mirror of https://github.com/vladmandic/human
add tfjs types and remove all instances of any
Vladimir Mandic
parent
0b907e56d9
commit
0fb63cea21
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@ -28,8 +28,6 @@
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"rules": {
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"@typescript-eslint/ban-ts-comment": "off",
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"@typescript-eslint/explicit-module-boundary-types": "off",
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"@typescript-eslint/ban-types": "off",
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"@typescript-eslint/no-explicit-any": "off",
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"@typescript-eslint/no-var-requires": "off",
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"camelcase": "off",
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"dot-notation": "off",
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@ -11,6 +11,7 @@ Repository: **<git+https://github.com/vladmandic/human.git>**
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### **HEAD -> main** 2021/05/22 mandic00@live.com
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- rebuild all for release
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### **1.9.2** 2021/05/22 mandic00@live.com
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@ -69,9 +69,9 @@
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"chokidar": "^3.5.1",
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"dayjs": "^1.10.4",
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"esbuild": "^0.12.1",
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"eslint": "^7.26.0",
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"eslint": "^7.27.0",
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"eslint-config-airbnb-base": "^14.2.1",
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"eslint-plugin-import": "^2.23.2",
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"eslint-plugin-import": "^2.23.3",
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"eslint-plugin-json": "^3.0.0",
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"eslint-plugin-node": "^11.1.0",
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"eslint-plugin-promise": "^5.1.0",
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@ -32,16 +32,16 @@ export interface DrawOptions {
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lineWidth: number,
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pointSize: number,
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roundRect: number,
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drawPoints: Boolean,
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drawLabels: Boolean,
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drawBoxes: Boolean,
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drawPolygons: Boolean,
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fillPolygons: Boolean,
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useDepth: Boolean,
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useCurves: Boolean,
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bufferedOutput: Boolean,
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useRawBoxes: Boolean,
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calculateHandBox: Boolean,
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drawPoints: boolean,
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drawLabels: boolean,
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drawBoxes: boolean,
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drawPolygons: boolean,
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fillPolygons: boolean,
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useDepth: boolean,
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useCurves: boolean,
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bufferedOutput: boolean,
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useRawBoxes: boolean,
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calculateHandBox: boolean,
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}
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export const options: DrawOptions = {
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@ -53,16 +53,16 @@ export const options: DrawOptions = {
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lineWidth: <number>6,
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pointSize: <number>2,
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roundRect: <number>28,
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drawPoints: <Boolean>false,
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drawLabels: <Boolean>true,
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drawBoxes: <Boolean>true,
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drawPolygons: <Boolean>true,
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fillPolygons: <Boolean>false,
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useDepth: <Boolean>true,
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useCurves: <Boolean>false,
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bufferedOutput: <Boolean>false, // not yet implemented
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useRawBoxes: <Boolean>false,
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calculateHandBox: <Boolean>true,
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drawPoints: <boolean>false,
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drawLabels: <boolean>true,
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drawBoxes: <boolean>true,
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drawPolygons: <boolean>true,
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fillPolygons: <boolean>false,
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useDepth: <boolean>true,
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useCurves: <boolean>false,
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bufferedOutput: <boolean>false, // not yet implemented
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useRawBoxes: <boolean>false,
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calculateHandBox: <boolean>true,
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};
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let bufferedResult: Result;
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@ -143,11 +143,11 @@ export async function gesture(inCanvas: HTMLCanvasElement, result: Array<Gesture
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ctx.fillStyle = localOptions.color;
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let i = 1;
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for (let j = 0; j < result.length; j++) {
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let where: any[] = []; // what&where is a record
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let what: any[] = []; // what&where is a record
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let where: unknown[] = []; // what&where is a record
