batch_size = {"train": 36, "val": 36, "test": 4} for i in range(10): batch_size["val" + str(i)] = 4 if visda == False: data_transforms = { 'train': tran.transform_train(resize_size=28, crop_size=28), 'val': tran.transform_train(resize_size=28, crop_size=28), } data_transforms = tran.transform_test(data_transforms=data_transforms, resize_size=28, crop_size=28) dsets = { "train": ImageList(open(src).readlines(), transform=data_transforms["train"]), "val": ImageList(open(tgt).readlines(), transform=data_transforms["val"]), "test": ImageList(open(tgt).readlines(), transform=data_transforms["val"]) } dset_loaders = { x: torch.utils.data.DataLoader(dsets[x], batch_size=batch_size[x], shuffle=True, num_workers=4) for x in ['train', 'val'] } dset_loaders["test"] = torch.utils.data.DataLoader( dsets["test"], batch_size=batch_size["test"],
rl="rr-real.txt" if args.src =='rl': source_path = rl elif args.src =='rc': source_path = rc if args.tgt =='rl': target_path = rl elif args.tgt =='rc': target_path = rc dsets = {"train": ImageList(open(source_path).readlines(), transform=data_transforms["train"]), "val": ImageList(open(target_path).readlines(),transform=data_transforms["val"]), "test": ImageList(open(target_path).readlines(),transform=data_transforms["test"])} dset_loaders = {x: torch.utils.data.DataLoader(dsets[x], batch_size=batch_size[x], shuffle=True, num_workers=0) for x in ['train', 'val']} dset_loaders["test"] = torch.utils.data.DataLoader(dsets["test"], batch_size=batch_size["test"], shuffle=False, num_workers=64) dset_sizes = {x: len(dsets[x]) for x in ['train', 'val','test']} device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') def Regression_test(loader, model): MSE=0 MAE=0 number = 0