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 class ResidualBlock(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） def forward(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 is None: # 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） class ResNet(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） def make_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 in range(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） def forward(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, 5.7865e-01, -2.2614e-01, 1.1027e-01, -8.9046e-01, -5.5591e-01, -1.9302e-01, -8.6779e-01, 5.6450e-01, 2.5317e-01, 1.8407e-01, 3.1509e-01, 3.0071e-01, 1.8821e-01, 1.9301e-01, 2.2245e-01, -7.0432e-02, -5.5133e-01, 1.3677e-01, -5.1272e-01, 1.4352e+00, 1.0956e-01, -5.4226e-02, 2.3303e-01, -1.8693e-01, 8.5983e-02, -1.6748e-02, 3.5629e-01, 6.9455e-01, -2.1752e-01, -9.7052e-01, -6.2566e-02, 1.7783e-02, -5.3616e-01, -1.6616e-01, 2.3980e-01, -6.5388e-01, 4.6447e-01, -1.1445e-01, 3.9800e-01, -6.1762e-01, 2.1847e-01, 3.0629e-01, 9.6355e-01, 4.8554e-01, -1.3560e-01, -1.1258e+00, 4.5508e-01, -6.5425e-02, 2.2604e-01, 8.4579e-01, 3.5011e-01, -4.8174e-02, 8.6373e-02, 8.3569e-01, 5.2538e-01, -3.7520e-01, -1.0723e+00, 1.7073e-01, -3.4342e-01, -1.0935e+00, 2.5121e-01, 7.0005e-01, 3.2427e-01, 5.2415e-01, -1.0085e+00, 6.7876e-01, -1.2413e-01, -2.9308e-01, 1.4041e-01, 8.0507e-01, -1.5743e-01, 7.9150e-01, 8.3295e-01, -3.7907e-01, 1.3265e+00, 3.8708e-02, -2.8288e-01, 3.4668e-01, -2.4022e-01, 8.8009e-01, 4.9280e-01, -5.6786e-01, 5.9342e-01, 1.7836e-02, 6.1642e-01, -3.7113e-01, 5.5412e-03, -7.4085e-01, 9.4929e-01, -1.7198e-01, -3.5788e-02, 6.8394e-01, -1.0366e+00, 1.5008e-01, -3.0477e-01, -2.1622e-01, -6.1331e-01, -5.3795e-01, -2.8243e-01, -3.9197e-02, 3.1893e-01, 3.7748e-01, 3.6212e-01, -6.8486e-02, -4.4467e-01, -2.1987e-01, 7.9024e-02, -7.7868e-02, 3.7589e-01, -1.2438e+00, -5.0136e-01, 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-6.7116e-01, 2.3342e-01, 2.5999e-01, -3.3351e-01, 2.3589e-01, 9.8910e-02, -8.1655e-01, 2.5841e-01, -8.8194e-01, 6.8509e-01, -2.4890e-02, 9.0165e-01, -5.3347e-01, 2.2197e-01, 1.3163e+00, 8.4227e-02, 6.3555e-01, 7.9277e-01, 1.2936e-01, 1.0270e+00, 8.6476e-01, 7.1398e-02, 1.4991e-01, 4.8816e-01, 4.8867e-01, 1.6423e-01, 1.4731e-01, 6.8859e-01, 1.5584e-01, 2.9952e-01, -7.8371e-01, 1.8490e-01, -2.1610e-02, -5.3605e-01, -2.5407e-01, 1.7297e-01, 2.2950e-01, -6.2010e-01, 4.6020e-01, 6.3796e-02, 3.1704e-01, -8.6192e-02, 1.5022e-01, -4.5151e-04, 2.5592e-01, -1.0011e+00, 1.0367e+00, -4.9935e-01, 2.3648e-01, -8.8412e-01, 4.8926e-01, -4.6301e-01, -2.2750e-01, -2.3098e-01, 6.1564e-01, -6.5003e-02, -8.3130e-01, -4.0774e-01, 1.0351e+00, -2.2826e-01, 1.8550e-01, -2.2818e-01, -9.0132e-01, -1.2123e-02, -8.1003e-01, -7.1771e-02, -3.9507e-01, -1.7745e+00, 3.3608e-01, 1.4656e+00, 7.2080e-01, 1.8197e-02, 6.9331e-01, 4.4406e-01, -6.4882e-01, -7.4701e-01, -2.8917e-01, -4.7285e-01, -5.2247e-01, 4.8074e-01, 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4.3156e-01, 1.5737e+00, 2.1686e-01, -3.5330e-01, -1.0931e+00, 3.6434e-01, -7.3331e-01, -3.8396e-01]], grad_fn=<AddmmBackward0>) Process finished with exit code 0
实际运行结果部分截图：
