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active_TQS.yaml
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active_TQS.yaml
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CLASS_NAMES: ['Vehicle']
DATA_CONFIG:
_BASE_CONFIG_: cfgs/dataset_configs/waymo/ADA/ada_waymo_dataset_gt_D.yaml
INFO_WITH_FAKELIDAR: False
DATA_AUGMENTOR:
DISABLE_AUG_LIST: ['placeholder']
AUG_CONFIG_LIST:
- NAME: random_world_flip
ALONG_AXIS_LIST: [ 'x', 'y' ]
- NAME: random_world_rotation
WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
- NAME: random_world_scaling
WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
DATA_CONFIG_TAR:
_BASE_CONFIG_: cfgs/dataset_configs/lyft/ADA/ada_lyft_dataset.yaml
FILE_PATH: ../data/lyft/lyft_infos_train.pkl
TARGET: True
CLASS_NAMES: ['car']
SHIFT_COOR: [0.0, 0.0, 1.8]
MAX_SWEEPS: 1
PRED_VELOCITY: False
BALANCED_RESAMPLING: False
GT_FILTER:
RANGE_FILTER: True
DATA_CONFIG_SAMPLE:
_BASE_CONFIG_: cfgs/dataset_configs/lyft/ADA/ada_lyft_dataset.yaml
TARGET: True
CLASS_NAMES: ['car']
SHIFT_COOR: [0.0, 0.0, 1.8]
MAX_SWEEPS: 1
PRED_VELOCITY: False
BALANCED_RESAMPLING: False
GT_FILTER:
RANGE_FILTER: True
MODEL:
NAME: VoxelRCNN_TQS
VFE:
NAME: DynMeanVFE
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: ActiveCenterHead
CLASS_AGNOSTIC: False
CLASS_NAMES_EACH_HEAD: [
[ 'Vehicle', 'car' ]
]
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
DISCRIMINATOR:
NAME: BEVDiscriminator_Center_TQS
FEATURE_DIM: 512
SIGMA: 0.4
MU: 0.75
ROI_HEAD:
NAME: VoxelRCNNHead_ABL
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
ROI_GRID_POOL:
FEATURES_SOURCE: ['x_conv2', 'x_conv3', 'x_conv4']
PRE_MLP: True
GRID_SIZE: 6
POOL_LAYERS:
x_conv2:
MLPS: [ [ 64, 64 ] ]
QUERY_RANGES: [ [ 3, 3, 2 ] ]
POOL_RADIUS: [ 0.4 ]
NSAMPLE: [ 16 ]
POOL_METHOD: max_pool
x_conv3:
MLPS: [ [ 64, 64 ] ]
QUERY_RANGES: [ [ 3, 3, 2 ] ]
POOL_RADIUS: [ 0.8 ]
NSAMPLE: [ 16 ]
POOL_METHOD: max_pool
x_conv4:
MLPS: [ [ 64, 64 ] ]
QUERY_RANGES: [ [ 3, 3, 2 ] ]
POOL_RADIUS: [ 1.6 ]
NSAMPLE: [ 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: kitti
NMS_CONFIG:
MULTI_CLASSES_NMS: False
NMS_TYPE: nms_gpu
NMS_THRESH: 0.1
NMS_PRE_MAXSIZE: 4096
NMS_POST_MAXSIZE: 500
OPTIMIZATION:
NUM_EPOCHS: 20
BATCH_SIZE_PER_GPU: 2
OPTIMIZER: adam_onecycle
LR: 0.001
WEIGHT_DECAY: 0.001
MOMENTUM: 0.9
MOMS: [0.95, 0.85]
PCT_START: 0.4
DIV_FACTOR: 10
DECAY_STEP_LIST: [10]
LR_DECAY: 0.1
LR_CLIP: 0.0000001
DISCRIMINATOR:
OPTIMIZER: adam
LR: 0.001
WEIGHT_DECAY: 0.
LR_CLIP: 0.0000001
LR_DECAY: 0.1
DECAY_STEP_LIST: [5]
LR_WARMUP: False
GRAD_NORM_CLIP: 10
MUL_CLS:
OPTIMIZER: adam
LR: 0.001
WEIGHT_DECAY: 0.
LR_CLIP: 0.0000001
LR_DECAY: 0.1
DECAY_STEP_LIST: [5]
LR_WARMUP: False
GRAD_NORM_CLIP: 10
LR_WARMUP: False
WARMUP_EPOCH: 1
GRAD_NORM_CLIP: 10
TAR_LOSS_SCALE: 1.0
SAMPLE_LOSS_SCALE: 1.0
ANNOTATION_BUDGET: 94
SAMPLE_EPOCHS: [0, 5]