Open Neural Network Exchange (ONNX)

This page provides a Java example of inferencing a model, built in Python with ONNX Runtime, a cross-platform, high performance scoring engine for machine learning models.

The Open Neural Network Exchange (ONNX) format is supported by a number of deep learning frameworks, including PyTorch, CNTK, MXNet, etc,.

import ai.konduit.serving.InferenceConfiguration;
import ai.konduit.serving.config.ServingConfig;
import ai.konduit.serving.configprovider.KonduitServingMain;
import ai.konduit.serving.model.PythonConfig;
import ai.konduit.serving.pipeline.step.ImageLoadingStep;
import ai.konduit.serving.pipeline.step.PythonStep;
import com.mashape.unirest.http.Unirest;
import com.mashape.unirest.http.exceptions.UnirestException;
import org.apache.commons.io.FileUtils;
import org.datavec.python.PythonVariables;
import org.nd4j.linalg.io.ClassPathResource;

A reference Java project is provided in the Example repository ( https://github.com/KonduitAI/konduit-serving-examples ) with a Maven pom.xml dependencies file. If using the IntelliJ IDEA IDE, open the java folder as a Maven project and run the main function of InferenceModelStepONNX class.

For the purposes of this example, we use ONNX model files from Ultra-Light-Fast-Generic-Face-Detector-1MB by Linzaer, a lightweight facedetection model designed for edge computing devices.

Python script with PyTorch and ONNX Runtime

Now that we have an optimized ONNX file, we can serve our model.

The following is the python script onnxFacedetect.py :

• transforms a PIL image into a 240 x 320 image,

• casts it into a PyTorch Tensor,

• adds an extra dimension with unsqueeze,

• casts the Tensor into a NumPy array, then

• returns the model's output with ONNX Runtime.

import os
import numpy as np
from PIL import Image
import torchvision.transforms as transforms
import onnxruntime
from matplotlib.image import imread
dl_path = os.path.abspath("./src/main/resources/data/facedetector/facedetector.onnx")
sys.path.append(dl_path)
a,b,c,d=inputimage.shape
inputimage=inputimage.reshape(b,c,d)
im=np.array(inputimage)
image = Image.fromarray(im.astype('uint8'), 'RGB')
resize = transforms.Resize([240, 320])
img_y = resize(image)
to_tensor = transforms.ToTensor()
img_y = to_tensor(img_y)
img_y.unsqueeze_(0)

def to_numpy(tensor):
    return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()

ort_session = onnxruntime.InferenceSession(dl_path)
ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(img_y)}
ort_outs = ort_session.run(None, ort_inputs)
_, boxes = ort_outs

Configure the step

Defining a PythonConfig

• Here we use the pythonCodePath argument instead of pythonCode, in order to specify the location of the Python script.

• Define the inputs and outputs name and type of the value as defined by the name() method of a PythonVariables.Type, here we use NDARRAY. See https://serving.oss.konduit.ai/python for supported data types.

• To run this example please install (PIL 6.21,numpy,matplotlib 3.1.2,onnxruntime 1.1.0, torchvision 0.4.2)and set the python path as pythonPath(pythonPath) in the python_config to refer the required Python libraries.

 String pythonCodePath = new ClassPathResource("scripts/onnxFacedetect.py").getFile().getAbsolutePath();

String pythonPath = Arrays.stream(cachePackages())
        .filter(Objects::nonNull)
        .map(File::getAbsolutePath)
        .collect(Collectors.joining(File.pathSeparator));

PythonConfig python_config = PythonConfig.builder()
        .pythonCodePath(pythonCodePath)
        .pythonInput("inputimage", PythonVariables.Type.NDARRAY.name())
        .pythonOutput("boxes", PythonVariables.Type.NDARRAY.name())
        .pythonPath(pythonPath)
        .build();

Define a pipeline step with the PythonStep class

In the .step() method, define the input configuration (python_config).

PythonStep onnx_step = new PythonStep().step(python_config);

Define a pipeline step with ImageLoadingStep class

A Pipeline Step for loading and transforming an image

ImageLoadingStep imageLoadingStep = ImageLoadingStep.builder()
                .inputName("inputimage")
                .dimensionsConfig("default", new Long[]{478L, 720L, 3L}) // Height, width, channels
                .build();

Configure the server

In the ServingConfig, specify any port number that is not reserved.

int port = Util.randInt(1000, 65535);

ServingConfig servingConfig = ServingConfig.builder().httpPort(port).
    build();

The ServingConfig has to be passed to Server in addition to the imageLoadingStep as a list. In this case, there is a single step: onnx_step.

InferenceConfiguration inferenceConfiguration = InferenceConfiguration.builder()
    .steps(Arrays.asList(imageLoadingStep, onnx_step)).servingConfig(servingConfig).build();

The inferenceConfiguration is stored as a JSON File.

File configFile = new File("config.json");
FileUtils.write(configFile, inferenceConfiguration.toJson(), Charset.defaultCharset());

Inference

Load a sample image and send the image as NDARRAY to the server for prediction.

