GitLocker: The Coding Marketplace

Description:

onnxruntime

OnnxRuntime Plugin #

Overview #
Flutter plugin for OnnxRuntime via dart:ffi provides an easy, flexible, and fast Dart API to integrate Onnx models in flutter apps across mobile and desktop platforms.

Platform
Android
iOS
Linux
macOS
Windows

Compatibility
API level 21+
*
*
*
*

Architecture
arm32/arm64
*
*
*
*

*: Consistent with Flutter
Key Features #

Multi-platform Support for Android, iOS, Linux, macOS, Windows, and Web(Coming soon).
Flexibility to use any Onnx Model.
Acceleration using multi-threading.
Similar structure as OnnxRuntime Java and C# API.
Inference speed is not slower than native Android/iOS Apps built using the Java/Objective-C API.
Run inference in different isolates to prevent jank in UI thread.

Getting Started #
In your flutter project add the dependency:
dependencies:
...
onnxruntime: x.y.z
copied to clipboard
Usage example #
Import #
import 'package:onnxruntime/onnxruntime.dart';
copied to clipboard
Initializing environment #
OrtEnv.instance.init();
copied to clipboard
Creating the Session #
final sessionOptions = OrtSessionOptions();
const assetFileName = 'assets/models/test.onnx';
final rawAssetFile = await rootBundle.load(assetFileName);
final bytes = rawAssetFile.buffer.asUint8List();
final session = OrtSession.fromBuffer(bytes, sessionOptions!);
copied to clipboard
Performing inference #
final shape = [1, 2, 3];
final inputOrt = OrtValueTensor.createTensorWithDataList(data, shape);
final inputs = {'input': inputOrt};
final runOptions = OrtRunOptions();
final outputs = await _session?.runAsync(runOptions, inputs);
inputOrt.release();
runOptions.release();
outputs?.forEach((element) {
element?.release();
});
copied to clipboard
Releasing environment #
OrtEnv.instance.release();
copied to clipboard