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resnet,inception等网络核心单元代码实现

article 2025/6/27 15:36:45

1、resnet残差网络
（1）较深的网络比较浅的网络性能更差。一个创新的想法：如果什么也没学到，那就比以前更糟：因此提出了identity map（也称为shortcut连接）。 H（x）= x + F（x），F（x）= H（x）-x，称为残差。

（2）ResNet的深度各不相同，从18层到34、50、101到非常深的152层。

（3）不同的快捷方式，如果不更改尺寸，则可以使用identity映射。但是在实践中，尺寸更改因此会产生“瓶颈”。（这在基线后记中很流行，因为它可以保存参数。）

残差单元：

在这里插入图片描述

跨层连接：要求输入x：feature map的shape与输出F(x)：feature map 的shape一样。

可能存在的情况：

①输入x与输出F(x) 的shape一样直接相加

②输入x输出F(x) 的feature map数量及feature map尺寸不一样

如：输入x：[1, 64, 224, 224]输出F(x) ：[1, 128, 112, 112]

需要对输入x通过卷积层各参数调整，确保与F(x)一致

相邻的x 与F(x)通道不一致，fp也相差一倍

import torch.nn as nn
import torch#残差单元
class Block(nn.Module):def __init__(self,inch,outch,stride):super(Block, self).__init__()self.con1=nn.Conv2d(inch,outch,3,stride,1)self.con2=nn.Conv2d(outch,outch,3,1,1)self.relu=nn.ReLU()self.bn=nn.BatchNorm2d(outch)if inch!=outch:self.downsample=nn.Conv2d(inch,outch,1,stride)#输入与输出通道不一致，调整通道并通过步长调整特征图else:self.downsample=Nonedef forward(self,x):pre=xout=self.con1(x)out=self.relu(out)out=self.bn(out)out=self.con2(out)out = self.bn(out)if self.downsample is not None:#pre=self.downsample(x)out+=pre#跨层连接相加：x+F(x)out = self.relu(out)return outclass mynet(nn.Module):def __init__(self,Block):super(mynet, self).__init__()self.layer1=nn.Sequential(nn.Conv2d(3,64,3,1,1),nn.BatchNorm2d(64),nn.MaxPool2d(2))#64,224self.layer2=self.layer(Block,64,64,3,1)self.layer3 = self.layer(Block, 64, 128,4, 2)#连续有4个残差单元除了第一个要增加通道，后面保持与上个一样self.layer4 = self.layer(Block, 128, 256, 6,2)self.layer5 = self.layer(Block, 256, 512, 3, 2)def layer(self,Block,inch,outch,blocknum,stride):layers=[]layers.append(Block(inch,outch,stride))#主要负责调整每个block的特征图及通道for i in range(blocknum-1):layers.append(Block(outch,outch,1))#普通的卷积操作并且通道一致return nn.Sequential(*layers)#以列表形式放在序列化中def forward(self,x):x1=self.layer1(x)print(x1.shape)x2 = self.layer2(x1)print(x2.shape)x3 = self.layer3(x2)print(x3.shape)x4 = self.layer4(x3)print(x4.shape)x5 = self.layer5(x4)print(x5.shape)return x1,x2x=torch.randn(1,3,448,448)
model=mynet(Block)
y1,y2=model(x)
# print(y1.shape,y2.shape)

