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# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.config import read_base
from mmpretrain.engine import EMAHook
with read_base():
from .._base_.datasets.imagenet_bs64_swin_384 import *
from .._base_.default_runtime import *
from .._base_.models.convnext_base import *
from .._base_.schedules.imagenet_bs1024_adamw_swin import *
# dataset setting
train_dataloader.update(batch_size=64)
# schedule setting
optim_wrapper.update(
optimizer=dict(lr=4e-3),
clip_grad=dict(max_norm=5.0),
)
# runtime setting
custom_hooks = [dict(type=EMAHook, momentum=4e-5, priority='ABOVE_NORMAL')]
# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
# base_batch_size = (64 GPUs) x (64 samples per GPU)
auto_scale_lr.update(base_batch_size=4096)
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