GLENet_C.yaml

# ad hoc
# If your want to train GLENet-C, you should replace pcdet/datasets/dataset.py with pcdet/datasets/dataset_van.py

CLASS_NAMES: ['Car']

DATA_CONFIG:
    _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml
    DATA_AUGMENTOR:
        DISABLE_AUG_LIST: ['placeholder']
        AUG_CONFIG_LIST:
            - NAME: gt_sampling
              USE_ROAD_PLANE: True
              DB_INFO_PATH:
                  - kitti_dbinfos_train.pkl
              PREPARE: {
                 filter_by_min_points: ['Car:5'],
                 filter_by_difficulty: [-1],
              }

              SAMPLE_GROUPS: ['Car:15']
              NUM_POINT_FEATURES: 4
              DATABASE_WITH_FAKELIDAR: False
              REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0]
              LIMIT_WHOLE_SCENE: False

            - NAME: noise_per_object
            #   GT_ROTATION_NOISE: [0, 0.0]
            #   GT_LOC_NOISE_STD: [0.0, 0.0, 0.0]
              GT_ROTATION_NOISE: [-0.785, 0.785]
              GT_LOC_NOISE_STD: [1.0, 1.0, 0.5]
              DATA_AUG_WITH_CONTEXT: -1.0
              DATA_AUG_RANDOM_DROP: -1.0

            - NAME: random_world_flip
              ALONG_AXIS_LIST: ['x']

            - NAME: random_world_rotation
              WORLD_ROT_ANGLE: [-0.78539816, 0.78539816]

            - NAME: random_world_scaling
              WORLD_SCALE_RANGE: [0.95, 1.05]


MODEL:
    NAME: SECONDNet

    VFE:
        NAME: MeanVFE

    BACKBONE_3D:
        NAME: VoxelBackBone8xCiassd

    MAP_TO_BEV:
        NAME: HeightCompression
        NUM_BEV_FEATURES: 128

    BACKBONE_2D:
        NAME: SSFA

        LAYER_NUMS: [5, 5]
        LAYER_STRIDES: [1, 2]
        NUM_FILTERS: [128, 256]
        UPSAMPLE_STRIDES: [1, 2]
        NUM_UPSAMPLE_FILTERS: [256, 256]

    DENSE_HEAD:
        NAME: AnchorHeadKLLabelIoU
        CLASS_AGNOSTIC: False

        USE_DIRECTION_CLASSIFIER: True
        DIR_OFFSET: 0.78539
        DIR_LIMIT_OFFSET: 0.0
        NUM_DIR_BINS: 2

        PRE_CLS_THRESH: 0.0
        PRE_IOU_THRESH: 0.0
        POW: 4

        ANCHOR_GENERATOR_CONFIG: [
            {
                'class_name': 'Car',
                'anchor_sizes': [[3.9, 1.6, 1.56]],
                'anchor_rotations': [0, 1.57],
                'anchor_bottom_heights': [-1.78],
                'align_center': False,
                'feature_map_stride': 8,
                'matched_threshold': 0.6,
                'unmatched_threshold': 0.45
            }
        ]

        TARGET_ASSIGNER_CONFIG:
            NAME: WeightedAxisAlignedTargetAssigner
            POS_FRACTION: -1.0
            SAMPLE_SIZE: 512
            NORM_BY_NUM_EXAMPLES: False
            MATCH_HEIGHT: False
            BOX_CODER: ResidualCoder

        LOSS_CONFIG:
            LOSS_WEIGHTS: {
                'cls_weight': 1.0,
                'loc_weight': 2.0,
                'dir_weight': 0.2,
                'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
            }

    POST_PROCESSING:
        RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
        # SCORE_THRESH: 1.0e-6 # 1.0e-6
        SCORE_THRESH: 0.055 # 1.0e-6
        OUTPUT_RAW_SCORE: False

        EVAL_METRIC: kitti

        NMS_CONFIG:
            MULTI_CLASSES_NMS: False
            NMS_TYPE: new_nms_gpu # nms_gpu
            NMS_THRESH: 0.01
            NMS_PRE_MAXSIZE: 4096
            NMS_POST_MAXSIZE: 500


OPTIMIZATION:
    BATCH_SIZE_PER_GPU: 4
    NUM_EPOCHS: 80

    OPTIMIZER: adam_onecycle
    LR: 0.003
    WEIGHT_DECAY: 0.01
    MOMENTUM: 0.9

    MOMS: [0.95, 0.85]
    PCT_START: 0.4
    DIV_FACTOR: 10
    DECAY_STEP_LIST: [35, 45]
    LR_DECAY: 0.1
    LR_CLIP: 0.0000001

    LR_WARMUP: False
    WARMUP_EPOCH: 1

    GRAD_NORM_CLIP: 10