From 4080b6914c3f01e6f364ed1ace048f6a8f777cce Mon Sep 17 00:00:00 2001
From: Rich <33289025+rijobro@users.noreply.github.com>
Date: Tue, 23 Feb 2021 15:27:24 +0000
Subject: [PATCH 1/4] move transforms out of compose file

Signed-off-by: Richard Brown <33289025+rijobro@users.noreply.github.com>
---
 monai/apps/deepgrow/transforms.py        |   2 +-
 monai/transforms/__init__.py             |   3 +-
 monai/transforms/compose.py              | 190 +--------------------
 monai/transforms/croppad/array.py        |   2 +-
 monai/transforms/croppad/dictionary.py   |   2 +-
 monai/transforms/intensity/array.py      |   2 +-
 monai/transforms/intensity/dictionary.py |   2 +-
 monai/transforms/io/array.py             |   2 +-
 monai/transforms/io/dictionary.py        |   2 +-
 monai/transforms/post/array.py           |   2 +-
 monai/transforms/post/dictionary.py      |   2 +-
 monai/transforms/spatial/array.py        |   2 +-
 monai/transforms/spatial/dictionary.py   |   2 +-
 monai/transforms/transform.py            | 206 +++++++++++++++++++++++
 monai/transforms/utility/array.py        |   2 +-
 monai/transforms/utility/dictionary.py   |   2 +-
 16 files changed, 224 insertions(+), 201 deletions(-)
 create mode 100644 monai/transforms/transform.py

diff --git a/monai/apps/deepgrow/transforms.py b/monai/apps/deepgrow/transforms.py
index f178360031..80b0d1648d 100644
--- a/monai/apps/deepgrow/transforms.py
+++ b/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.compose import MapTransform, Randomizable, Transform
+from monai.transforms.transform import MapTransform, Randomizable, Transform
 from monai.transforms.utils import generate_spatial_bounding_box
 from monai.utils import min_version, optional_import
 
diff --git a/monai/transforms/__init__.py b/monai/transforms/__init__.py
index 6f7c2a4f61..357e00c6dd 100644
--- a/monai/transforms/__init__.py
+++ b/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, MapTransform, Randomizable, Transform
+from .compose import Compose
 from .croppad.array import (
     BorderPad,
     BoundingRect,
@@ -234,6 +234,7 @@
     ZoomD,
     ZoomDict,
 )
+from .transform import MapTransform, Randomizable, Transform
 from .utility.array import (
     AddChannel,
     AddExtremePointsChannel,
diff --git a/monai/transforms/compose.py b/monai/transforms/compose.py
index 3e23377b36..ae06e81d21 100644
--- a/monai/transforms/compose.py
+++ b/monai/transforms/compose.py
@@ -13,135 +13,15 @@
 """
 
 import warnings
-from abc import ABC, abstractmethod
-from typing import Any, Callable, Hashable, Optional, Sequence, Tuple, Union
+from typing import Any, Callable, Optional, Sequence, Union
 
 import numpy as np
 
-from monai.config import KeysCollection
+from monai.transforms.transform import Randomizable, Transform
 from monai.transforms.utils import apply_transform
 from monai.utils import MAX_SEED, ensure_tuple, get_seed
 
