tftrt/benchmarking-python/image_classification/Colab-TFv2-TF-TRT-inference-from-Keras-saved-model.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Colab-TF20-TF-TRT-inference-from-Keras-saved-model.ipynb",
      "provenance": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "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": "3.6.8"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tensorflow/tensorrt/blob/r2.0/tftrt/examples/image_classification/TFv2-TF-TRT-inference-from-Keras-saved-model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "dR1W9kv7IPhE",
        "colab": {}
      },
      "source": [
        "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     http://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License.\n",
        "# =============================================================================="
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Yb3TdMZAkVNq"
      },
      "source": [
        "<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
        "\n",
        "# TF-TRT Inference from Keras Model with TensorFlow 2.0\n",
        "\n",
        "\n",
        "## Introduction\n",
        "The NVIDIA TensorRT is a C++ library that facilitates high performance inference on NVIDIA graphics processing units (GPUs). TensorRT takes a trained network, which consists of a network definition and a set of trained parameters, and produces a highly optimized runtime engine which performs inference for that network. \n",
        "\n",
        "TensorFlow™ integration with TensorRT™ (TF-TRT) optimizes and executes compatible subgraphs, allowing TensorFlow to execute the remaining graph. While you can still use TensorFlow's wide and flexible feature set, TensorRT will parse the model and apply optimizations to the portions of the graph wherever possible.\n",
        "\n",
        "In this notebook, we demonstrate the process of creating a TF-TRT optimized model from a ResNet-50 Keras saved model.\n",
        "\n",
        "## Requirement\n",
        "\n",
        "### GPU\n",
        "\n",
        "Before running this notebook, please set the Colab runtime environment to GPU via the menu *Runtime => Change runtime type => GPU*.\n",
        "\n",
        "This demo will work on any NVIDIA GPU with CUDA cores, though for improved FP16 and INT8 inference, a Volta, Turing or newer generation GPU with Tensor cores is desired.  On Google Colab, this normally means a T4 GPU. If you are assigned an older K80 GPU, another trial at another time might give you a T4 GPU.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8Fg4x4aomCY4",
        "colab": {}
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "LG4IBNn-2PWY"
      },
      "source": [
        "### Install TensorFlow-GPU 2.0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "rL9Sz1GhkVNz",
        "colab": {}
      },
      "source": [
        "!pip install pillow matplotlib\n",
        "!pip install tensorflow-gpu==2.0.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "v0mfnfqg3ned",
        "outputId": "11c043a0-b8e5-49e2-f907-5f1372c92a68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "print(\"Tensorflow version: \", tf.version.VERSION)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensorflow version:  2.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "KIBqZ3-Rlibh"
      },
      "source": [
        "### Install TensorRT Runtime"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BlaPmL1JllHC",
        "colab": {}
      },
      "source": [
        "%%bash\n",
        "wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb\n",
        "\n",
        "dpkg -i nvidia-machine-learning-repo-*.deb\n",
        "apt-get update\n",
        "\n",
        "sudo apt-get install libnvinfer5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "kyW580EYkVOP",
        "outputId": "71cd7d76-09ef-42fe-dfc7-dd0f9d166ea5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "# check TensorRT version\n",
        "print(\"TensorRT version: \")\n",
        "!dpkg -l | grep nvinfer"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorRT version: \n",
            "ii  libnvinfer5                             5.1.5-1+cuda10.1                                  amd64        TensorRT runtime libraries\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9U8b2394CZRu"
      },
      "source": [
        "A successfull TensorRT installation looks like:\n",
        "\n",
        "```\n",
        "TensorRT version: \n",
        "ii  libnvinfer5   5.1.5-1+cuda10.1   amd64        TensorRT runtime libraries\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "SeekwaXj4CbI"
      },
      "source": [
        "### Check Tensor core GPU\n",
        "The below code check whether a Tensor-core GPU is present."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "VHLld1VNkVOa",
        "outputId": "a0d2b13c-fe62-476c-9589-190bdcfff233",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from tensorflow.python.client import device_lib\n",
        "\n",
        "def check_tensor_core_gpu_present():\n",
        "    local_device_protos = device_lib.list_local_devices()\n",
        "    for line in local_device_protos:\n",
        "        if \"compute capability\" in str(line):\n",
        "            compute_capability = float(line.physical_device_desc.split(\"compute capability: \")[-1])\n",
        "            if compute_capability>=7.0:\n",
        "                return True\n",
        "    \n",
        "print(\"Tensor Core GPU Present:\", check_tensor_core_gpu_present())\n",
        "tensor_core_gpu = check_tensor_core_gpu_present()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor Core GPU Present: None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "nWYufTjPCMgW"
      },
      "source": [
        "### Importing required libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Yyzwxjlm37jx",
        "colab": {}
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "import os\n",
        "import time\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.python.compiler.tensorrt import trt_convert as trt\n",
        "from tensorflow.python.saved_model import tag_constants\n",
        "from tensorflow.keras.applications.resnet50 import ResNet50\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "v-R2iN4akVOi"
      },
      "source": [
        "## Data\n",
        "We download several random images for testing from the Internet."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "tVJ2-8rokVOl",
        "colab": {}
      },
      "source": [
        "!mkdir ./data\n",
        "!wget  -O ./data/img0.JPG \"https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\"\n",
        "!wget  -O ./data/img1.JPG \"https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\"\n",
        "!wget  -O ./data/img2.JPG \"https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\"\n",
        "!wget  -O ./data/img3.JPG \"https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "F_9n-AR1kVOv",
        "outputId": "e0ead6dc-e761-404e-a030-f6d3057a57da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "from tensorflow.keras.preprocessing import image\n",
        "\n",
        "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
        "\n",
        "for i in range(4):\n",
        "  img_path = './data/img%d.JPG'%i\n",
        "  img = image.load_img(img_path, target_size=(224, 224))\n",
        "  plt.subplot(2,2,i+1)\n",
        "  plt.imshow(img);\n",
        "  plt.axis('off');"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qSqwColDVkZmZ4aU/j89za4C6rulUCy6XM+LQuqGhVD9jjNgUYskkXw3PuJYK/uR+ZhxL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AsizT7znISBiaTr5YxnF6tDse3W6XNHaQtEtShVHKertDybYI/QhJUeh0fdYbTfK6Tt/tMTJW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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "xeV4r2YTkVO1"
      },
      "source": [
        "## Model\n",
        "\n",
        "We next download and test a ResNet-50 pre-trained model from the Keras model zoo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WwRBOikEkVO3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "2d63bc46-8bac-492f-b519-9ae5f19176bc"
      },
      "source": [
        "model = ResNet50(weights='imagenet')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/keras-team/keras-applications/releases/download/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5\n",
