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cba4e40bc1
明确受影响的模型(damo)： ONE-PEACE-4B ModuleNotFoundError: MyCustomPipeline: MyCustomModel: No module named 'one_peace'，缺少依赖。 cv_resnet50_face-reconstruction 不兼容tf2 nlp_automatic_post_editing_for_translation_en2de tf2.0兼容性问题，tf1.x需要 cv_resnet18_ocr-detection-word-level_damo tf2.x兼容性问题 cv_resnet18_ocr-detection-line-level_damo tf兼容性问题 cv_resnet101_detection_fewshot-defrcn 模型限制必须detection0.3+torch1.11.0" speech_dfsmn_ans_psm_48k_causal "librosa， numpy兼容性问题 cv_mdm_motion-generation "依赖numpy版本兼容性问题： File ""/opt/conda/lib/python3.8/site-packages/smplx/body_models.py"", cv_resnet50_ocr-detection-vlpt numpy兼容性问题 cv_clip-it_video-summarization_language-guided_en tf兼容性问题 Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/13744636 * numpy and pandas no version * modify compatible issue * fix numpy compatible issue * modify ci * fix lint issue * replace Image.ANTIALIAS to Image.Resampling.LANCZOS pillow compatible * skip uncompatible cases * fix numpy compatible issue, skip cases that can not compatbile numpy or tensorflow2.x * skip compatible cases * fix clip model issue * fix body 3d keypoints compatible issue
75 lines
2.9 KiB
Python
75 lines
2.9 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import unittest
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from modelscope.hub.snapshot_download import snapshot_download
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from modelscope.models import Model
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from modelscope.models.nlp import StarForTextToSql
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from modelscope.pipelines import pipeline
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from modelscope.pipelines.nlp import ConversationalTextToSqlPipeline
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from modelscope.preprocessors import ConversationalTextToSqlPreprocessor
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from modelscope.utils.constant import Tasks
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from modelscope.utils.nlp.space_T_en.utils import \
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text2sql_tracking_and_print_results
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from modelscope.utils.test_utils import test_level
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@unittest.skip(
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"For compatible issue, TypeError: edge_subgraph() got an unexpected keyword argument 'preserve_nodes'"
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)
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class ConversationalTextToSql(unittest.TestCase):
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def setUp(self) -> None:
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self.task = Tasks.table_question_answering
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self.model_id = 'damo/nlp_star_conversational-text-to-sql'
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model_id = 'damo/nlp_star_conversational-text-to-sql'
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test_case = {
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'database_id':
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'employee_hire_evaluation',
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'local_db_path':
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None,
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'utterance': [
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"I'd like to see Shop names.", 'Which of these are hiring?',
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'Which shop is hiring the highest number of employees? | do you want the name of the shop ? | Yes'
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]
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}
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_run_by_direct_model_download(self):
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cache_path = snapshot_download(self.model_id)
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preprocessor = ConversationalTextToSqlPreprocessor(
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model_dir=cache_path,
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database_id=self.test_case['database_id'],
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db_content=True)
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model = StarForTextToSql(
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model_dir=cache_path, config=preprocessor.config)
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pipelines = [
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ConversationalTextToSqlPipeline(
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model=model, preprocessor=preprocessor),
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pipeline(task=self.task, model=model, preprocessor=preprocessor)
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]
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text2sql_tracking_and_print_results(self.test_case, pipelines)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_run_with_model_from_modelhub(self):
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model = Model.from_pretrained(self.model_id)
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preprocessor = ConversationalTextToSqlPreprocessor(
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model_dir=model.model_dir)
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pipelines = [
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ConversationalTextToSqlPipeline(
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model=model, preprocessor=preprocessor),
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pipeline(task=self.task, model=model, preprocessor=preprocessor)
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]
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text2sql_tracking_and_print_results(self.test_case, pipelines)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_run_with_model_name(self):
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pipelines = [pipeline(task=self.task, model=self.model_id)]
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text2sql_tracking_and_print_results(self.test_case, pipelines)
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if __name__ == '__main__':
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unittest.main()
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