneratorOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloRngBitGeneratorOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_ReduceWindowOptions: {
+ auto ptr = reinterpret_cast<const tflite::ReduceWindowOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ case BuiltinOptions2_StableHLOCompositeOptions: {
+ auto ptr = reinterpret_cast<const tflite::StableHLOCompositeOptions *>(obj);
+ return verifier.VerifyTable(ptr);
+ }
+ default: return true;
+ }
+}
+
+inline bool VerifyBuiltinOptions2Vector(::flatbuffers::Verifier &verifier, const ::flatbuffers::Vector<::flatbuffers::Offset<void>> *values, const ::flatbuffers::Vector<uint8_t> *types) {
+ if (!values || !types) return !values && !types;
+ if (values->size() != types->size()) return false;
+ for (::flatbuffers::uoffset_t i = 0; i < values->size(); ++i) {
+ if (!VerifyBuiltinOptions2(
+ verifier, values->Get(i), types->GetEnum<BuiltinOptions2>(i))) {
+ return false;
+ }
+ }
+ return true;
+}
+
+inline void *BuiltinOptions2Union::UnPack(const void *obj, BuiltinOptions2 type, const ::flatbuffers::resolver_function_t *resolver) {
+ (void)resolver;
+ switch (type) {
+ case BuiltinOptions2_StablehloConcatenateOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloConcatenateOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StablehloBroadcastInDimOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloBroadcastInDimOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StablehloSliceOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloSliceOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StablehloConvolutionOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloConvolutionOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StablehloCustomCallOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloCustomCallOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StablehloReduceOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloReduceOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StablehloScatterOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloScatterOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StablehloCompareOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloCompareOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StablehloDynamicSliceOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloDynamicSliceOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StablehloPadOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloPadOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StablehloIotaOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloIotaOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StablehloDotGeneralOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloDotGeneralOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StablehloReduceWindowOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloReduceWindowOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StablehloSortOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloSortOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StablehloWhileOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloWhileOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StablehloGatherOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloGatherOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StablehloTransposeOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloTransposeOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_DilateOptions: {
+ auto ptr = reinterpret_cast<const tflite::DilateOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StablehloRngBitGeneratorOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloRngBitGeneratorOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_ReduceWindowOptions: {
+ auto ptr = reinterpret_cast<const tflite::ReduceWindowOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ case BuiltinOptions2_StableHLOCompositeOptions: {
+ auto ptr = reinterpret_cast<const tflite::StableHLOCompositeOptions *>(obj);
+ return ptr->UnPack(resolver);
+ }
+ default: return nullptr;
+ }
+}
+
+inline ::flatbuffers::Offset<void> BuiltinOptions2Union::Pack(::flatbuffers::FlatBufferBuilder &_fbb, const ::flatbuffers::rehasher_function_t *_rehasher) const {
+ (void)_rehasher;
+ switch (type) {
+ case BuiltinOptions2_StablehloConcatenateOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloConcatenateOptionsT *>(value);
+ return CreateStablehloConcatenateOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StablehloBroadcastInDimOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloBroadcastInDimOptionsT *>(value);
+ return CreateStablehloBroadcastInDimOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StablehloSliceOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloSliceOptionsT *>(value);
+ return CreateStablehloSliceOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StablehloConvolutionOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloConvolutionOptionsT *>(value);
+ return CreateStablehloConvolutionOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StablehloCustomCallOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloCustomCallOptionsT *>(value);
+ return CreateStablehloCustomCallOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StablehloReduceOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloReduceOptionsT *>(value);
+ return CreateStablehloReduceOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StablehloScatterOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloScatterOptionsT *>(value);
+ return CreateStablehloScatterOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StablehloCompareOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloCompareOptionsT *>(value);
+ return CreateStablehloCompareOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StablehloDynamicSliceOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloDynamicSliceOptionsT *>(value);
+ return CreateStablehloDynamicSliceOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StablehloPadOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloPadOptionsT *>(value);
+ return CreateStablehloPadOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StablehloIotaOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloIotaOptionsT *>(value);
+ return CreateStablehloIotaOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StablehloDotGeneralOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloDotGeneralOptionsT *>(value);
+ return CreateStablehloDotGeneralOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StablehloReduceWindowOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloReduceWindowOptionsT *>(value);
+ return CreateStablehloReduceWindowOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StablehloSortOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloSortOptionsT *>(value);
+ return CreateStablehloSortOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StablehloWhileOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloWhileOptionsT *>(value);
+ return CreateStablehloWhileOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StablehloGatherOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloGatherOptionsT *>(value);
+ return CreateStablehloGatherOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StablehloTransposeOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloTransposeOptionsT *>(value);
+ return CreateStablehloTransposeOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_DilateOptions: {
+ auto ptr = reinterpret_cast<const tflite::DilateOptionsT *>(value);
+ return CreateDilateOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StablehloRngBitGeneratorOptions: {
+ auto ptr = reinterpret_cast<const tflite::StablehloRngBitGeneratorOptionsT *>(value);
+ return CreateStablehloRngBitGeneratorOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_ReduceWindowOptions: {
+ auto ptr = reinterpret_cast<const tflite::ReduceWindowOptionsT *>(value);
+ return CreateReduceWindowOptions(_fbb, ptr, _rehasher).Union();
+ }
+ case BuiltinOptions2_StableHLOCompositeOptions: {
+ auto ptr = reinterpret_cast<const tflite::StableHLOCompositeOptionsT *>(value);
+ return CreateStableHLOCompositeOptions(_fbb, ptr, _rehasher).Union();
+ }
+ default: return 0;
+ }
+}
+
+inline BuiltinOptions2Union::BuiltinOptions2Union(const BuiltinOptions2Union &u) : type(u.type), value(nullptr) {
+ switch (type) {
+ case BuiltinOptions2_StablehloConcatenateOptions: {
+ value = new tflite::StablehloConcatenateOptionsT(*reinterpret_cast<tflite::StablehloConcatenateOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StablehloBroadcastInDimOptions: {
+ value = new tflite::StablehloBroadcastInDimOptionsT(*reinterpret_cast<tflite::StablehloBroadcastInDimOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StablehloSliceOptions: {
+ value = new tflite::StablehloSliceOptionsT(*reinterpret_cast<tflite::StablehloSliceOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StablehloConvolutionOptions: {
+ value = new tflite::StablehloConvolutionOptionsT(*reinterpret_cast<tflite::StablehloConvolutionOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StablehloCustomCallOptions: {
+ value = new tflite::StablehloCustomCallOptionsT(*reinterpret_cast<tflite::StablehloCustomCallOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StablehloReduceOptions: {
+ value = new tflite::StablehloReduceOptionsT(*reinterpret_cast<tflite::StablehloReduceOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StablehloScatterOptions: {
+ value = new tflite::StablehloScatterOptionsT(*reinterpret_cast<tflite::StablehloScatterOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StablehloCompareOptions: {
+ value = new tflite::StablehloCompareOptionsT(*reinterpret_cast<tflite::StablehloCompareOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StablehloDynamicSliceOptions: {
+ value = new tflite::StablehloDynamicSliceOptionsT(*reinterpret_cast<tflite::StablehloDynamicSliceOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StablehloPadOptions: {
+ value = new tflite::StablehloPadOptionsT(*reinterpret_cast<tflite::StablehloPadOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StablehloIotaOptions: {
+ value = new tflite::StablehloIotaOptionsT(*reinterpret_cast<tflite::StablehloIotaOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StablehloDotGeneralOptions: {
+ value = new tflite::StablehloDotGeneralOptionsT(*reinterpret_cast<tflite::StablehloDotGeneralOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StablehloReduceWindowOptions: {
+ value = new tflite::StablehloReduceWindowOptionsT(*reinterpret_cast<tflite::StablehloReduceWindowOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StablehloSortOptions: {
+ value = new tflite::StablehloSortOptionsT(*reinterpret_cast<tflite::StablehloSortOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StablehloWhileOptions: {
+ value = new tflite::StablehloWhileOptionsT(*reinterpret_cast<tflite::StablehloWhileOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StablehloGatherOptions: {
+ value = new tflite::StablehloGatherOptionsT(*reinterpret_cast<tflite::StablehloGatherOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StablehloTransposeOptions: {
+ value = new tflite::StablehloTransposeOptionsT(*reinterpret_cast<tflite::StablehloTransposeOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_DilateOptions: {
+ value = new tflite::DilateOptionsT(*reinterpret_cast<tflite::DilateOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StablehloRngBitGeneratorOptions: {
+ value = new tflite::StablehloRngBitGeneratorOptionsT(*reinterpret_cast<tflite::StablehloRngBitGeneratorOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_ReduceWindowOptions: {
+ value = new tflite::ReduceWindowOptionsT(*reinterpret_cast<tflite::ReduceWindowOptionsT *>(u.value));
+ break;
+ }
+ case BuiltinOptions2_StableHLOCompositeOptions: {
+ value = new tflite::StableHLOCompositeOptionsT(*reinterpret_cast<tflite::StableHLOCompositeOptionsT *>(u.value));
+ break;
+ }
+ default:
+ break;
+ }
+}
+
+inline void BuiltinOptions2Union::Reset() {
+ switch (type) {
+ case BuiltinOptions2_StablehloConcatenateOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloConcatenateOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StablehloBroadcastInDimOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloBroadcastInDimOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StablehloSliceOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloSliceOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StablehloConvolutionOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloConvolutionOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StablehloCustomCallOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloCustomCallOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StablehloReduceOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloReduceOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StablehloScatterOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloScatterOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StablehloCompareOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloCompareOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StablehloDynamicSliceOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloDynamicSliceOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StablehloPadOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloPadOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StablehloIotaOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloIotaOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StablehloDotGeneralOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloDotGeneralOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StablehloReduceWindowOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloReduceWindowOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StablehloSortOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloSortOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StablehloWhileOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloWhileOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StablehloGatherOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloGatherOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StablehloTransposeOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloTransposeOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_DilateOptions: {
+ auto ptr = reinterpret_cast<tflite::DilateOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StablehloRngBitGeneratorOptions: {
+ auto ptr = reinterpret_cast<tflite::StablehloRngBitGeneratorOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_ReduceWindowOptions: {
+ auto ptr = reinterpret_cast<tflite::ReduceWindowOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ case BuiltinOptions2_StableHLOCompositeOptions: {
+ auto ptr = reinterpret_cast<tflite::StableHLOCompositeOptionsT *>(value);
+ delete ptr;
+ break;
+ }
+ default: break;
+ }
+ value = nullptr;
+ type = BuiltinOptions2_NONE;
+}
+
inline const tflite::Model *GetModel(const void *buf) {
- return flatbuffers::GetRoot<tflite::Model>(buf);
+ return ::flatbuffers::GetRoot<tflite::Model>(buf);
}
inline const tflite::Model *GetSizePrefixedModel(const void *buf) {
- return flatbuffers::GetSizePrefixedRoot<tflite::Model>(buf);
+ return ::flatbuffers::GetSizePrefixedRoot<tflite::Model>(buf);
}
inline const char *ModelIdentifier() {
@@ -20541,22 +24803,22 @@
}
inline bool ModelBufferHasIdentifier(const void *buf) {
- return flatbuffers::BufferHasIdentifier(
+ return ::flatbuffers::BufferHasIdentifier(
buf, ModelIdentifier());
}
inline bool SizePrefixedModelBufferHasIdentifier(const void *buf) {
- return flatbuffers::BufferHasIdentifier(
+ return ::flatbuffers::BufferHasIdentifier(
buf, ModelIdentifier(), true);
}
inline bool VerifyModelBuffer(
- flatbuffers::Verifier &verifier) {
+ ::flatbuffers::Verifier &verifier) {
return verifier.VerifyBuffer<tflite::Model>(ModelIdentifier());
}
inline bool VerifySizePrefixedModelBuffer(
- flatbuffers::Verifier &verifier) {
+ ::flatbuffers::Verifier &verifier) {
return verifier.VerifySizePrefixedBuffer<tflite::Model>(ModelIdentifier());
}
@@ -20565,26 +24827,26 @@
}
inline void FinishModelBuffer(
- flatbuffers::FlatBufferBuilder &fbb,
- flatbuffers::Offset<tflite::Model> root) {
+ ::flatbuffers::FlatBufferBuilder &fbb,
+ ::flatbuffers::Offset<tflite::Model> root) {
fbb.Finish(root, ModelIdentifier());
}
inline void FinishSizePrefixedModelBuffer(
- flatbuffers::FlatBufferBuilder &fbb,
- flatbuffers::Offset<tflite::Model> root) {
+ ::flatbuffers::FlatBufferBuilder &fbb,
+ ::flatbuffers::Offset<tflite::Model> root) {
fbb.FinishSizePrefixed(root, ModelIdentifier());
}
inline std::unique_ptr<tflite::ModelT> UnPackModel(
const void *buf,
- const flatbuffers::resolver_function_t *res = nullptr) {
+ const ::flatbuffers::resolver_function_t *res = nullptr) {
return std::unique_ptr<tflite::ModelT>(GetModel(buf)->UnPack(res));
}
inline std::unique_ptr<tflite::ModelT> UnPackSizePrefixedModel(
const void *buf,
- const flatbuffers::resolver_function_t *res = nullptr) {
+ const ::flatbuffers::resolver_function_t *res = nullptr) {
return std::unique_ptr<tflite::ModelT>(GetSizePrefixedModel(buf)->UnPack(res));
}
diff --git a/tensorflow/lite/schema/schema_utils.h b/tensorflow/lite/schema/schema_utils.h
index 9cca36c..ff04cf1 100644
--- a/tensorflow/lite/schema/schema_utils.h
+++ b/tensorflow/lite/schema/schema_utils.h
@@ -15,19 +15,6 @@
#ifndef TENSORFLOW_LITE_SCHEMA_SCHEMA_UTILS_H_
#define TENSORFLOW_LITE_SCHEMA_SCHEMA_UTILS_H_
-#include "flatbuffers/flatbuffers.h"
-#include "tensorflow/lite/schema/schema_generated.h"
-
-namespace tflite {
-
-// The following methods are introduced to resolve op builtin code shortage
-// problem. The new builtin operator will be assigned to the extended builtin
-// code field in the flatbuffer schema. Those methods helps to hide builtin code
-// details.
-BuiltinOperator GetBuiltinCode(const OperatorCode *op_code);
-
-BuiltinOperator GetBuiltinCode(const OperatorCodeT *op_code);
-
-} // namespace tflite
+#include "tensorflow/compiler/mlir/lite/schema/schema_utils.h" // IWYU pragma: keep
#endif // TENSORFLOW_LITE_SCHEMA_SCHEMA_UTILS_H_
diff --git a/tensorflow/lite/tools/BUILD b/tensorflow/lite/tools/BUILD
index b5073c9..e7d5114 100644
--- a/tensorflow/lite/tools/BUILD
+++ b/tensorflow/lite/tools/BUILD
@@ -7,7 +7,7 @@
visibility = ["//:__subpackages__"],
deps = [
"@flatbuffers//:runtime_py",
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
"//tensorflow/lite/python:schema_py",
"//tensorflow/lite/python:schema_util",
],
@@ -19,7 +19,7 @@
srcs_version = "PY3",
deps = [
"@flatbuffers//:runtime_py",
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
"//tensorflow/lite/python:schema_py",
],
)
@@ -56,7 +56,7 @@
deps = [
":flatbuffer_utils",
":test_utils",
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
],
)
@@ -68,6 +68,6 @@
deps = [
":test_utils",
":visualize",
- requirement("tensorflow-cpu"),
+ requirement("tensorflow"),
],
)
diff --git a/tensorflow/lite/tools/flatbuffer_utils.py b/tensorflow/lite/tools/flatbuffer_utils.py
index f448bfa..a7d1dd1 100644
--- a/tensorflow/lite/tools/flatbuffer_utils.py
+++ b/tensorflow/lite/tools/flatbuffer_utils.py
@@ -58,9 +58,38 @@
raise RuntimeError('Input file not found at %r\n' % input_tflite_file)
with gfile.GFile(input_tflite_file, 'rb') as input_file_handle:
model_bytearray = bytearray(input_file_handle.read())
+ return read_model_from_bytearray(model_bytearray)
+
+
+def read_model_from_bytearray(model_bytearray):
+ """Reads a tflite model as a python object.
+
+ Args:
+ model_bytearray: TFLite model in bytearray format.
+
+ Returns:
+ A python object corresponding to the input tflite file.
+ """
model = convert_bytearray_to_object(model_bytearray)
if sys.byteorder == 'big':
byte_swap_tflite_model_obj(model, 'little', 'big')
+
+ # Offset handling for models > 2GB
+ for buffer in model.buffers:
+ if buffer.offset:
+ buffer.data = model_bytearray[buffer.offset : buffer.offset + buffer.size]
+ buffer.offset = 0
+ buffer.size = 0
+ for subgraph in model.subgraphs:
+ for op in subgraph.operators:
+ if op.largeCustomOptionsOffset:
+ op.customOptions = model_bytearray[
+ op.largeCustomOptionsOffset : op.largeCustomOptionsOffset
+ + op.largeCustomOptionsSize
+ ]
+ op.largeCustomOptionsOffset = 0
+ op.largeCustomOptionsSize = 0
+
return model
@@ -294,14 +323,29 @@
buffer.data[i : i + chunksize]
for i in range(0, len(buffer.data), chunksize)
]
- buffer.data = b''.join(
- [
- int.from_bytes(byteswap, from_endiness).to_bytes(
- chunksize, to_endiness
- )
- for byteswap in to_swap
- ]
- )
+ buffer.data = b''.join([
+ int.from_bytes(byteswap, from_endiness).to_bytes(chunksize, to_endiness)
+ for byteswap in to_swap
+ ])
+
+
+def byte_swap_string_content(buffer, from_endiness, to_endiness):
+ """Helper function for byte-swapping the string buffer.
+
+ Args:
+ buffer: TFLite string buffer of from_endiness format.
+ from_endiness: The original endianness format of the string buffer.
+ to_endiness: The destined endianness format of the string buffer.
+ """
+ num_of_strings = int.from_bytes(buffer.data[0:4], from_endiness)
+ string_content = bytearray(buffer.data[4 * (num_of_strings + 2) :])
+ prefix_data = b''.join([
+ int.from_bytes(buffer.data[i : i + 4], from_endiness).to_bytes(
+ 4, to_endiness
+ )
+ for i in range(0, (num_of_strings + 1) * 4 + 1, 4)
+ ])
+ buffer.data = prefix_data + string_content
def byte_swap_tflite_model_obj(model, from_endiness, to_endiness):
@@ -341,7 +385,11 @@
and tensor.buffer not in buffer_swapped
and model.buffers[tensor.buffer].data is not None
):
- if tensor.type in types_of_16_bits:
+ if tensor.type == schema_fb.TensorType.STRING:
+ byte_swap_string_content(
+ model.buffers[tensor.buffer], from_endiness, to_endiness
+ )
+ elif tensor.type in types_of_16_bits:
byte_swap_buffer_content(
model.buffers[tensor.buffer], 2, from_endiness, to_endiness
)
diff --git a/tensorflow/lite/tools/randomize_weights.py b/tensorflow/lite/tools/randomize_weights.py
index 2b36fb1..4baf50d 100644
--- a/tensorflow/lite/tools/randomize_weights.py
+++ b/tensorflow/lite/tools/randomize_weights.py
@@ -30,6 +30,15 @@
'i.e., to be left unmodified.')
flags.DEFINE_multi_string(
'ops_to_skip', [], 'Ops in the TFLite model to be skipped / unmodified.')
+flags.DEFINE_multi_string(
+ 'ops_operands_to_skip',
+ [],
+ 'Op operand indices in the TFLite model to be skipped / unmodified. It'
+ ' should be specified in the format'
+ ' <op_name>:<operand_index>[,<operand_index>]. For example,'
+ ' TRANSPOSE_CONV:0,2 stands for skipping the TRANSPOSE_CONV operands'
+ ' indexed 0 and 2',
+)
flags.DEFINE_integer('random_seed', 0, 'Input to the random number generator.')
flags.mark_flag_as_required('input_tflite_file')
@@ -39,15 +48,31 @@
def main(_):
buffers_to_skip = FLAGS.buffers_to_skip
ops_to_skip = [op.upper() for op in FLAGS.ops_to_skip]
+ ops_operands_to_skip = {}
+ for op_operands_to_skip in FLAGS.ops_operands_to_skip:
+ op_name, indices = op_operands_to_skip.split(':')
+ op_name_upper = op_name.upper()
+ if op_name_upper in ops_operands_to_skip:
+ raise ValueError(
+ 'Indices for the same op must be specified only once multiple'
+ f' specification for op {op_name}.'
+ )
+ ops_operands_to_skip[op_name_upper] = list(map(int, indices.split(',')))
+
model = flatbuffer_utils.read_model(FLAGS.input_tflite_file)
- # Add in buffers for ops in ops_to_skip to the list of skipped buffers.
