使用pytorch编写googlenet程序

以下是使用PyTorch实现GoogLeNet的代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class InceptionModule(nn.Module):
    def __init__(self, in_channels, filter1, filter3, filter5, filter_pool):
        super(InceptionModule, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, filter1, kernel_size=1)
        self.conv3 = nn.Conv2d(in_channels, filter3, kernel_size=3, padding=1)
        self.conv5 = nn.Conv2d(in_channels, filter5, kernel_size=5, padding=2)
        self.pool = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
        self.conv_pool = nn.Conv2d(in_channels, filter_pool, kernel_size=1)
        
    def forward(self, x):
        out1 = F.relu(self.conv1(x))
        out3 = F.relu(self.conv3(x))
        out5 = F.relu(self.conv5(x))
        pool = self.pool(x)
        out_pool = F.relu(self.conv_pool(pool))
        return torch.cat([out1, out3, out5, out_pool], dim=1)

class GoogLeNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(GoogLeNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.conv2a = nn.Conv2d(64, 64, kernel_size=1)
        self.conv2b = nn.Conv2d(64, 192, kernel_size=3, padding=1)
        self.pool2 = 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.pool3 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        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.pool4 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.inception5a = InceptionModule(832, 256, 160, 320, 32, 128, 128)
        self.inception5b = InceptionModule(832, 384, 192, 384, 48, 128, 128)
        self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1)
        self.dropout = nn.Dropout(0.4)
        self.fc = nn.Linear(1024, num_classes)
        
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool1(x)
        x = F.relu(self.conv2a(x))
        x = F.relu(self.conv2b(x))
        x = self.pool2(x)
        x = self.inception3a(x)
        x = self.inception3b(x)
        x = self.pool3(x)
        x = self.inception4a(x)
        x = self.inception4b(x)
        x = self.inception4c(x)
        x = self.inception4d(x)
        x = self.inception4e(x)
        x = self.pool4(x)
        x = self.inception5a(x)
        x = self.inception5b(x)
        x = self.avgpool(x)
        x = self.dropout(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

同样需要注意，这个代码也只是GoogLeNet的一个简单实现，还需要使用适当的数据集和优化算法对其进行训练。