81bbf66aab
* Add project code * Logger improvements * Improvements to web demo code * added create_wlasl_landmarks_dataset.py and xtract_mediapipe_landmarks.py * Fix rotation augmentation * fixed error in docstring, and removed unnecessary replace -1 -> 0 * Readme updates * Share base notebooks * Add notebooks and unify for different datasets * requirements update * fixes * Make evaluate more deterministic * Allow training with clearml * refactor preprocessing and apply linter * Minor fixes * Minor notebook tweaks * Readme updates * Fix PR comments * Remove unneeded code * Add banner to Readme --------- Co-authored-by: Gabriel Lema <gabriel.lema@xmartlabs.com>
61 lines
2.6 KiB
HTML
61 lines
2.6 KiB
HTML
<!DOCTYPE html>
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<html>
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<header>
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<title>ONNX Runtime JavaScript examples: Quick Start - Web (using script tag)</title>
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</header>
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<body>
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<button id="start-test">Start Test</button>
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<p id="output"></p>
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<!-- import ONNXRuntime Web from CDN -->
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<script src="https://cdn.jsdelivr.net/npm/onnxruntime-web/dist/ort.min.js"></script>
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<script>
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async function setupButtons() {
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let test_button = document.querySelector("#start-test");
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test_button.addEventListener('click', async function() {
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main();
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});
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}
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// use an async context to call onnxruntime functions.
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async function main() {
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try {
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// create a new session and load the specific model.
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//
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// the model in this example contains a single MatMul node
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// it has 2 inputs: 'a'(float32, 3x4) and 'b'(float32, 4x3)
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// it has 1 output: 'c'(float32, 3x3)
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const session = await ort.InferenceSession.create('./spoter.onnx');
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// Number of frames
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const N = 100
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// prepare inputs. a tensor need its corresponding TypedArray as data
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const dataA = new Float32Array(108 * N);
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dataA.fill(0.4);
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// const dataB = Float32Array.from([10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]);
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const tensorA = new ort.Tensor('float32', dataA, [1, N, 54, 2]);
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console.log(tensorA);
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// prepare feeds. use model input names as keys.
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const feeds = { input: tensorA };
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// feed inputs and run
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startTime = new Date();
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const results = await session.run(feeds);
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// read from results
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const dataC = results.output.data;
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endTime = new Date();
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let output = document.querySelector("#output");
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var timeDiff = endTime - startTime; //in ms
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output.innerText = `Data of result tensor 'output':\n ${dataC}` + "\nInference took " + timeDiff + " ms";
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} catch (e) {
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let output = document.querySelector("#output");
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output.innerText = `failed to inference ONNX model: ${e}.`;
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}
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}
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setupButtons();
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</script>
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</body>
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</html> |