Accepted input and output data formats are as follows:

• Input: JSON, ARROW, IMAGE, ND4J and NUMPY.

• Output: NUMPY, JSON, ND4J and ARROW. {% endhint %}

The Client should be configured to match the Konduit Serving instance. As this example is run on a local computer, the server is located at host 'http://localhost' and port port. And Finally, we run the Konduit Serving instance with the saved config.json file path as configPath and other necessary server configuration arguments.

A Callback Function onSuccess is implemented in order to post the Client request and get the HttpResponse, only after the successful run of the KonduitServingMain Server.

File imageOnnx = new ClassPathResource("data/facedetector/OnnxImageTest.jpg").getFile();

KonduitServingMain.builder()
    .onSuccess(() -> {
        try {
            HttpResponse<JsonNode> response = Unirest.post(String.format("http://localhost:%s/raw/json", port))
                    .header("Content-Type", "application/json")
                    .body("{\"first\" :\"value\"}").asJson();

            System.out.println(response.getBody().toString());
            System.exit(0);
        } catch (UnirestException e) {
            e.printStackTrace();
            System.exit(0);
        }
    })
    .build()
    .runMain("--configPath", configFile.getAbsolutePath());

Confirm the output

After executing the above, in order to confirm the successful start of the Server, check for the below output text:

Jan 08, 2020 6:33:47 PM ai.konduit.serving.configprovider.KonduitServingMain
INFO: Deployed verticle ai.konduit.serving.verticles.inference.InferenceVerticle

The Output of the program is as follows:

System.out.println(response.getBody().toString());

{
  "default" : {
    "batchId" : "78590ecd-3dd0-4fe4-9b9f-c6046e691207",
    "ndArray" : {
      "dataType" : "FLOAT",
      "shape" : [ 1, 4420, 4 ],
      "data" : [ 0.0053688805, 0.0025114473, 0.02034211, 0.03720106,
-0.0017913571, -0.008830132, 0.030541036, 0.062001586,
 -0.009721115, -0.02121478, 0.038849648,
......
......
 0.95745945, 1.198462, 0.379943, 0.39496967, 1.0312535,
 1.2312568, 0.7340108, 0.62931126, 1.0600785,
 1.100086, 0.65668, 0.49809134, 1.1420089, 1.2198907,
0.57574236, 0.38997138, 1.2035433, 1.2634758 ]
    }
  }
}

The complete inference configuration in JSON format is as follows:

System.out.println(inferenceConfiguration.toJson());

{
  "memMapConfig" : null,
  "servingConfig" : {
    "httpPort" : 26652,
    "listenHost" : "localhost",
    "logTimings" : false,
    "metricTypes" : [ "CLASS_LOADER", "JVM_MEMORY", "JVM_GC", "PROCESSOR", "JVM_THREAD", "LOGGING_METRICS", "NATIVE" ],
    "outputDataFormat" : "JSON",
    "uploadsDirectory" : "file-uploads/"
  },
  "steps" : [ {
    "@type" : "ImageLoadingStep",
    "dimensionsConfigs" : {
      "default" : [ 478, 720, 3 ]
    },
    "imageProcessingInitialLayout" : null,
    "imageProcessingRequiredLayout" : null,
    "imageTransformProcesses" : { },
    "inputColumnNames" : { },
    "inputNames" : [ "inputimage" ],
    "inputSchemas" : { },
    "objectDetectionConfig" : null,
    "originalImageHeight" : 0,
    "originalImageWidth" : 0,
    "outputColumnNames" : { },
    "outputNames" : [ ],
    "outputSchemas" : { },
    "updateOrderingBeforeTransform" : false
  }, {
    "@type" : "PythonStep",
    "inputColumnNames" : {
      "default" : [ "inputimage" ]
    },
    "inputNames" : [ "default" ],
    "inputSchemas" : {
      "default" : [ "NDArray" ]
    },
    "outputColumnNames" : {
      "default" : [ "boxes" ]
    },
    "outputNames" : [ "default" ],
    "outputSchemas" : {
      "default" : [ "NDArray" ]
    },
    "pythonConfigs" : {
      "default" : {
        "extraInputs" : { },
        "pythonCode" : null,
        "pythonCodePath" : "C:\\Projects\\konduit-serving-examples\\java\\target\\classes\\scripts\\onnxFacedetect.py",
        "pythonInputs" : {
          "inputimage" : "NDARRAY"
        },
        "pythonOutputs" : {
          "boxes" : "NDARRAY"
        },
        "pythonPath" : "C:\\Users\\AppData\\Local\\Programs\\Python\\Python37\\python37.zip;C:\\Users\\AppData\\Local\\Programs\\Python\\Python37\\DLLs;C:\\Users\\AppData\\Local\\Programs\\Python\\Python37\\lib;C:\\Users\\AppData\\Local\\Programs\\Python\\Python37;C:\\Users\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages;C:\\Users\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\pyyaml-5.2-py3.7-win-amd64.egg;c:\\projects\\konduit-serving\\python",
        "returnAllInputs" : false,
        "setupAndRun" : false
      }
    }
  } ]
}