add vision_efficient_tuning models

1）新增任务：vision_efficient_tuning；
2）新增该任务下四个模型：
vision_efficient_tuning_adapter、
vision_efficient_tuning_prefix、
vision_efficient_tuning_prompt、
vision_efficient_tuning_lora

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11575894

tests/pipelines/test_vision_efficient_tuning_adapter.py

@@ -0,0 +1,37 @@
+# Copyright 2022-2023 The Alibaba Fundamental Vision Team Authors. All rights reserved.
+import unittest
+
+from modelscope.models import Model
+from modelscope.models.cv.vision_efficient_tuning.vision_efficient_tuning import \
+    VisionEfficientTuningModel
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+from modelscope.utils.demo_utils import DemoCompatibilityCheck
+from modelscope.utils.test_utils import test_level
+
+
+class VisionEfficientTuningAdapterTest(unittest.TestCase,
+                                       DemoCompatibilityCheck):
+
+    def setUp(self) -> None:
+        self.task = Tasks.vision_efficient_tuning
+        self.model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-adapter'
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_run_pipeline(self):
+
+        petl_pipeline = pipeline(self.task, self.model_id)
+        result = petl_pipeline(
+            'data/test/images/vision_efficient_tuning_test_1.png')
+
+        print(f'Vision-efficient-tuning-adapter output: {result}.')
+
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
+    def test_load_model_from_pretrained(self):
+        model = Model.from_pretrained(
+            'damo/cv_vitb16_classification_vision-efficient-tuning-adapter')
+        self.assertTrue(model.__class__ == VisionEfficientTuningModel)
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/pipelines/test_vision_efficient_tuning_lora.py

@@ -0,0 +1,36 @@
+# Copyright 2022-2023 The Alibaba Fundamental Vision Team Authors. All rights reserved.
+import unittest
+
+from modelscope.models import Model
+from modelscope.models.cv.vision_efficient_tuning.vision_efficient_tuning import \
+    VisionEfficientTuningModel
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+from modelscope.utils.demo_utils import DemoCompatibilityCheck
+from modelscope.utils.test_utils import test_level
+
+
+class VisionEfficientTuningLoRATest(unittest.TestCase, DemoCompatibilityCheck):
+
+    def setUp(self) -> None:
+        self.task = Tasks.vision_efficient_tuning
+        self.model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-lora'
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_run_pipeline(self):
+
+        petl_pipeline = pipeline(self.task, self.model_id)
+        result = petl_pipeline(
+            'data/test/images/vision_efficient_tuning_test_1.png')
+
+        print(f'Vision-efficient-tuning-lora output: {result}.')
+
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
+    def test_load_model_from_pretrained(self):
+        model = Model.from_pretrained(
+            'damo/cv_vitb16_classification_vision-efficient-tuning-lora')
+        self.assertTrue(model.__class__ == VisionEfficientTuningModel)
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/pipelines/test_vision_efficient_tuning_prefix.py

@@ -0,0 +1,37 @@
+# Copyright 2022-2023 The Alibaba Fundamental Vision Team Authors. All rights reserved.
+import unittest
+
+from modelscope.models import Model
+from modelscope.models.cv.vision_efficient_tuning.vision_efficient_tuning import \
+    VisionEfficientTuningModel
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+from modelscope.utils.demo_utils import DemoCompatibilityCheck
+from modelscope.utils.test_utils import test_level
+
+
+class VisionEfficientTuningPrefixTest(unittest.TestCase,
+                                      DemoCompatibilityCheck):
+
+    def setUp(self) -> None:
+        self.task = Tasks.vision_efficient_tuning
+        self.model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-prefix'
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_run_pipeline(self):
+
+        petl_pipeline = pipeline(self.task, self.model_id)
+        result = petl_pipeline(
+            'data/test/images/vision_efficient_tuning_test_1.png')
+
+        print(f'Vision-efficient-tuning-prefix output: {result}.')
+
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
+    def test_load_model_from_pretrained(self):
+        model = Model.from_pretrained(
+            'damo/cv_vitb16_classification_vision-efficient-tuning-prefix')
+        self.assertTrue(model.__class__ == VisionEfficientTuningModel)
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/pipelines/test_vision_efficient_tuning_prompt.py

@@ -0,0 +1,37 @@
+# Copyright 2022-2023 The Alibaba Fundamental Vision Team Authors. All rights reserved.
+import unittest
+
+from modelscope.models import Model
+from modelscope.models.cv.vision_efficient_tuning.vision_efficient_tuning import \
+    VisionEfficientTuningModel
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+from modelscope.utils.demo_utils import DemoCompatibilityCheck
+from modelscope.utils.test_utils import test_level
+
+
+class VisionEfficientTuningPromptTest(unittest.TestCase,
+                                      DemoCompatibilityCheck):
+
+    def setUp(self) -> None:
+        self.task = Tasks.vision_efficient_tuning
+        self.model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-prompt'
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_run_pipeline(self):
+
+        petl_pipeline = pipeline(self.task, self.model_id)
+        result = petl_pipeline(
+            'data/test/images/vision_efficient_tuning_test_1.png')
+
+        print(f'Vision-efficient-tuning-prompt output: {result}.')
+
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
+    def test_load_model_from_pretrained(self):
+        model = Model.from_pretrained(
+            'damo/cv_vitb16_classification_vision-efficient-tuning-prompt')
+        self.assertTrue(model.__class__ == VisionEfficientTuningModel)
+
+
+if __name__ == '__main__':
+    unittest.main()