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https://github.com/modelscope/modelscope.git
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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
72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import subprocess
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import sys
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import tempfile
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import unittest
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import cv2
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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.utils.constant import Tasks
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from modelscope.utils.logger import get_logger
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from modelscope.utils.test_utils import test_level
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logger = get_logger()
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@unittest.skip('For need realesrgan')
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class Text2360PanoramaImageTest(unittest.TestCase):
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def setUp(self) -> None:
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logger.info('start install xformers')
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cmd = [
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sys.executable, '-m', 'pip', 'install', 'xformers', '-f',
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'https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html'
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]
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subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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logger.info('install xformers finished')
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self.task = Tasks.text_to_360panorama_image
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self.model_id = 'damo/cv_diffusion_text-to-360panorama-image_generation'
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self.prompt = 'The living room'
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self.upscale = False
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self.refinement = False
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self.input = {
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'prompt': self.prompt,
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'upscale': self.upscale,
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'refinement': self.refinement,
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}
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@unittest.skipUnless(test_level() >= 3, 'skip test due to gpu oom')
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def test_run_by_direct_model_download(self):
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from modelscope.pipelines.cv import Text2360PanoramaImagePipeline
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output_image_path = tempfile.NamedTemporaryFile(suffix='.png').name
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cache_path = snapshot_download(self.model_id)
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pipeline = Text2360PanoramaImagePipeline(cache_path)
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pipeline.group_key = self.task
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output = pipeline(inputs=self.input)[OutputKeys.OUTPUT_IMG]
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cv2.imwrite(output_image_path, output)
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print(
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'pipeline: the output image path is {}'.format(output_image_path))
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@unittest.skipUnless(test_level() >= 3, 'skip test due to gpu oom')
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def test_run_with_model_from_modelhub(self):
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from modelscope.pipelines.cv import Text2360PanoramaImagePipeline
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output_image_path = tempfile.NamedTemporaryFile(suffix='.png').name
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pipeline_ins = pipeline(
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task=Tasks.text_to_360panorama_image,
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model=self.model_id,
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model_revision='v1.0.0')
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output = pipeline_ins(inputs=self.input)[OutputKeys.OUTPUT_IMG]
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cv2.imwrite(output_image_path, output)
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print(
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'pipeline: the output image path is {}'.format(output_image_path))
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if __name__ == '__main__':
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unittest.main()
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