class_docstr: CornerNet.
  
  This detector is the implementation of the paper `CornerNet: Detecting
  Objects as Paired Keypoints <https://arxiv.org/abs/1808.01244>`_ .
docstr: Merge augmented detection bboxes and score.
  
  Args:
      aug_results (list[list[Tensor]]): Det_bboxes and det_labels of each
          image.
      img_metas (list[list[dict]]): Meta information of each image, e.g.,
          image size, scaling factor, etc.
  
  Returns:
      tuple: (bboxes, labels)
docstr_tok: ["Merge","augmented","detection","bboxes","and","score",".","Args",":","aug_results","(","list","[","list","[","Tensor","]","]",")",":","Det_bboxes","and","det_labels","of","each","image",".","img_metas","(","list","[","list","[","dict","]","]",")",":","Meta","information","of","each","image",",","e.g.",",","image","size",",","scaling","factor",",","etc",".","Returns",":","tuple",":","(","bboxes",",","labels",")"]
code: def merge_aug_results(self, aug_results, img_metas):
      """Merge augmented detection bboxes and score.
  
      Args:
          aug_results (list[list[Tensor]]): Det_bboxes and det_labels of each
              image.
          img_metas (list[list[dict]]): Meta information of each image, e.g.,
              image size, scaling factor, etc.
  
      Returns:
          tuple: (bboxes, labels)
      """
      recovered_bboxes, aug_labels = [], []
      for bboxes_labels, img_info in zip(aug_results, img_metas):
          img_shape = img_info[0][
              "img_shape"
          ]  # using shape before padding
          scale_factor = img_info[0]["scale_factor"]
          flip = img_info[0]["flip"]
          bboxes, labels = bboxes_labels
          bboxes, scores = bboxes[:, :4], bboxes[:, -1:]
          bboxes = bbox_mapping_back(
              bboxes, img_shape, scale_factor, flip
          )
          recovered_bboxes.append(torch.cat([bboxes, scores], dim=-1))
          aug_labels.append(labels)
  
      bboxes = torch.cat(recovered_bboxes, dim=0)
      labels = torch.cat(aug_labels)
  
      if bboxes.shape[0] > 0:
          out_bboxes, out_labels = self.bbox_head._bboxes_nms(
              bboxes, labels, self.bbox_head.test_cfg
          )
      else:
          out_bboxes, out_labels = bboxes, labels
  
      return out_bboxes, out_labels
  
file_path: insightface/detection/scrfd/mmdet/models/detectors/cornernet.py
imports: import torch
  from mmdet.core import bbox2result, bbox_mapping_back
  from ..builder import DETECTORS
  from .single_stage import SingleStageDetector
label_desc: Use image node_id 2 for calling the CornerNet obj's underlying member method code with example usage: obj.merge_aug_results(aug_results, img_metas) and returns: out_bboxes,  out_labels