torch.fft.ifft — PyTorch 2.7 documentation (original) (raw)

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

Computes the one dimensional inverse discrete Fourier transform of input.

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-padded or trimmed to this length before computing the IFFT.
• dim (int, optional) – The dimension along which to take the one dimensional IFFT.
• norm (str, optional) –
  Normalization mode. For the backward transform (ifft()), these correspond to:
  • "forward" - no normalization
  • "backward" - normalize by 1/n
  • "ortho" - normalize by 1/sqrt(n) (making the IFFT orthonormal)
    Calling the forward transform (fft()) with the same normalization mode will apply an overall normalization of 1/n between the two transforms. This is required to make ifft()the exact inverse.
    Default is "backward" (normalize by 1/n).

Keyword Arguments

out (Tensor, optional) – the output tensor.

Example

t = torch.tensor([ 6.+0.j, -2.+2.j, -2.+0.j, -2.-2.j]) torch.fft.ifft(t) tensor([0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j])