63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
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# Copyright (c) OpenMMLab. All rights reserved.
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from unittest.mock import MagicMock
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from mmfewshot.classification.datasets import (CUBDataset, MiniImageNetDataset,
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TieredImageNetDataset)
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def test_cub_dataset():
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CUBDataset.load_annotations = MagicMock(return_value=[])
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dataset = CUBDataset(
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data_prefix='',
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subset='train',
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pipeline=[dict(type='LoadImageFromFile')])
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assert len(dataset.CLASSES) == 100
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dataset = CUBDataset(
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data_prefix='',
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subset='val',
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pipeline=[dict(type='LoadImageFromFile')])
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assert len(dataset.CLASSES) == 50
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dataset = CUBDataset(
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data_prefix='',
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subset='test',
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pipeline=[dict(type='LoadImageFromFile')])
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assert len(dataset.CLASSES) == 50
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def test_mini_imagenet_dataset():
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MiniImageNetDataset.load_annotations = MagicMock(return_value=[])
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dataset = MiniImageNetDataset(
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data_prefix='',
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subset='train',
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pipeline=[dict(type='LoadImageFromFile')])
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assert len(dataset.CLASSES) == 64
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dataset = MiniImageNetDataset(
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data_prefix='',
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subset='val',
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pipeline=[dict(type='LoadImageFromFile')])
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assert len(dataset.CLASSES) == 16
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dataset = MiniImageNetDataset(
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data_prefix='',
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subset='test',
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pipeline=[dict(type='LoadImageFromFile')])
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assert len(dataset.CLASSES) == 20
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def test_tiered_imagenet_dataset():
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TieredImageNetDataset.load_annotations = MagicMock(return_value=[])
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dataset = TieredImageNetDataset(
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data_prefix='',
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subset='train',
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pipeline=[dict(type='LoadImageFromFile')])
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assert len(dataset.CLASSES) == 351
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dataset = TieredImageNetDataset(
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data_prefix='',
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subset='val',
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pipeline=[dict(type='LoadImageFromFile')])
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assert len(dataset.CLASSES) == 97
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dataset = TieredImageNetDataset(
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data_prefix='',
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subset='test',
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pipeline=[dict(type='LoadImageFromFile')])
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assert len(dataset.CLASSES) == 160
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