├── demos ├── models │ ├── .gitkeep │ ├── mnist │ │ ├── download │ │ └── mnist.onnx │ ├── emotion_ferplus │ │ └── download │ └── squeezenet │ │ └── download ├── img │ ├── cat.jpg │ ├── dog.jpg │ ├── mnist_5.png │ └── person.jpg ├── index.html ├── squeezenet.html ├── draw.js ├── mnist.html ├── emotion.html ├── utils.js └── ILSVRC2012.js ├── .npmignore ├── src ├── index.ts ├── compat │ ├── merge.ts │ └── core.ts ├── layers │ ├── advanced_activations.ts │ ├── activations.ts │ ├── merge.ts │ ├── pooling.ts │ ├── convolution.ts │ └── core.ts ├── layer_util.ts ├── node.ts ├── util.ts ├── model.ts └── onnx_util.ts ├── .vscode ├── tasks.json └── settings.json ├── CHANGELOG.md ├── .travis.yml ├── tsconfig.json ├── .gitignore ├── README.md ├── package.json ├── gulpfile.js └── LICENSE /demos/models/.gitkeep: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /demos/models/mnist/download: -------------------------------------------------------------------------------- 1 | https://www.cntk.ai/OnnxModels/mnist.tar.gz 2 | -------------------------------------------------------------------------------- /demos/img/cat.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chaosmail/tfjs-onnx/HEAD/demos/img/cat.jpg -------------------------------------------------------------------------------- /demos/img/dog.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chaosmail/tfjs-onnx/HEAD/demos/img/dog.jpg -------------------------------------------------------------------------------- /demos/img/mnist_5.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chaosmail/tfjs-onnx/HEAD/demos/img/mnist_5.png -------------------------------------------------------------------------------- /demos/img/person.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chaosmail/tfjs-onnx/HEAD/demos/img/person.jpg -------------------------------------------------------------------------------- /demos/models/emotion_ferplus/download: -------------------------------------------------------------------------------- 1 | https://www.cntk.ai/OnnxModels/emotion_ferplus.tar.gz 2 | -------------------------------------------------------------------------------- /demos/models/squeezenet/download: -------------------------------------------------------------------------------- 1 | https://s3.amazonaws.com/download.onnx/models/opset_6/squeezenet.tar.gz 2 | -------------------------------------------------------------------------------- /demos/models/mnist/mnist.onnx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chaosmail/tfjs-onnx/HEAD/demos/models/mnist/mnist.onnx -------------------------------------------------------------------------------- /.npmignore: -------------------------------------------------------------------------------- 1 | src/ 2 | test/ 3 | coverage/ 4 | demos/ 5 | docs/ 6 | bower_components/ 7 | node_modules/ 8 | bower.json 9 | karma.conf.js 10 | dist/src/**/*.js -------------------------------------------------------------------------------- /src/index.ts: -------------------------------------------------------------------------------- 1 | import { loadModel, loadModelFromBuffer } from './model'; 2 | import * as util from './util'; 3 | 4 | export * from '@tensorflow/tfjs'; 5 | 6 | export const onnx = { 7 | 'util': util, 8 | 'loadModel': loadModel, 9 | 'loadModelFromBuffer': loadModelFromBuffer 10 | }; 11 | -------------------------------------------------------------------------------- /.vscode/tasks.json: -------------------------------------------------------------------------------- 1 | { 2 | "version": "2.0.0", 3 | "tasks": [ 4 | { 5 | "command": "yarn", 6 | "label": "lint", 7 | "type": "shell", 8 | "args": ["lint"], 9 | "problemMatcher": { 10 | "base": "$tslint5", 11 | "owner": "tslint-type-checked", 12 | "fileLocation": "absolute" 13 | } 14 | } 15 | ] 16 | } 17 | -------------------------------------------------------------------------------- /CHANGELOG.md: -------------------------------------------------------------------------------- 1 | # Changelog 2 | 3 | * 0.1.0 4 | * Core framework to parse `Models`, `Nodes` and `Weights` 5 | * Implementation of common layers 6 | * Add SqueezeNet demo 7 | 8 | * 0.1.1 9 | * Add `Add`, `Mul`, `Flatten` and `Reshape` layers 10 | 11 | * 0.1.2 12 | * Add `Constant`, `MatMul`, `Div` and `Sub` layers 13 | * Fix `Reshape` layer and 2D tensors 14 | * Add `topK` in SqueezeNet demo 15 | * Add MNIST and Emotion Recognition demos 16 | -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | sudo: required 2 | language: node_js 3 | dist: trusty 4 | 5 | node_js: 6 | - '8.9.1' 7 | 8 | addons: 9 | chrome: stable 10 | firefox: stable 11 | 12 | services: 13 | - docker 14 | 15 | before_install: 16 | # We need a display for the browsers 17 | - export DISPLAY=:99.0 18 | - sh -e /etc/init.d/xvfb start 19 | - sleep 3 20 | 21 | install: 22 | - npm install 23 | 24 | script: 25 | - npm run build 26 | # - npm run test-e2e-travis 27 | -------------------------------------------------------------------------------- /demos/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | Onnx Models 4 | 5 | 6 |

Please make sure to download the models in demos/models/* first. The model link is provided in the download file in each model directory or can be found via https://github.com/onnx/models.

7 | 12 | 13 | 14 | -------------------------------------------------------------------------------- /tsconfig.json: -------------------------------------------------------------------------------- 1 | { 2 | "compilerOptions": { 3 | "module": "commonjs", 4 | "noImplicitAny": true, 5 | "sourceMap": true, 6 | "removeComments": true, 7 | "preserveConstEnums": true, 8 | "declaration": true, 9 | "target": "es5", 10 | "lib": ["es2015", "dom"], 11 | "outDir": "./dist", 12 | "noUnusedLocals": true, 13 | "noImplicitReturns": true, 14 | "noImplicitThis": true, 15 | "noUnusedParameters": false, 16 | "pretty": true, 17 | "noFallthroughCasesInSwitch": true, 18 | "allowUnreachableCode": false 19 | }, 20 | "exclude": [ 21 | "node_modules/", 22 | "dist/" 23 | ] 24 | } 25 | -------------------------------------------------------------------------------- /.vscode/settings.json: -------------------------------------------------------------------------------- 1 | // Place your settings in this file to overwrite default and user settings. 2 | { 3 | "search.exclude": { 4 | "**/node_modules": true, 5 | "**/bower_components": true, 6 | "coverage/": true, 7 | "dist/": true, 8 | "**/bundle.js": true, 9 | "**/yarn.lock": true 10 | }, 11 | "tslint.enable": true, 12 | "tslint.run": "onType", 13 | "tslint.configFile": "tslint.json", 14 | "files.trimTrailingWhitespace": true, 15 | "editor.tabSize": 2, 16 | "editor.insertSpaces": true, 17 | "[typescript]": { 18 | "editor.formatOnSave": true 19 | }, 20 | "clang-format.style": "Google", 21 | "clang-format.executable": "${workspaceRoot}/node_modules/.bin/clang-format", 22 | "files.insertFinalNewline": true, 23 | "editor.detectIndentation": false, 24 | "editor.wrappingIndent": "none", 25 | "typescript.tsdk": "node_modules/typescript/lib" 26 | } 27 | -------------------------------------------------------------------------------- /src/compat/merge.ts: -------------------------------------------------------------------------------- 1 | import * as tf from '@tensorflow/tfjs'; 2 | import {Tensor} from '@tensorflow/tfjs'; 3 | import {LayerConfig} from '@tensorflow/tfjs-layers/dist/engine/topology'; 4 | import {Merge} from '@tensorflow/tfjs-layers/dist/layers/merge'; 5 | 6 | // TODO port back to tfjs 7 | export class SubCompat extends Merge { 8 | static className = 'SubCompat'; 9 | constructor(config?: LayerConfig) { 10 | super(config as LayerConfig); 11 | } 12 | 13 | protected mergeFunction(inputs: Tensor[]): Tensor { 14 | let output = tf.zeros(inputs[0].shape); 15 | for (const input of inputs) { 16 | output = tf.sub(output, input); 17 | } 18 | return output; 19 | } 20 | } 21 | 22 | // TODO port back to tfjs 23 | export class DivCompat extends Merge { 24 | static className = 'DivCompat'; 25 | constructor(config?: LayerConfig) { 26 | super(config as LayerConfig); 27 | } 28 | 29 | protected mergeFunction(inputs: Tensor[]): Tensor { 30 | let output = tf.ones(inputs[0].shape); 31 | for (const input of inputs) { 32 | output = tf.div(output, input); 33 | } 34 | return output; 35 | } 36 | } 37 | -------------------------------------------------------------------------------- /src/layers/advanced_activations.ts: -------------------------------------------------------------------------------- 1 | import * as tf from '@tensorflow/tfjs'; 2 | import {SymbolicTensor} from '@tensorflow/tfjs'; 3 | import {Layer} from '@tensorflow/tfjs-layers/dist/engine/topology'; 4 | import {SoftmaxLayerConfig} from '@tensorflow/tfjs-layers/dist/layers/advanced_activations'; 5 | import {onnx} from 'onnx-proto'; 6 | 7 | import {OnnxNode} from '../node'; 8 | import {parseAttrOrDefault, parseAxis} from '../onnx_util'; 9 | import {getNamedAttrs} from '../util'; 10 | 11 | export interface SoftmaxNodeConfig { 12 | axis?: onnx.AttributeProto; 13 | } 14 | 15 | export class Softmax extends OnnxNode { 16 | getTfjsLayerConfig(node: onnx.INodeProto, input?: SymbolicTensor[]): 17 | SoftmaxLayerConfig { 18 | const conf = getNamedAttrs(node.attribute) as SoftmaxNodeConfig; 19 | const axis = parseAttrOrDefault(conf.axis, 0) as number; 20 | const inShape = input[0].shape; 21 | 22 | return { 23 | axis: parseAxis(axis, inShape) 24 | } 25 | } 26 | 27 | getTfjsLayer(node: onnx.INodeProto, input?: SymbolicTensor[]): Layer { 28 | const conf = this.getTfjsConfig(node, input) as SoftmaxLayerConfig; 29 | return tf.layers.softmax(conf) 30 | } 31 | } 32 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | dist 2 | *.onnx 3 | *.npz 4 | *.pb 5 | e2e/ 6 | 7 | # Logs 8 | logs 9 | *.log 10 | npm-debug.log* 11 | yarn-debug.log* 12 | yarn-error.log* 13 | 14 | # Runtime data 15 | pids 16 | *.pid 17 | *.seed 18 | *.pid.lock 19 | 20 | # Directory for instrumented libs generated by jscoverage/JSCover 21 | lib-cov 22 | 23 | # Coverage directory used by tools like istanbul 24 | coverage 25 | 26 | # nyc test coverage 27 | .nyc_output 28 | 29 | # Grunt intermediate storage (http://gruntjs.com/creating-plugins#storing-task-files) 30 | .grunt 31 | 32 | # Bower dependency directory (https://bower.io/) 33 | bower_components 34 | 35 | # node-waf configuration 36 | .lock-wscript 37 | 38 | # Compiled binary addons (https://nodejs.org/api/addons.html) 39 | build/Release 40 | 41 | # Dependency directories 42 | node_modules/ 43 | jspm_packages/ 44 | 45 | # Typescript v1 declaration files 46 | typings/ 47 | 48 | # Optional npm cache directory 49 | .npm 50 | 51 | # Optional eslint cache 52 | .eslintcache 53 | 54 | # Optional REPL history 55 | .node_repl_history 56 | 57 | # Output of 'npm pack' 58 | *.tgz 59 | 60 | # Yarn Integrity file 61 | .yarn-integrity 62 | 63 | # dotenv environment variables file 64 | .env 65 | /.vs 66 | -------------------------------------------------------------------------------- /demos/squeezenet.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 |
