#6094 - add warn flag to RandCropByLabelClasses

monai/transforms/croppad/array.py

@@ -1147,6 +1147,7 @@ class RandCropByLabelClasses(Randomizable, TraceableTransform, LazyTransform, Mu
         allow_smaller: if `False`, an exception will be raised if the image is smaller than
             the requested ROI in any dimension. If `True`, any smaller dimensions will remain
             unchanged.
+        warn: if `True` prints a warning if a class is not present in the label.
 
     """
 
@@ -1163,6 +1164,7 @@ def __init__(
         image_threshold: float = 0.0,
         indices: list[NdarrayOrTensor] | None = None,
         allow_smaller: bool = False,
+        warn: bool = True,
     ) -> None:
         self.spatial_size = spatial_size
         self.ratios = ratios
@@ -1174,6 +1176,7 @@ def __init__(
         self.centers: list[list[int]] | None = None
         self.indices = indices
         self.allow_smaller = allow_smaller
+        self.warn = warn
 
     def randomize(
         self,
@@ -1198,7 +1201,7 @@ def randomize(
         if _shape is None:
             raise ValueError("label or image must be provided to infer the output spatial shape.")
         self.centers = generate_label_classes_crop_centers(
-            self.spatial_size, self.num_samples, _shape, indices_, self.ratios, self.R, self.allow_smaller
+            self.spatial_size, self.num_samples, _shape, indices_, self.ratios, self.R, self.allow_smaller, self.warn
         )
 
     @LazyTransform.lazy_evaluation.setter  # type: ignore

monai/transforms/croppad/dictionary.py

@@ -961,6 +961,8 @@ class RandCropByLabelClassesd(Randomizable, MapTransform, LazyTransform, MultiSa
             the requested ROI in any dimension. If `True`, any smaller dimensions will remain
             unchanged.
         allow_missing_keys: don't raise exception if key is missing.
+        warn: if `True` prints a warning if a class is not present in the label.
+
 
     """
 
@@ -979,6 +981,7 @@ def __init__(
         indices_key: str | None = None,
         allow_smaller: bool = False,
         allow_missing_keys: bool = False,
+        warn: bool = True,
     ) -> None:
         MapTransform.__init__(self, keys, allow_missing_keys)
         self.label_key = label_key
@@ -991,6 +994,7 @@ def __init__(
             num_samples=num_samples,
             image_threshold=image_threshold,
             allow_smaller=allow_smaller,
+            warn=warn,
         )
 
     def set_random_state(

monai/transforms/utils.py

@@ -535,6 +535,7 @@ def generate_label_classes_crop_centers(
     ratios: list[float | int] | None = None,
     rand_state: np.random.RandomState | None = None,
     allow_smaller: bool = False,
+    warn: bool = True,
 ) -> list[list[int]]:
     """
     Generate valid sample locations based on the specified ratios of label classes.
@@ -551,6 +552,7 @@ def generate_label_classes_crop_centers(
         allow_smaller: if `False`, an exception will be raised if the image is smaller than
             the requested ROI in any dimension. If `True`, any smaller dimensions will be set to
             match the cropped size (i.e., no cropping in that dimension).
+        warn: if `True` prints a warning if a class is not present in the label.
 
     """
     if rand_state is None:
@@ -567,7 +569,7 @@ def generate_label_classes_crop_centers(
         raise ValueError(f"ratios should not contain negative number, got {ratios_}.")
 
     for i, array in enumerate(indices):
-        if len(array) == 0:
+        if len(array) == 0 and warn:
             warnings.warn(f"no available indices of class {i} to crop, set the crop ratio of this class to zero.")
             ratios_[i] = 0