编码器网络由一个卷积层（“Conv”）和四个卷积块（“ECB10”、“ECB20”、“ECB30”和“ECB40”）组成。每个块包含两个卷积层和一个最大池算子，可以确保编码器网络提取不同尺度的深度特征。解码器网络有六个卷积块（“DCB11”、“DCB12”、“DCB13”；“DCB21”、“DCB22”；“DCB31”）和一个卷积层（“Conv”）。python代码

以下是一个简单的 Python 代码示例，实现了上述所述的编码器和解码器网络结构：

import torch.nn as nn

class Encoder(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(Encoder, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
        self.ecb10 = nn.Sequential(
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.ecb20 = nn.Sequential(
            nn.Conv2d(out_channels, out_channels*2, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels*2, out_channels*2, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.ecb30 = nn.Sequential(
            nn.Conv2d(out_channels*2, out_channels*4, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels*4, out_channels*4, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.ecb40 = nn.Sequential(
            nn.Conv2d(out_channels*4, out_channels*8, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels*8, out_channels*8, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )

    def forward(self, x):
        x = self.conv(x)
        x = self.ecb10(x)
        x = self.ecb20(x)
        x = self.ecb30(x)
        x = self.ecb40(x)
        return x


class Decoder(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(Decoder, self).__init__()
        self.dcb11 = nn.Sequential(
            nn.Conv2d(in_channels, out_channels*4, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels*4, out_channels*4, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        )
        self.dcb12 = nn.Sequential(
            nn.Conv2d(out_channels*4, out_channels*2, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels*2, out_channels*2, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        )
        self.dcb13 = nn.Sequential(
            nn.Conv2d(out_channels*2, out_channels, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        )
        self.dcb21 = nn.Sequential(
            nn.Conv2d(out_channels, out_channels*2, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels*2, out_channels*2, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        )
        self.dcb22 = nn.Sequential(
            nn.Conv2d(out_channels*2, out_channels, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        )
        self.dcb31 = nn.Sequential(
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
        )
        self.conv = nn.Conv2d(out_channels, in_channels, kernel_size=3, padding=1)

    def forward(self, x):
        x = self.dcb11(x)
        x = self.dcb12(x)
        x = self.dcb13(x)
        x = self.dcb21(x)
        x = self.dcb22(x)
        x = self.dcb31(x)
        x = self.conv(x)
        return x