RMDS (Relative Mahalanobis Distance), a new OOD detector

.gitignore

@@ -19,6 +19,9 @@ __pycache__
 oodeel_dev_env
 oodeel_env*
 *_env
+.venv
+._tf
+.venv_tf
 
 # Files generated:
 logs
@@ -29,5 +32,6 @@ docs/notebooks/saved_models/*
 # mkdocs documentation
 /site
 .env
+.venv
 
 .DS_Store

README.md

@@ -177,6 +177,7 @@ Currently, **oodeel** includes the following baselines:
 | NMD | [Neural Mean Discrepancy for Efficient Out-of-Distribution Detection](https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Neural_Mean_Discrepancy_for_Efficient_Out-of-Distribution_Detection_CVPR_2022_paper.html) | CVPR 2022 | planned |
 | Gram | [Detecting Out-of-Distribution Examples with Gram Matrices](https://proceedings.mlr.press/v119/sastry20a.html) | ICML 2020 | avail [tensorflow](docs/notebooks/tensorflow/demo_gram_tf.ipynb) or  [torch](docs/notebooks/torch/demo_gram_torch.ipynb) |
 | GEN | [GEN: Pushing the Limits of Softmax-Based Out-of-Distribution Detection](https://openaccess.thecvf.com/content/CVPR2023/html/Liu_GEN_Pushing_the_Limits_of_Softmax-Based_Out-of-Distribution_Detection_CVPR_2023_paper.html) | CVPR 2023 | avail [tensorflow](docs/notebooks/tensorflow/demo_gen_tf.ipynb) or [torch](docs/notebooks/torch/demo_gen_torch.ipynb) |
+| RMDS | [A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection](https://arxiv.org/abs/2106.09022) | preprint | avail [tensorflow](docs/notebooks/tensorflow/demo_rmds_tf.ipynb) or [torch](docs/notebooks/torch/demo_rmds_torch.ipynb) |
 
 
 

mkdocs.yml

@@ -17,6 +17,7 @@ nav:
       - React: notebooks/tensorflow/demo_react_tf.ipynb
       - Gram: notebooks/tensorflow/demo_gram_tf.ipynb
       - GEN: notebooks/tensorflow/demo_gen_tf.ipynb
+      - RMDS: notebooks/tensorflow/demo_rmds_tf.ipynb
     - OOD Baselines (Torch):
       - MLS/MSP: notebooks/torch/demo_mls_msp_torch.ipynb
       - ODIN: notebooks/torch/demo_odin_torch.ipynb
@@ -28,6 +29,7 @@ nav:
       - React: notebooks/torch/demo_react_torch.ipynb
       - Gram: notebooks/torch/demo_gram_torch.ipynb
       - GEN: notebooks/torch/demo_gen_torch.ipynb
+      - RMDS: notebooks/torch/demo_rmds_torch.ipynb
     - Advanced Topics:
       - Seamlessly handling torch and tf datasets with DataHandler: pages/datahandler_tuto.md
       - Seamlessly handling torch and tf Tensors with Operator: pages/operator_tuto.md

oodeel/methods/__init__.py

@@ -28,6 +28,7 @@
 from .mahalanobis import Mahalanobis
 from .mls import MLS
 from .odin import ODIN
+from .rmds import RMDS
 from .vim import VIM
 
