def batched_nms(boxes, scores, idxs, nms_cfg, class_agnostic=False):
    """Performs non-maximum suppression in a batched fashion.

    Modified from https://github.com/pytorch/vision/blob
    /505cd6957711af790211896d32b40291bea1bc21/torchvision/ops/boxes.py#L39.
    In order to perform NMS independently per class, we add an offset to all
    the boxes. The offset is dependent only on the class idx, and is large
    enough so that boxes from different classes do not overlap.

    Arguments:
        boxes (torch.Tensor): boxes in shape (N, 4).
        scores (torch.Tensor): scores in shape (N, ).
        idxs (torch.Tensor): each index value correspond to a bbox cluster,
            and NMS will not be applied between elements of different idxs,
            shape (N, ).
        nms_cfg (dict): specify nms type and other parameters like iou_thr.
            Possible keys includes the following.

            - iou_thr (float): IoU threshold used for NMS.
            - split_thr (float): threshold number of boxes. In some cases the
                number of boxes is large (e.g., 200k). To avoid OOM during
                training, the users could set `split_thr` to a small value.
                If the number of boxes is greater than the threshold, it will
                perform NMS on each group of boxes separately and sequentially.
                Defaults to 10000.
        class_agnostic (bool): if true, nms is class agnostic,
            i.e. IoU thresholding happens over all boxes,
            regardless of the predicted class.

    Returns:
        tuple: kept dets and indice.
    """
    nms_cfg_ = nms_cfg.copy()
    class_agnostic = nms_cfg_.pop('class_agnostic', class_agnostic)
    if class_agnostic:
        boxes_for_nms = boxes
    else:
        max_coordinate = boxes.max()
        offsets = idxs.to(boxes) * (max_coordinate + torch.tensor(1).to(boxes))
        boxes_for_nms = boxes + offsets[:, None]

    nms_type = nms_cfg_.pop('type', 'nms')
    nms_op = eval(nms_type)

    split_thr = nms_cfg_.pop('split_thr', 10000)
    # Won't split to multiple nms nodes when exporting to onnx
    if boxes_for_nms.shape[0] < split_thr or torch.onnx.is_in_onnx_export():
        dets, keep = nms_op(boxes_for_nms, scores, **nms_cfg_)
        boxes = boxes[keep]
        # -1 indexing works abnormal in TensorRT
        # This assumes `dets` has 5 dimensions where
        # the last dimension is score.
        # TODO: more elegant way to handle the dimension issue.
        # Some type of nms would reweight the score, such as SoftNMS
        scores = dets[:, 4]
    else:
        max_num = nms_cfg_.pop('max_num', -1)
        total_mask = scores.new_zeros(scores.size(), dtype=torch.bool)
        # Some type of nms would reweight the score, such as SoftNMS
        scores_after_nms = scores.new_zeros(scores.size())
        for id in torch.unique(idxs):
            mask = (idxs == id).nonzero(as_tuple=False).view(-1)
            dets, keep = nms_op(boxes_for_nms[mask], scores[mask], **nms_cfg_)
            total_mask[mask[keep]] = True
            scores_after_nms[mask[keep]] = dets[:, -1]
        keep = total_mask.nonzero(as_tuple=False).view(-1)

        scores, inds = scores_after_nms[keep].sort(descending=True)
        keep = keep[inds]
        boxes = boxes[keep]

        if max_num > 0:
            keep = keep[:max_num]
            boxes = boxes[:max_num]
            scores = scores[:max_num]

    return torch.cat([boxes, scores[:, None]], -1), keep
Performs non-maximum suppression in a batched fashion.

    Modified from https://github.com/pytorch/vision/blob
    /505cd6957711af790211896d32b40291bea1bc21/torchvision/ops/boxes.py#L39.
    In order to perform NMS independently per class, we add an offset to all
    the boxes. The offset is dependent only on the class idx, and is large
    enough so that boxes from different classes do not overlap.

    Arguments:
        boxes (torch.Tensor): boxes in shape (N, 4).
        scores (torch.Tensor): scores in shape (N, ).
        idxs (torch.Tensor): each index value correspond to a bbox cluster,
            and NMS will not be applied between elements of different idxs,
            shape (N, ).
        nms_cfg (dict): specify nms type and other parameters like iou_thr.
            Possible keys includes the following.

            - iou_thr (float): IoU threshold used for NMS.
            - split_thr (float): threshold number of boxes. In some cases the
                number of boxes is large (e.g., 200k). To avoid OOM during
                training, the users could set `split_thr` to a small value.
                If the number of boxes is greater than the threshold, it will
                perform NMS on each group of boxes separately and sequentially.
                Defaults to 10000.
        class_agnostic (bool): if true, nms is class agnostic,
            i.e. IoU thresholding happens over all boxes,
            regardless of the predicted class.

    Returns:
        tuple: kept dets and indice.
    
ControlNet-v1-1-nightly/annotator/uniformer/mmcv/ops/nms.py