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model = dict( |
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type='VoteNet', |
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data_preprocessor=dict(type='Det3DDataPreprocessor'), |
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backbone=dict( |
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type='PointNet2SASSG', |
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in_channels=4, |
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num_points=(2048, 1024, 512, 256), |
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radius=(0.2, 0.4, 0.8, 1.2), |
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num_samples=(64, 32, 16, 16), |
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sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256), |
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(128, 128, 256)), |
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fp_channels=((256, 256), (256, 256)), |
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norm_cfg=dict(type='BN2d'), |
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sa_cfg=dict( |
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type='PointSAModule', |
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pool_mod='max', |
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use_xyz=True, |
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normalize_xyz=True)), |
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bbox_head=dict( |
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type='VoteHead', |
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vote_module_cfg=dict( |
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in_channels=256, |
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vote_per_seed=1, |
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gt_per_seed=3, |
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conv_channels=(256, 256), |
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conv_cfg=dict(type='Conv1d'), |
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norm_cfg=dict(type='BN1d'), |
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norm_feats=True, |
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vote_loss=dict( |
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type='ChamferDistance', |
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mode='l1', |
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reduction='none', |
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loss_dst_weight=10.0)), |
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vote_aggregation_cfg=dict( |
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type='PointSAModule', |
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num_point=256, |
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radius=0.3, |
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num_sample=16, |
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mlp_channels=[256, 128, 128, 128], |
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use_xyz=True, |
|
normalize_xyz=True), |
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pred_layer_cfg=dict( |
|
in_channels=128, shared_conv_channels=(128, 128), bias=True), |
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objectness_loss=dict( |
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type='mmdet.CrossEntropyLoss', |
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class_weight=[0.2, 0.8], |
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reduction='sum', |
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loss_weight=5.0), |
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center_loss=dict( |
|
type='ChamferDistance', |
|
mode='l2', |
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reduction='sum', |
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loss_src_weight=10.0, |
|
loss_dst_weight=10.0), |
|
dir_class_loss=dict( |
|
type='mmdet.CrossEntropyLoss', reduction='sum', loss_weight=1.0), |
|
dir_res_loss=dict( |
|
type='mmdet.SmoothL1Loss', reduction='sum', loss_weight=10.0), |
|
size_class_loss=dict( |
|
type='mmdet.CrossEntropyLoss', 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=10.0 / 3.0), |
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semantic_loss=dict( |
|
type='mmdet.CrossEntropyLoss', reduction='sum', loss_weight=1.0)), |
|
|
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train_cfg=dict( |
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pos_distance_thr=0.3, neg_distance_thr=0.6, sample_mode='vote'), |
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test_cfg=dict( |
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sample_mode='seed', |
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nms_thr=0.25, |
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score_thr=0.05, |
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per_class_proposal=True)) |
|
|
