File size: 6,369 Bytes
0b7b08a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
# YOLOX-CPP-MegEngine

Cpp file compile of YOLOX object detection base on [MegEngine](https://github.com/MegEngine/MegEngine).

## Tutorial

### 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](https://developer.android.com/ndk/downloads)
   	* after unzip download NDK, then export NDK_ROOT="path of NDK"

### Step2: build MegEngine

```shell
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:   
./scripts/cmake-build/host_build.sh
# or build host with cuda:
./scripts/cmake-build/host_build.sh -c
# or cross build for android aarch64: 
./scripts/cmake-build/cross_build_android_arm_inference.sh
# 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
```
* you can refs [build tutorial of MegEngine](https://github.com/MegEngine/MegEngine/blob/master/scripts/cmake-build/BUILD_README.md) to build other platform, eg, windows/macos/ etc!

### Step3: build OpenCV

```shell
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)
#          CHECK_INCLUDE_FILE(pthread.h HAVE_PTHREAD)
#          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

```shell
cd root_dir_of_opencv
mkdir -p build/install
cd build
cmake -DBUILD_JAVA=OFF -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX=$PWD/install 
make install -j32
```

* build for android-aarch64

```shell
cd root_dir_of_opencv
mkdir -p build_android/install
cd build_android

cmake -DCMAKE_TOOLCHAIN_FILE="$NDK_ROOT/build/cmake/android.toolchain.cmake" -DANDROID_NDK="$NDK_ROOT"  -DANDROID_ABI=arm64-v8a -DANDROID_NATIVE_API_LEVEL=21 -DBUILD_JAVA=OFF -DBUILD_ANDROID_PROJECTS=OFF -DBUILD_ANDROID_EXAMPLES=OFF -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX=$PWD/install ..

make install -j32
```

* after build OpenCV, you need export  `OPENCV_INSTALL_INCLUDE_PATH ` and `OPENCV_INSTALL_LIB_PATH`

```shell
# 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

```shell
run build.sh

# if host:
export CXX=g++
./build.sh
# or cross android aarch64
export CXX=aarch64-linux-android21-clang++
./build.sh
```

### 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](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_s.mge).
> * For users with code after 0.1.0 version, use [python code in megengine](../python) to generate mge file.

```shell
# 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
```

## Bechmark

* 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-6.0.1.5.sh @ 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 |

## Acknowledgement

* [MegEngine](https://github.com/MegEngine/MegEngine)
* [OpenCV](https://github.com/opencv/opencv)
* [NDK](https://developer.android.com/ndk)
* [CMAKE](https://cmake.org/)