Fix label list order for h-label classification

src/otx/algorithms/classification/adapters/openvino/task.py

@@ -36,6 +36,7 @@
 from otx.algorithms.classification.utils import (
     get_cls_deploy_config,
     get_cls_inferencer_configuration,
+    get_hierarchical_label_list,
 )
 from otx.algorithms.common.utils import OTXOpenVinoDataLoader
 from otx.algorithms.common.utils.ir import check_if_quantized
@@ -228,12 +229,18 @@ def add_prediction(id: int, predicted_scene: AnnotationSceneEntity, aux_data: tu
                 if saliency_map is not None and repr_vector is not None:
                     feature_vec_media = TensorEntity(name="representation_vector", numpy=repr_vector.reshape(-1))
                     dataset_item.append_metadata_item(feature_vec_media, model=self.model)
+                    label_list = self.task_environment.get_labels()
+                    # Fix the order for hierarchical labels to adjust classes with model outputs
+                    if self.inferencer.model.hierarchical:
+                        label_list = get_hierarchical_label_list(
+                            self.inferencer.model.hierarchical_info["cls_heads_info"], label_list
+                        )
 
                     add_saliency_maps_to_dataset_item(
                         dataset_item=dataset_item,
                         saliency_map=saliency_map,
                         model=self.model,
-                        labels=self.task_environment.get_labels(),
+                        labels=label_list,
                         predicted_scored_labels=item_labels,
                         explain_predicted_classes=explain_predicted_classes,
                         process_saliency_maps=process_saliency_maps,
@@ -284,6 +291,12 @@ def explain(
             explain_predicted_classes = explain_parameters.explain_predicted_classes
 
         dataset_size = len(dataset)
+        label_list = self.task_environment.get_labels()
+        # Fix the order for hierarchical labels to adjust classes with model outputs
+        if self.inferencer.model.hierarchical:
+            label_list = get_hierarchical_label_list(
+                self.inferencer.model.hierarchical_info["cls_heads_info"], label_list
+            )
         for i, dataset_item in enumerate(dataset, 1):
             predicted_scene, _, saliency_map, _, _ = self.inferencer.predict(dataset_item.numpy)
             if saliency_map is None:
@@ -298,7 +311,7 @@ def explain(
                 dataset_item=dataset_item,
                 saliency_map=saliency_map,
                 model=self.model,
-                labels=self.task_environment.get_labels(),
+                labels=label_list,
                 predicted_scored_labels=item_labels,
                 explain_predicted_classes=explain_predicted_classes,
                 process_saliency_maps=process_saliency_maps,

src/otx/algorithms/classification/task.py

@@ -28,6 +28,7 @@
     get_cls_deploy_config,
     get_cls_inferencer_configuration,
     get_cls_model_api_configuration,
+    get_hierarchical_label_list,
 )
 from otx.algorithms.classification.utils import (
     get_multihead_class_info as get_hierarchical_info,
@@ -345,6 +346,10 @@ def _add_predictions_to_dataset(
 
         dataset_size = len(dataset)
         pos_thr = 0.5
+        label_list = self._labels
+        # Fix the order for hierarchical labels to adjust classes with model outputs
+        if self._hierarchical:
+            label_list = get_hierarchical_label_list(self._hierarchical_info, label_list)
         for i, (dataset_item, prediction_items) in enumerate(zip(dataset, prediction_results)):
             prediction_item, feature_vector, saliency_map = prediction_items
             if any(np.isnan(prediction_item)):
@@ -373,7 +378,7 @@ def _add_predictions_to_dataset(
                     dataset_item=dataset_item,
                     saliency_map=saliency_map,
                     model=self._task_environment.model,
-                    labels=self._labels,
+                    labels=label_list,
                     predicted_scored_labels=item_labels,
                     explain_predicted_classes=explain_predicted_classes,
                     process_saliency_maps=process_saliency_maps,
@@ -436,13 +441,17 @@ def _add_explanations_to_dataset(
     ):
         """Loop over dataset again and assign saliency maps."""
         dataset_size = len(dataset)
+        label_list = self._labels
+        # Fix the order for hierarchical labels to adjust classes with model outputs
+        if self._hierarchical:
+            label_list = get_hierarchical_label_list(self._hierarchical_info, label_list)
         for i, (dataset_item, prediction_item, saliency_map) in enumerate(zip(dataset, predictions, saliency_maps)):
             item_labels = self._get_item_labels(prediction_item, pos_thr=0.5)
             add_saliency_maps_to_dataset_item(
                 dataset_item=dataset_item,
                 saliency_map=saliency_map,
                 model=self._task_environment.model,
-                labels=self._labels,
+                labels=label_list,
                 predicted_scored_labels=item_labels,
                 explain_predicted_classes=explain_predicted_classes,
                 process_saliency_maps=process_saliency_maps,

src/otx/algorithms/classification/utils/__init__.py

@@ -8,10 +8,12 @@
     get_cls_deploy_config,
     get_cls_inferencer_configuration,
     get_cls_model_api_configuration,
+    get_hierarchical_label_list,
     get_multihead_class_info,
 )
 
 __all__ = [
+    "get_hierarchical_label_list",
     "get_multihead_class_info",
     "get_cls_inferencer_configuration",
     "get_cls_deploy_config",

src/otx/algorithms/classification/utils/cls_utils.py

@@ -117,3 +117,24 @@ def get_cls_model_api_configuration(label_schema: LabelSchemaEntity, inference_c
 
     mapi_config[("model_info", "hierarchical_config")] = json.dumps(hierarchical_config)
     return mapi_config
+
+
+def get_hierarchical_label_list(hierarchical_info, labels):
+    """Return hierarchical labels list which is adjusted to model outputs classes."""
+    hierarchical_labels = []
+    for head_idx in range(hierarchical_info["num_multiclass_heads"]):
+        logits_begin, logits_end = hierarchical_info["head_idx_to_logits_range"][str(head_idx)]
+        for logit in range(0, logits_end - logits_begin):
+            label_str = hierarchical_info["all_groups"][head_idx][logit]
+            label_idx = hierarchical_info["label_to_idx"][label_str]
+            hierarchical_labels.append(labels[label_idx])
+
+    if hierarchical_info["num_multilabel_classes"]:
+        logits_begin = hierarchical_info["num_single_label_classes"]
+        logits_end = len(labels)
+        for logit_idx, logit in enumerate(range(0, logits_end - logits_begin)):
+            label_str_idx = hierarchical_info["num_multiclass_heads"] + logit_idx
+            label_str = hierarchical_info["all_groups"][label_str_idx][0]
+            label_idx = hierarchical_info["label_to_idx"][label_str]
+            hierarchical_labels.append(labels[label_idx])
+    return hierarchical_labels

tests/unit/algorithms/classification/tasks/test_classification_openvino_task.py

@@ -182,6 +182,7 @@ def test_explain(self, mocker):
                 self.fake_input,
             ),
         )
+        self.cls_ov_task.inferencer.model.hierarchical = False
         updpated_dataset = self.cls_ov_task.explain(self.dataset)
 
         assert updpated_dataset is not None