Simple torch.nn.module implementation of Alias-Free-GAN style filter and resample

Related tags

Deep Learningtorchpytorchresampleresample-audio-filesaliasingalias-free-gan
Overview

Alias-Free-Torch

Simple torch module implementation of Alias-Free GAN.

This repository including

Note: Since this repository is unofficial, filter and upsample could be different with official implementation.

Note: 2d lowpass filter is applying sinc instead of jinc (first order Bessel function of the first kind) in paper

Requirements

Due to torch.kaiser_window and torch.i0 are implemeted after 1.7.0, our repository need torch>=1.7.0.

  • Pytorch>=1.7.0

TODO

  • 2d sinc filter
  • 2d resample
  • devide 1d and 2d modules
  • pip packaging

Test results 1d

Filter sine Filter noise
filtersin filternoise
upsample downsample
up2 down10
up256 down100

Test results 2d

Filter L1 norm sine Filter noise
filter2dsin filter2dnoise
upsample downsample
up2d2 downsample2d2
up2d8 downsample2d4
Activation
act

References

  • Alias-Free GAN
  • adefossez/julius
  • A. V. Oppenheim and R. W. Schafer. Discrete-Time Signal Processing. Pearson, International Edition, 3rd edition, 2010

Acknowledgement

This work is done at MINDsLab Inc.

Thanks to teammates at MINDsLab Inc.

Comments
  • Batched resampling for the new implementation

    Batched resampling for the new implementation

    Hi, thank you very much for the contribution.

    I think the new implementation of resample.Upsample1d and resample.Downsample1d breaks batched resampling when using groups=C without expanding the filter to match the shape. Perhaps the implementation should be like the below (maybe similar goes to 2d):

    Upsample1d.forward()

        # x: [B,C,T]
    def forward(self, x):
        B, C, T = x.shape
        x = F.pad(x, (self.pad, self.pad), mode='reflect')
        # TConv with filter expanded to C with C groups for depthwise op
        x = self.ratio * F.conv_transpose1d(
            x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
        pad_left = self.pad * self.stride + (self.kernel_size -
                                             self.stride) // 2
        pad_right = self.pad * self.stride + (self.kernel_size - self.stride +
                                              1) // 2
        x = x[..., pad_left:-pad_right]

    LowPassFilter1d.forward()

        #input [B,C,T]
    def forward(self, x):
        B, C, T = x.shape
        if self.padding:
            x = F.pad(x, (self.left_pad, self.right_pad),
                      mode=self.padding_mode)
        # Conv with filter expanded to C with C groups for depthwise op
        out = F.conv1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C) # typo 'groupds' btw
        return out

    Could you check the correctness? Thanks again for the implementation!

    opened by L0SG 2
  • torch.speical.i1 typo

    torch.speical.i1 typo

    https://github.com/junjun3518/alias-free-torch/blob/f1fddd52fdd068ee475e82ae60c92e1bc24ffe02/src/alias_free_torch/filter.py#L22

    At this line I believe you wanted torch.special.i1.

    opened by torridgristle 2

  • "if self.pad / self.padding" in LowPassFilter2d

    https://github.com/junjun3518/alias-free-torch/blob/258551410ff7bf02e06ece7c597466dc970fe5c7/src/alias_free_torch/filter.py#L165 https://github.com/junjun3518/alias-free-torch/blob/258551410ff7bf02e06ece7c597466dc970fe5c7/src/alias_free_torch/filter.py#L173

    In LowPassFilter2d it looks like if self.pad: should change to if self.padding:, or self.padding = padding should change to self.pad = padding to match LowPassFilter1d.

    opened by torridgristle 1
  • Padding Bool typo

    Padding Bool typo

    https://github.com/junjun3518/alias-free-torch/blob/258551410ff7bf02e06ece7c597466dc970fe5c7/src/alias_free_torch/filter.py#L73

    padding: bool: True, should be padding: bool = True,

    I'm not sure if this causes an error with every version of PyTorch, but it does with PyTorch 1.12.0+cu113 on Python 3.7.13

    opened by torridgristle 1
  • 2D Filter Jinc appears to be wrong

    2D Filter Jinc appears to be wrong

    Here is a plot of the generated 1D sinc filter kernel. sinc looks right

    Here is a plot of the generated 2D jinc filter kernel. jinc looks wrong

    I'd expect it to look more like a series of rings or ripples, rather than a donut or torus.

    jinc filtered noise fft

    The FFT output for randn noise put through the 2D filter doesn't look right either.

    change jinc to sinc in 2d filter

    Changing filter_ = 2 * cutoff * window * jinc(2 * cutoff * time) to filter_ = 2 * cutoff * window * sinc(2 * cutoff * time) in kaiser_jinc_filter2d makes a more familiar kernel.

    change jinc to sinc in 2d filter fft out

    And the FFT output for randn noise put through this 2D filter looks about how I'd expect.

    opened by torridgristle 3
Releases(v0.0.6)
Owner
이준혁(Junhyeok Lee)
Audio/Speech Deep Learning Researcher @mindslab-ai
이준혁(Junhyeok Lee)
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