Fix: correctly apply fftshift to real-valued data inputs (#8407)

Correctly apply fftshift to real-valued data inputs

Fixes # .

### Description

Correctly apply fftshift to real-valued data inputs. The last dimension
of output of torch.view_as_real has size 2 for both real-valued and
complex-valued input.

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: Puyang Wang <[email protected]>
Co-authored-by: YunLiu <[email protected]>

Author: XwK-P

monai/networks/blocks/fft_utils_t.py

@@ -139,21 +139,17 @@ def ifftn_centered_t(ksp: Tensor, spatial_dims: int, is_complex: bool = True) ->
             output2 = ifftn_centered(ksp, spatial_dims=2, is_complex=True)
     """
     # define spatial dims to perform ifftshift, fftshift, and ifft
-    shift = list(range(-spatial_dims, 0))
+    dims = list(range(-spatial_dims, 0))
     if is_complex:
         if ksp.shape[-1] != 2:
             raise ValueError(f"ksp.shape[-1] is not 2 ({ksp.shape[-1]}).")
-        shift = list(range(-spatial_dims - 1, -1))
-    dims = list(range(-spatial_dims, 0))
-
-    x = ifftshift(ksp, shift)
-
-    if is_complex:
-        x = torch.view_as_real(torch.fft.ifftn(torch.view_as_complex(x), dim=dims, norm="ortho"))
+        x = torch.view_as_complex(ifftshift(ksp, [d - 1 for d in dims]))
     else:
-        x = torch.view_as_real(torch.fft.ifftn(x, dim=dims, norm="ortho"))
+        x = ifftshift(ksp, dims)
+
+    x = torch.view_as_real(torch.fft.ifftn(x, dim=dims, norm="ortho"))
 
-    out: Tensor = fftshift(x, shift)
+    out: Tensor = fftshift(x, [d - 1 for d in dims])
 
     return out
 
@@ -187,20 +183,16 @@ def fftn_centered_t(im: Tensor, spatial_dims: int, is_complex: bool = True) -> T
             output2 = fftn_centered(im, spatial_dims=2, is_complex=True)
     """
     # define spatial dims to perform ifftshift, fftshift, and fft
-    shift = list(range(-spatial_dims, 0))
+    dims = list(range(-spatial_dims, 0))
     if is_complex:
         if im.shape[-1] != 2:
             raise ValueError(f"img.shape[-1] is not 2 ({im.shape[-1]}).")
-        shift = list(range(-spatial_dims - 1, -1))
-    dims = list(range(-spatial_dims, 0))
-
-    x = ifftshift(im, shift)
-
-    if is_complex:
-        x = torch.view_as_real(torch.fft.fftn(torch.view_as_complex(x), dim=dims, norm="ortho"))
+        x = torch.view_as_complex(ifftshift(im, [d - 1 for d in dims]))
     else:
-        x = torch.view_as_real(torch.fft.fftn(x, dim=dims, norm="ortho"))
+        x = ifftshift(im, dims)
+
+    x = torch.view_as_real(torch.fft.fftn(x, dim=dims, norm="ortho"))
 
-    out: Tensor = fftshift(x, shift)
+    out: Tensor = fftshift(x, [d - 1 for d in dims])
 
     return out

tests/data/test_fft_utils.py

@@ -21,7 +21,7 @@
 #
 im = [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]
 res = [
-    [[[0.0, 0.0], [0.0, 3.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]]
+    [[[0.0, 0.0], [0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [3.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]]
 ]
 TESTS = []
 for p in TEST_NDARRAYS: