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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>
35 lines
1012 B
Python
35 lines
1012 B
Python
_base_ = [
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'../_base_/models/efficientnet_v2/efficientnetv2_s.py',
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'../_base_/datasets/imagenet_bs32.py',
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'../_base_/schedules/imagenet_bs256.py',
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'../_base_/default_runtime.py',
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]
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# 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=[127.5, 127.5, 127.5],
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std=[127.5, 127.5, 127.5],
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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='EfficientNetRandomCrop', scale=300, crop_padding=0),
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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='EfficientNetCenterCrop', crop_size=384, crop_padding=0),
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dict(type='PackInputs'),
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]
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train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
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val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
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test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
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