class_docstr: AutoAssignHead head used in AutoAssign.
  
  More details can be found in the `paper
  <https://arxiv.org/abs/2007.03496>`_ .
  
  Args:
      force_topk (bool): Used in center prior initialization to
          handle extremely small gt. Default is False.
      topk (int): The number of points used to calculate the
          center prior when no point falls in gt_bbox. Only work when
          force_topk if True. Defaults to 9.
      pos_loss_weight (float): The loss weight of positive loss
          and with default value 0.25.
      neg_loss_weight (float): The loss weight of negative loss
          and with default value 0.75.
      center_loss_weight (float): The loss weight of center prior
          loss and with default value 0.75.
class_docstr_tok: ["AutoAssignHead","head","used","in","AutoAssign",".","More","details","can","be","found","in","the","`","paper","<","https",":","\/\/arxiv.org\/abs\/2007.03496",">","`","_",".","Args",":","force_topk","(","bool",")",":","Used","in","center","prior","initialization","to","handle","extremely","small","gt",".","Default","is","False",".","topk","(","int",")",":","The","number","of","points","used","to","calculate","the","center","prior","when","no","point","falls","in","gt_bbox",".","Only","work","when","force_topk","if","True",".","Defaults","to","9.","pos_loss_weight","(","float",")",":","The","loss","weight","of","positive","loss","and","with","default","value","0.25.","neg_loss_weight","(","float",")",":","The","loss","weight","of","negative","loss","and","with","default","value","0.75.","center_loss_weight","(","float",")",":","The","loss","weight","of","center","prior","loss","and","with","default","value","0.75","."]
docstr: Initialize weights of the head.
  
  In particular, we have special initialization for classified conv's and
  regression conv's bias
docstr_tok: ["Initialize","weights","of","the","head",".","In","particular",",","we","have","special","initialization","for","classified","conv","'s","and","regression","conv","'s","bias"]
code: def init_weights(self) -> None:
      """Initialize weights of the head.
  
      In particular, we have special initialization for classified conv's and
      regression conv's bias
      """
  
      super(AutoAssignHead, self).init_weights()
      bias_cls = bias_init_with_prob(0.02)
      normal_init(self.conv_cls, std=0.01, bias=bias_cls)
      normal_init(self.conv_reg, std=0.01, bias=4.0)
  
imports: from typing import Dict, List, Sequence, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import Scale
  from mmengine.model import bias_init_with_prob, normal_init
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.structures.bbox import bbox_overlaps
  from mmdet.utils import InstanceList, OptInstanceList, reduce_mean
  from ..task_modules.prior_generators import MlvlPointGenerator
  from ..utils import levels_to_images, multi_apply
  from .fcos_head import FCOSHead
label_desc: Use image node_id 2 for calling the AutoAssignHead obj's underlying member method code with example usage: obj.init_weights() without return types