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INSTALL.md

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Installation

Requirements:

  • PyTorch 1.1 or 1.0.1.
  • torchvision 0.2.2.post3
  • cocoapi
  • yacs
  • matplotlib
  • GCC >= 4.9
  • OpenCV

Option 1: Step-by-step installation

# first, make sure that your conda is setup properly with the right environment
# for that, check that `which conda`, `which pip` and `which python` points to the
# right path. From a clean conda env, this is what you need to do

conda create --name RDPNet
conda activate RDPNet

# this installs the right pip and dependencies for the fresh python
conda install ipython

# follow PyTorch installation in https://pytorch.org/get-started/locally/
# we give the instructions for CUDA 9.0
# You should install the consistent CUDA vesion which is same with your system's CUDA version!!!
conda install -c pytorch pytorch==1.1 torchvision==0.2.2 cudatoolkit=9.0

export INSTALL_DIR=$PWD

# install pycocotools
pip install pycocotools

# install RDPNet
cd $INSTALL_DIR
git clone https://github.com/yuhuan-wu/RDPNet.git
cd RDPNet


# RDPNet and coco api dependencies
pip install -r requirements.txt

# the following will install the lib with
# symbolic links, so that you can modify
# the files if you want and won't need to
# re-build it
python setup.py build develop

cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" .

unset INSTALL_DIR

# or if you are on macOS
# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build develop

Option 2: Docker Image (Requires CUDA, Linux only)

The following steps are for original maskrcnn-benchmark. Please change the repository name if needed.

Build image with defaults (CUDA=9.0, CUDNN=7, FORCE_CUDA=1):

nvidia-docker build -t maskrcnn-benchmark docker/

Build image with other CUDA and CUDNN versions:

nvidia-docker build -t maskrcnn-benchmark --build-arg CUDA=9.2 --build-arg CUDNN=7 docker/

Build image with FORCE_CUDA disabled:

nvidia-docker build -t maskrcnn-benchmark --build-arg FORCE_CUDA=0 docker/

Build and run image with built-in jupyter notebook(note that the password is used to log in jupyter notebook):

nvidia-docker build -t maskrcnn-benchmark-jupyter docker/docker-jupyter/
nvidia-docker run -td -p 8888:8888 -e PASSWORD=<password> -v <host-dir>:<container-dir> maskrcnn-benchmark-jupyter