使用ncnn布署pytorch模型到Android手机

使用 ncnn 布署 pytorch 模型到 Android 手机

编译 NCNN 时要打开显卡支持 vulkan 是针对 gpu 的 -DNCNN_VULKAN=ON
MobileNetV3

編譯成 MT 時要打開 CMAKE 0091 特性

cmake_minimum_required(VERSION 3.20.0)
cmake_policy(SET CMP0091 NEW)
set(CMAKE_MSVC_RUNTIME_LIBRARY "MultiThreaded$<$<CONFIG:Debug>:Debug>")
project("client-project")

训练 YOLO

\Envs\torch\Scripts\activate.ps1
python train.py --batch 6 --workers 2 --imgsz 960 --epochs 300 --data "\Core\yaml\data.yaml" --cfg "\Core\yaml\cfg.yaml" --weights \Core\weights\best.pt --device 0

转换模型

from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.onnx
from libs import define
from libs.net import Net
from libs.dataset import ImageDataset
import os

test_data = ImageDataset(define.testPath,False)
test_loader = torch.utils.data.DataLoader( test_data, batch_size=1, shuffle=True)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Net(out_dim=19).to(device)
model.load_state_dict(torch.load( "./widget/last.pt" ))
model.eval()

def saveOnnx():
    for data, target in test_loader:
        data, target = data.to(device), target.to(device)
        label = target.long()
        y = model(data)
        # Export the model
        torch.onnx.export(model,                   # model being run
                        data,                      # model input (or a tuple for multiple inputs)
                        "./widget/best.onnx",            # where to save the model (can be a file or file-like object)
                        export_params=True,        # store the trained parameter weights inside the model file
                        opset_version=10,          # the ONNX version to export the model to
                        do_constant_folding=True,  # whether to execute constant folding for optimization
                        input_names = ['input'],   # the model's input names
                        output_names = ['output'],  # the model's output names
                        dynamic_axes={'input' : {0 : 'batch_size'},    # variable lenght axes
                                        'output' : {0 : 'batch_size'}})

        traced_script_module = torch.jit.trace(model, data)
        return

saveOnnx()
# 转换
os.system("python -m onnxsim ./widget/best.onnx ./widgetbest-sim.onnx")
os.system("./bin/onnx2ncnn.exe ./widget/best-sim.onnx ./widget/best.param ./widget/best.bin")
os.system("./bin/ncnnoptimize.exe ./widget/best.param ./widget/best.bin ./widget/best-opt.param ./widget/best-opt.bin 65536")

python .\export.py --weights weights/best.pt --img 960 --batch 1 --train
python -m onnxsim best.onnx best-sim.onnx
.\onnx2ncnn.exe best-sim.onnx best.param best.bin
ncnnoptimize best.param best.bin best-opt.param best-opt.bin 65536

Git clone ncnn repo with submodule

$ git clone https://github.com/Tencent/ncnn.git
$ cd ncnn
$ git submodule update --init

Build for Linux

Install required build dependencies:

git
g++
cmake
protocol buffer (protobuf) headers files and protobuf compiler
vulkan header files and loader library
glslang
(optional) opencv # For building examples

Generally if you have Intel, AMD or Nvidia GPU from last 10 years, Vulkan can be easily used.

On some systems there are no Vulkan drivers easily available at the moment (October 2020), so you might need to disable use of Vulkan on them. This applies to Raspberry Pi 3 (but there is experimental open source Vulkan driver in the works, which is not ready yet). Nvidia Tegra series devices (like Nvidia Jetson) should support Vulkan. Ensure you have most recent software installed for best expirience.

