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torch.fft.ifft
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 by1/n
"ortho"
- normalize by1/sqrt(n)
(making the IFFT orthonormal)
Calling the forward transform (
fft()
) with the same normalization mode will apply an overall normalization of1/n
between the two transforms. This is required to makeifft()
the exact inverse.Default is
"backward"
(normalize by1/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])
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https://pytorch.org/docs/2.1/generated/torch.fft.ifft.html