How to use the mmdet.core.delta2bbox function in mmdet

Returns:
            Tensor: Regressed bboxes, the same shape as input rois.
        """
        assert rois.size(1) == 4 or rois.size(1) == 5

        if not self.reg_class_agnostic:
            label = label * 4
            inds = torch.stack((label, label + 1, label + 2, label + 3), 1)
            bbox_pred = torch.gather(bbox_pred, 1, inds)
        assert bbox_pred.size(1) == 4

        if rois.size(1) == 4:
            new_rois = delta2bbox(rois, bbox_pred, self.target_means,
                                  self.target_stds, img_meta['img_shape'])
        else:
            bboxes = delta2bbox(rois[:, 1:], bbox_pred, self.target_means,
                                self.target_stds, img_meta['img_shape'])
            new_rois = torch.cat((rois[:, [0]], bboxes), dim=1)

        return new_rois

assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
            rpn_cls_score = rpn_cls_score.permute(1, 2, 0)
            if self.use_sigmoid_cls:
                rpn_cls_score = rpn_cls_score.reshape(-1)
                scores = rpn_cls_score.sigmoid()
            else:
                rpn_cls_score = rpn_cls_score.reshape(-1, 2)
                scores = rpn_cls_score.softmax(dim=1)[:, 1]
            rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1, 4)
            anchors = mlvl_anchors[idx]
            if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre:
                _, topk_inds = scores.topk(cfg.nms_pre)
                rpn_bbox_pred = rpn_bbox_pred[topk_inds, :]
                anchors = anchors[topk_inds, :]
                scores = scores[topk_inds]
            proposals = delta2bbox(anchors, rpn_bbox_pred, self.target_means,
                                   self.target_stds, img_shape)
            if cfg.min_bbox_size > 0:
                w = proposals[:, 2] - proposals[:, 0] + 1
                h = proposals[:, 3] - proposals[:, 1] + 1
                valid_inds = torch.nonzero((w >= cfg.min_bbox_size) &
                                           (h >= cfg.min_bbox_size)).squeeze()
                proposals = proposals[valid_inds, :]
                scores = scores[valid_inds]
            proposals = torch.cat([proposals, scores.unsqueeze(-1)], dim=-1)
            proposals, _ = nms(proposals, cfg.nms_thr)
            proposals = proposals[:cfg.nms_post, :]
            mlvl_proposals.append(proposals)
        proposals = torch.cat(mlvl_proposals, 0)
        if cfg.nms_across_levels:
            proposals, _ = nms(proposals, cfg.nms_thr)
            proposals = proposals[:cfg.max_num, :]

Returns:
            Tensor: Regressed bboxes, the same shape as input rois.
        """
        assert rois.size(1) == 4 or rois.size(1) == 5

        if not self.reg_class_agnostic:
            label = label * 4
            inds = torch.stack((label, label + 1, label + 2, label + 3), 1)
            bbox_pred = torch.gather(bbox_pred, 1, inds)
        assert bbox_pred.size(1) == 4

        if rois.size(1) == 4:
            new_rois = delta2bbox(rois, bbox_pred, self.target_means,
                                  self.target_stds, img_meta['img_shape'])
        else:
            bboxes = delta2bbox(rois[:, 1:], bbox_pred, self.target_means,
                                self.target_stds, img_meta['img_shape'])
            new_rois = torch.cat((rois[:, [0]], bboxes), dim=1)

        return new_rois

def loss_shape_single(self, shape_pred, bbox_anchors, bbox_gts,
                          anchor_weights, anchor_total_num):
        shape_pred = shape_pred.permute(0, 2, 3, 1).contiguous().view(-1, 2)
        bbox_anchors = bbox_anchors.contiguous().view(-1, 4)
        bbox_gts = bbox_gts.contiguous().view(-1, 4)
        anchor_weights = anchor_weights.contiguous().view(-1, 4)
        bbox_deltas = bbox_anchors.new_full(bbox_anchors.size(), 0)
        bbox_deltas[:, 2:] += shape_pred
        # filter out negative samples to speed-up weighted_bounded_iou_loss
        inds = torch.nonzero(anchor_weights[:, 0] > 0).squeeze(1)
        bbox_deltas_ = bbox_deltas[inds]
        bbox_anchors_ = bbox_anchors[inds]
        bbox_gts_ = bbox_gts[inds]
        anchor_weights_ = anchor_weights[inds]
        pred_anchors_ = delta2bbox(
            bbox_anchors_,
            bbox_deltas_,
            self.anchoring_means,
            self.anchoring_stds,
            wh_ratio_clip=1e-6)
        loss_shape = self.loss_shape(
            pred_anchors_,
            bbox_gts_,
            anchor_weights_,
            avg_factor=anchor_total_num)
        return loss_shape

if scores.dim() == 0:
                anchors = anchors.unsqueeze(0)
                scores = scores.unsqueeze(0)
                bbox_pred = bbox_pred.unsqueeze(0)
            # filter anchors, bbox_pred, scores w.r.t. scores
            nms_pre = cfg.get('nms_pre', -1)
            if nms_pre > 0 and scores.shape[0] > nms_pre:
                if self.use_sigmoid_cls:
                    max_scores, _ = scores.max(dim=1)
                else:
                    max_scores, _ = scores[:, 1:].max(dim=1)
                _, topk_inds = max_scores.topk(nms_pre)
                anchors = anchors[topk_inds, :]
                bbox_pred = bbox_pred[topk_inds, :]
                scores = scores[topk_inds, :]
            bboxes = delta2bbox(anchors, bbox_pred, self.target_means,
                                self.target_stds, img_shape)
            mlvl_bboxes.append(bboxes)
            mlvl_scores.append(scores)
        mlvl_bboxes = torch.cat(mlvl_bboxes)
        if rescale:
            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
        mlvl_scores = torch.cat(mlvl_scores)
        if self.use_sigmoid_cls:
            padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
            mlvl_scores = torch.cat([padding, mlvl_scores], dim=1)
        # multi class NMS
        det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
                                                cfg.score_thr, cfg.nms,
                                                cfg.max_per_img)
        return det_bboxes, det_labels

