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108 lines
3.6 KiB
Python
108 lines
3.6 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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import zipfile
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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.models.cv.movie_scene_segmentation import \
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MovieSceneSegmentationModel
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from modelscope.msdatasets import MsDataset
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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
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from modelscope.utils.test_utils import test_level
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class TestImageInstanceSegmentationTrainer(unittest.TestCase):
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model_id = 'damo/cv_resnet50-bert_video-scene-segmentation_movienet'
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def setUp(self):
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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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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cfg = Config.from_file(config_path)
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max_epochs = cfg.train.max_epochs
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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=cfg.preprocessor,
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test_mode=False)
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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=cfg.preprocessor,
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test_mode=True)
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self.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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assert next(
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iter(self.train_dataset.config_kwargs['split_config'].values()))
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self.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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assert next(
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iter(self.test_dataset.config_kwargs['split_config'].values()))
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self.max_epochs = max_epochs
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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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def tearDown(self):
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shutil.rmtree(self.tmp_dir)
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super().tearDown()
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@unittest.skipUnless(test_level() >= 1, '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.train_dataset,
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eval_dataset=self.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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self.assertIn(f'{trainer.timestamp}.log.json', results_files)
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_trainer_with_model_and_args(self):
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tmp_dir = tempfile.TemporaryDirectory().name
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if not os.path.exists(tmp_dir):
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os.makedirs(tmp_dir)
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cache_path = snapshot_download(self.model_id)
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model = MovieSceneSegmentationModel.from_pretrained(cache_path)
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kwargs = dict(
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cfg_file=os.path.join(cache_path, ModelFile.CONFIGURATION),
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model=model,
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train_dataset=self.train_dataset,
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eval_dataset=self.test_dataset,
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work_dir=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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self.assertIn(f'{trainer.timestamp}.log.json', results_files)
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if __name__ == '__main__':
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unittest.main()
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