mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-16 21:00:14 +00:00
152 lines
4.2 KiB
TypeScript
152 lines
4.2 KiB
TypeScript
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// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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import *as React from 'react';
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import{Button, Image, Text, View} from 'react-native';
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import{InferenceSession, Tensor} from 'onnxruntime-react-native';
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import MNIST, {MNISTInput, MNISTOutput, MNISTResult, } from './mnist-data-handler';
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import{Buffer} from 'buffer';
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interface Duration {
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preprocess:
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number;
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inference:
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number;
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postprocess:
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number;
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}
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interface State {
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session:
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InferenceSession | null;
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output:
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string | null;
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duration:
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Duration | null;
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imagePath:
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string | null;
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}
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// eslint-disable-next-line @typescript-eslint/ban-types
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export default class App extends React.PureComponent<{}, State> {
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// eslint-disable-next-line @typescript-eslint/ban-types
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constructor(props : {} | Readonly<{}>) {
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super(props);
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this.state = {
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session : null,
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output : null,
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duration : null,
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imagePath : null,
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};
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}
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// Load a model when an app is loading
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async componentDidMount() : Promise<void> {
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if (!this.state.session) {
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try {
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const imagePath = await MNIST.getImagePath();
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this.setState({imagePath});
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const modelPath = await MNIST.getLocalModelPath();
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const session : InferenceSession = await InferenceSession.create(modelPath);
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this.setState({session});
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} catch (err) {
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console.log(err.message);
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}
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}
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}
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// Run a model with a given image
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infer = async() : Promise<void> => {
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try {
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let preprocessTime = 0;
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let inferenceTime = 0;
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let postprocessTime = 0;
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const options : InferenceSession.RunOptions = {};
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let startTime = Date.now();
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const mnistInput : MNISTInput = await MNIST.preprocess(this.state.imagePath !);
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const input : {[name:string] : Tensor} = {};
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for (const key in mnistInput) {
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if (Object.hasOwnProperty.call(mnistInput, key)) {
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const buffer = Buffer.from(mnistInput[key].data, 'base64');
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const tensorData =
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new Float32Array(buffer.buffer, buffer.byteOffset, buffer.length / Float32Array.BYTES_PER_ELEMENT);
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input[key] = new Tensor(mnistInput[key].type as keyof Tensor.DataTypeMap, tensorData, mnistInput[key].dims);
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}
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}
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preprocessTime = Date.now() - startTime;
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startTime = Date.now();
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const output : InferenceSession.ReturnType =
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await this.state.session !.run(input, this.state.session !.outputNames, options);
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inferenceTime = Date.now() - startTime;
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startTime = Date.now();
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const mnistOutput : MNISTOutput = {};
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for (const key in output) {
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if (Object.hasOwnProperty.call(output, key)) {
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const buffer = (output[key].data as Float32Array).buffer;
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const tensorData = {
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data : Buffer.from(buffer, 0, buffer.byteLength).toString('base64'),
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};
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mnistOutput[key] = tensorData;
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}
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}
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const result : MNISTResult = await MNIST.postprocess(mnistOutput);
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postprocessTime = Date.now() - startTime;
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this.setState({
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output : result.result,
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duration : {
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preprocess : preprocessTime,
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inference : inferenceTime,
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postprocess : postprocessTime,
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},
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});
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} catch (err) {
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console.log(err.message);
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}
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};
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render() : JSX.Element {
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const {output, duration, imagePath} = this.state;
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return (
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<View>
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<Text>{'\n'}</Text>
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<Button
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title={'Run'}
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disabled={!this.state.session}
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onPress={this.infer}
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/>
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{imagePath && (
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<Image
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source={
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{
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uri:
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imagePath
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}}
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style={{
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height: 200,
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width: 200,
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resizeMode: 'stretch',
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}}
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/>
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)}
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{output && (
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<Text>
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Result: {output} {'\n'}
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{'--------------------------------\n'}
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Preprocess time taken: {duration?.preprocess} ms {'\n'}
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Inference time taken: {duration?.inference} ms {'\n'}
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Postprocess time taken: {duration?.postprocess} ms {'\n'}
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</Text>
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)}
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</View>
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);
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}
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}
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