Fedformer中的FEB模块与torch.nn.Parameter的简单理解

与2022.10.25日更新,关于torch.nn.Parameter看看下面这篇博客,同时你可以关注一下nn.Linear与nn.Parameter的区别,可以用nn.Parameter实现nn.Linear

[Pytorch系列-30]:神经网络基础 - torch.nn库五大基本功能:nn.Parameter、nn.Linear、nn.functioinal、nn.Module、nn.Sequentia_文火冰糖的硅基工坊的博客-CSDN博客

Fedformer中的FEB模块与torch.nn.Parameter的简单理解_第1张图片

这个是fedformer里面提的频率增强模块,其实就是通过傅立叶变换拿到频率特征然后乘上一个可学习参数,为该操作赋能,把提取到的频域信息整合到模型中。

代码 


# ########## fourier layer #############
class FourierBlock(nn.Module):
    def __init__(self, in_channels, out_channels, seq_len, modes=0, mode_select_method='random'):
        super(FourierBlock, self).__init__()
        print('fourier enhanced block used!')
        """
        1D Fourier block. It performs representation learning on frequency domain, 
        it does FFT, linear transform, and Inverse FFT.    
        """
        # get modes on frequency domain
        self.index = get_frequency_modes(seq_len, modes=modes, mode_select_method=mode_select_method)#得到随机打乱选取的基,后续进行DFT操作
        print('modes={}, index={}'.format(modes, self.index))

        self.scale = (1 / (in_channels * out_channels))
        self.weights1 = nn.Parameter(
            self.scale * torch.rand(8, in_channels // 8, out_channels // 8, len(self.index), dtype=torch.cfloat))

    # Complex multiplication 复数乘法
    def compl_mul1d(self, input, weights):
        # (batch, in_channel, x ), (in_channel, out_channel, x) -> (batch, out_channel, x)
        return torch.einsum("bhi,hio->bho", input, weights)#高维张量的计算
    #搞懂这个torch.einsum操作!!!

    def forward(self, q, k, v, mask):
        # size = [B, L, H, E]
        B, L, H, E = q.shape
        x = q.permute(0, 2, 3, 1)
        # Compute Fourier coefficients
        x_ft = torch.fft.rfft(x, dim=-1)
        # Perform Fourier neural operations
        out_ft = torch.zeros(B, H, E, L // 2 + 1, device=x.device, dtype=torch.cfloat)
        for wi, i in enumerate(self.index):
            out_ft[:, :, :, wi] = self.compl_mul1d(x_ft[:, :, :, i], self.weights1[:, :, :, wi])#这里就是点对点运算,每个频率点的幅值分别乘以一个可学习参数
        # Return to time domain
        x = torch.fft.irfft(out_ft, n=x.size(-1))
        return (x, None) 

torch.nn.Parameter理解_Stoneplay26的博客-CSDN博客_torch.nn.parameter

PyTorch里面的torch.nn.Parameter()_明泽.的博客-CSDN博客_torch.nn.parameter

参考资料

[Pytorch系列-30]:神经网络基础 - torch.nn库五大基本功能:nn.Parameter、nn.Linear、nn.functioinal、nn.Module、nn.Sequentia_文火冰糖的硅基工坊的博客-CSDN博客

torch.nn.Parameter()_chenzy_hust的博客-CSDN博客_nn.parameter()

PyTorch里面的torch.nn.Parameter() - 简书

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