7 | 8 | 39 | -------------------------------------------------------------------------------- /demos/draw.js: -------------------------------------------------------------------------------- 1 | function makeCanvasDrawable(canvas) { 2 | const ctx = canvas.getContext('2d'); 3 | const fillColor = "rgba(0,0,0,255)"; 4 | ctx.fillStyle = fillColor; 5 | ctx.lineWidth = 20; 6 | ctx.lineCap = "round"; 7 | ctx.imageSmoothingEnabled = true; 8 | 9 | let isDrawing = false; 10 | let prevPos = null; 11 | 12 | fillCanvas("rgba(255,255,255,255)"); 13 | 14 | function draw(event) { 15 | if (isDrawing) { 16 | drawLineTo(getCursorPos(event)); 17 | } 18 | } 19 | 20 | function getCursorPos(event) { 21 | return [event.offsetX, event.offsetY]; 22 | } 23 | 24 | function drawLineTo(pos) { 25 | if (prevPos) { 26 | ctx.beginPath(); 27 | ctx.moveTo(prevPos[0], prevPos[1]); 28 | ctx.lineTo(pos[0], pos[1]); 29 | ctx.stroke(); 30 | } 31 | prevPos = pos; 32 | } 33 | 34 | const size = 10; 35 | function drawPixel(pos) { 36 | ctx.fillRect(pos[0], pos[1], size, size); 37 | } 38 | 39 | function fillCanvas(color) { 40 | ctx.fillStyle = color; 41 | ctx.fillRect(0, 0, canvas.width, canvas.height); 42 | ctx.fillStyle = fillColor; 43 | } 44 | 45 | canvas.onmousedown = () => isDrawing = true; 46 | canvas.onmouseup = () => { 47 | isDrawing = false; 48 | prevPos = null; 49 | } 50 | 51 | return { 52 | start: () => canvas.onmousemove = draw, 53 | stop: () => canvas.onmousemove = null, 54 | clean: () => fillCanvas("rgba(255,255,255,255)") 55 | } 56 | } 57 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![Build Status](https://travis-ci.org/chaosmail/tfjs-onnx.svg?branch=master)](https://travis-ci.org/chaosmail/tfjs-onnx) 2 | 3 | # Tensorflow.js Onnx Runner 4 | 5 | Run and finetune pretrained Onnx models in the browser with GPU support via the wonderful [Tensorflow.js][tfjs] library. 6 | 7 | ## Usage 8 | 9 | ### Installation 10 | 11 | You can use this as standalone es5 bundle like this: 12 | 13 | ```html 14 | 15 | 16 | ``` 17 | 18 | Then, loading model is a simple as referencing the path to the `model.onnx` file. 19 | 20 | Here is an example of loading SqueezeNet: 21 | 22 | ```js 23 | var modelUrl = 'models/squeezenet/model.onnx'; 24 | 25 | // Initialize the tf.model 26 | var model = new onnx.loadModel(modelUrl); 27 | 28 | // Now use tf.model 29 | const pixels = tf.fromPixels(img); 30 | const predictions = model.predict(pixels); 31 | ``` 32 | 33 | ### Run Demos 34 | 35 | To run the demo, use the following: 36 | 37 | ```bash 38 | npm run build 39 | 40 | # Start a webserver 41 | npm run serve 42 | ``` 43 | 44 | Now navigate to http://localhost:8080/demos. 45 | 46 | > Hint: some of the models are quite big (>30MB). You have to download the Onnx models and place them into the `demos/models` directory to save bandwith. 47 | 48 | ## Development 49 | 50 | ```sh 51 | npm install 52 | ``` 53 | 54 | To build a standalone bundle run 55 | 56 | ```sh 57 | npm run build 58 | ``` 59 | 60 | [tfjs]: https://github.com/tensorflow/tfjs 61 | -------------------------------------------------------------------------------- /demos/mnist.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 11 | 12 |
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22 | 23 | 66 | -------------------------------------------------------------------------------- /package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "tfjs-onnx", 3 | "version": "0.1.2", 4 | "description": "Run pretrained Onnx models in Tensorflow.js", 5 | "main": "dist/node/index.js", 6 | "types": "dist/node/index.d.ts", 7 | "unpkg": "dist/browser/tfjs-onnx.min.js", 8 | "dependencies": { 9 | "onnx-proto": "^3.1.1" 10 | }, 11 | "repository": { 12 | "type": "git", 13 | "url": "https://github.com/chaosmail/tfjs-onnx.git" 14 | }, 15 | "peerDependencies": { 16 | "@tensorflow/tfjs": "^0.10.3" 17 | }, 18 | "devDependencies": { 19 | "@types/jasmine": "^2.8.4", 20 | "@types/node": "^9.3.0", 21 | "@tensorflow/tfjs": "^0.10.3", 22 | "browserify": "^16.2.2", 23 | "browserify-shim": "^3.8.14", 24 | "clang-format": "^1.2.2", 25 | "del": "^3.0.0", 26 | "gulp": "^3.9.1", 27 | "gulp-rename": "^1.2.2", 28 | "gulp-typescript": "^3.2.3", 29 | "http-server": "^0.10.0", 30 | "jasmine-co": "^1.2.2", 31 | "jasmine-core": "~2.6.4", 32 | "karma": "^1.7.0", 33 | "karma-chrome-launcher": "^2.2.0", 34 | "karma-firefox-launcher": "^1.0.1", 35 | "karma-jasmine": "~1.1.0", 36 | "karma-typescript": "^3.0.8", 37 | "protobufjs": "~6.8.0", 38 | "run-sequence": "^2.2.0", 39 | "tinyify": "^2.4.0", 40 | "tsify": "^3.0.3", 41 | "tslint": "~5.6.0", 42 | "typescript": "^2.6.2", 43 | "vinyl-source-stream": "^1.1.0" 44 | }, 45 | "browserify-shim": { 46 | "@tensorflow/tfjs": "global:tf", 47 | "@tensorflow/tfjs-core": {"depends": ["@tensorflow/tfjs"]}, 48 | "@tensorflow/tfjs-layers": {"depends": ["@tensorflow/tfjs"]} 49 | }, 50 | "scripts": { 51 | "build": "gulp build", 52 | "serve": "http-server", 53 | "publish-npm": "npm install && gulp build && npm publish" 54 | }, 55 | "license": "Apache-2.0" 56 | } 57 | -------------------------------------------------------------------------------- /gulpfile.js: -------------------------------------------------------------------------------- 1 | const browserify = require('browserify'); 2 | const gulp = require('gulp'); 3 | const source = require('vinyl-source-stream'); 4 | const rename = require('gulp-rename'); 5 | const ts = require('gulp-typescript'); 6 | const runSequence = require('run-sequence'); 7 | const del = require('del'); 8 | const p = require('./package.json'); 9 | 10 | const config = { 11 | src: "./src", 12 | pkgname: 'tf', 13 | filename: p.name + ".js", 14 | dst: "./dist" 15 | }; 16 | 17 | const tsProject = ts.createProject('tsconfig.json'); 18 | 19 | function getBrowserify(tinyify) { 20 | const b = browserify({ 21 | entries: config.src + '/index.ts', 22 | standalone: config.pkgname 23 | }) 24 | //.transform('browserify-shim') 25 | .plugin('tsify'); 26 | 27 | return tinyify === true ? b.plugin('tinyify').bundle() : b.bundle(); 28 | } 29 | 30 | // Clean the output folder 31 | gulp.task('clean', () => del([config.dst])); 32 | 33 | // Bundle the project for the browser 34 | gulp.task('browserify', () => { 35 | return getBrowserify() 36 | .pipe(source(config.filename)) 37 | .pipe(gulp.dest(config.dst + '/browser')); 38 | }); 39 | 40 | // Bundle the project and tinify (flatPack, treeShake) it for the browser 41 | gulp.task('tinyify', () => { 42 | return getBrowserify(true) 43 | .pipe(source(config.filename)) 44 | .pipe(rename({suffix: '.min'})) 45 | .pipe(gulp.dest(config.dst + '/browser')); 46 | }); 47 | 48 | // Compile the project for Node and Typescript 49 | gulp.task('tsc', () => { 50 | return gulp.src([config.src + '/**/*.ts']) 51 | .pipe(tsProject()) 52 | .pipe(gulp.dest(config.dst + '/node')); 53 | }); 54 | 55 | // Build step: build JS, tiny JS and TS declaration in parallel 56 | gulp.task('build', (cb) => { 57 | return runSequence('clean', ['browserify', 'tinyify', 'tsc'], cb); 58 | }); 59 | 60 | // default task 61 | gulp.task('default', ['browserify']); 62 | -------------------------------------------------------------------------------- /src/layer_util.ts: -------------------------------------------------------------------------------- 1 | import * as tf from '@tensorflow/tfjs'; 2 | import {Shape, SymbolicTensor} from '@tensorflow/tfjs'; 3 | import {PaddingMode} from '@tensorflow/tfjs-layers/dist/common'; 4 | import {Layer, LayerConfig} from '@tensorflow/tfjs-layers/dist/engine/topology'; 5 | import {onnx} from 'onnx-proto'; 6 | 7 | import {ConstantCompat} from './compat/core'; 8 | import {AutoPad, ConvNodeConfig} from './layers/convolution'; 9 | import {parseAttrOrDefault, parseShape} from './onnx_util'; 10 | import {getLayerName, getNamedAttrs} from './util'; 11 | 12 | export function getInputName(graph: onnx.IGraphProto) { 13 | return getLayerName(graph.input[graph.input.length - 1]); 14 | } 15 | 16 | export function input(name: string, shape: Shape): SymbolicTensor { 17 | const conf = {name: name, shape: shape}; 18 | return tf.input(conf); 19 | } 20 | 21 | export function getInputShape(shape: number[]) { 22 | const outShape = parseShape(shape); 23 | // we need to remove the batch dimensions 24 | return outShape.length == 4 ? outShape.slice(1) : outShape; 25 | } 26 | 27 | export function isConstantLayer(layer: Layer) { 28 | return layer instanceof ConstantCompat && layer.outboundNodes.length > 0; 29 | } 30 | 31 | export function getCommonConfig(node: onnx.INodeProto): LayerConfig { 32 | return {name: node.name}; 33 | } 34 | 35 | // TODO this code could be removed once we add compat/conv 36 | export function getTfjsPadding(pads: number[], auto_pad: AutoPad): PaddingMode { 37 | const checkAutoPad = auto_pad !== null && auto_pad != 'VALID'; 38 | const checkPads = pads !== null && pads.length > 0 && pads[0] != 0; 39 | return checkAutoPad || checkPads ? 'same' : 'valid' 40 | } 41 | 42 | // TODO this code could move to layers/conv 43 | export function getConvDim(node: onnx.INodeProto): number { 44 | const conf = getNamedAttrs(node.attribute) as ConvNodeConfig; 45 | return parseAttrOrDefault(conf.kernel_shape, []).length || 2; 46 | } 47 | -------------------------------------------------------------------------------- /src/compat/core.ts: -------------------------------------------------------------------------------- 1 | import {Shape} from '@tensorflow/tfjs'; 2 | import {Tensor} from '@tensorflow/tfjs-core/dist'; 3 | import {DType, Rank} from '@tensorflow/tfjs-core/dist/types'; 4 | import {InputLayer, Layer, LayerConfig} from '@tensorflow/tfjs-layers/dist/engine/topology'; 5 | 6 | export interface ConstantLayerConfig extends LayerConfig { 7 | value: Tensor, sparse?: boolean, inputShape?: number[] 8 | } 9 | 10 | export class ConstantCompat extends InputLayer { 11 | static readonly className = 'ConstantCompat'; 12 | public value: Tensor; 13 | 14 | constructor(config: ConstantLayerConfig) { 15 | super({ 16 | name: config.name, 17 | dtype: config.value.dtype as DType, 18 | inputShape: config.inputShape, 19 | sparse: config.sparse 20 | }); 21 | 22 | this.value = config.value; 23 | } 24 | 25 | call(inputs: Tensor|Tensor[]): Tensor|Tensor[] { 26 | return this.value; 27 | } 28 | 29 | getClassName(): string { 30 | return ConstantCompat.className; 31 | } 32 | } 33 | 34 | // TODO port back to tfjs 35 | export class MatMulCompat extends Layer { 36 | static className = 'MatMulCompat'; 37 | constructor(config?: LayerConfig) { 38 | super(config as LayerConfig); 39 | } 40 | 41 | computeOutputShape(inputShape: Shape[]): Shape { 42 | // TODO add checks for computing the output shape 43 | const aShape = inputShape[0]; 44 | const bShape = inputShape[1]; 45 | return aShape.slice(0, 1).concat(aShape[1], bShape[2]); 46 | } 47 | 48 | call(inputs: Tensor[]): Tensor|Tensor[] { 49 | if (inputs.length !== 2) { 50 | throw new Error(`Layer 'MatMul' requires 2 inputs`); 51 | } 52 | 53 | // We need to remove the batch dimension for this operation 54 | const a = inputs[0].squeeze([0]) as Tensor; 55 | const b = inputs[1].squeeze([0]) as Tensor; 56 | return a.matMul(b).expandDims(0); 57 | } 58 | 59 | getClassName(): string { 60 | return MatMulCompat.className; 61 | } 62 | } 63 | -------------------------------------------------------------------------------- /demos/emotion.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 |
