2701 Add dict version RandCoarseShuffle transform

docs/source/transforms.rst

@@ -977,6 +977,12 @@ Intensity (Dict)
     :members:
     :special-members: __call__
 
+`RandCoarseShuffled`
+""""""""""""""""""""
+.. autoclass:: RandCoarseShuffled
+    :members:
+    :special-members: __call__
+
 `HistogramNormalized`
 """""""""""""""""""""
  .. autoclass:: HistogramNormalized

monai/transforms/__init__.py

@@ -144,6 +144,9 @@
     RandCoarseDropoutd,
     RandCoarseDropoutD,
     RandCoarseDropoutDict,
+    RandCoarseShuffled,
+    RandCoarseShuffleD,
+    RandCoarseShuffleDict,
     RandGaussianNoised,
     RandGaussianNoiseD,
     RandGaussianNoiseDict,

monai/transforms/intensity/array.py

@@ -1786,6 +1786,21 @@ class RandCoarseShuffle(RandCoarseTransform):
     Kang, Guoliang, et al. "Patchshuffle regularization." arXiv preprint arXiv:1707.07103 (2017).
     https://arxiv.org/abs/1707.07103
 
+    Args:
+        holes: number of regions to dropout, if `max_holes` is not None, use this arg as the minimum number to
+            randomly select the expected number of regions.
+        spatial_size: spatial size of the regions to dropout, if `max_spatial_size` is not None, use this arg
+            as the minimum spatial size to randomly select size for every region.
+            if some components of the `spatial_size` are non-positive values, the transform will use the
+            corresponding components of input img size. For example, `spatial_size=(32, -1)` will be adapted
+            to `(32, 64)` if the second spatial dimension size of img is `64`.
+        max_holes: if not None, define the maximum number to randomly select the expected number of regions.
+        max_spatial_size: if not None, define the maximum spatial size to randomly select size for every region.
+            if some components of the `max_spatial_size` are non-positive values, the transform will use the
+            corresponding components of input img size. For example, `max_spatial_size=(32, -1)` will be adapted
+            to `(32, 64)` if the second spatial dimension size of img is `64`.
+        prob: probability of applying the transform.
+
     """
 
     def _transform_holes(self, img: np.ndarray):

monai/transforms/intensity/dictionary.py

@@ -34,6 +34,7 @@
     NormalizeIntensity,
     RandBiasField,
     RandCoarseDropout,
+    RandCoarseShuffle,
     RandGaussianNoise,
     RandKSpaceSpikeNoise,
     RandRicianNoise,
@@ -75,6 +76,7 @@
     "RandKSpaceSpikeNoised",
     "RandHistogramShiftd",
     "RandCoarseDropoutd",
+    "RandCoarseShuffled",
     "HistogramNormalized",
     "RandGaussianNoiseD",
     "RandGaussianNoiseDict",
@@ -126,6 +128,8 @@
     "RandRicianNoiseDict",
     "RandCoarseDropoutD",
     "RandCoarseDropoutDict",
+    "RandCoarseShuffleD",
+    "RandCoarseShuffleDict",
     "HistogramNormalizeD",
     "HistogramNormalizeDict",
 ]
@@ -1478,6 +1482,13 @@ def __init__(
             prob=prob,
         )
 
+    def set_random_state(
+        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None
+    ) -> "RandCoarseDropoutd":
+        self.dropper.set_random_state(seed, state)
+        super().set_random_state(seed, state)
+        return self
+
     def randomize(self, img_size: Sequence[int]) -> None:
         self.dropper.randomize(img_size=img_size)
 
