用torch写的双路特征提取代码,一个支路用一维卷积提取高光谱谱间特征,一路用二维卷积提取空间特征,最后用concat融合在一起
时间: 2024-03-09 12:47:32 浏览: 16
好的,您的问题是关于使用torch写双路特征提取代码的问题。下面是一个示例代码,其中一个支路使用一维卷积提取高光谱谱间特征,另一个支路使用二维卷积提取空间特征,最后使用concat函数将它们融合在一起。请注意,以下代码仅供参考:
```python
import torch.nn as nn
class DoubleConv(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(out_channels)
self.relu2 = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu2(x)
return x
class SpectralBranch(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size=3, padding=1)
self.bn = nn.BatchNorm1d(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
class SpatialBranch(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(out_channels)
self.relu2 = nn.ReLU(inplace=True)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu2(x)
x = self.pool(x)
return x
class DoubleBranch(nn.Module):
def __init__(self, spectral_in_channels, spectral_out_channels, spatial_in_channels, spatial_out_channels):
super().__init__()
self.spectral_branch = SpectralBranch(spectral_in_channels, spectral_out_channels)
self.spatial_branch = SpatialBranch(spatial_in_channels, spatial_out_channels)
self.concat = nn.Concat(dim=1)
def forward(self, x_spectral, x_spatial):
x_spectral = self.spectral_branch(x_spectral)
x_spatial = self.spatial_branch(x_spatial)
x = self.concat([x_spectral, x_spatial])
return x
```
在这个示例代码中,我们使用了nn.Module类来定义了三个子模块:DoubleConv、SpectralBranch和SpatialBranch。其中DoubleConv是一个简单的卷积块,用于提取特征。SpectralBranch是一个卷积支路,用于提取高光谱谱间特征,它使用一维卷积。SpatialBranch是另一个卷积支路,用于提取空间特征,它使用二维卷积。最后,我们定义了一个DoubleBranch模块,它将两个支路的输出连接在一起,并通过concat函数将它们融合在一起。
希望这能够解答您的问题。