import argparse import copy import os import os.path as osp import time import mmcv import torch from mmcv import Config, DictAction from mmcv.runner import get_dist_info, init_dist, set_random_seed from mmcv.utils import get_git_hash from models import * # noqa from models.apis import train_model from models.datasets import build_dataset from mmpose import __version__ from mmpose.models import build_posenet from mmpose.utils import collect_env, get_root_logger def parse_args(): parser = argparse.ArgumentParser(description='Train a pose model') parser.add_argument('--config', default=None, help='train config file path') parser.add_argument('--work-dir', default=None, help='the dir to save logs and models') parser.add_argument( '--resume-from', help='the checkpoint file to resume from') parser.add_argument( '--auto-resume', type=bool, default=True, help='automatically detect the latest checkpoint in word dir and resume from it.') 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=None, help='random seed') parser.add_argument( '--deterministic', action='store_true', help='whether to set deterministic options for CUDNN backend.') parser.add_argument( '--cfg-options', nargs='+', action=DictAction, default={}, help='override some settings in the used config, the key-value pair ' 'in xxx=yyy format will be merged into config file. For example, ' "'--cfg-options model.backbone.depth=18 model.backbone.with_cp=True'") 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') parser.add_argument( '--show', action='store_true', help='whether to display the prediction results in a window.') args = parser.parse_args() if 'LOCAL_RANK' not in os.environ: os.environ['LOCAL_RANK'] = str(args.local_rank) 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]) # auto resume if args.auto_resume: checkpoint = os.path.join(args.work_dir, 'latest.pth') if os.path.exists(checkpoint): cfg.resume_from = checkpoint 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)) # 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') logger = get_root_logger(log_file=log_file, log_level=cfg.log_level) # 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 # log some basic info logger.info(f'Distributed training: {distributed}') logger.info(f'Config:\n{cfg.pretty_text}') # set random seeds args.seed = 1 args.deterministic = True 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 model = build_posenet(cfg.model) train_datasets = [build_dataset(cfg.data.train)] # if len(cfg.workflow) == 2: # val_dataset = copy.deepcopy(cfg.data.val) # val_dataset.pipeline = cfg.data.train.pipeline # datasets.append(build_dataset(val_dataset)) val_dataset = copy.deepcopy(cfg.data.val) val_dataset = build_dataset(val_dataset, dict(test_mode=True)) if cfg.checkpoint_config is not None: # save mmpose version, config file content # checkpoints as meta data cfg.checkpoint_config.meta = dict( mmpose_version=__version__ + get_git_hash(digits=7), config=cfg.pretty_text, ) train_model( model, train_datasets, val_dataset, cfg, distributed=distributed, validate=(not args.no_validate), timestamp=timestamp, meta=meta) if __name__ == '__main__': main()