Refactor the task_datasets module

Refactor the task_datasets module:

1. Add new module modelscope.msdatasets.dataset_cls.custom_datasets.
2. Add new function: modelscope.msdatasets.ms_dataset.MsDataset.to_custom_dataset().
2. Add calling to_custom_dataset() func in MsDataset.load() to adapt new custom_datasets module.
3. Refactor the pipeline for loading custom dataset: 
	1) Only use MsDataset.load() function to load the custom datasets.
	2) Combine MsDataset.load() with class EpochBasedTrainer.
4. Add new entry func for building datasets in EpochBasedTrainer: see modelscope.trainers.trainer.EpochBasedTrainer.build_dataset()
5. Add new func to build the custom dataset from model configuration, see: modelscope.trainers.trainer.EpochBasedTrainer.build_dataset_from_cfg()
6. Add new registry function for building custom datasets, see: modelscope.msdatasets.dataset_cls.custom_datasets.builder.build_custom_dataset()
7. Refine the class SiameseUIETrainer to adapt the new custom_datasets module.
8. Add class TorchCustomDataset as a superclass for custom datasets classes.
9. To move modules/classes/functions:
	1) Move module msdatasets.audio to custom_datasets
	2) Move module msdatasets.cv to custom_datasets
	3) Move module bad_image_detecting to custom_datasets
	4) Move module damoyolo to custom_datasets
	5) Move module face_2d_keypoints to custom_datasets
	6) Move module hand_2d_keypoints to custom_datasets
	7) Move module human_wholebody_keypoint to custom_datasets
	8) Move module image_classification to custom_datasets
	9) Move module image_inpainting to custom_datasets
	10) Move module image_portrait_enhancement to custom_datasets
	11) Move module image_quality_assessment_degradation to custom_datasets
	12) Move module image_quality_assmessment_mos to custom_datasets
	13) Move class LanguageGuidedVideoSummarizationDataset to custom_datasets
	14) Move class MGeoRankingDataset to custom_datasets
	15) Move module movie_scene_segmentation custom_datasets
	16) Move module object_detection to custom_datasets
	17) Move module referring_video_object_segmentation to custom_datasets
	18) Move module sidd_image_denoising to custom_datasets
	19) Move module video_frame_interpolation to custom_datasets
	20) Move module video_stabilization to custom_datasets
	21) Move module video_super_resolution to custom_datasets
	22) Move class GoproImageDeblurringDataset to custom_datasets
	23) Move class EasyCVBaseDataset to custom_datasets
	24) Move class ImageInstanceSegmentationCocoDataset to custom_datasets
	25) Move class RedsImageDeblurringDataset to custom_datasets
	26) Move class TextRankingDataset to custom_datasets
	27) Move class VecoDataset to custom_datasets
	28) Move class VideoSummarizationDataset to custom_datasets
10. To delete modules/functions/classes:
	1) Del module task_datasets
	2) Del to_task_dataset() in EpochBasedTrainer
	3) Del build_dataset() in EpochBasedTrainer and renew a same name function.
11. Rename class Datasets to CustomDatasets in metainfo.py

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11872747

docs/source/api/modelscope.msdatasets.cv.rst

@@ -1,14 +0,0 @@
-modelscope.msdatasets.cv
-================================
-
-.. automodule:: modelscope.msdatasets.cv
-
-.. currentmodule:: modelscope.msdatasets.cv
-
-.. autosummary::
-    :toctree: generated
-    :nosignatures:
-    :template: classtemplate.rst
-
-    easycv_base.EasyCVBaseDataset
-    image_classification.ClsDataset

docs/source/api/modelscope.msdatasets.dataset_cls.custom_datasets.rst

@@ -0,0 +1,41 @@
+modelscope.msdatasets.dataset_cls.custom_datasets
+====================
+
+.. automodule:: modelscope.msdatasets.dataset_cls.custom_datasets
+
+.. currentmodule:: modelscope.msdatasets.dataset_cls.custom_datasets
+
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    EasyCVBaseDataset
+    TorchCustomDataset
+    MovieSceneSegmentationDataset
+    ImageInstanceSegmentationCocoDataset
+    GoproImageDeblurringDataset
+    LanguageGuidedVideoSummarizationDataset
+    MGeoRankingDataset
+    RedsImageDeblurringDataset
+    TextRankingDataset
+    VecoDataset
+    VideoSummarizationDataset
+    BadImageDetectingDataset
+    ImageInpaintingDataset
+    ImagePortraitEnhancementDataset
+    ImageQualityAssessmentDegradationDataset
+    ImageQualityAssessmentMosDataset
+    ReferringVideoObjectSegmentationDataset
+    SiddImageDenoisingDataset
+    VideoFrameInterpolationDataset
+    VideoStabilizationDataset
+    VideoSuperResolutionDataset
+    SegDataset
+    FaceKeypointDataset
+    HandCocoWholeBodyDataset
+    WholeBodyCocoTopDownDataset
+    ClsDataset
+    DetImagesMixDataset
+    DetDataset

docs/source/api/modelscope.msdatasets.dataset_cls.rst

@@ -0,0 +1,15 @@
+modelscope.msdatasets.dataset_cls
+====================
+
+.. automodule:: modelscope.msdatasets.dataset_cls
+
+.. currentmodule:: modelscope.msdatasets.dataset_cls
+
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    ExternalDataset
+    NativeIterableDataset

docs/source/api/modelscope.msdatasets.ms_dataset.rst

@@ -10,5 +10,4 @@ modelscope.msdatasets.ms_dataset
     :nosignatures:
     :template: classtemplate.rst
 
-    MsMapDataset
     MsDataset

modelscope/metainfo.py

@@ -1137,7 +1137,7 @@ class LR_Schedulers(object):
     ExponentialWarmup = 'ExponentialWarmup'
 
