Merge branch 'dev' into 4980-get-wsi-at-mpp

docs/source/transforms.rst

@@ -661,6 +661,27 @@ Post-processing
     :members:
     :special-members: __call__
 
+Regularization
+^^^^^^^^^^^^^^
+
+`CutMix`
+""""""""
+.. autoclass:: CutMix
+    :members:
+    :special-members: __call__
+
+`CutOut`
+""""""""
+.. autoclass:: CutOut
+    :members:
+    :special-members: __call__
+
+`MixUp`
+"""""""
+.. autoclass:: MixUp
+    :members:
+    :special-members: __call__
+
 Signal
 ^^^^^^^
 
@@ -1707,6 +1728,27 @@ Post-processing (Dict)
   :members:
   :special-members: __call__
 
+Regularization (Dict)
+^^^^^^^^^^^^^^^^^^^^^
+
+`CutMixd`
+"""""""""
+.. autoclass:: CutMixd
+    :members:
+    :special-members: __call__
+
+`CutOutd`
+"""""""""
+.. autoclass:: CutOutd
+    :members:
+    :special-members: __call__
+
+`MixUpd`
+""""""""
+.. autoclass:: MixUpd
+    :members:
+    :special-members: __call__
+
 Signal (Dict)
 ^^^^^^^^^^^^^
 

docs/source/transforms_idx.rst

@@ -74,6 +74,16 @@ Post-processing
    post.array
    post.dictionary
 
+Regularization
+^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: _gen
+   :nosignatures:
+
+   regularization.array
+   regularization.dictionary
+
 Signal
 ^^^^^^
 

monai/bundle/config_item.py

@@ -289,10 +289,7 @@ def instantiate(self, **kwargs: Any) -> object:
         mode = self.get_config().get("_mode_", CompInitMode.DEFAULT)
         args = self.resolve_args()
         args.update(kwargs)
-        try:
-            return instantiate(modname, mode, **args)
-        except Exception as e:
-            raise RuntimeError(f"Failed to instantiate {self}") from e
+        return instantiate(modname, mode, **args)
 
 
 class ConfigExpression(ConfigItem):

monai/transforms/__init__.py

@@ -336,6 +336,18 @@
     VoteEnsembled,
     VoteEnsembleDict,
 )
+from .regularization.array import CutMix, CutOut, MixUp
+from .regularization.dictionary import (
+    CutMixd,
+    CutMixD,
+    CutMixDict,
+    CutOutd,
+    CutOutD,
+    CutOutDict,
+    MixUpd,
+    MixUpD,
+    MixUpDict,
+)
 from .signal.array import (
     SignalContinuousWavelet,
     SignalFillEmpty,

monai/transforms/regularization/__init__.py

@@ -0,0 +1,10 @@
+# Copyright (c) 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.

