# Copyright 2015 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 tf.layers.pooling.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.layers import pooling as pooling_layers from tensorflow.python.ops import array_ops from tensorflow.python.ops import random_ops from tensorflow.python.platform import test class PoolingTest(test.TestCase): def testInvalidDataFormat(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) with self.assertRaisesRegexp(ValueError, 'data_format'): pooling_layers.max_pooling2d(images, 3, strides=2, data_format='invalid') def testInvalidStrides(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) with self.assertRaisesRegexp(ValueError, 'strides'): pooling_layers.max_pooling2d(images, 3, strides=(1, 2, 3)) with self.assertRaisesRegexp(ValueError, 'strides'): pooling_layers.max_pooling2d(images, 3, strides=None) def testInvalidPoolSize(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) with self.assertRaisesRegexp(ValueError, 'pool_size'): pooling_layers.max_pooling2d(images, (1, 2, 3), strides=2) with self.assertRaisesRegexp(ValueError, 'pool_size'): pooling_layers.max_pooling2d(images, None, strides=2) def testCreateMaxPooling2D(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 4)) layer = pooling_layers.MaxPooling2D([2, 2], strides=2) output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, 3, 4, 4]) def testCreateAveragePooling2D(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 4)) layer = pooling_layers.AveragePooling2D([2, 2], strides=2) output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, 3, 4, 4]) def testCreateMaxPooling2DChannelsFirst(self): height, width = 7, 9 images = random_ops.random_uniform((5, 2, height, width)) layer = pooling_layers.MaxPooling2D([2, 2], strides=1, data_format='channels_first') output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, 2, 6, 8]) def testCreateAveragePooling2DChannelsFirst(self): height, width = 5, 6 images = random_ops.random_uniform((3, 4, height, width)) layer = pooling_layers.AveragePooling2D((2, 2), strides=(1, 1), padding='valid', data_format='channels_first') output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [3, 4, 4, 5]) def testCreateAveragePooling2DChannelsFirstWithNoneBatch(self): height, width = 5, 6 images = array_ops.placeholder(dtype='float32', shape=(None, 4, height, width)) layer = pooling_layers.AveragePooling2D((2, 2), strides=(1, 1), padding='valid', data_format='channels_first') output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [None, 4, 4, 5]) def testCreateMaxPooling1D(self): width = 7 channels = 3 images = random_ops.random_uniform((5, width, channels)) layer = pooling_layers.MaxPooling1D(2, strides=2) output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, width // 2, channels]) def testCreateAveragePooling1D(self): width = 7 channels = 3 images = random_ops.random_uniform((5, width, channels)) layer = pooling_layers.AveragePooling1D(2, strides=2) output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, width // 2, channels]) def testCreateMaxPooling1DChannelsFirst(self): width = 7 channels = 3 images = random_ops.random_uniform((5, channels, width)) layer = pooling_layers.MaxPooling1D( 2, strides=2, data_format='channels_first') output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, channels, width // 2]) def testCreateAveragePooling1DChannelsFirst(self): width = 7 channels = 3 images = random_ops.random_uniform((5, channels, width)) layer = pooling_layers.AveragePooling1D( 2, strides=2, data_format='channels_first') output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, channels, width // 2]) def testCreateMaxPooling3D(self): depth, height, width = 6, 7, 9 images = random_ops.random_uniform((5, depth, height, width, 4)) layer = pooling_layers.MaxPooling3D([2, 2, 2], strides=2) output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, 3, 3, 4, 4]) def testCreateAveragePooling3D(self): depth, height, width = 6, 7, 9 images = random_ops.random_uniform((5, depth, height, width, 4)) layer = pooling_layers.AveragePooling3D([2, 2, 2], strides=2) output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, 3, 3, 4, 4]) def testMaxPooling3DChannelsFirst(self): depth, height, width = 6, 7, 9 images = random_ops.random_uniform((5, 2, depth, height, width)) layer = pooling_layers.MaxPooling3D( [2, 2, 2], strides=2, data_format='channels_first') output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, 2, 3, 3, 4]) def testAveragePooling3DChannelsFirst(self): depth, height, width = 6, 7, 9 images = random_ops.random_uniform((5, 2, depth, height, width)) layer = pooling_layers.AveragePooling3D( [2, 2, 2], strides=2, data_format='channels_first') output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, 2, 3, 3, 4]) def testCreateMaxPooling2DIntegerPoolSize(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 4)) layer = pooling_layers.MaxPooling2D(2, strides=2) output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, 3, 4, 4]) def testMaxPooling2DPaddingSame(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 4), seed=1) layer = pooling_layers.MaxPooling2D( images.get_shape()[1:3], strides=2, padding='same') output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, 4, 5, 4]) def testCreatePooling2DWithStrides(self): height, width = 6, 8 # Test strides tuple images = random_ops.random_uniform((5, height, width, 3), seed=1) layer = pooling_layers.MaxPooling2D([2, 2], strides=(2, 2), padding='same') output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, height / 2, width / 2, 3]) # Test strides integer layer = pooling_layers.MaxPooling2D([2, 2], strides=2, padding='same') output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, height / 2, width / 2, 3]) # Test unequal strides layer = pooling_layers.MaxPooling2D([2, 2], strides=(2, 1), padding='same') output = layer.apply(images) self.assertListEqual(output.get_shape().as_list(), [5, height / 2, width, 3]) if __name__ == '__main__': test.main()