mirror of
https://github.com/modelscope/modelscope.git
synced 2025-12-17 00:37:43 +01:00
67 lines
2.8 KiB
Python
67 lines
2.8 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
|
|
import unittest
|
|
|
|
import numpy as np
|
|
|
|
from modelscope.hub.snapshot_download import snapshot_download
|
|
from modelscope.models import Model
|
|
from modelscope.models.nlp import ModelForFeatureExtraction
|
|
from modelscope.outputs import OutputKeys
|
|
from modelscope.pipelines import pipeline
|
|
from modelscope.pipelines.nlp import FeatureExtractionPipeline
|
|
from modelscope.preprocessors import FillMaskTransformersPreprocessor
|
|
from modelscope.utils.constant import Tasks
|
|
from modelscope.utils.test_utils import test_level
|
|
|
|
|
|
class FeatureExtractionTaskModelTest(unittest.TestCase):
|
|
|
|
def setUp(self) -> None:
|
|
self.task = Tasks.feature_extraction
|
|
self.model_id = 'damo/pert_feature-extraction_base-test'
|
|
|
|
sentence1 = '测试embedding'
|
|
|
|
@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 = FillMaskTransformersPreprocessor(cache_path, padding=False)
|
|
model = ModelForFeatureExtraction.from_pretrained(self.model_id)
|
|
pipeline1 = FeatureExtractionPipeline(model, preprocessor=tokenizer)
|
|
pipeline2 = pipeline(
|
|
Tasks.feature_extraction, model=model, preprocessor=tokenizer)
|
|
result = pipeline1(input=self.sentence1)
|
|
|
|
print(f'sentence1: {self.sentence1}\n'
|
|
f'pipeline1:{np.shape(result[OutputKeys.TEXT_EMBEDDING])}')
|
|
result = pipeline2(input=self.sentence1)
|
|
print(f'sentence1: {self.sentence1}\n'
|
|
f'pipeline1: {np.shape(result[OutputKeys.TEXT_EMBEDDING])}')
|
|
|
|
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
|
|
def test_run_with_model_from_modelhub(self):
|
|
model = Model.from_pretrained(self.model_id)
|
|
tokenizer = FillMaskTransformersPreprocessor(
|
|
model.model_dir, padding=False)
|
|
pipeline_ins = pipeline(
|
|
task=Tasks.feature_extraction, model=model, preprocessor=tokenizer)
|
|
result = pipeline_ins(input=self.sentence1)
|
|
print(np.shape(result[OutputKeys.TEXT_EMBEDDING]))
|
|
|
|
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
|
|
def test_run_with_model_name(self):
|
|
pipeline_ins = pipeline(
|
|
task=Tasks.feature_extraction, model=self.model_id)
|
|
result = pipeline_ins(input=self.sentence1)
|
|
print(np.shape(result[OutputKeys.TEXT_EMBEDDING]))
|
|
|
|
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
|
|
def test_run_with_default_model(self):
|
|
pipeline_ins = pipeline(task=Tasks.feature_extraction)
|
|
result = pipeline_ins(input=self.sentence1)
|
|
print(np.shape(result[OutputKeys.TEXT_EMBEDDING]))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|