[to #42322933]支持从dataset json文件中获取参数

* dataset json file add args

modelscope/msdatasets/ms_dataset.py

@@ -248,15 +248,15 @@ class MsDataset:
                 break
         target_subset_name, target_dataset_structure = get_target_dataset_structure(
             dataset_json, subset_name, split)
-        meta_map, file_map = get_dataset_files(target_dataset_structure,
+        meta_map, file_map, args_map = get_dataset_files(
-                                               dataset_name, namespace,
+            target_dataset_structure, dataset_name, namespace, version)
-                                               version)
         builder = load_dataset_builder(
             dataset_name,
             subset_name,
             namespace,
             meta_data_files=meta_map,
             zip_data_files=file_map,
+            args_map=args_map,
             cache_dir=MS_DATASETS_CACHE,
             version=version,
             split=list(target_dataset_structure.keys()),

modelscope/msdatasets/task_datasets/image_instance_segmentation_coco_dataset.py

@@ -60,6 +60,8 @@ class ImageInstanceSegmentationCocoDataset(TorchTaskDataset):
                  classes=None,
                  seg_prefix=None,
                  folder_name=None,
+                 ann_file=None,
+                 img_prefix=None,
                  test_mode=False,
                  filter_empty_gt=True,
                  **kwargs):
@@ -69,11 +71,9 @@ class ImageInstanceSegmentationCocoDataset(TorchTaskDataset):
         self.split = next(iter(split_config.keys()))
         self.preprocessor = preprocessor
 
-        self.ann_file = osp.join(self.data_root,
+        self.ann_file = osp.join(self.data_root, ann_file)
-                                 DATASET_STRUCTURE[self.split]['annotation'])
 
-        self.img_prefix = osp.join(self.data_root,
+        self.img_prefix = osp.join(self.data_root, img_prefix)
-                                   DATASET_STRUCTURE[self.split]['images'])
         self.seg_prefix = seg_prefix
         self.test_mode = test_mode
         self.filter_empty_gt = filter_empty_gt

modelscope/msdatasets/utils/dataset_utils.py

@@ -1,6 +1,6 @@
 import os
 from collections import defaultdict
-from typing import Mapping, Optional, Sequence, Union
+from typing import Any, Mapping, Optional, Sequence, Union
 
 from datasets.builder import DatasetBuilder
 
@@ -92,6 +92,7 @@ def get_dataset_files(subset_split_into: dict,
     """
     meta_map = defaultdict(dict)
     file_map = defaultdict(dict)
+    args_map = defaultdict(dict)
     from modelscope.hub.api import HubApi
     modelscope_api = HubApi()
     for split, info in subset_split_into.items():
@@ -99,7 +100,8 @@ def get_dataset_files(subset_split_into: dict,
             info.get('meta', ''), dataset_name, namespace, revision)
         if info.get('file'):
             file_map[split] = info['file']
-    return meta_map, file_map
+        args_map[split] = info.get('args')
+    return meta_map, file_map, args_map
 
 
 def load_dataset_builder(dataset_name: str, subset_name: str, namespace: str,
@@ -107,12 +109,16 @@ def load_dataset_builder(dataset_name: str, subset_name: str, namespace: str,
                                                              Sequence[str]]],
                          zip_data_files: Mapping[str, Union[str,
                                                             Sequence[str]]],
-                         cache_dir: str, version: Optional[Union[str]],
+                         args_map: Mapping[str, Any], cache_dir: str,
-                         split: Sequence[str],
+                         version: Optional[Union[str]], split: Sequence[str],
                          **config_kwargs) -> DatasetBuilder:
     sub_dir = os.path.join(version, '_'.join(split))
     meta_data_file = next(iter(meta_data_files.values()))
     if not meta_data_file:
+        args_map = next(iter(args_map.values()))
+        if args_map is None:
+            args_map = {}
+        args_map.update(config_kwargs)
         builder_instance = TaskSpecificDatasetBuilder(
             dataset_name=dataset_name,
             namespace=namespace,
@@ -121,7 +127,7 @@ def load_dataset_builder(dataset_name: str, subset_name: str, namespace: str,
             meta_data_files=meta_data_files,
             zip_data_files=zip_data_files,
             hash=sub_dir,
-            **config_kwargs)
+            **args_map)
     elif meta_data_file.endswith('.csv'):
         builder_instance = MsCsvDatasetBuilder(
             dataset_name=dataset_name,

