def build_detection_train_loader(cfg: CfgNode, mapper=None):
    """
    A data loader is created in a way similar to that of Detectron2.
    The main differences are:
     - it allows to combine datasets with different but compatible object category sets

    The data loader is created by the following steps:
    1. Use the dataset names in config to query :class:`DatasetCatalog`, and obtain a list of dicts.
    2. Start workers to work on the dicts. Each worker will:
        * Map each metadata dict into another format to be consumed by the model.
        * Batch them by simply putting dicts into a list.
    The batched ``list[mapped_dict]`` is what this dataloader will return.

    Args:
        cfg (CfgNode): the config
        mapper (callable): a callable which takes a sample (dict) from dataset and
            returns the format to be consumed by the model.
            By default it will be `DatasetMapper(cfg, True)`.

    Returns:
        an infinite iterator of training data
    """

    _add_category_whitelists_to_metadata(cfg)
    _add_category_maps_to_metadata(cfg)
    _maybe_add_class_to_mesh_name_map_to_metadata(cfg.DATASETS.TRAIN, cfg)
    dataset_dicts = combine_detection_dataset_dicts(
        cfg.DATASETS.TRAIN,
        keep_instance_predicate=_get_train_keep_instance_predicate(cfg),
        proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
    )
    if mapper is None:
        mapper = DatasetMapper(cfg, True)
    return d2_build_detection_train_loader(cfg, dataset=dataset_dicts, mapper=mapper)

    A data loader is created in a way similar to that of Detectron2.
    The main differences are:
     - it allows to combine datasets with different but compatible object category sets

    The data loader is created by the following steps:
    1. Use the dataset names in config to query :class:`DatasetCatalog`, and obtain a list of dicts.
    2. Start workers to work on the dicts. Each worker will:
        * Map each metadata dict into another format to be consumed by the model.
        * Batch them by simply putting dicts into a list.
    The batched ``list[mapped_dict]`` is what this dataloader will return.

    Args:
        cfg (CfgNode): the config
        mapper (callable): a callable which takes a sample (dict) from dataset and
            returns the format to be consumed by the model.
            By default it will be `DatasetMapper(cfg, True)`.

    Returns:
        an infinite iterator of training data
    
EVA-02/det/projects/DensePose/densepose/data/build.py