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_base_ = [
'../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
crop_size = (512, 512)
data_preprocessor = dict(size=crop_size)
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa
model = dict(
data_preprocessor=data_preprocessor,
backbone=dict(
type='mmpretrain.ConvNeXt',
arch='tiny',
out_indices=[0, 1, 2, 3],
drop_path_rate=0.4,
layer_scale_init_value=1.0,
gap_before_final_norm=False,
init_cfg=dict(
type='Pretrained', checkpoint=checkpoint_file,
prefix='backbone.')),
decode_head=dict(
in_channels=[96, 192, 384, 768],
num_classes=150,
),
auxiliary_head=dict(in_channels=384, num_classes=150),
test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)),
)
optim_wrapper = dict(
_delete_=True,
type='AmpOptimWrapper',
optimizer=dict(
type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05),
paramwise_cfg={
'decay_rate': 0.9,
'decay_type': 'stage_wise',
'num_layers': 6
},
constructor='LearningRateDecayOptimizerConstructor',
loss_scale='dynamic')
param_scheduler = [
dict(
type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
dict(
type='PolyLR',
power=1.0,
begin=1500,
end=160000,
eta_min=0.0,
by_epoch=False,
)
]
# By default, models are trained on 8 GPUs with 2 images per GPU
train_dataloader = dict(batch_size=2)
val_dataloader = dict(batch_size=1)
test_dataloader = val_dataloader
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