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8cdbeec3f6
* Support swift model inference for llm pipeline * fix bug * Add 'swfit' llm_framework and fix stream bug * For pass the unittest run action
58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os
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import unittest
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import imageio
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from modelscope.models.cv.human3d_animation.utils import write_obj
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from modelscope.outputs import OutputKeys
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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.test_utils import test_level
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class Human3DRenderTest(unittest.TestCase):
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def setUp(self) -> None:
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self.model_id = 'damo/cv_3d-human-synthesis-library'
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self.task = Tasks.human3d_render
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def save_results(self, result, save_root):
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os.makedirs(save_root, exist_ok=True)
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mesh = result[OutputKeys.OUTPUT]['mesh']
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write_obj(os.path.join(save_root, 'mesh.obj'), mesh)
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frames_color = result[OutputKeys.OUTPUT]['frames_color']
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imageio.mimwrite(
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os.path.join(save_root, 'render_color.gif'),
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frames_color,
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duration=33)
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del frames_color
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frames_normals = result[OutputKeys.OUTPUT]['frames_normal']
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imageio.mimwrite(
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os.path.join(save_root, 'render_normals.gif'),
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frames_normals,
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duration=33)
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del frames_normals
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print(f'Output written to {os.path.abspath(save_root)}')
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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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human3d = pipeline(self.task, model=self.model_id)
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input = {
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'dataset_id': 'damo/3DHuman_synthetic_dataset',
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'case_id': '000039',
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'resolution': 1024,
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}
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output = human3d(input)
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self.save_results(output, './human3d_results')
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print('human3d_render.test_run_modelhub done')
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if __name__ == '__main__':
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unittest.main()
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