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cp-pillar-v0.48_sparsekd.yaml
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CLASS_NAMES: ['Vehicle', 'Pedestrian', 'Cyclist']
TEACHER_CKPT: '../output/model_zoo/cp-pillar/cp-pillar_5909.pth'
PRETRAINED_MODEL: '../output/model_zoo/cp-pillar/cp-pillar_5909.pth'
DATA_CONFIG:
_BASE_CONFIG_: cfgs/dataset_configs/waymo_dataset.yaml
POINT_CLOUD_RANGE: [-74.88, -74.88, -2, 74.88, 74.88, 4.0]
DATA_PROCESSOR:
- NAME: mask_points_and_boxes_outside_range
REMOVE_OUTSIDE_BOXES: True
- NAME: shuffle_points
SHUFFLE_ENABLED: {
'train': True,
'test': True
}
- NAME: transform_points_to_voxels
VOXEL_SIZE: [ 0.48, 0.48, 6.0 ]
MAX_POINTS_PER_VOXEL: 36
MAX_NUMBER_OF_VOXELS: {
'train': 150000,
'test': 150000
}
- NAME: transform_points_to_voxels_tea
VOXEL_SIZE: [ 0.32, 0.32, 6.0 ]
MAX_POINTS_PER_VOXEL: 20
MAX_NUMBER_OF_VOXELS: {
'train': 150000,
'test': 150000
}
MODEL:
NAME: CenterPoint
VFE:
NAME: PillarVFE
WITH_DISTANCE: False
USE_ABSLOTE_XYZ: True
USE_NORM: True
NUM_FILTERS: [ 64, 64 ]
MAP_TO_BEV:
NAME: PointPillarScatter
NUM_BEV_FEATURES: 64
BACKBONE_2D:
NAME: BaseBEVBackbone
LAYER_NUMS: [ 3, 5, 5 ]
LAYER_STRIDES: [ 1, 2, 2 ]
NUM_FILTERS: [ 64, 128, 256 ]
UPSAMPLE_STRIDES: [ 1, 2, 4 ]
NUM_UPSAMPLE_FILTERS: [ 128, 128, 128 ]
FOCUS: False
ACT_FN: ReLU
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: 1
NUM_MAX_OBJS: 500
GAUSSIAN_OVERLAP: 0.1
MIN_RADIUS: 2
SHARPER: False
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: [-80, -80, -10.0, 80, 80, 10.0]
MAX_OBJ_PER_SAMPLE: 500
NMS_CONFIG:
NMS_TYPE: nms_gpu
NMS_THRESH: 0.7
NMS_PRE_MAXSIZE: 4096
NMS_POST_MAXSIZE: 500
POST_PROCESSING:
RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
EVAL_METRIC: waymo
EVAL_CLASSES: {
'LEVEL_2/AP': [ 'OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2/AP',
'OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2/AP',
'OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2/AP'
],
'LEVEL_2/APH': [ 'OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2/APH',
'OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2/APH',
'OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2/APH'
]
}
MODEL_TEACHER:
NAME: CenterPoint
IS_TEACHER: True
VFE:
NAME: PillarVFE
WITH_DISTANCE: False
USE_ABSLOTE_XYZ: True
USE_NORM: True
NUM_FILTERS: [ 64, 64 ]
MAP_TO_BEV:
NAME: PointPillarScatter
NUM_BEV_FEATURES: 64
BACKBONE_2D:
NAME: BaseBEVBackbone
LAYER_NUMS: [ 3, 5, 5 ]
LAYER_STRIDES: [ 1, 2, 2 ]
NUM_FILTERS: [ 64, 128, 256 ]
UPSAMPLE_STRIDES: [ 1, 2, 4 ]
NUM_UPSAMPLE_FILTERS: [ 128, 128, 128 ]
FOCUS: False
ACT_FN: ReLU
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: 1
NUM_MAX_OBJS: 500
GAUSSIAN_OVERLAP: 0.1
MIN_RADIUS: 2
SHARPER: False
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: [-80, -80, -10.0, 80, 80, 10.0]
MAX_OBJ_PER_SAMPLE: 500
NMS_CONFIG:
NMS_TYPE: nms_gpu
NMS_THRESH: 0.7
NMS_PRE_MAXSIZE: 4096
NMS_POST_MAXSIZE: 500
POST_PROCESSING:
RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
EVAL_METRIC: waymo
EVAL_CLASSES: {
'LEVEL_2/AP': [ 'OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2/AP',
'OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2/AP',
'OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2/AP'
],
'LEVEL_2/APH': [ 'OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2/APH',
'OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2/APH',
'OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2/APH'
]
}
OPTIMIZATION:
BATCH_SIZE_PER_GPU: 2
NUM_EPOCHS: 30
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
KD:
ENABLED: True
TEACHER_MODE: train # train or eval
DIFF_VOXEL: True
MASK:
SCORE_MASK: False
FG_MASK: False
BOX_MASK: False
LOGIT_KD:
ENABLED: True
# decode prediction to bounding boxes or not in logit kd
MODE: raw_pred # [raw_pred, decoded_boxes, target]
ALIGN: {
MODE: interpolate,
target: teacher,
mode: bilinear, # nearest, linear, bilinear, bicubic, trilinear, area
align_corners: True,
align_channel: False
}
FEATURE_KD:
ENABLED: False
FEATURE_NAME: spatial_features_2d
FEATURE_NAME_TEA: spatial_features_2d
# Align feature map
ALIGN: {
ENABLED: False,
MODE: interpolate,
target: teacher,
# interpolate params
mode: bilinear, # nearest, linear, bilinear, bicubic, trilinear, area
align_corners: True,
align_channel: False,
# conv params
num_filters: [ 192, 384 ], # [in_channel, out_channel]
use_norm: True,
use_act: False,
kernel_size: 3,
groups: 1,
}
ROI_POOL:
ENABLED: True
GRID_SIZE: 7
DOWNSAMPLE_RATIO: 1
ROI: gt # ['gt', 'tea', 'stu']
THRESH: 0.0 # for teacher prediction for student prediction
LABEL_ASSIGN_KD:
ENABLED: True
SCORE_TYPE: cls
USE_GT: True
GT_FIRST: False # when concatenate the gt boxes and target predictions,
# target boxes selection
SCORE_THRESH: [ 0.6, 0.5, 0.5 ]
NMS_CONFIG:
ENABLED: False
NMS_TYPE: nms_gpu
NMS_THRESH: 0.7
NMS_PRE_MAXSIZE: 4096
NMS_POST_MAXSIZE: 500
KD_LOSS:
ENABLED: True
HM_LOSS: {
type: MSELoss,
weight: 7.0,
thresh: 0.0, # threshold for score PP Logit KD
fg_mask: True,
soft_mask: True,
rank: -1, # rank PP Logit KD, -1 means not used
}
REG_LOSS: {
type: RegLossCenterNet,
# for L1 loss only
code_weights: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
weight: 0.0
}
FEATURE_LOSS: {
mode: rois,
type: MSELoss, # [SmoothL1Loss, MSELoss]
weight: 0.1,
# weight mask
fg_mask: False,
score_mask: False,
score_thresh: 0.3
}