# model settings voxel_size = [0.05, 0.05, 0.1] point_cloud_range = [0, -40, -3, 70.4, 40, 1] model = dict( type='VoxelNet', voxel_layer=dict( max_num_points=5, point_cloud_range=point_cloud_range, voxel_size=voxel_size, max_voxels=(16000, 40000), # (training, testing) max_coxels ), voxel_encoder=dict(type='HardSimpleVFE'), middle_encoder=dict( type='SparseEncoder', in_channels=4, sparse_shape=[41, 1600, 1408], order=('conv', 'norm', 'act')), backbone=dict( type='SECOND', in_channels=256, layer_nums=[5, 5], layer_strides=[1, 2], out_channels=[128, 256], ), neck=dict( type='SECONDFPN', in_channels=[128, 256], upsample_strides=[1, 2], out_channels=[256, 256], ), bbox_head=dict( type='Anchor3DHead', num_classes=3, in_channels=512, feat_channels=512, use_direction_classifier=True, anchor_generator=dict( type='Anchor3DRangeGenerator', ranges=[ [0, -40.0, -0.6, 70.4, 40.0, -0.6], [0, -40.0, -0.6, 70.4, 40.0, -0.6], [0, -40.0, -1.78, 70.4, 40.0, -1.78], ], sizes=[[0.6, 0.8, 1.73], [0.6, 1.76, 1.73], [1.6, 3.9, 1.56]], rotations=[0, 1.57], reshape_out=False), diff_rad_by_sin=True, bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'), loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=2.0), loss_dir=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2), ), ) # model training and testing settings train_cfg = dict( assigner=[ dict( # for Pedestrian type='MaxIoUAssigner', iou_calculator=dict(type='BboxOverlapsNearest3D'), pos_iou_thr=0.35, neg_iou_thr=0.2, min_pos_iou=0.2, ignore_iof_thr=-1), dict( # for Cyclist type='MaxIoUAssigner', iou_calculator=dict(type='BboxOverlapsNearest3D'), pos_iou_thr=0.35, neg_iou_thr=0.2, min_pos_iou=0.2, ignore_iof_thr=-1), dict( # for Car 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) test_cfg = dict( use_rotate_nms=True, nms_across_levels=False, nms_thr=0.01, score_thr=0.1, min_bbox_size=0, nms_pre=100, max_num=50) # dataset settings dataset_type = 'KittiDataset' data_root = 'data/kitti/' class_names = ['Pedestrian', 'Cyclist', '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, object_rot_range=[0.0, 0.0], prepare=dict( filter_by_difficulty=[-1], filter_by_min_points=dict( Car=5, Pedestrian=10, Cyclist=10, )), classes=class_names, sample_groups=dict( Car=12, Pedestrian=6, Cyclist=6, )) file_client_args = dict(backend='disk') # file_client_args = dict( # backend='petrel', path_mapping=dict(data='s3://kitti_data/')) train_pipeline = [ dict( type='LoadPointsFromFile', load_dim=4, use_dim=4, file_client_args=file_client_args), dict( type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, file_client_args=file_client_args), dict(type='ObjectSample', db_sampler=db_sampler), dict( type='ObjectNoise', num_try=100, loc_noise_std=[1.0, 1.0, 0.5], global_rot_range=[0.0, 0.0], rot_uniform_noise=[-0.78539816, 0.78539816]), dict(type='RandomFlip3D', flip_ratio=0.5), dict( type='GlobalRotScale', rot_uniform_noise=[-0.78539816, 0.78539816], scaling_uniform_noise=[0.95, 1.05]), dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range), dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range), 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', load_dim=4, use_dim=4, file_client_args=file_client_args), 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( samples_per_gpu=6, workers_per_gpu=4, train=dict( type='RepeatDataset', times=2, dataset=dict( type=dataset_type, data_root=data_root, ann_file=data_root + 'kitti_infos_train.pkl', split='training', pts_prefix='velodyne_reduced', pipeline=train_pipeline, modality=input_modality, classes=class_names, test_mode=False)), val=dict( type=dataset_type, data_root=data_root, ann_file=data_root + 'kitti_infos_val.pkl', split='training', pts_prefix='velodyne_reduced', pipeline=test_pipeline, modality=input_modality, classes=class_names, test_mode=True), test=dict( type=dataset_type, data_root=data_root, ann_file=data_root + 'kitti_infos_val.pkl', split='training', pts_prefix='velodyne_reduced', pipeline=test_pipeline, modality=input_modality, classes=class_names, test_mode=True)) # optimizer lr = 0.0018 # max learning rate optimizer = dict(type='AdamW', lr=lr, betas=(0.95, 0.99), weight_decay=0.01) optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2)) lr_config = dict( policy='cyclic', target_ratio=(10, 1e-4), cyclic_times=1, step_ratio_up=0.4, ) momentum_config = dict( policy='cyclic', target_ratio=(0.85 / 0.95, 1), cyclic_times=1, step_ratio_up=0.4, ) checkpoint_config = dict(interval=1) evaluation = dict(interval=1) # yapf:disable log_config = dict( interval=50, hooks=[ dict(type='TextLoggerHook'), dict(type='TensorboardLoggerHook') ]) # yapf:enable # runtime settings total_epochs = 40 dist_params = dict(backend='nccl') log_level = 'INFO' work_dir = './work_dirs/sec_secfpn_80e' load_from = None resume_from = None workflow = [('train', 1)]