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 _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 _make_prediction_gan_model(generator_inputs, generator_fn, generator_scope):
"""Make a `GANModel` from just the generator."""
with variable_scope.variable_scope(generator_scope) as gen_scope:
generator_inputs = tfgan_train._convert_tensor_or_l_or_d(generator_inputs) # pylint:disable=protected-access
generated_data = generator_fn(generator_inputs)
generator_variables = variable_lib.get_trainable_variables(gen_scope)
return tfgan_tuples.GANModel(
generator_inputs,
generated_data,
generator_variables,
gen_scope,
generator_fn,
real_data=None,
discriminator_real_outputs=None,
discriminator_gen_outputs=None,
discriminator_variables=None,
discriminator_scope=None,
discriminator_fn=None)
def _make_prediction_gan_model(generator_inputs, generator_fn,
generator_scope):
"""Make a `GANModel` from just the generator."""
with variable_scope.variable_scope(generator_scope) as gen_scope:
generator_inputs = tfgan_train._convert_tensor_or_l_or_d(
generator_inputs) # pylint:disable=protected-access
generated_data = generator_fn(generator_inputs)
generator_variables = variable_lib.get_trainable_variables(gen_scope)
return tfgan_tuples.GANModel(generator_inputs,
generated_data,
generator_variables,
gen_scope,
generator_fn,
real_data=None,
discriminator_real_outputs=None,
discriminator_gen_outputs=None,
discriminator_variables=None,
discriminator_scope=None,
discriminator_fn=None)
def _make_prediction_gan_model(generator_inputs, generator_fn, generator_scope):
"""Make a `GANModel` 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:
generator_inputs = tfgan_train._convert_tensor_or_l_or_d(generator_inputs) # pylint:disable=protected-access
generated_data = generator_fn(generator_inputs)
generator_variables = variable_lib.get_trainable_variables(gen_scope)
return tfgan_tuples.GANModel(
generator_inputs,
generated_data,
generator_variables,
gen_scope,
generator_fn,
real_data=None,
discriminator_real_outputs=None,
discriminator_gen_outputs=None,
discriminator_variables=None,
discriminator_scope=None,
discriminator_fn=None)
def _make_prediction_gan_model(generator_inputs, generator_fn, generator_scope):
"""Make a `GANModel` 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:
generator_inputs = tfgan_train._convert_tensor_or_l_or_d(generator_inputs) # pylint:disable=protected-access
generated_data = generator_fn(generator_inputs)
generator_variables = variable_lib.get_trainable_variables(gen_scope)
return tfgan_tuples.GANModel(
generator_inputs,
generated_data,
generator_variables,
gen_scope,
generator_fn,
real_data=None,
discriminator_real_outputs=None,
discriminator_gen_outputs=None,
discriminator_variables=None,
discriminator_scope=None,
discriminator_fn=None)
