def _make_prediction_gan_model(input_data, input_data_domain_label,
generator_fn, generator_scope):
"""Make a `StarGANModel` from just the generator."""
# If `generator_fn` has an argument `mode`, pass mode to it.
if 'mode' in inspect.getargspec(generator_fn).args:
generator_fn = functools.partial(generator_fn,
mode=model_fn_lib.ModeKeys.PREDICT)
with variable_scope.variable_scope(generator_scope) as gen_scope:
# pylint:disable=protected-access
input_data = tfgan_train._convert_tensor_or_l_or_d(input_data)
input_data_domain_label = tfgan_train._convert_tensor_or_l_or_d(
input_data_domain_label)
# pylint:enable=protected-access
generated_data = generator_fn(input_data, input_data_domain_label)
generator_variables = variable_lib.get_trainable_variables(gen_scope)
return tfgan_tuples.StarGANModel(
input_data=input_data,
input_data_domain_label=None,
generated_data=generated_data,
generated_data_domain_target=input_data_domain_label,
reconstructed_data=None,
discriminator_input_data_source_predication=None,
discriminator_generated_data_source_predication=None,
discriminator_input_data_domain_predication=None,
discriminator_generated_data_domain_predication=None,
generator_variables=generator_variables,
generator_scope=generator_scope,
generator_fn=generator_fn,
discriminator_variables=None,
discriminator_scope=None,
discriminator_fn=None)
def get_dummy_gan_model():
"""Similar to get_gan_model()."""
# TODO(joelshor): Find a better way of creating a variable scope.
with variable_scope.variable_scope('generator') as gen_scope:
gen_var = variable_scope.get_variable('dummy_var', initializer=0.0)
with variable_scope.variable_scope('discriminator') as dis_scope:
dis_var = variable_scope.get_variable('dummy_var', initializer=0.0)
return tfgan_tuples.StarGANModel(
input_data=array_ops.ones([1, 2, 2, 3]),
input_data_domain_label=array_ops.ones([1, 2]),
generated_data=array_ops.ones([1, 2, 2, 3]),
generated_data_domain_target=array_ops.ones([1, 2]),
reconstructed_data=array_ops.ones([1, 2, 2, 3]),
discriminator_input_data_source_predication=array_ops.ones([1]) * dis_var,
discriminator_generated_data_source_predication=array_ops.ones(
[1]) * gen_var * dis_var,
discriminator_input_data_domain_predication=array_ops.ones([1, 2
]) * dis_var,
discriminator_generated_data_domain_predication=array_ops.ones([1, 2]) *
gen_var * dis_var,
generator_variables=[gen_var],
generator_scope=gen_scope,
generator_fn=None,
discriminator_variables=[dis_var],
discriminator_scope=dis_scope,
discriminator_fn=None)
def setUp(self):
super(StarGANLossWrapperTest, self).setUp()
self.input_data = array_ops.ones([1, 2, 2, 3])
self.input_data_domain_label = constant_op.constant([[0, 1]])
self.generated_data = array_ops.ones([1, 2, 2, 3])
self.discriminator_input_data_source_predication = array_ops.ones([1])
self.discriminator_generated_data_source_predication = array_ops.ones(
[1])
def _discriminator_fn(inputs, num_domains):
"""Differentiable dummy discriminator for StarGAN."""
hidden = layers.flatten(inputs)
output_src = math_ops.reduce_mean(hidden, axis=1)
output_cls = layers.fully_connected(inputs=hidden,
num_outputs=num_domains,
activation_fn=None,
normalizer_fn=None,
biases_initializer=None)
return output_src, output_cls
with variable_scope.variable_scope('discriminator') as dis_scope:
pass
self.model = namedtuples.StarGANModel(
input_data=self.input_data,
input_data_domain_label=self.input_data_domain_label,
generated_data=self.generated_data,
generated_data_domain_target=None,
reconstructed_data=None,
discriminator_input_data_source_predication=self.
discriminator_input_data_source_predication,
discriminator_generated_data_source_predication=self.
discriminator_generated_data_source_predication,
discriminator_input_data_domain_predication=None,
discriminator_generated_data_domain_predication=None,
generator_variables=None,
generator_scope=None,
generator_fn=None,
discriminator_variables=None,
discriminator_scope=dis_scope,
discriminator_fn=_discriminator_fn)
self.discriminator_fn = _discriminator_fn
self.discriminator_scope = dis_scope
def get_stargan_model():
"""Similar to 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.StarGANModel(
input_data=array_ops.ones([1, 2, 2, 3]),
input_data_domain_label=array_ops.ones([1, 2]),
generated_data=array_ops.ones([1, 2, 2, 3]),
generated_data_domain_target=array_ops.ones([1, 2]),
reconstructed_data=array_ops.ones([1, 2, 2, 3]),
discriminator_input_data_source_predication=array_ops.ones([1]),
discriminator_generated_data_source_predication=array_ops.ones([1]),
discriminator_input_data_domain_predication=array_ops.ones([1, 2]),
discriminator_generated_data_domain_predication=array_ops.ones([1, 2]),
generator_variables=None,
generator_scope=gen_scope,
generator_fn=stargan_generator_model,
discriminator_variables=None,
discriminator_scope=dis_scope,
discriminator_fn=stargan_discriminator_model)