nervn / voxelnext_3d_box /config.yaml
mart9992's picture
m
b793f0c
SAM_TYPE: "vit_h"
SAM_CHECKPOINT: "sam_vit_h_4b8939.pth"
POINT_CLOUD_RANGE: [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
USED_FEATURE_LIST: ['x', 'y', 'z', 'intensity', 'timestamp']
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
}
VOXELNEXT_CHECKPOINT: "voxelnext_nuscenes_kernel1.pth"
INPUT_CHANNELS: 5
GRID_SIZE: [1440, 1440, 40]
CLASS_NAMES: ['car','truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone']
KERNEL_SIZE_HEAD: 1
VOXEL_SIZE: [0.075, 0.075, 0.2]
CLASS_NAMES_EACH_HEAD: [
['car'],
['truck', 'construction_vehicle'],
['bus', 'trailer'],
['barrier'],
['motorcycle', 'bicycle'],
['pedestrian', 'traffic_cone'],
]
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},
}
POST_PROCESSING:
SCORE_THRESH: 0
POST_CENTER_LIMIT_RANGE: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
MAX_OBJ_PER_SAMPLE: 500