55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
_base_ = [
|
|
'../_base_/models/convnext_v2/huge.py',
|
|
'../_base_/datasets/imagenet_bs64_swin_384.py',
|
|
'../_base_/schedules/imagenet_bs1024_adamw_swin.py',
|
|
'../_base_/default_runtime.py',
|
|
]
|
|
|
|
# dataset setting
|
|
train_pipeline = [
|
|
dict(type='LoadImageFromFile'),
|
|
dict(
|
|
type='RandomResizedCrop',
|
|
scale=512,
|
|
backend='pillow',
|
|
interpolation='bicubic'),
|
|
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
|
|
dict(type='PackInputs'),
|
|
]
|
|
|
|
test_pipeline = [
|
|
dict(type='LoadImageFromFile'),
|
|
dict(type='Resize', scale=512, backend='pillow', interpolation='bicubic'),
|
|
dict(type='PackInputs'),
|
|
]
|
|
|
|
train_dataloader = dict(batch_size=32, 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(
|
|
optimizer=dict(lr=2.5e-3),
|
|
clip_grad=None,
|
|
)
|
|
|
|
# learning policy
|
|
param_scheduler = [
|
|
# warm up learning rate scheduler
|
|
dict(
|
|
type='LinearLR',
|
|
start_factor=1e-3,
|
|
by_epoch=True,
|
|
end=20,
|
|
# update by iter
|
|
convert_to_iter_based=True),
|
|
# main learning rate scheduler
|
|
dict(type='CosineAnnealingLR', eta_min=1e-5, by_epoch=True, begin=20)
|
|
]
|
|
|
|
# train, val, test setting
|
|
train_cfg = dict(by_epoch=True, max_epochs=100, val_interval=1)
|
|
|
|
# runtime setting
|
|
custom_hooks = [dict(type='EMAHook', momentum=1e-4, priority='ABOVE_NORMAL')]
|