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- _base_ = './hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py'
-
- point_cloud_range = [0, -40, -3, 70.4, 40, 1] # velodyne coordinates, x, y, z
-
- model = dict(
- rpn_head=dict(
- type='PartA2RPNHead',
- num_classes=1,
- anchor_generator=dict(
- _delete_=True,
- type='Anchor3DRangeGenerator',
- ranges=[[0, -40.0, -1.78, 70.4, 40.0, -1.78]],
- sizes=[[1.6, 3.9, 1.56]],
- rotations=[0, 1.57],
- reshape_out=False)),
- roi_head=dict(
- num_classes=1,
- semantic_head=dict(num_classes=1),
- bbox_head=dict(num_classes=1)),
- # model training and testing settings
- train_cfg=dict(
- _delete_=True,
- rpn=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- iou_calculator=dict(type='BboxOverlapsNearest3D'),
- pos_iou_thr=0.6,
- neg_iou_thr=0.45,
- min_pos_iou=0.45,
- ignore_iof_thr=-1),
- allowed_border=0,
- pos_weight=-1,
- debug=False),
- rpn_proposal=dict(
- nms_pre=9000,
- nms_post=512,
- max_num=512,
- nms_thr=0.8,
- score_thr=0,
- use_rotate_nms=False),
- rcnn=dict(
- assigner=dict( # for Car
- type='MaxIoUAssigner',
- iou_calculator=dict(type='BboxOverlaps3D', coordinate='lidar'),
- pos_iou_thr=0.55,
- neg_iou_thr=0.55,
- min_pos_iou=0.55,
- ignore_iof_thr=-1),
- sampler=dict(
- type='IoUNegPiecewiseSampler',
- num=128,
- pos_fraction=0.55,
- neg_piece_fractions=[0.8, 0.2],
- neg_iou_piece_thrs=[0.55, 0.1],
- neg_pos_ub=-1,
- add_gt_as_proposals=False,
- return_iou=True),
- cls_pos_thr=0.75,
- cls_neg_thr=0.25)),
- test_cfg=dict(
- rpn=dict(
- nms_pre=1024,
- nms_post=100,
- max_num=100,
- nms_thr=0.7,
- score_thr=0,
- use_rotate_nms=True),
- rcnn=dict(
- use_rotate_nms=True,
- use_raw_score=True,
- nms_thr=0.01,
- score_thr=0.1)))
-
- # dataset settings
- dataset_type = 'KittiDataset'
- data_root = 'data/kitti/'
- class_names = ['Car']
- input_modality = dict(use_lidar=True, use_camera=False)
- db_sampler = dict(
- data_root=data_root,
- info_path=data_root + 'kitti_dbinfos_train.pkl',
- rate=1.0,
- prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
- classes=class_names,
- sample_groups=dict(Car=15))
- train_pipeline = [
- dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
- dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
- dict(type='ObjectSample', db_sampler=db_sampler),
- dict(
- type='ObjectNoise',
- num_try=100,
- translation_std=[1.0, 1.0, 0.5],
- global_rot_range=[0.0, 0.0],
- rot_range=[-0.78539816, 0.78539816]),
- dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
- dict(
- type='GlobalRotScaleTrans',
- rot_range=[-0.78539816, 0.78539816],
- scale_ratio_range=[0.95, 1.05]),
- dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
- dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
- dict(type='ObjectNameFilter', classes=class_names),
- dict(type='PointShuffle'),
- dict(type='DefaultFormatBundle3D', class_names=class_names),
- dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
- ]
- test_pipeline = [
- dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
- dict(
- type='MultiScaleFlipAug3D',
- img_scale=(1333, 800),
- pts_scale_ratio=1,
- flip=False,
- transforms=[
- dict(
- type='GlobalRotScaleTrans',
- rot_range=[0, 0],
- scale_ratio_range=[1., 1.],
- translation_std=[0, 0, 0]),
- dict(type='RandomFlip3D'),
- dict(
- type='PointsRangeFilter', point_cloud_range=point_cloud_range),
- dict(
- type='DefaultFormatBundle3D',
- class_names=class_names,
- with_label=False),
- dict(type='Collect3D', keys=['points'])
- ])
- ]
-
- data = dict(
- train=dict(dataset=dict(pipeline=train_pipeline, classes=class_names)),
- val=dict(pipeline=test_pipeline, classes=class_names),
- test=dict(pipeline=test_pipeline, classes=class_names))
-
- find_unused_parameters = True
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