Merge branch 'dev' into dev

monai/data/wsi_datasets.py

@@ -23,7 +23,7 @@
 from monai.data.utils import iter_patch_position
 from monai.data.wsi_reader import BaseWSIReader, WSIReader
 from monai.transforms import ForegroundMask, Randomizable, apply_transform
-from monai.utils import convert_to_dst_type, ensure_tuple_rep
+from monai.utils import convert_to_dst_type, ensure_tuple, ensure_tuple_rep
 from monai.utils.enums import CommonKeys, ProbMapKeys, WSIPatchKeys
 
 __all__ = ["PatchWSIDataset", "SlidingPatchWSIDataset", "MaskedPatchWSIDataset"]
@@ -123,9 +123,9 @@ def _get_label(self, sample: dict):
     def _get_location(self, sample: dict):
         if self.center_location:
             size = self._get_size(sample)
-            return [sample[WSIPatchKeys.LOCATION][i] - size[i] // 2 for i in range(len(size))]
+            return ensure_tuple(sample[WSIPatchKeys.LOCATION][i] - size[i] // 2 for i in range(len(size)))
         else:
-            return sample[WSIPatchKeys.LOCATION]
+            return ensure_tuple(sample[WSIPatchKeys.LOCATION])
 
     def _get_level(self, sample: dict):
         if self.patch_level is None:

monai/losses/dice.py

@@ -666,6 +666,7 @@ def __init__(
         weight: torch.Tensor | None = None,
         lambda_dice: float = 1.0,
         lambda_ce: float = 1.0,
+        label_smoothing: float = 0.0,
     ) -> None:
         """
         Args:
@@ -704,6 +705,9 @@ def __init__(
                 Defaults to 1.0.
             lambda_ce: the trade-off weight value for cross entropy loss. The value should be no less than 0.0.
                 Defaults to 1.0.
+            label_smoothing: a value in [0, 1] range. If > 0, the labels are smoothed
+                by the given factor to reduce overfitting.
+                Defaults to 0.0.
 
         """
         super().__init__()
@@ -728,7 +732,12 @@ def __init__(
             batch=batch,
             weight=dice_weight,
         )
-        self.cross_entropy = nn.CrossEntropyLoss(weight=weight, reduction=reduction)
+        if pytorch_after(1, 10):
+            self.cross_entropy = nn.CrossEntropyLoss(
+                weight=weight, reduction=reduction, label_smoothing=label_smoothing
+            )
+        else:
+            self.cross_entropy = nn.CrossEntropyLoss(weight=weight, reduction=reduction)
         self.binary_cross_entropy = nn.BCEWithLogitsLoss(pos_weight=weight, reduction=reduction)
         if lambda_dice < 0.0:
             raise ValueError("lambda_dice should be no less than 0.0.")