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https://github.com/open-compass/opencompass.git
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101 lines
3.1 KiB
Python
101 lines
3.1 KiB
Python
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import argparse
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import copy
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import json
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import os.path as osp
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import mmengine
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from mmengine.config import Config, ConfigDict
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from opencompass.utils import build_dataset_from_cfg, get_infer_output_path
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def parse_args():
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parser = argparse.ArgumentParser(description='Run an evaluation task')
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parser.add_argument('config', help='Train config file path')
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parser.add_argument('-w',
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'--work-dir',
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help='Work path, all the outputs will be '
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'saved in this path, including the slurm logs, '
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'the evaluation results, the summary results, etc.'
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'If not specified, the work_dir will be set to '
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'./outputs/default.',
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default=None,
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type=str)
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args = parser.parse_args()
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return args
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class PredictionMerger:
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""""""
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def __init__(self, cfg: ConfigDict) -> None:
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self.cfg = cfg
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self.model_cfg = copy.deepcopy(self.cfg['model'])
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self.dataset_cfg = copy.deepcopy(self.cfg['dataset'])
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self.work_dir = self.cfg.get('work_dir')
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def run(self):
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filename = get_infer_output_path(
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self.model_cfg, self.dataset_cfg,
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osp.join(self.work_dir, 'predictions'))
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root, ext = osp.splitext(filename)
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partial_filename = root + '_0' + ext
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if osp.exists(osp.realpath(filename)):
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return
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if not osp.exists(osp.realpath(partial_filename)):
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print(f'{filename} not found')
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return
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# Load predictions
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partial_filenames = []
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if osp.exists(osp.realpath(filename)):
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preds = mmengine.load(filename)
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else:
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preds, offset = {}, 0
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i = 1
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while osp.exists(osp.realpath(partial_filename)):
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partial_filenames.append(osp.realpath(partial_filename))
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_preds = mmengine.load(partial_filename)
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partial_filename = root + f'_{i}' + ext
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i += 1
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for _o in range(len(_preds)):
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preds[str(offset)] = _preds[str(_o)]
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offset += 1
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dataset = build_dataset_from_cfg(self.dataset_cfg)
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if len(preds) != len(dataset.test):
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print('length mismatch')
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return
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print(f'Merge {partial_filenames} to {filename}')
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with open(filename, 'w', encoding='utf-8') as f:
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json.dump(preds, f, indent=4, ensure_ascii=False)
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def dispatch_tasks(cfg):
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for model in cfg['models']:
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for dataset in cfg['datasets']:
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PredictionMerger({
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'model': model,
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'dataset': dataset,
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'work_dir': cfg['work_dir']
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}).run()
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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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# set work_dir
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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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else:
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cfg.setdefault('work_dir', './outputs/default')
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dispatch_tasks(cfg)
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if __name__ == '__main__':
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main()
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