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# 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 maskrcnn_benchmark -y
conda activate maskrcnn_benchmark
# this installs the right pip and dependencies for the fresh python
conda install ipython pip
# maskrcnn_benchmark and coco api dependencies
pip install ninja yacs cython matplotlib tqdm opencv-python
# follow PyTorch installation in https://pytorch.org/get-started/locally/# we give the instructions for CUDA 9.0#conda install -c pytorch pytorch-nightly torchvision cudatoolkit
pip install pytorch==1.1.0
pip install torchvision==0.3.0
export INSTALL_DIR=$PWD
git clone https://github.com/jiaxi-wu/MPSR.git
# install pycocotools# modify "cocoeval.py" to store raw results in "~/coco_result.txt"cd$INSTALL_DIR
git clone https://github.com/cocodataset/cocoapi.git
cp MPSR/tools/fewshot_exp/cocoeval.py cocoapi/PythonAPI/pycocotools/
cd cocoapi/PythonAPI
python setup.py build_ext install
# install apex-96b017a8b40f137abb971c4555d61b2fcbb87648cd$INSTALL_DIR
git clone https://github.com/NVIDIA/apex.git
cd apex
git checkout 96b017a
python setup.py install --cuda_ext --cpp_ext
# install PyTorch Detectioncd$INSTALL_DIRcd MPSR
# 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