PC端NCNN部署

PC端NCNN部署

1 NCNN部署

1.在电脑端使用ncnn实现分类(alexnet)

s1，安装g++，cmake，protobuf，opencv

s2，对源码进行编译

git clone cd $ mkdir -p build$ cd build$ cmake ..$ make

s3，准备caffe模型文件(alexnet)

deploy.prototxt snapshot_10000.caffemodel

alexnet​​​deploy.prototxt​​​，​​​caffemodel​​

s4，使用ncnn转换工具将旧caffe版本的prototxt和caffemodel转新版本 将旧caffe版本的prototxt和caffemodel存放在caffe/build/tools目录下，执行如下命令完成转换

./upgrade_net_proto_text [old prototxt] [new prototxt]./upgrade_net_proto_binary [old caffemodel] [new caffemodel]

s5，将deploy.prototxt中输入层替换成input层(如果只读取一张图片将dim设置为1)

layer { name: "data" type: "Input" top: "data" input_param { shape: { dim: 1 dim: 3 dim: 227 dim: 227 } }}

s6，使用caffe2ncnn工具将caffe model转成ncnn model

./caffe2ncnn deploy.prototxt bvlc_alexnet.caffemodel alexnet.param alexnet.bin

在ncnn/build/tools目录下生成param和bin文件。

s7，对模型参数进行加密

./ncnn2mem alexnet.param alexnet.bin alexnet.id.h alexnet.mem.h

在ncnn/build/tools目录下生成.param、.bin和.h文件。 alexnet.param 网络的模型参数 alexnet.bin 网络的权重 alexnet.id.h 在预测图片的时候使用到

#include #include #include #include"gesture.id.h" #include "net.h" //使用ncnn，传入的参数第一个是你需要预测的数据，第二个参数是各个类别的得分vector，注意传入的是地址，这样才能在这个函数中改变其值 static int detect_squeezenet( float *data, std::vector& cls_scores) { //实例化ncnn：Net，注意include "net.h"，不要在意这时候因为找不到net.h文件而include报错，后文会介绍正确的打开方式 ncnn::Net squeezenet; //加载二进制文件，也是照写，后面会介绍对应文件应该放的正确位置 int a=squeezenet.load_param("demo.param"); int b=squeezenet.load_param_bin("demo.bin"); //实例化Mat，前三个参数是维度，第四个参数是传入的data，维度的设置根据你自己的数据进行设置，顺序是w、h、c ncnn::Mat in = ncnn::Mat(550, 8, 2, data); //实例化Extractor ncnn::Extractor ex = squeezenet.create_extractor(); ex.set_light_mode(true); //注意把"data"换成你deploy中的数据层名字 int d= ex.input("data", in); ncnn::Mat out; //这里是真正的终点，不多说了，只能仰天膜拜nihui大牛，重点是将prob换成你deploy中最后一层的名字 int c=ex.extract("prob", out); //将out中的值转化为我们的cls_scores，这样就可以返回不同类别的得分了 cls_scores.resize(out.w); for (int j=0; j cls_scores; //用来存储最终各类别的得分 //这个函数的实现在上面，快去看 detect_squeezenet(data, cls_scores); for (int i = 0; i < cls_scores.size(); ++i) { printf("%c : %f\n", a[i],cls_scores[i]); } return 0; }

代码是最简单的ncnn使用场景，可以根据自己需求加入代码。

s9，编译代码 (1) 编写CMakeLists.txt 在CMakeLists.txt增加如下两行代码

add_executable(demo demo.cpp)target_link_libraries(demo ncnn)

