Enable export of feature vectors for semantic segmentation task (#4055)

src/otx/algo/segmentation/huggingface_model.py

@@ -162,4 +162,8 @@ def _exporter(self) -> OTXModelExporter:
 
     def forward_for_tracing(self, image: torch.Tensor) -> torch.Tensor | dict[str, torch.Tensor]:
         """Model forward function used for the model tracing during model exportation."""
+        if self.explain_mode:
+            msg = "Explain mode is not supported for this model."
+            raise NotImplementedError(msg)
+
         return self.model(image)

src/otx/algo/segmentation/litehrnet.py

@@ -81,7 +81,7 @@ def _exporter(self) -> OTXModelExporter:
             swap_rgb=False,
             via_onnx=False,
             onnx_export_configuration={"operator_export_type": OperatorExportTypes.ONNX_ATEN_FALLBACK},
-            output_names=None,
+            output_names=["preds", "feature_vector"] if self.explain_mode else None,
         )
 
     @property

src/otx/algo/segmentation/segmentors/base_model.py

@@ -10,6 +10,8 @@
 import torch.nn.functional as f
 from torch import Tensor, nn
 
+from otx.algo.explain.explain_algo import feature_vector_fn
+
 if TYPE_CHECKING:
     from otx.core.data.entity.base import ImageInfo
 
@@ -58,7 +60,7 @@ def forward(
                 - If mode is "predict", returns the predicted outputs.
                 - Otherwise, returns the model outputs after interpolation.
         """
-        outputs = self.extract_features(inputs)
+        enc_feats, outputs = self.extract_features(inputs)
         outputs = f.interpolate(outputs, size=inputs.size()[2:], mode="bilinear", align_corners=True)
 
         if mode == "tensor":
@@ -76,12 +78,19 @@ def forward(
         if mode == "predict":
             return outputs.argmax(dim=1)
 
+        if mode == "explain":
+            feature_vector = feature_vector_fn(enc_feats)
+            return {
+                "preds": outputs,
+                "feature_vector": feature_vector,
+            }
+
         return outputs
 
-    def extract_features(self, inputs: Tensor) -> Tensor:
+    def extract_features(self, inputs: Tensor) -> tuple[Tensor, Tensor]:
         """Extract features from the backbone and head."""
         enc_feats = self.backbone(inputs)
-        return self.decode_head(enc_feats)
+        return enc_feats, self.decode_head(enc_feats)
 
     def calculate_loss(
         self,

src/otx/core/model/base.py

@@ -124,7 +124,6 @@ def __init__(
         self.input_size = input_size
         self.classification_layers: dict[str, dict[str, Any]] = {}
         self.model = self._create_model()
-        self._explain_mode = False
         self.optimizer_callable = ensure_callable(optimizer)
         self.scheduler_callable = ensure_callable(scheduler)
         self.metric_callable = ensure_callable(metric)

src/otx/core/model/segmentation.py

@@ -119,7 +119,12 @@ def _build_model(self) -> nn.Module:
         """
 
     def _customize_inputs(self, entity: SegBatchDataEntity) -> dict[str, Any]:
-        mode = "loss" if self.training else "predict"
+        if self.training:
+            mode = "loss"
+        elif self.explain_mode:
+            mode = "explain"
+        else:
+            mode = "predict"
 
         if self.train_type == OTXTrainType.SEMI_SUPERVISED and mode == "loss":
             if not isinstance(entity, dict):
@@ -155,6 +160,16 @@ def _customize_outputs(
                 losses[k] = v
             return losses
 
+        if self.explain_mode:
+            return SegBatchPredEntity(
+                batch_size=len(outputs["preds"]),
+                images=inputs.images,
+                imgs_info=inputs.imgs_info,
+                scores=[],
+                masks=outputs["preds"],
+                feature_vector=outputs["feature_vector"],
+            )
+
         return SegBatchPredEntity(
             batch_size=len(outputs),
             images=inputs.images,
@@ -199,14 +214,24 @@ def _exporter(self) -> OTXModelExporter:
             swap_rgb=False,
             via_onnx=False,
             onnx_export_configuration=None,
-            output_names=None,
+            output_names=["preds", "feature_vector"] if self.explain_mode else None,
         )
 
     def _convert_pred_entity_to_compute_metric(
         self,
         preds: SegBatchPredEntity,
         inputs: SegBatchDataEntity,
     ) -> MetricInput:
+        """Convert prediction and input entities to a format suitable for metric computation.
+
+        Args:
+            preds (SegBatchPredEntity): The predicted segmentation batch entity containing predicted masks.
+            inputs (SegBatchDataEntity): The input segmentation batch entity containing ground truth masks.
+
+        Returns:
+            MetricInput: A list of dictionaries where each dictionary contains 'preds' and 'target' keys
+            corresponding to the predicted and target masks for metric evaluation.
+        """
         return [
             {
                 "preds": pred_mask,
@@ -228,8 +253,26 @@ def _dispatch_label_info(label_info: LabelInfoTypes) -> LabelInfo:
 
