index.js

async function runExample() {
  // Create an ONNX inference session with default backend.
  const session = new onnx.InferenceSession();
  // Load an ONNX model. This model is Resnet50 that takes a 1*3*224*224 image and classifies it.
  await session.loadModel("./add.onnx");

  const x = new Float32Array(3 * 4 * 5).fill(1);
  const y = new Float32Array(3 * 4 * 5).fill(2);
  const tensorX = new onnx.Tensor(x, 'float32', [3, 4, 5]);
  const tensorY = new onnx.Tensor(y, 'float32', [3, 4, 5]);

  // Run model with Tensor inputs and get the result by output name defined in model.
  const outputMap = await session.run([tensorX, tensorY]);
  const outputData = outputMap.get('sum');

  // Check if result is expected.
  const predictions = document.getElementById('predictions');
  if (!outputData.data.every((value) => value === 3)) {
    predictions.innerHTML = `Error: Data mismatch!`;
    return;
  }
  if (outputData.data.length !== 3 * 4 * 5) {
    predictions.innerHTML = `Error: Expected length of ${3 * 4 * 5} but got ${outputData.data.length}`;
    return;
  }
  predictions.innerHTML = `Got an Tensor of size ${outputData.data.length} with all elements being ${outputData.data[0]}`;

}