Move ResNet related files to the ResNet folder (e.g. utils, CTL, CFit, etc.)

PiperOrigin-RevId: 301185629

official/benchmark/keras_imagenet_benchmark.py

@@ -23,7 +23,7 @@ import tensorflow as tf  # pylint: disable=g-bad-import-order
 
 from official.benchmark import keras_benchmark
 from official.utils.testing import benchmark_wrappers
-from official.vision.image_classification import resnet_imagenet_main
+from official.vision.image_classification.resnet import resnet_imagenet_main
 
 MIN_TOP_1_ACCURACY = 0.76
 MAX_TOP_1_ACCURACY = 0.77

official/benchmark/models/resnet_cifar_main.py

@@ -27,8 +27,8 @@ from official.utils.flags import core as flags_core
 from official.utils.logs import logger
 from official.utils.misc import distribution_utils
 from official.utils.misc import keras_utils
-from official.vision.image_classification import cifar_preprocessing
-from official.vision.image_classification import common
+from official.vision.image_classification.resnet import cifar_preprocessing
+from official.vision.image_classification.resnet import common
 
 
 LR_SCHEDULE = [  # (multiplier, epoch to start) tuples

official/benchmark/models/resnet_cifar_test.py

@@ -27,7 +27,7 @@ from tensorflow.python.platform import googletest
 from official.benchmark.models import resnet_cifar_main
 from official.utils.misc import keras_utils
 from official.utils.testing import integration
-from official.vision.image_classification import cifar_preprocessing
+from official.vision.image_classification.resnet import cifar_preprocessing
 
 
 class KerasCifarTest(googletest.TestCase):

official/benchmark/resnet_ctl_imagenet_benchmark.py

@@ -22,8 +22,8 @@ import time
 from absl import flags
 import tensorflow as tf
 
-from official.vision.image_classification import common
-from official.vision.image_classification import resnet_ctl_imagenet_main
+from official.vision.image_classification.resnet import common
+from official.vision.image_classification.resnet import resnet_ctl_imagenet_main
 from official.utils.testing.perfzero_benchmark import PerfZeroBenchmark
 from official.utils.testing import benchmark_wrappers
 from official.utils.flags import core as flags_core

official/vision/image_classification/common_test.py

-# 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.
-# ==============================================================================
-"""Tests for the common module."""
-from __future__ import absolute_import
-from __future__ import print_function
-
-# pylint: disable=g-bad-import-order
-from mock import Mock
-import numpy as np
-import tensorflow as tf
-
-from tensorflow.python.platform import googletest
-from official.utils.misc import keras_utils
-from official.vision.image_classification import common
-
-
-class KerasCommonTests(tf.test.TestCase):
-  """Tests for common."""
-
-  @classmethod
-  def setUpClass(cls):  # pylint: disable=invalid-name
-    super(KerasCommonTests, cls).setUpClass()
-
-  def test_build_stats(self):
-
-    history = self._build_history(1.145, cat_accuracy=.99988)
-    eval_output = self._build_eval_output(.56432111, 5.990)
-    th = keras_utils.TimeHistory(128, 100)
-
-    th.timestamp_log = [keras_utils.BatchTimestamp(0, 1),
-                        keras_utils.BatchTimestamp(1, 2),
-                        keras_utils.BatchTimestamp(2, 3)]
-    th.train_finish_time = 12345
-    stats = common.build_stats(history, eval_output, [th])
-
-    self.assertEqual(1.145, stats['loss'])
-    self.assertEqual(.99988, stats['training_accuracy_top_1'])
-
-    self.assertEqual(.56432111, stats['accuracy_top_1'])
-    self.assertEqual(5.990, stats['eval_loss'])
-
-    self.assertEqual(3, stats['step_timestamp_log'][2].timestamp)
-    self.assertEqual(12345, stats['train_finish_time'])
-
-  def test_build_stats_sparse(self):
-
-    history = self._build_history(1.145, cat_accuracy_sparse=.99988)
-    eval_output = self._build_eval_output(.928, 1.9844)
-    stats = common.build_stats(history, eval_output, None)
-
-    self.assertEqual(1.145, stats['loss'])
-    self.assertEqual(.99988, stats['training_accuracy_top_1'])
-
-    self.assertEqual(.928, stats['accuracy_top_1'])
-    self.assertEqual(1.9844, stats['eval_loss'])
-
-  def test_time_history(self):
-    th = keras_utils.TimeHistory(batch_size=128, log_steps=3)
-
-    th.on_train_begin()
-    th.on_batch_begin(0)
-    th.on_batch_end(0)
-    th.on_batch_begin(1)
-    th.on_batch_end(1)
-    th.on_batch_begin(2)
-    th.on_batch_end(2)
-    th.on_batch_begin(3)
-    th.on_batch_end(3)
-    th.on_batch_begin(4)
-    th.on_batch_end(4)
-    th.on_batch_begin(5)
-    th.on_batch_end(5)
-    th.on_batch_begin(6)
-    th.on_batch_end(6)
-    th.on_train_end()
-
-    self.assertEqual(3, len(th.timestamp_log))
-
-  def _build_history(self, loss, cat_accuracy=None,
-                     cat_accuracy_sparse=None):
-    history_p = Mock()
-    history = {}
-    history_p.history = history
-    history['loss'] = [np.float64(loss)]
-    if cat_accuracy:
-      history['categorical_accuracy'] = [np.float64(cat_accuracy)]
-    if cat_accuracy_sparse:
-      history['sparse_categorical_accuracy'] = [np.float64(cat_accuracy_sparse)]
-
-    return history_p
-
-  def _build_eval_output(self, top_1, eval_loss):
-    eval_output = [np.float64(eval_loss), np.float64(top_1)]
-    return eval_output
-
-if __name__ == '__main__':
-  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
-  googletest.main()