-__all__ = ["Transform", "Randomizable", "Compose", "MapTransform"]
-
-
-class Transform(ABC):
-    """
-    An abstract class of a ``Transform``.
-    A transform is callable that processes ``data``.
-
-    It could be stateful and may modify ``data`` in place,
-    the implementation should be aware of:
-
-        #. thread safety when mutating its own states.
-           When used from a multi-process context, transform's instance variables are read-only.
-        #. ``data`` content unused by this transform may still be used in the
-           subsequent transforms in a composed transform.
-        #. storing too much information in ``data`` may not scale.
-
-    See Also
-
-        :py:class:`monai.transforms.Compose`
-    """
-
-    @abstractmethod
-    def __call__(self, data: Any):
-        """
-        ``data`` is an element which often comes from an iteration over an
-        iterable, such as :py:class:`torch.utils.data.Dataset`. This method should
-        return an updated version of ``data``.
-        To simplify the input validations, most of the transforms assume that
-
-        - ``data`` is a Numpy ndarray, PyTorch Tensor or string
-        - the data shape can be:
-
-          #. string data without shape, `LoadImage` transform expects file paths
-          #. most of the pre-processing transforms expect: ``(num_channels, spatial_dim_1[, spatial_dim_2, ...])``,
-             except that `AddChannel` expects (spatial_dim_1[, spatial_dim_2, ...]) and
-             `AsChannelFirst` expects (spatial_dim_1[, spatial_dim_2, ...], num_channels)
-          #. most of the post-processing transforms expect
-             ``(batch_size, num_channels, spatial_dim_1[, spatial_dim_2, ...])``
-
-        - the channel dimension is not omitted even if number of channels is one
-
-        This method can optionally take additional arguments to help execute transformation operation.
-
-        Raises:
-            NotImplementedError: When the subclass does not override this method.
-
-        """
-        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
-
-
-class Randomizable(ABC):
-    """
-    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::
-
-        class RandShiftIntensity(Randomizable):
-            def randomize():
-                self._offset = self.R.uniform(low=0, high=100)
-            def __call__(self, img):
-                self.randomize()
-                return img + self._offset
-
-        transform = RandShiftIntensity()
-        transform.set_random_state(seed=0)
-
-    """
-
-    R: np.random.RandomState = np.random.RandomState()
-
-    def set_random_state(
-        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None
-    ) -> "Randomizable":
-        """
-        Set the random state locally, to control the randomness, the derived
-        classes should use :py:attr:`self.R` instead of `np.random` to introduce random
-        factors.
-
-        Args:
-            seed: set the random state with an integer seed.
-            state: set the random state with a `np.random.RandomState` object.
-
-        Raises:
-            TypeError: When ``state`` is not an ``Optional[np.random.RandomState]``.
-
-        Returns:
-            a Randomizable instance.
-
-        """
-        if seed is not None:
-            _seed = id(seed) if not isinstance(seed, (int, np.integer)) else seed
-            _seed = _seed % MAX_SEED
-            self.R = np.random.RandomState(_seed)
-            return self
-
-        if state is not None:
-            if not isinstance(state, np.random.RandomState):
-                raise TypeError(f"state must be None or a np.random.RandomState but is {type(state).__name__}.")
-            self.R = state
-            return self
-
-        self.R = np.random.RandomState()
-        return self
-
-    @abstractmethod
-    def randomize(self, data: Any) -> None:
-        """
-        Within this method, :py:attr:`self.R` should be used, instead of `np.random`, to introduce random factors.
-
-        all :py:attr:`self.R` calls happen here so that we have a better chance to
-        identify errors of sync the random state.
-
-        This method can generate the random factors based on properties of the input data.
-
-        Raises:
-            NotImplementedError: When the subclass does not override this method.
-
-        """
-        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
+__all__ = ["Compose"]
 