On Debian, Ubuntu or Raspberry Pi OS, you can install all required dependencies using:

sudo apt install build-essential git cmake libprotobuf-dev protobuf-compiler libvulkan-dev vulkan-utils libopencv-dev

To use Vulkan backend install Vulkan header files, a vulkan driver loader, GLSL to SPIR-V compiler and vulkaninfo tool. Preferably from your distribution repositories. Alternatively download and install full Vulkan SDK (about 200MB in size; it contains all header files, documentation and prebuilt loader, as well some extra tools and source code of everything) from https://vulkan.lunarg.com/sdk/home

wget https://sdk.lunarg.com/sdk/download/1.2.189.0/linux/vulkansdk-linux-x86_64-1.2.189.0.tar.gz?Human=true -O vulkansdk-linux-x86_64-1.2.189.0.tar.gz
tar -xf vulkansdk-linux-x86_64-1.2.189.0.tar.gz
export VULKAN_SDK=$(pwd)/1.2.189.0/x86_64

To use Vulkan after building ncnn later, you will also need to have Vulkan driver for your GPU. For AMD and Intel GPUs these can be found in Mesa graphics driver, which usually is installed by default on all distros (i.e. sudo apt install mesa-vulkan-drivers on Debian/Ubuntu). For Nvidia GPUs the proprietary Nvidia driver must be downloaded and installed (some distros will allow easier installation in some way). After installing Vulkan driver, confirm Vulkan libraries and driver are working, by using vulkaninfo or vulkaninfo | grep deviceType, it should list GPU device type. If there are more than one GPU installed (including the case of integrated GPU and discrete GPU, commonly found in laptops), you might need to note the order of devices to use later on.

Nvidia Jetson devices the Vulkan support should be present in Nvidia provided SDK (Jetpack) or prebuild OS images.

Raspberry Pi Vulkan drivers do exists, but are not mature. You are free to experiment at your own discretion, and report results and performance.

cd ncnn
mkdir -p build
cd build
cmake -DCMAKE_BUILD_TYPE=Release -DNCNN_VULKAN=ON -DNCNN_SYSTEM_GLSLANG=ON -DNCNN_BUILD_EXAMPLES=ON ..
make -j$(nproc)

You can add -GNinja to cmake above to use Ninja build system (invoke build using ninja or cmake --build .).

For Nvidia Jetson devices, add -DCMAKE_TOOLCHAIN_FILE=../toolchains/jetson.toolchain.cmake to cmake.

For Rasberry Pi 3, add -DCMAKE_TOOLCHAIN_FILE=../toolchains/pi3.toolchain.cmake -DPI3=ON to cmake. You can also consider disabling Vulkan support as the Vulkan drivers for Rasberry Pi are still not mature, but it doesn’t hurt to build the support in, but not use it.

Verify build by running some examples:

cd ../examples
../build/examples/squeezenet ../images/256-ncnn.png
[0 AMD RADV FIJI (LLVM 10.0.1)]  queueC=1[4]  queueG=0[1]  queueT=0[1]
[0 AMD RADV FIJI (LLVM 10.0.1)]  bugsbn1=0  buglbia=0  bugcopc=0  bugihfa=0
[0 AMD RADV FIJI (LLVM 10.0.1)]  fp16p=1  fp16s=1  fp16a=0  int8s=1  int8a=1
532 = 0.163452
920 = 0.093140
716 = 0.061584

You can also run benchmarks (the 4th argument is a GPU device index to use, refer to vulkaninfo, if you have more than one GPU):

cd ../benchmark
../build/benchmark/benchncnn 10 $(nproc) 0 0
[0 AMD RADV FIJI (LLVM 10.0.1)]  queueC=1[4]  queueG=0[1]  queueT=0[1]
[0 AMD RADV FIJI (LLVM 10.0.1)]  bugsbn1=0  buglbia=0  bugcopc=0  bugihfa=0
[0 AMD RADV FIJI (LLVM 10.0.1)]  fp16p=1  fp16s=1  fp16a=0  int8s=1  int8a=1
num_threads = 4
powersave = 0
gpu_device = 0
cooling_down = 1
          squeezenet  min =    4.68  max =    4.99  avg =    4.85
     squeezenet_int8  min =   38.52  max =   66.90  avg =   48.52
...

To run benchmarks on a CPU, set the 5th argument to -1.

