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swin-base-w6_8xb256-coslr-100e_in1k-192px.py
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swin-base-w6_8xb256-coslr-100e_in1k-192px.py
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_base_ = [
'../../_base_/models/swin_transformer/base_224.py',
'../../_base_/datasets/imagenet_bs256_swin_192.py',
'../../_base_/default_runtime.py'
]
# model settings
model = dict(
backbone=dict(
img_size=192,
drop_path_rate=0.1,
stage_cfgs=dict(block_cfgs=dict(window_size=6)),
init_cfg=dict(type='Pretrained', checkpoint='', prefix='backbone.')))
# optimizer settings
optim_wrapper = dict(
type='AmpOptimWrapper',
optimizer=dict(type='AdamW', lr=5e-3, weight_decay=0.05),
clip_grad=dict(max_norm=5.0),
constructor='LearningRateDecayOptimWrapperConstructor',
paramwise_cfg=dict(
layer_decay_rate=0.9,
custom_keys={
'.norm': dict(decay_mult=0.0),
'.bias': dict(decay_mult=0.0),
'.absolute_pos_embed': dict(decay_mult=0.0),
'.relative_position_bias_table': dict(decay_mult=0.0)
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=2.5e-7 / 1.25e-3,
by_epoch=True,
begin=0,
end=20,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=80,
eta_min=2.5e-7 * 2048 / 512,
by_epoch=True,
begin=20,
end=100,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=100)
val_cfg = dict()
test_cfg = dict()
default_hooks = dict(
# save checkpoint per epoch.
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3),
logger=dict(type='LoggerHook', interval=100))
randomness = dict(seed=0)