282 lines
9.9 KiB
Python
282 lines
9.9 KiB
Python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for pooling layers."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from absl.testing import parameterized
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import numpy as np
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from tensorflow.python import keras
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from tensorflow.python.eager import context
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from tensorflow.python.framework import constant_op
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from tensorflow.python.keras import combinations
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from tensorflow.python.keras import testing_utils
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from tensorflow.python.ops.ragged import ragged_factory_ops
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from tensorflow.python.platform import test
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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class GlobalPoolingTest(test.TestCase, parameterized.TestCase):
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def test_globalpooling_1d(self):
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testing_utils.layer_test(
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keras.layers.pooling.GlobalMaxPooling1D, input_shape=(3, 4, 5))
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testing_utils.layer_test(
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keras.layers.pooling.GlobalMaxPooling1D,
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kwargs={'data_format': 'channels_first'},
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input_shape=(3, 4, 5))
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testing_utils.layer_test(
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keras.layers.pooling.GlobalAveragePooling1D, input_shape=(3, 4, 5))
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testing_utils.layer_test(
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keras.layers.pooling.GlobalAveragePooling1D,
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kwargs={'data_format': 'channels_first'},
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input_shape=(3, 4, 5))
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def test_globalpooling_1d_masking_support(self):
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model = keras.Sequential()
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model.add(keras.layers.Masking(mask_value=0., input_shape=(None, 4)))
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model.add(keras.layers.GlobalAveragePooling1D())
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model.compile(loss='mae', optimizer='rmsprop')
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model_input = np.random.random((2, 3, 4))
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model_input[0, 1:, :] = 0
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output = model.predict(model_input)
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self.assertAllClose(output[0], model_input[0, 0, :])
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def test_globalpooling_1d_with_ragged(self):
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ragged_data = ragged_factory_ops.constant(
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[[[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]], [[1.0, 1.0], [2.0, 2.0]]],
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ragged_rank=1)
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dense_data = ragged_data.to_tensor()
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inputs = keras.Input(shape=(None, 2), dtype='float32', ragged=True)
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out = keras.layers.GlobalAveragePooling1D()(inputs)
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model = keras.models.Model(inputs=inputs, outputs=out)
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output_ragged = model.predict(ragged_data, steps=1)
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inputs = keras.Input(shape=(None, 2), dtype='float32')
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masking = keras.layers.Masking(mask_value=0., input_shape=(3, 2))(inputs)
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out = keras.layers.GlobalAveragePooling1D()(masking)
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model = keras.models.Model(inputs=inputs, outputs=out)
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output_dense = model.predict(dense_data, steps=1)
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self.assertAllEqual(output_ragged, output_dense)
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def test_globalpooling_2d_with_ragged(self):
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ragged_data = ragged_factory_ops.constant(
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[[[[1.0], [1.0]], [[2.0], [2.0]], [[3.0], [3.0]]],
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[[[1.0], [1.0]], [[2.0], [2.0]]]],
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ragged_rank=1)
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dense_data = ragged_data.to_tensor()
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inputs = keras.Input(shape=(None, 2, 1), dtype='float32', ragged=True)
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out = keras.layers.GlobalMaxPooling2D()(inputs)
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model = keras.models.Model(inputs=inputs, outputs=out)
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output_ragged = model.predict(ragged_data, steps=1)
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inputs = keras.Input(shape=(None, 2, 1), dtype='float32')
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out = keras.layers.GlobalMaxPooling2D()(inputs)
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model = keras.models.Model(inputs=inputs, outputs=out)
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output_dense = model.predict(dense_data, steps=1)
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self.assertAllEqual(output_ragged, output_dense)
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def test_globalpooling_3d_with_ragged(self):
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ragged_data = ragged_factory_ops.constant(
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[[[[[1.0]], [[1.0]]], [[[2.0]], [[2.0]]], [[[3.0]], [[3.0]]]],
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[[[[1.0]], [[1.0]]], [[[2.0]], [[2.0]]]]],
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ragged_rank=1)
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inputs = keras.Input(shape=(None, 2, 1, 1), dtype='float32', ragged=True)
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out = keras.layers.GlobalAveragePooling3D()(inputs)
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model = keras.models.Model(inputs=inputs, outputs=out)
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output_ragged = model.predict(ragged_data, steps=1)
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# Because GlobalAveragePooling3D doesn't support masking, the results
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# cannot be compared with its dense equivalent.
