eval_linear.py [102:131]:
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    val_dataset = datasets.ImageFolder(os.path.join(args.data_path, "val"))
    tr_normalize = transforms.Normalize(
        mean=[0.485, 0.456, 0.406], std=[0.228, 0.224, 0.225]
    )
    train_dataset.transform = transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        tr_normalize,
    ])
    val_dataset.transform = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        tr_normalize,
    ])
    sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
    train_loader = torch.utils.data.DataLoader(
        train_dataset,
        sampler=sampler,
        batch_size=args.batch_size,
        num_workers=args.workers,
        pin_memory=True,
    )
    val_loader = torch.utils.data.DataLoader(
        val_dataset,
        batch_size=args.batch_size,
        num_workers=args.workers,
        pin_memory=True,
    )
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eval_semisup.py [104:133]:
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    val_dataset = datasets.ImageFolder(os.path.join(args.data_path, "val"))
    tr_normalize = transforms.Normalize(
        mean=[0.485, 0.456, 0.406], std=[0.228, 0.224, 0.225]
    )
    train_dataset.transform = transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        tr_normalize,
    ])
    val_dataset.transform = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        tr_normalize,
    ])
    sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
    train_loader = torch.utils.data.DataLoader(
        train_dataset,
        sampler=sampler,
        batch_size=args.batch_size,
        num_workers=args.workers,
        pin_memory=True,
    )
    val_loader = torch.utils.data.DataLoader(
        val_dataset,
        batch_size=args.batch_size,
        num_workers=args.workers,
        pin_memory=True,
    )
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