From 291987a9e1f340ac6ef3584a0e0c329821afb575 Mon Sep 17 00:00:00 2001
From: Wenqi Li <wenqil@nvidia.com>
Date: Mon, 5 Apr 2021 22:32:31 +0100
Subject: [PATCH] remove unused RandomizableTransform

Signed-off-by: Wenqi Li <wenqil@nvidia.com>
---
 monai/apps/deepgrow/transforms.py        |  8 +++-----
 monai/data/dataset.py                    | 13 ++++++-------
 monai/data/image_dataset.py              |  5 ++---
 monai/data/test_time_augmentation.py     |  6 +++---
 monai/transforms/compose.py              |  6 +++---
 monai/transforms/croppad/array.py        | 10 +++++-----
 monai/transforms/croppad/dictionary.py   | 14 +++++---------
 monai/transforms/intensity/array.py      |  7 +++++++
 monai/transforms/intensity/dictionary.py |  9 ++++++---
 monai/transforms/spatial/array.py        |  8 ++++----
 monai/transforms/transform.py            | 16 +++++++++++++---
 monai/transforms/utility/array.py        |  4 ++--
 monai/transforms/utility/dictionary.py   |  8 ++++----
 tests/test_compose.py                    |  8 ++++----
 tests/test_rand_lambdad.py               |  4 ++--
 15 files changed, 69 insertions(+), 57 deletions(-)

diff --git a/monai/apps/deepgrow/transforms.py b/monai/apps/deepgrow/transforms.py
index c58d4c1123..3d8f08bc01 100644
--- a/monai/apps/deepgrow/transforms.py
+++ b/monai/apps/deepgrow/transforms.py
@@ -16,7 +16,7 @@
 from monai.config import IndexSelection, KeysCollection
 from monai.networks.layers import GaussianFilter
 from monai.transforms import Resize, SpatialCrop
-from monai.transforms.transform import MapTransform, RandomizableTransform, Transform
+from monai.transforms.transform import MapTransform, Randomizable, Transform
 from monai.transforms.utils import generate_spatial_bounding_box
 from monai.utils import InterpolateMode, ensure_tuple_rep, min_version, optional_import
 
@@ -61,7 +61,7 @@ def __call__(self, data):
         return d
 
 
-class AddInitialSeedPointd(RandomizableTransform):
+class AddInitialSeedPointd(Randomizable):
     """
     Add random guidance as initial seed point for a given label.
 
@@ -86,7 +86,6 @@ def __init__(
         sid: str = "sid",
         connected_regions: int = 5,
     ):
-        super().__init__(prob=1.0, do_transform=True)
         self.label = label
         self.sids_key = sids
         self.sid_key = sid
@@ -284,7 +283,7 @@ def __call__(self, data):
         return d
 
 
-class AddRandomGuidanced(RandomizableTransform):
+class AddRandomGuidanced(Randomizable):
     """
     Add random guidance based on discrepancies that were found between label and prediction.
 
@@ -320,7 +319,6 @@ def __init__(
         probability: str = "probability",
         batched: bool = True,
     ):
-        super().__init__(prob=1.0, do_transform=True)
         self.guidance = guidance
         self.discrepancy = discrepancy
         self.probability = probability
diff --git a/monai/data/dataset.py b/monai/data/dataset.py
index 12403bbff1..a09050e5bc 100644
--- a/monai/data/dataset.py
+++ b/monai/data/dataset.py
@@ -29,7 +29,6 @@
 
 from monai.data.utils import first, pickle_hashing
 from monai.transforms import Compose, Randomizable, Transform, apply_transform
-from monai.transforms.transform import RandomizableTransform
 from monai.utils import MAX_SEED, get_seed, min_version, optional_import
 
