请基于pytorch搭建一个用于交通标志识别的网络

好的，我可以为您提供一个基于PyTorch的交通标志识别模型的代码示例。这个模型使用卷积神经网络（CNN）来学习图像特征，并使用全连接层进行分类。

以下是示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets

# 定义模型
class TrafficSignNet(nn.Module):
    def __init__(self):
        super(TrafficSignNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(128 * 8 * 8, 512)
        self.fc2 = nn.Linear(512, 43)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = self.pool(nn.functional.relu(self.conv3(x)))
        x = x.view(-1, 128 * 8 * 8)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 加载数据集
transform = transforms.Compose([
    transforms.Resize((32, 32)),
    transforms.ToTensor()
])
train_set = datasets.ImageFolder('train_dir', transform=transform)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=32, shuffle=True)
test_set = datasets.ImageFolder('test_dir', transform=transform)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=32, shuffle=True)

# 训练模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = TrafficSignNet().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader):
        inputs, labels = data[0].to(device), data[1].to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print("Epoch %d loss: %.3f" % (epoch+1, running_loss/len(train_loader)))

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data[0].to(device), data[1].to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
print("Accuracy: %.2f%%" % (100 * correct / total))

在这个示例中，我们定义了一个名为`TrafficSignNet`的类，该类继承自`nn.Module`。该模型包含三个卷积层，每个卷积层后面跟着一个最大池化层。最后，我们使用两个全连接层进行分类。我们使用的是交叉熵损失函数和Adam优化器。我们使用的数据集是`ImageFolder`，它将图像和标签组织在不同的文件夹中。我们将训练集和测试集分别放在`train_dir`和`test_dir`文件夹中。在训练过程中，我们迭代数据批次，并使用反向传播来更新模型参数。在测试过程中，我们计算模型在测试集上的准确率。