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

with open(osp.join(args.out, 'config.yaml'), 'w') as f:
        yaml.safe_dump(args.__dict__, f, default_flow_style=False)

    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.FCN32s(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']

with open(osp.join(args.out, 'config.yaml'), 'w') as f:
        yaml.safe_dump(args.__dict__, f, default_flow_style=False)

    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']

with open(osp.join(args.out, 'config.yaml'), 'w') as f:
        yaml.safe_dump(args.__dict__, f, default_flow_style=False)

    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']

with open(osp.join(args.out, 'config.yaml'), 'w') as f:
        yaml.safe_dump(args.__dict__, f, default_flow_style=False)

    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']