 if TYPE_CHECKING:
@@ -182,7 +181,7 @@ def _pre_transform(self, item_transformed):
             raise ValueError("transform must be an instance of monai.transforms.Compose.")
         for _transform in self.transform.transforms:
             # execute all the deterministic transforms
-            if isinstance(_transform, RandomizableTransform) or not isinstance(_transform, Transform):
+            if isinstance(_transform, Randomizable) or not isinstance(_transform, Transform):
                 break
             item_transformed = apply_transform(_transform, item_transformed)
         return item_transformed
@@ -204,7 +203,7 @@ def _post_transform(self, item_transformed):
         for _transform in self.transform.transforms:
             if (
                 start_post_randomize_run
-                or isinstance(_transform, RandomizableTransform)
+                or isinstance(_transform, Randomizable)
                 or not isinstance(_transform, Transform)
             ):
                 start_post_randomize_run = True
@@ -547,7 +546,7 @@ def _load_cache_item(self, idx: int):
             raise ValueError("transform must be an instance of monai.transforms.Compose.")
         for _transform in self.transform.transforms:
             # execute all the deterministic transforms
-            if isinstance(_transform, RandomizableTransform) or not isinstance(_transform, Transform):
+            if isinstance(_transform, Randomizable) or not isinstance(_transform, Transform):
                 break
             item = apply_transform(_transform, item)
         return item
@@ -564,7 +563,7 @@ def _transform(self, index: int):
         if not isinstance(self.transform, Compose):
             raise ValueError("transform must be an instance of monai.transforms.Compose.")
         for _transform in self.transform.transforms:
-            if start_run or isinstance(_transform, RandomizableTransform) or not isinstance(_transform, Transform):
+            if start_run or isinstance(_transform, Randomizable) or not isinstance(_transform, Transform):
                 start_run = True
                 data = apply_transform(_transform, data)
         return data
@@ -967,10 +966,10 @@ def __getitem__(self, index: int):
             # set transforms of each zip component
             for dataset in self.dataset.data:
                 transform = getattr(dataset, "transform", None)
-                if isinstance(transform, RandomizableTransform):
+                if isinstance(transform, Randomizable):
                     transform.set_random_state(seed=self._seed)
         transform = getattr(self.dataset, "transform", None)
-        if isinstance(transform, RandomizableTransform):
+        if isinstance(transform, Randomizable):
             transform.set_random_state(seed=self._seed)
         return self.dataset[index]
 
diff --git a/monai/data/image_dataset.py b/monai/data/image_dataset.py
index 1074105508..1568e082ee 100644
--- a/monai/data/image_dataset.py
+++ b/monai/data/image_dataset.py
@@ -17,7 +17,6 @@
 from monai.config import DtypeLike
 from monai.data.image_reader import ImageReader
 from monai.transforms import LoadImage, Randomizable, apply_transform
-from monai.transforms.transform import RandomizableTransform
 from monai.utils import MAX_SEED, get_seed
 
 
@@ -107,14 +106,14 @@ def __getitem__(self, index: int):
             label = self.labels[index]
 
         if self.transform is not None:
-            if isinstance(self.transform, RandomizableTransform):
+            if isinstance(self.transform, Randomizable):
                 self.transform.set_random_state(seed=self._seed)
             img = apply_transform(self.transform, img)
 
         data = [img]
 
         if self.seg_transform is not None:
-            if isinstance(self.seg_transform, RandomizableTransform):
+            if isinstance(self.seg_transform, Randomizable):
                 self.seg_transform.set_random_state(seed=self._seed)
             seg = apply_transform(self.seg_transform, seg)
 
diff --git a/monai/data/test_time_augmentation.py b/monai/data/test_time_augmentation.py
index 51b95adc58..06e1f63da5 100644
--- a/monai/data/test_time_augmentation.py
+++ b/monai/data/test_time_augmentation.py
@@ -20,7 +20,7 @@
 from monai.data.utils import list_data_collate, pad_list_data_collate
 from monai.transforms.compose import Compose
 from monai.transforms.inverse import InvertibleTransform
-from monai.transforms.transform import RandomizableTransform
+from monai.transforms.transform import Randomizable
 from monai.transforms.utils import allow_missing_keys_mode
 from monai.utils.enums import CommonKeys, InverseKeys
 
