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import argparse |
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import copy |
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import os |
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import os.path as osp |
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import time |
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import mmcv |
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import torch |
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from mmcv.runner import init_dist |
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from mmcv.utils import Config, DictAction, get_git_hash |
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from mmseg import __version__ |
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from mmseg.apis import set_random_seed, train_segmentor |
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from mmseg.datasets import build_dataset |
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from mmseg.models import build_segmentor |
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from mmseg.utils import collect_env, get_root_logger |
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def parse_args(): |
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parser = argparse.ArgumentParser(description='Train a segmentor') |
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parser.add_argument('--config',default="/SEG/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x1024_40k_Recipe1M.py", help='train config file path') |
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parser.add_argument('--work-dir',default="/SEG/mmsegmentation/checkpoints/ccnet/recipe1m_train2", help='the dir to save logs and models') |
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parser.add_argument( |
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'--load-from', help='the checkpoint file to load weights from') |
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parser.add_argument( |
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'--resume-from', help='the checkpoint file to resume from') |
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parser.add_argument( |
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'--no-validate', |
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action='store_true', |
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help='whether not to evaluate the checkpoint during training') |
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group_gpus = parser.add_mutually_exclusive_group() |
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group_gpus.add_argument( |
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'--gpus', |
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type=int, |
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help='number of gpus to use ' |
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'(only applicable to non-distributed training)') |
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group_gpus.add_argument( |
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'--gpu-ids', |
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type=int, |
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nargs='+', |
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help='ids of gpus to use ' |
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'(only applicable to non-distributed training)') |
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parser.add_argument('--seed', type=int, default=None, help='random seed') |
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parser.add_argument( |
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'--deterministic', |
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action='store_true', |
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help='whether to set deterministic options for CUDNN backend.') |
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parser.add_argument( |
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'--options', nargs='+', action=DictAction, help='custom options') |
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parser.add_argument( |
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'--launcher', |
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choices=['none', 'pytorch', 'slurm', 'mpi'], |
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default='none', |
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help='job launcher') |
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parser.add_argument('--local_rank', type=int, default=0) |
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args = parser.parse_args() |
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if 'LOCAL_RANK' not in os.environ: |
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os.environ['LOCAL_RANK'] = str(args.local_rank) |
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return args |
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def main(): |
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args = parse_args() |
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cfg = Config.fromfile(args.config) |
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if args.options is not None: |
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cfg.merge_from_dict(args.options) |
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if cfg.get('cudnn_benchmark', False): |
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torch.backends.cudnn.benchmark = True |
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if args.work_dir is not None: |
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cfg.work_dir = args.work_dir |
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elif cfg.get('work_dir', None) is None: |
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cfg.work_dir = osp.join('./work_dirs', |
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osp.splitext(osp.basename(args.config))[0]) |
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if args.load_from is not None: |
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cfg.load_from = args.load_from |
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if args.resume_from is not None: |
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cfg.resume_from = args.resume_from |
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if args.gpu_ids is not None: |
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cfg.gpu_ids = args.gpu_ids |
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else: |
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cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus) |
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if args.launcher == 'none': |
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distributed = False |
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else: |
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distributed = True |
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init_dist(args.launcher, **cfg.dist_params) |
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mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) |
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cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config))) |
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timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) |
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log_file = osp.join(cfg.work_dir, f'{timestamp}.log') |
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logger = get_root_logger(log_file=log_file, log_level=cfg.log_level) |
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meta = dict() |
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env_info_dict = collect_env() |
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env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()]) |
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dash_line = '-' * 60 + '\n' |
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logger.info('Environment info:\n' + dash_line + env_info + '\n' + |
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dash_line) |
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meta['env_info'] = env_info |
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logger.info(f'Distributed training: {distributed}') |
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logger.info(f'Config:\n{cfg.pretty_text}') |
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if args.seed is not None: |
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logger.info(f'Set random seed to {args.seed}, deterministic: ' |
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f'{args.deterministic}') |
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set_random_seed(args.seed, deterministic=args.deterministic) |
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cfg.seed = args.seed |
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meta['seed'] = args.seed |
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meta['exp_name'] = osp.basename(args.config) |
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model = build_segmentor( |
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cfg.model, |
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train_cfg=cfg.get('train_cfg'), |
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test_cfg=cfg.get('test_cfg')) |
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logger.info(model) |
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datasets = [build_dataset(cfg.data.train)] |
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if len(cfg.workflow) == 2: |
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val_dataset = copy.deepcopy(cfg.data.val) |
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val_dataset.pipeline = cfg.data.train.pipeline |
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datasets.append(build_dataset(val_dataset)) |
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if cfg.checkpoint_config is not None: |
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cfg.checkpoint_config.meta = dict( |
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mmseg_version=f'{__version__}+{get_git_hash()[:7]}', |
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config=cfg.pretty_text, |
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CLASSES=datasets[0].CLASSES, |
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PALETTE=datasets[0].PALETTE) |
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model.CLASSES = datasets[0].CLASSES |
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train_segmentor( |
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model, |
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datasets, |
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cfg, |
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distributed=distributed, |
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validate=(not args.no_validate), |
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timestamp=timestamp, |
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meta=meta) |
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if __name__ == '__main__': |
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main() |
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