inference.ipynb

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   "source": [
    "# Running the Inference for lenet model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "import json\n",
    "import sys\n",
    "import os\n",
    "import time\n",
    "import numpy as np\n",
    "import cv2\n",
    "import onnx\n",
    "import onnxruntime\n",
    "from onnx import numpy_helper\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
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   "id": "88d91b8f",
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   "source": [
    "model=\"./lenet.onnx\"\n",
    "path=\"./mnist/mnist_00.png\"\n",
    "Image(filename=path, width=256, height=256)\n",
    "#path=sys.argv[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a1c4e46",
   "metadata": {},
   "source": [
    "## Preprocessing the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58558fc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread(path)\n",
    "img = np.dot(img[...,:3], [0.299, 0.587, 0.114])\n",
    "img = cv2.resize(img, dsize=(28, 28), interpolation=cv2.INTER_AREA)\n",
    "img.resize((1, 1, 28, 28))\n",
    "img = img / 255"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0399656",
   "metadata": {},
   "source": [
    "## Create the inference session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80825dfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = json.dumps({'data': img.tolist()})\n",
    "data = np.array(json.loads(data)['data']).astype('float32')\n",
    "session = onnxruntime.InferenceSession(model, None)\n",
    "input_name = session.get_inputs()[0].name\n",
    "output_name = session.get_outputs()[0].name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04945191",
   "metadata": {},
   "source": [
    "## Run model and print results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55e529ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = session.run([output_name], {input_name: data})\n",
    "prediction=int(np.argmax(np.array(result).squeeze(), axis=0))\n",
    "print(prediction)"
   ]
  }
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