 
-class Datasets(object):
+class CustomDatasets(object):
     """ Names for different datasets.
     """
     ClsDataset = 'ClsDataset'

modelscope/models/cv/tinynas_detection/damo/apis/detector_evaluater.py

@@ -8,8 +8,8 @@ from modelscope.models.cv.tinynas_detection.damo.apis.detector_inference import
     inference
 from modelscope.models.cv.tinynas_detection.damo.detectors.detector import \
     build_local_model
-from modelscope.msdatasets.task_datasets.damoyolo import (build_dataloader,
+from modelscope.msdatasets.dataset_cls.custom_datasets.damoyolo import (
-                                                          build_dataset)
+    build_dataloader, build_dataset)
 
 
 def mkdir(path):

modelscope/models/cv/tinynas_detection/damo/apis/detector_inference.py

@@ -5,7 +5,7 @@ import os
 import torch
 from tqdm import tqdm
 
-from modelscope.msdatasets.task_datasets.damoyolo.evaluation import evaluate
+from modelscope.msdatasets.dataset_cls.custom_datasets.damoyolo import evaluate
 from modelscope.utils.logger import get_logger
 from modelscope.utils.timer import Timer, get_time_str
 from modelscope.utils.torch_utils import (all_gather, get_world_size,

modelscope/msdatasets/__init__.py

@@ -1,3 +1,2 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
-from . import cv
 from .ms_dataset import MsDataset

modelscope/msdatasets/cv/__init__.py

@@ -1,3 +0,0 @@
-# Copyright (c) Alibaba, Inc. and its affiliates.
-from . import (image_classification, image_semantic_segmentation,
-               object_detection)

modelscope/msdatasets/data_loader/data_loader.py

@@ -13,6 +13,7 @@ from modelscope.msdatasets.context.dataset_context_config import \
     DatasetContextConfig
 from modelscope.msdatasets.data_files.data_files_manager import \
     DataFilesManager
+from modelscope.msdatasets.dataset_cls.dataset import ExternalDataset
 from modelscope.msdatasets.meta.data_meta_manager import DataMetaManager
 from modelscope.utils.constant import DatasetFormations
 
@@ -62,7 +63,8 @@ class OssDataLoader(BaseDataLoader):
 
         self.data_files_builder: Optional[DataFilesManager] = None
         self.dataset: Optional[Union[Dataset, IterableDataset, DatasetDict,
-                                     IterableDatasetDict]] = None
+                                     IterableDatasetDict,
+                                     ExternalDataset]] = None
         self.builder: Optional[DatasetBuilder] = None
         self.data_files_manager: Optional[DataFilesManager] = None
 
@@ -141,7 +143,8 @@ class OssDataLoader(BaseDataLoader):
                 self.builder)
 
     def _post_process(self) -> None:
-        ...
+        if isinstance(self.dataset, ExternalDataset):
+            self.dataset.custom_map = self.dataset_context_config.data_meta_config.meta_type_map
 
 
 class MaxComputeDataLoader(BaseDataLoader):

modelscope/msdatasets/dataset_cls/__init__.py

@@ -1 +1,3 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
+
+from .dataset import ExternalDataset, NativeIterableDataset

modelscope/msdatasets/dataset_cls/custom_datasets/__init__.py

@@ -0,0 +1,84 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .easycv_base import EasyCVBaseDataset
+    from .builder import CUSTOM_DATASETS, build_custom_dataset
+    from .torch_custom_dataset import TorchCustomDataset
+    from .movie_scene_segmentation.movie_scene_segmentation_dataset import MovieSceneSegmentationDataset
+    from .image_instance_segmentation_coco_dataset import ImageInstanceSegmentationCocoDataset
+    from .gopro_image_deblurring_dataset import GoproImageDeblurringDataset
+    from .language_guided_video_summarization_dataset import LanguageGuidedVideoSummarizationDataset
+    from .mgeo_ranking_dataset import MGeoRankingDataset
+    from .reds_image_deblurring_dataset import RedsImageDeblurringDataset
+    from .text_ranking_dataset import TextRankingDataset
+    from .veco_dataset import VecoDataset
+    from .video_summarization_dataset import VideoSummarizationDataset
+    from .audio import KWSDataset, KWSDataLoader, kws_nearfield_dataset
+    from .bad_image_detecting import BadImageDetectingDataset
+    from .image_inpainting import ImageInpaintingDataset
+    from .image_portrait_enhancement import ImagePortraitEnhancementDataset
+    from .image_quality_assessment_degradation import ImageQualityAssessmentDegradationDataset
+    from .image_quality_assmessment_mos import ImageQualityAssessmentMosDataset
+    from .referring_video_object_segmentation import ReferringVideoObjectSegmentationDataset
+    from .sidd_image_denoising import SiddImageDenoisingDataset
+    from .video_frame_interpolation import VideoFrameInterpolationDataset
+    from .video_stabilization import VideoStabilizationDataset
+    from .video_super_resolution import VideoSuperResolutionDataset
+    from .image_semantic_segmentation import SegDataset
+    from .face_2d_keypoins import FaceKeypointDataset
+    from .hand_2d_keypoints import HandCocoWholeBodyDataset
+    from .human_wholebody_keypoint import WholeBodyCocoTopDownDataset
+    from .image_classification import ClsDataset
+    from .object_detection import DetDataset, DetImagesMixDataset
+    from .ocr_detection import DataLoader, ImageDataset, QuadMeasurer
+    from .ocr_recognition_dataset import OCRRecognitionDataset
+else:
+    _import_structure = {
+        'easycv_base': ['EasyCVBaseDataset'],
+        'builder': ['CUSTOM_DATASETS', 'build_custom_dataset'],
+        'torch_custom_dataset': ['TorchCustomDataset'],
+        'movie_scene_segmentation_dataset': ['MovieSceneSegmentationDataset'],
+        'image_instance_segmentation_coco_dataset':
+        ['ImageInstanceSegmentationCocoDataset'],
+        'gopro_image_deblurring_dataset': ['GoproImageDeblurringDataset'],
+        'language_guided_video_summarization_dataset':
+        ['LanguageGuidedVideoSummarizationDataset'],
+        'mgeo_ranking_dataset': ['MGeoRankingDataset'],
+        'reds_image_deblurring_dataset': ['RedsImageDeblurringDataset'],
+        'text_ranking_dataset': ['TextRankingDataset'],
+        'veco_dataset': ['VecoDataset'],
+        'video_summarization_dataset': ['VideoSummarizationDataset'],
+        'audio': ['KWSDataset', 'KWSDataLoader', 'kws_nearfield_dataset'],
+        'bad_image_detecting': ['BadImageDetectingDataset'],
+        'image_inpainting': ['ImageInpaintingDataset'],
+        'image_portrait_enhancement': ['ImagePortraitEnhancementDataset'],
+        'image_quality_assessment_degradation':
+        ['ImageQualityAssessmentDegradationDataset'],
+        'image_quality_assmessment_mos': ['ImageQualityAssessmentMosDataset'],
+        'referring_video_object_segmentation':
+        ['ReferringVideoObjectSegmentationDataset'],
+        'sidd_image_denoising': ['SiddImageDenoisingDataset'],
+        'video_frame_interpolation': ['VideoFrameInterpolationDataset'],
+        'video_stabilization': ['VideoStabilizationDataset'],
+        'video_super_resolution': ['VideoSuperResolutionDataset'],
+        'image_semantic_segmentation': ['SegDataset'],
+        'face_2d_keypoins': ['FaceKeypointDataset'],
+        'hand_2d_keypoints': ['HandCocoWholeBodyDataset'],
+        'human_wholebody_keypoint': ['WholeBodyCocoTopDownDataset'],
+        'image_classification': ['ClsDataset'],
+        'object_detection': ['DetDataset', 'DetImagesMixDataset'],
+        'ocr_detection': ['DataLoader', 'ImageDataset', 'QuadMeasurer'],
+        'ocr_recognition_dataset': ['OCRRecognitionDataset'],
+    }
+
+    import sys
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/msdatasets/dataset_cls/custom_datasets/audio/kws_farfield_dataset.py

