Insertion Deletion AUC metric (#56)

* Draft pointing game implementation

* Add insertion deletion auc

* Add ADCC

* Update auc

* Introduce BaseMetric as a parent class

* Delete ADCC

* Remove adcc tests

* Fixes from comments

CHANGELOG.md

@@ -21,7 +21,8 @@
 * Upgrade OpenVINO to 2024.3.0 by @goodsong81 in https://github.com/openvinotoolkit/openvino_xai/pull/52
 * Add saliency map visualization with explanation.plot() by @GalyaZalesskaya in https://github.com/openvinotoolkit/openvino_xai/pull/53
 * Enable flexible naming for saved saliency maps and include confidence scores by @GalyaZalesskaya in https://github.com/openvinotoolkit/openvino_xai/pull/51
-* Add [Pointing Game](https://arxiv.org/abs/1608.00507) saliency map quality metric by @GalyaZalesskaya in https://github.com/openvinotoolkit/openvino_xai/pull/54
+* Add [Pointing Game](https://link.springer.com/article/10.1007/s11263-017-1059-x) saliency map quality metric by @GalyaZalesskaya in https://github.com/openvinotoolkit/openvino_xai/pull/54
+* Add [Insertion Deletion AUC](https://arxiv.org/abs/1806.07421) saliency map quality metric by @GalyaZalesskaya in https://github.com/openvinotoolkit/openvino_xai/pull/56
 
 ### Known Issues
 

openvino_xai/explainer/utils.py

@@ -119,22 +119,29 @@ def get_preprocess_fn(
     )
 
 
-def postprocess_fn(x: Mapping, logit_name="logits") -> np.ndarray:
-    """Postprocess function."""
-    return x.get(logit_name, x[0])  # Models from OVC has no output names at times
-
-
-def get_postprocess_fn(logit_name="logits") -> Callable[[], np.ndarray]:
-    """Returns partially initialized postprocess_fn."""
-    return partial(postprocess_fn, logit_name=logit_name)
-
-
 class ActivationType(Enum):
     SIGMOID = "sigmoid"
     SOFTMAX = "softmax"
     NONE = "none"
 
 
+def postprocess_fn(x: Mapping, logit_name="logits", activation: ActivationType = ActivationType.NONE) -> np.ndarray:
+    """Postprocess function."""
+    x = x.get(logit_name, x[0])  # Models from OVC has no output names at times
+    if activation == ActivationType.SOFTMAX:
+        return softmax(x)
+    if activation == ActivationType.SIGMOID:
+        return sigmoid(x)
+    return x
+
+
+def get_postprocess_fn(
+    logit_name="logits", activation: ActivationType = ActivationType.NONE
+) -> Callable[[], np.ndarray]:
+    """Returns partially initialized postprocess_fn."""
+    return partial(postprocess_fn, logit_name=logit_name, activation=activation)
+
+
 def get_score(x: np.ndarray, index: int, activation: ActivationType = ActivationType.NONE):
     """Returns activated score at index."""
     if activation == ActivationType.SOFTMAX:

openvino_xai/methods/base.py

@@ -5,7 +5,7 @@
 from typing import Callable, Dict, Mapping
 
 import numpy as np
-import openvino.runtime as ov
+import openvino as ov
 
 from openvino_xai.common.utils import IdentityPreprocessFN
 
@@ -25,7 +25,7 @@ def __init__(
         self._device_name = device_name
 
     @property
-    def model_compiled(self) -> ov.ie_api.CompiledModel | None:
+    def model_compiled(self) -> ov.CompiledModel | None:
         return self._model_compiled
 
     @abstractmethod

openvino_xai/metrics/base.py

@@ -0,0 +1,36 @@
+from abc import ABC, abstractmethod
+from typing import Any, Callable, Dict, List
+
+import numpy as np
+import openvino as ov
+
+from openvino_xai.common.utils import IdentityPreprocessFN
+from openvino_xai.explainer.explanation import Explanation
+
+
+class BaseMetric(ABC):
+    """Base class for XAI quality metric."""
+
+    def __init__(
+        self,
+        model_compiled: ov.CompiledModel = None,
+        preprocess_fn: Callable[[np.ndarray], np.ndarray] = IdentityPreprocessFN(),
+        postprocess_fn: Callable[[np.ndarray], np.ndarray] = None,
+    ):
+        # Pass model_predict to class initialization directly?
+        self.model_compiled = model_compiled
+        self.preprocess_fn = preprocess_fn
+        self.postprocess_fn = postprocess_fn
+
+    def model_predict(self, input: np.ndarray) -> np.ndarray:
+        logits = self.model_compiled([self.preprocess_fn(input)])
+        logits = self.postprocess_fn(logits)[0]
+        return logits
+
+    @abstractmethod
+    def __call__(self, saliency_map, *args: Any, **kwargs: Any) -> Dict[str, float]:
+        """Calculate the metric for the single saliency map"""
+
+    @abstractmethod
+    def evaluate(self, explanations: List[Explanation], *args: Any, **kwargs: Any) -> Dict[str, float]:
+        """Evaluate the quality of saliency maps over the list of images"""

