使用pytorch搭建GoogleNet模型

GoogleNet是一种深度卷积神经网络，它在2014年的ImageNet竞赛中取得了第一名的好成绩。下面是使用PyTorch搭建GoogleNet模型的代码：

import torch.nn as nn

class InceptionModule(nn.Module):
    def __init__(self, in_channels, out1x1, reduce3x3, out3x3, reduce5x5, out5x5, pool_proj):
        super(InceptionModule, self).__init__()
        
        # 1x1 conv branch
        self.branch1 = nn.Sequential(
            nn.Conv2d(in_channels, out1x1, kernel_size=1),
            nn.BatchNorm2d(out1x1),
            nn.ReLU(inplace=True)
        )
        
        # 1x1 conv -> 3x3 conv branch
        self.branch2 = nn.Sequential(
            nn.Conv2d(in_channels, reduce3x3, kernel_size=1),
            nn.BatchNorm2d(reduce3x3),
            nn.ReLU(inplace=True),
            nn.Conv2d(reduce3x3, out3x3, kernel_size=3, padding=1),
            nn.BatchNorm2d(out3x3),
            nn.ReLU(inplace=True)
        )
        
        # 1x1 conv -> 5x5 conv branch
        self.branch3 = nn.Sequential(
            nn.Conv2d(in_channels, reduce5x5, kernel_size=1),
            nn.BatchNorm2d(reduce5x5),
            nn.ReLU(inplace=True),
            nn.Conv2d(reduce5x5, out5x5, kernel_size=5, padding=2),
            nn.BatchNorm2d(out5x5),
            nn.ReLU(inplace=True)
        )
        
        # 3x3 max pooling -> 1x1 conv branch
        self.branch4 = nn.Sequential(
            nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
            nn.Conv2d(in_channels, pool_proj, kernel_size=1),
            nn.BatchNorm2d(pool_proj),
            nn.ReLU(inplace=True)
        )
        
    def forward(self, x):
        branch1_output = self.branch1(x)
        branch2_output = self.branch2(x)
        branch3_output = self.branch3(x)
        branch4_output = self.branch4(x)
        output = torch.cat([branch1_output, branch2_output, branch3_output, branch4_output], 1)
        return output

class GoogleNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(GoogleNet, self).__init__()
        
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )
        
        self.conv2 = nn.Sequential(
            nn.Conv2d(64, 64, kernel_size=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 192, kernel_size=3, padding=1),
            nn.BatchNorm2d(192),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )
        
        self.inception3a = InceptionModule(192, 64, 96, 128, 16, 32, 32)
        self.inception3b = InceptionModule(256, 128, 128, 192, 32, 96, 64)
        self.inception4a = InceptionModule(480, 192, 96, 208, 16, 48, 64)
        self.inception4b = InceptionModule(512, 160, 112, 224, 24, 64, 64)
        self.inception4c = InceptionModule(512, 128, 128, 256, 24, 64, 64)
        self.inception4d = InceptionModule(512, 112, 144, 288, 32, 64, 64)
        self.inception4e = InceptionModule(528, 256, 160, 320, 32, 128, 128)
        self.inception5a = InceptionModule(832, 256, 160, 320, 32, 128, 128)
        self.inception5b = InceptionModule(832, 384, 192, 384, 48, 128, 128)
        
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.dropout = nn.Dropout(p=0.4)
        self.fc = nn.Linear(1024, num_classes)
        
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.inception3a(x)
        x = self.inception3b(x)
        x = self.inception4a(x)
        x = self.inception4b(x)
        x = self.inception4c(x)
        x = self.inception4d(x)
        x = self.inception4e(x)
        x = self.inception5a(x)
        x = self.inception5b(x)
        x = self.avg_pool(x)
        x = x.view(x.size(0), -1)
        x = self.dropout(x)
        x = self.fc(x)
        return x

在这个例子中，我们实现了GoogleNet中的Inception模块和整个网络。通过将Inception模块堆叠在一起，就可以构建出GoogleNet模型了。