PointRCNN/tools/cfgs/default.yaml

CLASSES: Car

INCLUDE_SIMILAR_TYPE: True

# config of augmentation
AUG_DATA: True
AUG_METHOD_LIST: ['rotation', 'scaling', 'flip']
AUG_METHOD_PROB: [1.0, 1.0, 0.5]
AUG_ROT_RANGE: 18

GT_AUG_ENABLED: True
GT_EXTRA_NUM: 15
GT_AUG_RAND_NUM: True
GT_AUG_APPLY_PROB: 1.0
GT_AUG_HARD_RATIO: 0.6

PC_REDUCE_BY_RANGE: True
PC_AREA_SCOPE: [[-40, 40], [-1,   3], [0, 70.4]]  # x, y, z scope in rect camera coords
CLS_MEAN_SIZE: [[1.52563191462, 1.62856739989, 3.88311640418]]


# 1. config of rpn network
RPN:
    ENABLED: True
    FIXED: False

    # config of input
    USE_INTENSITY: False

    # config of bin-based loss
    LOC_XZ_FINE: True
    LOC_SCOPE: 3.0
    LOC_BIN_SIZE: 0.5
    NUM_HEAD_BIN: 12

    # config of network structure
    BACKBONE: pointnet2_msg
    USE_BN: True
    NUM_POINTS: 16384

    SA_CONFIG:
        NPOINTS: [4096, 1024, 256, 64]
        RADIUS: [[0.1, 0.5], [0.5, 1.0], [1.0, 2.0], [2.0, 4.0]]
        NSAMPLE: [[16, 32], [16, 32], [16, 32], [16, 32]]
        MLPS: [[[16, 16, 32], [32, 32, 64]],
              [[64, 64, 128], [64, 96, 128]],
              [[128, 196, 256], [128, 196, 256]],
              [[256, 256, 512], [256, 384, 512]]]
    FP_MLPS: [[128, 128], [256, 256], [512, 512], [512, 512]]
    CLS_FC: [128]
    REG_FC: [128]
    DP_RATIO: 0.5

    # config of training
    LOSS_CLS: SigmoidFocalLoss
    FG_WEIGHT: 15
    FOCAL_ALPHA: [0.25, 0.75]
    FOCAL_GAMMA: 2.0
    REG_LOSS_WEIGHT: [1.0, 1.0, 1.0, 1.0]
    LOSS_WEIGHT: [1.0, 1.0]
    NMS_TYPE: normal

    # config of testing
    SCORE_THRESH: 0.3

# 2. config of rcnn network
RCNN:
    ENABLED: True

    # config of input
    ROI_SAMPLE_JIT: True
    REG_AUG_METHOD: multiple  # multiple, single, normal
    ROI_FG_AUG_TIMES: 10

    USE_RPN_FEATURES: True
    USE_MASK: True
    MASK_TYPE: seg
    USE_INTENSITY: False
    USE_DEPTH: True
    USE_SEG_SCORE: False

    POOL_EXTRA_WIDTH: 1.0

    # config of bin-based loss
    LOC_SCOPE: 1.5
    LOC_BIN_SIZE: 0.5
    NUM_HEAD_BIN: 9
    LOC_Y_BY_BIN: False
    LOC_Y_SCOPE: 0.5
    LOC_Y_BIN_SIZE: 0.25
    SIZE_RES_ON_ROI: False

    # config of network structure
    USE_BN: False
    DP_RATIO: 0.0

    BACKBONE: pointnet  # pointnet
    XYZ_UP_LAYER: [128, 128]

    NUM_POINTS: 512
    SA_CONFIG:
        NPOINTS: [128, 32, -1]
        RADIUS: [0.2, 0.4, 100]
        NSAMPLE: [64, 64, 64]
        MLPS: [[128, 128, 128],
               [128, 128, 256],
               [256, 256, 512]]
    CLS_FC: [256, 256]
    REG_FC: [256, 256]

    # config of training
    LOSS_CLS: BinaryCrossEntropy
    FOCAL_ALPHA: [0.25, 0.75]
    FOCAL_GAMMA: 2.0
    CLS_WEIGHT: [1.0, 1.0, 1.0]
    CLS_FG_THRESH: 0.6
    CLS_BG_THRESH: 0.45
    CLS_BG_THRESH_LO: 0.05
    REG_FG_THRESH: 0.55
    FG_RATIO: 0.5
    ROI_PER_IMAGE: 64
    HARD_BG_RATIO: 0.8

    # config of testing
    SCORE_THRESH: 0.3
    NMS_THRESH: 0.1

# general training config
TRAIN:
    SPLIT: train
    VAL_SPLIT: smallval

    LR: 0.002
    LR_CLIP: 0.00001
    LR_DECAY: 0.5
    DECAY_STEP_LIST: [100, 150, 180, 200]
    LR_WARMUP: True
    WARMUP_MIN: 0.0002
    WARMUP_EPOCH: 1

    BN_MOMENTUM: 0.1
    BN_DECAY: 0.5
    BNM_CLIP: 0.01
    BN_DECAY_STEP_LIST: [1000]

    OPTIMIZER: adam_onecycle  # adam, adam_onecycle
    WEIGHT_DECAY: 0.001  # L2 regularization
    MOMENTUM: 0.9

    MOMS: [0.95, 0.85]
    DIV_FACTOR: 10.0
    PCT_START: 0.4

    GRAD_NORM_CLIP: 1.0

    RPN_PRE_NMS_TOP_N: 9000
    RPN_POST_NMS_TOP_N: 512
    RPN_NMS_THRESH: 0.85
    RPN_DISTANCE_BASED_PROPOSE: True

TEST:
    SPLIT: val
    RPN_PRE_NMS_TOP_N: 9000
    RPN_POST_NMS_TOP_N: 100
    RPN_NMS_THRESH: 0.8
    RPN_DISTANCE_BASED_PROPOSE: True