[DLMED] remove RandCompose

Signed-off-by: Nic Ma <[email protected]>

Author: Nic-Ma

docs/source/transforms.rst

@@ -38,12 +38,6 @@ Generic Interfaces
     :members:
     :special-members: __call__
 
-`RandCompose`
-^^^^^^^^^^^^^
-.. autoclass:: RandCompose
-    :members:
-    :special-members: __call__
-
 `InvertibleTransform`
 ^^^^^^^^^^^^^^^^^^^^^
 .. autoclass:: InvertibleTransform

monai/transforms/__init__.py

@@ -10,7 +10,7 @@
 # limitations under the License.
 
 from .adaptors import FunctionSignature, adaptor, apply_alias, to_kwargs
-from .compose import Compose, RandCompose
+from .compose import Compose
 from .croppad.array import (
     BorderPad,
     BoundingRect,

monai/transforms/compose.py

@@ -27,9 +27,9 @@
     Transform,
     apply_transform,
 )
-from monai.utils import MAX_SEED, ensure_tuple, ensure_tuple_rep, get_seed
+from monai.utils import MAX_SEED, ensure_tuple, get_seed
 
-__all__ = ["Compose", "RandCompose"]
+__all__ = ["Compose"]
 
 
 class Compose(Randomizable, InvertibleTransform):
@@ -143,7 +143,7 @@ def flatten(self):
         """
         new_transforms = []
         for t in self.transforms:
-            if type(t) == Compose:
+            if isinstance(t, Compose):
                 new_transforms += t.flatten().transforms
             else:
                 new_transforms.append(t)
@@ -172,34 +172,3 @@ def inverse(self, data):
         for t in reversed(invertible_transforms):
             data = apply_transform(t.inverse, data, self.map_items, self.unpack_items)
         return data
-
-
-class RandCompose(Compose):
-    def __init__(
-        self,
-        prob: Union[Sequence[float], float],
-        transforms: Optional[Union[Sequence[Callable], Callable]] = None,
-        map_items: bool = True,
-        unpack_items: bool = False,
-    ) -> None:
-        super().__init__(transforms=transforms, map_items=map_items, unpack_items=unpack_items)
-        self.prob = ensure_tuple_rep(prob, len(self.transforms))
-        self.applied: List[Callable] = []
-
-    def flatten(self):
-        raise NotImplementedError("flatten method not yet implemented for `RandCompose` class.")
-
-    def __len__(self):
-        return len(self.transforms)
-
-    def __call__(self, input_):
-        rands = self.R.rand(len(self))
-        self.applied = []
-        for _transform, r, p in zip(self.transforms, rands, self.prob):
-            if r < min(max(p, 0.0), 1.0):
-                input_ = apply_transform(_transform, input_, self.map_items, self.unpack_items)
-                self.applied.append(_transform)
-        return input_
-
-    def _get_applied_transforms(self):
-        return self.applied