junjun3518 / alias-free-torch Goto Github PK

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

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.

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

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

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

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

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

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.

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

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!