official/vision/image_classification/mnist_main.py

@@ -28,7 +28,7 @@ import tensorflow_datasets as tfds
 from official.utils.flags import core as flags_core
 from official.utils.misc import distribution_utils
 from official.utils.misc import model_helpers
-from official.vision.image_classification import common
+from official.vision.image_classification.resnet import common
 
 FLAGS = flags.FLAGS
 

official/vision/image_classification/cifar_preprocessing.py → official/vision/image_classification/resnet/cifar_preprocessing.py

@@ -22,7 +22,7 @@ import os
 from absl import logging
 import tensorflow as tf
 
-from official.vision.image_classification import imagenet_preprocessing
+from official.vision.image_classification.resnet import imagenet_preprocessing
 
 HEIGHT = 32
 WIDTH = 32

official/vision/image_classification/resnet_ctl_imagenet_main.py → official/vision/image_classification/resnet/resnet_ctl_imagenet_main.py

@@ -30,9 +30,9 @@ from official.utils.logs import logger
 from official.utils.misc import distribution_utils
 from official.utils.misc import keras_utils
 from official.utils.misc import model_helpers
-from official.vision.image_classification import common
-from official.vision.image_classification import imagenet_preprocessing
-from official.vision.image_classification import resnet_runnable
+from official.vision.image_classification.resnet import common
+from official.vision.image_classification.resnet import imagenet_preprocessing
+from official.vision.image_classification.resnet import resnet_runnable
 
 flags.DEFINE_boolean(name='use_tf_function', default=True,
                      help='Wrap the train and test step inside a '

official/vision/image_classification/resnet_imagenet_main.py → official/vision/image_classification/resnet/resnet_imagenet_main.py

@@ -34,8 +34,8 @@ from official.utils.logs import logger
 from official.utils.misc import distribution_utils
 from official.utils.misc import keras_utils
 from official.utils.misc import model_helpers
-from official.vision.image_classification import common
-from official.vision.image_classification import imagenet_preprocessing
+from official.vision.image_classification.resnet import common
+from official.vision.image_classification.resnet import imagenet_preprocessing
 from official.vision.image_classification.resnet import resnet_model
 
 

official/vision/image_classification/resnet/resnet_model.py

@@ -33,7 +33,7 @@ from tensorflow.python.keras import backend
 from tensorflow.python.keras import initializers
 from tensorflow.python.keras import models
 from tensorflow.python.keras import regularizers
-from official.vision.image_classification import imagenet_preprocessing
+from official.vision.image_classification.resnet import imagenet_preprocessing
 