@@ -5,7 +5,6 @@ import math
 import os.path
 import queue
 import threading
-import time
 
 import numpy as np
 import torch

modelscope/msdatasets/dataset_cls/custom_datasets/audio/kws_nearfield_dataset.py

@@ -18,7 +18,7 @@ import torch
 import torch.distributed as dist
 from torch.utils.data import IterableDataset
 
-import modelscope.msdatasets.task_datasets.audio.kws_nearfield_processor as processor
+import modelscope.msdatasets.dataset_cls.custom_datasets.audio.kws_nearfield_processor as processor
 from modelscope.trainers.audio.kws_utils.file_utils import (make_pair,
                                                             read_lists)
 from modelscope.utils.logger import get_logger

modelscope/msdatasets/dataset_cls/custom_datasets/bad_image_detecting/bad_image_detecting_dataset.py

@@ -1,12 +1,8 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
-import cv2
-import numpy as np
-
 from modelscope.metainfo import Models
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets import (
-from modelscope.msdatasets.task_datasets.torch_base_dataset import \
+    CUSTOM_DATASETS, TorchCustomDataset)
-    TorchTaskDataset
 from modelscope.outputs import OutputKeys
 from modelscope.preprocessors import LoadImage
 from modelscope.preprocessors.cv.bad_image_detecting_preprocessor import \
@@ -14,9 +10,9 @@ from modelscope.preprocessors.cv.bad_image_detecting_preprocessor import \
 from modelscope.utils.constant import Tasks
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     Tasks.bad_image_detecting, module_name=Models.bad_image_detecting)
-class BadImageDetectingDataset(TorchTaskDataset):
+class BadImageDetectingDataset(TorchCustomDataset):
     """Paired image dataset for bad image detecting.
     """
 

modelscope/msdatasets/dataset_cls/custom_datasets/builder.py

@@ -3,13 +3,13 @@
 from modelscope.utils.config import ConfigDict
 from modelscope.utils.registry import Registry, build_from_cfg
 
-TASK_DATASETS = Registry('task_datasets')
+CUSTOM_DATASETS = Registry('custom_datasets')
 
 
-def build_task_dataset(cfg: ConfigDict,
+def build_custom_dataset(cfg: ConfigDict,
-                       task_name: str = None,
+                         task_name: str,
-                       default_args: dict = None):
+                         default_args: dict = None):
-    """ Build task specific dataset processor given model config dict and the task name.
+    """ Build custom dataset for user-define dataset given model config and task name.
 
     Args:
         cfg (:obj:`ConfigDict`): config dict for model object.
@@ -18,4 +18,4 @@ def build_task_dataset(cfg: ConfigDict,
         default_args (dict, optional): Default initialization arguments.
     """
     return build_from_cfg(
-        cfg, TASK_DATASETS, group_key=task_name, default_args=default_args)
+        cfg, CUSTOM_DATASETS, group_key=task_name, default_args=default_args)

modelscope/msdatasets/dataset_cls/custom_datasets/damoyolo/__init__.py

@@ -1,2 +1,3 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 from .build import build_dataloader, build_dataset
+from .evaluation import evaluate

modelscope/msdatasets/dataset_cls/custom_datasets/damoyolo/evaluation/__init__.py

@@ -1,6 +1,6 @@
 # Copyright © Alibaba, Inc. and its affiliates.
 
