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Intel's Deep Learning Inference Engine backend

Dmitry Kurtaev edited this page Apr 8, 2019 · 28 revisions

Intel's Deep Learning Inference Engine (DL IE) is a part of Intel® OpenVINO™ toolkit. You can use it as a computational backend for OpenCV deep learning module.

Download

  • Download and install Intel® OpenVINO™ toolkit.

    Important note: if you want to transfer the installed Inference Engine binaries to another machine w/o running OpenVINO installer there, you need the redistributable files of Intel C++ compiler (use the latest update, 64-bit version), otherwise the Inference Engine or some of its essential plugins will refuse to load and run, which may result in an app crash.

OpenCV from OpenVINO

OpenVINO 2018 R2 and later comes with OpenCV built with DL IE support, so you can skip this step if you use fresh enough version.

Build OpenCV from source

Ubuntu

Setup environment variables to detect Inference Engine:

source /opt/intel/openvino/bin/setupvars.sh

Build OpenCV with extra flags:

cmake \
  -DWITH_INF_ENGINE=ON \
  -DENABLE_CXX11=ON \
  ...

Microsoft Windows

Setup environment variables to detect Inference Engine:

"C:\Program Files (x86)\IntelSWTools\openvino\bin\setupvars.bat"

Build OpenCV with extra flags:

cmake ^
  -DWITH_INF_ENGINE=ON ^
  -DENABLE_CXX11=ON ^
  ...

Raspbian Stretch

Use Docker to cross-compile OpenCV for Raspberry Pi. Check that uname -m detects armv7l CPU architecture (starts from Raspberry Pi 2 model B).

  1. Create a folder named debian_9_armhf with a file Dockerfile with the following content:
FROM debian:stretch

USER root

RUN dpkg --add-architecture armhf && \
    apt-get update && \
    apt-get install -y --no-install-recommends \
    crossbuild-essential-armhf \
    cmake \
    pkg-config \
    wget \
    xz-utils \
    libgtk2.0-dev:armhf \
    libpython-dev:armhf \
    libpython3-dev:armhf \
    python-numpy \
    python3-numpy \
    libgstreamer1.0-dev:armhf \
    libgstreamer-plugins-base1.0-dev:armhf

# Install Inference Engine
RUN wget --no-check-certificate https://download.01.org/opencv/2019/openvinotoolkit/l_openvino_toolkit_raspbi_p_2019.1.094.tgz && \
    tar -xf l_openvino_toolkit_raspbi_p_2019.1.094.tgz
  1. Build a Docker image
docker image build debian_9_armhf

Example output:

Successfully built 13404dac3614
  1. Tag a build and run Docker container mounting source code folder from host.
docker tag 13404dac3614 debian_9_armhf:latest
docker run -it -v /absolute/path/to/opencv:/opencv debian_9_armhf /bin/bash
  1. Build
cd opencv && mkdir opencv_build && mkdir opencv_install && cd opencv_build
cmake -DCMAKE_BUILD_TYPE=Release \
      -DCMAKE_INSTALL_PREFIX="../opencv_install" \
      -DOPENCV_CONFIG_INSTALL_PATH="cmake" \
      -DCMAKE_TOOLCHAIN_FILE="../platforms/linux/arm-gnueabi.toolchain.cmake" \
      -DWITH_IPP=OFF \
      -DBUILD_TESTS=OFF \
      -DBUILD_PERF_TESTS=OFF \
      -DOPENCV_ENABLE_PKG_CONFIG=ON \
      -DPKG_CONFIG_EXECUTABLE="/usr/bin/arm-linux-gnueabihf-pkg-config" \
      -DPYTHON2_INCLUDE_PATH="/usr/include/python2.7" \
      -DPYTHON2_NUMPY_INCLUDE_DIRS="/usr/local/lib/python2.7/dist-packages/numpy/core/include" \
      -DPYTHON3_INCLUDE_PATH="/usr/include/python3.5" \
      -DPYTHON3_NUMPY_INCLUDE_DIRS="/usr/local/lib/python3.5/dist-packages/numpy/core/include" \
      -DPYTHON3_CVPY_SUFFIX=".cpython-35m-arm-linux-gnueabihf.so" \
      -DENABLE_NEON=ON \
      -DCPU_BASELINE="NEON" \
      -DWITH_INF_ENGINE=ON \
      -DINF_ENGINE_LIB_DIRS="/inference_engine_vpu_arm/deployment_tools/inference_engine/lib/armv7l" \
      -DINF_ENGINE_INCLUDE_DIRS="/inference_engine_vpu_arm/deployment_tools/inference_engine/include" \
      -DCMAKE_FIND_ROOT_PATH="/inference_engine_vpu_arm/" \
      -DENABLE_CXX11=ON ..
make -j4 && make install
  1. Copy opencv_install to the board. Follow http://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_raspbian.html to install OpenVINO distribution for Raspberry Pi. Then type the following commands to specify new location of OpenCV:
export PYTHONPATH=/path/to/opencv_install/lib/python2.7/dist-packages/cv2/python-2.7/:$PYTHONPATH
export PYTHONPATH=/path/to/opencv_install/lib/python3.5/dist-packages/cv2/python-3.5/:$PYTHONPATH
export LD_LIBRARY_PATH=/path/to/opencv_install/lib/:$LD_LIBRARY_PATH

Usage

  • Enable Intel's Inference Engine backend right after cv::dnn::readNet invocation:

    net.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
           // the other possible options are
           // DNN_BACKEND_OPENCV (the default C++ implementation)
           // DNN_BACKEND_HALIDE (Halide-based implementation)

    note that the inference engine backend is used by default since OpenCV 3.4.2 (OpenVINO 2018.R2) when OpenCV is built with the Inference engine support, so the call above is not necessary. Also, the Inference engine backend is the only available option (also enabled by default) when the loaded model is represented in OpenVINO™ Model Optimizer format.

  • Then, optionally you can also set the device to use for the inference (by default it will use CPU):

    net.setPreferableTarget(DNN_TARGET_OPENCL);
           // the possible options are
           // DNN_TARGET_CPU,
           // DNN_TARGET_OPENCL, 
           // DNN_TARGET_OPENCL_FP16
           //   (fall back to OPENCL if the hardware does not support FP16),
           // DNN_TARGET_MYRIAD
  • You may also import pre-trained models from Open Model Zoo passing paths to .bin and .xml files to cv::dnn::readNet function.

Troubleshooting

  • OpenVINO 2018R5 and older:

    Set OMP_WAIT_POLICY to PASSIVE to prevent efficiency gaps due networks partitioning if there are layers unsupported by Inference Engine backend or you have several networks with preferable backend DNN_TARGET_CPU.

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