tools/train.py

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp

from mmengine.config import Config, DictAction
from mmengine.runner import Runner


def parse_args():
    parser = argparse.ArgumentParser(description='Train a pose model')
    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',
        nargs='?',
        type=str,
        const='auto',
        help='If specify checkpint path, resume from it, while if not '
        'specify, try to auto resume from the latest checkpoint '
        'in the work directory.')
    parser.add_argument(
        '--amp',
        action='store_true',
        default=False,
        help='enable automatic-mixed-precision training')
    parser.add_argument(
        '--no-validate',
        action='store_true',
        help='whether not to evaluate the checkpoint during training')
    parser.add_argument(
        '--auto-scale-lr',
        action='store_true',
        help='whether to auto scale the learning rate according to the '
        'actual batch size and the original batch size.')
    parser.add_argument(
        '--show-dir',
        help='directory where the visualization images will be saved.')
    parser.add_argument(
        '--show',
        action='store_true',
        help='whether to display the prediction results in a window.')
    parser.add_argument(
        '--interval',
        type=int,
        default=1,
        help='visualize per interval samples.')
    parser.add_argument(
        '--wait-time',
        type=float,
        default=1,
        help='display time of every window. (second)')
    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')
    # When using PyTorch version >= 2.0.0, the `torch.distributed.launch`
    # will pass the `--local-rank` parameter to `tools/train.py` instead
    # of `--local_rank`.
    parser.add_argument('--local_rank', '--local-rank', type=int, default=0)
    args = parser.parse_args()
    if 'LOCAL_RANK' not in os.environ:
        os.environ['LOCAL_RANK'] = str(args.local_rank)

    return args


def merge_args(cfg, args):
    """Merge CLI arguments to config."""
    if args.no_validate:
        cfg.val_cfg = None
        cfg.val_dataloader = None
        cfg.val_evaluator = None

    cfg.launcher = args.launcher

    # 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])

    # enable automatic-mixed-precision training
    if args.amp is True:
        from mmengine.optim import AmpOptimWrapper, OptimWrapper
        optim_wrapper = cfg.optim_wrapper.get('type', OptimWrapper)
        assert optim_wrapper in (OptimWrapper, AmpOptimWrapper,
                                 'OptimWrapper', 'AmpOptimWrapper'), \
            '`--amp` is not supported custom optimizer wrapper type ' \
            f'`{optim_wrapper}.'
        cfg.optim_wrapper.type = 'AmpOptimWrapper'
        cfg.optim_wrapper.setdefault('loss_scale', 'dynamic')

    # resume training
    if args.resume == 'auto':
        cfg.resume = True
        cfg.load_from = None
    elif args.resume is not None:
        cfg.resume = True
        cfg.load_from = args.resume

    # enable auto scale learning rate
    if args.auto_scale_lr:
        cfg.auto_scale_lr.enable = True

    # visualization
    if args.show or (args.show_dir is not None):
        assert 'visualization' in cfg.default_hooks, \
            'PoseVisualizationHook is not set in the ' \
            '`default_hooks` field of config. Please set ' \
            '`visualization=dict(type="PoseVisualizationHook")`'

        cfg.default_hooks.visualization.enable = True
        cfg.default_hooks.visualization.show = args.show
        if args.show:
            cfg.default_hooks.visualization.wait_time = args.wait_time
        cfg.default_hooks.visualization.out_dir = args.show_dir
        cfg.default_hooks.visualization.interval = args.interval

    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)

    return cfg


def main():
    args = parse_args()

    # load config
    cfg = Config.fromfile(args.config)

    # merge CLI arguments to config
    cfg = merge_args(cfg, args)

    # set preprocess configs to model
    if 'preprocess_cfg' in cfg:
        cfg.model.setdefault('data_preprocessor',
                             cfg.get('preprocess_cfg', {}))

    # build the runner from config
    runner = Runner.from_cfg(cfg)

    # start training
    runner.train()


if __name__ == '__main__':
    main()