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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
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,
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-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,
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-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,
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8.7046e-01, -2.5414e-01, -1.0958e+00, -2.5689e-01, 1.7565e-01,
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1.3876e-01, -5.7287e-01, 1.3552e+00, 9.9222e-01, 8.3570e-01,
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3.5264e-01, 2.4308e-01, -3.4107e-02, 1.0145e+00, -4.1692e-01,
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5.4346e-01, -9.5232e-01, -6.8879e-01, 7.4688e-01, -2.4348e-01,
1.3636e+00, 3.9611e-01, -7.5341e-01, 1.5457e-01, -1.2955e+00,
2.9039e-01, -1.8801e-02, 6.1748e-01, 4.4085e-01, 3.0806e-01,
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4.5906e-01, -2.5582e-01, 5.1523e-01, -8.1713e-02, 3.3685e-01,
-1.0462e+00, 7.3278e-01, -2.7332e-01, 1.8338e-01, -3.4347e-01,
-5.2576e-01, -6.6501e-01, -1.0592e-01, 3.9719e-01, -2.3533e-01,
3.6105e-01, 1.3998e+00, 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：
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