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2c3875c0e1
* fix msdataset
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9436292
* fix msdataset
125 lines
4.1 KiB
Python
125 lines
4.1 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 json
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import numpy as np
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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 modelscope.trainers import build_trainer
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from modelscope.utils.constant import LogKeys, ModelFile, TrainerStages
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from modelscope.utils.test_utils import create_dummy_test_dataset
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dummy_dataset = create_dummy_test_dataset(
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np.random.random(size=(5, )), np.random.randint(0, 4, (1, )), 10)
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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=dummy_dataset,
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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.data_loader = train_dataloader
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trainer.invoke_hook(TrainerStages.before_run)
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for i in range(trainer._epoch, trainer._max_epochs):
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trainer.invoke_hook(TrainerStages.before_train_epoch)
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for _, data_batch in enumerate(train_dataloader):
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trainer.invoke_hook(TrainerStages.before_train_iter)
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trainer.train_step(trainer.model, data_batch)
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trainer.invoke_hook(TrainerStages.after_train_iter)
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self.assertIn(LogKeys.DATA_LOAD_TIME,
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trainer.log_buffer.val_history)
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self.assertIn(LogKeys.ITER_TIME,
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trainer.log_buffer.val_history)
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self.assertIn(LogKeys.LOSS, trainer.log_buffer.val_history)
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trainer.invoke_hook(TrainerStages.after_train_epoch)
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target_len = 5
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self.assertEqual(
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len(trainer.log_buffer.val_history[LogKeys.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[LogKeys.ITER_TIME]),
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target_len)
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self.assertEqual(
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len(trainer.log_buffer.val_history[LogKeys.LOSS]), target_len)
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self.assertEqual(
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len(trainer.log_buffer.n_history[LogKeys.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[LogKeys.ITER_TIME]),
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target_len)
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self.assertEqual(
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len(trainer.log_buffer.n_history[LogKeys.LOSS]), target_len)
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trainer.invoke_hook(TrainerStages.after_run)
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if __name__ == '__main__':
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unittest.main()
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