Function main

tools/track_sort.py:127–268

(exp, args, num_gpu)

@logger.catch
def main(exp, args, num_gpu):
    if args.seed is not None:
        random.seed(args.seed)
        torch.manual_seed(args.seed)
        cudnn.deterministic = True
        warnings.warn(
            "You have chosen to seed testing. This will turn on the CUDNN deterministic setting, "
        )

    is_distributed = num_gpu > 1

    # set environment variables for distributed training
    cudnn.benchmark = True

    rank = args.local_rank
    # rank = get_local_rank()

    file_name = os.path.join(exp.output_dir, args.experiment_name)

    if rank == 0:
        os.makedirs(file_name, exist_ok=True)

    results_folder = os.path.join(file_name, "track_results_sort")
    os.makedirs(results_folder, exist_ok=True)

    setup_logger(file_name, distributed_rank=rank, filename="val_log.txt", mode="a")
    logger.info("Args: {}".format(args))

    if args.conf is not None:
        exp.test_conf = args.conf
    if args.nms is not None:
        exp.nmsthre = args.nms
    if args.tsize is not None:
        exp.test_size = (args.tsize, args.tsize)

    model = exp.get_model()
    logger.info("Model Summary: {}".format(get_model_info(model, exp.test_size)))
    #logger.info("Model Structure:\n{}".format(str(model)))

    #evaluator = exp.get_evaluator(args.batch_size, is_distributed, args.test)

    val_loader = exp.get_eval_loader(args.batch_size, is_distributed, args.test)
    evaluator = MOTEvaluator(
        args=args,
        dataloader=val_loader,
        img_size=exp.test_size,
        confthre=exp.test_conf,
        nmsthre=exp.nmsthre,
        num_classes=exp.num_classes,
        )

    torch.cuda.set_device(rank)
    model.cuda(rank)
    model.eval()

    if not args.speed and not args.trt:
        if args.ckpt is None:
            ckpt_file = os.path.join(file_name, "best_ckpt.pth.tar")