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https://github.com/open-mmlab/mmclassification.git
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a05c79e806
* [Refactor] Move transforms in mmselfsup to mmpretrain. * Update transform docs and configs. And register some mmcv transforms in mmpretrain. * Fix missing transform wrapper. * update selfsup transforms * Fix UT * Fix UT * update gaussianblur inconfigs --------- Co-authored-by: fangyixiao18 <fangyx18@hotmail.com>
54 lines
1.4 KiB
Python
54 lines
1.4 KiB
Python
# dataset settings
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dataset_type = 'ImageNet'
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data_preprocessor = dict(
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num_classes=1000,
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# RGB format normalization parameters
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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# convert image from BGR to RGB
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to_rgb=True,
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)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='RandomResizedCrop', scale=224),
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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='LoadImageFromFile'),
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dict(type='ResizeEdge', scale=256, edge='short'),
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dict(type='CenterCrop', crop_size=224),
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dict(type='PackInputs'),
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]
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train_dataloader = dict(
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batch_size=64,
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num_workers=5,
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dataset=dict(
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type=dataset_type,
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data_root='data/imagenet',
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ann_file='meta/train.txt',
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data_prefix='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=64,
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num_workers=5,
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dataset=dict(
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type=dataset_type,
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data_root='data/imagenet',
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ann_file='meta/val.txt',
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data_prefix='val',
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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, 5))
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# If you want standard test, please manually configure the test dataset
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test_dataloader = val_dataloader
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test_evaluator = val_evaluator
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