class_docstr: RTMDetHead with separated BN layers and shared conv layers.
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      share_conv (bool): Whether to share conv layers between stages.
          Defaults to True.
      use_depthwise (bool): Whether to use depthwise separable convolution in
          head. Defaults to False.
      norm_cfg (:obj:`ConfigDict` or dict)): Config dict for normalization
          layer. Defaults to dict(type='BN', momentum=0.03, eps=0.001).
      act_cfg (:obj:`ConfigDict` or dict)): Config dict for activation layer.
          Defaults to dict(type='SiLU').
      pred_kernel_size (int): Kernel size of prediction layer. Defaults to 1.
class_docstr_tok: ["RTMDetHead","with","separated","BN","layers","and","shared","conv","layers",".","Args",":","num_classes","(","int",")",":","Number","of","categories","excluding","the","background","category",".","in_channels","(","int",")",":","Number","of","channels","in","the","input","feature","map",".","share_conv","(","bool",")",":","Whether","to","share","conv","layers","between","stages",".","Defaults","to","True",".","use_depthwise","(","bool",")",":","Whether","to","use","depthwise","separable","convolution","in","head",".","Defaults","to","False",".","norm_cfg","(",":","obj",":","`","ConfigDict","`","or","dict",")",")",":","Config","dict","for","normalization","layer",".","Defaults","to","dict","(","type='BN","'",",","momentum=0.03",",","eps=0.001",")",".","act_cfg","(",":","obj",":","`","ConfigDict","`","or","dict",")",")",":","Config","dict","for","activation","layer",".","Defaults","to","dict","(","type='SiLU","'",")",".","pred_kernel_size","(","int",")",":","Kernel","size","of","prediction","layer",".","Defaults","to","1","."]
docstr: Initialize weights of the head.
docstr_tok: ["Initialize","weights","of","the","head","."]
code: def init_weights(self) -> None:
      """Initialize weights of the head."""
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              normal_init(m, mean=0, std=0.01)
          if is_norm(m):
              constant_init(m, 1)
      bias_cls = bias_init_with_prob(0.01)
      for rtm_cls, rtm_reg in zip(self.rtm_cls, self.rtm_reg):
          normal_init(rtm_cls, std=0.01, bias=bias_cls)
          normal_init(rtm_reg, std=0.01)
      if self.with_objectness:
          for rtm_obj in self.rtm_obj:
              normal_init(rtm_obj, std=0.01, bias=bias_cls)
  
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 3 for calling the RTMDetSepBNHead obj's underlying member method code with example usage: obj.init_weights() without return types