请提供代码满足使用pytorch训练的基于多尺度自编码网络的红外与可见光图像融合的模型,实现红外与可见光图像的融合
时间: 2023-10-29 16:05:10 浏览: 107
使用深度学习框架(Pytorch) 的 红外和可见光图像融合_Jupyter_python代码_下载
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以下是一个基于多尺度自编码网络的红外与可见光图像融合模型的PyTorch代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
def __init__(self, in_channels, out_channels):
super(Encoder, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = self.pool(x)
return x
class Decoder(nn.Module):
def __init__(self, in_channels, out_channels):
super(Decoder, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.upsample(x)
x = F.relu(self.conv2(x))
return x
class FusionNet(nn.Module):
def __init__(self, in_channels):
super(FusionNet, self).__init__()
self.encoder1 = Encoder(in_channels, 64)
self.encoder2 = Encoder(64, 128)
self.encoder3 = Encoder(128, 256)
self.decoder1 = Decoder(256, 128)
self.decoder2 = Decoder(128, 64)
self.decoder3 = Decoder(64, in_channels)
def forward(self, x1, x2):
# Encoding
x1_e1 = self.encoder1(x1)
x1_e2 = self.encoder2(x1_e1)
x1_e3 = self.encoder3(x1_e2)
x2_e1 = self.encoder1(x2)
x2_e2 = self.encoder2(x2_e1)
x2_e3 = self.encoder3(x2_e2)
# Decoding
x3_d1 = self.decoder1(torch.cat([x1_e3, x2_e3], dim=1))
x3_d2 = self.decoder2(torch.cat([x1_e2, x2_e2, x3_d1], dim=1))
x3_d3 = self.decoder3(torch.cat([x1_e1, x2_e1, x3_d2], dim=1))
return x3_d3
```
在上面的代码中,我们定义了一个 `Encoder` 类和一个 `Decoder` 类,它们分别用于提取图像的特征和重建图像。然后,我们定义了一个 `FusionNet` 类,它将两个输入图像分别编码为特征向量,然后将它们进行级联并传递给解码器,最终生成融合图像。
我们可以使用以下代码来进行训练:
```python
import torch.optim as optim
# 初始化模型
model = FusionNet(in_channels=3)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()
# 训练模型
for epoch in range(num_epochs):
running_loss = 0.0
for data in dataloader:
inputs1, inputs2, labels = data
optimizer.zero_grad()
outputs = model(inputs1, inputs2)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs1.size(0)
epoch_loss = running_loss / len(dataset)
print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, epoch_loss))
```
在上面的代码中,我们首先定义了优化器和损失函数。然后,我们通过迭代训练数据集来训练模型。每个batch的输入包括两个图像,我们将它们作为模型的输入并计算输出。然后,我们计算输出与标签之间的均方误差,反向传播误差并更新模型参数。最后,我们计算并打印出当前的损失。
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