@@ -47,7 +47,7 @@ class TestTimeAugmentation:
 
     Args:
         transform: transform (or composed) to be applied to each realisation. At least one transform must be of type
-            `RandomizableTransform`. All random transforms must be of type `InvertibleTransform`.
+            `Randomizable`. All random transforms must be of type `InvertibleTransform`.
         batch_size: number of realisations to infer at once.
         num_workers: how many subprocesses to use for data.
         inferrer_fn: function to use to perform inference.
@@ -96,7 +96,7 @@ def __init__(
     def _check_transforms(self):
         """Should be at least 1 random transform, and all random transforms should be invertible."""
         ts = [self.transform] if not isinstance(self.transform, Compose) else self.transform.transforms
-        randoms = np.array([isinstance(t, RandomizableTransform) for t in ts])
+        randoms = np.array([isinstance(t, Randomizable) for t in ts])
         invertibles = np.array([isinstance(t, InvertibleTransform) for t in ts])
         # check at least 1 random
         if sum(randoms) == 0:
diff --git a/monai/transforms/compose.py b/monai/transforms/compose.py
index dd40663e2a..ce965b8b18 100644
--- a/monai/transforms/compose.py
+++ b/monai/transforms/compose.py
@@ -32,7 +32,7 @@
 __all__ = ["Compose"]
 
 
-class Compose(RandomizableTransform, InvertibleTransform):
+class Compose(Randomizable, InvertibleTransform):
     """
     ``Compose`` provides the ability to chain a series of calls together in a
     sequence. Each transform in the sequence must take a single argument and
@@ -102,14 +102,14 @@ def __init__(self, transforms: Optional[Union[Sequence[Callable], Callable]] = N
     def set_random_state(self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None) -> "Compose":
         super().set_random_state(seed=seed, state=state)
         for _transform in self.transforms:
-            if not isinstance(_transform, RandomizableTransform):
+            if not isinstance(_transform, Randomizable):
                 continue
             _transform.set_random_state(seed=self.R.randint(MAX_SEED, dtype="uint32"))
         return self
 
     def randomize(self, data: Optional[Any] = None) -> None:
         for _transform in self.transforms:
-            if not isinstance(_transform, RandomizableTransform):
+            if not isinstance(_transform, Randomizable):
                 continue
             try:
                 _transform.randomize(data)
diff --git a/monai/transforms/croppad/array.py b/monai/transforms/croppad/array.py
index 159fa1a5f4..c8f7136334 100644
--- a/monai/transforms/croppad/array.py
+++ b/monai/transforms/croppad/array.py
@@ -20,7 +20,7 @@
 
 from monai.config import IndexSelection
 from monai.data.utils import get_random_patch, get_valid_patch_size
-from monai.transforms.transform import Randomizable, RandomizableTransform, Transform
+from monai.transforms.transform import Randomizable, Transform
 from monai.transforms.utils import (
     generate_pos_neg_label_crop_centers,
     generate_spatial_bounding_box,
@@ -279,7 +279,7 @@ def __call__(self, img: np.ndarray):
         return cropper(img)
 
 
-class RandSpatialCrop(RandomizableTransform):
+class RandSpatialCrop(Randomizable):
     """
     Crop image with random size or specific size ROI. It can crop at a random position as center
     or at the image center. And allows to set the minimum size to limit the randomly generated ROI.
@@ -324,7 +324,7 @@ def __call__(self, img: np.ndarray):
         return cropper(img)
 
 
-class RandSpatialCropSamples(RandomizableTransform):
+class RandSpatialCropSamples(Randomizable):
     """
     Crop image with random size or specific size ROI to generate a list of N samples.
     It can crop at a random position as center or at the image center. And allows to set
@@ -432,7 +432,7 @@ def __call__(self, img: np.ndarray):
         return cropped
 
 
-class RandWeightedCrop(RandomizableTransform):
+class RandWeightedCrop(Randomizable):
     """
     Samples a list of `num_samples` image patches according to the provided `weight_map`.
 