2、Googlenet中inception单元
(1)除了增加网络深度之外（对于googlenet v1为22层），还会增加网络的宽度

(2)引入较小的内核，1 * 1卷积，减小尺寸并保存参数。参数：AlexNet ~ 12 GoogLeNet, VGG ~ 3 AlexNet

(3)初始模块很容易添加或删除，它是模仿人的大脑以建立稀疏连接。
在这里插入图片描述

import torch
import torch.nn as nnclass inception(nn.Module):def __init__(self,inch,outch):super(inception, self).__init__()self.con1=nn.Conv2d(inch,outch,1,1)self.con2=nn.Conv2d(inch,outch,3,1,1)self.con3=nn.Conv2d(inch,outch,5,1,2)self.maxpool=nn.MaxPool2d(3,1,1)def forward(self,x):out1=self.con1(x)out2=self.con2(x)out3=self.con3(x)out4=self.maxpool(x)out=torch.cat((out1,out2,out3,out4),dim=1)#特征融合return outclass mynet(nn.Module):def __init__(self,inception):super(mynet, self).__init__()self.layer1=nn.Sequential(nn.Conv2d(3, 64, 3, 1, 1),nn.MaxPool2d(2))self.layer2=inception(64,64)def forward(self,x):x=self.layer1(x)x=self.layer2(x)return xx=torch.randn(1,3,448,448)
model=mynet(inception)
y=model(x)
print(y.shape)

3、Yolov3
①无yolo层的darknet53 主干网络backbone：
在这里插入图片描述

import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):def __init__(self,inch,outch):super(ResidualBlock, self).__init__()self.conv1=nn.Sequential(nn.Conv2d(inch,outch,1,1),nn.BatchNorm2d(outch),nn.LeakyReLU())self.conv2=nn.Sequential(nn.Conv2d(outch,inch,3,1,1),nn.BatchNorm2d(inch),nn.LeakyReLU())def forward(self, x):identity=xout=self.conv1(x)out=self.conv2(out)out+=identityreturn outclass Darknet53(nn.Module):def __init__(self,ResidualBlock):super(Darknet53, self).__init__()self.firstblock=nn.Sequential(nn.Conv2d(3,32,3,1,1),nn.BatchNorm2d(32),nn.LeakyReLU())self.conv1 = nn.Conv2d(32, 64, 3, 2, 1)self.resblock1=self.block(ResidualBlock,32,64,1)self.conv2 = nn.Conv2d(64, 128, 3, 2, 1)self.resblock2 = self.block(ResidualBlock, 64, 128, 2)self.conv3 = nn.Conv2d(128, 256, 3, 2, 1)self.resblock3 = self.block(ResidualBlock, 128, 256, 8)self.conv4 = nn.Conv2d(256, 512, 3, 2, 1)self.resblock4 = self.block(ResidualBlock, 256, 512, 8)self.conv5 = nn.Conv2d(512, 1024, 3, 2, 1)self.resblock5 = self.block(ResidualBlock, 512, 1024, 4)def block(self,ResidualBlock,inch, outch, numblock):layers=[]# layers.append(nn.Conv2d(inch,outch,3,2,1))for i in range(numblock - 1):layers.append(ResidualBlock(outch,inch))return nn.Sequential(*layers)def forward(self,x):out=self.firstblock(x)out=self.conv1(out)out=self.resblock1(out)out = self.conv2(out)out = self.resblock2(out)out = self.conv3(out)fp_52 = self.resblock3(out)out = self.conv4(fp_52)fp_26 = self.resblock4(out)out = self.conv5(fp_26)fp_13 = self.resblock5(out)return fp_52,fp_26,fp_13inputs=torch.randn(1,3,416,416)
model=Darknet53(ResidualBlock)
sc1,sc2,sc3=model(inputs)
print(sc1.shape,sc2.shape,sc3.shape)

