Python torchvision.datasets.CIFAR10 Examples

def main():
    best_acc = 0

    device = 'cuda' if torch.cuda.is_available() else 'cpu'

    print('==> Preparing data..')
    transforms_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))])

    dataset_train = CIFAR10(root='../data', train=True, download=True, 
                            transform=transforms_train)

    train_loader = DataLoader(dataset_train, batch_size=args.batch_size, 
                              shuffle=True, num_workers=args.num_worker)

    # there are 10 classes so the dataset name is cifar-10
    classes = ('plane', 'car', 'bird', 'cat', 'deer', 
               'dog', 'frog', 'horse', 'ship', 'truck')

    print('==> Making model..')

    net = pyramidnet()
    net = nn.DataParallel(net)
    net = net.to(device)
    num_params = sum(p.numel() for p in net.parameters() if p.requires_grad)
    print('The number of parameters of model is', num_params)

    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(net.parameters(), lr=args.lr)
    # optimizer = optim.SGD(net.parameters(), lr=args.lr, 
    #                       momentum=0.9, weight_decay=1e-4)
    
    train(net, criterion, optimizer, train_loader, device) 

def cifar_loaders(batch_size, shuffle_test=False): 
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.225, 0.225, 0.225])
    train = datasets.CIFAR10('./data', train=True, download=True, 
        transform=transforms.Compose([
            transforms.RandomHorizontalFlip(),
            transforms.RandomCrop(32, 4),
            transforms.ToTensor(),
            normalize,
        ]))
    test = datasets.CIFAR10('./data', train=False, 
        transform=transforms.Compose([transforms.ToTensor(), normalize]))
    train_loader = torch.utils.data.DataLoader(train, batch_size=batch_size,
        shuffle=True, pin_memory=True)
    test_loader = torch.utils.data.DataLoader(test, batch_size=batch_size,
        shuffle=shuffle_test, pin_memory=True)
    return train_loader, test_loader 

def get_data(train):
	data_raw = datasets.CIFAR10('../data/dl/', train=train, download=True,  transform=transforms.Compose([
							transforms.Grayscale(),
							transforms.Resize((20, 20)),
							transforms.ToTensor(),
							lambda x: x.numpy().flatten()]))

	data_x, data_y = zip(*data_raw)
	
	data_x = np.array(data_x)
	data_y = np.array(data_y, dtype='int32').reshape(-1, 1)

	# binarize
	label_0 = data_y < 5
	label_1 = ~label_0

	data_y[label_0] = 0
	data_y[label_1] = 1

	data = pd.DataFrame(data_x)
	data[COLUMN_LABEL] = data_y

	return data, data_x.mean(), data_x.std()

#--- 

def __getitem__(self, index):
        """Override the original method of the CIFAR10 class.
        Args:
            index (int): Index

        Returns:
            tuple: (image, target, semi_target, index)
        """
        img, target, semi_target = self.data[index], self.targets[index], int(self.semi_targets[index])

        # doing this so that it is consistent with all other datasets
        # to return a PIL Image
        img = Image.fromarray(img)

        if self.transform is not None:
            img = self.transform(img)

        if self.target_transform is not None:
            target = self.target_transform(target)

        return img, target, semi_target, index 

def get_data(train):
	data_raw = datasets.CIFAR10('../data/dl/', train=train, download=True,  transform=transforms.Compose([
							transforms.Grayscale(),
							transforms.Resize((20, 20)),
							transforms.ToTensor(),
							lambda x: x.numpy().flatten()]))

	data_x, data_y = zip(*data_raw)
	
	data_x = np.array(data_x)
	data_y = np.array(data_y, dtype='int32').reshape(-1, 1)

	data = pd.DataFrame(data_x)
	data[COLUMN_LABEL] = data_y

	return data, data_x.mean(), data_x.std()

