Experience Java Simple Inference Demo
Overview
This tutorial provides an example program for MindSpore Lite to perform inference. It demonstrates the basic process of performing inference on the device side using MindSpore Lite Java API by random inputting data, executing inference, and printing the inference result. You can quickly understand how to use the Java APIs related to inference on MindSpore Lite. In this tutorial, the randomly generated data is used as the input data to perform the inference on the MobileNetV2 model and print the output data. The code is stored in the mindspore/lite/examples/quick_start_java directory.
The MindSpore Lite inference steps are as follows:
Load the model(optional): Read the
.msmodel converted by the model conversion tool from the file system.Create and configure context: Create a configuration context MSContext to save some basic configuration parameters required by a session to guide graph build and execution. including
deviceType(device type),threadNum(number of threads),cpuBindMode(CPU binding mode), andenable_float16(whether to preferentially use the float16 operator).Build a graph: Before building a graph, the build interface of model needs to be called to build the graph, including subgraph partition and operator selection and scheduling. This takes a long time. Therefore, it is recommended that with one model created, one graph be built. In this case, the inference will be performed for multiple times.
Input data: Before the graph is executed, data needs to be filled in the
Input Tensor.Perform inference: Use the predict of the model to perform model inference.
Obtain the output: After the graph execution is complete, you can obtain the inference result by
outputting the tensor.Release the memory: If the MindSpore Lite inference framework is not required, release the created model.
To view the advanced usage of MindSpore Lite, see Using Runtime to Perform Inference (Java).
Building and Running
Environment requirements
Build
Run the build script in the
mindspore/lite/examples/quick_start_javadirectory to automatically download the MindSpore Lite inference framework library and model files and build the Demo.bash build.shIf the MindSpore Lite inference framework fails to be downloaded, manually download the MindSpore Lite model inference framework mindspore-lite-{version}-linux-x64.tar.gz whose hardware platform is CPU and operating system is Ubuntu-x64. Decompress the package and copy
runtime/lib/mindspore-lite-java.jarfile to themindspore/lite/examples/quick_start_java/libdirectory.If the MobileNetV2 model fails to be downloaded, manually download the model file mobilenetv2.ms and copy it to the
mindspore/lite/examples/quick_start_java/model/directory.After manually downloading and placing the file in the specified location, you need to execute the build.sh script again to complete the compilation.
Inference
After the build, go to the
mindspore/lite/examples/quick_start_java/targetdirectory and run the following command to experience MindSpore Lite inference on the MobileNetV2 model:java -classpath .:./quick_start_java.jar:../lib/mindspore-lite-java.jar com.mindspore.lite.demo.Main ../model/mobilenetv2.ms
After the execution, the following information is displayed, including the tensor name, tensor size, number of output tensors, and the first 50 pieces of data.
out tensor shape: [1,1000,] and out data: 5.4091015E-5 4.030303E-4 3.032344E-4 4.0029243E-4 2.2730739E-4 8.366581E-5 2.629827E-4 3.512394E-4 2.879536E-4 1.9557697E-4xxxxxxxxxx MindSpore Lite 1.1.0out tensor shape: [1,1000,] and out data: 5.4091015E-5 4.030303E-4 3.032344E-4 4.0029243E-4 2.2730739E-4 8.366581E-5 2.629827E-4 3.512394E-4 2.879536E-4 1.9557697E-4tensor name is:Default/Sigmoid-op204 tensor size is:2000 tensor elements num is:500output data is:3.31223e-05 1.99382e-05 3.01624e-05 0.000108345 1.19685e-05 4.25282e-06 0.00049955 0.000340809 0.00199094 0.000997094 0.00013585 1.57605e-05 4.34131e-05 1.56114e-05 0.000550819 2.9839e-05 4.70447e-06 6.91601e-06 0.000134483 2.06795e-06 4.11612e-05 2.4667e-05 7.26248e-06 2.37974e-05 0.000134513 0.00142482 0.00011707 0.000161848 0.000395011 3.01961e-05 3.95325e-05 3.12398e-06 3.57709e-05 1.36277e-06 1.01068e-05 0.000350805 5.09019e-05 0.000805241 6.60321e-05 2.13734e-05 9.88654e-05 2.1991e-06 3.24065e-05 3.9479e-05 4.45178e-05 0.00205024 0.000780899 2.0633e-05 1.89997e-05 0.00197261 0.000259391
Model Loading(optional)
Read the MindSpore Lite model from the file system.
// Load the .ms model.
MappedByteBuffer byteBuffer = null;
try {
fc = new RandomAccessFile(fileName, "r").getChannel();
byteBuffer = fc.map(FileChannel.MapMode.READ_ONLY, 0, fc.size()).load();
} catch (IOException e) {
e.printStackTrace();
}
Model Build
Model build includes context configuration creation and model compilation. current graph build support file and mappedbytebuffer format. The following [sample code] describes model compilation by reading from a file.
private static boolean compile(String modelPath) {
MSContext context = new MSContext();
// use default param init context
context.init();
boolean ret = context.addDeviceInfo(DeviceType.DT_CPU, false, 0);
if (!ret) {
System.err.println("Compile graph failed");
context.free();
return false;
}
// Create the MindSpore lite session.
model = new Model();
// Compile graph.
ret = model.build(modelPath, ModelType.MT_MINDIR, context);
if (!ret) {
System.err.println("Compile graph failed");
model.free();
return false;
}
return true;
}
Model Inference
Model inference includes data input, inference execution, and output obtaining. In this example, the input data is randomly generated, and the output result is printed after inference.
private static boolean run() {
MSTensor inputTensor = model.getInputByTensorName("graph_input-173");
if (inputTensor.getDataType() != DataType.kNumberTypeFloat32) {
System.err.println("Input tensor data type is not float, the data type is " + inputTensor.getDataType());
return false;
}
// Generator Random Data.
int elementNums = inputTensor.elementsNum();
float[] randomData = generateArray(elementNums);
ByteBuffer inputData = floatArrayToByteBuffer(randomData);
// Set Input Data.
inputTensor.setData(inputData);
// Run Inference.
boolean ret = model.predict();
if (!ret) {
inputTensor.free();
System.err.println("MindSpore Lite run failed.");
return false;
}
// Get Output Tensor Data.
MSTensor outTensor = model.getOutputByTensorName("Softmax-65");
// Print out Tensor Data.
StringBuilder msgSb = new StringBuilder();
msgSb.append("out tensor shape: [");
int[] shape = outTensor.getShape();
for (int dim : shape) {
msgSb.append(dim).append(",");
}
msgSb.append("]");
if (outTensor.getDataType() != DataType.kNumberTypeFloat32) {
inputTensor.free();
outTensor.free();
System.err.println("output tensor data type is not float, the data type is " + outTensor.getDataType());
return false;
}
float[] result = outTensor.getFloatData();
if (result == null) {
inputTensor.free();
outTensor.free();
System.err.println("decodeBytes return null");
return false;
}
msgSb.append(" and out data:");
for (int i = 0; i < 50 && i < outTensor.elementsNum(); i++) {
msgSb.append(" ").append(result[i]);
}
System.out.println(msgSb.toString());
// In/Out Tensor must be free
inputTensor.free();
outTensor.free();
return true;
}
Memory Release
If the MindSpore Lite inference framework is not required, release the created model.
// Delete model buffer.
model.free();