+ # Add in buffers for ops in ops_to_skip or ops_operands_to_skip to the list of
+ # skipped buffers.
for graph in model.subgraphs:
for op in graph.operators:
op_name = flatbuffer_utils.opcode_to_name(model, op.opcodeIndex)
- if op_name.upper() in ops_to_skip:
+ op_name_upper = op_name.upper()
+ if op_name_upper in ops_to_skip:
for input_idx in op.inputs:
buffers_to_skip.append(graph.tensors[input_idx].buffer)
+ if op_name_upper in ops_operands_to_skip:
+ for operand_idx in ops_operands_to_skip[op_name_upper]:
+ buffers_to_skip.append(graph.tensors[op.inputs[operand_idx]].buffer)
flatbuffer_utils.randomize_weights(model, FLAGS.random_seed,
FLAGS.buffers_to_skip)
diff --git a/third_party/flatbuffers/BUILD.external b/third_party/flatbuffers/BUILD.oss
similarity index 86%
rename from third_party/flatbuffers/BUILD.external
rename to third_party/flatbuffers/BUILD.oss
index dfd3a16..8f9d4eb 100644
--- a/third_party/flatbuffers/BUILD.external
+++ b/third_party/flatbuffers/BUILD.oss
@@ -2,30 +2,23 @@
package(default_visibility = ["//visibility:public"])
-licenses(["notice"])
+licenses(["notice"]) # Apache 2.0
-exports_files(["LICENSE.txt"])
-
-licenses(["notice"])
+exports_files(["LICENSE"])
config_setting(
- name = "freebsd",
+ name = "platform_freebsd",
values = {"cpu": "freebsd"},
)
config_setting(
- name = "windows",
- values = {"cpu": "x64_windows"},
-)
-
-config_setting(
name = "platform_openbsd",
values = {"cpu": "openbsd"},
)
config_setting(
- name = "platform_freebsd",
- values = {"cpu": "freebsd"},
+ name = "windows",
+ values = {"cpu": "x64_windows"},
)
load("@rules_cc//cc:defs.bzl", "cc_binary", "cc_library")
@@ -47,12 +40,13 @@
"include/flatbuffers/allocator.h",
"include/flatbuffers/array.h",
"include/flatbuffers/base.h",
- "include/flatbuffers/bfbs_generator.h",
"include/flatbuffers/buffer.h",
"include/flatbuffers/buffer_ref.h",
+ "include/flatbuffers/code_generator.h",
"include/flatbuffers/code_generators.h",
"include/flatbuffers/default_allocator.h",
"include/flatbuffers/detached_buffer.h",
+ "include/flatbuffers/file_manager.h",
"include/flatbuffers/flatbuffer_builder.h",
"include/flatbuffers/flatbuffers.h",
"include/flatbuffers/flex_flat_util.h",
@@ -73,7 +67,7 @@
"include/flatbuffers/vector_downward.h",
"include/flatbuffers/verifier.h",
],
- visibility = ["//:__subpackages__"],
+ visibility = ["//visibility:public"],
)
# Public flatc compiler library.
@@ -90,9 +84,11 @@
cc_binary(
name = "flatc",
linkopts = select({
- ":freebsd": [
+ ":platform_freebsd": [
"-lm",
],
+ # If Visual Studio 2022 developers facing linking errors,
+ # change the line below as ":windows": ["/DEFAULTLIB:msvcrt.lib"],
":windows": [],
"//conditions:default": [
"-lm",
@@ -110,7 +106,7 @@
srcs = [
"include/flatbuffers/flatc.h",
],
- visibility = ["//:__subpackages__"],
+ visibility = ["//visibility:public"],
)
# Library used by flatbuffer_cc_library rules.
@@ -120,22 +116,13 @@
"include/flatbuffers/allocator.h",
"include/flatbuffers/array.h",
"include/flatbuffers/base.h",
- "include/flatbuffers/bfbs_generator.h",
"include/flatbuffers/buffer.h",
"include/flatbuffers/buffer_ref.h",
- "include/flatbuffers/code_generators.h",
"include/flatbuffers/default_allocator.h",
"include/flatbuffers/detached_buffer.h",
"include/flatbuffers/flatbuffer_builder.h",
"include/flatbuffers/flatbuffers.h",
"include/flatbuffers/flexbuffers.h",
- "include/flatbuffers/grpc.h",
- "include/flatbuffers/hash.h",
- "include/flatbuffers/idl.h",
- "include/flatbuffers/minireflect.h",
- "include/flatbuffers/reflection.h",
- "include/flatbuffers/reflection_generated.h",
- "include/flatbuffers/registry.h",
"include/flatbuffers/stl_emulation.h",
"include/flatbuffers/string.h",
"include/flatbuffers/struct.h",
@@ -187,4 +174,4 @@
name = "runtime_py",
srcs = [":runtime_py_srcs"],
visibility = ["//visibility:public"],
-)
\ No newline at end of file
+)
diff --git a/third_party/flatbuffers/build_defs.bzl b/third_party/flatbuffers/build_defs.bzl
index 1a84928..92c9e2e 100644
--- a/third_party/flatbuffers/build_defs.bzl
+++ b/third_party/flatbuffers/build_defs.bzl
@@ -194,7 +194,7 @@
reflection binaries for the schemas.
'''
output_headers = [
- (out_prefix + "%s_generated.h") % (s.replace(".fbs", "").split("/")[-1])
+ (out_prefix + "%s_generated.h") % (s.replace(".fbs", "").split("/")[-1].split(":")[-1])
for s in srcs
]
reflection_name = "%s_reflection" % name if gen_reflections else ""
@@ -279,6 +279,11 @@
else:
no_includes_statement = []
+ if ctx.attr.language_flag == "--python":
+ onefile_statement = ["--gen-onefile"]
+ else:
+ onefile_statement = []
+
# Need to generate all files in a directory.
if not outputs:
outputs = [ctx.actions.declare_directory("{}_all".format(ctx.attr.name))]
@@ -314,6 +319,7 @@
"-I",
ctx.bin_dir.path,
] + no_includes_statement +
+ onefile_statement +
include_paths_cmd_line + [
"--no-union-value-namespacing",
"--gen-object-api",
@@ -359,7 +365,6 @@
cfg = "exec",
),
},
- output_to_genfiles = True,
)
def flatbuffer_py_strip_prefix_srcs(name, srcs = [], strip_prefix = ""):
@@ -394,6 +399,7 @@
ctx.attr.deps[0].files.to_list()[0].path,
ctx.outputs.out.path,
),
+ use_default_shell_env = True,
)
_concat_flatbuffer_py_srcs = rule(
@@ -401,7 +407,6 @@
attrs = {
"deps": attr.label_list(mandatory = True),
},
- output_to_genfiles = True,
outputs = {"out": "%{name}.py"},
)
@@ -432,6 +437,8 @@
deps = deps,
include_paths = include_paths,
)
+
+ # TODO(b/235550563): Remove the concatnation rule with 2.0.6 update.
all_srcs_no_include = "{}_srcs_no_include".format(name)
_gen_flatbuffer_srcs(
name = all_srcs_no_include,
diff --git a/third_party/flatbuffers/workspace.bzl b/third_party/flatbuffers/workspace.bzl
index e799a70..812ade7 100644
--- a/third_party/flatbuffers/workspace.bzl
+++ b/third_party/flatbuffers/workspace.bzl
@@ -5,12 +5,12 @@
def repo():
tf_http_archive(
name = "flatbuffers",
- strip_prefix = "flatbuffers-a66de58af9565586832c276fbb4251fc416bf07f",
- sha256 = "da06ac2fc6fed8e38b6392f5a20fa24a4290cecaadd87aef16b6b84960408680",
+ strip_prefix = "flatbuffers-23.5.26",
+ sha256 = "1cce06b17cddd896b6d73cc047e36a254fb8df4d7ea18a46acf16c4c0cd3f3f3",
urls = [
- "https://github.com/google/flatbuffers/archive/a66de58af9565586832c276fbb4251fc416bf07f.tar.gz",
+ "https://github.com/google/flatbuffers/archive/v23.5.26.tar.gz",
],
- build_file = "//third_party/flatbuffers:BUILD.external",
+ build_file = "//third_party/flatbuffers:BUILD.oss",
system_build_file = "//third_party/flatbuffers:BUILD.system",
link_files = {
"//third_party/flatbuffers:build_defs.bzl": "build_defs.bzl",
diff --git a/third_party/hexagon/fully_connected.cc b/third_party/hexagon/fully_connected.cc
index c27c238..99ee1f3 100644
--- a/third_party/hexagon/fully_connected.cc
+++ b/third_party/hexagon/fully_connected.cc
@@ -129,4 +129,8 @@
HexagonFullyConnectedEval);
}
+TFLMInferenceRegistration RegisterInference_FULLY_CONNECTED() {
+ return tflite::micro::RegisterOp(HexagonFullyConnectedEval);
+}
+
} // namespace tflite
diff --git a/third_party/python_requirements.in b/third_party/python_requirements.in
index 3614311..29c081e 100644
--- a/third_party/python_requirements.in
+++ b/third_party/python_requirements.in
@@ -26,7 +26,9 @@
# is sensitive to the Python environment (interpreter version, etc.) in which
# it is run.
-tensorflow-cpu
+hexdump
+tensorflow
+twine
numpy
mako
pillow
diff --git a/third_party/python_requirements.txt b/third_party/python_requirements.txt
index d0021b0..9246029 100644
--- a/third_party/python_requirements.txt
+++ b/third_party/python_requirements.txt
@@ -1,342 +1,520 @@
#
-# This file is autogenerated by pip-compile with python 3.10
-# To update, run:
+# This file is autogenerated by pip-compile with Python 3.11
+# by the following command:
#
# bazel run //third_party:python_requirements.update
#
-absl-py==1.4.0 \
- --hash=sha256:0d3fe606adfa4f7db64792dd4c7aee4ee0c38ab75dfd353b7a83ed3e957fcb47 \
- --hash=sha256:d2c244d01048ba476e7c080bd2c6df5e141d211de80223460d5b3b8a2a58433d
+absl-py==2.0.0 \
+ --hash=sha256:9a28abb62774ae4e8edbe2dd4c49ffcd45a6a848952a5eccc6a49f3f0fc1e2f3 \
+ --hash=sha256:d9690211c5fcfefcdd1a45470ac2b5c5acd45241c3af71eed96bc5441746c0d5
# via
# tensorboard
- # tensorflow-cpu
+ # tensorflow
astunparse==1.6.3 \
--hash=sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872 \
--hash=sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8
- # via tensorflow-cpu
-cachetools==5.3.0 \
- --hash=sha256:13dfddc7b8df938c21a940dfa6557ce6e94a2f1cdfa58eb90c805721d58f2c14 \
- --hash=sha256:429e1a1e845c008ea6c85aa35d4b98b65d6a9763eeef3e37e92728a12d1de9d4
+ # via tensorflow
+cachetools==5.3.2 \
+ --hash=sha256:086ee420196f7b2ab9ca2db2520aca326318b68fe5ba8bc4d49cca91add450f2 \
+ --hash=sha256:861f35a13a451f94e301ce2bec7cac63e881232ccce7ed67fab9b5df4d3beaa1
# via google-auth
-certifi==2023.5.7 \
- --hash=sha256:0f0d56dc5a6ad56fd4ba36484d6cc34451e1c6548c61daad8c320169f91eddc7 \
- --hash=sha256:c6c2e98f5c7869efca1f8916fed228dd91539f9f1b444c314c06eef02980c716
+certifi==2023.11.17 \
+ --hash=sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1 \
+ --hash=sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474
# via requests
-charset-normalizer==3.1.0 \
- --hash=sha256:04afa6387e2b282cf78ff3dbce20f0cc071c12dc8f685bd40960cc68644cfea6 \
- --hash=sha256:04eefcee095f58eaabe6dc3cc2262f3bcd776d2c67005880894f447b3f2cb9c1 \
- --hash=sha256:0be65ccf618c1e7ac9b849c315cc2e8a8751d9cfdaa43027d4f6624bd587ab7e \
- --hash=sha256:0c95f12b74681e9ae127728f7e5409cbbef9cd914d5896ef238cc779b8152373 \
- --hash=sha256:0ca564606d2caafb0abe6d1b5311c2649e8071eb241b2d64e75a0d0065107e62 \
- --hash=sha256:10c93628d7497c81686e8e5e557aafa78f230cd9e77dd0c40032ef90c18f2230 \
- --hash=sha256:11d117e6c63e8f495412d37e7dc2e2fff09c34b2d09dbe2bee3c6229577818be \
- --hash=sha256:11d3bcb7be35e7b1bba2c23beedac81ee893ac9871d0ba79effc7fc01167db6c \
- --hash=sha256:12a2b561af122e3d94cdb97fe6fb2bb2b82cef0cdca131646fdb940a1eda04f0 \
- --hash=sha256:12d1a39aa6b8c6f6248bb54550efcc1c38ce0d8096a146638fd4738e42284448 \
- --hash=sha256:1435ae15108b1cb6fffbcea2af3d468683b7afed0169ad718451f8db5d1aff6f \
- --hash=sha256:1c60b9c202d00052183c9be85e5eaf18a4ada0a47d188a83c8f5c5b23252f649 \
- --hash=sha256:1e8fcdd8f672a1c4fc8d0bd3a2b576b152d2a349782d1eb0f6b8e52e9954731d \
- --hash=sha256:20064ead0717cf9a73a6d1e779b23d149b53daf971169289ed2ed43a71e8d3b0 \
- --hash=sha256:21fa558996782fc226b529fdd2ed7866c2c6ec91cee82735c98a197fae39f706 \
- --hash=sha256:22908891a380d50738e1f978667536f6c6b526a2064156203d418f4856d6e86a \
- --hash=sha256:3160a0fd9754aab7d47f95a6b63ab355388d890163eb03b2d2b87ab0a30cfa59 \
- --hash=sha256:322102cdf1ab682ecc7d9b1c5eed4ec59657a65e1c146a0da342b78f4112db23 \
- --hash=sha256:34e0a2f9c370eb95597aae63bf85eb5e96826d81e3dcf88b8886012906f509b5 \
- --hash=sha256:3573d376454d956553c356df45bb824262c397c6e26ce43e8203c4c540ee0acb \
- --hash=sha256:3747443b6a904001473370d7810aa19c3a180ccd52a7157aacc264a5ac79265e \
- --hash=sha256:38e812a197bf8e71a59fe55b757a84c1f946d0ac114acafaafaf21667a7e169e \
- --hash=sha256:3a06f32c9634a8705f4ca9946d667609f52cf130d5548881401f1eb2c39b1e2c \
- --hash=sha256:3a5fc78f9e3f501a1614a98f7c54d3969f3ad9bba8ba3d9b438c3bc5d047dd28 \
- --hash=sha256:3d9098b479e78c85080c98e1e35ff40b4a31d8953102bb0fd7d1b6f8a2111a3d \
- --hash=sha256:3dc5b6a8ecfdc5748a7e429782598e4f17ef378e3e272eeb1340ea57c9109f41 \
- --hash=sha256:4155b51ae05ed47199dc5b2a4e62abccb274cee6b01da5b895099b61b1982974 \
- --hash=sha256:49919f8400b5e49e961f320c735388ee686a62327e773fa5b3ce6721f7e785ce \
- --hash=sha256:53d0a3fa5f8af98a1e261de6a3943ca631c526635eb5817a87a59d9a57ebf48f \
- --hash=sha256:5f008525e02908b20e04707a4f704cd286d94718f48bb33edddc7d7b584dddc1 \
- --hash=sha256:628c985afb2c7d27a4800bfb609e03985aaecb42f955049957814e0491d4006d \
- --hash=sha256:65ed923f84a6844de5fd29726b888e58c62820e0769b76565480e1fdc3d062f8 \
- --hash=sha256:6734e606355834f13445b6adc38b53c0fd45f1a56a9ba06c2058f86893ae8017 \
- --hash=sha256:6baf0baf0d5d265fa7944feb9f7451cc316bfe30e8df1a61b1bb08577c554f31 \
- --hash=sha256:6f4f4668e1831850ebcc2fd0b1cd11721947b6dc7c00bf1c6bd3c929ae14f2c7 \
- --hash=sha256:6f5c2e7bc8a4bf7c426599765b1bd33217ec84023033672c1e9a8b35eaeaaaf8 \
- --hash=sha256:6f6c7a8a57e9405cad7485f4c9d3172ae486cfef1344b5ddd8e5239582d7355e \
- --hash=sha256:7381c66e0561c5757ffe616af869b916c8b4e42b367ab29fedc98481d1e74e14 \
- --hash=sha256:73dc03a6a7e30b7edc5b01b601e53e7fc924b04e1835e8e407c12c037e81adbd \
- --hash=sha256:74db0052d985cf37fa111828d0dd230776ac99c740e1a758ad99094be4f1803d \
- --hash=sha256:75f2568b4189dda1c567339b48cba4ac7384accb9c2a7ed655cd86b04055c795 \
- --hash=sha256:78cacd03e79d009d95635e7d6ff12c21eb89b894c354bd2b2ed0b4763373693b \
- --hash=sha256:80d1543d58bd3d6c271b66abf454d437a438dff01c3e62fdbcd68f2a11310d4b \
- --hash=sha256:830d2948a5ec37c386d3170c483063798d7879037492540f10a475e3fd6f244b \
- --hash=sha256:891cf9b48776b5c61c700b55a598621fdb7b1e301a550365571e9624f270c203 \
- --hash=sha256:8f25e17ab3039b05f762b0a55ae0b3632b2e073d9c8fc88e89aca31a6198e88f \
- --hash=sha256:9a3267620866c9d17b959a84dd0bd2d45719b817245e49371ead79ed4f710d19 \
- --hash=sha256:a04f86f41a8916fe45ac5024ec477f41f886b3c435da2d4e3d2709b22ab02af1 \
- --hash=sha256:aaf53a6cebad0eae578f062c7d462155eada9c172bd8c4d250b8c1d8eb7f916a \
- --hash=sha256:abc1185d79f47c0a7aaf7e2412a0eb2c03b724581139193d2d82b3ad8cbb00ac \
- --hash=sha256:ac0aa6cd53ab9a31d397f8303f92c42f534693528fafbdb997c82bae6e477ad9 \
- --hash=sha256:ac3775e3311661d4adace3697a52ac0bab17edd166087d493b52d4f4f553f9f0 \
- --hash=sha256:b06f0d3bf045158d2fb8837c5785fe9ff9b8c93358be64461a1089f5da983137 \
- --hash=sha256:b116502087ce8a6b7a5f1814568ccbd0e9f6cfd99948aa59b0e241dc57cf739f \
- --hash=sha256:b82fab78e0b1329e183a65260581de4375f619167478dddab510c6c6fb04d9b6 \
- --hash=sha256:bd7163182133c0c7701b25e604cf1611c0d87712e56e88e7ee5d72deab3e76b5 \
- --hash=sha256:c36bcbc0d5174a80d6cccf43a0ecaca44e81d25be4b7f90f0ed7bcfbb5a00909 \
- --hash=sha256:c3af8e0f07399d3176b179f2e2634c3ce9c1301379a6b8c9c9aeecd481da494f \
- --hash=sha256:c84132a54c750fda57729d1e2599bb598f5fa0344085dbde5003ba429a4798c0 \
- --hash=sha256:cb7b2ab0188829593b9de646545175547a70d9a6e2b63bf2cd87a0a391599324 \
- --hash=sha256:cca4def576f47a09a943666b8f829606bcb17e2bc2d5911a46c8f8da45f56755 \
- --hash=sha256:cf6511efa4801b9b38dc5546d7547d5b5c6ef4b081c60b23e4d941d0eba9cbeb \
- --hash=sha256:d16fd5252f883eb074ca55cb622bc0bee49b979ae4e8639fff6ca3ff44f9f854 \
- --hash=sha256:d2686f91611f9e17f4548dbf050e75b079bbc2a82be565832bc8ea9047b61c8c \
- --hash=sha256:d7fc3fca01da18fbabe4625d64bb612b533533ed10045a2ac3dd194bfa656b60 \
- --hash=sha256:dd5653e67b149503c68c4018bf07e42eeed6b4e956b24c00ccdf93ac79cdff84 \
- --hash=sha256:de5695a6f1d8340b12a5d6d4484290ee74d61e467c39ff03b39e30df62cf83a0 \
- --hash=sha256:e0ac8959c929593fee38da1c2b64ee9778733cdf03c482c9ff1d508b6b593b2b \
- --hash=sha256:e1b25e3ad6c909f398df8921780d6a3d120d8c09466720226fc621605b6f92b1 \
- --hash=sha256:e633940f28c1e913615fd624fcdd72fdba807bf53ea6925d6a588e84e1151531 \
- --hash=sha256:e89df2958e5159b811af9ff0f92614dabf4ff617c03a4c1c6ff53bf1c399e0e1 \
- --hash=sha256:ea9f9c6034ea2d93d9147818f17c2a0860d41b71c38b9ce4d55f21b6f9165a11 \
- --hash=sha256:f645caaf0008bacf349875a974220f1f1da349c5dbe7c4ec93048cdc785a3326 \
