在C#中加载ONNX模型,你可以使用ML.NET,这是一个开源的机器学习框架,支持多种语言和平台,包括C#。以下是使用ML.NET加载ONNX模型的步骤:
Install-Package Microsoft.ML
Install-Package Microsoft.ML.OnnxRuntime
Input
的数据集,你可以创建一个名为Input
的类:public class Input
{
[LoadColumn(0)]
public float[] Feature1 { get; set; }
[LoadColumn(1)]
public float[] Feature2 { get; set; }
}
Output
的结果,你可以创建一个名为Output
的类:public class Output
{
[ColumnName("output_0")]
public float[] PredictedLabel { get; set; }
}
MLContext
类加载ONNX模型:using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.OnnxRuntime;
public class ONNXModelLoader
{
private readonly MLContext _mlContext;
private readonly string _modelPath;
public ONNXModelLoader(string modelPath)
{
_mlContext = new MLContext();
_modelPath = modelPath;
}
public ITransformer LoadModel()
{
var sessionOptions = new SessionOptions
{
InferenceEngineName = "onnxruntime"
};
using var model = _mlContext.Model.Load(_modelPath, out var modelInputSchema);
using var session = new InferenceSession(model, sessionOptions);
return session;
}
}
ITransformer
对象进行预测:public class ONNXModelPredictor
{
private readonly ITransformer _model;
public ONNXModelPredictor(ITransformer model)
{
_model = model;
}
public Output Predict(Input input)
{
var predictor = _model.CreatePredictionEngine<Input, Output>(_mlContext);
var prediction = predictor.Predict(input);
return prediction;
}
}
var modelLoader = new ONNXModelLoader("path/to/your/model.onnx");
var model = modelLoader.LoadModel();
var predictor = new ONNXModelPredictor(model);
var input = new Input
{
Feature1 = new float[] { 1.0f, 2.0f },
Feature2 = new float[] { 3.0f, 4.0f }
};
var output = predictor.Predict(input);
Console.WriteLine($"Predicted label: {string.Join(", ", output.PredictedLabel)}");
这样,你就可以在C#中使用ONNX模型进行预测了。