 L2_WEIGHT_DECAY = 1e-4
 BATCH_NORM_DECAY = 0.9

official/vision/image_classification/resnet_runnable.py → official/vision/image_classification/resnet/resnet_runnable.py

@@ -25,8 +25,8 @@ from official.staging.training import grad_utils
 from official.staging.training import standard_runnable
 from official.staging.training import utils
 from official.utils.flags import core as flags_core
-from official.vision.image_classification import common
-from official.vision.image_classification import imagenet_preprocessing
+from official.vision.image_classification.resnet import common
+from official.vision.image_classification.resnet import imagenet_preprocessing
 from official.vision.image_classification.resnet import resnet_model
 
 

official/vision/image_classification/resnet/tfhub_export.py

@@ -26,7 +26,7 @@ from absl import flags
 
 import tensorflow.compat.v2 as tf
 
-from official.vision.image_classification import imagenet_preprocessing
+from official.vision.image_classification.resnet import imagenet_preprocessing
 from official.vision.image_classification.resnet import resnet_model
 
 FLAGS = flags.FLAGS

official/vision/image_classification/resnet_ctl_imagenet_test.py

-# 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.
-# ==============================================================================
-"""Test the ResNet model with ImageNet data using CTL."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tempfile
-import os
-
-import tensorflow.compat.v2 as tf
-
-from tensorflow.python.eager import context
-from official.utils.testing import integration
-from official.vision.image_classification import common
-from official.vision.image_classification import imagenet_preprocessing
-from official.vision.image_classification import resnet_ctl_imagenet_main
-
-
-class CtlImagenetTest(tf.test.TestCase):
-  """Unit tests for Keras ResNet with ImageNet using CTL."""
-
-  _extra_flags = [
-      '-batch_size', '4',
-      '-train_steps', '4',
-      '-use_synthetic_data', 'true'
-  ]
-  _tempdir = None
-
-  def get_temp_dir(self):
-    if not self._tempdir:
-      self._tempdir = tempfile.mkdtemp(
-          dir=super(CtlImagenetTest, self).get_temp_dir())
-    return self._tempdir
-
-  @classmethod
-  def setUpClass(cls):
-    super(CtlImagenetTest, cls).setUpClass()
-    common.define_keras_flags()
-
-  def setUp(self):
-    super(CtlImagenetTest, self).setUp()
-    imagenet_preprocessing.NUM_IMAGES['validation'] = 4
-    self.policy = \
-        tf.compat.v2.keras.mixed_precision.experimental.global_policy()
-
-  def tearDown(self):
-    super(CtlImagenetTest, self).tearDown()
-    tf.io.gfile.rmtree(self.get_temp_dir())
-    tf.compat.v2.keras.mixed_precision.experimental.set_policy(self.policy)
-
-  def test_end_to_end_no_dist_strat(self):
-    """Test Keras model with 1 GPU, no distribution strategy."""
-
-    model_dir = os.path.join(self.get_temp_dir(), 'ctl_imagenet_no_dist_strat')
-    extra_flags = [
-        '-distribution_strategy', 'off',
-        '-model_dir', model_dir,
-        '-data_format', 'channels_last',
-    ]
-    extra_flags = extra_flags + self._extra_flags
-
-    integration.run_synthetic(
-        main=resnet_ctl_imagenet_main.run,
-        tmp_root=self.get_temp_dir(),
-        extra_flags=extra_flags
-    )
-
-  def test_end_to_end_2_gpu(self):
-    """Test Keras model with 2 GPUs."""
-    num_gpus = '2'
-    if context.num_gpus() < 2:
-      num_gpus = '0'
-
-    model_dir = os.path.join(self.get_temp_dir(), 'ctl_imagenet_2_gpu')
-    extra_flags = [
-        '-num_gpus', num_gpus,
-        '-distribution_strategy', 'mirrored',
-        '-model_dir', model_dir,
-        '-data_format', 'channels_last',
-    ]
-    extra_flags = extra_flags + self._extra_flags
-
-    integration.run_synthetic(
-        main=resnet_ctl_imagenet_main.run,
-        tmp_root=self.get_temp_dir(),
-        extra_flags=extra_flags
-    )
-
-if __name__ == '__main__':
-  assert tf.version.VERSION.startswith('2.')
-  tf.test.main()

