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cbgs_voxel0075_voxelnext_maxpool.yaml
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cbgs_voxel0075_voxelnext_maxpool.yaml
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CLASS_NAMES: ['car','truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone']
DATA_CONFIG:
_BASE_CONFIG_: cfgs/dataset_configs/nuscenes_dataset.yaml
POINT_CLOUD_RANGE: [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
INFO_PATH: {
'train': [nuscenes_infos_10sweeps_train.pkl],
'test': [nuscenes_infos_10sweeps_val.pkl],
}
DATA_AUGMENTOR:
DISABLE_AUG_LIST: ['placeholder']
AUG_CONFIG_LIST:
- NAME: gt_sampling
DB_INFO_PATH:
- nuscenes_dbinfos_10sweeps_withvelo.pkl
USE_SHARED_MEMORY: False #True # set it to True to speed up (it costs about 15GB shared memory)
DB_DATA_PATH:
- nuscenes_dbinfos_10sweeps_withvelo_global.pkl.npy
PREPARE: {
filter_by_min_points: [
'car:5','truck:5', 'construction_vehicle:5', 'bus:5', 'trailer:5',
'barrier:5', 'motorcycle:5', 'bicycle:5', 'pedestrian:5', 'traffic_cone:5'
],
}
SAMPLE_GROUPS: [
'car:2','truck:2', 'construction_vehicle:2', 'bus:2', 'trailer:2',
'barrier:2', 'motorcycle:2', 'bicycle:2', 'pedestrian:2', 'traffic_cone:2'
]
NUM_POINT_FEATURES: 5
DATABASE_WITH_FAKELIDAR: False
REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0]
LIMIT_WHOLE_SCENE: True
- 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: [0.9, 1.1]
- NAME: random_world_translation
NOISE_TRANSLATE_STD: [0.5, 0.5, 0.5]
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: [0.075, 0.075, 0.2]
MAX_POINTS_PER_VOXEL: 10
MAX_NUMBER_OF_VOXELS: {
'train': 120000,
'test': 160000
}
MODEL:
NAME: VoxelNeXt
VFE:
NAME: MeanVFE
BACKBONE_3D:
NAME: VoxelResBackBone8xVoxelNeXt
DENSE_HEAD:
NAME: VoxelNeXtHeadMaxPool
CLASS_AGNOSTIC: False
INPUT_FEATURES: 128
CLASS_NAMES_EACH_HEAD: [
['car'],
['truck', 'construction_vehicle'],
['bus', 'trailer'],
['barrier'],
['motorcycle', 'bicycle'],
['pedestrian', 'traffic_cone'],
]
VOXEL_SIZE_EACH_HEAD: [0.08, 0.12, 0.12, 0.08, 0.06, 0.06]
KERNEL_SIZE_EACH_HEAD: [15, 15, 15, 15, 3, 15]
EASY_HEAD: [4]
SHARED_CONV_CHANNEL: 128
KERNEL_SIZE_HEAD: 1
USE_BIAS_BEFORE_NORM: True
NUM_HM_CONV: 2
SEPARATE_HEAD_CFG:
HEAD_ORDER: ['center', 'center_z', 'dim', 'rot', 'vel']
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},
'vel': {'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': 0.25,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2, 1.0, 1.0]
}
POST_PROCESSING:
SCORE_THRESH: 0.1
POST_CENTER_LIMIT_RANGE: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
MAX_OBJ_PER_SAMPLE: 500
NMS_CONFIG:
NMS_TYPE: nms_gpu
NMS_THRESH: 0.2
NMS_PRE_MAXSIZE: 1000
NMS_POST_MAXSIZE: 83
POST_PROCESSING:
RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
EVAL_METRIC: kitti
OPTIMIZATION:
BATCH_SIZE_PER_GPU: 4
NUM_EPOCHS: 20
OPTIMIZER: adam_onecycle
LR: 0.001
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