 __all__ = [
@@ -39,5 +40,6 @@
     "Mahalanobis",
     "MLS",
     "ODIN",
+    "RMDS",
     "VIM",
 ]

oodeel/methods/rmds.py

@@ -0,0 +1,123 @@
+# -*- coding: utf-8 -*-
+# Copyright IRT Antoine de Saint Exupéry et Université Paul Sabatier Toulouse III - All
+# rights reserved. DEEL is a research program operated by IVADO, IRT Saint Exupéry,
+# CRIAQ and ANITI - https://www.deel.ai/
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import numpy as np
+
+from ..types import DatasetType
+from ..types import TensorType
+from ..types import Tuple
+from oodeel.methods.mahalanobis import Mahalanobis
+
+
+class RMDS(Mahalanobis):
+    """
+    "A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection"
+    https://arxiv.org/abs/2106.09022
+
+    Args:
+        eps (float): magnitude for gradient based input perturbation.
+            Defaults to 0.02.
+    """
+
+    def __init__(self, eps: float = 0.002):
+        super().__init__(eps=eps)
+
+    def _fit_to_dataset(self, fit_dataset: DatasetType) -> None:
+        """
+        Constructs the pear class means and the covariance matrice,
+        as well as the background mean and covariance matrice,
+        from ID data "fit_dataset".
+        The means and pseudo-inverses of the covariance matrices
+        will be used for RMDS score computation.
+
+        Args:
+            fit_dataset (Union[TensorType, DatasetType]): input dataset (ID)
+        """
+        # means and pseudo-inverse of the mean convariance matrice from Mahalanobis
+        super()._fit_to_dataset(fit_dataset)
+
+        # extract features
+        features, _ = self.feature_extractor.predict(fit_dataset)
+
+        # comput background mu and cov
+        _features_bg = self.op.flatten(features[0])
+        mu_bg = self.op.mean(_features_bg, dim=0)
+        _zero_f_bg = _features_bg - mu_bg
+        cov_bg = self.op.matmul(self.op.t(_zero_f_bg), _zero_f_bg) / _zero_f_bg.shape[0]
+
+        # background mu and pseudo-inverse of the mean covariance matrices
+        self._mu_bg = mu_bg
+        self._pinv_cov_bg = self.op.pinv(cov_bg)
+
+    def _score_tensor(self, inputs: TensorType) -> Tuple[np.ndarray]:
+        """
+        Computes an OOD score for input samples "inputs" based on the RMDS
+        distance with respect to the closest class-conditional Gaussian distribution,
+        and the background distribution.
+
+        Args:
+            inputs (TensorType): input samples
+
+        Returns:
+            Tuple[np.ndarray]: scores, logits
+        """
+        # input preprocessing (perturbation)
+        if self.eps > 0:
+            inputs_p = self._input_perturbation(inputs)
+        else:
+            inputs_p = inputs
+
+        # mahalanobis score on perturbed inputs
+        features_p, _ = self.feature_extractor.predict_tensor(inputs_p)
+        features_p = self.op.flatten(features_p[0])
+        gaussian_score_p = self._mahalanobis_score(features_p)
+
+        # background score on perturbed inputs
+        gaussian_score_bg = self._background_score(features_p)
+
+        # take the highest score for each sample
+        gaussian_score_corrected = self.op.max(
+            gaussian_score_bg - gaussian_score_p, dim=1
+        )
+        return -self.op.convert_to_numpy(gaussian_score_corrected)
+
+    def _background_score(self, out_features: TensorType) -> TensorType:
+        """
+        Mahalanobis distance-based background score. For each test sample, it computes
+        the log of the probability densities of some observations (assuming a
+        normal distribution) using the mahalanobis distance with respect to the
+        background distribution.
+
+        Args:
+            out_features (TensorType): test samples features
+
+        Returns:
+            TensorType: confidence scores (with respect to the background distribution)
+        """
+        mu = self._mu_bg
+        zero_f = out_features - mu
+        # gaussian log prob density (mahalanobis)
+        log_probs_f = -0.5 * self.op.diag(
+            self.op.matmul(self.op.matmul(zero_f, self._pinv_cov_bg), self.op.t(zero_f))
+        )
+        gaussian_score = self.op.reshape(log_probs_f, (-1, 1))
+        return gaussian_score

tests/tests_tensorflow/methods/test_tf_rmds.py

@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+# Copyright IRT Antoine de Saint Exupéry et Université Paul Sabatier Toulouse III - All
+# rights reserved. DEEL is a research program operated by IVADO, IRT Saint Exupéry,
+# CRIAQ and ANITI - https://www.deel.ai/
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import pytest
+
+from oodeel.methods import RMDS
+from tests.tests_tensorflow import eval_detector_on_blobs
+
+
+@pytest.mark.parametrize("auroc_thr,fpr95_thr", [(0.95, 0.05)])
+def test_rmds(auroc_thr, fpr95_thr):
+    """
+    Test RMDS on toy blobs OOD dataset-wise task
+
+    We check that the area under ROC is above a certain threshold, and that the FPR95TPR
+    is below an other threshold.
+    """
+    rmds = RMDS()
+    eval_detector_on_blobs(
+        detector=rmds,
+        auroc_thr=auroc_thr,
+        fpr95_thr=fpr95_thr,
+        batch_size=64,
+    )

tests/tests_torch/methods/test_torch_rmds.py

@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+# Copyright IRT Antoine de Saint Exupéry et Université Paul Sabatier Toulouse III - All
+# rights reserved. DEEL is a research program operated by IVADO, IRT Saint Exupéry,
+# CRIAQ and ANITI - https://www.deel.ai/
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import pytest
+
+from oodeel.methods import RMDS
+from tests.tests_torch import eval_detector_on_blobs
+
+
+@pytest.mark.parametrize("auroc_thr,fpr95_thr", [(0.95, 0.05)])
+def test_rmds(auroc_thr, fpr95_thr):
+    """
+    Test RMDS on toy blobs OOD dataset-wise task
+
+    We check that the area under ROC is above a certain threshold, and that the FPR95TPR
+    is below an other threshold.
+    """
+    rmds = RMDS()
+    eval_detector_on_blobs(
+        detector=rmds,
+        auroc_thr=auroc_thr,
+        fpr95_thr=fpr95_thr,
+    )