卷积神经网络模型之——GoogLeNet网络结构与代码实现

文章目录

• ​​GoogLeNet网络简介​​
• ​​GoogLeNet网络结构​​

• ​​Inception之前的几层结构​​
• ​​Inception结构​​

• ​​Inception3a模块​​
• ​​Inception3b + MaxPool​​
• ​​Inception4a​​
• ​​Inception4b​​
• ​​Inception4c​​
• ​​Inception4d​​
• ​​Inception4e+MaxPool​​
• ​​Inception5a​​
• ​​Inception5b​​

• ​​Inception之后的几层结构​​
• ​​辅助分类模块​​

• ​​辅助分类模块1​​
• ​​辅助分类模块2​​

• ​​整体网络结构​​

• ​​pytorch搭建完整代码​​
• ​​结构图​​

GoogLeNet网络简介

GoogLeNet原文地址：Going Deeper with Convolutions：

GoogLeNet在2014年由Christian Szegedy提出，它是一种全新的深度学习结构。

GoogLeNet网络的主要创新点在于：

2. 提出Inception结构在多个尺寸上同时进行卷积再聚合；



3. 使用1X1的卷积进行降维以及映射处理；
4. 添加两个辅助分类器帮助训练；
   辅助分类器是将中间某一层的输出用作分类,并按一个较小的权重加到最终分类结果中。
5. 使用平均池化层代替全连接层，大大减少了参数量。

GoogLeNet网络结构

GoogLeNet的完整网络结构如下所示：

下面我们将其逐层拆分讲解并结合代码分析

Inception之前的几层结构

在进入Inception结构之前，GoogLeNet网络先堆叠了两个卷积（实则3个，有一个1X1的卷积）和两个最大池化层。

# input(3,224,224)
self.front = nn.Sequential(
    nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),   # output(64,112,112)
    nn.ReLU(inplace=True),

    nn.MaxPool2d(kernel_size=3,stride=2,ceil_mode=True),    # output(64,56,56)

    nn.Conv2d(64,64,kernel_size=1),
    nn.Conv2d(64,192,kernel_size=3,stride=1,padding=1),     # output(192,56,56)
    nn.ReLU(inplace=True),

    nn.MaxPool2d(kernel_size=3,stride=2,ceil_mode=True),    # output(192,28,28)
)

Inception结构

Inception模块只会改变特征图的通道数，而不会改变尺寸大小。

Inception结构相对复杂，我们重新创建一个类来构建此结构，并通过参数不同的参数来控制各层的通道数。

class Inception(nn.Module):
    '''
     in_channels: 输入通道数
     out1x1：分支1输出通道数
     in3x3：分支2的3x3卷积的输入通道数
     out3x3：分支2的3x3卷积的输出通道数
     in5x5：分支3的5x5卷积的输入通道数
     out5x5：分支3的5x5卷积的输出通道数
     pool_proj：分支4的最大池化层输出通道数
    '''
    def __init__(self,in_channels,out1x1,in3x3,out3x3,in5x5,out5x5,pool_proj):
        super(Inception, self).__init__()

        self.branch1 = nn.Sequential(
            nn.Conv2d(in_channels, out1x1, kernel_size=1),
            nn.ReLU(inplace=True)
        )
        self.branch2 = nn.Sequential(
            nn.Conv2d(in_channels,in3x3,kernel_size=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(in3x3,out3x3,kernel_size=3,padding=1),
            nn.ReLU(inplace=True)
        )
        self.branch3 = nn.Sequential(
            nn.Conv2d(in_channels, in5x5, kernel_size=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(in5x5, out5x5, kernel_size=5, padding=2),
            nn.ReLU(inplace=True)
        )

        self.branch4 = nn.Sequential(
            nn.MaxPool2d(kernel_size=3,stride=1,padding=1),
            nn.Conv2d(in_channels,pool_proj,kernel_size=1),
            nn.ReLU(inplace=True)
        )

    def forward(self,x):
        branch1 = self.branch1(x)
        branch2 = self.branch2(x)
        branch3 = self.branch3(x)
        branch4 = self.branch4(x)

        outputs = [branch1,branch2,branch3,branch4]
        return torch.cat(outputs,1) # 按通道数叠加

