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115 lines
4.9 KiB
Python
115 lines
4.9 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import unittest
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import torch
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from packaging import version
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from modelscope.hub.snapshot_download import snapshot_download
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from modelscope.models import Model, TorchModel
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from modelscope.models.nlp import SbertForSequenceClassification
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from modelscope.pipelines import pipeline
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from modelscope.pipelines.nlp import TextClassificationPipeline
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from modelscope.preprocessors import TextClassificationTransformersPreprocessor
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from modelscope.utils.constant import Tasks
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from modelscope.utils.regress_test_utils import IgnoreKeyFn, MsRegressTool
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from modelscope.utils.test_utils import test_level
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class SentenceSimilarityTest(unittest.TestCase):
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def setUp(self) -> None:
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self.task = Tasks.sentence_similarity
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self.model_id = 'damo/nlp_structbert_sentence-similarity_chinese-base'
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self.model_id_retail = 'damo/nlp_structbert_sentence-similarity_chinese-retail-base'
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sentence1 = '今天气温比昨天高么？'
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sentence2 = '今天湿度比昨天高么？'
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regress_tool = MsRegressTool(baseline=False)
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_run(self):
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cache_path = snapshot_download(self.model_id)
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tokenizer = TextClassificationTransformersPreprocessor(cache_path)
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model = SbertForSequenceClassification.from_pretrained(cache_path)
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pipeline1 = TextClassificationPipeline(model, preprocessor=tokenizer)
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pipeline2 = pipeline(
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Tasks.sentence_similarity, model=model, preprocessor=tokenizer)
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print('test1')
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print(f'sentence1: {self.sentence1}\nsentence2: {self.sentence2}\n'
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f'pipeline1:{pipeline1(input=(self.sentence1, self.sentence2))}')
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print()
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print(
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f'sentence1: {self.sentence1}\nsentence2: {self.sentence2}\n'
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f'pipeline1: {pipeline2(input=(self.sentence1, self.sentence2))}')
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@unittest.skipUnless(test_level() >= 2, '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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tokenizer = TextClassificationTransformersPreprocessor(model.model_dir)
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pipeline_ins = pipeline(
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task=Tasks.sentence_similarity,
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model=model,
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preprocessor=tokenizer)
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print(pipeline_ins(input=(self.sentence1, self.sentence2)))
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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_batch(self):
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pipeline_ins = pipeline(
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task=Tasks.sentence_similarity, model=self.model_id)
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print(
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pipeline_ins(
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input=[(self.sentence1, self.sentence2),
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(self.sentence1[:4], self.sentence2[5:]),
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(self.sentence1[2:], self.sentence2[:8])],
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batch_size=2))
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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_batch_iter(self):
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pipeline_ins = pipeline(
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task=Tasks.sentence_similarity, model=self.model_id, padding=False)
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print(
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pipeline_ins(input=[(
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self.sentence1,
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self.sentence2), (self.sentence1[:4], self.sentence2[5:]
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), (self.sentence1[2:],
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self.sentence2[:8])]))
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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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pipeline_ins = pipeline(
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task=Tasks.sentence_similarity, model=self.model_id)
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with self.regress_tool.monitor_module_single_forward(
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pipeline_ins.model,
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'sbert_sen_sim',
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compare_fn=IgnoreKeyFn('.*intermediate_act_fn')):
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print(pipeline_ins(input=(self.sentence1, self.sentence2)))
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_retail_similarity_model(self):
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pipeline_ins = pipeline(
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task=Tasks.sentence_similarity,
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model=self.model_id_retail,
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model_revision='v1.0.0')
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print(pipeline_ins(input=(self.sentence1, self.sentence2)))
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@unittest.skipIf(
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version.parse(torch.__version__) < version.parse('2.0.0.dev'),
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'skip when torch version < 2.0')
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def test_compile(self):
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pipeline_ins = pipeline(
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task=Tasks.sentence_similarity,
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model=self.model_id_retail,
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model_revision='v1.0.0',
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compile=True)
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print(pipeline_ins(input=(self.sentence1, self.sentence2)))
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self.assertTrue(isinstance(pipeline_ins.model._orig_mod, TorchModel))
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_run_with_default_model(self):
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pipeline_ins = pipeline(task=Tasks.sentence_similarity)
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print(pipeline_ins(input=(self.sentence1, self.sentence2)))
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if __name__ == '__main__':
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unittest.main()
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