https://github.com/gt-ripl/multiagentperception
Official source code to CVPR'20 paper, "When2com: Multi-Agent Perception via Communication Graph Grouping"
https://github.com/gt-ripl/multiagentperception
cvpr2020 deep-learning multi-agent-systems multi-view semantic-segmentation
Last synced: 11 days ago
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Official source code to CVPR'20 paper, "When2com: Multi-Agent Perception via Communication Graph Grouping"
- Host: GitHub
- URL: https://github.com/gt-ripl/multiagentperception
- Owner: GT-RIPL
- License: mit
- Created: 2020-06-15T23:36:04.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-07-06T21:14:42.000Z (almost 2 years ago)
- Last Synced: 2025-04-11T22:52:14.442Z (11 days ago)
- Topics: cvpr2020, deep-learning, multi-agent-systems, multi-view, semantic-segmentation
- Language: Python
- Homepage: https://ycliu93.github.io/projects/multi-agent-perception.html
- Size: 1.22 MB
- Stars: 59
- Watchers: 3
- Forks: 12
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## When2com: Multi-Agent Perception via Communication Graph Grouping
[](https://opensource.org/licenses/MIT)
This is the PyTorch implementation of our paper:
**When2com: Multi-Agent Perception via Communication Graph Grouping**
[__***Yen-Cheng Liu***__](https://ycliu93.github.io/), [Junjiao Tian](https://www.linkedin.com/in/junjiao-tian-42b9758a/), [Nathaniel Glaser](https://sites.google.com/view/nathanglaser/), [Zsolt Kira](https://www.cc.gatech.edu/~zk15/)
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
[[Paper](http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_When2com_Multi-Agent_Perception_via_Communication_Graph_Grouping_CVPR_2020_paper.pdf)] [[GitHub](https://github.gatech.edu/RIPL/multi-agent-perception)] [[Project](https://ycliu93.github.io/projects/multi-agent-perception.html)]
## Prerequisites
- Python 3.6
- Pytorch 0.4.1
- Other required packages in `requirement.txt`
## Getting started
### Download and install miniconda
```
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
```
### Create conda environment
```
conda create -n semseg python=3.6
source actviate semseg
```
### Install the required packages
```
pip install -r requirements.txt
```
### Download AirSim-MAP dataset and unzip it.
- Download the zip file you would like to run
[](https://gtvault-my.sharepoint.com/:f:/g/personal/yliu3133_gatech_edu/Ett0G1_5YYdBpgojk0uWESgBi95dO79LkbYaKRhlBIkVJQ?e=vdjklb/)
### Move the datasets to the dataset path
```
mkdir dataset
mv (dataset folder name) dataset/
```
### Training
```
# [Single-request multi-support] All norm
python train.py --config configs/srms-allnorm.yml --gpu=0
# [Multi-request multi-support] when2com model
python train.py --config configs/mrms-when2com.yml --gpu=0
```
### Testing
```
# [Single-request multi-support] All norm
python test.py --config configs/srms-allnorm.yml --model_path --gpu=0
# [Multi-request multi-support] when2com model
python test.py --config configs/mrms-when2com.yml --model_path --gpu=0
```
## Acknowledgments
- This work was supported by ONR grant N00014-18-1-2829.
- This code is built upon the implementation from [Pytorch-semseg](https://github.com/meetshah1995/pytorch-semseg).
## Citation
If you find this repository useful, please cite our paper:
```
@inproceedings{liu2020when2com,
title={When2com: Multi-Agent Perception via Communication Graph Grouping},
author={Yen-Cheng Liu and Junjiao Tian and Nathaniel Glaser and Zsolt Kira},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2020}
}
```