GLENet

Overview

• Introduction
• Installation
• Dataset Preparation
• Generate Label Uncertainty with GLEnet
• Probabilistic Object Detectors
• License
• Acknowledgement

Introduction

Implementation of paper: "GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation". The implementation contains two parts, GLENet for generating label uncertainty and probability detectors part implemented based on [OpenPcdet 0.5.2].

Fig.1 Visual results of GLENet. The ground-truth and predictions are colored in red and green

Installation

Requrements

• Linux
• Python 3.6+
• PyTorch 1.1 or higher (tested on PyTorch 1.1, 1,3, 1,5~1.10)
• CUDA 9.0 or higher (PyTorch 1.3+ needs CUDA 9.2+)
• spconv v1.0 (commit 8da6f96) or spconv v1.2

Install

• a. install dependent python libraries:

cd GLENet;pip install -r requirements.txt 

• b. Install the SparseConv library, we use the implementation from [spconv].
  • If you use PyTorch 1.1, then make sure you install the spconv v1.0 with (commit 8da6f96) instead of the latest one.
  • If you use PyTorch 1.3+, then you need to install the spconv v1.2. As mentioned by the author of spconv, you need to use their docker if you use PyTorch 1.4+.
• c. Install this pcdet library and its dependent libraries by running the following command:

python setup.py develop

Dataset Preparation

KITTI Dataset

• Please download the official KITTI 3D object detection dataset and organize the downloaded files as follows (the road planes could be downloaded from [road plane], which are optional for data augmentation in the training):

GLENet
├── data
│   ├── kitti
│   │   │── ImageSets
│   │   │── training
│   │   │   ├──calib & velodyne & label_2 & image_2 & planes
│   │   │── testing
│   │   │   ├──calib & velodyne & image_2
├── cvae_uncertainty
├── pcdet
├── tools

• Generate the data infos by running the following command:

python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml

Waymo Open Dataset

• Please download the official Waymo Open Dataset(v1.2.0), including the training data training_0000.tar~training_0031.tar and the validation data validation_0000.tar~validation_0007.tar.
• Unzip all the above xxxx.tar files to the directory of data/waymo/raw_data as follows (You could get 798 train tfrecord and 202 val tfrecord ):

GLENet
├── data
│   ├── waymo
│   │   │── ImageSets
│   │   │── raw_data
│   │   │   │── segment-xxxxxxxx.tfrecord
|   |   |   |── ...
|   |   |── waymo_processed_data_v0_5_0
│   │   │   │── segment-xxxxxxxx/
|   |   |   |── ...
│   │   │── waymo_processed_data_v0_5_0_gt_database_train_sampled_1/
│   │   │── waymo_processed_data_v0_5_0_waymo_dbinfos_train_sampled_1.pkl
│   │   │── waymo_processed_data_v0_5_0_gt_database_train_sampled_1_global.npy (optional)
│   │   │── waymo_processed_data_v0_5_0_infos_train.pkl (optional)
│   │   │── waymo_processed_data_v0_5_0_infos_val.pkl (optional)
├── cvae_uncertainty
├── pcdet
├── tools

• Install the official waymo-open-dataset by running the following command:

pip3 install --upgrade pip
# tf 2.0.0
pip3 install waymo-open-dataset-tf-2-5-0 --user

• Extract point cloud data from tfrecord and generate data infos by running the following command (it takes several hours, and you could refer to data/waymo/waymo_processed_data_v0_5_0 to see how many records that have been processed):

python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos \
    --cfg_file tools/cfgs/dataset_configs/waymo_dataset.yaml

Note that you do not need to install waymo-open-dataset if you have already processed the data before and do not need to evaluate with official Waymo Metrics.

