ptt.yaml

CLASS_NAMES: Car    # ['Car', 'Pedestrian', 'Cyclist', 'Van']

DATA_CONFIG:
    DATASET: 'KittiTrackingDataset'
    DATA_PATH: '../data/kitti'
    DEBUG: False   # whether debug mode
    REF_COOR: 'lidar'   # lidar / camera
    USE_Z_AXIS: True   # whether use the z prediction of model
    LOAD_FROM_DATABASE: True   # whether use the memory to save time
    LIDAR_CROP_OFFSET: 10.0   # the crop offset when training
    NUM_CANDIDATES_PERFRAME: 4   # generate random offsets per frame
    SEARCH_INPUT_SIZE: 1024   # the regularized points number of search area
    TEMPLATE_INPUT_SIZE: 512   # the regularized points number of template area
    SEARCH_BB_OFFSET: 0.0
    SEARCH_BB_SCALE: 1.25
    MODEL_BB_OFFSET: 0.0
    MODEL_BB_SCALE: 1.25
    REFINE_BOX_SIZE: True   # whether scale the box by a ratio, adopted from p2b
    POINT_CLOUD_RANGE: [-1, -1, -1]   # -1 means unlimited
    DATA_SPLIT: {
        'train': train,
        'test': test
    }
    SAMPLED_INTERVAL: 1   # only for training
    INFO_PATH: {
        'train': kitti_infos_train.pkl,
        'test': kitti_infos_test.pkl,
    }
    FOV_POINTS_ONLY: False
    POINT_FEATURE_ENCODING: {
        encoding_type: absolute_coordinates_encoding,
        used_feature_list: [ 'x', 'y', 'z' ],
        src_feature_list: [ 'x', 'y', 'z', 'intensity' ],
    }

MODEL:
    NAME: PTT   # PTT / P2B
    BACKBONE_3D:
        NAME: PointNet2BackboneLight
        DEBUG: False   # whether debug mode
        SA_CONFIG:
            SAMPLE_METHOD: ['fps', 'sequence', 'sequence']  # fps/sequence/rs  ffps not support
            USE_XYZ: True
            NORMALIZE_XYZ: True
            NPOINTS_SEARCH: [512, 256, 128]
            NPOINTS_TEMPLATE: [256, 128, 64]
            RADIUS: [0.3, 0.5, 0.7]
            NSAMPLE: [32, 32, 32]
            MLPS: [[0, 64, 64, 128],
                   [128, 128, 128, 256],
                   [256, 128, 128, 256]]

    SIMILARITY_MODULE:
        NAME: CosineSimAug
        DEBUG: False   # whether debug mode
        MLP:
            CHANNELS: [260, 256, 256, 256]
            BN: True
        CONV:
            CHANNELS: [256, 256, 256]
            BN: True

    CENTROID_HEAD:
        NAME: CentroidVotingHead
        DEBUG: False   # whether debug mode
        CLS_USE_SEARCH_XYZ: False
        CLS_FC:
            CHANNELS: [256, 256, 256, 1]
        REG_FC:
            CHANNELS: [259, 256, 256, 259]

        TRANSFORMER_BLOCK:
            ENABLE: True
            NAME: TransformerBlock   # TransformerBlock / MulTransformerBlock
            DIM_INPUT: 256
            DIM_MODEL: 512
            KNN: 16
            N_HEADS: 1
            N_LAYERS: 1

        LOSS_CONFIG:
            CLS_LOSS: BinaryCrossEntropy
            CLS_LOSS_REDUCTION: 'mean'
            CLS_LOSS_POS_WEIGHT: 1.0
            REG_LOSS: smooth-l1
            LOSS_WEIGHTS: {
                'centroids_cls_weight': 0.2,
                'centroids_reg_weight': 1.0
            }

    BOX_HEAD:
        NAME: BoxVotingHead
        DEBUG: False   # whether debug mode
        FC: [256, 256, 256, 5]

        SA_CONFIG:
            NPOINTS: 64
            RADIUS: 0.3
            NSAMPLE: 16
            MLPS: [257, 256, 256, 256]
            USE_XYZ: True
            NORMALIZE_XYZ: True
            SAMPLE_METHOD: 'fps'

        TRANSFORMER_BLOCK:
            ENABLE: True
            NAME: TransformerBlock   # TransformerBlock / MulTransformerBlock
            DIM_INPUT: 256
            DIM_MODEL: 512
            KNN: 16
            N_HEADS: 1
            N_LAYERS: 1

        LOSS_CONFIG:
            CLS_LOSS: BinaryCrossEntropy
            CLS_LOSS_REDUCTION: 'none'
            CLS_LOSS_POS_WEIGHT: 2.0
            REG_LOSS: smooth-l1
            LOSS_WEIGHTS: {
                'boxes_cls_weight': 1.5,
                'boxes_reg_weight': 0.2
            }

OPTIMIZATION:
    DEBUG: False   # whether debug mode
    BATCH_SIZE_PER_GPU: 48
    NUM_EPOCHS: 60
    # optimizer
    OPTIMIZER: adam
    LR: 0.001
    WEIGHT_DECAY: 0
    BETAS: [0.5, 0.999]
    EPS: 1e-06
    # scheduler
    SCHEDULER: 'step'
    STEP_SIZE: 12
    GAMMA: 0.2
    # grad clip
    GRAD_NORM_CLIP: 10

TRAIN:
    WITH_EVAL:
        ENABLE: False
        START_EPOCH: 3
        INTERVAL: 1
TEST:
    VISUALIZE: False
    SAVE_PCD: False   # TODO check could use or not
    SHAPE_AGGREGATION: firstandprevious   # first/previous/firstandprevious/all
    REF_BOX: previous_result   # previous_result/previous_gt/current_gt