如何在mlnet c#中使用onnx模型，传递输入和获取输出

Question

我使用pytorch训练了一个模型，我将它导出为onnx格式，并在python中测试它是否工作(确实如此)。

我想知道如何在c#的ml.net中使用它

python中的用法如下所示

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netorn中的模型如下所示

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我找到了一个使用Microsoft.ML、Microsoft.ML.OnnxRuntime和Microsoft.ML.OnnxTransformer包的example

并且能够使用onnx模型，但它适用于图像，由于我对此非常陌生，我无法弄清楚如何加载模型并进行预测，并获得action的值，它是一个浮点数组大小为6的数组。

Answer 1

我发现this在这方面很有用

您还必须定义您的模型架构。

下面是我在这个模型中最终得到的类

using Microsoft.ML;
using Microsoft.ML.Data;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Numerics;
using System.Text;
using System.Threading.Tasks;

namespace godot_net_server
{
    class OnnxModelScorer
    {
        private readonly string modelLocation;
        private readonly MLContext mlContext;


        public OnnxModelScorer(string modelLocation, MLContext mlContext)
        {
            this.modelLocation = modelLocation;
            this.mlContext = mlContext;
        }

        public class ModelInput
        {
            [VectorType(10)]
            [ColumnName("input.1")]
            public float[] Features { get; set; }
        }

        private ITransformer LoadModel(string modelLocation)
        {
            Console.WriteLine("Read model");
            Console.WriteLine($"Model location: {modelLocation}");
            
            // Create IDataView from empty list to obtain input data schema
            var data = mlContext.Data.LoadFromEnumerable(new List<ModelInput>());

            // Define scoring pipeline
            var pipeline = mlContext.Transforms.ApplyOnnxModel(modelFile: modelLocation, outputColumnNames: new[] { "31","34" }, inputColumnNames: new[] { "input.1" });

            // Fit scoring pipeline
            var model = pipeline.Fit(data);

            return model;
        }
        public class Prediction
        {
            [VectorType(6)]
            [ColumnName("31")]
            public float[] action{ get; set; }
            [VectorType(1)]
            [ColumnName("34")]
            public float[]  state { get; set; }
        }
        private IEnumerable<float> PredictDataUsingModel(IDataView testData, ITransformer model)
        {
            Console.WriteLine("");
            Console.WriteLine("=====Identify the objects in the images=====");
            Console.WriteLine("");

            IDataView scoredData = model.Transform(testData);

            IEnumerable<float[]> probabilities = scoredData.GetColumn<float[]>("31");
            var a = probabilities.ToList();
            a.Count.ToString();
            return a[0];
        }

        public IEnumerable<float> Score(IDataView data)
        {
            var model = LoadModel(modelLocation);

            return PredictDataUsingModel(data, model);
        }
    }
}

这就是我如何使用它来获得预测

            MLContext mlContext = new MLContext();

            // Load trained model

            var modelScorer = new OnnxModelScorer("my_ppo_1_model.onnx", mlContext);
            List<ModelInput> input = new List<ModelInput>();
            input.Add(new ModelInput()
            {
                Features = new[]
                {
                    3.036393f,11.0f,2.958097f,0.0f,0.0f,0.0f,0.015607f,0.684984f,0.0f,0.0f
                }
            });
            var action = modelScorer.Score(mlContext.Data.LoadFromEnumerable(input));

结果正如我所期望的那样

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