def get_cifar10_data()

in utils.py [0:0]

• 41 lines of code
• 1 McCabe index (conditional complexity)

def get_cifar10_data(batch_size):
    print("==> Preparing CIFAR-10 data...")
    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)
            ),
        ]
    )

    trainset = torchvision.datasets.CIFAR10(
        root="./data", train=True, download=True, transform=transform_train
    )
    trainloader = torch.utils.data.DataLoader(
        trainset,
        batch_size=batch_size,
        shuffle=True,
        num_workers=2,
        pin_memory=True,
    )

    testset = torchvision.datasets.CIFAR10(
        root="./data", train=False, download=True, transform=transform_test
    )
    # Note that, we perform an analysis of BatchNorm statistics when validating,
    # so we *must* shuffle the validation set.
    testloader = torch.utils.data.DataLoader(
        testset,
        batch_size=batch_size,
        shuffle=True,
        num_workers=2,
        pin_memory=True,
    )

    return trainset, trainloader, testset, testloader