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https://github.com/PaddlePaddle/FastDeploy.git
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6343b0db47
* Add notes for tensors * Optimize some apis * move some warnings * Support build with Paddle2ONNX * Add protobuf support * Fix compile on mac * add clearn package script * Add paddle2onnx code * remove submodule * Add onnx ocde * remove softlink * add onnx code * fix error * Add cmake file * fix patchelf * update paddle2onnx * Delete .gitmodules --------- Co-authored-by: PaddleCI <paddle_ci@example.com> Co-authored-by: pangyoki <pangyoki@126.com> Co-authored-by: jiangjiajun <jiangjiajun@baidu.lcom>
47 lines
1.6 KiB
Python
Executable File
47 lines
1.6 KiB
Python
Executable File
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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import argparse
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import sys
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from paddle2onnx.utils import logging
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def parse_arguments():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--input_model',
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required=True,
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help='The path of input onnx model file.')
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parser.add_argument(
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'--output_model',
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required=True,
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help='The file path to write optimized onnx model file.')
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parser.add_argument(
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'--input_shape_dict',
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default="",
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help="The shape infos of inputs, e.g --input_shape_dict=\"{'image': [1, 3, 608, 608], 'scale_factor': [1, 2]}\""
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)
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return parser.parse_args()
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if __name__ == '__main__':
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args = parse_arguments()
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import paddle2onnx.paddle2onnx_cpp2py_export as c_p2o
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shape_dict = {}
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if args.input_shape_dict != "":
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shape_dict = eval(args.input_shape_dict)
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c_p2o.optimize(args.input_model, args.output_model, shape_dict)
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logging.info("Model optmized, saved in {}.".format(args.output_model))
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