harmony 鸿蒙使用MindSpore Lite实现图像分类（C/C++）
使用MindSpore Lite实现图像分类（C/C++）
场景说明
开发者可以使用MindSpore，在UI代码中直接集成MindSpore Lite能力，快速部署AI算法，进行AI模型推理，实现图像分类的应用。
图像分类可实现对图像中物体的识别，在医学影像分析、自动驾驶、电子商务、人脸识别等有广泛的应用。
基本概念
- N-API：用于构建ArkTS本地化组件的一套接口。可利用N-API，将C/C++开发的库封装成ArkTS模块。
开发流程
- 选择图像分类模型。
- 在端侧使用MindSpore Lite推理模型，实现对选择的图片进行分类。
环境准备
安装DevEco Studio，要求版本 >= 4.1，并更新SDK到API 11或以上。
开发步骤
本文以对相册的一张图片进行推理为例，提供使用MindSpore Lite实现图像分类的开发指导。
选择模型
本示例程序中使用的图像分类模型文件为mobilenetv2.ms，放置在entry/src/main/resources/rawfile工程目录下。
如果开发者有其他图像分类的预训练模型，请参考MindSpore Lite 模型转换介绍，将原始模型转换成.ms格式。
编写代码
图像输入和预处理
此处以获取相册图片为例，调用@ohos.file.picker 实现相册图片文件的选择。
根据模型的输入尺寸，调用@ohos.multimedia.image （实现图片处理）、@ohos.file.fs （实现基础文件操作） API对选择图片进行裁剪、获取图片buffer数据，并进行标准化处理。
// Index.ets
import { fileIo } from '@kit.CoreFileKit';
import { photoAccessHelper } from '@kit.MediaLibraryKit';
import { BusinessError } from '@kit.BasicServicesKit';
import { image } from '@kit.ImageKit';
@Entry
@Component
struct Index {
@State modelName: string = 'mobilenetv2.ms';
@State modelInputHeight: number = 224;
@State modelInputWidth: number = 224;
@State uris: Array<string> = [];
build() {
Row() {
Column() {
Button() {
Text('photo')
.fontSize(30)
.fontWeight(FontWeight.Bold)
}
.type(ButtonType.Capsule)
.margin({
top: 20
})
.backgroundColor('#0D9FFB')
.width('40%')
.height('5%')
.onClick(() => {
let resMgr = this.getUIContext()?.getHostContext()?.getApplicationContext().resourceManager;
// 获取相册图片
// 1.创建图片文件选择实例
let photoSelectOptions = new photoAccessHelper.PhotoSelectOptions();
// 2.设置选择媒体文件类型为IMAGE，设置选择媒体文件的最大数目
photoSelectOptions.MIMEType = photoAccessHelper.PhotoViewMIMETypes.IMAGE_TYPE;
photoSelectOptions.maxSelectNumber = 1;
// 3.创建图库选择器实例，调用select()接口拉起图库界面进行文件选择。文件选择成功后，返回photoSelectResult结果集。
let photoPicker = new photoAccessHelper.PhotoViewPicker();
photoPicker.select(photoSelectOptions,
async (err: BusinessError, photoSelectResult: photoAccessHelper.PhotoSelectResult) => {
if (err) {
console.error('MS_LITE_ERR: PhotoViewPicker.select failed with err: ' + JSON.stringify(err));
return;
}
console.info('MS_LITE_LOG: PhotoViewPicker.select successfully, photoSelectResult uri: ' +
JSON.stringify(photoSelectResult));
this.uris = photoSelectResult.photoUris;
console.info('MS_LITE_LOG: uri: ' + this.uris);
// 预处理图片数据
try {
// 1.使用fileIo.openSync接口，通过uri打开这个文件得到fd
let file = fileIo.openSync(this.uris[0], fileIo.OpenMode.READ_ONLY);
console.info('MS_LITE_LOG: file fd: ' + file.fd);
// 2.通过fd使用fileIo.readSync接口读取这个文件内的数据
let inputBuffer = new ArrayBuffer(4096000);
let readLen = fileIo.readSync(file.fd, inputBuffer);
console.info('MS_LITE_LOG: readSync data to file succeed and inputBuffer size is:' + readLen);
// 3.通过PixelMap预处理
let imageSource = image.createImageSource(file.fd);
imageSource.createPixelMap().then((pixelMap) => {
pixelMap.getImageInfo().then((info) => {
console.info('MS_LITE_LOG: info.width = ' + info.size.width);
console.info('MS_LITE_LOG: info.height = ' + info.size.height);
// 4.根据模型输入的尺寸，将图片裁剪为对应的size，获取图片buffer数据readBuffer
pixelMap.scale(256.0 / info.size.width, 256.0 / info.size.height).then(() => {
pixelMap.crop({
x: 16,
y: 16,
size: { height: this.modelInputHeight, width: this.modelInputWidth }
})
.then(async () => {
let info = await pixelMap.getImageInfo();
