mindspore-ci-bot
4 years ago
committed by
Gitee
commit
a380ee8bf2
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<?xml version="1.0" encoding="UTF-8"?>
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<project xmlns="http://maven.apache.org/POM/4.0.0"
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xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
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<modelVersion>4.0.0</modelVersion>
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<groupId>com.mindspore.lite</groupId>
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<artifactId>mindspore-lite-java-test</artifactId>
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<version>1.0</version>
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<dependencies>
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<dependency>
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<groupId>com.mindspore.lite</groupId>
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<artifactId>mindspore-lite-java</artifactId>
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<version>1.0</version>
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<scope>system</scope>
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<systemPath>${project.basedir}/lib/mindspore-lite-java.jar</systemPath>
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</dependency>
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</dependencies>
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<properties>
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<maven.compiler.source>8</maven.compiler.source>
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<maven.compiler.target>8</maven.compiler.target>
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</properties>
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<build>
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<finalName>${project.name}</finalName>
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<plugins>
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<plugin>
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<groupId>org.apache.maven.plugins</groupId>
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<artifactId>maven-assembly-plugin</artifactId>
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<configuration>
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<archive>
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<manifest>
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<mainClass>Benchmark</mainClass>
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</manifest>
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</archive>
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<descriptorRefs>
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<descriptorRef>jar-with-dependencies</descriptorRef>
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</descriptorRefs>
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</configuration>
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<executions>
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<execution>
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<id>make-assemble</id>
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<phase>package</phase>
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<goals>
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<goal>single</goal>
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</goals>
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</execution>
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</executions>
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</plugin>
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</plugins>
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</build>
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</project>
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@ -0,0 +1,175 @@
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import com.mindspore.lite.DataType;
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import com.mindspore.lite.LiteSession;
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import com.mindspore.lite.MSTensor;
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import com.mindspore.lite.Model;
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import com.mindspore.lite.config.DeviceType;
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import com.mindspore.lite.config.MSConfig;
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import java.io.*;
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public class Benchmark {
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private static Model model;
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private static LiteSession session;
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public static byte[] readBinFile(String fileName, int size) {
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try {
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DataInputStream is = new DataInputStream(
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new BufferedInputStream(new FileInputStream(
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fileName)));
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byte[] buf = new byte[size];
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is.read(buf);
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is.close();
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return buf;
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} catch (IOException e) {
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e.printStackTrace();
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}
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return null;
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}
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public static boolean compareData(String filePath, float accuracy) {
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double meanError = 0;
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File file = new File(filePath);
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if (file.exists()) {
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try {
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FileReader fileReader = new FileReader(file);
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BufferedReader br = new BufferedReader(fileReader);
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String lineContent = null;
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int line = 0;
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MSTensor outTensor = null;
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String name = null;
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while ((lineContent = br.readLine()) != null) {
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String[] strings = lineContent.split(" ");
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if (line++ % 2 == 0) {
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name = strings[0];
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outTensor = session.getOutputByTensorName(name);
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continue;
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}
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float[] benchmarkData = new float[strings.length];
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for (int i = 0; i < strings.length; i++) {
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benchmarkData[i] = Float.parseFloat(strings[i]);
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}
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float[] outData = outTensor.getFloatData();
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int errorCount = 0;
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for (int i = 0; i < benchmarkData.length; i++) {
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double relativeTolerance = 1e-5;
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double absoluteTolerance = 1e-8;
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double tolerance = absoluteTolerance + relativeTolerance * Math.abs(benchmarkData[i]);
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double absoluteError = Math.abs(outData[i] - benchmarkData[i]);
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if (absoluteError > tolerance) {
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if (Math.abs(benchmarkData[i] - 0.0f) < Float.MIN_VALUE)
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if (absoluteError > 1e-5) {
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meanError += absoluteError;
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errorCount++;
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} else {
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continue;
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}
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} else {
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meanError += absoluteError / (Math.abs(benchmarkData[i]) + Float.MIN_VALUE);
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errorCount++;
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}
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}
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if (meanError > 0.0f) {
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meanError /= errorCount;
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}
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if (meanError <= 0.0000001) {
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System.out.println("Mean bias of node/tensor " + name + " : 0%");
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} else {
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System.out.println("Mean bias of node/tensor " + name + " : " + meanError * 100 + "%");
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}
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}
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br.close();
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fileReader.close();
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} catch (IOException e) {
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e.printStackTrace();
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}
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}
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return meanError < accuracy;
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}
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private static boolean compile() {
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MSConfig msConfig = new MSConfig();
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boolean ret = msConfig.init(DeviceType.DT_CPU, 2);
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if (!ret) {
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System.err.println("Init context failed");
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return false;
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}
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// Create the MindSpore lite session.
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session = new LiteSession();
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ret = session.init(msConfig);
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msConfig.free();
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if (!ret) {
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System.err.println("Create session failed");
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model.free();
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return false;
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}
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// Compile graph.
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ret = session.compileGraph(model);
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if (!ret) {
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System.err.println("Compile graph failed");
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model.free();
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return false;
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}
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return true;
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}
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private static void freeBuffer() {
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session.free();
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model.free();
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}
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public static void main(String[] args) {
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if (args.length < 4) {
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System.err.println("We must pass parameters such as modelPath, inDataFile, benchmarkDataFile and accuracy.");
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return;
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}
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String modelPath = args[0];
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String[] inDataFile = args[1].split(",");
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String benchmarkDataFile = args[2];
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float accuracy = Float.parseFloat(args[3]);
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model = new Model();
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boolean ret = model.loadModel(modelPath);
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if (!ret) {
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System.err.println("Load model failed, model path is " + modelPath);
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return;
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}
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ret = compile();
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if (!ret) {
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System.err.println("MindSpore Lite compile failed.");
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return;
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}
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for (int i = 0; i < session.getInputs().size(); i++) {
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MSTensor inputTensor = session.getInputs().get(i);
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if (inputTensor.getDataType() != DataType.kNumberTypeFloat32) {
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System.err.println("Input tensor shape do not float, the data type is " + inputTensor.getDataType());
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freeBuffer();
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return;
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}
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// Set Input Data.
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byte[] data = readBinFile(inDataFile[i], (int) inputTensor.size());
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inputTensor.setData(data);
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}
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// Run Inference.
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if (!session.runGraph()) {
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System.err.println("MindSpore Lite run failed.");
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freeBuffer();
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return;
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}
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boolean benchmarkResult = compareData(benchmarkDataFile, accuracy);
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freeBuffer();
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if (!benchmarkResult) {
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System.err.println(modelPath + " accuracy error is too large.");
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System.exit(1);
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}
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}
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}
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