class_docstr: Detection Head of RTMDet.
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      with_objectness (bool): Whether to add an objectness branch.
          Defaults to True.
      act_cfg (:obj:`ConfigDict` or dict): Config dict for activation layer.
          Default: dict(type='ReLU')
class_docstr_tok: ["Detection","Head","of","RTMDet",".","Args",":","num_classes","(","int",")",":","Number","of","categories","excluding","the","background","category",".","in_channels","(","int",")",":","Number","of","channels","in","the","input","feature","map",".","with_objectness","(","bool",")",":","Whether","to","add","an","objectness","branch",".","Defaults","to","True",".","act_cfg","(",":","obj",":","`","ConfigDict","`","or","dict",")",":","Config","dict","for","activation","layer",".","Default",":","dict","(","type='ReLU","'",")"]
code: def __init__(
      self,
      num_classes: int,
      in_channels: int,
      with_objectness: bool = True,
      act_cfg: ConfigType = dict(type="ReLU"),
      **kwargs,
  ) -> None:
      self.act_cfg = act_cfg
      self.with_objectness = with_objectness
      super().__init__(num_classes, in_channels, **kwargs)
      if self.train_cfg:
          self.assigner = TASK_UTILS.build(self.train_cfg["assigner"])
  
imports: from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule, Scale, is_norm
  from mmengine.model import bias_init_with_prob, constant_init, normal_init
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures.bbox import distance2bbox
  from mmdet.utils import ConfigType, InstanceList, OptInstanceList, reduce_mean
  from ..layers.transformer import inverse_sigmoid
  from ..task_modules import anchor_inside_flags
  from ..utils import images_to_levels, multi_apply, sigmoid_geometric_mean, unmap
  from .atss_head import ATSSHead
label_desc: Use image node_id 1 to create a new RTMDetHead object from inherited base classes: ATSSHead with example: obj = RTMDetHead(num_classes, in_channels, with_objectness, act_cfg)