pytorch版GaborCNN的代码
时间: 2023-11-13 12:08:46 浏览: 37
下面是一个简单的PyTorch版GaborCNN的代码示例:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class GaborConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True):
super(GaborConv2d, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
# 定义卷积核参数
self.theta = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size))
self.sigma = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size))
self.lambd = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size))
self.psi = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size))
if bias:
self.bias = nn.Parameter(torch.randn(out_channels))
else:
self.bias = None
def forward(self, x):
batch_size, channels, height, width = x.shape
# 生成Gabor滤波器
gabor_filters = []
for i in range(self.out_channels):
theta = self.theta[i].unsqueeze(1).unsqueeze(1)
sigma = self.sigma[i].unsqueeze(1).unsqueeze(1)
lambd = self.lambd[i].unsqueeze(1).unsqueeze(1)
psi = self.psi[i].unsqueeze(1).unsqueeze(1)
kernel = self._gabor_kernel(theta, sigma, lambd, psi, self.kernel_size)
gabor_filters.append(kernel)
gabor_filters = torch.stack(gabor_filters, dim=0) # (out_channels, in_channels, kernel_size, kernel_size)
gabor_filters = gabor_filters.to(x.device)
# 卷积操作
x = x.unsqueeze(1) # (batch_size, 1, channels, height, width)
x = F.conv3d(x, gabor_filters.unsqueeze(2), padding=(0, self.padding, self.padding))
x = x.squeeze(2) # (batch_size, out_channels, height, width)
# 添加偏置项
if self.bias is not None:
x += self.bias.view(1, -1, 1, 1)
return x
def _gabor_kernel(self, theta, sigma, lambd, psi, kernel_size):
half_size = kernel_size // 2
x, y = torch.meshgrid(torch.linspace(-half_size, half_size, kernel_size), torch.linspace(-half_size, half_size, kernel_size))
x, y = x.float(), y.float()
x_theta = x * torch.cos(theta) + y * torch.sin(theta)
y_theta = -x * torch.sin(theta) + y * torch.cos(theta)
gb = torch.exp(-0.5 * (x_theta**2 + y_theta**2) / (sigma**2))
gb *= torch.cos(2 * math.pi * x_theta / lambd + psi)
return gb
```
这是一个简单的Gabor卷积层实现,它继承自nn.Module基类,并实现了forward方法。在forward方法中,我们首先生成Gabor滤波器,然后使用F.conv3d函数执行卷积操作。由于我们已经在生成Gabor滤波器时添加了可学习参数,因此不需要手动更新滤波器。
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