编写一个基于pytorch的segnet网络构架

以下是基于PyTorch的SegNet网络框架的示例代码：

import torch
import torch.nn as nn

class SegNet(nn.Module):
    def __init__(self, in_channels, n_classes):
        super(SegNet, self).__init__()
        
        # Encoder
        self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(128)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.bn3 = nn.BatchNorm2d(256)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.bn4 = nn.BatchNorm2d(512)
        self.conv5 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn5 = nn.BatchNorm2d(512)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, return_indices=True)
        
        # Decoder
        self.unpool = nn.MaxUnpool2d(kernel_size=2, stride=2)
        self.conv6 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.bn6 = nn.BatchNorm2d(512)
        self.conv7 = nn.Conv2d(512, 256, kernel_size=3, padding=1)
        self.bn7 = nn.BatchNorm2d(256)
        self.conv8 = nn.Conv2d(256, 128, kernel_size=3, padding=1)
        self.bn8 = nn.BatchNorm2d(128)
        self.conv9 = nn.Conv2d(128, 64, kernel_size=3, padding=1)
        self.bn9 = nn.BatchNorm2d(64)
        self.conv10 = nn.Conv2d(64, n_classes, kernel_size=3, padding=1)
        
    def forward(self, x):
        # Encoder
        x = self.conv1(x)
        x = self.bn1(x)
        x = torch.relu(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = torch.relu(x)
        x, indices1 = self.pool(x)
        x = self.conv3(x)
        x = self.bn3(x)
        x = torch.relu(x)
        x = self.conv4(x)
        x = self.bn4(x)
        x = torch.relu(x)
        x, indices2 = self.pool(x)
        x = self.conv5(x)
        x = self.bn5(x)
        x = torch.relu(x)
        x, indices3 = self.pool(x)
        
        # Decoder
        x = self.unpool(x, indices=indices3)
        x = self.conv6(x)
        x = self.bn6(x)
        x = torch.relu(x)
        x = self.conv7(x)
        x = self.bn7(x)
        x = torch.relu(x)
        x = self.conv8(x)
        x = self.bn8(x)
        x = torch.relu(x)
        x = self.unpool(x, indices=indices2)
        x = self.conv9(x)
        x = self.bn9(x)
        x = torch.relu(x)
        x = self.unpool(x, indices=indices1)
        x = self.conv10(x)
        
        return x

这个网络包括一个编码器和一个解码器。编码器由5个卷积层和一个最大池化层组成。解码器由3个最大反池化层和4个卷积层组成。在解码器中，我们使用最大反池化层来恢复编码器中的池化操作。

在forward方法中，我们首先通过编码器处理输入。在编码器中，我们将输入x传入每个卷积层后，使用批量归一化和ReLU激活函数进行处理。然后，我们使用最大池化层来减小特征图的大小，同时记录池化索引以在解码器中使用。在解码器中，我们使用最大反池化层来恢复池化操作。然后，我们分别传入每个卷积层，再次使用批量归一化和ReLU激活函数处理每个层的输出。最后，我们使用一个卷积层将解码器的输出转换为预测掩码。

该网络可以通过以下方式实例化：

in_channels = 3
n_classes = 2
model = SegNet(in_channels, n_classes)

其中，in_channels是输入图像的通道数，n_classes是要预测的类别数。