mirror of
https://github.com/modelscope/modelscope.git
synced 2026-07-10 04:22:33 +02:00
bf3a2b6c09
## 查看改动点 ↓↓↓
### vision efficient tuning finetune
- Model模块改造成适配训练的
- Model模块在支持训练同时向下兼容之前发布的modecard
- Pipline兼容modelcard加载的preprocessor或直接定义的
- 添加 ImageClassificationPreprocessor （非mmcv版本）
- 添加 VisionEfficientTuningTrainer
- ~~添加 opencv_transforms==0.0.6~~ (以源代码引入必要)
### Modelcard
- test pipeline和trainer合并到一起
- 新增3个模型的test
- 新增demo service
### 公共组件
- ms_dataset.py: fix warning, [UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or xxx]
- preprocessor添加common：ToNumpy、Rename、Identity
- preprocessor common对于dict进行key判断再取值。
- ~~修复learning rate在iter级别变化的逻辑。~~ （本次不做了）
- ~~修复非dist状态下train data没有进行shuffle的bug。~~ (Master已有人改了)
- 修复训练时调用util中非cv包的异常 zhconv。
### 其他
- 为防止新引入的preprocessor模块在config中被原代码加载，导致在其他人做CI时会报错；所以暂时没有添加新的tag，等CR完成后，会进行打tag再rerun CI。
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11762108
* support vision efficient tuning finetune
* update test case
* update shuffle on IterableDataset
* update bitfit & sidetuning
* compatible with base trainer
155 lines
8.1 KiB
Python
155 lines
8.1 KiB
Python
# Copyright 2022-2023 The Alibaba Fundamental Vision Team Authors. All rights reserved.
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import unittest
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from modelscope.models import Model
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from modelscope.models.cv.vision_efficient_tuning.model import \
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VisionEfficientTuningModel
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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from modelscope.utils.demo_utils import DemoCompatibilityCheck
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from modelscope.utils.test_utils import test_level
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class VisionEfficientTuningTest(unittest.TestCase, DemoCompatibilityCheck):
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def setUp(self) -> None:
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self.task = Tasks.vision_efficient_tuning
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_vision_efficient_tuning_adapter_run_pipeline(self):
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model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-adapter'
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img_path = 'data/test/images/vision_efficient_tuning_test_1.png'
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petl_pipeline = pipeline(self.task, model_id)
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result = petl_pipeline(img_path)
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print(f'Vision-efficient-tuning-adapter output: {result}.')
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_vision_efficient_tuning_adapter_load_model_from_pretrained(self):
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model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-adapter'
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model = Model.from_pretrained(model_id)
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self.assertTrue(model.__class__ == VisionEfficientTuningModel)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_vision_efficient_tuning_adapter_demo_compatibility(self):
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self.model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-adapter'
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self.compatibility_check()
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_vision_efficient_tuning_lora_run_pipeline(self):
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model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-lora'
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img_path = 'data/test/images/vision_efficient_tuning_test_1.png'
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petl_pipeline = pipeline(self.task, model_id)
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result = petl_pipeline(img_path)
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print(f'Vision-efficient-tuning-lora output: {result}.')
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_vision_efficient_tuning_lora_load_model_from_pretrained(self):
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model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-lora'
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model = Model.from_pretrained(model_id)
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self.assertTrue(model.__class__ == VisionEfficientTuningModel)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_vision_efficient_tuning_lora_demo_compatibility(self):
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self.model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-lora'
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self.compatibility_check()
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_vision_efficient_tuning_prefix_run_pipeline(self):
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model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-prefix'
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img_path = 'data/test/images/vision_efficient_tuning_test_1.png'
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petl_pipeline = pipeline(self.task, model_id)
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result = petl_pipeline(img_path)
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print(f'Vision-efficient-tuning-prefix output: {result}.')
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_vision_efficient_tuning_prefix_load_model_from_pretrained(self):
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model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-prefix'
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model = Model.from_pretrained(model_id)
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self.assertTrue(model.__class__ == VisionEfficientTuningModel)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_vision_efficient_tuning_prefix_demo_compatibility(self):
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self.model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-prefix'
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self.compatibility_check()
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_vision_efficient_tuning_prompt_run_pipeline(self):
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model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-prompt'
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img_path = 'data/test/images/vision_efficient_tuning_test_1.png'
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petl_pipeline = pipeline(self.task, model_id)
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result = petl_pipeline(img_path)
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print(f'Vision-efficient-tuning-prompt output: {result}.')
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_vision_efficient_tuning_prompt_load_model_from_pretrained(self):
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model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-prompt'
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model = Model.from_pretrained(model_id)
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self.assertTrue(model.__class__ == VisionEfficientTuningModel)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_vision_efficient_tuning_prompt_demo_compatibility(self):
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self.model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-prompt'
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self.compatibility_check()
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_vision_efficient_tuning_bitfit_run_pipeline(self):
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model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-bitfit'
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img_path = 'data/test/images/vision_efficient_tuning_test_1.png'
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petl_pipeline = pipeline(self.task, model_id)
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result = petl_pipeline(img_path)
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print(f'Vision-efficient-tuning-bitfit output: {result}.')
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_vision_efficient_tuning_bitfit_load_model_from_pretrained(self):
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model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-bitfit'
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model = Model.from_pretrained(model_id)
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self.assertTrue(model.__class__ == VisionEfficientTuningModel)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_vision_efficient_tuning_bitfit_demo_compatibility(self):
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self.model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-bitfit'
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self.compatibility_check()
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_vision_efficient_tuning_sidetuning_run_pipeline(self):
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model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-sidetuning'
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img_path = 'data/test/images/vision_efficient_tuning_test_1.png'
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petl_pipeline = pipeline(self.task, model_id)
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result = petl_pipeline(img_path)
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print(f'Vision-efficient-tuning-sidetuning output: {result}.')
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_vision_efficient_tuning_sidetuning_load_model_from_pretrained(
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self):
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model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-sidetuning'
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model = Model.from_pretrained(model_id)
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self.assertTrue(model.__class__ == VisionEfficientTuningModel)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_vision_efficient_tuning_sidetuning_demo_compatibility(self):
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self.model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-sidetuning'
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self.compatibility_check()
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_vision_efficient_tuning_utuning_run_pipeline(self):
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model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-utuning'
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img_path = 'data/test/images/vision_efficient_tuning_test_1.png'
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petl_pipeline = pipeline(self.task, model_id)
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result = petl_pipeline(img_path)
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print(f'Vision-efficient-tuning-utuning output: {result}.')
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_vision_efficient_tuning_utuning_load_model_from_pretrained(self):
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model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-utuning'
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model = Model.from_pretrained(model_id)
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self.assertTrue(model.__class__ == VisionEfficientTuningModel)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_vision_efficient_tuning_utuning_demo_compatibility(self):
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self.model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-utuning'
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self.compatibility_check()
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if __name__ == '__main__':
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unittest.main()
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