Build for Windows x64 using Visual Studio Community 2017

Download and Install Visual Studio Community 2017 from https://visualstudio.microsoft.com/vs/community/

Start the command prompt: Start → Programs → Visual Studio 2017 → Visual Studio Tools → x64 Native Tools Command Prompt for VS 2017

Download protobuf-3.4.0 from https://github.com/google/protobuf/archive/v3.4.0.zip

Build protobuf library:

cd <protobuf-root-dir>
mkdir build
cd build
cmake -G"NMake Makefiles" -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=%cd%/install -Dprotobuf_BUILD_TESTS=OFF -Dprotobuf_MSVC_STATIC_RUNTIME=OFF ../cmake
nmake
nmake install

(optional) Download and install Vulkan SDK from https://vulkan.lunarg.com/sdk/home

Build ncnn library (replace with a proper path):

cd <ncnn-root-dir>
mkdir -p build
cd build
cmake -G"NMake Makefiles" -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=%cd%/install -DProtobuf_INCLUDE_DIR=<protobuf-root-dir>/build/install/include -DProtobuf_LIBRARIES=<protobuf-root-dir>/build/install/lib/libprotobuf.lib -DProtobuf_PROTOC_EXECUTABLE=<protobuf-root-dir>/build/install/bin/protoc.exe -DNCNN_VULKAN=ON ..
nmake
nmake install

Note: To speed up compilation process on multi core machines, configuring cmake to use jom or ninja using -G flag is recommended.

Build for macOS

First install Xcode or Xcode Command Line Tools according to your needs.

Then install protobuf and libomp via homebrew

brew install protobuf libomp

Download and install Vulkan SDK from https://vulkan.lunarg.com/sdk/home

wget https://sdk.lunarg.com/sdk/download/1.2.189.0/mac/vulkansdk-macos-1.2.189.0.dmg?Human=true -O vulkansdk-macos-1.2.189.0.dmg
hdiutil attach vulkansdk-macos-1.2.189.0.dmg
sudo /Volumes/vulkansdk-macos-1.2.189.0/InstallVulkan.app/Contents/MacOS/InstallVulkan --root `pwd`/vulkansdk-macos-1.2.189.0 --accept-licenses --default-answer --confirm-command install
hdiutil detach /Volumes/vulkansdk-macos-1.2.189.0

# setup env
export VULKAN_SDK=`pwd`/vulkansdk-macos-1.2.189.0/macOS

cd <ncnn-root-dir>
mkdir -p build
cd build

cmake -DCMAKE_OSX_ARCHITECTURES="x86_64;arm64" \
    -DVulkan_INCLUDE_DIR=`pwd`/../vulkansdk-macos-1.2.189.0/MoltenVK/include \
    -DVulkan_LIBRARY=`pwd`/../vulkansdk-macos-1.2.189.0/MoltenVK/dylib/macOS/libMoltenVK.dylib \
    -DNCNN_VULKAN=ON -DNCNN_BUILD_EXAMPLES=ON ..

cmake --build . -j 4
cmake --build . --target install

Note: If you encounter libomp related errors during installation, you can also check our GitHub Actions at here to install and use openmp.

Build for ARM Cortex-A family with cross-compiling

Download ARM toolchain from https://developer.arm.com/open-source/gnu-toolchain/gnu-a/downloads

export PATH="<your-toolchain-compiler-path>:${PATH}"

Alternatively install a cross-compiler provided by the distribution (i.e. on Debian / Ubuntu, you can do sudo apt install g++-arm-linux-gnueabi g++-arm-linux-gnueabihf g++-aarch64-linux-gnu).

Depending on your needs build one or more of the below targets.