openvino_xai/metrics/insertion_deletion_auc.py

@@ -0,0 +1,109 @@
+from typing import Any, Dict, List, Tuple
+
+import numpy as np
+
+from openvino_xai.explainer.explanation import Explanation, Layout
+from openvino_xai.metrics.base import BaseMetric
+
+
+def AUC(arr: np.array) -> float:
+    """
+    Returns normalized Area Under Curve of the array.
+    """
+    return np.abs((arr.sum() - arr[0] / 2 - arr[-1] / 2) / (arr.shape[0] - 1))
+
+
+class InsertionDeletionAUC(BaseMetric):
+    """
+    Implementation of the Insertion and Deletion AUC by Petsiuk et al. 2018.
+
+    References:
+        Petsiuk, Vitali, Abir Das, and Kate Saenko. "Rise: Randomized input sampling
+        for explanation of black-box models." arXiv preprint arXiv:1806.07421 (2018).
+    """
+
+    @staticmethod
+    def step_image_insertion_deletion(
+        num_pixels: int, sorted_indices: Tuple[np.ndarray, np.ndarray], input_image: np.ndarray
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Return insertion/deletion image based on number of pixels to add/delete on this step.
+        """
+        # Values to start
+        image_insertion = np.full_like(input_image, 0)
+        image_deletion = input_image.copy()
+
+        x_indices = sorted_indices[0][:num_pixels]
+        y_indices = sorted_indices[1][:num_pixels]
+
+        # Insert the image on the places of the important pixels
+        image_insertion[x_indices, y_indices] = input_image[x_indices, y_indices]
+        # Remove image pixels on the places of the important pixels
+        image_deletion[x_indices, y_indices] = 0
+        return image_insertion, image_deletion
+
+    def __call__(
+        self, saliency_map: np.ndarray, class_idx: int, input_image: np.ndarray, steps: int = 100, **kwargs: Any
+    ) -> Dict[str, float]:
+        """
+        Calculate the Insertion and Deletion AUC metrics for one saliency map for one class.
+
+        Parameters:
+        :param saliency_map: Importance scores for each pixel (H, W).
+        :type saliency_map: np.ndarray
+        :param class_idx: The class of saliency map to evaluate.
+        :type class_idx: int
+        :param input_image: The input image to the model (H, W, C).
+        :type input_image: np.ndarray
+        :param steps: Number of steps for inserting pixels.
+        :type steps: int
+
+        Returns:
+        :return: A dictionary containing the AUC scores for insertion and deletion scores.
+        :rtype: Dict[str, float]
+        """
+        # Sort pixels by descending importance to find the most important pixels
+        sorted_indices = np.argsort(-saliency_map.flatten())
+        sorted_indices = np.unravel_index(sorted_indices, saliency_map.shape)
+
+        insertion_scores, deletion_scores = [], []
+        for i in range(steps + 1):
+            num_pixels = int(i * len(sorted_indices[0]) / steps)
+            step_image_insertion, step_image_deletion = self.step_image_insertion_deletion(
+                num_pixels, sorted_indices, input_image
+            )
+            # Predict on masked image
+            insertion_scores.append(self.model_predict(step_image_insertion)[class_idx])
+            deletion_scores.append(self.model_predict(step_image_deletion)[class_idx])
+        insertion = AUC(np.array(insertion_scores))
+        deletion = AUC(np.array(deletion_scores))
+        return {"insertion": insertion, "deletion": deletion}
+
+    def evaluate(
+        self, explanations: List[Explanation], input_images: List[np.ndarray], steps: int, **kwargs: Any
+    ) -> Dict[str, float]:
+        """
+        Evaluate the insertion and deletion AUC over the list of images and its saliency maps.
+
+        :param explanations: List of explanation objects containing saliency maps.
+        :type explanations: List[Explanation]
+        :param input_images: List of input images as numpy arrays.
+        :type input_images: List[np.ndarray]
+        :param steps: Number of steps for the insertion and deletion process.
+        :type steps: int
+
+        :return: A Dict containing the mean insertion AUC, mean deletion AUC, and their difference (delta) as values.
+        :rtype: float
+        """
+        for explanation in explanations:
+            assert explanation.layout in [Layout.MULTIPLE_MAPS_PER_IMAGE_GRAY, Layout.MULTIPLE_MAPS_PER_IMAGE_COLOR]
+
+        results = []
+        for input_image, explanation in zip(input_images, explanations):
+            for class_idx, saliency_map in explanation.saliency_map.items():
+                metric_dict = self(saliency_map, int(class_idx), input_image, steps)
+                results.append([metric_dict["insertion"], metric_dict["deletion"]])
+
+        insertion, deletion = np.mean(np.array(results), axis=0)
+        delta = insertion - deletion
+        return {"insertion": insertion, "deletion": deletion, "delta": delta}