tests/msdatasets/test_ms_dataset.py

@@ -36,9 +36,8 @@ class MsDatasetTest(unittest.TestCase):
         ms_ds_train = MsDataset.load(
             'pets_small',
             namespace=DEFAULT_DATASET_NAMESPACE,
-            split='train',
+            download_mode=DownloadMode.FORCE_REDOWNLOAD,
-            classes=('1', '2'),
+            split='train')
-            folder_name='Pets')
         print(ms_ds_train.config_kwargs)
         assert next(iter(ms_ds_train.config_kwargs['split_config'].values()))
 

tests/trainers/test_finetune_mplug.py

@@ -20,7 +20,6 @@ class TestFinetuneMPlug(unittest.TestCase):
         self.tmp_dir = tempfile.TemporaryDirectory().name
         if not os.path.exists(self.tmp_dir):
             os.makedirs(self.tmp_dir)
-
         from modelscope.utils.constant import DownloadMode
         datadict = MsDataset.load(
             'coco_captions_small_slice',

tests/trainers/test_image_instance_segmentation_trainer.py

@@ -15,7 +15,7 @@ from modelscope.msdatasets.task_datasets import \
     ImageInstanceSegmentationCocoDataset
 from modelscope.trainers import build_trainer
 from modelscope.utils.config import Config, ConfigDict
-from modelscope.utils.constant import ModelFile
+from modelscope.utils.constant import DownloadMode, ModelFile
 from modelscope.utils.test_utils import test_level
 
 
@@ -41,38 +41,26 @@ class TestImageInstanceSegmentationTrainer(unittest.TestCase):
         if train_data_cfg is None:
             # use default toy data
             train_data_cfg = ConfigDict(
-                name='pets_small',
+                name='pets_small', split='train', test_mode=False)
-                split='train',
-                classes=('Cat', 'Dog'),
-                folder_name='Pets',
-                test_mode=False)
         if val_data_cfg is None:
             val_data_cfg = ConfigDict(
-                name='pets_small',
+                name='pets_small', split='validation', test_mode=True)
-                split='validation',
-                classes=('Cat', 'Dog'),
-                folder_name='Pets',
-                test_mode=True)
 
         self.train_dataset = MsDataset.load(
             dataset_name=train_data_cfg.name,
             split=train_data_cfg.split,
-            classes=train_data_cfg.classes,
+            test_mode=train_data_cfg.test_mode,
-            folder_name=train_data_cfg.folder_name,
+            download_mode=DownloadMode.FORCE_REDOWNLOAD)
-            test_mode=train_data_cfg.test_mode)
+        assert self.train_dataset.config_kwargs['classes']
-        assert self.train_dataset.config_kwargs[
-            'classes'] == train_data_cfg.classes
         assert next(
             iter(self.train_dataset.config_kwargs['split_config'].values()))
 
         self.eval_dataset = MsDataset.load(
             dataset_name=val_data_cfg.name,
             split=val_data_cfg.split,
-            classes=val_data_cfg.classes,
+            test_mode=val_data_cfg.test_mode,
-            folder_name=val_data_cfg.folder_name,
+            download_mode=DownloadMode.FORCE_REDOWNLOAD)
-            test_mode=val_data_cfg.test_mode)
+        assert self.eval_dataset.config_kwargs['classes']
-        assert self.eval_dataset.config_kwargs[
-            'classes'] == val_data_cfg.classes
         assert next(
             iter(self.eval_dataset.config_kwargs['split_config'].values()))