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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
76 lines
3.0 KiB
Python
76 lines
3.0 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os
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import shutil
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import tempfile
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import unittest
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from modelscope.hub.snapshot_download import snapshot_download
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from modelscope.trainers import build_trainer
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from modelscope.utils.config import Config
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from modelscope.utils.constant import ModelFile
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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 tensorflow 2.x compatible')
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class LanguageGuidedVideoSummarizationTrainerTest(unittest.TestCase):
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def setUp(self):
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from modelscope.msdatasets.dataset_cls.custom_datasets import LanguageGuidedVideoSummarizationDataset
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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self.tmp_dir = tempfile.TemporaryDirectory().name
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if not os.path.exists(self.tmp_dir):
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os.makedirs(self.tmp_dir)
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self.model_id = 'damo/cv_clip-it_video-summarization_language-guided_en'
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self.cache_path = snapshot_download(self.model_id)
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self.config = Config.from_file(
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os.path.join(self.cache_path, ModelFile.CONFIGURATION))
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self.dataset_train = LanguageGuidedVideoSummarizationDataset(
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'train', self.config.dataset, self.cache_path)
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self.dataset_val = LanguageGuidedVideoSummarizationDataset(
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'test', self.config.dataset, self.cache_path)
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def tearDown(self):
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shutil.rmtree(self.tmp_dir, ignore_errors=True)
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super().tearDown()
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_trainer(self):
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kwargs = dict(
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model=self.model_id,
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train_dataset=self.dataset_train,
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eval_dataset=self.dataset_val,
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max_epochs=2,
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work_dir=self.tmp_dir)
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trainer = build_trainer(default_args=kwargs)
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trainer.train()
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results_files = os.listdir(self.tmp_dir)
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self.assertIn(f'{trainer.timestamp}.log.json', results_files)
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for i in range(2):
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self.assertIn(f'epoch_{i+1}.pth', results_files)
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_trainer_with_model_and_args(self):
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from modelscope.models.cv.language_guided_video_summarization import ClipItVideoSummarization
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model = ClipItVideoSummarization.from_pretrained(self.cache_path)
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kwargs = dict(
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cfg_file=os.path.join(self.cache_path, ModelFile.CONFIGURATION),
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model=model,
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train_dataset=self.dataset_train,
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eval_dataset=self.dataset_val,
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max_epochs=2,
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work_dir=self.tmp_dir)
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trainer = build_trainer(default_args=kwargs)
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trainer.train()
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results_files = os.listdir(self.tmp_dir)
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self.assertIn(f'{trainer.timestamp}.log.json', results_files)
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for i in range(2):
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self.assertIn(f'epoch_{i+1}.pth', results_files)
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if __name__ == '__main__':
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unittest.main()
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