-from modelscope.msdatasets.task_datasets.damoyolo import datasets
+from .. import datasets
 from .coco import coco_evaluation
 
 

modelscope/msdatasets/dataset_cls/custom_datasets/face_2d_keypoins/face_2d_keypoints_dataset.py

@@ -1,15 +1,16 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 from easycv.datasets.face import FaceKeypointDataset as _FaceKeypointDataset
 
-from modelscope.metainfo import Datasets
+from modelscope.metainfo import CustomDatasets
-from modelscope.msdatasets.cv.easycv_base import EasyCVBaseDataset
+from modelscope.msdatasets.dataset_cls.custom_datasets import CUSTOM_DATASETS
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets.easycv_base import \
+    EasyCVBaseDataset
 from modelscope.utils.constant import Tasks
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     group_key=Tasks.face_2d_keypoints,
-    module_name=Datasets.Face2dKeypointsDataset)
+    module_name=CustomDatasets.Face2dKeypointsDataset)
 class FaceKeypointDataset(EasyCVBaseDataset, _FaceKeypointDataset):
     """EasyCV dataset for face 2d keypoints.
 

modelscope/msdatasets/dataset_cls/custom_datasets/gopro_image_deblurring_dataset.py

@@ -3,14 +3,13 @@
 import cv2
 import numpy as np
 
-from modelscope.metainfo import Datasets
+from modelscope.metainfo import CustomDatasets
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets import (
-from modelscope.msdatasets.task_datasets.sidd_image_denoising.data_utils import (
+    CUSTOM_DATASETS, TorchCustomDataset)
+from modelscope.msdatasets.dataset_cls.custom_datasets.sidd_image_denoising.data_utils import (
     img2tensor, padding)
-from modelscope.msdatasets.task_datasets.sidd_image_denoising.transforms import (
+from modelscope.msdatasets.dataset_cls.custom_datasets.sidd_image_denoising.transforms import (
     augment, paired_random_crop)
-from modelscope.msdatasets.task_datasets.torch_base_dataset import \
-    TorchTaskDataset
 from modelscope.utils.constant import Tasks
 
 
@@ -18,9 +17,9 @@ def default_loader(path):
     return cv2.imread(path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.0
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
-    Tasks.image_deblurring, module_name=Datasets.PairedDataset)
+    Tasks.image_deblurring, module_name=CustomDatasets.PairedDataset)
-class GoproImageDeblurringDataset(TorchTaskDataset):
+class GoproImageDeblurringDataset(TorchCustomDataset):
     """Paired image dataset for image restoration.
     """
 

modelscope/msdatasets/dataset_cls/custom_datasets/hand_2d_keypoints/__init__.py

@@ -0,0 +1,22 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .hand_2d_keypoints_dataset import HandCocoWholeBodyDataset
+
+else:
+    _import_structure = {
+        'hand_2d_keypoints_dataset': ['HandCocoWholeBodyDataset']
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/msdatasets/dataset_cls/custom_datasets/hand_2d_keypoints/hand_2d_keypoints_dataset.py

@@ -2,15 +2,16 @@
 from easycv.datasets.pose import \
     HandCocoWholeBodyDataset as _HandCocoWholeBodyDataset
 
-from modelscope.metainfo import Datasets
+from modelscope.metainfo import CustomDatasets
-from modelscope.msdatasets.cv.easycv_base import EasyCVBaseDataset
+from modelscope.msdatasets.dataset_cls.custom_datasets import CUSTOM_DATASETS
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets.easycv_base import \
+    EasyCVBaseDataset
 from modelscope.utils.constant import Tasks
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     group_key=Tasks.hand_2d_keypoints,
-    module_name=Datasets.HandCocoWholeBodyDataset)
+    module_name=CustomDatasets.HandCocoWholeBodyDataset)
 class HandCocoWholeBodyDataset(EasyCVBaseDataset, _HandCocoWholeBodyDataset):
     """EasyCV dataset for human hand 2d keypoints.
 

modelscope/msdatasets/dataset_cls/custom_datasets/human_wholebody_keypoint/human_wholebody_keypoint_dataset.py

@@ -2,15 +2,16 @@
 from easycv.datasets.pose import \
     WholeBodyCocoTopDownDataset as _WholeBodyCocoTopDownDataset
 
-from modelscope.metainfo import Datasets
+from modelscope.metainfo import CustomDatasets
-from modelscope.msdatasets.cv.easycv_base import EasyCVBaseDataset
+from modelscope.msdatasets.dataset_cls.custom_datasets import CUSTOM_DATASETS
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets.easycv_base import \
+    EasyCVBaseDataset
 from modelscope.utils.constant import Tasks
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     group_key=Tasks.human_wholebody_keypoint,
-    module_name=Datasets.HumanWholeBodyKeypointDataset)
+    module_name=CustomDatasets.HumanWholeBodyKeypointDataset)
 class WholeBodyCocoTopDownDataset(EasyCVBaseDataset,
                                   _WholeBodyCocoTopDownDataset):
     """EasyCV dataset for human whole body 2d keypoints.

modelscope/msdatasets/dataset_cls/custom_datasets/image_classification/classification_dataset.py

@@ -1,14 +1,16 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 from easycv.datasets.classification import ClsDataset as _ClsDataset
 
-from modelscope.metainfo import Datasets
+from modelscope.metainfo import CustomDatasets
-from modelscope.msdatasets.cv.easycv_base import EasyCVBaseDataset
+from modelscope.msdatasets.dataset_cls.custom_datasets import CUSTOM_DATASETS
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets.easycv_base import \
+    EasyCVBaseDataset
 from modelscope.utils.constant import Tasks
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
-    group_key=Tasks.image_classification, module_name=Datasets.ClsDataset)
+    group_key=Tasks.image_classification,
+    module_name=CustomDatasets.ClsDataset)
 class ClsDataset(_ClsDataset):
     """EasyCV dataset for classification.
 

