scripts/train_instance_seg.py [466:487]:
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        exit(0)

    for epoch in range(starting_epoch, total_epochs):
        log_info("Starting epoch %d", epoch + 1)
        if not batch_update:
            scheduler.step(epoch)

        # Run training epoch
        global_step = train(model, optimizer, scheduler, train_dataloader, meters,
                            batch_update=batch_update, epoch=epoch, summary=summary, device=device,
                            log_interval=config["general"].getint("log_interval"), num_epochs=total_epochs,
                            global_step=global_step, loss_weights=config["optimizer"].getstruct("loss_weights"))

        # Save snapshot (only on rank 0)
        if rank == 0:
            snapshot_file = path.join(args.log_dir, "model_last.pth.tar")
            log_debug("Saving snapshot to %s", snapshot_file)
            meters_out_dict = {k + "_meter": v.state_dict() for k, v in meters.items()}
            save_snapshot(snapshot_file, config, epoch, 0, best_score, global_step,
                          body=model.module.body.state_dict(),
                          rpn_head=model.module.rpn_head.state_dict(),
                          roi_head=model.module.roi_head.state_dict(),
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scripts/train_panoptic.py [577:598]:
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        exit(0)

    for epoch in range(starting_epoch, total_epochs):
        log_info("Starting epoch %d", epoch + 1)
        if not batch_update:
            scheduler.step(epoch)

        # Run training epoch
        global_step = train(model, optimizer, scheduler, train_dataloader, meters,
                            batch_update=batch_update, epoch=epoch, summary=summary, device=device,
                            log_interval=config["general"].getint("log_interval"), num_epochs=total_epochs,
                            global_step=global_step, loss_weights=config["optimizer"].getstruct("loss_weights"))

        # Save snapshot (only on rank 0)
        if rank == 0:
            snapshot_file = path.join(args.log_dir, "model_last.pth.tar")
            log_debug("Saving snapshot to %s", snapshot_file)
            meters_out_dict = {k + "_meter": v.state_dict() for k, v in meters.items()}
            save_snapshot(snapshot_file, config, epoch, 0, best_score, global_step,
                          body=model.module.body.state_dict(),
                          rpn_head=model.module.rpn_head.state_dict(),
                          roi_head=model.module.roi_head.state_dict(),
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