AArch32 target with soft float (arm-linux-gnueabi)

cd <ncnn-root-dir>
mkdir -p build-arm-linux-gnueabi
cd build-arm-linux-gnueabi
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/arm-linux-gnueabi.toolchain.cmake ..
make -j$(nproc)

AArch32 target with hard float (arm-linux-gnueabihf)

cd <ncnn-root-dir>
mkdir -p build-arm-linux-gnueabihf
cd build-arm-linux-gnueabihf
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/arm-linux-gnueabihf.toolchain.cmake ..
make -j$(nproc)

AArch64 GNU/Linux target (aarch64-linux-gnu)

cd <ncnn-root-dir>
mkdir -p build-aarch64-linux-gnu
cd build-aarch64-linux-gnu
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/aarch64-linux-gnu.toolchain.cmake ..
make -j$(nproc)

Build for Hisilicon platform with cross-compiling

Download and install Hisilicon SDK. The toolchain should be in /opt/hisi-linux/x86-arm

cd <ncnn-root-dir>
mkdir -p build
cd build

# Choose one cmake toolchain file depends on your target platform
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/hisiv300.toolchain.cmake ..
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/hisiv500.toolchain.cmake ..
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/himix100.toolchain.cmake ..
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/himix200.toolchain.cmake ..

make -j$(nproc)
make install

Build for Android

You can use the pre-build ncnn-android-lib.zip from https://github.com/Tencent/ncnn/releases

Download Android NDK from http://developer.android.com/ndk/downloads/index.html and install it, for example:

unzip android-ndk-r21d-linux-x86_64.zip
export ANDROID_NDK=<your-ndk-root-path>

(optional) remove the hardcoded debug flag in Android NDK android-ndk issue

# open $ANDROID_NDK/build/cmake/android.toolchain.cmake
# delete "-g" line
list(APPEND ANDROID_COMPILER_FLAGS
  -g
  -DANDROID

Build armv7 library

cd <ncnn-root-dir>
mkdir -p build-android-armv7
cd build-android-armv7

cmake -DCMAKE_TOOLCHAIN_FILE="$ANDROID_NDK/build/cmake/android.toolchain.cmake" \
    -DANDROID_ABI="armeabi-v7a" -DANDROID_ARM_NEON=ON \
    -DANDROID_PLATFORM=android-14 ..

# If you want to enable Vulkan, platform api version >= android-24 is needed
cmake -DCMAKE_TOOLCHAIN_FILE="$ANDROID_NDK/build/cmake/android.toolchain.cmake" \
  -DANDROID_ABI="armeabi-v7a" -DANDROID_ARM_NEON=ON \
  -DANDROID_PLATFORM=android-24 -DNCNN_VULKAN=ON ..

make -j$(nproc)
make install

Pick build-android-armv7/install folder for further JNI usage.

Build aarch64 library:

cd <ncnn-root-dir>
mkdir -p build-android-aarch64
cd build-android-aarch64

cmake -DCMAKE_TOOLCHAIN_FILE="$ANDROID_NDK/build/cmake/android.toolchain.cmake"\
  -DANDROID_ABI="arm64-v8a" \
  -DANDROID_PLATFORM=android-21 ..

# If you want to enable Vulkan, platform api version >= android-24 is needed
cmake -DCMAKE_TOOLCHAIN_FILE="$ANDROID_NDK/build/cmake/android.toolchain.cmake" \
  -DANDROID_ABI="arm64-v8a" \
  -DANDROID_PLATFORM=android-24 -DNCNN_VULKAN=ON ..

make -j$(nproc)
make install

Pick build-android-aarch64/install folder for further JNI usage.

Build for iOS on macOS with xcode

You can use the pre-build ncnn.framework glslang.framework and openmp.framework from https://github.com/Tencent/ncnn/releases

Install xcode

You can replace -DENABLE_BITCODE=0 to -DENABLE_BITCODE=1 in the following cmake arguments if you want to build bitcode enabled libraries.

