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LockzhinerAI/PaddleDetection

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export_model.py 3.69 KB
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# Copyright (c) 2020 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
# add python path of PaddleDetection to sys.path
parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
sys.path.insert(0, parent_path)
# ignore warning log
import warnings
warnings.filterwarnings('ignore')
import paddle
from ppdet.core.workspace import load_config, merge_config
from ppdet.utils.check import check_gpu, check_version, check_config
from ppdet.utils.cli import ArgsParser
from ppdet.engine import Trainer
from ppdet.engine.trainer_ssod import Trainer_ARSL
from ppdet.slim import build_slim_model
from ppdet.utils.logger import setup_logger
logger = setup_logger('export_model')
def parse_args():
parser = ArgsParser()
parser.add_argument(
"--output_dir",
type=str,
default="output_inference",
help="Directory for storing the output model files.")
parser.add_argument(
"--export_serving_model",
type=bool,
default=False,
help="Whether to export serving model or not.")
parser.add_argument(
"--slim_config",
default=None,
type=str,
help="Configuration file of slim method.")
parser.add_argument("--for_fd", action='store_true')
args = parser.parse_args()
return args
def run(FLAGS, cfg):
ssod_method = cfg.get('ssod_method', None)
if ssod_method is not None and ssod_method == 'ARSL':
trainer = Trainer_ARSL(cfg, mode='test')
trainer.load_weights(cfg.weights, ARSL_eval=True)
# build detector
else:
trainer = Trainer(cfg, mode='test')
# load weights
if cfg.architecture in ['DeepSORT', 'ByteTrack']:
trainer.load_weights_sde(cfg.det_weights, cfg.reid_weights)
else:
trainer.load_weights(cfg.weights)
# export model
trainer.export(FLAGS.output_dir, for_fd=FLAGS.for_fd)
if FLAGS.export_serving_model:
assert not FLAGS.for_fd
from paddle_serving_client.io import inference_model_to_serving
model_name = os.path.splitext(os.path.split(cfg.filename)[-1])[0]
inference_model_to_serving(
dirname="{}/{}".format(FLAGS.output_dir, model_name),
serving_server="{}/{}/serving_server".format(FLAGS.output_dir,
model_name),
serving_client="{}/{}/serving_client".format(FLAGS.output_dir,
model_name),
model_filename="model.pdmodel",
params_filename="model.pdiparams")
def main():
paddle.set_device("cpu")
FLAGS = parse_args()
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
if FLAGS.slim_config:
cfg = build_slim_model(cfg, FLAGS.slim_config, mode='test')
# FIXME: Temporarily solve the priority problem of FLAGS.opt
merge_config(FLAGS.opt)
check_config(cfg)
if 'use_gpu' not in cfg:
cfg.use_gpu = False
check_gpu(cfg.use_gpu)
check_version()
run(FLAGS, cfg)
if __name__ == '__main__':
main()
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