I tried to train a dsvt-pillar model using the kitti dataset, below is my config:
CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist']
DATA_CONFIG:
_BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml
POINT_CLOUD_RANGE: [0, -40, -3, 70.4, 40, 1]
DATA_AUGMENTOR:
DISABLE_AUG_LIST: ['placeholder']
AUG_CONFIG_LIST:
- NAME: gt_sampling
USE_ROAD_PLANE: True
DB_INFO_PATH:
- kitti_dbinfos_train.pkl
PREPARE: {
filter_by_min_points: ['Car:5', 'Pedestrian:5', 'Cyclist:5'],
filter_by_difficulty: [-1],
}
SAMPLE_GROUPS: ['Car:15','Pedestrian:15', 'Cyclist:15']
NUM_POINT_FEATURES: 4
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: [0.95, 1.05]
- 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': False
}
- NAME: transform_points_to_voxels_placeholder
VOXEL_SIZE: [ 0.1505, 0.1709, 4 ]
MODEL:
NAME: CenterPoint
VFE:
NAME: DynPillarVFE3D
WITH_DISTANCE: False
USE_ABSLOTE_XYZ: True
USE_NORM: True
NUM_FILTERS: [192, 192]
BACKBONE_3D:
NAME: DSVT
INPUT_LAYER:
sparse_shape: [468, 468, 1]
downsample_stride: []
d_model: [192]
set_info: [[36, 4]]
window_shape: [[12, 12, 1]]
hybrid_factor: [2, 2, 1] # x, y, z
shifts_list: [[[0, 0, 0], [6, 6, 0]]]
normalize_pos: False
block_name: ['DSVTBlock']
set_info: [[36, 4]]
d_model: [192]
nhead: [8]
dim_feedforward: [384]
dropout: 0.0
activation: gelu
reduction_type: 'attention'
output_shape: [468, 468]
conv_out_channel: 192
# ues_checkpoint: True
MAP_TO_BEV:
NAME: PointPillarScatter3d
INPUT_SHAPE: [468, 468, 1]
NUM_BEV_FEATURES: 192
BACKBONE_2D:
NAME: BaseBEVResBackbone
LAYER_NUMS: [ 1, 2, 2 ]
LAYER_STRIDES: [ 1, 2, 2 ]
NUM_FILTERS: [ 128, 128, 256 ]
UPSAMPLE_STRIDES: [ 1, 2, 4 ]
NUM_UPSAMPLE_FILTERS: [ 128, 128, 128 ]
DENSE_HEAD:
NAME: CenterHead
CLASS_AGNOSTIC: False
CLASS_NAMES_EACH_HEAD: [
['Car', 'Pedestrian', 'Cyclist']
]
SHARED_CONV_CHANNEL: 64
USE_BIAS_BEFORE_NORM: False
NUM_HM_CONV: 2
BN_EPS: 0.001
BN_MOM: 0.01
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},
'iou': {'out_channels': 1, 'num_conv': 2},
}
TARGET_ASSIGNER_CONFIG:
FEATURE_MAP_STRIDE: 1
NUM_MAX_OBJS: 500
GAUSSIAN_OVERLAP: 0.1
MIN_RADIUS: 2
IOU_REG_LOSS: True
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: 0.5
POST_CENTER_LIMIT_RANGE: [-80, -80, -10.0, 80, 80, 10.0]
MAX_OBJ_PER_SAMPLE: 500
USE_IOU_TO_RECTIFY_SCORE: True
IOU_RECTIFIER: [0.68, 0.71, 0.65]
NMS_CONFIG:
# NMS_TYPE: multi_class_nms # only for centerhead, use mmdet3d version nms
# NMS_THRESH: [0.7, 0.6, 0.55]
# NMS_PRE_MAXSIZE: [4096, 4096, 4096]
# NMS_POST_MAXSIZE: [500, 500, 500]
NMS_TYPE: nms_gpu
NMS_THRESH: 0.1
NMS_PRE_MAXSIZE: 4096
NMS_POST_MAXSIZE: 500
POST_PROCESSING:
RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
EVAL_METRIC: kitti
OPTIMIZATION:
BATCH_SIZE_PER_GPU: 1
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
LOSS_SCALE_FP16: 32.0
HOOK:
DisableAugmentationHook:
DISABLE_AUG_LIST: ['gt_sampling','random_world_flip','random_world_rotation','random_world_scaling', 'random_world_translation']
NUM_LAST_EPOCHS: 1
I only modified the point cloud range to match with the kitti settings and the voxel size to match with the default sparce shape [468, 468, 1], but I am constantly getting an error:
RuntimeError: max(): Expected reduction dim to be specified for input.numel() == 0. Specify the reduction dim with the 'dim' argument.
I traced down the error happened in DynamicPillarVFE3D module where the batch_dict['points'] often return some empty tensor point. However, when I tried to use the default point cloud range from waymo settings: [-74.88, -74.88, -2, 74.88, 74.88, 4.0], this error disappered. Can u give me some guidance?
Hi,
I tried to train a dsvt-pillar model using the kitti dataset, below is my config:
I only modified the point cloud range to match with the kitti settings and the voxel size to match with the default sparce shape [468, 468, 1], but I am constantly getting an error:
I traced down the error happened in DynamicPillarVFE3D module where the batch_dict['points'] often return some empty tensor point. However, when I tried to use the default point cloud range from waymo settings: [-74.88, -74.88, -2, 74.88, 74.88, 4.0], this error disappered. Can u give me some guidance?
Thank you!