public TorchTensor ifft2(long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)
{
if (this.Dimensions < 2)
{
throw new ArgumentException("ifft2() input should be at least 2D");
}
if (dim == null)
{
dim = new long[] { -2, -1 }
}
;
unsafe
{
fixed(long *ps = s, pDim = dim)
{
var res = THSTensor_ifft2(handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm);
if (res == IntPtr.Zero)
{
Torch.CheckForErrors();
}
return(new TorchTensor(res));
}
}
}
/// <summary>
/// Computes the one dimensional inverse discrete Fourier transform of input.
/// </summary>
/// <param name="input">The input tensor</param>
/// <param name="n">Signal length. If given, the input will either be zero-padded or trimmed to this length before computing the IFFT.</param>
/// <param name="dim">The dimension along which to take the one dimensional IFFT.</param>
/// <param name="norm">Normalization mode.</param>
/// <returns></returns>
public static Tensor ifft(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward)
{
var res = THSTensor_ifft(input.Handle, n, dim, (sbyte)norm);
if (res == IntPtr.Zero)
{
torch.CheckForErrors();
}
return(new Tensor(res));
}
public TorchTensor ifft(long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward)
{
var res = THSTensor_ifft(handle, n, dim, (sbyte)norm);
if (res == IntPtr.Zero)
{
Torch.CheckForErrors();
}
return(new TorchTensor(res));
}
public TorchTensor irfftn(long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)
{
var slen = (s == null) ? 0 : s.Length;
var dlen = (dim == null) ? 0 : dim.Length;
unsafe
{
fixed(long *ps = s, pDim = dim)
{
var res = THSTensor_irfftn(handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm);
if (res == IntPtr.Zero)
{
Torch.CheckForErrors();
}
return(new TorchTensor(res));
}
}
}
