def test_define_train_ops(self):
hparams = self.hparams._replace(
batch_size=2, generator_lr=0.1, discriminator_lr=0.01)
images_shape = [hparams.batch_size, 4, 4, 3]
images = tf.zeros(images_shape, dtype=tf.float32)
labels = tf.one_hot([0] * hparams.batch_size, 2)
model = train_lib._define_model(images, labels)
loss = tfgan.stargan_loss(model)
train_ops = train_lib._define_train_ops(model, loss, hparams.generator_lr,
hparams.discriminator_lr,
hparams.adam_beta1,
hparams.adam_beta2,
hparams.max_number_of_steps)
self.assertIsInstance(train_ops, tfgan.GANTrainOps)
def test_define_model(self):
hparams = self.hparams._replace(batch_size=2)
images_shape = [hparams.batch_size, 4, 4, 3]
images_np = np.zeros(shape=images_shape)
images = tf.constant(images_np, dtype=tf.float32)
labels = tf.one_hot([0] * hparams.batch_size, 2)
model = train_lib._define_model(images, labels)
self.assertIsInstance(model, tfgan.StarGANModel)
self.assertShapeEqual(images_np, model.generated_data)
self.assertShapeEqual(images_np, model.reconstructed_data)
self.assertTrue(isinstance(model.discriminator_variables, list))
self.assertTrue(isinstance(model.generator_variables, list))
self.assertIsInstance(model.discriminator_scope, tf.compat.v1.VariableScope)
self.assertTrue(model.generator_scope, tf.compat.v1.VariableScope)
self.assertTrue(callable(model.discriminator_fn))
self.assertTrue(callable(model.generator_fn))