            "102973440/102967424 [==============================] - 4s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lFKQPoLO_ikd",
        "outputId": "c0b93de8-c94b-4977-992e-c780e12a3d52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "source": [
        "for i in range(4):\n",
        "  img_path = './data/img%d.JPG'%i\n",
        "  img = image.load_img(img_path, target_size=(224, 224))\n",
        "  x = image.img_to_array(img)\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "  x = preprocess_input(x)\n",
        "\n",
        "  preds = model.predict(x)\n",
        "  # decode the results into a list of tuples (class, description, probability)\n",
        "  # (one such list for each sample in the batch)\n",
        "  print('{} - Predicted: {}'.format(img_path, decode_predictions(preds, top=3)[0]))\n",
        "\n",
        "  plt.subplot(2,2,i+1)\n",
        "  plt.imshow(img);\n",
        "  plt.axis('off');\n",
        "  plt.title(decode_predictions(preds, top=3)[0][0][1])\n",
        "    "
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json\n",
            "40960/35363 [==================================] - 0s 0us/step\n",
            "./data/img0.JPG - Predicted: [('n02110185', 'Siberian_husky', 0.5566208), ('n02109961', 'Eskimo_dog', 0.4173726), ('n02110063', 'malamute', 0.020951588)]\n",
            "./data/img1.JPG - Predicted: [('n01820546', 'lorikeet', 0.30138952), ('n01537544', 'indigo_bunting', 0.16979568), ('n01828970', 'bee_eater', 0.16134141)]\n",
            "./data/img2.JPG - Predicted: [('n02481823', 'chimpanzee', 0.51498663), ('n02480495', 'orangutan', 0.15896711), ('n02480855', 'gorilla', 0.15318131)]\n",
            "./data/img3.JPG - Predicted: [('n01729977', 'green_snake', 0.4237962), ('n03627232', 'knot', 0.09050954), ('n01749939', 'green_mamba', 0.06557768)]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UrTEXnKABT8U60xCRuiLmQkAQDEwzeOwUYSH5sGzMZ6ilJAhrlKQOuavAsvKG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qyhcciy9ak2I9dczFS+kUd2fZIv7/4o5sAop957B2t1inY5mWZ1o7Bzch/LBxSbj1eTXr6q8grW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\nFg8tsLxxnZRkkCrkkALB2UsXePPd97HX2sO0Enj2CDkUZJIpkukUo94Qw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DEAACAASURB\nVKoFuEUJ2LbXORCN8XAyzdOddcbyOQqKoF+5nVTtGYQQ/MwP/QJi1OPcs1/j649/lX/5y5+kvbOL\noiv07B62bdOx+7z5fd/H049/gXIpSy7I8dGf/wW2m1V+4od/AC985fvuV8XEyK984jc/JhQFy9Ip\n5yxKpdI+K0xgWklymSKj0ZBet0u71+PK8jX6/T6Li/fgD4aU8kl0LclwOGTQbtFq97EyFu3aDp12\nG9cdUl1Zo1ws0u22uXrlMuMTM6xWNxmObHzPxTBNCuUxZBHxla99ncXZOX77//htLr34AnutPQLH\nYTTq02p3CYHhyGZkD3GHMYCg1enQ73WJJMFYpoAnu/zTn/wIFy9e4M1vepC777uL5194jsANMEwV\nPxA0G3sszi9R39nAB9zBgMiPZ0SHPYdkusiw20QXsX6mO+rh2TYREq4XYOgJBq5L0kjRa3XJZ7Mo\nsky1F0/YNNpN+qMhbuDjOC4RUUzU8ASB5BPJSgxTjYB9itvP/9RHXpsY+RbGb/0///ZjkhTL0SU5\nfqYlWULen5GQ9gkqihIrI2LDWxTTlqWYJSj27wOFFIEkSKWhWJzH92Q22nUGwZDR0CFdKFHdazOT\ny/Eby8t0rrb56VvvII3CYHeTyuIBzOUQ/dYer/eOUNpb4/8ejNgyiviDa4y6Te6bO8KmNOBktsIg\ncpkNZXIbWzjtHaJen+7qNvRb7G03cEYjUrKG5MWyp1w4pD9ss9dfpRj61D1oB0NOqglEFDCQQ2YU\niZGIKBgO9XaXs0LGmpymVj/H0fIYp9c73GFlMGcOsPLcf0DxQx6+54MsP/oImq4xNXmAhaUFPGeI\nkTDQ9Rg/JgI51tB2ukyUKrRabYopk8svvkAqk6FYKPH9P/xj3/TZflXsBFVZQXY93KHNXruJ49ok\nzQREFolEAs91CDwXXVOQhY4qGczPHqfT6qJrSZqNHnvtHoqhs7WxTiZfYmd9h+HIR7d0OrUe+UKG\nQAZDUTl26wnq27uokkxprEQqkUJGoWn3uHD+MieOHefeN76Ry1evcWVrC1NPUNvaIJMtEPkBQQgq\ngnavjZ5M4HccsqUcup7j4sWLHFo8xObKNs8+8TSSrPLoE1/ljtvu4N3vez+/8eufwPM82t0Ouq6y\nubWO64xIJVSq61USmkwyNU3CUvGCLqgqm1trjI9PUCiPIRyHgTsinUyRyebZrG7RiDzKuSLLK8v4\nCgw6TXK5EkgqXbu1P54VIkcS8WEsQJaU+NLdiZA1CISM8vcpuV6L/6y40SgtSRIRNxqW93FqshxP\nSEgy8s3q8Q1kQDwhAtxMgvH2ECQ8HK9B1iwxbSWx3SG2P2Rk9xnPGGzWtrnTTKJVYP6zvwWJNGoq\nxz32GC8k1hlcNPkz8bccz4yztvsCDFxwJHK+y3h3yNGuR99eRl5dZWc0ZOOJ0/hugEq0jzt9idAn\nSRLZ8SK5qTLGoaOIlEVS5FD8NerXq6QzEl5xnm0JrLTKgeQ4I8tC7zsskaSh9Dm/dp6jdomrRsjR\nOYWBq3CwcIJHZYMHTjxELpUk/dA78do1Dt7/EPUrL9Lda6JKE2iKhCIrSJHLoNNgZv4g/f6A1Pom\njU4Xu2cTuAG9ZvcV1+hVcScYhiGSpYOl4is+/X6XMAgwNA1NURFRgDPqo8gGuXSGZCpBe2+XzZWL\njAZNvCACVUIOAu57w52YukSukGfg9Min06SyKcqZHG63zdGDh2jW9jh25DiTk5NEPnTsLoEUcnRx\nke2dTQaDIf/1v/gorXqDI7MLSK7DwuFDpFIpBoMBg9GAjeoGatJk7coK585dwOmPaLe7pDM5nn/2\nOVo7dVwR4o8cNtc2OXygyNTkHDqwuDBHSjeJnADH7ROFEvlsgfvvvx8vikEHQ8dhZ32VN7/xjYxP\nFDh4YI6UpiEIGA5tdht1gkiQMEwOzy4ihxD6Ltg+k9kKW9vbdAbN/Wp7LPGRJSk+iin7zekiQjF1\nhKSiytJreP3/QhGDE16CKLx8igTAFxFeGHzD98LIv4nY/7t4eUUWDHbrPFFfZd3u0wkdDCuBOxpi\n2z5H0mNMCuhsb/HdDZvExhpBbYWKuQXdAQ95CtOtBvdVe2jVPea7HfKuz3SvTzDq0V/f4vIzz7L6\n+NfZfOochfkFUkcOk1xaRC0UIaPFCgtNRkais9Ni7fQlls+fZ+f6ClG9BX1Bz/dJDl2SrRbtrTXC\njVXc6jWCa5fwGztMjyLG230qHpiqwmecTcRQYsoUDIWMo1i8++7vJpvO4XSbtPc2EKGPKym0Ox0C\n12PQi+k3fqRQrExhpjLkxsZ480MPMTE1TS5XYHp6ljtfd8srrs+rYicYhoKUKpO1FCxDxnH7+GFm\nH6IYT2yEYQxbNBJQLuaYHqtguzajfhM5nySb0vBGMpcvr6GYOt1ah06nw7BcJnIcWq0GAoOt6jr1\neg0zE88SZvK5mz2Ka5tbzCwssL1V5eO/+HEeesc7Wa9uMvQ8LC/k/MULHJw/wMbqGiMRMGh2GJst\nE+LSbNVRlQS1Wg1JkTE0nXwmyc7WJgO7x8p6l6WlWTJjZRqNBv3RED/0cJ2ATrdJOmOBHzI1NUWn\n0yGZypFKWSxfOk97r8GWYrBT3yPcn+pQwoDtrW3y+TyWaZCanSYIR1TrTWYrFVZ2d8nlkviBS7vb\nRUW5yaMT8g1qkRQrG6O4f+3vE1S/Fv/5IYcyKDHkI5IiJBERKQGKHMMMbtCQQyHdxKnKSLFMKQq+\nwcUhRIiIFEzRYTZ3L3Q6RKpPs9FG0zTKuSSb9W0k3cTUFNrL17gnn6MRhJS9FHJjnULaY3uwSz81\noLizRUFEJJp7eM6Q1dEG7SsraKGFdfAEnqZT13VE5GK4kLYyDFotfKWDHDiEjouiqAgv4vqLVxHm\nKplsnrXBFSjk0EizTQ9XDsh4kHI8LKfBuJ+mY4YIw2HcTlM14ZAQ9MizljPRy3WKh96Grsn0+100\nU+D0R1x/+utUN6/R2mtRyuUpjJUJQkGylCd0PdLpNK7nkctluHLmDDMHZilNjnH10sVXXJ9XRRKU\nFQ01qZDK6mi6QDdjI1cgg+f6yJKD6zmISCWVzFGZmGZ14zpu1EFWCoSKRuDK1FodXCdi2GoRhA7l\nVJbry1fR9Rj5gxyyXW8QqjKh77O6sU6+WMCyUmxtbXH369/A+voqhmHwgQ/+E+q1bQxJYXyiwvOn\nTtGt1WhmM5QnCnhuiCQEE5Upcok07UGXyBFMT08jqaDJOucvXiCKIioTU1w4/yJvedcbSacsOvU6\nIgzJpVK0u3HLj2kkkKSI3c0ayWSSYa+NnUoTyQqSFFGt7zIMZAxdR0gyejJBu1VjcmKM5sjn2HyF\n02dtspkMw4HN3NwsuiI4t72BpiaR3BGSBIqsIckCIQkkAVKgECoBCNAl5T++WK/FPyi+wY8L8QfO\ny9BaIopQb9jlJGl/jC6Kx+xk9u/G973DUXwQFZJgqIS4ySS7O5uYno+hGfiuTWM0IJtI0XIc8nqC\nLWePiaGG1NpmtdmhONhjlB7htnbZUnaQWzVsoFnfQ2oPECOF1IFbaLoegaZhORFCT1CKPOyEymDU\njdN00iSyh0jeiHDQRrJURMJEHkZ0Bz5rzRoLckhFVdkaBciWQS6Txx75ZIVCOoSN5g6yIiEPN1hR\nI4ryDFFuwFd3lrk7Nc0Db/lBrjz9CAzOcd/3foBMuoI78giEzrULFzi4dBizUERzfNJjRfqtDoZp\n4ocew86AL/z1X3HXHScxZZibm3vFNXpVJEGBTzKdZHwyR7GcYO7ALAklR3fg0B+0cEc6nutjWjqS\nF6EgmJybIDssk7AKNKsand42gaZgJBWaewNCAubmZllf80hbSQqFAqfOPE+72SBh6Ohzhzi2uMhy\ndYeFAwdZubZMq11jY2eLtJVESCMOzk/g9qtUtzucvPUuLlvXkSOPMBSoskSz2aZYKNPq9VGCiFq3\ny3se/i6uXbvGWKXEsD/EHrbZvr7CkQPz/NHvfYbHHn+ccm6MUinP0HUwkxa9dodry5dRNJXpuQkI\nfNIpCxnBC2eewQuHjBWmODw/w/LyMhlTZ2uvRrk8ztrmFotLFo9//WvkkmmW11dJ5iyQIjpuiKIn\nUP0eoQggBE1VCKQY0x6GAiIPIlDCCF9+bWLkWx1CSARShLzvCZEBIQnCIO75k/XYjUuoxMAUEXty\nEQGyEguC5P1bK1mKcVsiipAFVM9+mtvv+mFWzp1mz7ZJSgJFkmg2mximxc7mBpphstHZQdM0lqvX\nUWRYZYBtD9hVVAatNqEnyC8eojg/zpXdJpHsEzR2IPJQDk3R3+ujzc2TG7kcHZ/n691N8KW4ENLr\nEDabiNEQvCEYAS8++zjGkddz/Og9XF+5TsIacMQtE4UhUiJkJpFGDSS8RJIFM8FGrYqpCqYUhWuB\nxJFKhU/ubvDjsszd9z5Aq9tnrDKNmVDpdYekJydw+n1CJ6BcmaTd7SD6IwLXY7NWJ1fKcfbUs/iy\nTHOvybEjC2hJ7RXX6FVxJygjkUhoSFKEZVlk0gWy2SLZTBlJUhj0uwShSxTGAvaMlUSSXHQrpF7t\nMRy06XaGdKvbDAZ9jJTJwuwkoTdAUeIy+oWLLzA/O8PCxCRWUkdRBJ4kSOsKQ9vGyuWYmJji7Q++\nhV7PRsgy6ztVLi9fY2q6wtW1NSRZ0O7bdBpdJM3ATCbQEjA5Oc380gEOHz1GtVYlU0yRKuTwZIlM\nrsD7f/SDpA2Drz57CkvVcb0R9mCAY7uEw4CB42AkTBRVoAoJ9oU7VjZBSgmQMOjYPep7u1hpnTAU\njGWzDN0ORlKh12pTLIyTLuTI53IoioEhZIQWYsggJJlEAFYkCCQJKYqbcSVZRTVNFF1HURT0/4iQ\n5rX4h8fLkVk3MPEvV2nemByJoggRhBBGiCi42T94447w5X9f7OgNkRWX587+DvP3v42UXqDbG7Lb\n6uAMXarrWwxtByuS8RyXlFDJ6ymyGBiSRl4kCAMFX05Tfv1beP3Re0gWTFQjoOXYqJUcejFD8vI6\n6b7HijdkZdCElMWDC3djmiYdV0JO5DFnFtHnDiFnC0RmGjQd/9wzrA1kvHd+gMniEl2rxpiRImcm\nSMkqiiZTTMS+azVtMZNOUXICjM4uBxyZWV0l6Y2wWy26WzVCNbbG6ZqCIkkEIWys7zAajUiYyZcm\ncIjY26lRLJe4cv4Kb3jgPuZPHOTiC6dfcY1eFUkwJJYxm4aBoSfQTI1cvkCxWMRQVEajEWYihaYp\nSAicQBB5kLYO47ohC4fLhJFDs9NFkULKySS7rRaupJHNZ7jz9tsppLPsrK7RHnQ4vHSEYa9PIVtA\nVjR6/T4HZ2e5dOUi21trJC2NX/rF/5nHvvAIQi3whT//c/K6ymSpyG233MrIH2IldIppk057BLLg\n3IXLFLIW3b0mju1y7oWzfPVv/5KphTmef+xJzly4wte+8gS5UhlCn6Hvk02naHXrSIFHNmmR1U0C\n1yGKAvr2gNZeCzWVRRAyPjWJbw/odrt89/d8T2zbG7j0WgPa/R6eF7Czs4MkCQqpFCL0iUKFrGlh\nRQJVVogQ6KpCYOpEmoqe0AkRaLKOCG8cy16Lb3WEQUQUxru6UHATn38jMYoovpsVIiQS3ktJ8e8U\nRIhCAjUgFD66EISSTjp7O/Q2edsHf5J3vult9LQEhmFQyJYYT+ZBCjkwNknBTFNOFzlUnmC8UGDx\n+H3c810/RSad4/XHjnFucx1PUjk2MU42clFlBX1qht3bFzl0x63cIyU5agvO1VY5t3GJe5cOocmA\nGe+wPEmCsQVSU5PIaJBKcfnf/w/0alf5dC7PZ7UKX9E0KloCEirprE7JsBAhjOkG01qaLXdIvteh\nVq9yR6Cz0rqI07SZmFtAV3QkWSYIXMKRy8bGBlvVNVwv2m/10ghDHy/0KY2VyOeKHJ6cIjsxTiQ0\nuv1XhoO8Ko7DiqQgGRq6liChm5hm/FK1JLV0Bn97E9/3SVo6mm6QTob0B2P0GytMTFdYvVal1elj\nGhpSoKNlE9w7P81ydQtVgXazxdTEFL3BgPFChWvXrpFMpBg1u/SHNjoh5VKOqOGz12zTaLTY3d3l\nHz38Hg6fOMbPP/5FysIjb+XIFQsEI49iKoWcK2APXTxnxK//v+y9d5Ckd3nv+3lz6DwzPXlmZ3c2\narVJ0ioBkghCIgowNjZwLSzfg499jPExNrbvKR/bOJUjGCPsa4MxJhgBBiEQCCRQjqvVanOa2Z0c\nOoc3x/tHr4Q498h/nMLFlkrfqa5+Z6qreup9fv30E7/fv/p9/uJjnwIJKvVVlpcWecWrriPxYwoD\nfQxPTnLTa67h5Imj2JpCJpRAlHAchyRJOL+4RKmQR5NhIDdIsZSh0+kQhjFRGrG6sEzGUAnDkPu+\n/wApMhnTpGF79BeKyMSYpkkSebRtC9tye0JRSUR/KUfQapGIBlVBQFVlQCYUBGRRQQocZE1D8l9e\nm/uxI7mgtXtB/FEUn4sE+ZEoL70wHJ3E8YUZwgurdgjPd40VVMQ4QBBUvMIg1132DjZNX4OiF2jV\n1vF33MiYlMc/cpA9w4Os2zb7L91Euu4jFzO07C7zkcCknGc1UZkqmzT7hvni0Tmunxzge488yGBf\nCS2bY09pjEdOHYOtGznYXEdWVEY3TdA5fw6jT+bEyiqxmEfM5UBoIKYSiawiRALpcErarpKxVBb+\n4o/hV3+HKHZ5tJHw6PwxbpuY5srBMoESYeZkxFim6dlookxZlFhaW2RTscS+3W9n1Foj8OILkXOK\n0+qwtraGpmnYzSZpGJHEMakQYdt2756pMgsz53jTLa/H7dYIAger031RE10UTlCRJFRRopjrQzHL\nRH5EkgrIokypb4Q0PdL79hQgjRQ8N8X3Q5KkSNANWVhcxtBkdmzbycz5E7TtLk6jTqkvh5YpYioi\nUSywvL5GYNlMTo5QbdgU8hojQ5uo1Woomo7nO9RX2+iSwtLSCuWpcb5597e46YYbUEs5/uBD/w9T\nOzaxeWIDx04dZm1hiUq9ThLFfO5f/gnEhHq7jd+N+bOP/h39eZ2HHriXNJJ43+X7mJreTqdWQ4wC\nVmstJFWEJMUsFimkCe12l0wmoT+NMXJ99OVyuJ5PgkKn28TtyoSyQJSkmJkyQlpnQNfp2l1UX0FS\nZIYGRqnV6pQ0k2ZlBaUgMVnKEOUUuoBthbTDBC0VERRATpEEjT4tQSmpP+mj8JJDGsWAQHJhIBp6\nYuHphZm/57Rf4jjppWVCSpTGPDet9CMKdIUSu69/D7vLO1G0IjEBDc+n0WrgJ3C2u4ac2YB71Rhn\nghZmeYy7a4vYnoVRi3DDPIOzJ/humjKRxEzfeC2J00WuVViqF/iNn3oPf/P1zxPki1QcB310iCg3\nSHT2aaLdV7FQnUMYGmXXZbt46qGnYWSUsYEyi0sBlEcQfIeu14diDmJOuLSrp2GxQa46h5fPE0kp\n6UiJf10/zT/PH+cDk5ewYWSUrKZjahpRItFs1vmZa2/i8cNHeOahJ5l80xUIpo4sy0BA0GmBEHHV\n5ftxPZvFmfMMb5pGKyo0m00MUyewXbJ9RexWnbNHD3P8+HE2Tl7kjREviSiVhjD0PEW1QBylPZFq\noac0J+sGaRoRejYLyyvoZp7RkXEWl1Y5vzKP7YdMDo9y/MQRBooZCrkimUyO/mI/opjwg4cfJAp8\niqoKSoooq3StRdpuF0lpoRsqp549jJEtsGFzH7VWnUu37+Tw4SMokszE5s3c953v8pG//iiPfP/7\n3HHHFyn1ZQjDGC/wkWUZu+ugKjo3v/71vOGN76BRrfDkg8cpDJd5xc2v5tTpGT7/b58jnzWpVhto\nqsLywhJO4GCEWbw4QlMkAsCPYqq1OoaQoKgSiaAwPDRKu9Ok27UwdBOn0yJCZHF5jmxhkImJaUKv\nSyKEdPwuaZqS0UyyhsZIv46sKHixhLXewq+1SaWelkUiRKh6RF9Rx1QviurISwpJkiKkAoIiIKQy\naSQ+r6vbiwqFC6SqQm9uSaAXJZKSpClyEtFWda7e/XbeeMN7iWOJQIxxnS4NN0YxFQhgodWh4zmM\nF03u/cZXWDxygL5tuwjTLHuthM7+TRw7fpartu7hcj1ic6py38NPYRx4luzN13D86Bly5zbxmr1X\n0lmt8ujqEphlojCF8gi7xoscXbVJDYXluRpi5KMGLs35BbLDfVjnV9Ent5KIXYJai3bsIyv9RH0p\n7jfvIvrlX4SSDmsLkBiUU5fVyGcLKaYikpCgmCaX73oVWt5k87bN9G+ZwOpUMUuDREJM5LoImRxG\nnGJOGbSaddYXm2QcB1QZU9dwOhYtUlbnF+m0m0RJF9v1mb5k+4va6KJwgqIsoGgZFEkmSlJ8N6TT\naZE1SqRhjGkUSJLevGBfqUTgR8zMz5MkCbqUsH/nTpZWVhkoFBDSiKmxjXR9Dz8MOXjkafrzRc6d\nn+HaKy7HCwPOnz9PX3+ZucUFysMT+JZFlMQokkSnsU4axrzrPe9h77YdyKLEHXf+O7VGA0OSeNd7\n3s1117+aowcf58kDz7JaXWLbpq1cvmcvu/dfg6ZpRL7FZ//9LgazZUJZIJfLQdgC36bpOqSChKao\nCLKELEr4rkWsKuiSQiGTpd1osmXLFqxWkyBwKfb341gdZEFGkCWq62tk8yVSAXIZE1XTCBybrmuR\nCBEiKZoiI8ZgyDpjg4Moqsxqy2agaDBfrSGnGkkaIgI506AwkGc4+7Lu8I8bcRwjX3B2SdITTfuR\nWl/aW4sTkXsO87mtkAuRYN/Ifj741t9lemqCWqdFxe/QtnwsBExZpzI/x3L1PKVGi6988i+Qcgrk\nRyhN7iRtd5H6DGr791F0qhSXq1Q2tAkOPcnhOGTvpfu47ObX8dSpMwSpyMHlk0xlxzi+XuGWa17D\noX+/n9WsRkjIglXjrcWt3HXiXpYvH4WxAt5aE6G/TLHrwliexDTwmzXIlBFFFyEtIeb6SGaPkzl7\nCmXPNC0xpSRJ7BoYRpMVNEWl3u0yWiyxb++1PHP4JB/547/k5vf+F37umina59cxS4N0u12CWg3f\n9ehWG6i6ShSnxAkXokSYnZ0lqxuISsrC/Hk6rTqZokmcOHzi7/6ct/3fH/zf2uii+OqXE9BEAU3U\nSRMBzwto1hs0Og0QAgwjg+f1xM4txyKVdAr5DM1aByPfj+9FmKZJHMds27Sdc6sLFPsGSUjRBGg3\na4yMjHDk+LEe+4tlEfkBfhCSMTS2bNxIf6mP/kIGJ4FCocT9997Lbb/wS8wuzZOkKf/X+2/jsccf\nZnlhlv6BAq99yzv5s4/+OZ///Ff58P/8Q17/jnfi+gHPHn6GodECe7fuwCgUUCUDIRX55t33sHHr\ndgqFnkNfWF7uiZ5HMWHk41senhcgqgqSKrF8bq4nPq+aZDMKju+haqDJQm8wVBIwNJ2BoREajQbo\nIqPlIULfJ2+W0FWVTCaDbzXQNI1sJke5v0BO0dEkCBMfUZRQ4gRTVygXSpSHXnaCP26kaUJCjHCh\nAfJcre+HuiMXHmkEifC8Rq5ASJyK3HrLb1LWZU6ePsXc2jJaotCcPczRg4/x+De+yO1//8fc/cXP\n8cnbP4ak53FjhTibxwkTrhyZwAliuqrF0WYLd8MoOS0i62fJlMoszJzi8bPH6Ogp77rtfYSLy8y0\n2mSHCty9MsPaldsQDhyEqSms8yvcZa/yX3/7rxCtNvrEdtSdO0iLGaauuB70EpEaQwbkYpGkf4wk\nY5AtD1MY2gyPH2ZrfhjsGEMIWOs2EVKIFYVyvh8Tg29/+xDrisbW17+F0c1beOLIDLW1Cl4QELSa\nLJ+fo9WocubAIWTFJD84Qr5UpFAqkCnmKZZGyBWKaGqW/pERRob72LJlgiNHn6HV8V/URhdFJCjr\nBkl6QVfVT/CTgDANCX2HXKGPfD6L63l4KZjZQc7Oz1E08wwNmrQ9mVp1HVkWKeSzrHY7vOXNb+Mb\nd3yJtuviR5BICsVClomhQRrtDvl8lnqrhecF1Nst+vuLaIrI8FCZ6noNy+rwyEMP8/G/+wRa1iSf\ny7Fv126++93vcs93v4sYBwz3l1jveiS+1xOCkuHU0jKqbvKtb32DV1x/PTe/4dWcPnGGteUl7vjy\n55naspHYC8ibBvmcRhrDTKcBkoIm9xbA3UYbxdSZq6yxrVBEJmF9ZR1BEqh0HBRkWl2LNEgQ+0q4\nzRah3aZer9JWdDQS4tQmUSTsVgfEBDdwyRdyeE2LREgJ0hgVBS8KSUnIFPMMlfMMDxV/0kfhJYde\nqisiCr3B6Of2iJ8bkxEF4fkmiSj0GJNEUSCJDN78ut/hG089Rr3lMvPMAcytW2lYDq/JjzLj10lD\nl3deeR3/ft93KIyNgCoTuDZ7+kY425xntisQHDlNefE8S3HA2KZpGqdOsCY12TV9A62H76fZseCy\nPTxw7Bzppku4Yv9+jn/hs0R7d8GRWXjlXn7z2hv5q698EtXI8Q+f+lu0QhavZUN/AZZmOVQagcQj\nFoqweTdqLBJV54hTg44TIA+OIrdVzt33APRL5OQ8rxzr6fHoggx6P/fONpg9cYjgmMrl1+7lG//0\nj1w5nWfr1CB6Po9Mipo1mTlymLVaDd006Rvqp7lUw/d9vMBFNWXSOGR1ZZ7K2iIqATsu3cGHf+vP\n+O8f+vUXtdFFEQmSJCRRiij2xID8IMS2O7TbbYLYQxAkTD2DIqnk8gabxwYQ0hZ9AxkUFDK6Qeha\nhHHEQL7IsSNH6DgupCFR6KIoEoVCgVJex7FsNM1AEMANA+yWxaFnnkGQJSqrawiKRCqqxHHIhz70\nIYqlDKqiceTYYSLP5XU33cxlV+7noaceZe70SU6cOMpTTz7OvY8+juuHXPnKV9L1LJrdLkPZIo89\n9hiKKnHjTTdjtVqkBBT6B4iDlHq7RTZfwDAMIhEkRcFLYnRdRTV0OlYbpojcrgAAIABJREFUTetR\n5BfyPaKHSr1Gt9tFNXO019fJZHNs3LKF1AvYPDmFJGrki33EdkCEgJxKON2ACBFJVEmCCCERiIIQ\nKUqQ4hhdBEPXUXh5Y+THjTRNiRIIkxgh7akiJkny/BhMkgYkF/Q0kqTHHpMkCZoc842Hv8tKdZFy\nZpAFNeb8/Dx24vLVR76Nstrm+KM/4NP3fIuW7aFbFu2lFaRGl/nqOYK6T3ulglRf44QAebPE6972\nGvwzJ/G7NrOnTzMzUMKb3sp7991I5/DDhOtVDh87gJ/VuSodhnIA7TYfv+9rTKYDBMMymf5RcsOb\nESdyqL7KL//2R0DXybzidejFLLgiXmcFc/MlZIvjCHqRaGyYvF4mWG5SSkWmC0PU3C6GonNubZ3a\nasDDtoV0+S72v+IalFaT/l27GM3C4IZN6KaBrGhopsbpY6foHyxh5E00M4OsSvi+S6ddxyzkCAWb\n08fOcuDpR0gIkYQIXZP55y994UVtdFE4wSRJSBMPN/CJoh5xqu3ZCEmAbbmYeo/JGTFlcHAYs79I\nxtSx7JSFxVksv0tfIYsgSfQP93N2/hye39MridOE/nyebdObaHoJsiKyvr5OrEpoukKQeL2B6maL\nWq2G67pksjqxkFIsF/mT3/tTsn1F7vjCHVxx5X6++dUvo5oZ+gfGWaiskqQRZn+RV73+tViOzfLc\nEsW+fs4dPMp3Hvg+P/Vz7yCWVAxBJgkE8rkSCwsLWKGNZdk0Gw0i30ZKoVQaAFLa7Ta7tu2m0+ng\nRQmSnmdlfZFUkugrD/X+X2uVTLkPUTexWh263TqnzxwjU8jT6XTYtnkLipig5os0XYvl5WUsL6Tp\n2aSJhJcGvQ9gGiIjYHkurhf9pI/CSxC94t6PSm8mPzok/QLihOe6wY6X0qq2WHjyWfrVGGNphQ1x\nwqWNNsObBmmaNq7XJW21UFQBL0lILJsg7p15B4fhUhHFlPFbK/hCwOd+78/Y+ZobuXp4I+ZghoJm\nQL3JPSfvZWC5zgbFxA4SxJ27eJJlRlsZ8mM55NGNuFdfjnSugz3ZR61UJDn4FIHZ5cSZg2Ck2E4N\nryjBziLp6BRBaOGYF3bTnz2FNp5jODvEFZlRNmkFcpKEIcGWTft4Mk3Z2d/PiFmkem4GpX+cS4bL\nlAv9mCODqKpKmkT4tsPCyip9xX5kQUQURQYGy7Qqld6tDmPEWGbdrxLkIPFDjhw+yZ/80e/j1Jov\naqGLwgkKgoCk6IROiBV4NNor+IFLGKeEjo3jOEiKjGHomLpB7CV4oYgs50n8kNQPWau3yWfyJG6E\n3WzjWjZWEDHcX2JiYoxm22atUqXZ6iCIMhoyQiIiJaAqGqIooUkyWpTgeyGlUonueoXf/K0PUijm\nue2293HfPd9j+pLtZPQM4xs2s2fPHsIoRRNlNDWDkclw/PhxSqUCf/n5TzG9YZLdmy/lzOEjPH30\nAIoUYeayZHMmYiiRyWUwZBnpwsye02kRhX5Ptzjymdq2jVgQaTZXsSwH27NJfR9D1ejUfVqNLu12\nG1WTGB4aQ1dVnE6bbrvJoaOHCIMUt2szv1ChUmmxtl7DdWLC0EehF3XHQYLrRURRTMOp/6SPwksQ\nvabIc+QV8EM26f/1Gn44EpMoIdXlGapzp/n6lz+N3+1Qtdeod5tElRrVUyfYum0nhCFJu0FTERi5\nei9bN2ykW6uiGQonz51g/823sLt/iPLpIySvu47C1A6e6NSxv/pNzKjNruv3Un/wIItXbKU0NoA6\nexqn0UAIBSr7L6NzZJlo8TRBVKew90rU+bPgdhFSyHghDy6fITuuIg0UeMfOPRjZAVJ7nkQvoeoK\nxiWXwPaNZNQ+ZF2DVkA96LJjcJSRcp6TjoxqZtgtx3z/nz/H4e/ewTPPPITSqqHl82iZbI9NJwwJ\n3IBqq4kqKXi+Qxh4CLqMXa0giCKqJFNvt2gmNQamB+lYIVt2XUvL9rjvjrv+AwtdBBAVEc/2sJwu\nVqNBu90iil2C0KbdriOIoGsK4+PjSIpMsVjs1UlmZhgZHsaPYfOmLQiCxPETR0nTmEBKiDwbL0xY\nq3d5/MgxTDNPFMXEacJ6tYJhGGi6ShD6VCrr+N0OluuQJgGapBF7EVddfT3//A//SLPTZWzDBOW+\nMg8/8QhvfNONdDoWg6MjhFHAd+75JpZlYWQ1rHqXf/3EJ5lfXuH82iK5/n4+8Ku/gZ7P067XmT93\nDt1UaaxV8NMQVBlJVPDDgFLfELYTszI3x+LMDK16g3z/UI+EdX2dTHmAwE/YuHEjkR+gqxoDpX68\nwGdteREfcMMIXdfJGxlkVOwufO+hJ3jy5BnOr62jIiLEEUQgRLC8uMyZo3OcOrHwkz4KLz2kIim9\nFDe60BQRecFKXCT3BNj/F4otCZE3Xf16jKlp9MFJtJ07UNQM6pZJUk0n9ANOz55i47ZtaE2fTAqr\nTx7ljFUlVDK8ZstlmFsmePjEIY7YNkoE/U8f5DsPfQ8OP0lr2yZqdZf4zu+hb9tANsoijIwQTF/C\nNU6WVI2JRId9t76XoN2h3V6j0XZJ6zE05khv2IqdPElOrGHVv8cGeYHvHL+DSwoNvvj+XyPTn8VD\nwtcUhIFRZoSUqK+Ptieye3yCvCxSVvKcmjlL9/QiPzhylJ9/+6u5+bqf5fs1CEoZNDmDIKR47RaW\n1UEi5p23vB437rC2tMTK0iJWu4bt2thWB0mT8PGo1W3+x61/RrVjI3gBf/SXf8PRmfkXNdFF4QT1\nRCayXBzLJvQdUAQkrScGJIoi3Y6FiICmqGTzRSyrQ7Pl0G63iUMLQbkgVRjErDVbFEwD3w0QJIlW\n22WtVierqnTtDgNDZSRJRlBUdFVFEmWGBofJZ3NIep786CjddgdVEpGyGbZu3crBpx+jXqsxVu7n\n3OxZBgdH+ehf/zW5Yo7Ai3nFK19Fsb+PvnyBNEqxQptqt8vP/sy7+PUP/DppYPPlf/kMpiwjShLD\nY8MokoKZySAIEoEXIkQOoRvSaTeQdQXbdRAkBTtw6LTaGFqWgeFh5s6eJFvKYXctBCkgtLt4UUgS\nhWhGntB1MHUNL/RwhZBISJmbO0djsUF1pULq+nhJRJRGCEJKmHq0bYe5lTXOzq3+pI/CSw5CCuIL\n9oRfmPL+yHXyw9eQisSJyTWvuZ5SMc/8yVN4kYUUpMyfO03j5Fmuu/EmyrpKtd3GnhrDJoHhHJJW\nJJUVjpyZITx0DqWUpyD7rI4UqZezDB6/H/GamyDwEbsOLSvEPbXM+L5LOB53yW7exOyeCUhVGO3j\n5LkFNl53LXRqXH79qwl3jqFk1rhciNkYRUycqnHDepHlz/2AHYfXeYOX593f/0u6MyuQtNGUmDSf\nJTYlSoPj1DWNIU2kYKY882yL+W6Dxsppxsa388yKxZzUJmk0KKUpodVCERVCq0PkWkgyFIdM8v05\nhDShub5Ku1rFKOjEvo/vODTqVe76p69TXzzD0eMnufOrn0U3JC6/fN+L2uiicIJBGuNLIoEvEKsS\nSRySJBGx4IMQ07UaRPRIJ5fmZjl07ByFnNmrjXXsXpGflKbbZjCn0Q4DBFFBlmWypQJ5U6PSajE2\nOMzi0iqQ9nZlRYkkCmk3qtiBR7NbQxMkZFWh3mmxZ8dmjp98moX5Je6+805WqzWGJsaYnBzHNE1q\ntRqyKjAzf568nsWNQzquy65de3jtq6/jrz7+MT7yR3/Axz9xO0MjZRAlbNumXq3iuL00X0h76ZIb\nJxRKebwoIgxDivkCvtMlCUIcq4MTRayt1iCVSIMEVJV8tszy+irNloWYRMiaTLteYaCvj3JhEDVR\nCKwOuVyR0Q2TyIJOnCS9eoqgXhCvEnHbPpX1Oktnln/SR+EliRemu89Fg0LyQwcYxzGkaU/qgJQ0\nBtsP+MN/+BusWMWdLNHfEFjshIxFAuEVOzmwdoqh0X4sIQWtiLxjG9Ta5PsMRjaNUBFt3HyesRPr\ntNUS6p7LYMVhJBkkOfQQbO7Dj5qsbMnDvss4dfh+5GcPYnlrVJ46yNWvfR1Uj+F15zl/4ixlfRMH\n7/4ibJhEGd/IoUfPskvZwen1kzx517eRl2c5cuRZ/vZvP8Jtl1zHZW++mg+8+xcIcpMgOqSRSLdt\nMSRrWPVZnj78KKcGhshLOTZfvpvTC4/x+KHH+OrjT7N/qsDCE/fSVy6QJgFe0Mbp1qgsLpJLUgh8\nJEUGEmqVVaQkIY48Oo06G8e2sHJygZGpad75zncwu7DCM9/8Cq9+03Uvap+LwgkipvjdENu2IE5I\n6IIcIhsCqST25gabTRzLYmZmjq1TkzhulzAVmF9eYbgwwPFz52k0WtQcj7NnzyJ4HVTdoGjq1CtV\noiTm8WPH2LJxgiCKUTSTMPRJ6L2HqfU6zPXlOfoLeZLApVKrsW/vfsZGRtAyCutzi4yMjPDko49g\nWRbTm7ezYcNGdm3fwdDIGD/3rndTUBX2799HuVCgXq1y6MABJocGOHLyJBlDxw9sLKtDu1XBUBWi\nJCZFwNRzOI6HIYlIokC1sYofhPiBhZBCKkQIic9AeYS608W2WgiywuaNOygWcqiqgdvtMNBXZnV9\nneXVFVqNOoIqMzo0ynpjhb5yESmJIAoRfB9Rli/QvqcEdkjgOD/pk/CShRALiLGAEF1Ym3tBVPhc\nvfCFjDM3Xv52Bo0iU0NFhgoxceTQPywj9EvIa1U0W+BYo82rL7uK4fOniJfWEHZuo+l0uVQZIEwD\nkEK0gTy7ZRPJakLocGxiAHHnJVzZykFBAt8FQ4DsAH9w64eQ1tZhzOTQyWcxqwnC8ChkctibMmAc\n43+MjHBZVSc5d4zvfPTj3NhS+PtbP8C//fKHOfnpL3Pb2BSX3/Ugb3zgByw21hAKFqLWx++cvo87\n3nMZf3XDtRx+domuOMioCb+1q48vnzzCQ3UFa3wbG4YHGPUDymqGQqlI5HYJnQ7LcwvMzc3xzXse\nIBJz5Ep9aJpBHCWkce+eDQ8PU1IMzi8f5xMfv52vfPUuHjt4mm8cfJYvffrvX9Q2F4UTTIKUOI3Q\nNQVJBFFQQUxIhARJUlAUBQGJdrvNsVMnUcSYyuo6EpDNGhTyWfw4wgt8vHaDnCSTKw7h2G3OnJul\n63oUDIPJkXGq63UyuSyarhMFAbZjEbgBge+gaDIjQ2NokoQkiDTbLfKmQXligvVKg4efeorZhTmu\nuXo/1157LXNnTvHo40/y0AOP8IEPfJDHHnuMMBVpt9vce/+DtNttHn/iEU6dmyEIPDRNI3RjBEGi\n3fWRFJV8Pk8U+8RSj0odWURORUgV9GyOMIhxQo9uvYltOTQ7bQZyQxSzZQaGRolEH9fp4gYOiiCw\nVl0gb2ZQMxpmIYeoGOQyA5SLw1gNizQB4oRYSBCCBDmVSOLoAhPHRTE2+pKEkPZW5UDoiV39R68V\nBN76hpsYyJUZzpep6RNUfQ93cYYjNQPRcyiurDE2fgn3L53Fyaeo9RZpuw0Zk269Sd/4ENgdTkc1\njiTnGa5UEcp5nO4ymt9g44ZNTK5kMffugYOPIAwN8+H77yRZX0GPQHj8IM7aMh/YuBfKAe/au5Hx\nSsKffuFLvHF6Jxw6yfTIMFs2TTA+PsKOq65mdmGGqyZ2sHXfLm67di/FL/wRD+YzxD91LTf/5m8z\nYl7KJbt38cG3/DRLi11OfPRP+fT378WcWUdCIH7gq0wpCayc5fLdO8hksrjdNp5j02m3WVxexswN\nMDgySK5QZGBgkMHBYYxiT7WxWq0yunML/RNb+MX3/iI/845beN/Pvxu74vDkyTMver8vCicoyeC6\nLnboE6QxkiIiyzJhGCKIPpqsIEopjt1m++ZpKpUKUZrQatdQVZW6bRO7PgU9g6nmUMwcbuSxYXIL\n2WyWjC6TN3JU1peo1ap4QUKjWqF/aBBR7L2XqujoWgHLc0kliQ0TE4iyTqXV4NypM8ycOIWiKHzu\nU5+hPD7Gs0eO8No33sSv/sr72bVvL7/yS+9n5tws+XyWIBG45zvfJoliOs0WV155BbqqUGnUUHQN\nlQRFFfB9n9APMPQsaiIhyz3NBtd3yWaztFotdE0hDXsKdWMjo4gidOrL1O0m586dQ0xTbC/AMAzM\nYglDNEiShC1j49huB0NRWaqfJzE1UCSQRJIwQowgjkMS30GSJARZQtVfFt38cUO4kPomJKRpQk+i\n6H/vBJ+PDsWIr3//PpzI4oGn7id34gix2EV0Y/TVE1gk2F7Eam0Gww8Z2Hc1ZTVHXpLRux5PWCvk\n1m2EjYOIpQEgR//YFtJam2R0MyO7LuWOY/ezMOwy8YNnkcdM0vUzvDlf5sO/8ntMeTbe9DS5K1/F\nyOYSm7odPvP1e7n1ll8l7Z7kL//gw+ghFEyVe++9ly9/5Uv8z9/9bwSNNv/y7Tv4+pe/yqGTx9g/\nso/Gyjy/8Ju/zomnn0ArxhQKKX3jA3SKU2hveSt3LzjIZkwsRzi7XsuO/i3s3jjB0M5pFF3Ealex\nOzbVyiq1egtJ1MiWysgZHSVrUhoaQdaySIbB1MQkfXqeL37mkzx24EEajQb1ao3sQIFW13tRG10U\nTjD2Y+IkREwTJBJURblQI4kJIw/DkMjoBpKo0Kyt0PF9irkigeej6xm8wCeIQhzXAk1BNhQyuTwL\nC3MIYUCs6bTsNkP9gyDLPS0NIWJtrYIiq0iaQSSkNLsdsqaCjMBapQJJQClXYHJ8GDt0GR4d4TWv\nvYHA9bjpDW/kwFMHyfYVmTlxnP1XX8Pk6Aiu28T3fWy7zcnZo2QLeY4/c5zADQgtF6drIQgQBh6B\n5xMFIb7jkIgQ+DZxGICQIEkSOcPE8kMEIe7NPBJTb1SBhHzBRHQsLK8nNK8oCr5tYxPQ7jY5OXMS\nq9Eidl3EVOlRkxkqSgKpAIKYIF/oRooxSJIE8ouz776M/zMI6YXHc8vA8PzGyHN4rhny3N/TNOXw\nkUNsmb4UdIWpDVvZPDrN69/6cwyNTqB4sJ7XmJ5p4Lot1mbWWRox6FN1PMUHWWKgNIR+rEWS2hiG\nwhNLJ2DTOBseO8D5uVkYHoThYVZVgcgxmZJKfOvsg/ztZ/+et7/3NuAQQf0Q6wsVdpU3kD8xy79+\n/pOMuzrWWovx4Sz5fJbrr38VExMTdOpN/vFT/y+/+t8/wHB5gLVag76+PiqNFtunL+H8mXl+9zc+\nyPWvuJpb3/8+jP4+GmKeqb4pnPF9bApVbhwaRs9Y7Nq+mUwpB4FDGDnIsspA/yCeF7Bx4zRGoUDi\nh0R+iJ7NouoaRq7AMwef5Nizh/iVD/xXvv61u3jqwBMcO3aEOI4xDONFbXRROMEkjZAUDU0RkZQY\nVVURJYk4DnETnzCN8B0XgYR8ocDqwgLt1joyAqppUGtaRIFPx/PoujZiLOC5barryzScADFMaLRa\nnFuaw8wUcCyb/mIJSVQIfQ/XtWm1O+TzOcIwpljIks9m2LxpGkGSCZMYJRU4eugZTpw+wxe/8CUe\ne+JxDEXma1/7GoPjG8jlMuzZs4dMX5l3veOncX0PJREYn9xAsdzP5m3bURUBTUlJJQ1ZlJAUGTEV\n0U0DYpFMtif6lEQha2tr2HaXfC7X0wUWRDpdizQMqbfbtKpNIi0l9CPiOKbthWQyOZRUQJEU/CDF\nyJj4vs/kYBk5EvCiiETqfSjTBBBSUiDJqqSaDJncT/gkvAQhCsiCyAs1rJ5zgGLKD1mmhaTXSUZA\nQmV4so8zM4dI44CW2qReW+arz9zPiOsSZGI6XovxPTsQWy6O6VDM5Hn9u97DhJ9ha7GPg0Edd/sI\nm5a6RH6HyJRBD0l2bCc9U0WLWsjdNp1NwzCSZ0HuQrGFWWjjHJpnrNGgr1LgY5//ex6882uMlxRK\nlTqrB04hyxCGIUEQcPbcLHff9z0qzTqtVoPb//ZjbNu+hY999G9YXV3lM5/5DCsrK8zNz5I3C3zo\ntz7CxI234s0vIZxeQB7dyJWGyoev28vP7R5i0KthZFQiz6ayPE+r0SWhl6XomszUtq1IaUzk+Uia\njiSJNFsV0ijmnm/fy2c/+1m+8C//xtT0Zl59w03c8uZbsDtdPO/FI8GLoggUiwmKGILaU2nTNBXE\ngDRKieOQWIhRpRDH8VhfX6e/VGB+boWubaPU2ziei65rRElImAh0ug1QBERNQTFUbNsmiXxMM08Y\nBsRxiB+K5AsmrYZPIZOl2+3Q7ToYqkgUKgRBzPJKBSmBQsYgiELcNYuFM7PEkYjrWVx91bUErQqC\nJPLskUOs1xv4zRa3/+Pt5Af6cOstrE6XSy+9hLNnZ6l3mrRd+wKjsISQRMiZDGGakM9lcHyfQrGM\n3aoRxD7FUj9xDLKs4rseruszUCog6Sa1dpPUB2OoQKdZJRuGRLJAoVRECGMcWcZxLCRF48T8LGOj\nG1hbsQiCCEESEaKIkARNUwglGUUUEPWX+QT/M5Cm6QX6rB9Fz/kJz18Lz19LNGqrLDQdDC3P8tFl\nfvpt7+LA0jEGpjaz/8QsVbHF/asrXL3ncs4fPURdbPCPd3+NfVfvwTl0tNfskCTkyY2Ei7MwrTAV\nayzLHmwvs9fVeVqZA83HKJZRzpxioD+F2OAz0YOkxyS6gwdBSblifCOthTmseoWff+t1pJLI0ZNn\nadsOaqhidSw0TSOf0XA8n9e++e0srjepVqtMTU0hyfDr/+3XSGWBx586yBNnPOqSyPZtV7CUxJQS\nFz0Bw+uwuLZKY2IEtZFhYXaJdrOJKkusVVdwLRvb6uI6AYqikMlq1DyPNPRZmZ9lesc27rvvHoLE\n56Yb346hJzz22KNomsbw4IuXei6KSFAWFXRDQRZjUrE3HkMqEkURghigSCqqnmNtbQlJlZmemkQz\ndHTTYL2yTBoGdO0WcZgQejZxFBF3HGI7JXI89EKWIIpxXZswTnq7unGA43v4cdJ7H8DqtNHUDE7X\nIWdmKA/0kdMVMoUSoZcQJwKbpia5Ys8OpkaHWFpcZGV+EUlOWVlaRhRFIhG+e/c9tKtNSuVh1lYW\nIAnwApdupYUQxpimjiyLKKbaW6gPEjzPY+PkBjrNOqKuI6XQWq+wvraMoPU0XoUkYrVepV1vkLg+\nVhAQ+DaaJmO5FqRCj4k67rFRK5pBu1VBTiW63S4xApAQxyGpLIMoEiEiRgmRLBC/rL3+Y8dzzk0i\npScx8kNnd2E0sCep+Rx7jCAgSCKddoOxPpORzUPIG8b44vxhlivn+NZj97I2VSZeWUcxJJ4M1kgD\nj0gXQEoIcLDWLMgG7Nq0hXMZCfbtY3tNZK57AqE/hjGRmmMzHLUY0SBnl/n9v/kc08XrmY/b2JXT\n/JdfuBUyJrdd9lrM0CN0LOI45qlDR1leXSNnKHQsG6fTJSVG1USanTaCKnPVVdfxkT/8c+699wdk\nMwa33PIWbv/ER/n6V77F1//1bqTqHNt2XIHQZ7JpZIBX7t+DEfssnT1Cs+0gOAGNlSpLyxUefPQx\njp04SbfbJbzARVap1/A8DyFNsK0OpeIguZzJoacP8O6f/VkeeugAleo8//BPt3Pw2BF03aSvVHpR\nG10UTlBVVXRTQ80q6JqELEukqYAkyyAK6EYGx/WRZJGBYom5xWXWV9eQjAz5Yh+SpCCpGhEpnt1B\nlAXEOEDKgB8GLJ05jZwKhI6H02nSVywSBzGypiMLMl0/IHYCZMUgjhMUSe1Nqlsd3KCLGKdEqUeq\nKyytrfDmN76Jhx54hE6tQiTK1CsNAkBXRSqVCk6rgRSEvPr6VxIhISs5EjcilgRIBFy7twaoiTr5\nfB40CYSIxZWeOphAhEyIaWhkNJk0iNAVlX17r2DD+ATlsRFETcRQZFzbBj2Dpur4gUOn2sTxA3RB\nRpVFMkYWXZbQZQlT1C7osEvESUIqCChST/TmOU3cl/GfgxcOSAMviPp6TlEUe7uwz/1uKBHdELSO\nC0IT029S8gXQRFiZw284hLLIaCbP637pF5Fc2DBY5sSRMyzvKrNnSeLswhGiyEGJK4z1T4HawTBX\n+MXxV/Jrf/oxHNFgl76NijXHb//Jhzl14jhxp4WUJhQNjTcMb6DjVsikPhvK/YReyP4r9tFsVBgf\nn6QvIyNrvW0nWVYplUq4docosdm7bzuX77sCUZA5/Owxbrr5zUxt2MiHfue96Bs3M2goGJGA0Gjh\nrq9g1RY5fnqWS3fvojQ5QSoKRCmougGaRsNxGSiU0TJZ7FqbNE3pdDq0Wi1c10USRW5561v5wQ/u\nJV/KccdX7qDTsWjW6heCjhdPei+KdFg1BQr5LAVDA1VGlCCKQiQZZFGjWCwyXM7j+wG27T6vyzGW\nzdGod0kzCpXGGoqgY1shkpYgyaAKKlISky2WaLsRAyMDhN0WlmWhIJJGIYNjI3TrdSygqArYtk2o\nSnixjYyOns/T7Vps3LiR08dOE6syx0+codCXwbIsLKvDUHmQtcU5hHSMy3ftYb1Sw5ItvnbXnYwO\nDHLvfd9GShN0SaHpWAz1D1BpNYhFEaflYxgGhXwfdtcmSRM8N0LTFGzbJggC8oVB4jjk5PljpFFM\nJl9GEkR0SelFg5VVimYOLWuiFBQqjRY5XaRea4IoMzm5gXa3zY7xKR4/ewjVNIhDH0VSSOOUVFaR\neNkB/mfj/9ccgecd33PPkiQhCAI6KmlOZn75PNv7SyxV1nD6Brkxa3L/yizR9hKvSHI8unaKr61X\nSCez1OonUFMPPw3YUNrOCecgSmaVW/e9i0/NLnND9mZKnsanHz9K9uFv8+Yr38f9Tz6BktZQs1le\nu/dKVvwV9IxBZeUsRdlgr5xwPAnYMDHGyFCZ48fOMDwwyuLiEgtrNvv3bmdxeZ1qpd4jJLF9JEHk\n0JHTbJvayTXXvYLd2ye49bZbuWTzlczNzXH6kquwhCOMX3EDupUSk9Js1lEVnW379iJIMrXZBRYW\nl1leXSMIIjRZ4fobXo1q5sjmy0RhiN9wiPyANIqp1VfJGBmu2n9zJ/F1AAAgAElEQVQN7/nZ9/DQ\n/Q/QtLscPX6Cbqf9H9rloogEFUVBEFIkTSejZpEVDVlUUCQdWcr0aOCTFMPI0O22aLfbaFkTs5Bj\nrbNObX0N3/Io5FQkBeIwon9olDiOCZMQ1czQ119CSERqnTbNegOXFNuN8X0XURbIaDJREKOrKmOj\no1QrTepWEz+wkWUBp94CWUFD5fbbb0dWDbqeBULKgScfRVEUJEHm2NkTvZU0MUWIU2zbRtUNHNfG\nDXxARFBkEBSyGQMzayBJvXEZNaOTLxUxTRNZ1UgF0HI5bLuLIilklByCbOB5Dl7bxgo8HNciSlOa\nXYuu5dJudog8hzBIKWaLhL5LvVJBxSS0bTJqjiTwkUQNUhFfEokFCAWBVLoovhNfYkh6C9pC0mt+\niC9kjxZ+WAsUBURRRhAkRAQcKaEYO4yOFGiJNhs3bMIT21TnqySGD6qNW7XJqTbeUJP3X/EqxgKR\niRGFm6cv5a5ih7ddeQtX9m/la0dPQm2RmVqbk7UG+OeQMiqnV55lsFxAyGS5bngbOTXBSzXEMOHw\nie/TnH+Wxcoqa40WTzx9kLnFFXZfvpv+LXmMrMCrrtzEo48eQtHBT8E0slhegB2lvOXGG9l37WWc\nnzvNnXfeydve+FPc8vbrGR/vZ8JxGd90KbuzOUI/pdRXZr3hsHV6K2a+iN2scm72LGvVReZXlzm3\nNMfE6EbGtmygmM2hSJAKMfW1FVRV5dzsWQzDRM0p1OvL/N3tH2fz9AZkKWXfnkuBhJGR8ota6KI4\n9akooGczGKaGIAskgoisa6QRmEpvvzZJJRRZIw5jlpdXyRoGMwtzREIKYo+oVNPN3kydIlGpVMgo\nBn4QYOoGLdsnttuUSmWSJOqpTwkK7WYLRZfpuh75XAbL7rC0EDE6Nsx6pYZre9QbLcI0IUl6K20p\nEQPZQRqdOmkqkNE0uoHP8uISWVPvLcvHKVkzix36GECUygQEJJHHer2OoiokCYhpgiAotNsdSsUC\nspmhW7XIZ7JoSq9hEaUpcRLRsTqoqkbQdegfLNFtdyhks5BAnKYIKZiGjJTqpIKE41nIYi+y8IMu\nUaISEqFqRq9bpgjISERphCSIJOl/PMT7Mv7PIYovqPld+En5UUf4I2kxoMcB/aObec3WS5hZXWM6\nfwkHjs9yjSJSLm3nHmGOsjXEFdEo//DEYV6573VEa8s8croCODy4vEjuRB1vPIssRkiKiOEKbB+c\nIJFiirLJULnEdJ9JGNrU3ZC4e5R6q00sOTTbWU63Q9qeh6bqOH7A6PAIX3r6/2vvzGPsuu77/jn3\n3P3d+7Z5s3EVScmSKMm2ZCW2E7uJvANtkbquE9eOmw1F0aZ/FEX/KFDkv7RoEKAtYrRNgKJFYqdO\nEzRxnM3xFllO7NqOZe2kxG04HHL2N2+9+zmnf9zHRbLZAG4CE+b7AAMMZ+48cnjv+55zfsv39wcY\nD7zC46En3sbaM9+gHXUYZ3D8+HFcq8EHfvyD3Hv8MF/808+TOQ1KKyHPLaJOg9iyeemzv0Px1nfx\nyOlH+csv/wnBtOT+t9yPhWEyHDCdTplkOQ03ZLW1ypve8oM0l3pok2HJ2h3pYGsLuxNz4uS97F+5\nzHMXXuDi+jXavQ5ZkXJlfY3uwiJR6LO/u33be3NniOBUowtVzxYVAlcaXBlSmBLXaRBHHfZ2D3A9\nG2MMybRgUqUsr67gjT2UEWzv77CxtYnnhIRBRD4ao2ILbVlMswJjDJPJCEd5WFoTOC6Vqv3ztAIX\nUKqi2WpR5RVJWuK6LkVREIYhRZajGyGD/kH9WvmkrnK3FFlaoDFIFFlZsNhskW+ns+BtiTZQZBNs\nY6EdG9+CsiiwfY90WiA8ycrKCjvbW8TtRSgVo2FC1AgYZRlBEDLNM6QBy1Q0ul2kqsjznLjdpT8c\nEHohKi/QjiTwY3Akw3Efh/pNNRpNiNsRru1QVBWe56GEhTYVkSVwWxFSfnsGc85fB/WwdIQ1+9xg\nLG6M4BRC3KwlnGWRLQTS16xfPsfFr5/hvve+mfz8PrFw8JoPsrWdYwUdjiyeZHdnSFMPeHHnMm9p\nHyJWEk8alpo2b/rhd/K1yQbaSlltLfHoiaOcGe8RiCnv+6EnuPDCy4wVfPPrX2DaMEhT1UXdWiAb\nFXrsUkxz7MjGMVBoXTtfa4OSOTvCkC8uMyozorLg7JkN/tuvfoy9nU2+8X++wUMP3c/Glct84XNP\nYjl/zsrhI+iFDr4D/XJMpxyR+h6OKIiWFyinYzYub7A/2KdKS47fs8K73/Zmlg8tEPo+44NdplnK\nYHOHSTohTyfk2ZTP/9kfc+3qLssrixhVcebMGaIgpNWMGI+HGH37cbJ3hAhOspKrm0PuO6GIYokt\nXYTt4eraTDUbD2iEXn1k9ByMpepAvqpb1HRVopQhbjUJLI/B4AA3iDgY7BNHCwzGQ3zHRVsVqpTE\nzRa7/QPiKCIdjZCeh3EcQsenyHPiqMV4NKLM69ic41iMBxmWgEorAsfFFDnSMphKEQYerlJMkFiW\nxe72DnlZ4GhNHPgURVHPOc4T/LCJbXsU+wOm05yiKlhq9dAaWr0lxsN9FhaWmeYJnlMnhiLXp9GO\nqYqcIktIDiYI2+D6AZ7jIwQk+RiETcPvoqRDMhnT6S7T39+iqupaNGF5pPkuUjpkZUHoegjbYnEh\n5p6jPdrzjpG/doQwCGEA8apkyPV44K0xwVv/fJ3VoEI+coRjYYN73/YGvnrlZSoHHmu62INNol6H\nd5w+zVfWLiIbgobX4olGh3GjxJcOjx6+n2PJcbYPrrC46JGtXaKTDNjde4lPbXwZpeoQTegblC7R\nWoHQKErsQDLcTW4kFZSw2N7duRE9VkB35RitaJGm7XC/DT/y5sd54ZlnSIdD3vr2t3L+zDmOHF6m\ndCze9tYf4sqll+hNB+wXCZtfvYR9rMc94ZQT7/gRwtBn+9JFptN9iqJAug5vffRxXveGh/GbAUk6\n5OLLL7L2ynlGacnhEyfYWL/KU09+jlMnT/Lo69/CNBvy1W98nd0XXuR1p47xwgvPEvkevfbts8N3\nhAhayjAcTdntj+ktrcyOAxpsFyFtClWgyjrOVinN8aOHeeblizi2jTEKR7pURcokKRChQ6nAjMe4\nvk8QhgwmE6bJGE82aDYjRpMxtpRoFFoaXNelnJSkVoXtOCRJAkIQdduk4xEm0URRSD/LqISh0IpI\nagplkI5Nmdc7QUc6lLpECAfbcrBQVEVeC+NwgOO5WHlF6ARIV1KoglYjxrYkve4C46xkZ3ONXnuB\nrKpwXYesP8RfbCKLCiPrzo9KOaiirGv8KLGFg21ZxI2IUVnQiRrEjWX2hgd0u0sUZUWuHIp8Shi4\npJmiGUVM0gkrnTZx4HG4G9MOb19VP+e74/qgdSMkFhphJEaKmcdW3R0ipaxjggikEa+qKSyk4r5j\nC8Shw9RRLGUOYa/D4j0NHrncpPAMaal5y/I95L7k0OElerbh7NpLnL/0DH/0zKfwQx8hBJuvmHrz\noDWOALSpR35SYURBPQZKoSqF1nUr2/LyKpsbmxRZzkIck08ybGNTGUMmKx5q9jgaGz77ud9j5fBx\n9vaP8eDrT3Pp5XN88ytfJGx2+MQn/xS7KvjKU19iko1oNAZEJx5FyJL0ynk6qwFuKyZPxhz0t7CN\nYJxmuNKm4TfwPQedjUmHBZcurrM1mBKEMRUWYLjnxFGqomSx0+Irf/gkOsmxmzHHT5xid3+fPB/T\nio/e9h7dESKIZ1NUdeY3TTKcMARmcRStSSdTpBMgjaDdbqOMoNVsgi3xfZ/BYITXcIjDiOl4gh27\nNGTIQX8PxxmwsrDI5tY67cUlsmntTbi40OPK5hUcIequEVmwGLaZJArfhkarw+61dbAEVakpyyHT\nwhBJl7QsKF2P0J+tkEqhS0CUuEZQZMlsNRcEjRaTKiGMGihdIKUgyzL8KKQaTKhcn0KUrK2tcfT4\nMYIgIu51OBiPMZWh1VrEdSWl0LTdACV8Kix2drcp84oirZDSwQ0CpkawqBW6UpSuRdhoMpxO8CwL\nx2twae0V7MUIKSVJnmBZNq7SLHUarMRtYi/83j0D36+IWZxV6NnibmFmRdKv3fVh3TwO38r5c09z\nwdicfuRxTpw+ThC2mJQF96y65FXC0889xdYrl1hcbXH+ObBFha0rbAvCMMSIV7fo3fQxVBijbvQ3\nV6aeemdm8e9m2OUdb3ovv/W/Po4UFv1+v7a6xwJR4FeQTgd84ctf4l9+5KPECx0Gac746gYqz/jR\n936AT/72r7O7u4tWgkNHDrNyZInLl7fIk5RD3WU60rDQ7mGE4eDaNleuXWGaZAghKJQhjEN0qRnu\n9bm6vsnmzjZJXm+QiiLn2ee+iRSGpd4yo+EB95w4ytWtTb729HlarqTdjJiMFZubt/fKvCNE0PM1\nndhG6CmTSUKjE+B5LlVWIWRAVhaoLKURdvD8iOHBAXt7e+zsj9FVgiNtXBEwzVKM0uSjlNJVNLs9\nVJmwub3FKMnwx2MavsOkKMmKlKoosWxJMpnSanaospyRNcIioNy/RlVVeHED1xOMBxVCJ1RSECDr\nAmvfpUgyHCHRlkLnIB2JMAILBUaSlClGKahKfDvElha5UWA8QBNKh2yaoIWNLV1cy2YyTQFFZhSh\n45ImST0oqtNhd2sDL+5x8uRp0smY/miPRhhwMB4TuWBsl4WoySifEgiL1JFklcK2DA88eJrL/T1G\n1RAD2I4mF4pyNuNEy9uPJZzz3aKBWgCvS5Go++PqbPEtCREsUfd185pyGsCWipfPfI1Xnv8a2iqR\nUiBnvca2EfRWG0ihMbouOVHI2ihj9m+4IX71WHeYuVsb6p9BGxBgjEKpCkNFt3WER07/AL86+BiN\nIMR2HYyu6HR77A+v0hSrvL4T8DP/4ue5snuVol/y+kffSK93iF/52H/iv//mf2Pn6ibdzgIPPPAw\n6xuX2NnZZntnE+kuEvuGxfgUbruNKTI2dzbZ2x+xOxhTakOz3caSkE1GDPb6bG9vsb23T6PVxQt8\nJtMBR46uMj5IWL92hYfvPcq5c+cpLfCkxf5en949h7jvvvs4f+7ibe/QHVEi02z5LB2OCdtuXR4i\nfTAOrtOo4yezma1CmJk5wZTA87FNgWc7OI5HqTIsA77vgxb1iD6rXoirLKPdaDHNpjSbTRqNEK01\nURTBzPq8GYVoS7DaXSbLMtKsYqG3hCccqjyjVAWduEmR1zsvx3JwpYWQVt0EKjRR6KGLAi/wWWwv\nYts2ltZ4jldPGNMlhRbooqIsJyANUdigEUWEYciVa1forixhW7WBRJZlHDm0ROg4NNptBoMBC91l\numGItCr8QOA1mqSjEXHgIbSg1ewgQxeJje1HdJs9AttiqhTbV6+yEkZ4loUtLEBQFAWjg4LN/V36\n+4Pv9aPwfYcQ9QQ/YWrhQRSzr892gpIbuz8pahehWgD1LJYIRlWzYWQV2qqwjABVJ/Sue+lpral0\niUbVx25VYmk9c68R9Q7vljnHymiUqfvOlSpQSlFVBaqs/fkQkh9+5AfpdlZYXFwkbjU5urqMRcnb\n73sfb1/8e/yte97Jmx56BCeKKNOCM2fO8PU/f4r//GsfY+PaNV56/gUeOP0Qli35/Bf/tO6HH405\nurrC4ydXONVqcvx1p3Btm72tTc6cO8+59S3Onltjb3efXifEqIJLF85zce0SVza3wHIRwiKOI775\nl9/iueef58Mf/TBx2ELbmvf/3Q+SJwLPt9jcGiC8iLW1NVoLS7e9R3eECPqeS9hwaEQOjaiJxKIo\n69GElmVjuRaVrvuIF9odGnGMH/oopUjyCml7CCFJpwlFUWHZEqkUk9EQ27Fq630sbD9gZ6fP7k4f\n29S7Oe06lEbR3ztAKcXBYERRGWxLsrWzw3465fTrH0a4PotLbVpxA42g1WoznmSzFjyB74UIIXH9\ngDwpmEwmNIIAI2z8uIGHRYUhSSYUCozy6yMRDrbnkmYVjXaToswwRuA4DovdVaTlEHW76KREWzaF\nVgymQ6KwwWQ4RZqKZm8RC0Gn1SaOW/RaPeJGkzB0sU3J3v6AbhQRNRdIihI78GpnY6VRU0WS5lzb\nPeDS1u0ncs35btG1UcWryo/0zQ9xc6d4neu7w9da8VvmpunC9Wl1N15Ra4ypj7f1K9ZO1dd3gdev\n0aj6Q1ffNuHOGHOzaJuQdHfKte1LGGPY29tjMplQGti51IfAJvBcdq5dpdXqsLU74EMf/gjj6YTN\nK5dodzvce++9PPPMN5mMB1RFyebmNlop7r/vdThZwfHVVaTnMh2PuXBxjXGSMphMSYpy9rsZtK4Y\nDAb0D0ZMJxm2bROGIdt7u5x+8EGubu/y27/1cZ541xP82ZOfYTwe8k9/7h+zurTMuIDpVHHf605z\n+fIdPmPE9kzthxeGxO0WrhNhITHSpjL1jZLkZHnC00//JcPxiOFeH1faWBiELnBth06ng2VBb6FF\n2IzRrkOa1atdUWYstNok2RhTTNgZ7iGlxFcC13IJ/Ubd+6sKLFOwPxzSDBxOHV7h6aefptlskhX1\nw5rnOfbMlXk8SW6sxHmeU1YFrl2XQSRZhlIlxTSl1Io4CFECpOtg2xa+G1DJnMBv0mk0oFT1LjcI\nKIXB813iKML3vLp8qKxwHYeFVhtTVhi7/nttyyFqtmi2IpTS7O7ucvjwCq0gQFouJ+89xXg4rC3H\nsim+saiEQquKLEkZDadcu7zLuQsb3+tH4fuU66JXI0SdBLlus2XNdn71967HCa26sFoIjLC+43wS\nAKFvFl7XwlhRVCUKM3ML0vVRV0mEVrWHpK6Pu9qoWXLEUKniFmFUHGs9hB4rPvnJTxB4PkZpKpXh\n4HH//ScIjEWz1eNLX/k6v/u7n+JDH/ko//7f/VvWdvcotcelS5dJ05Q0zXCdgGYroN2KKHJFMslZ\naS9y7PgqUmmurK/x7Isvce7CRdJJitaGpd4CVIY8S0jTnP3hiELVA8TCKMKSNs+ffY50mnH+0hZf\n/fxTjJOSzz35WV48+yzv/7H3E/uSV9Y3iZtLnDpx/LZ3544QwcB1b4hgIw6Jo3adVVMaS1tURqEE\npOmExx9/jB9606O1YBmF5VmMswFVVTAZD1FliWXqAmHXEiwuLFFVFY602N3exPUaOKJuWNdVSX98\nQBAEjEYD7MDDc2yarS65MewdjDnoj+uOlrJiPBjiSBvP9qhMeWMltqXEsuzaFNVAoxHTjCJsR9Zi\nWFVoRzIajer2Octmkg5QBhw7RBVTdJnWNutlhRe4TA/2ayOEqmA4HFLqHM/zkLaH48d0llZJC0Pg\nt5Cej2cHaBSrC12EkOz3x3hxi7jbRFaC5aVVlrs9AtvDEw6yqBCVIVcV/dGYsxeucvGl26+Wc75b\nbhXAmaHqLBYIdQwO9KyLpDZevW6v9aqkCdwc2K41WhuMqYe6l7NTk9bMvl4vjmVZP6OVKqhUQaln\nO8WZ2NVD4EsqVWBMNSvyz9Ea3vf29/H3f+JneOc7302pKlzf42CcsDMasRg1WOjEXNnZ4O1vewKd\nDfgfv/YrtLodLq5t8qPveILRaMDa2hpJkrC2fgXXDwiCgPtO3ctyb5FHH38QN4q4cOElvvCFL/D1\nZ77FZJrj+B6Hl1doRwGmzFm/ssl+/4BCabAkjThmkqQM9vY4d+YSDz14P74nefhtj1Fmkv3+hC89\n9RS//b//Jx/9yIfY3b7G5Y1r3Pe6N972Dt0RIugFLqETEgXNeofkRbO5C3VZjLQFli3QpmBvZ5v1\njSsgJa7vY6WalttCoHGEoLfQpioK0mkGlWbr4Bq271PkKUaB73lkuiKKw9pm37FRomRhaZlyWpFV\nhoPhkG7UBCDJUnRRD4NyXRdpW/iBS397H20EjuOhFXhhgO+HSNepHzRVYipNtxVi6ao2ZvA8pLYo\nJQR+k8By8GxB3OgymIzJipwwDLl8+QKNsA2FQmtYiFpEoYcbRuTZFD+IkdJheaHH4GCXVrvL3rSP\ntDxeubjONC/Y3N24IeBeIyJNUw6vHmWxu4IoCyK39mILbb923bEsSmH+ijs15/+Hm0fiW7K11CII\n3DgafyduHcd56+td7zrRWtcxQnNzXsnNAe9VPbjMaJSuULPOJ6VqM+NaGDU3j9SG5775VX7/c7/J\nF576LKPphCTPeMPrH8NJU5SZ0G41Gez3ObzUZmF5hSCOGY0nCGH4L7/6K4QND9u2OTiY0Ihj9voD\njDFEUcTjj70Jz5e4luTM08/wyuUNCl2XCuV5TuD5LC0uIIVha2eX/cGQsiyxpEMzbmOwGA4P+IV/\n8ws8+oY3srq6ymhri3e/9+/wxDvfw4lTD7By6Bh//hdP8tGP/EP6gxHbO7efqX1HZIdt26YRxgjL\nR2uwvdkAIFXf/OvHAiklWTalFTfrFS7PkY2AySRFqhKEYm8wIPZDOgtd0vEI1w7IrYJSKJIkpdls\nEjZajIcDbCdASsl0nLCyEFOEATaC3BhWjx3i4itn0VrjevXDJR2bVrfD5rUtVDqtkxHGwgn9unuk\nrIj9EMuVpMME6fpMk5I0r4/RSZbi2bV1VaFVncQRNv2DHYxtEXh1YbXKC2zXo9mMaXZ6jMZDpHAQ\njkTIui1PTccsLR5CCMHO1g6OtsiKkrjd4vUPP8jLZ86Sj/u0OieJgoRx4DJIJuQ2HDl8gufPPYOH\nJC1SZG30hJ73Dv+NcF38pNEYZh0ilkZYIHAAC2FqU1XELQXWxsIIXccBsdCiFihtVB0fvB5H1HUH\nCubmLBPFzaNuXfKiZ/HCm8kRTDkT1Tp+aIxGaAsjJXt7CdPxBuuX16hSF0coXnruef7ZT/84jdCj\n227iPx7w+c/8ESsrK2xubnJ1e4vpNMX3Yk4/8CCf++KTeG7A1uYA17N43fFlVnoLBKFE+i7rGy/z\n4lo94bAZtVAYoqCB73u0mgF+q8lL516kUiDduqdaRQs8du+D/O0feSu//LH/QKVS2s0FLlwasrm1\njhYuvU6PXrdJmaXkacLW1g62uL1X5h3x1LeaXcKgg+vEGGXNBlSDtgWOtGYxDoV0BVoqLEvgYUgN\n5Nn18g7wpEOjEVGoCpWOSYoclacYXfcd2yIhy3McV5CVgobno5WizDPCwCPrp9itJkU2ru2rvLgW\nYyORwqDKioYfUukKz3bJ8xLHtrAtlzRNqSyB5zlUSUZlwLKgKko8CcoIhO9hCZvQD/CpV76yzKmU\npN3qEkUR/b094k6XbDjAKM04GdPwA1qNkPXtbY6urjCZDhjnOf3BEDtssnp4CWkZXEuQFwk7Ozt4\njQZ7W9vsbF/l0KEjDLKc8TRBVhbShiiM2R8PkcaiFApLGAzfXqM2568HratZmYzAGHnj66898tbX\n6lcdh18bB3ztz5VW/X6xuXm9NrpOfnFLHBF14/u1se/16XYCber7r4XBMvWC/BMf/mmCMGb9pZf5\nxV/+1/yrn/9Z8mTCK2fP0whbDCdjpsmEl18+x9beDsuLRyi7Cds7fZ579gzLK8fY3tmirOA973sX\nrqk4fOQQSlekk5xnn3+Rnf1dXD+g21thOp2ytLCI77r1LlYolHFAKjzHQ3oBhdfkQDRI11/gzItn\nCYKAx37sB/jiZ/8EQ4YXdri0do7xeMqxo/fwwotP0/Bj7j1+323vzR1xHHaJsIWNK12KSW2UeD1W\nYozA9WwcKQFNqx3RasdMypJG5JOmGUHg4TguvuPWJgKuy9GjR2nEEUqVdNoRri2xLaiqijQpsW2X\nwHVpeDZRM+bC1TWyIiXNRqR5wf7BgCKbYnQtyo04otWM2N3e5uTJU5RGY3supYE0T3EDHyksJpMJ\nRircwMcWFr7v110lRhB4IUk6ZDg9wJYORZViCY3rGYosqXfBrkuV5XUP8lKHvc1Nyryg3+/T8D3y\nQuE4LtIKWeksENnQ71+lyhQvPv8c4yRnd3vMUrvNfffdjyMFV69ewbVtimnOiVMnKZKSbJwgbRsr\ndDjUbbFyuMvJe3vf60fh+w5BbZ56c67IzSxuve2rXWbMbO6woW5bwyiMrrjV6dYS9ixza3F9cl1d\n3GfVw5y0Rl0/1qq6ItAYg9LW7Cisb8QVr+8SldaUwtR95Bq0kQgjuXBxjX/yzz/ES898hpVjR/iP\nv/QLNGOfg/1dHjx9mk435uzZszz84AO89c1v4Zd+8Zc5dfIB1q9mvPs9H+DxN/8g17a26S0u0u0G\nrJ0/Rxj6CMvh2uYOX37yKc5c2GDt8gZGWORZweGVQ/R6PRYWOuSZZmvvgKqqsCwbjEvv8HEqr8lG\n5XL2xbMcOXqcn/rJn+LTn/40jVaTlaVTgE3c6rC4vMy17Wvs7eyz2jvCE+/50dveoztCBO3Zwlim\ndZB3MplQKoFSBq0EWlmYWU2eFLCy1MWokiSZ4Hk2ua5wVIXtB1R5gi0D1ChBGoFRFvl4yiDJsGY9\nkJYlabebBJ0maanx/JCm7RF32th4hM0W2WQE0kKpKZZlsT8e1hZenkeRpBzqLSF1LarCcWblPBa+\n16gTGFjYlsS2bYIgwOiS6XRK028jpUBLQRx1me70SSdDXNdlOh2TFymZUagqY6c/xPcc8qKePifQ\nGC3BsinJsAKPSZGx0j2E59ssHj6MHwZMqjFX9xMyXRHHMVEQUipF3G5x9oXnOHryJI899EaOdldp\neg6dboNjK0ucOnzse/gUfH9ihL7RHvdaP8Eb19zyvdpMQb9q13drVvg7U2eTb8T1BCij0QK0Ebe8\nhkArg9E3X7MSYhZHZGbjJRFa0Ixj0Ibf+/0/YpoWDHbWeeZb32Kh06XR6vHUl/8Cz3UpsyGf/syn\n+NyX/4QTx1f4jU/8Vy5uvMin/vB3kXbFwWCXMApACrKi5MwrF3n2hbNc3Tlga6dPqQSbV7cAizTJ\nCcMGxgjWN65w8dIaWV6Q5RVGSlIlOUhK9gcD7FYHUQkuXlIAzeQAAASDSURBVLnE8cP31L3Gtrix\n497fr3vwXbvDh37yw3QXVm/7v3dHHIeNtEALNIqiqKtANRW2baNlhURgW5IMCzeQ+L5DO4wYpRm2\n5eMgwJZUxtCImnieJMkns2O1QUsDVYVSFtoqcKXDcDyp439FwXKjwUgp8klB1GnhWppJkWNbDtMs\nI4psQsdHq5LxZIiybKZZinRtnEoiLIFSqj5GNENGuztE7S7JJCXNpwRegOeG2L5LPk0J3JilhQX6\nuzsMpwOazdod2yiFJyWOsMmERTUdI3uL+I5LniVgbIwNk+mELCs4tnKES2sXUZVgmpecOLLC1Y1N\nHn7kjQxHfRp+j4ZjkZmSg/MjVDqmt7TKwajPeDDGsurM/EIz4vBKi4Xu7QtK53x36FkHCPAaYRM3\nMsE3L1YgBEYIhJhlia1bf8amtgb/dkHUdbtHfZ0CsKhU3QliYTBaoKubx2NtoNTyZpveTCwFdWWF\nY9fvucFwQjYdsLN9wMHeAW98+CH+8A9+h6qEf/DB9/Prv/EJNrZ2+OJnPss4yfn0H/8exrKQtk83\niphYNr3lHuQVk7SgHOUkacpgPGZ0MCaMY/wgxLEcOt0WaZpQlFn9fkIQN9rk2mBJjxTJVn+XUSk5\nES/wwZ/8RxgSji4v8/GPf5xmGJBOx+ztT1ClQ38z5Wd/+ueIWx0arfZt79EdsRPUVYXEAWWhVJ0h\nMqoevm5UXTBtqrpY1PNDHFfSPxjiex7SBt+LsGQd+Ky0odNq0QhjLMtCUxeA+sLGi2Ns42D5HnEj\nRlg2SwtddKXJkxRtCtLRhL3NXYxtococW2iqqkAICy/wcWyfuBEhioosLzB2XZaQTmqHmDLJWFpc\nIHQadHsLuLYPlsRx64crasYYSzAajMjLgjCMEFoxnY4os4TJdEqWjMnTjFKX7A0PsAOHMAzJsoxk\nMmb/oE9jYYErm1vEzQ7GErQih/F0QivyOdjboyoNo8mQrcGAjfXLJNmUsoJzrzzHSqeD8lwee+MP\nsNRu0+2EnDh6L4tLt39Q5vzNcmvWF/7qOOBr+bYRnrd8boyhqqpX1RqqG8XUr77++ommqqq6zMx1\n0UpybTsnTVOe/tazrCwf5ic+9EHW19c5t77B6x++j7JMGSQJRZHR7/c5fqjHwqEltO0gpaQZRbXj\nk9Z4nkeSZAzHY4QtOXLkGMuLS1gIxuMxw+GQ0XhKmhe4nkccN5GuQ6UVG5vXyPOM5y9scM+pQzz3\nla8QxzEf+OAH2NrZrc1PqE+R73nXO7n3/iM0260bv+t3Qvy/t9lz5syZ8/3NHbETnDNnzpzvFXMR\nnDNnzl3NXATnzJlzVzMXwTlz5tzVzEVwzpw5dzVzEZwzZ85dzVwE58yZc1czF8E5c+bc1cxFcM6c\nOXc1cxGcM2fOXc1cBOfMmXNXMxfBOXPm3NXMRXDOnDl3NXMRnDNnzl3NXATnzJlzVzMXwTlz5tzV\nzEVwzpw5dzVzEZwzZ85dzVwE58yZc1czF8E5c+bc1cxFcM6cOXc1cxGcM2fOXc1cBOfMmXNX838B\nKIOiyA0oGCYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XrL3FEcdkVPA"
      },
      "source": [
        "TF-TRT takes input as aTensorFlow saved model, therefore, we re-export the Keras model as a TF saved model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WxlUF3rlkVPH",
        "outputId": "9f3864e7-f211-4c06-d2d2-585c1a477e34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        }
      },
      "source": [
        "# Save the entire model as a SavedModel.\n",
        "model.save('resnet50_saved_model') "
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "INFO:tensorflow:Assets written to: resnet50_saved_model/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RBu2RKs6kVPP",
        "outputId": "8e063261-7efb-47fd-fa6c-1bb5076d418c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        }
      },
      "source": [
        "!saved_model_cli show --all --dir resnet50_saved_model"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:\n",
            "\n",
            "signature_def['__saved_model_init_op']:\n",
            "  The given SavedModel SignatureDef contains the following input(s):\n",
            "  The given SavedModel SignatureDef contains the following output(s):\n",
            "    outputs['__saved_model_init_op'] tensor_info:\n",
            "        dtype: DT_INVALID\n",
            "        shape: unknown_rank\n",
            "        name: NoOp\n",
            "  Method name is: \n",
            "\n",
            "signature_def['serving_default']:\n",
            "  The given SavedModel SignatureDef contains the following input(s):\n",
            "    inputs['input_1'] tensor_info:\n",
            "        dtype: DT_FLOAT\n",
            "        shape: (-1, 224, 224, 3)\n",
            "        name: serving_default_input_1:0\n",
            "  The given SavedModel SignatureDef contains the following output(s):\n",
            "    outputs['probs'] tensor_info:\n",
            "        dtype: DT_FLOAT\n",
            "        shape: (-1, 1000)\n",
            "        name: StatefulPartitionedCall:0\n",
            "  Method name is: tensorflow/serving/predict\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qBQwBvlNm-J8"
      },
      "source": [
        "### Inference with native TF2.0 saved model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8zLN0GMCkVPe",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.load_model('resnet50_saved_model')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Fbj-UEOxkVPs",
        "outputId": "3a2b34f9-8034-48cb-b3fe-477f09966025",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        }
      },
      "source": [
        "img_path = './data/img0.JPG'  # Siberian_husky\n",
        "img = image.load_img(img_path, target_size=(224, 224))\n",
        "x = image.img_to_array(img)\n",
        "x = np.expand_dims(x, axis=0)\n",
        "x = preprocess_input(x)\n",
        "\n",
        "preds = model.predict(x)\n",
        "# decode the results into a list of tuples (class, description, probability)\n",
        "# (one such list for each sample in the batch)\n",
        "print('{} - Predicted: {}'.format(img_path, decode_predictions(preds, top=3)[0]))\n",
        "plt.subplot(2,2,1)\n",
        "plt.imshow(img);\n",
        "plt.axis('off');\n",
        "plt.title(decode_predictions(preds, top=3)[0][0][1])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "./data/img0.JPG - Predicted: [('n02110185', 'Siberian_husky', 0.55662125), ('n02109961', 'Eskimo_dog', 0.4173722), ('n02110063', 'malamute', 0.020951565)]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Siberian_husky')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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M4vwyxsZo3SbWbb76lW+wutLEK5e4eO4ipVKFN73xdbz6gUkkdgRtJUbHPk8+cZWXPbCf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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGc-dC6DvwRP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e0a22e05-f4fe-47b6-93e8-2b806bf7098a"
      },
      "source": [
        "batch_size = 8\n",
