|
primitive_z_cfg = dict( |
|
type='PrimitiveHead', |
|
num_dims=2, |
|
num_classes=18, |
|
primitive_mode='z', |
|
upper_thresh=100.0, |
|
surface_thresh=0.5, |
|
vote_module_cfg=dict( |
|
in_channels=256, |
|
vote_per_seed=1, |
|
gt_per_seed=1, |
|
conv_channels=(256, 256), |
|
conv_cfg=dict(type='Conv1d'), |
|
norm_cfg=dict(type='BN1d'), |
|
norm_feats=True, |
|
vote_loss=dict( |
|
type='ChamferDistance', |
|
mode='l1', |
|
reduction='none', |
|
loss_dst_weight=10.0)), |
|
vote_aggregation_cfg=dict( |
|
type='PointSAModule', |
|
num_point=1024, |
|
radius=0.3, |
|
num_sample=16, |
|
mlp_channels=[256, 128, 128, 128], |
|
use_xyz=True, |
|
normalize_xyz=True), |
|
feat_channels=(128, 128), |
|
conv_cfg=dict(type='Conv1d'), |
|
norm_cfg=dict(type='BN1d'), |
|
objectness_loss=dict( |
|
type='mmdet.CrossEntropyLoss', |
|
class_weight=[0.4, 0.6], |
|
reduction='mean', |
|
loss_weight=30.0), |
|
center_loss=dict( |
|
type='ChamferDistance', |
|
mode='l1', |
|
reduction='sum', |
|
loss_src_weight=0.5, |
|
loss_dst_weight=0.5), |
|
semantic_reg_loss=dict( |
|
type='ChamferDistance', |
|
mode='l1', |
|
reduction='sum', |
|
loss_src_weight=0.5, |
|
loss_dst_weight=0.5), |
|
semantic_cls_loss=dict( |
|
type='mmdet.CrossEntropyLoss', reduction='sum', loss_weight=1.0), |
|
train_cfg=dict( |
|
sample_mode='vote', |
|
dist_thresh=0.2, |
|
var_thresh=1e-2, |
|
lower_thresh=1e-6, |
|
num_point=100, |
|
num_point_line=10, |
|
line_thresh=0.2), |
|
test_cfg=dict(sample_mode='seed')) |
|
|
|
primitive_xy_cfg = dict( |
|
type='PrimitiveHead', |
|
num_dims=1, |
|
num_classes=18, |
|
primitive_mode='xy', |
|
upper_thresh=100.0, |
|
surface_thresh=0.5, |
|
vote_module_cfg=dict( |
|
in_channels=256, |
|
vote_per_seed=1, |
|
gt_per_seed=1, |
|
conv_channels=(256, 256), |
|
conv_cfg=dict(type='Conv1d'), |
|
norm_cfg=dict(type='BN1d'), |
|
norm_feats=True, |
|
vote_loss=dict( |
|
type='ChamferDistance', |
|
mode='l1', |
|
reduction='none', |
|
loss_dst_weight=10.0)), |
|
vote_aggregation_cfg=dict( |
|
type='PointSAModule', |
|
num_point=1024, |
|
radius=0.3, |
|
num_sample=16, |
|
mlp_channels=[256, 128, 128, 128], |
|
use_xyz=True, |
|
normalize_xyz=True), |
|
feat_channels=(128, 128), |
|
conv_cfg=dict(type='Conv1d'), |
|
norm_cfg=dict(type='BN1d'), |
|
objectness_loss=dict( |
|
type='mmdet.CrossEntropyLoss', |
|
class_weight=[0.4, 0.6], |
|
reduction='mean', |
|
loss_weight=30.0), |
|
center_loss=dict( |
|
type='ChamferDistance', |
|
mode='l1', |
|
reduction='sum', |
|
loss_src_weight=0.5, |
|
loss_dst_weight=0.5), |
|
semantic_reg_loss=dict( |
|
type='ChamferDistance', |
|
mode='l1', |
|
reduction='sum', |
|
loss_src_weight=0.5, |
|
loss_dst_weight=0.5), |
|
semantic_cls_loss=dict( |
|
type='mmdet.CrossEntropyLoss', reduction='sum', loss_weight=1.0), |
|
train_cfg=dict( |
|
sample_mode='vote', |
|
dist_thresh=0.2, |
|
var_thresh=1e-2, |
|
lower_thresh=1e-6, |
|
num_point=100, |
|
num_point_line=10, |
|
line_thresh=0.2), |
|
test_cfg=dict(sample_mode='seed')) |
|
|
|
primitive_line_cfg = dict( |
|
