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)); } } }