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39485426e7
* 修复了zip文件不同打包模式下返回路径错误问题。 * 修复了替换了数据集文件重新下载时校验失败问题。 * 修复dataset oss文件在 REUSE 模式下重复下载的问题。 * 修复了csv数据集的meta json文件中某个split的meta和file字段都为''时加载所有split失败的问题。 * 修复了不同版本datasets路径不一致的问题。
137 lines
4.9 KiB
Python
137 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 tempfile
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import unittest
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import zipfile
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from functools import partial
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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.models.cv.image_instance_segmentation import \
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CascadeMaskRCNNSwinModel
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from modelscope.msdatasets import MsDataset
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from modelscope.msdatasets.task_datasets import \
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ImageInstanceSegmentationCocoDataset
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from modelscope.trainers import build_trainer
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from modelscope.utils.config import Config, ConfigDict
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from modelscope.utils.constant import ModelFile
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from modelscope.utils.test_utils import test_level
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class TestImageInstanceSegmentationTrainer(unittest.TestCase):
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model_id = 'damo/cv_swin-b_image-instance-segmentation_coco'
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def setUp(self):
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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cache_path = snapshot_download(self.model_id)
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config_path = os.path.join(cache_path, ModelFile.CONFIGURATION)
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cfg = Config.from_file(config_path)
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max_epochs = cfg.train.max_epochs
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samples_per_gpu = cfg.train.dataloader.batch_size_per_gpu
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try:
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train_data_cfg = cfg.dataset.train
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val_data_cfg = cfg.dataset.val
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except Exception:
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train_data_cfg = None
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val_data_cfg = None
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if train_data_cfg is None:
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# use default toy data
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train_data_cfg = ConfigDict(
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name='pets_small',
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split='train',
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classes=('Cat', 'Dog'),
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folder_name='Pets',
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test_mode=False)
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if val_data_cfg is None:
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val_data_cfg = ConfigDict(
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name='pets_small',
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split='validation',
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classes=('Cat', 'Dog'),
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folder_name='Pets',
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test_mode=True)
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self.train_dataset = MsDataset.load(
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dataset_name=train_data_cfg.name,
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split=train_data_cfg.split,
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classes=train_data_cfg.classes,
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folder_name=train_data_cfg.folder_name,
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test_mode=train_data_cfg.test_mode)
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assert self.train_dataset.config_kwargs[
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'classes'] == train_data_cfg.classes
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assert next(
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iter(self.train_dataset.config_kwargs['split_config'].values()))
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self.eval_dataset = MsDataset.load(
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dataset_name=val_data_cfg.name,
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split=val_data_cfg.split,
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classes=val_data_cfg.classes,
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folder_name=val_data_cfg.folder_name,
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test_mode=val_data_cfg.test_mode)
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assert self.eval_dataset.config_kwargs[
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'classes'] == val_data_cfg.classes
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assert next(
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iter(self.eval_dataset.config_kwargs['split_config'].values()))
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from mmcv.parallel import collate
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self.collate_fn = partial(collate, samples_per_gpu=samples_per_gpu)
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self.max_epochs = max_epochs
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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() >= 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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data_collator=self.collate_fn,
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train_dataset=self.train_dataset,
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eval_dataset=self.eval_dataset,
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work_dir=self.tmp_dir)
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trainer = build_trainer(
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name=Trainers.image_instance_segmentation, 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(self.max_epochs):
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self.assertIn(f'epoch_{i+1}.pth', results_files)
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_trainer_with_model_and_args(self):
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tmp_dir = tempfile.TemporaryDirectory().name
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if not os.path.exists(tmp_dir):
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os.makedirs(tmp_dir)
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cache_path = snapshot_download(self.model_id)
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model = CascadeMaskRCNNSwinModel.from_pretrained(cache_path)
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kwargs = dict(
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cfg_file=os.path.join(cache_path, ModelFile.CONFIGURATION),
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model=model,
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data_collator=self.collate_fn,
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train_dataset=self.train_dataset,
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eval_dataset=self.eval_dataset,
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work_dir=self.tmp_dir)
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trainer = build_trainer(
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name=Trainers.image_instance_segmentation, 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(self.max_epochs):
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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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