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expected_output = constant_op.constant([[2.0], [1.5]])
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self.assertAllEqual(output_ragged, expected_output)
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def test_globalpooling_2d(self):
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testing_utils.layer_test(
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keras.layers.pooling.GlobalMaxPooling2D,
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kwargs={'data_format': 'channels_first'},
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input_shape=(3, 4, 5, 6))
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testing_utils.layer_test(
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keras.layers.pooling.GlobalMaxPooling2D,
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kwargs={'data_format': 'channels_last'},
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input_shape=(3, 5, 6, 4))
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testing_utils.layer_test(
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keras.layers.pooling.GlobalAveragePooling2D,
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kwargs={'data_format': 'channels_first'},
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input_shape=(3, 4, 5, 6))
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testing_utils.layer_test(
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keras.layers.pooling.GlobalAveragePooling2D,
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kwargs={'data_format': 'channels_last'},
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input_shape=(3, 5, 6, 4))
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def test_globalpooling_3d(self):
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testing_utils.layer_test(
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keras.layers.pooling.GlobalMaxPooling3D,
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kwargs={'data_format': 'channels_first'},
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input_shape=(3, 4, 3, 4, 3))
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testing_utils.layer_test(
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keras.layers.pooling.GlobalMaxPooling3D,
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kwargs={'data_format': 'channels_last'},
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input_shape=(3, 4, 3, 4, 3))
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testing_utils.layer_test(
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keras.layers.pooling.GlobalAveragePooling3D,
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kwargs={'data_format': 'channels_first'},
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input_shape=(3, 4, 3, 4, 3))
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testing_utils.layer_test(
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keras.layers.pooling.GlobalAveragePooling3D,
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kwargs={'data_format': 'channels_last'},
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input_shape=(3, 4, 3, 4, 3))
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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class Pooling2DTest(test.TestCase, parameterized.TestCase):
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def test_maxpooling_2d(self):
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pool_size = (3, 3)
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for strides in [(1, 1), (2, 2)]:
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testing_utils.layer_test(
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keras.layers.MaxPooling2D,
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kwargs={
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'strides': strides,
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'padding': 'valid',
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'pool_size': pool_size
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},
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input_shape=(3, 5, 6, 4))
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def test_averagepooling_2d(self):
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testing_utils.layer_test(
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keras.layers.AveragePooling2D,
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kwargs={
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'strides': (2, 2),
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'padding': 'same',
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'pool_size': (2, 2)
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},
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input_shape=(3, 5, 6, 4))
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testing_utils.layer_test(
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keras.layers.AveragePooling2D,
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kwargs={
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'strides': (2, 2),
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'padding': 'valid',
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'pool_size': (3, 3)
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},
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input_shape=(3, 5, 6, 4))
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# This part of the test can only run on GPU but doesn't appear
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# to be properly assigned to a GPU when running in eager mode.
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if not context.executing_eagerly():
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# Only runs on GPU with CUDA, channels_first is not supported on CPU.
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# TODO(b/62340061): Support channels_first on CPU.
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if test.is_gpu_available(cuda_only=True):
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testing_utils.layer_test(
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keras.layers.AveragePooling2D,
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kwargs={
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'strides': (1, 1),
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'padding': 'valid',
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'pool_size': (2, 2),
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'data_format': 'channels_first'
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},
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input_shape=(3, 4, 5, 6))
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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class Pooling3DTest(test.TestCase, parameterized.TestCase):
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def test_maxpooling_3d(self):
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pool_size = (3, 3, 3)
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testing_utils.layer_test(
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keras.layers.MaxPooling3D,
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kwargs={
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'strides': 2,
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'padding': 'valid',
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'pool_size': pool_size
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},
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input_shape=(3, 11, 12, 10, 4))
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testing_utils.layer_test(
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keras.layers.MaxPooling3D,
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kwargs={
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'strides': 3,
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'padding': 'valid',
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'data_format': 'channels_first',
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'pool_size': pool_size
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},
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input_shape=(3, 4, 11, 12, 10))
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def test_averagepooling_3d(self):
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pool_size = (3, 3, 3)
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testing_utils.layer_test(
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keras.layers.AveragePooling3D,
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kwargs={
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'strides': 2,
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'padding': 'valid',
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'pool_size': pool_size
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},
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input_shape=(3, 11, 12, 10, 4))
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testing_utils.layer_test(
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keras.layers.AveragePooling3D,
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kwargs={
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'strides': 3,
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'padding': 'valid',
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'data_format': 'channels_first',
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'pool_size': pool_size
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},
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input_shape=(3, 4, 11, 12, 10))
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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class Pooling1DTest(test.TestCase, parameterized.TestCase):
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def test_maxpooling_1d(self):
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for padding in ['valid', 'same']:
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for stride in [1, 2]:
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testing_utils.layer_test(
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keras.layers.MaxPooling1D,
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kwargs={
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'strides': stride,
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'padding': padding
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},
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input_shape=(3, 5, 4))
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testing_utils.layer_test(
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keras.layers.MaxPooling1D,
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kwargs={'data_format': 'channels_first'},
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input_shape=(3, 2, 6))
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def test_averagepooling_1d(self):
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for padding in ['valid', 'same']:
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for stride in [1, 2]:
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testing_utils.layer_test(
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keras.layers.AveragePooling1D,
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kwargs={
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'strides': stride,
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'padding': padding
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},
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input_shape=(3, 5, 4))
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testing_utils.layer_test(
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keras.layers.AveragePooling1D,
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kwargs={'data_format': 'channels_first'},
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input_shape=(3, 2, 6))
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if __name__ == '__main__':
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test.main()
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