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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()
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