Download and install openmp for multithreading inference feature on iPhoneOS

wget https://github.com/llvm/llvm-project/releases/download/llvmorg-11.0.0/openmp-11.0.0.src.tar.xz
tar -xf openmp-11.0.0.src.tar.xz
cd openmp-11.0.0.src

# apply some compilation fix
sed -i'' -e '/.size __kmp_unnamed_critical_addr/d' runtime/src/z_Linux_asm.S
sed -i'' -e 's/__kmp_unnamed_critical_addr/___kmp_unnamed_critical_addr/g' runtime/src/z_Linux_asm.S

mkdir -p build-ios
cd build-ios

cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/ios.toolchain.cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=install \
    -DIOS_PLATFORM=OS -DENABLE_BITCODE=0 -DENABLE_ARC=0 -DENABLE_VISIBILITY=0 -DIOS_ARCH="armv7;arm64;arm64e" \
    -DPERL_EXECUTABLE=/usr/local/bin/perl \
    -DLIBOMP_ENABLE_SHARED=OFF -DLIBOMP_OMPT_SUPPORT=OFF -DLIBOMP_USE_HWLOC=OFF ..

cmake --build . -j 4
cmake --build . --target install

# copy openmp library and header files to xcode toolchain sysroot
sudo cp install/include/* /Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS.sdk/usr/include
sudo cp install/lib/libomp.a /Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS.sdk/usr/lib

Download and install openmp for multithreading inference feature on iPhoneSimulator

wget https://github.com/llvm/llvm-project/releases/download/llvmorg-11.0.0/openmp-11.0.0.src.tar.xz
tar -xf openmp-11.0.0.src.tar.xz
cd openmp-11.0.0.src

# apply some compilation fix
sed -i'' -e '/.size __kmp_unnamed_critical_addr/d' runtime/src/z_Linux_asm.S
sed -i'' -e 's/__kmp_unnamed_critical_addr/___kmp_unnamed_critical_addr/g' runtime/src/z_Linux_asm.S

mkdir -p build-ios-sim
cd build-ios-sim

cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/ios.toolchain.cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=install \
    -DIOS_PLATFORM=SIMULATOR -DENABLE_BITCODE=0 -DENABLE_ARC=0 -DENABLE_VISIBILITY=0 -DIOS_ARCH="i386;x86_64" \
    -DPERL_EXECUTABLE=/usr/local/bin/perl \
    -DLIBOMP_ENABLE_SHARED=OFF -DLIBOMP_OMPT_SUPPORT=OFF -DLIBOMP_USE_HWLOC=OFF ..

cmake --build . -j 4
cmake --build . --target install

# copy openmp library and header files to xcode toolchain sysroot
sudo cp install/include/* /Applications/Xcode.app/Contents/Developer/Platforms/iPhoneSimulator.platform/Developer/SDKs/iPhoneSimulator.sdk/usr/include
sudo cp install/lib/libomp.a /Applications/Xcode.app/Contents/Developer/Platforms/iPhoneSimulator.platform/Developer/SDKs/iPhoneSimulator.sdk/usr/lib

Package openmp framework:

cd <openmp-root-dir>

mkdir -p openmp.framework/Versions/A/Headers
mkdir -p openmp.framework/Versions/A/Resources
ln -s A openmp.framework/Versions/Current
ln -s Versions/Current/Headers openmp.framework/Headers
ln -s Versions/Current/Resources openmp.framework/Resources
ln -s Versions/Current/openmp openmp.framework/openmp
lipo -create build-ios/install/lib/libomp.a build-ios-sim/install/lib/libomp.a -o openmp.framework/Versions/A/openmp
cp -r build-ios/install/include/* openmp.framework/Versions/A/Headers/
sed -e 's/__NAME__/openmp/g' -e 's/__IDENTIFIER__/org.llvm.openmp/g' -e 's/__VERSION__/11.0/g' Info.plist > openmp.framework/Versions/A/Resources/Info.plist

Download and install Vulkan SDK from https://vulkan.lunarg.com/sdk/home

wget https://sdk.lunarg.com/sdk/download/1.2.189.0/mac/vulkansdk-macos-1.2.189.0.dmg?Human=true -O vulkansdk-macos-1.2.189.0.dmg
hdiutil attach vulkansdk-macos-1.2.189.0.dmg
sudo /Volumes/vulkansdk-macos-1.2.189.0/InstallVulkan.app/Contents/MacOS/InstallVulkan --root `pwd`/vulkansdk-macos-1.2.189.0 --accept-licenses --default-answer --confirm-command install
hdiutil detach /Volumes/vulkansdk-macos-1.2.189.0

