mirror of
https://github.com/modelscope/modelscope.git
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88 lines
3.3 KiB
Python
88 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.models.cv.image_denoise import NAFNetForImageDenoise
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from modelscope.msdatasets import MsDataset
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from modelscope.msdatasets.task_datasets.sidd_image_denoising import \
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SiddImageDenoisingDataset
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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 DownloadMode, 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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class ImageDenoiseTrainerTest(unittest.TestCase):
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def setUp(self):
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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_nafnet_image-denoise_sidd'
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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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dataset_train = MsDataset.load(
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'SIDD',
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namespace='huizheng',
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subset_name='default',
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split='test',
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download_mode=DownloadMode.FORCE_REDOWNLOAD)._hf_ds
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dataset_val = MsDataset.load(
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'SIDD',
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namespace='huizheng',
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subset_name='default',
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split='test',
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download_mode=DownloadMode.FORCE_REDOWNLOAD)._hf_ds
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self.dataset_train = SiddImageDenoisingDataset(
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dataset_train, self.config.dataset, is_train=True)
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self.dataset_val = SiddImageDenoisingDataset(
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dataset_val, self.config.dataset, is_train=False)
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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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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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model = NAFNetForImageDenoise.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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