We incorporate modular and inheritance design into the config system to enable users conveniently modify the model/training/inference parameters. Specifically, we use yaml files to configure all the important parameters.
To train a model from scratch, all the yaml files should be saved in v2xvit/hypes_yaml, and users should use the load_yaml() function in v2xvit/hypes_yaml/yaml_utils.py to load the parameters into a dictionary.
To train from a saved checkpoint or test, the yaml files should be located with the checkpoint together in the v2xvit/logs/model_name.
We follow the below style to name config yaml files.
{backbone}_{fusion_strategy}.yamlCommunication noise simulation is defined in the wild_setting group in the yaml file.
wild_setting: # setting related to noise
async: true
async_mode: 'sim'
async_overhead: 100
backbone_delay: 10
data_size: 1.06
loc_err: true
ryp_std: 0.2
seed: 25
transmission_speed: 27
xyz_std: 0.2
async: whether add communication delay.
aysnc_mode: sim or real mode. In sim mode, the delay is a constant while in real mode, the delay has a uniform distribution.
The major experiment in the paper used sim mode whereas the 'Effects of transmission size' study used real
mode.
async_overhead: the communication delay in ms. In sim mode, it represents a constant number. In real mode,
the systematic async will be a random number from 0 to aysnc_overhead.
backbone_delay: an estimate of backbone computation time. Only useful in real mode.
data_size: transmission data size in Mb. Only used in real mode.
transmission_speed: data transmitting speed during communication. By default 27 Mb/s. Only used in real mode.
loc_err: whether to add localization error.
xyz_std: the standard deviation of positional GPS error.
ryp_std: the standard deviation of angular GPS error.
seed: random seed for noise simulation. please keep it as 25 during testing .
Now let's go through the point_pillar_opv2v_fusion.yaml as an example.
name: point_pillar_intermediate_fusion # this parameter together with the current timestamp will define the name of the saved folder for the model.
root_dir: "v2xset/train" # this is where the training data locate
validate_dir: "v2xset/validate" # during training, it defines the validation folder. during testing, it defines the testing folder path.
yaml_parser: "load_point_pillar_params" # we need specific loading functions for different backbones.
train_params: # the common training parameters
batch_size: &batch_size 2
epoches: 60
eval_freq: 1
save_freq: 1
wild_setting: # setting related to noise
async: true
async_mode: 'sim'
async_overhead: 100
backbone_delay: 10
data_size: 1.06
loc_err: true
ryp_std: 0.2
seed: 25
transmission_speed: 27
xyz_std: 0.2
fusion:
core_method: 'IntermediateFusionDataset' # LateFusionDataset, EarlyFusionDataset, and IntermediateFusionDataset are supported
args:
cur_ego_pose_flag: True
# when the cur_ego_pose_flag is set to True, there is no time gap
# between the time when the LiDAR data is captured by connected
# agents and when the extracted features are received by
# the ego vehicle, which is equal to implement STCM. When set to False,
# STCM has to be used. To validate STCM, V2X-ViT will set this as False.
# preprocess-related
preprocess:
# options: BasePreprocessor, SpVoxelPreprocessor, BevPreprocessor
core_method: 'SpVoxelPreprocessor'
args:
voxel_size: &voxel_size [0.4, 0.4, 4] # the voxel resolution for PointPillar
max_points_per_voxel: 32 # maximum points allowed in each voxel
max_voxel_train: 32000 # the maximum voxel number during training
max_voxel_test: 70000 # the maximum voxel number during testing
# LiDAR point cloud cropping range
cav_lidar_range: &cav_lidar [-140.8, -40, -3, 140.8, 40, 1]
# data augmentation options.
data_augment:
- NAME: random_world_flip
ALONG_AXIS_LIST: [ 'x' ]
- NAME: random_world_rotation
WORLD_ROT_ANGLE: [ -0.78539816, 0.78539816 ]
- NAME: random_world_scaling
WORLD_SCALE_RANGE: [ 0.95, 1.05 ]
# post processing related.
postprocess:
core_method: 'VoxelPostprocessor' # VoxelPostprocessor and BevPostprocessor are supported
anchor_args: # anchor generator parameters
cav_lidar_range: *cav_lidar # the range is consistent with the lidar cropping range to generate the correct ancrhors
l: 3.9 # the default length of the anchor
w: 1.6 # the default width
h: 1.56 # the default height
r: [0, 90] # the yaw angles. 0, 90 meaning for each voxel, two anchors will be generated with 0 and 90 degree yaw angle
feature_stride: 2 # the feature map is shrank twice compared the input voxel tensor
num: &achor_num 2 # for each location in the feature map, 2 anchors will be generated
target_args: # used to generate positive and negative samples for object detection
pos_threshold: 0.6
neg_threshold: 0.45
score_threshold: 0.20
order: 'hwl' # hwl or lwh
max_num: 100 # maximum number of objects in a single frame. use this number to make sure different frames have the same dimension in the same batch
nms_thresh: 0.15
# model related
model:
core_method: point_pillar_opv2v # trainer will load the corresponding model python file with the same name
args: # detailed parameters of the point pillar model
voxel_size: *voxel_size
lidar_range: *cav_lidar
anchor_number: *achor_num
pillar_vfe:
use_norm: true
with_distance: false
use_absolute_xyz: true
num_filters: [64]
point_pillar_scatter:
num_features: 64
base_bev_backbone:
layer_nums: [3, 5, 8]
layer_strides: [2, 2, 2]
num_filters: [64, 128, 256]
upsample_strides: [1, 2, 4]
num_upsample_filter: [128, 128, 128]
compression: 0 # whether to compress the features before fusion to reduce the bandwidth
backbone_fix: false # whether fix the pointpillar backbone weights during training.
anchor_num: *achor_num
loss: # loss function
core_method: point_pillar_loss # trainer will load the loss function with the same name
args:
cls_weight: 1.0 # classification weights
reg: 2.0 # regression weights
optimizer: # optimzer setup
core_method: Adam # the name has to exist in Pytorch optimizer library
lr: 0.002
args:
eps: 1e-10
weight_decay: 1e-4
lr_scheduler: # learning rate schedular
core_method: multistep #step, multistep and Exponential are supported
gamma: 0.1
step_size: [15, 30]