Pytorch实现ResNet50网络结构，包含ResNet18，ResNet34，ResNet50，ResNet101，ResNet152

创建各版本的ResNet模型，ResNet18，ResNet34，ResNet50，ResNet101，ResNet152

原文地址: https://arxiv.org/pdf/1512.03385.pdf

---

论文就不解读了，大部分解读都是翻译，看的似懂非懂，自己搞懂就行了。

最近想着实现一下经典的网络结构，看了原文之后，根据原文代码结构开始实现。起初去搜了下各种版本的实现，发现很多博客都是错误百出，有些博文都发布几年了，错误还是没人发现，评论区几十号人不知道是真懂还是装懂，颇有些无奈啊。因此打算自己手动实现网络结构，锻炼下自己的代码能力，也加深对网络结构的理解。写完之后也很欣慰，毕竟一直认为自己是个菜鸡，最近竟然接连不断的发现很多博文的错误之处，而且很多人看后都没发现的，想想自己似乎还有点小水平。

最后在一套代码里，实现了各版本ResNet，为了方便。

其实最后还是觉得应该每个网络分开写比较好。因为不同版本的网络内部操作是有很大差异的，本文下面的代码是将ResidualBlock和BottleNeckBlock分开写的，但是在维度的变换上差异还是很复杂，一方面想提高代码的复用性，另一方面也受制于复杂度。所以最后写出的算不上高复用性的精简代码。勉强能用。关于ResNet的结构，除各版本分开写之外，重复的block其实也可以分开写，因为BottleNeckBlock的维度变换太复杂，参数变换多，能分开就分开，复杂度小的地方可以复用。

