Randomizable constructor

docs/source/transforms.rst

@@ -27,6 +27,11 @@ Generic Interfaces
 .. autoclass:: Randomizable
     :members:
 
+`RandomizableTransform`
+^^^^^^^^^^^^^^^^^^^^^^^
+.. autoclass:: RandomizableTransform
+    :members:
+
 `Compose`
 ^^^^^^^^^
 .. autoclass:: Compose

monai/apps/deepgrow/transforms.py

@@ -17,7 +17,7 @@
 from monai.config import IndexSelection, KeysCollection
 from monai.networks.layers import GaussianFilter
 from monai.transforms import SpatialCrop
-from monai.transforms.transform import MapTransform, Randomizable, Transform
+from monai.transforms.transform import MapTransform, RandomizableTransform, Transform
 from monai.transforms.utils import generate_spatial_bounding_box
 from monai.utils import min_version, optional_import
 
@@ -62,7 +62,7 @@ def __call__(self, data):
         return d
 
 
-class AddInitialSeedPointd(Randomizable, Transform):
+class AddInitialSeedPointd(RandomizableTransform):
     """
     Add random guidance as initial seed point for a given label.
 
@@ -279,7 +279,7 @@ def __call__(self, data):
         return d
 
 
-class AddRandomGuidanced(Randomizable, Transform):
+class AddRandomGuidanced(RandomizableTransform):
     """
     Add random guidance based on discrepancies that were found between label and prediction.
 

monai/data/dataset.py

@@ -26,6 +26,7 @@
 
 from monai.data.utils import 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:
@@ -161,7 +162,7 @@ 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, Randomizable) or not isinstance(_transform, Transform):
+            if isinstance(_transform, RandomizableTransform) or not isinstance(_transform, Transform):
                 break
             item_transformed = apply_transform(_transform, item_transformed)
         return item_transformed
@@ -183,7 +184,7 @@ def _post_transform(self, item_transformed):
         for _transform in self.transform.transforms:
             if (
                 start_post_randomize_run
-                or isinstance(_transform, Randomizable)
+                or isinstance(_transform, RandomizableTransform)
                 or not isinstance(_transform, Transform)
             ):
                 start_post_randomize_run = True
@@ -522,7 +523,7 @@ 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, Randomizable) or not isinstance(_transform, Transform):
+            if isinstance(_transform, RandomizableTransform) or not isinstance(_transform, Transform):
                 break
             item = apply_transform(_transform, item)
         return item
@@ -539,7 +540,7 @@ def __getitem__(self, index):
         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, Randomizable) or not isinstance(_transform, Transform):
+            if start_run or isinstance(_transform, RandomizableTransform) or not isinstance(_transform, Transform):
                 start_run = True
                 data = apply_transform(_transform, data)
         return data
@@ -924,9 +925,9 @@ 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, Randomizable):
+                if isinstance(transform, RandomizableTransform):
                     transform.set_random_state(seed=self._seed)
         transform = getattr(self.dataset, "transform", None)
-        if isinstance(transform, Randomizable):
+        if isinstance(transform, RandomizableTransform):
             transform.set_random_state(seed=self._seed)
         return self.dataset[index]

monai/data/image_dataset.py

@@ -17,6 +17,7 @@
 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
 
 
@@ -106,14 +107,14 @@ def __getitem__(self, index: int):
             label = self.labels[index]
 
         if self.transform is not None:
-            if isinstance(self.transform, Randomizable):
+            if isinstance(self.transform, RandomizableTransform):
                 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, Randomizable):
+            if isinstance(self.seg_transform, RandomizableTransform):
                 self.seg_transform.set_random_state(seed=self._seed)
             seg = apply_transform(self.seg_transform, seg)
 

monai/transforms/__init__.py

@@ -237,7 +237,7 @@
     ZoomD,
     ZoomDict,
 )
-from .transform import MapTransform, Randomizable, Transform
+from .transform import MapTransform, Randomizable, RandomizableTransform, Transform
 from .utility.array import (
     AddChannel,
     AddExtremePointsChannel,

monai/transforms/compose.py

@@ -18,15 +18,14 @@
 import numpy as np
 
 # For backwards compatiblity (so this still works: from monai.transforms.compose import MapTransform)
-from monai.transforms.transform import MapTransform  # noqa: F401
-from monai.transforms.transform import Randomizable, Transform
+from monai.transforms.transform import MapTransform, Randomizable, RandomizableTransform, Transform  # noqa: F401
 from monai.transforms.utils import apply_transform
 from monai.utils import MAX_SEED, ensure_tuple, get_seed
 
