mirror of
https://github.com/modelscope/modelscope.git
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e288cf076e
* using get_model to validate hub path * support reading pipeline info from configuration file * add metainfo const * update model type and pipeline type and fix UT * relax requimrent for protobuf * skip two dataset tests due to temporal failure Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9118154
106 lines
3.7 KiB
Python
106 lines
3.7 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import shutil
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import unittest
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import zipfile
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from pathlib import Path
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from modelscope.fileio import File
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from modelscope.models import Model
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from modelscope.models.nlp import BertForSequenceClassification
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from modelscope.pipelines import SequenceClassificationPipeline, pipeline
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from modelscope.preprocessors import SequenceClassificationPreprocessor
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from modelscope.pydatasets import PyDataset
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from modelscope.utils.constant import Hubs, Tasks
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from modelscope.utils.test_utils import test_level
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class SequenceClassificationTest(unittest.TestCase):
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def setUp(self) -> None:
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self.model_id = 'damo/bert-base-sst2'
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def predict(self, pipeline_ins: SequenceClassificationPipeline):
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from easynlp.appzoo import load_dataset
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set = load_dataset('glue', 'sst2')
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data = set['test']['sentence'][:3]
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results = pipeline_ins(data[0])
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print(results)
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results = pipeline_ins(data[1])
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print(results)
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print(data)
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def printDataset(self, dataset: PyDataset):
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for i, r in enumerate(dataset):
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if i > 10:
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break
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print(r)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_run_with_model_from_modelhub(self):
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model = Model.from_pretrained(self.model_id)
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preprocessor = SequenceClassificationPreprocessor(
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model.model_dir, first_sequence='sentence', second_sequence=None)
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pipeline_ins = pipeline(
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task=Tasks.text_classification,
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model=model,
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preprocessor=preprocessor)
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self.predict(pipeline_ins)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_run_with_model_name(self):
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text_classification = pipeline(
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task=Tasks.text_classification, model=self.model_id)
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result = text_classification(
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PyDataset.load(
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'glue',
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subset_name='sst2',
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split='train',
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target='sentence',
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hub=Hubs.huggingface))
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self.printDataset(result)
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_run_with_default_model(self):
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text_classification = pipeline(task=Tasks.text_classification)
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result = text_classification(
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PyDataset.load(
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'glue',
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subset_name='sst2',
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split='train',
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target='sentence',
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hub=Hubs.huggingface))
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self.printDataset(result)
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_run_with_dataset(self):
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model = Model.from_pretrained(self.model_id)
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preprocessor = SequenceClassificationPreprocessor(
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model.model_dir, first_sequence='sentence', second_sequence=None)
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text_classification = pipeline(
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Tasks.text_classification, model=model, preprocessor=preprocessor)
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# loaded from huggingface dataset
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dataset = PyDataset.load(
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'glue',
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subset_name='sst2',
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split='train',
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target='sentence',
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hub=Hubs.huggingface)
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result = text_classification(dataset)
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self.printDataset(result)
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_run_with_modelscope_dataset(self):
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text_classification = pipeline(task=Tasks.text_classification)
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# loaded from modelscope dataset
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dataset = PyDataset.load(
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'squad', split='train', target='context', hub=Hubs.modelscope)
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result = text_classification(dataset)
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self.printDataset(result)
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if __name__ == '__main__':
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unittest.main()
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