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add PV-RCNN config with CenterHead
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sshaoshuai committed Nov 30, 2021
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215 changes: 215 additions & 0 deletions tools/cfgs/waymo_models/pv_rcnn_with_centerhead_rpn.yaml
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CLASS_NAMES: ['Vehicle', 'Pedestrian', 'Cyclist']

DATA_CONFIG:
_BASE_CONFIG_: cfgs/dataset_configs/waymo_dataset.yaml


MODEL:
NAME: PVRCNN

VFE:
NAME: MeanVFE

BACKBONE_3D:
NAME: VoxelBackBone8x

MAP_TO_BEV:
NAME: HeightCompression
NUM_BEV_FEATURES: 256

BACKBONE_2D:
NAME: BaseBEVBackbone

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

DENSE_HEAD:
NAME: CenterHead
CLASS_AGNOSTIC: False

CLASS_NAMES_EACH_HEAD: [
[ 'Vehicle', 'Pedestrian', 'Cyclist' ]
]

SHARED_CONV_CHANNEL: 64
USE_BIAS_BEFORE_NORM: True
NUM_HM_CONV: 2
SEPARATE_HEAD_CFG:
HEAD_ORDER: [ 'center', 'center_z', 'dim', 'rot' ]
HEAD_DICT: {
'center': { 'out_channels': 2, 'num_conv': 2 },
'center_z': { 'out_channels': 1, 'num_conv': 2 },
'dim': { 'out_channels': 3, 'num_conv': 2 },
'rot': { 'out_channels': 2, 'num_conv': 2 },
}

TARGET_ASSIGNER_CONFIG:
FEATURE_MAP_STRIDE: 8
NUM_MAX_OBJS: 500
GAUSSIAN_OVERLAP: 0.1
MIN_RADIUS: 2

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

POST_PROCESSING:
SCORE_THRESH: 0.1
POST_CENTER_LIMIT_RANGE: [ -75.2, -75.2, -2, 75.2, 75.2, 4 ]
MAX_OBJ_PER_SAMPLE: 500
NMS_CONFIG:
NMS_TYPE: nms_gpu
NMS_THRESH: 0.7
NMS_PRE_MAXSIZE: 4096
NMS_POST_MAXSIZE: 500

PFE:
NAME: VoxelSetAbstraction
POINT_SOURCE: raw_points
NUM_KEYPOINTS: 4096
NUM_OUTPUT_FEATURES: 90
SAMPLE_METHOD: FPS

FEATURES_SOURCE: ['bev', 'x_conv3', 'x_conv4', 'raw_points']
SA_LAYER:
raw_points:
MLPS: [[16, 16], [16, 16]]
POOL_RADIUS: [0.4, 0.8]
NSAMPLE: [16, 16]
x_conv1:
DOWNSAMPLE_FACTOR: 1
MLPS: [[16, 16], [16, 16]]
POOL_RADIUS: [0.4, 0.8]
NSAMPLE: [16, 16]
x_conv2:
DOWNSAMPLE_FACTOR: 2
MLPS: [[32, 32], [32, 32]]
POOL_RADIUS: [0.8, 1.2]
NSAMPLE: [16, 32]
x_conv3:
DOWNSAMPLE_FACTOR: 4
MLPS: [[64, 64], [64, 64]]
POOL_RADIUS: [1.2, 2.4]
NSAMPLE: [16, 32]
x_conv4:
DOWNSAMPLE_FACTOR: 8
MLPS: [[64, 64], [64, 64]]
POOL_RADIUS: [2.4, 4.8]
NSAMPLE: [16, 32]

POINT_HEAD:
NAME: PointHeadSimple
CLS_FC: [256, 256]
CLASS_AGNOSTIC: True
USE_POINT_FEATURES_BEFORE_FUSION: True
TARGET_CONFIG:
GT_EXTRA_WIDTH: [0.2, 0.2, 0.2]
LOSS_CONFIG:
LOSS_REG: smooth-l1
LOSS_WEIGHTS: {
'point_cls_weight': 1.0,
}

ROI_HEAD:
NAME: PVRCNNHead
CLASS_AGNOSTIC: True

SHARED_FC: [256, 256]
CLS_FC: [256, 256]
REG_FC: [256, 256]
DP_RATIO: 0.3

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: 1024
NMS_POST_MAXSIZE: 100
NMS_THRESH: 0.7
SCORE_THRESH: 0.1

# NMS_PRE_MAXSIZE: 4096
# NMS_POST_MAXSIZE: 500
# NMS_THRESH: 0.85


ROI_GRID_POOL:
GRID_SIZE: 6
MLPS: [[64, 64], [64, 64]]
POOL_RADIUS: [0.8, 1.6]
NSAMPLE: [16, 16]
POOL_METHOD: max_pool

TARGET_CONFIG:
BOX_CODER: ResidualCoder
ROI_PER_IMAGE: 128
FG_RATIO: 0.5

SAMPLE_ROI_BY_EACH_CLASS: True
CLS_SCORE_TYPE: roi_iou

CLS_FG_THRESH: 0.75
CLS_BG_THRESH: 0.25
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: waymo

NMS_CONFIG:
MULTI_CLASSES_NMS: False
NMS_TYPE: nms_gpu
NMS_THRESH: 0.7
NMS_PRE_MAXSIZE: 4096
NMS_POST_MAXSIZE: 500


OPTIMIZATION:
BATCH_SIZE_PER_GPU: 2
NUM_EPOCHS: 30

OPTIMIZER: adam_onecycle
LR: 0.01
WEIGHT_DECAY: 0.001
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

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