37 lines
1.0 KiB
Python
37 lines
1.0 KiB
Python
# dataset settings
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dataset_type = 'CIFAR100'
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img_norm_cfg = dict(
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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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to_rgb=False)
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train_pipeline = [
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dict(type='RandomCrop', size=32, padding=4),
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dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='ToTensor', keys=['gt_label']),
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dict(type='Collect', keys=['img', 'gt_label'])
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]
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test_pipeline = [
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img'])
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]
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data = dict(
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samples_per_gpu=16,
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workers_per_gpu=2,
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train=dict(
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type=dataset_type,
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data_prefix='data/cifar100',
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pipeline=train_pipeline),
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val=dict(
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type=dataset_type,
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data_prefix='data/cifar100',
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pipeline=test_pipeline,
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test_mode=True),
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test=dict(
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type=dataset_type,
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data_prefix='data/cifar100',
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pipeline=test_pipeline,
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test_mode=True))
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