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
时间: 2024-04-30 15:20:46 浏览: 14
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。
```python
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:
```python
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)
```
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