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125 lines
4.5 KiB
Python
125 lines
4.5 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 TestEfficientDiffusionTuningTrainerSwift(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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'style_custom_dataset',
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namespace='damo',
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split='train',
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subset_name='Anime').remap_columns({'Image:FILE': 'target:FILE'})
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self.max_epochs = 1
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self.lr = 0.0001
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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_efficient_diffusion_tuning_swift_lora_train(self):
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model_id = 'damo/multi-modal_efficient-diffusion-tuning-swift-lora'
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model_revision = 'v1.0.2'
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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.T_max = self.max_epochs
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cfg.train.optimizer.lr = self.lr
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cfg.model.inference = False
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cfg.model.pretrained_tuner = None
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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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cfg_modify_fn=cfg_modify_fn)
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trainer = build_trainer(
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name=Trainers.efficient_diffusion_tuning, default_args=kwargs)
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trainer.train()
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print('Efficient-diffusion-tuning-swift-lora 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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self.assertIn(f'epoch_{self.max_epochs}.pth', results_files)
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_efficient_diffusion_tuning_swift_adapter_train(self):
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model_id = 'damo/multi-modal_efficient-diffusion-tuning-swift-adapter'
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model_revision = 'v1.0.2'
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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.T_max = self.max_epochs
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cfg.train.optimizer.lr = self.lr
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cfg.model.inference = False
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cfg.model.pretrained_tuner = None
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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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cfg_modify_fn=cfg_modify_fn)
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trainer = build_trainer(
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name=Trainers.efficient_diffusion_tuning, default_args=kwargs)
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trainer.train()
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print('Efficient-diffusion-tuning-swift-adapter 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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self.assertIn(f'epoch_{self.max_epochs}.pth', results_files)
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_efficient_diffusion_tuning_swift_prompt_train(self):
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model_id = 'damo/multi-modal_efficient-diffusion-tuning-swift-prompt'
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model_revision = 'v1.0.2'
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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.T_max = self.max_epochs
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cfg.train.optimizer.lr = self.lr
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cfg.model.inference = False
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cfg.model.pretrained_tuner = None
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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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cfg_modify_fn=cfg_modify_fn)
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trainer = build_trainer(
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name=Trainers.efficient_diffusion_tuning, default_args=kwargs)
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trainer.train()
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print('Efficient-diffusion-tuning-swift-prompt 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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self.assertIn(f'epoch_{self.max_epochs}.pth', results_files)
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if __name__ == '__main__':
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unittest.main()
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