def test_works_with_get_collection(self):
"""Tests that gradient penalty works inside other scopes."""
# We ran the discriminator once in the setup, so there should be an op
# already in the collection.
self.assertEqual(
1,
len(
ops.get_collection('fake_update_ops',
self._kwargs['discriminator_scope'].name)))
# Make sure the op is added to the collection even if it's in a name scope.
with ops.name_scope('loss'):
tfgan_losses.wasserstein_gradient_penalty(**self._kwargs)
self.assertEqual(
2,
len(
ops.get_collection('fake_update_ops',
self._kwargs['discriminator_scope'].name)))
# Make sure the op is added to the collection even if it's in a variable
# scope.
with variable_scope.variable_scope('loss_vscope'):
tfgan_losses.wasserstein_gradient_penalty(**self._kwargs)
self.assertEqual(
3,
len(
ops.get_collection('fake_update_ops',
self._kwargs['discriminator_scope'].name)))
def test_works_with_get_collection(self):
"""Tests that gradient penalty works inside other scopes."""
# We ran the discriminator once in the setup, so there should be an op
# already in the collection.
self.assertEqual(1, len(ops.get_collection(
'fake_update_ops', self._kwargs['discriminator_scope'].name)))
# Make sure the op is added to the collection even if it's in a name scope.
with ops.name_scope('loss'):
tfgan_losses.wasserstein_gradient_penalty(**self._kwargs)
self.assertEqual(2, len(ops.get_collection(
'fake_update_ops', self._kwargs['discriminator_scope'].name)))
# Make sure the op is added to the collection even if it's in a variable
# scope.
with variable_scope.variable_scope('loss_vscope'):
tfgan_losses.wasserstein_gradient_penalty(**self._kwargs)
self.assertEqual(3, len(ops.get_collection(
'fake_update_ops', self._kwargs['discriminator_scope'].name)))
def grad_loss(self, real, fake):
def tfgan_dis(gen_data, not_used):
return self.dis.build(gen_data)
loss = gan_losses.wasserstein_gradient_penalty(
real_data=real,
generated_data=fake,
generator_inputs=real,
discriminator_fn=tfgan_dis,
discriminator_scope=self.dis.name,
one_sided=self.one_sided,
add_summaries=self.tb_verbose)
return self.grad_lambda * loss
def test_loss_with_placeholder(self):
generated_data = array_ops.placeholder(dtypes.float32,
shape=(None, None))
real_data = array_ops.placeholder(dtypes.float32, shape=(None, None))
loss = tfgan_losses.wasserstein_gradient_penalty(
generated_data, real_data, self._kwargs['generator_inputs'],
self._kwargs['discriminator_fn'],
self._kwargs['discriminator_scope'])
self.assertEqual(generated_data.dtype, loss.dtype)
with self.test_session() as sess:
variables.global_variables_initializer().run()
loss = sess.run(loss,
feed_dict={
generated_data: self._generated_data_np,
real_data: self._real_data_np,
})
self.assertAlmostEqual(self._expected_loss, loss, 5)
def test_loss_with_placeholder(self):
generated_data = array_ops.placeholder(dtypes.float32, shape=(None, None))
real_data = array_ops.placeholder(dtypes.float32, shape=(None, None))
loss = tfgan_losses.wasserstein_gradient_penalty(
generated_data,
real_data,
self._kwargs['generator_inputs'],
self._kwargs['discriminator_fn'],
self._kwargs['discriminator_scope'])
self.assertEqual(generated_data.dtype, loss.dtype)
with self.test_session() as sess:
variables.global_variables_initializer().run()
loss = sess.run(loss,
feed_dict={
generated_data: self._generated_data_np,
real_data: self._real_data_np,
})
self.assertAlmostEqual(self._expected_loss, loss, 5)
def test_loss_with_gradient_norm_target(self):
"""Test loss value with non default gradient norm target."""
generated_data = array_ops.placeholder(dtypes.float32,
shape=(None, None))
real_data = array_ops.placeholder(dtypes.float32, shape=(None, None))
loss = tfgan_losses.wasserstein_gradient_penalty(
generated_data,
real_data,
self._kwargs['generator_inputs'],
self._kwargs['discriminator_fn'],
self._kwargs['discriminator_scope'],
target=2.0)
with self.test_session() as sess:
variables.global_variables_initializer().run()
loss = sess.run(loss,
feed_dict={
generated_data: self._generated_data_np,
real_data: self._real_data_np,
})
self.assertAlmostEqual(1.0, loss, 5)
def test_loss_with_gradient_norm_target(self):
"""Test loss value with non default gradient norm target."""
generated_data = array_ops.placeholder(dtypes.float32, shape=(None, None))
real_data = array_ops.placeholder(dtypes.float32, shape=(None, None))
loss = tfgan_losses.wasserstein_gradient_penalty(
generated_data,
real_data,
self._kwargs['generator_inputs'],
self._kwargs['discriminator_fn'],
self._kwargs['discriminator_scope'],
target=2.0)
with self.cached_session() as sess:
variables.global_variables_initializer().run()
loss = sess.run(
loss,
feed_dict={
generated_data: self._generated_data_np,
real_data: self._real_data_np,
})
self.assertAlmostEqual(1.0, loss, 5)
def test_reuses_scope(self):
"""Test that gradient penalty reuses discriminator scope."""
num_vars = len(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES))
tfgan_losses.wasserstein_gradient_penalty(**self._kwargs)
self.assertEqual(
num_vars, len(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)))
def test_reuses_scope(self):
"""Test that gradient penalty reuses discriminator scope."""
num_vars = len(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES))
tfgan_losses.wasserstein_gradient_penalty(**self._kwargs)
self.assertEqual(
num_vars, len(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)))