type='PrimitiveHead', |
|
num_dims=0, |
|
num_classes=18, |
|
primitive_mode='line', |
|
upper_thresh=100.0, |
|
surface_thresh=0.5, |
|
vote_module_cfg=dict( |
|
in_channels=256, |
|
vote_per_seed=1, |
|
gt_per_seed=1, |
|
conv_channels=(256, 256), |
|
conv_cfg=dict(type='Conv1d'), |
|
norm_cfg=dict(type='BN1d'), |
|
norm_feats=True, |
|
vote_loss=dict( |
|
type='ChamferDistance', |
|
mode='l1', |
|
reduction='none', |
|
loss_dst_weight=10.0)), |
|
vote_aggregation_cfg=dict( |
|
type='PointSAModule', |
|
num_point=1024, |
|
radius=0.3, |
|
num_sample=16, |
|
mlp_channels=[256, 128, 128, 128], |
|
use_xyz=True, |
|
normalize_xyz=True), |
|
feat_channels=(128, 128), |
|
conv_cfg=dict(type='Conv1d'), |
|
norm_cfg=dict(type='BN1d'), |
|
objectness_loss=dict( |
|
type='mmdet.CrossEntropyLoss', |
|
class_weight=[0.4, 0.6], |
|
reduction='mean', |
|
loss_weight=30.0), |
|
center_loss=dict( |
|
type='ChamferDistance', |
|
mode='l1', |
|
reduction='sum', |
|
loss_src_weight=1.0, |
|
loss_dst_weight=1.0), |
|
semantic_reg_loss=dict( |
|
type='ChamferDistance', |
|
mode='l1', |
|
reduction='sum', |
|
loss_src_weight=1.0, |
|
loss_dst_weight=1.0), |
|
semantic_cls_loss=dict( |
|
type='mmdet.CrossEntropyLoss', reduction='sum', loss_weight=2.0), |
|
train_cfg=dict( |
|
sample_mode='vote', |
|
dist_thresh=0.2, |
|
var_thresh=1e-2, |
|
lower_thresh=1e-6, |
|
num_point=100, |
|
num_point_line=10, |
|
line_thresh=0.2), |
|
test_cfg=dict(sample_mode='seed')) |
|
|
|
model = dict( |
|
type='H3DNet', |
|
data_preprocessor=dict(type='Det3DDataPreprocessor'), |
|
backbone=dict( |
|
type='MultiBackbone', |
|
num_streams=4, |
|
suffixes=['net0', 'net1', 'net2', 'net3'], |
|
conv_cfg=dict(type='Conv1d'), |
|
norm_cfg=dict(type='BN1d', eps=1e-5, momentum=0.01), |
|
act_cfg=dict(type='ReLU'), |
|
backbones=dict( |
|
type='PointNet2SASSG', |
|
in_channels=4, |
|
num_points=(2048, 1024, 512, 256), |
|
radius=(0.2, 0.4, 0.8, 1.2), |
|
num_samples=(64, 32, 16, 16), |
|
sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256), |
|
(128, 128, 256)), |
|
fp_channels=((256, 256), (256, 256)), |
|
norm_cfg=dict(type='BN2d'), |
|
sa_cfg=dict( |
|
type='PointSAModule', |
|
pool_mod='max', |
|
use_xyz=True, |
|
normalize_xyz=True))), |
|
rpn_head=dict( |
|
type='VoteHead', |
|
vote_module_cfg=dict( |
|
in_channels=256, |
|
vote_per_seed=1, |
|
gt_per_seed=3, |
|
conv_channels=(256, 256), |
|
conv_cfg=dict(type='Conv1d'), |
|
norm_cfg=dict(type='BN1d'), |
|
norm_feats=True, |
|
vote_loss=dict( |
|
type='ChamferDistance', |
|
mode='l1', |
|
reduction='none', |
|
loss_dst_weight=10.0)), |
|
vote_aggregation_cfg=dict( |
|
type='PointSAModule', |
|
num_point=256, |
|
radius=0.3, |
|
num_sample=16, |
|
mlp_channels=[256, 128, 128, 128], |
|
use_xyz=True, |
|
normalize_xyz=True), |
|
pred_layer_cfg=dict( |
|
in_channels=128, shared_conv_channels=(128, 128), bias=True), |
|
objectness_loss=dict( |
|
type='mmdet.CrossEntropyLoss', |
|
class_weight=[0.2, 0.8], |
|
reduction='sum', |
|
loss_weight=5.0), |
|
center_loss=dict( |
