ONNX modifications with no op exit early

modelconverter/utils/config.py

@@ -229,6 +229,21 @@ def _parse_values(
             return [value, value, value]
         return value
 
+    def requires_onnx_input_modification(
+        self, *, reverse_only: bool = False
+    ) -> bool:
+        if self.encoding_mismatch:
+            return True
+        if reverse_only:
+            return False
+        return (
+            self.mean_values is not None
+            and any(v != 0 for v in self.mean_values)
+        ) or (
+            self.scale_values is not None
+            and any(v != 1 for v in self.scale_values)
+        )
+
 
 class TargetConfig(BaseModelExtraForbid):
     disable_calibration: bool = False

modelconverter/utils/metadata.py

@@ -45,10 +45,14 @@ def _get_metadata_dlc(path: Path) -> Metadata:
     if path.suffix == ".csv":
         csv_path = path
     else:
-        csv_path = Path("info.csv")
-        subprocess_run(
-            ["snpe-dlc-info", "-i", path, "-s", csv_path], silent=True
-        )
+        csv_path = path.with_suffix(".info.csv")
+        if (
+            not csv_path.exists()
+            or csv_path.stat().st_mtime < path.stat().st_mtime
+        ):
+            subprocess_run(
+                ["snpe-dlc-info", "-i", path, "-s", csv_path], silent=True
+            )
     content = csv_path.read_text()
 
     metadata = {}

modelconverter/utils/onnx_tools.py

@@ -111,6 +111,15 @@ def onnx_attach_normalization_to_inputs(
     *,
     reverse_only: bool = False,
 ) -> Path:
+    if not any(
+        cfg.requires_onnx_input_modification(reverse_only=reverse_only)
+        for cfg in input_configs.values()
+    ):
+        logger.info(
+            "No ONNX input normalization changes requested; using original model."
+        )
+        return model_path
+
     model = onnx.load(str(model_path))
     if model_path.with_suffix(".onnx_data").exists():
         model_data_path = str(model_path).replace(".onnx", ".onnx_data")
@@ -136,11 +145,7 @@ def onnx_attach_normalization_to_inputs(
         if input_name not in input_configs:
             continue
         cfg = input_configs[input_name]
-        if (
-            cfg.encoding.from_ == cfg.encoding.to
-            and cfg.mean_values is None
-            and cfg.scale_values is None
-        ):
+        if not cfg.requires_onnx_input_modification(reverse_only=reverse_only):
             continue
 
         shape = cfg.shape

tests/test_utils/test_config.py

@@ -843,6 +843,30 @@ def test_modified_onnx(keys: list[str], values: list[str]):
             assert input_configs[inp].scale_values is None or [1, 1, 1]
 
 
+def test_modified_onnx_noop_returns_original_model():
+    model_path = DATA_DIR / "dummy_model.onnx"
+    modified_path = DATA_DIR / "dummy_model_noop_modified.onnx"
+    config = Config.get_config(
+        None,
+        {
+            "input_model": str(model_path),
+            "encoding": "NONE",
+        },
+    )
+    input_configs = {
+        inp.name: inp for inp in next(iter(config.stages.values())).inputs
+    }
+
+    result = onnx_attach_normalization_to_inputs(
+        model_path,
+        modified_path,
+        input_configs,
+    )
+
+    assert result == model_path
+    assert not modified_path.exists()
+
+
 def test_output_nn_config_from_yaml_raw_input_with_archive_preprocess():
     model_path = DATA_DIR / "dummy_model.onnx"
     config = Config.get_config(