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)
