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=tf.estimator.ModeKeys.PREDICT)
with tf.compat.v1.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 = contrib.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."""
# 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=tf.estimator.ModeKeys.PREDICT)
with tf.compat.v1.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 = tf.compat.v1.get_collection(
tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, gen_scope.name)
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 cut_model(
# Lambdas defining models.
generator_fn,
discriminator_fn,
feat_discriminator_fn,
# Real data and conditioning.
real_data,
generator_inputs,
# Optional scopes.
generator_scope='Generator',
discriminator_scope='Discriminator',
feat_discriminator_scope='FeatDiscriminator',
# Options.
check_shapes=True):
"""Returns GAN model outputs and variables.
Args:
generator_fn: A python lambda that takes `generator_inputs` as inputs and
returns the outputs of the GAN generator.
discriminator_fn: A python lambda that takes `real_data`/`generated data`
and `generator_inputs`. Outputs a Tensor in the range [-inf, inf].
real_data: A Tensor representing the real data.
generator_inputs: A Tensor or list of Tensors to the generator. In the
vanilla GAN case, this might be a single noise Tensor. In the conditional
GAN case, this might be the generator's conditioning.
generator_scope: Optional generator variable scope. Useful if you want to
reuse a subgraph that has already been created.
discriminator_scope: Optional discriminator variable scope. Useful if you
want to reuse a subgraph that has already been created.
check_shapes: If `True`, check that generator produces Tensors that are the
same shape as real data. Otherwise, skip this check.
Returns:
A GANModel namedtuple.
Raises:
ValueError: If the generator outputs a Tensor that isn't the same shape as
`real_data`.
ValueError: If TF is executing eagerly.
"""
if tf.executing_eagerly():
raise ValueError('`vut_model` doesn\'t work when executing eagerly.')
# Create models
with tf.compat.v1.variable_scope(
generator_scope, reuse=tf.compat.v1.AUTO_REUSE) as gen_scope:
generator_inputs = _convert_tensor_or_l_or_d(generator_inputs)
generated_data = generator_fn(generator_inputs)
with tf.compat.v1.variable_scope(
discriminator_scope, reuse=tf.compat.v1.AUTO_REUSE) as dis_scope:
discriminator_gen_outputs = discriminator_fn(generated_data,
generator_inputs)
with tf.compat.v1.variable_scope(dis_scope, reuse=True):
real_data = _convert_tensor_or_l_or_d(real_data)
discriminator_real_outputs = discriminator_fn(real_data,
generator_inputs)
if check_shapes:
if not generated_data.shape.is_compatible_with(real_data.shape):
raise ValueError(
'Generator output shape (%s) must be the same shape as real data '
'(%s).' % (generated_data.shape, real_data.shape))
# Get model-specific variables.
generator_variables = get_trainable_variables(gen_scope)
discriminator_variables = get_trainable_variables(dis_scope)
return CUTModel(generator_inputs, generated_data, generator_variables,
gen_scope, generator_fn, real_data,
discriminator_real_outputs, discriminator_gen_outputs,
discriminator_variables, dis_scope, discriminator_fn)