1542 Add RandLambdad transform

docs/source/transforms.rst

@@ -939,6 +939,12 @@ Utility (Dict)
     :members:
     :special-members: __call__
 
+`RandLambdad`
+"""""""""""""
+.. autoclass:: RandLambdad
+    :members:
+    :special-members: __call__
+
 `LabelToMaskd`
 """"""""""""""
 .. autoclass:: LabelToMaskd

monai/transforms/__init__.py

@@ -298,6 +298,9 @@
     Lambdad,
     LambdaD,
     LambdaDict,
+    RandLambdad,
+    RandLambdaD,
+    RandLambdaDict,
     RepeatChanneld,
     RepeatChannelD,
     RepeatChannelDict,

monai/transforms/utility/dictionary.py

@@ -17,7 +17,7 @@
 
 import copy
 import logging
-from typing import Callable, Dict, Hashable, List, Mapping, Optional, Sequence, Tuple, Union
+from typing import Any, Callable, Dict, Hashable, List, Mapping, Optional, Sequence, Tuple, Union
 
 import numpy as np
 import torch
@@ -64,10 +64,12 @@
     "CopyItemsd",
     "ConcatItemsd",
     "Lambdad",
+    "RandLambdad",
     "LabelToMaskd",
     "FgBgToIndicesd",
     "ConvertToMultiChannelBasedOnBratsClassesd",
     "AddExtremePointsChanneld",
+    "TorchVisiond",
     "IdentityD",
     "IdentityDict",
     "AsChannelFirstD",
@@ -76,6 +78,8 @@
     "AsChannelLastDict",
     "AddChannelD",
     "AddChannelDict",
+    "RandLambdaD",
+    "RandLambdaDict",
     "RepeatChannelD",
     "RepeatChannelDict",
     "SplitChannelD",
@@ -106,7 +110,6 @@
     "ConvertToMultiChannelBasedOnBratsClassesDict",
     "AddExtremePointsChannelD",
     "AddExtremePointsChannelDict",
-    "TorchVisiond",
     "TorchVisionD",
     "TorchVisionDict",
 ]
@@ -621,6 +624,27 @@ def __call__(self, data):
         return d
 
 
+class RandLambdad(Lambdad, Randomizable):
+    """
+    Randomizable version :py:class:`monai.transforms.Lambdad`, the input `func` contains random logic.
+    It's a randomizable transform so `CacheDataset` will not execute it and cache the results.
+
+    Args:
+        keys: keys of the corresponding items to be transformed.
+            See also: :py:class:`monai.transforms.compose.MapTransform`
+        func: Lambda/function to be applied. It also can be a sequence of Callable,
+            each element corresponds to a key in ``keys``.
+        overwrite: whether to overwrite the original data in the input dictionary with lamdbda function output.
+            default to True. it also can be a sequence of bool, each element corresponds to a key in ``keys``.
+
+    For more details, please check :py:class:`monai.transforms.Lambdad`.
+
+    """
+
+    def randomize(self, data: Any) -> None:
+        pass
+
+
 class LabelToMaskd(MapTransform):
     """
     Dictionary-based wrapper of :py:class:`monai.transforms.LabelToMask`.
@@ -830,3 +854,4 @@ def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> Dict[Hashable, torc
 ) = ConvertToMultiChannelBasedOnBratsClassesd
 AddExtremePointsChannelD = AddExtremePointsChannelDict = AddExtremePointsChanneld
 TorchVisionD = TorchVisionDict = TorchVisiond
+RandLambdaD = RandLambdaDict = RandLambdad

tests/test_rand_lambdad.py

@@ -0,0 +1,48 @@
+# 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.
+
+import unittest
+
+import numpy as np
+
+from monai.transforms import Randomizable
+from monai.transforms.utility.dictionary import RandLambdad
+
+
+class RandTest(Randomizable):
+    """
+    randomisable transform for testing.
+    """
+
+    def randomize(self, data=None):
+        self._a = self.R.random()
+
+    def __call__(self, data):
+        self.randomize()
+        return data + self._a
+
+
+class TestRandLambdad(unittest.TestCase):
+    def test_rand_lambdad_identity(self):
+        img = np.zeros((10, 10))
+        data = {"img": img, "prop": 1.0}
+
+        test_func = RandTest()
+        test_func.set_random_state(seed=134)
+        expected = {"img": test_func(data["img"]), "prop": 1.0}
+        test_func.set_random_state(seed=134)
+        ret = RandLambdad(keys=["img", "prop"], func=test_func, overwrite=[True, False])(data)
+        np.testing.assert_allclose(expected["img"], ret["img"])
+        np.testing.assert_allclose(expected["prop"], ret["prop"])
+
+
+if __name__ == "__main__":
+    unittest.main()