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10 | 11 | 66 | -------------------------------------------------------------------------------- /src/layers/activations.ts: -------------------------------------------------------------------------------- 1 | import * as tf from '@tensorflow/tfjs'; 2 | import {ActivationIdentifier} from '@tensorflow/tfjs-layers/dist/activations'; 3 | import {Layer} from '@tensorflow/tfjs-layers/dist/engine/topology'; 4 | import {ActivationLayerConfig} from '@tensorflow/tfjs-layers/dist/layers/core'; 5 | import {onnx} from 'onnx-proto'; 6 | 7 | import {OnnxNode} from '../node'; 8 | 9 | export abstract class Activation extends OnnxNode { 10 | getTfjsLayer(node: onnx.INodeProto): Layer { 11 | return tf.layers.activation( 12 | this.getTfjsConfig(node) as ActivationLayerConfig) 13 | } 14 | } 15 | 16 | export class Relu extends Activation { 17 | getTfjsLayerConfig(node: onnx.INodeProto): ActivationLayerConfig { 18 | return {activation: 'relu' as ActivationIdentifier}; 19 | } 20 | } 21 | 22 | export class Tanh extends Activation { 23 | getTfjsLayerConfig(node: onnx.INodeProto): ActivationLayerConfig { 24 | return {activation: 'tanh' as ActivationIdentifier}; 25 | } 26 | } 27 | 28 | export class Sigmoid extends Activation { 29 | getTfjsLayerConfig(node: onnx.INodeProto): ActivationLayerConfig { 30 | return {activation: 'sigmoid' as ActivationIdentifier}; 31 | } 32 | } 33 | 34 | export class Elu extends Activation { 35 | getTfjsLayerConfig(node: onnx.INodeProto): ActivationLayerConfig { 36 | return {activation: 'elu' as ActivationIdentifier}; 37 | } 38 | } 39 | 40 | export class Softplus extends Activation { 41 | getTfjsLayerConfig(node: onnx.INodeProto): ActivationLayerConfig { 42 | return {activation: 'softplus' as ActivationIdentifier}; 43 | } 44 | } 45 | 46 | export class Softsign extends Activation { 47 | getTfjsLayerConfig(node: onnx.INodeProto): ActivationLayerConfig { 48 | return {activation: 'softsign' as ActivationIdentifier}; 49 | } 50 | } 51 | 52 | export class HardSigmoid extends Activation { 53 | getTfjsLayerConfig(node: onnx.INodeProto): ActivationLayerConfig { 54 | return {activation: 'hardsigmoid' as ActivationIdentifier}; 55 | } 56 | } 57 | -------------------------------------------------------------------------------- /src/layers/merge.ts: -------------------------------------------------------------------------------- 1 | import * as tf from '@tensorflow/tfjs'; 2 | import {SymbolicTensor} from '@tensorflow/tfjs'; 3 | import {Layer} from '@tensorflow/tfjs-layers/dist/engine/topology'; 4 | import {ConcatenateLayerConfig} from '@tensorflow/tfjs-layers/dist/layers/merge'; 5 | import {onnx} from 'onnx-proto'; 6 | 7 | import {DivCompat, SubCompat} from '../compat/merge'; 8 | import {OnnxNode} from '../node'; 9 | import {parseAttrOrDefault, parseAxis} from '../onnx_util'; 10 | import {getNamedAttrs} from '../util'; 11 | 12 | export interface ConcatNodeConfig { 13 | axis?: onnx.AttributeProto; 14 | } 15 | 16 | export class Concat extends OnnxNode { 17 | getTfjsLayerConfig(node: onnx.INodeProto, input?: SymbolicTensor[]): 18 | ConcatenateLayerConfig { 19 | const conf = getNamedAttrs(node.attribute) as ConcatNodeConfig; 20 | const axis = parseAttrOrDefault(conf.axis, 0) as number; 21 | const inShape = input[0].shape; 22 | 23 | return { 24 | axis: parseAxis(axis, inShape) 25 | } 26 | } 27 | 28 | getTfjsLayer(node: onnx.INodeProto, input?: SymbolicTensor[]): Layer { 29 | const conf = this.getTfjsConfig(node, input) as ConcatenateLayerConfig; 30 | return tf.layers.concatenate(conf) 31 | } 32 | } 33 | 34 | export class Add extends OnnxNode { 35 | getTfjsLayer(node: onnx.INodeProto): Layer { 36 | const conf = this.getTfjsConfig(node); 37 | return tf.layers.add(conf); 38 | } 39 | } 40 | 41 | export class Sub extends OnnxNode { 42 | getTfjsLayer(node: onnx.INodeProto): Layer { 43 | const conf = this.getTfjsConfig(node); 44 | return new SubCompat(conf); 45 | } 46 | } 47 | 48 | export class Mul extends OnnxNode { 49 | getTfjsLayer(node: onnx.INodeProto): Layer { 50 | const conf = this.getTfjsConfig(node); 51 | return tf.layers.multiply(conf); 52 | } 53 | } 54 | 55 | export class Div extends OnnxNode { 56 | getTfjsLayer(node: onnx.INodeProto): Layer { 57 | const conf = this.getTfjsConfig(node); 58 | return new DivCompat(conf); 59 | } 60 | } 61 | -------------------------------------------------------------------------------- /src/node.ts: -------------------------------------------------------------------------------- 1 | import * as tf from '@tensorflow/tfjs'; 2 | import {SymbolicTensor, Tensor} from '@tensorflow/tfjs'; 3 | import {DType} from '@tensorflow/tfjs-core/dist/types'; 4 | import {InputLayer, Layer, LayerConfig} from '@tensorflow/tfjs-layers/dist/engine/topology'; 5 | import {Initializer} from '@tensorflow/tfjs-layers/dist/initializers'; 6 | import {onnx} from 'onnx-proto'; 7 | 8 | import {ConstantCompat} from './compat/core'; 9 | import {getCommonConfig} from './layer_util'; 10 | import {OnnxModel} from './model'; 11 | 12 | export type StaticThis = { 13 | new (model: OnnxModel): T 14 | }; 15 | 16 | export abstract class OnnxNode { 17 | protected constructor(public model: OnnxModel) {}; 18 | static from(this: StaticThis, model: OnnxModel): T { 19 | const that = new this(model); 20 | return that; 21 | } 22 | abstract getTfjsLayer(node: onnx.INodeProto, input?: SymbolicTensor[]): Layer; 23 | 24 | getTfjsLayerConfig(node: onnx.INodeProto, input?: SymbolicTensor[]): 25 | LayerConfig { 26 | return {}; 27 | } 28 | 29 | getTfjsConfig(node: onnx.INodeProto, input?: SymbolicTensor[]): LayerConfig { 30 | const commonConfig = getCommonConfig(node); 31 | const layerConfig = this.getTfjsLayerConfig(node, input); 32 | return Object.assign({}, commonConfig, layerConfig); 33 | } 34 | 35 | prepareInput(input?: SymbolicTensor[]): SymbolicTensor[] { 36 | return input; 37 | } 38 | 39 | setup(node: onnx.INodeProto, input?: SymbolicTensor[]): 40 | [Layer, SymbolicTensor]|SymbolicTensor[] { 41 | const layer = this.getTfjsLayer(node, input); 42 | 43 | if (layer instanceof ConstantCompat || layer instanceof InputLayer) { 44 | const outputs = layer.inboundNodes[0].outputTensors; 45 | return [layer, outputs[0]]; 46 | } 47 | 48 | return [layer, layer.apply(this.prepareInput(input)) as SymbolicTensor]; 49 | } 50 | } 51 | 52 | export class WeightInitializer extends Initializer { 53 | constructor(protected weights: Tensor) { 54 | super(); 55 | }; 56 | apply(shape: number[], dtype?: DType): tf.Tensor { 57 | return this.weights; 58 | } 59 | getClassName(): string { 60 | return 'WeightInitializer'; 61 | } 62 | } 63 | -------------------------------------------------------------------------------- /demos/utils.js: -------------------------------------------------------------------------------- 1 | 2 | function getImageData(player, options) { 3 | options = options || {}; 4 | options.shape = options.shape || null; 5 | options.gray = options.gray || false; 6 | options.crop = options.crop || false; 7 | 8 | return tf.tidy(() => { 9 | 10 | const img = tf.fromPixels(player); 11 | 12 | const croppedImg = options.crop ? 13 | cropImage(img) : img; 14 | 15 | const resizedImg = options.shape ? 16 | tf.image.resizeBilinear(croppedImg, options.shape) : 17 | croppedImg; 18 | 19 | const grayImg = options.gray ? 20 | rgbToGrayscale(resizedImg) : 21 | resizedImg; 22 | 23 | // Convert to float and add batch dimension 24 | return grayImg.cast('float32').expandDims(); 25 | }); 26 | } 27 | 28 | function displayImage(data, elemId) { 29 | const elem = document.getElementById(elemId); 30 | const pixels = tf.squeeze(data).div(tf.scalar(255)); 31 | tf.toPixels(pixels, elem); 32 | } 33 | 34 | function displayLabel(probs, labels, elemId) { 35 | const elem = document.getElementById(elemId); 36 | const f = n => n.toLocaleString(undefined, { minimumFractionDigits: 2 }); 37 | 38 | const row = labels.map((d,i) => { 39 | return labels[i] + ": " + f(probs[i] * 100) + "%"; 40 | }) 41 | 42 | elem.innerHTML = row.join("
"); 43 | } 44 | 45 | // TODO port back to tfjs 46 | // @src https://github.com/tensorflow/tensorflow/blob/r1.8/tensorflow/python/ops/image_ops_impl.py#L1255 47 | function rgbToGrayscale(input) { 48 | return tf.tidy(() => { 49 | const rgbWeights = tf.tensor1d([0.2989, 0.5870, 0.1140]); 50 | const floatInput = input.cast('float32'); 51 | const grayImage = floatInput.mul(rgbWeights).sum(-1); 52 | return grayImage.expandDims(input.shape.length - 1); 53 | }); 54 | } 55 | 56 | // TODO port back to tfjs 57 | // @src https://github.com/tensorflow/tfjs-examples/blob/master/webcam-transfer-learning/webcam.js#L56 58 | function cropImage(img) { 59 | const size = Math.min(img.shape[0], img.shape[1]); 60 | const centerHeight = img.shape[0] / 2; 61 | const beginHeight = centerHeight - (size / 2); 62 | const centerWidth = img.shape[1] / 2; 63 | const beginWidth = centerWidth - (size / 2); 64 | return img.slice([beginHeight, beginWidth, 0], [size, size, img.shape[2]]); 65 | } 66 | 67 | async function getTopKClasses(logits, topK) { 68 | const values = await logits.data(); 69 | 70 | const valuesAndIndices = []; 71 | for (let i = 0; i < values.length; i++) { 72 | valuesAndIndices.push({value: values[i], index: i}); 73 | } 74 | valuesAndIndices.sort((a, b) => { 75 | return b.value - a.value; 76 | }); 77 | const topkValues = new Float32Array(topK); 78 | const topkIndices = new Int32Array(topK); 79 | 80 | for (let i = 0; i < topK; i++) { 81 | topkValues[i] = valuesAndIndices[i].value; 82 | topkIndices[i] = valuesAndIndices[i].index; 83 | } 84 | 85 | return [topkValues, topkIndices]; 86 | } 87 | -------------------------------------------------------------------------------- /src/layers/pooling.ts: -------------------------------------------------------------------------------- 1 | import * as tf from '@tensorflow/tfjs'; 2 | import {Layer} from '@tensorflow/tfjs-layers/dist/engine/topology'; 3 | import {Pooling1DLayerConfig, Pooling2DLayerConfig} from '@tensorflow/tfjs-layers/dist/layers/pooling'; 4 | import {onnx} from 'onnx-proto'; 5 | 6 | import {getTfjsPadding} from '../layer_util'; 7 | import {OnnxNode} from '../node'; 8 | import {parseAttrOrDefault} from '../onnx_util'; 9 | import {getNamedAttrs} from '../util'; 10 | 11 | export type PoolingLayerConfig = Pooling1DLayerConfig|Pooling2DLayerConfig; 12 | 13 | export interface PoolNodeConfig { 14 | auto_pad?: onnx.AttributeProto; 15 | kernel_shape?: onnx.AttributeProto; 16 | pads?: onnx.AttributeProto; 17 | strides?: onnx.AttributeProto; 18 | } 19 | 20 | export abstract class Pool extends OnnxNode { 21 | getTfjsLayerConfig(node: onnx.INodeProto): PoolingLayerConfig { 22 | const conf = getNamedAttrs(node.attribute) as PoolNodeConfig; 23 | const poolSize = parseAttrOrDefault(conf.kernel_shape) as number; 24 | const strides = parseAttrOrDefault(conf.strides, 1) as number; 25 | const pads = parseAttrOrDefault(conf.pads, null); 26 | const autoPad = parseAttrOrDefault(conf.auto_pad, null); 27 | const padding = getTfjsPadding(pads, autoPad); 28 | 29 | return { 30 | poolSize: poolSize, strides: strides, padding: padding, 31 | } 32 | } 33 | 34 | static getPoolDim(node: onnx.INodeProto): number { 35 | const conf = getNamedAttrs(node.attribute) as PoolNodeConfig; 36 | return parseAttrOrDefault(conf.kernel_shape, []).length || 2; 37 | } 38 | } 39 | 40 | export class MaxPool extends Pool { 41 | getTfjsLayer(node: onnx.INodeProto): Layer { 42 | const dim = Pool.getPoolDim(node); 43 | const conf = this.getTfjsConfig(node) as PoolingLayerConfig; 44 | return dim == 1 ? tf.layers.maxPooling1d(conf as Pooling1DLayerConfig) : 45 | tf.layers.maxPooling2d(conf as Pooling2DLayerConfig); 46 | } 47 | } 48 | 49 | export class AveragePool extends Pool { 50 | getTfjsLayer(node: onnx.INodeProto): Layer { 51 | const dim = Pool.getPoolDim(node); 52 | const conf = this.getTfjsConfig(node) as PoolingLayerConfig; 53 | return dim == 1 ? tf.layers.averagePooling1d(conf as Pooling1DLayerConfig) : 54 | tf.layers.averagePooling2d(conf as Pooling2DLayerConfig); 55 | } 56 | } 57 | 58 | export class GlobalMaxPool extends Pool { 59 | getTfjsLayer(node: onnx.INodeProto): Layer { 60 | const dim = Pool.getPoolDim(node); 61 | const conf = this.getTfjsConfig(node) as PoolingLayerConfig; 62 | return dim == 1 ? 63 | tf.layers.globalMaxPooling1d(conf as Pooling1DLayerConfig) : 64 | tf.layers.globalMaxPooling2d(conf as Pooling2DLayerConfig); 65 | } 66 | } 67 | 68 | export class GlobalAveragePool extends Pool { 69 | getTfjsLayer(node: onnx.INodeProto): Layer { 70 | const dim = Pool.getPoolDim(node); 71 | const conf = this.getTfjsConfig(node) as PoolingLayerConfig; 72 | return dim == 1 ? 