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centerpoint_pillars_016voxel_kitti_mini.yml
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# This is a training configuration for a simplified version of KITTI. It is just for a quick start,
# all the hyperparameters are not strictly tuned, so the training result is not optimal
batch_size: 4
epochs: 20
train_dataset:
type: KittiPCDataset
dataset_root: datasets/KITTI
transforms:
- type: LoadPointCloud
dim: 4
use_dim: 4
- type: RemoveCameraInvisiblePointsKITTI
- type: SamplingDatabase
min_num_points_in_box_per_class:
Car: 5
Cyclist: 5
Pedestrian: 5
max_num_samples_per_class:
Car: 15
Cyclist: 10
ignored_difficulty: [-1]
database_anno_path: datasets/KITTI/kitti_train_gt_database/anno_info_train.pkl
database_root: datasets/KITTI/
class_names: ["Car", "Cyclist", "Pedestrian"]
- type: RandomObjectPerturb
rotation_range: [-0.15707963267, 0.15707963267]
translation_std: [0.25, 0.25, 0.25]
max_num_attempts: 100
- type: RandomVerticalFlip
- type: GlobalRotate
min_rot: -0.78539816
max_rot: 0.78539816
- type: GlobalScale
min_scale: 0.95
max_scale: 1.05
- type: GlobalTranslate
translation_std: [0.2, 0.2, 0.2]
- type: ShufflePoint
- type: FilterBBoxOutsideRange
point_cloud_range: [0, -39.68, -3, 69.12, 39.68, 1]
- type: Gt2CenterPointTarget
tasks:
- num_class: 1
class_names: ["Car"]
- num_class: 2
class_names: ["Cyclist", "Pedestrian"]
down_ratio: 2
point_cloud_range: [0, -39.68, -3, 69.12, 39.68, 1]
voxel_size: [0.16, 0.16, 4]
gaussian_overlap: 0.1
max_objs: 500
min_radius: 2
mode: train
class_balanced_sampling: False
class_names: ["Car", "Cyclist", "Pedestrian"]
val_dataset:
type: KittiPCDataset
dataset_root: datasets/KITTI
transforms:
- type: LoadPointCloud
dim: 4
use_dim: 4
- type: RemoveCameraInvisiblePointsKITTI
mode: val
class_names: ["Car", "Cyclist", "Pedestrian"]
optimizer:
type: OneCycleAdam
beta2: 0.99
weight_decay: 0.01
grad_clip:
type: ClipGradByGlobalNorm
clip_norm: 35
beta1:
type: OneCycleDecayWarmupMomentum
momentum_peak: 0.95
momentum_trough: 0.85
step_ratio_peak: 0.4
lr_scheduler:
type: OneCycleWarmupDecayLr
base_learning_rate: 0.001
lr_ratio_peak: 10
lr_ratio_trough: 0.0001
step_ratio_peak: 0.4
model:
type: CenterPoint
voxelizer:
type: HardVoxelizer
point_cloud_range: [0, -39.68, -3, 69.12, 39.68, 1]
voxel_size: [0.16, 0.16, 4]
max_num_points_in_voxel: 100
max_num_voxels: [12000, 40000]
voxel_encoder:
type: PillarFeatureNet
in_channels: 4
feat_channels: [64, 64]
with_distance: False
max_num_points_in_voxel: 100
voxel_size: [0.16, 0.16, 4]
point_cloud_range: [0, -39.68, -3, 69.12, 39.68, 1]
legacy: False
middle_encoder:
type: PointPillarsScatter
in_channels: 64
voxel_size: [0.16, 0.16, 4]
point_cloud_range: [0, -39.68, -3, 69.12, 39.68, 1]
backbone:
type: SecondBackbone
in_channels: 64
out_channels: [64, 128, 256]
layer_nums: [3, 5, 5]
downsample_strides: [1, 2, 2]
neck:
type: SecondFPN
in_channels: [64, 128, 256]
out_channels: [128, 128, 128]
upsample_strides: [0.5, 1, 2]
use_conv_for_no_stride: True
bbox_head:
type: CenterHead
in_channels: 384 # sum([128, 128, 128])
tasks:
- num_class: 1
class_names: ["Car"]
- num_class: 2
class_names: ["Cyclist", "Pedestrian"]
weight: 2.5 # loc_loss weight
code_weights: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] # [x, y, z, w, h, l, sin(angle), cos(angle)] weight in loc loss
common_heads:
reg: [2, 2] # classes, num_conv
height: [1, 2]
dim: [3, 2]
rot: [2, 2]
test_cfg:
post_center_limit_range: [-10., -50., -10., 80., 50., 10.]
nms:
nms_pre_max_size: 1000
nms_post_max_size: 83
nms_iou_threshold: 0.1
score_threshold: 0.1
point_cloud_range: [0, -39.68, -3, 69.12, 39.68, 1]
down_ratio: 2
voxel_size: [0.16, 0.16, 4]
export:
transforms:
- type: LoadPointCloud
dim: 4
use_dim: 4