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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,
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,
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-1.5743e-01,  7.9150e-01,  8.3295e-01, -3.7907e-01,  1.3265e+00,
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-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,
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-2.1987e-01,  7.9024e-02, -7.7868e-02,  3.7589e-01, -1.2438e+00,
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-9.4433e-01,  1.7178e-01,  1.2179e+00, -8.5136e-02,  1.0335e+00,
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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, -1.1043e+00, -9.0061e-01, -1.3376e+00, -4.4920e-02,
2.6194e-02, -6.9110e-01,  1.8998e-01,  6.8125e-01,  2.6472e-01,
-1.9500e-01,  1.1790e-01,  6.9329e-01,  8.9654e-02,  1.0810e-01,
-1.2606e-01, -2.9633e-01,  9.3100e-01, -9.3621e-02, -2.2722e-01,
-6.8945e-01,  5.3516e-01,  3.4176e-01,  2.8460e-01, -9.2238e-02,
-5.1156e-01, -3.6413e-02,  1.6606e-01, -7.6782e-02,  1.1313e+00,
4.2051e-01,  5.6609e-01,  3.9709e-01, -1.2401e-01, -8.0785e-01,
-2.2174e-01,  1.7601e-01,  1.4581e-01,  4.0443e-01, -4.0146e-01,
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7.0356e-01,  3.2557e-01, -2.7923e-01,  1.1310e+00,  9.7589e-02,
3.4966e-01,  8.2451e-01,  5.8476e-01,  3.8739e-01, -2.3314e-02,
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4.4510e-01,  2.8889e-01, -8.4225e-01,  1.5495e-01,  8.1316e-01,
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2.9890e-01, -1.0674e-02,  7.2148e-02,  3.1490e-02,  3.1332e-01,
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4.4507e-01, -1.3242e-01, -1.7397e-01,  1.0680e+00, -5.7670e-02,
6.2292e-01,  3.0558e-01, -6.8610e-01, -4.7339e-01,  1.7880e-01,
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-9.1949e-01,  7.2047e-01,  7.1974e-01, -8.6021e-01, -5.0419e-01,
-6.3278e-01, -4.2891e-01,  6.0974e-01, -8.3915e-01,  1.0890e-01,
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grad_fn=<AddmmBackward0>)
Process finished with exit code 0

实际运行结果部分截图：