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8f6a0f64e2
1. add support for configuration for gpu_collect and cache_dir which is used for cpu result gathering, configuration example
```json
"evaluation": {
"gpu_collect": false,
"cache_dir": "path/to/your/local/cache"
}
```
2. fix logger file missing when log_file is passed to get_logger and add log_file for trainer
3. automatically create work_dir in rank0 worker
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11342068
* add support for configuration for tmpdir and gpu_collect
68 lines
2.2 KiB
Python
68 lines
2.2 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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from functools import partial
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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.audio.audio_utils import to_segment
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from modelscope.utils.constant import DownloadMode
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from modelscope.utils.hub import read_config
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from modelscope.utils.test_utils import test_level
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SEGMENT_LENGTH_TEST = 640
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class TestANSTrainer(unittest.TestCase):
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def setUp(self):
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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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self.model_id = 'damo/speech_frcrn_ans_cirm_16k'
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cfg = read_config(self.model_id)
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cfg.train.max_epochs = 2
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cfg.train.dataloader.batch_size_per_gpu = 1
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self.cfg_file = os.path.join(self.tmp_dir, 'train_config.json')
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cfg.dump(self.cfg_file)
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hf_ds = MsDataset.load(
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'ICASSP_2021_DNS_Challenge',
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split='test',
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download_mode=DownloadMode.FORCE_REDOWNLOAD).to_hf_dataset()
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mapped_ds = hf_ds.map(
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partial(to_segment, segment_length=SEGMENT_LENGTH_TEST),
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remove_columns=['duration'],
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batched=True,
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batch_size=2)
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self.dataset = MsDataset.from_hf_dataset(mapped_ds)
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def tearDown(self):
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shutil.rmtree(self.tmp_dir, ignore_errors=True)
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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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train_dataset=self.dataset,
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eval_dataset=self.dataset,
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max_epochs=2,
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train_iters_per_epoch=2,
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val_iters_per_epoch=1,
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cfg_file=self.cfg_file,
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work_dir=self.tmp_dir)
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trainer = build_trainer(
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Trainers.speech_frcrn_ans_cirm_16k, 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(2):
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self.assertIn(f'epoch_{i + 1}.pth', results_files)
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