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. Defaults to 2.
      corner_emb_channels (int): Channel of embedding vector. Defaults to 1.
      train_cfg (:obj:`ConfigDict` or dict, optional): Training config.
          Useless in CornerHead, but we keep this variable for
          SingleStageDetector.
      test_cfg (:obj:`ConfigDict` or dict, optional): Testing config of
          CornerHead.
      loss_heatmap (:obj:`ConfigDict` or dict): Config of corner heatmap
          loss. Defaults to GaussianFocalLoss.
      loss_embedding (:obj:`ConfigDict` or dict): Config of corner embedding
          loss. Defaults to AssociativeEmbeddingLoss.
      loss_offset (:obj:`ConfigDict` or dict): Config of corner offset loss.
          Defaults to SmoothL1Loss.
      init_cfg (:obj:`ConfigDict` or dict, optional): the config to control
          the initialization.
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code: def __init__(
      self,
      num_classes: int,
      in_channels: int,
      num_feat_levels: int = 2,
      corner_emb_channels: int = 1,
      train_cfg: OptConfigType = None,
      test_cfg: OptConfigType = None,
      loss_heatmap: ConfigType = dict(
          type="GaussianFocalLoss", alpha=2.0, gamma=4.0, loss_weight=1
      ),
      loss_embedding: ConfigType = dict(
          type="AssociativeEmbeddingLoss",
          pull_weight=0.25,
          push_weight=0.25,
      ),
      loss_offset: ConfigType = dict(
          type="SmoothL1Loss", beta=1.0, loss_weight=1
      ),
      init_cfg: OptMultiConfig = None,
  ) -> None:
      assert init_cfg is None, (
          "To prevent abnormal initialization "
          "behavior, init_cfg is not allowed to be set"
      )
      super().__init__(init_cfg=init_cfg)
      self.num_classes = num_classes
      self.in_channels = in_channels
      self.corner_emb_channels = corner_emb_channels
      self.with_corner_emb = self.corner_emb_channels > 0
      self.corner_offset_channels = 2
      self.num_feat_levels = num_feat_levels
      self.loss_heatmap = (
          MODELS.build(loss_heatmap)
          if loss_heatmap is not None
          else None
      )
      self.loss_embedding = (
          MODELS.build(loss_embedding)
          if loss_embedding is not None
          else None
      )
      self.loss_offset = (
          MODELS.build(loss_offset) if loss_offset is not None else None
      )
      self.train_cfg = train_cfg
      self.test_cfg = test_cfg
  
      self._init_layers()
  
imports: from logging import warning
  from math import ceil, log
  from typing import List, Optional, Sequence, Tuple
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule
  from mmcv.ops import CornerPool, batched_nms
  from mmengine.config import ConfigDict
  from mmengine.model import BaseModule, bias_init_with_prob
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.utils import ConfigType, InstanceList, OptConfigType, OptInstanceList, OptMultiConfig
  from ..utils import gather_feat, gaussian_radius, gen_gaussian_target, get_local_maximum, get_topk_from_heatmap, multi_apply, transpose_and_gather_feat
  from .base_dense_head import BaseDenseHead
label_desc: Use image node_id 1 to create a new CornerHead object from inherited base classes: BaseDenseHead with example: obj = CornerHead(num_classes, in_channels, num_feat_levels, corner_emb_channels, train_cfg, test_cfg, loss_heatmap, loss_embedding, loss_offset, init_cfg)