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let what: unknown[] = []; // what&where is a record
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[where, what] = Object.entries(result[j]);
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if ((what.length > 1) && (what[1].length > 0)) {
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const person = where[1] > 0 ? `#${where[1]}` : '';
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if ((what.length > 1) && ((what[1] as string).length > 0)) {
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const person = where[1] as number > 0 ? `#${where[1]}` : '';
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const label = `${where[0]} ${person}: ${what[1]}`;
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if (localOptions.shadowColor && localOptions.shadowColor !== '') {
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ctx.fillStyle = localOptions.shadowColor;
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@ -1,8 +1,9 @@
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import { log, join } from '../helpers';
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import * as tf from '../../dist/tfjs.esm.js';
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import { Body } from '../result';
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import { GraphModel } from '../tfjs/types';
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let model;
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let model: GraphModel;
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type Keypoints = { score: number, part: string, position: { x: number, y: number }, positionRaw: { x: number, y: number } };
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@ -13,10 +14,11 @@ const bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist',
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export async function load(config) {
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if (!model) {
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// @ts-ignore type mismatch on GraphModel
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model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));
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if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);
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else if (config.debug) log('load model:', model.modelUrl);
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} else if (config.debug) log('cached model:', model.modelUrl);
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if (!model || !model['modelUrl']) log('load model failed:', config.body.modelPath);
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else if (config.debug) log('load model:', model['modelUrl']);
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} else if (config.debug) log('cached model:', model['modelUrl']);
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return model;
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}
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@ -50,6 +52,7 @@ export async function predict(image, config): Promise<Body[]> {
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skipped = 0;
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return new Promise(async (resolve) => {
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const tensor = tf.tidy(() => {
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if (!model.inputs[0].shape) return null;
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const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
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const enhance = tf.mul(resize, 2);
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const norm = enhance.sub(1);
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@ -75,13 +78,13 @@ export async function predict(image, config): Promise<Body[]> {
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parts.push({
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score: Math.round(100 * score) / 100,
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part: bodyParts[id],
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positionRaw: {
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x: x / model.inputs[0].shape[2], // x normalized to 0..1
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y: y / model.inputs[0].shape[1], // y normalized to 0..1
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positionRaw: { // normalized to 0..1
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// @ts-ignore model is not undefined here
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x: x / model.inputs[0].shape[2], y: y / model.inputs[0].shape[1],
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},
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position: {
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x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size
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y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size
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position: { // normalized to input image size
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// @ts-ignore model is not undefined here
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x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]),
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},
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});
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}
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@ -1,20 +1,22 @@
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import { log, join } from '../helpers';
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import * as tf from '../../dist/tfjs.esm.js';
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import { Tensor, GraphModel } from '../tfjs/types';
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let model;
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let model: GraphModel;
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const last: Array<{ age: number}> = [];
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let lastCount = 0;
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let skipped = Number.MAX_SAFE_INTEGER;
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type Tensor = typeof tf.Tensor;
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type DB = Array<{ name: string, source: string, embedding: number[] }>;
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export async function load(config) {
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const modelUrl = join(config.modelBasePath, config.face.description.modelPath);
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if (!model) {
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model = await tf.loadGraphModel(join(config.modelBasePath, config.face.description.modelPath));
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if (!model || !model.modelUrl) log('load model failed:', config.face.description.modelPath);
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else if (config.debug) log('load model:', model.modelUrl);
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} else if (config.debug) log('cached model:', model.modelUrl);
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// @ts-ignore type mismatch for GraphModel
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model = await tf.loadGraphModel(modelUrl);
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if (!model) log('load model failed:', config.face.description.modelPath);
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else if (config.debug) log('load model:', modelUrl);
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} else if (config.debug) log('cached model:', modelUrl);
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return model;
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}
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@ -52,6 +54,7 @@ export function enhance(input): Tensor {
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// do a tight crop of image and resize it to fit the model
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const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right
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// const box = [[0.0, 0.0, 1.0, 1.0]]; // basically no crop for test
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if (!model.inputs[0].shape) return null; // model has no shape so no point continuing
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const crop = (tensor.shape.length === 3)
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? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing
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: tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);
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@ -1,9 +1,10 @@
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import * as tf from '../../dist/tfjs.esm.js';
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import * as box from './box';
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import * as anchors from './anchors';
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import { Tensor, GraphModel } from '../tfjs/types';
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export class HandDetector {
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model: any; // tf.GraphModel
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model: GraphModel;
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anchors: number[][];
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anchorsTensor: typeof tf.Tensor;
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inputSize: number;
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@ -14,6 +15,7 @@ export class HandDetector {
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this.model = model;
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this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);
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this.anchorsTensor = tf.tensor2d(this.anchors);
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// @ts-ignore model is not undefined here
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this.inputSize = this.model?.inputs[0].shape[2];
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this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);
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this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);
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@ -39,7 +41,7 @@ export class HandDetector {
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}
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async getBoxes(input, config) {
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const batched = this.model.predict(input);
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const batched = this.model.predict(input) as Tensor;
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const predictions = batched.squeeze();
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batched.dispose();
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const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());
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scoresT.dispose();
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filteredT.dispose();
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const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = []; // box and lardmarks are tensors here
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const hands: Array<{ box: Tensor, palmLandmarks: Tensor, confidence: number }> = [];
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for (const index of filtered) {
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if (scores[index] >= config.hand.minConfidence) {
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const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);
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@ -2,6 +2,7 @@ import * as tf from '../../dist/tfjs.esm.js';
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import * as box from './box';
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import * as util from './util';
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import * as detector from './handdetector';
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import { Tensor, GraphModel } from '../tfjs/types';
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const palmBoxEnlargeFactor = 5; // default 3
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const handBoxEnlargeFactor = 1.65; // default 1.65
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@ -11,7 +12,7 @@ const palmLandmarksMiddleFingerBase = 2;
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export class HandPipeline {
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handDetector: detector.HandDetector;
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handPoseModel: any; // tf.GraphModel
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handPoseModel: GraphModel;
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inputSize: number;
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storedBoxes: Array<{ startPoint: number[]; endPoint: number[]; palmLandmarks: number[]; confidence: number } | null>;
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skipped: number;
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@ -20,6 +21,7 @@ export class HandPipeline {
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constructor(handDetector, handPoseModel) {
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this.handDetector = handDetector;
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this.handPoseModel = handPoseModel;
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// @ts-ignore model is not undefined here
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this.inputSize = this.handPoseModel?.inputs[0].shape[2];
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this.storedBoxes = [];
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this.skipped = 0;
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@ -96,7 +98,7 @@ export class HandPipeline {
