Lane-GCN代码（轨迹预测）

# LaneGCN: Learning Lane Graph Representations for Motion Forecasting [Paper](https://arxiv.org/pdf/2007.13732) | [Slides](http://www.cs.toronto.edu/~byang/slides/LaneGCN.pdf) | [Project Page]() | [**ECCV 2020 Oral** Video](https://yun.sfo2.digitaloceanspaces.com/public/lanegcn/video.mp4) Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, Raquel Urtasun **Rank 1st** in [Argoverse Motion Forecasting Competition](https://evalai.cloudcv.org/web/challenges/challenge-page/454/leaderboard/1279)  Table of Contents ================= * [Install Dependancy](#install-dependancy) * [Prepare Data](#prepare-data-argoverse-motion-forecasting) * [Training](#training) * [Testing](#testing) * [Licence](#licence) * [Citation](#citation) ## Install Dependancy You need to install following packages in order to run the code: - [PyTorch>=1.3.1](https://pytorch.org/) - [Argoverse API](https://github.com/argoai/argoverse-api#installation) 1. Following is an example of create environment **from scratch** with anaconda, you can use pip as well: ```sh conda create --name lanegcn python=3.7 conda activate lanegcn conda install pytorch==1.5.1 torchvision cudatoolkit=10.2 -c pytorch # pytorch=1.5.1 when the code is release # install argoverse api pip install git+https://github.com/argoai/argoverse-api.git # install others dependancy pip install scikit-image IPython tqdm ipdb ``` 2. \[Optional but Recommended\] Install [Horovod](https://github.com/horovod/horovod#install) and `mpi4py` for distributed training. Horovod is more efficient than `nn.DataParallel` for mulit-gpu training and easier to use than `nn.DistributedDataParallel`. Before install horovod, make sure you have openmpi installed (`sudo apt-get install -y openmpi-bin`). ```sh pip install mpi4py # install horovod with GPU support, this may take a while HOROVOD_GPU_OPERATIONS=NCCL pip install horovod==0.19.4 # if you have only SINGLE GPU, install for code-compatibility pip install horovod ``` if you have any issues regarding horovod, please refer to [horovod github](https://github.com/horovod/horovod) ## Prepare Data: Argoverse Motion Forecasting You could check the scripts, and download the processed data instead of running it for hours. ```sh bash get_data.sh ``` ## Training ### [Recommended] Training with Horovod-multigpus ```sh # single node with 4 gpus horovodrun -np 4 -H localhost:4 python /path/to/train.py -m lanegcn # 2 nodes, each with 4 gpus horovodrun -np 8 -H serverA:4,serverB:4 python /path/to/train.py -m lanegcn ``` It takes 8 hours to train the model in 4 GPUS (RTX 5000) with horovod. We also supply [training log](misc/train_log.txt) for you to debug. ### [Recommended] Training/Debug with Horovod in single gpu ```sh python train.py -m lanegcn ``` ## Testing You can download pretrained model from [here](http://yun.sfo2.digitaloceanspaces.com/public/lanegcn/36.000.ckpt) ### Inference test set for submission ``` python test.py -m lanegcn --weight=/absolute/path/to/36.000.ckpt --split=test ``` ### Inference validation set for metrics ``` python test.py -m lanegcn --weight=36.000.ckpt --split=val ``` **Qualitative results** Labels(Red) Prediction (Green) Other agents(Blue) <p> <img src="misc/5304.gif" width = "30.333%" align="left" /> <img src="misc/25035.gif" width = "30.333%" align="center" /> <img src="misc/19406.gif" width = "30.333%" align="right" /> </p> ------ **Quantitative results**  ## Licence check [LICENSE](LICENSE) ## Citation If you use our source code, please consider citing the following: ```bibtex @InProceedings{liang2020learning, title={Learning lane graph representations for motion forecasting}, author={Liang, Ming and Yang, Bin and Hu, Rui and Chen, Yun and Liao, Renjie and Feng, Song and Urtasun, Raquel}, booktitle = {ECCV}, year={2020} } ``` If you have any questions regarding the code, please open an issue and [@chenyuntc](https://github.com/chenyuntc).