def main_worker()

in eval-action-recg-linear.py [0:0]

• 36 lines of code
• 9 McCabe index (conditional complexity)

def main_worker(gpu, ngpus, fold, args, cfg):
    args.gpu = gpu
    args.world_size = ngpus

    # Prepare folder and logger
    eval_dir, model_cfg, logger = eval_utils.prepare_environment(args, cfg, fold)

    # Model
    model, ckp_manager = eval_utils.build_model(model_cfg, cfg, eval_dir, args, logger)

    # Optimizer
    optimizer, scheduler = main_utils.build_optimizer(model.parameters(), cfg['optimizer'], logger)

    # Datasets
    train_loader, test_loader, dense_loader = eval_utils.build_dataloaders(
        cfg['dataset'], fold, cfg['num_workers'], args.distributed, logger)

    ################################ Train ################################
    start_epoch, end_epoch = 0, cfg['optimizer']['num_epochs']
    if (cfg['resume'] or args.test_only) and ckp_manager.checkpoint_exists(last=True):
        start_epoch = ckp_manager.restore(model, optimizer, scheduler, restore_last=True)
        logger.add_line("Loaded checkpoint '{}' (epoch {})".format(ckp_manager.last_checkpoint_fn(), start_epoch))

    if not cfg['test_only']:
        logger.add_line("=" * 30 + "   Training   " + "=" * 30)
        for epoch in range(start_epoch, end_epoch):
            scheduler.step(epoch=epoch)
            if args.distributed:
                train_loader.sampler.set_epoch(epoch)
                test_loader.sampler.set_epoch(epoch)

            logger.add_line('='*30 + ' Epoch {} '.format(epoch) + '='*30)
            logger.add_line('LR: {}'.format(scheduler.get_lr()))
            run_phase('train', train_loader, model, optimizer, epoch, args, cfg, logger)
            run_phase('test', test_loader, model, None, epoch, args, cfg, logger)
            ckp_manager.save(model, optimizer, scheduler, epoch)

    ################################ Eval ################################
    logger.add_line('\n' + '=' * 30 + ' Final evaluation ' + '=' * 30)
    cfg['dataset']['test']['clips_per_video'] = 25
    train_loader, test_loader, dense_loader = eval_utils.build_dataloaders(cfg['dataset'], fold, cfg['num_workers'], args.distributed, logger)
    top1_dense, top5_dense = run_phase('test_dense', dense_loader, model, None, end_epoch, args, cfg, logger)
    top1, top5 = run_phase('test', test_loader, model, None, end_epoch, args, cfg, logger)

    logger.add_line('\n' + '=' * 30 + ' Evaluation done ' + '=' * 30)
    for ft in top1:
        logger.add_line('')
        logger.add_line('[{}] Clip@1: {:6.2f}'.format(ft, top1[ft]))
        logger.add_line('[{}] Clip@5: {:6.2f}'.format(ft, top5[ft]))
        logger.add_line('[{}] Video@1: {:6.2f}'.format(ft, top1_dense[ft]))
        logger.add_line('[{}] Video@5: {:6.2f}'.format(ft, top5_dense[ft]))