fix cpu default

taufactor/metrics.py

@@ -57,7 +57,7 @@ def crop_area_of_interest_numpy(array, labels):
                       max(min_idx[2]-3, 0):min(max_idx[2]+4, array.shape[2])]
     return sub_array
 
-def gaussian_kernel_3d_torch(size=3, sigma=1.0, device=torch.device('cuda')):
+def gaussian_kernel_3d_torch(size=3, sigma=1.0, device):
     """Creates a 3D Gaussian kernel using PyTorch"""
     ax = torch.linspace(-(size // 2), size // 2, size)
     xx, yy, zz = torch.meshgrid(ax, ax, ax, indexing="ij")
@@ -90,6 +90,10 @@ def specific_surface_area(img, spacing=(1,1,1), phases={}, method='gradient', de
     [dx,dy,dz] = spacing
     surface_areas = {}
 
+    if torch.device(device).type.startswith('cuda') and not torch.cuda.is_available():
+        device = torch.device('cpu')
+        warnings.warn("CUDA not available, defaulting device to cpu.")
+
     if (method == 'gradient') | (method == 'face_counting'):
         if type(img) is not type(torch.tensor(1)):
             tensor = torch.tensor(img)
@@ -102,7 +106,7 @@ def specific_surface_area(img, spacing=(1,1,1), phases={}, method='gradient', de
             labels = torch.unique(tensor)
             labels = labels.int()
             phases = {str(label.item()): label.item() for label in labels}
-        gaussian = gaussian_kernel_3d_torch(device=device)
+        gaussian = gaussian_kernel_3d_torch(device)
 
         volume = torch.numel(tensor)
         for name, label in phases.items():