     def forward_for_tracing(self, image: Tensor) -> Tensor | dict[str, Tensor]:
         """Model forward function used for the model tracing during model exportation."""
-        raw_outputs = self.model(inputs=image, mode="tensor")
-        return torch.softmax(raw_outputs, dim=1)
+        if self.explain_mode:
+            outputs = self.model(inputs=image, mode="explain")
+            outputs["preds"] = torch.softmax(outputs["preds"], dim=1)
+            return outputs
+
+        outputs = self.model(inputs=image, mode="tensor")
+        return torch.softmax(outputs, dim=1)
+
+    def forward_explain(self, inputs: SegBatchDataEntity) -> SegBatchPredEntity:
+        """Model forward explain function."""
+        outputs = self.model(inputs=inputs.images, mode="explain")
+
+        return SegBatchPredEntity(
+            batch_size=len(outputs["preds"]),
+            images=inputs.images,
+            imgs_info=inputs.imgs_info,
+            scores=[],
+            masks=outputs["preds"],
+            feature_vector=outputs["feature_vector"],
+        )
 
     def get_dummy_input(self, batch_size: int = 1) -> SegBatchDataEntity:
         """Returns a dummy input for semantic segmentation model."""
@@ -308,32 +351,34 @@ def _customize_outputs(
         outputs: list[ImageResultWithSoftPrediction],
         inputs: SegBatchDataEntity,
     ) -> SegBatchPredEntity | OTXBatchLossEntity:
-        if outputs and outputs[0].saliency_map.size != 1:
-            predicted_s_maps = [out.saliency_map for out in outputs]
-            predicted_f_vectors = [out.feature_vector for out in outputs]
-            return SegBatchPredEntity(
-                batch_size=len(outputs),
-                images=inputs.images,
-                imgs_info=inputs.imgs_info,
-                scores=[],
-                masks=[tv_tensors.Mask(mask.resultImage, device=self.device) for mask in outputs],
-                saliency_map=predicted_s_maps,
-                feature_vector=predicted_f_vectors,
-            )
-
+        masks = [tv_tensors.Mask(mask.resultImage, device=self.device) for mask in outputs]
+        predicted_f_vectors = (
+            [out.feature_vector for out in outputs] if outputs and outputs[0].feature_vector.size != 1 else []
+        )
         return SegBatchPredEntity(
             batch_size=len(outputs),
             images=inputs.images,
             imgs_info=inputs.imgs_info,
             scores=[],
-            masks=[tv_tensors.Mask(mask.resultImage, device=self.device) for mask in outputs],
+            masks=masks,
+            feature_vector=predicted_f_vectors,
         )
 
     def _convert_pred_entity_to_compute_metric(
         self,
         preds: SegBatchPredEntity,
         inputs: SegBatchDataEntity,
     ) -> MetricInput:
+        """Convert prediction and input entities to a format suitable for metric computation.
+
+        Args:
+            preds (SegBatchPredEntity): The predicted segmentation batch entity containing predicted masks.
+            inputs (SegBatchDataEntity): The input segmentation batch entity containing ground truth masks.
+
+        Returns:
+            MetricInput: A list of dictionaries where each dictionary contains 'preds' and 'target' keys
+            corresponding to the predicted and target masks for metric evaluation.
+        """
         return [
             {
                 "preds": pred_mask,

tests/e2e/cli/test_cli.py

@@ -220,8 +220,8 @@ def test_otx_e2e_cli(
     # 5) otx export with XAI
     if "instance_segmentation/rtmdet_inst_tiny" in recipe:
         return
-    if ("_cls" not in task) and (task not in ["detection", "instance_segmentation"]):
-        return  # Supported only for classification, detection and instance segmentation task.
+    if ("_cls" not in task) and (task not in ["detection", "instance_segmentation", "semantic_segmentation"]):
+        return  # Supported only for classification, detection and segmentation tasks.
 
     unsupported_models = ["dino", "rtdetr"]
     if any(model in model_name for model in unsupported_models):

tests/integration/cli/test_cli.py

@@ -242,8 +242,8 @@ def test_otx_e2e(
     # 5) otx export with XAI
     if "instance_segmentation/rtmdet_inst_tiny" in recipe:
         return
-    if ("_cls" not in task) and (task not in ["detection", "instance_segmentation"]):
-        return  # Supported only for classification, detection and instance segmentation task.
+    if ("_cls" not in task) and (task not in ["detection", "instance_segmentation", "semantic_segmentation"]):
+        return  # Supported only for classification, detection and segmentation tasks.
 
     if "dino" in model_name:
         return  # DINO is not supported.

tests/unit/algo/segmentation/segmentors/test_base_model.py

@@ -43,8 +43,10 @@ def test_forward_returns_prediction(self, model, inputs):
     def test_extract_features(self, model, inputs):
         images = inputs[0]
         features = model.extract_features(images)
-        assert isinstance(features, torch.Tensor)
-        assert features.shape == (1, 2, 256, 256)
+        assert isinstance(features, tuple)
+        assert isinstance(features[0], torch.Tensor)
+        assert isinstance(features[1], torch.Tensor)
+        assert features[1].shape == (1, 2, 256, 256)
 
     def test_calculate_loss(self, model, inputs):
         model.criterion.name = "CrossEntropyLoss"