mirror of
https://github.com/YaoFANGUK/video-subtitle-remover.git
synced 2026-02-28 23:14:53 +08:00
80 lines
2.9 KiB
Python
80 lines
2.9 KiB
Python
import os
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import json
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import argparse
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import datetime
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import numpy as np
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from shutil import copyfile
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import torch
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import torch.multiprocessing as mp
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from core.trainer import Trainer
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from core.dist import (
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get_world_size,
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get_local_rank,
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get_global_rank,
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get_master_ip,
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)
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parser = argparse.ArgumentParser(description='STTN')
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parser.add_argument('-c', '--config', default='configs/youtube-vos.json', type=str)
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parser.add_argument('-m', '--model', default='sttn', type=str)
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parser.add_argument('-p', '--port', default='23455', type=str)
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parser.add_argument('-e', '--exam', action='store_true')
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args = parser.parse_args()
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def main_worker(rank, config):
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if 'local_rank' not in config:
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config['local_rank'] = config['global_rank'] = rank
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if config['distributed']:
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torch.cuda.set_device(int(config['local_rank']))
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torch.distributed.init_process_group(backend='nccl',
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init_method=config['init_method'],
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world_size=config['world_size'],
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rank=config['global_rank'],
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group_name='mtorch'
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)
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print('using GPU {}-{} for training'.format(
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int(config['global_rank']), int(config['local_rank'])))
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config['save_dir'] = os.path.join(config['save_dir'], '{}_{}'.format(config['model'],
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os.path.basename(args.config).split('.')[0]))
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if torch.cuda.is_available():
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config['device'] = torch.device("cuda:{}".format(config['local_rank']))
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else:
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config['device'] = 'cpu'
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if (not config['distributed']) or config['global_rank'] == 0:
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os.makedirs(config['save_dir'], exist_ok=True)
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config_path = os.path.join(
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config['save_dir'], config['config'].split('/')[-1])
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if not os.path.isfile(config_path):
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copyfile(config['config'], config_path)
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print('[**] create folder {}'.format(config['save_dir']))
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trainer = Trainer(config, debug=args.exam)
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trainer.train()
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if __name__ == "__main__":
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# loading configs
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config = json.load(open(args.config))
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config['model'] = args.model
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config['config'] = args.config
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# setting distributed configurations
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config['world_size'] = get_world_size()
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config['init_method'] = f"tcp://{get_master_ip()}:{args.port}"
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config['distributed'] = True if config['world_size'] > 1 else False
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# setup distributed parallel training environments
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if get_master_ip() == "127.0.0.1":
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# manually launch distributed processes
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mp.spawn(main_worker, nprocs=config['world_size'], args=(config,))
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else:
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# multiple processes have been launched by openmpi
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config['local_rank'] = get_local_rank()
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config['global_rank'] = get_global_rank()
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main_worker(-1, config)
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