|
|
|
norm_cfg = dict(type='SyncBN', requires_grad=True) |
|
model = dict( |
|
type='CascadeEncoderDecoder', |
|
num_stages=2, |
|
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=-1, |
|
num_classes=19, |
|
norm_cfg=norm_cfg, |
|
align_corners=False, |
|
loss_decode=dict( |
|
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), |
|
dict( |
|
type='PointHead', |
|
in_channels=[256], |
|
in_index=[0], |
|
channels=256, |
|
num_fcs=3, |
|
coarse_pred_each_layer=True, |
|
dropout_ratio=-1, |
|
num_classes=19, |
|
align_corners=False, |
|
loss_decode=dict( |
|
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)) |
|
], |
|
|
|
train_cfg=dict( |
|
num_points=2048, oversample_ratio=3, importance_sample_ratio=0.75), |
|
test_cfg=dict( |
|
mode='whole', |
|
subdivision_steps=2, |
|
subdivision_num_points=8196, |
|
scale_factor=2)) |
|
|
