mirror of
https://github.com/saymrwulf/onnxruntime.git
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35 lines
1.2 KiB
JavaScript
35 lines
1.2 KiB
JavaScript
'use strict';
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const ort = require('onnxruntime');
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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('./model.onnx');
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// prepare inputs. a tensor need its corresponding TypedArray as data
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const dataA = Float32Array.from([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
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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, [3, 4]);
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const tensorB = new ort.Tensor('float32', dataB, [4, 3]);
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// prepare feeds. use model input names as keys.
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const feeds = { a: tensorA, b: tensorB };
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// feed inputs and run
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const results = await session.run(feeds);
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// read from results
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const dataC = results.c.data;
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console.log(`data of result tensor 'c': ${dataC}`);
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} catch (e) {
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console.error(`failed to inference ONNX model: ${e}.`);
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}
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}
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main();
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