def test_upsample_value(self): image = tf.random.normal([10, 32, 32, 3]) big_image = generator.usample(image) expected_image = tf.compat.v1.image.resize_nearest_neighbor( image, [32 * 2, 32 * 2]) with self.cached_session() as sess: big_image_np, expected_image_np = sess.run([big_image, expected_image]) self.assertAllEqual(big_image_np, expected_image_np)
def test_usample_shapes_value_placeholder(self): """Tests usample with partially shaped inputs.""" if tf.executing_eagerly(): # tf.placeholder() is not compatible with eager execution. return image = tf.placeholder(tf.float32, shape=[None, 32, 32, 3]) big_image = generator.usample(image) self.assertEqual([None, 64, 64, 3], big_image.shape.as_list()) expected_image = tf.image.resize_nearest_neighbor( image, [32 * 2, 32 * 2]) with self.cached_session() as sess: big_image_np, expected_image_np = sess.run( [big_image, expected_image], {image: np.random.normal(size=[10, 32, 32, 3])}) self.assertAllEqual(big_image_np, expected_image_np)
def test_usample_shapes(self): """Tests that upsampling has the desired effect on shape.""" image = tf.random.normal([10, 32, 32, 3]) big_image = generator.usample(image) self.assertEqual([10, 64, 64, 3], big_image.shape.as_list())