_base_ = [
    '../_base_/models/poolformer/poolformer_m36.py',
    '../_base_/datasets/imagenet_bs128_poolformer_medium_224.py',
    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
    '../_base_/default_runtime.py',
]

# schedule settings
optim_wrapper = dict(
    optimizer=dict(lr=4e-3),
    clip_grad=dict(max_norm=5.0),
)

# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
# base_batch_size = (32 GPUs) x (128 samples per GPU)
auto_scale_lr = dict(base_batch_size=4096)

377640afc173b2ce95271ce2f45cd5d3279144ae
<mask token>

_base_ = ['../_base_/models/poolformer/poolformer_m36.py',
    '../_base_/datasets/imagenet_bs128_poolformer_medium_224.py',
    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
    '../_base_/default_runtime.py']
optim_wrapper = dict(optimizer=dict(lr=0.004), clip_grad=dict(max_norm=5.0))
auto_scale_lr = dict(base_batch_size=4096)

_base_ = [
    '../_base_/models/poolformer/poolformer_m36.py',
    '../_base_/datasets/imagenet_bs128_poolformer_medium_224.py',
    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
    '../_base_/default_runtime.py',
]

# schedule settings
optim_wrapper = dict(
    optimizer=dict(lr=4e-3),
    clip_grad=dict(max_norm=5.0),
)

# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
# base_batch_size = (32 GPUs) x (128 samples per GPU)
auto_scale_lr = dict(base_batch_size=4096)