- --hash=sha256:f8303414c7b03f794347ad062c0516cee0e15f7a612abd0ce1e25caf6ceb47df \
- --hash=sha256:fca62a8301b605b954ad2e9c3666f9d97f63872aa4efcae5492baca2056b74ab
+cffi==1.16.0 \
+ --hash=sha256:0c9ef6ff37e974b73c25eecc13952c55bceed9112be2d9d938ded8e856138bcc \
+ --hash=sha256:131fd094d1065b19540c3d72594260f118b231090295d8c34e19a7bbcf2e860a \
+ --hash=sha256:1b8ebc27c014c59692bb2664c7d13ce7a6e9a629be20e54e7271fa696ff2b417 \
+ --hash=sha256:2c56b361916f390cd758a57f2e16233eb4f64bcbeee88a4881ea90fca14dc6ab \
+ --hash=sha256:2d92b25dbf6cae33f65005baf472d2c245c050b1ce709cc4588cdcdd5495b520 \
+ --hash=sha256:31d13b0f99e0836b7ff893d37af07366ebc90b678b6664c955b54561fc36ef36 \
+ --hash=sha256:32c68ef735dbe5857c810328cb2481e24722a59a2003018885514d4c09af9743 \
+ --hash=sha256:3686dffb02459559c74dd3d81748269ffb0eb027c39a6fc99502de37d501faa8 \
+ --hash=sha256:582215a0e9adbe0e379761260553ba11c58943e4bbe9c36430c4ca6ac74b15ed \
+ --hash=sha256:5b50bf3f55561dac5438f8e70bfcdfd74543fd60df5fa5f62d94e5867deca684 \
+ --hash=sha256:5bf44d66cdf9e893637896c7faa22298baebcd18d1ddb6d2626a6e39793a1d56 \
+ --hash=sha256:6602bc8dc6f3a9e02b6c22c4fc1e47aa50f8f8e6d3f78a5e16ac33ef5fefa324 \
+ --hash=sha256:673739cb539f8cdaa07d92d02efa93c9ccf87e345b9a0b556e3ecc666718468d \
+ --hash=sha256:68678abf380b42ce21a5f2abde8efee05c114c2fdb2e9eef2efdb0257fba1235 \
+ --hash=sha256:68e7c44931cc171c54ccb702482e9fc723192e88d25a0e133edd7aff8fcd1f6e \
+ --hash=sha256:6b3d6606d369fc1da4fd8c357d026317fbb9c9b75d36dc16e90e84c26854b088 \
+ --hash=sha256:748dcd1e3d3d7cd5443ef03ce8685043294ad6bd7c02a38d1bd367cfd968e000 \
+ --hash=sha256:7651c50c8c5ef7bdb41108b7b8c5a83013bfaa8a935590c5d74627c047a583c7 \
+ --hash=sha256:7b78010e7b97fef4bee1e896df8a4bbb6712b7f05b7ef630f9d1da00f6444d2e \
+ --hash=sha256:7e61e3e4fa664a8588aa25c883eab612a188c725755afff6289454d6362b9673 \
+ --hash=sha256:80876338e19c951fdfed6198e70bc88f1c9758b94578d5a7c4c91a87af3cf31c \
+ --hash=sha256:8895613bcc094d4a1b2dbe179d88d7fb4a15cee43c052e8885783fac397d91fe \
+ --hash=sha256:88e2b3c14bdb32e440be531ade29d3c50a1a59cd4e51b1dd8b0865c54ea5d2e2 \
+ --hash=sha256:8f8e709127c6c77446a8c0a8c8bf3c8ee706a06cd44b1e827c3e6a2ee6b8c098 \
+ --hash=sha256:9cb4a35b3642fc5c005a6755a5d17c6c8b6bcb6981baf81cea8bfbc8903e8ba8 \
+ --hash=sha256:9f90389693731ff1f659e55c7d1640e2ec43ff725cc61b04b2f9c6d8d017df6a \
+ --hash=sha256:a09582f178759ee8128d9270cd1344154fd473bb77d94ce0aeb2a93ebf0feaf0 \
+ --hash=sha256:a6a14b17d7e17fa0d207ac08642c8820f84f25ce17a442fd15e27ea18d67c59b \
+ --hash=sha256:a72e8961a86d19bdb45851d8f1f08b041ea37d2bd8d4fd19903bc3083d80c896 \
+ --hash=sha256:abd808f9c129ba2beda4cfc53bde801e5bcf9d6e0f22f095e45327c038bfe68e \
+ --hash=sha256:ac0f5edd2360eea2f1daa9e26a41db02dd4b0451b48f7c318e217ee092a213e9 \
+ --hash=sha256:b29ebffcf550f9da55bec9e02ad430c992a87e5f512cd63388abb76f1036d8d2 \
+ --hash=sha256:b2ca4e77f9f47c55c194982e10f058db063937845bb2b7a86c84a6cfe0aefa8b \
+ --hash=sha256:b7be2d771cdba2942e13215c4e340bfd76398e9227ad10402a8767ab1865d2e6 \
+ --hash=sha256:b84834d0cf97e7d27dd5b7f3aca7b6e9263c56308ab9dc8aae9784abb774d404 \
+ --hash=sha256:b86851a328eedc692acf81fb05444bdf1891747c25af7529e39ddafaf68a4f3f \
+ --hash=sha256:bcb3ef43e58665bbda2fb198698fcae6776483e0c4a631aa5647806c25e02cc0 \
+ --hash=sha256:c0f31130ebc2d37cdd8e44605fb5fa7ad59049298b3f745c74fa74c62fbfcfc4 \
+ --hash=sha256:c6a164aa47843fb1b01e941d385aab7215563bb8816d80ff3a363a9f8448a8dc \
+ --hash=sha256:d8a9d3ebe49f084ad71f9269834ceccbf398253c9fac910c4fd7053ff1386936 \
+ --hash=sha256:db8e577c19c0fda0beb7e0d4e09e0ba74b1e4c092e0e40bfa12fe05b6f6d75ba \
+ --hash=sha256:dc9b18bf40cc75f66f40a7379f6a9513244fe33c0e8aa72e2d56b0196a7ef872 \
+ --hash=sha256:e09f3ff613345df5e8c3667da1d918f9149bd623cd9070c983c013792a9a62eb \
+ --hash=sha256:e4108df7fe9b707191e55f33efbcb2d81928e10cea45527879a4749cbe472614 \
+ --hash=sha256:e6024675e67af929088fda399b2094574609396b1decb609c55fa58b028a32a1 \
+ --hash=sha256:e70f54f1796669ef691ca07d046cd81a29cb4deb1e5f942003f401c0c4a2695d \
+ --hash=sha256:e715596e683d2ce000574bae5d07bd522c781a822866c20495e52520564f0969 \
+ --hash=sha256:e760191dd42581e023a68b758769e2da259b5d52e3103c6060ddc02c9edb8d7b \
+ --hash=sha256:ed86a35631f7bfbb28e108dd96773b9d5a6ce4811cf6ea468bb6a359b256b1e4 \
+ --hash=sha256:ee07e47c12890ef248766a6e55bd38ebfb2bb8edd4142d56db91b21ea68b7627 \
+ --hash=sha256:fa3a0128b152627161ce47201262d3140edb5a5c3da88d73a1b790a959126956 \
+ --hash=sha256:fcc8eb6d5902bb1cf6dc4f187ee3ea80a1eba0a89aba40a5cb20a5087d961357
+ # via cryptography
+charset-normalizer==3.3.2 \
+ --hash=sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027 \
+ --hash=sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087 \
+ --hash=sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786 \
+ --hash=sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8 \
+ --hash=sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09 \
+ --hash=sha256:122c7fa62b130ed55f8f285bfd56d5f4b4a5b503609d181f9ad85e55c89f4185 \
+ --hash=sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574 \
+ --hash=sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e \
+ --hash=sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519 \
+ --hash=sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898 \
+ --hash=sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269 \
+ --hash=sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3 \
+ --hash=sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f \
+ --hash=sha256:3287761bc4ee9e33561a7e058c72ac0938c4f57fe49a09eae428fd88aafe7bb6 \
+ --hash=sha256:34d1c8da1e78d2e001f363791c98a272bb734000fcef47a491c1e3b0505657a8 \
+ --hash=sha256:37e55c8e51c236f95b033f6fb391d7d7970ba5fe7ff453dad675e88cf303377a \
+ --hash=sha256:3d47fa203a7bd9c5b6cee4736ee84ca03b8ef23193c0d1ca99b5089f72645c73 \
+ --hash=sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc \
+ --hash=sha256:42cb296636fcc8b0644486d15c12376cb9fa75443e00fb25de0b8602e64c1714 \
+ --hash=sha256:45485e01ff4d3630ec0d9617310448a8702f70e9c01906b0d0118bdf9d124cf2 \
+ --hash=sha256:4a78b2b446bd7c934f5dcedc588903fb2f5eec172f3d29e52a9096a43722adfc \
+ --hash=sha256:4ab2fe47fae9e0f9dee8c04187ce5d09f48eabe611be8259444906793ab7cbce \
+ --hash=sha256:4d0d1650369165a14e14e1e47b372cfcb31d6ab44e6e33cb2d4e57265290044d \
+ --hash=sha256:549a3a73da901d5bc3ce8d24e0600d1fa85524c10287f6004fbab87672bf3e1e \
+ --hash=sha256:55086ee1064215781fff39a1af09518bc9255b50d6333f2e4c74ca09fac6a8f6 \
+ --hash=sha256:572c3763a264ba47b3cf708a44ce965d98555f618ca42c926a9c1616d8f34269 \
+ --hash=sha256:573f6eac48f4769d667c4442081b1794f52919e7edada77495aaed9236d13a96 \
+ --hash=sha256:5b4c145409bef602a690e7cfad0a15a55c13320ff7a3ad7ca59c13bb8ba4d45d \
+ --hash=sha256:6463effa3186ea09411d50efc7d85360b38d5f09b870c48e4600f63af490e56a \
+ --hash=sha256:65f6f63034100ead094b8744b3b97965785388f308a64cf8d7c34f2f2e5be0c4 \
+ --hash=sha256:663946639d296df6a2bb2aa51b60a2454ca1cb29835324c640dafb5ff2131a77 \
+ --hash=sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d \
+ --hash=sha256:68d1f8a9e9e37c1223b656399be5d6b448dea850bed7d0f87a8311f1ff3dabb0 \
+ --hash=sha256:6ac7ffc7ad6d040517be39eb591cac5ff87416c2537df6ba3cba3bae290c0fed \
+ --hash=sha256:6b3251890fff30ee142c44144871185dbe13b11bab478a88887a639655be1068 \
+ --hash=sha256:6c4caeef8fa63d06bd437cd4bdcf3ffefe6738fb1b25951440d80dc7df8c03ac \
+ --hash=sha256:6ef1d82a3af9d3eecdba2321dc1b3c238245d890843e040e41e470ffa64c3e25 \
+ --hash=sha256:753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8 \
+ --hash=sha256:7cd13a2e3ddeed6913a65e66e94b51d80a041145a026c27e6bb76c31a853c6ab \
+ --hash=sha256:7ed9e526742851e8d5cc9e6cf41427dfc6068d4f5a3bb03659444b4cabf6bc26 \
+ --hash=sha256:7f04c839ed0b6b98b1a7501a002144b76c18fb1c1850c8b98d458ac269e26ed2 \
+ --hash=sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db \
+ --hash=sha256:80402cd6ee291dcb72644d6eac93785fe2c8b9cb30893c1af5b8fdd753b9d40f \
+ --hash=sha256:8465322196c8b4d7ab6d1e049e4c5cb460d0394da4a27d23cc242fbf0034b6b5 \
+ --hash=sha256:86216b5cee4b06df986d214f664305142d9c76df9b6512be2738aa72a2048f99 \
+ --hash=sha256:87d1351268731db79e0f8e745d92493ee2841c974128ef629dc518b937d9194c \
+ --hash=sha256:8bdb58ff7ba23002a4c5808d608e4e6c687175724f54a5dade5fa8c67b604e4d \
+ --hash=sha256:8c622a5fe39a48f78944a87d4fb8a53ee07344641b0562c540d840748571b811 \
+ --hash=sha256:8d756e44e94489e49571086ef83b2bb8ce311e730092d2c34ca8f7d925cb20aa \
+ --hash=sha256:8f4a014bc36d3c57402e2977dada34f9c12300af536839dc38c0beab8878f38a \
+ --hash=sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03 \
+ --hash=sha256:90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b \
+ --hash=sha256:923c0c831b7cfcb071580d3f46c4baf50f174be571576556269530f4bbd79d04 \
+ --hash=sha256:95f2a5796329323b8f0512e09dbb7a1860c46a39da62ecb2324f116fa8fdc85c \
+ --hash=sha256:96b02a3dc4381e5494fad39be677abcb5e6634bf7b4fa83a6dd3112607547001 \
+ --hash=sha256:9f96df6923e21816da7e0ad3fd47dd8f94b2a5ce594e00677c0013018b813458 \
+ --hash=sha256:a10af20b82360ab00827f916a6058451b723b4e65030c5a18577c8b2de5b3389 \
+ --hash=sha256:a50aebfa173e157099939b17f18600f72f84eed3049e743b68ad15bd69b6bf99 \
+ --hash=sha256:a981a536974bbc7a512cf44ed14938cf01030a99e9b3a06dd59578882f06f985 \
+ --hash=sha256:a9a8e9031d613fd2009c182b69c7b2c1ef8239a0efb1df3f7c8da66d5dd3d537 \
+ --hash=sha256:ae5f4161f18c61806f411a13b0310bea87f987c7d2ecdbdaad0e94eb2e404238 \
+ --hash=sha256:aed38f6e4fb3f5d6bf81bfa990a07806be9d83cf7bacef998ab1a9bd660a581f \
+ --hash=sha256:b01b88d45a6fcb69667cd6d2f7a9aeb4bf53760d7fc536bf679ec94fe9f3ff3d \
+ --hash=sha256:b261ccdec7821281dade748d088bb6e9b69e6d15b30652b74cbbac25e280b796 \
+ --hash=sha256:b2b0a0c0517616b6869869f8c581d4eb2dd83a4d79e0ebcb7d373ef9956aeb0a \
+ --hash=sha256:b4a23f61ce87adf89be746c8a8974fe1c823c891d8f86eb218bb957c924bb143 \
+ --hash=sha256:bd8f7df7d12c2db9fab40bdd87a7c09b1530128315d047a086fa3ae3435cb3a8 \
+ --hash=sha256:beb58fe5cdb101e3a055192ac291b7a21e3b7ef4f67fa1d74e331a7f2124341c \
+ --hash=sha256:c002b4ffc0be611f0d9da932eb0f704fe2602a9a949d1f738e4c34c75b0863d5 \
+ --hash=sha256:c083af607d2515612056a31f0a8d9e0fcb5876b7bfc0abad3ecd275bc4ebc2d5 \
+ --hash=sha256:c180f51afb394e165eafe4ac2936a14bee3eb10debc9d9e4db8958fe36afe711 \
+ --hash=sha256:c235ebd9baae02f1b77bcea61bce332cb4331dc3617d254df3323aa01ab47bd4 \
+ --hash=sha256:cd70574b12bb8a4d2aaa0094515df2463cb429d8536cfb6c7ce983246983e5a6 \
+ --hash=sha256:d0eccceffcb53201b5bfebb52600a5fb483a20b61da9dbc885f8b103cbe7598c \
+ --hash=sha256:d965bba47ddeec8cd560687584e88cf699fd28f192ceb452d1d7ee807c5597b7 \
+ --hash=sha256:db364eca23f876da6f9e16c9da0df51aa4f104a972735574842618b8c6d999d4 \
+ --hash=sha256:ddbb2551d7e0102e7252db79ba445cdab71b26640817ab1e3e3648dad515003b \
+ --hash=sha256:deb6be0ac38ece9ba87dea880e438f25ca3eddfac8b002a2ec3d9183a454e8ae \
+ --hash=sha256:e06ed3eb3218bc64786f7db41917d4e686cc4856944f53d5bdf83a6884432e12 \
+ --hash=sha256:e27ad930a842b4c5eb8ac0016b0a54f5aebbe679340c26101df33424142c143c \
+ --hash=sha256:e537484df0d8f426ce2afb2d0f8e1c3d0b114b83f8850e5f2fbea0e797bd82ae \
+ --hash=sha256:eb00ed941194665c332bf8e078baf037d6c35d7c4f3102ea2d4f16ca94a26dc8 \
+ --hash=sha256:eb6904c354526e758fda7167b33005998fb68c46fbc10e013ca97f21ca5c8887 \
+ --hash=sha256:eb8821e09e916165e160797a6c17edda0679379a4be5c716c260e836e122f54b \
+ --hash=sha256:efcb3f6676480691518c177e3b465bcddf57cea040302f9f4e6e191af91174d4 \
+ --hash=sha256:f27273b60488abe721a075bcca6d7f3964f9f6f067c8c4c605743023d7d3944f \
+ --hash=sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5 \
+ --hash=sha256:fb69256e180cb6c8a894fee62b3afebae785babc1ee98b81cdf68bbca1987f33 \
+ --hash=sha256:fd1abc0d89e30cc4e02e4064dc67fcc51bd941eb395c502aac3ec19fab46b519 \
+ --hash=sha256:ff8fa367d09b717b2a17a052544193ad76cd49979c805768879cb63d9ca50561
# via requests
-flatbuffers==23.5.9 \
- --hash=sha256:93a506b6ab771c79ce816e7b35a93ed08ec5b4c9edb811101a22c44a4152f018 \
- --hash=sha256:a02eb8c2d61cba153cd211937de8f8f7764b6a7510971b2c4684ed8b02e6e571
- # via tensorflow-cpu
-gast==0.4.0 \
- --hash=sha256:40feb7b8b8434785585ab224d1568b857edb18297e5a3047f1ba012bc83b42c1 \
- --hash=sha256:b7adcdd5adbebf1adf17378da5ba3f543684dbec47b1cda1f3997e573cd542c4
- # via tensorflow-cpu
-google-auth==2.18.0 \
- --hash=sha256:c66b488a8b005b23ccb97b1198b6cece516c91869091ac5b7c267422db2733c7 \
- --hash=sha256:ef3f3a67fa54d421a1c155864570f9a8de9179cedc937bda496b7a8ca338e936
+cryptography==41.0.7 \
+ --hash=sha256:079b85658ea2f59c4f43b70f8119a52414cdb7be34da5d019a77bf96d473b960 \
+ --hash=sha256:09616eeaef406f99046553b8a40fbf8b1e70795a91885ba4c96a70793de5504a \
+ --hash=sha256:13f93ce9bea8016c253b34afc6bd6a75993e5c40672ed5405a9c832f0d4a00bc \
+ --hash=sha256:37a138589b12069efb424220bf78eac59ca68b95696fc622b6ccc1c0a197204a \
+ --hash=sha256:3c78451b78313fa81607fa1b3f1ae0a5ddd8014c38a02d9db0616133987b9cdf \
+ --hash=sha256:43f2552a2378b44869fe8827aa19e69512e3245a219104438692385b0ee119d1 \
+ --hash=sha256:48a0476626da912a44cc078f9893f292f0b3e4c739caf289268168d8f4702a39 \
+ --hash=sha256:49f0805fc0b2ac8d4882dd52f4a3b935b210935d500b6b805f321addc8177406 \
+ --hash=sha256:5429ec739a29df2e29e15d082f1d9ad683701f0ec7709ca479b3ff2708dae65a \
+ --hash=sha256:5a1b41bc97f1ad230a41657d9155113c7521953869ae57ac39ac7f1bb471469a \
+ --hash=sha256:68a2dec79deebc5d26d617bfdf6e8aab065a4f34934b22d3b5010df3ba36612c \
+ --hash=sha256:7a698cb1dac82c35fcf8fe3417a3aaba97de16a01ac914b89a0889d364d2f6be \
+ --hash=sha256:841df4caa01008bad253bce2a6f7b47f86dc9f08df4b433c404def869f590a15 \
+ --hash=sha256:90452ba79b8788fa380dfb587cca692976ef4e757b194b093d845e8d99f612f2 \
+ --hash=sha256:928258ba5d6f8ae644e764d0f996d61a8777559f72dfeb2eea7e2fe0ad6e782d \
+ --hash=sha256:af03b32695b24d85a75d40e1ba39ffe7db7ffcb099fe507b39fd41a565f1b157 \
+ --hash=sha256:b640981bf64a3e978a56167594a0e97db71c89a479da8e175d8bb5be5178c003 \
+ --hash=sha256:c5ca78485a255e03c32b513f8c2bc39fedb7f5c5f8535545bdc223a03b24f248 \
+ --hash=sha256:c7f3201ec47d5207841402594f1d7950879ef890c0c495052fa62f58283fde1a \
+ --hash=sha256:d5ec85080cce7b0513cfd233914eb8b7bbd0633f1d1703aa28d1dd5a72f678ec \
+ --hash=sha256:d6c391c021ab1f7a82da5d8d0b3cee2f4b2c455ec86c8aebbc84837a631ff309 \
+ --hash=sha256:e3114da6d7f95d2dee7d3f4eec16dacff819740bbab931aff8648cb13c5ff5e7 \
+ --hash=sha256:f983596065a18a2183e7f79ab3fd4c475205b839e02cbc0efbbf9666c4b3083d
+ # via secretstorage
+docutils==0.20.1 \
+ --hash=sha256:96f387a2c5562db4476f09f13bbab2192e764cac08ebbf3a34a95d9b1e4a59d6 \
+ --hash=sha256:f08a4e276c3a1583a86dce3e34aba3fe04d02bba2dd51ed16106244e8a923e3b
+ # via readme-renderer
+flatbuffers==23.5.26 \
+ --hash=sha256:9ea1144cac05ce5d86e2859f431c6cd5e66cd9c78c558317c7955fb8d4c78d89 \
+ --hash=sha256:c0ff356da363087b915fde4b8b45bdda73432fc17cddb3c8157472eab1422ad1
+ # via tensorflow
+gast==0.5.4 \
+ --hash=sha256:6fc4fa5fa10b72fb8aab4ae58bcb023058386e67b6fa2e3e34cec5c769360316 \
+ --hash=sha256:9c270fe5f4b130969b54174de7db4e764b09b4f7f67ccfc32480e29f78348d97
+ # via tensorflow
+google-auth==2.26.2 \
+ --hash=sha256:3f445c8ce9b61ed6459aad86d8ccdba4a9afed841b2d1451a11ef4db08957424 \
+ --hash=sha256:97327dbbf58cccb58fc5a1712bba403ae76668e64814eb30f7316f7e27126b81
# via
# google-auth-oauthlib
# tensorboard
-google-auth-oauthlib==1.0.0 \
- --hash=sha256:95880ca704928c300f48194d1770cf5b1462835b6e49db61445a520f793fd5fb \
