pointrcnn_hindsight_p2_squashlevel.yaml

CLASS_NAMES: ['Car', 'Pedestrian']

DATA_CONFIG:
    _BASE_CONFIG_: cfgs/dataset_configs/lyft_dataset_hindsight.yaml

    GET_ITEM_LIST: ["points", "p2_score", "history_scans"]
    load_p2_score: /home/yy785/datasets/lyft_release_test/training/pp_score_fw70_2m_r0.3
    load_p2_test_score: /home/yy785/datasets/lyft_release_test/training/pp_score_fw70_2m_r0.3_test
    
    DATA_PROCESSOR:
        -   NAME: mask_points_and_boxes_outside_range
            REMOVE_OUTSIDE_BOXES: True

        -   NAME: sample_points
            NUM_POINTS: {
                'train': 16384,
                'test': 16384
            }

        -   NAME: shuffle_points
            SHUFFLE_ENABLED: {
                'train': True,
                'test': False
            }

MODEL:
    NAME: PointRCNN

    HISTORY_QUERY:
        NAME: SparseResUQueryNet
        history_backbone: Res16UNet14E
        history_backbone_config:
            bn_momentum: 0.05
            conv1_kernel_size: 3
            final_feature_size: 64

        simple_conv_kernel_size: 5
        extra_conv: False
        mode: update_point_features
        P2_LOSS_CONFIG:
            LOSS_FN: l1

    BACKBONE_3D:
        NAME: PointNet2MSG
        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]]

    POINT_HEAD:
        NAME: PointHeadBox
        CLS_FC: [256, 256]
        REG_FC: [256, 256]
        CLASS_AGNOSTIC: False
        USE_POINT_FEATURES_BEFORE_FUSION: False
        TARGET_CONFIG:
            GT_EXTRA_WIDTH: [0.2, 0.2, 0.2]
            BOX_CODER: PointResidualCoder
            BOX_CODER_CONFIG: {
                'use_mean_size': True,
                'mean_size': [
                    [3.9, 1.6, 1.56],
                    [0.8, 0.6, 1.73],
                    [1.76, 0.6, 1.73]
                ]
            }

        LOSS_CONFIG:
            LOSS_REG: WeightedSmoothL1Loss
            LOSS_WEIGHTS: {
                'point_cls_weight': 1.0,
                'point_box_weight': 1.0,
                'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
            }

    ROI_HEAD:
        NAME: PointRCNNHead
        CLASS_AGNOSTIC: True

        ROI_POINT_POOL:
            POOL_EXTRA_WIDTH: [0.0, 0.0, 0.0]
            NUM_SAMPLED_POINTS: 512
            DEPTH_NORMALIZER: 70.0

        XYZ_UP_LAYER: [128, 128]
        CLS_FC: [256, 256]
        REG_FC: [256, 256]
        DP_RATIO: 0.0
        USE_BN: False

        SA_CONFIG:
            NPOINTS: [128, 32, -1]
            RADIUS: [0.2, 0.4, 100]
            NSAMPLE: [16, 16, 16]
            MLPS: [[128, 128, 128],
                   [128, 128, 256],
                   [256, 256, 512]]

        NMS_CONFIG:
            TRAIN:
                NMS_TYPE: nms_gpu
                MULTI_CLASSES_NMS: False
                NMS_PRE_MAXSIZE: 9000
                NMS_POST_MAXSIZE: 512
                NMS_THRESH: 0.8
            TEST:
                NMS_TYPE: nms_gpu
                MULTI_CLASSES_NMS: False
                NMS_PRE_MAXSIZE: 9000
                NMS_POST_MAXSIZE: 100
                NMS_THRESH: 0.85

        TARGET_CONFIG:
            BOX_CODER: ResidualCoder
            ROI_PER_IMAGE: 128
            FG_RATIO: 0.5

            SAMPLE_ROI_BY_EACH_CLASS: True
            CLS_SCORE_TYPE: cls

            CLS_FG_THRESH: 0.6
            CLS_BG_THRESH: 0.45
            CLS_BG_THRESH_LO: 0.1
            HARD_BG_RATIO: 0.8

            REG_FG_THRESH: 0.55

        LOSS_CONFIG:
            CLS_LOSS: BinaryCrossEntropy
            REG_LOSS: smooth-l1
            CORNER_LOSS_REGULARIZATION: True
            LOSS_WEIGHTS: {
                'rcnn_cls_weight': 1.0,
                'rcnn_reg_weight': 1.0,
                'rcnn_corner_weight': 1.0,
                '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: 0.1
        OUTPUT_RAW_SCORE: False

        EVAL_METRIC: kitti

        NMS_CONFIG:
            MULTI_CLASSES_NMS: False
            NMS_TYPE: nms_gpu
            NMS_THRESH: 0.1
            NMS_PRE_MAXSIZE: 4096
            NMS_POST_MAXSIZE: 500


OPTIMIZATION:
    BATCH_SIZE_PER_GPU: 2
    NUM_EPOCHS: 60

    OPTIMIZER: adam_onecycle
    LR: 0.01
    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