Merge branch 'main' of github.com:magic-research/Sa2VA

projects/llava_sam2/evaluation/dataset/base_eval_dataset.py

@@ -0,0 +1,69 @@
+from torch.utils.data import Dataset
+import copy
+from collections.abc import Mapping
+from typing import Union
+from mmengine.config import Config
+import logging
+from mmengine.fileio import list_from_file
+from mmengine.logging import print_log
+from abc import abstractmethod
+
+
+class BaseEvalDataset(Dataset):
+
+    METAINFO: dict = dict(name='default')
+
+    def __init__(self, metainfo: Union[Mapping, Config, None] = None):
+        self._metainfo = self._load_metainfo(copy.deepcopy(metainfo))
+
+    @classmethod
+    def _load_metainfo(cls,
+                       metainfo: Union[Mapping, Config, None] = None) -> dict:
+        """Collect meta information from the dictionary of meta.
+
+        Args:
+            metainfo (Mapping or Config, optional): Meta information dict.
+                If ``metainfo`` contains existed filename, it will be
+                parsed by ``list_from_file``.
+
+        Returns:
+            dict: Parsed meta information.
+        """
+        # avoid `cls.METAINFO` being overwritten by `metainfo`
+        cls_metainfo = copy.deepcopy(cls.METAINFO)
+        if metainfo is None:
+            return cls_metainfo
+        if not isinstance(metainfo, (Mapping, Config)):
+            raise TypeError('metainfo should be a Mapping or Config, '
+                            f'but got {type(metainfo)}')
+
+        for k, v in metainfo.items():
+            if isinstance(v, str):
+                # If type of value is string, and can be loaded from
+                # corresponding backend. it means the file name of meta file.
+                try:
+                    cls_metainfo[k] = list_from_file(v)
+                except (TypeError, FileNotFoundError):
+                    print_log(
+                        f'{v} is not a meta file, simply parsed as meta '
+                        'information',
+                        logger='current',
+                        level=logging.WARNING)
+                    cls_metainfo[k] = v
+            else:
+                cls_metainfo[k] = v
+        return cls_metainfo
+
+    @property
+    def metainfo(self) -> dict:
+        """Get meta information of dataset.
+
+        Returns:
+            dict: meta information collected from ``BaseDataset.METAINFO``,
+            annotation file and metainfo argument during instantiation.
+        """
+        return copy.deepcopy(self._metainfo)
+
+    @abstractmethod
+    def evaluate(self, results, work_dir):
+        pass

projects/llava_sam2/evaluation/dataset/refVOS.py

@@ -8,7 +8,7 @@
 
 from mmengine.dist import master_only
 
-from vlm.datasets.evaluation.base_eval_dataset import BaseEvalDataset
+from .base_eval_dataset import BaseEvalDataset
 
 SEG_PROMPT = "<image>\nPlease segment {}."
 

projects/llava_sam2/evaluation/ref_vos_eval.py

@@ -12,8 +12,8 @@
 import tqdm
 from transformers import AutoModel, AutoTokenizer
 
-from projects.llava_sam2.hf_evaluation.dataset import RefVOSDataset
-from projects.llava_sam2.hf_evaluation.utils import _init_dist_pytorch, _init_dist_slurm, get_dist_info, get_rank, collect_results_cpu, barrier
+from projects.llava_sam2.evaluation.dataset import RefVOSDataset
+from projects.llava_sam2.evaluation.utils import _init_dist_pytorch, _init_dist_slurm, get_dist_info, get_rank, collect_results_cpu
 
 import concurrent.futures
 from pycocotools import mask as cocomask