Function _train_loader_from_config

detectron2/data/build.py:301–336

(cfg, *, mapper=None, dataset=None, sampler=None)

def _train_loader_from_config(cfg, *, mapper=None, dataset=None, sampler=None):
    if dataset is None:
        dataset = get_detection_dataset_dicts(
            cfg.DATASETS.TRAIN,
            filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
            min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
            if cfg.MODEL.KEYPOINT_ON
            else 0,
            proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
        )

    if mapper is None:
        mapper = DatasetMapper(cfg, True)

    if sampler is None:
        sampler_name = cfg.DATALOADER.SAMPLER_TRAIN
        logger = logging.getLogger(__name__)
        logger.info("Using training sampler {}".format(sampler_name))
        if sampler_name == "TrainingSampler":
            sampler = TrainingSampler(len(dataset))
        elif sampler_name == "RepeatFactorTrainingSampler":
            repeat_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency(
                dataset, cfg.DATALOADER.REPEAT_THRESHOLD
            )
            sampler = RepeatFactorTrainingSampler(repeat_factors)
        else:
            raise ValueError("Unknown training sampler: {}".format(sampler_name))

    return {
        "dataset": dataset,
        "sampler": sampler,
        "mapper": mapper,
        "total_batch_size": cfg.SOLVER.IMS_PER_BATCH,
        "aspect_ratio_grouping": cfg.DATALOADER.ASPECT_RATIO_GROUPING,
        "num_workers": cfg.DATALOADER.NUM_WORKERS,
    }


# TODO can allow dataset as an iterable or IterableDataset to make this function more general