 
 class Compose(Randomizable, Transform):
@@ -235,67 +115,3 @@ def __call__(self, input_):
         for _transform in self.transforms:
             input_ = apply_transform(_transform, input_)
         return input_
-
-
-class MapTransform(Transform):
-    """
-    A subclass of :py:class:`monai.transforms.Transform` with an assumption
-    that the ``data`` input of ``self.__call__`` is a MutableMapping such as ``dict``.
-
-    The ``keys`` parameter will be used to get and set the actual data
-    item to transform.  That is, the callable of this transform should
-    follow the pattern:
-
-        .. code-block:: python
-
-            def __call__(self, data):
-                for key in self.keys:
-                    if key in data:
-                        # update output data with some_transform_function(data[key]).
-                    else:
-                        # do nothing or some exceptions handling.
-                return data
-
-    Raises:
-        ValueError: When ``keys`` is an empty iterable.
-        TypeError: When ``keys`` type is not in ``Union[Hashable, Iterable[Hashable]]``.
-
-    """
-
-    def __init__(self, keys: KeysCollection) -> None:
-        self.keys: Tuple[Hashable, ...] = ensure_tuple(keys)
-        if not self.keys:
-            raise ValueError("keys must be non empty.")
-        for key in self.keys:
-            if not isinstance(key, Hashable):
-                raise TypeError(f"keys must be one of (Hashable, Iterable[Hashable]) but is {type(keys).__name__}.")
-
-    @abstractmethod
-    def __call__(self, data):
-        """
-        ``data`` often comes from an iteration over an iterable,
-        such as :py:class:`torch.utils.data.Dataset`.
-
-        To simplify the input validations, this method assumes:
-
-        - ``data`` is a Python dictionary
-        - ``data[key]`` is a Numpy ndarray, PyTorch Tensor or string, where ``key`` is an element
-          of ``self.keys``, the data shape can be:
-
-          #. string data without shape, `LoadImaged` transform expects file paths
-          #. most of the pre-processing transforms expect: ``(num_channels, spatial_dim_1[, spatial_dim_2, ...])``,
-             except that `AddChanneld` expects (spatial_dim_1[, spatial_dim_2, ...]) and
-             `AsChannelFirstd` expects (spatial_dim_1[, spatial_dim_2, ...], num_channels)
-          #. most of the post-processing transforms expect
-             ``(batch_size, num_channels, spatial_dim_1[, spatial_dim_2, ...])``
-
-        - the channel dimension is not omitted even if number of channels is one
-
-        Raises:
-            NotImplementedError: When the subclass does not override this method.
-
-        returns:
-            An updated dictionary version of ``data`` by applying the transform.
-
-        """
-        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
diff --git a/monai/transforms/croppad/array.py b/monai/transforms/croppad/array.py
index b4444803a4..ef5e0019bd 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.compose import Randomizable, Transform
+from monai.transforms.transform import Randomizable, Transform
 from monai.transforms.utils import (
     generate_pos_neg_label_crop_centers,
     generate_spatial_bounding_box,
diff --git a/monai/transforms/croppad/dictionary.py b/monai/transforms/croppad/dictionary.py
index 1faed25605..20ae6ac1ed 100644
--- a/monai/transforms/croppad/dictionary.py
+++ b/monai/transforms/croppad/dictionary.py
@@ -21,7 +21,6 @@
 
 from monai.config import IndexSelection, KeysCollection
 from monai.data.utils import get_random_patch, get_valid_patch_size
-from monai.transforms.compose import MapTransform, Randomizable
 from monai.transforms.croppad.array import (
     BorderPad,
     BoundingRect,
@@ -31,6 +30,7 @@
     SpatialCrop,
     SpatialPad,
 )
+from monai.transforms.transform import MapTransform, Randomizable
 from monai.transforms.utils import (
     generate_pos_neg_label_crop_centers,
     generate_spatial_bounding_box,
diff --git a/monai/transforms/intensity/array.py b/monai/transforms/intensity/array.py
index 87091f6237..40bef064eb 100644
--- a/monai/transforms/intensity/array.py
+++ b/monai/transforms/intensity/array.py
@@ -22,7 +22,7 @@
 
 from monai.config import DtypeLike
 from monai.networks.layers import GaussianFilter, HilbertTransform, SavitzkyGolayFilter
-from monai.transforms.compose import Randomizable, Transform
+from monai.transforms.transform import Randomizable, Transform
 from monai.transforms.utils import rescale_array
 from monai.utils import PT_BEFORE_1_7, InvalidPyTorchVersionError, dtype_torch_to_numpy, ensure_tuple_size
 
diff --git a/monai/transforms/intensity/dictionary.py b/monai/transforms/intensity/dictionary.py
index 1c9b31c120..54a85a57b0 100644
--- a/monai/transforms/intensity/dictionary.py
+++ b/monai/transforms/intensity/dictionary.py
@@ -22,7 +22,6 @@
 import torch
 
 from monai.config import DtypeLike, KeysCollection
-from monai.transforms.compose import MapTransform, Randomizable
 from monai.transforms.intensity.array import (
     AdjustContrast,
     GaussianSharpen,
@@ -35,6 +34,7 @@
     ShiftIntensity,
     ThresholdIntensity,
 )
+from monai.transforms.transform import MapTransform, Randomizable
 from monai.utils import dtype_torch_to_numpy, ensure_tuple_rep, ensure_tuple_size
 
