用python构建EDSR超分辨率模型，给出模型代码，要求输入输出通道数为1

好的，我可以帮你回答这个问题。下面是用Python构建EDSR超分辨率模型的代码：

import torch.nn as nn

class ResBlock(nn.Module):
    def __init__(self, num_channels):
        super(ResBlock, self).__init__()
        self.block = nn.Sequential(
            nn.Conv2d(num_channels, num_channels, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(num_channels, num_channels, kernel_size=3, stride=1, padding=1)
        )
        
    def forward(self, x):
        return x + self.block(x)

class EDSR(nn.Module):
    def __init__(self, num_channels=64, num_resblocks=8, res_scale=0.1):
        super(EDSR, self).__init__()
        
        self.head = nn.Conv2d(1, num_channels, kernel_size=3, stride=1, padding=1)
        
        res_blocks = []
        for _ in range(num_resblocks):
            res_blocks.append(ResBlock(num_channels))
        self.res_blocks = nn.Sequential(*res_blocks)
        
        self.body = nn.Conv2d(num_channels, num_channels, kernel_size=3, stride=1, padding=1)
        
        self.tail = nn.Conv2d(num_channels, 1, kernel_size=3, stride=1, padding=1)
        
        self.res_scale = res_scale
        
    def forward(self, x):
        x = self.head(x)
        residual = x
        
        x = self.res_blocks(x)
        x = self.body(x)
        
        x = self.res_scale * x + residual
        
        x = self.tail(x)
        
        return x

这是一个简单的EDSR模型，包含了一个头部卷积层、多个残差块和一个尾部卷积层。输入和输出通道数都是1。