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unitr+lss_cache_plus.yaml
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unitr+lss_cache_plus.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],
}
CAMERA_CONFIG:
USE_CAMERA: True
IMAGE:
FINAL_DIM: [256,704]
RESIZE_LIM_TRAIN: [0.38, 0.55]
RESIZE_LIM_TEST: [0.48, 0.48]
DATA_AUGMENTOR:
DISABLE_AUG_LIST: ['placeholder']
AUG_CONFIG_LIST:
- NAME: gt_sampling
IMG_AUG_TYPE: nuscenes
IMG_AUG_MIXUP: 0.7
DB_INFO_PATH:
- nuscenes_dbinfos_10sweeps_withvelo.pkl
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'
],
}
USE_SHARED_MEMORY: False # set it to True to speed up (it costs about 24+150GB shared memory)
DB_DATA_PATH:
- nuscenes_10sweeps_withvelo_lidar.npy
- nuscenes_10sweeps_withvelo_img.npy
SAMPLE_GROUPS: [
'car:2','truck:3', 'construction_vehicle:7', 'bus:4', 'trailer:6',
'barrier:2', 'motorcycle:6', 'bicycle:6', '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
# hf cfg
use_hf: False
fr_path: /private_dataset/nuscenes_pcdet/v1.0-trainval/gt_database_10sweeps_withvelo
fr_num: 54899
- 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]
- NAME: imgaug
ROT_LIM: [-5.4, 5.4]
RAND_FLIP: True
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_placeholder
VOXEL_SIZE: [0.3, 0.3, 8.0]
- NAME: image_calibrate
- NAME: image_normalize
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
MODEL:
NAME: UniTR
MM_BACKBONE:
NAME: UniTR
ACCELERATE: True # option of cache unitr backbone
PATCH_EMBED:
in_channels: 3
image_size: [256, 704]
embed_dims: 128
patch_size: 8
patch_norm: True
norm_cfg: {'type': 'LN'}
IMAGE_INPUT_LAYER:
sparse_shape: [32, 88, 1]
d_model: [128]
set_info: [[90, 4]]
window_shape: [[30, 30, 1]]
hybrid_factor: [1, 1, 1] # x, y, z
shifts_list: [[[0, 0, 0], [15, 15, 0]]]
input_image: True
LIDAR_INPUT_LAYER:
sparse_shape: [360, 360, 1]
d_model: [128]
set_info: [[90, 4]]
window_shape: [[30, 30, 1]]
hybrid_factor: [1, 1, 1] # x, y, z
shifts_list: [[[0, 0, 0], [15, 15, 0]]]
set_info: [[90, 4]]
d_model: [128]
nhead: [8]
dim_feedforward: [256]
dropout: 0.0
activation: gelu
checkpoint_blocks: [0,1,2,3] # here can save 50% CUDA memory with marginal speed drop
layer_cfg: {'use_bn': False, 'split_ffn': True, 'split_residual': True}
# fuse backbone config
FUSE_BACKBONE:
IMAGE2LIDAR:
block_start: 3
block_end: 4
point_cloud_range: [-54.0, -54.0, -10.0, 54.0, 54.0, 10.0]
voxel_size: [0.3,0.3,20.0]
sample_num: 20
image2lidar_layer:
sparse_shape: [360, 360, 1]
d_model: [128]
set_info: [[90, 1]]
window_shape: [[30, 30, 1]]
hybrid_factor: [1, 1, 1]
shifts_list: [[[0, 0, 0], [15, 15, 0]]]
expand_max_voxels: 10
LIDAR2IMAGE:
block_start: 1
block_end: 3
point_cloud_range: [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
voxel_size: [0.3,0.3,8.0]
sample_num: 1
lidar2image_layer:
