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<!DOCTYPE html>
<html>
<head>
<title>ONNX Runtime JavaScript examples: Quick Start - Web (using script tag)</title>
</head>
<body>
<!-- see also advanced usage of importing ONNX Runtime Web: -->
<!-- https://github.com/microsoft/onnxruntime-inference-examples/tree/main/js/importing_onnxruntime-web -->
<!-- import ONNXRuntime Web from CDN -->
<script src="https://cdn.jsdelivr.net/npm/onnxruntime-web/dist/ort.min.js"></script>
<script>
// use an async context to call onnxruntime functions.
async function main() {
try {
// create a new session and load the specific model.
//
// the model in this example contains a single MatMul node
// it has 2 inputs: 'a'(float32, 3x4) and 'b'(float32, 4x3)
// it has 1 output: 'c'(float32, 3x3)
const session = await ort.InferenceSession.create('./model.onnx');
// prepare inputs. a tensor need its corresponding TypedArray as data
const dataA = Float32Array.from([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
const dataB = Float32Array.from([10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]);
const tensorA = new ort.Tensor('float32', dataA, [3, 4]);
const tensorB = new ort.Tensor('float32', dataB, [4, 3]);
// prepare feeds. use model input names as keys.
const feeds = { a: tensorA, b: tensorB };
// feed inputs and run
const results = await session.run(feeds);
// read from results
const dataC = results.c.data;
document.write(`data of result tensor 'c': ${dataC}`);
} catch (e) {
document.write(`failed to inference ONNX model: ${e}.`);
}
}
main();
</script>
</body>
</html>