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)