Facebook Detectron2 Mask-RCNN 安装踩坑

使用FaceBook官方github repo(网址),按照官方教程一步一步来,有几个地方有问题,记录一下,蓝色是官方命令,红色是修改的命令

# 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=9.0
conda install pytorch torchvision cudatoolkit=10.2 -c pytorch-nightly

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 cityscapesScripts
cd $INSTALL_DIR
git clone https://github.com/mcordts/cityscapesScripts.git
cd cityscapesScripts/
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/facebookresearch/maskrcnn-benchmark.git
cd maskrcnn-benchmark

export CUDA_HOME=/usr/local/cuda
cuda_dir="maskrcnn_benchmark/csrc/cuda"
perl -i -pe ‘s/AT_CHECK/TORCH_CHECK/‘ $cuda_dir/deform_pool_cuda.cu $cuda_dir/deform_conv_cuda.cu

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

 

Facebook Detectron2 Mask-RCNN 安装踩坑

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