41 lines
1.0 KiB
Python
41 lines
1.0 KiB
Python
_base_ = [
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'../_base_/models/vit-base-p32.py',
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'../_base_/datasets/imagenet_bs64_pil_resize.py',
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'../_base_/schedules/imagenet_bs4096_AdamW.py',
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'../_base_/default_runtime.py'
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]
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# model setting
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model = dict(backbone=dict(pre_norm=True))
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# data settings
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='RandomResizedCrop',
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scale=448,
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backend='pillow',
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interpolation='bicubic'),
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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(
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type='ResizeEdge',
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scale=448,
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edge='short',
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backend='pillow',
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interpolation='bicubic'),
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dict(type='CenterCrop', crop_size=448),
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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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# schedule setting
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optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
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