File size: 3,623 Bytes
da806fb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | import argparse
import copy
import json
import os
import mmengine
from mmengine.config import Config, ConfigDict
from opencompass.utils import build_dataset_from_cfg, get_infer_output_path
def parse_args():
parser = argparse.ArgumentParser(
description='Merge patitioned predictions')
parser.add_argument('config', help='Train config file path')
parser.add_argument('-w', '--work-dir', default=None, type=str)
parser.add_argument('-r', '--reuse', default='latest', type=str)
parser.add_argument('-c', '--clean', action='store_true')
parser.add_argument('-f', '--force', action='store_true')
args = parser.parse_args()
return args
class PredictionMerger:
def __init__(self, cfg: ConfigDict) -> None:
self.cfg = cfg
self.model_cfg = copy.deepcopy(self.cfg['model'])
self.dataset_cfg = copy.deepcopy(self.cfg['dataset'])
self.work_dir = self.cfg.get('work_dir')
def run(self):
filename = get_infer_output_path(
self.model_cfg, self.dataset_cfg,
os.path.join(self.work_dir, 'predictions'))
root, ext = os.path.splitext(filename)
partial_filename = root + '_0' + ext
if os.path.exists(
os.path.realpath(filename)) and not self.cfg['force']:
return
if not os.path.exists(os.path.realpath(partial_filename)):
print(f'{filename} not found')
return
# Load predictions
partial_filenames = []
preds, offset = {}, 0
i = 1
while os.path.exists(os.path.realpath(partial_filename)):
partial_filenames.append(os.path.realpath(partial_filename))
_preds = mmengine.load(partial_filename)
partial_filename = root + f'_{i}' + ext
i += 1
for _o in range(len(_preds)):
preds[str(offset)] = _preds[str(_o)]
offset += 1
dataset = build_dataset_from_cfg(self.dataset_cfg)
if len(preds) != len(dataset.test):
print('length mismatch')
return
print(f'Merge {partial_filenames} to {filename}')
with open(filename, 'w', encoding='utf-8') as f:
json.dump(preds, f, indent=4, ensure_ascii=False)
if self.cfg['clean']:
for partial_filename in partial_filenames:
print(f'Remove {partial_filename}')
os.remove(partial_filename)
def dispatch_tasks(cfg):
for model in cfg['models']:
for dataset in cfg['datasets']:
PredictionMerger({
'model': model,
'dataset': dataset,
'work_dir': cfg['work_dir'],
'clean': cfg['clean'],
'force': cfg['force'],
}).run()
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set work_dir
if args.work_dir is not None:
cfg['work_dir'] = args.work_dir
else:
cfg.setdefault('work_dir', './outputs/default')
if args.reuse:
if args.reuse == 'latest':
if not os.path.exists(cfg.work_dir) or not os.listdir(
cfg.work_dir):
print('No previous results to reuse!')
return
else:
dirs = os.listdir(cfg.work_dir)
dir_time_str = sorted(dirs)[-1]
else:
dir_time_str = args.reuse
cfg['work_dir'] = os.path.join(cfg.work_dir, dir_time_str)
cfg['clean'] = args.clean
cfg['force'] = args.force
dispatch_tasks(cfg)
if __name__ == '__main__':
main()
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