        "batched_input = np.zeros((batch_size, 224, 224, 3), dtype=np.float32)\n",
        "\n",
        "for i in range(batch_size):\n",
        "  img_path = './data/img%d.JPG' % (i % 4)\n",
        "  img = image.load_img(img_path, target_size=(224, 224))\n",
        "  x = image.img_to_array(img)\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "  x = preprocess_input(x)\n",
        "  batched_input[i, :] = x\n",
        "batched_input = tf.constant(batched_input)\n",
        "print('batched_input shape: ', batched_input.shape)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "batched_input shape:  (8, 224, 224, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "rFBV6hQR7N3z",
        "colab": {}
      },
      "source": [
        "# Benchmarking throughput\n",
        "N_warmup_run = 50\n",
        "N_run = 1000\n",
        "elapsed_time = []\n",
        "\n",
        "for i in range(N_warmup_run):\n",
        "  preds = model.predict(batched_input)\n",
        "\n",
        "for i in range(N_run):\n",
        "  start_time = time.time()\n",
        "  preds = model.predict(batched_input)\n",
        "  end_time = time.time()\n",
        "  elapsed_time = np.append(elapsed_time, end_time - start_time)\n",
        "  if i % 50 == 0:\n",
        "    print('Step {}: {:4.1f}ms'.format(i, (elapsed_time[-50:].mean()) * 1000))\n",
        "\n",
        "print('Throughput: {:.0f} images/s'.format(N_run * batch_size / elapsed_time.sum()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vC_RN0BAkVPy"
      },
      "source": [
        "### TF-TRT FP32 model\n",
        "\n",
        "We first convert the TF native FP32 model to a TF-TRT FP32 model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0eLImSJ-kVPz",
        "outputId": "e2c353c7-8e4b-49aa-ab97-f4d82797d4d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "print('Converting to TF-TRT FP32...')\n",
        "conversion_params = trt.DEFAULT_TRT_CONVERSION_PARAMS._replace(precision_mode=trt.TrtPrecisionMode.FP32,\n",
        "                                                               max_workspace_size_bytes=8000000000)\n",
        "\n",
        "converter = trt.TrtGraphConverterV2(input_saved_model_dir='resnet50_saved_model',\n",
        "                                    conversion_params=conversion_params)\n",
        "converter.convert()\n",
        "converter.save(output_saved_model_dir='resnet50_saved_model_TFTRT_FP32')\n",
        "print('Done Converting to TF-TRT FP32')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Converting to TF-TRT FP32...\n",
            "INFO:tensorflow:Linked TensorRT version: (5, 1, 5)\n",
            "INFO:tensorflow:Loaded TensorRT version: (5, 1, 5)\n",
            "INFO:tensorflow:Running against TensorRT version 5.1.5\n",
            "INFO:tensorflow:Assets written to: resnet50_saved_model_TFTRT_FP32/assets\n",
            "Done Converting to TF-TRT FP32\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "dlue_3npkVQC",
        "outputId": "4dd6a366-fe9a-43c8-aad0-dd357bba41bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        }
      },
      "source": [
        "!saved_model_cli show --all --dir resnet50_saved_model_TFTRT_FP32"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:\n",
            "\n",
            "signature_def['__saved_model_init_op']:\n",
            "  The given SavedModel SignatureDef contains the following input(s):\n",
            "  The given SavedModel SignatureDef contains the following output(s):\n",
            "    outputs['__saved_model_init_op'] tensor_info:\n",
            "        dtype: DT_INVALID\n",
            "        shape: unknown_rank\n",
            "        name: NoOp\n",
            "  Method name is: \n",
            "\n",
            "signature_def['serving_default']:\n",
            "  The given SavedModel SignatureDef contains the following input(s):\n",
            "    inputs['input_1'] tensor_info:\n",
            "        dtype: DT_FLOAT\n",
            "        shape: (-1, 224, 224, 3)\n",
            "        name: serving_default_input_1:0\n",
            "  The given SavedModel SignatureDef contains the following output(s):\n",
            "    outputs['probs'] tensor_info:\n",
            "        dtype: DT_FLOAT\n",
            "        shape: unknown_rank\n",
            "        name: PartitionedCall:0\n",
            "  Method name is: tensorflow/serving/predict\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Vd2DoGUp8ivj"
      },
      "source": [
        "Next, we load and test the TF-TRT FP32 model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rf97K_rxvwRm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict_tftrt(input_saved_model):\n",
        "    \"\"\"Runs prediction on a single image and shows the result.\n",
        "    input_saved_model (string): Name of the input model stored in the current dir\n",
        "    \"\"\"\n",
        "    img_path = './data/img0.JPG'  # Siberian_husky\n",
        "    img = image.load_img(img_path, target_size=(224, 224))\n",
        "    x = image.img_to_array(img)\n",
        "    x = np.expand_dims(x, axis=0)\n",
        "    x = preprocess_input(x)\n",
        "    x = tf.constant(x)\n",
        "    \n",
        "    saved_model_loaded = tf.saved_model.load(input_saved_model, tags=[tag_constants.SERVING])\n",
        "    signature_keys = list(saved_model_loaded.signatures.keys())\n",
        "    print(signature_keys)\n",
        "\n",
        "    infer = saved_model_loaded.signatures['serving_default']\n",
        "    print(infer.structured_outputs)\n",
        "\n",
        "    labeling = infer(x)\n",
        "    preds = labeling['probs'].numpy()\n",
        "    print('{} - Predicted: {}'.format(img_path, decode_predictions(preds, top=3)[0]))\n",
        "    plt.subplot(2,2,1)\n",
        "    plt.imshow(img);\n",
        "    plt.axis('off');\n",
        "    plt.title(decode_predictions(preds, top=3)[0][0][1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "pRK0pRE-snvb",
        "outputId": "1f7ab6c1-dbfa-4e3e-a21d-df9975c70455",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "predict_tftrt('resnet50_saved_model_TFTRT_FP32')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['serving_default']\n",
            "{'probs': TensorSpec(shape=<unknown>, dtype=tf.float32, name='probs')}\n",
            "./data/img0.JPG - Predicted: [('n02110185', 'Siberian_husky', 0.55662125), ('n02109961', 'Eskimo_dog', 0.41737217), ('n02110063', 'malamute', 0.020951524)]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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B8Oyzz3Lm9PN02i0e+9uv0Wm1CYKARqPO8ePH2LdvL0ePHmWoUcfD0NSKYrnM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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z9b5j6jMvwRt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def benchmark_tftrt(input_saved_model):\n",
        "    saved_model_loaded = tf.saved_model.load(input_saved_model, tags=[tag_constants.SERVING])\n",
        "    infer = saved_model_loaded.signatures['serving_default']\n",
        "\n",
        "    N_warmup_run = 50\n",
        "    N_run = 1000\n",
        "    elapsed_time = []\n",
        "\n",
        "    for i in range(N_warmup_run):\n",
        "      labeling = infer(batched_input)\n",
        "\n",
        "    for i in range(N_run):\n",
        "      start_time = time.time()\n",
        "      labeling = infer(batched_input)\n",
        "      #prob = labeling['probs'].numpy()\n",
        "      end_time = time.time()\n",
        "      elapsed_time = np.append(elapsed_time, end_time - start_time)\n",
        "      if i % 50 == 0:\n",
        "        print('Step {}: {:4.1f}ms'.format(i, (elapsed_time[-50:].mean()) * 1000))\n",
        "\n",
        "    print('Throughput: {:.0f} images/s'.format(N_run * batch_size / elapsed_time.sum()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ai6bxNcNszHc",
        "colab": {}
      },
      "source": [
        "benchmark_tftrt('resnet50_saved_model_TFTRT_FP32')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "G2F8t6cPkVQS"
      },
      "source": [
        "### TF-TRT FP16 model\n",
        "We next convert the native TF FP32 saved model to TF-TRT FP16 model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0ia_AlSDkVQT",
