mirror of
https://github.com/modelscope/modelscope.git
synced 2025-12-21 02:29:23 +01:00
231f400133
co-contributed with 夕陌&雨泓 * add torch epoch based trainer and dis utils * add hooks including optimizer, lrscheduler, logging, checkpoint, evaluation, time profiling * add torch mdoel base and test * add optimizer and lrscheduler module * add sbert for text classification example * add task_dataset for dataset-level processor Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9338412
109 lines
2.9 KiB
Python
109 lines
2.9 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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from abc import ABCMeta
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import json
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import torch
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from torch import nn
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from torch.utils.data import Dataset
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from modelscope.trainers import build_trainer
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from modelscope.utils.constant import ModelFile
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class DummyDataset(Dataset, metaclass=ABCMeta):
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def __len__(self):
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return 20
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def __getitem__(self, idx):
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return dict(feat=torch.rand((5, )), label=torch.randint(0, 4, (1, )))
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class DummyModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.linear = nn.Linear(5, 4)
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self.bn = nn.BatchNorm1d(4)
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def forward(self, feat, labels):
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x = self.linear(feat)
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x = self.bn(x)
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loss = torch.sum(x)
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return dict(logits=x, loss=loss)
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class CheckpointHookTest(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.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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super().tearDown()
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shutil.rmtree(self.tmp_dir)
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def test_checkpoint_hook(self):
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json_cfg = {
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'task': 'image_classification',
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'train': {
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'work_dir': self.tmp_dir,
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'dataloader': {
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'batch_size_per_gpu': 2,
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'workers_per_gpu': 1
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},
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'optimizer': {
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'type': 'SGD',
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'lr': 0.01,
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'options': {
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'grad_clip': {
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'max_norm': 2.0
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}
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}
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},
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'lr_scheduler': {
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'type': 'StepLR',
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'step_size': 2,
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'options': {
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'warmup': {
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'type': 'LinearWarmup',
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'warmup_iters': 2
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}
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}
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},
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'hooks': [{
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'type': 'CheckpointHook',
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'interval': 1
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}]
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}
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}
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config_path = os.path.join(self.tmp_dir, ModelFile.CONFIGURATION)
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with open(config_path, 'w') as f:
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json.dump(json_cfg, f)
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trainer_name = 'EpochBasedTrainer'
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kwargs = dict(
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cfg_file=config_path,
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model=DummyModel(),
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data_collator=None,
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train_dataset=DummyDataset(),
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max_epochs=2)
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trainer = build_trainer(trainer_name, kwargs)
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trainer.train()
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results_files = os.listdir(self.tmp_dir)
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self.assertIn('epoch_1.pth', results_files)
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self.assertIn('epoch_2.pth', results_files)
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if __name__ == '__main__':
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unittest.main()
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