Change load_stat_dict to on_load_checkpoint

src/otx/core/model/detection.py

@@ -137,7 +137,7 @@
             ],
         }
 
-    def load_state_dict(self, ckpt: dict[str, Any], *args, **kwargs) -> None:
+    def on_load_checkpoint(self, ckpt: dict[str, Any]) -> None:
         """Load state_dict from checkpoint.
 
         For detection, it is need to update confidence threshold information when
@@ -148,7 +148,7 @@
             and (best_confidence_threshold := hyper_parameters.get("best_confidence_threshold", None))
         ):
             self.hparams["best_confidence_threshold"] = best_confidence_threshold
-        super().load_state_dict(ckpt, *args, **kwargs)
+        super().on_load_checkpoint(ckpt)
 
     def _log_metrics(self, meter: Metric, key: Literal["val", "test"], **compute_kwargs) -> None:
         if key == "val":
@@ -539,7 +539,7 @@
 
         if model_adapter.model.has_rt_info(["model_info", "confidence_threshold"]):
             best_confidence_threshold = model_adapter.model.get_rt_info(["model_info", "confidence_threshold"]).value
-            self.hparams["best_confidence_threshold"] = best_confidence_threshold
+            self.hparams["best_confidence_threshold"] = float(best_confidence_threshold)
         else:
             msg = (
                 "Cannot get best_confidence_threshold from OpenVINO IR's rt_info. "

src/otx/core/model/instance_segmentation.py

@@ -113,12 +113,12 @@
         return super()._export_parameters.wrap(
             model_type="MaskRCNN",
             task_type="instance_segmentation",
-            confidence_threshold=self.hparams.get("best_confidence_threshold", 0.0),
+            confidence_threshold=self.hparams.get("best_confidence_threshold", None),
             iou_threshold=0.5,
             tile_config=self.tile_config if self.tile_config.enable_tiler else None,
         )
 
-    def load_state_dict(self, ckpt: dict[str, Any], *args, **kwargs) -> None:
+    def on_load_checkpoint(self, ckpt: dict[str, Any]) -> None:
         """Load state_dict from checkpoint.
 
         For detection, it is need to update confidence threshold information when
@@ -129,7 +129,7 @@
             and (best_confidence_threshold := hyper_parameters.get("best_confidence_threshold", None))
         ):
             self.hparams["best_confidence_threshold"] = best_confidence_threshold
-        super().load_state_dict(ckpt, *args, **kwargs)
+        super().on_load_checkpoint(ckpt)
 
     def _log_metrics(self, meter: Metric, key: Literal["val", "test"], **compute_kwargs) -> None:
         if key == "val":
@@ -597,16 +597,16 @@
 
         if model_adapter.model.has_rt_info(["model_info", "confidence_threshold"]):
             best_confidence_threshold = model_adapter.model.get_rt_info(["model_info", "confidence_threshold"]).value
-            self.hparams["best_confidence_threshold"] = best_confidence_threshold
+            self.hparams["best_confidence_threshold"] = float(best_confidence_threshold)
         else:
             msg = (
                 "Cannot get best_confidence_threshold from OpenVINO IR's rt_info. "
                 "Please check whether this model is trained by OTX or not. "
                 "Without this information, it can produce a wrong F1 metric score. "
-                "At this time, it will be set as the default value = 0.0."
+                "At this time, it will be set as the default value = None."
             )
             log.warning(msg)
-            self.hparams["best_confidence_threshold"] = 0.0
+            self.hparams["best_confidence_threshold"] = None
 
         return Model.create_model(model_adapter, model_type=self.model_type, configuration=self.model_api_configuration)
 
@@ -729,6 +729,6 @@
         return {"preds": pred_info, "target": target_info}
 
     def _log_metrics(self, meter: Metric, key: Literal["val", "test"], **compute_kwargs) -> None:
-        best_confidence_threshold = self.hparams.get("best_confidence_threshold", 0.0)
+        best_confidence_threshold = self.hparams.get("best_confidence_threshold", None)
         compute_kwargs = {"best_confidence_threshold": best_confidence_threshold}
         return super()._log_metrics(meter, key, **compute_kwargs)

tests/unit/core/model/test_detection.py

@@ -72,7 +72,7 @@ def test_configure_metric_with_ckpt(
             metric=FMeasureCallable,
         )
 
-        model.load_state_dict(mock_ckpt)
+        model.on_load_checkpoint(mock_ckpt)
 
         assert model.hparams["best_confidence_threshold"] == 0.35