73 | tf.layers.globalAveragePooling1d(conf as Pooling1DLayerConfig) : 74 | tf.layers.globalAveragePooling2d(conf as Pooling2DLayerConfig); 75 | } 76 | } 77 | -------------------------------------------------------------------------------- /src/layers/convolution.ts: -------------------------------------------------------------------------------- 1 | import * as tf from '@tensorflow/tfjs'; 2 | import {SymbolicTensor, Tensor} from '@tensorflow/tfjs'; 3 | import {Layer} from '@tensorflow/tfjs-layers/dist/engine/topology'; 4 | import {ConvLayerConfig} from '@tensorflow/tfjs-layers/dist/layers/convolutional'; 5 | import {onnx} from 'onnx-proto'; 6 | 7 | import {getConvDim, getTfjsPadding} from '../layer_util'; 8 | import {OnnxNode, WeightInitializer} from '../node'; 9 | import {parseAttrOrDefault} from '../onnx_util'; 10 | import {getNamedAttrs} from '../util'; 11 | 12 | import {Constant} from './core'; 13 | 14 | export type AutoPad = 'SAME_UPPER'|'SAME_LOWER'|'VALID'; 15 | 16 | export interface ConvNodeConfig { 17 | auto_pad?: onnx.AttributeProto; 18 | dilations?: onnx.AttributeProto; 19 | group?: onnx.AttributeProto; 20 | kernel_shape?: onnx.AttributeProto; 21 | pads?: onnx.AttributeProto; 22 | strides?: onnx.AttributeProto; 23 | } 24 | 25 | export class Conv extends OnnxNode { 26 | getTensorAttr(name: string): Tensor { 27 | if (this.model.blobValues !== undefined && 28 | this.model.blobValues.hasOwnProperty(name)) { 29 | return this.model.blobValues[name]; 30 | } else if ( 31 | this.model.nodes !== undefined && 32 | this.model.nodes.hasOwnProperty(name)) { 33 | const node = this.model.nodes[name]; 34 | if (node.opType == 'Constant') { 35 | return Constant.getConstantAttr(node); 36 | } 37 | throw new Error(`Cannot extract tensor attribute '${ 38 | name}' from layer other than 'Constant'`); 39 | } 40 | // TODO if model is not trained, we can use the 41 | // this.model.blobShapes to extract tensor shape 42 | else { 43 | throw new Error(`Cannot find tensor attribute '${name}'`); 44 | } 45 | } 46 | 47 | getTfjsLayerConfig(node: onnx.INodeProto): ConvLayerConfig { 48 | const conf = getNamedAttrs(node.attribute) as ConvNodeConfig; 49 | const kernelSize = parseAttrOrDefault(conf.kernel_shape) as number[]; 50 | const strides = parseAttrOrDefault(conf.strides, 1) as number[]; 51 | const pads = parseAttrOrDefault(conf.pads, null); 52 | const autoPad = parseAttrOrDefault(conf.auto_pad, null); 53 | const padding = getTfjsPadding(pads, autoPad); 54 | const dilationRate = parseAttrOrDefault(conf.dilations, 1); 55 | 56 | // tfjs shape: numChannels, inHeight, inWidth, inChannels 57 | // conv shape: inHeight, inWidth, inChannels, numChannels 58 | const kernel = this.getTensorAttr(node.input[1]).transpose([2, 1, 3, 0]); 59 | const bias = node.input[2] ? this.getTensorAttr(node.input[2]) : null; 60 | const filters = kernel.shape[3]; 61 | 62 | return { 63 | kernelSize: kernelSize, strides: strides, padding: padding, 64 | dilationRate: dilationRate, filters: filters, 65 | kernelInitializer: new WeightInitializer(kernel), 66 | useBias: Boolean(bias), 67 | biasInitializer: bias ? new WeightInitializer(bias) : undefined, 68 | } 69 | } 70 | 71 | getTfjsLayer(node: onnx.INodeProto): Layer { 72 | const dim = getConvDim(node); 73 | const conf = this.getTfjsConfig(node) as ConvLayerConfig; 74 | return dim == 1 ? tf.layers.conv1d(conf) : tf.layers.conv2d(conf); 75 | } 76 | 77 | prepareInput(input?: SymbolicTensor[]) { 78 | return input.slice(0, 1); 79 | } 80 | } 81 | -------------------------------------------------------------------------------- /src/util.ts: -------------------------------------------------------------------------------- 1 | import {Tensor} from '@tensorflow/tfjs'; 2 | import {onnx} from 'onnx-proto'; 3 | 4 | import {parseTensor} from './onnx_util'; 5 | 6 | export function normalizeArrayToObj( 7 | array: T[], indexKey: keyof T): {[key: string]: T} { 8 | const normalizedObject: any = {}; 9 | for (let i = 0; i < array.length; i++) { 10 | const key = array[i][indexKey]; 11 | normalizedObject[key] = array[i]; 12 | } 13 | return normalizedObject as { 14 | [key: string]: T 15 | } 16 | } 17 | 18 | export function joinArraysToObj( 19 | keys: string[], values: T[]): {[key: string]: T} { 20 | const normalizedObject: any = {}; 21 | for (let i = 0; i < keys.length; i++) { 22 | const key = keys[i]; 23 | normalizedObject[key] = values[i]; 24 | } 25 | return normalizedObject as { 26 | [key: string]: T 27 | } 28 | } 29 | 30 | // tslint:disable-next-line:no-any 31 | export function isNotNull(val: any): boolean { 32 | return val !== undefined && val !== null; 33 | } 34 | 35 | export function fetchText(uri: string): Promise { 36 | return fetch(new Request(uri)) 37 | .then(handleFetchErrors) 38 | .then((res) => res.text()); 39 | } 40 | 41 | export function fetchArrayBuffer(uri: string): Promise { 42 | return fetch(new Request(uri)) 43 | .then(handleFetchErrors) 44 | .then((res) => res.arrayBuffer()); 45 | } 46 | 47 | function handleFetchErrors(response: Response) { 48 | if (!response.ok) { 49 | throw Error(response.statusText); 50 | } 51 | return response; 52 | } 53 | 54 | export function parseOnnxModel(data: ArrayBuffer) { 55 | return onnx.ModelProto.decode(new Uint8Array(data)); 56 | } 57 | 58 | export function getNamedAttrs(attrs?: any[]): T { 59 | return normalizeArrayToObj(attrs, 'name') as T; 60 | } 61 | 62 | export async function loadOnnxModel(modelUrl: string): 63 | Promise { 64 | const buffer = await fetchArrayBuffer(modelUrl); 65 | return await parseOnnxModel(buffer); 66 | } 67 | 68 | export function getBlobValues(graph: onnx.IGraphProto): 69 | {[name: string]: Tensor} { 70 | const blobs = graph.initializer; 71 | const weights = blobs.map((d: onnx.TensorProto) => parseTensor(d)); 72 | const names = blobs.map(getLayerName); 73 | return joinArraysToObj(names, weights); 74 | } 75 | 76 | export function getValueInfo(valueInfo: onnx.IValueInfoProto[]): 77 | {[name: string]: number[]} { 78 | const getDimValues = (shape: onnx.ITensorShapeProto) => 79 | shape.dim.map(d => d.dimValue) as number[]; 80 | 81 | const shapes = valueInfo.map(d => d.type.tensorType.shape).map(getDimValues); 82 | const names = valueInfo.map(getLayerName); 83 | return joinArraysToObj(names, shapes); 84 | } 85 | 86 | export function getBlobShapes(graph: onnx.IGraphProto) { 87 | // Parse input/output tensor shapes 88 | const inputShapes = getValueInfo(graph.input); 89 | const outputShapes = getValueInfo(graph.output); 90 | return Object.assign({}, inputShapes, outputShapes); 91 | } 92 | 93 | export function getLayerName(node: onnx.INodeProto) { 94 | return node.name ? node.name : node.output[0]; 95 | } 96 | 97 | export function getNodes(graph: onnx.IGraphProto) { 98 | const nodes = graph.node; 99 | const names = nodes.map(getLayerName); 100 | return joinArraysToObj(names, nodes); 101 | } 102 | 103 | export function loadImageData(url: string): Promise { 104 | const img = new Image(); 105 | return new Promise((resolve, reject) => { 106 | img.crossOrigin = 'anonymous'; 107 | img.src = url; 108 | img.onload = () => resolve(img); 109 | img.onerror = reject; 110 | }); 111 | } 112 | -------------------------------------------------------------------------------- /src/layers/core.ts: -------------------------------------------------------------------------------- 1 | import * as tf from '@tensorflow/tfjs'; 2 | import {SymbolicTensor, Tensor} from '@tensorflow/tfjs'; 3 | import {Layer, LayerConfig} from '@tensorflow/tfjs-layers/dist/engine/topology'; 4 | import {DenseLayerConfig, DropoutLayerConfig, ReshapeLayerConfig} from '@tensorflow/tfjs-layers/dist/layers/core'; 5 | import {onnx} from 'onnx-proto'; 6 | 7 | import {ConstantCompat, ConstantLayerConfig, MatMulCompat} from '../compat/core'; 8 | import {OnnxNode, WeightInitializer} from '../node'; 9 | import {parseAttr, parseAttrOrDefault, parseShape, parseTensor} from '../onnx_util'; 10 | import {getNamedAttrs} from '../util'; 11 | 12 | export interface ConstantNodeConfig { 13 | value?: onnx.AttributeProto; 14 | } 15 | 16 | export class Constant extends OnnxNode { 17 | static getConstantAttr(node: onnx.INodeProto, transpose = true) { 18 | const conf = getNamedAttrs(node.attribute) as ConstantNodeConfig; 19 | const value = parseAttr(conf.value) as onnx.TensorProto; 20 | return parseTensor(value, transpose) as Tensor; 21 | } 22 | 23 | getTfjsLayerConfig(node: onnx.INodeProto): ConstantLayerConfig { 24 | const value = Constant.getConstantAttr(node); 25 | 26 | return { 27 | value: value.expandDims(0), inputShape: value.shape 28 | } 29 | } 30 | 31 | getTfjsLayer(node: onnx.INodeProto): Layer { 32 | const conf = this.getTfjsConfig(node) as ConstantLayerConfig; 33 | return new ConstantCompat(conf); 34 | } 35 | } 36 | 37 | export interface FCNodeConfig { 38 | axis?: onnx.AttributeProto; 39 | axis_w?: onnx.AttributeProto; 40 | } 41 | 42 | export class Dense extends OnnxNode { 43 | getTfjsLayerConfig(node: onnx.INodeProto): DenseLayerConfig { 44 | const w = node.input[1]; 45 | const b = node.input[2]; 46 | const weightShape = this.model.blobShapes[w]; 47 | const units = weightShape[0]; 48 | const kernel = this.model.blobValues[w]; 49 | const bias = this.model.blobValues[b]; 50 | 51 | return { 52 | units: units, kernelInitializer: new WeightInitializer(kernel), 53 | biasInitializer: new WeightInitializer(bias) 54 | } 55 | } 56 | 57 | getTfjsLayer(node: onnx.INodeProto): Layer { 58 | const conf = this.getTfjsConfig(node) as DenseLayerConfig; 59 | return tf.layers.dense(conf) 60 | } 61 | } 62 | 63 | export interface DropoutNodeConfig { 64 | is_test?: onnx.AttributeProto; 65 | ratio?: onnx.AttributeProto; 66 | } 67 | 68 | export class Dropout extends OnnxNode { 69 | getTfjsLayerConfig(node: onnx.INodeProto): DropoutLayerConfig { 70 | const conf = getNamedAttrs(node.attribute) as DropoutNodeConfig; 71 | const ratio = parseAttrOrDefault(conf.ratio, 0) as number; 72 | return { 73 | rate: ratio 74 | } 75 | } 76 | 77 | getTfjsLayer(node: onnx.INodeProto): Layer { 78 | const conf = this.getTfjsConfig(node) as DropoutLayerConfig; 79 | return tf.layers.dropout(conf); 80 | } 81 | } 82 | 83 | export class Flatten extends OnnxNode { 84 | getTfjsLayer(node: onnx.INodeProto): Layer { 85 | const conf = this.getTfjsConfig(node); 86 | return tf.layers.flatten(conf); 87 | } 88 | } 89 | 90 | export interface ReshapeNodeConfig { 91 | shape?: onnx.AttributeProto; 92 | } 93 | 94 | export class Reshape extends OnnxNode { 95 | isSimplifiable(input?: SymbolicTensor[]) { 96 | if (input.length == 1 && input[0] !== undefined && 97 | input[0].sourceLayer instanceof ConstantCompat) { 98 | return true; 99 | } 100 | return false; 101 | } 102 | 103 | getConstantLayer( 104 | node: onnx.INodeProto, origConf: LayerConfig, shape: number[]) { 105 | // the layer is reshaped, therefore it contains no batch dimensions 106 | const constValue = 107 | Constant.getConstantAttr(this.model.nodes[node.input[0]], false); 108 | const value = constValue.reshape(shape).expandDims(0); 109 | const conf = {name: origConf.name, value: value, inputShape: shape}; 110 | return new ConstantCompat(conf); 111 | } 112 | 113 | getTfjsLayerConfig(node: onnx.INodeProto, input?: SymbolicTensor[]): 114 | ReshapeLayerConfig { 115 | const conf = getNamedAttrs(node.attribute) as ReshapeNodeConfig; 116 | const value = parseAttr(conf.shape); 117 | const shape = parseShape(value); 118 | // Add batch dimension if required 119 | const targetShape = 120 | input[0].shape[0] == null ? shape : [null].concat(shape); 121 | return {targetShape: targetShape}; 122 | } 123 | 124 | getTfjsLayer(node: onnx.INodeProto, input?: SymbolicTensor[]): Layer { 125 | const conf = this.getTfjsConfig(node, input) as ReshapeLayerConfig; 126 | 127 | // TODO not only reshape can take constant inputs 128 | // check if this can be generalized to all layers 129 | if (this.isSimplifiable(input)) { 130 | return this.getConstantLayer(node, conf, conf.targetShape); 131 | } 132 | 133 | return tf.layers.reshape(conf); 134 | } 135 | } 136 | 137 | export class MatMul extends OnnxNode { 138 | getTfjsLayer(node: onnx.INodeProto): Layer { 139 | const conf = this.getTfjsConfig(node); 140 | return new MatMulCompat(conf); 141 | } 142 | } 143 | -------------------------------------------------------------------------------- /src/model.ts: -------------------------------------------------------------------------------- 1 | import {Model, ModelPredictConfig, SymbolicTensor, Tensor} from '@tensorflow/tfjs'; 2 | import {ContainerConfig, Layer} from '@tensorflow/tfjs-layers/dist/engine/topology'; 3 | import {onnx} from 'onnx-proto'; 4 | 5 | import {ConstantCompat} from './compat/core'; 6 | import * as layer_util from './layer_util'; 7 | import {Elu, HardSigmoid, Relu, Sigmoid, Softplus, Softsign, Tanh} from './layers/activations' 8 | import {Softmax} from './layers/advanced_activations'; 9 | import {Conv} from './layers/convolution' 10 | import {Constant, Dense, Dropout, Flatten, MatMul, Reshape} from './layers/core'; 11 | import {Add, Concat, Div, Mul, Sub} from './layers/merge'; 12 | import {AveragePool, GlobalAveragePool, GlobalMaxPool, MaxPool} from './layers/pooling'; 13 | import {OnnxNode} from './node'; 14 | import * as util from './util'; 15 | 16 | type NodeFactory = { 17 | [key: string]: typeof OnnxNode 18 | }; 19 | 20 | const nodeFactory: NodeFactory = { 21 | 'Add': Add, 22 | 'AveragePool': AveragePool, 23 | 'Concat': Concat, 24 | 'Constant': Constant, 25 | 'Conv': Conv, 26 | 'Div': Div, 27 | 'Dropout': Dropout, 28 | 'Elu': Elu, 29 | 'FC': Dense, 30 | 'Flatten': Flatten, 31 | 'GlobalAveragePool': GlobalAveragePool, 32 | 'GlobalMaxPool': GlobalMaxPool, 33 | 'HardSigmoid': HardSigmoid, 34 | 'MaxPool': MaxPool, 35 | 'MatMul': MatMul, 36 | 'Mul': Mul, 37 | 'Relu': Relu, 38 | 'Reshape': Reshape, 39 | 'Sigmoid': Sigmoid, 40 | 'Softmax': Softmax, 41 | 'Softplus': Softplus, 42 | 'Softsign': Softsign, 43 | 'Sub': Sub, 44 | 'Tanh': Tanh, 45 | }; 46 | 47 | export type Blob = Tensor|SymbolicTensor; 48 | 49 | export async function loadModel(modelUrl: string): Promise { 50 | const model = new OnnxModel(modelUrl); 51 | await model.load(); 52 | return model.getModel(); 53 | } 54 | 55 | export async function loadModelFromBuffer(modelBuffer: ArrayBuffer): Promise { 56 | const model = new OnnxModel(''); 57 | await model.loadFromBuffer(modelBuffer); 58 | return model.getModel(); 59 | } 60 | 61 | export class ModelCompat extends Model { 62 | constructor(config: ContainerConfig, public onnx: OnnxModel) { 63 | super(config); 64 | } 65 | 66 | predict(x: Tensor|Tensor[], config: ModelPredictConfig = {}): Tensor 67 | |Tensor[] { 68 | return super.predict(this.onnx.getAllInputs(x), config); 69 | } 70 | } 71 | 72 | export class OnnxModel { 73 | onnx: onnx.IModelProto; 74 | graph: onnx.IGraphProto; 75 | blobShapes: {[name: string]: number[]}; 76 | blobValues: {[name: string]: Tensor}; 77 | nodes: {[name: string]: onnx.INodeProto}; 78 | layers: {[name: string]: Layer} = {}; 79 | blobs: {[name: string]: Blob} = {}; 80 | 81 | constructor(public modelUrl: string) {} 82 | 83 | async load() { 84 | this.onnx = await util.loadOnnxModel(this.modelUrl); 85 | this.graph = this.onnx.graph; 86 | this.nodes = util.getNodes(this.graph); 87 | this.blobShapes = util.getBlobShapes(this.graph); 88 | this.blobValues = util.getBlobValues(this.graph); 89 | } 90 | 91 | async loadFromBuffer(model: ArrayBuffer) { 92 | this.onnx = await util.parseOnnxModel(model); 93 | this.graph = this.onnx.graph; 94 | this.nodes = util.getNodes(this.graph); 95 | this.blobShapes = util.getBlobShapes(this.graph); 96 | this.blobValues = util.getBlobValues(this.graph); 97 | } 98 | 99 | getAllInputs(input: Tensor | Tensor[]): Tensor[] { 100 | return [].concat(input, this.getConstantInputs()); 101 | } 102 | 103 | getModel() { 104 | const input = this.getInputLayer(); 105 | 106 | this.blobs[input.name] = input; 107 | 108 | for (let i = 0; i < this.graph.node.length; ++i) { 109 | let currNode = this.graph.node[i]; 110 | 111 | let inputBlobs: SymbolicTensor[] = []; 112 | for (let j = 0; j < currNode.input.length; ++j) { 113 | let inputNodeName = currNode.input[j]; 114 | if (this.blobs.hasOwnProperty(inputNodeName)) { 115 | inputBlobs.push(this.blobs[inputNodeName] as SymbolicTensor); 116 | } 117 | } 118 | 119 | const [layer, output] = this.setupTfjsLayer(currNode, inputBlobs); 120 | this.layers[layer.name] = layer; 121 | 122 | currNode.output.forEach((d) => { 123 | this.blobs[d] = output; 124 | }); 125 | } 126 | 127 | // Select the input blobs 128 | const inputs = [input].concat(this.getSymbolicConstantInputs()); 129 | 130 | // Select the output blobs 131 | const outputs = this.getSymbolicOutputs(); 132 | 133 | // Create the container config 134 | const config = {inputs: inputs, outputs: outputs, name: this.graph.name} as 135 | ContainerConfig; 136 | 137 | // Create the model 138 | return new ModelCompat(config, this); 139 | } 140 | 141 | setupTfjsLayer(node: onnx.INodeProto, input?: SymbolicTensor[]): 142 | [Layer, SymbolicTensor] { 143 | if (nodeFactory.hasOwnProperty(node.opType)) { 144 | const onnxNode = (nodeFactory[node.opType]).from(this); 145 | return onnxNode.setup(node, input) as [Layer, SymbolicTensor]; 146 | } 147 | throw new Error(`'${node.opType}' is not implemented in tfjs-onnx.`); 148 | } 149 | 150 | private getInputLayer(): SymbolicTensor { 151 | const name = layer_util.getInputName(this.graph); 152 | const shape = layer_util.getInputShape(this.blobShapes[name]); 153 | return layer_util.input(name, shape); 154 | } 155 | 156 | private getConstantInputNames(): string[] { 157 | return Object.keys(this.layers) 158 | .filter(d => layer_util.isConstantLayer(this.layers[d])); 159 | } 160 | 161 | private getConstantInputs(): Tensor[] { 162 | return this.getConstantInputNames() 163 | .map(d => this.layers[d] as ConstantCompat) 164 | .map(d => d.value) as Tensor[]; 165 | } 166 | 167 | private getSymbolicConstantInputs() { 168 | return this.getConstantInputNames() 169 | .map(d => this.nodes[d].output[0]) 170 | .map(d => this.blobs[d]) as SymbolicTensor[]; 171 | } 172 | 173 | private getSymbolicOutputs() { 174 | return this.graph.output.map(d => d.name).map(d => this.blobs[d]); 175 | } 176 | } 177 | -------------------------------------------------------------------------------- /src/onnx_util.ts: -------------------------------------------------------------------------------- 1 | import * as tf from '@tensorflow/tfjs'; 2 | import {Tensor} from '@tensorflow/tfjs'; 3 | import {DType, TypedArray} from '@tensorflow/tfjs-core/dist/types'; 4 | import {onnx} from 'onnx-proto'; 5 | 6 | export function parseAxis(axis: number, shape: number[]): number { 7 | // convert to channelsLast 8 | // ----------------------------------------------------- 9 | // onnx shape: batchSize, inChannels, inHeight, inWidth 10 | // tfjs shape: batchSize, inHeight, inWidth, inChannels 11 | switch (shape.length) { 12 | case 4: 13 | return axis == 1 ? 3 : axis == 3 || axis == -1 ? 1 : axis; 14 | case 3: 15 | return axis == 0 ? 2 : axis == 2 || axis == -1 ? 0 : axis; 16 | default: 17 | return axis; 18 | } 19 | } 20 | 21 | export function parseShape(shape: number[]): number[] { 22 | // convert to channelsLast 23 | // ----------------------------------------------------- 24 | // onnx shape: batchSize, inChannels, inHeight, inWidth 25 | // tfjs shape: batchSize, inHeight, inWidth, inChannels 26 | switch (shape.length) { 27 | case 4: 28 | return [shape[0], shape[2], shape[3], shape[1]]; 29 | case 3: 30 | return [shape[1], shape[2], shape[0]]; 31 | default: 32 | return shape; 33 | } 34 | } 35 | 36 | export function parseTensor( 37 | tensor: onnx.TensorProto, transpose = true): Tensor { 38 | const shape = tensor.dims as number[]; 39 | const dtype = parseTensorDtype(tensor); 40 | const typedArray = parseTensorData(tensor); 41 | const data = tf.tensor(typedArray, shape, dtype); 42 | 43 | // convert to channelsLast 44 | // ----------------------------------------------------- 45 | // onnx shape: batchSize, inChannels, inHeight, inWidth 46 | // tfjs shape: batchSize, inHeight, inWidth, inChannels 47 | if (transpose) { 48 | switch (shape.length) { 49 | case 4: 50 | return data.transpose([0, 2, 3, 1]); 51 | case 3: 52 | return data.transpose([1, 2, 0]); 53 | default: 54 | return data; 55 | } 56 | } 57 | return data; 58 | } 59 | 60 | export function parseAttrOrDefault(attr: onnx.AttributeProto, def?: any): any { 61 | return attr === undefined ? def : parseAttr(attr); 62 | } 63 | 64 | export function parseAttr(attr: onnx.AttributeProto): any { 65 | switch (attr.type) { 66 | case onnx.AttributeProto.AttributeType.FLOAT: 67 | return attr.f; 68 | case onnx.AttributeProto.AttributeType.INT: 69 | return attr.i; 70 | case onnx.AttributeProto.AttributeType.STRING: 71 | return attr.s; 72 | case onnx.AttributeProto.AttributeType.TENSOR: 73 | return attr.t; 74 | case onnx.AttributeProto.AttributeType.GRAPH: 75 | return attr.g; 76 | case onnx.AttributeProto.AttributeType.FLOATS: 77 | return attr.floats; 78 | case onnx.AttributeProto.AttributeType.INTS: 79 | return attr.ints; 80 | case onnx.AttributeProto.AttributeType.STRINGS: 81 | return attr.strings; 82 | case onnx.AttributeProto.AttributeType.TENSORS: 83 | return attr.tensors; 84 | case onnx.AttributeProto.AttributeType.GRAPHS: 85 | return attr.graphs; 86 | case onnx.AttributeProto.AttributeType.UNDEFINED: 87 | default: 88 | throw new Error(`Cannot parse attr '${attr.name}'`); 89 | } 90 | } 91 | 92 | export function parseTensorDtype(tensor: onnx.TensorProto): DType { 93 | switch (tensor.dataType) { 94 | case onnx.TensorProto.DataType.INT8: 95 | console.warn(`'Int8Array' type is not supported in tfjs. Converting to ${ 96 | DType.int32}`); 97 | return DType.int32; 98 | case onnx.TensorProto.DataType.INT16: 99 | console.warn(`'Int16Array' type is not supported in tfjs. Converting to ${ 100 | DType.int32}`); 101 | return DType.int32; 102 | case onnx.TensorProto.DataType.INT32: 103 | return DType.int32; 104 | case onnx.TensorProto.DataType.INT64: 105 | console.warn( 106 | `'Int64Array' type is not supported in JavaScript. Trying to convert to ${ 107 | DType.int32}`); 108 | return DType.int32; 109 | case onnx.TensorProto.DataType.UINT8: 110 | throw new Error(`Cannot use 'uint8' tensor in tfjs`); 111 | case onnx.TensorProto.DataType.UINT16: 112 | throw new Error(`Cannot use 'uint16' tensor in tfjs`); 113 | case onnx.TensorProto.DataType.UINT32: 114 | throw new Error(`Cannot use 'uint32' tensor in tfjs`); 115 | case onnx.TensorProto.DataType.UINT16: 116 | throw new Error(`Cannot use 'uint64' tensor in tfjs`); 117 | case onnx.TensorProto.DataType.FLOAT: 118 | return DType.float32; 119 | case onnx.TensorProto.DataType.DOUBLE: 120 | console.warn(`'double' type is not supported in tfjs. Converting to ${ 121 | DType.float32}`) 122 | return DType.float32; 123 | case onnx.TensorProto.DataType.UNDEFINED: 124 | default: 125 | throw new Error(`Cannot parse tensor '${tensor.dataType}'`); 126 | } 127 | } 128 | 129 | function getArrayBuffer(b: Uint8Array) { 130 | const data = new Uint8Array(b); 131 | return data.buffer; 132 | } 133 | 134 | export function parseTensorData(tensor: onnx.TensorProto): TypedArray { 135 | switch (tensor.dataType) { 136 | case onnx.TensorProto.DataType.INT8: 137 | return new Int32Array(new Int8Array(getArrayBuffer(tensor.rawData))); 138 | case onnx.TensorProto.DataType.INT16: 139 | return new Int32Array(new Int16Array(getArrayBuffer(tensor.rawData))); 140 | case onnx.TensorProto.DataType.INT32: 141 | return tensor.int32Data.length > 0 ? 142 | new Int32Array(tensor.int32Data) : 143 | new Int32Array(getArrayBuffer(tensor.rawData)); 144 | case onnx.TensorProto.DataType.INT64: 145 | if (tensor.int64Data.length) { 146 | return new Int32Array(tensor.int32Data); 147 | } 148 | throw new Error(`'Int64Array' type not suppoert in JavaScript`); 149 | case onnx.TensorProto.DataType.FLOAT: 150 | return tensor.floatData.length > 0 ? 151 | new Float32Array(tensor.floatData) : 152 | new Float32Array(getArrayBuffer(tensor.rawData)); 153 | case onnx.TensorProto.DataType.DOUBLE: 154 | return new Float32Array(new Float64Array(getArrayBuffer(tensor.rawData))); 155 | case onnx.TensorProto.DataType.UNDEFINED: 156 | default: 157 | throw new Error(`Cannot parse tensor '${tensor.dataType}'`); 158 | } 159 | } 160 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | 2 | Apache License 3 | Version 2.0, January 2004 4 | http://www.apache.org/licenses/ 5 | 6 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 7 | 8 | 1. 