如何将deblurgan-v2中生成器部分的fpn-inception模块换成超像素分割+超像素池化学习特征模块，要求用pytorch

将deblurgan-v2中生成器部分的fpn-inception模块换成超像素分割+超像素池化学习特征模块，需要进行以下几个步骤：

1. 安装必要的库

!pip install torch torchvision numpy opencv-python matplotlib scikit-image

2. 加载模型

import torch
import torch.nn as nn
from torchvision import models

class DeblurGANv2(nn.Module):
    def __init__(self):
        super(DeblurGANv2, self).__init__()
        self.encoder = models.vgg19(pretrained=True).features[:36]
        self.inception = models.inception_v3(pretrained=True).features[:17]
        self.fpn = FPN()
        self.decoder = Decoder()
        self.conv_hr = nn.Sequential(
            nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1),
            nn.Tanh()
        )

        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        # Encoder
        x = self.encoder(x)

        # Inception
        x_incep = self.inception(x)

        # FPN
        x_fpn = self.fpn(x)

        # Concatenate features
        x = torch.cat((x_incep, x_fpn), dim=1)

        # Decoder
        x = self.decoder(x)

        # Output
        output = self.conv_hr(x)

        return output

3. 定义超像素分割+超像素池化学习特征模块

class SuperPixelSegmentation(nn.Module):
    def __init__(self, in_channels, out_channels, num_superpixels):
        super(SuperPixelSegmentation, self).__init__()
        self.num_superpixels = num_superpixels
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, x):
        # Get superpixels
        superpixels = slic(x.cpu().numpy(), n_segments=self.num_superpixels, compactness=10.0, sigma=1.0)
        superpixels = torch.from_numpy(superpixels).float().to(x.device)

        # Encode superpixels
        superpixel_one_hot = self.softmax(superpixels.unsqueeze(1))
        superpixel_encoded = torch.matmul(x.view(x.size(0), x.size(1), -1), superpixel_one_hot.view(superpixel_one_hot.size(0), -1, self.num_superpixels))
        superpixel_encoded = superpixel_encoded.view(x.size(0), -1, self.num_superpixels)
        superpixel_encoded = self.conv(superpixel_encoded)

        # Pool superpixels
        superpixel_pooled = F.adaptive_avg_pool2d(superpixel_encoded, 1)

        return superpixel_pooled.squeeze()

4. 定义FPN

class FPN(nn.Module):
    def __init__(self):
        super(FPN, self).__init__()
        self.conv1 = nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0)
        self.conv2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.conv4 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.conv5 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.conv6 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.conv7 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)

        self.sp1 = SuperPixelSegmentation(512, 256, num_superpixels=100)
        self.sp2 = SuperPixelSegmentation(256, 256, num_superpixels=200)
        self.sp3 = SuperPixelSegmentation(256, 256, num_superpixels=400)
        self.sp4 = SuperPixelSegmentation(256, 256, num_superpixels=800)

    def forward(self, x):
        # Top-down pathway
        x5 = self.conv1(x)
        x4 = self.conv2(x5) + F.interpolate(self.sp1(x5), scale_factor=2, mode='bilinear', align_corners=True)
        x3 = self.conv3(x4) + F.interpolate(self.sp2(x4), scale_factor=2, mode='bilinear', align_corners=True)
        x2 = self.conv4(x3) + F.interpolate(self.sp3(x3), scale_factor=2, mode='bilinear', align_corners=True)
        x1 = self.conv5(x2) + F.interpolate(self.sp4(x2), scale_factor=2, mode='bilinear', align_corners=True)

        # Bottom-up pathway
        x2 = self.conv6(x1) + F.interpolate(x2, scale_factor=0.5, mode='bilinear', align_corners=True)
        x3 = self.conv7(x2) + F.interpolate(x3, scale_factor=0.5, mode='bilinear', align_corners=True)

        return x1, x2, x3, x4, x5

5. 定义Decoder

class Decoder(nn.Module):
    def __init__(self):
        super(Decoder, self).__init__()
        self.conv1 = nn.Conv2d(256 + 2048, 256, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(256 + 1024, 256, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(256 + 512, 256, kernel_size=3, stride=1, padding=1)
        self.conv4 = nn.Conv2d(256 + 256, 256, kernel_size=3, stride=1, padding=1)
        self.conv5 = nn.Conv2d(256 + 128, 256, kernel_size=3, stride=1, padding=1)
        self.conv6 = nn.Conv2d(256 + 64, 256, kernel_size=3, stride=1, padding=1)

        self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)

    def forward(self, x):
        x1, x2, x3, x4, x5 = x

        # Top-down pathway
        x5 = self.conv1(torch.cat((x5, self.up(x4)), dim=1))
        x4 = self.conv2(torch.cat((x4, self.up(x3)), dim=1))
        x3 = self.conv3(torch.cat((x3, self.up(x2)), dim=1))
        x2 = self.conv4(torch.cat((x2, self.up(x1)), dim=1))

        # Bottom-up pathway
        x2 = self.conv5(torch.cat((x2, self.up(x3)), dim=1))
        x1 = self.conv6(torch.cat((x1, self.up(x2)), dim=1))

        return x1

6. 替换模型中的FPN-inception模块

class DeblurGANv2(nn.Module):
    def __init__(self):
        super(DeblurGANv2, self).__init__()
        self.encoder = models.vgg19(pretrained=True).features[:36]
        self.fpn = FPN()
        self.decoder = Decoder()
        self.conv_hr = nn.Sequential(
            nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1),
            nn.Tanh()
        )

        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        # Encoder
        x = self.encoder(x)

        # FPN
        x_fpn = self.fpn(x)

        # Decoder
        x = self.decoder(x_fpn)

        # Output
        output = self.conv_hr(x)

        return output

7. 测试新的模型

model = DeblurGANv2().cuda()
input = torch.randn((1, 3, 256, 256)).cuda()
output = model(input)
print(output.shape)