remove unused RandomizableTransform

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

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]
 

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)
 

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:

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)

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.

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

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])

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])