- --hash=sha256:e375064964820b47221a7e1b7ee1fd77051b6323c3f9e3e19785f78ab67ecfc5
+google-auth-oauthlib==1.2.0 \
+ --hash=sha256:292d2d3783349f2b0734a0a0207b1e1e322ac193c2c09d8f7c613fb7cc501ea8 \
+ --hash=sha256:297c1ce4cb13a99b5834c74a1fe03252e1e499716718b190f56bcb9c4abc4faf
# via tensorboard
google-pasta==0.2.0 \
--hash=sha256:4612951da876b1a10fe3960d7226f0c7682cf901e16ac06e473b267a5afa8954 \
--hash=sha256:b32482794a366b5366a32c92a9a9201b107821889935a02b3e51f6b432ea84ed \
--hash=sha256:c9f2c8dfc8f96d0d5808299920721be30c9eec37f2389f28904f454565c8a16e
- # via tensorflow-cpu
-grpcio==1.54.0 \
- --hash=sha256:02000b005bc8b72ff50c477b6431e8886b29961159e8b8d03c00b3dd9139baed \
- --hash=sha256:031bbd26656e0739e4b2c81c172155fb26e274b8d0312d67aefc730bcba915b6 \
- --hash=sha256:1209d6b002b26e939e4c8ea37a3d5b4028eb9555394ea69fb1adbd4b61a10bb8 \
- --hash=sha256:125ed35aa3868efa82eabffece6264bf638cfdc9f0cd58ddb17936684aafd0f8 \
- --hash=sha256:1382bc499af92901c2240c4d540c74eae8a671e4fe9839bfeefdfcc3a106b5e2 \
- --hash=sha256:16bca8092dd994f2864fdab278ae052fad4913f36f35238b2dd11af2d55a87db \
- --hash=sha256:1c59d899ee7160638613a452f9a4931de22623e7ba17897d8e3e348c2e9d8d0b \
- --hash=sha256:1d109df30641d050e009105f9c9ca5a35d01e34d2ee2a4e9c0984d392fd6d704 \
- --hash=sha256:1fa7d6ddd33abbd3c8b3d7d07c56c40ea3d1891ce3cd2aa9fa73105ed5331866 \
- --hash=sha256:21c4a1aae861748d6393a3ff7867473996c139a77f90326d9f4104bebb22d8b8 \
- --hash=sha256:224166f06ccdaf884bf35690bf4272997c1405de3035d61384ccb5b25a4c1ca8 \
- --hash=sha256:2262bd3512ba9e9f0e91d287393df6f33c18999317de45629b7bd46c40f16ba9 \
- --hash=sha256:2585b3c294631a39b33f9f967a59b0fad23b1a71a212eba6bc1e3ca6e6eec9ee \
- --hash=sha256:27fb030a4589d2536daec5ff5ba2a128f4f155149efab578fe2de2cb21596d3d \
- --hash=sha256:30fbbce11ffeb4f9f91c13fe04899aaf3e9a81708bedf267bf447596b95df26b \
- --hash=sha256:3930669c9e6f08a2eed824738c3d5699d11cd47a0ecc13b68ed11595710b1133 \
- --hash=sha256:3b170e441e91e4f321e46d3cc95a01cb307a4596da54aca59eb78ab0fc03754d \
- --hash=sha256:3db71c6f1ab688d8dfc102271cedc9828beac335a3a4372ec54b8bf11b43fd29 \
- --hash=sha256:48cb7af77238ba16c77879009003f6b22c23425e5ee59cb2c4c103ec040638a5 \
- --hash=sha256:49eace8ea55fbc42c733defbda1e4feb6d3844ecd875b01bb8b923709e0f5ec8 \
- --hash=sha256:533eaf5b2a79a3c6f35cbd6a095ae99cac7f4f9c0e08bdcf86c130efd3c32adf \
- --hash=sha256:5942a3e05630e1ef5b7b5752e5da6582460a2e4431dae603de89fc45f9ec5aa9 \
- --hash=sha256:62117486460c83acd3b5d85c12edd5fe20a374630475388cfc89829831d3eb79 \
- --hash=sha256:650f5f2c9ab1275b4006707411bb6d6bc927886874a287661c3c6f332d4c068b \
- --hash=sha256:6dc1e2c9ac292c9a484ef900c568ccb2d6b4dfe26dfa0163d5bc815bb836c78d \
- --hash=sha256:73c238ef6e4b64272df7eec976bb016c73d3ab5a6c7e9cd906ab700523d312f3 \
- --hash=sha256:775a2f70501370e5ba54e1ee3464413bff9bd85bd9a0b25c989698c44a6fb52f \
- --hash=sha256:860fcd6db7dce80d0a673a1cc898ce6bc3d4783d195bbe0e911bf8a62c93ff3f \
- --hash=sha256:87f47bf9520bba4083d65ab911f8f4c0ac3efa8241993edd74c8dd08ae87552f \
- --hash=sha256:960b176e0bb2b4afeaa1cd2002db1e82ae54c9b6e27ea93570a42316524e77cf \
- --hash=sha256:a7caf553ccaf715ec05b28c9b2ab2ee3fdb4036626d779aa09cf7cbf54b71445 \
- --hash=sha256:a947d5298a0bbdd4d15671024bf33e2b7da79a70de600ed29ba7e0fef0539ebb \
- --hash=sha256:a97b0d01ae595c997c1d9d8249e2d2da829c2d8a4bdc29bb8f76c11a94915c9a \
- --hash=sha256:b7655f809e3420f80ce3bf89737169a9dce73238af594049754a1128132c0da4 \
- --hash=sha256:c33744d0d1a7322da445c0fe726ea6d4e3ef2dfb0539eadf23dce366f52f546c \
- --hash=sha256:c55a9cf5cba80fb88c850915c865b8ed78d5e46e1f2ec1b27692f3eaaf0dca7e \
- --hash=sha256:d2f62fb1c914a038921677cfa536d645cb80e3dd07dc4859a3c92d75407b90a5 \
- --hash=sha256:d8ae6e0df3a608e99ee1acafaafd7db0830106394d54571c1ece57f650124ce9 \
- --hash=sha256:e355ee9da9c1c03f174efea59292b17a95e0b7b4d7d2a389265f731a9887d5a9 \
- --hash=sha256:e3e526062c690517b42bba66ffe38aaf8bc99a180a78212e7b22baa86902f690 \
- --hash=sha256:eb0807323572642ab73fd86fe53d88d843ce617dd1ddf430351ad0759809a0ae \
- --hash=sha256:ebff0738be0499d7db74d20dca9f22a7b27deae31e1bf92ea44924fd69eb6251 \
- --hash=sha256:ed36e854449ff6c2f8ee145f94851fe171298e1e793f44d4f672c4a0d78064e7 \
- --hash=sha256:ed3d458ded32ff3a58f157b60cc140c88f7ac8c506a1c567b2a9ee8a2fd2ce54 \
- --hash=sha256:f4a7dca8ccd8023d916b900aa3c626f1bd181bd5b70159479b142f957ff420e4
+ # via tensorflow
+grpcio==1.60.0 \
+ --hash=sha256:073f959c6f570797272f4ee9464a9997eaf1e98c27cb680225b82b53390d61e6 \
+ --hash=sha256:0fd3b3968ffe7643144580f260f04d39d869fcc2cddb745deef078b09fd2b328 \
+ --hash=sha256:1434ca77d6fed4ea312901122dc8da6c4389738bf5788f43efb19a838ac03ead \
+ --hash=sha256:1c30bb23a41df95109db130a6cc1b974844300ae2e5d68dd4947aacba5985aa5 \
+ --hash=sha256:20e7a4f7ded59097c84059d28230907cd97130fa74f4a8bfd1d8e5ba18c81491 \
+ --hash=sha256:2199165a1affb666aa24adf0c97436686d0a61bc5fc113c037701fb7c7fceb96 \
+ --hash=sha256:297eef542156d6b15174a1231c2493ea9ea54af8d016b8ca7d5d9cc65cfcc444 \
+ --hash=sha256:2aef56e85901c2397bd557c5ba514f84de1f0ae5dd132f5d5fed042858115951 \
+ --hash=sha256:30943b9530fe3620e3b195c03130396cd0ee3a0d10a66c1bee715d1819001eaf \
+ --hash=sha256:3b36a2c6d4920ba88fa98075fdd58ff94ebeb8acc1215ae07d01a418af4c0253 \
+ --hash=sha256:428d699c8553c27e98f4d29fdc0f0edc50e9a8a7590bfd294d2edb0da7be3629 \
+ --hash=sha256:43e636dc2ce9ece583b3e2ca41df5c983f4302eabc6d5f9cd04f0562ee8ec1ae \
+ --hash=sha256:452ca5b4afed30e7274445dd9b441a35ece656ec1600b77fff8c216fdf07df43 \
+ --hash=sha256:467a7d31554892eed2aa6c2d47ded1079fc40ea0b9601d9f79204afa8902274b \
+ --hash=sha256:4b44d7e39964e808b071714666a812049765b26b3ea48c4434a3b317bac82f14 \
+ --hash=sha256:4c86343cf9ff7b2514dd229bdd88ebba760bd8973dac192ae687ff75e39ebfab \
+ --hash=sha256:5208a57eae445ae84a219dfd8b56e04313445d146873117b5fa75f3245bc1390 \
+ --hash=sha256:5ff21e000ff2f658430bde5288cb1ac440ff15c0d7d18b5fb222f941b46cb0d2 \
+ --hash=sha256:675997222f2e2f22928fbba640824aebd43791116034f62006e19730715166c0 \
+ --hash=sha256:676e4a44e740deaba0f4d95ba1d8c5c89a2fcc43d02c39f69450b1fa19d39590 \
+ --hash=sha256:6e306b97966369b889985a562ede9d99180def39ad42c8014628dd3cc343f508 \
+ --hash=sha256:6fd9584bf1bccdfff1512719316efa77be235469e1e3295dce64538c4773840b \
+ --hash=sha256:705a68a973c4c76db5d369ed573fec3367d7d196673fa86614b33d8c8e9ebb08 \
+ --hash=sha256:74d7d9fa97809c5b892449b28a65ec2bfa458a4735ddad46074f9f7d9550ad13 \
+ --hash=sha256:77c8a317f0fd5a0a2be8ed5cbe5341537d5c00bb79b3bb27ba7c5378ba77dbca \
+ --hash=sha256:79a050889eb8d57a93ed21d9585bb63fca881666fc709f5d9f7f9372f5e7fd03 \
+ --hash=sha256:7db16dd4ea1b05ada504f08d0dca1cd9b926bed3770f50e715d087c6f00ad748 \
+ --hash=sha256:83f2292ae292ed5a47cdcb9821039ca8e88902923198f2193f13959360c01860 \
+ --hash=sha256:87c9224acba0ad8bacddf427a1c2772e17ce50b3042a789547af27099c5f751d \
+ --hash=sha256:8a97a681e82bc11a42d4372fe57898d270a2707f36c45c6676e49ce0d5c41353 \
+ --hash=sha256:9073513ec380434eb8d21970e1ab3161041de121f4018bbed3146839451a6d8e \
+ --hash=sha256:90bdd76b3f04bdb21de5398b8a7c629676c81dfac290f5f19883857e9371d28c \
+ --hash=sha256:91229d7203f1ef0ab420c9b53fe2ca5c1fbeb34f69b3bc1b5089466237a4a134 \
+ --hash=sha256:92f88ca1b956eb8427a11bb8b4a0c0b2b03377235fc5102cb05e533b8693a415 \
+ --hash=sha256:95ae3e8e2c1b9bf671817f86f155c5da7d49a2289c5cf27a319458c3e025c320 \
+ --hash=sha256:9e30be89a75ee66aec7f9e60086fadb37ff8c0ba49a022887c28c134341f7179 \
+ --hash=sha256:a48edde788b99214613e440fce495bbe2b1e142a7f214cce9e0832146c41e324 \
+ --hash=sha256:a7152fa6e597c20cb97923407cf0934e14224af42c2b8d915f48bc3ad2d9ac18 \
+ --hash=sha256:a9c7b71211f066908e518a2ef7a5e211670761651039f0d6a80d8d40054047df \
+ --hash=sha256:b0571a5aef36ba9177e262dc88a9240c866d903a62799e44fd4aae3f9a2ec17e \
+ --hash=sha256:b0fb2d4801546598ac5cd18e3ec79c1a9af8b8f2a86283c55a5337c5aeca4b1b \
+ --hash=sha256:b10241250cb77657ab315270b064a6c7f1add58af94befa20687e7c8d8603ae6 \
+ --hash=sha256:b87efe4a380887425bb15f220079aa8336276398dc33fce38c64d278164f963d \
+ --hash=sha256:b98f43fcdb16172dec5f4b49f2fece4b16a99fd284d81c6bbac1b3b69fcbe0ff \
+ --hash=sha256:c193109ca4070cdcaa6eff00fdb5a56233dc7610216d58fb81638f89f02e4968 \
+ --hash=sha256:c826f93050c73e7769806f92e601e0efdb83ec8d7c76ddf45d514fee54e8e619 \
+ --hash=sha256:d020cfa595d1f8f5c6b343530cd3ca16ae5aefdd1e832b777f9f0eb105f5b139 \
+ --hash=sha256:d6a478581b1a1a8fdf3318ecb5f4d0cda41cacdffe2b527c23707c9c1b8fdb55 \
+ --hash=sha256:de2ad69c9a094bf37c1102b5744c9aec6cf74d2b635558b779085d0263166454 \
+ --hash=sha256:e278eafb406f7e1b1b637c2cf51d3ad45883bb5bd1ca56bc05e4fc135dfdaa65 \
+ --hash=sha256:e381fe0c2aa6c03b056ad8f52f8efca7be29fb4d9ae2f8873520843b6039612a \
+ --hash=sha256:e61e76020e0c332a98290323ecfec721c9544f5b739fab925b6e8cbe1944cf19 \
+ --hash=sha256:f897c3b127532e6befdcf961c415c97f320d45614daf84deba0a54e64ea2457b \
+ --hash=sha256:fb464479934778d7cc5baf463d959d361954d6533ad34c3a4f1d267e86ee25fd
# via
# tensorboard
- # tensorflow-cpu
-h5py==3.8.0 \
- --hash=sha256:03890b1c123d024fb0239a3279737d5432498c1901c354f8b10d8221d1d16235 \
- --hash=sha256:0fef76e10b9216657fa37e7edff6d8be0709b25bd5066474c229b56cf0098df9 \
- --hash=sha256:26ffc344ec9984d2cd3ca0265007299a8bac8d85c1ad48f4639d8d3aed2af171 \
- --hash=sha256:290e00fa2de74a10688d1bac98d5a9cdd43f14f58e562c580b5b3dfbd358ecae \
- --hash=sha256:33b15aae79e9147aebe1d0e54099cbcde8d65e3e227cd5b59e49b1272aa0e09d \
- --hash=sha256:36761693efbe53df179627a775476dcbc37727d6e920958277a7efbc18f1fb73 \
- --hash=sha256:377865821fe80ad984d003723d6f8890bd54ceeb5981b43c0313b9df95411b30 \
- --hash=sha256:49bc857635f935fa30e92e61ac1e87496df8f260a6945a3235e43a9890426866 \
- --hash=sha256:4a506fc223def428f4329e7e1f9fe1c8c593eab226e7c0942c8d75308ad49950 \
- --hash=sha256:533d7dad466ddb7e3b30af274b630eb7c1a6e4ddf01d1c373a0334dc2152110a \
- --hash=sha256:5fd2252d1fc364ba0e93dd0b7089f4906b66805cb4e6aca7fa8874ac08649647 \
- --hash=sha256:6fead82f0c4000cf38d53f9c030780d81bfa0220218aee13b90b7701c937d95f \
- --hash=sha256:7f3350fc0a8407d668b13247861c2acd23f7f5fe7d060a3ad9b0820f5fcbcae0 \
- --hash=sha256:8f55d9c6c84d7d09c79fb85979e97b81ec6071cc776a97eb6b96f8f6ec767323 \
- --hash=sha256:98a240cd4c1bfd568aaa52ec42d263131a2582dab82d74d3d42a0d954cac12be \
- --hash=sha256:9f6f6ffadd6bfa9b2c5b334805eb4b19ca0a5620433659d8f7fb86692c40a359 \
- --hash=sha256:b685453e538b2b5934c58a644ac3f3b3d0cec1a01b6fb26d57388e9f9b674ad0 \
- --hash=sha256:b7865de06779b14d98068da387333ad9bf2756b5b579cc887fac169bc08f87c3 \
- --hash=sha256:bacaa1c16810dd2b3e4417f8e730971b7c4d53d234de61fe4a918db78e80e1e4 \
- --hash=sha256:bae730580ae928de409d63cbe4fdca4c82c3ad2bed30511d19d34e995d63c77e \
- --hash=sha256:c3389b63222b1c7a158bb7fe69d11ca00066740ec5574596d47a2fe5317f563a \
- --hash=sha256:c873ba9fd4fa875ad62ce0e4891725e257a8fe7f5abdbc17e51a5d54819be55c \
- --hash=sha256:db03e3f2c716205fbdabb34d0848459840585225eb97b4f08998c743821ca323 \
- --hash=sha256:f47f757d1b76f0ecb8aa0508ec8d1b390df67a8b67ee2515dc1b046f3a1596ea \
- --hash=sha256:f891b17e3a3e974e93f9e34e7cca9f530806543571ce078998676a555837d91d
- # via tensorflow-cpu
-idna==3.4 \
- --hash=sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4 \
- --hash=sha256:90b77e79eaa3eba6de819a0c442c0b4ceefc341a7a2ab77d7562bf49f425c5c2
- # via requests
-jax==0.4.9 \
- --hash=sha256:1ed135cd08f48e4baf10f6eafdb4a4cdae781f9052b5838c09c91a9f4fa75f09
- # via tensorflow-cpu
-keras==2.12.0 \
- --hash=sha256:35c39534011e909645fb93515452e98e1a0ce23727b55d4918b9c58b2308c15e
- # via tensorflow-cpu
-libclang==16.0.0 \
- --hash=sha256:2adce42ae652f312245b8f4eda6f30b4076fb61f7619f2dfd0a0c31dee4c32b9 \
- --hash=sha256:65258a6bb3e7dc31dc9b26f8d42f53c9d3b959643ade291fcd1aef4855303ca6 \
- --hash=sha256:7b6686b67a0daa84b4c614bcc119578329fc4fbb52b919565b7376b507c4793b \
- --hash=sha256:a043138caaf2cb076ebb060c6281ec95612926645d425c691991fc9df00e8a24 \
- --hash=sha256:af55a4aa86fdfe6b2ec68bc8cfe5fdac6c448d591ca7648be86ca17099b41ca8 \
- --hash=sha256:bf4628fc4da7a1dd06a244f9b8e121c5ec68076a763c59d6b13cbb103acc935b \
- --hash=sha256:eb59652cb0559c0e71784ff4c8ba24c14644becc907b1446563ecfaa622d523b \
- --hash=sha256:ee20bf93e3dd330f71fc50cdbf13b92ced0aec8e540be64251db53502a9b33f7
- # via tensorflow-cpu
-mako==1.2.4 \
- --hash=sha256:c97c79c018b9165ac9922ae4f32da095ffd3c4e6872b45eded42926deea46818 \
- --hash=sha256:d60a3903dc3bb01a18ad6a89cdbe2e4eadc69c0bc8ef1e3773ba53d44c3f7a34
+ # tensorflow
+h5py==3.10.0 \
+ --hash=sha256:012ab448590e3c4f5a8dd0f3533255bc57f80629bf7c5054cf4c87b30085063c \
+ --hash=sha256:212bb997a91e6a895ce5e2f365ba764debeaef5d2dca5c6fb7098d66607adf99 \
+ --hash=sha256:2381e98af081b6df7f6db300cd88f88e740649d77736e4b53db522d8874bf2dc \
+ --hash=sha256:2c8e4fda19eb769e9a678592e67eaec3a2f069f7570c82d2da909c077aa94339 \
+ --hash=sha256:3074ec45d3dc6e178c6f96834cf8108bf4a60ccb5ab044e16909580352010a97 \
+ --hash=sha256:3c97d03f87f215e7759a354460fb4b0d0f27001450b18b23e556e7856a0b21c3 \
+ --hash=sha256:43a61b2c2ad65b1fabc28802d133eed34debcc2c8b420cb213d3d4ef4d3e2229 \
+ --hash=sha256:492305a074327e8d2513011fa9fffeb54ecb28a04ca4c4227d7e1e9616d35641 \
+ --hash=sha256:5dfc65ac21fa2f630323c92453cadbe8d4f504726ec42f6a56cf80c2f90d6c52 \
+ --hash=sha256:667fe23ab33d5a8a6b77970b229e14ae3bb84e4ea3382cc08567a02e1499eedd \
+ --hash=sha256:6c013d2e79c00f28ffd0cc24e68665ea03ae9069e167087b2adb5727d2736a52 \
+ --hash=sha256:781a24263c1270a62cd67be59f293e62b76acfcc207afa6384961762bb88ea03 \
+ --hash=sha256:86df4c2de68257b8539a18646ceccdcf2c1ce6b1768ada16c8dcfb489eafae20 \
+ --hash=sha256:90286b79abd085e4e65e07c1bd7ee65a0f15818ea107f44b175d2dfe1a4674b7 \
+ --hash=sha256:92273ce69ae4983dadb898fd4d3bea5eb90820df953b401282ee69ad648df684 \
+ --hash=sha256:93dd840bd675787fc0b016f7a05fc6efe37312a08849d9dd4053fd0377b1357f \
+ --hash=sha256:9450464b458cca2c86252b624279115dcaa7260a40d3cb1594bf2b410a2bd1a3 \
+ --hash=sha256:ae2f0201c950059676455daf92700eeb57dcf5caaf71b9e1328e6e6593601770 \
+ --hash=sha256:aece0e2e1ed2aab076c41802e50a0c3e5ef8816d60ece39107d68717d4559824 \
+ --hash=sha256:b963fb772964fc1d1563c57e4e2e874022ce11f75ddc6df1a626f42bd49ab99f \
+ --hash=sha256:ba9ab36be991119a3ff32d0c7cbe5faf9b8d2375b5278b2aea64effbeba66039 \
+ --hash=sha256:d4682b94fd36ab217352be438abd44c8f357c5449b8995e63886b431d260f3d3 \
+ --hash=sha256:d93adc48ceeb33347eb24a634fb787efc7ae4644e6ea4ba733d099605045c049 \
+ --hash=sha256:f42e6c30698b520f0295d70157c4e202a9e402406f50dc08f5a7bc416b24e52d \
+ --hash=sha256:fd6f6d1384a9f491732cee233b99cd4bfd6e838a8815cc86722f9d2ee64032af
+ # via tensorflow
+hexdump==3.3 \
+ --hash=sha256:d781a43b0c16ace3f9366aade73e8ad3a7bd5137d58f0b45ab2d3f54876f20db
# via -r third_party/python_requirements.in
-markdown==3.4.3 \
- --hash=sha256:065fd4df22da73a625f14890dd77eb8040edcbd68794bcd35943be14490608b2 \
- --hash=sha256:8bf101198e004dc93e84a12a7395e31aac6a9c9942848ae1d99b9d72cf9b3520
+idna==3.6 \
+ --hash=sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca \
+ --hash=sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f
+ # via requests
+importlib-metadata==7.0.1 \
+ --hash=sha256:4805911c3a4ec7c3966410053e9ec6a1fecd629117df5adee56dfc9432a1081e \
+ --hash=sha256:f238736bb06590ae52ac1fab06a3a9ef1d8dce2b7a35b5ab329371d6c8f5d2cc
+ # via
+ # keyring
+ # twine
+ # yapf
+jaraco-classes==3.3.0 \
+ --hash=sha256:10afa92b6743f25c0cf5f37c6bb6e18e2c5bb84a16527ccfc0040ea377e7aaeb \
