帮我用pytorch定义一个loggabor_fn函数，用于生成LogGabor滤波器的参数，以及一个LogGaborConv2d类，用于创建一个神经网络层，其中需使用LogGabor滤波器

好的，以下是定义 loggabor_fn 函数的代码：

import torch
import math

def loggabor_fn(sigma, theta, lambd, gamma, psi, ksize):
    """
    生成 LogGabor 滤波器的参数
    :param sigma: float, 控制高斯包络的标准差
    :param theta: float, 控制滤波器的方向
    :param lambd: float, 控制滤波器的波长
    :param gamma: float, 控制高斯包络的宽度
    :param psi: float, 控制相位
    :param ksize: int, 滤波器的大小
    :return: LogGabor 滤波器的参数
    """
    half_size = ksize // 2
    x, y = torch.meshgrid(torch.arange(-half_size, half_size + 1), torch.arange(-half_size, half_size + 1))
    x_theta = x * math.cos(theta) + y * math.sin(theta)
    y_theta = -x * math.sin(theta) + y * math.cos(theta)
    sigma_x = sigma
    sigma_y = sigma / gamma
    envelope = torch.exp(-0.5 * ((x_theta ** 2) / (sigma_x ** 2) + (y_theta ** 2) / (sigma_y ** 2)))
    wavelength = lambd
    phase = torch.exp(1j * 2 * math.pi * x_theta / wavelength + psi)
    return envelope * phase

接下来是定义 LogGaborConv2d 类的代码：

import torch.nn as nn

class LogGaborConv2d(nn.Module):
    """
    创建一个神经网络层，其中使用 LogGabor 滤波器
    """
    def __init__(self, in_channels, out_channels, kernel_size, sigma, theta, lambd, gamma, psi, stride=1, padding=0, dilation=1, groups=1, bias=True):
        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
        self.dilation = dilation
        self.groups = groups
        self.weight = nn.Parameter(torch.zeros((out_channels, in_channels, kernel_size, kernel_size), dtype=torch.complex64))
        self.bias = nn.Parameter(torch.zeros(out_channels)) if bias else None
        for i in range(out_channels):
            for j in range(in_channels):
                self.weight[i, j] = loggabor_fn(sigma=sigma, theta=theta, lambd=lambd, gamma=gamma, psi=psi, ksize=kernel_size)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.kaiming_uniform_(self.weight.real, a=math.sqrt(5))
        nn.init.zeros_(self.weight.imag)
        if self.bias is not None:
            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight.real)
            bound = 1 / math.sqrt(fan_in)
            nn.init.uniform_(self.bias, -bound, bound)

    def forward(self, input):
        return torch.conv2d(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)

希望这些代码能够帮到你！