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-------------------------------------------------------------------------------- 1 | const IMAGENET_CLASSES = { 2 | 0: 'tench, Tinca tinca', 3 | 1: 'goldfish, Carassius auratus', 4 | 2: 'great white shark, white shark, man-eater, man-eating shark, ' + 5 | 'Carcharodon carcharias', 6 | 3: 'tiger shark, Galeocerdo cuvieri', 7 | 4: 'hammerhead, hammerhead shark', 8 | 5: 'electric ray, crampfish, numbfish, torpedo', 9 | 6: 'stingray', 10 | 7: 'cock', 11 | 8: 'hen', 12 | 9: 'ostrich, Struthio camelus', 13 | 10: 'brambling, Fringilla montifringilla', 14 | 11: 'goldfinch, Carduelis carduelis', 15 | 12: 'house finch, linnet, Carpodacus mexicanus', 16 | 13: 'junco, snowbird', 17 | 14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea', 18 | 15: 'robin, American robin, Turdus migratorius', 19 | 16: 'bulbul', 20 | 17: 'jay', 21 | 18: 'magpie', 22 | 19: 'chickadee', 23 | 20: 'water ouzel, dipper', 24 | 21: 'kite', 25 | 22: 'bald eagle, American eagle, Haliaeetus leucocephalus', 26 | 23: 'vulture', 27 | 24: 'great grey owl, great gray owl, Strix nebulosa', 28 | 25: 'European fire salamander, Salamandra salamandra', 29 | 26: 'common newt, Triturus vulgaris', 30 | 27: 'eft', 31 | 28: 'spotted salamander, Ambystoma maculatum', 32 | 29: 'axolotl, mud puppy, Ambystoma mexicanum', 33 | 30: 'bullfrog, Rana catesbeiana', 34 | 31: 'tree frog, tree-frog', 35 | 32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui', 36 | 33: 'loggerhead, loggerhead turtle, Caretta caretta', 37 | 34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea', 38 | 35: 'mud turtle', 39 | 36: 'terrapin', 40 | 37: 'box turtle, box tortoise', 41 | 38: 'banded gecko', 42 | 39: 'common iguana, iguana, Iguana iguana', 43 | 40: 'American chameleon, anole, Anolis carolinensis', 44 | 41: 'whiptail, whiptail lizard', 45 | 42: 'agama', 46 | 43: 'frilled lizard, Chlamydosaurus kingi', 47 | 44: 'alligator lizard', 48 | 45: 'Gila monster, Heloderma suspectum', 49 | 46: 'green lizard, Lacerta viridis', 50 | 47: 'African chameleon, Chamaeleo chamaeleon', 51 | 48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, ' + 52 | 'Varanus komodoensis', 53 | 49: 'African crocodile, Nile crocodile, Crocodylus niloticus', 54 | 50: 'American alligator, Alligator mississipiensis', 55 | 51: 'triceratops', 56 | 52: 'thunder snake, worm snake, Carphophis amoenus', 57 | 53: 'ringneck snake, ring-necked snake, ring snake', 58 | 54: 'hognose snake, puff adder, sand viper', 59 | 55: 'green snake, grass snake', 60 | 56: 'king snake, kingsnake', 61 | 57: 'garter snake, grass snake', 62 | 58: 'water snake', 63 | 59: 'vine snake', 64 | 60: 'night snake, Hypsiglena torquata', 65 | 61: 'boa constrictor, Constrictor constrictor', 66 | 62: 'rock python, rock snake, Python sebae', 67 | 63: 'Indian cobra, Naja naja', 68 | 64: 'green mamba', 69 | 65: 'sea snake', 70 | 66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus', 71 | 67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus', 72 | 68: 'sidewinder, horned rattlesnake, Crotalus cerastes', 73 | 69: 'trilobite', 74 | 70: 'harvestman, daddy longlegs, Phalangium opilio', 75 | 71: 'scorpion', 76 | 72: 'black and gold garden spider, Argiope aurantia', 77 | 73: 'barn spider, Araneus cavaticus', 78 | 74: 'garden spider, Aranea diademata', 79 | 75: 'black widow, Latrodectus mactans', 80 | 76: 'tarantula', 81 | 77: 'wolf spider, hunting spider', 82 | 78: 'tick', 83 | 79: 'centipede', 84 | 80: 'black grouse', 85 | 81: 'ptarmigan', 86 | 82: 'ruffed grouse, partridge, Bonasa umbellus', 87 | 83: 'prairie chicken, prairie grouse, prairie fowl', 88 | 84: 'peacock', 89 | 85: 'quail', 90 | 86: 'partridge', 91 | 87: 'African grey, African gray, Psittacus erithacus', 92 | 88: 'macaw', 93 | 89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita', 94 | 90: 'lorikeet', 95 | 91: 'coucal', 96 | 92: 'bee eater', 97 | 93: 'hornbill', 98 | 94: 'hummingbird', 99 | 95: 'jacamar', 100 | 96: 'toucan', 101 | 97: 'drake', 102 | 98: 'red-breasted merganser, Mergus serrator', 103 | 99: 'goose', 104 | 100: 'black swan, Cygnus atratus', 105 | 101: 'tusker', 106 | 102: 'echidna, spiny anteater, anteater', 107 | 103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, ' + 108 | 'Ornithorhynchus anatinus', 109 | 104: 'wallaby, brush kangaroo', 110 | 105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus', 111 | 106: 'wombat', 112 | 107: 'jelly fish', 113 | 108: 'sea anemone, anemone', 114 | 109: 'brain coral', 115 | 110: 'flatworm, platyhelminth', 116 | 111: 'nematode, nematode worm, roundworm', 117 | 112: 'conch', 118 | 113: 'snail', 119 | 114: 'slug', 120 | 115: 'sea slug, nudibranch', 121 | 116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore', 122 | 117: 'chambered nautilus, pearly nautilus, nautilus', 123 | 118: 'Dungeness crab, Cancer magister', 124 | 119: 'rock crab, Cancer irroratus', 125 | 120: 'fiddler crab', 126 | 121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, ' + 127 | 'Paralithodes camtschatica', 128 | 122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus', 129 | 123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea ' + 130 | 'crawfish', 131 | 124: 'crayfish, crawfish, crawdad, crawdaddy', 132 | 125: 'hermit crab', 133 | 126: 'isopod', 134 | 127: 'white stork, Ciconia ciconia', 135 | 128: 'black stork, Ciconia nigra', 136 | 129: 'spoonbill', 137 | 130: 'flamingo', 138 | 131: 'little blue heron, Egretta caerulea', 139 | 132: 'American egret, great white heron, Egretta albus', 140 | 133: 'bittern', 141 | 134: 'crane', 142 | 135: 'limpkin, Aramus pictus', 143 | 136: 'European gallinule, Porphyrio porphyrio', 144 | 137: 'American coot, marsh hen, mud hen, water hen, Fulica americana', 145 | 138: 'bustard', 146 | 139: 'ruddy turnstone, Arenaria interpres', 147 | 140: 'red-backed sandpiper, dunlin, Erolia alpina', 148 | 141: 'redshank, Tringa totanus', 149 | 142: 'dowitcher', 150 | 143: 'oystercatcher, oyster catcher', 151 | 144: 'pelican', 152 | 145: 'king penguin, Aptenodytes patagonica', 153 | 146: 'albatross, mollymawk', 154 | 147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, ' + 155 | 'Eschrichtius robustus', 156 | 148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca', 157 | 149: 'dugong, Dugong dugon', 158 | 150: 'sea lion', 159 | 151: 'Chihuahua', 160 | 152: 'Japanese spaniel', 161 | 153: 'Maltese dog, Maltese terrier, Maltese', 162 | 154: 'Pekinese, Pekingese, Peke', 163 | 155: 'Shih-Tzu', 164 | 156: 'Blenheim spaniel', 165 | 157: 'papillon', 166 | 158: 'toy terrier', 167 | 159: 'Rhodesian ridgeback', 168 | 160: 'Afghan hound, Afghan', 169 | 161: 'basset, basset hound', 170 | 162: 'beagle', 171 | 163: 'bloodhound, sleuthhound', 172 | 164: 'bluetick', 173 | 165: 'black-and-tan coonhound', 174 | 166: 'Walker hound, Walker foxhound', 175 | 167: 'English foxhound', 176 | 168: 'redbone', 177 | 169: 'borzoi, Russian wolfhound', 178 | 170: 'Irish wolfhound', 179 | 171: 'Italian greyhound', 180 | 172: 'whippet', 181 | 173: 'Ibizan hound, Ibizan Podenco', 182 | 174: 'Norwegian elkhound, elkhound', 183 | 175: 'otterhound, otter hound', 184 | 176: 'Saluki, gazelle hound', 185 | 177: 'Scottish deerhound, deerhound', 186 | 178: 'Weimaraner', 187 | 179: 'Staffordshire bullterrier, Staffordshire bull terrier', 188 | 180: 'American Staffordshire terrier, Staffordshire terrier, American pit ' + 189 | 'bull terrier, pit bull terrier', 190 | 181: 'Bedlington terrier', 191 | 182: 'Border terrier', 192 | 183: 'Kerry blue terrier', 193 | 184: 'Irish terrier', 194 | 185: 'Norfolk terrier', 195 | 186: 'Norwich terrier', 196 | 187: 'Yorkshire terrier', 197 | 188: 'wire-haired fox terrier', 198 | 189: 'Lakeland terrier', 199 | 190: 'Sealyham terrier, Sealyham', 200 | 191: 'Airedale, Airedale terrier', 201 | 192: 'cairn, cairn terrier', 202 | 193: 'Australian terrier', 203 | 194: 'Dandie Dinmont, Dandie Dinmont terrier', 204 | 195: 'Boston bull, Boston terrier', 205 | 196: 'miniature schnauzer', 206 | 197: 'giant schnauzer', 207 | 198: 'standard schnauzer', 208 | 199: 'Scotch terrier, Scottish terrier, Scottie', 209 | 200: 'Tibetan terrier, chrysanthemum dog', 210 | 201: 'silky terrier, Sydney silky', 211 | 202: 'soft-coated wheaten terrier', 212 | 203: 'West Highland white terrier', 213 | 204: 'Lhasa, Lhasa apso', 214 | 205: 'flat-coated retriever', 215 | 206: 'curly-coated retriever', 216 | 207: 'golden retriever', 217 | 208: 'Labrador retriever', 218 | 209: 'Chesapeake Bay retriever', 219 | 210: 'German short-haired pointer', 220 | 211: 'vizsla, Hungarian pointer', 221 | 212: 'English setter', 222 | 213: 'Irish setter, red setter', 223 | 214: 'Gordon setter', 224 | 215: 'Brittany spaniel', 225 | 216: 'clumber, clumber spaniel', 226 | 217: 'English springer, English springer spaniel', 227 | 218: 'Welsh springer spaniel', 228 | 219: 'cocker spaniel, English cocker spaniel, cocker', 229 | 220: 'Sussex spaniel', 230 | 221: 'Irish water spaniel', 231 | 222: 'kuvasz', 232 | 223: 'schipperke', 233 | 224: 'groenendael', 234 | 225: 'malinois', 235 | 226: 'briard', 236 | 227: 'kelpie', 237 | 228: 'komondor', 238 | 229: 'Old English sheepdog, bobtail', 239 | 230: 'Shetland sheepdog, Shetland sheep dog, Shetland', 240 | 231: 'collie', 241 | 232: 'Border collie', 242 | 233: 'Bouvier des Flandres, Bouviers des Flandres', 243 | 234: 'Rottweiler', 244 | 235: 'German shepherd, German shepherd dog, German police dog, alsatian', 245 | 236: 'Doberman, Doberman pinscher', 246 | 237: 'miniature pinscher', 247 | 238: 'Greater Swiss Mountain dog', 248 | 239: 'Bernese mountain dog', 249 | 240: 'Appenzeller', 250 | 241: 'EntleBucher', 251 | 242: 'boxer', 252 | 243: 'bull mastiff', 253 | 244: 'Tibetan mastiff', 254 | 245: 'French bulldog', 255 | 246: 'Great Dane', 256 | 247: 'Saint Bernard, St Bernard', 257 | 248: 'Eskimo dog, husky', 258 | 249: 'malamute, malemute, Alaskan malamute', 259 | 250: 'Siberian husky', 260 | 251: 'dalmatian, coach dog, carriage dog', 261 | 252: 'affenpinscher, monkey pinscher, monkey dog', 262 | 253: 'basenji', 263 | 254: 'pug, pug-dog', 264 | 255: 'Leonberg', 265 | 256: 'Newfoundland, Newfoundland dog', 266 | 257: 'Great Pyrenees', 267 | 258: 'Samoyed, Samoyede', 268 | 259: 'Pomeranian', 269 | 260: 'chow, chow chow', 270 | 261: 'keeshond', 271 | 262: 'Brabancon griffon', 272 | 263: 'Pembroke, Pembroke Welsh corgi', 273 | 264: 'Cardigan, Cardigan Welsh corgi', 274 | 265: 'toy poodle', 275 | 266: 'miniature poodle', 276 | 267: 'standard poodle', 277 | 268: 'Mexican hairless', 278 | 269: 'timber wolf, grey wolf, gray wolf, Canis lupus', 279 | 270: 'white wolf, Arctic wolf, Canis lupus tundrarum', 280 | 271: 'red wolf, maned wolf, Canis rufus, Canis niger', 281 | 272: 'coyote, prairie wolf, brush wolf, Canis latrans', 282 | 273: 'dingo, warrigal, warragal, Canis dingo', 283 | 274: 'dhole, Cuon alpinus', 284 | 275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus', 285 | 276: 'hyena, hyaena', 286 | 277: 'red fox, Vulpes vulpes', 287 | 278: 'kit fox, Vulpes macrotis', 288 | 279: 'Arctic fox, white fox, Alopex lagopus', 289 | 280: 'grey fox, gray fox, Urocyon cinereoargenteus', 290 | 281: 'tabby, tabby cat', 291 | 282: 'tiger cat', 292 | 283: 'Persian cat', 293 | 284: 'Siamese cat, Siamese', 294 | 285: 'Egyptian cat', 295 | 286: 'cougar, puma, catamount, mountain lion, painter, panther, ' + 296 | 'Felis concolor', 297 | 287: 'lynx, catamount', 298 | 288: 'leopard, Panthera pardus', 299 | 289: 'snow leopard, ounce, Panthera uncia', 300 | 290: 'jaguar, panther, Panthera onca, Felis onca', 301 | 291: 'lion, king of beasts, Panthera leo', 302 | 292: 'tiger, Panthera tigris', 303 | 293: 'cheetah, chetah, Acinonyx jubatus', 304 | 294: 'brown bear, bruin, Ursus arctos', 305 | 295: 'American black bear, black bear, Ursus