#--- 

def __init__(self):
        super(CIFAR10MetaInfo, self).__init__()
        self.label = "CIFAR10"
        self.short_label = "cifar"
        self.root_dir_name = "cifar10"
        self.dataset_class = CIFAR10Fine
        self.num_training_samples = 50000
        self.in_channels = 3
        self.num_classes = 10
        self.input_image_size = (32, 32)
        self.train_metric_capts = ["Train.Err"]
        self.train_metric_names = ["Top1Error"]
        self.train_metric_extra_kwargs = [{"name": "err"}]
        self.val_metric_capts = ["Val.Err"]
        self.val_metric_names = ["Top1Error"]
        self.val_metric_extra_kwargs = [{"name": "err"}]
        self.saver_acc_ind = 0
        self.train_transform = cifar10_train_transform
        self.val_transform = cifar10_val_transform
        self.test_transform = cifar10_val_transform
        self.ml_type = "imgcls" 

def load_data():
    transform_train = transforms.Compose(
        [transforms.Resize(227),
         transforms.ToTensor(),
         transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])
    transform_test = transforms.Compose(
        [transforms.Resize(227),
         transforms.ToTensor(),
         transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])
    trainset = datasets.CIFAR10(root='./data', train=True, download=True,
                                transform=transform_train)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=100,
                                              shuffle=False, num_workers=0)

    testset = datasets.CIFAR10(root='./data', train=False, download=True,
                               transform=transform_test)
    testloader = torch.utils.data.DataLoader(testset, batch_size=100,
                                             shuffle=False, num_workers=0)
    return trainloader, testloader 

def init_dataset():
    transform_train = transforms.Compose(
        [transforms.Resize(256),
         transforms.RandomCrop(227),
         transforms.RandomHorizontalFlip(),
         transforms.ToTensor(),
         transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])
    transform_test = transforms.Compose(
        [transforms.Resize(227),
         transforms.ToTensor(),
         transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])
    trainset = datasets.CIFAR10(root='./data', train=True, download=True,
                                transform=transform_train)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,
                                              shuffle=True, num_workers=0)

    testset = datasets.CIFAR10(root='./data', train=False, download=True,
                               transform=transform_test)
    testloader = torch.utils.data.DataLoader(testset, batch_size=100,
                                             shuffle=True, num_workers=0)
    return trainloader, testloader 

def get_datasets(initial_pool):
    transform = transforms.Compose(
        [transforms.Resize((224, 224)),
         transforms.RandomHorizontalFlip(),
         transforms.RandomRotation(30),
         transforms.ToTensor(),
         transforms.Normalize(3 * [0.5], 3 * [0.5]), ])
    test_transform = transforms.Compose(
        [
            transforms.Resize((224, 224)),
            transforms.ToTensor(),
            transforms.Normalize(3 * [0.5], 3 * [0.5]),
        ]
    )
    # Note: We use the test set here as an example. You should make your own validation set.
    train_ds = datasets.CIFAR10('.', train=True,
                                transform=transform, target_transform=None, download=True)
    test_set = datasets.CIFAR10('.', train=False,
                                transform=test_transform, target_transform=None, download=True)

    active_set = ActiveLearningDataset(train_ds, pool_specifics={'transform': test_transform})

    # We start labeling randomly.
    active_set.label_randomly(initial_pool)
    return active_set, test_set 

def __getitem__(self, index):
        """Override the original method of the CIFAR10 class.
        Args:
            index (int): Index
        Returns:
            triple: (image, target, index) where target is index of the target class.
        """
        if self.train:
            img, target = self.train_data[index], self.train_labels[index]
        else:
            img, target = self.test_data[index], self.test_labels[index]

        # doing this so that it is consistent with all other datasets
        # to return a PIL Image
        img = Image.fromarray(img)

        if self.transform is not None:
            img = self.transform(img)

        if self.target_transform is not None:
            target = self.target_transform(target)

        return img, target, index  # only line changed 

def build_datasets(self):
        """ You must to rewrite this method to load your own datasets.

        * :attr:`self.dataset_train` . Assign a training ``dataset`` to this.
        * :attr:`self.dataset_valid` . Assign a valid_epoch ``dataset`` to this.
        * :attr:`self.dataset_test` is optional. Assign a test ``dataset`` to this.
          If not, it will be replaced by ``self.dataset_valid`` .

        Example::

            self.dataset_train = datasets.CIFAR10(root, train=True, download=True,
                                                  transform=transforms.Compose(self.train_transform_list))
            self.dataset_valid = datasets.CIFAR10(root, train=False, download=True,
                                                  transform=transforms.Compose(self.valid_transform_list))
        """
        pass 

