text: # Copyright 2016 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 pooling layers."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.python.eager import context
  from tensorflow.python.framework import test_util as tf_test_util
  from tensorflow.python.keras._impl import keras
  from tensorflow.python.keras._impl.keras import testing_utils
  from tensorflow.python.platform import test
  
  
  class GlobalPoolingTest(test.TestCase):
  
    @tf_test_util.run_in_graph_and_eager_modes()
    def test_globalpooling_1d(self):
      testing_utils.layer_test(keras.layers.pooling.GlobalMaxPooling1D,
                               input_shape=(3, 4, 5))
      testing_utils.layer_test(
          keras.layers.pooling.GlobalAveragePooling1D, input_shape=(3, 4, 5))
  
    @tf_test_util.run_in_graph_and_eager_modes()
    def test_globalpooling_2d(self):
      testing_utils.layer_test(
          keras.layers.pooling.GlobalMaxPooling2D,
          kwargs={'data_format': 'channels_first'},
          input_shape=(3, 4, 5, 6))
      testing_utils.layer_test(
          keras.layers.pooling.GlobalMaxPooling2D,
          kwargs={'data_format': 'channels_last'},
          input_shape=(3, 5, 6, 4))
      testing_utils.layer_test(
          keras.layers.pooling.GlobalAveragePooling2D,
          kwargs={'data_format': 'channels_first'},
          input_shape=(3, 4, 5, 6))
      testing_utils.layer_test(
          keras.layers.pooling.GlobalAveragePooling2D,
          kwargs={'data_format': 'channels_last'},
          input_shape=(3, 5, 6, 4))
  
    @tf_test_util.run_in_graph_and_eager_modes()
    def test_globalpooling_3d(self):
      testing_utils.layer_test(
          keras.layers.pooling.GlobalMaxPooling3D,
          kwargs={'data_format': 'channels_first'},
          input_shape=(3, 4, 3, 4, 3))
      testing_utils.layer_test(
          keras.layers.pooling.GlobalMaxPooling3D,
          kwargs={'data_format': 'channels_last'},
          input_shape=(3, 4, 3, 4, 3))
      testing_utils.layer_test(
          keras.layers.pooling.GlobalAveragePooling3D,
          kwargs={'data_format': 'channels_first'},
          input_shape=(3, 4, 3, 4, 3))
      testing_utils.layer_test(
          keras.layers.pooling.GlobalAveragePooling3D,
          kwargs={'data_format': 'channels_last'},
          input_shape=(3, 4, 3, 4, 3))
  
  
  class Pooling2DTest(test.TestCase):
  
    @tf_test_util.run_in_graph_and_eager_modes()
    def test_maxpooling_2d(self):
      pool_size = (3, 3)
      for strides in [(1, 1), (2, 2)]:
        testing_utils.layer_test(
            keras.layers.MaxPooling2D,
            kwargs={
                'strides': strides,
                'padding': 'valid',
                'pool_size': pool_size
            },
            input_shape=(3, 5, 6, 4))
  
    @tf_test_util.run_in_graph_and_eager_modes()
    def test_averagepooling_2d(self):
      testing_utils.layer_test(
          keras.layers.AveragePooling2D,
          kwargs={'strides': (2, 2),
                  'padding': 'same',
                  'pool_size': (2, 2)},
          input_shape=(3, 5, 6, 4))
      testing_utils.layer_test(
          keras.layers.AveragePooling2D,
          kwargs={'strides': (2, 2),
                  'padding': 'valid',
                  'pool_size': (3, 3)},
          input_shape=(3, 5, 6, 4))
  
      # This part of the test can only run on GPU but doesn't appear
      # to be properly assigned to a GPU when running in eager mode.
      if not context.executing_eagerly():
        # Only runs on GPU with CUDA, channels_first is not supported on CPU.
        # TODO(b/62340061): Support channels_first on CPU.
        if test.is_gpu_available(cuda_only=True):
          testing_utils.layer_test(
              keras.layers.AveragePooling2D,
              kwargs={
                  'strides': (1, 1),
                  'padding': 'valid',
                  'pool_size': (2, 2),
                  'data_format': 'channels_first'
              },
              input_shape=(3, 4, 5, 6))
  
  
  class Pooling3DTest(test.TestCase):
  
    @tf_test_util.run_in_graph_and_eager_modes()
    def test_maxpooling_3d(self):
      pool_size = (3, 3, 3)
      testing_utils.layer_test(
          keras.layers.MaxPooling3D,
          kwargs={'strides': 2,
                  'padding': 'valid',
                  'pool_size': pool_size},
          input_shape=(3, 11, 12, 10, 4))
      testing_utils.layer_test(
          keras.layers.MaxPooling3D,
          kwargs={
              'strides': 3,
              'padding': 'valid',
              'data_format': 'channels_first',
              'pool_size': pool_size
          },
          input_shape=(3, 4, 11, 12, 10))
  
    @tf_test_util.run_in_graph_and_eager_modes()
    def test_averagepooling_3d(self):
      pool_size = (3, 3, 3)
      testing_utils.layer_test(
          keras.layers.AveragePooling3D,
          kwargs={'strides': 2,
                  'padding': 'valid',
                  'pool_size': pool_size},
          input_shape=(3, 11, 12, 10, 4))
      testing_utils.layer_test(
          keras.layers.AveragePooling3D,
          kwargs={
              'strides': 3,
              'padding': 'valid',
              'data_format': 'channels_first',
              'pool_size': pool_size
          },
          input_shape=(3, 4, 11, 12, 10))
  
  
  class Pooling1DTest(test.TestCase):
  
    @tf_test_util.run_in_graph_and_eager_modes()
    def test_maxpooling_1d(self):
      for padding in ['valid', 'same']:
        for stride in [1, 2]:
          testing_utils.layer_test(
              keras.layers.MaxPooling1D,
              kwargs={'strides': stride,
                      'padding': padding},
              input_shape=(3, 5, 4))
  
    @tf_test_util.run_in_graph_and_eager_modes()
    def test_averagepooling_1d(self):
      for padding in ['valid', 'same']:
        for stride in [1, 2]:
          testing_utils.layer_test(
              keras.layers.AveragePooling1D,
              kwargs={'strides': stride,
                      'padding': padding},
              input_shape=(3, 5, 4))
  
  
  if __name__ == '__main__':
    test.main()
  