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- # Copyright (c) OpenMMLab. All rights reserved.
- from __future__ import division
-
- import argparse
- import copy
- import mmcv
- import os
- import time
- import torch
- import warnings
- from mmcv import Config, DictAction
- from mmcv.runner import get_dist_info, init_dist
- from os import path as osp
-
- from mmdet import __version__ as mmdet_version
- from mmdet3d import __version__ as mmdet3d_version
- from mmdet3d.apis import train_model
- from mmdet3d.datasets import build_dataset
- from mmdet3d.models import build_model
- from mmdet3d.utils import collect_env, get_root_logger
- from mmdet.apis import set_random_seed
- from mmseg import __version__ as mmseg_version
-
-
- def parse_args():
- parser = argparse.ArgumentParser(description='Train a detector')
- parser.add_argument('config', help='train config file path')
- parser.add_argument('--work-dir', help='the dir to save logs and models')
- parser.add_argument(
- '--resume-from', help='the checkpoint file to resume from')
- parser.add_argument(
- '--no-validate',
- action='store_true',
- help='whether not to evaluate the checkpoint during training')
- group_gpus = parser.add_mutually_exclusive_group()
- group_gpus.add_argument(
- '--gpus',
- type=int,
- help='number of gpus to use '
- '(only applicable to non-distributed training)')
- group_gpus.add_argument(
- '--gpu-ids',
- type=int,
- nargs='+',
- help='ids of gpus to use '
- '(only applicable to non-distributed training)')
- parser.add_argument('--seed', type=int, default=0, help='random seed')
- parser.add_argument(
- '--deterministic',
- action='store_true',
- help='whether to set deterministic options for CUDNN backend.')
- parser.add_argument(
- '--options',
- nargs='+',
- action=DictAction,
- help='override some settings in the used config, the key-value pair '
- 'in xxx=yyy format will be merged into config file (deprecate), '
- 'change to --cfg-options instead.')
- parser.add_argument(
- '--cfg-options',
- nargs='+',
- action=DictAction,
- help='override some settings in the used config, the key-value pair '
- 'in xxx=yyy format will be merged into config file. If the value to '
- 'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
- 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
- 'Note that the quotation marks are necessary and that no white space '
- 'is allowed.')
- parser.add_argument(
- '--launcher',
- choices=['none', 'pytorch', 'slurm', 'mpi'],
- default='none',
- help='job launcher')
- parser.add_argument('--local_rank', type=int, default=0)
- parser.add_argument(
- '--autoscale-lr',
- action='store_true',
- help='automatically scale lr with the number of gpus')
- args = parser.parse_args()
- if 'LOCAL_RANK' not in os.environ:
- os.environ['LOCAL_RANK'] = str(args.local_rank)
-
- if args.options and args.cfg_options:
- raise ValueError(
- '--options and --cfg-options cannot be both specified, '
- '--options is deprecated in favor of --cfg-options')
- if args.options:
- warnings.warn('--options is deprecated in favor of --cfg-options')
- args.cfg_options = args.options
-
- return args
-
-
- def main():
- args = parse_args()
-
- cfg = Config.fromfile(args.config)
- if args.cfg_options is not None:
- cfg.merge_from_dict(args.cfg_options)
-
- # set cudnn_benchmark
- if cfg.get('cudnn_benchmark', False):
- torch.backends.cudnn.benchmark = True
-
- # work_dir is determined in this priority: CLI > segment in file > filename
- if args.work_dir is not None:
- # update configs according to CLI args if args.work_dir is not None
- cfg.work_dir = args.work_dir
- elif cfg.get('work_dir', None) is None:
- # use config filename as default work_dir if cfg.work_dir is None
- cfg.work_dir = osp.join('./work_dirs',
- osp.splitext(osp.basename(args.config))[0])
- if args.resume_from is not None:
- cfg.resume_from = args.resume_from
- if args.gpu_ids is not None:
- cfg.gpu_ids = args.gpu_ids
- else:
- cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
-
- if args.autoscale_lr:
- # apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
- cfg.optimizer['lr'] = cfg.optimizer['lr'] * len(cfg.gpu_ids) / 8
-
- # init distributed env first, since logger depends on the dist info.
- if args.launcher == 'none':
- distributed = False
- else:
- distributed = True
- init_dist(args.launcher, **cfg.dist_params)
- # re-set gpu_ids with distributed training mode
- _, world_size = get_dist_info()
- cfg.gpu_ids = range(world_size)
-
- # create work_dir
- mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
- # dump config
- cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
- # init the logger before other steps
- timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
- log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
- # specify logger name, if we still use 'mmdet', the output info will be
- # filtered and won't be saved in the log_file
- # TODO: ugly workaround to judge whether we are training det or seg model
- if cfg.model.type in ['EncoderDecoder3D']:
- logger_name = 'mmseg'
- else:
- logger_name = 'mmdet'
- logger = get_root_logger(
- log_file=log_file, log_level=cfg.log_level, name=logger_name)
-
- # init the meta dict to record some important information such as
- # environment info and seed, which will be logged
- meta = dict()
- # log env info
- env_info_dict = collect_env()
- env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])
- dash_line = '-' * 60 + '\n'
- logger.info('Environment info:\n' + dash_line + env_info + '\n' +
- dash_line)
- meta['env_info'] = env_info
- meta['config'] = cfg.pretty_text
-
- # log some basic info
- logger.info(f'Distributed training: {distributed}')
- logger.info(f'Config:\n{cfg.pretty_text}')
-
- # set random seeds
- if args.seed is not None:
- logger.info(f'Set random seed to {args.seed}, '
- f'deterministic: {args.deterministic}')
- set_random_seed(args.seed, deterministic=args.deterministic)
- cfg.seed = args.seed
- meta['seed'] = args.seed
- meta['exp_name'] = osp.basename(args.config)
-
- model = build_model(
- cfg.model,
- train_cfg=cfg.get('train_cfg'),
- test_cfg=cfg.get('test_cfg'))
- model.init_weights()
-
- logger.info(f'Model:\n{model}')
- datasets = [build_dataset(cfg.data.train)]
- if len(cfg.workflow) == 2:
- val_dataset = copy.deepcopy(cfg.data.val)
- # in case we use a dataset wrapper
- if 'dataset' in cfg.data.train:
- val_dataset.pipeline = cfg.data.train.dataset.pipeline
- else:
- val_dataset.pipeline = cfg.data.train.pipeline
- # set test_mode=False here in deep copied config
- # which do not affect AP/AR calculation later
- # refer to https://mmdetection3d.readthedocs.io/en/latest/tutorials/customize_runtime.html#customize-workflow # noqa
- val_dataset.test_mode = False
- datasets.append(build_dataset(val_dataset))
- if cfg.checkpoint_config is not None:
- # save mmdet version, config file content and class names in
- # checkpoints as meta data
- cfg.checkpoint_config.meta = dict(
- mmdet_version=mmdet_version,
- mmseg_version=mmseg_version,
- mmdet3d_version=mmdet3d_version,
- config=cfg.pretty_text,
- CLASSES=datasets[0].CLASSES,
- PALETTE=datasets[0].PALETTE # for segmentors
- if hasattr(datasets[0], 'PALETTE') else None)
- # add an attribute for visualization convenience
- model.CLASSES = datasets[0].CLASSES
- train_model(
- model,
- datasets,
- cfg,
- distributed=distributed,
- validate=(not args.no_validate),
- timestamp=timestamp,
- meta=meta)
-
-
- if __name__ == '__main__':
- main()
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