|
|
|
norm_cfg = dict(type='SyncBN', requires_grad=True) |
|
model = dict( |
|
type='CascadeEncoderDecoder', |
|
num_stages=2, |
|
pretrained='open-mmlab://msra/hrnetv2_w18', |
|
backbone=dict( |
|
type='HRNet', |
|
norm_cfg=norm_cfg, |
|
norm_eval=False, |
|
extra=dict( |
|
stage1=dict( |
|
num_modules=1, |
|
num_branches=1, |
|
block='BOTTLENECK', |
|
num_blocks=(4, ), |
|
num_channels=(64, )), |
|
stage2=dict( |
|
num_modules=1, |
|
num_branches=2, |
|
block='BASIC', |
|
num_blocks=(4, 4), |
|
num_channels=(18, 36)), |
|
stage3=dict( |
|
num_modules=4, |
|
num_branches=3, |
|
block='BASIC', |
|
num_blocks=(4, 4, 4), |
|
num_channels=(18, 36, 72)), |
|
stage4=dict( |
|
num_modules=3, |
|
num_branches=4, |
|
block='BASIC', |
|
num_blocks=(4, 4, 4, 4), |
|
num_channels=(18, 36, 72, 144)))), |
|
decode_head=[ |
|
dict( |
|
type='FCNHead', |
|
in_channels=[18, 36, 72, 144], |
|
channels=sum([18, 36, 72, 144]), |
|
in_index=(0, 1, 2, 3), |
|
input_transform='resize_concat', |
|
kernel_size=1, |
|
num_convs=1, |
|
concat_input=False, |
|
dropout_ratio=-1, |
|
num_classes=19, |
|
norm_cfg=norm_cfg, |
|
align_corners=False, |
|
loss_decode=dict( |
|
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), |
|
dict( |
|
type='OCRHead', |
|
in_channels=[18, 36, 72, 144], |
|
in_index=(0, 1, 2, 3), |
|
input_transform='resize_concat', |
|
channels=512, |
|
ocr_channels=256, |
|
dropout_ratio=-1, |
|
num_classes=19, |
|
norm_cfg=norm_cfg, |
|
align_corners=False, |
|
loss_decode=dict( |
|
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), |
|
], |
|
|
|
train_cfg=dict(), |
|
test_cfg=dict(mode='whole')) |
|
|
