mirror of
https://github.com/modelscope/modelscope.git
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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
129 lines
3.9 KiB
Python
129 lines
3.9 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.optim import SGD
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from torch.optim.lr_scheduler import MultiStepLR
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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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"""Base Dataset
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"""
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def __len__(self):
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return 10
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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 IterTimerHookTest(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_iter_time_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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'hooks': [{
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'type': 'IterTimerHook',
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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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model = DummyModel()
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optimizer = SGD(model.parameters(), lr=0.01)
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lr_scheduler = MultiStepLR(optimizer, milestones=[2, 4])
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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=model,
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train_dataset=DummyDataset(),
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optimizers=(optimizer, lr_scheduler),
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max_epochs=5)
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trainer = build_trainer(trainer_name, kwargs)
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train_dataloader = trainer._build_dataloader_with_dataset(
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trainer.train_dataset, **trainer.cfg.train.get('dataloader', {}))
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trainer.register_optimizers_hook()
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trainer.register_hook_from_cfg(trainer.cfg.train.hooks)
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trainer.invoke_hook('before_run')
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for i in range(trainer._epoch, trainer._max_epochs):
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trainer.invoke_hook('before_train_epoch')
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for _, data_batch in enumerate(train_dataloader):
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trainer.invoke_hook('before_train_iter')
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trainer.train_step(trainer.model, data_batch)
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trainer.invoke_hook('after_train_iter')
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self.assertIn('data_load_time', trainer.log_buffer.val_history)
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self.assertIn('time', trainer.log_buffer.val_history)
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self.assertIn('loss', trainer.log_buffer.val_history)
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trainer.invoke_hook('after_train_epoch')
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target_len = 5 * (i + 1)
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self.assertEqual(
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len(trainer.log_buffer.val_history['data_load_time']),
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target_len)
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self.assertEqual(
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len(trainer.log_buffer.val_history['time']), target_len)
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self.assertEqual(
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len(trainer.log_buffer.val_history['loss']), target_len)
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self.assertEqual(
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len(trainer.log_buffer.n_history['data_load_time']),
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target_len)
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self.assertEqual(
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len(trainer.log_buffer.n_history['time']), target_len)
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self.assertEqual(
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len(trainer.log_buffer.n_history['loss']), target_len)
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trainer.invoke_hook('after_run')
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if __name__ == '__main__':
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unittest.main()
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