mmselfsup/configs/selfsup/barlowtwins/barlowtwins_resnet50_8xb256...

43 lines
1.2 KiB
Python

_base_ = [
'../_base_/models/barlowtwins.py',
'../_base_/datasets/imagenet_byol.py',
'../_base_/schedules/lars_coslr-200e_in1k.py',
'../_base_/default_runtime.py',
]
data = dict(samples_per_gpu=256)
# optimizer
optimizer = dict(
type='LARS',
lr=1.6,
momentum=0.9,
weight_decay=1e-6,
paramwise_options={
'(bn|gn)(\\d+)?.(weight|bias)':
dict(weight_decay=0, lr_mult=0.024, lars_exclude=True),
'bias':
dict(weight_decay=0, lr_mult=0.024, lars_exclude=True),
# bn layer in ResNet block downsample module
'downsample.1':
dict(weight_decay=0, lr_mult=0.024, lars_exclude=True),
})
# learning policy
lr_config = dict(
policy='CosineAnnealing',
by_epoch=False,
min_lr=0.0016,
warmup='linear',
warmup_iters=10,
warmup_ratio=1.6e-4, # cannot be 0
warmup_by_epoch=True)
# runtime settings
# the max_keep_ckpts controls the max number of ckpt file in your work_dirs
# if it is 3, when CheckpointHook (in mmcv) saves the 4th ckpt
# it will remove the oldest one to keep the number of total ckpts as 3
checkpoint_config = dict(interval=10, max_keep_ckpts=3)
runner = dict(type='EpochBasedRunner', max_epochs=300)