写出CNN与transformer混合框架图像分割代码

以下是一个基于CNN与Transformer混合框架的图像分割代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

class ConvBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(ConvBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(self.bn1(x))
        x = self.conv2(x)
        x = F.relu(self.bn2(x))
        return x

class Encoder(nn.Module):
    def __init__(self, in_channels, out_channels, n_blocks):
        super(Encoder, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, 3, padding=1)
        self.bn = nn.BatchNorm2d(out_channels)
        self.blocks = nn.Sequential(*[ConvBlock(out_channels, out_channels) for _ in range(n_blocks)])
        self.pool = nn.MaxPool2d(2, 2)

    def forward(self, x):
        x = self.conv(x)
        x = F.relu(self.bn(x))
        x = self.blocks(x)
        p = x
        x = self.pool(x)
        return x, p

class Decoder(nn.Module):
    def __init__(self, in_channels, out_channels, n_blocks):
        super(Decoder, self).__init__()
        self.blocks = nn.Sequential(*[ConvBlock(in_channels, in_channels) for _ in range(n_blocks)])
        self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, 2, stride=2)
        self.conv = nn.Conv2d(in_channels, out_channels, 3, padding=1)

    def forward(self, x, p):
        x = self.blocks(x)
        x = self.up(x)
        x = torch.cat([x, p], dim=1)
        x = self.conv(x)
        return x

class CNNTransformer(nn.Module):
    def __init__(self, n_classes):
        super(CNNTransformer, self).__init__()
        self.enc1 = Encoder(3, 64, 2)
        self.enc2 = Encoder(64, 128, 2)
        self.enc3 = Encoder(128, 256, 3)
        self.enc4 = Encoder(256, 512, 3)
        self.dec4 = Decoder(512, 256, 3)
        self.dec3 = Decoder(256, 128, 2)
        self.dec2 = Decoder(128, 64, 2)
        self.dec1 = Decoder(64, 64, 2)
        self.conv = nn.Conv2d(64, n_classes, 1)

        self.pos_enc = nn.Parameter(torch.zeros(1, 64, 64))
        nn.init.normal_(self.pos_enc, mean=0, std=0.1)

        self.transformer = nn.Transformer(d_model=64, nhead=8, num_encoder_layers=6,
                                           num_decoder_layers=6, dim_feedforward=2048,
                                           dropout=0.1, activation='relu')

    def forward(self, x):
        x, p1 = self.enc1(x)
        x, p2 = self.enc2(x)
        x, p3 = self.enc3(x)
        x, p4 = self.enc4(x)
        x = self.dec4(x, p4)
        x = self.dec3(x, p3)
        x = self.dec2(x, p2)
        x = self.dec1(x, p1)
        x = self.conv(x)

        b, c, h, w = x.size()

        x = x.view(b, c, -1)
        x = x.permute(2, 0, 1)
        x = x + self.pos_enc
        x = self.transformer(x, x)
        x = x.permute(1, 2, 0)
        x = x.contiguous().view(b, c, h, w)
        return x

该模型使用了四个编码器和四个解码器，每个编码器和解码器包含了多个卷积块，来提取图像特征和进行上采样。同时，该模型还使用了一个Transformer来进一步处理图像特征，并提高分割结果的准确性。在前向传播过程中，每个编码器的输出都会传递给相应的解码器，以进行上采样和融合特征。最终，模型输出一个与输入图像大小相同的分割结果。