 __all__ = [
diff --git a/monai/transforms/io/array.py b/monai/transforms/io/array.py
index 9c14f7a689..855621e432 100644
--- a/monai/transforms/io/array.py
+++ b/monai/transforms/io/array.py
@@ -22,7 +22,7 @@
 from monai.data.image_reader import ImageReader, ITKReader, NibabelReader, NumpyReader, PILReader
 from monai.data.nifti_saver import NiftiSaver
 from monai.data.png_saver import PNGSaver
-from monai.transforms.compose import Transform
+from monai.transforms.transform import Transform
 from monai.utils import GridSampleMode, GridSamplePadMode
 from monai.utils import ImageMetaKey as Key
 from monai.utils import InterpolateMode, ensure_tuple, optional_import
diff --git a/monai/transforms/io/dictionary.py b/monai/transforms/io/dictionary.py
index d3220aa682..55707f750e 100644
--- a/monai/transforms/io/dictionary.py
+++ b/monai/transforms/io/dictionary.py
@@ -21,8 +21,8 @@
 
 from monai.config import DtypeLike, KeysCollection
 from monai.data.image_reader import ImageReader
-from monai.transforms.compose import MapTransform
 from monai.transforms.io.array import LoadImage, SaveImage
+from monai.transforms.transform import MapTransform
 from monai.utils import GridSampleMode, GridSamplePadMode, InterpolateMode
 
 __all__ = [
diff --git a/monai/transforms/post/array.py b/monai/transforms/post/array.py
index 0c60b0cc89..8b4f71093b 100644
--- a/monai/transforms/post/array.py
+++ b/monai/transforms/post/array.py
@@ -21,7 +21,7 @@
 import torch.nn.functional as F
 
 from monai.networks import one_hot
-from monai.transforms.compose import Transform
+from monai.transforms.transform import Transform
 from monai.transforms.utils import get_largest_connected_component_mask
 from monai.utils import ensure_tuple
 
diff --git a/monai/transforms/post/dictionary.py b/monai/transforms/post/dictionary.py
index 60cda11a91..aff4ae3572 100644
--- a/monai/transforms/post/dictionary.py
+++ b/monai/transforms/post/dictionary.py
@@ -21,7 +21,6 @@
 import torch
 
 from monai.config import KeysCollection
-from monai.transforms.compose import MapTransform
 from monai.transforms.post.array import (
     Activations,
     AsDiscrete,
@@ -30,6 +29,7 @@
     MeanEnsemble,
     VoteEnsemble,
 )
+from monai.transforms.transform import MapTransform
 from monai.utils import ensure_tuple_rep
 
 __all__ = [
diff --git a/monai/transforms/spatial/array.py b/monai/transforms/spatial/array.py
index df10480188..0610982847 100644
--- a/monai/transforms/spatial/array.py
+++ b/monai/transforms/spatial/array.py
@@ -22,8 +22,8 @@
 from monai.config import USE_COMPILED, DtypeLike
 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.compose import Randomizable, Transform
 from monai.transforms.croppad.array import CenterSpatialCrop
+from monai.transforms.transform import Randomizable, Transform
 from monai.transforms.utils import (
     create_control_grid,
     create_grid,
diff --git a/monai/transforms/spatial/dictionary.py b/monai/transforms/spatial/dictionary.py
index e612a25ef8..8b546e5e97 100644
--- a/monai/transforms/spatial/dictionary.py
+++ b/monai/transforms/spatial/dictionary.py
@@ -22,7 +22,6 @@
 
