def _test_correct_helper(self, use_weight_factor):
variable_list = [variables.Variable(1.0)]
main_loss = variable_list[0] * 2
adversarial_loss = variable_list[0] * 3
gradient_ratio_epsilon = 1e-6
if use_weight_factor:
weight_factor = constant_op.constant(2.0)
gradient_ratio = None
adv_coeff = 2.0
expected_loss = 1.0 * 2 + adv_coeff * 1.0 * 3
else:
weight_factor = None
gradient_ratio = constant_op.constant(0.5)
adv_coeff = 2.0 / (3 * 0.5 + gradient_ratio_epsilon)
expected_loss = 1.0 * 2 + adv_coeff * 1.0 * 3
combined_loss = tfgan_losses.combine_adversarial_loss(
main_loss,
adversarial_loss,
weight_factor=weight_factor,
gradient_ratio=gradient_ratio,
gradient_ratio_epsilon=gradient_ratio_epsilon,
variables=variable_list)
with self.test_session(use_gpu=True):
variables.global_variables_initializer().run()
self.assertNear(expected_loss, combined_loss.eval(), 1e-5)
def _test_correct_helper(self, use_weight_factor):
variable_list = [variables.Variable(1.0)]
main_loss = variable_list[0] * 2
adversarial_loss = variable_list[0] * 3
gradient_ratio_epsilon = 1e-6
if use_weight_factor:
weight_factor = constant_op.constant(2.0)
gradient_ratio = None
adv_coeff = 2.0
expected_loss = 1.0 * 2 + adv_coeff * 1.0 * 3
else:
weight_factor = None
gradient_ratio = constant_op.constant(0.5)
adv_coeff = 2.0 / (3 * 0.5 + gradient_ratio_epsilon)
expected_loss = 1.0 * 2 + adv_coeff * 1.0 * 3
combined_loss = tfgan_losses.combine_adversarial_loss(
main_loss,
adversarial_loss,
weight_factor=weight_factor,
gradient_ratio=gradient_ratio,
gradient_ratio_epsilon=gradient_ratio_epsilon,
variables=variable_list)
with self.test_session(use_gpu=True):
variables.global_variables_initializer().run()
self.assertNear(expected_loss, combined_loss.eval(), 1e-5)
def _test_no_weight_skips_adversarial_loss_helper(self, use_weight_factor):
"""Test the 0 adversarial weight or grad ratio skips adversarial loss."""
main_loss = constant_op.constant(1.0)
adversarial_loss = constant_op.constant(1.0)
weight_factor = 0.0 if use_weight_factor else None
gradient_ratio = None if use_weight_factor else 0.0
combined_loss = tfgan_losses.combine_adversarial_loss(
main_loss,
adversarial_loss,
weight_factor=weight_factor,
gradient_ratio=gradient_ratio,
gradient_summaries=False)
with self.test_session(use_gpu=True):
self.assertEqual(1.0, combined_loss.eval())
def _test_no_weight_skips_adversarial_loss_helper(self, use_weight_factor):
"""Test the 0 adversarial weight or grad ratio skips adversarial loss."""
main_loss = constant_op.constant(1.0)
adversarial_loss = constant_op.constant(1.0)
weight_factor = 0.0 if use_weight_factor else None
gradient_ratio = None if use_weight_factor else 0.0
combined_loss = tfgan_losses.combine_adversarial_loss(
main_loss,
adversarial_loss,
weight_factor=weight_factor,
gradient_ratio=gradient_ratio,
gradient_summaries=False)
with self.test_session(use_gpu=True):
self.assertEqual(1.0, combined_loss.eval())
def combine_adversarial_loss(gan_loss,
gan_model,
non_adversarial_loss,
weight_factor=None,
gradient_ratio=None,
gradient_ratio_epsilon=1e-6,
scalar_summaries=True,
gradient_summaries=True):
"""Combine adversarial loss and main loss.
Uses `combine_adversarial_loss` to combine the losses, and returns
a modified GANLoss namedtuple.
Args:
gan_loss: A GANLoss namedtuple. Assume the GANLoss.generator_loss is the
adversarial loss.
gan_model: A GANModel namedtuple. Used to access the generator's variables.
non_adversarial_loss: Same as `main_loss` from
`combine_adversarial_loss`.
weight_factor: Same as `weight_factor` from
`combine_adversarial_loss`.
gradient_ratio: Same as `gradient_ratio` from
`combine_adversarial_loss`.
gradient_ratio_epsilon: Same as `gradient_ratio_epsilon` from
`combine_adversarial_loss`.
scalar_summaries: Same as `scalar_summaries` from
`combine_adversarial_loss`.
gradient_summaries: Same as `gradient_summaries` from
`combine_adversarial_loss`.
Returns:
A modified GANLoss namedtuple, with `non_adversarial_loss` included
appropriately.
"""
combined_loss = losses_impl.combine_adversarial_loss(
non_adversarial_loss,
gan_loss.generator_loss,
weight_factor,
gradient_ratio,
gradient_ratio_epsilon,
gan_model.generator_variables,
scalar_summaries,
gradient_summaries)
return gan_loss._replace(generator_loss=combined_loss)
def combine_adversarial_loss(gan_loss,
gan_model,
non_adversarial_loss,
weight_factor=None,
gradient_ratio=None,
gradient_ratio_epsilon=1e-6,
scalar_summaries=True,
gradient_summaries=True):
"""Combine adversarial loss and main loss.
Uses `combine_adversarial_loss` to combine the losses, and returns
a modified GANLoss namedtuple.
Args:
gan_loss: A GANLoss namedtuple. Assume the GANLoss.generator_loss is the
adversarial loss.
gan_model: A GANModel namedtuple. Used to access the generator's variables.
non_adversarial_loss: Same as `main_loss` from
`combine_adversarial_loss`.
weight_factor: Same as `weight_factor` from
`combine_adversarial_loss`.
gradient_ratio: Same as `gradient_ratio` from
`combine_adversarial_loss`.
gradient_ratio_epsilon: Same as `gradient_ratio_epsilon` from
`combine_adversarial_loss`.
scalar_summaries: Same as `scalar_summaries` from
`combine_adversarial_loss`.
gradient_summaries: Same as `gradient_summaries` from
`combine_adversarial_loss`.
Returns:
A modified GANLoss namedtuple, with `non_adversarial_loss` included
appropriately.
"""
combined_loss = losses_impl.combine_adversarial_loss(
non_adversarial_loss,
gan_loss.generator_loss,
weight_factor,
gradient_ratio,
gradient_ratio_epsilon,
gan_model.generator_variables,
scalar_summaries,
gradient_summaries)
return gan_loss._replace(generator_loss=combined_loss)
