帮我用pytorch定义一个loggabor_fn函数，用于生成对数Gabor滤波器的参数，以及一个对数GaborConv2d类，用于创建一个神经网络层，其中需使用对数Gabor滤波器

好的，以下是定义 loggabor_fn 函数的代码：

import torch
import torch.nn.functional as F

def loggabor_fn(kernel_size, sigma, theta, lambd, gamma):
    """
    定义对数Gabor滤波器的参数
    :param kernel_size: 滤波器大小
    :param sigma: 高斯函数的标准差
    :param theta: 滤波器的方向
    :param lambd: 滤波器的波长
    :param gamma: 滤波器的椭圆度
    :return: 对数Gabor滤波器的参数
    """
    sigma_x = sigma
    sigma_y = float(sigma) / gamma

    # Bounding box
    nstds = 3  # Number of standard deviation sigma
    xmax = max(abs(nstds * sigma_x * torch.cos(theta)), abs(nstds * sigma_y * torch.sin(theta)))
    xmax = torch.ceil(max(1, xmax))
    ymax = max(abs(nstds * sigma_x * torch.sin(theta)), abs(nstds * sigma_y * torch.cos(theta)))
    ymax = torch.ceil(max(1, ymax))
    xmin = -xmax
    ymin = -ymax
    (y, x) = torch.meshgrid(torch.arange(ymin, ymax + 1), torch.arange(xmin, xmax + 1))

    # Rotation
    x_theta = x * torch.cos(theta) + y * torch.sin(theta)
    y_theta = -x * torch.sin(theta) + y * torch.cos(theta)

    # Log Gabor
    gb = torch.exp(-0.5 * (torch.pow(x_theta, 2) / torch.pow(sigma_x, 2) + torch.pow(y_theta, 2) / torch.pow(sigma_y, 2)))
    gb *= torch.cos(2 * math.pi * x_theta / lambd + math.pi)

    # Normalize
    gb -= torch.mean(gb)
    gb /= torch.std(gb)

    return gb

接下来是定义 LogGaborConv2d 类的代码：

import math
import torch
import torch.nn as nn
import torch.nn.functional as F

class LogGaborConv2d(nn.Module):
    """
    定义一个神经网络层，使用对数Gabor滤波器
    """
    def __init__(self, in_channels, out_channels, kernel_size, sigma, theta, lambd, gamma, stride=1, padding=0):
        super(LogGaborConv2d, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding

        # 定义对数Gabor滤波器的参数
        self.weight = nn.Parameter(torch.zeros(out_channels, in_channels, kernel_size, kernel_size))
        for i in range(out_channels):
            for j in range(in_channels):
                self.weight[i, j, :, :] = loggabor_fn(kernel_size, sigma[i], theta[i], lambd[i], gamma[i])

        self.bias = nn.Parameter(torch.zeros(out_channels))

    def forward(self, x):
        return F.conv2d(x, self.weight, self.bias, stride=self.stride, padding=self.padding)

希望这些代码能够帮到你！