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cp-voxel-xs_sparsekd.yaml
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cp-voxel-xs_sparsekd.yaml
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CLASS_NAMES: ['Vehicle', 'Pedestrian', 'Cyclist']
TEACHER_CKPT: '../output/model_zoo/cp-voxel/cp-voxel_6429.pth'
PRETRAINED_MODEL: '../output/model_zoo/cp-voxel/cp-voxel_6429.pth'
DATA_CONFIG:
_BASE_CONFIG_: cfgs/dataset_configs/waymo_dataset.yaml
MODEL:
NAME: CenterPoint
VFE:
NAME: MeanVFE
BACKBONE_3D:
NAME: VoxelResBackBone8x
ACT_FN: ReLU
# [input, conv1, conv2, conv3, conv4, output]
NUM_FILTERS: [16, 16, 32, 64, 128, 128]
LAYER_NUMS: [1, 2, 3, 3, 3, 1]
WIDTH: 0.75
MAP_TO_BEV:
NAME: HeightCompression
NUM_BEV_FEATURES: 192
BACKBONE_2D:
NAME: BaseBEVBackbone
ACT_FN: ReLU
NORM_TYPE: BatchNorm2d
WIDTH: 0.5
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
ACT_FN: ReLU
NORM_TYPE: BatchNorm2d
CLASS_NAMES_EACH_HEAD: [
['Vehicle', 'Pedestrian', 'Cyclist']
]
SHARED_CONV_CHANNEL: 32
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
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: MeanVFE
BACKBONE_3D:
NAME: VoxelResBackBone8x
ACT_FN: ReLU
# [input, conv1, conv2, conv3, conv4, output]
NUM_FILTERS: [16, 16, 32, 64, 128, 128]
LAYER_NUMS: [1, 2, 3, 3, 3, 1]
WIDTH: 1.0
MAP_TO_BEV:
NAME: HeightCompression
NUM_BEV_FEATURES: 256
BACKBONE_2D:
NAME: BaseBEVBackbone
ACT_FN: ReLU
NORM_TYPE: BatchNorm2d
WIDTH: 1.0
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
ACT_FN: ReLU
NORM_TYPE: BatchNorm2d
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
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: 4
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
REMAP_PRETRAIN:
ENABLED: True
WAY: FNAV2
BN_SCALE:
ABS: True
OFA:
l1_norm: max
KD:
ENABLED: True
TEACHER_MODE: train # train or eval
DIFF_VOXEL: False
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, 0.6, 0.6 ]
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, # [FocalLossCenterNet, SmoothL1Loss, MSELoss]
weight: 15.0,
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: 0.2
}
FEATURE_LOSS: {
mode: rois,
type: MSELoss, # [SmoothL1Loss, MSELoss]
weight: 0.1,
# weight mask
fg_mask: False,
score_mask: False,
score_thresh: 0.3
}