auto updates (#7077)

Author: monai-bot

monai/metrics/hausdorff_distance.py

@@ -211,10 +211,10 @@ def _compute_percentile_hausdorff_distance(
         return torch.tensor(torch.nan, dtype=torch.float, device=surface_distance.device)
 
     if not percentile:
-        return surface_distance.max()  # type: ignore[no-any-return]
+        return surface_distance.max()
 
     if 0 <= percentile <= 100:
-        return torch.quantile(surface_distance, percentile / 100)  # type: ignore[no-any-return]
+        return torch.quantile(surface_distance, percentile / 100)
     raise ValueError(f"percentile should be a value between 0 and 100, get {percentile}.")
 
 

monai/metrics/surface_dice.py

@@ -269,7 +269,7 @@ def compute_surface_dice(
         else:
             areas_pred, areas_gt = areas  # type: ignore
             areas_gt, areas_pred = areas_gt[edges_gt], areas_pred[edges_pred]
-            boundary_complete = areas_gt.sum() + areas_pred.sum()  # type: ignore
+            boundary_complete = areas_gt.sum() + areas_pred.sum()
             gt_true = areas_gt[distances_gt_pred <= class_thresholds[c]].sum() if len(areas_gt) > 0 else 0.0
             pred_true = areas_pred[distances_pred_gt <= class_thresholds[c]].sum() if len(areas_pred) > 0 else 0.0
             boundary_correct = gt_true + pred_true

monai/metrics/utils.py

@@ -204,7 +204,7 @@ def get_mask_edges(
     if crop:
         or_vol = seg_pred | seg_gt
         if not or_vol.any():
-            pred, gt = lib.zeros(seg_pred.shape, dtype=bool), lib.zeros(seg_gt.shape, dtype=bool)  # type: ignore
+            pred, gt = lib.zeros(seg_pred.shape, dtype=bool), lib.zeros(seg_gt.shape, dtype=bool)
             return (pred, gt) if spacing is None else (pred, gt, pred, gt)  # type: ignore
         channel_first = [seg_pred[None], seg_gt[None], or_vol[None]]
         if spacing is None and not use_cucim:  # cpu only erosion
@@ -275,11 +275,11 @@ def get_surface_distance(
     """
     lib: ModuleType = torch if isinstance(seg_pred, torch.Tensor) else np
     if not seg_gt.any():
-        dis = lib.inf * lib.ones_like(seg_gt, dtype=lib.float32)  # type: ignore
+        dis = lib.inf * lib.ones_like(seg_gt, dtype=lib.float32)
     else:
-        if not lib.any(seg_pred):  # type: ignore
-            dis = lib.inf * lib.ones_like(seg_gt, dtype=lib.float32)  # type: ignore
-            dis = dis[seg_gt]  # type: ignore
+        if not lib.any(seg_pred):
+            dis = lib.inf * lib.ones_like(seg_gt, dtype=lib.float32)
+            dis = dis[seg_gt]
             return convert_to_dst_type(dis, seg_pred, dtype=dis.dtype)[0]
         if distance_metric == "euclidean":
             dis = monai_distance_transform_edt((~seg_gt)[None, ...], sampling=spacing)[0]  # type: ignore

tests/min_tests.py

@@ -52,6 +52,7 @@ def run_testsuit():
         "test_csv_dataset",
         "test_csv_iterable_dataset",
         "test_cumulative_average_dist",
+        "test_sampler_dist",
         "test_dataset",
         "test_dataset_summary",
         "test_deepedit_transforms",