mirror of
https://github.com/open-compass/opencompass.git
synced 2025-05-30 16:03:24 +08:00
78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
![]() |
# flake8: noqa: E501
|
||
|
import os.path as osp
|
||
|
|
||
|
import mmengine
|
||
|
|
||
|
from opencompass.utils import dataset_abbr_from_cfg
|
||
|
|
||
|
|
||
|
def get_outdir(cfg, time_str):
|
||
|
"""Get out put path.
|
||
|
|
||
|
Args:
|
||
|
cfg (ConfigDict): The running config.
|
||
|
time_str (str): Current time.
|
||
|
"""
|
||
|
work_dir = cfg['work_dir']
|
||
|
output_path = osp.join(work_dir, 'summary', f'summary_{time_str}.txt')
|
||
|
output_dir = osp.join(osp.split(output_path)[0], f'{time_str}')
|
||
|
mmengine.mkdir_or_exist(output_dir)
|
||
|
results_folder = osp.join(work_dir, 'results')
|
||
|
return output_dir, results_folder
|
||
|
|
||
|
|
||
|
def get_judgeanswer_and_reference(dataset, subdir_path, post_process):
|
||
|
"""Extract judgements (scores) and references.
|
||
|
|
||
|
Args:
|
||
|
dataset (ConfigDict): Dataset config.
|
||
|
subdir_path (str): Model path in results dir.
|
||
|
post_process (function): The pre-defined extract function.
|
||
|
"""
|
||
|
dataset_abbr = dataset_abbr_from_cfg(dataset)
|
||
|
filename = osp.join(subdir_path, dataset_abbr + '.json')
|
||
|
partial_filename = osp.join(subdir_path, dataset_abbr + '_0.json')
|
||
|
if osp.exists(osp.realpath(filename)):
|
||
|
result = mmengine.load(filename)
|
||
|
elif osp.exists(osp.realpath(partial_filename)):
|
||
|
filename = partial_filename
|
||
|
result = {}
|
||
|
i = 1
|
||
|
partial_dict_flag = 0
|
||
|
while osp.exists(osp.realpath(filename)):
|
||
|
res = mmengine.load(filename)
|
||
|
for k, v in res.items():
|
||
|
result[partial_dict_flag] = v
|
||
|
partial_dict_flag += 1
|
||
|
filename = osp.join(subdir_path,
|
||
|
dataset_abbr + '_' + str(i) + '.json')
|
||
|
i += 1
|
||
|
else:
|
||
|
result = {}
|
||
|
|
||
|
if len(result) == 0:
|
||
|
print('*' * 100)
|
||
|
print('There are no results for ' + filename + ' or ' +
|
||
|
partial_filename)
|
||
|
print('*' * 100)
|
||
|
assert len(result) > 0
|
||
|
|
||
|
judged_answers = []
|
||
|
references = []
|
||
|
for k, v in result.items():
|
||
|
processed_judge = post_process(v['prediction'])
|
||
|
if processed_judge is not None:
|
||
|
judged_answers.append(processed_judge)
|
||
|
references.append(v['gold'])
|
||
|
print(
|
||
|
f'Among {len(result)} judgements, successfully extracted {len(judged_answers)} judgements.'
|
||
|
)
|
||
|
if len(judged_answers) == 0:
|
||
|
print('*' * 100)
|
||
|
print(
|
||
|
'There are no extracted judgements, please change your judge model or check your prompt!!!'
|
||
|
)
|
||
|
print('*' * 100)
|
||
|
assert len(judged_answers) > 0
|
||
|
return judged_answers, references
|