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95 lines
3.7 KiB
Python
95 lines
3.7 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.referring_video_object_segmentation import \
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ReferringVideoObjectSegmentation
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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_swin-t_referring_video-object-segmentation'
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dataset_name = 'referring_vos_toydata'
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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(name=self.dataset_name,
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split='train',
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test_mode=False,
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cfg=cfg.dataset)
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test_data_cfg = ConfigDict(name=self.dataset_name,
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split='test',
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test_mode=True,
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cfg=cfg.dataset)
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self.train_dataset = MsDataset.load(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(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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@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(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='./work_dir')
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trainer = build_trainer(
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name=Trainers.referring_video_object_segmentation,
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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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cache_path = snapshot_download(self.model_id)
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model = ReferringVideoObjectSegmentation.from_pretrained(cache_path)
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kwargs = dict(cfg_file=os.path.join(cache_path,
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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='./work_dir')
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trainer = build_trainer(
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name=Trainers.referring_video_object_segmentation,
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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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