integrations/tensorflow/e2e/keras/applications/BUILD

# Copyright 2020 The IREE Authors
#
# Licensed under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception

# ***THIS FILE DOES NOT BUILD WITH BAZEL***
#
# It is open sourced to enable Bazel->CMake conversion to maintain test coverage
# of our integration tests in open source while we figure out a long term plan
# for our integration testing.

load(
    "@iree//integrations/tensorflow/e2e:iree_e2e_cartesian_product_test_suite.bzl",
    "iree_e2e_cartesian_product_test_suite",
)

package(
    default_visibility = ["//visibility:public"],
    features = ["layering_check"],
    licenses = ["notice"],  # Apache 2.0
)

# @unused
DOC = """
applications_test_manual is for manual testing of all keras vision models.
Test will run only manually with all parameters specified manually, for example:
bazel run -c opt integrations/tensorflow/e2e/keras/applications:applications_test_manual -- \
--target_backends=tf,iree_llvmaot \
--data=imagenet \
--url=https://storage.googleapis.com/iree_models/ \
--model=ResNet50

Command arguments description:
--target_backends: can be combination of these: tf,iree_llvmaot
--data: can be 'imagenet' or 'cifar10'.
    imagenet - input image size (1, 224, 224, 3)
    cifar10 - input image size (1, 32, 32, 3) - it is used for quick tests
            and needs pretrained weights, we pretrained models: ResNet50, MobileNet, MobileNetV2
--include_top: Whether or not to include the final (top) layers of the model.
--url: we need it only for cifar10 models to load weights from https://storage.googleapis.com/iree_models/
       imagenet pretrained weights url is specified by keras
--model: supports ResNet50, MobileNet, MobileNetV2, ResNet101, ResNet152,
    ResNet50V2, ResNet101V2, ResNet152V2, VGG16, VGG19, Xception,
    InceptionV3, InceptionResNetV2, DenseNet121, DenseNet169,
    DenseNet201, NASNetMobile, NASNetLarge
    All above models works with 'imagenet' data sets.
    ResNet50, MobileNet, MobileNetV2 work with both 'imagenet' and 'cifar10' data sets.
"""

[
    py_binary(
        name = src.replace(".py", "_manual"),
        srcs = [src],
        main = src,
        python_version = "PY3",
        deps = [
            "//third_party/py/absl:app",
            "//third_party/py/absl/flags",
            "//third_party/py/iree:pylib_tf_support",
            "//third_party/py/numpy",
            "//third_party/py/tensorflow",
            "//util/debuginfo:signalsafe_addr2line_installer",
        ],
    )
    for src in glob(["*_test.py"])
]

KERAS_APPLICATIONS_MODELS = [
    "DenseNet121",
    "DenseNet169",
    "DenseNet201",
    "EfficientNetB0",
    "EfficientNetB1",
    "EfficientNetB2",
    "EfficientNetB3",
    "EfficientNetB4",
    "EfficientNetB5",
    "EfficientNetB6",
    "EfficientNetB7",
    "InceptionResNetV2",
    "InceptionV3",
    "MobileNet",
    "MobileNetV2",
    "MobileNetV3Large",
    "MobileNetV3Small",
    "NASNetLarge",
    "NASNetMobile",
    "ResNet101",
    "ResNet101V2",
    "ResNet152",
    "ResNet152V2",
    "ResNet50",
    "ResNet50V2",
    "VGG16",
    "VGG19",
    "Xception",
]

iree_e2e_cartesian_product_test_suite(
    name = "large_cifar10_tests",
    size = "large",
    failing_configurations = [
        # Frequently OOMs
        {
            "target_backends": [
                "tflite",
                "iree_llvmaot",
                "iree_vulkan",
            ],
            "model": [
                "VGG16",
                "VGG19",
            ],
        },
    ],
    matrix = {
        "src": "applications_test.py",
        "reference_backend": "tf",
        "data": "cifar10",
        "model": [
            # All models with runtime shorter than ResNet50.
            "MobileNet",  # Max: Vulkan 61.0s
            "MobileNetV2",  # Max: LLVM 96.3s
            "ResNet50",  # Max: LLVM 145.6s
            "VGG16",  # Max: LLVM 89.5s
            "VGG19",  # Max: LLVM 94.7s
        ],
        "target_backends": [
            "tf",
            "tflite",
            "iree_llvmaot",
            "iree_vulkan",
        ],
    },
    tags = ["manual"],
    deps = [
        "//third_party/py/absl:app",
        "//third_party/py/absl/flags",
        "//third_party/py/iree:pylib_tf_support",
        "//third_party/py/numpy",
        "//third_party/py/tensorflow",
        "//util/debuginfo:signalsafe_addr2line_installer",
    ],
)

