Enhance XAI (CLI, ExplainParameters, test)

CHANGELOG.md

@@ -12,6 +12,7 @@ All notable changes to this project will be documented in this file.
 
 - Clean up and refactor the output of the OTX CLI (<https://github.com/openvinotoolkit/training_extensions/pull/1946>)
 - Enhance DetCon logic and SupCon for semantic segmentation(<https://github.com/openvinotoolkit/training_extensions/pull/1958>)
+- Extend OTX explain CLI (<https://github.com/openvinotoolkit/training_extensions/pull/1941>)
 
 ### Bug fixes
 

docs/source/guide/get_started/quick_start_guide/cli_commands.rst

@@ -397,7 +397,7 @@ The command below will evaluate the trained model on the provided dataset:
 Explanation
 ***********
 
-``otx explain`` runs the explainable AI (XAI) algorithm of a model on the specific dataset. It helps explain the model's decision-making process in a way that is easily understood by humans.
+``otx explain`` runs the explainable AI (XAI) algorithm on a specific model-dataset pair. It helps explain the model's decision-making process in a way that is easily understood by humans.
 
 With the ``--help`` command, you can list additional information, such as its parameters common to all model templates:
 
@@ -422,8 +422,12 @@ With the ``--help`` command, you can list additional information, such as its pa
                             Load model weights from previously saved checkpoint.
       --explain-algorithm EXPLAIN_ALGORITHM
                             Explain algorithm name, currently support ['activationmap', 'eigencam', 'classwisesaliencymap']. For Openvino task, default method will be selected.
+      --process-saliency-maps PROCESS_SALIENCY_MAPS
+                            Processing of saliency map includes (1) resizing to input image resolution and (2) applying a colormap. Depending on the number of targets to explain, this might take significant time.
+      --explain-all-classes EXPLAIN_ALL_CLASSES
+                            Provides explanations for all classes. Otherwise, explains only predicted classes. This feature is supported by algorithms that can generate explanations per each class.
       --overlay-weight OVERLAY_WEIGHT
-                            Weight of the saliency map when overlaying the saliency map.
+                            Weight of the saliency map when overlaying the input image with saliency map.
 
 
 The command below will generate saliency maps (heatmaps with red colored areas of focus) of the trained model on the provided dataset and save the resulting images to ``save-explanation-to`` path:

otx/algorithms/classification/tasks/inference.py

@@ -36,6 +36,7 @@
 from otx.algorithms.common.utils import embed_ir_model_data
 from otx.algorithms.common.utils.logger import get_logger
 from otx.api.entities.datasets import DatasetEntity
+from otx.api.entities.explain_parameters import ExplainParameters
 from otx.api.entities.inference_parameters import (
     InferenceParameters,
     default_progress_callback,
@@ -163,7 +164,7 @@ def infer(
     def explain(
         self,
         dataset: DatasetEntity,
-        explain_parameters: Optional[InferenceParameters] = None,
+        explain_parameters: Optional[ExplainParameters] = None,
     ) -> DatasetEntity:
         """Main explain function of OTX Classification Task."""
         logger.info("called explain()")

otx/algorithms/classification/tasks/openvino.py

@@ -39,6 +39,7 @@
 )
 from otx.api.entities.annotation import AnnotationSceneEntity
 from otx.api.entities.datasets import DatasetEntity
+from otx.api.entities.explain_parameters import ExplainParameters
 from otx.api.entities.inference_parameters import (
     InferenceParameters,
     default_progress_callback,
@@ -254,7 +255,7 @@ def infer(
     def explain(
         self,
         dataset: DatasetEntity,
-        explain_parameters: Optional[InferenceParameters] = None,
+        explain_parameters: Optional[ExplainParameters] = None,
     ) -> DatasetEntity:
         """Explain function of ClassificationOpenVINOTask."""
 
