def get_cifar_loader():
    CIFAR_MEAN = np.array([0.49139968, 0.48215827, 0.44653124])
    CIFAR_STD = np.array([0.24703233, 0.24348505, 0.26158768])
    normTransform = transforms.Normalize(CIFAR_MEAN, CIFAR_STD)
    trainTransform = transforms.Compose([transforms.RandomCrop(32), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normTransform])
    testTransform = transforms.Compose([transforms.ToTensor(), normTransform])
    trainset = torchvision.datasets.CIFAR10(root='../data', train=True, download=True, transform=trainTransform)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True, num_workers=2)
    testset = torchvision.datasets.CIFAR10(root='../data', train=False, download=True, transform=testTransform)
    testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False, num_workers=2)
    classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
    return (trainloader, testloader, classes)
def get_cifar_loader():
    CIFAR_MEAN = np.array([0.49139968, 0.48215827, 0.44653124])
    CIFAR_STD = np.array([0.24703233, 0.24348505, 0.26158768])
    normTransform = transforms.Normalize(CIFAR_MEAN, CIFAR_STD)
    trainTransform = transforms.Compose([transforms.RandomCrop(32), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normTransform])
    testTransform = transforms.Compose([transforms.ToTensor(), normTransform])
    trainset = torchvision.datasets.CIFAR10(root='../data', train=True, download=True, transform=trainTransform)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True, num_workers=2)
    testset = torchvision.datasets.CIFAR10(root='../data', train=False, download=True, transform=testTransform)
    testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False, num_workers=2)
    classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
    return (trainloader, testloader, classes)