json_fn = os.path.join(base_outdir, "param.json")
check_if_done(json_fn)
args.machine = os.uname()[1]
save_dic_to_json(args.__dict__, json_fn)
train_img_shape = tuple([int(x) for x in train_args.train_img_shape])
test_img_shape = tuple([int(x) for x in args.test_img_shape])
# TODO
if "normalize_way" in train_args.__dict__.keys():
img_transform = get_img_transform(img_shape=train_img_shape, normalize_way=train_args.normalize_way)
else:
img_transform = get_img_transform(img_shape=train_img_shape)
if "background_id" in train_args.__dict__.keys():
label_transform = get_lbl_transform(img_shape=train_img_shape, n_class=train_args.n_class,
background_id=train_args.background_id)
else:
label_transform = get_lbl_transform(img_shape=train_img_shape, n_class=train_args.n_class)
tgt_dataset = get_dataset(dataset_name=args.tgt_dataset, split=args.split, img_transform=img_transform,
label_transform=label_transform, test=True, input_ch=train_args.input_ch)
target_loader = data.DataLoader(tgt_dataset, batch_size=1, pin_memory=True)
G_3ch, G_1ch, F1, F2 = get_models(net_name=train_args.net, res=train_args.res, input_ch=train_args.input_ch,
n_class=train_args.n_class,
method=detailed_method, is_data_parallel=train_args.is_data_parallel)
G_3ch.load_state_dict(checkpoint['g_3ch_state_dict'])
G_1ch.load_state_dict(checkpoint['g_1ch_state_dict'])
F1.load_state_dict(checkpoint['f1_state_dict'])
check_if_done(json_fn)
save_dic_to_json(args.__dict__, json_fn)
train_img_shape = tuple([int(x) for x in args.train_img_shape])
use_crop = True if args.crop_size > 0 else False
joint_transform = get_joint_transform(
crop_size=args.crop_size,
rotate_angle=args.rotate_angle) if use_crop else None
img_transform = get_img_transform(img_shape=train_img_shape,
normalize_way=args.normalize_way,
use_crop=use_crop)
label_transform = get_lbl_transform(img_shape=train_img_shape,
n_class=args.n_class,
background_id=args.background_id,
use_crop=use_crop)
src_dataset = get_dataset(dataset_name=args.src_dataset,
split=args.src_split,
img_transform=img_transform,
label_transform=label_transform,
test=False,
input_ch=args.input_ch)
tgt_dataset = get_dataset(dataset_name=args.tgt_dataset,
split=args.tgt_split,
img_transform=img_transform,
label_transform=label_transform,
test=False,
input_ch=args.input_ch)