@@ -1492,6 +1503,72 @@ def __call__(self, data):
         return d
 
 
+class RandCoarseShuffled(Randomizable, MapTransform):
+    """
+    Dictionary-based wrapper of :py:class:`monai.transforms.RandCoarseShuffle`.
+    Expect all the data specified by `keys` have same spatial shape and will randomly dropout the same regions
+    for every key, if want to shuffle different regions for every key, please use this transform separately.
+
+    Args:
+        keys: keys of the corresponding items to be transformed.
+            See also: :py:class:`monai.transforms.compose.MapTransform`
+        holes: number of regions to dropout, if `max_holes` is not None, use this arg as the minimum number to
+            randomly select the expected number of regions.
+        spatial_size: spatial size of the regions to dropout, if `max_spatial_size` is not None, use this arg
+            as the minimum spatial size to randomly select size for every region.
+            if some components of the `spatial_size` are non-positive values, the transform will use the
+            corresponding components of input img size. For example, `spatial_size=(32, -1)` will be adapted
+            to `(32, 64)` if the second spatial dimension size of img is `64`.
+        max_holes: if not None, define the maximum number to randomly select the expected number of regions.
+        max_spatial_size: if not None, define the maximum spatial size to randomly select size for every region.
+            if some components of the `max_spatial_size` are non-positive values, the transform will use the
+            corresponding components of input img size. For example, `max_spatial_size=(32, -1)` will be adapted
+            to `(32, 64)` if the second spatial dimension size of img is `64`.
+        prob: probability of applying the transform.
+        allow_missing_keys: don't raise exception if key is missing.
+
+    """
+
+    def __init__(
+        self,
+        keys: KeysCollection,
+        holes: int,
+        spatial_size: Union[Sequence[int], int],
+        max_holes: Optional[int] = None,
+        max_spatial_size: Optional[Union[Sequence[int], int]] = None,
+        prob: float = 0.1,
+        allow_missing_keys: bool = False,
+    ):
+        MapTransform.__init__(self, keys, allow_missing_keys)
+        self.shuffle = RandCoarseShuffle(
+            holes=holes,
+            spatial_size=spatial_size,
+            max_holes=max_holes,
+            max_spatial_size=max_spatial_size,
+            prob=prob,
+        )
+
+    def set_random_state(
+        self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None
+    ) -> "RandCoarseShuffled":
+        self.shuffle.set_random_state(seed, state)
+        super().set_random_state(seed, state)
+        return self
+
+    def randomize(self, img_size: Sequence[int]) -> None:
+        self.shuffle.randomize(img_size=img_size)
+
+    def __call__(self, data):
+        d = dict(data)
+        # expect all the specified keys have same spatial shape
+        self.randomize(d[self.keys[0]].shape[1:])
+        if self.shuffle._do_transform:
+            for key in self.key_iterator(d):
+                d[key] = self.shuffle(img=d[key])
+
+        return d
+
+
 class HistogramNormalized(MapTransform):
     """
     Dictionary-based wrapper of :py:class:`monai.transforms.HistogramNormalize`.
@@ -1562,3 +1639,4 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.nda
 RandKSpaceSpikeNoiseD = RandKSpaceSpikeNoiseDict = RandKSpaceSpikeNoised
 RandCoarseDropoutD = RandCoarseDropoutDict = RandCoarseDropoutd
 HistogramNormalizeD = HistogramNormalizeDict = HistogramNormalized
+RandCoarseShuffleD = RandCoarseShuffleDict = RandCoarseShuffled

tests/test_rand_coarse_shuffle.py

@@ -45,7 +45,7 @@
 
 class TestRandCoarseShuffle(unittest.TestCase):
     @parameterized.expand(TEST_CASES)
-    def test_local_patch_shuffle(self, input_param, input_data, expected_val):
+    def test_shuffle(self, input_param, input_data, expected_val):
         g = RandCoarseShuffle(**input_param)
         g.set_random_state(seed=12)
         result = g(**input_data)

tests/test_rand_coarse_shuffled.py

@@ -0,0 +1,56 @@
+# 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 parameterized import parameterized
+
+from monai.transforms import RandCoarseShuffled
+
+TEST_CASES = [
+    [
+        {"keys": "img", "holes": 5, "spatial_size": 1, "max_spatial_size": -1, "prob": 0.0},
+        {"img": np.arange(8).reshape((1, 2, 2, 2))},
+        np.arange(8).reshape((1, 2, 2, 2)),
+    ],
+    [
+        {"keys": "img", "holes": 10, "spatial_size": 1, "max_spatial_size": -1, "prob": 1.0},
+        {"img": np.arange(27).reshape((1, 3, 3, 3))},
+        np.asarray(
+            [
+                [
+                    [[13, 17, 5], [6, 16, 25], [12, 15, 22]],
+                    [[24, 7, 3], [9, 2, 23], [0, 4, 26]],
+                    [[19, 11, 14], [1, 20, 8], [18, 10, 21]],
+                ]
+            ]
+        ),
+    ],
+    [
+        {"keys": "img", "holes": 2, "spatial_size": 1, "max_spatial_size": -1, "prob": 1.0},
+        {"img": np.arange(16).reshape((2, 2, 2, 2))},
+        np.asarray([[[[7, 2], [1, 4]], [[5, 0], [3, 6]]], [[[8, 13], [10, 15]], [[14, 12], [11, 9]]]]),
+    ],
+]
+
+
+class TestRandCoarseShuffled(unittest.TestCase):
+    @parameterized.expand(TEST_CASES)
+    def test_shuffle(self, input_param, input_data, expected_val):
+        g = RandCoarseShuffled(**input_param)
+        g.set_random_state(seed=12)
+        result = g(input_data)
+        np.testing.assert_allclose(result["img"], expected_val, rtol=1e-4, atol=1e-4)
+
+
+if __name__ == "__main__":
+    unittest.main()