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- # Copyright (c) OpenMMLab. All rights reserved.
- import argparse
- import copy
- import os
- import os.path as osp
- import time
- import warnings
-
- import mmcv
- import torch
- import torch.distributed as dist
- from mmcv import Config, DictAction
- from mmcv.runner import get_dist_info, init_dist
-
- from mmcls import __version__
- from mmcls.apis import init_random_seed, set_random_seed, train_model
- from mmcls.datasets import build_dataset
- from mmcls.models import build_classifier
- from mmcls.utils import (auto_select_device, collect_env, get_root_logger,
- setup_multi_processes)
-
-
- def parse_args():
- parser = argparse.ArgumentParser(description='Train a 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-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(
- '--device', help='device used for training. (Deprecated)')
- group_gpus.add_argument(
- '--gpus',
- type=int,
- help='(Deprecated, please use --gpu-id) number of gpus to use '
- '(only applicable to non-distributed training)')
- group_gpus.add_argument(
- '--gpu-ids',
- type=int,
- nargs='+',
- help='(Deprecated, please use --gpu-id) ids of gpus to use '
- '(only applicable to non-distributed training)')
- group_gpus.add_argument(
- '--gpu-id',
- type=int,
- default=0,
- help='id of gpu to use '
- '(only applicable to non-distributed training)')
- parser.add_argument(
- '--ipu-replicas',
- type=int,
- default=None,
- help='num of ipu replicas to use')
- parser.add_argument('--seed', type=int, default=None, help='random seed')
- parser.add_argument(
- '--diff-seed',
- action='store_true',
- help='Whether or not set different seeds for different ranks')
- parser.add_argument(
- '--deterministic',
- action='store_true',
- help='whether to set deterministic options for CUDNN backend.')
- 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)
-
- 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 multi-process settings
- setup_multi_processes(cfg)
-
- # 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.gpus is not None:
- cfg.gpu_ids = range(1)
- warnings.warn('`--gpus` is deprecated because we only support '
- 'single GPU mode in non-distributed training. '
- 'Use `gpus=1` now.')
- if args.gpu_ids is not None:
- cfg.gpu_ids = args.gpu_ids[0:1]
- warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
- 'Because we only support single GPU mode in '
- 'non-distributed training. Use the first GPU '
- 'in `gpu_ids` now.')
- if args.gpus is None and args.gpu_ids is None:
- cfg.gpu_ids = [args.gpu_id]
-
- if args.ipu_replicas is not None:
- cfg.ipu_replicas = args.ipu_replicas
- args.device = 'ipu'
-
- # 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)
- _, 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
-
- # log some basic info
- logger.info(f'Distributed training: {distributed}')
- logger.info(f'Config:\n{cfg.pretty_text}')
-
- # set random seeds
- cfg.device = args.device or auto_select_device()
- seed = init_random_seed(args.seed, device=cfg.device)
- seed = seed + dist.get_rank() if args.diff_seed else 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
-
- model = build_classifier(cfg.model)
- model.init_weights()
-
- 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))
-
- # save mmcls version, config file content and class names in
- # runner as meta data
- meta.update(
- dict(
- mmcls_version=__version__,
- config=cfg.pretty_text,
- CLASSES=datasets[0].CLASSES))
-
- # add an attribute for visualization convenience
- train_model(
- model,
- datasets,
- cfg,
- distributed=distributed,
- validate=(not args.no_validate),
- timestamp=timestamp,
- device=cfg.device,
- meta=meta)
-
-
- if __name__ == '__main__':
- main()
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