def get_loss(self):
        """
        Args:
            img (Tensor): Input images of shape (N, C, H, W).
                Typically these should be mean centered and std scaled.
            img_metas (list[dict]): A List of image info dict where each dict
                has: 'img_shape', 'scale_factor', 'flip', and may also contain
                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
                For details on the values of these keys see
                :class:`mmdet.datasets.pipelines.Collect`.
            gt_bboxes (list[Tensor]): Each item are the truth boxes for each
                image in [tl_x, tl_y, br_x, br_y] format.
            gt_labels (list[Tensor]): Class indices corresponding to each box.
            gt_areas (list[Tensor]): mask areas corresponding to each box.
            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
                boxes can be ignored when computing the loss.

        Returns:
            dict[str, Tensor]: A dictionary of loss components.
        """
        img = self.inputs['image']
        batch_size, _, height, width = img.shape
        img_metas, gt_bboxes, gt_labels = self.get_inputs()
        gt_bboxes_ignore = getattr(self.inputs, 'gt_bboxes_ignore', None)
        x = self.extract_feat(self.inputs)
        losses = dict()

        def upd_loss(losses, idx, weight=1):
            new_losses = dict()
            for k, v in losses.items():
                new_k = '{}{}'.format(k, idx)
                if isinstance(v, list) or isinstance(v, tuple):
                    new_losses[new_k] = [i * weight for i in v]
                else:
                    new_losses[new_k] = v * weight
            return new_losses

        # DETR encoder and decoder forward
        if self.query_head is not None:
            bbox_losses, x = self.query_head.forward_train(x, img_metas, gt_bboxes,
                                                          gt_labels, gt_bboxes_ignore)
            losses.update(bbox_losses)

        if self.rpn_head is not None:
            rois, rois_num, rpn_loss = self.rpn_head(x, self.inputs)
            rpn_loss['loss_rpn_cls'] *= self.rpn_head.loss_rpn_bbox.loss_weight
            losses.update(rpn_loss)
            
        positive_coords = []
        if self.roi_head is not None:
            roi_losses, _ = self.roi_head(x, rois, rois_num,
                                self.inputs)
            if self.with_pos_coord:
                positive_coords.append(roi_losses.pop('pos_coords'))
            else: 
                if 'pos_coords' in roi_losses.keys():
                    tmp = roi_losses.pop('pos_coords')  
            roi_losses = upd_loss(roi_losses, idx=0)
            losses.update(roi_losses)

        if self.bbox_head is not None:
            bbox_losses = self.bbox_head.forward_train(x,img_metas,gt_bboxes,gt_labels)
            if self.with_pos_coord:
                positive_coords.append(bbox_losses.pop('pos_coords'))
            else: 
                if 'pos_coords' in bbox_losses.keys():
                    tmp = bbox_losses.pop('pos_coords')  
            bbox_losses = upd_loss(bbox_losses, idx=1)
            losses.update(bbox_losses)
        
        if self.with_pos_coord and len(positive_coords)>0:
            for i in range(len(positive_coords)):
                bbox_losses = self.query_head.forward_train_aux(x, img_metas, gt_bboxes,
                                                            gt_labels, gt_bboxes_ignore, positive_coords[i], i)
                bbox_losses = upd_loss(bbox_losses, idx=i)
                losses.update(bbox_losses)

        return self._parse_losses(losses)

        Args:
            img (Tensor): Input images of shape (N, C, H, W).
                Typically these should be mean centered and std scaled.
            img_metas (list[dict]): A List of image info dict where each dict
                has: 'img_shape', 'scale_factor', 'flip', and may also contain
                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
                For details on the values of these keys see
                :class:`mmdet.datasets.pipelines.Collect`.
            gt_bboxes (list[Tensor]): Each item are the truth boxes for each
                image in [tl_x, tl_y, br_x, br_y] format.
            gt_labels (list[Tensor]): Class indices corresponding to each box.
            gt_areas (list[Tensor]): mask areas corresponding to each box.
            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
                boxes can be ignored when computing the loss.

        Returns:
            dict[str, Tensor]: A dictionary of loss components.
        
ppdet/modeling/architectures/co_detr.py