default="./Test/t1.png")
parser.add_argument('--scale',
help='Scaling factor of the model',
default=2)
parser.add_argument('--epoch',
help='Number of epochs during training',
default=100)
parser.add_argument('--lr', help='Sets the learning rate', default=0.01)
args = parser.parse_args()
ARGS = dict()
ARGS["SCALE"] = int(args.scale)
main_ckpt_dir = "./checkpoints"
if not os.path.exists(main_ckpt_dir):
os.makedirs(main_ckpt_dir)
ARGS["CKPT_dir"] = main_ckpt_dir + "/checkpoint" + "_sc" + str(args.scale)
ARGS["CKPT"] = ARGS["CKPT_dir"] + "/ESPCN_ckpt_sc" + str(args.scale)
ARGS["TRAINDIR"] = args.traindir
ARGS["EPOCH_NUM"] = int(args.epoch)
ARGS["TESTIMG"] = args.testimg
ARGS["LRATE"] = float(args.lr)
if args.train:
run.training(ARGS)
elif args.test:
run.test(ARGS)
elif args.export:
run.export(ARGS)
exit()
# Set gpu
config = tf.ConfigProto() #log_device_placement=True
config.gpu_options.allow_growth = True
# Create run instance
run = run.run(config, lr_size, ckpt_path, scale, args.batch, args.epochs,
args.lr, args.fromscratch, fsrcnn_params, small,
args.validdir)
if args.train:
# if finetune, load model and train on general100
if args.finetune:
traindir = args.finetunedir
augmented_path = "./augmented_general100"
# augment (if not done before) and then load images
data_utils.augment(traindir, save_path=augmented_path)
run.train(augmented_path)
if args.test:
run.testFromPb(args.image)
#run.test(args.image)
#run.upscale(args.image)
if args.export:
run.export()
print("I ran successfully.")
else:
print(
"No checkpoint directory. Choose scale 2, 3 or 4. Or add checkpoint directory for this scale."
)
exit()
# Set gpu
os.environ["CUDA_VISIBLE_DEVICES"] = "4"
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
print("Num GPUs Available: ",
len(tf.config.experimental.list_physical_devices('GPU')))
# Create run instance
run = run.run(config, ckpt_path, scale, args.batch, args.epochs, args.B,
args.F, args.lr, args.fromscratch, meanbgr)
if args.train:
run.train(args.traindir, args.validdir)
if args.test:
run.test()
if args.upscale:
print('Test image: ', args.image)
run.upscaleFromPb(args.image)
#run.upscale(args.image)
if args.export:
run.export(args.quant)