modelscope/msdatasets/dataset_cls/custom_datasets/image_inpainting/__init__.py

@@ -4,13 +4,11 @@ from typing import TYPE_CHECKING
 from modelscope.utils.import_utils import LazyImportModule
 
 if TYPE_CHECKING:
-    from .hand_2d_keypoints_dataset import Hand2DKeypointDataset
+    from .image_inpainting_dataset import ImageInpaintingDataset
-
 else:
     _import_structure = {
-        'hand_2d_keypoints_dataset': ['Hand2DKeypointDataset']
+        'image_inpainting_dataset': ['ImageInpaintingDataset'],
     }
-
     import sys
 
     sys.modules[__name__] = LazyImportModule(

modelscope/msdatasets/dataset_cls/custom_datasets/image_inpainting/image_inpainting_dataset.py

@@ -3,20 +3,16 @@ Part of the implementation is borrowed and modified from LaMa,
 publicly available at https://github.com/saic-mdal/lama
 """
 import glob
-import os
 import os.path as osp
 from enum import Enum
 
 import albumentations as A
 import cv2
-import json
 import numpy as np
-import torch
 
 from modelscope.metainfo import Models
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets import (
-from modelscope.msdatasets.task_datasets.torch_base_dataset import \
+    CUSTOM_DATASETS, TorchCustomDataset)
-    TorchTaskDataset
 from modelscope.utils.constant import Tasks
 from modelscope.utils.logger import get_logger
 from .aug import IAAAffine2, IAAPerspective2
@@ -296,9 +292,9 @@ def get_transforms(test_mode, out_size):
     return transform
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     Tasks.image_inpainting, module_name=Models.image_inpainting)
-class ImageInpaintingDataset(TorchTaskDataset):
+class ImageInpaintingDataset(TorchCustomDataset):
 
     def __init__(self, **kwargs):
         split_config = kwargs['split_config']

modelscope/msdatasets/dataset_cls/custom_datasets/image_instance_segmentation_coco_dataset.py

@@ -6,9 +6,9 @@ import numpy as np
 from pycocotools.coco import COCO
 
 from modelscope.metainfo import Models
+from modelscope.msdatasets.dataset_cls.custom_datasets import (
+    CUSTOM_DATASETS, TorchCustomDataset)
 from modelscope.utils.constant import Tasks
-from .builder import TASK_DATASETS
-from .torch_base_dataset import TorchTaskDataset
 
 DATASET_STRUCTURE = {
     'train': {
@@ -22,10 +22,10 @@ DATASET_STRUCTURE = {
 }
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     module_name=Models.cascade_mask_rcnn_swin,
     group_key=Tasks.image_segmentation)
-class ImageInstanceSegmentationCocoDataset(TorchTaskDataset):
+class ImageInstanceSegmentationCocoDataset(TorchCustomDataset):
     """Coco-style dataset for image instance segmentation.
 
     Args:

modelscope/msdatasets/dataset_cls/custom_datasets/image_portrait_enhancement/image_portrait_enhancement_dataset.py

@@ -3,10 +3,9 @@
 import cv2
 import numpy as np
 
-from modelscope.metainfo import Datasets, Models
+from modelscope.metainfo import CustomDatasets
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets import (
-from modelscope.msdatasets.task_datasets.torch_base_dataset import \
+    CUSTOM_DATASETS, TorchCustomDataset)
-    TorchTaskDataset
 from modelscope.utils.constant import Tasks
 from .data_utils import img2tensor
 
@@ -15,9 +14,9 @@ def default_loader(path):
     return cv2.imread(path, cv2.IMREAD_COLOR).astype(np.float32) / 255.0
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
-    Tasks.image_portrait_enhancement, module_name=Datasets.PairedDataset)
+    Tasks.image_portrait_enhancement, module_name=CustomDatasets.PairedDataset)
-class ImagePortraitEnhancementDataset(TorchTaskDataset):
+class ImagePortraitEnhancementDataset(TorchCustomDataset):
     """Paired image dataset for image portrait enhancement.
     """
 

modelscope/msdatasets/dataset_cls/custom_datasets/image_quality_assessment_degradation/image_quality_assessment_degradation_dataset.py

@@ -1,21 +1,18 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
-import cv2
-import numpy as np
 from torchvision import transforms
 
 from modelscope.metainfo import Models
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets import (
-from modelscope.msdatasets.task_datasets.torch_base_dataset import \
+    CUSTOM_DATASETS, TorchCustomDataset)
-    TorchTaskDataset
 from modelscope.preprocessors import LoadImage
 from modelscope.utils.constant import Tasks
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     Tasks.image_quality_assessment_degradation,
     module_name=Models.image_quality_assessment_degradation)
-class ImageQualityAssessmentDegradationDataset(TorchTaskDataset):
+class ImageQualityAssessmentDegradationDataset(TorchCustomDataset):
     """Paired image dataset for image quality assessment degradation.
     """
 

modelscope/msdatasets/dataset_cls/custom_datasets/image_quality_assmessment_mos/image_quality_assessment_mos_dataset.py

@@ -1,20 +1,16 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
-import cv2
-import numpy as np
-
 from modelscope.metainfo import Models
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets import (
-from modelscope.msdatasets.task_datasets.torch_base_dataset import \
+    CUSTOM_DATASETS, TorchCustomDataset)
-    TorchTaskDataset
 from modelscope.preprocessors.cv import ImageQualityAssessmentMosPreprocessor
 from modelscope.utils.constant import Tasks
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     Tasks.image_quality_assessment_mos,
     module_name=Models.image_quality_assessment_mos)
-class ImageQualityAssessmentMosDataset(TorchTaskDataset):
+class ImageQualityAssessmentMosDataset(TorchCustomDataset):
     """Paired image dataset for image quality assessment mos.
     """
 

modelscope/msdatasets/dataset_cls/custom_datasets/image_semantic_segmentation/segmentation_dataset.py

@@ -1,14 +1,15 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 from easycv.datasets.segmentation import SegDataset as _SegDataset
 
