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_base_ = [
'../_base_/models/upernet_beit.py', '../_base_/datasets/ade20k_640x640.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_320k.py'
]
crop_size = (640, 640)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
pretrained='pretrain/beit_large_patch16_224_pt22k_ft22k.pth',
backbone=dict(
type='BEiT',
embed_dims=1024,
num_layers=24,
num_heads=16,
mlp_ratio=4,
qv_bias=True,
init_values=1e-6,
drop_path_rate=0.2,
out_indices=[7, 11, 15, 23]),
neck=dict(embed_dim=1024, rescales=[4, 2, 1, 0.5]),
decode_head=dict(
in_channels=[1024, 1024, 1024, 1024], num_classes=150, channels=1024),
auxiliary_head=dict(in_channels=1024, num_classes=150),
test_cfg=dict(mode='slide', crop_size=(640, 640), stride=(426, 426)))
optim_wrapper = dict(
_delete_=True,
type='AmpOptimWrapper',
optimizer=dict(
type='AdamW', lr=2e-5, betas=(0.9, 0.999), weight_decay=0.05),
constructor='LayerDecayOptimizerConstructor',
paramwise_cfg=dict(num_layers=24, layer_decay_rate=0.95),
accumulative_counts=2)
param_scheduler = [
dict(
type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=3000),
dict(
type='PolyLR',
power=1.0,
begin=3000,
end=160000,
eta_min=0.0,
by_epoch=False,
)
]
train_dataloader = dict(batch_size=1)
val_dataloader = dict(batch_size=1)
test_dataloader = val_dataloader
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