CMakeLists.txt如下

find_package(OpenCV QUIET COMPONENTS core highgui imgproc imgcodecs) if(NOT OpenCV_FOUND) find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc) endif() include_directories(${CMAKE_CURRENT_SOURCE_DIR}/../src) include_directories(${CMAKE_CURRENT_BINARY_DIR}/../src) add_executable(squeezenet squeezenet.cpp) target_link_libraries(squeezenet ncnn ${OpenCV_LIBS}) add_executable(fasterrcnn fasterrcnn.cpp) target_link_libraries(fasterrcnn ncnn ${OpenCV_LIBS}) add_executable(demo demo.cpp) target_link_libraries(demo ncnn) add_subdirectory(ssd)

(2) 在ncnn根目录下CMakeLists.txt中编译examples语句的注释去掉

############################################### add_subdirectory(examples)# add_subdirectory(benchmark)add_subdirectory(src)if(NOT ANDROID AND NOT IOS)add_subdirectory(tools)endif()

(3)ncnn/build目录下进行编译，生成demo可执行文件

make

s10，执行 将生成的.param和.bin文件复制到ncnn/build/examples目录下，然后终端cd到ncnn/build/examples，执行：

./demo data_path1 data_path2

2. Win x64 (Visual Studio Community 2017)

s1，安装Visual Studio Community 2017

download Visual Studio Community 2017 from itStart → Programs → Visual Studio 2017 → Visual Studio Tools → x64 Native Tools Command Prompt for VS 2017

s2，编译protobuf

download protobuf-3.4.0 from cd > mkdir build-vs2017> cd build-vs2017> 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

s3，编译ncnn库

> cd > mkdir -p build-vs2017> cd build-vs2017> cmake -G"NMake Makefiles" -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=%cd%/install -DProtobuf_INCLUDE_DIR=/build-vs2017/install/include -DProtobuf_LIBRARIES=/build-vs2017/install/lib/libprotobuf.lib -DProtobuf_PROTOC_EXECUTABLE=/build-vs2017/install/bin/protoc.exe ..> nmake> nmake installpick build-vs2017/install folder for

3. Android端使用ncnn

2）配置ndk的环境变量

打开profile

sudo

在profile文件末尾添加ndk路径

export NDK_HOME=sdkroot/ndk-bundlePATH=$NDK_HOME:$PATH

保存退出，使用source命令使环境变量生效

source

验证ndk是否配置成功

ndk-build -v

s2，编译ncnn sdk 通过如下命令编译ncnn sdk，会在ncnn/build-android下生成install文件夹，其中包括：include(调用ncnn所需的头文件)和lib(编译得到的ncnn库libncnn.a)

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

参数设置请参考：是架构名字，"armeabi-v7a" 支持绝大部分手机硬件ANDROID_ARM_NEON 是否使用 NEON 指令集，设为 ON 支持绝大部分手机硬件ANDROID_PLATFORM 指定最低系统版本，"android-14" 就是 android-4.0

s3，对源码进行编译

git clone cd $ mkdir -p build$ cd build$ cmake ..$ make

s4，准备caffe模型文件(alexnet)

deploy.prototxt snapshot_10000.caffemodel

alexnet ​​deploy.prototxt​​​， ​​caffemodel​​

s5，使用ncnn转换工具将旧caffe版本的prototxt和caffemodel转新版本

将旧caffe版本的prototxt和caffemodel存放在caffe/build/tools目录下，执行如下命令完成转换

./upgrade_net_proto_text [old prototxt] [new prototxt]./upgrade_net_proto_binary [old caffemodel] [new caffemodel]

s6，将deploy.prototxt中输入层替换成input层(如果只读取一张图片将dim设置为1)

layer { name: "data" type: "Input" top: "data" input_param { shape: { dim: 1 dim: 3 dim: 227 dim: 227 } }}

