def test_define_model(self, mock_eval):
self.hparams = self.hparams._replace(batch_size=2)
images_shape = [self.hparams.batch_size, 4, 4, 3]
images_x_np = np.zeros(shape=images_shape)
images_y_np = np.zeros(shape=images_shape)
images_x = tf.constant(images_x_np, dtype=tf.float32)
images_y = tf.constant(images_y_np, dtype=tf.float32)
cyclegan_model = train_lib._define_model(images_x, images_y)
self.assertIsInstance(cyclegan_model, tfgan.CycleGANModel)
self.assertShapeEqual(images_x_np, cyclegan_model.reconstructed_x)
self.assertShapeEqual(images_y_np, cyclegan_model.reconstructed_y)
def test_define_train_ops(self):
self.hparams = self.hparams._replace(batch_size=2,
generator_lr=0.1,
discriminator_lr=0.01)
images_shape = [self.hparams.batch_size, 4, 4, 3]
images_x = tf.zeros(images_shape, dtype=tf.float32)
images_y = tf.zeros(images_shape, dtype=tf.float32)
cyclegan_model = train_lib._define_model(images_x, images_y)
cyclegan_loss = tfgan.cyclegan_loss(cyclegan_model,
cycle_consistency_loss_weight=10.0)
train_ops = train_lib._define_train_ops(cyclegan_model, cyclegan_loss,
self.hparams)
self.assertIsInstance(train_ops, tfgan.GANTrainOps)