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INSTALL.md

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Installation

Most of the requirements of this projects are exactly the same as maskrcnn-benchmark. If you have any problem of your environment, you should check their issues page first. Hope you will find the answer.

Requirements:

  • PyTorch >= 1.2 (Mine 1.4.0 (CUDA 10.1))
  • torchvision >= 0.4 (Mine 0.5.0 (CUDA 10.1))
  • cocoapi
  • yacs
  • matplotlib
  • GCC >= 4.9
  • OpenCV

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 scene_graph_benchmark
conda activate scene_graph_benchmark

# this installs the right pip and dependencies for the fresh python
conda install ipython
conda install scipy
conda install h5py

# scene_graph_benchmark and coco api dependencies
pip install ninja yacs cython matplotlib tqdm opencv-python overrides

# follow PyTorch installation in https://pytorch.org/get-started/locally/
# we give the instructions for CUDA 10.1
conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.1 -c pytorch

export INSTALL_DIR=$PWD

# install pycocotools
cd $INSTALL_DIR
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
python setup.py build_ext install

# install apex
cd $INSTALL_DIR
git clone https://github.com/NVIDIA/apex.git
cd apex
python setup.py install --cuda_ext --cpp_ext

# install PyTorch Detection
cd $INSTALL_DIR
git clone https://github.com/KaihuaTang/Scene-Graph-Benchmark.pytorch.git
cd scene-graph-benchmark

# 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


unset INSTALL_DIR