def train()

in tools/train_net.py [0:0]

• 34 lines of code
• 3 McCabe index (conditional complexity)

def train(cfg, train_dir, local_rank, distributed, logger):

    # build model
    model = build_siammot(cfg)
    device = torch.device(cfg.MODEL.DEVICE)
    model.to(device)

    optimizer = make_optimizer(cfg, model)
    scheduler = make_lr_scheduler(cfg, optimizer)

    # Initialize mixed-precision training
    use_mixed_precision = cfg.DTYPE == "float16"
    amp_opt_level = 'O1' if use_mixed_precision else 'O0'
    model, optimizer = amp.initialize(model, optimizer, opt_level=amp_opt_level)

    if distributed:
        model = torch.nn.parallel.DistributedDataParallel(
            model, device_ids=[local_rank], output_device=local_rank,
            broadcast_buffers=False, find_unused_parameters=True
        )

    arguments = {}
    arguments["iteration"] = 0

    save_to_disk = get_rank() == 0
    checkpointer = DetectronCheckpointer(cfg, model, optimizer,
                                         scheduler, train_dir, save_to_disk
                                         )
    extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
    arguments.update(extra_checkpoint_data)

    data_loader = build_train_data_loader(
        cfg,
        is_distributed=distributed,
        start_iter=arguments["iteration"],
    )

    checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD

    tensorboard_writer = TensorboardWriter(cfg, train_dir)

    do_train(model, data_loader, optimizer, scheduler,
             checkpointer, device, checkpoint_period, arguments,
             logger, tensorboard_writer
             )

    return model