Handle case with only background/normal images in scoring

src/anomalib/metrics/spro.py

@@ -109,7 +109,7 @@ def spro_score(predictions: torch.Tensor, targets: torch.Tensor,
         predictions = predictions > threshold
 
     score = torch.tensor(0.0)
-
+    m = 0
     # Iterate for each image in the batch
     for i, target in enumerate(targets):
         unique_labels = torch.unique(target)
@@ -137,6 +137,9 @@ def spro_score(predictions: torch.Tensor, targets: torch.Tensor,
 
             # Update score with minimum of true_pos/saturation_threshold and 1.0
             score += torch.minimum(true_pos / saturation_threshold, torch.tensor(1.0))
+            m += 1
 
-    # Calculate the mean score
-    return torch.mean(score)
+    # If there are only backgrounds
+    if m == 0:
+        return torch.tensor(1.0)
+    return score / m