|
type='ChamferDistance', |
|
mode='l2', |
|
reduction='sum', |
|
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), |
|
size_res_loss=dict( |
|
type='mmdet.SmoothL1Loss', reduction='sum', loss_weight=10.0), |
|
semantic_loss=dict( |
|
type='mmdet.CrossEntropyLoss', reduction='sum', loss_weight=1.0)), |
|
roi_head=dict( |
|
type='H3DRoIHead', |
|
primitive_list=[primitive_z_cfg, primitive_xy_cfg, primitive_line_cfg], |
|
bbox_head=dict( |
|
type='H3DBboxHead', |
|
gt_per_seed=3, |
|
num_proposal=256, |
|
suface_matching_cfg=dict( |
|
type='PointSAModule', |
|
num_point=256 * 6, |
|
radius=0.5, |
|
num_sample=32, |
|
mlp_channels=[128 + 6, 128, 64, 32], |
|
use_xyz=True, |
|
normalize_xyz=True), |
|
line_matching_cfg=dict( |
|
type='PointSAModule', |
|
num_point=256 * 12, |
|
radius=0.5, |
|
num_sample=32, |
|
mlp_channels=[128 + 12, 128, 64, 32], |
|
use_xyz=True, |
|
normalize_xyz=True), |
|
primitive_refine_channels=[128, 128, 128], |
|
upper_thresh=100.0, |
|
surface_thresh=0.5, |
|
line_thresh=0.5, |
|
conv_cfg=dict(type='Conv1d'), |
|
norm_cfg=dict(type='BN1d'), |
|
objectness_loss=dict( |
|
type='mmdet.CrossEntropyLoss', |
|
class_weight=[0.2, 0.8], |
|
reduction='sum', |
|
loss_weight=5.0), |
|
center_loss=dict( |
|
type='ChamferDistance', |
|
mode='l2', |
|
reduction='sum', |
|
loss_src_weight=10.0, |
|
loss_dst_weight=10.0), |
|
dir_class_loss=dict( |
|
type='mmdet.CrossEntropyLoss', |
|
reduction='sum', |
|
loss_weight=0.1), |
|
dir_res_loss=dict( |
|
type='mmdet.SmoothL1Loss', reduction='sum', loss_weight=10.0), |
|
size_class_loss=dict( |
|
type='mmdet.CrossEntropyLoss', |
|
reduction='sum', |
|
loss_weight=0.1), |
|
size_res_loss=dict( |
|
type='mmdet.SmoothL1Loss', reduction='sum', loss_weight=10.0), |
|
semantic_loss=dict( |
|
type='mmdet.CrossEntropyLoss', |
|
reduction='sum', |
|
loss_weight=0.1), |
|
cues_objectness_loss=dict( |
|
type='mmdet.CrossEntropyLoss', |
|
class_weight=[0.3, 0.7], |
|
reduction='mean', |
|
loss_weight=5.0), |
|
cues_semantic_loss=dict( |
|
type='mmdet.CrossEntropyLoss', |
|
class_weight=[0.3, 0.7], |
|
reduction='mean', |
|
loss_weight=5.0), |
|
proposal_objectness_loss=dict( |
|
type='mmdet.CrossEntropyLoss', |
|
class_weight=[0.2, 0.8], |
|
reduction='none', |
|
loss_weight=5.0), |
|
primitive_center_loss=dict( |
|
type='mmdet.MSELoss', reduction='none', loss_weight=1.0))), |
|
|
|
train_cfg=dict( |
|
rpn=dict( |
|
pos_distance_thr=0.3, neg_distance_thr=0.6, sample_mode='vote'), |
|
rpn_proposal=dict(use_nms=False), |
|
rcnn=dict( |
|
pos_distance_thr=0.3, |
|
neg_distance_thr=0.6, |
|
sample_mode='vote', |
|
far_threshold=0.6, |
|
near_threshold=0.3, |
|
mask_surface_threshold=0.3, |
|
label_surface_threshold=0.3, |
|
mask_line_threshold=0.3, |
|
label_line_threshold=0.3)), |
|
test_cfg=dict( |
|
rpn=dict( |
|
sample_mode='seed', |
|
nms_thr=0.25, |
|
score_thr=0.05, |
|
per_class_proposal=True, |
|
use_nms=False), |
|
rcnn=dict( |
|
sample_mode='seed', |
|
nms_thr=0.25, |
|
score_thr=0.05, |
|
per_class_proposal=True))) |
|
|
