Detectron Customized

Detectron is released under the Apache 2.0 license. See the NOTICE file for additional details.

Installation

sudo apt-get update sudo apt-get upgrade

Add the ppa repo for NVIDIA graphics driver

sudo add-apt-repository ppa:graphics-drivers/ppa sudo apt-get update

Install the recommended driver (currently nvidia-390)

sudo ubuntu-drivers autoinstall sudo reboot

#check if drivers were installed nvidia-smi

wget -O cuda_8.0.61_375.26_linux-run https://drive.google.com/open?id=1k_FdTU39MBpBI73FmDDUCGCGHfvTej2K sudo chmod +x cuda_9.0.176_384.81_linux-run ./cuda_9.0.176_384.81_linux-run

Install cuDNN

wget -O cudnn-9.0-linux-x64-v7.tgz https://drive.google.com/open?id=1nhAMvluxJIjEeamuMETEi36J7nGNrZNw tar -zxvf cudnn-9.0-linux-x64-v7.tgz

copy libs to /usr/local/cuda folder

sudo cp -P cuda/include/cudnn.h /usr/local/cuda/include sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64 sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

Install Caffe2

sudo apt-get update sudo apt-get install -y --no-install-recommends
build-essential
cmake
git
libgoogle-glog-dev
libgtest-dev
libiomp-dev
libleveldb-dev
liblmdb-dev
libopencv-dev
libopenmpi-dev
libsnappy-dev
libprotobuf-dev
openmpi-bin
openmpi-doc
protobuf-compiler
python-dev
python-pip
sudo pip install
future
numpy
protobuf

for Ubuntu 16.04

sudo apt-get install -y --no-install-recommends libgflags-dev

git clone --recursive https://github.com/caffe2/caffe2.git && cd caffe2

This will build Caffe2 in an isolated directory so that Caffe2 source is

unaffected

mkdir build && cd build

This configures the build and finds which libraries it will include in the

Caffe2 installation. The output of this command is very helpful in debugging

cmake ..

This actually builds and installs Caffe2 from makefiles generated from the

above configuration step

sudo make install

cd ~ && python -c 'from caffe2.python import core' 2>/dev/null && echo "Success" || echo "Failure"

python caffe2/python/operator_test/relu_op_test.py

python2 -c 'from caffe2.python import workspace; print(workspace.NumCudaDevices())'

add to ~/.bashrc

export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64" export CUDA_HOME=/usr/local/cuda alias python2=python export PATH=/usr/bin:$PATH

export PYTHONPATH=/usr/local:/home/global/detectron/lib export PYTHONPATH="/home/global/caffe2/build:$PYTHONPATH" export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH export COCOAPI=/home/global/cocoapi

COCOAPI=/path/to/clone/cocoapi

git clone https://github.com/cocodataset/cocoapi.git $COCOAPI cd $COCOAPI/PythonAPI

Install into global site-packages

make install

Alternatively, if you do not have permissions or prefer

not to install the COCO API into global site-packages

python2 setup.py install --user

DETECTRON=/path/to/clone/detectron

git clone https://github.com/facebookresearch/detectron $DETECTRON cd $DETECTRON/lib && make python2 $DETECTRON/tests/test_spatial_narrow_as_op.py

Running on PC (ubuntu)

python tools/infer_simple.py --cfg configs/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_2x.yaml --output-dir /home/global/Downloads/detectron-visualizations --image-ext jpg --wts https://s3-us-west-2.amazonaws.com/detectron/35861858/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_2x.yaml.02_32_51.SgT4y1cO/output/train/coco_2014_train:coco_2014_valminusminival/generalized_rcnn/model_final.pkl demo