mirror of
https://github.com/saymrwulf/onnxruntime.git
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b5c2932ae8
* Add minimal build option to build.py Group some of the build settings so binary size reduction options are all together Make some cmake variable naming more consistent Replace usage of std::hash with murmurhash3 for kernel. std::hash is implementation dependent so can't be used. Add initial doco and ONNX to ORT model conversion script Misc cleanups of minimal build breaks.
62 lines
2.7 KiB
Python
62 lines
2.7 KiB
Python
#!/usr/bin/env python
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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import argparse
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import onnxruntime as ort
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import os
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import re
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def convert(model: str):
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if not model.endswith('.onnx'):
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raise ValueError("Model filename must end in .onnx.")
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onnx_target_path = re.sub('.onnx$', '.optimized.onnx', model)
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ort_target_path = re.sub('.onnx$', '.ort', model)
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so = ort.SessionOptions()
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so.optimized_model_filepath = onnx_target_path
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so.add_session_config_entry('session.save_model_format', 'ONNX')
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so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED # Skip NCHWc optimizations
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print("Optimizing ONNX model {} and saving in ONNX format to {}".format(model, onnx_target_path))
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# creating the session will result in the optimized model being saved
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_ = ort.InferenceSession(model, sess_options=so)
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# Second, convert optimized ONNX model to ORT format
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so.optimized_model_filepath = ort_target_path
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so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL # Convert model as-is so we don't change the kernels in this step # noqa
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so.add_session_config_entry('session.save_model_format', 'ORT')
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print("Converting optimized ONNX model {} to ORT format model {}".format(onnx_target_path, ort_target_path))
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_ = ort.InferenceSession(onnx_target_path, sess_options=so)
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orig_size = os.path.getsize(onnx_target_path)
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new_size = os.path.getsize(ort_target_path)
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print("Serialized {} to {}. Sizes: orig={} new={} diff={} new:old={:.4f}:1.0".format(
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onnx_target_path, ort_target_path, orig_size, new_size, new_size - orig_size, new_size / orig_size))
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def parse_args():
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parser = argparse.ArgumentParser(os.path.basename(__file__),
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description='''Convert an onnx model -> optimized onnx model -> ORT format model.
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Expects a .onnx file as input. Optimized onnx model will be saved in the same
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directory with an extension of .optimized.onnx.
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An ORT format model will be created from the optimized onnx model.
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The optimized onnx model should be used as input to a minimal build so that
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any post-optimization kernels are included in the build.'''
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)
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parser.add_argument('model', help='Provide path to ONNX model to convert. Must have .onnx extension.')
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return parser.parse_args()
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def main():
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args = parse_args()
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convert(args.model)
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if __name__ == '__main__':
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main()
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