- PyTorch >= 1.0. Installation instructions can be found in https://pytorch.org/get-started/locally/.
- torchvision
- cocoapi
- yacs
- matplotlib
- GCC >= 4.9,< 6.0
- (optional) OpenCV for the webcam demo
# 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 FCOS
conda activate FCOS
# this installs the right pip and dependencies for the fresh python
conda install ipython
# FCOS and coco api dependencies
pip install ninja yacs cython matplotlib tqdm
# follow PyTorch installation in https://pytorch.org/get-started/locally/
# we give the instructions for CUDA 10.2
conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
export INSTALL_DIR=$PWD
# install pycocotools. Please make sure you have installed cython.
cd $INSTALL_DIR
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
python setup.py build_ext install
# install PyTorch Detection
cd $INSTALL_DIR
git clone https://github.com/tianzhi0549/FCOS.git
cd FCOS
# 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 --no-deps
unset INSTALL_DIR
# or if you are on macOS
# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build developThe 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