@@ -484,7 +484,7 @@ def __call__(self, img: np.ndarray, weight_map: Optional[np.ndarray] = None) ->
         return results
 
 
-class RandCropByPosNegLabel(RandomizableTransform):
+class RandCropByPosNegLabel(Randomizable):
     """
     Crop random fixed sized regions with the center being a foreground or background voxel
     based on the Pos Neg Ratio.
diff --git a/monai/transforms/croppad/dictionary.py b/monai/transforms/croppad/dictionary.py
index 1d4fcfdb1f..c8d5ceea40 100644
--- a/monai/transforms/croppad/dictionary.py
+++ b/monai/transforms/croppad/dictionary.py
@@ -34,7 +34,7 @@
     SpatialPad,
 )
 from monai.transforms.inverse import InvertibleTransform
-from monai.transforms.transform import MapTransform, Randomizable, RandomizableTransform
+from monai.transforms.transform import MapTransform, Randomizable
 from monai.transforms.utils import (
     generate_pos_neg_label_crop_centers,
     generate_spatial_bounding_box,
@@ -386,7 +386,7 @@ def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndar
         return d
 
 
-class RandSpatialCropd(RandomizableTransform, MapTransform, InvertibleTransform):
+class RandSpatialCropd(Randomizable, MapTransform, InvertibleTransform):
     """
     Dictionary-based version :py:class:`monai.transforms.RandSpatialCrop`.
     Crop image with random size or specific size ROI. It can crop at a random position as
@@ -413,7 +413,6 @@ def __init__(
         random_size: bool = True,
         allow_missing_keys: bool = False,
     ) -> None:
-        RandomizableTransform.__init__(self, prob=1.0, do_transform=True)
         MapTransform.__init__(self, keys, allow_missing_keys)
         self.roi_size = roi_size
         self.random_center = random_center
@@ -477,7 +476,7 @@ def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndar
         return d
 
 
-class RandSpatialCropSamplesd(RandomizableTransform, MapTransform):
+class RandSpatialCropSamplesd(Randomizable, MapTransform):
     """
     Dictionary-based version :py:class:`monai.transforms.RandSpatialCropSamples`.
     Crop image with random size or specific size ROI to generate a list of N samples.
@@ -515,7 +514,6 @@ def __init__(
         meta_key_postfix: str = "meta_dict",
         allow_missing_keys: bool = False,
     ) -> None:
-        RandomizableTransform.__init__(self, prob=1.0, do_transform=True)
         MapTransform.__init__(self, keys, allow_missing_keys)
         if num_samples < 1:
             raise ValueError(f"num_samples must be positive, got {num_samples}.")
@@ -626,7 +624,7 @@ def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndar
         return d
 
 
-class RandWeightedCropd(RandomizableTransform, MapTransform):
+class RandWeightedCropd(Randomizable, MapTransform):
     """
     Samples a list of `num_samples` image patches according to the provided `weight_map`.
 
@@ -654,7 +652,6 @@ def __init__(
         center_coord_key: Optional[str] = None,
         allow_missing_keys: bool = False,
     ):
-        RandomizableTransform.__init__(self, prob=1.0, do_transform=True)
         MapTransform.__init__(self, keys, allow_missing_keys)
         self.spatial_size = ensure_tuple(spatial_size)
         self.w_key = w_key
@@ -693,7 +690,7 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> List[Dict[Hashable, n
         return results
 
