torch.irfft¶

torch.irfft(input, signal_ndim, normalized=False, onesided=True, signal_sizes=None) → Tensor¶

Complex-to-real Inverse Discrete Fourier Transform.

Warning

The function torch.irfft() is deprecated and will be removed in a future PyTorch release. Use the new torch.fft module functions, instead, by importing torch.fft and calling torch.fft.irfft() for one-sided input, or torch.fft.ifft() for two-sided input.

This method computes the complex-to-real inverse discrete Fourier transform. It is mathematically equivalent with ifft() with differences only in formats of the input and output.

The argument specifications are almost identical with ifft(). Similar to ifft(), if normalized is set to True, this normalizes the result by multiplying it with $\sqrt{\prod_{i=1}^K N_i}$ so that the operator is unitary, where $N_i$ is the size of signal dimension $i$ .

Note

Due to the conjugate symmetry, input do not need to contain the full complex frequency values. Roughly half of the values will be sufficient, as is the case when input is given by rfft() with rfft(signal, onesided=True). In such case, set the onesided argument of this method to True. Moreover, the original signal shape information can sometimes be lost, optionally set signal_sizes to be the size of the original signal (without the batch dimensions if in batched mode) to recover it with correct shape.

Therefore, to invert an rfft(), the normalized and onesided arguments should be set identically for irfft(), and preferably a signal_sizes is given to avoid size mismatch. See the example below for a case of size mismatch.

See rfft() for details on conjugate symmetry.

The inverse of this function is rfft().

Warning

Generally speaking, input to this function should contain values following conjugate symmetry. Note that even if onesided is True, often symmetry on some part is still needed. When this requirement is not satisfied, the behavior of irfft() is undefined. Since torch.autograd.gradcheck() estimates numerical Jacobian with point perturbations, irfft() will almost certainly fail the check.

Note

For CUDA tensors, an LRU cache is used for cuFFT plans to speed up repeatedly running FFT methods on tensors of same geometry with same configuration. See cuFFT plan cache for more details on how to monitor and control the cache.

Warning

Due to limited dynamic range of half datatype, performing this operation in half precision may cause the first element of result to overflow for certain inputs.

Warning

For CPU tensors, this method is currently only available with MKL. Use torch.backends.mkl.is_available() to check if MKL is installed.

Parameters

• input (Tensor) – the input tensor of at least signal_ndim + 1 dimensions

• signal_ndim (int) – the number of dimensions in each signal. signal_ndim can only be 1, 2 or 3

• normalized (bool, optional) – controls whether to return normalized results. Default: False

• onesided (bool, optional) – controls whether input was halfed to avoid redundancy, e.g., by rfft(). Default: True

• signal_sizes (list or torch.Size, optional) – the size of the original signal (without batch dimension). Default: None

Returns

A tensor containing the complex-to-real inverse Fourier transform result

Return type

Tensor

Example:

>>> x = torch.randn(4, 4)
>>> torch.rfft(x, 2, onesided=True).shape
torch.Size([4, 3, 2])
>>>
>>> # notice that with onesided=True, output size does not determine the original signal size
>>> x = torch.randn(4, 5)

>>> torch.rfft(x, 2, onesided=True).shape
torch.Size([4, 3, 2])
>>>
>>> # now we use the original shape to recover x
>>> x
tensor([[-0.8992,  0.6117, -1.6091, -0.4155, -0.8346],
        [-2.1596, -0.0853,  0.7232,  0.1941, -0.0789],
        [-2.0329,  1.1031,  0.6869, -0.5042,  0.9895],
        [-0.1884,  0.2858, -1.5831,  0.9917, -0.8356]])
>>> y = torch.rfft(x, 2, onesided=True)
>>> torch.irfft(y, 2, onesided=True, signal_sizes=x.shape)  # recover x
tensor([[-0.8992,  0.6117, -1.6091, -0.4155, -0.8346],
        [-2.1596, -0.0853,  0.7232,  0.1941, -0.0789],
        [-2.0329,  1.1031,  0.6869, -0.5042,  0.9895],
        [-0.1884,  0.2858, -1.5831,  0.9917, -0.8356]])