integrations/tensorflow/e2e/keras/applications/BUILD - 3p/openxla/iree - Git at Google

 # Copyright 2020 Google LLC
 #
 # 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
 #
 #      https://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 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_vmla \
 --data=imagenet \
 --url=https://storage.googleapis.com/iree_models/ \
 --model=ResNet50

 Command arguments description:
 --target_backends: can be combination of these: tf,iree_vmla
 --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",
             "model": "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_vmla",
             "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_vmla",
             "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_vmla",
             "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_vmla",
             "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",
     ],
 )
	# Copyright 2020 Google LLC
	#
	# 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
	#
	# https://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 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_vmla \
	--data=imagenet \
	--url=https://storage.googleapis.com/iree_models/ \
	--model=ResNet50

	Command arguments description:
	--target_backends: can be combination of these: tf,iree_vmla
	--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",
	"model": "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_vmla",
	"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_vmla",
	"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_vmla",
	"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_vmla",
	"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",
	],
	)