# model settings | |
norm_cfg = dict(type='SyncBN', requires_grad=True) | |
model = dict( | |
type='EncoderDecoder', | |
pretrained='open-mmlab://resnet50_v1c', | |
backbone=dict( | |
type='ResNetV1c', | |
depth=50, | |
num_stages=4, | |
out_indices=(0, 1, 2, 3), | |
dilations=(1, 1, 1, 1), | |
strides=(1, 2, 2, 2), | |
norm_cfg=norm_cfg, | |
norm_eval=False, | |
style='pytorch', | |
contract_dilation=True), | |
neck=dict( | |
type='FPN', | |
in_channels=[256, 512, 1024, 2048], | |
out_channels=256, | |
num_outs=4), | |
decode_head=dict( | |
type='FPNHead', | |
in_channels=[256, 256, 256, 256], | |
in_index=[0, 1, 2, 3], | |
feature_strides=[4, 8, 16, 32], | |
channels=128, | |
dropout_ratio=0.1, | |
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')) | |