def make_dataset():
    if opt.dataset in ("imagenet", "dog_and_cat_64", "dog_and_cat_128"):
        trans = tfs.Compose([
            tfs.Resize(opt.img_width),
            tfs.ToTensor(),
            tfs.Normalize(mean=[.5, .5, .5], std=[.5, .5, .5])])
        data = ImageFolder(opt.root, transform=trans)
        loader = DataLoader(data, batch_size=100, shuffle=False, num_workers=opt.workers)
    elif opt.dataset == "cifar10":
        trans = tfs.Compose([
            tfs.Resize(opt.img_width),
            tfs.ToTensor(),
            tfs.Normalize(mean=[.5, .5, .5], std=[.5, .5, .5])])
        data = CIFAR10(root=opt.root, train=True, download=False, transform=trans)
        loader = DataLoader(data, batch_size=100, shuffle=True, num_workers=opt.workers)
    else:
        raise ValueError(f"Unknown dataset: {opt.dataset}")
    return loader 

def __init__(self, data_dir, batch_size, shuffle, validation_split, num_workers, training=True):
        self.data_dir = data_dir
        self.dataset = datasets.CIFAR10(self.data_dir, train=training, download=True,
         transform=self._tansform_)
        super(CIFAR10DataLoader, self).__init__(self.dataset, batch_size, shuffle, validation_split, num_workers) 

def __init__(self, path):
        # type: (str) -> None
        """
        Class constructor.

        :param path: The folder in which to download CIFAR10.
        """
        super(CIFAR10, self).__init__()

        self.path = path

        self.normal_class = None

        # Get train and test split
        self.train_split = datasets.CIFAR10(self.path, train=True, download=True, transform=None)
        self.test_split = datasets.CIFAR10(self.path, train=False, download=True, transform=None)

        # Shuffle training indexes to build a validation set (see val())
        train_idx = np.arange(len(self.train_split))
        np.random.shuffle(train_idx)
        self.shuffled_train_idx = train_idx

        # Transform zone
        self.val_transform = transforms.Compose([ToFloatTensor2D()])
        self.test_transform = transforms.Compose([ToFloat32(), OCToFloatTensor2D()])
        self.transform = None

        # Other utilities
        self.mode = None
        self.length = None
        self.val_idxs = None 

def test(self, normal_class):
        # type: (int) -> None
        """
        Sets CIFAR10 in test mode.

        :param normal_class: the class to be considered normal.
        """
        self.normal_class = int(normal_class)

        # Update mode, length and transform
        self.mode = 'test'
        self.transform = self.test_transform
        self.length = len(self.test_split) 

def val(self, normal_class):
        # type: (int) -> None
        """
        Sets CIFAR10 in validation mode.

        :param normal_class: the class to be considered normal.
        """
        self.normal_class = int(normal_class)

        # Update mode, indexes, length and transform
        self.mode = 'val'
        self.transform = self.val_transform
        self.val_idxs = self.shuffled_train_idx[int(0.9 * len(self.shuffled_train_idx)):]
        self.val_idxs = [idx for idx in self.val_idxs if self.train_split[idx][1] == self.normal_class]
        self.length = len(self.val_idxs) 

def __repr__(self):
        return f'ONE-CLASS CIFAR10 (normal class = {self.normal_class})' 

def build_datasets(self):
        self.dataset_train = datasets.CIFAR10(self.root, train=True, download=True,
                                              transform=transforms.Compose(self.train_transform_list))
        self.dataset_valid = datasets.CIFAR10(self.root, train=False, download=True,
                                              transform=transforms.Compose(self.valid_transform_list)) 

def build_datasets(self):
        self.dataset_train = datasets.CIFAR10(self.root, train=True, download=True,
                                              transform=transforms.Compose(self.train_transform_list))
        self.dataset_valid = datasets.CIFAR10(self.root, train=False, download=True,
                                              transform=transforms.Compose(self.valid_transform_list)) 

def test(model):
    kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
    if args.dataset == 'cifar10':
        test_loader = torch.utils.data.DataLoader(
            datasets.CIFAR10('./data.cifar10', train=False, transform=transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])),
            batch_size=args.test_batch_size, shuffle=False, **kwargs)
    elif args.dataset == 'cifar100':
        test_loader = torch.utils.data.DataLoader(
            datasets.CIFAR100('./data.cifar100', train=False, transform=transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])),
            batch_size=args.test_batch_size, shuffle=False, **kwargs)
    else:
        raise ValueError("No valid dataset is given.")
    model.eval()
    correct = 0
    for data, target in test_loader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data, volatile=True), Variable(target)
        output = model(data)
        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
        correct += pred.eq(target.data.view_as(pred)).cpu().sum()

    print('\nTest set: Accuracy: {}/{} ({:.1f}%)\n'.format(
        correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset)))
    return correct / float(len(test_loader.dataset)) 

