def get_cyclegan_model():
with variable_scope.variable_scope('x2y'):
model_x2y = get_gan_model()
with variable_scope.variable_scope('y2x'):
model_y2x = get_gan_model()
return namedtuples.CycleGANModel(
model_x2y=model_x2y,
model_y2x=model_y2x,
reconstructed_x=array_ops.zeros([3, 30, 35, 6]),
reconstructed_y=array_ops.zeros([3, 30, 35, 6]))
def test_correct_loss(self):
"""Test the output of `cycle_consistency_loss`."""
loss = tfgan_losses.cycle_consistency_loss(
namedtuples.CycleGANModel(
model_x2y=self._model_x2y,
model_y2x=self._model_y2x,
reconstructed_x=constant_op.constant([9, 8],
dtype=dtypes.float32),
reconstructed_y=constant_op.constant([7, 2],
dtype=dtypes.float32)))
with self.test_session(use_gpu=True):
variables.global_variables_initializer().run()
self.assertNear(5.0, loss.eval(), 1e-5)
def cyclegan_model(
# Lambdas defining models.
generator_fn,
discriminator_fn,
# data X and Y.
data_x,
data_y,
# Optional scopes.
generator_scope='Generator',
discriminator_scope='Discriminator',
model_x2y_scope='ModelX2Y',
model_y2x_scope='ModelY2X',
# Options.
check_shapes=True):
"""Returns a CycleGAN model outputs and variables.
See https://arxiv.org/abs/1703.10593 for more details.
Args:
generator_fn: A python lambda that takes `data_x` or `data_y` 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].
data_x: A `Tensor` of dataset X. Must be the same shape as `data_y`.
data_y: A `Tensor` of dataset Y. Must be the same shape as `data_x`.
generator_scope: Optional generator variable scope. Useful if you want to
reuse a subgraph that has already been created. Defaults to 'Generator'.
discriminator_scope: Optional discriminator variable scope. Useful if you
want to reuse a subgraph that has already been created. Defaults to
'Discriminator'.
model_x2y_scope: Optional variable scope for model x2y variables. Defaults
to 'ModelX2Y'.
model_y2x_scope: Optional variable scope for model y2x variables. Defaults
to 'ModelY2X'.
check_shapes: If `True`, check that generator produces Tensors that are the
same shape as `data_x` (`data_y`). Otherwise, skip this check.
Returns:
A `CycleGANModel` namedtuple.
Raises:
ValueError: If `check_shapes` is True and `data_x` or the generator output
does not have the same shape as `data_y`.
"""
# Create models.
def _define_partial_model(input_data, output_data):
return gan_model(generator_fn=generator_fn,
discriminator_fn=discriminator_fn,
real_data=output_data,
generator_inputs=input_data,
generator_scope=generator_scope,
discriminator_scope=discriminator_scope,
check_shapes=check_shapes)
with variable_scope.variable_scope(model_x2y_scope):
model_x2y = _define_partial_model(data_x, data_y)
with variable_scope.variable_scope(model_y2x_scope):
model_y2x = _define_partial_model(data_y, data_x)
with variable_scope.variable_scope(model_y2x.generator_scope, reuse=True):
reconstructed_x = model_y2x.generator_fn(model_x2y.generated_data)
with variable_scope.variable_scope(model_x2y.generator_scope, reuse=True):
reconstructed_y = model_x2y.generator_fn(model_y2x.generated_data)
return namedtuples.CycleGANModel(model_x2y, model_y2x, reconstructed_x,
reconstructed_y)
def get_callable_cyclegan_model():
return namedtuples.CycleGANModel(model_x2y=get_callable_gan_model(),
model_y2x=get_callable_gan_model(),
reconstructed_x=array_ops.ones([1, 2, 3]),
reconstructed_y=array_ops.zeros([1, 2,
3]))
