OpenCompass/opencompass/partitioners/sub_naive.py

77 lines
2.7 KiB
Python
Raw Normal View History

2023-10-13 19:50:54 +08:00
from itertools import combinations
from typing import Dict, List, Optional, Tuple
from mmengine.config import ConfigDict
from opencompass.registry import PARTITIONERS
from .naive import NaivePartitioner
@PARTITIONERS.register_module()
class SubjectiveNaivePartitioner(NaivePartitioner):
"""Naive task partitioner for subjective evaluation. Compared to
NaivePartitioner, this partitioner squashes multiple models into a task.
Args:
out_dir (str): The output directory of tasks.
keep_keys (List[str]): The keys to be kept from the experiment config
to the task config.
"""
def __init__(self,
mode: str,
out_dir: str,
model_pairs: Optional[List[Tuple]] = None,
keep_keys: List[str] = ['eval.runner.task.judge_cfg']):
super().__init__(out_dir=out_dir, keep_keys=keep_keys)
assert mode in ['all', 'one_to_n', 'fixed']
self.mode = mode
self.model_pairs = model_pairs
def get_model_combinations(self, models: List[ConfigDict]) -> List:
if self.mode == 'all':
return combinations(models, 2)
elif self.mode == 'one_to_n':
pass
elif self.mode == 'fixed':
pass
def partition(self,
models: List[ConfigDict],
datasets: List[ConfigDict],
work_dir: str,
out_dir: str,
add_cfg: Dict = {}) -> List[Dict]:
"""Partition model-dataset pairs into tasks. Each task is defined as a
dict and will run independently as a unit. Its structure is as
follows:
.. code-block:: python
{
'models': [], # a list of model configs
'datasets': [[]], # a nested list of dataset configs, each
list corresponds to a model
'work_dir': '', # the work dir
}
Args:
models (List[ConfigDict]): A list of model configs.
datasets (List[ConfigDict]): A list of dataset configs.
work_dir (str): The work dir for the task.
out_dir (str): The full output path for the task, intended for
Partitioners to check whether the task is finished via the
existency of result file in this directory.
Returns:
List[Dict]: A list of tasks.
"""
models = self.get_model_combinations(models)
return super().partition(models=models,
datasets=datasets,
work_dir=work_dir,
out_dir=out_dir,
add_cfg=add_cfg)