 from monai.config import DtypeLike, KeysCollection
 from monai.networks.layers.simplelayers import GaussianFilter
-from monai.transforms.compose import MapTransform, Randomizable
 from monai.transforms.croppad.array import CenterSpatialCrop
 from monai.transforms.spatial.array import (
     Flip,
@@ -36,6 +35,7 @@
     Spacing,
     Zoom,
 )
+from monai.transforms.transform import MapTransform, Randomizable
 from monai.transforms.utils import create_grid
 from monai.utils import (
     GridSampleMode,
diff --git a/monai/transforms/transform.py b/monai/transforms/transform.py
new file mode 100644
index 0000000000..e5841cbe97
--- /dev/null
+++ b/monai/transforms/transform.py
@@ -0,0 +1,206 @@
+# Copyright 2020 - 2021 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+A collection of generic interfaces for MONAI transforms.
+"""
+
+from abc import ABC, abstractmethod
+from typing import Any, Hashable, Optional, Tuple
+
+import numpy as np
+
+from monai.config import KeysCollection
+from monai.utils import MAX_SEED, ensure_tuple
+
+__all__ = ["Randomizable", "Transform", "MapTransform"]
+
+
+class Randomizable(ABC):
+    """
+    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::
+
+        class RandShiftIntensity(Randomizable):
+            def randomize():
+                self._offset = self.R.uniform(low=0, high=100)
+            def __call__(self, img):
+                self.randomize()
+                return img + self._offset
+
+        transform = RandShiftIntensity()
+        transform.set_random_state(seed=0)
+
+    """
+
+    R: np.random.RandomState = np.random.RandomState()
+
+    def set_random_state(
+        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None
+    ) -> "Randomizable":
+        """
+        Set the random state locally, to control the randomness, the derived
+        classes should use :py:attr:`self.R` instead of `np.random` to introduce random
+        factors.
+
+        Args:
+            seed: set the random state with an integer seed.
+            state: set the random state with a `np.random.RandomState` object.
+
+        Raises:
+            TypeError: When ``state`` is not an ``Optional[np.random.RandomState]``.
+
+        Returns:
+            a Randomizable instance.
+
+        """
+        if seed is not None:
+            _seed = id(seed) if not isinstance(seed, (int, np.integer)) else seed
+            _seed = _seed % MAX_SEED
+            self.R = np.random.RandomState(_seed)
+            return self
+
+        if state is not None:
+            if not isinstance(state, np.random.RandomState):
+                raise TypeError(f"state must be None or a np.random.RandomState but is {type(state).__name__}.")
+            self.R = state
+            return self
+
+        self.R = np.random.RandomState()
+        return self
+
+    @abstractmethod
+    def randomize(self, data: Any) -> None:
+        """
+        Within this method, :py:attr:`self.R` should be used, instead of `np.random`, to introduce random factors.
+
+        all :py:attr:`self.R` calls happen here so that we have a better chance to
+        identify errors of sync the random state.
+
+        This method can generate the random factors based on properties of the input data.
+
+        Raises:
+            NotImplementedError: When the subclass does not override this method.
+
+        """
+        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
+
+
+class Transform(ABC):
+    """
+    An abstract class of a ``Transform``.
+    A transform is callable that processes ``data``.
+
+    It could be stateful and may modify ``data`` in place,
+    the implementation should be aware of:
+
+        #. thread safety when mutating its own states.
+           When used from a multi-process context, transform's instance variables are read-only.
+        #. ``data`` content unused by this transform may still be used in the
+           subsequent transforms in a composed transform.
+        #. storing too much information in ``data`` may not scale.
+
+    See Also
+
+        :py:class:`monai.transforms.Compose`
+    """
+
+    @abstractmethod
+    def __call__(self, data: Any):
