def main(_): with tf.Session() as sess: vdsr = VDSR(sess, image_size = FLAGS.image_size, label_size = FLAGS.label_size, layer = FLAGS.layer, c_dim = FLAGS.c_dim) if FLAGS.is_train: vdsr.train(FLAGS) else: FLAGS.c_dim = 3 vdsr.test(FLAGS)
g.as_default() with tf.Session(graph=g, config=config) as sess: # ----------------------------------- # build model # ----------------------------------- model_path = args.checkpoint_dir vdsr = VDSR(sess, args=args) # ----------------------------------- # train, test, inferecnce # ----------------------------------- if args.mode == "train": vdsr.train() elif args.mode == "test": vdsr.test() elif args.mode == "inference": #load image image_path = os.path.join(os.getcwd(), "test", args.infer_subdir, args.infer_imgpath) infer_image = plt.imread(image_path) if np.max(infer_image) > 1: infer_image = infer_image / 255 infer_image = imresize(infer_image, scalar_scale=1, output_shape=None, mode="vec") sr_img = vdsr.inference(infer_image, depth=args.train_depth, scale=args.scale)