| | |
| | 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)) |
| |
|
