def train()

in tools/train_net.py [0:0]

• 49 lines of code
• 5 McCabe index (conditional complexity)

def train(cfg, local_rank, distributed):
    model = build_detection_model(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,
            # this should be removed if we update BatchNorm stats
            broadcast_buffers=False,
        )

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

    output_dir = cfg.OUTPUT_DIR

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

    data_loader = make_data_loader(
        cfg,
        is_train=True,
        is_distributed=distributed,
        start_iter=arguments["iteration"],
    )

    test_period = cfg.SOLVER.TEST_PERIOD
    if test_period > 0:
        data_loader_val = make_data_loader(cfg, is_train=False, is_distributed=distributed, is_for_period=True)
    else:
        data_loader_val = None

    checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD

    do_train(
        cfg,
        model,
        data_loader,
        data_loader_val,
        optimizer,
        scheduler,
        checkpointer,
        device,
        checkpoint_period,
        test_period,
        arguments,
    )

    return model