torch.manual_seed(0)
torch.cuda.manual_seed(0)
np.random.seed(0)

transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (1.0, 1.0, 1.0)),
    #transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (1.0, 1.0, 1.0)),
    #transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])


trainset = torchvision.datasets.CIFAR10(root='./data', train=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batchsize, shuffle=True, num_workers=0)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=200, shuffle=False, num_workers=0)

network = model.Cifar()
network.cuda()