s7，使用caffe2ncnn工具将caffe model转成ncnn model

./caffe2ncnn deploy.prototxt bvlc_alexnet.caffemodel alexnet.param alexnet.bin

在ncnn/build/tools目录下生成param和bin文件。

s8，对模型参数进行加密

./ncnn2mem alexnet.param alexnet.bin alexnet.id.h alexnet.mem.h

在ncnn/build/tools目录下生成.param、.bin和.h文件。

alexnet.param 网络的模型参数

alexnet.bin 网络的权重

alexnet.id.h 在预测图片的时候使用到

s9，开发安卓项目

(1)在Android Studio上创建一个NCNN1，并选择Include C++ support

(2)在main目录下创建assets目录，并将alexnet.param、alexnet.bin、label.txt拷贝其中

(3)将include文件夹和 alexnet.id.h拷贝到cpp目录下

(4)在main目录下创建jniLibs/armeabi-v7a/目录，并将alexnet.id.h 拷贝其中

(5)在cpp文件夹下创建C++文件，用于加载模型和预测图片

#include #include #include #include #include // ncnn#include "include/net.h" #include "alexnet.id.h" #include #include static ncnn::UnlockedPoolAllocator g_blob_pool_allocator; static ncnn::PoolAllocator g_workspace_pool_allocator; static ncnn::Mat ncnn_param; static ncnn::Mat ncnn_bin; static ncnn::Net ncnn_net; extern "C" { // public native boolean Init(byte[] param, byte[] bin, byte[] words); JNIEXPORT jboolean JNICALL Java_com_example_ncnn1_NcnnJni_Init(JNIEnv *env, jobject thiz, jbyteArray param, jbyteArray bin) { // init param { int len = env->GetArrayLength(param); ncnn_param.create(len, (size_t) 1u); env->GetByteArrayRegion(param, 0, len, (jbyte *) ncnn_param); int ret = ncnn_net.load_param((const unsigned char *) ncnn_param); __android_log_print(ANDROID_LOG_DEBUG, "NcnnJni", "load_param %d %d", ret, len); } // init bin { int len = env->GetArrayLength(bin); ncnn_bin.create(len, (size_t) 1u); env->GetByteArrayRegion(bin, 0, len, (jbyte *) ncnn_bin); int ret = ncnn_net.load_model((const unsigned char *) ncnn_bin); __android_log_print(ANDROID_LOG_DEBUG, "NcnnJni", "load_model %d %d", ret, len); } ncnn::Option opt; opt.lightmode = true; opt.num_threads = 4; opt.blob_allocator = &g_blob_pool_allocator; opt.workspace_allocator = &g_workspace_pool_allocator; ncnn::set_default_option(opt); return JNI_TRUE; } // public native String Detect(Bitmap bitmap); JNIEXPORT jfloatArray JNICALL Java_com_example_ncnn1_NcnnJni_Detect(JNIEnv* env, jobject thiz, jobject bitmap) { // ncnn from bitmap ncnn::Mat in; { AndroidBitmapInfo info; AndroidBitmap_getInfo(env, bitmap, &info); int width = info.width; int height = info.height; if (info.format != ANDROID_BITMAP_FORMAT_RGBA_8888) return NULL; void* indata; AndroidBitmap_lockPixels(env, bitmap, &indata); // 把像素转换成data，并指定通道顺序 in = ncnn::Mat::from_pixels((const unsigned char*)indata, ncnn::Mat::PIXEL_RGBA2BGR, width, height); AndroidBitmap_unlockPixels(env, bitmap); } // ncnn_net std::vector cls_scores; { // 减去均值和乘上比例 const float mean_vals[3] = {103.94f, 116.78f, 123.68f}; const float scale[3] = {0.017f, 0.017f, 0.017f}; in.substract_mean_normalize(mean_vals, scale); ncnn::Extractor ex = ncnn_net.create_extractor(); // 如果时不加密是使用ex.input("data", in); ex.input(mobilenet_v2_param_id::BLOB_data, in); ncnn::Mat out; // 如果时不加密是使用ex.extract("prob", out); ex.extract(mobilenet_v2_param_id::BLOB_prob, out); int output_size = out.w; jfloat *output[output_size]; for (int j = 0; j < out.w; j++) { output[j] = &out[j]; } jfloatArray jOutputData = env->NewFloatArray(output_size); if (jOutputData == nullptr) return nullptr; env->SetFloatArrayRegion(jOutputData, 0, output_size, reinterpret_cast(*output)); // copy return jOutputData; } } }