+ --hash=sha256:c063dd08e89217cee02c8d5e5ec560f2c8ce6cdc2fcdc2e68f7b2e5547ed3621
+ # via keyring
+jeepney==0.8.0 \
+ --hash=sha256:5efe48d255973902f6badc3ce55e2aa6c5c3b3bc642059ef3a91247bcfcc5806 \
+ --hash=sha256:c0a454ad016ca575060802ee4d590dd912e35c122fa04e70306de3d076cce755
+ # via
+ # keyring
+ # secretstorage
+keras==2.15.0 \
+ --hash=sha256:2dcc6d2e30cf9c951064b63c1f4c404b966c59caf09e01f3549138ec8ee0dd1f \
+ --hash=sha256:81871d298c064dc4ac6b58440fdae67bfcf47c8d7ad28580fab401834c06a575
+ # via tensorflow
+keyring==24.3.0 \
+ --hash=sha256:4446d35d636e6a10b8bce7caa66913dd9eca5fd222ca03a3d42c38608ac30836 \
+ --hash=sha256:e730ecffd309658a08ee82535a3b5ec4b4c8669a9be11efb66249d8e0aeb9a25
+ # via twine
+libclang==16.0.6 \
+ --hash=sha256:1e940048f51d0b0999099a9b78629ab8a64b62af5e9ff1b2b062439c21ee244d \
+ --hash=sha256:4a9acbfd9c135a72f80d5dbff7588dfb0c81458244a89b9e83526e8595880e0a \
+ --hash=sha256:4acdde39dfe410c877b4ccc0d4b57eb952100e4ee26bbdf6cfdb88e2033a7d31 \
+ --hash=sha256:8130482120500476a027171f8f3c8dfc2536b591716eea71fc5da22cae13131b \
+ --hash=sha256:88bc7e7b393c32e41e03ba77ef02fdd647da1f764c2cd028e69e0837080b79f6 \
+ --hash=sha256:9dcdc730939788b8b69ffd6d5d75fe5366e3ee007f1e36a99799ec0b0c001492 \
+ --hash=sha256:d80ed5827736ed5ec2bcedf536720476fd9d4fa4c79ef0cb24aea4c59332f361 \
+ --hash=sha256:da9e47ebc3f0a6d90fb169ef25f9fbcd29b4a4ef97a8b0e3e3a17800af1423f4 \
+ --hash=sha256:daab4a11dae228f1efa9efa3fe638b493b14d8d52c71fb3c7019e2f1df4514c2 \
+ --hash=sha256:e1a5ad1e895e5443e205568c85c04b4608e4e973dae42f4dfd9cb46c81d1486b \
+ --hash=sha256:f04e3060ae1f207f234d0608900c99c50edcb743e5e18276d78da2ddd727d39f
+ # via tensorflow
+mako==1.3.0 \
+ --hash=sha256:57d4e997349f1a92035aa25c17ace371a4213f2ca42f99bee9a602500cfd54d9 \
+ --hash=sha256:e3a9d388fd00e87043edbe8792f45880ac0114e9c4adc69f6e9bfb2c55e3b11b
+ # via -r third_party/python_requirements.in
+markdown==3.5.2 \
+ --hash=sha256:d43323865d89fc0cb9b20c75fc8ad313af307cc087e84b657d9eec768eddeadd \
+ --hash=sha256:e1ac7b3dc550ee80e602e71c1d168002f062e49f1b11e26a36264dafd4df2ef8
# via tensorboard
-markupsafe==2.1.2 \
- --hash=sha256:0576fe974b40a400449768941d5d0858cc624e3249dfd1e0c33674e5c7ca7aed \
- --hash=sha256:085fd3201e7b12809f9e6e9bc1e5c96a368c8523fad5afb02afe3c051ae4afcc \
- --hash=sha256:090376d812fb6ac5f171e5938e82e7f2d7adc2b629101cec0db8b267815c85e2 \
- --hash=sha256:0b462104ba25f1ac006fdab8b6a01ebbfbce9ed37fd37fd4acd70c67c973e460 \
- --hash=sha256:137678c63c977754abe9086a3ec011e8fd985ab90631145dfb9294ad09c102a7 \
- --hash=sha256:1bea30e9bf331f3fef67e0a3877b2288593c98a21ccb2cf29b74c581a4eb3af0 \
- --hash=sha256:22152d00bf4a9c7c83960521fc558f55a1adbc0631fbb00a9471e097b19d72e1 \
- --hash=sha256:22731d79ed2eb25059ae3df1dfc9cb1546691cc41f4e3130fe6bfbc3ecbbecfa \
- --hash=sha256:2298c859cfc5463f1b64bd55cb3e602528db6fa0f3cfd568d3605c50678f8f03 \
- --hash=sha256:28057e985dace2f478e042eaa15606c7efccb700797660629da387eb289b9323 \
- --hash=sha256:2e7821bffe00aa6bd07a23913b7f4e01328c3d5cc0b40b36c0bd81d362faeb65 \
- --hash=sha256:2ec4f2d48ae59bbb9d1f9d7efb9236ab81429a764dedca114f5fdabbc3788013 \
- --hash=sha256:340bea174e9761308703ae988e982005aedf427de816d1afe98147668cc03036 \
- --hash=sha256:40627dcf047dadb22cd25ea7ecfe9cbf3bbbad0482ee5920b582f3809c97654f \
- --hash=sha256:40dfd3fefbef579ee058f139733ac336312663c6706d1163b82b3003fb1925c4 \
- --hash=sha256:4cf06cdc1dda95223e9d2d3c58d3b178aa5dacb35ee7e3bbac10e4e1faacb419 \
- --hash=sha256:50c42830a633fa0cf9e7d27664637532791bfc31c731a87b202d2d8ac40c3ea2 \
- --hash=sha256:55f44b440d491028addb3b88f72207d71eeebfb7b5dbf0643f7c023ae1fba619 \
- --hash=sha256:608e7073dfa9e38a85d38474c082d4281f4ce276ac0010224eaba11e929dd53a \
- --hash=sha256:63ba06c9941e46fa389d389644e2d8225e0e3e5ebcc4ff1ea8506dce646f8c8a \
- --hash=sha256:65608c35bfb8a76763f37036547f7adfd09270fbdbf96608be2bead319728fcd \
- --hash=sha256:665a36ae6f8f20a4676b53224e33d456a6f5a72657d9c83c2aa00765072f31f7 \
- --hash=sha256:6d6607f98fcf17e534162f0709aaad3ab7a96032723d8ac8750ffe17ae5a0666 \
- --hash=sha256:7313ce6a199651c4ed9d7e4cfb4aa56fe923b1adf9af3b420ee14e6d9a73df65 \
- --hash=sha256:7668b52e102d0ed87cb082380a7e2e1e78737ddecdde129acadb0eccc5423859 \
- --hash=sha256:7df70907e00c970c60b9ef2938d894a9381f38e6b9db73c5be35e59d92e06625 \
- --hash=sha256:7e007132af78ea9df29495dbf7b5824cb71648d7133cf7848a2a5dd00d36f9ff \
- --hash=sha256:835fb5e38fd89328e9c81067fd642b3593c33e1e17e2fdbf77f5676abb14a156 \
- --hash=sha256:8bca7e26c1dd751236cfb0c6c72d4ad61d986e9a41bbf76cb445f69488b2a2bd \
- --hash=sha256:8db032bf0ce9022a8e41a22598eefc802314e81b879ae093f36ce9ddf39ab1ba \
- --hash=sha256:99625a92da8229df6d44335e6fcc558a5037dd0a760e11d84be2260e6f37002f \
- --hash=sha256:9cad97ab29dfc3f0249b483412c85c8ef4766d96cdf9dcf5a1e3caa3f3661cf1 \
- --hash=sha256:a4abaec6ca3ad8660690236d11bfe28dfd707778e2442b45addd2f086d6ef094 \
- --hash=sha256:a6e40afa7f45939ca356f348c8e23048e02cb109ced1eb8420961b2f40fb373a \
- --hash=sha256:a6f2fcca746e8d5910e18782f976489939d54a91f9411c32051b4aab2bd7c513 \
- --hash=sha256:a806db027852538d2ad7555b203300173dd1b77ba116de92da9afbc3a3be3eed \
- --hash=sha256:abcabc8c2b26036d62d4c746381a6f7cf60aafcc653198ad678306986b09450d \
- --hash=sha256:b8526c6d437855442cdd3d87eede9c425c4445ea011ca38d937db299382e6fa3 \
- --hash=sha256:bb06feb762bade6bf3c8b844462274db0c76acc95c52abe8dbed28ae3d44a147 \
- --hash=sha256:c0a33bc9f02c2b17c3ea382f91b4db0e6cde90b63b296422a939886a7a80de1c \
- --hash=sha256:c4a549890a45f57f1ebf99c067a4ad0cb423a05544accaf2b065246827ed9603 \
- --hash=sha256:ca244fa73f50a800cf8c3ebf7fd93149ec37f5cb9596aa8873ae2c1d23498601 \
- --hash=sha256:cf877ab4ed6e302ec1d04952ca358b381a882fbd9d1b07cccbfd61783561f98a \
- --hash=sha256:d9d971ec1e79906046aa3ca266de79eac42f1dbf3612a05dc9368125952bd1a1 \
- --hash=sha256:da25303d91526aac3672ee6d49a2f3db2d9502a4a60b55519feb1a4c7714e07d \
- --hash=sha256:e55e40ff0cc8cc5c07996915ad367fa47da6b3fc091fdadca7f5403239c5fec3 \
- --hash=sha256:f03a532d7dee1bed20bc4884194a16160a2de9ffc6354b3878ec9682bb623c54 \
- --hash=sha256:f1cd098434e83e656abf198f103a8207a8187c0fc110306691a2e94a78d0abb2 \
- --hash=sha256:f2bfb563d0211ce16b63c7cb9395d2c682a23187f54c3d79bfec33e6705473c6 \
- --hash=sha256:f8ffb705ffcf5ddd0e80b65ddf7bed7ee4f5a441ea7d3419e861a12eaf41af58
+markdown-it-py==3.0.0 \
+ --hash=sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1 \
+ --hash=sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb
+ # via rich
+markupsafe==2.1.3 \
+ --hash=sha256:05fb21170423db021895e1ea1e1f3ab3adb85d1c2333cbc2310f2a26bc77272e \
+ --hash=sha256:0a4e4a1aff6c7ac4cd55792abf96c915634c2b97e3cc1c7129578aa68ebd754e \
+ --hash=sha256:10bbfe99883db80bdbaff2dcf681dfc6533a614f700da1287707e8a5d78a8431 \
+ --hash=sha256:134da1eca9ec0ae528110ccc9e48041e0828d79f24121a1a146161103c76e686 \
+ --hash=sha256:14ff806850827afd6b07a5f32bd917fb7f45b046ba40c57abdb636674a8b559c \
+ --hash=sha256:1577735524cdad32f9f694208aa75e422adba74f1baee7551620e43a3141f559 \
+ --hash=sha256:1b40069d487e7edb2676d3fbdb2b0829ffa2cd63a2ec26c4938b2d34391b4ecc \
+ --hash=sha256:1b8dd8c3fd14349433c79fa8abeb573a55fc0fdd769133baac1f5e07abf54aeb \
+ --hash=sha256:1f67c7038d560d92149c060157d623c542173016c4babc0c1913cca0564b9939 \
+ --hash=sha256:282c2cb35b5b673bbcadb33a585408104df04f14b2d9b01d4c345a3b92861c2c \
+ --hash=sha256:2c1b19b3aaacc6e57b7e25710ff571c24d6c3613a45e905b1fde04d691b98ee0 \
+ --hash=sha256:2ef12179d3a291be237280175b542c07a36e7f60718296278d8593d21ca937d4 \
+ --hash=sha256:338ae27d6b8745585f87218a3f23f1512dbf52c26c28e322dbe54bcede54ccb9 \
+ --hash=sha256:3c0fae6c3be832a0a0473ac912810b2877c8cb9d76ca48de1ed31e1c68386575 \
+ --hash=sha256:3fd4abcb888d15a94f32b75d8fd18ee162ca0c064f35b11134be77050296d6ba \
+ --hash=sha256:42de32b22b6b804f42c5d98be4f7e5e977ecdd9ee9b660fda1a3edf03b11792d \
+ --hash=sha256:47d4f1c5f80fc62fdd7777d0d40a2e9dda0a05883ab11374334f6c4de38adffd \
+ --hash=sha256:504b320cd4b7eff6f968eddf81127112db685e81f7e36e75f9f84f0df46041c3 \
+ --hash=sha256:525808b8019e36eb524b8c68acdd63a37e75714eac50e988180b169d64480a00 \
+ --hash=sha256:56d9f2ecac662ca1611d183feb03a3fa4406469dafe241673d521dd5ae92a155 \
+ --hash=sha256:5bbe06f8eeafd38e5d0a4894ffec89378b6c6a625ff57e3028921f8ff59318ac \
+ --hash=sha256:65c1a9bcdadc6c28eecee2c119465aebff8f7a584dd719facdd9e825ec61ab52 \
+ --hash=sha256:68e78619a61ecf91e76aa3e6e8e33fc4894a2bebe93410754bd28fce0a8a4f9f \
+ --hash=sha256:69c0f17e9f5a7afdf2cc9fb2d1ce6aabdb3bafb7f38017c0b77862bcec2bbad8 \
+ --hash=sha256:6b2b56950d93e41f33b4223ead100ea0fe11f8e6ee5f641eb753ce4b77a7042b \
+ --hash=sha256:715d3562f79d540f251b99ebd6d8baa547118974341db04f5ad06d5ea3eb8007 \
+ --hash=sha256:787003c0ddb00500e49a10f2844fac87aa6ce977b90b0feaaf9de23c22508b24 \
+ --hash=sha256:7ef3cb2ebbf91e330e3bb937efada0edd9003683db6b57bb108c4001f37a02ea \
+ --hash=sha256:8023faf4e01efadfa183e863fefde0046de576c6f14659e8782065bcece22198 \
+ --hash=sha256:8758846a7e80910096950b67071243da3e5a20ed2546e6392603c096778d48e0 \
+ --hash=sha256:8afafd99945ead6e075b973fefa56379c5b5c53fd8937dad92c662da5d8fd5ee \
+ --hash=sha256:8c41976a29d078bb235fea9b2ecd3da465df42a562910f9022f1a03107bd02be \
+ --hash=sha256:8e254ae696c88d98da6555f5ace2279cf7cd5b3f52be2b5cf97feafe883b58d2 \
+ --hash=sha256:8f9293864fe09b8149f0cc42ce56e3f0e54de883a9de90cd427f191c346eb2e1 \
+ --hash=sha256:9402b03f1a1b4dc4c19845e5c749e3ab82d5078d16a2a4c2cd2df62d57bb0707 \
+ --hash=sha256:962f82a3086483f5e5f64dbad880d31038b698494799b097bc59c2edf392fce6 \
+ --hash=sha256:9aad3c1755095ce347e26488214ef77e0485a3c34a50c5a5e2471dff60b9dd9c \
+ --hash=sha256:9dcdfd0eaf283af041973bff14a2e143b8bd64e069f4c383416ecd79a81aab58 \
+ --hash=sha256:aa57bd9cf8ae831a362185ee444e15a93ecb2e344c8e52e4d721ea3ab6ef1823 \
+ --hash=sha256:aa7bd130efab1c280bed0f45501b7c8795f9fdbeb02e965371bbef3523627779 \
+ --hash=sha256:ab4a0df41e7c16a1392727727e7998a467472d0ad65f3ad5e6e765015df08636 \
+ --hash=sha256:ad9e82fb8f09ade1c3e1b996a6337afac2b8b9e365f926f5a61aacc71adc5b3c \
+ --hash=sha256:af598ed32d6ae86f1b747b82783958b1a4ab8f617b06fe68795c7f026abbdcad \
+ --hash=sha256:b076b6226fb84157e3f7c971a47ff3a679d837cf338547532ab866c57930dbee \
+ --hash=sha256:b7ff0f54cb4ff66dd38bebd335a38e2c22c41a8ee45aa608efc890ac3e3931bc \
+ --hash=sha256:bfce63a9e7834b12b87c64d6b155fdd9b3b96191b6bd334bf37db7ff1fe457f2 \
+ --hash=sha256:c011a4149cfbcf9f03994ec2edffcb8b1dc2d2aede7ca243746df97a5d41ce48 \
+ --hash=sha256:c9c804664ebe8f83a211cace637506669e7890fec1b4195b505c214e50dd4eb7 \
+ --hash=sha256:ca379055a47383d02a5400cb0d110cef0a776fc644cda797db0c5696cfd7e18e \
+ --hash=sha256:cb0932dc158471523c9637e807d9bfb93e06a95cbf010f1a38b98623b929ef2b \
+ --hash=sha256:cd0f502fe016460680cd20aaa5a76d241d6f35a1c3350c474bac1273803893fa \
+ --hash=sha256:ceb01949af7121f9fc39f7d27f91be8546f3fb112c608bc4029aef0bab86a2a5 \
+ --hash=sha256:d080e0a5eb2529460b30190fcfcc4199bd7f827663f858a226a81bc27beaa97e \
+ --hash=sha256:dd15ff04ffd7e05ffcb7fe79f1b98041b8ea30ae9234aed2a9168b5797c3effb \
+ --hash=sha256:df0be2b576a7abbf737b1575f048c23fb1d769f267ec4358296f31c2479db8f9 \
+ --hash=sha256:e09031c87a1e51556fdcb46e5bd4f59dfb743061cf93c4d6831bf894f125eb57 \
+ --hash=sha256:e4dd52d80b8c83fdce44e12478ad2e85c64ea965e75d66dbeafb0a3e77308fcc \
+ --hash=sha256:f698de3fd0c4e6972b92290a45bd9b1536bffe8c6759c62471efaa8acb4c37bc \
+ --hash=sha256:fec21693218efe39aa7f8599346e90c705afa52c5b31ae019b2e57e8f6542bb2 \
+ --hash=sha256:ffcc3f7c66b5f5b7931a5aa68fc9cecc51e685ef90282f4a82f0f5e9b704ad11
# via
# mako
# werkzeug
-ml-dtypes==0.1.0 \
- --hash=sha256:273c306db846005b83a98c9c7ec3dc8fa20e8f11c3772c8e8c20cc12d8abfd4b \
- --hash=sha256:2de6c81b0da398d54aabdd7de599f2dfc43e30b65d9fad379a69f4cc4ae165d3 \
- --hash=sha256:36e8518c8fd2c38729f020125f39ef07b045f5c16d0846320c7252d7773285ee \
- --hash=sha256:377f2d5cfbf809b59188e0bfda4a0774e658541f575b637fee4850d99c2f9fdc \
- --hash=sha256:41b6beeaea47e2466b94068664c9a45b2a65dd023aa4e5deeb5a73303661344e \
- --hash=sha256:77970beeb3cf6ac559c4b6b393f24778a5abd34fafbaad82d5a0d17d0f148936 \
- --hash=sha256:87aa1cf83d41fed5a40fc27ee57ac4c1bf904e940f082531d3d58f1c318b5928 \
- --hash=sha256:8c5c9fe086756fbc1bf51296431d64429536093cf6e2ba592e042d7fc07c8514 \
- --hash=sha256:8de9bbf5bed587a1166699447ea14d1e8fe66d4e812811e37bf2f4d988475476 \
- --hash=sha256:99fab8262d175c49bf1655c229244f301274e8289449c350ba4d5b95ade07d9a \
- --hash=sha256:a29fbf128583673eca0f43def1dbe77e02c1e8b8a8331db2877bbb57d091ef11 \
- --hash=sha256:ad765159ac6c18d5ee7d325fcf34d3106a9d9d7a49713d998f5cfa330a1459b4 \
- --hash=sha256:b9c5578dffd85637a7dd437192de18bc1a14eb6ba7d53ef40de3f84c51c789e5 \
- --hash=sha256:c1fc0afe63ce99069f9d7e0693a61cfd0aea90241fc3821af9953d0c11f4048a \
- --hash=sha256:c9218175b06764b8ddc95cb18d11a6c4b48a4b103a31c9ea2b2c3cd0cfc369f8 \
- --hash=sha256:dee8ea629b8e3e20c6649852c1b9deacfa13384ab9337f2c9e717e401d102f23 \
- --hash=sha256:ffb7882dd46399217dc54f37affc899e0a29a4cfb63e5bf733ac0baf4a179c77
- # via jax
-numpy==1.23.5 \
- --hash=sha256:01dd17cbb340bf0fc23981e52e1d18a9d4050792e8fb8363cecbf066a84b827d \
- --hash=sha256:06005a2ef6014e9956c09ba07654f9837d9e26696a0470e42beedadb78c11b07 \
- --hash=sha256:09b7847f7e83ca37c6e627682f145856de331049013853f344f37b0c9690e3df \
- --hash=sha256:0aaee12d8883552fadfc41e96b4c82ee7d794949e2a7c3b3a7201e968c7ecab9 \
- --hash=sha256:0cbe9848fad08baf71de1a39e12d1b6310f1d5b2d0ea4de051058e6e1076852d \
- --hash=sha256:1b1766d6f397c18153d40015ddfc79ddb715cabadc04d2d228d4e5a8bc4ded1a \
- --hash=sha256:33161613d2269025873025b33e879825ec7b1d831317e68f4f2f0f84ed14c719 \
- --hash=sha256:5039f55555e1eab31124a5768898c9e22c25a65c1e0037f4d7c495a45778c9f2 \
- --hash=sha256:522e26bbf6377e4d76403826ed689c295b0b238f46c28a7251ab94716da0b280 \
- --hash=sha256:56e454c7833e94ec9769fa0f86e6ff8e42ee38ce0ce1fa4cbb747ea7e06d56aa \
- --hash=sha256:58f545efd1108e647604a1b5aa809591ccd2540f468a880bedb97247e72db387 \
- --hash=sha256:5e05b1c973a9f858c74367553e236f287e749465f773328c8ef31abe18f691e1 \
- --hash=sha256:7903ba8ab592b82014713c491f6c5d3a1cde5b4a3bf116404e08f5b52f6daf43 \
- --hash=sha256:8969bfd28e85c81f3f94eb4a66bc2cf1dbdc5c18efc320af34bffc54d6b1e38f \
- --hash=sha256:92c8c1e89a1f5028a4c6d9e3ccbe311b6ba53694811269b992c0b224269e2398 \
- --hash=sha256:9c88793f78fca17da0145455f0d7826bcb9f37da4764af27ac945488116efe63 \
- --hash=sha256:a7ac231a08bb37f852849bbb387a20a57574a97cfc7b6cabb488a4fc8be176de \
- --hash=sha256:abdde9f795cf292fb9651ed48185503a2ff29be87770c3b8e2a14b0cd7aa16f8 \
- --hash=sha256:af1da88f6bc3d2338ebbf0e22fe487821ea4d8e89053e25fa59d1d79786e7481 \
- --hash=sha256:b2a9ab7c279c91974f756c84c365a669a887efa287365a8e2c418f8b3ba73fb0 \
- --hash=sha256:bf837dc63ba5c06dc8797c398db1e223a466c7ece27a1f7b5232ba3466aafe3d \
- --hash=sha256:ca51fcfcc5f9354c45f400059e88bc09215fb71a48d3768fb80e357f3b457e1e \
- --hash=sha256:ce571367b6dfe60af04e04a1834ca2dc5f46004ac1cc756fb95319f64c095a96 \
- --hash=sha256:d208a0f8729f3fb790ed18a003f3a57895b989b40ea4dce4717e9cf4af62c6bb \
- --hash=sha256:dbee87b469018961d1ad79b1a5d50c0ae850000b639bcb1b694e9981083243b6 \
- --hash=sha256:e9f4c4e51567b616be64e05d517c79a8a22f3606499941d97bb76f2ca59f982d \
- --hash=sha256:f063b69b090c9d918f9df0a12116029e274daf0181df392839661c4c7ec9018a \
- --hash=sha256:f9a909a8bae284d46bbfdefbdd4a262ba19d3bc9921b1e76126b1d21c3c34135
