mirror of
https://github.com/modelscope/modelscope.git
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128 lines
4.4 KiB
Python
128 lines
4.4 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os
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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.metainfo import Trainers
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from modelscope.msdatasets import MsDataset
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from modelscope.pipelines import pipeline
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from modelscope.trainers import build_trainer
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from modelscope.utils.config import Config, ConfigDict
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from modelscope.utils.constant import ModelFile, Tasks
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from modelscope.utils.test_utils import test_level
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class MovieSceneSegmentationTest(unittest.TestCase):
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def setUp(self) -> None:
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self.task = Tasks.movie_scene_segmentation
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self.model_id = 'damo/cv_resnet50-bert_video-scene-segmentation_movienet'
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cache_path = snapshot_download(self.model_id)
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config_path = os.path.join(cache_path, ModelFile.CONFIGURATION)
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self.cfg = Config.from_file(config_path)
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self.tmp_dir = tempfile.TemporaryDirectory().name
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_movie_scene_segmentation(self):
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input_location = 'data/test/videos/movie_scene_segmentation_test_video.mp4'
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movie_scene_segmentation_pipeline = pipeline(
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Tasks.movie_scene_segmentation, model=self.model_id)
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result = movie_scene_segmentation_pipeline(input_location)
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if result:
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print(result)
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else:
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raise ValueError('process error')
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_movie_scene_segmentation_finetune(self):
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train_data_cfg = ConfigDict(
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name='movie_scene_seg_toydata',
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split='train',
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cfg=self.cfg.preprocessor,
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test_mode=False)
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train_dataset = MsDataset.load(
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dataset_name=train_data_cfg.name,
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split=train_data_cfg.split,
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cfg=train_data_cfg.cfg,
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test_mode=train_data_cfg.test_mode)
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test_data_cfg = ConfigDict(
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name='movie_scene_seg_toydata',
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split='test',
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cfg=self.cfg.preprocessor,
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test_mode=True)
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test_dataset = MsDataset.load(
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dataset_name=test_data_cfg.name,
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split=test_data_cfg.split,
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cfg=test_data_cfg.cfg,
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test_mode=test_data_cfg.test_mode)
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kwargs = dict(
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model=self.model_id,
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train_dataset=train_dataset,
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eval_dataset=test_dataset,
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work_dir=self.tmp_dir)
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trainer = build_trainer(
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name=Trainers.movie_scene_segmentation, default_args=kwargs)
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trainer.train()
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results_files = os.listdir(trainer.work_dir)
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print(results_files)
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_movie_scene_segmentation_finetune_with_custom_dataset(self):
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data_cfg = ConfigDict(
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dataset_name='movie_scene_seg_toydata',
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namespace='modelscope',
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train_split='train',
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test_split='test',
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model_cfg=self.cfg)
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train_dataset = MsDataset.load(
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dataset_name=data_cfg.dataset_name,
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namespace=data_cfg.namespace,
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split=data_cfg.train_split,
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custom_cfg=data_cfg.model_cfg,
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test_mode=False)
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test_dataset = MsDataset.load(
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dataset_name=data_cfg.dataset_name,
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namespace=data_cfg.namespace,
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split=data_cfg.test_split,
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custom_cfg=data_cfg.model_cfg,
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test_mode=True)
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kwargs = dict(
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model=self.model_id,
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train_dataset=train_dataset,
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eval_dataset=test_dataset,
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work_dir=self.tmp_dir)
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trainer = build_trainer(
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name=Trainers.movie_scene_segmentation, default_args=kwargs)
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trainer.train()
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results_files = os.listdir(trainer.work_dir)
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print(results_files)
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_movie_scene_segmentation_with_default_task(self):
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input_location = 'data/test/videos/movie_scene_segmentation_test_video.mp4'
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movie_scene_segmentation_pipeline = pipeline(
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Tasks.movie_scene_segmentation)
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result = movie_scene_segmentation_pipeline(input_location)
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if result:
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print(result)
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else:
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raise ValueError('process error')
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if __name__ == '__main__':
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unittest.main()
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