class_docstr: RPN head.
  
  Args:
      in_channels (int): Number of channels in the input feature map.
class_docstr_tok: ["RPN","head",".","Args",":","in_channels","(","int",")",":","Number","of","channels","in","the","input","feature","map","."]
docstr: Compute losses of the head.
  
  Args:
      cls_scores (list[Tensor]): Box scores for each scale level
          Has shape (N, num_anchors * num_classes, H, W)
      bbox_preds (list[Tensor]): Box energies / deltas for each scale
          level with shape (N, num_anchors * 4, H, W)
      gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
          shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
      img_metas (list[dict]): Meta information of each image, e.g.,
          image size, scaling factor, etc.
      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.
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code: def loss(
      self,
      cls_scores,
      bbox_preds,
      gt_bboxes,
      img_metas,
      gt_bboxes_ignore=None,
  ):
      """Compute losses of the head.
  
      Args:
          cls_scores (list[Tensor]): Box scores for each scale level
              Has shape (N, num_anchors * num_classes, H, W)
          bbox_preds (list[Tensor]): Box energies / deltas for each scale
              level with shape (N, num_anchors * 4, H, W)
          gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
              shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
          img_metas (list[dict]): Meta information of each image, e.g.,
              image size, scaling factor, etc.
          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.
      """
      losses = super(RPNHead, self).loss(
          cls_scores,
          bbox_preds,
          gt_bboxes,
          None,
          img_metas,
          gt_bboxes_ignore=gt_bboxes_ignore,
      )
      return dict(
          loss_rpn_cls=losses["loss_cls"],
          loss_rpn_bbox=losses["loss_bbox"],
      )
  
file_path: insightface/detection/scrfd/mmdet/models/dense_heads/rpn_head.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import normal_init
  from mmcv.ops import batched_nms
  from ..builder import HEADS
  from .anchor_head import AnchorHead
  from .rpn_test_mixin import RPNTestMixin
label_desc: Use image node_id 5 for calling the RPNHead obj's underlying member method code with example usage: obj.loss(cls_scores, bbox_preds, gt_bboxes, img_metas, gt_bboxes_ignore) and returns: dict