official/vision/image_classification/resnet_imagenet_test.py

-# 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.
-# ==============================================================================
-"""Test the keras ResNet model with ImageNet data."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from absl.testing import parameterized
-import tensorflow as tf
-
-from tensorflow.python.eager import context
-from official.utils.misc import keras_utils
-from official.utils.testing import integration
-from official.vision.image_classification import imagenet_preprocessing
-from official.vision.image_classification import resnet_imagenet_main
-
-
-@parameterized.parameters(
-    "resnet",
-    "resnet_polynomial_decay",
-    "mobilenet",
-    "mobilenet_polynomial_decay")
-class KerasImagenetTest(tf.test.TestCase):
-  """Unit tests for Keras Models with ImageNet."""
-  _default_flags_dict = [
-      "-batch_size", "4",
-      "-train_steps", "1",
-      "-use_synthetic_data", "true",
-      "-data_format", "channels_last",
-  ]
-  _extra_flags_dict = {
-      "resnet": [
-          "-model", "resnet50_v1.5",
-          "-optimizer", "resnet50_default",
-      ],
-      "resnet_polynomial_decay": [
-          "-model", "resnet50_v1.5",
-          "-optimizer", "resnet50_default",
-          "-pruning_method", "polynomial_decay",
-      ],
-      "mobilenet": [
-          "-model", "mobilenet",
-          "-optimizer", "mobilenet_default",
-      ],
-      "mobilenet_polynomial_decay": [
-          "-model", "mobilenet",
-          "-optimizer", "mobilenet_default",
-          "-pruning_method", "polynomial_decay",
-      ],
-  }
-  _tempdir = None
-
-  @classmethod
-  def setUpClass(cls):  # pylint: disable=invalid-name
-    super(KerasImagenetTest, cls).setUpClass()
-    resnet_imagenet_main.define_imagenet_keras_flags()
-
-  def setUp(self):
-    super(KerasImagenetTest, self).setUp()
-    imagenet_preprocessing.NUM_IMAGES["validation"] = 4
-    self.policy = \
-        tf.compat.v2.keras.mixed_precision.experimental.global_policy()
-
-  def tearDown(self):
-    super(KerasImagenetTest, self).tearDown()
-    tf.io.gfile.rmtree(self.get_temp_dir())
-    tf.compat.v2.keras.mixed_precision.experimental.set_policy(self.policy)
-
-  def get_extra_flags_dict(self, flags_key):
-    return self._extra_flags_dict[flags_key] + self._default_flags_dict
-
-  def test_end_to_end_no_dist_strat(self, flags_key):
-    """Test Keras model with 1 GPU, no distribution strategy."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
-
-    extra_flags = [
-        "-distribution_strategy", "off",
-    ]
-    extra_flags = extra_flags + self.get_extra_flags_dict(flags_key)
-
-    integration.run_synthetic(
-        main=resnet_imagenet_main.run,
-        tmp_root=self.get_temp_dir(),
-        extra_flags=extra_flags
-    )
-
-  def test_end_to_end_graph_no_dist_strat(self, flags_key):
-    """Test Keras model in legacy graph mode with 1 GPU, no dist strat."""
-    extra_flags = [
-        "-enable_eager", "false",
-        "-distribution_strategy", "off",
-    ]
-    extra_flags = extra_flags + self.get_extra_flags_dict(flags_key)
-
-    integration.run_synthetic(
-        main=resnet_imagenet_main.run,
-        tmp_root=self.get_temp_dir(),
-        extra_flags=extra_flags
-    )
-
-  def test_end_to_end_1_gpu(self, flags_key):
-    """Test Keras model with 1 GPU."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
-
-    if context.num_gpus() < 1:
-      self.skipTest(
-          "{} GPUs are not available for this test. {} GPUs are available".
-          format(1, context.num_gpus()))
-
-    extra_flags = [
-        "-num_gpus", "1",
-        "-distribution_strategy", "mirrored",
-        "-enable_checkpoint_and_export", "1",
-    ]
-    extra_flags = extra_flags + self.get_extra_flags_dict(flags_key)
-
-    integration.run_synthetic(
-        main=resnet_imagenet_main.run,
