结合并联 Transformer 和残差 U-Net 网络的水下图像增强模型Python代码

合并Transformer和残差U-Net网络结构在水下图像增强中可以利用Transformer的全局上下文理解和残差U-Net的细节保留能力。以下是一个简化版的Python代码示例，使用PyTorch库来实现这种融合：

import torch
import torch.nn as nn

class ResidualUNetBlock(nn.Module):
    # 残差U-Net块
    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size, stride=stride, padding=padding)
        
    def forward(self, x):
        identity = x
        x = self.conv1(x)
        x = self.relu(x)
        x = self.conv2(x)
        return x + identity   # 残差连接

class AttentionBlock(nn.Module):
    # 自注意力块，类似Transformer编码器的一部分
    def __init__(self, channels):
        super().__init__()
        self.channel_attention = nn.Sequential(
            nn.Linear(channels, channels // 8),
            nn.ReLU(),
            nn.Linear(channels // 8, channels),
            nn.Sigmoid()
        )
        self.spatial_attention = nn.Sequential(
            nn.Conv2d(channels, 1, kernel_size=1),
            nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        channel_attn = self.channel_attention(x.view(b, c, -1)).view(b, c, 1, 1)
        spatial_attn = self.spatial_attention(x)
        combined_attn = channel_attn * spatial_attn
        return x * combined_attn

# 合并网络
class CombinedModel(nn.Module):
    def __init__(self, input_channels, num_classes, num_transformer_blocks=6):
        super().__init__()
        self.u_net = nn.Sequential(ResidualUNetBlock(input_channels, 32), ...)  # 根据需要添加更多的层
        self.transformer_encoder = nn.TransformerEncoderLayer(d_model=input_channels//4, nhead=8)  # 调整通道数
        self.transformer = nn.TransformerEncoder(self.transformer_encoder, num_transformer_blocks)

    def forward(self, x):
        u_net_out = self.u_net(x)
        transformer_out = self.transformer(u_net_out.permute(0, 2, 1))  # 将输入从CHW转到HWC
        fused_output = u_net_out + transformer_out.permute(0, 2, 1)  # 再将特征图拼接
        return fused_output

# 使用示例
model = CombinedModel(input_channels=3, num_classes=1)  # 输入3通道，输出单通道
input_image = torch.randn(1, 3, 512, 512)  # 假设输入大小为512x512
output = model(input_image)