|
|
|
norm_cfg = dict(type='SyncBN', eps=1e-03, requires_grad=True) |
|
model = dict( |
|
type='EncoderDecoder', |
|
backbone=dict( |
|
type='CGNet', |
|
norm_cfg=norm_cfg, |
|
in_channels=3, |
|
num_channels=(32, 64, 128), |
|
num_blocks=(3, 21), |
|
dilations=(2, 4), |
|
reductions=(8, 16)), |
|
decode_head=dict( |
|
type='FCNHead', |
|
in_channels=256, |
|
in_index=2, |
|
channels=256, |
|
num_convs=0, |
|
concat_input=False, |
|
dropout_ratio=0, |
|
num_classes=19, |
|
norm_cfg=norm_cfg, |
|
loss_decode=dict( |
|
type='CrossEntropyLoss', |
|
use_sigmoid=False, |
|
loss_weight=1.0, |
|
class_weight=[ |
|
2.5959933, 6.7415504, 3.5354059, 9.8663225, 9.690899, 9.369352, |
|
10.289121, 9.953208, 4.3097677, 9.490387, 7.674431, 9.396905, |
|
10.347791, 6.3927646, 10.226669, 10.241062, 10.280587, |
|
10.396974, 10.055647 |
|
])), |
|
|
|
train_cfg=dict(sampler=None), |
|
test_cfg=dict(mode='whole')) |
|
|
