RunMobileNet.cs

using Unity.InferenceEngine;
using UnityEngine;

public class RunMobileNet : MonoBehaviour
{
    public ModelAsset modelAsset;

    //The image to classify here:
    public Texture2D inputImage;

    //Link class_desc.txt here:
    public TextAsset labelsAsset;

    //The input tensor
    Tensor<float> input = new Tensor<float>(new TensorShape(1, 3, 224, 224));

    const BackendType backend = BackendType.GPUCompute;

    Worker worker;
    string[] labels;

    //Used to normalise the input RGB values
    Tensor<float> mulRGB = new Tensor<float>(new TensorShape(1, 3, 1, 1), new[] { 1 / 0.229f, 1 / 0.224f, 1 / 0.225f });
    Tensor<float> shiftRGB = new Tensor<float>(new TensorShape(1, 3, 1, 1), new[] { 0.485f, 0.456f, 0.406f });

    void Start()
    {
        //Parse neural net labels
        labels = labelsAsset.text.Split('\n');

        //Load model from asset
        var model = ModelLoader.Load(modelAsset);

        //We modify the model to normalise the input RGB values and select the highest prediction
        //probability and item number
        var graph = new FunctionalGraph();
        var image = graph.AddInput(model, 0);
        var normalizedInput = (image - Functional.Constant(shiftRGB)) * Functional.Constant(mulRGB);
        var probability = Functional.Forward(model, normalizedInput)[0];
        var value = Functional.ReduceMax(probability, 1);
        var index = Functional.ArgMax(probability, 1);
        graph.AddOutput(value, "value");
        graph.AddOutput(index, "index");
        var model2 = graph.Compile();

        //Set up the worker to run the model
        worker = new Worker(model2, backend);

        //Execute inference
        ExecuteML();
    }

    public void ExecuteML()
    {
        //Preprocess image for input
        TextureConverter.ToTensor(inputImage, input);

        //Schedule neural net
        worker.Schedule(input);

        //Read output tensors
        using var value = (worker.PeekOutput("value") as Tensor<float>).ReadbackAndClone();
        using var index = (worker.PeekOutput("index") as Tensor<int>).ReadbackAndClone();

        //Select the best output class and print the results
        var accuracy = value[0];
        var ID = index[0];

        //The result is output to the console window
        int percent = Mathf.FloorToInt(accuracy * 100f + 0.5f);
        Debug.Log($"Prediction: {labels[ID]} {percent}﹪");

        //Clean memory
        Resources.UnloadUnusedAssets();
    }

    void OnDestroy()
    {
        input?.Dispose();
        mulRGB?.Dispose();
        shiftRGB?.Dispose();
        worker?.Dispose();
    }
}