# setup env
export VULKAN_SDK=`pwd`/vulkansdk-macos-1.2.189.0/macOS

Build library for iPhoneOS:

cd <ncnn-root-dir>
mkdir -p build-ios
cd build-ios

cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/ios.toolchain.cmake -DIOS_PLATFORM=OS -DIOS_ARCH="armv7;arm64;arm64e" \
    -DENABLE_BITCODE=0 -DENABLE_ARC=0 -DENABLE_VISIBILITY=0 \
    -DOpenMP_C_FLAGS="-Xclang -fopenmp" -DOpenMP_CXX_FLAGS="-Xclang -fopenmp" \
    -DOpenMP_C_LIB_NAMES="libomp" -DOpenMP_CXX_LIB_NAMES="libomp" \
    -DOpenMP_libomp_LIBRARY="/Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS.sdk/usr/lib/libomp.a" \
    -DNCNN_BUILD_BENCHMARK=OFF ..

# vulkan is only available on arm64 devices
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/ios.toolchain.cmake -DIOS_PLATFORM=OS64 -DIOS_ARCH="arm64;arm64e" \
    -DENABLE_BITCODE=0 -DENABLE_ARC=0 -DENABLE_VISIBILITY=0 \
    -DOpenMP_C_FLAGS="-Xclang -fopenmp" -DOpenMP_CXX_FLAGS="-Xclang -fopenmp" \
    -DOpenMP_C_LIB_NAMES="libomp" -DOpenMP_CXX_LIB_NAMES="libomp" \
    -DOpenMP_libomp_LIBRARY="/Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS.sdk/usr/lib/libomp.a" \
    -DVulkan_INCLUDE_DIR=`pwd`/../vulkansdk-macos-1.2.189.0/MoltenVK/include \
    -DVulkan_LIBRARY=`pwd`/../vulkansdk-macos-1.2.189.0/MoltenVK/dylib/iOS/libMoltenVK.dylib \
    -DNCNN_VULKAN=ON -DNCNN_BUILD_BENCHMARK=OFF ..

cmake --build . -j 4
cmake --build . --target install

Build library for iPhoneSimulator:

cd <ncnn-root-dir>
mkdir -p build-ios-sim
cd build-ios-sim

cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/ios.toolchain.cmake -DIOS_PLATFORM=SIMULATOR -DIOS_ARCH="i386;x86_64" \
    -DENABLE_BITCODE=0 -DENABLE_ARC=0 -DENABLE_VISIBILITY=0 \
    -DOpenMP_C_FLAGS="-Xclang -fopenmp" -DOpenMP_CXX_FLAGS="-Xclang -fopenmp" \
    -DOpenMP_C_LIB_NAMES="libomp" -DOpenMP_CXX_LIB_NAMES="libomp" \
    -DOpenMP_libomp_LIBRARY="/Applications/Xcode.app/Contents/Developer/Platforms/iPhoneSimulator.platform/Developer/SDKs/iPhoneSimulator.sdk/usr/lib/libomp.a" \
    -DNCNN_BUILD_BENCHMARK=OFF ..

cmake --build . -j 4
cmake --build . --target install

Package glslang framework:

