2627 Enhance surface metrics warning

monai/metrics/hausdorff_distance.py

@@ -157,6 +157,11 @@ def compute_hausdorff_distance(
     hd = np.empty((batch_size, n_class))
     for b, c in np.ndindex(batch_size, n_class):
         (edges_pred, edges_gt) = get_mask_edges(y_pred[b, c], y[b, c])
+        if not np.any(edges_gt):
+            warnings.warn(f"the ground truth of class {c} is all 0, this may result in nan/inf distance.")
+        if not np.any(edges_pred):
+            warnings.warn(f"the prediction of class {c} is all 0, this may result in nan/inf distance.")
+
         distance_1 = compute_percent_hausdorff_distance(edges_pred, edges_gt, distance_metric, percentile)
         if directed:
             hd[b, c] = distance_1

monai/metrics/surface_distance.py

@@ -152,6 +152,11 @@ def compute_average_surface_distance(
 
     for b, c in np.ndindex(batch_size, n_class):
         (edges_pred, edges_gt) = get_mask_edges(y_pred[b, c], y[b, c])
+        if not np.any(edges_gt):
+            warnings.warn(f"the ground truth of class {c} is all 0, this may result in nan/inf distance.")
+        if not np.any(edges_pred):
+            warnings.warn(f"the prediction of class {c} is all 0, this may result in nan/inf distance.")
+
         surface_distance = get_surface_distance(edges_pred, edges_gt, distance_metric=distance_metric)
         if surface_distance.shape == (0,):
             avg_surface_distance = np.nan

monai/metrics/utils.py

@@ -9,7 +9,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import warnings
 from typing import Tuple, Union
 
 import numpy as np
@@ -188,15 +187,17 @@ def get_surface_distance(
             - ``"euclidean"``, uses Exact Euclidean distance transform.
             - ``"chessboard"``, uses `chessboard` metric in chamfer type of transform.
             - ``"taxicab"``, uses `taxicab` metric in chamfer type of transform.
+
+    Note:
+        If seg_pred or seg_gt is all 0, may result in nan/inf distance.
+
     """
 
     if not np.any(seg_gt):
         dis = np.inf * np.ones_like(seg_gt)
-        warnings.warn("ground truth is all 0, this may result in nan/inf distance.")
     else:
         if not np.any(seg_pred):
             dis = np.inf * np.ones_like(seg_gt)
-            warnings.warn("prediction is all 0, this may result in nan/inf distance.")
             return np.asarray(dis[seg_gt])
         if distance_metric == "euclidean":
             dis = distance_transform_edt(~seg_gt)