def test_stargan_model_generator_output(self):
batch_size = 2
img_size = 16
c_size = 3
num_domains = 5
input_tensor, label_tensor = StarGANModelTest.create_input_and_label_tensor(
batch_size, img_size, c_size, num_domains)
model = train.stargan_model(
generator_fn=stargan_generator_model,
discriminator_fn=stargan_discriminator_model,
input_data=input_tensor,
input_data_domain_label=label_tensor)
with self.test_session(use_gpu=True) as sess:
sess.run(variables.global_variables_initializer())
input_data, generated_data, reconstructed_data = sess.run([
model.input_data, model.generated_data,
model.reconstructed_data
])
self.assertTupleEqual(
(batch_size * num_domains, img_size, img_size, c_size),
input_data.shape)
self.assertTupleEqual(
(batch_size * num_domains, img_size, img_size, c_size),
generated_data.shape)
self.assertTupleEqual(
(batch_size * num_domains, img_size, img_size, c_size),
reconstructed_data.shape)
def test_stargan_model_generator_output(self):
batch_size = 2
img_size = 16
c_size = 3
num_domains = 5
input_tensor, label_tensor = StarGANModelTest.create_input_and_label_tensor(
batch_size, img_size, c_size, num_domains)
model = train.stargan_model(
generator_fn=stargan_generator_model,
discriminator_fn=stargan_discriminator_model,
input_data=input_tensor,
input_data_domain_label=label_tensor)
with self.test_session(use_gpu=True) as sess:
sess.run(variables.global_variables_initializer())
input_data, generated_data, reconstructed_data = sess.run(
[model.input_data, model.generated_data, model.reconstructed_data])
self.assertTupleEqual(
(batch_size * num_domains, img_size, img_size, c_size),
input_data.shape)
self.assertTupleEqual(
(batch_size * num_domains, img_size, img_size, c_size),
generated_data.shape)
self.assertTupleEqual(
(batch_size * num_domains, img_size, img_size, c_size),
reconstructed_data.shape)
def test_stargan_model_discriminator_output(self):
batch_size = 2
img_size = 16
c_size = 3
num_domains = 5
input_tensor, label_tensor = StarGANModelTest.create_input_and_label_tensor(
batch_size, img_size, c_size, num_domains)
model = train.stargan_model(
generator_fn=stargan_generator_model,
discriminator_fn=stargan_discriminator_model,
input_data=input_tensor,
input_data_domain_label=label_tensor)
with self.test_session(use_gpu=True) as sess:
sess.run(variables.global_variables_initializer())
disc_input_data_source_pred, disc_gen_data_source_pred = sess.run([
model.discriminator_input_data_source_predication,
model.discriminator_generated_data_source_predication
])
self.assertEqual(1, len(disc_input_data_source_pred.shape))
self.assertEqual(batch_size * num_domains,
disc_input_data_source_pred.shape[0])
self.assertEqual(1, len(disc_gen_data_source_pred.shape))
self.assertEqual(batch_size * num_domains,
disc_gen_data_source_pred.shape[0])
input_label, disc_input_label, gen_label, disc_gen_label = sess.run(
[
model.input_data_domain_label,
model.discriminator_input_data_domain_predication,
model.generated_data_domain_target,
model.discriminator_generated_data_domain_predication
])
self.assertTupleEqual((batch_size * num_domains, num_domains),
input_label.shape)
self.assertTupleEqual((batch_size * num_domains, num_domains),
disc_input_label.shape)
self.assertTupleEqual((batch_size * num_domains, num_domains),
gen_label.shape)
self.assertTupleEqual((batch_size * num_domains, num_domains),
disc_gen_label.shape)
def test_stargan_model_discriminator_output(self):
batch_size = 2
img_size = 16
c_size = 3
num_domains = 5
input_tensor, label_tensor = StarGANModelTest.create_input_and_label_tensor(
batch_size, img_size, c_size, num_domains)
model = train.stargan_model(
generator_fn=stargan_generator_model,
discriminator_fn=stargan_discriminator_model,
input_data=input_tensor,
input_data_domain_label=label_tensor)
with self.test_session(use_gpu=True) as sess:
sess.run(variables.global_variables_initializer())
disc_input_data_source_pred, disc_gen_data_source_pred = sess.run([
model.discriminator_input_data_source_predication,
model.discriminator_generated_data_source_predication
])
self.assertEqual(1, len(disc_input_data_source_pred.shape))
self.assertEqual(batch_size * num_domains,
disc_input_data_source_pred.shape[0])
self.assertEqual(1, len(disc_gen_data_source_pred.shape))
self.assertEqual(batch_size * num_domains,
disc_gen_data_source_pred.shape[0])
input_label, disc_input_label, gen_label, disc_gen_label = sess.run([
model.input_data_domain_label,
model.discriminator_input_data_domain_predication,
model.generated_data_domain_target,
model.discriminator_generated_data_domain_predication
])
self.assertTupleEqual((batch_size * num_domains, num_domains),
input_label.shape)
self.assertTupleEqual((batch_size * num_domains, num_domains),
disc_input_label.shape)
self.assertTupleEqual((batch_size * num_domains, num_domains),
gen_label.shape)
self.assertTupleEqual((batch_size * num_domains, num_domains),
disc_gen_label.shape)
def _make_gan_model(generator_fn, discriminator_fn, input_data,
input_data_domain_label, generator_scope, add_summaries,
mode):
"""Construct a `StarGANModel`, and optionally pass in `mode`."""