def test(model):
    kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
    if args.dataset == 'cifar10':
        test_loader = torch.utils.data.DataLoader(
            datasets.CIFAR10('./data.cifar10', train=False, transform=transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])),
            batch_size=args.test_batch_size, shuffle=True, **kwargs)
    elif args.dataset == 'cifar100':
        test_loader = torch.utils.data.DataLoader(
            datasets.CIFAR100('./data.cifar100', train=False, transform=transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])),
            batch_size=args.test_batch_size, shuffle=True, **kwargs)
    else:
        raise ValueError("No valid dataset is given.")
    model.eval()
    correct = 0
    for data, target in test_loader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data, volatile=True), Variable(target)
        output = model(data)
        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
        correct += pred.eq(target.data.view_as(pred)).cpu().sum()

    print('\nTest set: Accuracy: {}/{} ({:.1f}%)\n'.format(
        correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset)))
    return correct / float(len(test_loader.dataset)) 

def fetch_bylabel(label):
    if label == 10:
        normalizer = transforms.Normalize(mean=[0.4914, 0.4824, 0.4467],
                                          std=[0.2471, 0.2435, 0.2616])
        data_cls = datasets.CIFAR10
    else:
        normalizer = transforms.Normalize(mean=[0.5071, 0.4867, 0.4408],
                                          std=[0.2675, 0.2565, 0.2761])
        data_cls = datasets.CIFAR100
    return normalizer, data_cls 

def val_dataloader(self):
        transform_val = transforms.Compose([transforms.ToTensor(),
                                            transforms.Normalize(self.mean, self.std)])
        dataset = CIFAR10(root=self.hparams.data_dir, train=False, transform=transform_val)
        dataloader = DataLoader(dataset, batch_size=self.hparams.batch_size, num_workers=4, pin_memory=True)
        return dataloader 

def train_dataloader(self):
        transform_train = transforms.Compose([transforms.RandomCrop(32, padding=4),
                                              transforms.RandomHorizontalFlip(),
                                              transforms.ToTensor(),
                                              transforms.Normalize(self.mean, self.std)])
        dataset = CIFAR10(root=self.hparams.data_dir, train=True, transform=transform_train)
        dataloader = DataLoader(dataset, batch_size=self.hparams.batch_size, num_workers=4, shuffle=True, drop_last=True, pin_memory=True)
        return dataloader 

def load_dataset_test(data_dir, dataset, batch_size):
    list_classes_test = []

    fas=False

    path = os.path.join(data_dir, 'Datasets', dataset)
    
    if dataset == 'mnist':
        dataset_test = datasets.MNIST(path, train=False, download=True, transform=transforms.Compose([transforms.ToTensor()]))
    elif dataset == 'fashion':
        if fas:
            dataset_test = DataLoader(
                datasets.FashionMNIST(path, train=False, download=True, transform=transforms.Compose(
                    [transforms.ToTensor()])),
                batch_size=batch_size)
        else:
            dataset_test = fashion(path, train=False, download=True, transform=transforms.ToTensor())

    elif dataset == 'cifar10':
        transform = transforms.Compose(
                [transforms.ToTensor(),
                    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

        dataset_test = datasets.CIFAR10(root=path, train=False,
                   download=True, transform=transform)

    elif dataset == 'celebA':
        dataset_test = utils.load_celebA(path + 'celebA', transform=transforms.Compose(
            [transforms.CenterCrop(160), transforms.Scale(64), transforms.ToTensor()]), batch_size=batch_size)
    elif dataset == 'timagenet':
        dataset_test, labels = get_test_image_folders(path)
        list_classes_test = np.asarray([labels[i] for i in range(len(dataset_test))])
        dataset_test = Subset(dataset_test, np.where(list_classes_test < 10)[0])
        list_classes_test = np.where(list_classes_test < 10)[0]

    list_classes_test = np.asarray([dataset_test[i][1] for i in range(len(dataset_test))])

    return dataset_test, list_classes_test 

def load_cifar10(self):
        transform_train = transforms.Compose([
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
        ])
        dataset_train = datasets.CIFAR10(root='./Datasets', train=True, download=True, transform=transform_train)
        tensor_data = torch.Tensor(len(dataset_train),3,32,32)
        tensor_label = torch.LongTensor(len(dataset_train))

        for i in range(len(dataset_train)):
            tensor_data[i] = dataset_train[i][0]
            tensor_label[i] = dataset_train[i][1]