 
-class RandCropByPosNegLabeld(RandomizableTransform, MapTransform):
+class RandCropByPosNegLabeld(Randomizable, MapTransform):
     """
     Dictionary-based version :py:class:`monai.transforms.RandCropByPosNegLabel`.
     Crop random fixed sized regions with the center being a foreground or background voxel
@@ -751,7 +748,6 @@ def __init__(
         meta_key_postfix: str = "meta_dict",
         allow_missing_keys: bool = False,
     ) -> None:
-        RandomizableTransform.__init__(self)
         MapTransform.__init__(self, keys, allow_missing_keys)
         self.label_key = label_key
         self.spatial_size: Union[Tuple[int, ...], Sequence[int], int] = spatial_size
diff --git a/monai/transforms/intensity/array.py b/monai/transforms/intensity/array.py
index f89e381daa..62350d4ab0 100644
--- a/monai/transforms/intensity/array.py
+++ b/monai/transforms/intensity/array.py
@@ -122,6 +122,7 @@ def __init__(self, offsets: Union[Tuple[float, float], float], prob: float = 0.1
             if len(offsets) != 2:
                 raise AssertionError("offsets should be a number or pair of numbers.")
             self.offsets = (min(offsets), max(offsets))
+        self._offset = self.offsets[0]
 
     def randomize(self, data: Optional[Any] = None) -> None:
         self._offset = self.R.uniform(low=self.offsets[0], high=self.offsets[1])
@@ -217,6 +218,7 @@ def __init__(
             if len(factors) != 2:
                 raise AssertionError("factors should be a number or pair of numbers.")
             self.factors = (min(factors), max(factors))
+        self.factor = self.factors[0]
         self.nonzero = nonzero
         self.channel_wise = channel_wise
         self.dtype = dtype
@@ -294,6 +296,7 @@ def __init__(self, factors: Union[Tuple[float, float], float], prob: float = 0.1
             if len(factors) != 2:
                 raise AssertionError("factors should be a number or pair of numbers.")
             self.factors = (min(factors), max(factors))
+        self.factor = self.factors[0]
 
     def randomize(self, data: Optional[Any] = None) -> None:
         self.factor = self.R.uniform(low=self.factors[0], high=self.factors[1])
@@ -874,6 +877,10 @@ def __init__(
         self.sigma_z = sigma_z
         self.approx = approx
 
+        self.x = self.sigma_x[0]
+        self.y = self.sigma_y[0]
+        self.z = self.sigma_z[0]
+
     def randomize(self, data: Optional[Any] = None) -> None:
         super().randomize(None)
         self.x = self.R.uniform(low=self.sigma_x[0], high=self.sigma_x[1])
diff --git a/monai/transforms/intensity/dictionary.py b/monai/transforms/intensity/dictionary.py
index 517c34cbf2..a35e5c8ea6 100644
--- a/monai/transforms/intensity/dictionary.py
+++ b/monai/transforms/intensity/dictionary.py
@@ -206,6 +206,7 @@ def __init__(
             if len(offsets) != 2:
                 raise AssertionError("offsets should be a number or pair of numbers.")
             self.offsets = (min(offsets), max(offsets))
+        self._offset = self.offsets[0]
 
     def randomize(self, data: Optional[Any] = None) -> None:
         self._offset = self.R.uniform(low=self.offsets[0], high=self.offsets[1])
@@ -293,6 +294,7 @@ def __init__(
             if len(factors) != 2:
                 raise AssertionError("factors should be a number or pair of numbers.")
             self.factors = (min(factors), max(factors))
+        self.factor = self.factors[0]
         self.nonzero = nonzero
         self.channel_wise = channel_wise
         self.dtype = dtype
@@ -380,6 +382,7 @@ def __init__(
             if len(factors) != 2:
                 raise AssertionError("factors should be a number or pair of numbers.")
             self.factors = (min(factors), max(factors))
+        self.factor = self.factors[0]
 
     def randomize(self, data: Optional[Any] = None) -> None:
         self.factor = self.R.uniform(low=self.factors[0], high=self.factors[1])
@@ -760,11 +763,11 @@ def __init__(
     ) -> None:
         MapTransform.__init__(self, keys, allow_missing_keys)
         RandomizableTransform.__init__(self, prob)
-        self.sigma_x = sigma_x
-        self.sigma_y = sigma_y
-        self.sigma_z = sigma_z
+        self.sigma_x, self.sigma_y, self.sigma_z = sigma_x, sigma_y, sigma_z
         self.approx = approx
 
