add h3d head

configs/_base_/models/h3dnet.py

@@ -0,0 +1,157 @@
+proposal_module_cfg = dict(
+ suface_matching_cfg=dict(
+ 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(
+ 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,
+ train_cfg=dict(
+ 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),
+ cues_objectness_loss=dict(
+ type='CrossEntropyLoss',
+ class_weight=[0.3, 0.7],
+ reduction='mean',
+ loss_weight=5.0),
+ cues_semantic_loss=dict(
+ type='CrossEntropyLoss',
+ class_weight=[0.3, 0.7],
+ reduction='mean',
+ loss_weight=5.0),
+ proposal_objectness_loss=dict(
+ type='CrossEntropyLoss',
+ class_weight=[0.2, 0.8],
+ reduction='none',
+ loss_weight=5.0),
+)
+
+model = dict(
+ type='H3DNet',
+ backbone=dict(
+ type='MultiBackbone',
+ suffixes=['net0', 'net1', 'net2', 'net3'],
+ num_streams=4,
+ 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'),
+ pool_mod='max')),
+ rpn_head=dict(
+ type='VoteHead',
+ vote_moudule_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(
+ num_point=256,
+ 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='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='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
+ dir_res_loss=dict(
+ type='SmoothL1Loss', reduction='sum', loss_weight=10.0),
+ size_class_loss=dict(
+ type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
+ size_res_loss=dict(
+ type='SmoothL1Loss', reduction='sum', loss_weight=10.0),
+ semantic_loss=dict(
+ type='CrossEntropyLoss', reduction='sum', loss_weight=1.0)),
+ roi_head=dict(
+ type='H3DRoIHead',
+ bbox_head=dict(
+ type='H3dHead',
+ gt_per_seed=3,
+ num_proposal=256,
+ proposal_module_cfg=proposal_module_cfg,
+ feat_channels=(128, 128),
+ conv_cfg=dict(type='Conv1d'),
+ norm_cfg=dict(type='BN1d'),
+ objectness_loss=dict(
+ type='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='CrossEntropyLoss', reduction='sum', loss_weight=0.1),
+ dir_res_loss=dict(
+ type='SmoothL1Loss', reduction='sum', loss_weight=10.0),
+ size_class_loss=dict(
+ type='CrossEntropyLoss', reduction='sum', loss_weight=0.1),
+ size_res_loss=dict(
+ type='SmoothL1Loss', reduction='sum', loss_weight=10.0),
+ semantic_loss=dict(
+ type='CrossEntropyLoss', reduction='sum', loss_weight=0.1))))
+
+# model training and testing settings
+train_cfg = dict(
+ rpn=dict(pos_distance_thr=0.3, neg_distance_thr=0.6, sample_mod='vote'),
+ rcnn=dict(pos_distance_thr=0.3, neg_distance_thr=0.6, sample_mod='vote'))
+
+test_cfg = dict(
+ rpn=dict(
+ sample_mod='seed',
+ nms_thr=0.25,
+ score_thr=0.05,
+ per_class_proposal=True),
+ rcnn=dict(
+ sample_mod='seed',
+ nms_thr=0.25,
+ score_thr=0.05,
+ per_class_proposal=True))