|
|
|
norm_cfg = dict(type='SyncBN', requires_grad=True) |
|
model = dict( |
|
type='EncoderDecoder', |
|
pretrained=None, |
|
backbone=dict( |
|
type='UNet', |
|
in_channels=3, |
|
base_channels=64, |
|
num_stages=5, |
|
strides=(1, 1, 1, 1, 1), |
|
enc_num_convs=(2, 2, 2, 2, 2), |
|
dec_num_convs=(2, 2, 2, 2), |
|
downsamples=(True, True, True, True), |
|
enc_dilations=(1, 1, 1, 1, 1), |
|
dec_dilations=(1, 1, 1, 1), |
|
with_cp=False, |
|
conv_cfg=None, |
|
norm_cfg=norm_cfg, |
|
act_cfg=dict(type='ReLU'), |
|
upsample_cfg=dict(type='InterpConv'), |
|
norm_eval=False), |
|
decode_head=dict( |
|
type='FCNHead', |
|
in_channels=64, |
|
in_index=4, |
|
channels=64, |
|
num_convs=1, |
|
concat_input=False, |
|
dropout_ratio=0.1, |
|
num_classes=2, |
|
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, |
|
in_index=3, |
|
channels=64, |
|
num_convs=1, |
|
concat_input=False, |
|
dropout_ratio=0.1, |
|
num_classes=2, |
|
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='slide', crop_size=256, stride=170)) |
|
|
