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96 lines
3.3 KiB
Python
96 lines
3.3 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.metainfo import Trainers
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from modelscope.models.cv.ocr_recognition import OCRRecognition
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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 DownloadMode, ModelFile
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from modelscope.utils.test_utils import test_level
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class TestOCRRecognitionTrainer(unittest.TestCase):
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model_id = 'damo/cv_crnn_ocr-recognition-general_damo'
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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, revision='v2.2.2')
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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='ICDAR13_HCTR_Dataset', split='test', namespace='damo')
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test_data_cfg = ConfigDict(
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name='ICDAR13_HCTR_Dataset', split='test', namespace='damo')
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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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namespace=train_data_cfg.namespace,
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download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS)
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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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namespace=train_data_cfg.namespace,
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download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS)
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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() >= 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.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.ocr_recognition, default_args=kwargs)
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trainer.train()
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@unittest.skipUnless(test_level() >= 0, '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, revision='v2.2.2')
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model = OCRRecognition.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.ocr_recognition, default_args=kwargs)
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trainer.train()
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if __name__ == '__main__':
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unittest.main()
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