torch.fft.fft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) → Tensor

Computes the 2 dimensional discrete Fourier transform of input. Equivalent to fftn() but FFTs only the last two dimensions by default.


The Fourier domain representation of any real signal satisfies the Hermitian property: X[i, j] = conj(X[-i, -j]). This function always returns all positive and negative frequency terms even though, for real inputs, half of these values are redundant. rfft2() returns the more compact one-sided representation where only the positive frequencies of the last dimension are returned.

  • input (Tensor) – the input tensor

  • s (Tuple[int], optional) – Signal size in the transformed dimensions. If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the FFT. If a length -1 is specified, no padding is done in that dimension. Default: s = [input.size(d) for d in dim]

  • dim (Tuple[int], optional) – Dimensions to be transformed. Default: last two dimensions.

  • norm (str, optional) –

    Normalization mode. For the forward transform (fft2()), these correspond to:

    • "forward" - normalize by 1/n

    • "backward" - no normalization

    • "ortho" - normalize by 1/sqrt(n) (making the FFT orthonormal)

    Where n = prod(s) is the logical FFT size. Calling the backward transform (ifft2()) with the same normalization mode will apply an overall normalization of 1/n between the two transforms. This is required to make ifft2() the exact inverse.

    Default is "backward" (no normalization).

Keyword Arguments

out (Tensor, optional) – the output tensor.


>>> x = torch.rand(10, 10, dtype=torch.complex64)
>>> fft2 = torch.fft.fft2(x)

The discrete Fourier transform is separable, so fft2() here is equivalent to two one-dimensional fft() calls:

>>> two_ffts = torch.fft.fft(torch.fft.fft(x, dim=0), dim=1)
>>> torch.allclose(fft2, two_ffts)


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