|
|
|
norm_cfg = dict(type='SyncBN', requires_grad=True) |
|
model = dict( |
|
type='EncoderDecoder', |
|
pretrained='open-mmlab://resnet50_v1c', |
|
backbone=dict( |
|
type='ResNetV1c', |
|
depth=50, |
|
num_stages=4, |
|
out_indices=(0, 1, 2, 3), |
|
dilations=(1, 1, 2, 4), |
|
strides=(1, 2, 1, 1), |
|
norm_cfg=norm_cfg, |
|
norm_eval=False, |
|
style='pytorch', |
|
contract_dilation=True), |
|
decode_head=dict( |
|
type='EncHead', |
|
in_channels=[512, 1024, 2048], |
|
in_index=(1, 2, 3), |
|
channels=512, |
|
num_codes=32, |
|
use_se_loss=True, |
|
add_lateral=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), |
|
loss_se_decode=dict( |
|
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.2)), |
|
auxiliary_head=dict( |
|
type='FCNHead', |
|
in_channels=1024, |
|
in_index=2, |
|
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=0.4)), |
|
|
|
train_cfg=dict(), |
|
test_cfg=dict(mode='whole')) |
|
|
