7c83acdf42df2a9bf4d4cfaf4af7d8e848d9f78f,examples/classification.py,InputLoader,__init__,#InputLoader#,28

Before Change

        if args.dataset == "cifar10":
            dataset = datasets.CIFAR10

        train_loader = torch.utils.data.DataLoader(
            dataset(dataset_folder, train=True, download=True,
                           transform=transforms.Compose([
                               transforms.ToTensor()
                           ])),
            batch_size=args.batch_size, shuffle=True, **kwargs)

        test_loader = torch.utils.data.DataLoader(
            dataset(dataset_folder, train=False, transform=transforms.Compose([
                transforms.ToTensor()

After Change

            //transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
        ])
        if args.augment:
            transform_train = transforms.Compose([
                transforms.RandomCrop(32, padding=4),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
            ])

            transform_test = transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
            ])
        train_loader = torch.utils.data.DataLoader(
            dataset(dataset_folder, train=True, download=True,
                           transform=transform_train),
            batch_size=args.batch_size, shuffle=True, **kwargs)

        test_loader = torch.utils.data.DataLoader(
            dataset(dataset_folder, train=False, transform=transform_test),
            batch_size=args.batch_size, shuffle=False, **kwargs)

Instances