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// for (const possible of boxes) this.storedBoxes.push(possible);
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if (this.storedBoxes.length > 0) useFreshBox = true;
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}
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const hands: Array<{}> = [];
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const hands: Array<{ landmarks?: number[], confidence: number, box: { topLeft: number[], bottomRight: number[] } }> = [];
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// go through working set of boxes
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for (let i = 0; i < this.storedBoxes.length; i++) {
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@ -113,7 +115,7 @@ export class HandPipeline {
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const handImage = croppedInput.div(255);
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croppedInput.dispose();
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rotatedImage.dispose();
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const [confidenceT, keypoints] = await this.handPoseModel.predict(handImage);
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const [confidenceT, keypoints] = await this.handPoseModel.predict(handImage) as Array<Tensor>;
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handImage.dispose();
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const confidence = confidenceT.dataSync()[0];
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confidenceT.dispose();
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@ -3,6 +3,7 @@ import * as tf from '../../dist/tfjs.esm.js';
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import * as handdetector from './handdetector';
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import * as handpipeline from './handpipeline';
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import { Hand } from '../result';
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import { GraphModel } from '../tfjs/types';
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const meshAnnotations = {
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thumb: [1, 2, 3, 4],
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@ -13,9 +14,9 @@ const meshAnnotations = {
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palmBase: [0],
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};
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let handDetectorModel;
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let handPoseModel;
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let handPipeline;
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let handDetectorModel: GraphModel | null;
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let handPoseModel: GraphModel | null;
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let handPipeline: handpipeline.HandPipeline;
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export async function predict(input, config): Promise<Hand[]> {
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const predictions = await handPipeline.estimateHands(input, config);
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@ -25,6 +26,7 @@ export async function predict(input, config): Promise<Hand[]> {
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const annotations = {};
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if (predictions[i].landmarks) {
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for (const key of Object.keys(meshAnnotations)) {
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// @ts-ignore landmarks are not undefined
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annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);
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}
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}
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@ -40,26 +42,28 @@ export async function predict(input, config): Promise<Hand[]> {
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(predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / input.shape[2],
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(predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / input.shape[1],
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];
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hands.push({ id: i, confidence: Math.round(100 * predictions[i].confidence) / 100, box, boxRaw, landmarks: predictions[i].landmarks, annotations });
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const landmarks = predictions[i].landmarks as number[];
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hands.push({ id: i, confidence: Math.round(100 * predictions[i].confidence) / 100, box, boxRaw, landmarks, annotations });
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}
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return hands;
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}
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export async function load(config): Promise<[Object, Object]> {
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export async function load(config): Promise<[unknown, unknown]> {
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if (!handDetectorModel || !handPoseModel) {
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// @ts-ignore type mismatch on GraphModel
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[handDetectorModel, handPoseModel] = await Promise.all([
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config.hand.enabled ? tf.loadGraphModel(join(config.modelBasePath, config.hand.detector.modelPath), { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,
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config.hand.landmarks ? tf.loadGraphModel(join(config.modelBasePath, config.hand.skeleton.modelPath), { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,
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]);
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if (config.hand.enabled) {
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if (!handDetectorModel || !handDetectorModel.modelUrl) log('load model failed:', config.hand.detector.modelPath);
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else if (config.debug) log('load model:', handDetectorModel.modelUrl);
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if (!handPoseModel || !handPoseModel.modelUrl) log('load model failed:', config.hand.skeleton.modelPath);
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else if (config.debug) log('load model:', handPoseModel.modelUrl);
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if (!handDetectorModel || !handDetectorModel['modelUrl']) log('load model failed:', config.hand.detector.modelPath);
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else if (config.debug) log('load model:', handDetectorModel['modelUrl']);
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if (!handPoseModel || !handPoseModel['modelUrl']) log('load model failed:', config.hand.skeleton.modelPath);