@@ -269,6 +270,12 @@ def explain(
         dataset_size = len(dataset)
         for i, dataset_item in enumerate(dataset, 1):
             predicted_scene, _, saliency_map, _, _ = self.inferencer.predict(dataset_item.numpy)
+            if saliency_map is None:
+                raise RuntimeError(
+                    "There is no Saliency Map in OpenVINO IR model output. "
+                    "Please export model to OpenVINO IR with dump_features"
+                )
+
             item_labels = predicted_scene.annotations[0].get_labels()
             dataset_item.append_labels(item_labels)
             add_saliency_maps_to_dataset_item(

otx/algorithms/common/tasks/base_task.py

@@ -31,6 +31,7 @@
 from otx.algorithms.common.utils import UncopiableDefaultDict
 from otx.algorithms.common.utils.logger import get_logger
 from otx.api.entities.datasets import DatasetEntity
+from otx.api.entities.explain_parameters import ExplainParameters
 from otx.api.entities.inference_parameters import InferenceParameters
 from otx.api.entities.label import LabelEntity
 from otx.api.entities.metrics import MetricsGroup
@@ -208,7 +209,7 @@ def export(
     def explain(
         self,
         dataset: DatasetEntity,
-        explain_parameters: Optional[InferenceParameters] = None,
+        explain_parameters: Optional[ExplainParameters] = None,
     ) -> DatasetEntity:
         """Main explain function of OTX Task."""
         raise NotImplementedError

otx/algorithms/detection/adapters/mmdet/hooks/__init__.py

@@ -3,6 +3,6 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-from .det_saliency_map_hook import DetSaliencyMapHook
+from .det_class_probability_map_hook import DetClassProbabilityMapHook
 
-__all__ = ["DetSaliencyMapHook"]
+__all__ = ["DetClassProbabilityMapHook"]

otx/algorithms/detection/adapters/mmdet/hooks/det_saliency_map_hook.py → otx/algorithms/detection/adapters/mmdet/hooks/det_class_probability_map_hook.py

@@ -26,7 +26,7 @@
 # pylint: disable=too-many-locals
 
 
-class DetSaliencyMapHook(BaseRecordingForwardHook):
+class DetClassProbabilityMapHook(BaseRecordingForwardHook):
     """Saliency map hook for object detection models."""
 
     def __init__(self, module: torch.nn.Module) -> None:
@@ -116,7 +116,7 @@ def forward_single(x, cls_convs, conv_cls):
             else:
                 raise NotImplementedError(
                     "Not supported detection head provided. "
-                    "DetSaliencyMapHook supports only the following single stage detectors: "
+                    "DetClassProbabilityMap supports only the following single stage detectors: "
                     "YOLOXHead, ATSSHead, SSDHead, VFNetHead."
                 )
         return cls_scores

otx/algorithms/detection/adapters/mmdet/models/detectors/custom_atss_detector.py

@@ -15,8 +15,8 @@
 from otx.algorithms.common.adapters.mmdeploy.utils import is_mmdeploy_enabled
 from otx.algorithms.common.utils.logger import get_logger
 from otx.algorithms.common.utils.task_adapt import map_class_names
-from otx.algorithms.detection.adapters.mmdet.hooks.det_saliency_map_hook import (
-    DetSaliencyMapHook,
+from otx.algorithms.detection.adapters.mmdet.hooks.det_class_probability_map_hook import (
+    DetClassProbabilityMapHook,
 )
 
 from .l2sp_detector_mixin import L2SPDetectorMixin
@@ -99,7 +99,7 @@ def custom_atss__simple_test(ctx, self, img, img_metas, **kwargs):
         if ctx.cfg["dump_features"]:
             feature_vector = FeatureVectorHook.func(feat)
             cls_scores = outs[0]
-            saliency_map = DetSaliencyMapHook(self).func(feature_map=cls_scores, cls_scores_provided=True)
+            saliency_map = DetClassProbabilityMapHook(self).func(feature_map=cls_scores, cls_scores_provided=True)
             return (*bbox_results, feature_vector, saliency_map)
 
         return bbox_results

otx/algorithms/detection/adapters/mmdet/models/detectors/custom_single_stage_detector.py

