def train(args):
    if args.config_file != "":
        cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()

    output_dir = cfg.OUTPUT_DIR
    if output_dir and not os.path.exists(output_dir):
        os.makedirs(output_dir)
    shutil.copy(args.config_file, cfg.OUTPUT_DIR)

    num_gpus = torch.cuda.device_count()

    logger = setup_logger('reid_baseline', output_dir, 0)
    logger.info('Using {} GPUS'.format(num_gpus))
    logger.info(args)
    logger.info('Running with config:\n{}'.format(cfg))

    train_dl, val_dl, num_query, num_classes = make_dataloader(cfg, num_gpus) 

    model = build_model(cfg, num_classes)

    loss_func = make_loss(cfg, num_classes)

    trainer = BaseTrainer(cfg, model, train_dl, val_dl,
                          loss_func, num_query, num_gpus)

    for epoch in range(trainer.epochs):
        for batch in trainer.train_dl:
            trainer.step(batch)
            trainer.handle_new_batch()
        trainer.handle_new_epoch() 

def main():
    parser = argparse.ArgumentParser(description="ReID Baseline Inference")
    parser.add_argument(
        "--config_file", default="", help="path to config file", type=str
    )
    parser.add_argument("opts", help="Modify config options using the command-line", default=None,
                        nargs=argparse.REMAINDER)

    args = parser.parse_args()

    num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1

    if args.config_file != "":
        cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()

    output_dir = cfg.OUTPUT_DIR
    if output_dir and not os.path.exists(output_dir):
        mkdir(output_dir)

    logger = setup_logger("reid_baseline", output_dir, 0)
    logger.info("Using {} GPUS".format(num_gpus))
    logger.info(args)

    if args.config_file != "":
        logger.info("Loaded configuration file {}".format(args.config_file))
        with open(args.config_file, 'r') as cf:
            config_str = "\n" + cf.read()
            logger.info(config_str)
    logger.info("Running with config:\n{}".format(cfg))

    if cfg.MODEL.DEVICE == "cuda":
        os.environ['CUDA_VISIBLE_DEVICES'] = cfg.MODEL.DEVICE_ID
    cudnn.benchmark = True

    train_loader, val_loader, num_query, num_classes = make_data_loader(cfg)
    model = build_model(cfg, num_classes)
    model.load_param(cfg.TEST.WEIGHT)

    inference(cfg, model, val_loader, num_query) 

def main():
    parser = argparse.ArgumentParser(description="ReID Baseline Training")
    parser.add_argument(
        "--config_file", default="./configs/tiger.yml", help="path to config file", type=str
    )
    parser.add_argument("opts", help="Modify config options using the command-line", default=None,
                        nargs=argparse.REMAINDER)
    parser.add_argument("--index_flod", help="Index of k-flod", default=3, type=int)                         #k-flod

    args = parser.parse_args()

    num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1

    if args.config_file != "":
        cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.DATASETS.INDEX_FLOD = args.index_flod
    cfg.freeze()

    output_dir = cfg.OUTPUT_DIR
    if output_dir and not os.path.exists(output_dir):
        os.makedirs(output_dir)

    logger = setup_logger("reid_baseline", output_dir, 0)
    logger.info("Using {} GPUS".format(num_gpus))
    logger.info(args)

    if args.config_file != "":
        logger.info("Loaded configuration file {}".format(args.config_file))
        with open(args.config_file, 'r') as cf:
            config_str = "\n" + cf.read()
            logger.info(config_str)
    logger.info("Running with config:\n{}".format(cfg))

    if cfg.MODEL.DEVICE == "cuda":
        os.environ['CUDA_VISIBLE_DEVICES'] = cfg.MODEL.DEVICE_ID    # new add by gu
    cudnn.benchmark = True
    train(cfg) 

def main():
    args = parse_args()
    update_config(cfg, args)

    cfg.defrost()
    cfg.RANK = args.rank
    cfg.freeze()

    logger, final_output_dir, tb_log_dir = create_logger(
        cfg, args.cfg, 'train'
    )

    logger.info(pprint.pformat(args))
    logger.info(cfg)

    if args.gpu is not None:
        warnings.warn('You have chosen a specific GPU. This will completely '
                      'disable data parallelism.')

    if args.dist_url == "env://" and args.world_size == -1:
        args.world_size = int(os.environ["WORLD_SIZE"])

    args.distributed = args.world_size > 1 or cfg.MULTIPROCESSING_DISTRIBUTED

    ngpus_per_node = torch.cuda.device_count()
    if cfg.MULTIPROCESSING_DISTRIBUTED:
        # Since we have ngpus_per_node processes per node, the total world_size
        # needs to be adjusted accordingly
        args.world_size = ngpus_per_node * args.world_size
        # Use torch.multiprocessing.spawn to launch distributed processes: the
        # main_worker process function
        mp.spawn(
            main_worker,
            nprocs=ngpus_per_node,
            args=(ngpus_per_node, args, final_output_dir, tb_log_dir)
        )
    else:
        # Simply call main_worker function
        main_worker(
            ','.join([str(i) for i in cfg.GPUS]),
            ngpus_per_node,
            args,
            final_output_dir,
            tb_log_dir
        )