# model settings | |
norm_cfg = dict(type='SyncBN', requires_grad=True) | |
model = dict( | |
type='EncoderDecoder', | |
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], | |
in_index=(0, 1, 2, 3), | |
channels=sum([18, 36, 72, 144]), | |
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=1.0)), | |
# model training and testing settings | |
train_cfg=dict(), | |
test_cfg=dict(mode='whole')) | |