Step2_keras-jetson-ImageNet-predict.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: Make prediction with TensorRT graph.\n",
    "\n",
    "Run this notebook on Jetson Nano.\n",
    "**For more detail, checkout [How to run Keras model on Jetson Nano](https://www.dlology.com/blog/how-to-run-keras-model-on-jetson-nano/) | DLology Blog**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Input and output nodes names printed in the output during freezing Keras model in previous step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_names = ['Logits/Softmax']\n",
    "input_names = ['input_1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the `TensorRT` frozen graph `.pb` file\n",
    "This might take a while."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-2-9908f6ec841a>:6: FastGFile.__init__ (from tensorflow.python.platform.gfile) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.gfile.GFile.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.contrib.tensorrt as trt\n",
    "import numpy as np\n",
    "\n",
    "def get_frozen_graph(graph_file):\n",
    "    \"\"\"Read Frozen Graph file from disk.\"\"\"\n",
    "    with tf.gfile.FastGFile(graph_file, \"rb\") as f:\n",
    "        graph_def = tf.GraphDef()\n",
    "        graph_def.ParseFromString(f.read())\n",
    "    return graph_def\n",
    "\n",
    "\n",
    "trt_graph = get_frozen_graph('./model/trt_graph.pb')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create session and load graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create session and load graph\n",
    "tf_config = tf.ConfigProto()\n",
    "tf_config.gpu_options.allow_growth = True\n",
    "tf_sess = tf.Session(config=tf_config)\n",
    "tf.import_graph_def(trt_graph, name='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image_size: [224, 224, 3]\n"
     ]
    }
   ],
   "source": [
    "# Get graph input size\n",
    "for node in trt_graph.node:\n",
    "    if 'input_' in node.name:\n",
    "        size = node.attr['shape'].shape\n",
    "        image_size = [size.dim[i].size for i in range(1, 4)]\n",
    "        break\n",
    "print(\"image_size: {}\".format(image_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_tensor_name: input_1:0\n",
      "output_tensor_name: Logits/Softmax:0\n"
     ]
    }
   ],
   "source": [
    "input_tensor_name = input_names[0] + \":0\"\n",
    "output_tensor_name = output_names[0] + \":0\"\n",
    "\n",
    "print(\"input_tensor_name: {}\\noutput_tensor_name: {}\".format(\n",
    "    input_tensor_name, output_tensor_name))\n",
    "\n",
    "output_tensor = tf_sess.graph.get_tensor_by_name(output_tensor_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict a real image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optional image to test model prediction.\n",
    "img_path = './data/elephant.jpg'\n",
    "\n",
    "img = image.load_img(img_path, target_size=image_size[:2])\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "x = preprocess_input(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1000)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feed_dict = {\n",
    "    input_tensor_name: x\n",
    "}\n",
    "preds = tf_sess.run(output_tensor, feed_dict)\n",
    "\n",
    "preds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: [('n02504458', 'African_elephant', 0.6990039), ('n01871265', 'tusker', 0.16008943), ('n02504013', 'Indian_elephant', 0.03917188)]\n"
     ]
    }
   ],
   "source": [
    "# decode the results into a list of tuples (class, description, probability)\n",
    "# (one such list for each sample in the batch)\n",
    "print('Predicted:', decode_predictions(preds, top=3)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmark inference speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average(sec):0.04,fps:27.18\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "times = []\n",
    "for i in range(20):\n",
    "    start_time = time.time()\n",
    "    one_prediction = tf_sess.run(output_tensor, feed_dict)\n",
    "    delta = (time.time() - start_time)\n",
    "    times.append(delta)\n",
    "mean_delta = np.array(times).mean()\n",
    "fps = 1 / mean_delta\n",
    "print('average(sec):{:.2f},fps:{:.2f}'.format(mean_delta, fps))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Close the session to release resources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_sess.close()"
   ]
  }
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