How to use the mmdet.datasets.build_dataloader function in mmdet

type='Corrupt',
                    corruption=corruption,
                    severity=corruption_severity)
                # TODO: hard coded "1", we assume that the first step is
                # loading images, which needs to be fixed in the future
                test_data_cfg['pipeline'].insert(1, corruption_trans)

            # print info
            print('\nTesting {} at severity {}'.format(corruption,
                                                       corruption_severity))

            # build the dataloader
            # TODO: support multiple images per gpu
            #       (only minor changes are needed)
            dataset = build_dataset(test_data_cfg)
            data_loader = build_dataloader(
                dataset,
                imgs_per_gpu=1,
                workers_per_gpu=args.workers,
                dist=distributed,
                shuffle=False)

            # build the model and load checkpoint
            model = build_detector(
                cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
            fp16_cfg = cfg.get('fp16', None)
            if fp16_cfg is not None:
                wrap_fp16_model(model)
            checkpoint = load_checkpoint(
                model, args.checkpoint, map_location='cpu')
            # old versions did not save class info in checkpoints,
            # this walkaround is for backward compatibility

def _non_dist_train(model,
                    dataset,
                    cfg,
                    validate=False,
                    logger=None,
                    timestamp=None):
    if validate:
        raise NotImplementedError('Built-in validation is not implemented '
                                  'yet in not-distributed training. Use '
                                  'distributed training or test.py and '
                                  '*eval.py scripts instead.')
    # prepare data loaders
    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
    data_loaders = [
        build_dataloader(
            ds,
            cfg.data.imgs_per_gpu,
            cfg.data.workers_per_gpu,
            cfg.gpus,
            dist=False) for ds in dataset
    ]
    # put model on gpus
    model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()

    # build runner
    optimizer = build_optimizer(model, cfg.optimizer)
    runner = Runner(
        model, batch_processor, optimizer, cfg.work_dir, logger=logger)
    # an ugly walkaround to make the .log and .log.json filenames the same
    runner.timestamp = timestamp
    # fp16 setting

# an ugly walkaround to make the .log and .log.json filenames the same
    runner.timestamp = timestamp
    # fp16 setting
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        optimizer_config = Fp16OptimizerHook(
            **cfg.optimizer_config, **fp16_cfg, distributed=False)
    else:
        optimizer_config = cfg.optimizer_config
    runner.register_training_hooks(cfg.lr_config, optimizer_config,
                                   cfg.checkpoint_config, cfg.log_config)

    # register eval hooks
    if validate:
        val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
        val_dataloader = build_dataloader(
            val_dataset,
            imgs_per_gpu=1,
            workers_per_gpu=cfg.data.workers_per_gpu,
            dist=False,
            shuffle=False)
        eval_cfg = cfg.get('evaluation', {})
        runner.register_hook(EvalHook(val_dataloader, **eval_cfg))

    if cfg.resume_from:
        runner.resume(cfg.resume_from)
    elif cfg.load_from:
        runner.load_checkpoint(cfg.load_from)
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)

def _dist_train(model,
                dataset,
                cfg,
                validate=False,
                logger=None,
                timestamp=None):
    # prepare data loaders
    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
    data_loaders = [
        build_dataloader(
            ds, cfg.data.imgs_per_gpu, cfg.data.workers_per_gpu, dist=True)
        for ds in dataset
    ]
    # put model on gpus
    model = MMDistributedDataParallel(model.cuda())

    # build runner
    optimizer = build_optimizer(model, cfg.optimizer)
    runner = Runner(
        model, batch_processor, optimizer, cfg.work_dir, logger=logger)
    # an ugly walkaround to make the .log and .log.json filenames the same
    runner.timestamp = timestamp

