train.py

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

import mmcv
import mmcv_custom  # noqa: F401,F403
import mmdet_custom  # noqa: F401,F403
import torch
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from mmcv.utils import get_git_hash
from mmdet import __version__
from mmdet.apis import init_random_seed, set_random_seed, train_detector
from mmdet.datasets import build_dataset
from mmdet.models import build_detector
from mmdet.utils import collect_env, get_root_logger
from mmcv.cnn.utils import revert_sync_batchnorm
import wandb
from change_key_name import change_key_name_from_timm_to_evo


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('--auto-resume',
                        action='store_true',
                        help='resume from the latest checkpoint automatically')
    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(
        '--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)
    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
    cfg.auto_resume = args.auto_resume
    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)

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
        if len(cfg.gpu_ids) > 1:
            warnings.warn(
                f'We treat {cfg.gpu_ids} as gpu-ids, and reset to '
                f'{cfg.gpu_ids[0:1]} as gpu-ids to avoid potential error in '
                'non-distribute training time.')
            cfg.gpu_ids = cfg.gpu_ids[0:1]
    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')
    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
    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
    seed = init_random_seed(args.seed)
    logger.info(f'Set random seed to {seed}, '
                f'deterministic: {args.deterministic}')
    set_random_seed(seed, deterministic=args.deterministic)
    cfg.seed = seed
    meta['seed'] = seed
    meta['exp_name'] = osp.basename(args.config)

    cfg.log_config.hooks = [
        dict(type='WandbLoggerHook',
             init_kwargs={'entity': 'team52', 'project': 'mm seg&detect',
                          'name': cfg.exp_name, 'config': cfg._cfg_dict.to_dict(),
                          'reinit': False, 'group': cfg.exp_name},
             interval=50,
             ignore_last=True,
             reset_flag=False,
             commit=True,
             by_epoch=False,
             with_step=False,
             ),
        dict(type='TextLoggerHook', by_epoch=False)
    ]
    wandb.init(entity='team52', project='mm seg&detect',
               name=cfg.exp_name, config=cfg._cfg_dict.to_dict(), reinit=False)
    wandb.save(args.config, policy='now')

    model = build_detector(cfg.model,
                           train_cfg=cfg.get('train_cfg'),
                           test_cfg=cfg.get('test_cfg'))
    model.init_weights()

    # SyncBN is not support for DP
    if not distributed:
        warnings.warn(
            'SyncBN is only supported with DDP. To be compatible with DP, '
            'we convert SyncBN to BN. Please use dist_train.sh which can '
            'avoid this error.')
        model = revert_sync_batchnorm(model)

    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))
    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=__version__ +
                                          get_git_hash()[:7],
                                          CLASSES=datasets[0].CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = datasets[0].CLASSES

    if cfg.get('change_key_name_evo', False):
        cfg.change_key_name_func = change_key_name_from_timm_to_evo
    if cfg.load_from and hasattr(cfg, 'change_key_name') and cfg.change_key_name:
        from mmcv.runner.checkpoint import _load_checkpoint
        checkpoint = _load_checkpoint(cfg.load_from)
        revised_ckpt_path = cfg.load_from.split('/')
        revised_ckpt_path[-1] = 'revised_' + revised_ckpt_path[-1]
        revised_ckpt_path = '/'.join(revised_ckpt_path)
        cfg.load_from = revised_ckpt_path
        if not osp.exists(revised_ckpt_path):
            from change_key_name import revise_keys
            from collections import OrderedDict
            import re
            for p, r in revise_keys:
                checkpoint['state_dict'] = OrderedDict(
                    {re.sub(p, r, k): v
                     for k, v in checkpoint['state_dict'].items()})
            torch.save(checkpoint, revised_ckpt_path)
    cfg.device = 'cuda'
    train_detector(model,
                   datasets,
                   cfg,
                   distributed=distributed,
                   validate=(not args.no_validate),
                   timestamp=timestamp,
                   meta=meta)


if __name__ == '__main__':
    main()