def _test_multiple_steps_helper(self, get_hooks_fn_fn):
train_ops = self._gan_train_ops(generator_add=10,
discriminator_add=100)
train_steps = namedtuples.GANTrainSteps(generator_train_steps=3,
discriminator_train_steps=4)
final_step = train.gan_train(
train_ops,
get_hooks_fn=get_hooks_fn_fn(train_steps),
logdir='',
hooks=[basic_session_run_hooks.StopAtStepHook(num_steps=1)])
self.assertTrue(np.isscalar(final_step))
self.assertEqual(1 + 3 * 10 + 4 * 100, final_step)
def _test_multiple_steps_helper(self, get_hooks_fn_fn):
train_ops = self._gan_train_ops(generator_add=10, discriminator_add=100)
train_steps = namedtuples.GANTrainSteps(
generator_train_steps=3,
discriminator_train_steps=4)
final_step = train.gan_train(
train_ops,
get_hooks_fn=get_hooks_fn_fn(train_steps),
logdir='',
hooks=[basic_session_run_hooks.StopAtStepHook(num_steps=1)])
self.assertTrue(np.isscalar(final_step))
self.assertEqual(1 + 3 * 10 + 4 * 100, final_step)
def _test_run_helper(self, create_gan_model_fn):
random_seed.set_random_seed(1234)
model = create_gan_model_fn()
loss = train.gan_loss(model)
g_opt = gradient_descent.GradientDescentOptimizer(1.0)
d_opt = gradient_descent.GradientDescentOptimizer(1.0)
train_ops = train.gan_train_ops(model, loss, g_opt, d_opt)
final_step = train.gan_train(
train_ops,
logdir='',
hooks=[basic_session_run_hooks.StopAtStepHook(num_steps=2)])
self.assertTrue(np.isscalar(final_step))
self.assertEqual(2, final_step)
def _test_run_helper(self, create_gan_model_fn):
random_seed.set_random_seed(1234)
model = create_gan_model_fn()
loss = train.gan_loss(model)
g_opt = gradient_descent.GradientDescentOptimizer(1.0)
d_opt = gradient_descent.GradientDescentOptimizer(1.0)
train_ops = train.gan_train_ops(model, loss, g_opt, d_opt)
final_step = train.gan_train(
train_ops,
logdir='',
hooks=[basic_session_run_hooks.StopAtStepHook(num_steps=2)])
self.assertTrue(np.isscalar(final_step))
self.assertEqual(2, final_step)
def test_patchgan(self, create_gan_model_fn):
"""Ensure that patch-based discriminators work end-to-end."""
random_seed.set_random_seed(1234)
model = create_gan_model_fn()
loss = train.gan_loss(model)
g_opt = gradient_descent.GradientDescentOptimizer(1.0)
d_opt = gradient_descent.GradientDescentOptimizer(1.0)
train_ops = train.gan_train_ops(model, loss, g_opt, d_opt)
final_step = train.gan_train(
train_ops,
logdir='',
hooks=[basic_session_run_hooks.StopAtStepHook(num_steps=2)])
self.assertTrue(np.isscalar(final_step))
self.assertEqual(2, final_step)
def test_patchgan(self, create_gan_model_fn):
"""Ensure that patch-based discriminators work end-to-end."""
random_seed.set_random_seed(1234)
model = create_gan_model_fn()
loss = train.gan_loss(model)
g_opt = gradient_descent.GradientDescentOptimizer(1.0)
d_opt = gradient_descent.GradientDescentOptimizer(1.0)
train_ops = train.gan_train_ops(model, loss, g_opt, d_opt)
final_step = train.gan_train(
train_ops,
logdir='',
hooks=[basic_session_run_hooks.StopAtStepHook(num_steps=2)])
self.assertTrue(np.isscalar(final_step))
self.assertEqual(2, final_step)