    # fp16 setting
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:

def _dist_train(model,
                dataset,
                cfg,
                validate=False,
                logger=None,
                timestamp=None,
                meta=None):
    # prepare data loaders
    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
    data_loaders = [
        build_dataloader(
            ds,
            cfg.data.imgs_per_gpu,
            cfg.data.workers_per_gpu,
            dist=True,
            seed=cfg.seed) for ds in dataset
    ]
    # put model on gpus
    find_unused_parameters = cfg.get('find_unused_parameters', False)
    # Sets the `find_unused_parameters` parameter in
    # torch.nn.parallel.DistributedDataParallel
    model = MMDistributedDataParallel(
        model.cuda(),
        device_ids=[torch.cuda.current_device()],
        broadcast_buffers=False,
        find_unused_parameters=find_unused_parameters)

def _dist_train(model, dataset, cfg, validate=False):
    # prepare data loaders
    data_loaders = [
        build_dataloader(
            dataset,
            cfg.data.imgs_per_gpu,
            cfg.data.workers_per_gpu,
            dist=True)
    ]
    # put model on gpus
    model = MMDistributedDataParallel(model.cuda())

    # build runner
    optimizer = build_optimizer(model, cfg.optimizer)
    runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
                    cfg.log_level)

    # fp16 setting
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:

# fp16 setting
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
                                             **fp16_cfg)
    else:
        optimizer_config = DistOptimizerHook(**cfg.optimizer_config)

    # register hooks
    runner.register_training_hooks(cfg.lr_config, optimizer_config,
                                   cfg.checkpoint_config, cfg.log_config)
    runner.register_hook(DistSamplerSeedHook())
    # register eval hooks
    if validate:
        val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
        val_dataloader = build_dataloader(
            val_dataset,
            imgs_per_gpu=1,
            workers_per_gpu=cfg.data.workers_per_gpu,
            dist=True,
            shuffle=False)
        eval_cfg = cfg.get('evaluation', {})
        runner.register_hook(DistEvalHook(val_dataloader, **eval_cfg))

    if cfg.resume_from:
        runner.resume(cfg.resume_from)
    elif cfg.load_from:
        runner.load_checkpoint(cfg.load_from)
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)

def _dist_train(model, dataset, cfg, validate=False):
    # prepare data loaders
    data_loaders = [
        build_dataloader(
            dataset,
            cfg.data.imgs_per_gpu,
            cfg.data.workers_per_gpu,
            dist=True)
    ]
    # put model on gpus
    model = MMDistributedDataParallel(model.cuda())
    # build runner
    runner = MultiLRRunner(model, batch_processor, cfg.optimizer, cfg.work_dir,
                    cfg.log_level)
    # register hooks
    optimizer_config = DistOptimizerHook(**cfg.optimizer_config)
    runner.register_training_hooks(cfg.lr_config, optimizer_config,
                                   cfg.checkpoint_config, cfg.log_config)
    runner.register_hook(DistSamplerSeedHook())
    # register eval hooks

if cfg.checkpoint_config is not None:
            # save mmdet version, config file content and class names in
            # checkpoints as meta data
            cfg.checkpoint_config.meta = dict(
                mmdet_version=__version__,
                config=cfg.text,
                CLASSES=train_dataset.CLASSES)
            cfg.checkpoint_config.out_dir = self.cfg.getWeightsOutPath()
            cfg.checkpoint_config.filename_tmpl = f"best-{ec.fold}.{ec.stage}.weights"
        logger = get_root_logger(cfg.log_level)
        model.setClasses(train_dataset.CLASSES)

        distributed = False
        # prepare data loaders
        data_loaders = [
            build_dataloader(
                train_dataset,
                cfg.data.imgs_per_gpu,
                cfg.data.workers_per_gpu,
                num_gpus=cfg.gpus,
                dist=distributed)
        ]

        runner = train_detector(
            model.model,
            train_dataset,
            cfg,
            distributed=distributed,  # distributed,
            validate=True,  # args_validate,
            logger=logger)

        runner._epoch = initial_epoch