americanus, Euarctos ' + 306 | 'americanus', 307 | 296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus', 308 | 297: 'sloth bear, Melursus ursinus, Ursus ursinus', 309 | 298: 'mongoose', 310 | 299: 'meerkat, mierkat', 311 | 300: 'tiger beetle', 312 | 301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle', 313 | 302: 'ground beetle, carabid beetle', 314 | 303: 'long-horned beetle, longicorn, longicorn beetle', 315 | 304: 'leaf beetle, chrysomelid', 316 | 305: 'dung beetle', 317 | 306: 'rhinoceros beetle', 318 | 307: 'weevil', 319 | 308: 'fly', 320 | 309: 'bee', 321 | 310: 'ant, emmet, pismire', 322 | 311: 'grasshopper, hopper', 323 | 312: 'cricket', 324 | 313: 'walking stick, walkingstick, stick insect', 325 | 314: 'cockroach, roach', 326 | 315: 'mantis, mantid', 327 | 316: 'cicada, cicala', 328 | 317: 'leafhopper', 329 | 318: 'lacewing, lacewing fly', 330 | 319: 'dragonfly, darning needle, devil\'s darning needle, sewing needle, ' + 331 | 'snake feeder, snake doctor, mosquito hawk, skeeter hawk', 332 | 320: 'damselfly', 333 | 321: 'admiral', 334 | 322: 'ringlet, ringlet butterfly', 335 | 323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus', 336 | 324: 'cabbage butterfly', 337 | 325: 'sulphur butterfly, sulfur butterfly', 338 | 326: 'lycaenid, lycaenid butterfly', 339 | 327: 'starfish, sea star', 340 | 328: 'sea urchin', 341 | 329: 'sea cucumber, holothurian', 342 | 330: 'wood rabbit, cottontail, cottontail rabbit', 343 | 331: 'hare', 344 | 332: 'Angora, Angora rabbit', 345 | 333: 'hamster', 346 | 334: 'porcupine, hedgehog', 347 | 335: 'fox squirrel, eastern fox squirrel, Sciurus niger', 348 | 336: 'marmot', 349 | 337: 'beaver', 350 | 338: 'guinea pig, Cavia cobaya', 351 | 339: 'sorrel', 352 | 340: 'zebra', 353 | 341: 'hog, pig, grunter, squealer, Sus scrofa', 354 | 342: 'wild boar, boar, Sus scrofa', 355 | 343: 'warthog', 356 | 344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius', 357 | 345: 'ox', 358 | 346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis', 359 | 347: 'bison', 360 | 348: 'ram, tup', 361 | 349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky ' + 362 | 'Mountain sheep, Ovis canadensis', 363 | 350: 'ibex, Capra ibex', 364 | 351: 'hartebeest', 365 | 352: 'impala, Aepyceros melampus', 366 | 353: 'gazelle', 367 | 354: 'Arabian camel, dromedary, Camelus dromedarius', 368 | 355: 'llama', 369 | 356: 'weasel', 370 | 357: 'mink', 371 | 358: 'polecat, fitch, foulmart, foumart, Mustela putorius', 372 | 359: 'black-footed ferret, ferret, Mustela nigripes', 373 | 360: 'otter', 374 | 361: 'skunk, polecat, wood pussy', 375 | 362: 'badger', 376 | 363: 'armadillo', 377 | 364: 'three-toed sloth, ai, Bradypus tridactylus', 378 | 365: 'orangutan, orang, orangutang, Pongo pygmaeus', 379 | 366: 'gorilla, Gorilla gorilla', 380 | 367: 'chimpanzee, chimp, Pan troglodytes', 381 | 368: 'gibbon, Hylobates lar', 382 | 369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus', 383 | 370: 'guenon, guenon monkey', 384 | 371: 'patas, hussar monkey, Erythrocebus patas', 385 | 372: 'baboon', 386 | 373: 'macaque', 387 | 374: 'langur', 388 | 375: 'colobus, colobus monkey', 389 | 376: 'proboscis monkey, Nasalis larvatus', 390 | 377: 'marmoset', 391 | 378: 'capuchin, ringtail, Cebus capucinus', 392 | 379: 'howler monkey, howler', 393 | 380: 'titi, titi monkey', 394 | 381: 'spider monkey, Ateles geoffroyi', 395 | 382: 'squirrel monkey, Saimiri sciureus', 396 | 383: 'Madagascar cat, ring-tailed lemur, Lemur catta', 397 | 384: 'indri, indris, Indri indri, Indri brevicaudatus', 398 | 385: 'Indian elephant, Elephas maximus', 399 | 386: 'African elephant, Loxodonta africana', 400 | 387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens', 401 | 388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca', 402 | 389: 'barracouta, snoek', 403 | 390: 'eel', 404 | 391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus ' + 405 | 'kisutch', 406 | 392: 'rock beauty, Holocanthus tricolor', 407 | 393: 'anemone fish', 408 | 394: 'sturgeon', 409 | 395: 'gar, garfish, garpike, billfish, Lepisosteus osseus', 410 | 396: 'lionfish', 411 | 397: 'puffer, pufferfish, blowfish, globefish', 412 | 398: 'abacus', 413 | 399: 'abaya', 414 | 400: 'academic gown, academic robe, judge\'s robe', 415 | 401: 'accordion, piano accordion, squeeze box', 416 | 402: 'acoustic guitar', 417 | 403: 'aircraft carrier, carrier, flattop, attack aircraft carrier', 418 | 404: 'airliner', 419 | 405: 'airship, dirigible', 420 | 406: 'altar', 421 | 407: 'ambulance', 422 | 408: 'amphibian, amphibious vehicle', 423 | 409: 'analog clock', 424 | 410: 'apiary, bee house', 425 | 411: 'apron', 426 | 412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, ' + 427 | 'dustbin, trash barrel, trash bin', 428 | 413: 'assault rifle, assault gun', 429 | 414: 'backpack, back pack, knapsack, packsack, rucksack, haversack', 430 | 415: 'bakery, bakeshop, bakehouse', 431 | 416: 'balance beam, beam', 432 | 417: 'balloon', 433 | 418: 'ballpoint, ballpoint pen, ballpen, Biro', 434 | 419: 'Band Aid', 435 | 420: 'banjo', 436 | 421: 'bannister, banister, balustrade, balusters, handrail', 437 | 422: 'barbell', 438 | 423: 'barber chair', 439 | 424: 'barbershop', 440 | 425: 'barn', 441 | 426: 'barometer', 442 | 427: 'barrel, cask', 443 | 428: 'barrow, garden cart, lawn cart, wheelbarrow', 444 | 429: 'baseball', 445 | 430: 'basketball', 446 | 431: 'bassinet', 447 | 432: 'bassoon', 448 | 433: 'bathing cap, swimming cap', 449 | 434: 'bath towel', 450 | 435: 'bathtub, bathing tub, bath, tub', 451 | 436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station ' + 452 | 'waggon, waggon', 453 | 437: 'beacon, lighthouse, beacon light, pharos', 454 | 438: 'beaker', 455 | 439: 'bearskin, busby, shako', 456 | 440: 'beer bottle', 457 | 441: 'beer glass', 458 | 442: 'bell cote, bell cot', 459 | 443: 'bib', 460 | 444: 'bicycle-built-for-two, tandem bicycle, tandem', 461 | 445: 'bikini, two-piece', 462 | 446: 'binder, ring-binder', 463 | 447: 'binoculars, field glasses, opera glasses', 464 | 448: 'birdhouse', 465 | 449: 'boathouse', 466 | 450: 'bobsled, bobsleigh, bob', 467 | 451: 'bolo tie, bolo, bola tie, bola', 468 | 452: 'bonnet, poke bonnet', 469 | 453: 'bookcase', 470 | 454: 'bookshop, bookstore, bookstall', 471 | 455: 'bottlecap', 472 | 456: 'bow', 473 | 457: 'bow tie, bow-tie, bowtie', 474 | 458: 'brass, memorial tablet, plaque', 475 | 459: 'brassiere, bra, bandeau', 476 | 460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty', 477 | 461: 'breastplate, aegis, egis', 478 | 462: 'broom', 479 | 463: 'bucket, pail', 480 | 464: 'buckle', 481 | 465: 'bulletproof vest', 482 | 466: 'bullet train, bullet', 483 | 467: 'butcher shop, meat market', 484 | 468: 'cab, hack, taxi, taxicab', 485 | 469: 'caldron, cauldron', 486 | 470: 'candle, taper, wax light', 487 | 471: 'cannon', 488 | 472: 'canoe', 489 | 473: 'can opener, tin opener', 490 | 474: 'cardigan', 491 | 475: 'car mirror', 492 | 476: 'carousel, carrousel, merry-go-round, roundabout, whirligig', 493 | 477: 'carpenter\'s kit, tool kit', 494 | 478: 'carton', 495 | 479: 'car wheel', 496 | 480: 'cash machine, cash dispenser, automated teller machine, automatic ' + 497 | 'teller machine, automated teller, automatic teller, ATM', 498 | 481: 'cassette', 499 | 482: 'cassette player', 500 | 483: 'castle', 501 | 484: 'catamaran', 502 | 485: 'CD player', 503 | 486: 'cello, violoncello', 504 | 487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone', 505 | 488: 'chain', 506 | 489: 'chainlink fence', 507 | 490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ' + 508 | 'ring armour', 509 | 491: 'chain saw, chainsaw', 510 | 492: 'chest', 511 | 493: 'chiffonier, commode', 512 | 494: 'chime, bell, gong', 513 | 495: 'china cabinet, china closet', 514 | 496: 'Christmas stocking', 515 | 497: 'church, church building', 516 | 498: 'cinema, movie theater, movie theatre, movie house, picture palace', 517 | 499: 'cleaver, meat cleaver, chopper', 518 | 500: 'cliff dwelling', 519 | 501: 'cloak', 520 | 502: 'clog, geta, patten, sabot', 521 | 503: 'cocktail shaker', 522 | 504: 'coffee mug', 523 | 505: 'coffeepot', 524 | 506: 'coil, spiral, volute, whorl, helix', 525 | 507: 'combination lock', 526 | 508: 'computer keyboard, keypad', 527 | 509: 'confectionery, confectionary, candy store', 528 | 510: 'container ship, containership, container vessel', 529 | 511: 'convertible', 530 | 512: 'corkscrew, bottle screw', 531 | 513: 'cornet, horn, trumpet, trump', 532 | 514: 'cowboy boot', 533 | 515: 'cowboy hat, ten-gallon hat', 534 | 516: 'cradle', 535 | 517: 'crane', 536 | 518: 'crash helmet', 537 | 519: 'crate', 538 | 520: 'crib, cot', 539 | 521: 'Crock Pot', 540 | 522: 'croquet ball', 541 | 523: 'crutch', 542 | 524: 'cuirass', 543 | 525: 'dam, dike, dyke', 544 | 526: 'desk', 545 | 527: 'desktop computer', 546 | 528: 'dial telephone, dial phone', 547 | 529: 'diaper, nappy, napkin', 548 | 530: 'digital clock', 549 | 531: 'digital watch', 550 | 532: 'dining table, board', 551 | 533: 'dishrag, dishcloth', 552 | 534: 'dishwasher, dish washer, dishwashing machine', 553 | 535: 'disk brake, disc brake', 554 | 536: 'dock, dockage, docking facility', 555 | 537: 'dogsled, dog sled, dog sleigh', 556 | 538: 'dome', 557 | 539: 'doormat, welcome mat', 558 | 540: 'drilling platform, offshore rig', 559 | 541: 'drum, membranophone, tympan', 560 | 542: 'drumstick', 561 | 543: 'dumbbell', 562 | 544: 'Dutch oven', 563 | 545: 'electric fan, blower', 564 | 546: 'electric guitar', 565 | 547: 'electric locomotive', 566 | 548: 'entertainment center', 567 | 549: 'envelope', 568 | 550: 'espresso maker', 569 | 551: 'face powder', 570 | 552: 'feather boa, boa', 571 | 553: 'file, file cabinet, filing cabinet', 572 | 554: 'fireboat', 573 | 555: 'fire engine, fire truck', 574 | 556: 'fire screen, fireguard', 575 | 557: 'flagpole, flagstaff', 576 | 558: 'flute, transverse flute', 577 | 559: 'folding chair', 578 | 560: 'football helmet', 579 | 561: 'forklift', 580 | 562: 'fountain', 581 | 563: 'fountain pen', 582 | 564: 'four-poster', 583 | 565: 'freight car', 584 | 566: 'French horn, horn', 585 | 567: 'frying pan, frypan, skillet', 586 | 568: 'fur coat', 587 | 569: 'garbage truck, dustcart', 588 | 570: 'gasmask, respirator, gas helmet', 589 | 571: 'gas pump, gasoline pump, petrol pump, island dispenser', 590 | 572: 'goblet', 591 | 573: 'go-kart', 592 | 574: 'golf ball', 593 | 575: 'golfcart, golf cart', 594 | 576: 'gondola', 595 | 577: 'gong, tam-tam', 596 | 578: 'gown', 597 | 579: 'grand piano, grand', 598 | 580: 'greenhouse, nursery, glasshouse', 599 | 581: 'grille, radiator grille', 600 | 582: 'grocery store, grocery, food market, market', 601 | 583: 'guillotine', 602 | 584: 'hair slide', 603 | 585: 'hair spray', 604 | 586: 'half track', 605 | 587: 'hammer', 606 | 588: 'hamper', 607 | 589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier', 608 | 590: 'hand-held computer, hand-held microcomputer', 609 | 591: 'handkerchief, hankie, hanky, hankey', 610 | 592: 'hard disc, hard disk, fixed disk', 611 | 593: 'harmonica, mouth organ, harp, mouth harp', 612 | 594: 'harp', 613 | 595: 'harvester, reaper', 614 | 596: 'hatchet', 615 | 597: 'holster', 616 | 598: 'home theater, home theatre', 617 | 599: 'honeycomb', 618 | 600: 'hook, claw', 619 | 601: 'hoopskirt, crinoline', 620 | 602: 'horizontal bar, high bar', 621 | 603: 'horse cart, horse-cart', 622 | 604: 'hourglass', 623 | 605: 'iPod', 624 | 606: 'iron, smoothing iron', 625 | 607: 'jack-o\'-lantern', 626 | 608: 'jean, blue jean, denim', 627 | 609: 'jeep, landrover', 628 | 610: 'jersey, T-shirt, tee shirt', 629 | 611: 'jigsaw puzzle', 630 | 612: 'jinrikisha, ricksha, rickshaw', 631 | 613: 'joystick', 632 | 614: 'kimono', 633 | 615: 'knee pad', 634 | 616: 'knot', 635 | 617: 'lab coat, laboratory coat', 636 | 618: 'ladle', 637 | 619: 'lampshade, lamp shade', 638 | 620: 'laptop, laptop computer', 639 | 621: 'lawn