行业知识
安卓调用onnx模型并计算
2026-11-03 12:43:26
来源：互联网转载
安卓平台可以通过调用onnx模型来进行计算，这为移动设备提供了更多的计算能力和应用场景。通过使用onnx模型，安卓设备可以进行复杂的计算任务，例如图像识别、语音识别等。这为移动应用的功能和性能提升提供了新的可能性。同时，开发者可以利用onnx模型来开发更加智能和高效的安卓应用，为用户提供更好的体验。总的来说，安卓调用onnx模型并进行计算的能力为移动设备的发展带来了新的机遇和挑战。
依赖
build.gradle
plugins { id 'com.android.application'}repositories { jcenter() maven { url "https://oss.sonatype.org/content/repositories/snapshots" }}android { signingConfigs { release { storeFile file('myapp.keystore') storePassword '123456' keyAlias 'myapp' keyPassword '123456' } } packagingOptions { pickFirst 'lib/arm64-v8a/libc++_shared.so' } configurations { extractForNativeBuild } compileSdkVersion 28 buildToolsVersion "30.0.3" defaultConfig { applicationId "com.mobvoi.myapp" minSdkVersion 21 targetSdkVersion 28 versionCode 1 versionName "1.0" testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner" externalNativeBuild { cmake { targets "myapp", "decoder_main" cppFlags "-std=c++11", "-DC10_USE_GLOG", "-DC10_USE_MINIMAL_GLOG", "-DANDROID", "-Wno-c++11-narrowing", "-fexceptions" } } ndkVersion '21.3.6528147' ndk { abiFilters 'arm64-v8a' } } buildTypes { release { minifyEnabled false signingConfig signingConfigs.release proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro' } } externalNativeBuild { cmake { path "src/main/cpp/CMakeLists.txt" } } compileOptions { sourceCompatibility JavaVersion.VERSION_1_8 targetCompatibility JavaVersion.VERSION_1_8 } ndkVersion '21.3.6528147'}dependencies { implementation 'androidx.appcompat:appcompat:1.2.0' implementation 'com.google.android.material:material:1.2.1' implementation 'androidx.constraintlayout:constraintlayout:2.0.4' testImplementation 'junit:junit:4.+' androidTestImplementation 'androidx.test.ext:junit:1.1.2' androidTestImplementation 'androidx.test.espresso:espresso-core:3.3.0' implementation 'org.pytorch:pytorch_android:1.10.0' extractForNativeBuild 'org.pytorch:pytorch_android:1.10.0' implementation group: 'com.microsoft.onnxruntime', name: 'onnxruntime-android', version: '1.15.1'}task extractAARForNativeBuild { doLast { configurations.extractForNativeBuild.files.each { def file = it.absoluteFile copy { from zipTree(file) into "$buildDir/$file.name" include "headers/**" include "jni/**" } } }}tasks.whenTaskAdded { task -> if (task.name.contains('externalNativeBuild')) { task.dependsOn(extractAARForNativeBuild) }} 准备好onnx放在assert目录下
api介绍
地址
api文档https://javadoc.io/doc/com.microsoft.onnxruntime/onnxruntime/latest/index.html
常用的api
| api | 作用 |
OrtEnvironment.getEnvironment() | 创建onnx上下文的运行环境 |
new OrtSession.SessionOptions() | 创建会话（配置） |
environment.createSession(bytes, options) | 创建会话，第一个参数是模型数据，第二个是配置的参数 |
LongBuffer.wrap(inputValues) | 将输入转换成onnx识别的输入，输入是模型识别的数据 |
OnnxTensor.createTensor(environment, wrap, new long[]{1, inputValues.length}) | 创建tensor，第一个参数是上面定义的环境，第二个参数是输入转换成模型的格式，第三个根据实际设置，为入参的矩阵格式 |
session.run(map) | 推理，map是整合起来的数据 |
(long[][]) output.get(1).getValue() | 获取推理结果，这里以二维数组为例 |
使用案例
private String getOnnx(String text) { OrtEnvironment environment = OrtEnvironment.getEnvironment(); AssetManager assetManager = getAssets(); try { // 创建会话 OrtSession.SessionOptions options = new OrtSession.SessionOptions(); // 读取模型 InputStream stream = assetManager.open("youonnx.onnx"); ByteArrayOutputStream byteStream = new ByteArrayOutputStream(); byte[] buffer = new byte[4096]; int bytesRead; while ((bytesRead = stream.read(buffer)) != -1) { byteStream.write(buffer, 0, bytesRead); } byteStream.flush(); byte[] bytes = byteStream.toByteArray(); OrtSession session = environment.createSession(bytes, options); String vocab = "vocab"; String puncVocab = "punc_vocab"; Map vocabMap = getFormFile(vocab, new String[]{"", ""}); Map puncVocabMap = getFormFile(vocab, new String[]{" "}); DataSet.NoPuncTextDataset dataset = new DataSet.NoPuncTextDataset(vocabMap, puncVocabMap); List list = dataset.word2seq(text); // 准备输入数据 long[] inputValues = new long[list.size()]; for (int i = 0; i 通过调用此函数，可以实现安卓调用onnx