+        self.x, self.y, self.z = self.sigma_x[0], self.sigma_y[0], self.sigma_z[0]
+
     def randomize(self, data: Optional[Any] = None) -> None:
         super().randomize(None)
         self.x = self.R.uniform(low=self.sigma_x[0], high=self.sigma_x[1])
diff --git a/monai/transforms/spatial/array.py b/monai/transforms/spatial/array.py
index 1c096ba743..a3eb055f7e 100644
--- a/monai/transforms/spatial/array.py
+++ b/monai/transforms/spatial/array.py
@@ -23,7 +23,7 @@
 from monai.data.utils import compute_shape_offset, to_affine_nd, zoom_affine
 from monai.networks.layers import AffineTransform, GaussianFilter, grid_pull
 from monai.transforms.croppad.array import CenterSpatialCrop
-from monai.transforms.transform import RandomizableTransform, Transform
+from monai.transforms.transform import Randomizable, RandomizableTransform, Transform
 from monai.transforms.utils import (
     create_control_grid,
     create_grid,
@@ -790,7 +790,7 @@ class RandAxisFlip(RandomizableTransform):
     """
 
     def __init__(self, prob: float = 0.1) -> None:
-        RandomizableTransform.__init__(self, min(max(prob, 0.0), 1.0))
+        RandomizableTransform.__init__(self, prob)
         self._axis: Optional[int] = None
 
     def randomize(self, data: np.ndarray) -> None:
@@ -1004,7 +1004,7 @@ def __call__(
         return grid if self.as_tensor_output else np.asarray(grid.cpu().numpy()), affine
 
 
-class RandAffineGrid(RandomizableTransform):
+class RandAffineGrid(Randomizable):
     """
     Generate randomised affine grid.
     """
@@ -1101,7 +1101,7 @@ def get_transformation_matrix(self) -> Optional[Union[np.ndarray, torch.Tensor]]
         return self.affine
 
 
-class RandDeformGrid(RandomizableTransform):
+class RandDeformGrid(Randomizable):
     """
     Generate random deformation grid.
     """
diff --git a/monai/transforms/transform.py b/monai/transforms/transform.py
index 6a22db1076..ff5f021739 100644
--- a/monai/transforms/transform.py
+++ b/monai/transforms/transform.py
@@ -180,17 +180,27 @@ class RandomizableTransform(Randomizable, Transform):
     """
     An interface for handling random state locally, currently based on a class variable `R`,
     which is an instance of `np.random.RandomState`.
-    This is mainly for randomized data augmentation transforms. For example::
+    This class introduces a randomized flag `_do_transform`, is mainly for randomized data augmentation transforms.
+    For example:
 
-        class RandShiftIntensity(RandomizableTransform):
-            def randomize():
+    .. code-block:: python
+
+        from monai.transforms import RandomizableTransform
+
+        class RandShiftIntensity100(RandomizableTransform):
+            def randomize(self):
+                super().randomize(None)
                 self._offset = self.R.uniform(low=0, high=100)
+
             def __call__(self, img):
                 self.randomize()
+                if not self._do_transform:
+                    return img
                 return img + self._offset
 
         transform = RandShiftIntensity()
         transform.set_random_state(seed=0)
+        print(transform(10))
 
     """
 
diff --git a/monai/transforms/utility/array.py b/monai/transforms/utility/array.py
index 8e0dabafb2..6903b2628d 100644
--- a/monai/transforms/utility/array.py
+++ b/monai/transforms/utility/array.py
@@ -22,7 +22,7 @@
 import torch
 
 from monai.config import DtypeLike, NdarrayTensor
-from monai.transforms.transform import RandomizableTransform, Transform
+from monai.transforms.transform import Randomizable, Transform
 from monai.transforms.utils import extreme_points_to_image, get_extreme_points, map_binary_to_indices
 from monai.utils import ensure_tuple, min_version, optional_import
 