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

        dataset_test = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)

        tensor_test = torch.Tensor(len(dataset_test),3,32,32)
        tensor_label_test = torch.LongTensor(len(dataset_test))

        for i in range(len(dataset_test)):
            tensor_test[i] = dataset_test[i][0]
            tensor_label_test[i] = dataset_test[i][1]

        #testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)

        return tensor_data, tensor_label, tensor_test, tensor_label_test 

def test(model):
    kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
    if args.dataset == 'cifar10':
        test_loader = torch.utils.data.DataLoader(
            datasets.CIFAR10('./data.cifar10', train=False, transform=transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])),
            batch_size=args.test_batch_size, shuffle=False, **kwargs)
    elif args.dataset == 'cifar100':
        test_loader = torch.utils.data.DataLoader(
            datasets.CIFAR100('./data.cifar100', train=False, transform=transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])),
            batch_size=args.test_batch_size, shuffle=False, **kwargs)
    else:
        raise ValueError("No valid dataset is given.")
    model.eval()
    correct = 0
    for data, target in test_loader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data, volatile=True), Variable(target)
        output = model(data)
        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
        correct += pred.eq(target.data.view_as(pred)).cpu().sum()

    print('\nTest set: Accuracy: {}/{} ({:.1f}%)\n'.format(
        correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset)))
    return correct / float(len(test_loader.dataset)) 

def __init__(self, opt):
		kwargs = {
		  'num_workers': opt.workers,
		  'batch_size' : opt.batch_size,
		  'shuffle' : True,
		  'pin_memory': True}

		self.train_loader = torch.utils.data.DataLoader(
			datasets.CIFAR10(opt.data_dir, train=True, download=True,
					transform=transforms.Compose([
						transforms.RandomCrop(32, padding=4),
						transforms.RandomHorizontalFlip(),
						transforms.ToTensor(),
						transforms.Normalize(mean=[x/255.0 for x in [125.3, 123.0, 113.9]],
std=[x/255.0 for x in [63.0, 62.1, 66.7]])
					   ])),
			 **kwargs)

		self.val_loader = torch.utils.data.DataLoader(
			datasets.CIFAR10(opt.data_dir, train=False,
			  transform=transforms.Compose([
						   transforms.ToTensor(),
						   transforms.Normalize(mean=[x/255.0 for x in [125.3, 123.0, 113.9]],
std=[x/255.0 for x in [63.0, 62.1, 66.7]])
					   ])),
		  **kwargs) 

def test(model):
    kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
    if args.dataset == 'cifar10':
        test_loader = torch.utils.data.DataLoader(
            datasets.CIFAR10('./data.cifar10', train=False, transform=transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])),
            batch_size=args.test_batch_size, shuffle=False, **kwargs)
    elif args.dataset == 'cifar100':
        test_loader = torch.utils.data.DataLoader(
            datasets.CIFAR100('./data.cifar100', train=False, transform=transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])),
            batch_size=args.test_batch_size, shuffle=False, **kwargs)
    else:
        raise ValueError("No valid dataset is given.")
    model.eval()
    correct = 0
    for data, target in test_loader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data, volatile=True), Variable(target)
        output = model(data)
        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
        correct += pred.eq(target.data.view_as(pred)).cpu().sum()

    print('\nTest set: Accuracy: {}/{} ({:.1f}%)\n'.format(
        correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset)))
    return correct / float(len(test_loader.dataset)) 

def test(model):
    kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
    if args.dataset == 'cifar10':
        test_loader = torch.utils.data.DataLoader(
            datasets.CIFAR10('./data.cifar10', train=False, transform=transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])),
            batch_size=args.test_batch_size, shuffle=False, **kwargs)
    elif args.dataset == 'cifar100':
        test_loader = torch.utils.data.DataLoader(
            datasets.CIFAR100('./data.cifar100', train=False, transform=transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])),
            batch_size=args.test_batch_size, shuffle=False, **kwargs)
    else:
        raise ValueError("No valid dataset is given.")
    model.eval()
    correct = 0
    for data, target in test_loader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data, volatile=True), Variable(target)
        output = model(data)
        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
        correct += pred.eq(target.data.view_as(pred)).cpu().sum()

    print('\nTest set: Accuracy: {}/{} ({:.1f}%)\n'.format(
        correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset)))
    return correct / float(len(test_loader.dataset))