@@ -15,8 +15,8 @@
 from otx.algorithms.common.adapters.mmdeploy.utils import is_mmdeploy_enabled
 from otx.algorithms.common.utils.logger import get_logger
 from otx.algorithms.common.utils.task_adapt import map_class_names
-from otx.algorithms.detection.adapters.mmdet.hooks.det_saliency_map_hook import (
-    DetSaliencyMapHook,
+from otx.algorithms.detection.adapters.mmdet.hooks.det_class_probability_map_hook import (
+    DetClassProbabilityMapHook,
 )
 
 from .l2sp_detector_mixin import L2SPDetectorMixin
@@ -156,7 +156,7 @@ def custom_single_stage_detector__simple_test(ctx, self, img, img_metas, **kwarg
         if ctx.cfg["dump_features"]:
             feature_vector = FeatureVectorHook.func(feat)
             cls_scores = outs[0]
-            saliency_map = DetSaliencyMapHook(self).func(cls_scores, cls_scores_provided=True)
+            saliency_map = DetClassProbabilityMapHook(self).func(cls_scores, cls_scores_provided=True)
             return (*bbox_results, feature_vector, saliency_map)
 
         return bbox_results

otx/algorithms/detection/adapters/mmdet/models/detectors/custom_yolox_detector.py

@@ -15,8 +15,8 @@
 from otx.algorithms.common.adapters.mmdeploy.utils import is_mmdeploy_enabled
 from otx.algorithms.common.utils.logger import get_logger
 from otx.algorithms.common.utils.task_adapt import map_class_names
-from otx.algorithms.detection.adapters.mmdet.hooks.det_saliency_map_hook import (
-    DetSaliencyMapHook,
+from otx.algorithms.detection.adapters.mmdet.hooks.det_class_probability_map_hook import (
+    DetClassProbabilityMapHook,
 )
 
 from .l2sp_detector_mixin import L2SPDetectorMixin
@@ -137,7 +137,7 @@ def custom_yolox__simple_test(ctx, self, img, img_metas, **kwargs):
         if ctx.cfg["dump_features"]:
             feature_vector = FeatureVectorHook.func(feat)
             cls_scores = outs[0]
-            saliency_map = DetSaliencyMapHook(self).func(cls_scores, cls_scores_provided=True)
+            saliency_map = DetClassProbabilityMapHook(self).func(cls_scores, cls_scores_provided=True)
             return (*bbox_results, feature_vector, saliency_map)
 
         return bbox_results

otx/algorithms/detection/adapters/mmdet/task.py

@@ -60,8 +60,8 @@
     SemiSLDetectionConfigurer,
 )
 from otx.algorithms.detection.adapters.mmdet.datasets import ImageTilingDataset
-from otx.algorithms.detection.adapters.mmdet.hooks.det_saliency_map_hook import (
-    DetSaliencyMapHook,
+from otx.algorithms.detection.adapters.mmdet.hooks.det_class_probability_map_hook import (
+    DetClassProbabilityMapHook,
 )
 from otx.algorithms.detection.adapters.mmdet.utils.builder import build_detector
 from otx.algorithms.detection.adapters.mmdet.utils.config_utils import (
@@ -74,6 +74,7 @@
 from otx.api.configuration import cfg_helper
 from otx.api.configuration.helper.utils import config_to_bytes, ids_to_strings
 from otx.api.entities.datasets import DatasetEntity
+from otx.api.entities.explain_parameters import ExplainParameters
 from otx.api.entities.inference_parameters import InferenceParameters
 from otx.api.entities.model import (
     ModelEntity,
@@ -392,7 +393,7 @@ def hook(module, inp, outp):
             if isinstance(raw_model, TwoStageDetector):
                 saliency_hook = ActivationMapHook(feature_model)
             else:
-                saliency_hook = DetSaliencyMapHook(feature_model)
+                saliency_hook = DetClassProbabilityMapHook(feature_model)
 
         if not dump_features:
             feature_vector_hook: Union[nullcontext, BaseRecordingForwardHook] = nullcontext()
@@ -540,12 +541,12 @@ def export(
     def explain(
         self,
         dataset: DatasetEntity,
-        explain_parameters: Optional[InferenceParameters] = None,
+        explain_parameters: Optional[ExplainParameters] = None,
     ) -> DatasetEntity:
         """Main explain function of MMDetectionTask."""
 
