Torchvision loads cifar10 data slowly

Hi, I used ds = torchvision.datasets.CIFAR10(root='./CIFARdata', train=False, download=True, transform=transform) to load cifar10 dataset, and I found the training was slow and the max cuda utilization was about 50%. Later, I changed to custom data loader below, and found the training was faster and cuda utilization could be ~70%.

def image_preprocess(img, config):
    img = img.type(torch.float) / 255
    img_amp = img.view(1, 3, img.shape[1], img.shape[2])
    img_amp = F.interpolate(img_amp, size=(config.x_num,
                            config.y_num), mode='bilinear', align_corners=True)

    img_padx = (config.total_x_num - config.x_num) // 2
    img_pady = (config.total_y_num - config.y_num) // 2

    img_amp = F.pad(img_amp, (img_pady, img_pady, img_padx, img_padx))

    return img_amp.view(3, config.total_x_num, config.total_y_num)

class Cifar10Dataset(data.Dataset):
    def __init__(self, config, is_training=True):
        super(Cifar10Dataset, self).__init__()
            is_training: network in training phase or not
        self.config = config
        self.image_dir = config.image_data_path
        self.image_transform = config.image_transform
        self.is_training = is_training  # training set or test set

        self.image_data, self.targets = self._load_data()
    def _load_data(self):
        image_data = []
        targets = []
        if self.is_training:
            image_file = ['data_batch_1', 'data_batch_2', 'data_batch_3', 'data_batch_4', 'data_batch_5']
            image_file = ['test_batch']
        for file_name in image_file:
            file_path = os.path.join(self.image_dir, file_name)
            with open(file_path, 'rb') as f:
                entry = pickle.load(f, encoding='latin1')
                if 'labels' in entry:
        image_data = np.vstack(image_data).reshape(-1, 3, 32, 32)
        return torch.tensor(image_data).cuda(), torch.tensor(targets).cuda()

    def _transformation(self, input_img):
        data_transform = transforms.Compose([
        input_img = torch.squeeze(data_transform(torch.unsqueeze(input_img, dim=0)))
        return input_img

    def __getitem__(self, index):
        input_img, target = self.image_data[index], self.targets[index]  # Tensor

        if self.is_training and self.image_transform is not None:
            input_img = self._transformation(input_img)

        prop_img = image_preprocess(input_img, self.config)

        return prop_img, target

    def __len__(self) -> int:
        return len(self.image_data)

Currently, I suspect it’s due to the transform that torchvision.datasets.CIFAR10 uses CPU and custom one uses GPU.
Anyone has some ideas? Thank you!