1. Merge(add) daily regression from github PR (daily_regression.yaml)
2. Add lora stable diffusion from github PR
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/13010802
* fix: device arg not work, rename device to ngpu (#272)
* Correcting the lora stable diffusion example script (#300)
* add vad model and punc model in README.md
add vad model and punc model
* Merge pull request #302 from modelscope/langgz-patch-1
add vad model and punc model in README.md
* add 1.6
* modify ignore
* Merge pull request #307 from modelscope/dev_rs_16
Merge release 1.6
* undo datetime to 2099
* Merge pull request #311 from modelscope/fix_master_version
undo datetime to 2099
* add daily regression workflow
* modify workflow name
* fix cron format issue
* lora trainer
* Merge pull request #315 from liuyhwangyh/add_regression_workflow
add daily regression workflow
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
co-contributed with 夕陌&雨泓
* add torch epoch based trainer and dis utils
* add hooks including optimizer, lrscheduler, logging, checkpoint, evaluation, time profiling
* add torch mdoel base and test
* add optimizer and lrscheduler module
* add sbert for text classification example
* add task_dataset for dataset-level processor
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9338412
* make audio requirements optional
* add changelog for version v0.2
* add numpy constraint for compatibility with tensorflow1.15
* update faq
* fix nlp requiring tensorflow
* add torchvision to multimodal dependency
* bump version from 0.2.1 to 0.2.2
* add warning msg when tensorflow is not installed
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9268278
1. refine quick start and pipeline doc
2. remove tf pytorch easynlp from requirements
3. lazy import for torch and tensorflow
4. test successfully on linux and mac intel cpu
5. update api doc
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/8882373