console.info('MS_LITE_LOG: crop info.width = ' + info.size.width);
console.info('MS_LITE_LOG: crop info.height = ' + info.size.height);
// 需要创建的像素buffer大小
let readBuffer = new ArrayBuffer(this.modelInputHeight * this.modelInputWidth * 4);
await pixelMap.readPixelsToBuffer(readBuffer);
console.info('MS_LITE_LOG: Succeeded in reading image pixel data, buffer: ' +
readBuffer.byteLength);
// 处理readBuffer，转换成float32格式，并进行标准化处理
const imageArr =
new Uint8Array(readBuffer.slice(0, this.modelInputHeight * this.modelInputWidth * 4));
console.info('MS_LITE_LOG: imageArr length: ' + imageArr.length);
let means = [0.485, 0.456, 0.406];
let stds = [0.229, 0.224, 0.225];
let float32View = new Float32Array(this.modelInputHeight * this.modelInputWidth * 3);
let index = 0;
for (let i = 0; i < imageArr.length; i++) {
if ((i + 1) % 4 == 0) {
float32View[index] = (imageArr[i - 3] / 255.0 - means[0]) / stds[0]; // B
float32View[index+1] = (imageArr[i - 2] / 255.0 - means[1]) / stds[1]; // G
float32View[index+2] = (imageArr[i - 1] / 255.0 - means[2]) / stds[2]; // R
index += 3;
}
}
console.info('MS_LITE_LOG: float32View length: ' + float32View.length);
let printStr = 'float32View data:';
for (let i = 0; i < 20; i++) {
printStr += ' ' + float32View[i];
}
console.info('MS_LITE_LOG: float32View data: ' + printStr);
})
})
})
})
} catch (err) {
console.error('MS_LITE_LOG: uri: open file fd failed.' + err);
}
})
})
}.width('100%')
}
.height('100%')
}
}
编写推理代码
调用MindSpore实现端侧推理，推理代码流程如下。
- 引用对应的头文件
#include <iostream>
#include <sstream>
#include <stdlib.h>
#include <hilog/log.h>
#include <rawfile/raw_file_manager.h>
#include <mindspore/types.h>
#include <mindspore/model.h>
#include <mindspore/context.h>
#include <mindspore/status.h>
#include <mindspore/tensor.h>
#include "napi/native_api.h"
- 读取模型文件
#define LOGI(...) ((void)OH_LOG_Print(LOG_APP, LOG_INFO, LOG_DOMAIN, "[MSLiteNapi]", __VA_ARGS__))
#define LOGD(...) ((void)OH_LOG_Print(LOG_APP, LOG_DEBUG, LOG_DOMAIN, "[MSLiteNapi]", __VA_ARGS__))
#define LOGW(...) ((void)OH_LOG_Print(LOG_APP, LOG_WARN, LOG_DOMAIN, "[MSLiteNapi]", __VA_ARGS__))
#define LOGE(...) ((void)OH_LOG_Print(LOG_APP, LOG_ERROR, LOG_DOMAIN, "[MSLiteNapi]", __VA_ARGS__))
void *ReadModelFile(NativeResourceManager *nativeResourceManager, const std::string &modelName, size_t *modelSize) {
auto rawFile = OH_ResourceManager_OpenRawFile(nativeResourceManager, modelName.c_str());
if (rawFile == nullptr) {
LOGE("MS_LITE_ERR: Open model file failed");
return nullptr;
}
long fileSize = OH_ResourceManager_GetRawFileSize(rawFile);
void *modelBuffer = malloc(fileSize);
if (modelBuffer == nullptr) {
LOGE("MS_LITE_ERR: OH_ResourceManager_ReadRawFile failed");
}
int ret = OH_ResourceManager_ReadRawFile(rawFile, modelBuffer, fileSize);
if (ret == 0) {
LOGI("MS_LITE_LOG: OH_ResourceManager_ReadRawFile failed");
OH_ResourceManager_CloseRawFile(rawFile);
return nullptr;
}
OH_ResourceManager_CloseRawFile(rawFile);
*modelSize = fileSize;
return modelBuffer;
}
- 创建上下文，设置线程数、设备类型等参数，并加载模型。本样例模型，不支持使用NNRt推理。
void DestroyModelBuffer(void **buffer) {
if (buffer == nullptr) {
return;
}
free(*buffer);
*buffer = nullptr;
}
OH_AI_ContextHandle CreateMSLiteContext(void *modelBuffer) {
// Set executing context for model.