@@ -667,7 +667,7 @@ def __call__(self, img: np.ndarray) -> np.ndarray:
         return np.stack(result, axis=0)
 
 
-class AddExtremePointsChannel(RandomizableTransform):
+class AddExtremePointsChannel(Randomizable):
     """
     Add extreme points of label to the image as a new channel. This transform generates extreme
     point from label and applies a gaussian filter. The pixel values in points image are rescaled
diff --git a/monai/transforms/utility/dictionary.py b/monai/transforms/utility/dictionary.py
index c437cd055b..9464faa503 100644
--- a/monai/transforms/utility/dictionary.py
+++ b/monai/transforms/utility/dictionary.py
@@ -23,7 +23,7 @@
 import torch
 
 from monai.config import DtypeLike, KeysCollection, NdarrayTensor
-from monai.transforms.transform import MapTransform, RandomizableTransform
+from monai.transforms.transform import MapTransform, Randomizable
 from monai.transforms.utility.array import (
     AddChannel,
     AsChannelFirst,
@@ -731,9 +731,9 @@ def __call__(self, data):
         return d
 
 
-class RandLambdad(Lambdad, RandomizableTransform):
+class RandLambdad(Lambdad, Randomizable):
     """
-    RandomizableTransform version :py:class:`monai.transforms.Lambdad`, the input `func` contains random logic.
+    Randomizable version :py:class:`monai.transforms.Lambdad`, the input `func` contains random logic.
     It's a randomizable transform so `CacheDataset` will not execute it and cache the results.
 
     Args:
@@ -853,7 +853,7 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.nda
         return d
 
 
-class AddExtremePointsChanneld(RandomizableTransform, MapTransform):
+class AddExtremePointsChanneld(Randomizable, MapTransform):
     """
     Dictionary-based wrapper of :py:class:`monai.transforms.AddExtremePointsChannel`.
 
diff --git a/tests/test_compose.py b/tests/test_compose.py
index bb8a5f08c5..97b044af8f 100644
--- a/tests/test_compose.py
+++ b/tests/test_compose.py
@@ -14,11 +14,11 @@
 
 from monai.data import DataLoader, Dataset
 from monai.transforms import AddChannel, Compose
-from monai.transforms.transform import RandomizableTransform
+from monai.transforms.transform import Randomizable
 from monai.utils import set_determinism
 
 
-class _RandXform(RandomizableTransform):
+class _RandXform(Randomizable):
     def randomize(self):
         self.val = self.R.random_sample()
 
@@ -80,7 +80,7 @@ def c(d):  # transform to handle dict data
             self.assertDictEqual(item, {"a": 2, "b": 1, "c": 2})
 
     def test_random_compose(self):
-        class _Acc(RandomizableTransform):
+        class _Acc(Randomizable):
             self.rand = 0.0
 
             def randomize(self, data=None):
@@ -99,7 +99,7 @@ def __call__(self, data):
         self.assertAlmostEqual(c(1), 1.90734751)
 
     def test_randomize_warn(self):
-        class _RandomClass(RandomizableTransform):
+        class _RandomClass(Randomizable):
             def randomize(self, foo1, foo2):
                 pass
 
diff --git a/tests/test_rand_lambdad.py b/tests/test_rand_lambdad.py
index 2ddfeefae0..a450b67413 100644
--- a/tests/test_rand_lambdad.py
+++ b/tests/test_rand_lambdad.py
@@ -13,11 +13,11 @@
 
 import numpy as np
 
-from monai.transforms.transform import RandomizableTransform
+from monai.transforms.transform import Randomizable
 from monai.transforms.utility.dictionary import RandLambdad
 
 
-class RandTest(RandomizableTransform):
+class RandTest(Randomizable):
     """
     randomisable transform for testing.
     """