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8f18274f75
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/12643554 * add teardown for tests * add teardown for dialog_modeling_trainer,document_grounded_dialog_generate_trainer,document_grounded_dialog_rerank_trainer,document_grounded_dialog_retrieval_trainer,training_args,translation_evaluation_trainer,translation_trainer
140 lines
4.9 KiB
Python
140 lines
4.9 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 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.trainers import build_trainer
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from modelscope.utils.test_utils import test_level
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def _setup():
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model_id = 'damo/cv_tinynas_object-detection_damoyolo'
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cache_path = snapshot_download(model_id)
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return cache_path
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class TestTinynasDamoyoloTrainerSingleGPU(unittest.TestCase):
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def setUp(self):
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self.model_id = 'damo/cv_tinynas_object-detection_damoyolo'
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self.cache_path = _setup()
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def tearDown(self) -> None:
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super().tearDown()
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shutil.rmtree('./workdirs')
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_trainer_from_scratch_singleGPU(self):
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kwargs = dict(
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cfg_file=os.path.join(self.cache_path, 'configuration.json'),
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gpu_ids=[
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0,
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],
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batch_size=2,
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max_epochs=3,
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num_classes=80,
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base_lr_per_img=0.001,
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cache_path=self.cache_path,
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train_image_dir='./data/test/images/image_detection/images',
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val_image_dir='./data/test/images/image_detection/images',
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train_ann=
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'./data/test/images/image_detection/annotations/coco_sample.json',
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val_ann=
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'./data/test/images/image_detection/annotations/coco_sample.json',
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work_dir='./workdirs',
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exp_name='damoyolo_s',
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)
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trainer = build_trainer(
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name=Trainers.tinynas_damoyolo, default_args=kwargs)
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trainer.train()
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trainer.evaluate(
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checkpoint_path=os.path.join('./workdirs/damoyolo_s',
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'epoch_3_ckpt.pth'))
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_trainer_from_scratch_singleGPU_model_id(self):
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kwargs = dict(
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model=self.model_id,
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gpu_ids=[
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0,
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],
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batch_size=2,
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max_epochs=3,
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num_classes=80,
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load_pretrain=True,
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base_lr_per_img=0.001,
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train_image_dir='./data/test/images/image_detection/images',
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val_image_dir='./data/test/images/image_detection/images',
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train_ann=
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'./data/test/images/image_detection/annotations/coco_sample.json',
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val_ann=
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'./data/test/images/image_detection/annotations/coco_sample.json',
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work_dir='./workdirs',
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exp_name='damoyolo_s',
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)
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trainer = build_trainer(
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name=Trainers.tinynas_damoyolo, default_args=kwargs)
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trainer.train()
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trainer.evaluate(
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checkpoint_path=os.path.join(self.cache_path,
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'damoyolo_tinynasL25_S.pt'))
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@unittest.skip('multiGPU test is varified offline')
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def test_trainer_from_scratch_multiGPU(self):
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kwargs = dict(
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cfg_file=os.path.join(self.cache_path, 'configuration.json'),
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gpu_ids=[
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0,
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1,
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],
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batch_size=32,
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max_epochs=3,
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num_classes=1,
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cache_path=self.cache_path,
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train_image_dir='./data/test/images/image_detection/images',
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val_image_dir='./data/test/images/image_detection/images',
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train_ann=
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'./data/test/images/image_detection/annotations/coco_sample.json',
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val_ann=
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'./data/test/images/image_detection/annotations/coco_sample.json',
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work_dir='./workdirs',
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exp_name='damoyolo_s',
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)
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trainer = build_trainer(
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name=Trainers.tinynas_damoyolo, 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_finetune_singleGPU(self):
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kwargs = dict(
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cfg_file=os.path.join(self.cache_path, 'configuration.json'),
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gpu_ids=[
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0,
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],
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batch_size=16,
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max_epochs=3,
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num_classes=1,
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load_pretrain=True,
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pretrain_model=os.path.join(self.cache_path,
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'damoyolo_tinynasL25_S.pt'),
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cache_path=self.cache_path,
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train_image_dir='./data/test/images/image_detection/images',
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val_image_dir='./data/test/images/image_detection/images',
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train_ann=
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'./data/test/images/image_detection/annotations/coco_sample.json',
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val_ann=
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'./data/test/images/image_detection/annotations/coco_sample.json',
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work_dir='./workdirs',
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exp_name='damoyolo_s',
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)
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trainer = build_trainer(
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name=Trainers.tinynas_damoyolo, 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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