Installation

System requirements

  • Nvidia gpu (GTX 10x0 or later, earlier may work but not tested)
  • OS: Linux (We prefer Ubuntu 16.04)

We provide the Dockerfile and docker-compose.yml for you to install the enviroment. First we need to install docker in host machine

Install Docker

  1. Install Docker

    Install Docker from Homepage

  2. Install CUDA on Host Machine

    Install Cuda 8.0 on Ubuntu 16.04

  3. Install docker-compose

    https://docs.docker.com/compose/install/

  4. Install Nvidia-docker

    In order to passthrough GPU to docker we need to install Nvidia-docker

Create Docker image

  1. Clone from github:

    git clone git@github.com:anhlt/faster_rcnn.git

  2. Use docker-compose to create a docker image

    cd ~/workspace/faster_rcnn
docker-compose up --build

Compile Cython module

There are 3 modules need to be compiled, nms, roi_pooling, utils. We need to exec \bin\bash on Docker image to build those modules

    cd ~/workspace/faster_rcnn
    docker-compose exec python /bin/bash

  • Compile nms

    cd /data/faster_rcnn/nms
python setup.py build_ext --inplace 
rm -rf build

  • Compile utils

    cd /data/faster_rcnn/utils
python setup.py build_ext --inplace

  • Compile roi_pooling

    cd /data/faster_rcnn/roi_pooling/src/cuda/  
nvcc -c -o roi_pooling.cu.o roi_pooling_kernel.cu -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC -arch=sm_61

cd /data/faster_rcnn/roi_pooling
python setup.py build_ext --inplace