{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n\n# Load and predict with ONNX Runtime and a very simple model\n\nThis example demonstrates how to load a model and compute\nthe output for an input vector. It also shows how to\nretrieve the definition of its inputs and outputs.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy\n\nimport onnxruntime as rt\nfrom onnxruntime.datasets import get_example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's load a very simple model.\nThe model is available on github [onnx...test_sigmoid](https://github.com/onnx/onnx/blob/main/onnx/backend/test/data/node/test_sigmoid).\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "example1 = get_example(\"sigmoid.onnx\")\nsess = rt.InferenceSession(example1, providers=rt.get_available_providers())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's see the input name and shape.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "input_name = sess.get_inputs()[0].name\nprint(\"input name\", input_name)\ninput_shape = sess.get_inputs()[0].shape\nprint(\"input shape\", input_shape)\ninput_type = sess.get_inputs()[0].type\nprint(\"input type\", input_type)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's see the output name and shape.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "output_name = sess.get_outputs()[0].name\nprint(\"output name\", output_name)\noutput_shape = sess.get_outputs()[0].shape\nprint(\"output shape\", output_shape)\noutput_type = sess.get_outputs()[0].type\nprint(\"output type\", output_type)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's compute its outputs (or predictions if it is a machine learned model).\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy.random\n\nx = numpy.random.random((3, 4, 5))\nx = x.astype(numpy.float32)\nres = sess.run([output_name], {input_name: x})\nprint(res)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.6"
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  },
  "nbformat": 4,
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