Inception3a模块

# input(192,28,28)
self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)    # output(256,28,28)

Inception3b + MaxPool

# input(256,28,28)
self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)    # output(480,28,28)
self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)     # output(480,14,14)

Inception4a

# input(480,14,14)
self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)   # output(512,14,14)

Inception4b

# input(512,14,14)
self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)    # output(512,14,14)

Inception4c

# input(512,14,14)
self.inception4c = Inception(512, 160, 112, 224, 24, 64, 64)    # output(512,14,14)

Inception4d

# input(512,14,14)
self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)    # output(528,14,14)

Inception4e+MaxPool

# input(528,14,14)
self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)  # output(832,14,14)
self.maxpool4 = nn.MaxPool2d(3, stride=2, ceil_mode=True)   # output(832,7,7)

Inception5a

# input(832,7,7)
self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)    # output(832,7,7)

Inception5b

# input(832,7,7)
self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)    # output(1024,7,7)

Inception之后的几层结构

辅助分类模块

除了以上主干网络结构以外，GoogLeNet还提供了两个辅助分类模块，用于将中间某一层的输出用作分类，并按一个较小的权重(0.3)加到最终分类结果。

与Inception模块一样，我们也重新创建一个类来搭建辅助分类模块结构。

class AccClassify(nn.Module):
    # in_channels: 输入通道
    # num_classes： 分类数
    def __init__(self,in_channels,num_classes):
        self.avgpool = nn.AvgPool2d(kernel_size=5, stride=3)
        self.conv = nn.MaxPool2d(in_channels, 128, kernel_size=1)  # output[batch, 128, 4, 4]
        self.relu = nn.ReLU(inplace=True)

        self.fc1 = nn.Linear(2048, 1024)
        self.fc2 = nn.Linear(1024, num_classes)

    def forward(self,x):
        x = self.avgpool(x)
        x = self.conv(x)
        x = self.relu(x)
        x = torch.flatten(x, 1)
        x = F.dropout(x, 0.5, training=self.training)
        x = F.relu(self.fc1(x), inplace=True)
        x = F.dropout(x, 0.5, training=self.training)
        x = self.fc2(x)

        return x

辅助分类模块1

第一个中间层输出位于Inception4a之后，将Inception4a的输出经过平均池化，1X1卷积和全连接后等到分类结果。

self.acc_classify1 = AccClassify(512,num_classes)

辅助分类模块2

self.acc_classify2 = AccClassify(528,num_classes)

整体网络结构

pytorch搭建完整代码

"""
#-*-coding:utf-8-*- 
# @author: wangyu a beginner programmer, striving to be the strongest.
# @date: 2022/7/5 18:37
"""
import torch.nn as nn
import torch
import torch.nn.functional as F


class GoogLeNet(nn.Module):
    def __init__(self,num_classes=1000,aux_logits=True):
        super(GoogLeNet, self).__init__()
        self.aux_logits = aux_logits

        # input(3,224,224)
        self.front = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),   # output(64,112,112)
            nn.ReLU(inplace=True),

            nn.MaxPool2d(kernel_size=3,stride=2,ceil_mode=True),    # output(64,56,56)

            nn.Conv2d(64,64,kernel_size=1),
            nn.Conv2d(64,192,kernel_size=3,stride=1,padding=1),     # output(192,56,56)
            nn.ReLU(inplace=True),

            nn.MaxPool2d(kernel_size=3,stride=2,ceil_mode=True),    # output(192,28,28)
        )

