给出Inception v2的pytorch代码

以下是一个简单的Inception v2的pytorch代码实现，仅供参考：

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

class InceptionModule(nn.Module):
    def __init__(self, in_channels, out_channels1, out_channels2, out_channels3, out_channels4):
        super(InceptionModule, self).__init__()
        
        # 第一个分支
        self.branch1 = nn.Conv2d(in_channels, out_channels1, kernel_size=1)
        
        # 第二个分支
        self.branch2 = nn.Sequential(
            nn.Conv2d(in_channels, out_channels2[0], kernel_size=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels2[0], out_channels2[1], kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        
        # 第三个分支
        self.branch3 = nn.Sequential(
            nn.Conv2d(in_channels, out_channels3[0], kernel_size=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels3[0], out_channels3[1], kernel_size=5, padding=2),
            nn.ReLU(inplace=True)
        )
        
        # 第四个分支
        self.branch4 = nn.Sequential(
            nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
            nn.Conv2d(in_channels, out_channels4, kernel_size=1)
        )
        
    def forward(self, x):
        out1 = self.branch1(x)
        out2 = self.branch2(x)
        out3 = self.branch3(x)
        out4 = self.branch4(x)
        out = torch.cat([out1, out2, out3, out4], dim=1)
        return out
        
class InceptionV2(nn.Module):
    def __init__(self, num_classes=1000):
        super(InceptionV2, self).__init__()
        
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
        self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.conv2 = nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1)
        self.maxpool2 = 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.maxpool3 = 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.maxpool4 = 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.AdaptiveAvgPool2d((1, 1))
        self.dropout = nn.Dropout(0.4)
        self.fc = nn.Linear(1024, num_classes)
        
    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x, inplace=True)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = F.relu(x, inplace=True)
        x = self.maxpool2(x)
        x = self.inception3a(x)
        x = self.inception3b(x)
        x = self.maxpool3(x)
        x = self.inception4a(x)
        x = self.inception4b(x)
        x = self.inception4c(x)
        x = self.inception4d(x)
        x = self.inception4e(x)
        x = self.maxpool4(x)
        x = self.inception5a(x)
        x = self.inception5b(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.dropout(x)
        x = self.fc(x)
        return x

这里实现的是一个简单的Inception v2，仅包含了网络的主要结构。如果需要使用该模型进行训练或测试，还需要添加一些额外的代码，如数据处理、优化器、损失函数等。