log-Gabor的pytorch代码

我可以回答这个问题，以下是一个log-Gabor的pytorch代码示例：

import torch
import torch.nn.functional as F
import numpy as np

def log_gabor_filter(size, sigma, theta, lambd, gamma):
    """Generate a log-Gabor filter."""
    x, y = np.meshgrid(np.linspace(-size/2, size/2, size), np.linspace(-size/2, size/2, size))
    x_theta = x * np.cos(theta) + y * np.sin(theta)
    y_theta = -x * np.sin(theta) + y * np.cos(theta)
    gb = np.exp(-(np.log(x_theta**2 + y_theta**2) / 2 / np.log(sigma))**2) * np.cos(2 * np.pi * x_theta / lambd + gamma)
    return gb

class LogGaborFilter(torch.nn.Module):
    """Log-Gabor filter layer."""
    def __init__(self, size, sigma, theta, lambd, gamma):
        super(LogGaborFilter, self).__init__()
        self.filter = torch.nn.Parameter(torch.Tensor(log_gabor_filter(size, sigma, theta, lambd, gamma)))
        self.filter.requiresGrad = False

    def forward(self, x):
        return F.conv2d(x, self.filter.expand(x.shape[1], -1, -1, -1), padding=self.filter.shape[1]//2)

这个代码实现了一个log-Gabor滤波器层，可以用于图像处理任务。