LEAStereo
This repository contains the code for our NeurIPS 2020 paper Hierarchical Neural Architecture Searchfor Deep Stereo Matching [NeurIPS 20]
Requirements
Environment
- Python 3.8.*
- CUDA 10.0
- PyTorch
- TorchVision
Install
Create a virtual environment and activate it.
conda create -n leastereo python=3.8
conda activate leastereo
The code has been tested with PyTorch 1.6 and Cuda 10.2.
conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.2 -c pytorch
conda install matplotlib path.py tqdm
conda install tensorboard tensorboardX
conda install scipy scikit-image opencv
Install Nvidia Apex
Follow the instructions here. Apex is required for mixed precision training.
Please do not use pip install apex - this will not install the correct package.
Dataset
To evaluate/train our LEAStereo network, you will need to download the required datasets.
Change the first column path in file create_link.sh with your actual dataset location. Then run create_link.sh that will create symbolic links to wherever the datasets were downloaded in the datasets folder. For Middlebury 2014 dataset, we perform our network on half resolution images.
├── datasets
├── SceneFlow
├── camera_data
├── disparity
├── frames_finalpass
├── kitti2012
├── testing
├── training
├── kitti2015
├── testing
├── training
├── MiddEval3
├── testH
├── trainingH
Prediction
You can evaluate a trained model using prediction.sh for each dataset, that would help you generate *.png or *.pfm images correspoding to different datasets.
sh predict_sf.sh
sh predict_md.sh
sh predict_kitti12.sh
sh predict_kitti15.sh
Results of our model on three benchmark datasets could also be found here
Architecture Search
Three steps for the architecture search:
1. Search
sh search.sh
2. decode
sh decode.sh
3. retrain
sh train_sf.sh
sh train_md.sh
sh train_kitti12.sh
sh train_kitti15.sh
Acknowledgements
This repository makes liberal use of code from [AutoDeeplab] [pytorch code(Non-official)].
Citing
If you find this code useful, please consider to cite our work.
@article{cheng2020hierarchical,
title={Hierarchical Neural Architecture Search for Deep Stereo Matching},
author={Cheng, Xuelian and Zhong, Yiran and Harandi, Mehrtash and Dai, Yuchao and Chang, Xiaojun and Li, Hongdong and Drummond, Tom and Ge, Zongyuan},
journal={Advances in Neural Information Processing Systems},
volume={33},
year={2020}
}
