gt_image = np.expand_dims(gt_image, axis=0) input_image = np.where(mask == 1, 1, gt_image) prediction_coarse, prediction_refine = generator( [input_image, mask], training=False) prediction_refine = prediction_refine * mask + gt_image * ( 1 - mask) save_images(input_image[0, ...], gt_image[0, ...], prediction_coarse[0, ...], prediction_refine[0, ...], os.path.join(config.testing_dir, file)) count += 1 if count == config.test_num: return print('-' * 20) if __name__ == '__main__': # Loading the arguments config = TestOptions().parse() model = Model() generator = model.build_generator() checkpoint = tf.train.Checkpoint(generator=generator) checkpoint.restore( os.path.join(config.pretrained_model_dir, config.checkpoint_prefix)) test(config)