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1fe211ffe5
* fix pipeline builder when model is not supported * fix ci & skip --------- Co-authored-by: suluyan.sly@alibaba-inc.com <suluyan.sly@alibaba-inc.com>
92 lines
2.8 KiB
Python
92 lines
2.8 KiB
Python
# Copyright 2022-2023 The Alibaba Fundamental Vision Team Authors. All rights reserved.
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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.metainfo import Trainers
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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.constant import DownloadMode
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from modelscope.utils.test_utils import test_level
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class TestLoraDiffusionTrainer(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.train_dataset = MsDataset.load(
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'buptwq/lora-stable-diffusion-finetune',
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split='train',
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download_mode=DownloadMode.FORCE_REDOWNLOAD)
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self.eval_dataset = MsDataset.load(
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'buptwq/lora-stable-diffusion-finetune',
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split='validation',
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download_mode=DownloadMode.FORCE_REDOWNLOAD)
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self.max_epochs = 5
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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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# need diffusers==0.24.0, skip in ci
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@unittest.skip
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def test_lora_diffusion_train(self):
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model_id = 'AI-ModelScope/stable-diffusion-v1-5'
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model_revision = 'v1.0.9'
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def cfg_modify_fn(cfg):
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cfg.train.max_epochs = self.max_epochs
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cfg.train.lr_scheduler = {
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'type': 'LambdaLR',
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'lr_lambda': lambda _: 1,
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'last_epoch': -1
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}
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cfg.train.optimizer.lr = 1e-4
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return cfg
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kwargs = dict(
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model=model_id,
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model_revision=model_revision,
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work_dir=self.tmp_dir,
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train_dataset=self.train_dataset,
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eval_dataset=self.eval_dataset,
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cfg_modify_fn=cfg_modify_fn)
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trainer = build_trainer(
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name=Trainers.lora_diffusion, default_args=kwargs)
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trainer.train()
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result = trainer.evaluate()
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print(f'Lora-diffusion train output: {result}.')
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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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# need diffusers==0.24.0, skip in ci
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@unittest.skip
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def test_lora_diffusion_eval(self):
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model_id = 'AI-ModelScope/stable-diffusion-v1-5'
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model_revision = 'v1.0.9'
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kwargs = dict(
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model=model_id,
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model_revision=model_revision,
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work_dir=self.tmp_dir,
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train_dataset=None,
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eval_dataset=self.eval_dataset)
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trainer = build_trainer(
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name=Trainers.lora_diffusion, default_args=kwargs)
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result = trainer.evaluate()
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print(f'Lora-diffusion eval output: {result}.')
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if __name__ == '__main__':
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unittest.main()
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