Spaces:
Running
Running
File size: 5,895 Bytes
0b8359d |
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 |
# Copyright 2016 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 DSN components."""
import numpy as np
import tensorflow as tf
#from models.domain_adaptation.domain_separation
import models
class SharedEncodersTest(tf.test.TestCase):
def _testSharedEncoder(self,
input_shape=[5, 28, 28, 1],
model=models.dann_mnist,
is_training=True):
images = tf.to_float(np.random.rand(*input_shape))
with self.test_session() as sess:
logits, _ = model(images)
sess.run(tf.global_variables_initializer())
logits_np = sess.run(logits)
return logits_np
def testBuildGRLMnistModel(self):
logits = self._testSharedEncoder(model=getattr(models,
'dann_mnist'))
self.assertEqual(logits.shape, (5, 10))
self.assertTrue(np.any(logits))
def testBuildGRLSvhnModel(self):
logits = self._testSharedEncoder(model=getattr(models,
'dann_svhn'))
self.assertEqual(logits.shape, (5, 10))
self.assertTrue(np.any(logits))
def testBuildGRLGtsrbModel(self):
logits = self._testSharedEncoder([5, 40, 40, 3],
getattr(models, 'dann_gtsrb'))
self.assertEqual(logits.shape, (5, 43))
self.assertTrue(np.any(logits))
def testBuildPoseModel(self):
logits = self._testSharedEncoder([5, 64, 64, 4],
getattr(models, 'dsn_cropped_linemod'))
self.assertEqual(logits.shape, (5, 11))
self.assertTrue(np.any(logits))
def testBuildPoseModelWithBatchNorm(self):
images = tf.to_float(np.random.rand(10, 64, 64, 4))
with self.test_session() as sess:
logits, _ = getattr(models, 'dsn_cropped_linemod')(
images, batch_norm_params=models.default_batch_norm_params(True))
sess.run(tf.global_variables_initializer())
logits_np = sess.run(logits)
self.assertEqual(logits_np.shape, (10, 11))
self.assertTrue(np.any(logits_np))
class EncoderTest(tf.test.TestCase):
def _testEncoder(self, batch_norm_params=None, channels=1):
images = tf.to_float(np.random.rand(10, 28, 28, channels))
with self.test_session() as sess:
end_points = models.default_encoder(
images, 128, batch_norm_params=batch_norm_params)
sess.run(tf.global_variables_initializer())
private_code = sess.run(end_points['fc3'])
self.assertEqual(private_code.shape, (10, 128))
self.assertTrue(np.any(private_code))
self.assertTrue(np.all(np.isfinite(private_code)))
def testEncoder(self):
self._testEncoder()
def testEncoderMultiChannel(self):
self._testEncoder(None, 4)
def testEncoderIsTrainingBatchNorm(self):
self._testEncoder(models.default_batch_norm_params(True))
def testEncoderBatchNorm(self):
self._testEncoder(models.default_batch_norm_params(False))
class DecoderTest(tf.test.TestCase):
def _testDecoder(self,
height=64,
width=64,
channels=4,
batch_norm_params=None,
decoder=models.small_decoder):
codes = tf.to_float(np.random.rand(32, 100))
with self.test_session() as sess:
output = decoder(
codes,
height=height,
width=width,
channels=channels,
batch_norm_params=batch_norm_params)
sess.run(tf.global_variables_initializer())
output_np = sess.run(output)
self.assertEqual(output_np.shape, (32, height, width, channels))
self.assertTrue(np.any(output_np))
self.assertTrue(np.all(np.isfinite(output_np)))
def testSmallDecoder(self):
self._testDecoder(28, 28, 4, None, getattr(models, 'small_decoder'))
def testSmallDecoderThreeChannels(self):
self._testDecoder(28, 28, 3)
def testSmallDecoderBatchNorm(self):
self._testDecoder(28, 28, 4, models.default_batch_norm_params(False))
def testSmallDecoderIsTrainingBatchNorm(self):
self._testDecoder(28, 28, 4, models.default_batch_norm_params(True))
def testLargeDecoder(self):
self._testDecoder(32, 32, 4, None, getattr(models, 'large_decoder'))
def testLargeDecoderThreeChannels(self):
self._testDecoder(32, 32, 3, None, getattr(models, 'large_decoder'))
def testLargeDecoderBatchNorm(self):
self._testDecoder(32, 32, 4,
models.default_batch_norm_params(False),
getattr(models, 'large_decoder'))
def testLargeDecoderIsTrainingBatchNorm(self):
self._testDecoder(32, 32, 4,
models.default_batch_norm_params(True),
getattr(models, 'large_decoder'))
def testGtsrbDecoder(self):
self._testDecoder(40, 40, 3, None, getattr(models, 'large_decoder'))
def testGtsrbDecoderBatchNorm(self):
self._testDecoder(40, 40, 4,
models.default_batch_norm_params(False),
getattr(models, 'gtsrb_decoder'))
def testGtsrbDecoderIsTrainingBatchNorm(self):
self._testDecoder(40, 40, 4,
models.default_batch_norm_params(True),
getattr(models, 'gtsrb_decoder'))
if __name__ == '__main__':
tf.test.main()
|