Top 10 Examples of "onnxjs in functional component" in JavaScript
Dive into secure and efficient coding practices with our curated list of the top 10 examples showcasing 'onnxjs' in functional components in JavaScript. Our advanced machine learning engine meticulously scans each line of code, cross-referencing millions of open source libraries to ensure your implementation is not just functional, but also robust and secure. Elevate your React applications to new heights by mastering the art of handling side effects, API calls, and asynchronous operations with confidence and precision.
preprocess(data, width, height) {
// data processing
const dataTensor = ndarray(new Float32Array(data), [width, height, 4]);
const dataProcessedTensor = ndarray(new Float32Array(width * height * 3), [1, 3, width, height]);
ops.divseq(dataTensor, 128.0);
ops.subseq(dataTensor, 1.0);
ops.assign(dataProcessedTensor.pick(0, 0, null, null), dataTensor.pick(null, null, 2));
ops.assign(dataProcessedTensor.pick(0, 1, null, null), dataTensor.pick(null, null, 1));
ops.assign(dataProcessedTensor.pick(0, 2, null, null), dataTensor.pick(null, null, 0));
const tensor = new onnx.Tensor(dataProcessedTensor.data, 'float32', [1, 3, width, height]);
return tensor;
}
}async postprocess(tensor: Tensor, inferenceTime: number) {
try {
const originalOutput = new Tensor(tensor.data as Float32Array, 'float32', [1, 125, 13, 13]);
const outputTensor = yoloTransforms.transpose(originalOutput, [0, 2, 3, 1]);
// postprocessing
const boxes = await yolo.postprocess(outputTensor, 20);
boxes.forEach(box => {
const {
top, left, bottom, right, classProb, className,
} = box;
this.drawRect(left, top, right-left, bottom-top,
`${className} Confidence: ${Math.round(classProb * 100)}% Time: ${inferenceTime.toFixed(1)}ms`);
});
} catch (e) {
alert('Model is not valid!');
}
}preprocess(ctx: CanvasRenderingContext2D): Tensor {
const imageData = ctx.getImageData(0, 0, ctx.canvas.width, ctx.canvas.height);
const { data, width, height } = imageData;
// data processing
const dataTensor = ndarray(new Float32Array(data), [width, height, 4]);
const dataProcessedTensor = ndarray(new Float32Array(width * height * 3), [1, 3, width, height]);
ops.assign(dataProcessedTensor.pick(0, 0, null, null), dataTensor.pick(null, null, 0));
ops.assign(dataProcessedTensor.pick(0, 1, null, null), dataTensor.pick(null, null, 1));
ops.assign(dataProcessedTensor.pick(0, 2, null, null), dataTensor.pick(null, null, 2));
const tensor = new Tensor(new Float32Array(width* height* 3), 'float32', [1, 3, width, height]);
(tensor.data as Float32Array).set(dataProcessedTensor.data);
return tensor;
}async init(backend, modelPath, imageSize) {
this.imageSize = imageSize;
const hint = {backendHint: backend };
this.model = new onnx.InferenceSession(hint);
await this.model.loadModel(modelPath);
}
async runModel(data) {this.session = this.gpuSession;
return;
}
this.modelLoading = true;
this.modelInitializing = true;
this.gpuSession = new InferenceSession({backendHint: this.sessionBackend});
this.session = this.gpuSession;
}
if (this.sessionBackend === 'wasm') {
if (this.cpuSession) {
this.session = this.cpuSession;
return;
}
this.modelLoading = true;
this.modelInitializing = true;
this.cpuSession = new InferenceSession({backendHint: this.sessionBackend});
this.session = this.cpuSession;
}
try {
await this.session!.loadModel(this.modelFile);
} catch (e){
this.modelLoading = false;
this.modelInitializing = false;
if (this.sessionBackend === 'webgl') {
this.gpuSession = undefined;
} else {
this.cpuSession = undefined;
}
throw new Error('Error: Backend not supported. ');
}
