paddlepaddle--paddlenlp
wehub-resource-sync
69 行
2.6 KiB
Python
69 行
2.6 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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import os
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from typing import List, Optional, Tuple
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import paddle
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from ..utils.log import logger
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from .model_utils import PretrainedModel, unwrap_model
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__all__ = ["export_model"]
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def export_model(
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model: "PretrainedModel", input_spec=None, path: Optional[str] = None, model_format: Optional[str] = "paddle"
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) -> Tuple[List[str], List[str]]:
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"""
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Export paddle inference model or onnx model.
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Args:
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model ([`PretrainedModel`]:
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The model to export.
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input_spec (paddle.static.InputSpec, optional):
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Describes the input of the saved model’s forward method, which can be described
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by InputSpec or example Tensor. Default None.
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path (Optional[str], optional):
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Output dir to save the exported model. Defaults to None.
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model_format (Optional[str], optional):
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Export model format. There are two options: paddle or onnx, defaults to paddle.
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"""
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if path is None:
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path = "./"
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logger.info("Export path is missing, set default path to current dir.")
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if issubclass(type(model), PretrainedModel):
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model = unwrap_model(model)
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model.eval()
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model_format = model_format.lower()
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file_prefix = "model"
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if model_format == "paddle":
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# Convert to static graph with specific input description
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model = paddle.jit.to_static(model, input_spec=input_spec)
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# Save in static graph model.
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save_path = os.path.join(path, file_prefix)
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logger.info("Exporting inference model to %s" % save_path)
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paddle.jit.save(model, save_path)
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logger.info("Inference model exported.")
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elif model_format == "onnx":
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# Export ONNX model.
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save_path = os.path.join(path, file_prefix)
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logger.info("Exporting ONNX model to %s" % save_path)
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paddle.onnx.export(model, save_path, input_spec=input_spec)
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logger.info("ONNX model exported.")
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else:
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logger.info("This export format is not supported, please select paddle or onnx!")
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