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else if (config.debug) log('load model:', handPoseModel['modelUrl']);
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}
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} else {
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if (config.debug) log('cached model:', handDetectorModel.modelUrl);
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if (config.debug) log('cached model:', handPoseModel.modelUrl);
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if (config.debug) log('cached model:', handDetectorModel['modelUrl']);
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if (config.debug) log('cached model:', handPoseModel['modelUrl']);
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}
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const handDetector = new handdetector.HandDetector(handDetectorModel);
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handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);
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21
src/human.ts
21
src/human.ts
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@ -18,9 +18,7 @@ import * as image from './image/image';
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import * as draw from './draw/draw';
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import * as sample from './sample';
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import * as app from '../package.json';
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/** Generic Tensor object type */
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export type Tensor = typeof tf.Tensor;
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import { Tensor } from './tfjs/types';
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export type { Config } from './config';
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export type { Result, Face, Hand, Body, Item, Gesture } from './result';
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@ -36,7 +34,7 @@ export type Error = { error: string };
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export type TensorFlow = typeof tf;
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/** Generic Model object type, holds instance of individual models */
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type Model = Object;
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type Model = unknown;
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/**
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* **Human** library main class
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@ -114,7 +112,7 @@ export class Human {
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/** Platform and agent information detected by Human */
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sysinfo: { platform: string, agent: string };
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/** Performance object that contains values for all recently performed operations */
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perf: any; // perf members are dynamically defined as needed
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perf: Record<string, unknown>; // perf members are dynamically defined as needed
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#numTensors: number;
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#analyzeMemoryLeaks: boolean;
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#checkSanity: boolean;
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@ -128,7 +126,7 @@ export class Human {
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* Creates instance of Human library that is futher used for all operations
|
||||
* - @param userConfig: {@link Config}
|
||||
*/
|
||||
constructor(userConfig: Config | Object = {}) {
|
||||
constructor(userConfig: Config | Record<string, unknown> = {}) {
|
||||
this.tf = tf;
|
||||
this.draw = draw;
|
||||
this.version = app.version;
|
||||
|
|
@ -215,6 +213,7 @@ export class Human {
|
|||
*/
|
||||
// eslint-disable-next-line class-methods-use-this
|
||||
enhance(input: Tensor): Tensor | null {
|
||||
// @ts-ignore type mismach for Tensor
|
||||
return faceres.enhance(input);
|
||||
}
|
||||
|
||||
|
|
@ -233,7 +232,7 @@ export class Human {
|
|||
/** Load method preloads all configured models on-demand
|
||||
* - Not explicitly required as any required model is load implicitly on it's first run
|
||||
*/
|
||||
async load(userConfig: Config | Object = {}) {
|
||||
async load(userConfig: Config | Record<string, unknown> = {}) {
|
||||
this.state = 'load';
|
||||
const timeStamp = now();
|
||||
if (userConfig) this.config = mergeDeep(this.config, userConfig);
|
||||
|
|
@ -287,7 +286,7 @@ export class Human {
|
|||
}
|
||||
|
||||
const current = Math.trunc(now() - timeStamp);
|
||||
if (current > (this.perf.load || 0)) this.perf.load = current;
|
||||
if (current > (this.perf.load as number || 0)) this.perf.load = current;
|
||||
}
|
||||
|
||||
// check if backend needs initialization if it changed
|
||||
|
|
@ -385,7 +384,7 @@ export class Human {
|
|||
* - Run inference for all configured models
|
||||
* - Process and return result: {@link Result}
|
||||
*/
|
||||
async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {
|
||||
async detect(input: Input, userConfig: Config | Record<string, unknown> = {}): Promise<Result | Error> {
|
||||
// detection happens inside a promise
|
||||
return new Promise(async (resolve) => {
|
||||
this.state = 'config';
|
||||
|
|
@ -442,7 +441,7 @@ export class Human {
|
|||
this.config.skipFrame = await this.#skipFrame(process.tensor);
|
||||
if (!this.perf.frames) this.perf.frames = 0;
|
||||
if (!this.perf.cached) this.perf.cached = 0;
|
||||
this.perf.frames++;
|
||||
(this.perf.frames as number)++;
|
||||
// @ts-ignore hidden dynamic property that is not part of definitions
|
||||
if (this.config.skipFrame) this.perf.cached++;
|
||||
this.perf.changed = Math.trunc(now() - timeStamp);
|
||||
|
|
@ -629,7 +628,7 @@ export class Human {
|
|||
* - can take significant time on startup
|
||||
* - only used for `webgl` and `humangl` backends
|
||||
*/
|
||||
async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {
|
||||
async warmup(userConfig: Config | Record<string, unknown> = {}): Promise<Result | { error }> {
|
||||
const t0 = now();
|
||||
if (userConfig) this.config = mergeDeep(this.config, userConfig);
|
||||
if (!this.config.warmup || this.config.warmup === 'none') return { error: 'null' };
|
||||
|
|
|
|||
|
|
@ -127,7 +127,9 @@ export function decode(offsets, scores, displacementsFwd, displacementsBwd, maxD
|
|||
// The top element in the queue is the next root candidate.
|
||||
const root = queue.dequeue();
|
||||
// Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.
|
||||
// @ts-ignore this one is tree walk
|
||||
const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);
|
||||
// @ts-ignore this one is tree walk
|
||||
if (withinRadius(poses, rootImageCoords, root.part.id)) continue;
|
||||
// Else start a new detection instance at the position of the root.
