forked from tensorflow/models
-
Notifications
You must be signed in to change notification settings - Fork 0
/
cifar10_test.py
170 lines (134 loc) · 6.03 KB
/
cifar10_test.py
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
# 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tempfile import mkstemp
import numpy as np
import tensorflow as tf # pylint: disable=g-bad-import-order
from official.resnet import cifar10_main
from official.utils.testing import integration
tf.logging.set_verbosity(tf.logging.ERROR)
_BATCH_SIZE = 128
_HEIGHT = 32
_WIDTH = 32
_NUM_CHANNELS = 3
class BaseTest(tf.test.TestCase):
"""Tests for the Cifar10 version of Resnet.
"""
@classmethod
def setUpClass(cls): # pylint: disable=invalid-name
super(BaseTest, cls).setUpClass()
cifar10_main.define_cifar_flags()
def tearDown(self):
super(BaseTest, self).tearDown()
tf.gfile.DeleteRecursively(self.get_temp_dir())
def test_dataset_input_fn(self):
fake_data = bytearray()
fake_data.append(7)
for i in range(_NUM_CHANNELS):
for _ in range(_HEIGHT * _WIDTH):
fake_data.append(i)
_, filename = mkstemp(dir=self.get_temp_dir())
data_file = open(filename, 'wb')
data_file.write(fake_data)
data_file.close()
fake_dataset = tf.data.FixedLengthRecordDataset(
filename, cifar10_main._RECORD_BYTES) # pylint: disable=protected-access
fake_dataset = fake_dataset.map(
lambda val: cifar10_main.parse_record(val, False, tf.float32))
image, label = fake_dataset.make_one_shot_iterator().get_next()
self.assertAllEqual(label.shape, ())
self.assertAllEqual(image.shape, (_HEIGHT, _WIDTH, _NUM_CHANNELS))
with self.test_session() as sess:
image, label = sess.run([image, label])
self.assertEqual(label, 7)
for row in image:
for pixel in row:
self.assertAllClose(pixel, np.array([-1.225, 0., 1.225]), rtol=1e-3)
def cifar10_model_fn_helper(self, mode, resnet_version, dtype):
input_fn = cifar10_main.get_synth_input_fn(dtype)
dataset = input_fn(True, '', _BATCH_SIZE)
iterator = dataset.make_initializable_iterator()
features, labels = iterator.get_next()
spec = cifar10_main.cifar10_model_fn(
features, labels, mode, {
'dtype': dtype,
'resnet_size': 32,
'data_format': 'channels_last',
'batch_size': _BATCH_SIZE,
'resnet_version': resnet_version,
'loss_scale': 128 if dtype == tf.float16 else 1,
'fine_tune': False,
})
predictions = spec.predictions
self.assertAllEqual(predictions['probabilities'].shape,
(_BATCH_SIZE, 10))
self.assertEqual(predictions['probabilities'].dtype, tf.float32)
self.assertAllEqual(predictions['classes'].shape, (_BATCH_SIZE,))
self.assertEqual(predictions['classes'].dtype, tf.int64)
if mode != tf.estimator.ModeKeys.PREDICT:
loss = spec.loss
self.assertAllEqual(loss.shape, ())
self.assertEqual(loss.dtype, tf.float32)
if mode == tf.estimator.ModeKeys.EVAL:
eval_metric_ops = spec.eval_metric_ops
self.assertAllEqual(eval_metric_ops['accuracy'][0].shape, ())
self.assertAllEqual(eval_metric_ops['accuracy'][1].shape, ())
self.assertEqual(eval_metric_ops['accuracy'][0].dtype, tf.float32)
self.assertEqual(eval_metric_ops['accuracy'][1].dtype, tf.float32)
def test_cifar10_model_fn_train_mode_v1(self):
self.cifar10_model_fn_helper(tf.estimator.ModeKeys.TRAIN, resnet_version=1,
dtype=tf.float32)
def test_cifar10_model_fn_trainmode__v2(self):
self.cifar10_model_fn_helper(tf.estimator.ModeKeys.TRAIN, resnet_version=2,
dtype=tf.float32)
def test_cifar10_model_fn_eval_mode_v1(self):
self.cifar10_model_fn_helper(tf.estimator.ModeKeys.EVAL, resnet_version=1,
dtype=tf.float32)
def test_cifar10_model_fn_eval_mode_v2(self):
self.cifar10_model_fn_helper(tf.estimator.ModeKeys.EVAL, resnet_version=2,
dtype=tf.float32)
def test_cifar10_model_fn_predict_mode_v1(self):
self.cifar10_model_fn_helper(tf.estimator.ModeKeys.PREDICT,
resnet_version=1, dtype=tf.float32)
def test_cifar10_model_fn_predict_mode_v2(self):
self.cifar10_model_fn_helper(tf.estimator.ModeKeys.PREDICT,
resnet_version=2, dtype=tf.float32)
def _test_cifar10model_shape(self, resnet_version):
batch_size = 135
num_classes = 246
model = cifar10_main.Cifar10Model(32, data_format='channels_last',
num_classes=num_classes,
resnet_version=resnet_version)
fake_input = tf.random_uniform([batch_size, _HEIGHT, _WIDTH, _NUM_CHANNELS])
output = model(fake_input, training=True)
self.assertAllEqual(output.shape, (batch_size, num_classes))
def test_cifar10model_shape_v1(self):
self._test_cifar10model_shape(resnet_version=1)
def test_cifar10model_shape_v2(self):
self._test_cifar10model_shape(resnet_version=2)
def test_cifar10_end_to_end_synthetic_v1(self):
integration.run_synthetic(
main=cifar10_main.run_cifar, tmp_root=self.get_temp_dir(),
extra_flags=['-resnet_version', '1']
)
def test_cifar10_end_to_end_synthetic_v2(self):
integration.run_synthetic(
main=cifar10_main.run_cifar, tmp_root=self.get_temp_dir(),
extra_flags=['-resnet_version', '2']
)
if __name__ == '__main__':
tf.test.main()