请用pytorch写出小波散射网络

好的，以下是使用PyTorch实现小波散射网络的代码：

import torch
import torch.nn as nn

class Scattering(nn.Module):
    def __init__(self, J, L):
        super(Scattering, self).__init__()
        self.J = J
        self.L = L
        self.filters = self.build_filters()

    def build_filters(self):
        filters = []
        for j in range(0, self.J + 1):
            filters.append(self.morlet_wavelet(j))
        return filters

    def morlet_wavelet(self, j):
        scale = 2 ** j
        B = 8 * scale / self.L
        Q = 1 / (2 ** (j + 1))
        x, y = torch.meshgrid(torch.linspace(-1, 1, self.L), torch.linspace(-1, 1, self.L))
        r = torch.sqrt(x ** 2 + y ** 2)
        f = torch.sin(B * r) * torch.exp(-Q * r ** 2)
        f = f - torch.mean(f)
        f = f / torch.norm(f)
        return f.unsqueeze(0)

    def conv_and_pool(self, x, filter):
        conv = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=filter.shape[2], padding=1, stride=1)
        conv.weight.data = filter
        conv.bias.data.fill_(0)
        y = conv(x)
        y = nn.functional.avg_pool2d(y, kernel_size=2)
        return y

    def forward(self, x):
        S = []
        U = nn.functional.avg_pool2d(x, kernel_size=2)
        S.append(self.conv_and_pool(U, self.filters[0]))
        for j in range(1, self.J + 1):
            V = self.conv_and_pool(U, self.filters[j])
            W = nn.functional.avg_pool2d(V, kernel_size=2)
            S.append(self.conv_and_pool(W, self.filters[j - 1]))
            U = V
        return torch.cat(S, dim=1)

其中，`Scattering`类定义了小波散射网络的结构，`J`和`L`分别表示小波散射网络的深度和输入图像的大小。`build_filters`方法用于构建小波滤波器，`morlet_wavelet`方法用于生成Morlet小波，并返回一个张量。`conv_and_pool`方法用于卷积和池化操作。`forward`方法将输入图像传递给小波散射网络，并返回小波散射系数。