Generate Label Uncertainty with GLEnet

step 1.0: prepare data for GLENet

ln -s data/kitti cvae_uncertainty
ln -s data/waymo cvae_uncertainty

step1.1: Train GLENet:

cd cvae_uncertainty;mkdir -p logs;
exp_id=exp20 # you can set other exp_id
for iter in `seq 0 9`;do
    sed "s@# FOLD_IDX: 0@FOLD_IDX: ${iter}@" cfgs/${exp_id}_gen_ori.yaml > cfgs/${exp_id}_gen.yaml
    grep FOLD cfgs/${exp_id}_gen.yaml
    CUDA_VISIBLE_DEVICES=0,1 bash scripts/dist_train.sh 2 --cfg_file cfgs/${exp_id}_gen.yaml --tcp_port 18889  --max_ckpt_save_num 10  --workers 1 --extra_tag fold_${iter} &>> logs/${exp_id}_gen_fold_${iter}.log
done

step 1.2: GLENet Prediction:

cd cvae_uncertainty;
exp_id=exp20
for iter in `seq 0 9`;do
    sed "s@# FOLD_IDX: 0@FOLD_IDX: ${iter}@" cfgs/${exp_id}_gen_ori.yaml > cfgs/${exp_id}_gen.yaml
    grep FOLD cfgs/${exp_id}_gen.yaml
    sh predict.sh ${exp_id}_gen fold_${iter} 400 0
done

step 1.3: Generate and Save Label Uncertainty

• mkdir -p output/uncertainty_dump
• python mapping_uncertainty.py
• python change_gt_infos.py

Then you can use the new *.pkl that contains label uncertainty to replace the origin file.

We provide the kitti_infos_train.pkl and kitti_dbinfos_train.pkl that contain label uncertainty.

Probabilistic Object Detectors

Training

cd tools;
python train.py --cfg_file ./cfgs/kitti_models/GLENet_VR.yaml

Multi gpu training, assuming you have 4 gpus:

CUDA_VISIBLE_DEVICES=0,1,2,3 bash scripts/dist_train.sh 4 --cfg_file ./cfgs/kitti_models/GLENet_VR.yaml

Testing

cd tools/

Single gpu testing for all saved checkpoints, assuming you have 4 gpus:

python test.py --eval_all --cfg_file ./cfgs/kitti_models/GLENet_VR.yaml

Multi gpu testing for all saved checkpoints, assuming you have 4 gpus:

CUDA_VISIBLE_DEVICES=0,1,2,3 bash scripts/dist_test.sh 4 --eval_all --cfg_file ./cfgs/kitti_models/GLENet_VR.yaml

Multi gpu testing a specific checkpoint, assuming you have 4 gpus and checkpoint_39 is your best checkpoint :

CUDA_VISIBLE_DEVICES=0,1,2,3 bash scripts/dist_test.sh 4  --cfg_file ./cfgs/kitti_models/GLENet_VR.yaml --ckpt ../output/GLENet_VR/default/ckpt/checkpoint_epoch_80.pth

Pretrained Models

We provide the pre-trained models for car class on the KITTI dataset.

Method	Simple@R11	Moderate@R11	Hard@R11	Moderate@R40	Download
SECOND(Baseline)	88.61	78.62	77.22	79.94	-
GLENet-S(Ours)	88.60	84.41	78.42	84.81	Download
CIA-SSD(Baseline)	90.04	79.81	78.80	84.16	-
GLENet-C(Ours)	89.81	84.54	78.82	85.19	Download
Voxel R-CNN(Baseline)	89.41	84.52	78.93	85.29	-
GLENet-VR(Ours)	89.95	86.49	79.18	86.23	Download

Citation

If you find this work useful in your research, please consider citing:

@article{zhang2023glenet,
    title={GLENet: Boosting 3D object detectors with generative label uncertainty estimation},
    author={Zhang, Yifan and Zhang, Qijian and Zhu, Zhiyu and Hou, Junhui and Yuan, Yixuan},
    journal={International Journal of Computer Vision},
    volume={131},
    number={12},
    pages={3332--3352},
    year={2023}
}

License

GLENet is released under the Apache 2.0 license.

Acknowledgement

Thanks for the OpenPCDet, the implementation of probabilistic object detectors part is mainly based on the pcdet v0.5.2.