深度残差网络（ResNet）之ResNet34的实现和个人浅见
一、残差网络简介
残差网络是由来自Microsoft Research的4位学者提出的卷积神经网络，在2015年的ImageNet大规模视觉识别竞赛（ImageNet Large Scale Visual Recognition Challenge, ILSVRC）中获得了图像分类和物体识别的优胜。 残差网络的特点是容易优化，并且能够通过增加相当的深度来提高准确率。其内部的残差块使用了跳跃连接（shortcut），缓解了在深度神经网络中增加深度带来的梯度消失问题。残差网络（ResNet）的网络结构图举例如下：
二、shortcut和Residual Block的简介
深度残差网络（ResNet）除最开始的卷积池化和最后池化的全连接之外，网络中有很多结构相似的单元，这些重复的单元的共同点就是有个跨层直连的shortcut，同时将这些单元称作Residual Block。Residual Block的构造图如下（图中 x identity 标注的曲线表示 shortcut）：
三、实现逻辑
逻辑实现顺序按照下面代码中的的标号按顺序执行理解，但注意一定要搞明白通道数的一个变化和forward调用及Sequential调用的方式相同。
四、代码及结果
from torch import nn
import torch as t
from torch.nn import functional as F
from torch.autograd import Variable as V
classResidualBlock(nn.Module):# 定义ResidualBlock类 （11）"""实现子modual：residualblock"""def__init__(self,inchannel,outchannel,stride=1,shortcut=None):# 初始化，自动执行 （12）super(ResidualBlock, self).__init__()# 继承nn.Module （13）
self.left = nn.Sequential(# 左网络，构建Sequential，属于特殊的module，类似于forward前向传播函数，同样的方式调用执行 （14）（31）
nn.Conv2d(inchannel,outchannel,3,stride,1,bias=False),
nn.BatchNorm2d(outchannel),
nn.ReLU(inplace=True),
nn.Conv2d(outchannel,outchannel,3,1,1,bias=False),
nn.BatchNorm2d(outchannel))
self.right = shortcut # 右网络，也属于Sequential，见（8）步可知，并且充当残差和非残差的判断标志。 （15）defforward(self,x):# ResidualBlock的前向传播函数 （29）
out = self.left(x)# # 和调用forward一样如此调用left这个Sequential（30）if self.right isNone:# 残差（ResidualBlock）（32）
residual = x #（33）else:# 非残差（非ResidualBlock） （34）
residual = self.right(x)# （35）
out += residual # 结果相加 （36）print(out.size())# 检查每单元的输出的通道数 （37）return F.relu(out)# 返回激活函数执行后的结果作为下个单元的输入 （38）classResNet(nn.Module):# 定义ResNet类，也就是构建残差网络结构 （2）"""实现主module：ResNet34"""def__init__(self,numclasses=1000):# 创建实例时直接初始化 （3）super(ResNet, self).__init__()# 表示ResNet继承nn.Module （4）
self.pre = nn.Sequential(# 构建Sequential，属于特殊的module，类似于forward前向传播函数，同样的方式调用执行 （5）（26）
nn.Conv2d(3,64,7,2,3,bias=False),# 卷积层，输入通道数为3，输出通道数为64，包含在Sequential的子module，层层按顺序自动执行
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(3,2,1))
self.layer1 = self.make_layer(64,128,4)# 输入通道数为64，输出为128，根据残差网络结构将一个非Residual Block加上多个Residual Block构造成一层layer（6）
self.layer2 = self.make_layer(128,256,4,stride=2)# 输入通道数为128，输出为256 （18，流程重复所以标注省略7-17过程）
self.layer3 = self.make_layer(256,256,6,stride=2)# 输入通道数为256，输出为256 （19，流程重复所以标注省略7-17过程）
self.layer4 = self.make_layer(256,512,3,stride=2)# 输入通道数为256，输出为512 （20，流程重复所以标注省略7-17过程）
self.fc = nn.Linear(512,numclasses)# 全连接层，属于残差网络结构的最后一层，输入通道数为512，输出为numclasses （21）defmake_layer(self,inchannel,outchannel,block_num,stride=1):# 创建layer层，（block_num-1）表示此层中Residual Block的个数 （7）"""构建layer，包含多个residualblock"""
shortcut = nn.Sequential(# 构建Sequential，属于特殊的module，类似于forward前向传播函数，同样的方式调用执行 （8）
nn.Conv2d(inchannel,outchannel,1,stride,bias=False),
nn.BatchNorm2d(outchannel))
layers =[]# 创建一个列表，将非Residual Block和多个Residual Block装进去 （9）
layers.append(ResidualBlock(inchannel,outchannel,stride,shortcut))# 非残差也就是非Residual Block创建及入列表 （10）for i inrange(1,block_num):
layers.append(ResidualBlock(outchannel,outchannel))# 残差也就是Residual Block创建及入列表 （16）return nn.Sequential(*layers)# 通过nn.Sequential函数将列表通过非关键字参数的形式传入，并构成一个新的网络结构以Sequential形式构成，一个非Residual Block和多个Residual Block分别成为此Sequential的子module，层层按顺序自动执行，并且类似于forward前向传播函数，同样的方式调用执行 （17） （28）defforward(self,x):# ResNet类的前向传播函数 （24）
x = self.pre(x)# 和调用forward一样如此调用pre这个Sequential（25）
x = self.layer1(x)# 和调用forward一样如此调用layer1这个Sequential（27）
x = self.layer2(x)# 和调用forward一样如此调用layer2这个Sequential（39，流程重复所以标注省略28-38过程）
x = self.layer3(x)# 和调用forward一样如此调用layer3这个Sequential（40，流程重复所以标注省略28-38过程）
x = self.layer4(x)# 和调用forward一样如此调用layer4这个Sequential（41，流程重复所以标注省略28-38过程）
x = F.avg_pool2d(x,7)# 平均池化 （42）
x = x.view(x.size(0),-1)# 设置返回结果的尺度 （43）return self.fc(x)# 返回结果 （44）
model = ResNet()# 创建ResNet残差网络结构的模型的实例 (1)input= V(t.randn(1,3,224,224))# 输入数据的创建，注意要报证通道数与残差网络结构每层需要的通道数一致，此数据通道数为3 （22）
output = model(input)# 把数据输入残差模型，等同于开始调用ResNet类的前向传播函数 （23）print(output)# 输出运行的结果 （45）
全部运行结果如下：
D:\Anaconda\python.exe E:/pythonProjecttest/ResNet34.py
torch.Size([1, 128, 56, 56])
torch.Size([1, 128, 56, 56])
torch.Size([1, 128, 56, 56])
torch.Size([1, 128, 56, 56])
torch.Size([1, 256, 28, 28])
torch.Size([1, 256, 28, 28])
torch.Size([1, 256, 28, 28])
torch.Size([1, 256, 28, 28])
torch.Size([1, 256, 14, 14])
torch.Size([1, 256, 14, 14])
torch.Size([1, 256, 14, 14])
torch.Size([1, 256, 14, 14])
torch.Size([1, 256, 14, 14])
torch.Size([1, 256, 14, 14])
torch.Size([1, 512, 7, 7])
torch.Size([1, 512, 7, 7])
torch.Size([1, 512, 7, 7])
tensor([[-6.9146e-01, 4.2167e-02, 6.7924e-01, 3.3181e-02, -8.8691e-01,
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grad_fn=<AddmmBackward0>)
Process finished with exit code 0
实际运行结果部分截图：
本文转载自: https://blog.csdn.net/rothschild666/article/details/123497166
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版权归原作者 rothschildlhl 所有， 如有侵权，请联系我们删除。