-from modelscope.metainfo import Datasets
+from modelscope.metainfo import CustomDatasets
-from modelscope.msdatasets.cv.easycv_base import EasyCVBaseDataset
+from modelscope.msdatasets.dataset_cls.custom_datasets import CUSTOM_DATASETS
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets.easycv_base import \
+    EasyCVBaseDataset
 from modelscope.utils.constant import Tasks
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
-    group_key=Tasks.image_segmentation, module_name=Datasets.SegDataset)
+    group_key=Tasks.image_segmentation, module_name=CustomDatasets.SegDataset)
 class SegDataset(EasyCVBaseDataset, _SegDataset):
     """EasyCV dataset for Sementic segmentation.
     For more details, please refer to :

modelscope/msdatasets/dataset_cls/custom_datasets/language_guided_video_summarization_dataset.py

@@ -25,16 +25,15 @@ import numpy as np
 import torch
 
 from modelscope.metainfo import Models
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets import (
-from modelscope.msdatasets.task_datasets.torch_base_dataset import \
+    CUSTOM_DATASETS, TorchCustomDataset)
-    TorchTaskDataset
 from modelscope.utils.constant import Tasks
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     Tasks.language_guided_video_summarization,
     module_name=Models.language_guided_video_summarization)
-class LanguageGuidedVideoSummarizationDataset(TorchTaskDataset):
+class LanguageGuidedVideoSummarizationDataset(TorchCustomDataset):
 
     def __init__(self, mode, opt, root_dir):
         self.mode = mode

modelscope/msdatasets/dataset_cls/custom_datasets/mgeo_ranking_dataset.py

@@ -1,24 +1,20 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import random
-from dataclasses import dataclass
+from typing import Any, List, Union
-from typing import Any, Dict, List, Tuple, Union
 
 import json
 import torch
-from datasets import Dataset, IterableDataset, concatenate_datasets
 from torch.utils.data import ConcatDataset
-from transformers import DataCollatorWithPadding
 
 from modelscope.metainfo import Models
+from modelscope.msdatasets.dataset_cls.custom_datasets import (
+    CUSTOM_DATASETS, TorchCustomDataset)
 from modelscope.utils.constant import ModeKeys, Tasks
-from .base import TaskDataset
-from .builder import TASK_DATASETS
-from .torch_base_dataset import TorchTaskDataset
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     group_key=Tasks.text_ranking, module_name=Models.mgeo)
-class MGeoRankingDataset(TorchTaskDataset):
+class MGeoRankingDataset(TorchCustomDataset):
 
     def __init__(self,
                  datasets: Union[Any, List[Any]],

modelscope/msdatasets/dataset_cls/custom_datasets/movie_scene_segmentation/__init__.py

@@ -0,0 +1,20 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .movie_scene_segmentation_dataset import MovieSceneSegmentationDataset
+else:
+    _import_structure = {
+        'movie_scene_segmentation_dataset': ['MovieSceneSegmentationDataset'],
+    }
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/msdatasets/dataset_cls/custom_datasets/movie_scene_segmentation/movie_scene_segmentation_dataset.py

@@ -10,9 +10,8 @@ import torch
 from torchvision.datasets.folder import pil_loader
 
 from modelscope.metainfo import Models
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets.builder import \
-from modelscope.msdatasets.task_datasets.torch_base_dataset import \
+    CUSTOM_DATASETS
-    TorchTaskDataset
 from modelscope.utils.constant import Tasks
 from . import sampler
 
@@ -30,9 +29,9 @@ DATASET_STRUCTURE = {
 }
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
-    Tasks.movie_scene_segmentation, module_name=Models.resnet50_bert)
+    group_key=Tasks.movie_scene_segmentation, module_name=Models.resnet50_bert)
-class MovieSceneSegmentationDataset(TorchTaskDataset):
+class MovieSceneSegmentationDataset(torch.utils.data.Dataset):
     """dataset for movie scene segmentation.
 
     Args:

modelscope/msdatasets/dataset_cls/custom_datasets/object_detection/detection_dataset.py

@@ -1,20 +1,21 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
-import os.path as osp
 
 from easycv.datasets.detection import DetDataset as _DetDataset
 from easycv.datasets.detection import \
     DetImagesMixDataset as _DetImagesMixDataset
 
-from modelscope.metainfo import Datasets
+from modelscope.metainfo import CustomDatasets
-from modelscope.msdatasets.cv.easycv_base import EasyCVBaseDataset
+from modelscope.msdatasets.dataset_cls.custom_datasets import CUSTOM_DATASETS
-from modelscope.msdatasets.task_datasets import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets.easycv_base import \
+    EasyCVBaseDataset
 from modelscope.utils.constant import Tasks
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
-    group_key=Tasks.image_object_detection, module_name=Datasets.DetDataset)
+    group_key=Tasks.image_object_detection,
-@TASK_DATASETS.register_module(
+    module_name=CustomDatasets.DetDataset)
-    group_key=Tasks.image_segmentation, module_name=Datasets.DetDataset)
+@CUSTOM_DATASETS.register_module(
+    group_key=Tasks.image_segmentation, module_name=CustomDatasets.DetDataset)
 class DetDataset(EasyCVBaseDataset, _DetDataset):
     """EasyCV dataset for object detection.
     For more details, please refer to https://github.com/alibaba/EasyCV/blob/master/easycv/datasets/detection/raw.py .
@@ -47,12 +48,12 @@ class DetDataset(EasyCVBaseDataset, _DetDataset):
         _DetDataset.__init__(self, *args, **kwargs)
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     group_key=Tasks.image_object_detection,
-    module_name=Datasets.DetImagesMixDataset)
+    module_name=CustomDatasets.DetImagesMixDataset)
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     group_key=Tasks.domain_specific_object_detection,
-    module_name=Datasets.DetImagesMixDataset)
+    module_name=CustomDatasets.DetImagesMixDataset)
 class DetImagesMixDataset(EasyCVBaseDataset, _DetImagesMixDataset):
     """EasyCV dataset for object detection, a wrapper of multiple images mixed dataset.
     Suitable for training on multiple images mixed data augmentation like