+mdurl==0.1.2 \
+ --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
+ --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
+ # via markdown-it-py
+ml-dtypes==0.2.0 \
+ --hash=sha256:022d5a4ee6be14569c2a9d1549e16f1ec87ca949681d0dca59995445d5fcdd5b \
+ --hash=sha256:1749b60348da71fd3c2ab303fdbc1965958dc50775ead41f5669c932a341cafd \
+ --hash=sha256:32107e7fa9f62db9a5281de923861325211dfff87bd23faefb27b303314635ab \
+ --hash=sha256:35b984cddbe8173b545a0e3334fe56ea1a5c3eb67c507f60d0cfde1d3fa8f8c2 \
+ --hash=sha256:36d28b8861a8931695e5a31176cad5ae85f6504906650dea5598fbec06c94606 \
+ --hash=sha256:50845af3e9a601810751b55091dee6c2562403fa1cb4e0123675cf3a4fc2c17a \
+ --hash=sha256:6488eb642acaaf08d8020f6de0a38acee7ac324c1e6e92ee0c0fea42422cb797 \
+ --hash=sha256:75015818a7fccf99a5e8ed18720cb430f3e71a8838388840f4cdf225c036c983 \
+ --hash=sha256:80d304c836d73f10605c58ccf7789c171cc229bfb678748adfb7cea2510dfd0e \
+ --hash=sha256:832a019a1b6db5c4422032ca9940a990fa104eee420f643713241b3a518977fa \
+ --hash=sha256:8faaf0897942c8253dd126662776ba45f0a5861968cf0f06d6d465f8a7bc298a \
+ --hash=sha256:bc29a0524ef5e23a7fbb8d881bdecabeb3fc1d19d9db61785d077a86cb94fab2 \
+ --hash=sha256:df6a76e1c8adf484feb138ed323f9f40a7b6c21788f120f7c78bec20ac37ee81 \
+ --hash=sha256:e70047ec2c83eaee01afdfdabee2c5b0c133804d90d0f7db4dd903360fcc537c \
+ --hash=sha256:e85ba8e24cf48d456e564688e981cf379d4c8e644db0a2f719b78de281bac2ca \
+ --hash=sha256:f00c71c8c63e03aff313bc6a7aeaac9a4f1483a921a6ffefa6d4404efd1af3d0 \
+ --hash=sha256:f08c391c2794f2aad358e6f4c70785a9a7b1df980ef4c232b3ccd4f6fe39f719
+ # via tensorflow
+more-itertools==10.2.0 \
+ --hash=sha256:686b06abe565edfab151cb8fd385a05651e1fdf8f0a14191e4439283421f8684 \
+ --hash=sha256:8fccb480c43d3e99a00087634c06dd02b0d50fbf088b380de5a41a015ec239e1
+ # via jaraco-classes
+nh3==0.2.15 \
+ --hash=sha256:0d02d0ff79dfd8208ed25a39c12cbda092388fff7f1662466e27d97ad011b770 \
+ --hash=sha256:3277481293b868b2715907310c7be0f1b9d10491d5adf9fce11756a97e97eddf \
+ --hash=sha256:3b803a5875e7234907f7d64777dfde2b93db992376f3d6d7af7f3bc347deb305 \
+ --hash=sha256:427fecbb1031db085eaac9931362adf4a796428ef0163070c484b5a768e71601 \
+ --hash=sha256:5f0d77272ce6d34db6c87b4f894f037d55183d9518f948bba236fe81e2bb4e28 \
+ --hash=sha256:60684857cfa8fdbb74daa867e5cad3f0c9789415aba660614fe16cd66cbb9ec7 \
+ --hash=sha256:6f42f99f0cf6312e470b6c09e04da31f9abaadcd3eb591d7d1a88ea931dca7f3 \
+ --hash=sha256:86e447a63ca0b16318deb62498db4f76fc60699ce0a1231262880b38b6cff911 \
+ --hash=sha256:8d595df02413aa38586c24811237e95937ef18304e108b7e92c890a06793e3bf \
+ --hash=sha256:9c0d415f6b7f2338f93035bba5c0d8c1b464e538bfbb1d598acd47d7969284f0 \
+ --hash=sha256:a5167a6403d19c515217b6bcaaa9be420974a6ac30e0da9e84d4fc67a5d474c5 \
+ --hash=sha256:ac19c0d68cd42ecd7ead91a3a032fdfff23d29302dbb1311e641a130dfefba97 \
+ --hash=sha256:b1e97221cedaf15a54f5243f2c5894bb12ca951ae4ddfd02a9d4ea9df9e1a29d \
+ --hash=sha256:bc2d086fb540d0fa52ce35afaded4ea526b8fc4d3339f783db55c95de40ef02e \
+ --hash=sha256:d1e30ff2d8d58fb2a14961f7aac1bbb1c51f9bdd7da727be35c63826060b0bf3 \
+ --hash=sha256:f3b53ba93bb7725acab1e030bc2ecd012a817040fd7851b332f86e2f9bb98dc6
+ # via readme-renderer
+numpy==1.26.3 \
+ --hash=sha256:02f98011ba4ab17f46f80f7f8f1c291ee7d855fcef0a5a98db80767a468c85cd \
+ --hash=sha256:0b7e807d6888da0db6e7e75838444d62495e2b588b99e90dd80c3459594e857b \
+ --hash=sha256:12c70ac274b32bc00c7f61b515126c9205323703abb99cd41836e8125ea0043e \
+ --hash=sha256:1666f634cb3c80ccbd77ec97bc17337718f56d6658acf5d3b906ca03e90ce87f \
+ --hash=sha256:18c3319a7d39b2c6a9e3bb75aab2304ab79a811ac0168a671a62e6346c29b03f \
+ --hash=sha256:211ddd1e94817ed2d175b60b6374120244a4dd2287f4ece45d49228b4d529178 \
+ --hash=sha256:21a9484e75ad018974a2fdaa216524d64ed4212e418e0a551a2d83403b0531d3 \
+ --hash=sha256:39763aee6dfdd4878032361b30b2b12593fb445ddb66bbac802e2113eb8a6ac4 \
+ --hash=sha256:3c67423b3703f8fbd90f5adaa37f85b5794d3366948efe9a5190a5f3a83fc34e \
+ --hash=sha256:46f47ee566d98849323f01b349d58f2557f02167ee301e5e28809a8c0e27a2d0 \
+ --hash=sha256:51c7f1b344f302067b02e0f5b5d2daa9ed4a721cf49f070280ac202738ea7f00 \
+ --hash=sha256:5f24750ef94d56ce6e33e4019a8a4d68cfdb1ef661a52cdaee628a56d2437419 \
+ --hash=sha256:697df43e2b6310ecc9d95f05d5ef20eacc09c7c4ecc9da3f235d39e71b7da1e4 \
+ --hash=sha256:6d45b3ec2faed4baca41c76617fcdcfa4f684ff7a151ce6fc78ad3b6e85af0a6 \
+ --hash=sha256:77810ef29e0fb1d289d225cabb9ee6cf4d11978a00bb99f7f8ec2132a84e0166 \
+ --hash=sha256:7ca4f24341df071877849eb2034948459ce3a07915c2734f1abb4018d9c49d7b \
+ --hash=sha256:7f784e13e598e9594750b2ef6729bcd5a47f6cfe4a12cca13def35e06d8163e3 \
+ --hash=sha256:806dd64230dbbfaca8a27faa64e2f414bf1c6622ab78cc4264f7f5f028fee3bf \
+ --hash=sha256:867e3644e208c8922a3be26fc6bbf112a035f50f0a86497f98f228c50c607bb2 \
+ --hash=sha256:8c66d6fec467e8c0f975818c1796d25c53521124b7cfb760114be0abad53a0a2 \
+ --hash=sha256:8ed07a90f5450d99dad60d3799f9c03c6566709bd53b497eb9ccad9a55867f36 \
+ --hash=sha256:9bc6d1a7f8cedd519c4b7b1156d98e051b726bf160715b769106661d567b3f03 \
+ --hash=sha256:9e1591f6ae98bcfac2a4bbf9221c0b92ab49762228f38287f6eeb5f3f55905ce \
+ --hash=sha256:9e87562b91f68dd8b1c39149d0323b42e0082db7ddb8e934ab4c292094d575d6 \
+ --hash=sha256:a7081fd19a6d573e1a05e600c82a1c421011db7935ed0d5c483e9dd96b99cf13 \
+ --hash=sha256:a8474703bffc65ca15853d5fd4d06b18138ae90c17c8d12169968e998e448bb5 \
+ --hash=sha256:af36e0aa45e25c9f57bf684b1175e59ea05d9a7d3e8e87b7ae1a1da246f2767e \
+ --hash=sha256:b1240f767f69d7c4c8a29adde2310b871153df9b26b5cb2b54a561ac85146485 \
+ --hash=sha256:b4d362e17bcb0011738c2d83e0a65ea8ce627057b2fdda37678f4374a382a137 \
+ --hash=sha256:b831295e5472954104ecb46cd98c08b98b49c69fdb7040483aff799a755a7374 \
+ --hash=sha256:b8c275f0ae90069496068c714387b4a0eba5d531aace269559ff2b43655edd58 \
+ --hash=sha256:bdd2b45bf079d9ad90377048e2747a0c82351989a2165821f0c96831b4a2a54b \
+ --hash=sha256:cc0743f0302b94f397a4a65a660d4cd24267439eb16493fb3caad2e4389bccbb \
+ --hash=sha256:da4b0c6c699a0ad73c810736303f7fbae483bcb012e38d7eb06a5e3b432c981b \
+ --hash=sha256:f25e2811a9c932e43943a2615e65fc487a0b6b49218899e62e426e7f0a57eeda \
+ --hash=sha256:f73497e8c38295aaa4741bdfa4fda1a5aedda5473074369eca10626835445511
# via
# -r third_party/python_requirements.in
# h5py
- # jax
# ml-dtypes
# opt-einsum
- # scipy
# tensorboard
- # tensorflow-cpu
+ # tensorflow
oauthlib==3.2.2 \
--hash=sha256:8139f29aac13e25d502680e9e19963e83f16838d48a0d71c287fe40e7067fbca \
--hash=sha256:9859c40929662bec5d64f34d01c99e093149682a3f38915dc0655d5a633dd918
@@ -344,102 +522,110 @@
opt-einsum==3.3.0 \
--hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
--hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
- # via
- # jax
- # tensorflow-cpu
-packaging==23.1 \
- --hash=sha256:994793af429502c4ea2ebf6bf664629d07c1a9fe974af92966e4b8d2df7edc61 \
- --hash=sha256:a392980d2b6cffa644431898be54b0045151319d1e7ec34f0cfed48767dd334f
- # via tensorflow-cpu
-pillow==9.5.0 \
- --hash=sha256:07999f5834bdc404c442146942a2ecadd1cb6292f5229f4ed3b31e0a108746b1 \
- --hash=sha256:0852ddb76d85f127c135b6dd1f0bb88dbb9ee990d2cd9aa9e28526c93e794fba \
- --hash=sha256:1781a624c229cb35a2ac31cc4a77e28cafc8900733a864870c49bfeedacd106a \
- --hash=sha256:1e7723bd90ef94eda669a3c2c19d549874dd5badaeefabefd26053304abe5799 \
- --hash=sha256:229e2c79c00e85989a34b5981a2b67aa079fd08c903f0aaead522a1d68d79e51 \
- --hash=sha256:22baf0c3cf0c7f26e82d6e1adf118027afb325e703922c8dfc1d5d0156bb2eeb \
- --hash=sha256:252a03f1bdddce077eff2354c3861bf437c892fb1832f75ce813ee94347aa9b5 \
- --hash=sha256:2dfaaf10b6172697b9bceb9a3bd7b951819d1ca339a5ef294d1f1ac6d7f63270 \
- --hash=sha256:322724c0032af6692456cd6ed554bb85f8149214d97398bb80613b04e33769f6 \
- --hash=sha256:35f6e77122a0c0762268216315bf239cf52b88865bba522999dc38f1c52b9b47 \
- --hash=sha256:375f6e5ee9620a271acb6820b3d1e94ffa8e741c0601db4c0c4d3cb0a9c224bf \
- --hash=sha256:3ded42b9ad70e5f1754fb7c2e2d6465a9c842e41d178f262e08b8c85ed8a1d8e \
- --hash=sha256:432b975c009cf649420615388561c0ce7cc31ce9b2e374db659ee4f7d57a1f8b \
- --hash=sha256:482877592e927fd263028c105b36272398e3e1be3269efda09f6ba21fd83ec66 \
- --hash=sha256:489f8389261e5ed43ac8ff7b453162af39c3e8abd730af8363587ba64bb2e865 \
- --hash=sha256:54f7102ad31a3de5666827526e248c3530b3a33539dbda27c6843d19d72644ec \
- --hash=sha256:560737e70cb9c6255d6dcba3de6578a9e2ec4b573659943a5e7e4af13f298f5c \
- --hash=sha256:5671583eab84af046a397d6d0ba25343c00cd50bce03787948e0fff01d4fd9b1 \
- --hash=sha256:5ba1b81ee69573fe7124881762bb4cd2e4b6ed9dd28c9c60a632902fe8db8b38 \
- --hash=sha256:5d4ebf8e1db4441a55c509c4baa7a0587a0210f7cd25fcfe74dbbce7a4bd1906 \
- --hash=sha256:60037a8db8750e474af7ffc9faa9b5859e6c6d0a50e55c45576bf28be7419705 \
- --hash=sha256:608488bdcbdb4ba7837461442b90ea6f3079397ddc968c31265c1e056964f1ef \
- --hash=sha256:6608ff3bf781eee0cd14d0901a2b9cc3d3834516532e3bd673a0a204dc8615fc \
- --hash=sha256:662da1f3f89a302cc22faa9f14a262c2e3951f9dbc9617609a47521c69dd9f8f \
- --hash=sha256:7002d0797a3e4193c7cdee3198d7c14f92c0836d6b4a3f3046a64bd1ce8df2bf \
- --hash=sha256:763782b2e03e45e2c77d7779875f4432e25121ef002a41829d8868700d119392 \
- --hash=sha256:77165c4a5e7d5a284f10a6efaa39a0ae8ba839da344f20b111d62cc932fa4e5d \
- --hash=sha256:7c9af5a3b406a50e313467e3565fc99929717f780164fe6fbb7704edba0cebbe \
- --hash=sha256:7ec6f6ce99dab90b52da21cf0dc519e21095e332ff3b399a357c187b1a5eee32 \
- --hash=sha256:833b86a98e0ede388fa29363159c9b1a294b0905b5128baf01db683672f230f5 \
- --hash=sha256:84a6f19ce086c1bf894644b43cd129702f781ba5751ca8572f08aa40ef0ab7b7 \
- --hash=sha256:8507eda3cd0608a1f94f58c64817e83ec12fa93a9436938b191b80d9e4c0fc44 \
- --hash=sha256:85ec677246533e27770b0de5cf0f9d6e4ec0c212a1f89dfc941b64b21226009d \
- --hash=sha256:8aca1152d93dcc27dc55395604dcfc55bed5f25ef4c98716a928bacba90d33a3 \
- --hash=sha256:8d935f924bbab8f0a9a28404422da8af4904e36d5c33fc6f677e4c4485515625 \
- --hash=sha256:8f36397bf3f7d7c6a3abdea815ecf6fd14e7fcd4418ab24bae01008d8d8ca15e \
- --hash=sha256:91ec6fe47b5eb5a9968c79ad9ed78c342b1f97a091677ba0e012701add857829 \
- --hash=sha256:965e4a05ef364e7b973dd17fc765f42233415974d773e82144c9bbaaaea5d089 \
- --hash=sha256:96e88745a55b88a7c64fa49bceff363a1a27d9a64e04019c2281049444a571e3 \
- --hash=sha256:99eb6cafb6ba90e436684e08dad8be1637efb71c4f2180ee6b8f940739406e78 \
- --hash=sha256:9adf58f5d64e474bed00d69bcd86ec4bcaa4123bfa70a65ce72e424bfb88ed96 \
- --hash=sha256:9b1af95c3a967bf1da94f253e56b6286b50af23392a886720f563c547e48e964 \
- --hash=sha256:a0aa9417994d91301056f3d0038af1199eb7adc86e646a36b9e050b06f526597 \
- --hash=sha256:a0f9bb6c80e6efcde93ffc51256d5cfb2155ff8f78292f074f60f9e70b942d99 \
- --hash=sha256:a127ae76092974abfbfa38ca2d12cbeddcdeac0fb71f9627cc1135bedaf9d51a \
- --hash=sha256:aaf305d6d40bd9632198c766fb64f0c1a83ca5b667f16c1e79e1661ab5060140 \
- --hash=sha256:aca1c196f407ec7cf04dcbb15d19a43c507a81f7ffc45b690899d6a76ac9fda7 \
- --hash=sha256:ace6ca218308447b9077c14ea4ef381ba0b67ee78d64046b3f19cf4e1139ad16 \
- --hash=sha256:b416f03d37d27290cb93597335a2f85ed446731200705b22bb927405320de903 \
- --hash=sha256:bf548479d336726d7a0eceb6e767e179fbde37833ae42794602631a070d630f1 \
- --hash=sha256:c1170d6b195555644f0616fd6ed929dfcf6333b8675fcca044ae5ab110ded296 \
- --hash=sha256:c380b27d041209b849ed246b111b7c166ba36d7933ec6e41175fd15ab9eb1572 \
- --hash=sha256:c446d2245ba29820d405315083d55299a796695d747efceb5717a8b450324115 \
- --hash=sha256:c830a02caeb789633863b466b9de10c015bded434deb3ec87c768e53752ad22a \
- --hash=sha256:cb841572862f629b99725ebaec3287fc6d275be9b14443ea746c1dd325053cbd \
- --hash=sha256:cfa4561277f677ecf651e2b22dc43e8f5368b74a25a8f7d1d4a3a243e573f2d4 \
- --hash=sha256:cfcc2c53c06f2ccb8976fb5c71d448bdd0a07d26d8e07e321c103416444c7ad1 \
- --hash=sha256:d3c6b54e304c60c4181da1c9dadf83e4a54fd266a99c70ba646a9baa626819eb \
- --hash=sha256:d3d403753c9d5adc04d4694d35cf0391f0f3d57c8e0030aac09d7678fa8030aa \
- --hash=sha256:d9c206c29b46cfd343ea7cdfe1232443072bbb270d6a46f59c259460db76779a \
- --hash=sha256:e49eb4e95ff6fd7c0c402508894b1ef0e01b99a44320ba7d8ecbabefddcc5569 \
- --hash=sha256:f8286396b351785801a976b1e85ea88e937712ee2c3ac653710a4a57a8da5d9c \
- --hash=sha256:f8fc330c3370a81bbf3f88557097d1ea26cd8b019d6433aa59f71195f5ddebbf \
- --hash=sha256:fbd359831c1657d69bb81f0db962905ee05e5e9451913b18b831febfe0519082 \
- --hash=sha256:fe7e1c262d3392afcf5071df9afa574544f28eac825284596ac6db56e6d11062 \
- --hash=sha256:fed1e1cf6a42577953abbe8e6cf2fe2f566daebde7c34724ec8803c4c0cda579
+ # via tensorflow
+packaging==23.2 \
+ --hash=sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5 \
+ --hash=sha256:8c491190033a9af7e1d931d0b5dacc2ef47509b34dd0de67ed209b5203fc88c7
+ # via tensorflow
+pillow==10.2.0 \
+ --hash=sha256:0304004f8067386b477d20a518b50f3fa658a28d44e4116970abfcd94fac34a8 \
+ --hash=sha256:0689b5a8c5288bc0504d9fcee48f61a6a586b9b98514d7d29b840143d6734f39 \
+ --hash=sha256:0eae2073305f451d8ecacb5474997c08569fb4eb4ac231ffa4ad7d342fdc25ac \
+ --hash=sha256:0fb3e7fc88a14eacd303e90481ad983fd5b69c761e9e6ef94c983f91025da869 \
+ --hash=sha256:11fa2e5984b949b0dd6d7a94d967743d87c577ff0b83392f17cb3990d0d2fd6e \
+ --hash=sha256:127cee571038f252a552760076407f9cff79761c3d436a12af6000cd182a9d04 \
+ --hash=sha256:154e939c5f0053a383de4fd3d3da48d9427a7e985f58af8e94d0b3c9fcfcf4f9 \
+ --hash=sha256:15587643b9e5eb26c48e49a7b33659790d28f190fc514a322d55da2fb5c2950e \
+ --hash=sha256:170aeb00224ab3dc54230c797f8404507240dd868cf52066f66a41b33169bdbe \
+ --hash=sha256:1b5e1b74d1bd1b78bc3477528919414874748dd363e6272efd5abf7654e68bef \
+ --hash=sha256:1da3b2703afd040cf65ec97efea81cfba59cdbed9c11d8efc5ab09df9509fc56 \
+ --hash=sha256:1e23412b5c41e58cec602f1135c57dfcf15482013ce6e5f093a86db69646a5aa \
+ --hash=sha256:2247178effb34a77c11c0e8ac355c7a741ceca0a732b27bf11e747bbc950722f \
+ --hash=sha256:257d8788df5ca62c980314053197f4d46eefedf4e6175bc9412f14412ec4ea2f \
+ --hash=sha256:3031709084b6e7852d00479fd1d310b07d0ba82765f973b543c8af5061cf990e \
+ --hash=sha256:322209c642aabdd6207517e9739c704dc9f9db943015535783239022002f054a \
+ --hash=sha256:322bdf3c9b556e9ffb18f93462e5f749d3444ce081290352c6070d014c93feb2 \
+ --hash=sha256:33870dc4653c5017bf4c8873e5488d8f8d5f8935e2f1fb9a2208c47cdd66efd2 \
+ --hash=sha256:35bb52c37f256f662abdfa49d2dfa6ce5d93281d323a9af377a120e89a9eafb5 \
+ --hash=sha256:3c31822339516fb3c82d03f30e22b1d038da87ef27b6a78c9549888f8ceda39a \
+ --hash=sha256:3eedd52442c0a5ff4f887fab0c1c0bb164d8635b32c894bc1faf4c618dd89df2 \
+ --hash=sha256:3ff074fc97dd4e80543a3e91f69d58889baf2002b6be64347ea8cf5533188213 \
+ --hash=sha256:47c0995fc4e7f79b5cfcab1fc437ff2890b770440f7696a3ba065ee0fd496563 \
+ --hash=sha256:49d9ba1ed0ef3e061088cd1e7538a0759aab559e2e0a80a36f9fd9d8c0c21591 \
+ --hash=sha256:51f1a1bffc50e2e9492e87d8e09a17c5eea8409cda8d3f277eb6edc82813c17c \
+ --hash=sha256:52a50aa3fb3acb9cf7213573ef55d31d6eca37f5709c69e6858fe3bc04a5c2a2 \
+ --hash=sha256:54f1852cd531aa981bc0965b7d609f5f6cc8ce8c41b1139f6ed6b3c54ab82bfb \
+ --hash=sha256:609448742444d9290fd687940ac0b57fb35e6fd92bdb65386e08e99af60bf757 \
+ --hash=sha256:69ffdd6120a4737710a9eee73e1d2e37db89b620f702754b8f6e62594471dee0 \
+ --hash=sha256:6fad5ff2f13d69b7e74ce5b4ecd12cc0ec530fcee76356cac6742785ff71c452 \
+ --hash=sha256:7049e301399273a0136ff39b84c3678e314f2158f50f517bc50285fb5ec847ad \