openvino_xai/metrics/pointing_game.py

@@ -1,15 +1,16 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import Dict, List, Tuple
+from typing import Any, Dict, List, Tuple
 
 import numpy as np
 
 from openvino_xai.common.utils import logger
 from openvino_xai.explainer.explanation import Explanation
+from openvino_xai.metrics.base import BaseMetric
 
 
-class PointingGame:
+class PointingGame(BaseMetric):
     """
     Implementation of the Pointing Game by Zhang et al., 2018.
 
@@ -29,18 +30,21 @@ class PointingGame:
     """
 
     @staticmethod
-    def pointing_game(saliency_map: np.ndarray, image_gt_bboxes: List[Tuple[int, int, int, int]]) -> bool:
+    def __call__(saliency_map: np.ndarray, image_gt_bboxes: List[Tuple[int, int, int, int]]) -> Dict[str, float]:
         """
-        Implements the Pointing Game metric using a saliency map and bounding boxes of the same image and class.
-        Returns a boolean indicating if any of the most salient points fall within the ground truth bounding boxes.
+        Calculate the Pointing Game metric for one saliency map for one class.
+
+        This implementation uses a saliency map and bounding boxes of the same image and class.
+        Returns a dictionary with the result of the Pointing Game metric.
+        1.0 if any of the most salient points fall within the ground truth bounding boxes, 0.0 otherwise.
 
         :param saliency_map: A 2D numpy array representing the saliency map for the image.
         :type saliency_map: np.ndarray
         :param image_gt_bboxes: A list of tuples (x, y, w, h) representing the bounding boxes of the ground truth objects.
         :type image_gt_bboxes: List[Tuple[int, int, int, int]]
 
-        :return: True if any of the most salient points fall within any of the ground truth bounding boxes, False otherwise.
-        :rtype: bool
+        :return: A dictionary with the result of the Pointing Game metric.
+        :rtype: Dict[str, float]
         """
         # TODO: Optimize calculation by generating a mask from annotation and finding the intersection
         # Find the most salient points in the saliency map
@@ -51,12 +55,15 @@ def pointing_game(saliency_map: np.ndarray, image_gt_bboxes: List[Tuple[int, int
             for max_point_y, max_point_x in max_indices:
                 # Check if this point is within the ground truth bounding box
                 if x <= max_point_x <= x + w and y <= max_point_y <= y + h:
-                    return True
-        return False
+                    return {"pointing_game": 1.0}
+        return {"pointing_game": 0.0}
 
     def evaluate(
-        self, explanations: List[Explanation], gt_bboxes: List[Dict[str, List[Tuple[int, int, int, int]]]]
-    ) -> float:
+        self,
+        explanations: List[Explanation],
+        gt_bboxes: List[Dict[str, List[Tuple[int, int, int, int]]]],
+        **kwargs: Any,
+    ) -> Dict[str, float]:
         """
         Evaluates the Pointing Game metric over a set of images. Skips saliency maps if the gt bboxes for this class are absent.
 
@@ -65,15 +72,15 @@ def evaluate(
         :param gt_bboxes: A list of dictionaries {label_name: lists of bounding boxes} for each image.
         :type gt_bboxes: List[Dict[str, List[Tuple[int, int, int, int]]]]
 
-        :return: Pointing game score over a list of images
-        :rtype: float
+        :return: Dict with "Pointing game" as a key and score over a list of images as as a value.
+        :rtype: Dict[str, float]
         """
 
         assert len(explanations) == len(
             gt_bboxes
         ), "Number of explanations and ground truth bounding boxes must match and equal to number of images."
 
-        hits = 0
+        hits = 0.0
         num_sal_maps = 0
         for explanation, image_gt_bboxes in zip(explanations, gt_bboxes):
             label_names = explanation.label_names
@@ -90,7 +97,8 @@ def evaluate(
                     continue
 
                 class_gt_bboxes = image_gt_bboxes[label_name]
-                hits += self.pointing_game(class_sal_map, class_gt_bboxes)
+                hits += self(class_sal_map, class_gt_bboxes)["pointing_game"]
                 num_sal_maps += 1
 
-        return hits / num_sal_maps if num_sal_maps > 0 else 0.0
+        score = hits / num_sal_maps if num_sal_maps > 0 else 0.0
+        return {"pointing_game": score}