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https://github.com/open-mmlab/mmclassification.git
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496e098b21
* feat: support some downstream classification datasets * update sun397 * sum * update sun397 * [CI] Add test mim CI. (#879) * feat: support some downstream classification datasets * update sun397 * sum * update sun397 * rebase * feat: support some downstream classification datasets * update sun397 * update sun397 * update sun397 * update sun397 * fix unittest * update docstring * rm * update * update * refactor names of datasets * refactor some implements of datasets * refactor some implements of datasets * fix datasets unittest * refactor cub and stanford cars * refactor cub and cifar * refactor cub and cifar * refactor cub and cifar * update downstream datasets and docs * update docstring --------- Co-authored-by: Ma Zerun <mzr1996@163.com> Co-authored-by: Ezra-Yu <18586273+Ezra-Yu@users.noreply.github.com>
46 lines
1.1 KiB
Python
46 lines
1.1 KiB
Python
# dataset settings
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dataset_type = 'CIFAR100'
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data_preprocessor = dict(
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num_classes=100,
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# RGB format normalization parameters
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mean=[129.304, 124.070, 112.434],
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std=[68.170, 65.392, 70.418],
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# loaded images are already RGB format
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to_rgb=False)
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train_pipeline = [
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dict(type='RandomCrop', crop_size=32, padding=4),
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dict(type='RandomFlip', prob=0.5, direction='horizontal'),
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dict(type='PackInputs'),
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]
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test_pipeline = [
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dict(type='PackInputs'),
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]
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train_dataloader = dict(
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batch_size=16,
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num_workers=2,
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dataset=dict(
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type=dataset_type,
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data_root='data/cifar100',
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split='train',
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pipeline=train_pipeline),
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sampler=dict(type='DefaultSampler', shuffle=True),
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)
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val_dataloader = dict(
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batch_size=16,
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num_workers=2,
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dataset=dict(
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type=dataset_type,
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data_root='data/cifar100/',
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split='test',
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pipeline=test_pipeline),
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sampler=dict(type='DefaultSampler', shuffle=False),
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)
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val_evaluator = dict(type='Accuracy', topk=(1, ))
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test_dataloader = val_dataloader
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test_evaluator = val_evaluator
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