PulseFocusPlatform/tools/train.py

140 lines
3.9 KiB
Python

# 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 PadleDetection to sys.path
parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
if parent_path not in sys.path:
sys.path.append(parent_path)
# ignore warning log
import warnings
warnings.filterwarnings('ignore')
import paddle
from ppdet.core.workspace import load_config, merge_config
from ppdet.engine import Trainer, init_parallel_env, set_random_seed, init_fleet_env
from ppdet.slim import build_slim_model
import ppdet.utils.cli as cli
import ppdet.utils.check as check
from ppdet.utils.logger import setup_logger
logger = setup_logger('train')
def parse_args():
parser = cli.ArgsParser()
parser.add_argument(
"--eval",
action='store_true',
default=False,
help="Whether to perform evaluation in train")
parser.add_argument(
"-r", "--resume", default=None, help="weights path for resume")
parser.add_argument(
"--slim_config",
default=None,
type=str,
help="Configuration file of slim method.")
parser.add_argument(
"--enable_ce",
type=bool,
default=False,
help="If set True, enable continuous evaluation job."
"This flag is only used for internal test.")
parser.add_argument(
"--fp16",
action='store_true',
default=False,
help="Enable mixed precision training.")
parser.add_argument(
"--fleet", action='store_true', default=False, help="Use fleet or not")
parser.add_argument(
"--use_vdl",
type=bool,
default=False,
help="whether to record the data to VisualDL.")
parser.add_argument(
'--vdl_log_dir',
type=str,
default="vdl_log_dir/scalar",
help='VisualDL logging directory for scalar.')
parser.add_argument(
'--save_prediction_only',
action='store_true',
default=False,
help='Whether to save the evaluation results only')
args = parser.parse_args()
return args
def run(FLAGS, cfg):
# init fleet environment
if cfg.fleet:
init_fleet_env()
else:
# init parallel environment if nranks > 1
init_parallel_env()
if FLAGS.enable_ce:
set_random_seed(0)
# build trainer
trainer = Trainer(cfg, mode='train')
# load weights
if FLAGS.resume is not None:
trainer.resume_weights(FLAGS.resume)
elif 'pretrain_weights' in cfg and cfg.pretrain_weights:
trainer.load_weights(cfg.pretrain_weights)
# training
trainer.train(FLAGS.eval)
def main():
FLAGS = parse_args()
cfg = load_config(FLAGS.config)
cfg['fp16'] = FLAGS.fp16
cfg['fleet'] = FLAGS.fleet
cfg['use_vdl'] = FLAGS.use_vdl
cfg['vdl_log_dir'] = FLAGS.vdl_log_dir
cfg['save_prediction_only'] = FLAGS.save_prediction_only
merge_config(FLAGS.opt)
place = paddle.set_device('gpu' if cfg.use_gpu else 'cpu')
if 'norm_type' in cfg and cfg['norm_type'] == 'sync_bn' and not cfg.use_gpu:
cfg['norm_type'] = 'bn'
if FLAGS.slim_config:
cfg = build_slim_model(cfg, FLAGS.slim_config)
check.check_config(cfg)
check.check_gpu(cfg.use_gpu)
check.check_version()
run(FLAGS, cfg)
if __name__ == "__main__":
main()