sparse_shape: [96, 264, 6]
d_model: [128]
set_info: [[90, 2]]
window_shape: [[30, 30, 1]]
hybrid_factor: [1, 1, 1]
shifts_list: [[[0, 0, 0], [15, 15, 0]]]
expand_max_voxels: 30
out_indices: [0, 1, 2, 3]
NECK:
NAME: GeneralizedLSSFPN
IN_CHANNELS: [128, 128, 128, 128]
OUT_CHANNELS: 256
START_LEVEL: 0
END_LEVEL: -1
NUM_OUTS: 4
USE_BIAS: True
ALIGN_CORNERS: True
VTRANSFORM:
NAME: LSSTransform
ACCELERATE: True # option of cache LSS
IMAGE_SIZE: [256,704]
IN_CHANNEL: 256
OUT_CHANNEL: 80
FEATURE_SIZE: [32,88]
XBOUND: [-54.0, 54.0, 0.3]
YBOUND: [-54.0, 54.0, 0.3]
ZBOUND: [-10.0, 10.0, 20.0]
DBOUND: [1.0, 60.0, 0.5]
DOWNSAMPLE: 1
FUSER:
NAME: ConvFuser
IN_CHANNEL: 208
OUT_CHANNEL: 128
VFE:
NAME: DynPillarVFE
WITH_DISTANCE: False
USE_ABSLOTE_XYZ: True
USE_NORM: True
NUM_FILTERS: [ 128, 128 ]
MAP_TO_BEV:
NAME: PointPillarScatter3d
INPUT_SHAPE: [360, 360, 1]
NUM_BEV_FEATURES: 128
BACKBONE_2D:
NAME: BaseBEVResBackbone
LAYER_NUMS: [ 1, 2, 2, 2] #
LAYER_STRIDES: [1, 2, 2, 2]
NUM_FILTERS: [128, 128, 256, 256]
UPSAMPLE_STRIDES: [0.5, 1, 2, 4]
NUM_UPSAMPLE_FILTERS: [128, 128, 128, 128]
DENSE_HEAD:
CLASS_AGNOSTIC: False
NAME: TransFusionHead
QUERY_RADIUS: 20
QUERY_LOCAL: True
USE_BIAS_BEFORE_NORM: True
NUM_PROPOSALS: 200
HIDDEN_CHANNEL: 128
NUM_CLASSES: 10
NUM_HEADS: 8
NMS_KERNEL_SIZE: 3
FFN_CHANNEL: 256
DROPOUT: 0.1
BN_MOMENTUM: 0.1
ACTIVATION: relu
NUM_HM_CONV: 2
SEPARATE_HEAD_CFG:
HEAD_ORDER: ['center', 'height', 'dim', 'rot', 'vel','iou']
HEAD_DICT: {
'center': {'out_channels': 2, 'num_conv': 2},
'height': {'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},
'iou': {'out_channels': 1, 'num_conv': 2}
}
TARGET_ASSIGNER_CONFIG:
FEATURE_MAP_STRIDE: 2
DATASET: nuScenes
GAUSSIAN_OVERLAP: 0.1
MIN_RADIUS: 2
HUNGARIAN_ASSIGNER:
cls_cost: {'gamma': 2.0, 'alpha': 0.25, 'weight': 0.15}
reg_cost: {'weight': 0.25}
iou_cost: {'weight': 0.25}
LOSS_CONFIG:
LOSS_WEIGHTS: {
'cls_weight': 1.0,
'bbox_weight': 0.25,
'hm_weight': 1.0,
'iou_weight': 0.5,
'iou_reg_weight': 0.5,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2]
}
LOSS_CLS:
use_sigmoid: True
gamma: 2.0
alpha: 0.25
LOSS_IOU: True
LOSS_IOU_REG: True
POST_PROCESSING:
SCORE_THRESH: 0.0
POST_CENTER_RANGE: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
USE_IOU_TO_RECTIFY_SCORE: True
IOU_RECTIFIER: [0.5]
NMS_CONFIG:
NMS_TYPE: nms_gpu
NMS_THRESH: 0.2
NMS_PRE_MAXSIZE: 1000
NMS_POST_MAXSIZE: 100
SCORE_THRES: 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: True
NMS_TYPE: nms_gpu
NMS_THRESH: 0.2
NMS_PRE_MAXSIZE: 1000
NMS_POST_MAXSIZE: 83
OPTIMIZATION:
BATCH_SIZE_PER_GPU: 3
NUM_EPOCHS: 10
OPTIMIZER: adam_onecycle
LR: 0.003
WEIGHT_DECAY: 0.03
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
LOSS_SCALE_FP16: 32
HOOK:
DisableAugmentationHook:
DISABLE_AUG_LIST: ['gt_sampling']
NUM_LAST_EPOCHS: 2