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# model settings
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)),
# model training and testing settings
train_cfg=dict(),
test_cfg=dict(mode='slide', crop_size=256, stride=170))
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