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SuMuzi 4bc8454aa2 | 1 year ago | |
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assets | 2 years ago | |
configs | 2 years ago | |
rodnet | 1 year ago | |
tools | 1 year ago | |
.gitignore | 2 years ago | |
LICENSE | 3 years ago | |
README.md | 1 year ago | |
cmake_build.py | 1 year ago | |
requirements.txt | 2 years ago | |
setup.py | 1 year ago | |
setup_tdc.py | 1 year ago | |
setup_wo_tdc.py | 2 years ago |
镜像 dockerhub.pcl.ac.cn:5000/user-images/openi:rod
数据集 rot_openi.tar.gz
git clone https://github.com/yizhou-wang/cruw-devkit.git
cd cruw-devkit
pip install .
cd /code
python setup.py develop
ln -s /dataset/rod_openi /ROD2021
python tools/train.py --config configs/config_rodnet_cdc_win16.py --data_dir /dataset/rod_openi/ --log_dir /model
This is the official implementation of our RODNet papers
at WACV 2021
and IEEE J-STSP 2021.
[Arxiv]
[Dataset]
[ROD2021 Challenge]
[Presentation]
[Demo]
Please cite our paper if this repository is helpful for your research:
@inproceedings{wang2021rodnet,
author={Wang, Yizhou and Jiang, Zhongyu and Gao, Xiangyu and Hwang, Jenq-Neng and Xing, Guanbin and Liu, Hui},
title={RODNet: Radar Object Detection Using Cross-Modal Supervision},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month={January},
year={2021},
pages={504-513}
}
@article{wang2021rodnet,
title={RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization},
author={Wang, Yizhou and Jiang, Zhongyu and Li, Yudong and Hwang, Jenq-Neng and Xing, Guanbin and Liu, Hui},
journal={IEEE Journal of Selected Topics in Signal Processing},
volume={15},
number={4},
pages={954--967},
year={2021},
publisher={IEEE}
}
Clone RODNet code.
cd $RODNET_ROOT
git clone https://github.com/yizhou-wang/RODNet.git
Create a conda environment for RODNet. Tested under Python 3.6, 3.7, 3.8.
conda create -n rodnet python=3.* -y
conda activate rodnet
Install pytorch.
Note: If you are using Temporal Deformable Convolution (TDC), we only tested under pytorch<=1.4
and CUDA=10.1
.
Without TDC, you should be able to choose the latest versions.
If you met some issues with environment, feel free to raise an issue.
conda install pytorch=1.4 torchvision cudatoolkit=10.1 -c pytorch # if using TDC
# OR
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch # if not using TDC
Install cruw-devkit
package.
Please refer to cruw-devit
repository for detailed instructions.
git clone https://github.com/yizhou-wang/cruw-devkit.git
cd cruw-devkit
pip install .
cd ..
Setup RODNet package.
pip install -e .
Note: If you are not using TDC, you can rename script setup_wo_tdc.py
as setup.py
, and run the above command.
This should allow you to use the latest cuda and pytorch version.
Download ROD2021 dataset.
Follow this script to reorganize files as below.
data_root
- sequences
| - train
| | - <SEQ_NAME>
| | | - IMAGES_0
| | | | - <FRAME_ID>.jpg
| | | | - ***.jpg
| | | - RADAR_RA_H
| | | - <FRAME_ID>_<CHIRP_ID>.npy
| | | - ***.npy
| | - ***
| |
| - test
| - <SEQ_NAME>
| | - RADAR_RA_H
| | - <FRAME_ID>_<CHIRP_ID>.npy
| | - ***.npy
| - ***
|
- annotations
| - train
| | - <SEQ_NAME>.txt
| | - ***.txt
| - test
| - <SEQ_NAME>.txt
| - ***.txt
- calib
Convert data and annotations to .pkl
files.
python tools/prepare_dataset/prepare_data.py \
--config configs/<CONFIG_FILE> \
--data_root <DATASET_ROOT> \
--split train,test \
--out_data_dir data/<DATA_FOLDER_NAME>
python tools/train.py --config configs/<CONFIG_FILE> \
--data_dir data/<DATA_FOLDER_NAME> \
--log_dir checkpoints/
python tools/test.py --config configs/<CONFIG_FILE> \
--data_dir data/<DATA_FOLDER_NAME> \
--checkpoint <CHECKPOINT_PATH> \
--res_dir results/
深蓝环境感知项目,4\18训练
SVG Python Cuda C++
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