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
|
# 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.
# ==============================================================================
"""Tests for TFGAN summaries."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.gan.python import namedtuples
from tensorflow.contrib.gan.python.eval.python import summaries_impl as summaries
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
from tensorflow.python.summary import summary
def generator_model(inputs):
return variable_scope.get_variable('dummy_g', initializer=2.0) * inputs
def discriminator_model(inputs, _):
return variable_scope.get_variable('dummy_d', initializer=2.0) * inputs
def stargan_generator_model(inputs, _):
return generator_model(inputs)
def get_gan_model():
# TODO(joelshor): Find a better way of creating a variable scope.
with variable_scope.variable_scope('generator') as gen_scope:
pass
with variable_scope.variable_scope('discriminator') as dis_scope:
pass
return namedtuples.GANModel(
generator_inputs=array_ops.zeros([4, 32, 32, 3]),
generated_data=array_ops.zeros([4, 32, 32, 3]),
generator_variables=[variables.Variable(0), variables.Variable(1)],
generator_scope=gen_scope,
generator_fn=generator_model,
real_data=array_ops.ones([4, 32, 32, 3]),
discriminator_real_outputs=array_ops.ones([1, 2, 3]),
discriminator_gen_outputs=array_ops.ones([1, 2, 3]),
discriminator_variables=[variables.Variable(0)],
discriminator_scope=dis_scope,
discriminator_fn=discriminator_model)
def get_stargan_model():
"""Similar to get_gan_model()."""
# TODO(joelshor): Find a better way of creating a variable scope.
with variable_scope.variable_scope('discriminator') as dis_scope:
pass
with variable_scope.variable_scope('generator') as gen_scope:
return namedtuples.StarGANModel(
input_data=array_ops.ones([1, 2, 2, 3]),
input_data_domain_label=array_ops.ones([1, 2]),
generated_data=stargan_generator_model(
array_ops.ones([1, 2, 2, 3]), None),
generated_data_domain_target=array_ops.ones([1, 2]),
reconstructed_data=array_ops.ones([1, 2, 2, 3]),
discriminator_input_data_source_predication=array_ops.ones([1]),
discriminator_generated_data_source_predication=array_ops.ones([1]),
discriminator_input_data_domain_predication=array_ops.ones([1, 2]),
discriminator_generated_data_domain_predication=array_ops.ones([1, 2]),
generator_variables=None,
generator_scope=gen_scope,
generator_fn=stargan_generator_model,
discriminator_variables=None,
discriminator_scope=dis_scope,
discriminator_fn=discriminator_model)
def get_cyclegan_model():
with variable_scope.variable_scope('x2y'):
model_x2y = get_gan_model()
with variable_scope.variable_scope('y2x'):
model_y2x = get_gan_model()
return namedtuples.CycleGANModel(
model_x2y=model_x2y,
model_y2x=model_y2x,
reconstructed_x=array_ops.zeros([4, 32, 32, 3]),
reconstructed_y=array_ops.zeros([4, 32, 32, 3]))
class SummariesTest(test.TestCase):
def _test_add_gan_model_image_summaries_impl(
self, get_model_fn, expected_num_summary_ops, model_summaries):
summaries.add_gan_model_image_summaries(get_model_fn(), grid_size=2,
model_summaries=model_summaries)
self.assertEquals(expected_num_summary_ops,
len(ops.get_collection(ops.GraphKeys.SUMMARIES)))
with self.test_session(use_gpu=True):
variables.global_variables_initializer().run()
summary.merge_all().eval()
def test_add_gan_model_image_summaries(self):
self._test_add_gan_model_image_summaries_impl(get_gan_model, 5, True)
def test_add_gan_model_image_summaries_no_model(self):
self._test_add_gan_model_image_summaries_impl(get_gan_model, 2, False)
def test_cyclegan_image_summaries_dont_work(self):
with self.assertRaises(ValueError):
summaries.add_gan_model_image_summaries(get_cyclegan_model())
def _test_add_gan_model_summaries_impl(self, get_model_fn,
expected_num_summary_ops):
summaries.add_gan_model_summaries(get_model_fn())
self.assertEquals(expected_num_summary_ops,
len(ops.get_collection(ops.GraphKeys.SUMMARIES)))
with self.test_session(use_gpu=True):
variables.global_variables_initializer().run()
summary.merge_all().eval()
def test_add_gan_model_summaries(self):
self._test_add_gan_model_summaries_impl(get_gan_model, 3)
def test_add_gan_model_summaries_for_cyclegan(self):
self._test_add_gan_model_summaries_impl(get_cyclegan_model, 6)
def _test_add_regularization_loss_summaries_impl(self, get_model_fn,
expected_num_summary_ops):
summaries.add_regularization_loss_summaries(get_model_fn())
self.assertEquals(expected_num_summary_ops,
len(ops.get_collection(ops.GraphKeys.SUMMARIES)))
with self.test_session(use_gpu=True):
summary.merge_all().eval()
def test_add_regularization_loss_summaries(self):
self._test_add_regularization_loss_summaries_impl(get_gan_model, 2)
def test_add_regularization_loss_summaries_for_cyclegan(self):
self._test_add_regularization_loss_summaries_impl(get_cyclegan_model, 4)
# TODO(joelshor): Add correctness test.
def _test_add_image_comparison_summaries_impl(self, get_model_fn,
expected_num_summary_ops):
summaries.add_image_comparison_summaries(get_model_fn(), display_diffs=True)
self.assertEquals(expected_num_summary_ops,
len(ops.get_collection(ops.GraphKeys.SUMMARIES)))
with self.test_session(use_gpu=True):
summary.merge_all().eval()
def test_add_image_comparison_summaries(self):
self._test_add_image_comparison_summaries_impl(get_gan_model, 1)
def test_add_image_comparison_summaries_for_cyclegan(self):
summaries.add_cyclegan_image_summaries(get_cyclegan_model())
self.assertEquals(2, len(ops.get_collection(ops.GraphKeys.SUMMARIES)))
with self.test_session(use_gpu=True):
summary.merge_all().eval()
def test_add_image_comparison_summaries_for_stargan(self):
summaries.add_stargan_image_summaries(get_stargan_model())
self.assertEquals(1, len(ops.get_collection(ops.GraphKeys.SUMMARIES)))
with self.test_session(use_gpu=True) as sess:
sess.run(variables.global_variables_initializer())
summary.merge_all().eval()
if __name__ == '__main__':
test.main()
|
