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https://github.com/modelscope/modelscope.git
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4dca4773db
1. Support csanmt exporting to savedmodel format 2. Create a new base class for text-ranking preprocessors, and move some parameters of mgeo_ranking_preprocessor to init method 3. Avoid Model & Preprocessor classes coupled with pytorch 4. Regression test supports comparing only model output 5. Support zero-shot exporting to onnx and torchscript Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11522461
109 lines
3.1 KiB
Python
109 lines
3.1 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import glob
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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 modelscope.metainfo import Trainers
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from modelscope.models.base import TorchModel
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from modelscope.trainers import build_trainer
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from modelscope.utils.constant import LogKeys, ModelFile
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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, )), 20)
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class DummyModel(TorchModel):
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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 TensorboardHookTest(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_tensorboard_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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},
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'lr_scheduler': {
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'type': 'StepLR',
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'step_size': 2,
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},
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'hooks': [{
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'type': 'TensorboardHook',
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'interval': 2
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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 = Trainers.default
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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=dummy_dataset,
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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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tb_out_dir = os.path.join(self.tmp_dir, 'tensorboard_output')
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events_files = glob.glob(
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os.path.join(tb_out_dir, 'events.out.tfevents.*'))
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self.assertEqual(len(events_files), 1)
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from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
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ea = EventAccumulator(events_files[0])
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ea.Reload()
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self.assertEqual(len(ea.Scalars(LogKeys.LOSS)), 10)
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self.assertEqual(len(ea.Scalars(LogKeys.LR)), 10)
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for i in range(5):
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self.assertAlmostEqual(
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ea.Scalars(LogKeys.LR)[i].value, 0.01, delta=0.001)
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for i in range(5, 10):
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self.assertAlmostEqual(
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ea.Scalars(LogKeys.LR)[i].value, 0.01, delta=0.0001)
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if __name__ == '__main__':
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unittest.main()
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