-        tmp_root=self.get_temp_dir(),
-        extra_flags=extra_flags
-    )
-
-  def test_end_to_end_1_gpu_fp16(self, flags_key):
-    """Test Keras model with 1 GPU and fp16."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
-
-    if context.num_gpus() < 1:
-      self.skipTest(
-          "{} GPUs are not available for this test. {} GPUs are available"
-          .format(1, context.num_gpus()))
-
-    extra_flags = [
-        "-num_gpus", "1",
-        "-dtype", "fp16",
-        "-distribution_strategy", "mirrored",
-    ]
-    extra_flags = extra_flags + self.get_extra_flags_dict(flags_key)
-
-    if "polynomial_decay" in extra_flags:
-      self.skipTest("Pruning with fp16 is not currently supported.")
-
-    integration.run_synthetic(
-        main=resnet_imagenet_main.run,
-        tmp_root=self.get_temp_dir(),
-        extra_flags=extra_flags
-    )
-
-  def test_end_to_end_2_gpu(self, flags_key):
-    """Test Keras model with 2 GPUs."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
-
-    if context.num_gpus() < 2:
-      self.skipTest(
-          "{} GPUs are not available for this test. {} GPUs are available".
-          format(2, context.num_gpus()))
-
-    extra_flags = [
-        "-num_gpus", "2",
-        "-distribution_strategy", "mirrored",
-    ]
-    extra_flags = extra_flags + self.get_extra_flags_dict(flags_key)
-
-    integration.run_synthetic(
-        main=resnet_imagenet_main.run,
-        tmp_root=self.get_temp_dir(),
-        extra_flags=extra_flags
-    )
-
-  def test_end_to_end_xla_2_gpu(self, flags_key):
-    """Test Keras model with XLA and 2 GPUs."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
-
-    if context.num_gpus() < 2:
-      self.skipTest(
-          "{} GPUs are not available for this test. {} GPUs are available".
-          format(2, context.num_gpus()))
-
-    extra_flags = [
-        "-num_gpus", "2",
-        "-enable_xla", "true",
-        "-distribution_strategy", "mirrored",
-    ]
-    extra_flags = extra_flags + self.get_extra_flags_dict(flags_key)
-
-    integration.run_synthetic(
-        main=resnet_imagenet_main.run,
-        tmp_root=self.get_temp_dir(),
-        extra_flags=extra_flags
-    )
-
-  def test_end_to_end_2_gpu_fp16(self, flags_key):
-    """Test Keras model with 2 GPUs and fp16."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
-
-    if context.num_gpus() < 2:
-      self.skipTest(
-          "{} GPUs are not available for this test. {} GPUs are available".
-          format(2, context.num_gpus()))
-
-    extra_flags = [
-        "-num_gpus", "2",
-        "-dtype", "fp16",
-        "-distribution_strategy", "mirrored",
-    ]
-    extra_flags = extra_flags + self.get_extra_flags_dict(flags_key)
-
-    if "polynomial_decay" in extra_flags:
-      self.skipTest("Pruning with fp16 is not currently supported.")
-
-    integration.run_synthetic(
-        main=resnet_imagenet_main.run,
-        tmp_root=self.get_temp_dir(),
-        extra_flags=extra_flags
-    )
-
-  def test_end_to_end_xla_2_gpu_fp16(self, flags_key):
-    """Test Keras model with XLA, 2 GPUs and fp16."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
-
-    if context.num_gpus() < 2:
-      self.skipTest(
-          "{} GPUs are not available for this test. {} GPUs are available".
-          format(2, context.num_gpus()))
-
-    extra_flags = [
-        "-num_gpus", "2",
-        "-dtype", "fp16",
-        "-enable_xla", "true",
-        "-distribution_strategy", "mirrored",
-    ]
-    extra_flags = extra_flags + self.get_extra_flags_dict(flags_key)
-
-    if "polynomial_decay" in extra_flags:
-      self.skipTest("Pruning with fp16 is not currently supported.")
-
-    integration.run_synthetic(
-        main=resnet_imagenet_main.run,
-        tmp_root=self.get_temp_dir(),
-        extra_flags=extra_flags
-    )
-
-
-if __name__ == "__main__":
-  tf.compat.v1.enable_v2_behavior()
-  tf.test.main()