modelscope/msdatasets/dataset_cls/custom_datasets/ocr_detection/__init__.py

@@ -1,3 +1,4 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 from .data_loader import DataLoader
 from .image_dataset import ImageDataset
+from .measures import QuadMeasurer

modelscope/msdatasets/dataset_cls/custom_datasets/ocr_recognition_dataset.py

@@ -9,9 +9,10 @@ import torch
 from PIL import Image
 
 from modelscope.metainfo import Models
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets.builder import \
-from modelscope.msdatasets.task_datasets.torch_base_dataset import \
+    CUSTOM_DATASETS
-    TorchTaskDataset
+from modelscope.msdatasets.dataset_cls.custom_datasets.torch_custom_dataset import \
+    TorchCustomDataset
 from modelscope.utils.constant import Tasks
 from modelscope.utils.logger import get_logger
 
@@ -29,9 +30,9 @@ def Q2B(uchar):
     return chr(inside_code)
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     Tasks.ocr_recognition, module_name=Models.ocr_recognition)
-class OCRRecognitionDataset(TorchTaskDataset):
+class OCRRecognitionDataset(TorchCustomDataset):
 
     def __init__(self, **kwargs):
         split_config = kwargs['split_config']

modelscope/msdatasets/dataset_cls/custom_datasets/reds_image_deblurring_dataset.py

@@ -3,14 +3,13 @@
 import cv2
 import numpy as np
 
-from modelscope.metainfo import Datasets
+from modelscope.metainfo import CustomDatasets
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets import (
-from modelscope.msdatasets.task_datasets.sidd_image_denoising.data_utils import (
+    CUSTOM_DATASETS, TorchCustomDataset)
+from modelscope.msdatasets.dataset_cls.custom_datasets.sidd_image_denoising.data_utils import (
     img2tensor, padding)
-from modelscope.msdatasets.task_datasets.sidd_image_denoising.transforms import (
+from modelscope.msdatasets.dataset_cls.custom_datasets.sidd_image_denoising.transforms import (
     augment, paired_random_crop)
-from modelscope.msdatasets.task_datasets.torch_base_dataset import \
-    TorchTaskDataset
 from modelscope.utils.constant import Tasks
 
 
@@ -18,9 +17,9 @@ def default_loader(path):
     return cv2.imread(path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.0
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
-    Tasks.image_deblurring, module_name=Datasets.PairedDataset)
+    Tasks.image_deblurring, module_name=CustomDatasets.PairedDataset)
-class RedsImageDeblurringDataset(TorchTaskDataset):
+class RedsImageDeblurringDataset(TorchCustomDataset):
     """Paired image dataset for image restoration.
     """
 

modelscope/msdatasets/dataset_cls/custom_datasets/referring_video_object_segmentation/__init__.py

@@ -0,0 +1,21 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .referring_video_object_segmentation_dataset import ReferringVideoObjectSegmentationDataset
+else:
+    _import_structure = {
+        'referring_video_object_segmentation_dataset':
+        ['MovieSceneSegmentationDataset'],
+    }
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/msdatasets/dataset_cls/custom_datasets/referring_video_object_segmentation/referring_video_object_segmentation_dataset.py

@@ -18,9 +18,8 @@ from tqdm import tqdm
 from modelscope.metainfo import Models
 from modelscope.models.cv.referring_video_object_segmentation.utils import \
     nested_tensor_from_videos_list
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets import (
-from modelscope.msdatasets.task_datasets.torch_base_dataset import \
+    CUSTOM_DATASETS, TorchCustomDataset)
-    TorchTaskDataset
 from modelscope.utils.constant import Tasks
 from modelscope.utils.logger import get_logger
 from . import transformers as T
@@ -33,10 +32,10 @@ def get_image_id(video_id, frame_idx, ref_instance_a2d_id):
     return image_id
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     Tasks.referring_video_object_segmentation,
     module_name=Models.referring_video_object_segmentation)
-class ReferringVideoObjectSegmentationDataset(TorchTaskDataset):
+class ReferringVideoObjectSegmentationDataset(TorchCustomDataset):
 
     def __init__(self, **kwargs):
         split_config = kwargs['split_config']

modelscope/msdatasets/dataset_cls/custom_datasets/sidd_image_denoising/sidd_image_denoising_dataset.py

@@ -4,9 +4,8 @@ import cv2
 import numpy as np
 
 from modelscope.metainfo import Models
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets import (
-from modelscope.msdatasets.task_datasets.torch_base_dataset import \
+    CUSTOM_DATASETS, TorchCustomDataset)
-    TorchTaskDataset
 from modelscope.utils.constant import Tasks
 from .data_utils import img2tensor, padding
 from .transforms import augment, paired_random_crop
@@ -16,9 +15,9 @@ def default_loader(path):
     return cv2.imread(path, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255.0
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     Tasks.image_denoising, module_name=Models.nafnet)
-class SiddImageDenoisingDataset(TorchTaskDataset):
+class SiddImageDenoisingDataset(TorchCustomDataset):
     """Paired image dataset for image restoration.
     """
 

modelscope/msdatasets/dataset_cls/custom_datasets/text_ranking_dataset.py

@@ -1,25 +1,21 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import random
-from dataclasses import dataclass
+from typing import Any, List, Union
-from typing import Any, Dict, List, Tuple, Union
 
 import torch
-from datasets import Dataset, IterableDataset, concatenate_datasets
 from torch.utils.data import ConcatDataset
-from transformers import DataCollatorWithPadding
 
