scripts/train_detection.py [383:407]:
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def main(args):
    # Initialize multi-processing
    distributed.init_process_group(backend='nccl', init_method='env://')
    device_id, device = args.local_rank, torch.device(args.local_rank)
    rank, world_size = distributed.get_rank(), distributed.get_world_size()
    torch.cuda.set_device(device_id)

    # Initialize logging
    if rank == 0:
        logging.init(args.log_dir, "training" if not args.eval else "eval")
        summary = tensorboard.SummaryWriter(args.log_dir)
    else:
        summary = None

    # Load configuration
    config = make_config(args)

    # Create dataloaders
    train_dataloader, val_dataloader = make_dataloader(args, config, rank, world_size)

    # Create model
    model = make_model(config, train_dataloader.dataset.num_thing, train_dataloader.dataset.num_stuff)
    if args.resume:
        assert not args.pre_train, "resume and pre_train are mutually exclusive"
        log_debug("Loading snapshot from %s", args.resume)
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scripts/train_panoptic.py [491:515]:
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def main(args):
    # Initialize multi-processing
    distributed.init_process_group(backend='nccl', init_method='env://')
    device_id, device = args.local_rank, torch.device(args.local_rank)
    rank, world_size = distributed.get_rank(), distributed.get_world_size()
    torch.cuda.set_device(device_id)

    # Initialize logging
    if rank == 0:
        logging.init(args.log_dir, "training" if not args.eval else "eval")
        summary = tensorboard.SummaryWriter(args.log_dir)
    else:
        summary = None

    # Load configuration
    config = make_config(args)

    # Create dataloaders
    train_dataloader, val_dataloader = make_dataloader(args, config, rank, world_size)

    # Create model
    model = make_model(config, train_dataloader.dataset.num_thing, train_dataloader.dataset.num_stuff)
    if args.resume:
        assert not args.pre_train, "resume and pre_train are mutually exclusive"
        log_debug("Loading snapshot from %s", args.resume)
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