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42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os.path as osp
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import numpy as np
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import pytest
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from mmselfsup.datasets import SingleViewDataset
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# dataset settings
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data_source = 'ImageNet'
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dataset_type = 'MultiViewDataset'
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img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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train_pipeline = [dict(type='RandomResizedCrop', size=4)]
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# prefetch
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prefetch = False
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if not prefetch:
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train_pipeline.extend(
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[dict(type='ToTensor'),
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dict(type='Normalize', **img_norm_cfg)])
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def test_one_view_dataset():
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data = dict(
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data_source=dict(
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type=data_source,
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data_prefix=osp.join(osp.dirname(__file__), '../../data'),
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ann_file=osp.join(
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osp.dirname(__file__), '../../data/data_list.txt'),
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),
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pipeline=train_pipeline,
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prefetch=prefetch)
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dataset = SingleViewDataset(**data)
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fake_results = {'test': np.array([[0.7, 0, 0.3], [0.5, 0.3, 0.2]])}
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with pytest.raises(AssertionError):
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eval_res = dataset.evaluate({'test': np.array([[0.7, 0, 0.3]])},
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topk=(1))
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eval_res = dataset.evaluate(fake_results, topk=(1, 2))
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assert eval_res['test_top1'] == 1 * 100.0 / 2
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assert eval_res['test_top2'] == 2 * 100.0 / 2
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