cd <ncnn-root-dir>

mkdir -p glslang.framework/Versions/A/Headers
mkdir -p glslang.framework/Versions/A/Resources
ln -s A glslang.framework/Versions/Current
ln -s Versions/Current/Headers glslang.framework/Headers
ln -s Versions/Current/Resources glslang.framework/Resources
ln -s Versions/Current/glslang glslang.framework/glslang
libtool -static build-ios/install/lib/libglslang.a build-ios/install/lib/libSPIRV.a build-ios/install/lib/libOGLCompiler.a build-ios/install/lib/libOSDependent.a -o build-ios/install/lib/libglslang_combined.a
libtool -static build-ios-sim/install/lib/libglslang.a build-ios-sim/install/lib/libSPIRV.a build-ios-sim/install/lib/libOGLCompiler.a build-ios-sim/install/lib/libOSDependent.a -o build-ios-sim/install/lib/libglslang_combined.a
lipo -create build-ios/install/lib/libglslang_combined.a build-ios-sim/install/lib/libglslang_combined.a -o glslang.framework/Versions/A/glslang
cp -r build/install/include/glslang glslang.framework/Versions/A/Headers/
sed -e 's/__NAME__/glslang/g' -e 's/__IDENTIFIER__/org.khronos.glslang/g' -e 's/__VERSION__/1.0/g' Info.plist > glslang.framework/Versions/A/Resources/Info.plist

Package ncnn framework:

cd <ncnn-root-dir>

mkdir -p ncnn.framework/Versions/A/Headers
mkdir -p ncnn.framework/Versions/A/Resources
ln -s A ncnn.framework/Versions/Current
ln -s Versions/Current/Headers ncnn.framework/Headers
ln -s Versions/Current/Resources ncnn.framework/Resources
ln -s Versions/Current/ncnn ncnn.framework/ncnn
lipo -create build-ios/install/lib/libncnn.a build-ios-sim/install/lib/libncnn.a -o ncnn.framework/Versions/A/ncnn
cp -r build-ios/install/include/* ncnn.framework/Versions/A/Headers/
sed -e 's/__NAME__/ncnn/g' -e 's/__IDENTIFIER__/com.tencent.ncnn/g' -e 's/__VERSION__/1.0/g' Info.plist > ncnn.framework/Versions/A/Resources/Info.plist

Pick ncnn.framework glslang.framework and openmp.framework folder for app development.

Build for WebAssembly

Install Emscripten

git clone https://github.com/emscripten-core/emsdk.git
cd emsdk
./emsdk install 2.0.8
./emsdk activate 2.0.8

source emsdk/emsdk_env.sh

Build without any extension for general compatibility:

mkdir -p build
cd build
cmake -DCMAKE_TOOLCHAIN_FILE=../emsdk/upstream/emscripten/cmake/Modules/Platform/Emscripten.cmake \
    -DNCNN_THREADS=OFF -DNCNN_OPENMP=OFF -DNCNN_SIMPLEOMP=OFF -DNCNN_RUNTIME_CPU=OFF -DNCNN_SSE2=OFF -DNCNN_AVX2=OFF -DNCNN_AVX=OFF \
    -DNCNN_BUILD_TOOLS=OFF -DNCNN_BUILD_EXAMPLES=OFF -DNCNN_BUILD_BENCHMARK=OFF ..
cmake --build . -j 4
cmake --build . --target install

Build with WASM SIMD extension:

mkdir -p build-simd
cd build-simd
cmake -DCMAKE_TOOLCHAIN_FILE=../emsdk/upstream/emscripten/cmake/Modules/Platform/Emscripten.cmake \
    -DNCNN_THREADS=OFF -DNCNN_OPENMP=OFF -DNCNN_SIMPLEOMP=OFF -DNCNN_RUNTIME_CPU=OFF -DNCNN_SSE2=ON -DNCNN_AVX2=OFF -DNCNN_AVX=OFF \
    -DNCNN_BUILD_TOOLS=OFF -DNCNN_BUILD_EXAMPLES=OFF -DNCNN_BUILD_BENCHMARK=OFF ..
cmake --build . -j 4
cmake --build . --target install

Build with WASM Thread extension:

mkdir -p build-threads
cd build-threads
cmake -DCMAKE_TOOLCHAIN_FILE=../emsdk/upstream/emscripten/cmake/Modules/Platform/Emscripten.cmake \
    -DNCNN_THREADS=ON -DNCNN_OPENMP=ON -DNCNN_SIMPLEOMP=ON -DNCNN_RUNTIME_CPU=OFF -DNCNN_SSE2=OFF -DNCNN_AVX2=OFF -DNCNN_AVX=OFF \
    -DNCNN_BUILD_TOOLS=OFF -DNCNN_BUILD_EXAMPLES=OFF -DNCNN_BUILD_BENCHMARK=OFF ..
cmake --build . -j 4
cmake --build . --target install

