210 lines
6.3 KiB
Python
210 lines
6.3 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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# add python path of PaddleDetection to sys.path
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parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
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sys.path.insert(0, parent_path)
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# ignore warning log
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import warnings
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warnings.filterwarnings('ignore')
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import paddle
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from ppdet.core.workspace import load_config, merge_config
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from ppdet.engine import Trainer, TrainerCot, init_parallel_env, set_random_seed, init_fleet_env
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from ppdet.engine.trainer_ssod import Trainer_DenseTeacher, Trainer_ARSL, Trainer_Semi_RTDETR
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from ppdet.slim import build_slim_model
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from ppdet.utils.cli import ArgsParser, merge_args
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import ppdet.utils.check as check
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from ppdet.utils.logger import setup_logger
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logger = setup_logger('train')
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def parse_args():
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parser = ArgsParser()
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parser.add_argument(
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"--eval",
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action='store_true',
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default=False,
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help="Whether to perform evaluation in train")
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parser.add_argument(
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"-r", "--resume", default=None, help="weights path for resume")
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parser.add_argument(
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"--slim_config",
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default=None,
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type=str,
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help="Configuration file of slim method.")
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parser.add_argument(
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"--enable_ce",
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type=bool,
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default=False,
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help="If set True, enable continuous evaluation job."
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"This flag is only used for internal test.")
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parser.add_argument(
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"--amp",
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action='store_true',
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default=False,
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help="Enable auto mixed precision training.")
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parser.add_argument(
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"--fleet", action='store_true', default=False, help="Use fleet or not")
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parser.add_argument(
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"--use_vdl",
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type=bool,
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default=False,
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help="whether to record the data to VisualDL.")
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parser.add_argument(
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'--vdl_log_dir',
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type=str,
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default="vdl_log_dir/scalar",
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help='VisualDL logging directory for scalar.')
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parser.add_argument(
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"--use_wandb",
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type=bool,
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default=False,
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help="whether to record the data to wandb.")
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parser.add_argument(
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'--save_prediction_only',
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action='store_true',
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default=False,
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help='Whether to save the evaluation results only')
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parser.add_argument(
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'--profiler_options',
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type=str,
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default=None,
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help="The option of profiler, which should be in "
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"format \"key1=value1;key2=value2;key3=value3\"."
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"please see ppdet/utils/profiler.py for detail.")
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parser.add_argument(
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'--save_proposals',
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action='store_true',
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default=False,
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help='Whether to save the train proposals')
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parser.add_argument(
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'--proposals_path',
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type=str,
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default="sniper/proposals.json",
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help='Train proposals directory')
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parser.add_argument(
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"--to_static",
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action='store_true',
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default=False,
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help="Enable dy2st to train.")
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args = parser.parse_args()
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return args
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def run(FLAGS, cfg):
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# init fleet environment
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if cfg.fleet:
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init_fleet_env(cfg.get('find_unused_parameters', False))
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else:
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# init parallel environment if nranks > 1
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init_parallel_env()
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if FLAGS.enable_ce:
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set_random_seed(0)
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# build trainer
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ssod_method = cfg.get('ssod_method', None)
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if ssod_method is not None:
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if ssod_method == 'DenseTeacher':
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trainer = Trainer_DenseTeacher(cfg, mode='train')
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elif ssod_method == 'ARSL':
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trainer = Trainer_ARSL(cfg, mode='train')
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elif ssod_method == 'Semi_RTDETR':
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trainer = Trainer_Semi_RTDETR(cfg, mode='train')
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else:
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raise ValueError(
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"Semi-Supervised Object Detection only no support this method.")
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elif cfg.get('use_cot', False):
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trainer = TrainerCot(cfg, mode='train')
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else:
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trainer = Trainer(cfg, mode='train')
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# load weights
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if FLAGS.resume is not None:
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trainer.resume_weights(FLAGS.resume)
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elif 'pretrain_student_weights' in cfg and 'pretrain_teacher_weights' in cfg \
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and cfg.pretrain_teacher_weights and cfg.pretrain_student_weights:
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trainer.load_semi_weights(cfg.pretrain_teacher_weights,
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cfg.pretrain_student_weights)
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elif 'pretrain_weights' in cfg and cfg.pretrain_weights:
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trainer.load_weights(cfg.pretrain_weights)
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# training
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trainer.train(FLAGS.eval)
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def main():
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FLAGS = parse_args()
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cfg = load_config(FLAGS.config)
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merge_args(cfg, FLAGS)
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merge_config(FLAGS.opt)
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# disable npu in config by default
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if 'use_npu' not in cfg:
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cfg.use_npu = False
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# disable xpu in config by default
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if 'use_xpu' not in cfg:
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cfg.use_xpu = False
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if 'use_gpu' not in cfg:
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cfg.use_gpu = False
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# disable mlu in config by default
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if 'use_mlu' not in cfg:
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cfg.use_mlu = False
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if cfg.use_gpu:
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place = paddle.set_device('gpu')
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elif cfg.use_npu:
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place = paddle.set_device('npu')
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elif cfg.use_xpu:
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place = paddle.set_device('xpu')
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elif cfg.use_mlu:
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place = paddle.set_device('mlu')
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else:
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place = paddle.set_device('cpu')
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if FLAGS.slim_config:
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cfg = build_slim_model(cfg, FLAGS.slim_config)
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# FIXME: Temporarily solve the priority problem of FLAGS.opt
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merge_config(FLAGS.opt)
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check.check_config(cfg)
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check.check_gpu(cfg.use_gpu)
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check.check_npu(cfg.use_npu)
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check.check_xpu(cfg.use_xpu)
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check.check_mlu(cfg.use_mlu)
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check.check_version()
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run(FLAGS, cfg)
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if __name__ == "__main__":
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main()
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