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28 lines
867 B
Python
28 lines
867 B
Python
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# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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from mmengine.evaluator import Evaluator
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from mmpretrain.structures import DataSample
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class TestScienceQAMetric:
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def test_evaluate(self):
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meta_info = {
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'choices': ['A', 'B', 'C', 'D'],
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'prediction': 'A',
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'grade': 'grade1',
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'subject': 'language science',
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'answer': 1,
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'hint': 'hint',
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'image': torch.ones((3, 224, 224))
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}
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data_sample = DataSample(metainfo=meta_info)
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data_samples = [data_sample for _ in range(10)]
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evaluator = Evaluator(dict(type='mmpretrain.ScienceQAMetric'))
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evaluator.process(data_samples)
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res = evaluator.evaluate(4)
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assert res['acc_grade_1_6'] == 0.0
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assert res['acc_language'] == 0.0
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assert res['all_acc'] == 0.0
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