41 lines
1.1 KiB
Python
41 lines
1.1 KiB
Python
# dataset settings
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custom_imports = dict(imports='mmcls.datasets', allow_failed_imports=False)
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dataset_type = 'mmcls.ImageNet'
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data_root = 'data/imagenet/'
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file_client_args = dict(backend='disk')
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train_pipeline = [
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dict(type='LoadImageFromFile', file_client_args=file_client_args),
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dict(
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type='RandomResizedCrop', size=224, scale=(0.2, 1.), backend='pillow'),
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dict(
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type='RandomGrayscale',
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prob=0.2,
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keep_channels=True,
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channel_weights=(0.114, 0.587, 0.2989)),
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dict(
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type='ColorJitter',
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brightness=0.4,
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contrast=0.4,
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saturation=0.4,
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hue=0.4),
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dict(type='RandomFlip', prob=0.5),
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dict(
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type='PackSelfSupInputs',
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algorithm_keys=['sample_idx'],
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meta_keys=['img_path'])
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]
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train_dataloader = dict(
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batch_size=32,
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num_workers=8,
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drop_last=True,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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ann_file='meta/train.txt',
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data_prefix=dict(img_path='train/'),
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pipeline=train_pipeline))
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