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
1. Refactor training_args
2. Refactor hooks
3. Add train_id for push_to_hub
4. Support both output_dir/output_sub_dir for checkpoint_hooks
5. Support copy when hardlink fails when checkpointing
6. Support mixed dataset config file as a CLI argument
7. Add eval txt in output folder
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/12384253
* support the ignorance of file pattern
1. backward compatible with to_task_dataset function for DefaultTrainer in adaseq repo
2. fix registry issue for RedsImageDeblurringDataset and GoproImageDeblurringDataset
3. add ut TestCustomDatasetsCompatibility
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11981956
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
1. Support two checkpoint hooks saving final checkpoints in two difference folders
2. Remove the check of checkpoint hooks
3. Fix a incorrect modification in UT
4. Fix bug: Checkpoint.load_checkpoint has been moved out
5. Add UT for new style configuration
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11630170
1. Support csanmt exporting to savedmodel format
2. Create a new base class for text-ranking preprocessors, and move some parameters of mgeo_ranking_preprocessor to init method
3. Avoid Model & Preprocessor classes coupled with pytorch
4. Regression test supports comparing only model output
5. Support zero-shot exporting to onnx and torchscript
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11522461
