args.machine = os.uname()[1]
save_dic_to_json(args.__dict__, json_fn)
start_epoch = 0
train_img_shape = tuple([int(x) for x in args.train_img_shape])
img_transform_list = [
Scale(train_img_shape, Image.BILINEAR),
ToTensor(),
# Normalize([.485, .456, .406], [.229, .224, .225])
]
if args.augment:
aug_list = [
RandomRotation(),
# RandomVerticalFlip(), # non-realistic
RandomHorizontalFlip(),
RandomSizedCrop()
]
img_transform_list = aug_list + img_transform_list
img_transform = Compose(img_transform_list)
label_transform = Compose([
Scale(train_img_shape, Image.NEAREST),
ToLabel(),
ReLabel(255, args.n_class - 1),
])
src_dataset = get_dataset(dataset_name=args.src_dataset,
# Save param dic
if resume_flg:
json_fn = os.path.join(args.outdir, "param-%s_resume.json" % model_name)
else:
json_fn = os.path.join(outdir, "param-%s.json" % model_name)
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])
img_transform_list = [
Scale(train_img_shape, Image.BILINEAR),
ToTensor(),
Normalize([.485, .456, .406], [.229, .224, .225])
]
if args.augment:
aug_list = [RandomRotation(), RandomHorizontalFlip(), RandomSizedCrop()]
img_transform_list = aug_list + img_transform_list
img_transform = Compose(img_transform_list)
label_transform = Compose([
Scale(train_img_shape, Image.NEAREST),
ToLabel(),
ReLabel(255,
args.n_class - 1), # Last Class is "Void" or "Background" class
])
src_dataset = get_dataset(dataset_name=args.src_dataset,
split=args.src_split,
img_transform=img_transform,
label_transform=label_transform,