My first ONNX Run with .Net

Inspirational Post: Use ONNX Runtime in Flutter My .net Application

Grab the simplest ONNX model on the shelf

It's not a model package yet: it's python source, you need to compile it! 

To use python in Windows it's better for your health to use WSL (linux subsitem for windows) with any Ubuntu, follow readme for compile instructions.

Inspect the resulting .onnx file with netron.app

Tweak the model nodes and say "hey mama! I won the machile learning!"

 graph = helper.make_graph(  
   [ # nodes  
     helper.make_node("Add", ["A", "B"], ["C"], "Add1"),  
     helper.make_node("Add", ["C", "E"], ["F"], "Add2"),  
   ],  
   "SingleAdd", # name  
   [ # inputs  
     helper.make_tensor_value_info('A', TensorProto.FLOAT, [1]),  
     helper.make_tensor_value_info('B', TensorProto.FLOAT, [1]),  
     helper.make_tensor_value_info('E', TensorProto.FLOAT, [1]),  
   ],  
   [ # outputs  
     helper.make_tensor_value_info('C', TensorProto.FLOAT, [1]),  
     helper.make_tensor_value_info('F', TensorProto.FLOAT, [1]),  
   ])  

Use it on C# [ONNX with C# documentation]

 const string modelPath = "./single_add.onnx"; 
 using var session = new InferenceSession(modelPath);
 
 var inputDataA = new List<float> { 1.0f }; // Example value
 var inputDataB = new List<float> { 2.0f }; // Example value
 var inputDataE = new List<float> { 3.0f }; // Example value

 var tensorA = new DenseTensor<float>(inputDataA.ToArray(), new[] { 1 });
 var tensorB = new DenseTensor<float>(inputDataB.ToArray(), new[] { 1 });
 var tensorE = new DenseTensor<float>(inputDataE.ToArray(), new[] { 1 });

 // Create input container
 var inputs = new List<NamedOnnxValue>
 {
     NamedOnnxValue.CreateFromTensor("A", tensorA),
     NamedOnnxValue.CreateFromTensor("B", tensorB),
     NamedOnnxValue.CreateFromTensor("E", tensorE)
 };

 // Run the model
 using var results = session.Run(inputs);

 // Extract and display results
 foreach (var result in results)
 {
     Console.WriteLine($"Output: {result.Name}");
     var tensor = result.AsTensor<float>();
     Console.WriteLine(tensor.GetValue(0));
 }