refactor

machine-learning/export/onnx/models/optimize.py

@@ -0,0 +1,49 @@
+from pathlib import Path
+
+import onnx
+import onnxruntime as ort
+import onnxsim
+
+
+def save_onnx(model: onnx.ModelProto, output_path: Path | str) -> None:
+    try:
+        onnx.save(model, output_path)
+    except ValueError as e:
+        if "The proto size is larger than the 2 GB limit." in str(e):
+            onnx.save(model, output_path, save_as_external_data=True, size_threshold=1_000_000)
+        else:
+            raise e
+
+
+def optimize_onnxsim(model_path: Path | str, output_path: Path | str) -> None:
+    model_path = Path(model_path)
+    output_path = Path(output_path)
+    model = onnx.load(model_path.as_posix())
+    model, check = onnxsim.simplify(model)
+    assert check, "Simplified ONNX model could not be validated"
+    for file in model_path.parent.iterdir():
+        if file.name.startswith("Constant") or "onnx" in file.name or file.suffix == ".weight":
+            file.unlink()
+    save_onnx(model, output_path)
+
+
+def optimize_ort(
+    model_path: Path | str,
+    output_path: Path | str,
+    level: ort.GraphOptimizationLevel = ort.GraphOptimizationLevel.ORT_ENABLE_BASIC,
+) -> None:
+    model_path = Path(model_path)
+    output_path = Path(output_path)
+
+    sess_options = ort.SessionOptions()
+    sess_options.graph_optimization_level = level
+    sess_options.optimized_model_filepath = output_path.as_posix()
+
+    ort.InferenceSession(model_path.as_posix(), providers=["CPUExecutionProvider"], sess_options=sess_options)
+
+
+def optimize(model_path: Path | str) -> None:
+    model_path = Path(model_path)
+
+    optimize_ort(model_path, model_path)
+    optimize_onnxsim(model_path, model_path)