|
||||
let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);
|
||||
|
|
|
|||
|
|
@ -49,9 +49,9 @@ export function scalePoses(poses, [height, width], [inputResolutionHeight, input
|
|||
|
||||
// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort
|
||||
export class MaxHeap {
|
||||
priorityQueue: Array<any>; // don't touch
|
||||
priorityQueue: Array<unknown>; // don't touch
|
||||
numberOfElements: number;
|
||||
getElementValue: any; // function call
|
||||
getElementValue: unknown; // function call
|
||||
|
||||
constructor(maxSize, getElementValue) {
|
||||
this.priorityQueue = new Array(maxSize);
|
||||
|
|
@ -98,6 +98,7 @@ export class MaxHeap {
|
|||
}
|
||||
|
||||
getValueAt(i) {
|
||||
// @ts-ignore getter is of unknown type
|
||||
return this.getElementValue(this.priorityQueue[i]);
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -2,16 +2,19 @@ import { log } from './helpers';
|
|||
|
||||
export const data = {};
|
||||
|
||||
export function run(modelName: string, profileData: any): void { // profileData is tfjs internal type
|
||||
export function run(modelName: string, profileData: Record<string, unknown>): void { // profileData is tfjs internal type
|
||||
if (!profileData || !profileData.kernels) return;
|
||||
const maxDetected = 5;
|
||||
// @ts-ignore profileData.kernels is tfjs internal type
|
||||
const time = profileData.kernels
|
||||
.filter((a) => a.kernelTimeMs > 0)
|
||||
.reduce((a, b) => a += b.kernelTimeMs, 0);
|
||||
// @ts-ignore profileData.kernels is tfjs internal type
|
||||
const slowest = profileData.kernels
|
||||
.map((a, i) => { a.id = i; return a; })
|
||||
.filter((a) => a.kernelTimeMs > 0)
|
||||
.sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);
|
||||
// @ts-ignore profileData.kernels is tfjs internal type
|
||||
const largest = profileData.kernels
|
||||
.map((a, i) => { a.id = i; return a; })
|
||||
.filter((a) => a.totalBytesSnapshot > 0)
|
||||
|
|
@ -23,7 +26,7 @@ export function run(modelName: string, profileData: any): void { // profileData
|
|||
newBytes: profileData.newBytes,
|
||||
newTensors: profileData.newTensors,
|
||||
peakBytes: profileData.peakBytes,
|
||||
numKernelOps: profileData.kernels.length,
|
||||
numKernelOps: (profileData['kernels'] as Array<unknown>).length,
|
||||
timeKernelOps: time,
|
||||
slowestKernelOps: slowest,
|
||||
largestKernelOps: largest,
|
||||
|
|
|
|||
|
|
@ -99,9 +99,7 @@ export interface Hand {
|
|||
confidence: number,
|
||||
box: [number, number, number, number],
|
||||
boxRaw: [number, number, number, number],
|
||||
landmarks: Array<[number, number, number]>,
|
||||
// annotations: Array<{ part: string, points: Array<[number, number, number]> }>,
|
||||
// annotations: Annotations,
|
||||
landmarks: number[],
|
||||
annotations: Record<string, Array<{ part: string, points: Array<[number, number, number]> }>>,
|
||||
}
|
||||
|
||||
|
|
@ -151,7 +149,7 @@ export interface Result {
|
|||
gesture: Array<Gesture>,
|
||||
/** {@link Object}: detection & analysis results */
|
||||
object: Array<Item>
|
||||
performance: { any },
|
||||
performance: Record<string, unknown>,
|
||||
canvas: OffscreenCanvas | HTMLCanvasElement,
|
||||
timestamp: number,
|
||||
}
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ export const config = {
|
|||
name: 'humangl',
|
||||
priority: 99,
|
||||
canvas: <null | OffscreenCanvas | HTMLCanvasElement>null,
|
||||
gl: <any>null,
|
||||
gl: <unknown>null,
|
||||
width: 1024,
|
||||
height: 1024,
|
||||
webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2
|
||||
|
|
|
|||
|
|
@ -0,0 +1,3 @@
|
|||
// export common types
|
||||
export { Tensor } from '@tensorflow/tfjs-core/dist/index';
|
||||
export { GraphModel } from '@tensorflow/tfjs-converter/dist/index';
|
||||
Loading…
Reference in New Issue