Cpp file compile of YOLOX object detection base on MegEngine.


Step1: install toolchain

* host: sudo apt install gcc/g++ (gcc/g++, which version >= 6) build-essential git git-lfs gfortran libgfortran-6-dev autoconf gnupg flex bison gperf curl zlib1g-dev gcc-multilib g++-multilib cmake
  • cross build android: download NDK

    • after unzip download NDK, then export NDK_ROOT=”path of NDK”

Step2: build MegEngine

git clone https://github.com/MegEngine/MegEngine.git

# then init third_party
export megengine_root="path of MegEngine"
cd $megengine_root && ./third_party/prepare.sh && ./third_party/install-mkl.sh

# build example:
# build host without cuda:   
# or build host with cuda:
./scripts/cmake-build/host_build.sh -c
# or cross build for android aarch64: 
# or cross build for android aarch64(with V8.2+fp16): 
./scripts/cmake-build/cross_build_android_arm_inference.sh -f

# after build MegEngine, you need export the `MGE_INSTALL_PATH`
# host without cuda: 
export MGE_INSTALL_PATH=${megengine_root}/build_dir/host/MGE_WITH_CUDA_OFF/MGE_INFERENCE_ONLY_ON/Release/install
# or host with cuda: 
export MGE_INSTALL_PATH=${megengine_root}/build_dir/host/MGE_WITH_CUDA_ON/MGE_INFERENCE_ONLY_ON/Release/install
# or cross build for android aarch64: 
export MGE_INSTALL_PATH=${megengine_root}/build_dir/android/arm64-v8a/Release/install

Step3: build OpenCV

git clone https://github.com/opencv/opencv.git

git checkout 3.4.15 (we test at 3.4.15, if test other version, may need modify some build)
  • patch diff for android:

# ```
#     diff --git a/CMakeLists.txt b/CMakeLists.txt
#     index f6a2da5310..10354312c9 100644
#     --- a/CMakeLists.txt
#     +++ b/CMakeLists.txt
#     @@ -643,7 +643,7 @@ if(UNIX)
#        if(NOT APPLE)
#          if(ANDROID)
#     -      set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} dl m log)
#     +      set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} dl m log z)
#          elseif(CMAKE_SYSTEM_NAME MATCHES "FreeBSD|NetBSD|DragonFly|OpenBSD|Haiku")
#            set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} m pthread)
#          elseif(EMSCRIPTEN)
# ```
  • build for host

cd root_dir_of_opencv
mkdir -p build/install
cd build
make install -j32
  • build for android-aarch64

cd root_dir_of_opencv
mkdir -p build_android/install
cd build_android


make install -j32
  • after build OpenCV, you need export OPENCV_INSTALL_INCLUDE_PATH and OPENCV_INSTALL_LIB_PATH

# host build: 
export OPENCV_INSTALL_INCLUDE_PATH=${path of opencv}/build/install/include
export OPENCV_INSTALL_LIB_PATH=${path of opencv}/build/install/lib
# or cross build for android aarch64:
export OPENCV_INSTALL_INCLUDE_PATH=${path of opencv}/build_android/install/sdk/native/jni/include
export OPENCV_INSTALL_LIB_PATH=${path of opencv}/build_android/install/sdk/native/libs/arm64-v8a

Step4: build test demo

run build.sh

# if host:
export CXX=g++
# or cross android aarch64
export CXX=aarch64-linux-android21-clang++

Step5: run demo

Note: two ways to get yolox_s.mge model file

  • reference to python demo’s dump.py script.

  • For users with code before 0.1.0 version, wget yolox-s weights here.

  • For users with code after 0.1.0 version, use python code in megengine to generate mge file.

# if host:
LD_LIBRARY_PATH=$MGE_INSTALL_PATH/lib/:$OPENCV_INSTALL_LIB_PATH ./yolox yolox_s.mge ../../../assets/dog.jpg cuda/cpu/multithread <warmup_count> <thread_number>

# or cross android
adb push/scp $MGE_INSTALL_PATH/lib/libmegengine.so android_phone
adb push/scp $OPENCV_INSTALL_LIB_PATH/*.so android_phone
adb push/scp ./yolox yolox_s.mge android_phone
adb push/scp ../../../assets/dog.jpg android_phone

# login in android_phone by adb or ssh
# then run: 
LD_LIBRARY_PATH=. ./yolox yolox_s.mge dog.jpg cpu/multithread <warmup_count> <thread_number> <use_fast_run> <use_weight_preprocess>  <run_with_fp16>

# * <warmup_count> means warmup count, valid number >=0
# * <thread_number> means thread number, valid number >=1, only take effect `multithread` device
# * <use_fast_run> if >=1 , will use fastrun to choose best algo
# * <use_weight_preprocess> if >=1, will handle weight preprocess before exe
# * <run_with_fp16> if >=1, will run with fp16 mode


  • model info: yolox-s @ input(1,3,640,640)

  • test devices

  * x86_64  -- Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz					
  * aarch64 -- xiamo phone mi9					
  * cuda    -- 1080TI @ cuda-10.1-cudnn-v7.6.3-TensorRT- @ Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
megengine @ tag1.4(fastrun + weight_preprocess)/sec 1 thread
x86_64 0.516245
aarch64(fp32+chw44) 0.587857
CUDA @ 1080TI/sec 1 batch 2 batch 4 batch 8 batch 16 batch 32 batch 64 batch
megengine(fp32+chw) 0.00813703 0.0132893 0.0236633 0.0444699 0.0864917 0.16895 0.334248