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   "source": [
    "# Importing models from ONNX to Caffe2\n",
    "\n",
    "In this tutorial we are going to show you how to import ONNX models to Caffe2. You can either\n",
    "\n",
    "- Directly run an ONNX model with Caffe2 in Python\n",
    "\n",
    "or\n",
    "\n",
    "- Convert an ONNX model file to a Caffe2 model file, and then later run the converted Caffe2 model in any environment that Caffe2 supports, e.g. on mobile phones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installation\n",
    "\n",
    "`onnx-caffe2` is now integrated as part of `caffe2` under `caffe2/python/onnx`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run an ONNX model with Caffe2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {},
   "outputs": [],
   "source": [
    "import onnx\n",
    "import caffe2.python.onnx.backend\n",
    "\n",
    "# Prepare the inputs, here we use numpy to generate some random inputs for demo purpose\n",
    "import numpy as np\n",
    "img = np.random.randn(1, 3, 224, 224).astype(np.float32)\n",
    "\n",
    "# Load the ONNX model\n",
    "model = onnx.load('assets/squeezenet.onnx')\n",
    "# Run the ONNX model with Caffe2\n",
    "outputs = caffe2.python.onnx.backend.run_model(model, [img])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert ONNX model file to Caffe2 model file\n",
    "`onnx-caffe2` has bundled a shell command `convert-onnx-to-caffe2` for converting ONNX model file to Caffe2 model file. \n",
    "\n",
    "\n",
    "```shell\n",
    "\n",
    "$ convert-onnx-to-caffe2 assets/squeezenet.onnx --output predict_net.pb --init-net-output init_net.pb\n",
    "\n",
    "```\n",
    "\n",
    "Note in ONNX model file, parameters and network structure are all stored in one model file, while in Caffe2, they are normally stored in separated `init_net.pb` (parameters) and `predict_net.pb` (network structure) files."
   ]
  }
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