this.modelLoading = false;async initSession() {
this.sessionRunning = false;
this.modelLoadingError = false;
if (this.sessionBackend === 'webgl') {
if (this.gpuSession) {
this.session = this.gpuSession;
return;
}
this.modelLoading = true;
this.modelInitializing = true;
this.gpuSession = new InferenceSession({backendHint: this.sessionBackend});
this.session = this.gpuSession;
}
if (this.sessionBackend === 'wasm') {
if (this.cpuSession) {
this.session = this.cpuSession;
return;
}
this.modelLoading = true;
this.modelInitializing = true;
this.cpuSession = new InferenceSession({backendHint: this.sessionBackend});
this.session = this.cpuSession;
}
try {
await this.session!.loadModel(this.modelFile);
} catch (e){async initSession() {
this.sessionRunning = false;
this.modelLoadingError = false;
if (this.sessionBackend === 'webgl') {
if (this.gpuSession) {
this.session = this.gpuSession;
return;
}
this.modelLoading = true;
this.modelInitializing = true;
this.gpuSession = new InferenceSession({backendHint: this.sessionBackend});
this.session = this.gpuSession;
}
if (this.sessionBackend === 'wasm') {
if (this.cpuSession) {
this.session = this.cpuSession;
return;
}
this.modelLoading = true;
this.modelInitializing = true;
this.cpuSession = new InferenceSession({backendHint: this.sessionBackend});
this.session = this.cpuSession;
}
try {
await this.session!.loadModel(this.modelFile);
} catch (e){async initSession() {
this.sessionRunning = false;
this.modelLoadingError = false;
if (this.sessionBackend === 'webgl') {
if (this.gpuSession) {
this.session = this.gpuSession;
return;
}
this.modelLoading = true;
this.modelInitializing = true;
this.gpuSession = new InferenceSession({backendHint: this.sessionBackend});
this.session = this.gpuSession;
}
if (this.sessionBackend === 'wasm') {
if (this.cpuSession) {
this.session = this.cpuSession;
return;
}
this.modelLoading = true;
this.modelInitializing = true;
this.cpuSession = new InferenceSession({backendHint: this.sessionBackend});
this.session = this.cpuSession;
}
try {
await this.session!.loadModel(this.modelFile);
} catch (e){this.session = this.gpuSession;
return;
}
this.modelLoading = true;
this.modelInitializing = true;
this.gpuSession = new InferenceSession({backendHint: this.sessionBackend});
this.session = this.gpuSession;
}
if (this.sessionBackend === 'wasm') {
if (this.cpuSession) {
this.session = this.cpuSession;
return;
}
this.modelLoading = true;
this.modelInitializing = true;
this.cpuSession = new InferenceSession({backendHint: this.sessionBackend});
this.session = this.cpuSession;
}
try {
await this.session!.loadModel(this.modelFile);
} catch (e){
this.modelLoading = false;
this.modelInitializing = false;
if (this.sessionBackend === 'webgl') {
this.gpuSession = undefined;
} else {
this.cpuSession = undefined;
}
throw new Error('Error: Backend not supported. ');
}
this.modelLoading = false;this.session = this.gpuSession;
return;
}
this.modelLoading = true;
this.modelInitializing = true;
this.gpuSession = new InferenceSession({backendHint: this.sessionBackend});
this.session = this.gpuSession;
}
if (this.sessionBackend === 'wasm') {
if (this.cpuSession) {
this.session = this.cpuSession;
return;
}
this.modelLoading = true;
this.modelInitializing = true;
this.cpuSession = new InferenceSession({backendHint: this.sessionBackend});
this.session = this.cpuSession;
}
try {
await this.session!.loadModel(this.modelFile);
} catch (e){
this.modelLoading = false;
this.modelInitializing = false;
if (this.sessionBackend === 'webgl') {
this.gpuSession = undefined;
} else {
this.cpuSession = undefined;
}
throw new Error('Error: Backend not supported. ');
}
this.modelLoading = false;