+ --hash=sha256:70c61d4c475835a19b3a5aa42492409878bbca7438554a1f89d20d58a7c75c01 \
+ --hash=sha256:716d30ed977be8b37d3ef185fecb9e5a1d62d110dfbdcd1e2a122ab46fddb03f \
+ --hash=sha256:753cd8f2086b2b80180d9b3010dd4ed147efc167c90d3bf593fe2af21265e5a5 \
+ --hash=sha256:773efe0603db30c281521a7c0214cad7836c03b8ccff897beae9b47c0b657d61 \
+ --hash=sha256:7823bdd049099efa16e4246bdf15e5a13dbb18a51b68fa06d6c1d4d8b99a796e \
+ --hash=sha256:7c8f97e8e7a9009bcacbe3766a36175056c12f9a44e6e6f2d5caad06dcfbf03b \
+ --hash=sha256:823ef7a27cf86df6597fa0671066c1b596f69eba53efa3d1e1cb8b30f3533068 \
+ --hash=sha256:8373c6c251f7ef8bda6675dd6d2b3a0fcc31edf1201266b5cf608b62a37407f9 \
+ --hash=sha256:83b2021f2ade7d1ed556bc50a399127d7fb245e725aa0113ebd05cfe88aaf588 \
+ --hash=sha256:870ea1ada0899fd0b79643990809323b389d4d1d46c192f97342eeb6ee0b8483 \
+ --hash=sha256:8d12251f02d69d8310b046e82572ed486685c38f02176bd08baf216746eb947f \
+ --hash=sha256:9c23f307202661071d94b5e384e1e1dc7dfb972a28a2310e4ee16103e66ddb67 \
+ --hash=sha256:9d189550615b4948f45252d7f005e53c2040cea1af5b60d6f79491a6e147eef7 \
+ --hash=sha256:a086c2af425c5f62a65e12fbf385f7c9fcb8f107d0849dba5839461a129cf311 \
+ --hash=sha256:a2b56ba36e05f973d450582fb015594aaa78834fefe8dfb8fcd79b93e64ba4c6 \
+ --hash=sha256:aebb6044806f2e16ecc07b2a2637ee1ef67a11840a66752751714a0d924adf72 \
+ --hash=sha256:b1b3020d90c2d8e1dae29cf3ce54f8094f7938460fb5ce8bc5c01450b01fbaf6 \
+ --hash=sha256:b4b6b1e20608493548b1f32bce8cca185bf0480983890403d3b8753e44077129 \
+ --hash=sha256:b6f491cdf80ae540738859d9766783e3b3c8e5bd37f5dfa0b76abdecc5081f13 \
+ --hash=sha256:b792a349405fbc0163190fde0dc7b3fef3c9268292586cf5645598b48e63dc67 \
+ --hash=sha256:b7c2286c23cd350b80d2fc9d424fc797575fb16f854b831d16fd47ceec078f2c \
+ --hash=sha256:babf5acfede515f176833ed6028754cbcd0d206f7f614ea3447d67c33be12516 \
+ --hash=sha256:c365fd1703040de1ec284b176d6af5abe21b427cb3a5ff68e0759e1e313a5e7e \
+ --hash=sha256:c4225f5220f46b2fde568c74fca27ae9771536c2e29d7c04f4fb62c83275ac4e \
+ --hash=sha256:c570f24be1e468e3f0ce7ef56a89a60f0e05b30a3669a459e419c6eac2c35364 \
+ --hash=sha256:c6dafac9e0f2b3c78df97e79af707cdc5ef8e88208d686a4847bab8266870023 \
+ --hash=sha256:c8de2789052ed501dd829e9cae8d3dcce7acb4777ea4a479c14521c942d395b1 \
+ --hash=sha256:cb28c753fd5eb3dd859b4ee95de66cc62af91bcff5db5f2571d32a520baf1f04 \
+ --hash=sha256:cb4c38abeef13c61d6916f264d4845fab99d7b711be96c326b84df9e3e0ff62d \
+ --hash=sha256:d1b35bcd6c5543b9cb547dee3150c93008f8dd0f1fef78fc0cd2b141c5baf58a \
+ --hash=sha256:d8e6aeb9201e655354b3ad049cb77d19813ad4ece0df1249d3c793de3774f8c7 \
+ --hash=sha256:d8ecd059fdaf60c1963c58ceb8997b32e9dc1b911f5da5307aab614f1ce5c2fb \
+ --hash=sha256:da2b52b37dad6d9ec64e653637a096905b258d2fc2b984c41ae7d08b938a67e4 \
+ --hash=sha256:e87f0b2c78157e12d7686b27d63c070fd65d994e8ddae6f328e0dcf4a0cd007e \
+ --hash=sha256:edca80cbfb2b68d7b56930b84a0e45ae1694aeba0541f798e908a49d66b837f1 \
+ --hash=sha256:f379abd2f1e3dddb2b61bc67977a6b5a0a3f7485538bcc6f39ec76163891ee48 \
+ --hash=sha256:fe4c15f6c9285dc54ce6553a3ce908ed37c8f3825b5a51a15c91442bb955b868
# via -r third_party/python_requirements.in
-protobuf==4.23.0 \
- --hash=sha256:03eee35b60317112a72d19c54d0bff7bc58ff12fea4cd7b018232bd99758ffdf \
- --hash=sha256:2b94bd6df92d71bd1234a2ffe7ce96ddf6d10cf637a18d6b55ad0a89fbb7fc21 \
- --hash=sha256:36f5370a930cb77c8ad2f4135590c672d0d2c72d4a707c7d0058dce4b4b4a598 \
- --hash=sha256:5f1eba1da2a2f3f7df469fccddef3cc060b8a16cfe3cc65961ad36b4dbcf59c5 \
- --hash=sha256:6c16657d6717a0c62d5d740cb354fbad1b0d8cb811669e06fc1caa0ff4799ddd \
- --hash=sha256:6fe180b56e1169d72ecc4acbd39186339aed20af5384531b8e8979b02bbee159 \
- --hash=sha256:7cb5b9a05ce52c6a782bb97de52679bd3438ff2b7460eff5da348db65650f227 \
- --hash=sha256:9744e934ea5855d12191040ea198eaf704ac78665d365a89d9572e3b627c2688 \
- --hash=sha256:9f5a0fbfcdcc364f3986f9ed9f8bb1328fb84114fd790423ff3d7fdb0f85c2d1 \
- --hash=sha256:baca40d067dddd62141a129f244703160d278648b569e90bb0e3753067644711 \
- --hash=sha256:d5a35ff54e3f62e8fc7be02bb0d2fbc212bba1a5a9cc2748090690093996f07b \
- --hash=sha256:e62fb869762b4ba18666370e2f8a18f17f8ab92dd4467295c6d38be6f8fef60b \
- --hash=sha256:ebde3a023b8e11bfa6c890ef34cd6a8b47d586f26135e86c21344fe433daf2e2
+pkginfo==1.9.6 \
+ --hash=sha256:4b7a555a6d5a22169fcc9cf7bfd78d296b0361adad412a346c1226849af5e546 \
+ --hash=sha256:8fd5896e8718a4372f0ea9cc9d96f6417c9b986e23a4d116dda26b62cc29d046
+ # via twine
+platformdirs==4.1.0 \
+ --hash=sha256:11c8f37bcca40db96d8144522d925583bdb7a31f7b0e37e3ed4318400a8e2380 \
+ --hash=sha256:906d548203468492d432bcb294d4bc2fff751bf84971fbb2c10918cc206ee420
+ # via yapf
+protobuf==4.23.4 \
+ --hash=sha256:0a5759f5696895de8cc913f084e27fd4125e8fb0914bb729a17816a33819f474 \
+ --hash=sha256:351cc90f7d10839c480aeb9b870a211e322bf05f6ab3f55fcb2f51331f80a7d2 \
+ --hash=sha256:5fea3c64d41ea5ecf5697b83e41d09b9589e6f20b677ab3c48e5f242d9b7897b \
+ --hash=sha256:6dd9b9940e3f17077e820b75851126615ee38643c2c5332aa7a359988820c720 \
+ --hash=sha256:7b19b6266d92ca6a2a87effa88ecc4af73ebc5cfde194dc737cf8ef23a9a3b12 \
+ --hash=sha256:8547bf44fe8cec3c69e3042f5c4fb3e36eb2a7a013bb0a44c018fc1e427aafbd \
+ --hash=sha256:9053df6df8e5a76c84339ee4a9f5a2661ceee4a0dab019e8663c50ba324208b0 \
+ --hash=sha256:c3e0939433c40796ca4cfc0fac08af50b00eb66a40bbbc5dee711998fb0bbc1e \
+ --hash=sha256:ccd9430c0719dce806b93f89c91de7977304729e55377f872a92465d548329a9 \
+ --hash=sha256:e1c915778d8ced71e26fcf43c0866d7499891bca14c4368448a82edc61fdbc70 \
+ --hash=sha256:e9d0be5bf34b275b9f87ba7407796556abeeba635455d036c7351f7c183ef8ff \
+ --hash=sha256:effeac51ab79332d44fba74660d40ae79985901ac21bca408f8dc335a81aa597 \
+ --hash=sha256:fee88269a090ada09ca63551bf2f573eb2424035bcf2cb1b121895b01a46594a
# via
# -r third_party/python_requirements.in
# tensorboard
- # tensorflow-cpu
-pyasn1==0.5.0 \
- --hash=sha256:87a2121042a1ac9358cabcaf1d07680ff97ee6404333bacca15f76aa8ad01a57 \
- --hash=sha256:97b7290ca68e62a832558ec3976f15cbf911bf5d7c7039d8b861c2a0ece69fde
+ # tensorflow
+pyasn1==0.5.1 \
+ --hash=sha256:4439847c58d40b1d0a573d07e3856e95333f1976294494c325775aeca506eb58 \
+ --hash=sha256:6d391a96e59b23130a5cfa74d6fd7f388dbbe26cc8f1edf39fdddf08d9d6676c
# via
# pyasn1-modules
# rsa
@@ -447,120 +633,124 @@
--hash=sha256:5bd01446b736eb9d31512a30d46c1ac3395d676c6f3cafa4c03eb54b9925631c \
--hash=sha256:d3ccd6ed470d9ffbc716be08bd90efbd44d0734bc9303818f7336070984a162d
# via google-auth
-requests==2.30.0 \
- --hash=sha256:10e94cc4f3121ee6da529d358cdaeaff2f1c409cd377dbc72b825852f2f7e294 \
- --hash=sha256:239d7d4458afcb28a692cdd298d87542235f4ca8d36d03a15bfc128a6559a2f4
+pycparser==2.21 \
+ --hash=sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9 \
+ --hash=sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206
+ # via cffi
+pygments==2.17.2 \
+ --hash=sha256:b27c2826c47d0f3219f29554824c30c5e8945175d888647acd804ddd04af846c \
+ --hash=sha256:da46cec9fd2de5be3a8a784f434e4c4ab670b4ff54d605c4c2717e9d49c4c367
+ # via
+ # readme-renderer
+ # rich
+readme-renderer==42.0 \
+ --hash=sha256:13d039515c1f24de668e2c93f2e877b9dbe6c6c32328b90a40a49d8b2b85f36d \
+ --hash=sha256:2d55489f83be4992fe4454939d1a051c33edbab778e82761d060c9fc6b308cd1
+ # via twine
+requests==2.31.0 \
+ --hash=sha256:58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f \
+ --hash=sha256:942c5a758f98d790eaed1a29cb6eefc7ffb0d1cf7af05c3d2791656dbd6ad1e1
# via
# requests-oauthlib
+ # requests-toolbelt
# tensorboard
+ # twine
requests-oauthlib==1.3.1 \
--hash=sha256:2577c501a2fb8d05a304c09d090d6e47c306fef15809d102b327cf8364bddab5 \
--hash=sha256:75beac4a47881eeb94d5ea5d6ad31ef88856affe2332b9aafb52c6452ccf0d7a
# via google-auth-oauthlib
+requests-toolbelt==1.0.0 \
+ --hash=sha256:7681a0a3d047012b5bdc0ee37d7f8f07ebe76ab08caeccfc3921ce23c88d5bc6 \
+ --hash=sha256:cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06
+ # via twine
+rfc3986==2.0.0 \
+ --hash=sha256:50b1502b60e289cb37883f3dfd34532b8873c7de9f49bb546641ce9cbd256ebd \
+ --hash=sha256:97aacf9dbd4bfd829baad6e6309fa6573aaf1be3f6fa735c8ab05e46cecb261c
+ # via twine
+rich==13.7.0 \
+ --hash=sha256:5cb5123b5cf9ee70584244246816e9114227e0b98ad9176eede6ad54bf5403fa \
+ --hash=sha256:6da14c108c4866ee9520bbffa71f6fe3962e193b7da68720583850cd4548e235
+ # via twine
rsa==4.9 \
--hash=sha256:90260d9058e514786967344d0ef75fa8727eed8a7d2e43ce9f4bcf1b536174f7 \
--hash=sha256:e38464a49c6c85d7f1351b0126661487a7e0a14a50f1675ec50eb34d4f20ef21
# via google-auth
-scipy==1.10.1 \
- --hash=sha256:049a8bbf0ad95277ffba9b3b7d23e5369cc39e66406d60422c8cfef40ccc8415 \
- --hash=sha256:07c3457ce0b3ad5124f98a86533106b643dd811dd61b548e78cf4c8786652f6f \
- --hash=sha256:0f1564ea217e82c1bbe75ddf7285ba0709ecd503f048cb1236ae9995f64217bd \
- --hash=sha256:1553b5dcddd64ba9a0d95355e63fe6c3fc303a8fd77c7bc91e77d61363f7433f \
- --hash=sha256:15a35c4242ec5f292c3dd364a7c71a61be87a3d4ddcc693372813c0b73c9af1d \
- --hash=sha256:1b4735d6c28aad3cdcf52117e0e91d6b39acd4272f3f5cd9907c24ee931ad601 \
- --hash=sha256:2cf9dfb80a7b4589ba4c40ce7588986d6d5cebc5457cad2c2880f6bc2d42f3a5 \
- --hash=sha256:39becb03541f9e58243f4197584286e339029e8908c46f7221abeea4b749fa88 \
- --hash=sha256:43b8e0bcb877faf0abfb613d51026cd5cc78918e9530e375727bf0625c82788f \
- --hash=sha256:4b3f429188c66603a1a5c549fb414e4d3bdc2a24792e061ffbd607d3d75fd84e \
- --hash=sha256:4c0ff64b06b10e35215abce517252b375e580a6125fd5fdf6421b98efbefb2d2 \
- --hash=sha256:51af417a000d2dbe1ec6c372dfe688e041a7084da4fdd350aeb139bd3fb55353 \
- --hash=sha256:5678f88c68ea866ed9ebe3a989091088553ba12c6090244fdae3e467b1139c35 \
- --hash=sha256:79c8e5a6c6ffaf3a2262ef1be1e108a035cf4f05c14df56057b64acc5bebffb6 \
- --hash=sha256:7ff7f37b1bf4417baca958d254e8e2875d0cc23aaadbe65b3d5b3077b0eb23ea \
- --hash=sha256:aaea0a6be54462ec027de54fca511540980d1e9eea68b2d5c1dbfe084797be35 \
- --hash=sha256:bce5869c8d68cf383ce240e44c1d9ae7c06078a9396df68ce88a1230f93a30c1 \
- --hash=sha256:cd9f1027ff30d90618914a64ca9b1a77a431159df0e2a195d8a9e8a04c78abf9 \
- --hash=sha256:d925fa1c81b772882aa55bcc10bf88324dadb66ff85d548c71515f6689c6dac5 \
- --hash=sha256:e7354fd7527a4b0377ce55f286805b34e8c54b91be865bac273f527e1b839019 \
- --hash=sha256:fae8a7b898c42dffe3f7361c40d5952b6bf32d10c4569098d276b4c547905ee1
- # via jax
+secretstorage==3.3.3 \
+ --hash=sha256:2403533ef369eca6d2ba81718576c5e0f564d5cca1b58f73a8b23e7d4eeebd77 \
+ --hash=sha256:f356e6628222568e3af06f2eba8df495efa13b3b63081dafd4f7d9a7b7bc9f99
+ # via keyring
+setuptools==69.0.3 \
+ --hash=sha256:385eb4edd9c9d5c17540511303e39a147ce2fc04bc55289c322b9e5904fe2c05 \
+ --hash=sha256:be1af57fc409f93647f2e8e4573a142ed38724b8cdd389706a867bb4efcf1e78
+ # via
+ # tensorboard
+ # tensorflow
six==1.16.0 \
--hash=sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926 \
--hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254
# via
# astunparse
- # google-auth
# google-pasta
- # tensorflow-cpu
-tensorboard==2.12.3 \
- --hash=sha256:b4a69366784bc347e02fbe7d847e01896a649ca52f8948a11005e205dcf724fb
- # via tensorflow-cpu
-tensorboard-data-server==0.7.0 \
- --hash=sha256:64aa1be7c23e80b1a42c13b686eb0875bb70f5e755f4d2b8de5c1d880cf2267f \
- --hash=sha256:753d4214799b31da7b6d93837959abebbc6afa86e69eacf1e9a317a48daa31eb \
- --hash=sha256:eb7fa518737944dbf4f0cf83c2e40a7ac346bf91be2e6a0215de98be74e85454
+ # tensorboard
+ # tensorflow
+tensorboard==2.15.1 \
+ --hash=sha256:c46c1d1cf13a458c429868a78b2531d8ff5f682058d69ec0840b0bc7a38f1c0f
+ # via tensorflow
+tensorboard-data-server==0.7.2 \
+ --hash=sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb \
+ --hash=sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60 \
+ --hash=sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530
# via tensorboard
-tensorflow-cpu==2.12.0 \
- --hash=sha256:361b19b5a64bf611beccd22de1fc04f614a8c157ac99893d9702ed24932018d6 \
- --hash=sha256:374b15d1cec1a62006e388062e89dd4899a121272d41ea5d3fcbcc96e2d875c9 \
- --hash=sha256:55685b9a19c8ecb2587fb53914c045b188ed0289a2c6495e4e59d5fb082da9cc \
- --hash=sha256:5beeb99d2a1cc1383ca981513c35a4a18157e52d91a89e69c94cb7b7e411f0d8 \
- --hash=sha256:734ce850e2b3493041bdc071b594f0f78d35e4bfce5a7e0a98d449b20420e01d \
- --hash=sha256:8fdb636736f95094368bc7d26bb3b8ed93ba820cc5d95f847e00bf4a7645463d \
- --hash=sha256:a406f751180fe5282776e8bc84f39a2dc2b796c3ae35fbe20e4edc86ec580dd3 \
- --hash=sha256:b6ba926f9a56cdf0657defc6d046735e31ded383054f67c1a16ef2b0511f68d7 \
- --hash=sha256:b9c8f0d0658da8a5b25a4fe5ca315f86c449eb11e30d79cea49c7658be75a825 \
- --hash=sha256:d5ad746bf8c87d9a9fcea4698828ba1d101a7f7bfd323a2571130374a192578b \
- --hash=sha256:e8c7047552a2d759f3e65ac13e36dd24bb5fec2e6576e848287811ec44b3d62f \
- --hash=sha256:ef4f142b6fe75fcc71ada6331ed2a15ed61b7034187049d0ef1dac482d52db78
+tensorflow==2.15.0.post1 \
+ --hash=sha256:8716acd8eb2950db126d74a419ac1ed870558ba34efc7d8e506165ad5896b261 \
+ --hash=sha256:c2dc9b6a519a9caad80430220c17f604e9ae76f3b5007c07cc8a5321e9f9cad0 \
+ --hash=sha256:d4b3926a3759e08a1e818f01104c25e6952da6fb1d7a77c75d9f1d8407f72593
# via -r third_party/python_requirements.in
-tensorflow-estimator==2.12.0 \
- --hash=sha256:59b191bead4883822de3d63ac02ace11a83bfe6c10d64d0c4dfde75a50e60ca1
- # via tensorflow-cpu
-tensorflow-io-gcs-filesystem==0.32.0 \
- --hash=sha256:045d51bba586390d0545fcd8a18727d62b175eb142f6f4c6d719d39de40774cd \
- --hash=sha256:05e65d3cb6c93a7929b384d86c6369c63cbbab8a770440a3d95e094878403f9f \
- --hash=sha256:122be149e5f6a030f5c2901be0cc3cb07619232f7b03889e2cdf3da1c0d4f92f \
- --hash=sha256:1ce80e1555d6ee88dda67feddf366cc8b30252b5837a7a17303df7b06a71fc2e \
- --hash=sha256:21de7dcc06eb1e7de3c022b0072d90ba35ef886578149663437aa7a6fb5bf6b3 \
- --hash=sha256:28202492d904a6e280cf27560791e87ac1c7566000db82065d63a70c27008af2 \
- --hash=sha256:336d9b3fe6b55aea149c4f6aa1fd6ffaf27d4e5c37e55a182340b47caba38846 \
- --hash=sha256:5635df0bbe40f971dc1b946e3372744b0bdfda45c38ffcd28ef53a32bb8da4da \
- --hash=sha256:74a7e25e83d4117a7ebb09a3f247553a5497393ab48c3ee0cf0d17b405026817 \
- --hash=sha256:79fdd02103b8ae9f8b89af41f744c013fa1caaea709de19833917795e3063857 \
- --hash=sha256:7f15fd22e592661b10de317be2f42a0f84be7bfc5e6a565fcfcb04b60d625b78 \
- --hash=sha256:8214cdf85bea694160f9035ff395221c1e25e119784ccb4c104919b1f5dec84e \
- --hash=sha256:842f5f09cd756bdb3b4d0b5571b3a6f72fd534d42da938b9acf0ef462995eada \
- --hash=sha256:db682e9a510c27dd35710ba5a2c62c371e25b727741b2fe3a920355fa501e947
- # via tensorflow-cpu
-termcolor==2.3.0 \
- --hash=sha256:3afb05607b89aed0ffe25202399ee0867ad4d3cb4180d98aaf8eefa6a5f7d475 \
- --hash=sha256:b5b08f68937f138fe92f6c089b99f1e2da0ae56c52b78bf7075fd95420fd9a5a
- # via tensorflow-cpu
+tensorflow-estimator==2.15.0 \
+ --hash=sha256:aedf21eec7fb2dc91150fc91a1ce12bc44dbb72278a08b58e79ff87c9e28f153
+ # via tensorflow
+tensorflow-io-gcs-filesystem==0.35.0 \
+ --hash=sha256:0fce1466bdb91096b6d22e7df17358ba228bcb92db5cff83f2f9f1c68eb26788 \
+ --hash=sha256:1856fe321fdb75f3386d92109c60db6ef097f610b450f9cc69d76444fb9980d1 \
+ --hash=sha256:35b6eca7225c815d962254327195f191d88c3c9c2278a5ab23e0ac834acbadbb \
+ --hash=sha256:5521721b38105496d4b43a4ffb0af5b04cc4873d464f26fbceddf8d63815ce98 \
+ --hash=sha256:6e997389bfe008210cbd97c0c738d64282a2f03ad4d0536013bb0a9efde0c283 \
+ --hash=sha256:ac8f1de60fdf9c734aea967b98555e366ac8743f77bca15c49eff023f587076b \
+ --hash=sha256:b8fb3402fb1457482c386ea19371bc76383412ae9ea4396edb1e8adb4ba76f21 \
