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67 lines
3.0 KiB
Python
67 lines
3.0 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import unittest
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from modelscope.pipelines.multi_modal.gridvlp_pipeline import (
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GridVlpClassificationPipeline, GridVlpEmbeddingPipeline)
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from modelscope.utils.test_utils import test_level
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class GridVlpClassificationTest(unittest.TestCase):
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def setUp(self) -> None:
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self.model_id = 'rgtjf1/multi-modal_gridvlp_classification_chinese-base-ecom-cate'
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text = '女装快干弹力轻型短裤448575'
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image = 'https://yejiabo-public.oss-cn-zhangjiakou.aliyuncs.com/alinlp/clothes.png'
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_run_with_pipeline(self):
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gridvlp_classification_pipeline = GridVlpClassificationPipeline(
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'rgtjf1/multi-modal_gridvlp_classification_chinese-base-ecom-cate')
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input_params = {'text': self.text, 'image': self.image}
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inputs = gridvlp_classification_pipeline.preprocess(input_params)
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outputs = gridvlp_classification_pipeline.forward(inputs)
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print(f'text: {self.text}\nimage: {self.image}\n'
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f'outputs: {outputs}')
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gridvlp_classification_pipeline = GridVlpClassificationPipeline(
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'rgtjf1/multi-modal_gridvlp_classification_chinese-base-ecom-cate-large'
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)
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input_params = {'text': self.text, 'image': self.image}
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inputs = gridvlp_classification_pipeline.preprocess(input_params)
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outputs = gridvlp_classification_pipeline.forward(inputs)
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print(f'text: {self.text}\nimage: {self.image}\n'
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f'outputs: {outputs}')
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gridvlp_classification_pipeline = GridVlpClassificationPipeline(
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'rgtjf1/multi-modal_gridvlp_classification_chinese-base-ecom-brand'
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)
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input_params = {'text': self.text, 'image': self.image}
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inputs = gridvlp_classification_pipeline.preprocess(input_params)
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outputs = gridvlp_classification_pipeline.forward(inputs)
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print(f'text: {self.text}\nimage: {self.image}\n'
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f'outputs: {outputs}')
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gridvlp_classification_pipeline = GridVlpClassificationPipeline(
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'rgtjf1/multi-modal_gridvlp_classification_chinese-base-similarity'
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)
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input_params = {'text': self.text, 'image': self.image}
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inputs = gridvlp_classification_pipeline.preprocess(input_params)
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outputs = gridvlp_classification_pipeline.forward(inputs)
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print(f'text: {self.text}\nimage: {self.image}\n'
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f'outputs: {outputs}')
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gridvlp_embedding_pipeline = GridVlpEmbeddingPipeline(
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'rgtjf1/multi-modal_gridvlp_classification_chinese-base-ecom-embedding'
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)
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input_params = {'text': self.text, 'image': self.image}
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inputs = gridvlp_embedding_pipeline.preprocess(input_params)
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outputs = gridvlp_embedding_pipeline.forward(inputs)
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print(f'text: {self.text}\nimage: {self.image}\n'
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f'outputs shape: {outputs.shape}')
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if __name__ == '__main__':
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unittest.main()
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