pytorchsinc卷积代码

下面是一个使用 PyTorch 实现的 Sinc 卷积的代码示例：

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

class SincConv1d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, sample_rate):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.sample_rate = sample_rate
        
        if kernel_size % 2 == 0:
            raise ValueError('Kernel size must be odd')
        
        self.pad = (kernel_size - 1) // 2
        self.sinc = nn.Parameter(torch.zeros((out_channels, in_channels, kernel_size)))
        self.half = kernel_size % 2 == 0
        
        self.reset_parameters()

    def reset_parameters(self):
        self.sinc.data[:, :, self.kernel_size // 2] = 1
        lp_cutoff = 0.45 * self.sample_rate
        self.sinc.data = torch.tensor([self.sinc_window(i, lp_cutoff) for i in range(self.kernel_size)])
        
    def sinc_window(self, i, cutoff):
        sinc = torch.sin(torch.tensor(2 * cutoff * (i - self.kernel_size // 2)).float()) / (i - self.kernel_size // 2)
        if i == self.kernel_size // 2:
            sinc = 2 * cutoff
        window = 0.54 - 0.46 * torch.cos(torch.tensor(2 * i * np.pi / self.kernel_size)).float()
        return sinc * window

    def forward(self, x):
        x = F.pad(x, (self.pad, self.pad), mode='reflect')
        self.sinc.data[:, :, self.kernel_size // 2] = 1
        if self.half:
            self.sinc.data[:, :, self.kernel_size // 2 + 1:] = 0
        x = F.conv1d(x, self.sinc, stride=1, padding=0, dilation=1, groups=self.in_channels)
        return x

这个代码实现了一个 Sinc 卷积层，可以在神经网络中使用。你可以通过调整 `in_channels`、`out_channels`、`kernel_size` 和 `sample_rate` 参数来创建不同的 Sinc 卷积层。