auto context = OH_AI_ContextCreate();
if (context == nullptr) {
DestroyModelBuffer(&modelBuffer);
LOGE("MS_LITE_ERR: Create MSLite context failed.\n");
return nullptr;
}
// 本样例模型，不支持配置OH_AI_DeviceInfoCreate(OH_AI_DEVICETYPE_NNRT)
auto cpu_device_info = OH_AI_DeviceInfoCreate(OH_AI_DEVICETYPE_CPU);
OH_AI_DeviceInfoSetEnableFP16(cpu_device_info, true);
OH_AI_ContextAddDeviceInfo(context, cpu_device_info);
LOGI("MS_LITE_LOG: Build MSLite context success.\n");
return context;
}
OH_AI_ModelHandle CreateMSLiteModel(void *modelBuffer, size_t modelSize, OH_AI_ContextHandle context) {
// Create model
auto model = OH_AI_ModelCreate();
if (model == nullptr) {
DestroyModelBuffer(&modelBuffer);
LOGE("MS_LITE_ERR: Allocate MSLite Model failed.\n");
return nullptr;
}
// Build model object
auto build_ret = OH_AI_ModelBuild(model, modelBuffer, modelSize, OH_AI_MODELTYPE_MINDIR, context);
DestroyModelBuffer(&modelBuffer);
if (build_ret != OH_AI_STATUS_SUCCESS) {
OH_AI_ModelDestroy(&model);
LOGE("MS_LITE_ERR: Build MSLite model failed.\n");
return nullptr;
}
LOGI("MS_LITE_LOG: Build MSLite model success.\n");
return model;
}
- 设置模型输入数据，执行模型推理。
constexpr int K_NUM_PRINT_OF_OUT_DATA = 20;
// 设置模型输入数据
int FillInputTensor(OH_AI_TensorHandle input, std::vector<float> input_data) {
if (OH_AI_TensorGetDataType(input) == OH_AI_DATATYPE_NUMBERTYPE_FLOAT32) {
float *data = (float *)OH_AI_TensorGetMutableData(input);
for (size_t i = 0; i < OH_AI_TensorGetElementNum(input); i++) {
data[i] = input_data[i];
}
return OH_AI_STATUS_SUCCESS;
} else {
return OH_AI_STATUS_LITE_ERROR;
}
}
// 执行模型推理
int RunMSLiteModel(OH_AI_ModelHandle model, std::vector<float> input_data) {
// Set input data for model.
auto inputs = OH_AI_ModelGetInputs(model);
auto ret = FillInputTensor(inputs.handle_list[0], input_data);
if (ret != OH_AI_STATUS_SUCCESS) {
LOGE("MS_LITE_ERR: RunMSLiteModel set input error.\n");
return OH_AI_STATUS_LITE_ERROR;
}
// Get model output.
auto outputs = OH_AI_ModelGetOutputs(model);
// Predict model.
auto predict_ret = OH_AI_ModelPredict(model, inputs, &outputs, nullptr, nullptr);
if (predict_ret != OH_AI_STATUS_SUCCESS) {
LOGE("MS_LITE_ERR: MSLite Predict error.\n");
return OH_AI_STATUS_LITE_ERROR;
}
LOGI("MS_LITE_LOG: Run MSLite model Predict success.\n");
// Print output tensor data.
