def test_stargan_model_generator_output(self):
if tf.executing_eagerly():
# None of the usual utilities work in eager.
return
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 = tfgan.stargan_model(
generator_fn=stargan_generator_model,
discriminator_fn=stargan_discriminator_model,
input_data=input_tensor,
input_data_domain_label=label_tensor)
with self.cached_session() as sess:
sess.run(tf.compat.v1.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_output_type(self):
if tf.executing_eagerly():
# None of the usual utilities work in eager.
return
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 = tfgan.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, tfgan.StarGANModel)
self.assertTrue(isinstance(model.discriminator_variables, list))
self.assertTrue(isinstance(model.generator_variables, list))
self.assertIsInstance(model.discriminator_scope, tf.compat.v1.VariableScope)
self.assertTrue(model.generator_scope, tf.compat.v1.VariableScope)
self.assertTrue(callable(model.discriminator_fn))
self.assertTrue(callable(model.generator_fn))
def _define_model(images, labels):
"""Create the StarGAN Model.
Args:
images: `Tensor` or list of `Tensor` of shape (N, H, W, C).
labels: `Tensor` or list of `Tensor` of shape (N, num_domains).
Returns:
`StarGANModel` namedtuple.
"""
return tfgan.stargan_model(generator_fn=network.generator,
discriminator_fn=network.discriminator,
input_data=images,
input_data_domain_label=labels)
def test_stargan_model_discriminator_output(self):
if tf.executing_eagerly():
# None of the usual utilities work in eager.
return
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 = tfgan.stargan_model(
generator_fn=stargan_generator_model,
discriminator_fn=stargan_discriminator_model,
input_data=input_tensor,
input_data_domain_label=label_tensor)
with self.cached_session() as sess:
sess.run(tf.compat.v1.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 create_callable_stargan_model():
return tfgan.stargan_model(StarGANGenerator(), StarGANDiscriminator(),
tf.ones([1, 2, 2, 3]), tf.ones([1, 2]))
def create_stargan_model():
return tfgan.stargan_model(stargan_generator_model,
stargan_discriminator_model, tf.ones([1, 2, 2, 3]),
tf.ones([1, 2]))