Added as_contiguous arg following recommedations from community.

monai/transforms/utils.py

@@ -1633,14 +1633,15 @@ class Fourier:
  """
 
  @staticmethod
- def shift_fourier(x: NdarrayOrTensor, spatial_dims: int) -> NdarrayOrTensor:
+ def shift_fourier(x: NdarrayOrTensor, spatial_dims: int, as_contiguous: bool = False) -> NdarrayOrTensor:
  """
  Applies fourier transform and shifts the zero-frequency component to the
  center of the spectrum. Only the spatial dimensions get transformed.
 
  Args:
  x: Image to transform.
  spatial_dims: Number of spatial dimensions.
+ as_contiguous: Whether to convert the cached NumPy array or PyTorch tensor to be contiguous.
 
  Returns
  k: K-space data.
@@ -1649,23 +1650,24 @@ def shift_fourier(x: NdarrayOrTensor, spatial_dims: int) -> NdarrayOrTensor:
  k: NdarrayOrTensor
  if isinstance(x, torch.Tensor):
  if hasattr(torch.fft, "fftshift"): # `fftshift` is new in torch 1.8.0
- k = torch.fft.fftshift(torch.fft.fftn(x, dim=dims), dim=dims).contiguous()
+ k = torch.fft.fftshift(torch.fft.fftn(x, dim=dims), dim=dims)
  else:
  # if using old PyTorch, will convert to numpy array and return
- k = np.ascontiguousarray(np.fft.fftshift(np.fft.fftn(x.cpu().numpy(), axes=dims), axes=dims))
+ k = np.fft.fftshift(np.fft.fftn(x.cpu().numpy(), axes=dims), axes=dims)
  else:
- k = np.ascontiguousarray(np.fft.fftshift(np.fft.fftn(x, axes=dims), axes=dims))
- return k
+ k = np.fft.fftshift(np.fft.fftn(x, axes=dims), axes=dims)
+ return ascontiguousarray(k) if as_contiguous else k
 
  @staticmethod
- def inv_shift_fourier(k: NdarrayOrTensor, spatial_dims: int, n_dims: int | None = None) -> NdarrayOrTensor:
+ def inv_shift_fourier(k: NdarrayOrTensor, spatial_dims: int, n_dims: int | None = None, as_contiguous: bool = False) -> NdarrayOrTensor:
  """
  Applies inverse shift and fourier transform. Only the spatial
  dimensions are transformed.
 
  Args:
  k: K-space data.
  spatial_dims: Number of spatial dimensions.
+ as_contiguous: Whether to convert the cached NumPy array or PyTorch tensor to be contiguous.
 
  Returns:
  x: Tensor in image space.
@@ -1674,13 +1676,13 @@ def inv_shift_fourier(k: NdarrayOrTensor, spatial_dims: int, n_dims: int | None
  out: NdarrayOrTensor
  if isinstance(k, torch.Tensor):
  if hasattr(torch.fft, "ifftshift"): # `ifftshift` is new in torch 1.8.0
- out = torch.fft.ifftn(torch.fft.ifftshift(k, dim=dims), dim=dims, norm="backward").real.contiguous()
+ out = torch.fft.ifftn(torch.fft.ifftshift(k, dim=dims), dim=dims, norm="backward").real
  else:
  # if using old PyTorch, will convert to numpy array and return
- out = np.ascontiguousarray(np.fft.ifftn(np.fft.ifftshift(k.cpu().numpy(), axes=dims), axes=dims).real)
+ out = np.fft.ifftn(np.fft.ifftshift(k.cpu().numpy(), axes=dims), axes=dims).real
  else:
- out = np.ascontiguousarray(np.fft.ifftn(np.fft.ifftshift(k, axes=dims), axes=dims).real)
- return out
+ out = np.fft.ifftn(np.fft.ifftshift(k, axes=dims), axes=dims).real
+ return ascontiguousarray(out) if as_contiguous else out
 
 
 def get_number_image_type_conversions(transform: Compose, test_data: Any, key: Hashable | None = None) -> int: