set dtype in proper place to make use of blown up value range (and wi…

…th is precision) in Gaussian

nnunetv2/inference/sliding_window_prediction.py

@@ -17,10 +17,10 @@ def compute_gaussian(tile_size: Union[Tuple[int, ...], List[int]], sigma_scale:
     tmp[tuple(center_coords)] = 1
     gaussian_importance_map = gaussian_filter(tmp, sigmas, 0, mode='constant', cval=0)
 
-    gaussian_importance_map = torch.from_numpy(gaussian_importance_map).type(dtype).to(device)
+    gaussian_importance_map = torch.from_numpy(gaussian_importance_map)
 
     gaussian_importance_map = gaussian_importance_map / torch.max(gaussian_importance_map) * value_scaling_factor
-    gaussian_importance_map = gaussian_importance_map.type(dtype)
+    gaussian_importance_map = gaussian_importance_map.type(dtype).to(device)
 
     # gaussian_importance_map cannot be 0, otherwise we may end up with nans!
     gaussian_importance_map[gaussian_importance_map == 0] = torch.min(