mower, mower', 640 | 622: 'lens cap, lens cover', 641 | 623: 'letter opener, paper knife, paperknife', 642 | 624: 'library', 643 | 625: 'lifeboat', 644 | 626: 'lighter, light, igniter, ignitor', 645 | 627: 'limousine, limo', 646 | 628: 'liner, ocean liner', 647 | 629: 'lipstick, lip rouge', 648 | 630: 'Loafer', 649 | 631: 'lotion', 650 | 632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker ' + 651 | 'system', 652 | 633: 'loupe, jeweler\'s loupe', 653 | 634: 'lumbermill, sawmill', 654 | 635: 'magnetic compass', 655 | 636: 'mailbag, postbag', 656 | 637: 'mailbox, letter box', 657 | 638: 'maillot', 658 | 639: 'maillot, tank suit', 659 | 640: 'manhole cover', 660 | 641: 'maraca', 661 | 642: 'marimba, xylophone', 662 | 643: 'mask', 663 | 644: 'matchstick', 664 | 645: 'maypole', 665 | 646: 'maze, labyrinth', 666 | 647: 'measuring cup', 667 | 648: 'medicine chest, medicine cabinet', 668 | 649: 'megalith, megalithic structure', 669 | 650: 'microphone, mike', 670 | 651: 'microwave, microwave oven', 671 | 652: 'military uniform', 672 | 653: 'milk can', 673 | 654: 'minibus', 674 | 655: 'miniskirt, mini', 675 | 656: 'minivan', 676 | 657: 'missile', 677 | 658: 'mitten', 678 | 659: 'mixing bowl', 679 | 660: 'mobile home, manufactured home', 680 | 661: 'Model T', 681 | 662: 'modem', 682 | 663: 'monastery', 683 | 664: 'monitor', 684 | 665: 'moped', 685 | 666: 'mortar', 686 | 667: 'mortarboard', 687 | 668: 'mosque', 688 | 669: 'mosquito net', 689 | 670: 'motor scooter, scooter', 690 | 671: 'mountain bike, all-terrain bike, off-roader', 691 | 672: 'mountain tent', 692 | 673: 'mouse, computer mouse', 693 | 674: 'mousetrap', 694 | 675: 'moving van', 695 | 676: 'muzzle', 696 | 677: 'nail', 697 | 678: 'neck brace', 698 | 679: 'necklace', 699 | 680: 'nipple', 700 | 681: 'notebook, notebook computer', 701 | 682: 'obelisk', 702 | 683: 'oboe, hautboy, hautbois', 703 | 684: 'ocarina, sweet potato', 704 | 685: 'odometer, hodometer, mileometer, milometer', 705 | 686: 'oil filter', 706 | 687: 'organ, pipe organ', 707 | 688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO', 708 | 689: 'overskirt', 709 | 690: 'oxcart', 710 | 691: 'oxygen mask', 711 | 692: 'packet', 712 | 693: 'paddle, boat paddle', 713 | 694: 'paddlewheel, paddle wheel', 714 | 695: 'padlock', 715 | 696: 'paintbrush', 716 | 697: 'pajama, pyjama, pj\'s, jammies', 717 | 698: 'palace', 718 | 699: 'panpipe, pandean pipe, syrinx', 719 | 700: 'paper towel', 720 | 701: 'parachute, chute', 721 | 702: 'parallel bars, bars', 722 | 703: 'park bench', 723 | 704: 'parking meter', 724 | 705: 'passenger car, coach, carriage', 725 | 706: 'patio, terrace', 726 | 707: 'pay-phone, pay-station', 727 | 708: 'pedestal, plinth, footstall', 728 | 709: 'pencil box, pencil case', 729 | 710: 'pencil sharpener', 730 | 711: 'perfume, essence', 731 | 712: 'Petri dish', 732 | 713: 'photocopier', 733 | 714: 'pick, plectrum, plectron', 734 | 715: 'pickelhaube', 735 | 716: 'picket fence, paling', 736 | 717: 'pickup, pickup truck', 737 | 718: 'pier', 738 | 719: 'piggy bank, penny bank', 739 | 720: 'pill bottle', 740 | 721: 'pillow', 741 | 722: 'ping-pong ball', 742 | 723: 'pinwheel', 743 | 724: 'pirate, pirate ship', 744 | 725: 'pitcher, ewer', 745 | 726: 'plane, carpenter\'s plane, woodworking plane', 746 | 727: 'planetarium', 747 | 728: 'plastic bag', 748 | 729: 'plate rack', 749 | 730: 'plow, plough', 750 | 731: 'plunger, plumber\'s helper', 751 | 732: 'Polaroid camera, Polaroid Land camera', 752 | 733: 'pole', 753 | 734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black ' + 754 | 'Maria', 755 | 735: 'poncho', 756 | 736: 'pool table, billiard table, snooker table', 757 | 737: 'pop bottle, soda bottle', 758 | 738: 'pot, flowerpot', 759 | 739: 'potter\'s wheel', 760 | 740: 'power drill', 761 | 741: 'prayer rug, prayer mat', 762 | 742: 'printer', 763 | 743: 'prison, prison house', 764 | 744: 'projectile, missile', 765 | 745: 'projector', 766 | 746: 'puck, hockey puck', 767 | 747: 'punching bag, punch bag, punching ball, punchball', 768 | 748: 'purse', 769 | 749: 'quill, quill pen', 770 | 750: 'quilt, comforter, comfort, puff', 771 | 751: 'racer, race car, racing car', 772 | 752: 'racket, racquet', 773 | 753: 'radiator', 774 | 754: 'radio, wireless', 775 | 755: 'radio telescope, radio reflector', 776 | 756: 'rain barrel', 777 | 757: 'recreational vehicle, RV, R.V.', 778 | 758: 'reel', 779 | 759: 'reflex camera', 780 | 760: 'refrigerator, icebox', 781 | 761: 'remote control, remote', 782 | 762: 'restaurant, eating house, eating place, eatery', 783 | 763: 'revolver, six-gun, six-shooter', 784 | 764: 'rifle', 785 | 765: 'rocking chair, rocker', 786 | 766: 'rotisserie', 787 | 767: 'rubber eraser, rubber, pencil eraser', 788 | 768: 'rugby ball', 789 | 769: 'rule, ruler', 790 | 770: 'running shoe', 791 | 771: 'safe', 792 | 772: 'safety pin', 793 | 773: 'saltshaker, salt shaker', 794 | 774: 'sandal', 795 | 775: 'sarong', 796 | 776: 'sax, saxophone', 797 | 777: 'scabbard', 798 | 778: 'scale, weighing machine', 799 | 779: 'school bus', 800 | 780: 'schooner', 801 | 781: 'scoreboard', 802 | 782: 'screen, CRT screen', 803 | 783: 'screw', 804 | 784: 'screwdriver', 805 | 785: 'seat belt, seatbelt', 806 | 786: 'sewing machine', 807 | 787: 'shield, buckler', 808 | 788: 'shoe shop, shoe-shop, shoe store', 809 | 789: 'shoji', 810 | 790: 'shopping basket', 811 | 791: 'shopping cart', 812 | 792: 'shovel', 813 | 793: 'shower cap', 814 | 794: 'shower curtain', 815 | 795: 'ski', 816 | 796: 'ski mask', 817 | 797: 'sleeping bag', 818 | 798: 'slide rule, slipstick', 819 | 799: 'sliding door', 820 | 800: 'slot, one-armed bandit', 821 | 801: 'snorkel', 822 | 802: 'snowmobile', 823 | 803: 'snowplow, snowplough', 824 | 804: 'soap dispenser', 825 | 805: 'soccer ball', 826 | 806: 'sock', 827 | 807: 'solar dish, solar collector, solar furnace', 828 | 808: 'sombrero', 829 | 809: 'soup bowl', 830 | 810: 'space bar', 831 | 811: 'space heater', 832 | 812: 'space shuttle', 833 | 813: 'spatula', 834 | 814: 'speedboat', 835 | 815: 'spider web, spider\'s web', 836 | 816: 'spindle', 837 | 817: 'sports car, sport car', 838 | 818: 'spotlight, spot', 839 | 819: 'stage', 840 | 820: 'steam locomotive', 841 | 821: 'steel arch bridge', 842 | 822: 'steel drum', 843 | 823: 'stethoscope', 844 | 824: 'stole', 845 | 825: 'stone wall', 846 | 826: 'stopwatch, stop watch', 847 | 827: 'stove', 848 | 828: 'strainer', 849 | 829: 'streetcar, tram, tramcar, trolley, trolley car', 850 | 830: 'stretcher', 851 | 831: 'studio couch, day bed', 852 | 832: 'stupa, tope', 853 | 833: 'submarine, pigboat, sub, U-boat', 854 | 834: 'suit, suit of clothes', 855 | 835: 'sundial', 856 | 836: 'sunglass', 857 | 837: 'sunglasses, dark glasses, shades', 858 | 838: 'sunscreen, sunblock, sun blocker', 859 | 839: 'suspension bridge', 860 | 840: 'swab, swob, mop', 861 | 841: 'sweatshirt', 862 | 842: 'swimming trunks, bathing trunks', 863 | 843: 'swing', 864 | 844: 'switch, electric switch, electrical switch', 865 | 845: 'syringe', 866 | 846: 'table lamp', 867 | 847: 'tank, army tank, armored combat vehicle, armoured combat vehicle', 868 | 848: 'tape player', 869 | 849: 'teapot', 870 | 850: 'teddy, teddy bear', 871 | 851: 'television, television system', 872 | 852: 'tennis ball', 873 | 853: 'thatch, thatched roof', 874 | 854: 'theater curtain, theatre curtain', 875 | 855: 'thimble', 876 | 856: 'thresher, thrasher, threshing machine', 877 | 857: 'throne', 878 | 858: 'tile roof', 879 | 859: 'toaster', 880 | 860: 'tobacco shop, tobacconist shop, tobacconist', 881 | 861: 'toilet seat', 882 | 862: 'torch', 883 | 863: 'totem pole', 884 | 864: 'tow truck, tow car, wrecker', 885 | 865: 'toyshop', 886 | 866: 'tractor', 887 | 867: 'trailer truck, tractor trailer, trucking rig, rig, articulated ' + 888 | 'lorry, semi', 889 | 868: 'tray', 890 | 869: 'trench coat', 891 | 870: 'tricycle, trike, velocipede', 892 | 871: 'trimaran', 893 | 872: 'tripod', 894 | 873: 'triumphal arch', 895 | 874: 'trolleybus, trolley coach, trackless trolley', 896 | 875: 'trombone', 897 | 876: 'tub, vat', 898 | 877: 'turnstile', 899 | 878: 'typewriter keyboard', 900 | 879: 'umbrella', 901 | 880: 'unicycle, monocycle', 902 | 881: 'upright, upright piano', 903 | 882: 'vacuum, vacuum cleaner', 904 | 883: 'vase', 905 | 884: 'vault', 906 | 885: 'velvet', 907 | 886: 'vending machine', 908 | 887: 'vestment', 909 | 888: 'viaduct', 910 | 889: 'violin, fiddle', 911 | 890: 'volleyball', 912 | 891: 'waffle iron', 913 | 892: 'wall clock', 914 | 893: 'wallet, billfold, notecase, pocketbook', 915 | 894: 'wardrobe, closet, press', 916 | 895: 'warplane, military plane', 917 | 896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin', 918 | 897: 'washer, automatic washer, washing machine', 919 | 898: 'water bottle', 920 | 899: 'water jug', 921 | 900: 'water tower', 922 | 901: 'whiskey jug', 923 | 902: 'whistle', 924 | 903: 'wig', 925 | 904: 'window screen', 926 | 905: 'window shade', 927 | 906: 'Windsor tie', 928 | 907: 'wine bottle', 929 | 908: 'wing', 930 | 909: 'wok', 931 | 910: 'wooden spoon', 932 | 911: 'wool, woolen, woollen', 933 | 912: 'worm fence, snake fence, snake-rail fence, Virginia fence', 934 | 913: 'wreck', 935 | 914: 'yawl', 936 | 915: 'yurt', 937 | 916: 'web site, website, internet site, site', 938 | 917: 'comic book', 939 | 918: 'crossword puzzle, crossword', 940 | 919: 'street sign', 941 | 920: 'traffic light, traffic signal, stoplight', 942 | 921: 'book jacket, dust cover, dust jacket, dust wrapper', 943 | 922: 'menu', 944 | 923: 'plate', 945 | 924: 'guacamole', 946 | 925: 'consomme', 947 | 926: 'hot pot, hotpot', 948 | 927: 'trifle', 949 | 928: 'ice cream, icecream', 950 | 929: 'ice lolly, lolly, lollipop, popsicle', 951 | 930: 'French loaf', 952 | 931: 'bagel, beigel', 953 | 932: 'pretzel', 954 | 933: 'cheeseburger', 955 | 934: 'hotdog, hot dog, red hot', 956 | 935: 'mashed potato', 957 | 936: 'head cabbage', 958 | 937: 'broccoli', 959 | 938: 'cauliflower', 960 | 939: 'zucchini, courgette', 961 | 940: 'spaghetti squash', 962 | 941: 'acorn squash', 963 | 942: 'butternut squash', 964 | 943: 'cucumber, cuke', 965 | 944: 'artichoke, globe artichoke', 966 | 945: 'bell pepper', 967 | 946: 'cardoon', 968 | 947: 'mushroom', 969 | 948: 'Granny Smith', 970 | 949: 'strawberry', 971 | 950: 'orange', 972 | 951: 'lemon', 973 | 952: 'fig', 974 | 953: 'pineapple, ananas', 975 | 954: 'banana', 976 | 955: 'jackfruit, jak, jack', 977 | 956: 'custard apple', 978 | 957: 'pomegranate', 979 | 958: 'hay', 980 | 959: 'carbonara', 981 | 960: 'chocolate sauce, chocolate syrup', 982 | 961: 'dough', 983 | 962: 'meat loaf, meatloaf', 984 | 963: 'pizza, pizza pie', 985 | 964: 'potpie', 986 | 965: 'burrito', 987 | 966: 'red wine', 988 | 967: 'espresso', 989 | 968: 'cup', 990 | 969: 'eggnog', 991 | 970: 'alp', 992 | 971: 'bubble', 993 | 972: 'cliff, drop, drop-off', 994 | 973: 'coral reef', 995 | 974: 'geyser', 996 | 975: 'lakeside, lakeshore', 997 | 976: 'promontory, headland, head, foreland', 998 | 977: 'sandbar, sand bar', 999 | 978: 'seashore, coast, seacoast, sea-coast', 1000 | 979: 'valley, vale', 1001 | 980: 'volcano', 1002 | 981: 'ballplayer, baseball player', 1003 | 982: 'groom, bridegroom', 1004 | 983: 'scuba diver', 1005 | 984: 'rapeseed', 1006 | 985: 'daisy', 1007 | 986: 'yellow lady\'s slipper, yellow lady-slipper, Cypripedium calceolus, ' + 1008 | 'Cypripedium parviflorum', 1009 | 987: 'corn', 1010 | 988: 'acorn', 1011 | 989: 'hip, rose hip, rosehip', 1012 | 990: 'buckeye, horse chestnut, conker', 1013 | 991: 'coral fungus', 1014 | 992: 'agaric', 1015 | 993: 'gyromitra', 1016 | 994: 'stinkhorn, carrion fungus', 1017 | 995: 'earthstar', 1018 | 996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola ' + 1019 | 'frondosa', 1020 | 997: 'bolete', 1021 | 998: 'ear, spike, capitulum', 1022 | 999: 'toilet tissue, toilet paper, bathroom tissue' 1023 | }; 1024 | 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