def _input_and_output_same_shape_helper(self, kernel_size): img_batch = tf.placeholder(tf.float32, shape=[None, 32, 32, 3]) output_img_batch, _ = cyclegan.cyclegan_generator_resnet( img_batch, kernel_size=kernel_size) self.assertAllEqual(img_batch.shape.as_list(), output_img_batch.shape.as_list())
def test_generator_inference(self): """Check one inference step.""" img_batch = tf.zeros([2, 32, 32, 3]) model_output, _ = cyclegan.cyclegan_generator_resnet(img_batch) with self.test_session() as sess: sess.run(tf.global_variables_initializer()) sess.run(model_output)
def generator(input_images): """Thin wrapper around CycleGAN generator to conform to the TFGAN API. Args: input_images: A batch of images to translate. Images should be normalized already. Shape is [batch, height, width, channels]. Returns: Returns generated image batch. """ input_images.shape.assert_has_rank(4) with tf.contrib.framework.arg_scope(cyclegan.cyclegan_arg_scope()): output_images, _ = cyclegan.cyclegan_generator_resnet(input_images) return output_images
def test_generator_unknown_batch_dim(self): """Check that generator can take unknown batch dimension inputs.""" img = tf.placeholder(tf.float32, shape=[None, 32, None, 3]) output_imgs, _ = cyclegan.cyclegan_generator_resnet(img) self.assertAllEqual([None, 32, None, 3], output_imgs.shape.as_list())
def _test_generator_graph_helper(self, shape): """Check that generator can take small and non-square inputs.""" output_imgs, _ = cyclegan.cyclegan_generator_resnet(tf.ones(shape)) self.assertAllEqual(shape, output_imgs.shape.as_list())