EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching (ECCV 2022) [arXiv] [Video] PyTorch implementation of searching an efficient network architecture for stereo matching. Please cite the paper below if you use this project. Any suggestion, fork, and pull request is welcome.
@inproceedings{
wang2022easnet,
title={EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching},
author={Qiang Wang and Shaohuai Shi and Kaiyong Zhao and Xiaowen Chu},
booktitle={European Conference on Computer Vision},
year={2022},
url={https://arxiv.org/pdf/xxxx.xxxxx.pdf}
}
Python 3 dependencies:
- PyTorch 1.8+
- OpenMPI 4.0.1+
- matplotlib
- numpy
- imageio
- other necessary packages
We use the deformation module from AANet. Install the ``deform_conv'' package as follow.
cd ofa/stereo_matching/networks/deform_conv
sh build.sh
Our training scripts apply MPI to accelerate the training procedure. Please install OpenMPI 4.0.1 or above.
The main commands are summarized in ``train.sh''. One can use them accordingly.
# two nodes, four GPUs per node
mpirun -np 8 -H host1:4,host2:4 \
-bind-to none -map-by slot \
-x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH -x PATH \
python train_ofa_stere.py \
--task large
# two nodes, four GPUs per node
export TASK=kernel # 'kernel', 'depth', 'width', 'scale'
mpirun -np 8 -H host1:4,host2:4 \
-bind-to none -map-by slot \
-x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH -x PATH \
python train_ofa_stere.py \
--task $TASK
# two nodes, four GPUs per node
export TASK=kitti12 # 'kitti12', 'kitti2015'
mpirun -np 8 -H host1:4,host2:4 \
-bind-to none -map-by slot \
-x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH -x PATH \
python train_ofa_stere.py \
--task $TASK