class_docstr: Head of CentripetalNet: Pursuing High-quality Keypoint Pairs for Object
  Detection.
  
  CentripetalHead inherits from :class:`CornerHead`. It removes the
  embedding branch and adds guiding shift and centripetal shift branches.
  More details can be found in the `paper
  <https://arxiv.org/abs/2003.09119>`_ .
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      num_feat_levels (int): Levels of feature from the previous module. 2
          for HourglassNet-104 and 1 for HourglassNet-52. HourglassNet-104
          outputs the final feature and intermediate supervision feature and
          HourglassNet-52 only outputs the final feature. Default: 2.
      corner_emb_channels (int): Channel of embedding vector. Default: 1.
      train_cfg (dict | None): Training config. Useless in CornerHead,
          but we keep this variable for SingleStageDetector. Default: None.
      test_cfg (dict | None): Testing config of CornerHead. Default: None.
      loss_heatmap (dict | None): Config of corner heatmap loss. Default:
          GaussianFocalLoss.
      loss_embedding (dict | None): Config of corner embedding loss. Default:
          AssociativeEmbeddingLoss.
      loss_offset (dict | None): Config of corner offset loss. Default:
          SmoothL1Loss.
      loss_guiding_shift (dict): Config of guiding shift loss. Default:
          SmoothL1Loss.
      loss_centripetal_shift (dict): Config of centripetal shift loss.
          Default: SmoothL1Loss.
docstr: Transform network output for a batch into bbox predictions.
  
  Args:
      tl_heats (list[Tensor]): Top-left corner heatmaps for each level
          with shape (N, num_classes, H, W).
      br_heats (list[Tensor]): Bottom-right corner heatmaps for each
          level with shape (N, num_classes, H, W).
      tl_offs (list[Tensor]): Top-left corner offsets for each level
          with shape (N, corner_offset_channels, H, W).
      br_offs (list[Tensor]): Bottom-right corner offsets for each level
          with shape (N, corner_offset_channels, H, W).
      tl_guiding_shifts (list[Tensor]): Top-left guiding shifts for each
          level with shape (N, guiding_shift_channels, H, W). Useless in
          this function, we keep this arg because it's the raw output
          from CentripetalHead.
      br_guiding_shifts (list[Tensor]): Bottom-right guiding shifts for
          each level with shape (N, guiding_shift_channels, H, W).
          Useless in this function, we keep this arg because it's the
          raw output from CentripetalHead.
      tl_centripetal_shifts (list[Tensor]): Top-left centripetal shifts
          for each level with shape (N, centripetal_shift_channels, H,
          W).
      br_centripetal_shifts (list[Tensor]): Bottom-right centripetal
          shifts for each level with shape (N,
          centripetal_shift_channels, H, W).
      img_metas (list[dict]): Meta information of each image, e.g.,
          image size, scaling factor, etc.
      rescale (bool): If True, return boxes in original image space.
          Default: False.
      with_nms (bool): If True, do nms before return boxes.
          Default: True.
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code: def get_bboxes(
      self,
      tl_heats,
      br_heats,
      tl_offs,
      br_offs,
      tl_guiding_shifts,
      br_guiding_shifts,
      tl_centripetal_shifts,
      br_centripetal_shifts,
      img_metas,
      rescale=False,
      with_nms=True,
  ):
      """Transform network output for a batch into bbox predictions.
  
      Args:
          tl_heats (list[Tensor]): Top-left corner heatmaps for each level
              with shape (N, num_classes, H, W).
          br_heats (list[Tensor]): Bottom-right corner heatmaps for each
              level with shape (N, num_classes, H, W).
          tl_offs (list[Tensor]): Top-left corner offsets for each level
              with shape (N, corner_offset_channels, H, W).
          br_offs (list[Tensor]): Bottom-right corner offsets for each level
              with shape (N, corner_offset_channels, H, W).
          tl_guiding_shifts (list[Tensor]): Top-left guiding shifts for each
              level with shape (N, guiding_shift_channels, H, W). Useless in
              this function, we keep this arg because it's the raw output
              from CentripetalHead.
          br_guiding_shifts (list[Tensor]): Bottom-right guiding shifts for
              each level with shape (N, guiding_shift_channels, H, W).
              Useless in this function, we keep this arg because it's the
              raw output from CentripetalHead.
          tl_centripetal_shifts (list[Tensor]): Top-left centripetal shifts
              for each level with shape (N, centripetal_shift_channels, H,
              W).
          br_centripetal_shifts (list[Tensor]): Bottom-right centripetal
              shifts for each level with shape (N,
              centripetal_shift_channels, H, W).
          img_metas (list[dict]): Meta information of each image, e.g.,
              image size, scaling factor, etc.
          rescale (bool): If True, return boxes in original image space.
              Default: False.
          with_nms (bool): If True, do nms before return boxes.
              Default: True.
      """
      assert (
          tl_heats[-1].shape[0]
          == br_heats[-1].shape[0]
          == len(img_metas)
      )
      result_list = []
      for img_id in range(len(img_metas)):
          result_list.append(
              self._get_bboxes_single(
                  tl_heats[-1][img_id : img_id + 1, :],
                  br_heats[-1][img_id : img_id + 1, :],
                  tl_offs[-1][img_id : img_id + 1, :],
                  br_offs[-1][img_id : img_id + 1, :],
                  img_metas[img_id],
                  tl_emb=None,
                  br_emb=None,
                  tl_centripetal_shift=tl_centripetal_shifts[-1][
                      img_id : img_id + 1, :
                  ],
                  br_centripetal_shift=br_centripetal_shifts[-1][
                      img_id : img_id + 1, :
                  ],
                  rescale=rescale,
                  with_nms=with_nms,
              )
          )
  
      return result_list
  
file_path: insightface/detection/scrfd/mmdet/models/dense_heads/centripetal_head.py
imports: import torch.nn
  from mmcv.cnn import ConvModule, normal_init
  from mmcv.ops import DeformConv2d
  from mmdet.core import multi_apply
  from ..builder import HEADS, build_loss
  from .corner_head import CornerHead
label_desc: Use image node_id 8 for calling the CentripetalHead obj's underlying member method code with example usage: obj.get_bboxes(tl_heats, br_heats, tl_offs, br_offs, tl_guiding_shifts, br_guiding_shifts, tl_centripetal_shifts, br_centripetal_shifts, img_metas, rescale, with_nms) and returns: result_list