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model = dict( |
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type='SSD3DNet', |
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data_preprocessor=dict(type='Det3DDataPreprocessor'), |
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backbone=dict( |
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type='PointNet2SAMSG', |
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in_channels=4, |
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num_points=(4096, 512, (256, 256)), |
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radii=((0.2, 0.4, 0.8), (0.4, 0.8, 1.6), (1.6, 3.2, 4.8)), |
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num_samples=((32, 32, 64), (32, 32, 64), (32, 32, 32)), |
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sa_channels=(((16, 16, 32), (16, 16, 32), (32, 32, 64)), |
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((64, 64, 128), (64, 64, 128), (64, 96, 128)), |
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((128, 128, 256), (128, 192, 256), (128, 256, 256))), |
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aggregation_channels=(64, 128, 256), |
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fps_mods=(('D-FPS'), ('FS'), ('F-FPS', 'D-FPS')), |
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fps_sample_range_lists=((-1), (-1), (512, -1)), |
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norm_cfg=dict(type='BN2d', eps=1e-3, momentum=0.1), |
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sa_cfg=dict( |
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type='PointSAModuleMSG', |
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pool_mod='max', |
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use_xyz=True, |
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normalize_xyz=False)), |
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bbox_head=dict( |
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type='SSD3DHead', |
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vote_module_cfg=dict( |
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in_channels=256, |
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num_points=256, |
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gt_per_seed=1, |
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conv_channels=(128, ), |
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conv_cfg=dict(type='Conv1d'), |
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norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.1), |
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with_res_feat=False, |
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vote_xyz_range=(3.0, 3.0, 2.0)), |
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vote_aggregation_cfg=dict( |
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type='PointSAModuleMSG', |
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num_point=256, |
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radii=(4.8, 6.4), |
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sample_nums=(16, 32), |
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mlp_channels=((256, 256, 256, 512), (256, 256, 512, 1024)), |
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norm_cfg=dict(type='BN2d', eps=1e-3, momentum=0.1), |
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use_xyz=True, |
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normalize_xyz=False, |
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bias=True), |
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pred_layer_cfg=dict( |
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in_channels=1536, |
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shared_conv_channels=(512, 128), |
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cls_conv_channels=(128, ), |
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reg_conv_channels=(128, ), |
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conv_cfg=dict(type='Conv1d'), |
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norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.1), |
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bias=True), |
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objectness_loss=dict( |
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type='mmdet.CrossEntropyLoss', |
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use_sigmoid=True, |
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reduction='sum', |
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loss_weight=1.0), |
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center_loss=dict( |
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type='mmdet.SmoothL1Loss', reduction='sum', loss_weight=1.0), |
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dir_class_loss=dict( |
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type='mmdet.CrossEntropyLoss', reduction='sum', loss_weight=1.0), |
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dir_res_loss=dict( |
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type='mmdet.SmoothL1Loss', reduction='sum', loss_weight=1.0), |
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size_res_loss=dict( |
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type='mmdet.SmoothL1Loss', reduction='sum', loss_weight=1.0), |
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corner_loss=dict( |
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type='mmdet.SmoothL1Loss', reduction='sum', loss_weight=1.0), |
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vote_loss=dict( |
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type='mmdet.SmoothL1Loss', reduction='sum', loss_weight=1.0)), |
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|
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train_cfg=dict( |
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sample_mode='spec', pos_distance_thr=10.0, expand_dims_length=0.05), |
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test_cfg=dict( |
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nms_cfg=dict(type='nms', iou_thr=0.1), |
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sample_mode='spec', |
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score_thr=0.0, |
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per_class_proposal=True, |
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max_output_num=100)) |
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