def test_tensor_pool_adjusted_model_no_pool(self):
"""Test `_tensor_pool_adjusted_model` for no pool size."""
model = create_gan_model()
new_model = train._tensor_pool_adjusted_model(model, None)
# Check values.
self.assertIs(new_model.discriminator_gen_outputs,
model.discriminator_gen_outputs)
def test_tensor_pool_adjusted_model_gan(self):
model = create_gan_model()
new_model = train._tensor_pool_adjusted_model(model, None)
# 'Generator/dummy_g:0' and 'Discriminator/dummy_d:0'
self.assertEqual(2, len(ops.get_collection(ops.GraphKeys.VARIABLES)))
self.assertIs(new_model.discriminator_gen_outputs,
model.discriminator_gen_outputs)
pool_size = 5
new_model = train._tensor_pool_adjusted_model(
model, get_tensor_pool_fn(pool_size=pool_size))
self.assertIsNot(new_model.discriminator_gen_outputs,
model.discriminator_gen_outputs)
# Check values.
self._check_tensor_pool_adjusted_model_outputs(
model.discriminator_gen_outputs, new_model.discriminator_gen_outputs,
pool_size)
def test_tensor_pool_adjusted_model_gan(self):
model = create_gan_model()
new_model = train._tensor_pool_adjusted_model(model, None)
# 'Generator/dummy_g:0' and 'Discriminator/dummy_d:0'
self.assertLen(ops.get_collection(ops.GraphKeys.VARIABLES), 2)
self.assertIs(new_model.discriminator_gen_outputs,
model.discriminator_gen_outputs)
pool_size = 5
new_model = train._tensor_pool_adjusted_model(
model, get_tensor_pool_fn(pool_size=pool_size))
self.assertIsNot(new_model.discriminator_gen_outputs,
model.discriminator_gen_outputs)
# Check values.
self._check_tensor_pool_adjusted_model_outputs(
model.discriminator_gen_outputs,
new_model.discriminator_gen_outputs, pool_size)
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_adjusted_model_acgan(self):
model = create_acgan_model()
pool_size = 5
new_model = train._tensor_pool_adjusted_model(
model, get_tensor_pool_fn(pool_size=pool_size))
# 'Generator/dummy_g:0' and 'Discriminator/dummy_d:0'
self.assertLen(ops.get_collection(ops.GraphKeys.VARIABLES), 2)
self.assertIsNot(new_model.discriminator_gen_outputs,
model.discriminator_gen_outputs)
self.assertIsNot(new_model.discriminator_gen_classification_logits,
model.discriminator_gen_classification_logits)
# Check values.
self._check_tensor_pool_adjusted_model_outputs(
model.discriminator_gen_outputs, new_model.discriminator_gen_outputs,
pool_size)
def test_tensor_pool_adjusted_model_acgan(self):
model = create_acgan_model()
pool_size = 5
new_model = train._tensor_pool_adjusted_model(
model, get_tensor_pool_fn(pool_size=pool_size))
# '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)
self.assertIsNot(new_model.discriminator_gen_classification_logits,
model.discriminator_gen_classification_logits)
# Check values.
self._check_tensor_pool_adjusted_model_outputs(
model.discriminator_gen_outputs,
new_model.discriminator_gen_outputs, pool_size)
