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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
48 lines
1.7 KiB
Python
48 lines
1.7 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os.path as osp
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import sys
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import unittest
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from modelscope.hub.snapshot_download import snapshot_download
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from modelscope.outputs import OutputKeys
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from modelscope.pipelines import pipeline
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from modelscope.pipelines.base import Pipeline
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from modelscope.utils.constant import Tasks
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from modelscope.utils.test_utils import test_level
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sys.path.append('.')
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@unittest.skip('For numpy compatible trimesh numpy bool')
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class HumanReconstructionTest(unittest.TestCase):
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def setUp(self) -> None:
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self.task = Tasks.human_reconstruction
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self.model_id = 'damo/cv_hrnet_image-human-reconstruction'
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self.test_image = 'data/test/images/human_reconstruction.jpg'
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def pipeline_inference(self, pipeline: Pipeline, input_location: str):
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result = pipeline(input_location)
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mesh = result[OutputKeys.OUTPUT]
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print(
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f'Output to {osp.abspath("human_reconstruction.obj")}, vertices num: {mesh["vertices"].shape}'
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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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model_dir = snapshot_download(self.model_id)
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human_reconstruction = pipeline(
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Tasks.human_reconstruction, model=model_dir)
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print('running')
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self.pipeline_inference(human_reconstruction, self.test_image)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_run_modelhub(self):
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human_reconstruction = pipeline(
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Tasks.human_reconstruction, model=self.model_id)
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self.pipeline_inference(human_reconstruction, self.test_image)
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if __name__ == '__main__':
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unittest.main()
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