| |
| 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)) |
|
|
