add support for set prediction path (#984)

configs/datasets/subjective/compassarena/compassarena_compare.py

@@ -9,7 +9,7 @@ subjective_reader_cfg = dict(
     output_column='judge',
     )
 
-data_path ="data/subjective/"
+data_path ="data/subjective/compass_arena"
 
 subjective_datasets = []
 

opencompass/partitioners/base.py

@@ -28,8 +28,8 @@ class BasePartitioner:
         self.out_dir = out_dir
         if keep_keys is None:
             self.keep_keys = [
-                'eval.runner.task.judge_cfg',
-                'eval.runner.task.dump_details',
+                'eval.runner.task.judge_cfg', 'eval.runner.task.dump_details',
+                'eval.given_pred'
             ]
         else:
             self.keep_keys = keep_keys

opencompass/tasks/subjective_eval.py

@@ -50,6 +50,7 @@ class SubjectiveEvalTask(BaseTask):
         self.num_gpus = run_cfg.get('num_gpus', 0)
         self.num_procs = run_cfg.get('num_procs', 1)
         self.judge_cfg = copy.deepcopy(judge_cfg)
+        self.given_pred = cfg.eval.get('given_pred', [])
 
     def get_command(self, cfg_path, template):
         """Get the command template for the task.
@@ -89,12 +90,16 @@ class SubjectiveEvalTask(BaseTask):
                     continue
                 self._score(model_cfg, dataset_cfg, eval_cfg, output_column)
 
-    def _load_model_pred(self, model_cfg: Union[ConfigDict, List[ConfigDict]],
+    def _load_model_pred(
+        self,
+        model_cfg: Union[ConfigDict, List[ConfigDict]],
         dataset_cfg: ConfigDict,
-                         eval_cfg: ConfigDict) -> Union[None, List[str]]:
+        eval_cfg: ConfigDict,
+        given_preds: List[dict],
+    ) -> Union[None, List[str]]:
         if isinstance(model_cfg, (tuple, list)):
             return [
-                self._load_model_pred(m, dataset_cfg, eval_cfg)
+                self._load_model_pred(m, dataset_cfg, eval_cfg, given_preds)
                 for m in model_cfg
             ]
 
@@ -119,7 +124,11 @@ class SubjectiveEvalTask(BaseTask):
         else:
             filename = get_infer_output_path(
                 model_cfg, dataset_cfg, osp.join(self.work_dir, 'predictions'))
-
+        for given_pred in given_preds:
+            abbr = given_pred['abbr']
+            path = given_pred['path']
+            if abbr == model_cfg['abbr']:
+                filename = osp.join(path, osp.basename(filename))
         # Get partition name
         root, ext = osp.splitext(filename)
         partial_filename = root + '_0' + ext
@@ -209,7 +218,7 @@ class SubjectiveEvalTask(BaseTask):
         if len(new_model_cfg) == 1:
             new_model_cfg = new_model_cfg[0]
         model_preds = self._load_model_pred(new_model_cfg, dataset_cfg,
-                                            eval_cfg)
+                                            eval_cfg, self.given_pred)
         if not self.judge_cfg:
             raise ValueError('missing "eval.runner.task.judge_cfg"')
         eval_cfg['evaluator']['judge_cfg'] = self.judge_cfg