69 lines
2.0 KiB
Python
69 lines
2.0 KiB
Python
# model settings
|
|
norm_cfg = dict(type='SyncBN', requires_grad=True)
|
|
model = dict(
|
|
type='EncoderDecoder',
|
|
backbone=dict(
|
|
type='BiSeNetV1',
|
|
in_channels=3,
|
|
context_channels=(128, 256, 512),
|
|
spatial_channels=(64, 64, 64, 128),
|
|
out_indices=(0, 1, 2),
|
|
out_channels=256,
|
|
backbone_cfg=dict(
|
|
type='ResNet',
|
|
in_channels=3,
|
|
depth=18,
|
|
num_stages=4,
|
|
out_indices=(0, 1, 2, 3),
|
|
dilations=(1, 1, 1, 1),
|
|
strides=(1, 2, 2, 2),
|
|
norm_cfg=norm_cfg,
|
|
norm_eval=False,
|
|
style='pytorch',
|
|
contract_dilation=True),
|
|
norm_cfg=norm_cfg,
|
|
align_corners=False,
|
|
init_cfg=None),
|
|
decode_head=dict(
|
|
type='FCNHead',
|
|
in_channels=256,
|
|
in_index=0,
|
|
channels=256,
|
|
num_convs=1,
|
|
concat_input=False,
|
|
dropout_ratio=0.1,
|
|
num_classes=19,
|
|
norm_cfg=norm_cfg,
|
|
align_corners=False,
|
|
loss_decode=dict(
|
|
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
|
|
auxiliary_head=[
|
|
dict(
|
|
type='FCNHead',
|
|
in_channels=128,
|
|
channels=64,
|
|
num_convs=1,
|
|
num_classes=19,
|
|
in_index=1,
|
|
norm_cfg=norm_cfg,
|
|
concat_input=False,
|
|
align_corners=False,
|
|
loss_decode=dict(
|
|
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
|
|
dict(
|
|
type='FCNHead',
|
|
in_channels=128,
|
|
channels=64,
|
|
num_convs=1,
|
|
num_classes=19,
|
|
in_index=2,
|
|
norm_cfg=norm_cfg,
|
|
concat_input=False,
|
|
align_corners=False,
|
|
loss_decode=dict(
|
|
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
|
|
],
|
|
# model training and testing settings
|
|
train_cfg=dict(),
|
|
test_cfg=dict(mode='whole'))
|