torch.fft.fft#

torch.fft.fft(input,n=None,dim=-1,norm=None,*,out=None)→Tensor#

Computes the one dimensional discrete Fourier transform ofinput.

Note

The Fourier domain representation of any real signal satisfies theHermitian property:X[i] = conj(X[-i]). This function always returns boththe positive and negative frequency terms even though, for real inputs, thenegative frequencies are redundant.rfft() returns themore compact one-sided representation where only the positive frequenciesare returned.

Note

Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater.However it only supports powers of 2 signal length in every transformed dimension.

Parameters:

• input (Tensor) – the input tensor

• n (int,optional) – Signal length. If given, the input will either be zero-paddedor trimmed to this length before computing the FFT.

• dim (int,optional) – The dimension along which to take the one dimensional FFT.

• norm (str,optional) –

  Normalization mode. For the forward transform(fft()), these correspond to:

  • "forward" - normalize by1/n

  • "backward" - no normalization

  • "ortho" - normalize by1/sqrt(n) (making the FFT orthonormal)

  Calling the backward transform (ifft()) with the samenormalization mode will apply an overall normalization of1/n betweenthe two transforms. This is required to makeifft()the exact inverse.

  Default is"backward" (no normalization).

Keyword Arguments:

out (Tensor,optional) – the output tensor.

Example

>>>t=torch.arange(4)>>>ttensor([0, 1, 2, 3])>>>torch.fft.fft(t)tensor([ 6.+0.j, -2.+2.j, -2.+0.j, -2.-2.j])

>>>t=torch.tensor([0.+1.j,2.+3.j,4.+5.j,6.+7.j])>>>torch.fft.fft(t)tensor([12.+16.j, -8.+0.j, -4.-4.j,  0.-8.j])