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100 lines
3.0 KiB
100 lines
3.0 KiB
# Copyright (c) 2021 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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import os
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from typing import List
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from typing import Optional
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import paddle
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from paddle.inference import Config
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from paddle.inference import create_predictor
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def init_predictor(model_dir: Optional[os.PathLike]=None,
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model_file: Optional[os.PathLike]=None,
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params_file: Optional[os.PathLike]=None,
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predictor_conf: dict=None):
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"""Create predictor with Paddle inference
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Args:
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model_dir (Optional[os.PathLike], optional): The path of the static model saved in the model layer. Defaults to None.
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model_file (Optional[os.PathLike], optional): *.pdmodel file path. Defaults to None.
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params_file (Optional[os.PathLike], optional): *.pdiparams file path.. Defaults to None.
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predictor_conf (dict, optional): The configuration parameters of predictor. Defaults to None.
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Returns:
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predictor (PaddleInferPredictor): created predictor
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"""
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if model_dir is not None:
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config = Config(args.model_dir)
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else:
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config = Config(model_file, params_file)
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# set device
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if predictor_conf["device"]:
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device = predictor_conf["device"]
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else:
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device = paddle.get_device()
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if "gpu" in device:
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gpu_id = device.split(":")[-1]
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config.enable_use_gpu(1000, int(gpu_id))
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# IR optim
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if predictor_conf["switch_ir_optim"]:
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config.switch_ir_optim()
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# glog
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if not predictor_conf["glog_info"]:
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config.disable_glog_info()
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# config summary
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if predictor_conf["summary"]:
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print(config.summary())
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# memory optim
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config.enable_memory_optim()
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predictor = create_predictor(config)
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return predictor
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def run_model(predictor, input: List) -> List:
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""" run predictor
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Args:
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predictor: paddle inference predictor
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input (list): The input of predictor
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Returns:
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list: result list
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"""
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input_names = predictor.get_input_names()
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for i, name in enumerate(input_names):
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input_handle = predictor.get_input_handle(name)
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input_handle.copy_from_cpu(input[i])
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# do the inference
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predictor.run()
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results = []
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# get out data from output tensor
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output_names = predictor.get_output_names()
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for i, name in enumerate(output_names):
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output_handle = predictor.get_output_handle(name)
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output_data = output_handle.copy_to_cpu()
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results.append(output_data)
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return results
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