monai/transforms/regularization/array.py

@@ -0,0 +1,173 @@
+# Copyright (c) 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.
+
+from __future__ import annotations
+
+from abc import abstractmethod
+from math import ceil, sqrt
+
+import torch
+
+from ..transform import RandomizableTransform
+
+__all__ = ["MixUp", "CutMix", "CutOut", "Mixer"]
+
+
+class Mixer(RandomizableTransform):
+    def __init__(self, batch_size: int, alpha: float = 1.0) -> None:
+        """
+        Mixer is a base class providing the basic logic for the mixup-class of
+        augmentations. In all cases, we need to sample the mixing weights for each
+        sample (lambda in the notation used in the papers). Also, pairs of samples
+        being mixed are picked by randomly shuffling the batch samples.
+
+        Args:
+            batch_size (int): number of samples per batch. That is, samples are expected tp
+                be of size batchsize x channels [x depth] x height x width.
+            alpha (float, optional): mixing weights are sampled from the Beta(alpha, alpha)
+                distribution. Defaults to 1.0, the uniform distribution.
+        """
+        super().__init__()
+        if alpha <= 0:
+            raise ValueError(f"Expected positive number, but got {alpha = }")
+        self.alpha = alpha
+        self.batch_size = batch_size
+
+    @abstractmethod
+    def apply(self, data: torch.Tensor):
+        raise NotImplementedError()
+
+    def randomize(self, data=None) -> None:
+        """
+        Sometimes you need may to apply the same transform to different tensors.
+        The idea is to get a sample and then apply it with apply() as often
+        as needed. You need to call this method everytime you apply the transform to a new
+        batch.
+        """
+        self._params = (
+            torch.from_numpy(self.R.beta(self.alpha, self.alpha, self.batch_size)).type(torch.float32),
+            self.R.permutation(self.batch_size),
+        )
+
+
+class MixUp(Mixer):
+    """MixUp as described in:
+    Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz.
+    mixup: Beyond Empirical Risk Minimization, ICLR 2018
+
+    Class derived from :py:class:`monai.transforms.Mixer`. See corresponding
+    documentation for details on the constructor parameters.
+    """
+
+    def apply(self, data: torch.Tensor):
+        weight, perm = self._params
+        nsamples, *dims = data.shape
+        if len(weight) != nsamples:
+            raise ValueError(f"Expected batch of size: {len(weight)}, but got {nsamples}")
+
+        if len(dims) not in [3, 4]:
+            raise ValueError("Unexpected number of dimensions")
+
+        mixweight = weight[(Ellipsis,) + (None,) * len(dims)]
+        return mixweight * data + (1 - mixweight) * data[perm, ...]
+
+    def __call__(self, data: torch.Tensor, labels: torch.Tensor | None = None):
+        self.randomize()
+        if labels is None:
+            return self.apply(data)
+        return self.apply(data), self.apply(labels)
+
+
+class CutMix(Mixer):
+    """CutMix augmentation as described in:
+        Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, Youngjoon Yoo.
+        CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features,
+        ICCV 2019
+
+        Class derived from :py:class:`monai.transforms.Mixer`. See corresponding
+        documentation for details on the constructor parameters. Here, alpha not only determines
+        the mixing weight but also the size of the random rectangles used during for mixing.
+        Please refer to the paper for details.
+
+        The most common use case is something close to:
+
+    .. code-block:: python
+
+        cm = CutMix(batch_size=8, alpha=0.5)
+        for batch in loader:
+            images, labels = batch
+            augimg, auglabels = cm(images, labels)
+            output = model(augimg)
+            loss = loss_function(output, auglabels)
+            ...
+
+    """
+
+    def apply(self, data: torch.Tensor):
+        weights, perm = self._params
+        nsamples, _, *dims = data.shape
+        if len(weights) != nsamples:
+            raise ValueError(f"Expected batch of size: {len(weights)}, but got {nsamples}")
+
+        mask = torch.ones_like(data)
+        for s, weight in enumerate(weights):
+            coords = [torch.randint(0, d, size=(1,)) for d in dims]
+            lengths = [d * sqrt(1 - weight) for d in dims]
+            idx = [slice(None)] + [slice(c, min(ceil(c + ln), d)) for c, ln, d in zip(coords, lengths, dims)]
+            mask[s][idx] = 0
+
+        return mask * data + (1 - mask) * data[perm, ...]
+
+    def apply_on_labels(self, labels: torch.Tensor):
+        weights, perm = self._params
+        nsamples, *dims = labels.shape
+        if len(weights) != nsamples:
+            raise ValueError(f"Expected batch of size: {len(weights)}, but got {nsamples}")
+
+        mixweight = weights[(Ellipsis,) + (None,) * len(dims)]
+        return mixweight * labels + (1 - mixweight) * labels[perm, ...]
+
+    def __call__(self, data: torch.Tensor, labels: torch.Tensor | None = None):
+        self.randomize()
+        augmented = self.apply(data)
+        return (augmented, self.apply_on_labels(labels)) if labels is not None else augmented
+
+
+class CutOut(Mixer):
+    """Cutout as described in the paper:
+    Terrance DeVries, Graham W. Taylor.
+    Improved Regularization of Convolutional Neural Networks with Cutout,
+    arXiv:1708.04552
+
+    Class derived from :py:class:`monai.transforms.Mixer`. See corresponding
+    documentation for details on the constructor parameters. Here, alpha not only determines
+    the mixing weight but also the size of the random rectangles being cut put.
+    Please refer to the paper for details.
+    """
+
+    def apply(self, data: torch.Tensor):
+        weights, _ = self._params
+        nsamples, _, *dims = data.shape
+        if len(weights) != nsamples:
+            raise ValueError(f"Expected batch of size: {len(weights)}, but got {nsamples}")
+
+        mask = torch.ones_like(data)
+        for s, weight in enumerate(weights):
+            coords = [torch.randint(0, d, size=(1,)) for d in dims]
+            lengths = [d * sqrt(1 - weight) for d in dims]
+            idx = [slice(None)] + [slice(c, min(ceil(c + ln), d)) for c, ln, d in zip(coords, lengths, dims)]
+            mask[s][idx] = 0
+
+        return mask * data
+
+    def __call__(self, data: torch.Tensor):
+        self.randomize()
+        return self.apply(data)

