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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.engine import Trainer, TrainerCot, init_parallel_env, set_random_seed, init_fleet_env
from ppdet.engine.trainer_ssod import Trainer_DenseTeacher, Trainer_ARSL, Trainer_Semi_RTDETR
from ppdet.slim import build_slim_model
from ppdet.utils.cli import ArgsParser, merge_args
import ppdet.utils.check as check
from ppdet.utils.logger import setup_logger
logger = setup_logger('train')
def parse_args():
parser = 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(
"--amp",
action='store_true',
default=False,
help="Enable auto 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(
"--use_wandb",
type=bool,
default=False,
help="whether to record the data to wandb.")
parser.add_argument(
'--save_prediction_only',
action='store_true',
default=False,
help='Whether to save the evaluation results only')
parser.add_argument(
'--profiler_options',
type=str,
default=None,
help="The option of profiler, which should be in "
"format \"key1=value1;key2=value2;key3=value3\"."
"please see ppdet/utils/profiler.py for detail.")
parser.add_argument(
'--save_proposals',
action='store_true',
default=False,
help='Whether to save the train proposals')
parser.add_argument(
'--proposals_path',
type=str,
default="sniper/proposals.json",
help='Train proposals directory')
parser.add_argument(
"--to_static",
action='store_true',
default=False,
help="Enable dy2st to train.")
args = parser.parse_args()
return args
def run(FLAGS, cfg):
# init fleet environment
if cfg.fleet:
init_fleet_env(cfg.get('find_unused_parameters', False))
else:
# init parallel environment if nranks > 1
init_parallel_env()
if FLAGS.enable_ce:
set_random_seed(0)
# build trainer
ssod_method = cfg.get('ssod_method', None)
if ssod_method is not None:
if ssod_method == 'DenseTeacher':
trainer = Trainer_DenseTeacher(cfg, mode='train')
elif ssod_method == 'ARSL':
trainer = Trainer_ARSL(cfg, mode='train')
elif ssod_method == 'Semi_RTDETR':
trainer = Trainer_Semi_RTDETR(cfg, mode='train')
else:
raise ValueError(
"Semi-Supervised Object Detection only no support this method.")
elif cfg.get('use_cot', False):
trainer = TrainerCot(cfg, mode='train')
else:
trainer = Trainer(cfg, mode='train')
# load weights
if FLAGS.resume is not None:
trainer.resume_weights(FLAGS.resume)
elif 'pretrain_student_weights' in cfg and 'pretrain_teacher_weights' in cfg \
and cfg.pretrain_teacher_weights and cfg.pretrain_student_weights:
trainer.load_semi_weights(cfg.pretrain_teacher_weights,
cfg.pretrain_student_weights)
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)
merge_args(cfg, FLAGS)
merge_config(FLAGS.opt)
# disable npu in config by default
if 'use_npu' not in cfg:
cfg.use_npu = False
# disable xpu in config by default
if 'use_xpu' not in cfg:
cfg.use_xpu = False
if 'use_gpu' not in cfg:
cfg.use_gpu = False
# disable mlu in config by default
if 'use_mlu' not in cfg:
cfg.use_mlu = False
if cfg.use_gpu:
place = paddle.set_device('gpu')
elif cfg.use_npu:
place = paddle.set_device('npu')
elif cfg.use_xpu:
place = paddle.set_device('xpu')
elif cfg.use_mlu:
place = paddle.set_device('mlu')
else:
place = paddle.set_device('cpu')
if FLAGS.slim_config:
cfg = build_slim_model(cfg, FLAGS.slim_config)
# FIXME: Temporarily solve the priority problem of FLAGS.opt
merge_config(FLAGS.opt)
check.check_config(cfg)
check.check_gpu(cfg.use_gpu)
check.check_npu(cfg.use_npu)
check.check_xpu(cfg.use_xpu)
check.check_mlu(cfg.use_mlu)
check.check_version()
run(FLAGS, cfg)
if __name__ == "__main__":
main()
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