Created
May 3, 2019 17:06
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onnx sample
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| <html> | |
| <script src='../dist/webml-polyfill.js'></script> | |
| <script src='third_party/protobuf.min.js'></script> | |
| <script src='util/base.js'></script> | |
| <script src='util/onnx/onnx.js'></script> | |
| <script src='util/onnx/OnnxModelUtils.js'></script> | |
| <script src='util/onnx/OnnxModelImporter.js'></script> | |
| <script> | |
| (async () => { | |
| const res = await fetch('path/to/model.onnx'); | |
| const bytes = await res.arrayBuffer(); | |
| const onnxModel = onnx.ModelProto.decode(new Uint8Array(bytes)); | |
| const model = new OnnxModelImporter({ | |
| rawModel: onnxModel, | |
| backend: 'WebML', | |
| prefer: 'fast', | |
| }); | |
| await model.createCompiledModel(); | |
| const inputs = [new Float32Array(224*224*3)]; // input tensors in NHWC format | |
| const outputs = [new Float32Array(1000)]; // placeholder for outputs | |
| const start = performance.now(); | |
| await model.compute(inputs, outputs); // outputs will be ready once fulfills | |
| const inferenceTime = performance.now() - start; | |
| console.log(outputs[0]); | |
| })(); | |
| </script> | |
| </html> |
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