The ONNX Runtime extensions library was not found. The Microsoft.ML.OnnxRuntime.Extensions NuGet package must be referenced by the project to use OrtExtensions.RegisterCustomOps.

Introduction:

The ONNX Runtime is an open-source machine learning inference engine that supports running models trained in various frameworks such as TensorFlow, PyTorch, and Caffe. The ONNX Runtime also provides a set of extensions that allow users to customize the behavior of the inference engine. In this blog post, we will discuss the ONNX Runtime extensions library and how to use it with the Microsoft.ML.OnnxRuntime.Extensions NuGet package.

What are ONNX Runtime Extensions?

ONNX Runtime Extensions is a set of libraries that provide additional functionality to the ONNX Runtime inference engine. These extensions allow users to customize the behavior of the inference engine and add new features to it. The ONNX Runtime Extensions library includes several categories of extensions, such as:

1. Custom Operations: This category provides a set of custom operations that can be used to extend the functionality of the ONNX Runtime inference engine. These operations allow users to define their own custom logic and integrate it with the inference engine.

2. Data Processing: This category provides a set of data processing extensions that can be used to preprocess or postprocess input data before or after inference. These extensions allow users to perform tasks such as normalization, resizing, and data augmentation.

3. Model Optimization: This category provides a set of model optimization extensions that can be used to optimize the performance of ONNX models. These extensions allow users to perform tasks such as pruning, quantization, and model compression.

4. Interoperability: This category provides a set of interoperability extensions that allow users to integrate ONNX models with other frameworks or libraries. These extensions allow users to use ONNX models in their existing workflows without having to retrain them.

How to Use ONNX Runtime Extensions with Microsoft.ML.OnnxRuntime.Extensions NuGet Package?

To use the ONNX Runtime Extensions with the Microsoft.ML.OnnxRuntime.Extensions NuGet package, follow these steps:

1. Install the Microsoft.ML.OnnxRuntime.Extensions NuGet package in your project by running the following command in the Package Manager Console:

Install-Package Microsoft.ML.OnnxRuntime.Extensions

2. Import the ONNX Runtime Extensions library into your project by adding the following using directive at the top of your code file:

csharp
using Microsoft.ML.OnnxRuntime.Extensions;

3. Register the custom operations that you want to use with the ONNX Runtime inference engine by calling the OrtExtensions.RegisterCustomOps method and passing in a list of custom operation classes as an argument:

csharp
using (var session = new InferenceSession())
{
    var customOperations = new List
    {
        new MyCustomOperation(),
        new AnotherCustomOperation()
    };
    session.RegisterCustomOps(customOperations);
}

4. Load the ONNX model that you want to use with the inference engine by calling the Infer method and passing in the input data as an argument:

csharp
using (var session = new InferenceSession())
{
    var inputData = new MyInputData();
    var outputData = session.Infer(inputData);
}

5. Use the custom operations that you registered with the inference engine by calling the custom operation methods and passing in the input data as an argument:

csharp
using (var session = new InferenceSession())
{
    var inputData = new MyInputData();
    var outputData = session.MyCustomOperation(inputData);
}

Conclusion:

The ONNX Runtime Extensions library provides a powerful set of tools for customizing the behavior of the inference engine and adding new features to it. By using the Microsoft.ML.OnnxRuntime.Extensions NuGet package, developers can easily register custom operations with the inference engine and use them to extend the functionality of their ONNX models. With the ONNX Runtime Extensions library, developers can build more powerful and flexible machine learning applications that can be easily integrated into their existing workflows.