You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
83 lines
2.7 KiB
83 lines
2.7 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import os
|
|
from typing import List
|
|
from typing import Optional
|
|
|
|
from paddle.inference import Config
|
|
from paddle.inference import create_predictor
|
|
|
|
|
|
def init_predictor(model_dir: Optional[os.PathLike]=None,
|
|
model_file: Optional[os.PathLike]=None,
|
|
params_file: Optional[os.PathLike]=None,
|
|
predictor_conf: dict=None):
|
|
"""Create predictor with Paddle inference
|
|
|
|
Args:
|
|
model_dir (Optional[os.PathLike], optional): The path of the static model saved in the model layer. Defaults to None.
|
|
model_file (Optional[os.PathLike], optional): *.pdmodel file path. Defaults to None.
|
|
params_file (Optional[os.PathLike], optional): *.pdiparams file path.. Defaults to None.
|
|
predictor_conf (dict, optional): The configuration parameters of predictor. Defaults to None.
|
|
|
|
Returns:
|
|
predictor (PaddleInferPredictor): created predictor
|
|
"""
|
|
|
|
if model_dir is not None:
|
|
config = Config(args.model_dir)
|
|
else:
|
|
config = Config(model_file, params_file)
|
|
|
|
config.enable_memory_optim()
|
|
if predictor_conf["use_gpu"]:
|
|
config.enable_use_gpu(1000, 0)
|
|
if predictor_conf["enable_mkldnn"]:
|
|
config.enable_mkldnn()
|
|
if predictor_conf["switch_ir_optim"]:
|
|
config.switch_ir_optim()
|
|
|
|
predictor = create_predictor(config)
|
|
|
|
return predictor
|
|
|
|
|
|
def run_model(predictor, input: List) -> List:
|
|
""" run predictor
|
|
|
|
Args:
|
|
predictor: paddle inference predictor
|
|
input (list): The input of predictor
|
|
|
|
Returns:
|
|
list: result list
|
|
"""
|
|
input_names = predictor.get_input_names()
|
|
for i, name in enumerate(input_names):
|
|
input_handle = predictor.get_input_handle(name)
|
|
input_handle.copy_from_cpu(input[i])
|
|
|
|
# do the inference
|
|
predictor.run()
|
|
|
|
results = []
|
|
# get out data from output tensor
|
|
output_names = predictor.get_output_names()
|
|
for i, name in enumerate(output_names):
|
|
output_handle = predictor.get_output_handle(name)
|
|
output_data = output_handle.copy_to_cpu()
|
|
results.append(output_data)
|
|
|
|
return results
|