将Resnet更改为适用于接受一组低分辨率图像的输入，输出单幅高分辨率图像完整程序示例

以下是一个示例程序，将ResNet更改为接受低分辨率图像列表的输入，然后输出一张高分辨率图像。

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

class ResidualBlock(nn.Module):
    def __init__(self, in_channels):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(in_channels)
        self.conv2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(in_channels)

    def forward(self, x):
        identity = x
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += identity
        out = F.relu(out)
        return out

class UpsampleBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(UpsampleBlock, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.shuf = nn.PixelShuffle(2)
        self.bn = nn.BatchNorm2d(out_channels)

    def forward(self, x):
        out = self.conv(x)
        out = self.shuf(out)
        out = F.relu(self.bn(out))
        return out

class ResNetSR(nn.Module):
    def __init__(self, num_blocks=16, input_channels=3, output_channels=3):
        super(ResNetSR, self).__init__()
        self.conv1 = nn.Conv2d(input_channels, 64, kernel_size=9, stride=1, padding=4, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.res_blocks = nn.Sequential(*[ResidualBlock(64) for _ in range(num_blocks)])
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(64)
        self.upsample = nn.Sequential(
            UpsampleBlock(64, 256),
            UpsampleBlock(64, 256),
            nn.Conv2d(64, output_channels, kernel_size=9, stride=1, padding=4, bias=False)
        )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.res_blocks(out)
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.upsample(out)
        return out

在这个例子中，我们定义了三个子模块：

- `ResidualBlock`是ResNet的基本块，用于提取图像的特征。
- `UpsampleBlock`是用于上采样特征的块。
- `ResNetSR`是我们的超分辨率模型，它由`ResidualBlock`和`UpsampleBlock`组成。

在`ResNetSR`中，我们首先使用一个大的卷积层（`conv1`）来提取特征，然后使用多个`ResidualBlock`来增加深度。之后，我们使用另一个卷积层（`conv2`）来进一步提取特征，并使用多个`UpsampleBlock`来上采样特征。最后，我们使用一个大的卷积层（`upsample`）来生成高分辨率图像输出。

这个模型接受一个低分辨率图像列表作为输入，并输出一张高分辨率图像。在训练和测试期间，我们需要将低分辨率图像列表堆叠在一起，以便将它们输入到模型中。例如：

lr_image_list = [lr_image1, lr_image2, lr_image3]
lr_images = torch.stack(lr_image_list, dim=0)
model = ResNetSR()
sr_image = model(lr_images)