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148 lines (124 loc) · 5.85 KB
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# 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 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 testCreateMaxPooling1D(self):
width = 7
images = random_ops.random_uniform((5, width, 4))
layer = pooling_layers.MaxPooling1D(2, strides=2)
output = layer.apply(images)
self.assertListEqual(output.get_shape().as_list(), [5, 3, 4])
def testCreateAveragePooling1D(self):
width = 7
images = random_ops.random_uniform((5, width, 4))
layer = pooling_layers.AveragePooling1D(2, strides=2)
output = layer.apply(images)
self.assertListEqual(output.get_shape().as_list(), [5, 3, 4])
def testCreateMaxPooling1DChannelsFirst(self):
width = 7
images = random_ops.random_uniform((5, width, 4))
layer = pooling_layers.MaxPooling1D(
2, strides=2, data_format='channels_first')
output = layer.apply(images)
self.assertListEqual(output.get_shape().as_list(), [5, 3, 4])
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, 4, 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, 4, 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()