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
|
# 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 convolution related functionality in tensorflow.ops.nn."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
class AtrousConv2DTest(tf.test.TestCase):
def _upsample_filters(self, filters, rate):
"""Upsamples the filters by a factor of rate along the spatial dimensions.
Args:
filters: [h, w, in_depth, out_depth]. Original filters.
rate: An int, specifying the upsampling rate.
Returns:
filters_up: [h_up, w_up, in_depth, out_depth]. Upsampled filters with
h_up = h + (h - 1) * (rate - 1)
w_up = w + (w - 1) * (rate - 1)
containing (rate - 1) zeros between consecutive filter values along
the filters' spatial dimensions.
"""
if rate == 1:
return filters
# [h, w, in_depth, out_depth] -> [in_depth, out_depth, h, w]
filters_up = np.transpose(filters, [2, 3, 0, 1])
ker = np.zeros([rate, rate])
ker[0, 0] = 1
filters_up = np.kron(filters_up, ker)[:, :, :-(rate-1), :-(rate-1)]
# [in_depth, out_depth, h_up, w_up] -> [h_up, w_up, in_depth, out_depth]
filters_up = np.transpose(filters_up, [2, 3, 0, 1])
self.assertEqual(np.sum(filters), np.sum(filters_up))
return filters_up
def testAtrousConv2DForward(self):
with self.test_session(use_gpu=True):
# Input: [batch, height, width, input_depth]
height = 9
for width in [9, 10]: # Test both odd and even width.
x_shape = [2, height, width, 2]
x = np.arange(np.prod(x_shape), dtype=np.float32).reshape(x_shape)
# Filter: [kernel_height, kernel_width, input_depth, output_depth]
for kernel_height in range(1, 4):
for kernel_width in range(1, 4):
f_shape = [kernel_height, kernel_width, 2, 2]
f = np.arange(np.prod(f_shape), dtype=np.float32).reshape(f_shape)
for rate in range(1, 4):
f_up = self._upsample_filters(f, rate)
for padding in ["SAME", "VALID"]:
y1 = tf.nn.atrous_conv2d(x, f, rate, padding=padding)
y2 = tf.nn.conv2d(x, f_up, strides=[1, 1, 1, 1],
padding=padding)
self.assertAllClose(y1.eval(), y2.eval(), rtol=1e-2,
atol=1e-2)
def testAtrousSequence(self):
"""Tests optimization of sequence of atrous convolutions.
Verifies that a sequence of `atrous_conv2d` operations with identical `rate`
parameters, 'SAME' `padding`, and `filters` with odd heights/ widths:
net = atrous_conv2d(net, filters1, rate, padding="SAME")
net = atrous_conv2d(net, filters2, rate, padding="SAME")
...
net = atrous_conv2d(net, filtersK, rate, padding="SAME")
is equivalent to:
pad = ... # padding so that the input dims are multiples of rate
net = space_to_batch(net, paddings=pad, block_size=rate)
net = conv2d(net, filters1, strides=[1, 1, 1, 1], padding="SAME")
net = conv2d(net, filters2, strides=[1, 1, 1, 1], padding="SAME")
...
net = conv2d(net, filtersK, strides=[1, 1, 1, 1], padding="SAME")
net = batch_to_space(net, crops=pad, block_size=rate)
"""
padding = "SAME" # The padding needs to be "SAME"
np.random.seed(1) # Make it reproducible.
with self.test_session(use_gpu=True):
# Input: [batch, height, width, input_depth]
for height in range(15, 17):
for width in range(15, 17):
x_shape = [3, height, width, 2]
x = np.random.random_sample(x_shape).astype(np.float32)
for kernel in [1, 3, 5]: # The kernel size needs to be odd.
# Filter: [kernel_height, kernel_width, input_depth, output_depth]
f_shape = [kernel, kernel, 2, 2]
f = 1e-2 * np.random.random_sample(f_shape).astype(np.float32)
for rate in range(2, 4):
# y1: three atrous_conv2d in a row.
y1 = tf.nn.atrous_conv2d(x, f, rate, padding=padding)
y1 = tf.nn.atrous_conv2d(y1, f, rate, padding=padding)
y1 = tf.nn.atrous_conv2d(y1, f, rate, padding=padding)
# y2: space_to_batch, three conv2d in a row, batch_to_space
pad_bottom = 0 if height % rate == 0 else rate - height % rate
pad_right = 0 if width % rate == 0 else rate - width % rate
pad = [[0, pad_bottom], [0, pad_right]]
y2 = tf.space_to_batch(x, paddings=pad, block_size=rate)
y2 = tf.nn.conv2d(y2, f, strides=[1, 1, 1, 1], padding=padding)
y2 = tf.nn.conv2d(y2, f, strides=[1, 1, 1, 1], padding=padding)
y2 = tf.nn.conv2d(y2, f, strides=[1, 1, 1, 1], padding=padding)
y2 = tf.batch_to_space(y2, crops=pad, block_size=rate)
self.assertAllClose(y1.eval(), y2.eval(), rtol=1e-2, atol=1e-2)
def testGradient(self):
with self.test_session(use_gpu=True):
# Input: [batch, height, width, input_depth]
x_shape = [2, 5, 6, 2]
# Filter: [kernel_height, kernel_width, input_depth, output_depth]
f_shape = [3, 3, 2, 2]
# Output: [batch, height, width, output_depth]
y_shape = [2, 5, 6, 2]
np.random.seed(1) # Make it reproducible.
x_val = np.random.random_sample(x_shape).astype(np.float32)
f_val = np.random.random_sample(f_shape).astype(np.float32)
x = tf.constant(x_val, name="x", dtype=tf.float32)
f = tf.constant(f_val, name="f", dtype=tf.float32)
for rate in range(1, 4):
output = tf.nn.atrous_conv2d(x, f, rate=rate, padding="SAME")
err = tf.test.compute_gradient_error(
[x, f], [x_shape, f_shape], output, y_shape)
print("atrous_conv2d gradient err = %g " % err)
err_tolerance = 1e-3
self.assertLess(err, err_tolerance)
if __name__ == "__main__":
tf.test.main()
|
