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[//]: # (## Work In Progress. Feedbacks are welcome!) ### Description This PR adds a few properties, methods and factories to Tensor type to support IO-binding feature. This will allow user to create tensor from GPU/CPU bound data without a force transferring of data between CPU and GPU. This change is a way to resolve #15312 ### Change Summary 1. Add properties to `Tensor` type: a. `location`: indicating where the data is sitting. valid values are `cpu`, `cpu-pinned`, `texture`, `gpu-buffer`. b. `texture`: sit side to `data`, a readonly property of `WebGLTexture` type. available only when `location === 'texture'` c. `gpuBuffer`: sit side to `data`, a readonly property of `GPUBuffer` type. available only when `location === 'gpu-buffer'` 2. Add methods to `Tensor` type (usually dealing with inference outputs): - async function `getData()` allows user to download data from GPU to CPU manually. - function `dispose()` allows user to release GPU resources manually. 3. Add factories for creating `Tensor` instances: a. `fromTexture()` to create a WebGL texture bound tensor data b. `fromGpuBuffer()` to create a WebGPUBuffer bound tensor data c. `fromPinnedBuffer()` to create a tensor using a CPU pinned buffer ### Examples: create tensors from texture and pass to inference session as inputs ```js // when create session, specify we prefer 'image_output:0' to be stored on GPU as texture const session = await InferenceSession.create('./my_model.onnx', { executionProviders: [ 'webgl' ], preferredOutputLocation: { 'image_output:0': 'texture' } }); ... const myImageTexture = getTexture(); // user's function to get a texture const myFeeds = { input0: Tensor.fromTexture(myImageTexture, { width: 224, height: 224 }) }; // shape [1, 224, 224, 4], RGBA format. const results = await session.run(myFeeds); const myOutputTexture = results['image_output:0'].texture; ``` |
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onnxruntime-react-native
ONNX Runtime React Native provides a JavaScript library for running ONNX models in a React Native app.
Why ONNX models
The Open Neural Network Exchange (ONNX) is an open standard for representing machine learning models. The biggest advantage of ONNX is that it allows interoperability across different open source AI frameworks, which itself offers more flexibility for AI frameworks adoption.
Why ONNX Runtime React Native
With ONNX Runtime React Native, React Native developers can score pre-trained ONNX models directly in React Native apps by leveraging ONNX Runtime, so it provides a light-weight inference solution for Android and iOS.
Installation
yarn add onnxruntime-react-native
Usage
import { InferenceSession } from "onnxruntime-react-native";
// load a model
const session: InferenceSession = await InferenceSession.create(modelPath);
// input as InferenceSession.OnnxValueMapType
const result = session.run(input, ['num_detection:0', 'detection_classes:0'])
Refer to ONNX Runtime JavaScript examples for samples and tutorials. The ONNX Runtime React Native library does not currently support the following features:
- Tensors with unsigned data types, with the exception of uint8 on Android devices
- Model loading using ArrayBuffer
Operator and type support
ONNX Runtime React Native version 1.13 supports both ONNX and ORT format models, and includes all operators and types.
Previous ONNX Runtime React Native packages use the ONNX Runtime Mobile package, and support operators and types used in popular mobile models. See here for the list of supported operators and types.
License
License information can be found here.