以下是网络结构和实现代码，检验后都是对的；水平有限，如发现有错误，欢迎评论告知！

1 残差结构图

2 VGG-19与ResNet34结构比较

3 ResNet各版本的结构

4 代码实现各版本

import torch.nn as nn from torch.nn import functional as F class ResNetModel(nn.Module): """
    实现通用的ResNet模块，可根据需要定义
    """ def __init__(self, num_classes=1000, layer_num=[],bottleneck = False): super(ResNetModel, self).__init__() #conv1 self.pre = nn.Sequential( #in 224*224*3 nn.Conv2d(3,64,7,2,3,bias=False), #输入通道3，输出通道64，卷积核7*7*64，步长2,根据以上计算出padding=3 #out 112*112*64 nn.BatchNorm2d(64), #输入通道C = 64 nn.ReLU(inplace=True), #inplace=True, 进行覆盖操作 # out 112*112*64 nn.MaxPool2d(3,2,1), #池化核3*3，步长2,计算得出padding=1; # out 56*56*64 ) if bottleneck: #resnet50以上使用BottleNeckBlock self.residualBlocks1 = self.add_layers(64, 256, layer_num[0], 64, bottleneck=bottleneck) self.residualBlocks2 = self.add_layers(128, 512, layer_num[1], 256, 2,bottleneck) self.residualBlocks3 = self.add_layers(256, 1024, layer_num[2], 512, 2,bottleneck) self.residualBlocks4 = self.add_layers(512, 2048, layer_num[3], 1024, 2,bottleneck) self.fc = nn.Linear(2048, num_classes) else: #resnet34使用普通ResidualBlock self.residualBlocks1 = self.add_layers(64,64,layer_num[0]) self.residualBlocks2 = self.add_layers(64,128,layer_num[1]) self.residualBlocks3 = self.add_layers(128,256,layer_num[2]) self.residualBlocks4 = self.add_layers(256,512,layer_num[3]) self.fc = nn.Linear(512, num_classes) def add_layers(self, inchannel, outchannel, nums, pre_channel=64, stride=1, bottleneck=False): layers = [] if bottleneck is False: #添加大模块首层, 首层需要判断inchannel == outchannel ? #跨维度需要stride=2，shortcut也需要1*1卷积扩维 layers.append(ResidualBlock(inchannel,outchannel)) #添加剩余nums-1层 for i in range(1,nums): layers.append(ResidualBlock(outchannel,outchannel)) return nn.Sequential(*layers) else: #resnet50使用bottleneck #传递每个block的shortcut，shortcut可以根据是否传递pre_channel进行推断 #添加首层,首层需要传递上一批blocks的channel layers.append(BottleNeckBlock(inchannel,outchannel,pre_channel,stride)) for i in range(1,nums): #添加n-1个剩余blocks，正常通道转换，不传递pre_channel layers.append(BottleNeckBlock(inchannel,outchannel)) return nn.Sequential(*layers) def forward(self, x): x = self.pre(x) x = self.residualBlocks1(x) x = self.residualBlocks2(x) x = self.residualBlocks3(x) x = self.residualBlocks4(x) x = F.avg_pool2d(x, 7) x = x.view(x.size(0), -1) return self.fc(x) class ResidualBlock(nn.Module): '''
    定义普通残差模块
    resnet34为普通残差块，resnet50为瓶颈结构
    ''' def __init__(self, inchannel, outchannel, stride=1, padding=1, shortcut=None): super(ResidualBlock, self).__init__() #resblock的首层，首层如果跨维度，卷积stride=2，shortcut需要1*1卷积扩维 if inchannel != outchannel: stride= 2 shortcut=nn.Sequential( nn.Conv2d(inchannel,outchannel,1,stride,bias=False), nn.BatchNorm2d(outchannel) ) # 定义残差块的左部分 self.left = nn.Sequential( nn.Conv2d(inchannel, outchannel, 3, stride, padding, bias=False), nn.BatchNorm2d(outchannel), nn.ReLU(inplace=True), nn.Conv2d(outchannel, outchannel, 3, 1, padding, bias=False), nn.BatchNorm2d(outchannel), ) #定义右部分 self.right = shortcut def forward(self, x): out = self.left(x) residual = x if self.right is None else self.right(x) out = out + residual return F.relu(out) class BottleNeckBlock(nn.Module): '''
    定义resnet50的瓶颈结构
    ''' def __init__(self,inchannel,outchannel, pre_channel=None, stride=1,shortcut=None): super(BottleNeckBlock, self).__init__() #首个bottleneck需要承接上一批blocks的输出channel if pre_channel is None: #为空则表示不是首个bottleneck， pre_channel = outchannel #正常通道转换 else: # 传递了pre_channel,表示为首个block，需要shortcut shortcut = nn.Sequential( nn.Conv2d(pre_channel,outchannel,1,stride,0,bias=False), nn.BatchNorm2d(outchannel) ) self.left = nn.Sequential( #1*1,inchannel nn.Conv2d(pre_channel, inchannel, 1, stride, 0, bias=False), nn.BatchNorm2d(inchannel), nn.ReLU(inplace=True), #3*3,inchannel nn.Conv2d(inchannel,inchannel,3,1,1,bias=False), nn.BatchNorm2d(inchannel), nn.ReLU(inplace=True), #1*1,outchannel nn.Conv2d(inchannel,outchannel,1,1,0,bias=False), nn.BatchNorm2d(outchannel), nn.ReLU(inplace=True), ) self.right = shortcut def forward(self,x): out = self.left(x) residual = x if self.right is None else self.right(x) return F.relu(out+residual) if __name__ == '__main__': # channel_nums = [64,128,256,512,1024,2048] num_classes = 6 #layers = 18, 34, 50, 101, 152 layer_nums = [[2,2,2,2],[3,4,6,3],[3,4,6,3],[3,4,23,3],[3,8,36,3]] #选择resnet版本， # resnet18 ——0；resnet34——1,resnet-50——2,resnet-101——3,resnet-152——4 i = 3; bottleneck = i >= 2 #i<2, false,使用普通的ResidualBlock; i>=2，true,使用BottleNeckBlock model = ResNetModel(num_classes,layer_nums[i],bottleneck) print(model) 

本文地址：https://blog.csdn.net/weixin_44277280/article/details/109035746