1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
|
# 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
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, 4, width))
layer = pooling_layers.MaxPooling1D(
2, strides=2, data_format='channels_first')
output = layer.apply(images)
self.assertListEqual(output.get_shape().as_list(), [5, 4, 3])
def testCreateAveragePooling1DChannelsFirst(self):
width = 7
images = random_ops.random_uniform((5, 4, width))
layer = pooling_layers.AveragePooling1D(
2, strides=2, data_format='channels_first')
output = layer.apply(images)
self.assertListEqual(output.get_shape().as_list(), [5, 4, 3])
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()
|