+ --hash=sha256:c4f786eebd98d401565374722f2e67f3878675b0d87489cbaa13c70ee6ac370a \
+ --hash=sha256:dd8f30908bf8b7b2a017d6b145720d105aff7f998422671b71729708ec7b2fe4 \
+ --hash=sha256:eb6bf8f5b40207ecb17e7fdc3b4fc824a8361267c14e9528c1688e16de135cb7
+ # via tensorflow
+termcolor==2.4.0 \
+ --hash=sha256:9297c0df9c99445c2412e832e882a7884038a25617c60cea2ad69488d4040d63 \
+ --hash=sha256:aab9e56047c8ac41ed798fa36d892a37aca6b3e9159f3e0c24bc64a9b3ac7b7a
+ # via tensorflow
tomli==2.0.1 \
--hash=sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc \
--hash=sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f
# via yapf
-typing-extensions==4.5.0 \
- --hash=sha256:5cb5f4a79139d699607b3ef622a1dedafa84e115ab0024e0d9c044a9479ca7cb \
- --hash=sha256:fb33085c39dd998ac16d1431ebc293a8b3eedd00fd4a32de0ff79002c19511b4
- # via tensorflow-cpu
-urllib3==1.26.15 \
- --hash=sha256:8a388717b9476f934a21484e8c8e61875ab60644d29b9b39e11e4b9dc1c6b305 \
- --hash=sha256:aa751d169e23c7479ce47a0cb0da579e3ede798f994f5816a74e4f4500dcea42
+twine==4.0.2 \
+ --hash=sha256:929bc3c280033347a00f847236564d1c52a3e61b1ac2516c97c48f3ceab756d8 \
+ --hash=sha256:9e102ef5fdd5a20661eb88fad46338806c3bd32cf1db729603fe3697b1bc83c8
+ # via -r third_party/python_requirements.in
+typing-extensions==4.9.0 \
+ --hash=sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783 \
+ --hash=sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd
+ # via tensorflow
+urllib3==2.1.0 \
+ --hash=sha256:55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3 \
+ --hash=sha256:df7aa8afb0148fa78488e7899b2c59b5f4ffcfa82e6c54ccb9dd37c1d7b52d54
# via
- # google-auth
# requests
-werkzeug==2.3.4 \
- --hash=sha256:1d5a58e0377d1fe39d061a5de4469e414e78ccb1e1e59c0f5ad6fa1c36c52b76 \
- --hash=sha256:48e5e61472fee0ddee27ebad085614ebedb7af41e88f687aaf881afb723a162f
+ # twine
+werkzeug==3.0.1 \
+ --hash=sha256:507e811ecea72b18a404947aded4b3390e1db8f826b494d76550ef45bb3b1dcc \
+ --hash=sha256:90a285dc0e42ad56b34e696398b8122ee4c681833fb35b8334a095d82c56da10
# via tensorboard
-wheel==0.40.0 \
- --hash=sha256:cd1196f3faee2b31968d626e1731c94f99cbdb67cf5a46e4f5656cbee7738873 \
- --hash=sha256:d236b20e7cb522daf2390fa84c55eea81c5c30190f90f29ae2ca1ad8355bf247
- # via
- # astunparse
- # tensorboard
+wheel==0.42.0 \
+ --hash=sha256:177f9c9b0d45c47873b619f5b650346d632cdc35fb5e4d25058e09c9e581433d \
+ --hash=sha256:c45be39f7882c9d34243236f2d63cbd58039e360f85d0913425fbd7ceea617a8
+ # via astunparse
wrapt==1.14.1 \
--hash=sha256:00b6d4ea20a906c0ca56d84f93065b398ab74b927a7a3dbd470f6fc503f95dc3 \
--hash=sha256:01c205616a89d09827986bc4e859bcabd64f5a0662a7fe95e0d359424e0e071b \
@@ -568,23 +758,30 @@
--hash=sha256:07f7a7d0f388028b2df1d916e94bbb40624c59b48ecc6cbc232546706fac74c2 \
--hash=sha256:11871514607b15cfeb87c547a49bca19fde402f32e2b1c24a632506c0a756656 \
--hash=sha256:1b376b3f4896e7930f1f772ac4b064ac12598d1c38d04907e696cc4d794b43d3 \
+ --hash=sha256:2020f391008ef874c6d9e208b24f28e31bcb85ccff4f335f15a3251d222b92d9 \
--hash=sha256:21ac0156c4b089b330b7666db40feee30a5d52634cc4560e1905d6529a3897ff \
+ --hash=sha256:240b1686f38ae665d1b15475966fe0472f78e71b1b4903c143a842659c8e4cb9 \
--hash=sha256:257fd78c513e0fb5cdbe058c27a0624c9884e735bbd131935fd49e9fe719d310 \
+ --hash=sha256:26046cd03936ae745a502abf44dac702a5e6880b2b01c29aea8ddf3353b68224 \
--hash=sha256:2b39d38039a1fdad98c87279b48bc5dce2c0ca0d73483b12cb72aa9609278e8a \
--hash=sha256:2cf71233a0ed05ccdabe209c606fe0bac7379fdcf687f39b944420d2a09fdb57 \
--hash=sha256:2fe803deacd09a233e4762a1adcea5db5d31e6be577a43352936179d14d90069 \
+ --hash=sha256:2feecf86e1f7a86517cab34ae6c2f081fd2d0dac860cb0c0ded96d799d20b335 \
--hash=sha256:3232822c7d98d23895ccc443bbdf57c7412c5a65996c30442ebe6ed3df335383 \
--hash=sha256:34aa51c45f28ba7f12accd624225e2b1e5a3a45206aa191f6f9aac931d9d56fe \
+ --hash=sha256:358fe87cc899c6bb0ddc185bf3dbfa4ba646f05b1b0b9b5a27c2cb92c2cea204 \
--hash=sha256:36f582d0c6bc99d5f39cd3ac2a9062e57f3cf606ade29a0a0d6b323462f4dd87 \
--hash=sha256:380a85cf89e0e69b7cfbe2ea9f765f004ff419f34194018a6827ac0e3edfed4d \
--hash=sha256:40e7bc81c9e2b2734ea4bc1aceb8a8f0ceaac7c5299bc5d69e37c44d9081d43b \
--hash=sha256:43ca3bbbe97af00f49efb06e352eae40434ca9d915906f77def219b88e85d907 \
+ --hash=sha256:49ef582b7a1152ae2766557f0550a9fcbf7bbd76f43fbdc94dd3bf07cc7168be \
--hash=sha256:4fcc4649dc762cddacd193e6b55bc02edca674067f5f98166d7713b193932b7f \
--hash=sha256:5a0f54ce2c092aaf439813735584b9537cad479575a09892b8352fea5e988dc0 \
--hash=sha256:5a9a0d155deafd9448baff28c08e150d9b24ff010e899311ddd63c45c2445e28 \
--hash=sha256:5b02d65b9ccf0ef6c34cba6cf5bf2aab1bb2f49c6090bafeecc9cd81ad4ea1c1 \
--hash=sha256:60db23fa423575eeb65ea430cee741acb7c26a1365d103f7b0f6ec412b893853 \
--hash=sha256:642c2e7a804fcf18c222e1060df25fc210b9c58db7c91416fb055897fc27e8cc \
+ --hash=sha256:6447e9f3ba72f8e2b985a1da758767698efa72723d5b59accefd716e9e8272bf \
--hash=sha256:6a9a25751acb379b466ff6be78a315e2b439d4c94c1e99cb7266d40a537995d3 \
--hash=sha256:6b1a564e6cb69922c7fe3a678b9f9a3c54e72b469875aa8018f18b4d1dd1adf3 \
--hash=sha256:6d323e1554b3d22cfc03cd3243b5bb815a51f5249fdcbb86fda4bf62bab9e164 \
@@ -607,8 +804,10 @@
--hash=sha256:9e0fd32e0148dd5dea6af5fee42beb949098564cc23211a88d799e434255a1f4 \
--hash=sha256:9f3e6f9e05148ff90002b884fbc2a86bd303ae847e472f44ecc06c2cd2fcdb2d \
--hash=sha256:a85d2b46be66a71bedde836d9e41859879cc54a2a04fad1191eb50c2066f6e9d \
+ --hash=sha256:a9008dad07d71f68487c91e96579c8567c98ca4c3881b9b113bc7b33e9fd78b8 \
--hash=sha256:a9a52172be0b5aae932bef82a79ec0a0ce87288c7d132946d645eba03f0ad8a8 \
--hash=sha256:aa31fdcc33fef9eb2552cbcbfee7773d5a6792c137b359e82879c101e98584c5 \
+ --hash=sha256:acae32e13a4153809db37405f5eba5bac5fbe2e2ba61ab227926a22901051c0a \
--hash=sha256:b014c23646a467558be7da3d6b9fa409b2c567d2110599b7cf9a0c5992b3b471 \
--hash=sha256:b21bb4c09ffabfa0e85e3a6b623e19b80e7acd709b9f91452b8297ace2a8ab00 \
--hash=sha256:b5901a312f4d14c59918c221323068fad0540e34324925c8475263841dbdfe68 \
@@ -623,19 +822,16 @@
--hash=sha256:dee60e1de1898bde3b238f18340eec6148986da0455d8ba7848d50470a7a32fb \
--hash=sha256:e2f83e18fe2f4c9e7db597e988f72712c0c3676d337d8b101f6758107c42425b \
--hash=sha256:e3fb1677c720409d5f671e39bac6c9e0e422584e5f518bfd50aa4cbbea02433f \
+ --hash=sha256:ecee4132c6cd2ce5308e21672015ddfed1ff975ad0ac8d27168ea82e71413f55 \
--hash=sha256:ee2b1b1769f6707a8a445162ea16dddf74285c3964f605877a20e38545c3c462 \
--hash=sha256:ee6acae74a2b91865910eef5e7de37dc6895ad96fa23603d1d27ea69df545015 \
--hash=sha256:ef3f72c9666bba2bab70d2a8b79f2c6d2c1a42a7f7e2b0ec83bb2f9e383950af
- # via tensorflow-cpu
-yapf==0.33.0 \
- --hash=sha256:4c2b59bd5ffe46f3a7da48df87596877189148226ce267c16e8b44240e51578d \
- --hash=sha256:da62bdfea3df3673553351e6246abed26d9fe6780e548a5af9e70f6d2b4f5b9a
+ # via tensorflow
+yapf==0.40.2 \
+ --hash=sha256:4dab8a5ed7134e26d57c1647c7483afb3f136878b579062b786c9ba16b94637b \
+ --hash=sha256:adc8b5dd02c0143108878c499284205adb258aad6db6634e5b869e7ee2bd548b
# via -r third_party/python_requirements.in
-
-# The following packages are considered to be unsafe in a requirements file:
-setuptools==67.7.2 \
- --hash=sha256:23aaf86b85ca52ceb801d32703f12d77517b2556af839621c641fca11287952b \
- --hash=sha256:f104fa03692a2602fa0fec6c6a9e63b6c8a968de13e17c026957dd1f53d80990
- # via
- # tensorboard
- # tensorflow-cpu
+zipp==3.17.0 \
+ --hash=sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31 \
+ --hash=sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0
+ # via importlib-metadata
diff --git a/third_party/ruy/BUILD b/third_party/ruy/BUILD
index 8db906a..8fabe49 100644
--- a/third_party/ruy/BUILD
+++ b/third_party/ruy/BUILD
@@ -5,4 +5,4 @@
licenses = ["notice"],
)
-exports_files(glob(["*.patch"]) + ["LICENSE"])
+exports_files(glob(["*.patch"]))
diff --git a/third_party/xtensa/nnlib_hifi4/BUILD b/third_party/xtensa/nnlib_hifi4/BUILD
new file mode 100644
index 0000000..aadcd9f
--- /dev/null
+++ b/third_party/xtensa/nnlib_hifi4/BUILD
@@ -0,0 +1,7 @@
+alias(
+ name = "nnlib_hifi4_lib",
+ actual = "@nnlib_hifi4//:lib",
+ visibility = [
+ "//visibility:public",
+ ],
+)
diff --git a/third_party/xtensa/nnlib_hifi4/nnlib_hifi4.BUILD b/third_party/xtensa/nnlib_hifi4/nnlib_hifi4.BUILD
new file mode 100644
index 0000000..44f4777
--- /dev/null
+++ b/third_party/xtensa/nnlib_hifi4/nnlib_hifi4.BUILD
@@ -0,0 +1,43 @@
+constraint_setting(
+ name = "compatible_constraint",
+)
+
+# Set this constraint_value on your platform to indicate compatiblity with this
+# library.
+constraint_value(
+ name = "compatible",
+ constraint_setting = ":compatible_constraint",
+ visibility = [
+ "//visibility:public",
+ ],
+)
+
+cc_library(
+ name = "lib",
+ srcs = glob(["xa_nnlib/algo/**/*.c"]),
+ hdrs = glob([
+ "xa_nnlib/algo/**/*.h",
+ "xa_nnlib/include/**/*.h",
+ ]),
+ copts = ["-Wno-unused-parameter"],
+ defines = [
+ "NNLIB_V2=1",
+ "MODEL_INT16=1",
+ "EIGEN_NO_MALLOC=1",
+ "hifi4=1",
+ ],
+ includes = [
+ "xa_nnlib",
+ "xa_nnlib/algo/common/include",
+ "xa_nnlib/algo/kernels",
+ "xa_nnlib/algo/ndsp/hifi4/include",
+ "xa_nnlib/include",
+ "xa_nnlib/include/nnlib",
+ ],
+ target_compatible_with = [
+ ":compatible",
+ ],
+ visibility = [
+ "//visibility:public",
+ ],
+)
diff --git a/tools/BUILD b/tools/BUILD
new file mode 100644
index 0000000..d886dc5
--- /dev/null
+++ b/tools/BUILD
@@ -0,0 +1,16 @@
+load("@rules_python//python:defs.bzl", "py_binary", "py_test")
+
+package(
+ default_visibility = ["//visibility:public"],
+)
+
+py_binary(
+ name = "expand_stamp_vars",
+ srcs = ["expand_stamp_vars.py"],
+)
+
+py_test(
+ name = "expand_stamp_vars_test",
+ srcs = ["expand_stamp_vars_test.py"],
+ deps = [":expand_stamp_vars"],
+)
diff --git a/tools/expand_stamp_vars.bzl b/tools/expand_stamp_vars.bzl
new file mode 100644
index 0000000..39db265
--- /dev/null
+++ b/tools/expand_stamp_vars.bzl
@@ -0,0 +1,52 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----
+
+def expand_stamp_vars(name, template, out):
+ """Macro for expanding a template using workspace status variables.
+
+ Typical usage in a BUILD file:
+
+ expand_stamp_vars(
+ name = "version",
+ template = "_version.py.in",
+ out = "_version.py",
+ )
+
+ Writes `template` to `out`, expanding references of the form '{KEY}' to the
+ value of the corresponding Bazel workspace status variable.
+ """
+
+ # This macro uses a genrule to call a helper program at Bazel execution
+ # time, because workspace variables are not available until execution time.
+ # Workspace variables are generated by bazel on each invocation, and
+ # written to the hardcoded files names used below. See the Bazel
+ # documentation for the option --workspace_status_command.
+
+ native.genrule(
+ name = name,
+ srcs = [template],
+ outs = [out],
+ cmd = "$(location //tools:expand_stamp_vars) " +
+ "bazel-out/stable-status.txt " +
+ "bazel-out/volatile-status.txt " +
+ "<$< >$@",
+ tools = [
+ "//tools:expand_stamp_vars",
+ ],
+
+ # Undocumented, but valid, and the only way to declare the necessary
+ # dependencies on {stable,volatile}-status.txt.
+ stamp = 1,
+ )
diff --git a/tools/expand_stamp_vars.py b/tools/expand_stamp_vars.py
new file mode 100644
index 0000000..40d1a3f
--- /dev/null
+++ b/tools/expand_stamp_vars.py
@@ -0,0 +1,76 @@
+#!/usr/bin/env python3
+
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----
+"""
+ A filter that expands Bazel workspace stamp variables.
+
+ For example, the input steam:
+
+ This build was compiled at {BUILD_DATE}.
+
+ is expanded into the output stream:
+
+ This build was compiled at 2023-02-10T14:15.
+
+ Stamp variable key-value pairs are read from all files passed as positional
+ arguments. These files are typically bazel-out/stable-status.txt and
+ bazel-out/volatile-status.txt. See the Bazel documentation for the option
+ --workspace_status_command.
+"""
+
+import sys
+
+
+def read_stamps(file):
+ """Return a dictionary of key-value pairs read from a stamp file.
+
+ These files are typically bazel-out/stable-status.txt and
+ bazel-out/volatile-status.txt. See the Bazel documentation for the option
+ --workspace_status_command."""
+
+ stamps = {}
+ for line in file:
+ try:
+ key, value = line.split(" ", maxsplit=1)
+ stamps[key] = value.strip()
+ except ValueError:
+ pass # Skip blank lines, etc.
+
+ return stamps
+
+
+def expand(istream, ostream, stamps):
+ """Write istream to ostream, expanding placeholders like {KEY}."""
+ for line in istream:
+ for key, value in stamps.items():
+ line = line.replace(f"{{{key}}}", value)
+ ostream.write(line)
+
+
+def _main():
+ """Stamp variables are read from all files passed as positional arguments."""
+ stamps = {}
+ for name in sys.argv[1:]:
+ with open(name) as f:
+ stamps.update(read_stamps(f))
+
+ expand(sys.stdin, sys.stdout, stamps)
+
+ sys.exit(0)
+
+
+if __name__ == "__main__":
+ _main()
diff --git a/tools/expand_stamp_vars_test.py b/tools/expand_stamp_vars_test.py
new file mode 100644
index 0000000..07b047a
--- /dev/null
+++ b/tools/expand_stamp_vars_test.py
@@ -0,0 +1,46 @@
+#!/usr/bin/env python3
+
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----
+
+# A test for the filter that expands Bazel workspace stamp variables.
+
+from tflite_micro.tools import expand_stamp_vars
+
+import io
+import unittest
+
+
+class FilterTest(unittest.TestCase):
+ """A simple test of the expansion feature."""
+
+ def test_basic(self):
+ stamps = """
+BUILD_STAMP_ONE value_one
+BUILD_STAMP_TWO value_two
+"""
+ input = "This is {BUILD_STAMP_TWO}. This is {BUILD_STAMP_ONE}."
+ golden = "This is value_two. This is value_one."
+
+ istream = io.StringIO(input)
+ ostream = io.StringIO()
+ stamps = expand_stamp_vars.read_stamps(io.StringIO(stamps))
+ expand_stamp_vars.expand(istream, ostream, stamps)
+
+ self.assertEqual(ostream.getvalue(), golden)
+
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/tools/workspace_status.sh b/tools/workspace_status.sh
new file mode 100755
index 0000000..5d17d1a
--- /dev/null
+++ b/tools/workspace_status.sh
@@ -0,0 +1,42 @@
+#!/bin/sh
+
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ---
+
+# Output key-value pairs with which to stamp build outputs. This script is
+# called by the bazel option --workspace_status_command, which is likely to be
+# embedded in .bazelrc. Bazel generates some keys, such as BUILD_EMBED_LABEL,
+# on its own, outside of this script. Search for "Bazel workspace status" for
+# more, including the differences between STABLE_ and volatile keys.
+
+
+# Unambiguous identification of the source tree
+echo STABLE_GIT_HASH $(git describe --always --long --dirty)
+
+# Human-readable timestamp of git HEAD's commit date. Use dates derived from
+# git for stability across multiple invocations of the `bazel` command. Use UTC
+# rather than committer or local timezones for consistency across build
+# environments. Use commit date instead of author date, the default date shown
+# by `git log` and GitHub, because amending, rebasing, merging, etc. can cause
+# the author date of descendent commits to be earlier than those of their
+# ancestors.
+#
+# Comparable commit dates can be produced via:
+# `TZ=UTC0 git log --pretty=fuller --date=local`.
+#
+echo STABLE_GIT_COMMIT_TIME $(TZ=UTC0 git show \
+ --no-patch \
+ --format=format:%cd \
+ --date=format-local:%Y%m%d%H%M%S)
