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
|
# Copyright 2017 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.
# ==============================================================================
"""Functional tests for fused batch norm operations."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.compiler.tests.xla_test import XLATestCase
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_nn_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import nn
from tensorflow.python.platform import test
class FusedBatchNormTest(XLATestCase):
def _reference_training(self, x, scale, offset, epsilon, data_format):
if data_format != "NHWC":
raise ValueError("data_format must be NHWC, got %s." % data_format)
x_square = x * x
x_square_sum = np.sum(x_square, (0, 1, 2))
x_sum = np.sum(x, axis=(0, 1, 2))
element_count = np.size(x) / int(np.shape(x)[0])
mean = x_sum / element_count
var = x_square_sum / element_count - mean * mean
normalized = (x - mean) / np.sqrt(var + epsilon)
return (normalized * scale + offset), mean, var
def _reference_grad(self, x, grad_y, scale, mean, var, epsilon, data_format):
# Use the following formulas to calculate gradients:
# grad_scale =
# sum(grad_y * (x - mean)) * rsqrt(var + epsilon)
#
# grad_offset = sum(output_y)
#
# grad_x =
# 1/N * scale * rsqrt(var + epsilon) * (N * grad_y - sum(grad_y) -
# (x - mean) * sum(grad_y * (x - mean)) / (var + epsilon))
if data_format != "NHWC":
raise ValueError("data_format must be NHWC, got %s." % data_format)
grad_x = scale * (grad_y - np.mean(grad_y, axis=(0, 1, 2)) -
(x - mean) * np.mean(grad_y *
(x - mean), axis=(0, 1, 2)) /
(var + epsilon)) / np.sqrt(var + epsilon)
grad_scale = np.sum(
grad_y * (x - mean) / np.sqrt(var + epsilon), axis=(0, 1, 2))
grad_offset = np.sum(grad_y, axis=(0, 1, 2))
return grad_x, grad_scale, grad_offset
def testInference(self):
x_shape = [2, 2, 6, 2]
scale_shape = [2]
x_val = np.random.random_sample(x_shape).astype(np.float32)
scale_val = np.random.random_sample(scale_shape).astype(np.float32)
offset_val = np.random.random_sample(scale_shape).astype(np.float32)
data_format = "NHWC"
with self.test_session() as sess, self.test_scope():
# To avoid constant folding
t_val = array_ops.placeholder(np.float32, shape=x_shape, name="x")
scale = array_ops.placeholder(np.float32, shape=[2], name="scale")
offset = array_ops.placeholder(np.float32, shape=[2], name="offset")
epsilon = 0.001
y_ref, mean_ref, var_ref = self._reference_training(
x_val, scale_val, offset_val, epsilon, data_format)
y, mean, variance = nn.fused_batch_norm(
t_val,
scale,
offset,
mean=mean_ref,
variance=var_ref,
epsilon=epsilon,
data_format=data_format,
is_training=False)
y_val, _, _ = sess.run(
[y, mean,
variance], {t_val: x_val,
scale: scale_val,
offset: offset_val})
self.assertAllClose(y_val, y_ref, atol=1e-3)
def _testLearning(self, use_gradient_checker):
x_shape = [2, 2, 6, 2]
scale_shape = [2]
x_val = np.random.random_sample(x_shape).astype(np.float32)
scale_val = np.random.random_sample(scale_shape).astype(np.float32)
offset_val = np.random.random_sample(scale_shape).astype(np.float32)
mean_val = np.random.random_sample(scale_shape).astype(np.float32)
var_val = np.random.random_sample(scale_shape).astype(np.float32)
data_format = "NHWC"
with self.test_session() as sess, self.test_scope():
# To avoid constant folding
t_val = array_ops.placeholder(np.float32, shape=x_shape, name="x")
scale = array_ops.placeholder(np.float32, shape=[2], name="scale")
offset = array_ops.placeholder(np.float32, shape=[2], name="offset")
epsilon = 0.001
y, mean, var = nn.fused_batch_norm(
t_val,
scale,
offset,
mean=None,
variance=None,
epsilon=epsilon,
data_format=data_format,
is_training=True)
# Check gradient.
if use_gradient_checker:
err = gradient_checker.compute_gradient_error(
t_val,
x_shape,
y,
x_shape,
extra_feed_dict={
t_val: x_val,
scale: scale_val,
offset: offset_val
})
self.assertLess(err, 1e-3)
y_val, mean_val, var_val = sess.run(
[y, mean, var], {t_val: x_val,
scale: scale_val,
offset: offset_val})
y_ref, mean_ref, var_ref = self._reference_training(
x_val, scale_val, offset_val, epsilon, data_format)
self.assertAllClose(mean_val, mean_ref, atol=1e-3)
self.assertAllClose(y_val, y_ref, atol=1e-3)
self.assertAllClose(var_val, var_ref, atol=1e-3)
def testLearning(self):
self._testLearning(False)
def testLearningWithGradientChecker(self):
self._testLearning(True)
def testGradient(self):
# TODO(b/64270657): Use gradient_checker here in addition to comparing with
# this reference implementation.
x_shape = [2, 2, 6, 2]
scale_shape = [2]
grad_val = np.random.random_sample(x_shape).astype(np.float32)
x_val = np.random.random_sample(x_shape).astype(np.float32)
scale_val = np.random.random_sample(scale_shape).astype(np.float32)
mean_val = np.random.random_sample(scale_shape).astype(np.float32)
var_val = np.random.random_sample(scale_shape).astype(np.float32)
epsilon = 0.001
with self.test_session() as sess, self.test_scope():
grad = array_ops.placeholder(np.float32, shape=x_shape, name="grad")
x = array_ops.placeholder(np.float32, shape=x_shape, name="x")
mean = array_ops.placeholder(np.float32, shape=scale_shape, name="mean")
var = array_ops.placeholder(np.float32, shape=scale_shape, name="var")
scale = array_ops.placeholder(np.float32, shape=scale_shape, name="scale")
grad_x, grad_scale, grad_offset, _, _ = gen_nn_ops.fused_batch_norm_grad(
grad, x, scale, mean, var, data_format="NHWC")
grad_x_val, grad_scale_val, grad_offset_val = sess.run(
[grad_x, grad_scale, grad_offset], {
grad: grad_val,
x: x_val,
mean: mean_val,
var: var_val,
scale: scale_val
})
grad_x_ref, grad_scale_ref, grad_offset_ref = self._reference_grad(
x_val, grad_val, scale_val, mean_val, var_val, epsilon, "NHWC")
self.assertAllClose(grad_x_val, grad_x_ref, atol=1e-2)
self.assertAllClose(grad_scale_val, grad_scale_ref, atol=1e-2)
self.assertAllClose(grad_offset_val, grad_offset_ref, atol=1e-3)
if __name__ == "__main__":
test.main()
|
