class_docstr: Bridging the Gap Between Anchor-based and Anchor-free Detection via
  Adaptive Training Sample Selection.
  
  ATSS head structure is similar with FCOS, however ATSS use anchor boxes
  and assign label by Adaptive Training Sample Selection instead max-iou.
  
  https://arxiv.org/abs/1912.02424
class_docstr_tok: ["Bridging","the","Gap","Between","Anchor-based","and","Anchor-free","Detection","via","Adaptive","Training","Sample","Selection",".","ATSS","head","structure","is","similar","with","FCOS",",","however","ATSS","use","anchor","boxes","and","assign","label","by","Adaptive","Training","Sample","Selection","instead","max-iou",".","https",":","\/\/arxiv.org\/abs\/1912.02424"]
docstr: Initialize weights of the head.
docstr_tok: ["Initialize","weights","of","the","head","."]
code: def init_weights(self):
      """Initialize weights of the head."""
      for m in self.cls_convs:
          normal_init(m.conv, std=0.01)
      for m in self.reg_convs:
          normal_init(m.conv, std=0.01)
      bias_cls = bias_init_with_prob(0.01)
      normal_init(self.atss_cls, std=0.01, bias=bias_cls)
      normal_init(self.atss_reg, std=0.01)
      normal_init(self.atss_centerness, std=0.01)
  
imports: import torch
  import torch.nn
  from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
  from mmcv.runner import force_fp32
  from mmdet.core import anchor_inside_flags, build_assigner, build_sampler, images_to_levels, multi_apply, multiclass_nms, reduce_mean, unmap
  from ..builder import HEADS, build_loss
  from .anchor_head import AnchorHead
label_desc: Use image node_id 3 for calling the ATSSHead obj's underlying member method code with example usage: obj.init_weights() without return types