iree_e2e_cartesian_product_test_suite(
    name = "enormous_cifar10_tests",
    size = "enormous",
    matrix = {
        "src": "applications_test.py",
        "reference_backend": "tf",
        "data": "cifar10",
        "model": [
            "DenseNet121",
            "DenseNet169",
            "DenseNet201",
            "NASNetLarge",
            "NASNetMobile",
            "ResNet50V2",
            "ResNet101",
            "ResNet101V2",
            "ResNet152",
            "ResNet152V2",
        ],
        "target_backends": [
            "tf",
            "tflite",
            "iree_llvmaot",
            "iree_vulkan",
        ],
    },
    tags = [
        "guitar",
        "manual",
        "nokokoro",
        "notap",
    ],
    deps = [
        "//third_party/py/absl:app",
        "//third_party/py/absl/flags",
        "//third_party/py/iree:pylib_tf_support",
        "//third_party/py/numpy",
        "//third_party/py/tensorflow",
        "//util/debuginfo:signalsafe_addr2line_installer",
    ],
)

# 'non_hermetic' tests use real model weights to test numerical correctness.
iree_e2e_cartesian_product_test_suite(
    name = "cifar10_non_hermetic_tests",
    size = "large",
    matrix = {
        "src": "applications_test.py",
        "reference_backend": "tf",
        "data": "cifar10",
        "url": "https://storage.googleapis.com/iree_models/",
        "use_external_weights": True,
        "model": [
            "MobileNet",
            "MobileNetV2",
            "ResNet50",
        ],
        "target_backends": [
            "tf",
            "tflite",
            "iree_llvmaot",
            "iree_vulkan",
        ],
    },
    tags = [
        "external",
        "guitar",
        "manual",
        "no-remote",
        "nokokoro",
        "notap",
    ],
    deps = [
        "//third_party/py/absl:app",
        "//third_party/py/absl/flags",
        "//third_party/py/iree:pylib_tf_support",
        "//third_party/py/numpy",
        "//third_party/py/tensorflow",
        "//util/debuginfo:signalsafe_addr2line_installer",
    ],
)

# 'non_hermetic' tests use real model weights to test numerical correctness.
iree_e2e_cartesian_product_test_suite(
    name = "imagenet_non_hermetic_tests",
    size = "enormous",
    failing_configurations = [
        # TODO(b/186579218): Fix linalg-on-tensors failures in these.
        {
            "target_backends": [
                "iree_llvmaot",
                "iree_vulkan",
            ],
            "model": [
                "InceptionResNetV2",
                "InceptionV3",
                "NASNetLarge",
                "NASNetMobile",
                "ResNet152V2",
            ],
        },
    ],
    matrix = {
        "src": "applications_test.py",
        "reference_backend": "tf",
        "data": "imagenet",
        "use_external_weights": True,
        "model": KERAS_APPLICATIONS_MODELS,
        "target_backends": [
            "tf",
            "tflite",
            "iree_llvmaot",
            "iree_vulkan",
        ],
    },
    tags = [
        "external",
        "guitar",
        "manual",
        "nokokoro",
        "notap",
    ],
    deps = [
        "//third_party/py/absl:app",
        "//third_party/py/absl/flags",
        "//third_party/py/iree:pylib_tf_support",
        "//third_party/py/numpy",
        "//third_party/py/tensorflow",
        "//util/debuginfo:signalsafe_addr2line_installer",
    ],
)

# It is used to produce weights for keras vision models with input image size
# 32x32. These models are not optimized for accuracy or latency (they are for
# debugging only). They have the same neural net topology with keras vision
# models trained on imagenet data sets
py_binary(
    name = "train_vision_models_on_cifar",
    srcs = ["train_vision_models_on_cifar.py"],
    python_version = "PY3",
    srcs_version = "PY2AND3",
    deps = [
        "//third_party/py/absl:app",
        "//third_party/py/absl/flags",
        "//third_party/py/iree:pylib_tf_support",
        "//third_party/py/numpy",
        "//third_party/py/tensorflow",
        "//util/debuginfo:signalsafe_addr2line_installer",
    ],
)