| model = dict( | |
| type='MinkUNet', | |
| data_preprocessor=dict( | |
| type='Det3DDataPreprocessor', | |
| voxel=True, | |
| voxel_type='minkunet', | |
| batch_first=False, | |
| max_voxels=80000, | |
| voxel_layer=dict( | |
| max_num_points=-1, | |
| point_cloud_range=[-100, -100, -20, 100, 100, 20], | |
| voxel_size=[0.05, 0.05, 0.05], | |
| max_voxels=(-1, -1))), | |
| backbone=dict( | |
| type='SPVCNNBackbone', | |
| in_channels=4, | |
| num_stages=4, | |
| base_channels=32, | |
| encoder_channels=[32, 64, 128, 256], | |
| encoder_blocks=[2, 2, 2, 2], | |
| decoder_channels=[256, 128, 96, 96], | |
| decoder_blocks=[2, 2, 2, 2], | |
| block_type='basic', | |
| sparseconv_backend='torchsparse', | |
| drop_ratio=0.3), | |
| decode_head=dict( | |
| type='MinkUNetHead', | |
| channels=96, | |
| num_classes=19, | |
| dropout_ratio=0, | |
| loss_decode=dict(type='mmdet.CrossEntropyLoss', avg_non_ignore=True), | |
| ignore_index=19), | |
| train_cfg=dict(), | |
| test_cfg=dict()) | |