EASNet

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}
}

TL;DR quickstart

Setup

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.

Searching EASNet

The main commands are summarized in ``train.sh''. One can use them accordingly.

Train the largest supernet.

# 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

Shrink the kernel size/depth/width/scale.

# 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

Finetuning on KITTI 2012/2015

# 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

Pretrained Models