diff --git a/tests/pipelines/test_nli.py b/tests/pipelines/test_nli.py index 0fb0bd2b..0c8da8b4 100644 --- a/tests/pipelines/test_nli.py +++ b/tests/pipelines/test_nli.py @@ -7,6 +7,7 @@ from modelscope.models.nlp import SbertForNLI from modelscope.pipelines import NLIPipeline, pipeline from modelscope.preprocessors import NLIPreprocessor from modelscope.utils.constant import Tasks +from modelscope.utils.test_utils import test_level class NLITest(unittest.TestCase): @@ -14,8 +15,8 @@ class NLITest(unittest.TestCase): sentence1 = '四川商务职业学院和四川财经职业学院哪个好?' sentence2 = '四川商务职业学院商务管理在哪个校区?' - @unittest.skip('skip temporarily to save test time') - def test_run_from_local(self): + @unittest.skipUnless(test_level() >= 2, 'skip test in current test level') + def test_run_with_direct_file_download(self): cache_path = snapshot_download(self.model_id) tokenizer = NLIPreprocessor(cache_path) model = SbertForNLI(cache_path, tokenizer=tokenizer) @@ -28,6 +29,7 @@ class NLITest(unittest.TestCase): f'sentence1: {self.sentence1}\nsentence2: {self.sentence2}\n' f'pipeline1: {pipeline2(input=(self.sentence1, self.sentence2))}') + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') def test_run_with_model_from_modelhub(self): model = Model.from_pretrained(self.model_id) tokenizer = NLIPreprocessor(model.model_dir) @@ -35,10 +37,12 @@ class NLITest(unittest.TestCase): task=Tasks.nli, model=model, preprocessor=tokenizer) print(pipeline_ins(input=(self.sentence1, self.sentence2))) + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') def test_run_with_model_name(self): pipeline_ins = pipeline(task=Tasks.nli, model=self.model_id) print(pipeline_ins(input=(self.sentence1, self.sentence2))) + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') def test_run_with_default_model(self): pipeline_ins = pipeline(task=Tasks.nli) print(pipeline_ins(input=(self.sentence1, self.sentence2))) diff --git a/tests/pipelines/test_sentiment_classification.py b/tests/pipelines/test_sentiment_classification.py index 7e55cd4c..0ba22d5c 100644 --- a/tests/pipelines/test_sentiment_classification.py +++ b/tests/pipelines/test_sentiment_classification.py @@ -7,13 +7,15 @@ from modelscope.models.nlp import SbertForSentimentClassification from modelscope.pipelines import SentimentClassificationPipeline, pipeline from modelscope.preprocessors import SentimentClassificationPreprocessor from modelscope.utils.constant import Tasks +from modelscope.utils.test_utils import test_level class SentimentClassificationTest(unittest.TestCase): model_id = 'damo/nlp_structbert_sentiment-classification_chinese-base' sentence1 = '启动的时候很大声音,然后就会听到1.2秒的卡察的声音,类似齿轮摩擦的声音' - def test_run_from_local(self): + @unittest.skipUnless(test_level() >= 2, 'skip test in current test level') + def test_run_with_direct_file_download(self): cache_path = snapshot_download(self.model_id) tokenizer = SentimentClassificationPreprocessor(cache_path) model = SbertForSentimentClassification( @@ -30,6 +32,7 @@ class SentimentClassificationTest(unittest.TestCase): print(f'sentence1: {self.sentence1}\n' f'pipeline1: {pipeline2(input=self.sentence1)}') + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') def test_run_with_model_from_modelhub(self): model = Model.from_pretrained(self.model_id) tokenizer = SentimentClassificationPreprocessor(model.model_dir) @@ -39,11 +42,13 @@ class SentimentClassificationTest(unittest.TestCase): preprocessor=tokenizer) print(pipeline_ins(input=self.sentence1)) + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') def test_run_with_model_name(self): pipeline_ins = pipeline( task=Tasks.sentiment_classification, model=self.model_id) print(pipeline_ins(input=self.sentence1)) + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') def test_run_with_default_model(self): pipeline_ins = pipeline(task=Tasks.sentiment_classification) print(pipeline_ins(input=self.sentence1)) diff --git a/tests/pipelines/test_zero_shot_classification.py b/tests/pipelines/test_zero_shot_classification.py index 278ce419..236013aa 100644 --- a/tests/pipelines/test_zero_shot_classification.py +++ b/tests/pipelines/test_zero_shot_classification.py @@ -7,6 +7,7 @@ from modelscope.models.nlp import SbertForZeroShotClassification from modelscope.pipelines import ZeroShotClassificationPipeline, pipeline from modelscope.preprocessors import ZeroShotClassificationPreprocessor from modelscope.utils.constant import Tasks +from modelscope.utils.test_utils import test_level class ZeroShotClassificationTest(unittest.TestCase): @@ -15,7 +16,8 @@ class ZeroShotClassificationTest(unittest.TestCase): labels = ['文化', '体育', '娱乐', '财经', '家居', '汽车', '教育', '科技', '军事'] template = '这篇文章的标题是{}' - def test_run_from_local(self): + @unittest.skipUnless(test_level() >= 2, 'skip test in current test level') + def test_run_with_direct_file_download(self): cache_path = snapshot_download(self.model_id) tokenizer = ZeroShotClassificationPreprocessor(cache_path) model = SbertForZeroShotClassification(cache_path, tokenizer=tokenizer) @@ -36,6 +38,7 @@ class ZeroShotClassificationTest(unittest.TestCase): f'pipeline2: {pipeline2(self.sentence,candidate_labels=self.labels,hypothesis_template=self.template)}' ) + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') def test_run_with_model_from_modelhub(self): model = Model.from_pretrained(self.model_id) tokenizer = ZeroShotClassificationPreprocessor(model.model_dir) @@ -45,11 +48,13 @@ class ZeroShotClassificationTest(unittest.TestCase): preprocessor=tokenizer) print(pipeline_ins(input=self.sentence, candidate_labels=self.labels)) + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') def test_run_with_model_name(self): pipeline_ins = pipeline( task=Tasks.zero_shot_classification, model=self.model_id) print(pipeline_ins(input=self.sentence, candidate_labels=self.labels)) + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') def test_run_with_default_model(self): pipeline_ins = pipeline(task=Tasks.zero_shot_classification) print(pipeline_ins(input=self.sentence, candidate_labels=self.labels))