def get_dummy_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.GANModel(
generator_inputs=None,
generated_data=array_ops.ones([3, 4]),
generator_variables=[gen_var],
generator_scope=gen_scope,
generator_fn=None,
real_data=array_ops.zeros([3, 4]),
discriminator_real_outputs=array_ops.ones([1, 2, 3]) * dis_var,
discriminator_gen_outputs=array_ops.ones([1, 2, 3]) * gen_var * dis_var,
discriminator_variables=[dis_var],
discriminator_scope=dis_scope,
discriminator_fn=None)
def 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.GANModel(
generator_inputs=None,
generated_data=None,
generator_variables=None,
generator_scope=gen_scope,
generator_fn=generator_model,
real_data=array_ops.ones([1, 2, 3]),
discriminator_real_outputs=array_ops.ones([1, 2, 3]),
discriminator_gen_outputs=array_ops.ones([1, 2, 3]),
discriminator_variables=None,
discriminator_scope=dis_scope,
discriminator_fn=discriminator_model)
def get_gan_model():
# TODO (joelshor): Find a better way of creating a variable scope. id:731
# https://github.com/imdone/tensorflow/issues/732
with variable_scope.variable_scope('generator') as gen_scope:
pass
with variable_scope.variable_scope('discriminator') as dis_scope:
pass
return namedtuples.GANModel(
generator_inputs=array_ops.zeros([4, 32, 32, 3]),
generated_data=array_ops.zeros([4, 32, 32, 3]),
generator_variables=[variables.Variable(0),
variables.Variable(1)],
generator_scope=gen_scope,
generator_fn=generator_model,
real_data=array_ops.ones([4, 32, 32, 3]),
discriminator_real_outputs=array_ops.ones([1, 2, 3]),
discriminator_gen_outputs=array_ops.ones([1, 2, 3]),
discriminator_variables=[variables.Variable(0)],
discriminator_scope=dis_scope,
discriminator_fn=discriminator_model)
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 gan_model(
# Lambdas defining models.
generator_fn,
discriminator_fn,
# Real data and conditioning.
real_data,
generator_inputs,
# Optional scopes.
generator_scope='Generator',
discriminator_scope='Discriminator',
# 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`.
"""
# Create models
with variable_scope.variable_scope(generator_scope) as gen_scope:
generator_inputs = _convert_tensor_or_l_or_d(generator_inputs)
generated_data = generator_fn(generator_inputs)
with variable_scope.variable_scope(discriminator_scope) as dis_scope:
discriminator_gen_outputs = discriminator_fn(generated_data,
generator_inputs)
with variable_scope.variable_scope(dis_scope, reuse=True):
real_data = ops.convert_to_tensor(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 = variables_lib.get_trainable_variables(gen_scope)
discriminator_variables = variables_lib.get_trainable_variables(dis_scope)
return namedtuples.GANModel(generator_inputs, generated_data,
generator_variables, gen_scope, generator_fn,
real_data, discriminator_real_outputs,
discriminator_gen_outputs,
discriminator_variables, dis_scope,
discriminator_fn)
def _partial_model(generator_inputs_np):
model = namedtuples.GANModel(*[None] * 11)
return model._replace(generator_inputs=constant_op.constant(
generator_inputs_np, dtype=dtypes.float32))
