How to use the torchfcn.Trainer function in torchfcn

# 3. optimizer

    optim = torch.optim.SGD(
        [
            {'params': get_parameters(model, bias=False)},
            {'params': get_parameters(model, bias=True),
             'lr': args.lr * 2, 'weight_decay': 0},
        ],
        lr=args.lr,
        momentum=args.momentum,
        weight_decay=args.weight_decay)
    if args.resume:
        optim.load_state_dict(checkpoint['optim_state_dict'])

    trainer = torchfcn.Trainer(
        cuda=cuda,
        model=model,
        optimizer=optim,
        train_loader=train_loader,
        val_loader=val_loader,
        out=args.out,
        max_iter=args.max_iteration,
        interval_validate=4000,
    )
    trainer.epoch = start_epoch
    trainer.iteration = start_iteration
    trainer.train()

# 3. optimizer

    optim = torch.optim.SGD(
        [
            {'params': get_parameters(model, bias=False)},
            {'params': get_parameters(model, bias=True),
             'lr': args.lr * 2, 'weight_decay': 0},
        ],
        lr=args.lr,
        momentum=args.momentum,
        weight_decay=args.weight_decay)
    if args.resume:
        optim.load_state_dict(checkpoint['optim_state_dict'])

    trainer = torchfcn.Trainer(
        cuda=cuda,
        model=model,
        optimizer=optim,
        train_loader=train_loader,
        val_loader=val_loader,
        out=args.out,
        max_iter=args.max_iteration,
        interval_validate=4000,
    )
    trainer.epoch = start_epoch
    trainer.iteration = start_iteration
    trainer.train()

vgg16 = torchfcn.models.VGG16(pretrained=True)
        model.copy_params_from_vgg16(vgg16, copy_fc8=False, init_upscore=False)
    if cuda:
        if torch.cuda.device_count() == 1:
            model = model.cuda()
        else:
            model = torch.nn.DataParallel(model).cuda()

    # 3. optimizer

    optim = torch.optim.Adam(model.parameters(), lr=cfg['lr'],
                             weight_decay=cfg['weight_decay'])
    if resume:
        optim.load_state_dict(checkpoint['optim_state_dict'])

    trainer = torchfcn.Trainer(
        cuda=cuda,
        model=model,
        optimizer=optim,
        train_loader=train_loader,
        val_loader=valid_loader,
        out=out,
        max_iter=max_iter,
        interval_validate=cfg['interval_validate'],
    )
    trainer.epoch = start_epoch
    trainer.iteration = start_epoch * len(train_loader)
    trainer.train()

# 3. optimizer

    optim = torch.optim.SGD(
        [
            {'params': get_parameters(model, bias=False)},
            {'params': get_parameters(model, bias=True),
             'lr': cfg['lr'] * 2, 'weight_decay': 0},
        ],
        lr=cfg['lr'],
        momentum=cfg['momentum'],
        weight_decay=cfg['weight_decay'])
    if resume:
        optim.load_state_dict(checkpoint['optim_state_dict'])

    trainer = torchfcn.Trainer(
        cuda=cuda,
        model=model,
        optimizer=optim,
        train_loader=train_loader,
        val_loader=val_loader,
        out=out,
        max_iter=cfg['max_iteration'],
        interval_validate=cfg.get('interval_validate', len(train_loader)),
    )
    trainer.epoch = start_epoch
    trainer.iteration = start_iteration
    trainer.train()

# 3. optimizer

    optim = torch.optim.SGD(
        [
            {'params': get_parameters(model, bias=False)},
            {'params': get_parameters(model, bias=True),
             'lr': args.lr * 2, 'weight_decay': 0},
        ],
        lr=args.lr,
        momentum=args.momentum,
        weight_decay=args.weight_decay)
    if args.resume:
        optim.load_state_dict(checkpoint['optim_state_dict'])

    trainer = torchfcn.Trainer(
        cuda=cuda,
        model=model,
        optimizer=optim,
        train_loader=train_loader,
        val_loader=val_loader,
        out=args.out,
        max_iter=args.max_iteration,
        interval_validate=4000,
    )
    trainer.epoch = start_epoch
    trainer.iteration = start_iteration
    trainer.train()