Spaces:
Runtime error
Runtime error
File size: 1,679 Bytes
412c852 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
_base_ = [
'../_base_/models/upernet_swin.py', '../_base_/datasets/levir_256x256.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py'
]
crop_size = (256, 256)
norm_cfg = dict(type='BN', requires_grad=True)
data_preprocessor = dict(
size=crop_size,
type='SegDataPreProcessor',
mean=[123.675, 116.28, 103.53, 123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375, 58.395, 57.12, 57.375])
model = dict(
data_preprocessor=data_preprocessor,
backbone=dict(
in_channels=6,
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
use_abs_pos_embed=False,
drop_path_rate=0.3,
patch_norm=True),
decode_head=dict(in_channels=[96, 192, 384, 768], num_classes=2),
auxiliary_head=dict(in_channels=384, num_classes=2))
# AdamW optimizer, no weight decay for position embedding & layer norm
# in backbone
optim_wrapper = dict(
_delete_=True,
type='OptimWrapper',
optimizer=dict(
type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01),
paramwise_cfg=dict(
custom_keys={
'absolute_pos_embed': dict(decay_mult=0.),
'relative_position_bias_table': dict(decay_mult=0.),
'norm': dict(decay_mult=0.)
}))
param_scheduler = [
dict(
type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
dict(
type='PolyLR',
eta_min=0.0,
power=1.0,
begin=1500,
end=20000,
by_epoch=False,
)
]
train_dataloader = dict(batch_size=4)
val_dataloader = dict(batch_size=1)
test_dataloader = val_dataloader
|