Initialisation du repository de Beta

venv/lib/python3.12/site-packages/onnxruntime/tools/optimize_onnx_model.py

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+#!/usr/bin/env python3
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+from __future__ import annotations
+
+import argparse
+import os
+import pathlib
+
+from .onnx_model_utils import get_optimization_level, optimize_model
+
+
+def optimize_model_helper():
+    parser = argparse.ArgumentParser(
+        f"{os.path.basename(__file__)}:{optimize_model_helper.__name__}",
+        description="""
+                                     Optimize an ONNX model using ONNX Runtime to the specified level.
+                                     See https://onnxruntime.ai/docs/performance/model-optimizations/graph-optimizations.html for more
+                                     details of the optimization levels.""",
+    )
+
+    parser.add_argument(
+        "--opt_level",
+        default="basic",
+        choices=["disable", "basic", "extended", "layout", "all"],
+        help="Optimization level to use.",
+    )
+    parser.add_argument(
+        "--log_level",
+        choices=["debug", "info", "warning", "error"],
+        type=str,
+        required=False,
+        default="error",
+        help="Log level. Defaults to Error so we don't get output about unused initializers "
+        "being removed. Warning or Info may be desirable in some scenarios.",
+    )
+
+    parser.add_argument("input_model", type=pathlib.Path, help="Provide path to ONNX model to update.")
+    parser.add_argument("output_model", type=pathlib.Path, help="Provide path to write optimized ONNX model to.")
+
+    args = parser.parse_args()
+
+    if args.log_level == "error":
+        log_level = 3
+    elif args.log_level == "debug":
+        log_level = 0  # ORT verbose level
+    elif args.log_level == "info":
+        log_level = 1
+    elif args.log_level == "warning":
+        log_level = 2
+
+    optimize_model(args.input_model, args.output_model, get_optimization_level(args.opt_level), log_level)
+
+
+if __name__ == "__main__":
+    optimize_model_helper()