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: Initialize corner keypoint layers.
  
  Including corner heatmap branch and corner offset branch. Each branch
  has two parts: prefix `tl_` for top-left and `br_` for bottom-right.
docstr_tok: ["Initialize","corner","keypoint","layers",".","Including","corner","heatmap","branch","and","corner","offset","branch",".","Each","branch","has","two","parts",":","prefix","`","tl_","`","for","top-left","and","`","br_","`","for","bottom-right","."]
code: def _init_corner_kpt_layers(self):
      """Initialize corner keypoint layers.
  
      Including corner heatmap branch and corner offset branch. Each branch
      has two parts: prefix `tl_` for top-left and `br_` for bottom-right.
      """
      self.tl_pool, self.br_pool = nn.ModuleList(), nn.ModuleList()
      self.tl_heat, self.br_heat = nn.ModuleList(), nn.ModuleList()
      self.tl_off, self.br_off = nn.ModuleList(), nn.ModuleList()
  
      for _ in range(self.num_feat_levels):
          self.tl_pool.append(
              BiCornerPool(
                  self.in_channels,
                  ["top", "left"],
                  out_channels=self.in_channels,
              )
          )
          self.br_pool.append(
              BiCornerPool(
                  self.in_channels,
                  ["bottom", "right"],
                  out_channels=self.in_channels,
              )
          )
  
          self.tl_heat.append(
              self._make_layers(
                  out_channels=self.num_classes,
                  in_channels=self.in_channels,
              )
          )
          self.br_heat.append(
              self._make_layers(
                  out_channels=self.num_classes,
                  in_channels=self.in_channels,
              )
          )
  
          self.tl_off.append(
              self._make_layers(
                  out_channels=self.corner_offset_channels,
                  in_channels=self.in_channels,
              )
          )
          self.br_off.append(
              self._make_layers(
                  out_channels=self.corner_offset_channels,
                  in_channels=self.in_channels,
              )
          )
  
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 3 for calling the CornerHead obj's underlying member method code with example usage: obj._init_corner_kpt_layers() without return types