LOGI("MS_LITE_LOG: Get model outputs:\n");
for (size_t i = 0; i < outputs.handle_num; i++) {
auto tensor = outputs.handle_list[i];
LOGI("MS_LITE_LOG: - Tensor %{public}d name is: %{public}s.\n", static_cast<int>(i),
OH_AI_TensorGetName(tensor));
LOGI("MS_LITE_LOG: - Tensor %{public}d size is: %{public}d.\n", static_cast<int>(i),
(int)OH_AI_TensorGetDataSize(tensor));
LOGI("MS_LITE_LOG: - Tensor data is:\n");
auto out_data = reinterpret_cast<const float *>(OH_AI_TensorGetData(tensor));
std::stringstream outStr;
for (int i = 0; (i < OH_AI_TensorGetElementNum(tensor)) && (i <= K_NUM_PRINT_OF_OUT_DATA); i++) {
outStr << out_data[i] << " ";
}
LOGI("MS_LITE_LOG: %{public}s", outStr.str().c_str());
}
return OH_AI_STATUS_SUCCESS;
}
- 调用以上方法，实现完整的模型推理流程。
static napi_value RunDemo(napi_env env, napi_callback_info info) {
LOGI("MS_LITE_LOG: Enter runDemo()");
napi_value error_ret;
napi_create_int32(env, -1, &error_ret);
// 传入数据处理
size_t argc = 2;
napi_value argv[2] = {nullptr};
napi_get_cb_info(env, info, &argc, argv, nullptr, nullptr);
bool isArray = false;
napi_is_array(env, argv[0], &isArray);
uint32_t length = 0;
// 获取数组的长度
napi_get_array_length(env, argv[0], &length);
LOGI("MS_LITE_LOG: argv array length = %{public}d", length);
std::vector<float> input_data;
double param = 0;
for (int i = 0; i < length; i++) {
napi_value value;
napi_get_element(env, argv[0], i, &value);
napi_get_value_double(env, value, ¶m);
input_data.push_back(static_cast<float>(param));
}
std::stringstream outstr;
for (int i = 0; i < K_NUM_PRINT_OF_OUT_DATA; i++) {
outstr << input_data[i] << " ";
}
LOGI("MS_LITE_LOG: input_data = %{public}s", outstr.str().c_str());
// Read model file
const std::string modelName = "mobilenetv2.ms";
LOGI("MS_LITE_LOG: Run model: %{public}s", modelName.c_str());
size_t modelSize;
auto resourcesManager = OH_ResourceManager_InitNativeResourceManager(env, argv[1]);
auto modelBuffer = ReadModelFile(resourcesManager, modelName, &modelSize);
if (modelBuffer == nullptr) {
LOGE("MS_LITE_ERR: Read model failed");
return error_ret;
}
LOGI("MS_LITE_LOG: Read model file success");
auto context = CreateMSLiteContext(modelBuffer);
if (context == nullptr) {
LOGE("MS_LITE_ERR: MSLiteFwk Build context failed.\n");
return error_ret;
}
auto model = CreateMSLiteModel(modelBuffer, modelSize, context);
if (model == nullptr) {
OH_AI_ContextDestroy(&context);
LOGE("MS_LITE_ERR: MSLiteFwk Build model failed.\n");
return error_ret;
}
int ret = RunMSLiteModel(model, input_data);
if (ret != OH_AI_STATUS_SUCCESS) {
OH_AI_ModelDestroy(&model);
OH_AI_ContextDestroy(&context);
LOGE("MS_LITE_ERR: RunMSLiteModel failed.\n");
return error_ret;
}
napi_value out_data;
napi_create_array(env, &out_data);
auto outputs = OH_AI_ModelGetOutputs(model);
OH_AI_TensorHandle output_0 = outputs.handle_list[0];
float *output0Data = reinterpret_cast<float *>(OH_AI_TensorGetMutableData(output_0));
for (size_t i = 0; i < OH_AI_TensorGetElementNum(output_0); i++) {
napi_value element;
napi_create_double(env, static_cast<double>(output0Data[i]), &element);
napi_set_element(env, out_data, i, element);
}
OH_AI_ModelDestroy(&model);
OH_AI_ContextDestroy(&context);
LOGI("MS_LITE_LOG: Exit runDemo()");
return out_data;
