mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
22d91a73c6
* init simple serving * simple serving is working * ppyoloe demo * Update README_CN.md * update readme * complete vision result to json
58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
# coding:utf-8
|
|
# copyright (c) 2022 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
|
|
import time
|
|
import json
|
|
import logging
|
|
import threading
|
|
# from .predictor import Predictor
|
|
from .handler import BaseModelHandler
|
|
from .utils import lock_predictor
|
|
|
|
|
|
class ModelManager:
|
|
def __init__(self, model_handler, predictor):
|
|
self._model_handler = model_handler
|
|
self._predictors = []
|
|
self._predictor_locks = []
|
|
self._register(predictor)
|
|
|
|
def _register(self, predictor):
|
|
# Get the model handler
|
|
if not issubclass(self._model_handler, BaseModelHandler):
|
|
raise TypeError(
|
|
"The model_handler must be subclass of BaseModelHandler, please check the type."
|
|
)
|
|
|
|
# TODO: Create multiple predictors to run on different GPUs or different CPU threads
|
|
self._predictors.append(predictor)
|
|
self._predictor_locks.append(threading.Lock())
|
|
|
|
def _get_predict_id(self):
|
|
t = time.time()
|
|
t = int(round(t * 1000))
|
|
predictor_id = t % len(self._predictors)
|
|
logging.info("The predictor id: {} is selected by running the model.".
|
|
format(predictor_id))
|
|
return predictor_id
|
|
|
|
def predict(self, data, parameters):
|
|
predictor_id = self._get_predict_id()
|
|
with lock_predictor(self._predictor_locks[predictor_id]):
|
|
model_output = self._model_handler.process(
|
|
self._predictors[predictor_id], data, parameters)
|
|
return model_output
|