def load_training_roidbs(self, names):
        """
        Args:
            names (list[str]): name of the training datasets, e.g.  ['train2014', 'valminusminival2014']

        Returns:
            roidbs (list[dict]):

        Produce "roidbs" as a list of dict, each dict corresponds to one image with k>=0 instances.
        and the following keys are expected for training:

        file_name: str, full path to the image
        boxes: numpy array of kx4 floats, each row is [x1, y1, x2, y2]
        class: numpy array of k integers, in the range of [1, #categories], NOT [0, #categories)
        is_crowd: k booleans. Use k False if you don't know what it means.
        segmentation: k lists of numpy arrays (one for each instance).
            Each list of numpy arrays corresponds to the mask for one instance.
            Each numpy array in the list is a polygon of shape Nx2,
            because one mask can be represented by N polygons.

            If your segmentation annotations are originally masks rather than polygons,
            either convert it, or the augmentation will need to be changed or skipped accordingly.

            Include this field only if training Mask R-CNN.
        """
        return COCODetection.load_many(
            cfg.DATA.BASEDIR, names, add_gt=True, add_mask=cfg.MODE_MASK)
Args:
            names (list[str]): name of the training datasets, e.g.  ['train2014', 'valminusminival2014']

        Returns:
            roidbs (list[dict]):

        Produce "roidbs" as a list of dict, each dict corresponds to one image with k>=0 instances.
        and the following keys are expected for training:

        file_name: str, full path to the image
        boxes: numpy array of kx4 floats, each row is [x1, y1, x2, y2]
        class: numpy array of k integers, in the range of [1, #categories], NOT [0, #categories)
        is_crowd: k booleans. Use k False if you don't know what it means.
        segmentation: k lists of numpy arrays (one for each instance).
            Each list of numpy arrays corresponds to the mask for one instance.
            Each numpy array in the list is a polygon of shape Nx2,
            because one mask can be represented by N polygons.

            If your segmentation annotations are originally masks rather than polygons,
            either convert it, or the augmentation will need to be changed or skipped accordingly.

            Include this field only if training Mask R-CNN.