}
- 编写CMake脚本，链接MindSpore Lite动态库。
# the minimum version of CMake.
cmake_minimum_required(VERSION 3.4.1)
project(MindSporeLiteCDemo)
set(NATIVERENDER_ROOT_PATH ${CMAKE_CURRENT_SOURCE_DIR})
if(DEFINED PACKAGE_FIND_FILE)
include(${PACKAGE_FIND_FILE})
endif()
include_directories(${NATIVERENDER_ROOT_PATH}
${NATIVERENDER_ROOT_PATH}/include)
add_library(entry SHARED mslite_napi.cpp)
target_link_libraries(entry PUBLIC mindspore_lite_ndk)
target_link_libraries(entry PUBLIC hilog_ndk.z)
target_link_libraries(entry PUBLIC rawfile.z)
target_link_libraries(entry PUBLIC ace_napi.z)
使用N-API将C++动态库封装成ArkTS模块
- 在 entry/src/main/cpp/types/libentry/Index.d.ts，定义ArkTS接口
runDemo()。内容如下：
export const runDemo: (a: number[], b:Object) => Array<number>;
- 在 oh-package.json5 文件，将API与so相关联，成为一个完整的ArkTS模块：
{
"name": "libentry.so",
"types": "./Index.d.ts",
"version": "1.0.0",
"description": "MindSpore Lite inference module"
}
调用封装的ArkTS模块进行推理并输出结果
在 entry/src/main/ets/pages/Index.ets 中，调用封装的ArkTS模块，最后对推理结果进行处理。
// Index.ets
import msliteNapi from 'libentry.so'
@Entry
@Component
struct Index {
@State modelInputHeight: number = 224;
@State modelInputWidth: number = 224;
@State max: number = 0;
@State maxIndex: number = 0;
@State maxArray: Array<number> = [];
@State maxIndexArray: Array<number> = [];
build() {
Row() {
Column() {
Button() {
Text('photo')
.fontSize(30)
.fontWeight(FontWeight.Bold)
}
.type(ButtonType.Capsule)
.margin({
top: 20
})
.backgroundColor('#0D9FFB')
.width('40%')
.height('5%')
.onClick(() => {
let resMgr = this.getUIContext()?.getHostContext()?.getApplicationContext().resourceManager;
let float32View = new Float32Array(this.modelInputHeight * this.modelInputWidth * 3);
// 图像输入和预处理。
// 调用c++的runDemo方法，完成图像输入和预处理后的buffer数据保存在float32View，具体可见上文图像输入和预处理中float32View的定义和处理。
console.info('MS_LITE_LOG: *** Start MSLite Demo ***');
let output: Array<number> = msliteNapi.runDemo(Array.from(float32View), resMgr);
// 取分类占比的最大值
this.max = 0;
this.maxIndex = 0;
this.maxArray = [];
this.maxIndexArray = [];
let newArray = output.filter(value => value !== this.max);
for (let n = 0; n < 5; n++) {
this.max = output[0];
this.maxIndex = 0;
for (let m = 0; m < newArray.length; m++) {
if (newArray[m] > this.max) {
this.max = newArray[m];
this.maxIndex = m;
}
}
this.maxArray.push(Math.round(this.max * 10000));
this.maxIndexArray.push(this.maxIndex);
// filter数组过滤函数
newArray = newArray.filter(value => value !== this.max);
}
console.info('MS_LITE_LOG: max:' + this.maxArray);
console.info('MS_LITE_LOG: maxIndex:' + this.maxIndexArray);
console.info('MS_LITE_LOG: *** Finished MSLite Demo ***');
})
}.width('100%')
}
.height('100%')
}
}
调测验证
- 在DevEco Studio中连接设备，点击Run entry，编译Hap，有如下显示：
Launching com.samples.mindsporelitecdemo
$ hdc shell aa force-stop com.samples.mindsporelitecdemo
$ hdc shell mkdir data/local/tmp/xxx
$ hdc file send C:\Users\xxx\MindSporeLiteCDemo\entry\build\default\outputs\default\entry-default-signed.hap "data/local/tmp/xxx"
$ hdc shell bm install -p data/local/tmp/xxx
$ hdc shell rm -rf data/local/tmp/xxx
$ hdc shell aa start -a EntryAbility -b com.samples.mindsporelitecdemo
- 在设备屏幕点击photo按钮，选择图片，点击确定。设备屏幕显示所选图片的分类结果，在日志打印结果中，过滤关键字”MS_LITE“，可得到如下结果：
08-05 17:15:52.001 4684-4684 A03d00/JSAPP pid-4684 I MS_LITE_LOG: PhotoViewPicker.select successfully, photoSelectResult uri: {"photoUris":["file://media/Photo/13/IMG_1501955351_012/plant.jpg"]}
...
