def _make_new_model_and_check(self, model, pool_size):
pool_fn = lambda x: random_tensor_pool.tensor_pool(x, pool_size=pool_size)
new_model = train._tensor_pool_adjusted_model(model, pool_fn)
# 'Generator/dummy_g:0' and 'Discriminator/dummy_d:0'
self.assertEqual(2, len(ops.get_collection(ops.GraphKeys.VARIABLES)))
self.assertIsNot(new_model.discriminator_gen_outputs,
model.discriminator_gen_outputs)
return new_model
def _make_new_model_and_check(self, model, pool_size):
pool_fn = lambda x: random_tensor_pool.tensor_pool(x, pool_size=pool_size)
new_model = train._tensor_pool_adjusted_model(model, pool_fn)
# 'Generator/dummy_g:0' and 'Discriminator/dummy_d:0'
self.assertEqual(2, len(ops.get_collection(ops.GraphKeys.VARIABLES)))
self.assertIsNot(new_model.discriminator_gen_outputs,
model.discriminator_gen_outputs)
return new_model
def test_tensor_pool(self, create_gan_model_fn):
"""Test tensor pool option."""
model = create_gan_model_fn()
tensor_pool_fn = lambda x: random_tensor_pool.tensor_pool(x, pool_size=5)
loss = train.gan_loss(model, tensor_pool_fn=tensor_pool_fn)
self.assertIsInstance(loss, namedtuples.GANLoss)
# Check values.
with self.test_session(use_gpu=True) as sess:
variables.global_variables_initializer().run()
for _ in range(10):
sess.run([loss.generator_loss, loss.discriminator_loss])
def test_tensor_pool(self, create_gan_model_fn):
"""Test tensor pool option."""
model = create_gan_model_fn()
tensor_pool_fn = lambda x: random_tensor_pool.tensor_pool(x, pool_size=5)
loss = train.gan_loss(model, tensor_pool_fn=tensor_pool_fn)
self.assertIsInstance(loss, namedtuples.GANLoss)
# Check values.
with self.test_session(use_gpu=True) as sess:
variables.global_variables_initializer().run()
for _ in range(10):
sess.run([loss.generator_loss, loss.discriminator_loss])
def tensor_pool_fn_impl(input_values):
generated_data, generator_inputs = input_values
output_values = random_tensor_pool.tensor_pool(
[generated_data] + generator_inputs, pool_size=pool_size)
return output_values[0], output_values[1:]
def tensor_pool_fn_impl(input_values): return random_tensor_pool.tensor_pool(input_values, pool_size=pool_size)
def tensor_pool_fn_impl(input_values):
generated_data, generator_inputs = input_values
output_values = random_tensor_pool.tensor_pool([generated_data] +
generator_inputs,
pool_size=pool_size)
return output_values[0], output_values[1:]
def tensor_pool_fn_impl(input_values):
return random_tensor_pool.tensor_pool(input_values,
pool_size=pool_size)