+        """
+        ``data`` is an element which often comes from an iteration over an
+        iterable, such as :py:class:`torch.utils.data.Dataset`. This method should
+        return an updated version of ``data``.
+        To simplify the input validations, most of the transforms assume that
+
+        - ``data`` is a Numpy ndarray, PyTorch Tensor or string
+        - the data shape can be:
+
+          #. string data without shape, `LoadImage` transform expects file paths
+          #. most of the pre-processing transforms expect: ``(num_channels, spatial_dim_1[, spatial_dim_2, ...])``,
+             except that `AddChannel` expects (spatial_dim_1[, spatial_dim_2, ...]) and
+             `AsChannelFirst` expects (spatial_dim_1[, spatial_dim_2, ...], num_channels)
+          #. most of the post-processing transforms expect
+             ``(batch_size, num_channels, spatial_dim_1[, spatial_dim_2, ...])``
+
+        - the channel dimension is not omitted even if number of channels is one
+
+        This method can optionally take additional arguments to help execute transformation operation.
+
+        Raises:
+            NotImplementedError: When the subclass does not override this method.
+
+        """
+        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
+
+
+class MapTransform(Transform):
+    """
+    A subclass of :py:class:`monai.transforms.Transform` with an assumption
+    that the ``data`` input of ``self.__call__`` is a MutableMapping such as ``dict``.
+
+    The ``keys`` parameter will be used to get and set the actual data
+    item to transform.  That is, the callable of this transform should
+    follow the pattern:
+
+        .. code-block:: python
+
+            def __call__(self, data):
+                for key in self.keys:
+                    if key in data:
+                        # update output data with some_transform_function(data[key]).
+                    else:
+                        # do nothing or some exceptions handling.
+                return data
+
+    Raises:
+        ValueError: When ``keys`` is an empty iterable.
+        TypeError: When ``keys`` type is not in ``Union[Hashable, Iterable[Hashable]]``.
+
+    """
+
+    def __init__(self, keys: KeysCollection) -> None:
+        self.keys: Tuple[Hashable, ...] = ensure_tuple(keys)
+        if not self.keys:
+            raise ValueError("keys must be non empty.")
+        for key in self.keys:
+            if not isinstance(key, Hashable):
+                raise TypeError(f"keys must be one of (Hashable, Iterable[Hashable]) but is {type(keys).__name__}.")
+
+    @abstractmethod
+    def __call__(self, data):
+        """
+        ``data`` often comes from an iteration over an iterable,
+        such as :py:class:`torch.utils.data.Dataset`.
+
+        To simplify the input validations, this method assumes:
+
+        - ``data`` is a Python dictionary
+        - ``data[key]`` is a Numpy ndarray, PyTorch Tensor or string, where ``key`` is an element
+          of ``self.keys``, the data shape can be:
+
+          #. string data without shape, `LoadImaged` transform expects file paths
+          #. most of the pre-processing transforms expect: ``(num_channels, spatial_dim_1[, spatial_dim_2, ...])``,
+             except that `AddChanneld` expects (spatial_dim_1[, spatial_dim_2, ...]) and
+             `AsChannelFirstd` expects (spatial_dim_1[, spatial_dim_2, ...], num_channels)
+          #. most of the post-processing transforms expect
+             ``(batch_size, num_channels, spatial_dim_1[, spatial_dim_2, ...])``
+
+        - the channel dimension is not omitted even if number of channels is one
+
+        Raises:
+            NotImplementedError: When the subclass does not override this method.
+
+        returns:
+            An updated dictionary version of ``data`` by applying the transform.
+
+        """
+        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
diff --git a/monai/transforms/utility/array.py b/monai/transforms/utility/array.py
index c0ae40de59..8b161a9223 100644
--- a/monai/transforms/utility/array.py
+++ b/monai/transforms/utility/array.py
@@ -21,7 +21,7 @@
 import torch
 
