Yolov7重参数化 RepConv 详解

参考：https://blog.youkuaiyun.com/YXD0514/article/details/132466512

1. RepConv简介
   RepConv是一种模型重参化技术，它可以在推理阶段将多个计算模块合并为一个，提高模型的效率和性能。它最初是用于VGG网络的，但后来也被应用到其他网络结构，如ResNet和DenseNet。RepConv的主要思想是在训练时使用多分支的卷积层，然后在推理时将分支的参数重参数化到主分支上，从而减少计算量和内存消耗。RepConv在目标检测等任务上取得了很好的效果。
   
   

import torch
import torch.nn as nn

class repconv3x3(nn.Module):
     def __init__(self, c1, c2):
          super().__init__()
          self.conv1 = nn.Conv2d(c1,c2,3,1,1)
          self.conv2 = nn.Conv2d(c1,c2,1,1,0)
          self.conv_fuse=nn.Conv2d(c1,c2,3,1,1)

     def fuse_1x1conv_3x3conv(self, conv1, conv2):
          conv1x1_weight = nn.functional.pad(conv2.weight, [1,1,1,1])
          conv_weight = conv1x1_weight + conv1.weight
          conv_bias = conv2.bias + conv1.bias

          return conv_weight,conv_bias
    
     def forward(self, x):
          x = self.conv1(x) + self.conv2(x)
          return x
     def forward_fuse(self, x):
          self.conv_fuse.weight.data,self.conv_fuse.bias.data=self.fuse_1x1conv_3x3conv(self.conv1,self.conv2)
          return self.conv_fuse(x)
    
inputs = torch.rand((1,1,3,3))
# 重点 模型调到 eval 模式
model = repconv3x3(1,2).eval()

out1 = model.forward(inputs)
out2 = model.forward_fuse(inputs)

print("difference:",((out2-out1)**2).sum().item())

difference: 2.4980018054066022e-15

import torch
import torch.nn as nn

class repconv3x3(nn.Module):
     def __init__(self, c1, c2):
          super().__init__()
          self.conv1x1 = nn.Conv2d(c1,c2,1,1,0)
          self.conv1x3 = nn.Conv2d(c2,c2,(1,3),1,(0,1))
          self.conv3x1 = nn.Conv2d(c2,c2,(3,1),1,(1,0))
          self.conv_fuse=nn.Conv2d(c1,c2,3,1,1)

     def fuse_1x1conv_1x3conv_3x1conv(self, conv1, conv2, conv3):
          weight=nn.functional.pad(conv1.weight.data,(1,1,1,1))+nn.functional.pad(conv2.weight.data,(0,0,1,1))+nn.functional.pad(conv3.weight.data,(1,1,0,0))
          bias=conv1.bias.data+conv2.bias.data+conv3.bias.data
          return weight,bias
    
     def forward(self, x):
          x = self.conv3x1(x)+self.conv1x3(x)+self.conv1x1(x)
          return x
     def forward_fuse(self, x):
          self.conv_fuse.weight.data, self.conv_fuse.bias.data=self.fuse_1x1conv_1x3conv_3x1conv(self.conv1x1,self.conv1x3,self.conv3x1)
          return self.conv_fuse(x)

inputs = torch.rand((1,2,3,3))
# 重点 模型调到 eval 模式
model = repconv3x3(2,2).eval()

out1 = model.forward(inputs)
out2 = model.forward_fuse(inputs)

print("difference:",((out2-out1)**2).sum().item())

difference: 7.327471962526033e-15

6. AvgPooling 转换 Conv
   池化层是针对各个输入通道的(对单层特征图进行池化操作)，而卷积层会将所有输入通道的结果相加。平均池化层可以等价一个固定权重的卷积层，假设池化核的大小为 K，那么可以设置卷积层权重为 1/K。池化权重另外要注意的是卷积层会将所有输入通道结果相加，所以我们需要对当前输入通道设置固定的权重，对其他通道权重设置为0。

import torch
import torch.nn as nn

class repconv3x3(nn.Module):
     def __init__(self, c1):
          super().__init__()
          self.avg = nn.AvgPool2d(3,1,1)
          self.conv_fuse=nn.Conv2d(c1,c1,3,1,1,bias=False)

     def fuse_avg(self):
          self.conv_fuse.weight.data[:]=0
          for i in range(self.conv_fuse.in_channels):
               self.conv_fuse.weight.data[i,i,:,:]=1/(torch.prod(torch.tensor(self.conv_fuse.kernel_size)))
    
     def forward(self, x):
          x = self.avg(x)
          return x
     def forward_fuse(self, x):
          self.fuse_avg()
          return self.conv_fuse(x)


inputs = torch.rand((1,2,3,3))
# 重点 模型调到 eval 模式
model = repconv3x3(2).eval()

out1 = model.forward(inputs)
out2 = model.forward_fuse(inputs)

print("difference:",((out2-out1)**2).sum().item())

difference: 1.0658141036401503e-14

2.RepConv优缺点
https://mp.weixin.qq.com/s?__biz=MzU1NjEwMTY0Mw==&mid=2247558125&idx=1&sn=4992c109ea00d4b87db8dcdc4404c02b&chksm=fbc99a89ccbe139f1bee2b622c5529c3374f2b92a74a57979c3bde9356b9215bde413201e1e0&scene=27