mirror of
https://github.com/modelscope/modelscope.git
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103 lines
3.3 KiB
Python
103 lines
3.3 KiB
Python
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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.pipelines import pipeline
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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 TestConesDiffusionTrainer(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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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_cones2_diffusion_train(self):
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model_id = 'damo/Cones2'
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model_revision = 'v1.0.1'
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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 = 5e-6
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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.cones2_inference, default_args=kwargs)
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trainer.train()
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result = trainer.evaluate()
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print(f'Cones-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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pipe = pipeline(
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task=Tasks.text_to_image_synthesis, model=f'{self.tmp_dir}/output')
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output = pipe({
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'text': 'a mug and a dog on the beach',
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'subject_list': [['mug', 2], ['dog', 5]],
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'color_context': {
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'255,192,0': ['mug', 2.5],
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'255,0,0': ['dog', 2.5]
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},
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'layout': 'data/test/images/mask_example.png'
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})
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cv2.imwrite('./cones.png', output['output_imgs'][0])
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_cones2_diffusion_eval(self):
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model_id = 'damo/Cones2'
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model_revision = 'v1.0.1'
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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.cones2_inference, default_args=kwargs)
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result = trainer.evaluate()
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print(f'Cones-diffusion eval output: {result}.')
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if __name__ == '__main__':
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unittest.main()
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