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# 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 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_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([3, 30, 35, 6]),
reconstructed_y=array_ops.zeros([3, 30, 35, 6]))
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_add_gan_model_image_summaries_for_cyclegan(self):
self._test_add_gan_model_image_summaries_impl(get_cyclegan_model, 10, True)
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):
self._test_add_image_comparison_summaries_impl(get_cyclegan_model, 2)
if __name__ == '__main__':
test.main()
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