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124 lines
3.9 KiB
124 lines
3.9 KiB
8 months ago
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<html>
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<head>
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<style>
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#result { font-size: 48px; }
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#time { font-size: 16px; color: grey; }
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#mybox { padding: 20px; }
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#resultbox { padding: 50px; }
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.bigggg { font-size: 18px; margin-top: 10px; }
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.bigg { font-size: 18px; }
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#url { font-size: 18px; width: 70%; }
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a { text-decoration: none; }
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h1 { padding: 50px; padding-bottom: 0px; font-size: 36px; font-weight: normal; }
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#imagebox { height:224px; width:224px; border: 1px dotted black; }
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#video { height:0px; width:0px; border: 1px dotted black; object-fit: cover;}
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canvas { display: none; }
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* { text-align: center; font-family: monospace; }
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</style>
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<title>tinygrad has WebGPU</title>
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<link rel="icon" type="image/x-icon" href="https://raw.githubusercontent.com/tinygrad/tinygrad/master/docs/logo.png">
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<script type="module">
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import model from "../../net.js";
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window.model = model;
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</script>
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</head>
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<body>
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<h1>WebGPU <a href="https://github.com/geohot/tinygrad">tinygrad</a> EfficientNet!</h1>
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<div id="mybox">
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<input type="text" id="url" placeholder="put url here" value="https://upload.wikimedia.org/wikipedia/commons/d/da/Norwegian_hen.jpg">
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<input class="bigg" type="button" onclick="runNetWResource(document.getElementById('url').value)" value="Use URL">
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</div>
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<br/>
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<img id="imagebox"></img>
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<canvas id="canvas" width="200" height="200"> </canvas>
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<div id="resultbox">
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<div id="result">result will go here</div>
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<div id="time"></div>
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</div>
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<script>
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const ctx = document.getElementById("canvas").getContext("2d", { willReadFrequently: true });
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const resultText = document.getElementById('result');
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let labels, net;
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const error = (err) => {
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resultText.innerHTML = `Error: ${err}`;
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throw new Error(err);
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}
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const getDevice = async () => {
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if (!navigator.gpu) error("WebGPU not supported.");
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const adapter = await navigator.gpu.requestAdapter();
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return await adapter.requestDevice();
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};
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const timer = async (func, label = "") => {
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document.getElementById('time').innerHTML = "";
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const start = performance.now();
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const out = await func();
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const delta = (performance.now() - start).toFixed(1)
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console.log(`${delta} ms ${label}`);
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document.getElementById('time').innerHTML = `${delta} ms ${label}`;
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return out;
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}
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const getLabels = async () => (await fetch("https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json")).json();
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const reorderChannelsAndRemoveAlpha = (data) => {
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const out = [];
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let i = 0;
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for (let c = 0; c < 3; c++) {
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for (let x = 0; x < 224 * 224; x++) {
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out[i] = data[x * 4 + c];
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i++;
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}
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}
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return out;
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};
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const runNetWResource = async (resource) => {
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resultText.innerHTML = "pending..."
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if (resource == "") error("sir. please type in a URL");
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const response = await fetch(resource)
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if (!response.ok) error("sir. that is not a good URL. try a new one");
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document.getElementById("imagebox").src = resource
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const img = new Image();
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img.crossOrigin = "Anonymous";
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img.onload = () => {
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URL.revokeObjectURL(img.src);
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ctx.drawImage(img, 0, 0, 224, 224);
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const data = ctx.getImageData(0, 0, 224, 224).data;
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runNet(data)
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};
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img.src = resource;
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}
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const loadLet = async () => {
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try {
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resultText.innerHTML = "loading..."
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labels = await getLabels();
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const device = await getDevice();
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net = await timer(() => model.load(device, '../../net.safetensors'), "(compilation)");
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resultText.innerHTML = "ready"
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} catch (e) {
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error(e)
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}
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}
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const runNet = async (data) => {
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if (!net) error("Net not loaded yet.");
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const input = reorderChannelsAndRemoveAlpha(Array.from(data).map((pix) => (pix / 255.0) * 0.45 - 0.225));
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const out = await timer(() => net(new Float32Array(input)));
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const arr = Array.from(new Float32Array(out[0]));
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const index = arr.indexOf(Math.max(...arr));
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resultText.textContent = labels[index];
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};
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loadLet();
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</script>
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</body>
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</html>
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