lenet加入transformer的pytorch代码

很抱歉，但是我只能为您提供文字回答，无法提供完整的代码。以下是一个使用LeNet结构并加入Transformer的PyTorch代码的示例：

import torch
import torch.nn as nn

class LeNetWithTransformer(nn.Module):
    def __init__(self):
        super(LeNetWithTransformer, self).__init__()

        # LeNet layers
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(2)

        # Transformer layers
        self.transformer = nn.Transformer(
            d_model=16 * 5 * 5, nhead=8, num_encoder_layers=6, num_decoder_layers=6)

        # Fully-connected layers
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.relu3 = nn.ReLU()
        self.fc2 = nn.Linear(120, 84)
        self.relu4 = nn.ReLU()
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool1(self.relu1(self.conv1(x)))
        x = self.pool2(self.relu2(self.conv2(x)))
        x = x.view(x.size(0), -1)  # Flatten the tensor

        # Apply transformer
        x = self.transformer(x, x)

        x = self.relu3(self.fc1(x))
        x = self.relu4(self.fc2(x))
        x = self.fc3(x)
        return x

# 创建LeNetWithTransformer的实例
model = LeNetWithTransformer()

# 打印模型结构
print(model)

希望这能对你有所帮助！如果你还有其他问题，请随时提出。