monai/transforms/regularization/dictionary.py

@@ -0,0 +1,97 @@
+# Copyright (c) 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.
+
+from __future__ import annotations
+
+from monai.config import KeysCollection
+from monai.utils.misc import ensure_tuple
+
+from ..transform import MapTransform
+from .array import CutMix, CutOut, MixUp
+
+__all__ = ["MixUpd", "MixUpD", "MixUpDict", "CutMixd", "CutMixD", "CutMixDict", "CutOutd", "CutOutD", "CutOutDict"]
+
+
+class MixUpd(MapTransform):
+    """
+    Dictionary-based version :py:class:`monai.transforms.MixUp`.
+
+    Notice that the mixup transformation will be the same for all entries
+    for consistency, i.e. images and labels must be applied the same augmenation.
+    """
+
+    def __init__(
+        self, keys: KeysCollection, batch_size: int, alpha: float = 1.0, allow_missing_keys: bool = False
+    ) -> None:
+        super().__init__(keys, allow_missing_keys)
+        self.mixup = MixUp(batch_size, alpha)
+
+    def __call__(self, data):
+        self.mixup.randomize()
+        result = dict(data)
+        for k in self.keys:
+            result[k] = self.mixup.apply(data[k])
+        return result
+
+
+class CutMixd(MapTransform):
+    """
+    Dictionary-based version :py:class:`monai.transforms.CutMix`.
+
+    Notice that the mixture weights will be the same for all entries
+    for consistency, i.e. images and labels must be aggregated with the same weights,
+    but the random crops are not.
+    """
+
+    def __init__(
+        self,
+        keys: KeysCollection,
+        batch_size: int,
+        label_keys: KeysCollection | None = None,
+        alpha: float = 1.0,
+        allow_missing_keys: bool = False,
+    ) -> None:
+        super().__init__(keys, allow_missing_keys)
+        self.mixer = CutMix(batch_size, alpha)
+        self.label_keys = ensure_tuple(label_keys) if label_keys is not None else []
+
+    def __call__(self, data):
+        self.mixer.randomize()
+        result = dict(data)
+        for k in self.keys:
+            result[k] = self.mixer.apply(data[k])
+        for k in self.label_keys:
+            result[k] = self.mixer.apply_on_labels(data[k])
+        return result
+
+
+class CutOutd(MapTransform):
+    """
+    Dictionary-based version :py:class:`monai.transforms.CutOut`.
+
+    Notice that the cutout is different for every entry in the dictionary.
+    """
+
+    def __init__(self, keys: KeysCollection, batch_size: int, allow_missing_keys: bool = False) -> None:
+        super().__init__(keys, allow_missing_keys)
+        self.cutout = CutOut(batch_size)
+
+    def __call__(self, data):
+        result = dict(data)
+        self.cutout.randomize()
+        for k in self.keys:
+            result[k] = self.cutout(data[k])
+        return result
+
+
+MixUpD = MixUpDict = MixUpd
+CutMixD = CutMixDict = CutMixd
+CutOutD = CutOutDict = CutOutd

monai/utils/module.py

@@ -272,7 +272,7 @@ def instantiate(__path: str, __mode: str, **kwargs: Any) -> Any:
             return pdb.runcall(component, **kwargs)
     except Exception as e:
         raise RuntimeError(
-            f"Failed to instantiate component '{__path}' with kwargs: {kwargs}"
+            f"Failed to instantiate component '{__path}' with keywords: {','.join(kwargs.keys())}"
             f"\n set '_mode_={CompInitMode.DEBUG}' to enter the debugging mode."
         ) from e