Force onnx names on exported Hailo models

modelconverter/packages/hailo/exporter.py

@@ -23,6 +23,7 @@ class HailoExporter(Exporter):
 
     def __init__(self, config: SingleStageConfig, output_dir: Path):
         super().__init__(config=config, output_dir=output_dir)
+        self.force_onnx_names = config.hailo.force_onnx_names
         self.optimization_level = config.hailo.optimization_level
         self.compression_level = config.hailo.compression_level
         self.batch_size = config.hailo.batch_size
@@ -69,6 +70,10 @@ def export(self) -> Path:
         logger.info("Model translated to Hailo IR.")
         har_path = self.input_model.with_suffix(".har")
         runner.save_har(har_path)
+
+        if self.force_onnx_names:
+            har_path = self._force_onnx_names(har_path)
+
         if self._disable_calibration:
             self._inference_model_path = self.output_dir / Path(
                 self.original_model_name
@@ -96,6 +101,55 @@ def export(self) -> Path:
             hef_file.write(hef)
         return hef_path
 
+    def _force_onnx_names(self, har_path: Path) -> Path:
+        """Force ONNX layer names into a .har model."""
+
+        runner = ClientRunner(hw_arch=self.hw_arch, har=str(har_path))
+        hn = runner.get_hn()
+        npz = dict(runner.get_params())
+
+        hn_layers = hn["layers"]
+
+        map_list = []
+        for name, layer in hn_layers.items():
+            if layer["type"] == "output_layer":
+                input_name = layer["input"][0]
+                context = input_name.split("/")[0]
+                orig_name = layer["original_names"][0]
+                new_name = f"{context}/{orig_name}"
+                map_list.append((input_name, new_name))
+
+        name_map = dict(map_list)
+
+        new_layers = {}
+        for name, layer in hn_layers.items():
+            new_name = name_map.get(name, name)
+            if "input" in layer:
+                layer["input"] = [name_map.get(i, i) for i in layer["input"]]
+            if "output" in layer:
+                layer["output"] = [name_map.get(o, o) for o in layer["output"]]
+            new_layers[new_name] = layer
+
+        hn["layers"] = new_layers
+
+        updated_npz = {}
+        for key, val in npz.items():
+            for old, new in name_map.items():
+                if old in key:
+                    key = key.replace(old, new)
+                    break
+            updated_npz[key] = val
+        npz = updated_npz
+
+        outputs = hn["net_params"]["output_layers_order"]
+        hn["net_params"]["output_layers_order"] = [name_map.get(o, o) for o in outputs]
+
+        runner.set_hn(hn)
+        runner.load_params(npz)
+        runner.save_har(har_path)
+
+        return har_path
+
     def _get_calibration_data(
         self, runner: ClientRunner
     ) -> dict[str, np.ndarray]:

modelconverter/utils/config.py

@@ -229,6 +229,7 @@ class TargetConfig(CustomBaseModel):
 
 
 class HailoConfig(TargetConfig):
+    force_onnx_names: bool = True
     optimization_level: Literal[-100, 0, 1, 2, 3, 4] = 2
     compression_level: Literal[0, 1, 2, 3, 4, 5] = 2
     batch_size: int = 8

shared_with_container/configs/defaults.yaml

@@ -123,6 +123,9 @@ stages:
 
     # --- Hailo-Specific Arguments ---
     hailo:
+      # Force onnx names on the final .har and .hef model
+      force_onnx_names: true
+
       # Specifies the optimization level.
       # Either a number between 0 and 4 or -100 to disable all optimizations.
       optimization_level: 2

tests/test_utils/test_config.py

@@ -69,6 +69,7 @@
         "optimization_level": 2,
     },
     "hailo": {
+        "force_onnx_names": True,
         "optimization_level": 2,
         "compression_level": 2,
         "batch_size": 8,
@@ -369,6 +370,7 @@ def test_correct():
             },
             "rvc4": {**DEFAULT_TARGET_CONFIGS["rvc4"]},
             "hailo": {
+                "force_onnx_names": True,
                 "disable_calibration": False,
                 "optimization_level": 3,
                 "compression_level": 3,