 from modelscope.metainfo import Models
+from modelscope.msdatasets.dataset_cls.custom_datasets import (
+    CUSTOM_DATASETS, TorchCustomDataset)
 from modelscope.utils.constant import ModeKeys, Tasks
-from .base import TaskDataset
-from .builder import TASK_DATASETS
-from .torch_base_dataset import TorchTaskDataset
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     group_key=Tasks.text_ranking, module_name=Models.bert)
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     group_key=Tasks.sentence_embedding, module_name=Models.bert)
-class TextRankingDataset(TorchTaskDataset):
+class TextRankingDataset(TorchCustomDataset):
 
     def __init__(self,
                  datasets: Union[Any, List[Any]],

modelscope/msdatasets/dataset_cls/custom_datasets/torch_custom_dataset.py

@@ -0,0 +1,51 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import Any, List, Union
+
+import torch.utils.data
+from torch.utils.data import ConcatDataset as TorchConcatDataset
+
+from modelscope.utils.constant import ModeKeys
+
+
+class TorchCustomDataset(torch.utils.data.Dataset):
+    """The custom dataset base class for all the torch-based task processors.
+    """
+
+    def __init__(self,
+                 datasets: Union[Any, List[Any]],
+                 mode=ModeKeys.TRAIN,
+                 preprocessor=None,
+                 **kwargs):
+        self.trainer = None
+        self.mode = mode
+        self.preprocessor = preprocessor
+        self._inner_dataset = self.prepare_dataset(datasets)
+
+    def __getitem__(self, index) -> Any:
+        return self.preprocessor(
+            self._inner_dataset[index]
+        ) if self.preprocessor else self._inner_dataset[index]
+
+    def __len__(self):
+        return len(self._inner_dataset)
+
+    def prepare_dataset(self, datasets: Union[Any, List[Any]]) -> Any:
+        """Prepare a dataset.
+
+        User can process the input datasets in a whole dataset perspective.
+        This method gives a default implementation of datasets merging, user can override this
+        method to write custom logics.
+
+        Args:
+            datasets: The original dataset(s)
+
+        Returns: A single dataset, which may be created after merging.
+
+        """
+        if isinstance(datasets, List):
+            if len(datasets) == 1:
+                return datasets[0]
+            elif len(datasets) > 1:
+                return TorchConcatDataset(datasets)
+        else:
+            return datasets

modelscope/msdatasets/dataset_cls/custom_datasets/veco_dataset.py

@@ -5,13 +5,13 @@ import numpy as np
 from datasets import Dataset, IterableDataset, concatenate_datasets
 
 from modelscope.metainfo import Models
+from modelscope.msdatasets.dataset_cls.custom_datasets import (
+    CUSTOM_DATASETS, TorchCustomDataset)
 from modelscope.utils.constant import Tasks
-from .builder import TASK_DATASETS
-from .torch_base_dataset import TorchTaskDataset
 
 
-@TASK_DATASETS.register_module(module_name=Models.veco, group_key=Tasks.nli)
+@CUSTOM_DATASETS.register_module(module_name=Models.veco, group_key=Tasks.nli)
-class VecoDataset(TorchTaskDataset):
+class VecoDataset(TorchCustomDataset):
 
     def __init__(self,
                  datasets: Union[Any, List[Any]],

modelscope/msdatasets/dataset_cls/custom_datasets/video_frame_interpolation/video_frame_interpolation_dataset.py

@@ -1,16 +1,13 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
-from collections import defaultdict
-
 import cv2
 import numpy as np
 import torch
 
 from modelscope.metainfo import Models
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets import (
-from modelscope.msdatasets.task_datasets.torch_base_dataset import \
+    CUSTOM_DATASETS, TorchCustomDataset)
-    TorchTaskDataset
+from modelscope.msdatasets.dataset_cls.custom_datasets.video_frame_interpolation.data_utils import (
-from modelscope.msdatasets.task_datasets.video_frame_interpolation.data_utils import (
     img2tensor, img_padding)
 from modelscope.utils.constant import Tasks
 
@@ -19,10 +16,10 @@ def default_loader(path):
     return cv2.imread(path, cv2.IMREAD_UNCHANGED).astype(np.float32)
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     Tasks.video_frame_interpolation,
     module_name=Models.video_frame_interpolation)
-class VideoFrameInterpolationDataset(TorchTaskDataset):
+class VideoFrameInterpolationDataset(TorchCustomDataset):
     """Dataset for video frame-interpolation.
     """
 

modelscope/msdatasets/dataset_cls/custom_datasets/video_stabilization/video_stabilization_dataset.py

@@ -1,15 +1,14 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
 from modelscope.metainfo import Models
-from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.msdatasets.dataset_cls.custom_datasets import (
-from modelscope.msdatasets.task_datasets.torch_base_dataset import \
+    CUSTOM_DATASETS, TorchCustomDataset)
-    TorchTaskDataset
 from modelscope.utils.constant import Tasks
 
 
-@TASK_DATASETS.register_module(
+@CUSTOM_DATASETS.register_module(
     Tasks.video_stabilization, module_name=Models.video_stabilization)
-class VideoStabilizationDataset(TorchTaskDataset):
+class VideoStabilizationDataset(TorchCustomDataset):
     """Paired video dataset for video stabilization.
     """
 

modelscope/msdatasets/dataset_cls/custom_datasets/video_summarization_dataset.py

@@ -8,11 +8,11 @@ import json
 import numpy as np
 import torch
 
-from modelscope.msdatasets.task_datasets.torch_base_dataset import \
+from modelscope.msdatasets.dataset_cls.custom_datasets import \
-    TorchTaskDataset
+    TorchCustomDataset
 
 
-class VideoSummarizationDataset(TorchTaskDataset):
+class VideoSummarizationDataset(TorchCustomDataset):
 
     def __init__(self, mode, opt, root_dir):
         self.mode = mode