7088 compatible metric util torch inf nan (#7080)

Fixes #7088


### 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: Wenqi Li <wenqil@nvidia.com>

Author: wyli

monai/metrics/hausdorff_distance.py

@@ -208,7 +208,7 @@ def _compute_percentile_hausdorff_distance(
 
     # for both pred and gt do not have foreground
     if surface_distance.shape == (0,):
-        return torch.tensor(torch.nan, dtype=torch.float, device=surface_distance.device)
+        return torch.tensor(np.nan, dtype=torch.float, device=surface_distance.device)
 
     if not percentile:
         return surface_distance.max()

monai/metrics/surface_dice.py

@@ -275,7 +275,7 @@ def compute_surface_dice(
             boundary_correct = gt_true + pred_true
         if boundary_complete == 0:
             # the class is neither present in the prediction, nor in the reference segmentation
-            nsd[b, c] = torch.nan
+            nsd[b, c] = torch.tensor(np.nan)
         else:
             nsd[b, c] = boundary_correct / boundary_complete
 

monai/metrics/surface_distance.py

@@ -181,6 +181,6 @@ def compute_average_surface_distance(
             class_index=c,
         )
         surface_distance = torch.cat(distances)
-        asd[b, c] = torch.nan if surface_distance.shape == (0,) else surface_distance.mean()
+        asd[b, c] = torch.tensor(np.nan) if surface_distance.shape == (0,) else surface_distance.mean()
 
     return convert_data_type(asd, output_type=torch.Tensor, device=y_pred.device, dtype=torch.float)[0]