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# model settings
norm_cfg = dict(type='SyncBN', eps=1e-03, requires_grad=True)
data_preprocessor = dict(
type='SegDataPreProcessor',
mean=[72.39239876, 82.90891754, 73.15835921],
std=[1, 1, 1],
bgr_to_rgb=True,
pad_val=0,
seg_pad_val=255)
model = dict(
type='EncoderDecoder',
data_preprocessor=data_preprocessor,
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
])),
# model training and testing settings
train_cfg=dict(sampler=None),
test_cfg=dict(mode='whole'))
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