move transforms out of compose file

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
 

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,

monai/transforms/compose.py

@@ -13,135 +13,17 @@
 """
 
 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
+# 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
 
-__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):
@@ -255,67 +137,3 @@ 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.")

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,

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,

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
 

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__ = [

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

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__ = [

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
 

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__ = [

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,

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,