onnxruntime/docs/tutorials/csharp/basic_csharp.md

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C# Tutorial: Basic

Here is simple tutorial for getting started with running inference on an existing ONNX model for a given input data. The model is typically trained using any of the well-known training frameworks and exported into the ONNX format.

To start scoring using the model, open a session using the InferenceSession class, passing in the file path to the model as a parameter.

var session = new InferenceSession("model.onnx");

Once a session is created, you can execute queries using the Run method of the InferenceSession object. Currently, only Tensor type of input and outputs are supported. The results of the Run method are represented as a collection of .Net Tensor objects (as defined in System.Numerics.Tensor).

Tensor<float> t1, t2;  // let's say data is fed into the Tensor objects
var inputs = new List<NamedOnnxValue>()
             {
                 NamedOnnxValue.CreateFromTensor<float>("name1", t1),
                 NamedOnnxValue.CreateFromTensor<float>("name2", t2)
             };
using (var results = session.Run(inputs))
{
    // manipulate the results
}

You can load your input data into Tensor objects in several ways. A simple example is to create the Tensor from arrays.

float[] sourceData;  // assume your data is loaded into a flat float array
int[] dimensions;    // and the dimensions of the input is stored here
Tensor<float> t1 = new DenseTensor<float>(sourceData, dimensions);