python 盲道语义分割的代码

以下是一个基于PyTorch的盲道语义分割模型的示例代码：

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

class BlindSegNet(nn.Module):
    def __init__(self):
        super(BlindSegNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.conv5 = nn.Conv2d(512, 512, kernel_size=3, padding=1)

        self.upconv1 = nn.ConvTranspose2d(512, 512, kernel_size=2, stride=2)
        self.upconv2 = nn.ConvTranspose2d(1024, 256, kernel_size=2, stride=2)
        self.upconv3 = nn.ConvTranspose2d(512, 128, kernel_size=2, stride=2)
        self.upconv4 = nn.ConvTranspose2d(256, 64, kernel_size=2, stride=2)
        self.upconv5 = nn.ConvTranspose2d(128, 1, kernel_size=1)

    def forward(self, x):
        # Encoder
        conv1 = F.relu(self.conv1(x))
        conv2 = F.relu(self.conv2(conv1))
        conv3 = F.relu(self.conv3(conv2))
        conv4 = F.relu(self.conv4(conv3))
        conv5 = F.relu(self.conv5(conv4))

        # Decoder
        upconv1 = F.relu(self.upconv1(conv5))
        upconv2 = F.relu(self.upconv2(torch.cat([conv4, upconv1], dim=1)))
        upconv3 = F.relu(self.upconv3(torch.cat([conv3, upconv2], dim=1)))
        upconv4 = F.relu(self.upconv4(torch.cat([conv2, upconv3], dim=1)))
        upconv5 = torch.sigmoid(self.upconv5(torch.cat([conv1, upconv4], dim=1)))

        return upconv5

该模型使用了5个卷积层和5个反卷积层来构建一个基本的编码器-解码器模型，其中使用ReLU激活函数和Sigmoid激活函数进行特征提取和输出。在训练过程中，可以使用二元交叉熵损失函数来计算预测结果和真实标签之间的差异。