41 lines
1.0 KiB
Python
41 lines
1.0 KiB
Python
_base_ = [
|
|
'../_base_/models/vit-base-p16.py',
|
|
'../_base_/datasets/imagenet_bs64_pil_resize.py',
|
|
'../_base_/schedules/imagenet_bs4096_AdamW.py',
|
|
'../_base_/default_runtime.py'
|
|
]
|
|
|
|
# model setting
|
|
model = dict(backbone=dict(pre_norm=True))
|
|
|
|
# data settings
|
|
train_pipeline = [
|
|
dict(type='LoadImageFromFile'),
|
|
dict(
|
|
type='RandomResizedCrop',
|
|
scale=224,
|
|
backend='pillow',
|
|
interpolation='bicubic'),
|
|
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
|
|
dict(type='PackInputs'),
|
|
]
|
|
|
|
test_pipeline = [
|
|
dict(type='LoadImageFromFile'),
|
|
dict(
|
|
type='ResizeEdge',
|
|
scale=224,
|
|
edge='short',
|
|
backend='pillow',
|
|
interpolation='bicubic'),
|
|
dict(type='CenterCrop', crop_size=224),
|
|
dict(type='PackInputs'),
|
|
]
|
|
|
|
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
|
|
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
|
|
test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
|
|
|
|
# schedule setting
|
|
optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
|