How to use the torchfcn.datasets.VOC2011ClassSeg function in torchfcn

os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
    cuda = torch.cuda.is_available()

    torch.manual_seed(1337)
    if cuda:
        torch.cuda.manual_seed(1337)

    # 1. dataset

    root = osp.expanduser('~/data/datasets')
    kwargs = {'num_workers': 4, 'pin_memory': True} if cuda else {}
    train_loader = torch.utils.data.DataLoader(
        torchfcn.datasets.SBDClassSeg(root, split='train', transform=True),
        batch_size=1, shuffle=True, **kwargs)
    val_loader = torch.utils.data.DataLoader(
        torchfcn.datasets.VOC2011ClassSeg(
            root, split='seg11valid', transform=True),
        batch_size=1, shuffle=False, **kwargs)

    # 2. model

    model = torchfcn.models.FCN8s(n_class=21)
    start_epoch = 0
    start_iteration = 0
    if args.resume:
        checkpoint = torch.load(args.resume)
        model.load_state_dict(checkpoint['model_state_dict'])
        start_epoch = checkpoint['epoch']
        start_iteration = checkpoint['iteration']
    else:
        fcn16s = torchfcn.models.FCN16s()
        state_dict = torch.load(args.pretrained_model)

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('model_file', help='Model path')
    parser.add_argument('-g', '--gpu', type=int, default=0)
    args = parser.parse_args()

    os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
    model_file = args.model_file

    root = osp.expanduser('~/data/datasets')
    val_loader = torch.utils.data.DataLoader(
        torchfcn.datasets.VOC2011ClassSeg(
            root, split='seg11valid', transform=True),
        batch_size=1, shuffle=False,
        num_workers=4, pin_memory=True)

    n_class = len(val_loader.dataset.class_names)

    if osp.basename(model_file).startswith('fcn32s'):
        model = torchfcn.models.FCN32s(n_class=21)
    elif osp.basename(model_file).startswith('fcn16s'):
        model = torchfcn.models.FCN16s(n_class=21)
    elif osp.basename(model_file).startswith('fcn8s'):
        if osp.basename(model_file).startswith('fcn8s-atonce'):
            model = torchfcn.models.FCN8sAtOnce(n_class=21)
        else:
            model = torchfcn.models.FCN8s(n_class=21)
    else:

os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
    cuda = torch.cuda.is_available()

    torch.manual_seed(1337)
    if cuda:
        torch.cuda.manual_seed(1337)

    # 1. dataset

    root = osp.expanduser('~/data/datasets')
    kwargs = {'num_workers': 4, 'pin_memory': True} if cuda else {}
    train_loader = torch.utils.data.DataLoader(
        torchfcn.datasets.SBDClassSeg(root, split='train', transform=True),
        batch_size=1, shuffle=True, **kwargs)
    val_loader = torch.utils.data.DataLoader(
        torchfcn.datasets.VOC2011ClassSeg(
            root, split='seg11valid', transform=True),
        batch_size=1, shuffle=False, **kwargs)

    # 2. model

    model = torchfcn.models.FCN16s(n_class=21)
    start_epoch = 0
    start_iteration = 0
    if args.resume:
        checkpoint = torch.load(args.resume)
        model.load_state_dict(checkpoint['model_state_dict'])
        start_epoch = checkpoint['epoch']
        start_iteration = checkpoint['iteration']
    else:
        fcn32s = torchfcn.models.FCN32s()
        state_dict = torch.load(args.pretrained_model)

cuda = torch.cuda.is_available()

    torch.manual_seed(1337)
    if cuda:
        torch.cuda.manual_seed(1337)

    # 1. dataset

    root = osp.expanduser('~/data/datasets')
    kwargs = {'num_workers': 4, 'pin_memory': True} if cuda else {}
    train_loader = torch.utils.data.DataLoader(
        torchfcn.datasets.SBDClassSeg(root, split='train', transform=True),
        batch_size=1, shuffle=True, **kwargs)
    val_loader = torch.utils.data.DataLoader(
        torchfcn.datasets.VOC2011ClassSeg(
            root, split='seg11valid', transform=True),
        batch_size=1, shuffle=False, **kwargs)

    # 2. model

    model = torchfcn.models.FCN32s(n_class=21)
    start_epoch = 0
    start_iteration = 0
    if resume:
        checkpoint = torch.load(resume)
        model.load_state_dict(checkpoint['model_state_dict'])
        start_epoch = checkpoint['epoch']
        start_iteration = checkpoint['iteration']
    else:
        vgg16_fcn32s = torchfcn.models.FCN32s(n_class=21)
        vgg16_fcn32s.load_state_dict(torch.load(osp.expanduser('~/data/models/torch/vgg16-fcn32s.pth')))

os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
    cuda = torch.cuda.is_available()

    torch.manual_seed(1337)
    if cuda:
        torch.cuda.manual_seed(1337)

    # 1. dataset

    root = osp.expanduser('~/data/datasets')
    kwargs = {'num_workers': 4, 'pin_memory': True} if cuda else {}
    train_loader = torch.utils.data.DataLoader(
        torchfcn.datasets.SBDClassSeg(root, split='train', transform=True),
        batch_size=1, shuffle=True, **kwargs)
    val_loader = torch.utils.data.DataLoader(
        torchfcn.datasets.VOC2011ClassSeg(
            root, split='seg11valid', transform=True),
        batch_size=1, shuffle=False, **kwargs)

    # 2. model

    model = torchfcn.models.FCN8sAtOnce(n_class=21)
    start_epoch = 0
    start_iteration = 0
    if args.resume:
        checkpoint = torch.load(args.resume)
        model.load_state_dict(checkpoint['model_state_dict'])
        start_epoch = checkpoint['epoch']
        start_iteration = checkpoint['iteration']
    else:
        vgg16 = torchfcn.models.VGG16(pretrained=True)
        model.copy_params_from_vgg16(vgg16)