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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/lenet/README.md b/lenet/README.md new file mode 100644 index 00000000..a1ad5179 --- /dev/null +++ b/lenet/README.md @@ -0,0 +1,13 @@ +## WebNN API LeNet Example +The sample uses the LeNet classifications network as an example. + +### Setup +Install dependencies: +```sh +> npm install +``` + +Please download the [lenet.bin](https://github.com/openvinotoolkit/openvino/blob/2020/inference-engine/samples/ngraph_function_creation_sample/lenet.bin) before launch the example. + +### Screenshot +![screenshot](screenshot.png) \ No newline at end of file diff --git a/lenet/index.html b/lenet/index.html new file mode 100644 index 00000000..47f07c05 --- /dev/null +++ b/lenet/index.html @@ -0,0 +1,114 @@ + + + + + WebNN LeNet Example + + + + + +
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+ + + + + + + + + + + \ No newline at end of file diff --git a/lenet/lenet.js b/lenet/lenet.js new file mode 100644 index 00000000..98f695aa --- /dev/null +++ b/lenet/lenet.js @@ -0,0 +1,113 @@ + +var nn = navigator.ml.getNeuralNetworkContext('v2'); + +function sizeOfShape(shape) { + return shape.reduce((a, b) => { return a * b; }); +} + +class Lenet { + constructor(url) { + this.url_ = url; + this.model_ = null; + this.compilation_ = null; + } + + async load() { + const response = await fetch(this.url_); + const arrayBuffer = await response.arrayBuffer(); + if (arrayBuffer.byteLength !== 1724336) { + throw new Error('Incorrect weights file'); + } + + const inputShape = [1, 1, 28, 28]; + const input = nn.input('input', {type: 'tensor-float32', dimensions: inputShape}); + + const conv1FitlerShape = [20, 1, 5, 5]; + let byteOffset = 0; + const conv1FilterData = new Float32Array(arrayBuffer, byteOffset, sizeOfShape(conv1FitlerShape)); + const conv1Filter = nn.constant({type: 'tensor-float32', dimensions: conv1FitlerShape}, + conv1FilterData); + byteOffset += sizeOfShape(conv1FitlerShape) * Float32Array.BYTES_PER_ELEMENT; + const conv1 = nn.conv2d(input, conv1Filter); + + const add1BiasShape = [1, 20, 1, 1]; + const add1BiasData = new Float32Array(arrayBuffer, byteOffset, sizeOfShape(add1BiasShape)); + const add1Bias = nn.constant({type: 'tensor-float32', dimensions: add1BiasShape}, + add1BiasData); + byteOffset += sizeOfShape(add1BiasShape) * Float32Array.BYTES_PER_ELEMENT; + const add1 = nn.add(conv1, add1Bias); + + const pool1WindowShape = [2, 2]; + const pool1Strides = [2, 2]; + const pool1 = nn.maxPool2d(add1, pool1WindowShape, [0, 0, 0, 0], pool1Strides); + + const conv2FilterShape = [50, 20, 5, 5]; + const conv2Filter = nn.constant({type: 'tensor-float32', dimensions: conv2FilterShape}, + new Float32Array(arrayBuffer, byteOffset, sizeOfShape(conv2FilterShape))); + byteOffset += sizeOfShape(conv2FilterShape) * Float32Array.BYTES_PER_ELEMENT; + const conv2 = nn.conv2d(pool1, conv2Filter); + + const add2BiasShape = [1, 50, 1, 1]; + const add2Bias = nn.constant({type: 'tensor-float32', dimensions: add2BiasShape}, + new Float32Array(arrayBuffer, byteOffset, sizeOfShape(add2BiasShape))); + byteOffset += sizeOfShape(add2BiasShape) * Float32Array.BYTES_PER_ELEMENT; + const add2 = nn.add(conv2, add2Bias); + + const pool2WindowShape = [2, 2]; + const pool2Strides = [2, 2]; + const pool2 = nn.maxPool2d(add2, pool2WindowShape, [0, 0, 0, 0], pool2Strides); + + const reshape1Shape = [1, -1]; + const reshape1 = nn.reshape(pool2, reshape1Shape); + + // skip the new shape + byteOffset += 2 * BigInt64Array.BYTES_PER_ELEMENT; + + const matmul1Shape = [500, 800]; + const matmul1Weights = nn.constant({type: 'tensor-float32', dimensions: matmul1Shape}, + new Float32Array(arrayBuffer, byteOffset, sizeOfShape(matmul1Shape))); + byteOffset += sizeOfShape(matmul1Shape) * Float32Array.BYTES_PER_ELEMENT; + const matmul1WeightsTransposed = nn.transpose(matmul1Weights); + const matmul1 = nn.matmul(reshape1, matmul1WeightsTransposed); + + const add3BiasShape = [1, 500]; + const add3Bias = nn.constant({type: 'tensor-float32', dimensions: add3BiasShape}, + new Float32Array(arrayBuffer, byteOffset, sizeOfShape(add3BiasShape))); + byteOffset += sizeOfShape(add3BiasShape) * Float32Array.BYTES_PER_ELEMENT; + const add3 = nn.add(matmul1, add3Bias); + + const relu = nn.relu(add3); + + const reshape2Shape = [1, -1]; + const reshape2 = nn.reshape(relu, reshape2Shape); + + const matmul2Shape = [10, 500]; + const matmul2Weights = nn.constant({type: 'tensor-float32', dimensions: matmul2Shape}, + new Float32Array(arrayBuffer, byteOffset, sizeOfShape(matmul2Shape))); + byteOffset += sizeOfShape(matmul2Shape) * Float32Array.BYTES_PER_ELEMENT; + const matmul2WeightsTransposed = nn.transpose(matmul2Weights); + const matmul2 = nn.matmul(reshape2, matmul2WeightsTransposed); + + const add4BiasShape = [1, 10]; + const add4Bias = nn.constant({type: 'tensor-float32', dimensions: add4BiasShape}, + new Float32Array(arrayBuffer, byteOffset, sizeOfShape(add4BiasShape))); + const add4 = nn.add(matmul2, add4Bias); + + const softmax = nn.softmax(add4) + + this.model_ = await nn.createModel([{name: 'output', operand: softmax}]); + } + + async compile(options) { + this.compilation_ = await this.model_.createCompilation(options); + } + + async predict(inputBuffer) { + const outputBuffer = new Float32Array(10); + const execution = await this.compilation_.createExecution(); + execution.setInput('input', inputBuffer); + execution.setOutput('output', outputBuffer); + await execution.startCompute(); + return Array.from(outputBuffer); + } +} diff --git a/lenet/main.js b/lenet/main.js new file mode 100644 index 00000000..ad1e7a19 --- /dev/null +++ b/lenet/main.js @@ -0,0 +1,121 @@ +const predictButton = document.getElementById('predict'); +const nextButton = document.getElementById('next'); +const clearButton = document.getElementById('clear'); +const visualCanvas = document.getElementById('visual_canvas'); +const visualContext = visualCanvas.getContext('2d'); +const digitCanvas = document.createElement('canvas'); +digitCanvas.setAttribute('height', 28); +digitCanvas.setAttribute('width', 28); +digitCanvas.style.backgroundColor = 'black'; +const digitContext = digitCanvas.getContext('2d'); + +function drawNextDigitFromMnist() { + const n = Math.floor(Math.random() * 10); + const digit = mnist[n].get(); + mnist.draw(digit, digitContext); + visualContext.drawImage(digitCanvas, 0, 0, visualCanvas.width, visualCanvas.height); +} + +function getInputFromCanvas() { + digitContext.clearRect(0, 0, digitCanvas.width, digitCanvas.height); + digitContext.drawImage(visualCanvas, 0, 0, digitCanvas.width, digitCanvas.height); + const imageData = digitContext.getImageData(0, 0, digitCanvas.width, digitCanvas.height); + const input = new Float32Array(digitCanvas.width * digitCanvas.height); + for (var i = 0; i < input.length; i++) { + input[i] = imageData.data[i * 4]; + } + return input; +} + +function clearResult() { + for (let i = 0; i < 3; ++i) { + let labelElement = document.getElementById(`label${i}`); + let probElement = document.getElementById(`prob${i}`); + labelElement.innerHTML = ''; + probElement.innerHTML = ''; + } +} + +async function main() { + drawNextDigitFromMnist(); + let pen = new Pen(visualCanvas); + const lenet = new Lenet('lenet.bin'); + try { + let start = performance.now(); + await lenet.load(); + console.log(`loading elapsed time: ${(performance.now() - start).toFixed(2)} ms`); + + start = performance.now(); + await lenet.compile(); + console.log(`compilation elapsed time: ${(performance.now() - start).toFixed(2)} ms`); + + predictButton.removeAttribute('disabled'); + } catch (error) { + console.log(error); + addWarning(error.message); + } + predictButton.addEventListener('click', async function (e) { + try { + const input = getInputFromCanvas(); + let start = performance.now(); + const result = await lenet.predict(input); + console.log(`execution elapsed time: ${(performance.now() - start).toFixed(2)} ms`); + console.log(`execution result: ${result}`); + const classes = topK(result); + classes.forEach((c, i) => { + console.log(`\tlabel: ${c.label}, probability: ${c.prob}%`); + let labelElement = document.getElementById(`label${i}`); + let probElement = document.getElementById(`prob${i}`); + labelElement.innerHTML = `${c.label}`; + probElement.innerHTML = `${c.prob}%`; + }); + } catch (error) { + console.log(error); + addWarning(error.message); + } + }); + nextButton.addEventListener('click', () => { + drawNextDigitFromMnist(); + clearResult(); + }); + + clearButton.addEventListener('click', () => { + pen.clear(); + clearResult(); + }) +} + +function topK(probs, k = 3) { + const sorted = probs.map((prob, index) => [prob, index]).sort((a, b) => { + if (a[0] === b[0]) { + return 0; + } + return a[0] < b[0] ? -1 : 1; + }); + sorted.reverse(); + + const classes = []; + for (let i = 0; i < k; ++i) { + let c = { + label: sorted[i][1], + prob: (sorted[i][0] * 100).toFixed(2) + } + classes.push(c); + } + + return classes; +} + +function addWarning(msg) { + let div = document.createElement('div'); + div.setAttribute('class', 'alert alert-warning alert-dismissible fade show'); + div.setAttribute('role', 'alert'); + div.innerHTML = msg; + let container = document.getElementById('container'); + container.insertBefore(div, container.childNodes[0]); +} + +function removeWarning() { + $('.alert').alert('close') +} + diff --git a/lenet/package.json b/lenet/package.json new file mode 100644 index 00000000..17d2f29e --- /dev/null +++ b/lenet/package.json @@ -0,0 +1,11 @@ +{ + "name": "webnn-lenet-example", + "version": "0.0.1", + "description": "The LeNet-based handwritten digits classification example", + "main": "main.js", + "author": "ningxin.hu@intel.com", + "license": "Apache-2.0", + "dependencies": { + "mnist": "^1.1.0" + } +} diff --git a/lenet/pen.js b/lenet/pen.js new file mode 100644 index 00000000..d5848763 --- /dev/null +++ b/lenet/pen.js @@ -0,0 +1,48 @@ +class Pen { + constructor(cavans) { + this.canvas = cavans; + this.canvas.style.backgroundColor = 'black'; + this.canvas.style.cursor = 'crosshair'; + this.context = cavans.getContext('2d'); + this.down = false; + this.start = {}; + const self = this; + this.canvas.addEventListener('mousedown', e => { + self.down = true; + self.start = self.getPosition(e); + }); + this.canvas.addEventListener('mouseup', e => { + self.down = false; + }); + this.canvas.addEventListener('mousemove', e => { + if (self.down) { + const end = self.getPosition(e); + self.draw(self.start, end); + self.start = end; + } + }) + } + + getPosition(e) { + const rect = this.canvas.getBoundingClientRect(); + const x = e.clientX- rect.left;; + const y = e.clientY- rect.top; + return {x: x, y: y}; + } + + draw(start, end) { + this.context.strokeStyle = 'white'; + this.context.lineJoin = 'round'; + this.context.lineWidth = 20; + + this.context.beginPath(); + this.context.moveTo(start.x, start.y); + this.context.lineTo(end.x, end.y); + this.context.closePath(); + this.context.stroke(); + } + + clear() { + this.context.clearRect(0, 0, this.canvas.width, this.canvas.height); + } +} \ No newline at end of file diff --git a/lenet/screenshot.png b/lenet/screenshot.png new file mode 100644 index 00000000..9a0374dc Binary files /dev/null and b/lenet/screenshot.png differ