class_docstr: A regression head for use in RetinaNet.
  
  Args:
      in_channels (int): number of channels of the input feature
      num_anchors (int): number of anchors to be predicted
class_docstr_tok: ["A","regression","head","for","use","in","RetinaNet",".","Args",":","in_channels","(","int",")",":","number","of","channels","of","the","input","feature","num_anchors","(","int",")",":","number","of","anchors","to","be","predicted"]
code: def __init__(self, in_channels, num_anchors):
      super(RetinaNetRegressionHead, self).__init__()
  
      # box subnet是由四个3x3的卷积层(激活函数为ReLU) + 一个3x3的卷积层(边界框回归器)
      conv = []
      for _ in range(4):
          conv.append(
              nn.Conv2d(
                  in_channels,
                  in_channels,
                  kernel_size=3,
                  stride=1,
                  padding=1,
              )
          )
          conv.append(nn.ReLU(inplace=True))
      self.conv = nn.Sequential(*conv)
  
      self.bbox_reg = nn.Conv2d(
          in_channels,
          num_anchors * 4,
          kernel_size=3,
          stride=1,
          padding=1,
      )
  
      # initial weights
      for layer in self.conv.children():
          if isinstance(layer, nn.Conv2d):
              torch.nn.init.normal_(layer.weight, std=0.01)
              torch.nn.init.zeros_(layer.bias)
  
      self.bbox_coder = det_utils.BoxCoder(weights=(1.0, 1.0, 1.0, 1.0))
  
imports: import math
  import warnings
  from collections import OrderedDict
  from typing import Dict, List, Tuple, Optional, Union
  import torch
  from torch import nn, Tensor
  from .None import det_utils
  from .anchor_utils import AnchorsGenerator
  from .None import boxes
  from .losses import sigmoid_focal_loss
  from .transform import GeneralizedRCNNTransform
label_desc: Use image node_id 1 to create a new RetinaNetRegressionHead object from inherited base classes: nn with example: obj = RetinaNetRegressionHead(in_channels, num_anchors)