        # input(192,28,28)
        self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)  # output(64+128+32+32=256,28,28)
        self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)  # output(480,28,28)
        self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)  # output(480,14,14)

        self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)  # output(512,14,14)
        self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)  # output(512,14,14)
        self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)  # output(512,14,14)
        self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)  # output(528,14,14)
        self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)  # output(832,14,14)
        self.maxpool4 = nn.MaxPool2d(3, stride=2, ceil_mode=True)  # output(832,7,7)

        self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)  # output(832,7,7)
        self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)  # output(1024,7,7)

        if self.training and self.aux_logits:
            self.acc_classify1 = AccClassify(512,num_classes)
            self.acc_classify2 = AccClassify(528,num_classes)

        self.avgpool = nn.AdaptiveAvgPool2d((1,1))        # output(1024,1,1)
        self.dropout = nn.Dropout(0.4)
        self.fc = nn.Linear(1024,num_classes)


    def forward(self,x):
        # input(3,224,224)
        x = self.front(x)       # output(192,28,28)

        x= self.inception3a(x)  # output(256,28,28)
        x = self.inception3b(x)
        x = self.maxpool3(x)

        x = self.inception4a(x)

        if self.training and self.aux_logits:
            classify1 = self.acc_classify1(x)

        x = self.inception4b(x)
        x = self.inception4c(x)
        x = self.inception4d(x)

        if self.training and self.aux_logits:
            classify2 = self.acc_classify2(x)

        x = self.inception4e(x)
        x = self.maxpool4(x)

        x = self.inception5a(x)
        x = self.inception5b(x)

        x = self.avgpool(x)
        x = torch.flatten(x,dims=1)
        x = self.dropout(x)
        x= self.fc(x)

        if self.training and self.aux_logits:
            return x,classify1,classify2

        return x


class Inception(nn.Module):
    '''
     in_channels: 输入通道数
     out1x1：分支1输出通道数
     in3x3：分支2的3x3卷积的输入通道数
     out3x3：分支2的3x3卷积的输出通道数
     in5x5：分支3的5x5卷积的输入通道数
     out5x5：分支3的5x5卷积的输出通道数
     pool_proj：分支4的最大池化层输出通道数
    '''
    def __init__(self,in_channels,out1x1,in3x3,out3x3,in5x5,out5x5,pool_proj):
        super(Inception, self).__init__()

        # input(192,28,28)
        self.branch1 = nn.Sequential(
            nn.Conv2d(in_channels, out1x1, kernel_size=1),
            nn.ReLU(inplace=True)
        )
        self.branch2 = nn.Sequential(
            nn.Conv2d(in_channels,in3x3,kernel_size=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(in3x3,out3x3,kernel_size=3,padding=1),
            nn.ReLU(inplace=True)
        )
        self.branch3 = nn.Sequential(
            nn.Conv2d(in_channels, in5x5, kernel_size=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(in5x5, out5x5, kernel_size=5, padding=2),
            nn.ReLU(inplace=True)
        )

        self.branch4 = nn.Sequential(
            nn.MaxPool2d(kernel_size=3,stride=1,padding=1),
            nn.Conv2d(in_channels,pool_proj,kernel_size=1),
            nn.ReLU(inplace=True)
        )

    def forward(self,x):
        branch1 = self.branch1(x)
        branch2 = self.branch2(x)
        branch3 = self.branch3(x)
        branch4 = self.branch4(x)

        outputs = [branch1,branch2,branch3,branch4]
        return torch.cat(outputs,1)


class AccClassify(nn.Module):
    def __init__(self,in_channels,num_classes):
        self.avgpool = nn.AvgPool2d(kernel_size=5, stride=3)
        self.conv = nn.MaxPool2d(in_channels, 128, kernel_size=1)  # output[batch, 128, 4, 4]
        self.relu = nn.ReLU(inplace=True)

        self.fc1 = nn.Linear(2048, 1024)
        self.fc2 = nn.Linear(1024, num_classes)

    def forward(self,x):
        x = self.avgpool(x)
        x = self.conv(x)
        x = self.relu(x)
        x = torch.flatten(x, 1)
        x = F.dropout(x, 0.5, training=self.training)
        x = F.relu(self.fc1(x), inplace=True)
        x = F.dropout(x, 0.5, training=self.training)
        x = self.fc2(x)

        return x

# print(GoogLeNet())

结构图