Build with WASM SIMD and Thread extension:

mkdir -p build-simd-threads
cd build-simd-threads
cmake -DCMAKE_TOOLCHAIN_FILE=../emsdk/upstream/emscripten/cmake/Modules/Platform/Emscripten.cmake \
    -DNCNN_THREADS=ON -DNCNN_OPENMP=ON -DNCNN_SIMPLEOMP=ON -DNCNN_RUNTIME_CPU=OFF -DNCNN_SSE2=ON -DNCNN_AVX2=OFF -DNCNN_AVX=OFF \
    -DNCNN_BUILD_TOOLS=OFF -DNCNN_BUILD_EXAMPLES=OFF -DNCNN_BUILD_BENCHMARK=OFF ..
cmake --build . -j 4
cmake --build . --target install

Pick build-XYZ/install folder for further usage.

Build for AllWinner D1

Download c906 toolchain package from https://occ.t-head.cn/community/download?id=3913221581316624384

tar -xf riscv64-linux-x86_64-20210512.tar.gz
export RISCV_ROOT_PATH=/home/nihui/osd/riscv64-linux-x86_64-20210512

Build ncnn with riscv-v vector and simpleocv enabled:

mkdir -p build-c906
cd build-c906
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/c906.toolchain.cmake \
    -DCMAKE_BUILD_TYPE=relwithdebinfo -DNCNN_OPENMP=OFF -DNCNN_THREADS=OFF -DNCNN_RUNTIME_CPU=OFF -DNCNN_RVV=ON \
    -DNCNN_SIMPLEOCV=ON -DNCNN_BUILD_EXAMPLES=ON ..
cmake --build . -j 4
cmake --build . --target install

Pick build-c906/install folder for further usage.

You can upload binary inside build-c906/examples folder and run on D1 board for testing.

Build for Loongson 2K1000

For gcc version < 8.5, you need to fix msa.h header for workaround msa fmadd bug.

Open /usr/lib/gcc/mips64el-linux-gnuabi64/8/include/msa.h, find __msa_fmadd_w and apply changes as the following

// #define __msa_fmadd_w __builtin_msa_fmadd_w
#define __msa_fmadd_w(a, b, c) __builtin_msa_fmadd_w(c, b, a)

Build ncnn with mips msa and simpleocv enabled:

mkdir -p build
cd build
cmake -DNCNN_DISABLE_RTTI=ON -DNCNN_DISABLE_EXCEPTION=ON -DNCNN_RUNTIME_CPU=OFF -DNCNN_MSA=ON -DNCNN_MMI=ON -DNCNN_SIMPLEOCV=ON ..
cmake --build . -j 2
cmake --build . --target install

Pick build/install folder for further usage.

You can run binary inside build/examples folder for testing.

Build for Termux on Android

Install app Termux on your phone,and install Ubuntu in Termux.

If you want use ssh, just install openssh in Termux

pkg install proot-distro
proot-distro install ubuntu

or you can see what system can be installed using proot-distro list

while you install ubuntu successfully, using proot-distro login ubuntu to login Ubuntu.

Then make ncnn,no need to install any other dependencies.

git clone https://github.com/Tencent/ncnn.git
cd ncnn
git submodule update --init
mkdir -p build
cd build
cmake -DCMAKE_BUILD_TYPE=Release -DNCNN_BUILD_EXAMPLES=ON -DNCNN_PLATFORM_API=OFF -DNCNN_SIMPLEOCV=ON ..
make -j$(nproc)

Then you can run a test

on my Pixel 3 XL using Qualcomm 845,cant load 256-ncnn.png

cd ../examples
../build/examples/squeezenet ../images/128-ncnn.png