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* [Feature]: Add GQA dataset * [Feature]: Add GQA * [Feature]: Add GQA UT * [Fix]: Fix hint * [Feature]: Add BLIP2 GQA * [Fix]: Fix lint * [Feature]: Update anno link * [Fix]: Update docstring * [Feature]: Update all links
31 lines
959 B
Python
31 lines
959 B
Python
# Copyright (c) OpenMMLab. All rights reserved.
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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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'pred_answer': 'dog',
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'gt_answer': 'dog',
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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.GQAAcc'))
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evaluator.process(data_samples)
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res = evaluator.evaluate(4)
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assert res['GQA/acc'] == 1.0
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meta_info = {
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'pred_answer': 'dog',
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'gt_answer': 'cat',
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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.GQAAcc'))
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evaluator.process(data_samples)
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res = evaluator.evaluate(4)
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assert res['GQA/acc'] == 0.0
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