安卓调用onnx模型并盘算
安卓平台可以通过调用onnx模型来举行盘算，这为移动设备提供了更多的盘算本事和应用场景。通过使用onnx模型，安卓设备可以举行复杂的盘算任务，比方图像识别、语音识别等。这为移动应用的功能和性能提拔提供了新的可能性。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模型并举行盘算的本事为移动设备的发展带来了新的机会和挑战。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
https://img-blog.csdnimg.cn/5176642116b64404b6e85f5406d5ec2e.png
api介绍
地点
api文档https://csdnimg.cn/release/blog_editor_html/release2.3.6/ckeditor/plugins/CsdnLink/icons/icon-default.png?t=N7T8https://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;
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<String, Integer> vocabMap = getFormFile(vocab, new String[]{"<UNK>", "<END>"});
Map<String, Integer> puncVocabMap = getFormFile(vocab, new String[]{" "});
DataSet.NoPuncTextDataset dataset = new DataSet.NoPuncTextDataset(vocabMap, puncVocabMap);
List<Integer> list = dataset.word2seq(text);
// 准备输入数据
long[] inputValues = new long;
for (int i = 0; i < list.size(); i++) {
inputValues = list.get(i);
}
LongBuffer wrap = LongBuffer.wrap(inputValues);
OnnxTensor inputTensor = OnnxTensor.createTensor(environment, wrap, new long[]{1, inputValues.length});
long[] len = new long[]{inputValues.length};
LongBuffer wrap2 = LongBuffer.wrap(len);
OnnxTensor inputTensor_len = OnnxTensor.createTensor(environment, wrap2, new long[]{1});
// 准备数据
Map<String, OnnxTensor> map = new HashMap<>();
map.put("inputs", inputTensor);
map.put("inputs_len", inputTensor_len);
// 运行推理
OrtSession.Result output = session.run(map);
// 获取输出结果
long[][] value = (long[][]) output.get(1).getValue();
// 处理输出结果
// todo
session.close();
return "you_answer"
} catch (IOException | OrtException e) {
throw new RuntimeException(e);
}
} 通过调用此函数，可以实现安卓调用onnx
免责声明：如果侵犯了您的权益，请联系站长，我们会及时删除侵权内容，谢谢合作！更多信息从访问主页：qidao123.com:ToB企服之家，中国第一个企服评测及商务社交产业平台。
页:
[1]