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e02a260c93
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
maintain docs
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build docs
# in root directory: make docs -
doc string format
We adopt the google style docstring format as the standard, please refer to the following documents.
- Google Python style guide docstring link
- Google docstring example link
- sample：torch.nn.modules.conv link
- load function as an example：
def load(file, file_format=None, **kwargs): """Load data from json/yaml/pickle files. This method provides a unified api for loading data from serialized files. Args: file (str or :obj:`Path` or file-like object): Filename or a file-like object. file_format (str, optional): If not specified, the file format will be inferred from the file extension, otherwise use the specified one. Currently supported formats include "json", "yaml/yml". Examples: >>> load('/path/of/your/file') # file is stored in disk >>> load('https://path/of/your/file') # file is stored on internet >>> load('oss://path/of/your/file') # file is stored in petrel Returns: The content from the file. """