scores = cls_score.sigmoid()
            else:
                scores = cls_score.softmax(-1)
            bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
            nms_pre = cfg.get('nms_pre', -1)
            if nms_pre > 0 and scores.shape[0] > nms_pre:
                # Get maximum scores for foreground classes.
                if self.use_sigmoid_cls:
                    max_scores, _ = scores.max(dim=1)
                else:
                    max_scores, _ = scores[:, 1:].max(dim=1)
                _, topk_inds = max_scores.topk(nms_pre)
                anchors = anchors[topk_inds, :]
                bbox_pred = bbox_pred[topk_inds, :]
                scores = scores[topk_inds, :]
            bboxes = delta2bbox(anchors, bbox_pred, self.target_means,
                                self.target_stds, img_shape)
            mlvl_bboxes.append(bboxes)
            mlvl_scores.append(scores)
        mlvl_bboxes = torch.cat(mlvl_bboxes)
        if rescale:
            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
        mlvl_scores = torch.cat(mlvl_scores)
        if self.use_sigmoid_cls:
            # Add a dummy background class to the front when using sigmoid
            padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
            mlvl_scores = torch.cat([padding, mlvl_scores], dim=1)
        det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
                                                cfg.score_thr, cfg.nms,
                                                cfg.max_per_img)
        return det_bboxes, det_labels

if self.use_sigmoid_cls:
                scores = cls_score.sigmoid()
            else:
                scores = cls_score.softmax(-1)
            bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
            nms_pre = cfg.get('nms_pre', -1)
            if nms_pre > 0 and scores.shape[0] > nms_pre:
                if self.use_sigmoid_cls:
                    max_scores, _ = scores.max(dim=1)
                else:
                    max_scores, _ = scores[:, 1:].max(dim=1)
                _, topk_inds = max_scores.topk(nms_pre)
                anchors = anchors[topk_inds, :]
                bbox_pred = bbox_pred[topk_inds, :]
                scores = scores[topk_inds, :]
            bboxes = delta2bbox(anchors, bbox_pred, self.target_means,
                                self.target_stds, img_shape)
            mlvl_bboxes.append(bboxes)
            mlvl_scores.append(scores)
        mlvl_bboxes = torch.cat(mlvl_bboxes)
        if rescale:
            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
        mlvl_scores = torch.cat(mlvl_scores)
        if self.use_sigmoid_cls:
            padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
            mlvl_scores = torch.cat([padding, mlvl_scores], dim=1)
        det_bboxes, det_labels = multiclass_nms(
            mlvl_bboxes, mlvl_scores, cfg.score_thr, cfg.nms, cfg.max_per_img)
        return det_bboxes, det_labels

def loss_shape_single(self, shape_pred, bbox_anchors, bbox_gts,
                          anchor_weights, anchor_total_num):
        shape_pred = shape_pred.permute(0, 2, 3, 1).contiguous().view(-1, 2)
        bbox_anchors = bbox_anchors.contiguous().view(-1, 4)
        bbox_gts = bbox_gts.contiguous().view(-1, 4)
        anchor_weights = anchor_weights.contiguous().view(-1, 4)
        bbox_deltas = bbox_anchors.new_full(bbox_anchors.size(), 0)
        bbox_deltas[:, 2:] += shape_pred
        # filter out negative samples to speed-up weighted_bounded_iou_loss
        inds = torch.nonzero(anchor_weights[:, 0] > 0).squeeze(1)
        bbox_deltas_ = bbox_deltas[inds]
        bbox_anchors_ = bbox_anchors[inds]
        bbox_gts_ = bbox_gts[inds]
        anchor_weights_ = anchor_weights[inds]
        pred_anchors_ = delta2bbox(
            bbox_anchors_,
            bbox_deltas_,
            self.anchoring_means,
            self.anchoring_stds,
            wh_ratio_clip=1e-6)
        loss_shape = self.loss_shape(
            pred_anchors_,
            bbox_gts_,
            anchor_weights_,
            avg_factor=anchor_total_num)
        return loss_shape

cfg, rescale, cls_out_channels, use_sigmoid_cls,
                      target_means, target_stds):
    cls_scores = cls_scores.view(-1, cls_out_channels)
    bbox_preds = bbox_preds.view(-1, 4)
    priors = priors.view(-1, 4)
    nms_pre = cfg.get('nms_pre', -1)
    if nms_pre > 0 and cls_scores.shape[0] > nms_pre:
        if use_sigmoid_cls:
            max_scores, _ = cls_scores.max(dim=1)
        else:
            max_scores, _ = cls_scores[:, 1:].max(dim=1)
        _, topk_inds = max_scores.topk(nms_pre)
        priors = priors[topk_inds, :]
        bbox_preds = bbox_preds[topk_inds, :]
        cls_scores = cls_scores[topk_inds, :]
    mlvl_bboxes = delta2bbox(priors, bbox_preds, target_means,
                             target_stds, img_shape)
    if rescale:
        mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
    if use_sigmoid_cls:
        padding = cls_scores.new_zeros(cls_scores.shape[0], 1)
        cls_scores = torch.cat([padding, cls_scores], dim=1)
    det_bboxes, det_labels = multiclass_nms(
        mlvl_bboxes, cls_scores, cfg.score_thr, cfg.nms, cfg.max_per_img)
    return det_bboxes, det_labels