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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>
56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# ATTENTION: The code in this file is highly EXPERIMENTAL.
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# Adventurous users should note that the APIs will probably change.
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"""onnx optimizer
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This enables users to optimize their models.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import onnx
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import onnxoptimizer.onnx_opt_cpp2py_export as C
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from onnx import ModelProto
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from typing import Text, Sequence, Optional
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get_available_passes = C.get_available_passes
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get_fuse_and_elimination_passes = C.get_fuse_and_elimination_passes
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def optimize(model, passes=None, fixed_point=False): # type: (ModelProto, Optional[Sequence[Text]], bool) -> ModelProto
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"""Apply the optimization on the serialized ModelProto.
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Arguments:
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input (ModelProto): model
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names (list of string): list of optimization names
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Return:
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return (ModelProto) optimized model
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"""
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if passes is None:
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print('WARNING: defualt optimization passes will be enlarged to all fuse and elimination passes in the next version')
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passes = ['eliminate_nop_transpose',
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'eliminate_nop_pad',
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'fuse_consecutive_transposes',
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'fuse_transpose_into_gemm']
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if not isinstance(model, ModelProto):
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raise ValueError(
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'Optimizer only accepts ModelProto, incorrect type: {}'.format(type(model)))
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model_str = model.SerializeToString()
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if fixed_point:
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optimized_model_str = C.optimize_fixedpoint(model_str, passes)
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else:
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optimized_model_str = C.optimize(model_str, passes)
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return onnx.load_from_string(optimized_model_str)
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__all__ = ['optimize', 'get_available_passes', 'get_fuse_and_elimination_passes']
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