 from monai.config import DtypeLike, NdarrayTensor
-from monai.transforms.compose import Randomizable, 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
 
diff --git a/monai/transforms/utility/dictionary.py b/monai/transforms/utility/dictionary.py
index f374b82d76..c4bd7d4cba 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.compose import MapTransform, Randomizable
+from monai.transforms.transform import MapTransform, Randomizable
 from monai.transforms.utility.array import (
     AddChannel,
     AsChannelFirst,

From 201ee51e886373a1015c79ee016380ec1fa5b110 Mon Sep 17 00:00:00 2001
From: Rich <33289025+rijobro@users.noreply.github.com>
Date: Wed, 24 Feb 2021 16:49:02 +0000
Subject: [PATCH 2/4] backwards compatibility

Signed-off-by: Richard Brown <33289025+rijobro@users.noreply.github.com>
---
 monai/transforms/compose.py | 2 ++
 tests/test_compose.py       | 3 +++
 2 files changed, 5 insertions(+)

diff --git a/monai/transforms/compose.py b/monai/transforms/compose.py
index d71618d88f..36b187fbf1 100644
--- a/monai/transforms/compose.py
+++ b/monai/transforms/compose.py
@@ -18,6 +18,8 @@
 import numpy as np
 
 from monai.transforms.transform import Randomizable, Transform
+# For backwards compatiblity (so this still works: from monai.transforms.compose import MapTransform)
+from monai.transforms.transform import MapTransform  # noqa: F401
 from monai.transforms.utils import apply_transform
 from monai.utils import MAX_SEED, ensure_tuple, get_seed
 
diff --git a/tests/test_compose.py b/tests/test_compose.py
index c049044a97..8d51614e2c 100644
--- a/tests/test_compose.py
+++ b/tests/test_compose.py
@@ -167,6 +167,9 @@ def test_flatten_and_len(self):
         # test len
         self.assertEqual(len(t1), 8)
 
+    def test_backwards_compatbile_imports(self):
+        from monai.transforms.compose import MapTransform, Transform, Randomizable  # noqa: F401
+
 
 if __name__ == "__main__":
     unittest.main()

From 6f0164b316198100ac5cc654fc65da9993b77a95 Mon Sep 17 00:00:00 2001
From: Richard Brown <33289025+rijobro@users.noreply.github.com>
Date: Wed, 24 Feb 2021 16:53:08 +0000
Subject: [PATCH 3/4] typo

Signed-off-by: Richard Brown <33289025+rijobro@users.noreply.github.com>
---
 tests/test_compose.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_compose.py b/tests/test_compose.py
index 8d51614e2c..46c93b027d 100644
--- a/tests/test_compose.py
+++ b/tests/test_compose.py
@@ -167,7 +167,7 @@ def test_flatten_and_len(self):
         # test len
         self.assertEqual(len(t1), 8)
 
-    def test_backwards_compatbile_imports(self):
+    def test_backwards_compatible_imports(self):
         from monai.transforms.compose import MapTransform, Transform, Randomizable  # noqa: F401
 
 

From 0684752a65332c43bde7ba327cc3b95fccb91b0b Mon Sep 17 00:00:00 2001
From: Rich <33289025+rijobro@users.noreply.github.com>
Date: Wed, 24 Feb 2021 17:01:40 +0000
Subject: [PATCH 4/4] code format

Signed-off-by: Richard Brown <33289025+rijobro@users.noreply.github.com>
---
 monai/transforms/compose.py | 2 +-
 tests/test_compose.py       | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/monai/transforms/compose.py b/monai/transforms/compose.py
index 36b187fbf1..2d612ad2e3 100644
--- a/monai/transforms/compose.py
+++ b/monai/transforms/compose.py
@@ -17,9 +17,9 @@
 
 import numpy as np
 
-from monai.transforms.transform import Randomizable, Transform
 # 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.utils import apply_transform
 from monai.utils import MAX_SEED, ensure_tuple, get_seed
 
diff --git a/tests/test_compose.py b/tests/test_compose.py
index 46c93b027d..3a0a6ea5bb 100644
--- a/tests/test_compose.py
+++ b/tests/test_compose.py
@@ -168,7 +168,7 @@ def test_flatten_and_len(self):
         self.assertEqual(len(t1), 8)
 
     def test_backwards_compatible_imports(self):
-        from monai.transforms.compose import MapTransform, Transform, Randomizable  # noqa: F401
+        from monai.transforms.compose import MapTransform, Randomizable, Transform  # noqa: F401
 
 
 if __name__ == "__main__":