class_docstr: Anchor-free head used in `FCOS <https://arxiv.org/abs/1904.01355>`_.
  
  The FCOS head does not use anchor boxes. Instead bounding boxes are
  predicted at each pixel and a centerness measure is used to supress
  low-quality predictions.
  Here norm_on_bbox, centerness_on_reg, dcn_on_last_conv are training
  tricks used in official repo, which will bring remarkable mAP gains
  of up to 4.9. Please see https://github.com/tianzhi0549/FCOS for
  more detail.
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      strides (list[int] | list[tuple[int, int]]): Strides of points
          in multiple feature levels. Default: (4, 8, 16, 32, 64).
      regress_ranges (tuple[tuple[int, int]]): Regress range of multiple
          level points.
      center_sampling (bool): If true, use center sampling. Default: False.
      center_sample_radius (float): Radius of center sampling. Default: 1.5.
      norm_on_bbox (bool): If true, normalize the regression targets
          with FPN strides. Default: False.
      centerness_on_reg (bool): If true, position centerness on the
          regress branch. Please refer to https://github.com/tianzhi0549/FCOS/issues/89#issuecomment-516877042.
          Default: False.
      conv_bias (bool | str): If specified as `auto`, it will be decided by the
          norm_cfg. Bias of conv will be set as True if `norm_cfg` is None, otherwise
          False. Default: "auto".
      loss_cls (dict): Config of classification loss.
      loss_bbox (dict): Config of localization loss.
      loss_centerness (dict): Config of centerness loss.
      norm_cfg (dict): dictionary to construct and config norm layer.
          Default: norm_cfg=dict(type='GN', num_groups=32, requires_grad=True).
  
  Example:
      >>> self = FCOSHead(11, 7)
      >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
      >>> cls_score, bbox_pred, centerness = self.forward(feats)
      >>> assert len(cls_score) == len(self.scales)
class_docstr_tok: ["Anchor-free","head","used","in","`","FCOS","<","https",":","\/\/arxiv.org\/abs\/1904.01355",">","`","_",".","The","FCOS","head","does","not","use","anchor","boxes",".","Instead","bounding","boxes","are","predicted","at","each","pixel","and","a","centerness","measure","is","used","to","supress","low-quality","predictions",".","Here","norm_on_bbox",",","centerness_on_reg",",","dcn_on_last_conv","are","training","tricks","used","in","official","repo",",","which","will","bring","remarkable","mAP","gains","of","up","to","4.9",".","Please","see","https",":","\/\/github.com\/tianzhi0549\/FCOS","for","more","detail",".","Args",":","num_classes","(","int",")",":","Number","of","categories","excluding","the","background","category",".","in_channels","(","int",")",":","Number","of","channels","in","the","input","feature","map",".","strides","(","list","[","int","]","|","list","[","tuple","[","int",",","int","]","]",")",":","Strides","of","points","in","multiple","feature","levels",".","Default",":","(","4",",","8",",","16",",","32",",","64",")",".","regress_ranges","(","tuple","[","tuple","[","int",",","int","]","]",")",":","Regress","range","of","multiple","level","points",".","center_sampling","(","bool",")",":","If","true",",","use","center","sampling",".","Default",":","False",".","center_sample_radius","(","float",")",":","Radius","of","center","sampling",".","Default",":","1.5.","norm_on_bbox","(","bool",")",":","If","true",",","normalize","the","regression","targets","with","FPN","strides",".","Default",":","False",".","centerness_on_reg","(","bool",")",":","If","true",",","position","centerness","on","the","regress","branch",".","Please","refer","to","https",":","\/\/github.com\/tianzhi0549\/FCOS\/issues\/89","#","issuecomment-516877042",".","Default",":","False",".","conv_bias","(","bool","|","str",")",":","If","specified","as","`","auto","`",",","it","will","be","decided","by","the","norm_cfg",".","Bias","of","conv","will","be","set","as","True","if","`","norm_cfg","`","is","None",",","otherwise","False",".","Default",":","``","auto","''",".","loss_cls","(","dict",")",":","Config","of","classification","loss",".","loss_bbox","(","dict",")",":","Config","of","localization","loss",".","loss_centerness","(","dict",")",":","Config","of","centerness","loss",".","norm_cfg","(","dict",")",":","dictionary","to","construct","and","config","norm","layer",".","Default",":","norm_cfg=dict","(","type='GN","'",",","num_groups=32",",","requires_grad=True",")",".","Example",":",">",">",">","self","=","FCOSHead","(","11",",","7",")",">",">",">","feats","=","[","torch.rand","(","1",",","7",",","s",",","s",")","for","s","in","[","4",",","8",",","16",",","32",",","64","]","]",">",">",">","cls_score",",","bbox_pred",",","centerness","=","self.forward","(","feats",")",">",">",">","assert","len","(","cls_score",")","==","len","(","self.scales",")"]
docstr: Initialize weights of the head.
docstr_tok: ["Initialize","weights","of","the","head","."]
code: def init_weights(self):
      """Initialize weights of the head."""
      super().init_weights()
      normal_init(self.conv_centerness, std=0.01)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import Scale, normal_init
  from mmcv.runner import force_fp32
  from mmdet.core import distance2bbox, multi_apply, multiclass_nms
  from ..builder import HEADS, build_loss
  from .anchor_free_head import AnchorFreeHead
label_desc: Use image node_id 3 for calling the FCOSHead obj's underlying member method code with example usage: obj.init_weights() without return types