②含有YOLO层的网络
在这里插入图片描述
将输出调整为：（batch,3*(x,y,w,h,conf,cls1…cls20),fp,fp）

这里20类加上两个坐标点，宽高，置信度共25，fp为 feature map的大小。

import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):def __init__(self,inch,outch):super(ResidualBlock, self).__init__()self.conv1=nn.Sequential(nn.Conv2d(inch,outch,1,1),nn.BatchNorm2d(outch),nn.LeakyReLU())self.conv2=nn.Sequential(nn.Conv2d(outch,inch,3,1,1),nn.BatchNorm2d(inch),nn.LeakyReLU())def forward(self, x):identity=xout=self.conv1(x)out=self.conv2(out)out+=identityreturn out
class UpsampleLayer(nn.Module):def __init__(self):super(UpsampleLayer, self).__init__()def forward(self, x):return F.interpolate(x, scale_factor=2, mode='nearest')
class ConvolutionalSetLayer(nn.Module):def __init__(self,inch,outch):super(ConvolutionalSetLayer, self).__init__()self.conv=nn.Sequential(nn.Conv2d(inch, outch, 1, 1),nn.Conv2d(outch, inch, 3, 1, 1),nn.Conv2d(inch, outch, 1, 1),nn.Conv2d(outch, inch, 3, 1, 1),nn.Conv2d(inch, outch, 1, 1))def forward(self,x):return self.conv(x)
class Darknet53(nn.Module):def __init__(self,ResidualBlock):super(Darknet53, self).__init__()self.firstblock=nn.Sequential(nn.Conv2d(3,32,3,1,1),nn.BatchNorm2d(32),nn.LeakyReLU())self.conv1 = nn.Conv2d(32, 64, 3, 2, 1)self.resblock1=self.block(ResidualBlock,32,64,1)self.conv2 = nn.Conv2d(64, 128, 3, 2, 1)self.resblock2 = self.block(ResidualBlock, 64, 128, 2)self.conv3 = nn.Conv2d(128, 256, 3, 2, 1)self.resblock3 = self.block(ResidualBlock, 128, 256, 8)self.conv4 = nn.Conv2d(256, 512, 3, 2, 1)self.resblock4 = self.block(ResidualBlock, 256, 512, 8)self.conv5 = nn.Conv2d(512, 1024, 3, 2, 1)self.resblock5 = self.block(ResidualBlock, 512, 1024, 4)self.conset13=nn.Sequential(ConvolutionalSetLayer(1024,512))self.conset26 = nn.Sequential(ConvolutionalSetLayer(768, 256))self.conset52 = nn.Sequential(ConvolutionalSetLayer(384, 128))self.yololy1=nn.Sequential(nn.Conv2d(512, 1024, 3, 1, 1),nn.BatchNorm2d(1024),nn.LeakyReLU(),nn.Conv2d(1024,75,1,1))self.yololy2 = nn.Sequential(nn.Conv2d(256, 512, 3, 1, 1),nn.BatchNorm2d(512),nn.LeakyReLU(),nn.Conv2d(512, 75, 1, 1))self.yololy3 = nn.Sequential(nn.Conv2d(128, 256, 3, 1, 1),nn.BatchNorm2d(256),nn.LeakyReLU(),nn.Conv2d(256, 75, 1, 1))self.up1=nn.Sequential(nn.Conv2d(512, 256, 1, 1),UpsampleLayer())self.up2 = nn.Sequential(nn.Conv2d(256, 128,1, 1),UpsampleLayer())def block(self,ResidualBlock,inch, outch, numblock):layers=[]# layers.append(nn.Conv2d(inch,outch,3,2,1))for i in range(numblock - 1):layers.append(ResidualBlock(outch,inch))return nn.Sequential(*layers)def forward(self,x):out=self.firstblock(x)out=self.conv1(out)out=self.resblock1(out)out = self.conv2(out)out = self.resblock2(out)out = self.conv3(out)fp_52 = self.resblock3(out)out = self.conv4(fp_52)fp_26 = self.resblock4(out)out = self.conv5(fp_26)fp_13 = self.resblock5(out)convset13=self.conset13(fp_13)yoloout1=self.yololy1(convset13)up1=self.up1(convset13)route1=torch.cat((up1,fp_26),1)convset26=self.conset26(route1)yoloout2=self.yololy2(convset26)up2=self.up2(convset26)route2=torch.cat((up2,fp_52),1)convset52=self.conset52(route2)yoloout3=self.yololy3(convset52)return yoloout1,yoloout2,yoloout3
inputs=torch.randn(1,3,416,416)
model=Darknet53(ResidualBlock)
print(model)
sc1,sc2,sc3=model(inputs)
print(sc1.shape,sc2.shape,sc3.shape)