08-05 17:15:52.627 4684-4684 A03d00/JSAPP pid-4684 I MS_LITE_LOG: crop info.width = 224
08-05 17:15:52.627 4684-4684 A03d00/JSAPP pid-4684 I MS_LITE_LOG: crop info.height = 224
08-05 17:15:52.628 4684-4684 A03d00/JSAPP pid-4684 I MS_LITE_LOG: Succeeded in reading image pixel data, buffer: 200704
08-05 17:15:52.971 4684-4684 A03d00/JSAPP pid-4684 I MS_LITE_LOG: float32View data: float32View data: 1.2385478019714355 1.308123230934143 1.4722440242767334 1.2385478019714355 1.308123230934143 1.4722440242767334 1.2385478019714355 1.308123230934143 1.4722440242767334 1.2385478019714355 1.308123230934143 1.4722440242767334 1.2385478019714355 1.308123230934143 1.4722440242767334 1.2385478019714355 1.308123230934143 1.4722440242767334 1.2385478019714355 1.308123230934143
08-05 17:15:52.971 4684-4684 A03d00/JSAPP pid-4684 I MS_LITE_LOG: *** Start MSLite Demo ***
08-05 17:15:53.454 4684-4684 A00000/[MSLiteNapi] pid-4684 I MS_LITE_LOG: Build MSLite model success.
08-05 17:15:53.753 4684-4684 A00000/[MSLiteNapi] pid-4684 I MS_LITE_LOG: Run MSLite model Predict success.
08-05 17:15:53.753 4684-4684 A00000/[MSLiteNapi] pid-4684 I MS_LITE_LOG: Get model outputs:
08-05 17:15:53.753 4684-4684 A00000/[MSLiteNapi] pid-4684 I MS_LITE_LOG: - Tensor 0 name is: Default/head-MobileNetV2Head/Sigmoid-op466.
08-05 17:15:53.753 4684-4684 A00000/[MSLiteNapi] pid-4684 I MS_LITE_LOG: - Tensor data is:
08-05 17:15:53.753 4684-4684 A00000/[MSLiteNapi] pid-4684 I MS_LITE_LOG: 3.43385e-06 1.40285e-05 9.11969e-07 4.91007e-05 9.50266e-07 3.94537e-07 0.0434676 3.97196e-05 0.00054832 0.000246202 1.576e-05 3.6494e-06 1.23553e-05 0.196977 5.3028e-05 3.29346e-05 4.90475e-07 1.66109e-06 7.03273e-06 8.83677e-07 3.1365e-06
08-05 17:15:53.781 4684-4684 A03d00/JSAPP pid-4684 W MS_LITE_WARN: output length = 500 ;value = 0.0000034338463592575863,0.000014028532859811094,9.119685273617506e-7,0.000049100715841632336,9.502661555416125e-7,3.945370394831116e-7,0.04346757382154465,0.00003971960904891603,0.0005483203567564487,0.00024620210751891136,0.000015759984307806008,0.0000036493988773145247,0.00001235533181898063,0.1969769448041916,0.000053027983085485175,0.000032934600312728435,4.904751449430478e-7,0.0000016610861166554969,0.000007032729172351537,8.836767619868624e-7
08-05 17:15:53.831 4684-4684 A03d00/JSAPP pid-4684 I MS_LITE_LOG: max:9497,7756,1970,435,46
08-05 17:15:53.831 4684-4684 A03d00/JSAPP pid-4684 I MS_LITE_LOG: maxIndex:323,46,13,6,349
08-05 17:15:53.831 4684-4684 A03d00/JSAPP pid-4684 I MS_LITE_LOG: *** Finished MSLite Demo ***
效果示意
在设备上，点击photo按钮，选择相册中的一张图片，点击确定。在图片下方显示此图片占比前4的分类信息。
相关实例
针对使用MindSpore Lite进行图像分类应用的开发，有以下相关实例可供参考：
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