dbpn pytorch

嗨！关于DBPN（Deep Back-Projection Networks）在PyTorch中的实现，您可以参考以下代码片段：

首先，您需要导入所需的库和模块：

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

然后，定义DBPN模型的主要组件：

class ResidualBlock(nn.Module):
    def __init__(self, channels):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=1)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=1)
        
    def forward(self, x):
        residual = x
        out = self.relu(self.conv1(x))
        out = self.conv2(out)
        out += residual
        return out

class UpsampleBlock(nn.Module):
    def __init__(self, in_channels, out_channels, scale_factor):
        super(UpsampleBlock, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels * scale_factor ** 2, kernel_size=3, stride=1, padding=1)
        self.pixel_shuffle = nn.PixelShuffle(scale_factor)
        
    def forward(self, x):
        out = self.conv(x)
        out = self.pixel_shuffle(out)
        return out

class DBPN(nn.Module):
    def __init__(self, upscale_factor, num_channels, base_channels, num_stages, num_residuals):
        super(DBPN, self).__init__()
        self.upscale_factor = upscale_factor
        self.num_stages = num_stages
        
        self.entry = nn.Conv2d(num_channels, base_channels, kernel_size=3, stride=1, padding=1)
        self.feature_extraction = nn.Conv2d(base_channels, base_channels, kernel_size=3, stride=1, padding=1)
        
        self.residual_layers = nn.ModuleList([ResidualBlock(base_channels) for _ in range(num_residuals)])
        
        self.upsampling_layers = nn.ModuleList([UpsampleBlock(base_channels, base_channels, upscale_factor) for _ in range(num_stages)])
        
        self.exit = nn.Conv2d(base_channels, num_channels, kernel_size=3, stride=1, padding=1)
        
    def forward(self, x):
        x = self.entry(x)
        residual = x
        
        x = self.feature_extraction(x)
        
        out = []
        for i in range(self.num_stages):
            x = self.residual_layers[i](x)
            out.append(self.upsampling_layers[i](x))
        
        out = torch.cat(out, dim=1)
        out = self.exit(out + residual)
        
        return out

最后，您可以根据需要实例化DBPN模型并使用它进行训练或推断：

upscale_factor = 2
num_channels = 3
base_channels = 64
num_stages = 7
num_residuals = 5

model = DBPN(upscale_factor, num_channels, base_channels, num_stages, num_residuals)

这只是一个基本的DBPN模型实现示例，您可以根据自己的需求进行修改和调整。希望对您有所帮助！如果您有其他问题，请随时提问。