 __all__ = ["Compose"]
 
 
-class Compose(Randomizable, Transform):
+class Compose(RandomizableTransform):
     """
     ``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
@@ -96,14 +95,14 @@ 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, Randomizable):
+            if not isinstance(_transform, RandomizableTransform):
                 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, Randomizable):
+            if not isinstance(_transform, RandomizableTransform):
                 continue
             try:
                 _transform.randomize(data)

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, Transform
+from monai.transforms.transform import Randomizable, RandomizableTransform, Transform
 from monai.transforms.utils import (
     generate_pos_neg_label_crop_centers,
     generate_spatial_bounding_box,
@@ -276,7 +276,7 @@ def __call__(self, img: np.ndarray):
         return cropper(img)
 
 
-class RandSpatialCrop(Randomizable, Transform):
+class RandSpatialCrop(RandomizableTransform):
     """
     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.
@@ -321,7 +321,7 @@ def __call__(self, img: np.ndarray):
         return cropper(img)
 
 
-class RandSpatialCropSamples(Randomizable, Transform):
+class RandSpatialCropSamples(RandomizableTransform):
     """
     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
@@ -429,7 +429,7 @@ def __call__(self, img: np.ndarray):
         return cropped
 
 
-class RandWeightedCrop(Randomizable, Transform):
+class RandWeightedCrop(RandomizableTransform):
     """
     Samples a list of `num_samples` image patches according to the provided `weight_map`.
 
@@ -481,7 +481,7 @@ def __call__(self, img: np.ndarray, weight_map: Optional[np.ndarray] = None) ->
         return results
 
 
-class RandCropByPosNegLabel(Randomizable, Transform):
+class RandCropByPosNegLabel(RandomizableTransform):
     """
     Crop random fixed sized regions with the center being a foreground or background voxel
     based on the Pos Neg Ratio.

monai/transforms/croppad/dictionary.py

@@ -30,7 +30,7 @@
     SpatialCrop,
     SpatialPad,
 )
-from monai.transforms.transform import MapTransform, Randomizable
+from monai.transforms.transform import MapTransform, Randomizable, RandomizableTransform
 from monai.transforms.utils import (
     generate_pos_neg_label_crop_centers,
     generate_spatial_bounding_box,
@@ -258,7 +258,7 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.nda
         return d
 
 
-class RandSpatialCropd(Randomizable, MapTransform):
+class RandSpatialCropd(RandomizableTransform, MapTransform):
     """
     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
@@ -283,7 +283,8 @@ def __init__(
         random_center: bool = True,
         random_size: bool = True,
     ) -> None:
-        super().__init__(keys)
+        RandomizableTransform.__init__(self)
+        MapTransform.__init__(self, keys)
         self.roi_size = roi_size
         self.random_center = random_center
         self.random_size = random_size
@@ -312,7 +313,7 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.nda
         return d
 
 
-class RandSpatialCropSamplesd(Randomizable, MapTransform):
+class RandSpatialCropSamplesd(RandomizableTransform, 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.
@@ -344,7 +345,8 @@ def __init__(
         random_center: bool = True,
         random_size: bool = True,
     ) -> None:
-        super().__init__(keys)
+        RandomizableTransform.__init__(self)
+        MapTransform.__init__(self, keys)
         if num_samples < 1:
             raise ValueError(f"num_samples must be positive, got {num_samples}.")
         self.num_samples = num_samples
@@ -420,7 +422,7 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.nda
         return d
 
 
-class RandWeightedCropd(Randomizable, MapTransform):
+class RandWeightedCropd(RandomizableTransform, MapTransform):
     """
     Samples a list of `num_samples` image patches according to the provided `weight_map`.
 
@@ -446,7 +448,8 @@ def __init__(
         num_samples: int = 1,
         center_coord_key: Optional[str] = None,
     ):
-        super().__init__(keys)
+        RandomizableTransform.__init__(self)
+        MapTransform.__init__(self, keys)
         self.spatial_size = ensure_tuple(spatial_size)
         self.w_key = w_key
         self.num_samples = int(num_samples)
@@ -484,7 +487,7 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> List[Dict[Hashable, n
         return results
 
 
-class RandCropByPosNegLabeld(Randomizable, MapTransform):
+class RandCropByPosNegLabeld(RandomizableTransform, MapTransform):
     """
     Dictionary-based version :py:class:`monai.transforms.RandCropByPosNegLabel`.
     Crop random fixed sized regions with the center being a foreground or background voxel
@@ -534,7 +537,8 @@ def __init__(
         fg_indices_key: Optional[str] = None,
         bg_indices_key: Optional[str] = None,
     ) -> None:
-        super().__init__(keys)
+        RandomizableTransform.__init__(self)
+        MapTransform.__init__(self, keys)
         self.label_key = label_key
         self.spatial_size: Union[Tuple[int, ...], Sequence[int], int] = spatial_size
         if pos < 0 or neg < 0: