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Add pv_rcnn_plusplus config for KITTI (open-mmlab#1422)
* Add pv_rcnn_plusplus config for KITTI * Rename pv_rcnn_plusplus config file
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‎tools/cfgs/kitti_models/pv_rcnn_plusplus_reproduced_by_community.yaml‎

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CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist']
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DATA_CONFIG:
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_BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml
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DATA_AUGMENTOR:
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DISABLE_AUG_LIST: ['placeholder']
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AUG_CONFIG_LIST:
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- NAME: gt_sampling
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USE_ROAD_PLANE: True
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DB_INFO_PATH:
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- kitti_dbinfos_train.pkl
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PREPARE: {
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filter_by_min_points: ['Car:5', 'Pedestrian:5', 'Cyclist:5'],
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filter_by_difficulty: [-1],
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}
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SAMPLE_GROUPS: ['Car:15','Pedestrian:10', 'Cyclist:10']
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NUM_POINT_FEATURES: 4
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DATABASE_WITH_FAKELIDAR: False
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REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0]
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LIMIT_WHOLE_SCENE: False
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- NAME: random_world_flip
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ALONG_AXIS_LIST: ['x']
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- NAME: random_world_rotation
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WORLD_ROT_ANGLE: [-0.78539816, 0.78539816]
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- NAME: random_world_scaling
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WORLD_SCALE_RANGE: [0.95, 1.05]
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MODEL:
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NAME: PVRCNNPlusPlus
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VFE:
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NAME: MeanVFE
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BACKBONE_3D:
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NAME: VoxelBackBone8x
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MAP_TO_BEV:
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NAME: HeightCompression
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NUM_BEV_FEATURES: 256
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BACKBONE_2D:
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NAME: BaseBEVBackbone
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LAYER_NUMS: [5, 5]
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LAYER_STRIDES: [1, 2]
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NUM_FILTERS: [128, 256]
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UPSAMPLE_STRIDES: [1, 2]
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NUM_UPSAMPLE_FILTERS: [256, 256]
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DENSE_HEAD:
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NAME: CenterHead
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CLASS_AGNOSTIC: False
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CLASS_NAMES_EACH_HEAD: [
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[ 'Car', 'Pedestrian', 'Cyclist' ]
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]
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SHARED_CONV_CHANNEL: 64
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USE_BIAS_BEFORE_NORM: True
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NUM_HM_CONV: 2
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SEPARATE_HEAD_CFG:
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HEAD_ORDER: [ 'center', 'center_z', 'dim', 'rot' ]
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HEAD_DICT: {
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'center': { 'out_channels': 2, 'num_conv': 2 },
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'center_z': { 'out_channels': 1, 'num_conv': 2 },
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'dim': { 'out_channels': 3, 'num_conv': 2 },
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'rot': { 'out_channels': 2, 'num_conv': 2 },
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}
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TARGET_ASSIGNER_CONFIG:
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FEATURE_MAP_STRIDE: 8
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NUM_MAX_OBJS: 500
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GAUSSIAN_OVERLAP: 0.1
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MIN_RADIUS: 2
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LOSS_CONFIG:
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LOSS_WEIGHTS: {
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'cls_weight': 1.0,
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'loc_weight': 2.0,
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'code_weights': [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ]
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}
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POST_PROCESSING:
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SCORE_THRESH: 0.01
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POST_CENTER_LIMIT_RANGE: [ -75.2, -75.2, -2, 75.2, 75.2, 4 ]
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MAX_OBJ_PER_SAMPLE: 500
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NMS_CONFIG:
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NMS_TYPE: nms_gpu
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NMS_THRESH: 0.7
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NMS_PRE_MAXSIZE: 4096
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NMS_POST_MAXSIZE: 500
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PFE:
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NAME: VoxelSetAbstraction
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POINT_SOURCE: raw_points
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NUM_KEYPOINTS: 4096
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NUM_OUTPUT_FEATURES: 90
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SAMPLE_METHOD: SPC
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SPC_SAMPLING:
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NUM_SECTORS: 6
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SAMPLE_RADIUS_WITH_ROI: 1.6
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FEATURES_SOURCE: ['bev', 'x_conv3', 'x_conv4', 'raw_points']
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SA_LAYER:
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raw_points:
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NAME: VectorPoolAggregationModuleMSG
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NUM_GROUPS: 2
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LOCAL_AGGREGATION_TYPE: local_interpolation
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NUM_REDUCED_CHANNELS: 1
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NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32
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MSG_POST_MLPS: [ 32 ]
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FILTER_NEIGHBOR_WITH_ROI: True
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RADIUS_OF_NEIGHBOR_WITH_ROI: 2.4
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GROUP_CFG_0:
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NUM_LOCAL_VOXEL: [ 2, 2, 2 ]
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MAX_NEIGHBOR_DISTANCE: 0.2
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NEIGHBOR_NSAMPLE: -1
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POST_MLPS: [ 32, 32 ]
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GROUP_CFG_1:
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NUM_LOCAL_VOXEL: [ 3, 3, 3 ]
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MAX_NEIGHBOR_DISTANCE: 0.4
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NEIGHBOR_NSAMPLE: -1
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POST_MLPS: [ 32, 32 ]
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x_conv3:
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DOWNSAMPLE_FACTOR: 4
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INPUT_CHANNELS: 64
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NAME: VectorPoolAggregationModuleMSG
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NUM_GROUPS: 2
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LOCAL_AGGREGATION_TYPE: local_interpolation
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NUM_REDUCED_CHANNELS: 32
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NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32
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MSG_POST_MLPS: [128]
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FILTER_NEIGHBOR_WITH_ROI: True
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RADIUS_OF_NEIGHBOR_WITH_ROI: 4.0
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GROUP_CFG_0:
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NUM_LOCAL_VOXEL: [3, 3, 3]
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MAX_NEIGHBOR_DISTANCE: 1.2
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NEIGHBOR_NSAMPLE: -1
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POST_MLPS: [64, 64]
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GROUP_CFG_1:
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NUM_LOCAL_VOXEL: [ 3, 3, 3 ]
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MAX_NEIGHBOR_DISTANCE: 2.4
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NEIGHBOR_NSAMPLE: -1
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POST_MLPS: [ 64, 64 ]
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x_conv4:
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DOWNSAMPLE_FACTOR: 8
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INPUT_CHANNELS: 64
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NAME: VectorPoolAggregationModuleMSG
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NUM_GROUPS: 2
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LOCAL_AGGREGATION_TYPE: local_interpolation
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NUM_REDUCED_CHANNELS: 32
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NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32
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MSG_POST_MLPS: [ 128 ]
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FILTER_NEIGHBOR_WITH_ROI: True
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RADIUS_OF_NEIGHBOR_WITH_ROI: 6.4
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GROUP_CFG_0:
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NUM_LOCAL_VOXEL: [ 3, 3, 3 ]
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MAX_NEIGHBOR_DISTANCE: 2.4
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NEIGHBOR_NSAMPLE: -1
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POST_MLPS: [ 64, 64 ]
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GROUP_CFG_1:
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NUM_LOCAL_VOXEL: [ 3, 3, 3 ]
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MAX_NEIGHBOR_DISTANCE: 4.8
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NEIGHBOR_NSAMPLE: -1
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POST_MLPS: [ 64, 64 ]
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POINT_HEAD:
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NAME: PointHeadSimple
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CLS_FC: [256, 256]
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CLASS_AGNOSTIC: True
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USE_POINT_FEATURES_BEFORE_FUSION: True
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TARGET_CONFIG:
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GT_EXTRA_WIDTH: [0.2, 0.2, 0.2]
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LOSS_CONFIG:
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LOSS_REG: smooth-l1
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LOSS_WEIGHTS: {
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'point_cls_weight': 1.0,
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}
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ROI_HEAD:
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NAME: PVRCNNHead
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CLASS_AGNOSTIC: True
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SHARED_FC: [256, 256]
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CLS_FC: [256, 256]
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REG_FC: [256, 256]
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DP_RATIO: 0.3
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NMS_CONFIG:
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TRAIN:
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NMS_TYPE: nms_gpu
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MULTI_CLASSES_NMS: False
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NMS_PRE_MAXSIZE: 9000
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NMS_POST_MAXSIZE: 512
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NMS_THRESH: 0.8
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TEST:
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NMS_TYPE: nms_gpu
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MULTI_CLASSES_NMS: False
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NMS_PRE_MAXSIZE: 1024
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NMS_POST_MAXSIZE: 100
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NMS_THRESH: 0.7
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SCORE_THRESH: 0.1
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# NMS_PRE_MAXSIZE: 4096
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# NMS_POST_MAXSIZE: 500
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# NMS_THRESH: 0.85
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ROI_GRID_POOL:
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GRID_SIZE: 6
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NAME: VectorPoolAggregationModuleMSG
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NUM_GROUPS: 2
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LOCAL_AGGREGATION_TYPE: voxel_random_choice
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NUM_REDUCED_CHANNELS: 30
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NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32
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MSG_POST_MLPS: [ 128 ]
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GROUP_CFG_0:
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NUM_LOCAL_VOXEL: [ 3, 3, 3 ]
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MAX_NEIGHBOR_DISTANCE: 0.8
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NEIGHBOR_NSAMPLE: 32
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POST_MLPS: [ 64, 64 ]
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GROUP_CFG_1:
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NUM_LOCAL_VOXEL: [ 3, 3, 3 ]
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MAX_NEIGHBOR_DISTANCE: 1.6
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NEIGHBOR_NSAMPLE: 32
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POST_MLPS: [ 64, 64 ]
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TARGET_CONFIG:
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BOX_CODER: ResidualCoder
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ROI_PER_IMAGE: 128
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FG_RATIO: 0.5
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SAMPLE_ROI_BY_EACH_CLASS: True
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CLS_SCORE_TYPE: roi_iou
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CLS_FG_THRESH: 0.75
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CLS_BG_THRESH: 0.25
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CLS_BG_THRESH_LO: 0.1
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HARD_BG_RATIO: 0.8
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REG_FG_THRESH: 0.55
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LOSS_CONFIG:
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CLS_LOSS: BinaryCrossEntropy
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REG_LOSS: smooth-l1
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CORNER_LOSS_REGULARIZATION: True
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LOSS_WEIGHTS: {
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'rcnn_cls_weight': 1.0,
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'rcnn_reg_weight': 1.0,
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'rcnn_corner_weight': 1.0,
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'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
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}
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POST_PROCESSING:
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RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
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SCORE_THRESH: 0.1
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OUTPUT_RAW_SCORE: False
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EVAL_METRIC: kitti
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NMS_CONFIG:
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MULTI_CLASSES_NMS: False
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NMS_TYPE: nms_gpu
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NMS_THRESH: 0.7
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NMS_PRE_MAXSIZE: 4096
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NMS_POST_MAXSIZE: 500
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OPTIMIZATION:
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BATCH_SIZE_PER_GPU: 2
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NUM_EPOCHS: 140
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OPTIMIZER: adam_onecycle
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LR: 0.01
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WEIGHT_DECAY: 0.001
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MOMENTUM: 0.9
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MOMS: [0.95, 0.85]
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PCT_START: 0.4
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DIV_FACTOR: 10
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DECAY_STEP_LIST: [35, 45]
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LR_DECAY: 0.1
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LR_CLIP: 0.0000001
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LR_WARMUP: False
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WARMUP_EPOCH: 1
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GRAD_NORM_CLIP: 10

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