Spaces:
Running
Running
File size: 6,027 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 |
# 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 model assembly functions."""
import numpy as np
import tensorflow as tf
import dsn
class HelperFunctionsTest(tf.test.TestCase):
def testBasicDomainSeparationStartPoint(self):
with self.test_session() as sess:
# Test for when global_step < domain_separation_startpoint
step = tf.contrib.slim.get_or_create_global_step()
sess.run(tf.global_variables_initializer()) # global_step = 0
params = {'domain_separation_startpoint': 2}
weight = dsn.dsn_loss_coefficient(params)
weight_np = sess.run(weight)
self.assertAlmostEqual(weight_np, 1e-10)
step_op = tf.assign_add(step, 1)
step_np = sess.run(step_op) # global_step = 1
weight = dsn.dsn_loss_coefficient(params)
weight_np = sess.run(weight)
self.assertAlmostEqual(weight_np, 1e-10)
# Test for when global_step >= domain_separation_startpoint
step_np = sess.run(step_op) # global_step = 2
tf.logging.info(step_np)
weight = dsn.dsn_loss_coefficient(params)
weight_np = sess.run(weight)
self.assertAlmostEqual(weight_np, 1.0)
class DsnModelAssemblyTest(tf.test.TestCase):
def _testBuildDefaultModel(self):
images = tf.to_float(np.random.rand(32, 28, 28, 1))
labels = {}
labels['classes'] = tf.one_hot(
tf.to_int32(np.random.randint(0, 9, (32))), 10)
params = {
'use_separation': True,
'layers_to_regularize': 'fc3',
'weight_decay': 0.0,
'ps_tasks': 1,
'domain_separation_startpoint': 1,
'alpha_weight': 1,
'beta_weight': 1,
'gamma_weight': 1,
'recon_loss_name': 'sum_of_squares',
'decoder_name': 'small_decoder',
'encoder_name': 'default_encoder',
}
return images, labels, params
def testBuildModelDann(self):
images, labels, params = self._testBuildDefaultModel()
with self.test_session():
dsn.create_model(images, labels,
tf.cast(tf.ones([32,]), tf.bool), images, labels,
'dann_loss', params, 'dann_mnist')
loss_tensors = tf.contrib.losses.get_losses()
self.assertEqual(len(loss_tensors), 6)
def testBuildModelDannSumOfPairwiseSquares(self):
images, labels, params = self._testBuildDefaultModel()
with self.test_session():
dsn.create_model(images, labels,
tf.cast(tf.ones([32,]), tf.bool), images, labels,
'dann_loss', params, 'dann_mnist')
loss_tensors = tf.contrib.losses.get_losses()
self.assertEqual(len(loss_tensors), 6)
def testBuildModelDannMultiPSTasks(self):
images, labels, params = self._testBuildDefaultModel()
params['ps_tasks'] = 10
with self.test_session():
dsn.create_model(images, labels,
tf.cast(tf.ones([32,]), tf.bool), images, labels,
'dann_loss', params, 'dann_mnist')
loss_tensors = tf.contrib.losses.get_losses()
self.assertEqual(len(loss_tensors), 6)
def testBuildModelMmd(self):
images, labels, params = self._testBuildDefaultModel()
with self.test_session():
dsn.create_model(images, labels,
tf.cast(tf.ones([32,]), tf.bool), images, labels,
'mmd_loss', params, 'dann_mnist')
loss_tensors = tf.contrib.losses.get_losses()
self.assertEqual(len(loss_tensors), 6)
def testBuildModelCorr(self):
images, labels, params = self._testBuildDefaultModel()
with self.test_session():
dsn.create_model(images, labels,
tf.cast(tf.ones([32,]), tf.bool), images, labels,
'correlation_loss', params, 'dann_mnist')
loss_tensors = tf.contrib.losses.get_losses()
self.assertEqual(len(loss_tensors), 6)
def testBuildModelNoDomainAdaptation(self):
images, labels, params = self._testBuildDefaultModel()
params['use_separation'] = False
with self.test_session():
dsn.create_model(images, labels,
tf.cast(tf.ones([32,]), tf.bool), images, labels, 'none',
params, 'dann_mnist')
loss_tensors = tf.contrib.losses.get_losses()
self.assertEqual(len(loss_tensors), 1)
self.assertEqual(len(tf.contrib.losses.get_regularization_losses()), 0)
def testBuildModelNoAdaptationWeightDecay(self):
images, labels, params = self._testBuildDefaultModel()
params['use_separation'] = False
params['weight_decay'] = 1e-5
with self.test_session():
dsn.create_model(images, labels,
tf.cast(tf.ones([32,]), tf.bool), images, labels, 'none',
params, 'dann_mnist')
loss_tensors = tf.contrib.losses.get_losses()
self.assertEqual(len(loss_tensors), 1)
self.assertTrue(len(tf.contrib.losses.get_regularization_losses()) >= 1)
def testBuildModelNoSeparation(self):
images, labels, params = self._testBuildDefaultModel()
params['use_separation'] = False
with self.test_session():
dsn.create_model(images, labels,
tf.cast(tf.ones([32,]), tf.bool), images, labels,
'dann_loss', params, 'dann_mnist')
loss_tensors = tf.contrib.losses.get_losses()
self.assertEqual(len(loss_tensors), 2)
if __name__ == '__main__':
tf.test.main()
|