(6)在项目包com.example.ncnn1下，修改MainActivity.java中的代码

package com.example.ncnn1; import android.Manifest; import android.app.Activity; import android.content.Intent; import android.content.pm.PackageManager; import android.content.res.AssetManager; import android.graphics.Bitmap; import android.graphics.BitmapFactory; import android-.Uri; import android.os.Bundle; import android.support.annotation.NonNull; import android.support.annotation.Nullable; import android.support.v4.app.ActivityCompat; import android.support.v4.content.ContextCompat; import android.text.method.ScrollingMovementMethod; import android.util.Log; import android.view.View; import android.widget.Button; import android.widget.ImageView; import android.widget.TextView; import android.widget.Toast; import com.bumptech.glide.Glide; import com.bumptech.glide.load.engine.DiskCacheStrategy; import com.bumptech.glide.request.RequestOptions; import java.io.BufferedReader; import java.io.FileNotFoundException; import java.io.IOException; import java.io.InputStream; import java.io.InputStreamReader; import java.util.ArrayList; import java.util.Arrays; import java.util.List; public class MainActivity extends Activity { private static final String TAG = MainActivity.class.getName(); private static final int USE_PHOTO = 1001; private String camera_image_path; private ImageView show_image; private TextView result_text; private boolean load_result = false; private int[] ddims = {1, 3, 224, 224}; private int model_index = 1; private List resultLabel = new ArrayList<>(); private NcnnJni squeezencnn = new NcnnJni(); @Override public void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.activity_main); try { initSqueezeNcnn(); } catch (IOException e) { Log.e("MainActivity", "initSqueezeNcnn error"); } init_view(); readCacheLabelFromLocalFile(); } private void initSqueezeNcnn() throws IOException { byte[] param = null; byte[] bin = null; { InputStream assetsInputStream = getAssets().open("mobilenet_v2.param.bin"); int available = assetsInputStream.available(); param = new byte[available]; int byteCode = assetsInputStream.read(param); assetsInputStream.close(); } { InputStream assetsInputStream = getAssets().open("mobilenet_v2.bin"); int available = assetsInputStream.available(); bin = new byte[available]; int byteCode = assetsInputStream.read(bin); assetsInputStream.close(); } load_result = squeezencnn.Init(param, bin); Log.d("load model", "result:" + load_result); } // initialize view private void init_view() { request_permissions(); show_image = (ImageView) findViewById(R.id.show_image); result_text = (TextView) findViewById(R.id.result_text); result_text.setMovementMethod(ScrollingMovementMethod.getInstance()); Button use_photo = (Button) findViewById(R.id.use_photo); // use photo click use_photo.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View view) { if (!load_result) { Toast.makeText(MainActivity.this, "never load model", Toast.LENGTH_SHORT).show(); return; } PhotoUtil.use_photo(MainActivity.this, USE_PHOTO); } }); } // load label's name private void readCacheLabelFromLocalFile() { try { AssetManager assetManager = getApplicationContext().getAssets(); BufferedReader reader = new BufferedReader(new InputStreamReader(assetManager.open("synset.txt"))); String readLine = null; while ((readLine = reader.readLine()) != null) { resultLabel.add(readLine); } reader.close(); } catch (Exception e) { Log.e("labelCache", "error " + e); } } @Override protected void onActivityResult(int requestCode, int resultCode, @Nullable Intent data) { String image_path; RequestOptions options = new RequestOptions().skipMemoryCache(true).diskCacheStrategy(DiskCacheStrategy.NONE); if (resultCode == Activity.RESULT_OK) { switch (requestCode) { case USE_PHOTO: if (data == null) { Log.w(TAG, "user photo data is null"); return; } Uri image_uri = data.getData(); Glide.with(MainActivity.this).load(image_uri).apply(options).into(show_image); // get image path from uri image_path = PhotoUtil.get_path_from_URI(MainActivity.this, image_uri); // predict image predict_image(image_path); break; } } } // predict image private void predict_image(String image_path) { // picture to float array Bitmap bmp = PhotoUtil.getScaleBitmap(image_path); Bitmap rgba = bmp.copy(Bitmap.Config.ARGB_8888, true); // resize to 227x227 Bitmap input_bmp = Bitmap.createScaledBitmap(rgba, ddims[2], ddims[3], false); try { // Data format conversion takes too long // Log.d("inputData", Arrays.toString(inputData)); long start = System.currentTimeMillis(); // get predict result float[] result = squeezencnn.Detect(input_bmp); long end = System.currentTimeMillis(); Log.d(TAG, "origin predict result:" + Arrays.toString(result)); long time = end - start; Log.d("result length", String.valueOf(result.length)); // show predict result and time int r = get_max_result(result); String show_text = "result：" + r + "\nname：" + resultLabel.get(r) + "\nprobability：" + result[r] + "\ntime：" + time + "ms"; result_text.setText(show_text); } catch (Exception e) { e.printStackTrace(); } } // get max probability label private int get_max_result(float[] result) { float probability = result[0]; int r = 0; for (int i = 0; i < result.length; i++) { if (probability < result[i]) { probability = result[i]; r = i; } } return r; } // request permissions private void request_permissions() { List permissionList = new ArrayList<>(); if (ContextCompat.checkSelfPermission(this, Manifest.permission.CAMERA) != PackageManager.PERMISSION_GRANTED) { permissionList.add(Manifest.permission.CAMERA); } if (ContextCompat.checkSelfPermission(this, Manifest.permission.WRITE_EXTERNAL_STORAGE) != PackageManager.PERMISSION_GRANTED) { permissionList.add(Manifest.permission.WRITE_EXTERNAL_STORAGE); } if (ContextCompat.checkSelfPermission(this, Manifest.permission.READ_EXTERNAL_STORAGE) != PackageManager.PERMISSION_GRANTED) { permissionList.add(Manifest.permission.READ_EXTERNAL_STORAGE); } // if list is not empty will request permissions if (!permissionList.isEmpty()) { ActivityCompat.requestPermissions(this, permissionList.toArray(new String[permissionList.size()]), 1); } } @Override public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions, @NonNull int[] grantResults) { super.onRequestPermissionsResult(requestCode, permissions, grantResults); switch (requestCode) { case 1: if (grantResults.length > 0) { for (int i = 0; i < grantResults.length; i++) { int grantResult = grantResults[i]; if (grantResult == PackageManager.PERMISSION_DENIED) { String s = permissions[i]; Toast.makeText(this, s + " permission was denied", Toast.LENGTH_SHORT).show(); } } } break; } } }

