class_docstr: Head of CornerNet: Detecting Objects as Paired Keypoints.
  
  Code is modified from the `official github repo
  <https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/
  kp.py#L73>`_ .
  
  More details can be found in the `paper
  <https://arxiv.org/abs/1808.01244>`_ .
  
  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. Because
          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.
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docstr: Compute losses of the head.
  
  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_embs (list[Tensor]): Top-left corner embeddings for each level
          with shape (N, corner_emb_channels, H, W).
      br_embs (list[Tensor]): Bottom-right corner embeddings for each
          level with shape (N, corner_emb_channels, 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).
      gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
          shape (num_gts, 4) in [left, top, right, bottom] format.
      gt_labels (list[Tensor]): Class indices corresponding to each box.
      img_metas (list[dict]): Meta information of each image, e.g.,
          image size, scaling factor, etc.
      gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
          boxes can be ignored when computing the loss.
  
  Returns:
      dict[str, Tensor]: A dictionary of loss components. Containing the
      following losses:
  
          - det_loss (list[Tensor]): Corner keypoint losses of all
            feature levels.
          - pull_loss (list[Tensor]): Part one of AssociativeEmbedding
            losses of all feature levels.
          - push_loss (list[Tensor]): Part two of AssociativeEmbedding
            losses of all feature levels.
          - off_loss (list[Tensor]): Corner offset losses of all feature
            levels.
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code: def loss(
      self,
      tl_heats,
      br_heats,
      tl_embs,
      br_embs,
      tl_offs,
      br_offs,
      gt_bboxes,
      gt_labels,
      img_metas,
      gt_bboxes_ignore=None,
  ):
      """Compute losses of the head.
  
      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_embs (list[Tensor]): Top-left corner embeddings for each level
              with shape (N, corner_emb_channels, H, W).
          br_embs (list[Tensor]): Bottom-right corner embeddings for each
              level with shape (N, corner_emb_channels, 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).
          gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
              shape (num_gts, 4) in [left, top, right, bottom] format.
          gt_labels (list[Tensor]): Class indices corresponding to each box.
          img_metas (list[dict]): Meta information of each image, e.g.,
              image size, scaling factor, etc.
          gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
              boxes can be ignored when computing the loss.
  
      Returns:
          dict[str, Tensor]: A dictionary of loss components. Containing the
          following losses:
  
              - det_loss (list[Tensor]): Corner keypoint losses of all
                feature levels.
              - pull_loss (list[Tensor]): Part one of AssociativeEmbedding
                losses of all feature levels.
              - push_loss (list[Tensor]): Part two of AssociativeEmbedding
                losses of all feature levels.
              - off_loss (list[Tensor]): Corner offset losses of all feature
                levels.
      """
      targets = self.get_targets(
          gt_bboxes,
          gt_labels,
          tl_heats[-1].shape,
          img_metas[0]["pad_shape"],
          with_corner_emb=self.with_corner_emb,
      )
      mlvl_targets = [targets for _ in range(self.num_feat_levels)]
      det_losses, pull_losses, push_losses, off_losses = multi_apply(
          self.loss_single,
          tl_heats,
          br_heats,
          tl_embs,
          br_embs,
          tl_offs,
          br_offs,
          mlvl_targets,
      )
      loss_dict = dict(det_loss=det_losses, off_loss=off_losses)
      if self.with_corner_emb:
          loss_dict.update(pull_loss=pull_losses, push_loss=push_losses)
      return loss_dict
  
imports: from math import ceil, log
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, bias_init_with_prob
  from mmcv.ops import CornerPool, batched_nms
  from mmdet.core import multi_apply
  from ..builder import HEADS, build_loss
  from ..utils import gaussian_radius, gen_gaussian_target
  from .base_dense_head import BaseDenseHead
label_desc: Use image node_id 10 for calling the CornerHead obj's underlying member method code with example usage: obj.loss(tl_heats, br_heats, tl_embs, br_embs, tl_offs, br_offs, gt_bboxes, gt_labels, img_metas, gt_bboxes_ignore) and returns: loss_dict