         explainer_hook_selector = {
-            "classwisesaliencymap": DetSaliencyMapHook,
+            "classwisesaliencymap": DetClassProbabilityMapHook,
             "eigencam": EigenCamHook,
             "activationmap": ActivationMapHook,
         }

otx/algorithms/detection/adapters/openvino/task.py

@@ -41,6 +41,7 @@
 from otx.api.configuration.helper.utils import config_to_bytes
 from otx.api.entities.annotation import AnnotationSceneEntity
 from otx.api.entities.datasets import DatasetEntity
+from otx.api.entities.explain_parameters import ExplainParameters
 from otx.api.entities.inference_parameters import (
     InferenceParameters,
     default_progress_callback,
@@ -434,7 +435,7 @@ def infer(
     def explain(
         self,
         dataset: DatasetEntity,
-        explain_parameters: Optional[InferenceParameters] = None,
+        explain_parameters: Optional[ExplainParameters] = None,
     ) -> DatasetEntity:
         """Explain function of OpenVINODetectionTask."""
         logger.info("Start OpenVINO explain")
@@ -453,6 +454,11 @@ def explain(
             dataset_item.append_annotations(predicted_scene.annotations)
             update_progress_callback(int(i / dataset_size * 100), None)
             _, saliency_map = features
+            if saliency_map is None:
+                raise RuntimeError(
+                    "There is no Saliency Map in OpenVINO IR model output. "
+                    "Please export model to OpenVINO IR with dump_features"
+                )
 
             labels = self.task_environment.get_labels().copy()
             if saliency_map.shape[0] == len(labels) + 1:

otx/algorithms/detection/task.py

@@ -36,6 +36,7 @@
 from otx.api.configuration.helper.utils import ids_to_strings
 from otx.api.entities.annotation import Annotation
 from otx.api.entities.datasets import DatasetEntity
+from otx.api.entities.explain_parameters import ExplainParameters
 from otx.api.entities.id import ID
 from otx.api.entities.inference_parameters import InferenceParameters
 from otx.api.entities.label import Domain, LabelEntity
@@ -259,7 +260,7 @@ def export(
     def explain(
         self,
         dataset: DatasetEntity,
-        explain_parameters: Optional[InferenceParameters] = None,
+        explain_parameters: Optional[ExplainParameters] = None,
     ) -> DatasetEntity:
         """Main explain function of OTX Task."""
         raise NotImplementedError

otx/api/entities/explain_parameters.py

@@ -0,0 +1,33 @@
+"""This module define the Explain entity."""
+# Copyright (C) 2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+
+from dataclasses import dataclass
+from typing import Any, Callable, Optional
+
+
+# pylint: disable=unused-argument
+def default_progress_callback(progress: int, score: Optional[float] = None):
+    """This is the default progress callback for OptimizationParameters."""
+
+
+@dataclass
+class ExplainParameters:
+    """Explain parameters.
+
+    Attributes:
+        explainer: Explain algorithm to be used in explanation mode.
+            Will be converted automatically to lowercase.
+        process_saliency_maps: Processing of saliency map includes (1) resize to input image resolution
+            and (2) apply a colormap.
+        explain_predicted_classes: Provides explanations only for predicted classes.
+            Otherwise, explain all classes.
+    """
+
+    update_progress: Callable[[int, Optional[float]], Any] = default_progress_callback
+
+    explainer: str = ""
+    process_saliency_maps: bool = False
+    explain_predicted_classes: bool = True

otx/api/entities/inference_parameters.py

@@ -36,7 +36,6 @@ class InferenceParameters:
     is_evaluation: bool = False
     update_progress: Callable[[int, Optional[float]], Any] = default_progress_callback
 
-    # TODO(negvet): use separate ExplainParameters dataclass for this
     explainer: str = ""
     process_saliency_maps: bool = False
     explain_predicted_classes: bool = True

otx/api/usecases/tasks/interfaces/explain_interface.py

@@ -8,7 +8,7 @@
 import abc
 
 from otx.api.entities.datasets import DatasetEntity
-from otx.api.entities.inference_parameters import InferenceParameters
+from otx.api.entities.explain_parameters import ExplainParameters
 
 
 class IExplainTask(metaclass=abc.ABCMeta):
@@ -18,7 +18,7 @@ class IExplainTask(metaclass=abc.ABCMeta):
     def explain(
         self,
         dataset: DatasetEntity,
-        explain_parameters: InferenceParameters,
+        explain_parameters: ExplainParameters,
     ) -> DatasetEntity:
         """This is the method that is called upon explanation.