wehub-resource-sync
124 lines
4.9 KiB
Python
124 lines
4.9 KiB
Python
# Copyright (c) 2020 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 annotations
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import os
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from typing import TYPE_CHECKING
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from paddle.utils import try_import
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if TYPE_CHECKING:
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from collections.abc import Sequence
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from typing_extensions import Unpack
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from paddle import Tensor
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from paddle.jit.api import _SaveOptions
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from paddle.nn import Layer
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from paddle.static import InputSpec
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__all__ = []
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def export(
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layer: Layer,
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path: str,
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input_spec: Sequence[InputSpec | Tensor | object] | None = None,
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opset_version: int = 9,
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**configs: Unpack[_SaveOptions],
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) -> None:
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"""
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Export Layer to ONNX format, which can use for inference via onnxruntime or other backends.
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For more details, Please refer to `paddle2onnx <https://github.com/PaddlePaddle/paddle2onnx>`_ .
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Args:
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layer (Layer): The Layer to be exported.
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path (str): The path prefix to export model. The format is ``dirname/file_prefix`` or ``file_prefix`` ,
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and the exported ONNX file suffix is ``.onnx`` .
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input_spec (list[InputSpec|Tensor]|None, optional): Describes the input of the exported model's forward
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method, which can be described by InputSpec or example Tensor. If None, all input variables of
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the original Layer's forward method would be the inputs of the exported ``ONNX`` model. Default: None.
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opset_version(int, optional): Opset version of exported ONNX model.
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Now, stable supported opset version include 9, 10, 11. Default: 9.
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**configs (dict, optional): Other export configuration options for compatibility. We do not
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recommend using these configurations, they may be removed in the future. If not necessary,
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DO NOT use them. Default None.
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The following options are currently supported:
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(1) output_spec (list[Tensor]): Selects the output targets of the exported model.
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By default, all return variables of original Layer's forward method are kept as the
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output of the exported model. If the provided ``output_spec`` list is not all output variables,
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the exported model will be pruned according to the given ``output_spec`` list.
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Returns:
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None
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> class LinearNet(paddle.nn.Layer):
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... def __init__(self):
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... super().__init__()
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... self._linear = paddle.nn.Linear(128, 10)
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...
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... def forward(self, x):
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... return self._linear(x)
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>>> # Export model with 'InputSpec' to support dynamic input shape.
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>>> def export_linear_net():
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... model = LinearNet()
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... x_spec = paddle.static.InputSpec(shape=[None, 128], dtype='float32')
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... paddle.onnx.export(model, 'linear_net', input_spec=[x_spec])
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>>> # doctest: +SKIP('Need install Paddle2ONNX')
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>>> export_linear_net()
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>>> class Logic(paddle.nn.Layer):
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... def __init__(self):
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... super().__init__()
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...
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... def forward(self, x, y, z):
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... if z:
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... return x
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... else:
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... return y
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>>> # Export model with 'Tensor' to support pruned model by set 'output_spec'.
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>>> def export_logic():
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... model = Logic()
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... x = paddle.to_tensor([1])
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... y = paddle.to_tensor([2])
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... # Static and run model.
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... paddle.jit.to_static(model)
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... out = model(x, y, z=True)
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... paddle.onnx.export(model, 'pruned', input_spec=[x, y, True], output_spec=[out], input_names_after_prune=[x.name])
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>>> export_logic()
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"""
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p2o = try_import('paddle2onnx')
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file_prefix = os.path.basename(path)
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if file_prefix == "":
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raise ValueError(
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"The input path MUST be format of dirname/file_prefix "
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"[dirname\\file_prefix in Windows system], but "
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f"the file_prefix is empty in received path: {path}"
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)
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save_file = path + '.onnx'
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p2o.dygraph2onnx(
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layer,
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save_file,
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input_spec=input_spec,
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opset_version=opset_version,
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**configs,
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)
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