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# 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 grl_ops."""
#from models.domain_adaptation.domain_separation import grl_op_grads # pylint: disable=unused-import
#from models.domain_adaptation.domain_separation import grl_op_shapes # pylint: disable=unused-import
import tensorflow as tf
import grl_op_grads
import grl_ops
FLAGS = tf.app.flags.FLAGS
class GRLOpsTest(tf.test.TestCase):
def testGradientReversalOp(self):
with tf.Graph().as_default():
with self.test_session():
# Test that in forward prop, gradient reversal op acts as the
# identity operation.
examples = tf.constant([5.0, 4.0, 3.0, 2.0, 1.0])
output = grl_ops.gradient_reversal(examples)
expected_output = examples
self.assertAllEqual(output.eval(), expected_output.eval())
# Test that shape inference works as expected.
self.assertAllEqual(output.get_shape(), expected_output.get_shape())
# Test that in backward prop, gradient reversal op multiplies
# gradients by -1.
examples = tf.constant([[1.0]])
w = tf.get_variable(name='w', shape=[1, 1])
b = tf.get_variable(name='b', shape=[1])
init_op = tf.global_variables_initializer()
init_op.run()
features = tf.nn.xw_plus_b(examples, w, b)
# Construct two outputs: features layer passes directly to output1, but
# features layer passes through a gradient reversal layer before
# reaching output2.
output1 = features
output2 = grl_ops.gradient_reversal(features)
gold = tf.constant([1.0])
loss1 = gold - output1
loss2 = gold - output2
opt = tf.train.GradientDescentOptimizer(learning_rate=0.01)
grads_and_vars_1 = opt.compute_gradients(loss1,
tf.trainable_variables())
grads_and_vars_2 = opt.compute_gradients(loss2,
tf.trainable_variables())
self.assertAllEqual(len(grads_and_vars_1), len(grads_and_vars_2))
for i in range(len(grads_and_vars_1)):
g1 = grads_and_vars_1[i][0]
g2 = grads_and_vars_2[i][0]
# Verify that gradients of loss1 are the negative of gradients of
# loss2.
self.assertAllEqual(tf.negative(g1).eval(), g2.eval())
if __name__ == '__main__':
tf.test.main()
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