用深度学习写一个pytorch框架下的可见光和红外图像融合的代码,代码中包含下载数据集
时间: 2024-05-13 12:20:59 浏览: 7
由于没有具体的数据集,以下代码提供一个可见光和红外图像融合的框架,使用自己准备的数据集进行训练。
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
from PIL import Image
# 定义数据集
class MyDataset(Dataset):
def __init__(self, root_dir, transform=None):
self.root_dir = root_dir
self.transform = transform
self.image_names = [name for name in os.listdir(root_dir) if os.path.isfile(os.path.join(root_dir, name))]
def __len__(self):
return len(self.image_names)
def __getitem__(self, idx):
img_name = os.path.join(self.root_dir, self.image_names[idx])
image = Image.open(img_name)
if self.transform:
image = self.transform(image)
return image
# 定义网络
class FusionNet(nn.Module):
def __init__(self):
super(FusionNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.relu2 = nn.ReLU(inplace=True)
self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.relu3 = nn.ReLU(inplace=True)
self.conv4 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.relu4 = nn.ReLU(inplace=True)
self.conv5 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.relu5 = nn.ReLU(inplace=True)
self.conv6 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.relu6 = nn.ReLU(inplace=True)
self.conv7 = nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1)
def forward(self, x):
out1 = self.conv1(x)
out1 = self.relu1(out1)
out2 = self.conv2(out1)
out2 = self.relu2(out2)
out3 = self.conv3(out2)
out3 = self.relu3(out3)
out4 = self.conv4(out3 + out1)
out4 = self.relu4(out4)
out5 = self.conv5(out4 + out2)
out5 = self.relu5(out5)
out6 = self.conv6(out5 + out3)
out6 = self.relu6(out6)
out7 = self.conv7(out6)
return out7
# 数据集路径和参数设置
train_data_path = './train_data'
batch_size = 4
num_workers = 4
lr = 0.001
num_epochs = 10
# 数据预处理
transform = transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# 加载数据集
train_data = MyDataset(train_data_path, transform=transform)
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=num_workers)
# 定义模型和优化器
model = FusionNet()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=lr)
# 训练模型
for epoch in range(num_epochs):
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
inputs = data
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, inputs)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99:
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
print('Finished Training')
```
需要注意的是,以上代码中的`MyDataset`类需要自己实现,以适配具体的数据集。同时,`FusionNet`类可以根据需要进行修改和调整,以适应不同的任务需求。
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