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Use PyTorch to compute Fourier transforms (#389)
* Use PyTorch for FFT if available * Make sure tensor in on CPU before calling numpy()
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Original file line number | Diff line number | Diff line change |
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@@ -1,16 +1,27 @@ | ||
import torch | ||
import numpy as np | ||
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class FourierTransform: | ||
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@staticmethod | ||
def fourier_transform(array: np.ndarray) -> np.ndarray: | ||
transformed = np.fft.fftn(array) | ||
fshift = np.fft.fftshift(transformed) | ||
return fshift | ||
def fourier_transform(tensor: torch.Tensor) -> torch.Tensor: | ||
try: | ||
import torch.fft | ||
return torch.fft.fftn(tensor) | ||
except ModuleNotFoundError: | ||
import torch | ||
transformed = np.fft.fftn(tensor) | ||
fshift = np.fft.fftshift(transformed) | ||
return torch.from_numpy(fshift) | ||
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@staticmethod | ||
def inv_fourier_transform(fshift: np.ndarray) -> np.ndarray: | ||
f_ishift = np.fft.ifftshift(fshift) | ||
img_back = np.fft.ifftn(f_ishift) | ||
return img_back | ||
def inv_fourier_transform(tensor: torch.Tensor) -> torch.Tensor: | ||
try: | ||
import torch.fft | ||
return torch.fft.ifftn(tensor) | ||
except ModuleNotFoundError: | ||
import torch | ||
f_ishift = np.fft.ifftshift(tensor) | ||
img_back = np.fft.ifftn(f_ishift) | ||
return torch.from_numpy(img_back) |