(7)在项目的包com.example.ncnn1下，创建一个NcnnJni.java类，用于提供JNI接口，代码如下：

package com.example.ncnn1; import android.graphics.Bitmap; public class NcnnJni { public native boolean Init(byte[] param, byte[] bin); public native float[] Detect(Bitmap bitmap); static { System.loadLibrary("ncnn_jni"); } }

(8)在项目的包com.example.ncnn1下，创建一个PhotoUtil.java类，这个是图片的工具类，代码如下：

package com.example.ncnn1; import android.app.Activity; import android.content.Context; import android.content.Intent; import android.database.Cursor; import android.graphics.Bitmap; import android.graphics.BitmapFactory; import android-.Uri; import android.provider.MediaStore; import java.nio.FloatBuffer; public class PhotoUtil { // get picture in photo public static void use_photo(Activity activity, int requestCode) { Intent intent = new Intent(Intent.ACTION_PICK); intent.setType("image/*"); activity.startActivityForResult(intent, requestCode); } // get photo from Uri public static String get_path_from_URI(Context context, Uri uri) { String result; Cursor cursor = context.getContentResolver().query(uri, null, null, null, null); if (cursor == null) { result = uri.getPath(); } else { cursor.moveToFirst(); int idx = cursor.getColumnIndex(MediaStore.Images.ImageColumns.DATA); result = cursor.getString(idx); cursor.close(); } return result; } // compress picture public static Bitmap getScaleBitmap(String filePath) { BitmapFactory.Options opt = new BitmapFactory.Options(); opt.inJustDecodeBounds = true; BitmapFactory.decodeFile(filePath, opt); int bmpWidth = opt.outWidth; int bmpHeight = opt.outHeight; int maxSize = 500; // compress picture with inSampleSize opt.inSampleSize = 1; while (true) { if (bmpWidth / opt.inSampleSize < maxSize || bmpHeight / opt.inSampleSize < maxSize) { break; } opt.inSampleSize *= 2; } opt.inJustDecodeBounds = false; return BitmapFactory.decodeFile(filePath, opt); } }

(9)修改启动页面的布局，修改如下：

(10)修改APP目录下的CMakeLists.txt文件，修改如下：

# For more information about using CMake with Android Studio, read the # documentation: # Sets the minimum version of CMake required to build the native library. cmake_minimum_required(VERSION 3.4.1) # Creates and names a library, sets it as either STATIC # or SHARED, and provides the relative paths to its source code. # You can define multiple libraries, and CMake builds them for you. # Gradle automatically packages shared libraries with your APK. set(ncnn_lib ${CMAKE_SOURCE_DIR}/src/main/jniLibs/armeabi-v7a/libncnn.a) add_library (ncnn_lib STATIC IMPORTED) set_target_properties(ncnn_lib PROPERTIES IMPORTED_LOCATION ${ncnn_lib}) add_library( # Sets the name of the library. ncnn_jni # Sets the library as a shared library. SHARED # Provides a relative path to your source file(s). src/main/cpp/ncnn_jni.cpp ) # Searches for a specified prebuilt library and stores the path as a # variable. Because CMake includes system libraries in the search path by # default, you only need to specify the name of the public NDK library # you want to add. CMake verifies that the library exists before # completing its build. find_library( # Sets the name of the path variable. log-lib # Specifies the name of the NDK library that # you want CMake to locate. log ) # Specifies libraries CMake should link to your target library. You # can link multiple libraries, such as libraries you define in this # build script, prebuilt third-party libraries, or system libraries. target_link_libraries( # Specifies the target library. ncnn_jni ncnn_lib jnigraphics # Links the target library to the log library # included in the NDK. ${log-lib} )

(11)修改APP目录下的build.gradle文件，修改如下：

apply plugin: 'com.android.application' android { compileSdkVersion 28 defaultConfig { applicationId "com.example.ncnn1" minSdkVersion 21 targetSdkVersion 28 versionCode 1 versionName "1.0" testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner" externalNativeBuild { cmake { cppFlags "-std=c++11 -fopenmp" abiFilters "armeabi-v7a" } } } buildTypes { release { minifyEnabled false proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro' } } externalNativeBuild { cmake { path "CMakeLists.txt" } } sourceSets { main { jniLibs.srcDirs = ["src/main/jniLibs"] jni.srcDirs = ['src/cpp'] } } } dependencies { implementation fileTree(dir: 'libs', include: ['*.jar']) implementation 'com.android.support:appcompat-v7:28.0.0-rc02' implementation 'com.android.support.constraint:constraint-layout:1.1.3' testImplementation 'junit:junit:4.12' implementation 'com.github.bumptech.glide:glide:4.3.1' androidTestImplementation 'com.android.support.test:runner:1.0.2' androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2' }

(12)最后在配置文件中添加权限

(13)编译完成

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