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# torch.fft.ihfft¶

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

Computes the inverse of hfft().

input must be a real-valued signal, interpreted in the Fourier domain. The IFFT of a real signal is Hermitian-symmetric, X[i] = conj(X[-i]). ihfft() represents this in the one-sided form where only the positive frequencies below the Nyquist frequency are included. To compute the full output, use ifft().

Note

Supports torch.half 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 real input tensor

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

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

• norm (str, optional) –

Normalization mode. For the backward transform (ihfft()), 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 (hfft()) with the same normalization mode will apply an overall normalization of 1/n between the two transforms. This is required to make ihfft() the exact inverse.

Default is "backward" (normalize by 1/n).

Keyword Arguments

out (Tensor, optional) – the output tensor.

Example

>>> t = torch.arange(5)
>>> t
tensor([0, 1, 2, 3, 4])
>>> torch.fft.ihfft(t)
tensor([ 2.0000-0.0000j, -0.5000-0.6882j, -0.5000-0.1625j])


Compare against the full output from ifft():

>>> torch.fft.ifft(t)
tensor([ 2.0000-0.0000j, -0.5000-0.6882j, -0.5000-0.1625j, -0.5000+0.1625j,
-0.5000+0.6882j]) ## Docs

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