# If network functions have an argument `mode`, pass mode to it.
if 'mode' in inspect.getargspec(generator_fn).args:
generator_fn = functools.partial(generator_fn, mode=mode)
if 'mode' in inspect.getargspec(discriminator_fn).args:
discriminator_fn = functools.partial(discriminator_fn, mode=mode)
gan_model = tfgan_train.stargan_model(generator_fn,
discriminator_fn,
input_data,
input_data_domain_label,
generator_scope=generator_scope)
if add_summaries:
if not isinstance(add_summaries, (tuple, list)):
add_summaries = [add_summaries]
with ops.name_scope(None):
for summary_type in add_summaries:
_summary_type_map[summary_type](gan_model)
return gan_model
def test_stargan_model_output_type(self):
batch_size = 2
img_size = 16
c_size = 3
num_domains = 5
input_tensor, label_tensor = StarGANModelTest.create_input_and_label_tensor(
batch_size, img_size, c_size, num_domains)
model = train.stargan_model(
generator_fn=stargan_generator_model,
discriminator_fn=stargan_discriminator_model,
input_data=input_tensor,
input_data_domain_label=label_tensor)
self.assertIsInstance(model, namedtuples.StarGANModel)
self.assertTrue(isinstance(model.discriminator_variables, list))
self.assertTrue(isinstance(model.generator_variables, list))
self.assertIsInstance(model.discriminator_scope,
variable_scope.VariableScope)
self.assertTrue(model.generator_scope, variable_scope.VariableScope)
self.assertTrue(callable(model.discriminator_fn))
self.assertTrue(callable(model.generator_fn))
def test_stargan_model_output_type(self):
batch_size = 2
img_size = 16
c_size = 3
num_domains = 5
input_tensor, label_tensor = StarGANModelTest.create_input_and_label_tensor(
batch_size, img_size, c_size, num_domains)
model = train.stargan_model(
generator_fn=stargan_generator_model,
discriminator_fn=stargan_discriminator_model,
input_data=input_tensor,
input_data_domain_label=label_tensor)
self.assertIsInstance(model, namedtuples.StarGANModel)
self.assertTrue(isinstance(model.discriminator_variables, list))
self.assertTrue(isinstance(model.generator_variables, list))
self.assertIsInstance(model.discriminator_scope,
variable_scope.VariableScope)
self.assertTrue(model.generator_scope, variable_scope.VariableScope)
self.assertTrue(callable(model.discriminator_fn))
self.assertTrue(callable(model.generator_fn))
def _make_gan_model(generator_fn, discriminator_fn, input_data,
input_data_domain_label, generator_scope, add_summaries,
mode):
"""Construct a `StarGANModel`, and optionally pass in `mode`."""
# If network functions have an argument `mode`, pass mode to it.
if 'mode' in inspect.getargspec(generator_fn).args:
generator_fn = functools.partial(generator_fn, mode=mode)
if 'mode' in inspect.getargspec(discriminator_fn).args:
discriminator_fn = functools.partial(discriminator_fn, mode=mode)
gan_model = tfgan_train.stargan_model(
generator_fn,
discriminator_fn,
input_data,
input_data_domain_label,
generator_scope=generator_scope)
if add_summaries:
if not isinstance(add_summaries, (tuple, list)):
add_summaries = [add_summaries]
with ops.name_scope(None):
for summary_type in add_summaries:
_summary_type_map[summary_type](gan_model)
return gan_model
def create_callable_stargan_model():
return train.stargan_model(StarGANGenerator(), StarGANDiscriminator(),
array_ops.ones([1, 2, 2, 3]),
array_ops.ones([1, 2]))
def create_stargan_model():
return train.stargan_model(stargan_generator_model,
stargan_discriminator_model,
array_ops.ones([1, 2, 2, 3]),
array_ops.ones([1, 2]))
def create_callable_stargan_model():
return train.stargan_model(StarGANGenerator(), StarGANDiscriminator(),
array_ops.ones([1, 2, 2, 3]),
array_ops.ones([1, 2]))
def create_stargan_model():
return train.stargan_model(
stargan_generator_model, stargan_discriminator_model,
array_ops.ones([1, 2, 2, 3]), array_ops.ones([1, 2]))
