给我一个Googlenet采用SGD优化器的pytorch代码

以下是一个简单的GoogLeNet模型代码，使用PyTorch实现，采用SGD优化器：

import torch
import torch.nn as nn
import torch.optim as optim

class InceptionModule(nn.Module):
    def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):
        super(InceptionModule, self).__init__()
        # 1x1 conv branch
        self.branch1 = nn.Sequential(
            nn.Conv2d(in_channels, ch1x1, kernel_size=1),
            nn.BatchNorm2d(ch1x1),
            nn.ReLU(inplace=True)
        )

        # 1x1 conv -> 3x3 conv branch
        self.branch2 = nn.Sequential(
            nn.Conv2d(in_channels, ch3x3red, kernel_size=1),
            nn.BatchNorm2d(ch3x3red),
            nn.ReLU(inplace=True),
            nn.Conv2d(ch3x3red, ch3x3, kernel_size=3, padding=1),
            nn.BatchNorm2d(ch3x3),
            nn.ReLU(inplace=True)
        )

        # 1x1 conv -> 5x5 conv branch
        self.branch3 = nn.Sequential(
            nn.Conv2d(in_channels, ch5x5red, kernel_size=1),
            nn.BatchNorm2d(ch5x5red),
            nn.ReLU(inplace=True),
            nn.Conv2d(ch5x5red, ch5x5, kernel_size=5, padding=2),
            nn.BatchNorm2d(ch5x5),
            nn.ReLU(inplace=True)
        )

        # 3x3 pool -> 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 = 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 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.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=1)
        self.conv3 = nn.Conv2d(64, 192, kernel_size=3, 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(p=0.4)
        self.fc = nn.Linear(1024, num_classes)

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        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

# 使用SGD优化器训练GoogLeNet
model = GoogLeNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 训练模型
for epoch in range(num_epochs):
    for i, data in enumerate(train_loader):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        # 打印训练过程中的损失和准确率等信息
        if i % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, total_step, loss.item()))

需要注意的是，这只是一个简单的GoogLeNet模型实现，你可以根据实际情况进行修改和优化。