mirror of
https://github.com/open-compass/opencompass.git
synced 2025-05-30 16:03:24 +08:00
77 lines
2.7 KiB
Python
77 lines
2.7 KiB
Python
![]() |
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)
|