mirror of
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
118 lines
3.2 KiB
Python
118 lines
3.2 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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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.metrics.builder import METRICS, MetricKeys
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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 ModelFile
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from modelscope.utils.registry import default_group
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from modelscope.utils.test_utils import create_dummy_test_dataset
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def create_dummy_metric():
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@METRICS.register_module(
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group_key=default_group, module_name='DummyMetric', force=True)
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class DummyMetric:
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def add(*args, **kwargs):
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pass
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def evaluate(self):
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return {MetricKeys.ACCURACY: 0.5}
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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 EvaluationHookTest(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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create_dummy_metric()
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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_evaluation_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': 'EvaluationHook',
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'interval': 1,
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}]
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},
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'evaluation': {
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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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'shuffle': False
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},
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'metrics': ['DummyMetric']
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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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eval_dataset=dummy_dataset,
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max_epochs=1)
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trainer = build_trainer(trainer_name, kwargs)
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trainer.train()
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self.assertDictEqual(trainer.metric_values, {'accuracy': 0.5})
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if __name__ == '__main__':
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unittest.main()
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