        "outputId": "d29eb6de-101b-4b9a-8ebf-4880a2e469bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "print('Converting to TF-TRT FP16...')\n",
        "conversion_params = trt.DEFAULT_TRT_CONVERSION_PARAMS._replace(\n",
        "    precision_mode=trt.TrtPrecisionMode.FP16,\n",
        "    max_workspace_size_bytes=8000000000)\n",
        "converter = trt.TrtGraphConverterV2(\n",
        "   input_saved_model_dir='resnet50_saved_model', conversion_params=conversion_params)\n",
        "converter.convert()\n",
        "converter.save(output_saved_model_dir='resnet50_saved_model_TFTRT_FP16')\n",
        "print('Done Converting to TF-TRT FP16')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Converting to TF-TRT FP16...\n",
            "INFO:tensorflow:Linked TensorRT version: (5, 1, 5)\n",
            "INFO:tensorflow:Loaded TensorRT version: (5, 1, 5)\n",
            "INFO:tensorflow:Running against TensorRT version 5.1.5\n",
            "INFO:tensorflow:Assets written to: resnet50_saved_model_TFTRT_FP16/assets\n",
            "Done Converting to TF-TRT FP16\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yTDbB6DWn0kJ",
        "outputId": "33cd23f8-9c8b-4cdd-9c7a-4939aae12f56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "predict_tftrt('resnet50_saved_model_TFTRT_FP16')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['serving_default']\n",
            "{'probs': TensorSpec(shape=<unknown>, dtype=tf.float32, name='probs')}\n",
            "./data/img0.JPG - Predicted: [('n02110185', 'Siberian_husky', 0.55662024), ('n02109961', 'Eskimo_dog', 0.417373), ('n02110063', 'malamute', 0.020951627)]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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B8Oyzz3Lm9PN02i0e+9uv0Wm1CYKARqPO8ePH2LdvL0ePHmWoUcfD0NSKYrnM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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "G_gerN5j9l6U",
        "colab": {}
      },
      "source": [
        "benchmark_tftrt('resnet50_saved_model_TFTRT_FP16')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qKSJ-oizkVQY"
      },
      "source": [
        "### TF-TRT INT8 model\n",
        "\n",
        "Creating TF-TRT INT8 model requires a small calibration dataset. This data set ideally should represent the test data in production well, and will be used to create a value histogram for each layer in the neural network for effective 8-bit quantization.  \n",
        "\n",
        "Herein, for demonstration purposes, we take only the 4 images that we downloaded for calibration. In production, this set should be more representative of the production data.\n",
        "\n",
        "Due to Colab memory limit which sometime causes TensorRT to crash, to proceed, first, restart the runtime by pressing CTRL+M or select *Runtime -> Restart runtime...* or simply execute the next cell."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oRXVurzh6o5T",
        "colab": {}
      },
      "source": [
        "import os\n",
        "os.kill(os.getpid(), 9)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DGXjp3K_6x59",
        "colab": {}
      },
      "source": [
        "\n",
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "import os\n",
        "import time\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.python.compiler.tensorrt import trt_convert as trt\n",
        "from tensorflow.python.saved_model import tag_constants\n",
        "from tensorflow.keras.applications.resnet50 import ResNet50\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01lqtLJzvwSS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cca3c2d1-a697-4a25-c597-d16619da56d1"
      },
      "source": [
        "batch_size = 8\n",
        "batched_input = np.zeros((batch_size, 224, 224, 3), dtype=np.float32)\n",
        "\n",
        "for i in range(batch_size):\n",
        "  img_path = './data/img%d.JPG' % (i % 4)\n",
        "  img = image.load_img(img_path, target_size=(224, 224))\n",
        "  x = image.img_to_array(img)\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "  x = preprocess_input(x)\n",
        "  batched_input[i, :] = x\n",
        "batched_input = tf.constant(batched_input)\n",
        "print('batched_input shape: ', batched_input.shape)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "batched_input shape:  (8, 224, 224, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iM8DshiYkVQe",
        "outputId": "d3e88347-e060-4fd2-de2b-79cb38fc8442",
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "print('Converting to TF-TRT INT8...')\n",
        "conversion_params = trt.DEFAULT_TRT_CONVERSION_PARAMS._replace(\n",
        "    precision_mode=trt.TrtPrecisionMode.INT8, \n",
        "    max_workspace_size_bytes=8000000000, \n",
        "    use_calibration=True)\n",
        "converter = trt.TrtGraphConverterV2(\n",
        "    input_saved_model_dir='resnet50_saved_model', \n",
        "    conversion_params=conversion_params)\n",
        "\n",
        "def calibration_input_fn():\n",
        "    yield (batched_input, )\n",
        "converter.convert(calibration_input_fn=calibration_input_fn)\n",
        "\n",
        "converter.save(output_saved_model_dir='resnet50_saved_model_TFTRT_INT8')\n",
        "print('Done Converting to TF-TRT INT8')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Converting to TF-TRT INT8...\n",
            "INFO:tensorflow:Linked TensorRT version: (5, 1, 5)\n",
            "INFO:tensorflow:Loaded TensorRT version: (5, 1, 5)\n",
            "INFO:tensorflow:Running against TensorRT version 5.1.5\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "INFO:tensorflow:Assets written to: resnet50_saved_model_TFTRT_INT8/assets\n",
            "Done Converting to TF-TRT INT8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gu0CSNGVvwSY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict_tftrt(input_saved_model):\n",
        "    \"\"\"Runs prediction on a single image and shows the result.\n",
        "    input_saved_model (string): Name of the input model stored in the current dir\n",
        "    \"\"\"\n",
        "    img_path = './data/img0.JPG'  # Siberian_husky\n",
        "    img = image.load_img(img_path, target_size=(224, 224))\n",
        "    x = image.img_to_array(img)\n",
        "    x = np.expand_dims(x, axis=0)\n",
        "    x = preprocess_input(x)\n",
        "    x = tf.constant(x)\n",
        "    \n",
        "    saved_model_loaded = tf.saved_model.load(input_saved_model, tags=[tag_constants.SERVING])\n",
        "    signature_keys = list(saved_model_loaded.signatures.keys())\n",
        "    print(signature_keys)\n",
        "\n",
        "    infer = saved_model_loaded.signatures['serving_default']\n",
        "    print(infer.structured_outputs)\n",
        "\n",
        "    labeling = infer(x)\n",
        "    preds = labeling['probs'].numpy()\n",
        "    print('{} - Predicted: {}'.format(img_path, decode_predictions(preds, top=3)[0]))\n",
        "    plt.subplot(2,2,1)\n",
        "    plt.imshow(img);\n",
        "    plt.axis('off');\n",
        "    plt.title(decode_predictions(preds, top=3)[0][0][1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DSKfqPDGEv7U",
        "outputId": "028545e1-26e5-4fb6-930f-7b10af2c82fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "predict_tftrt('resnet50_saved_model_TFTRT_INT8')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['serving_default']\n",
            "{'probs': TensorSpec(shape=<unknown>, dtype=tf.float32, name='probs')}\n",
            "./data/img0.JPG - Predicted: [('n02110185', 'Siberian_husky', 0.5563322), ('n02109961', 'Eskimo_dog', 0.4175399), ('n02110063', 'malamute', 0.021040814)]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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B8Oyzz3Lm9PN02i0e+9uv0Wm1CYKARqPO8ePH2LdvL0ePHmWoUcfD0NSKYrnM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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DMVOHSo_vwSd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def benchmark_tftrt(input_saved_model):\n",
        "    saved_model_loaded = tf.saved_model.load(input_saved_model, tags=[tag_constants.SERVING])\n",
        "    infer = saved_model_loaded.signatures['serving_default']\n",
        "\n",
        "    N_warmup_run = 50\n",
        "    N_run = 1000\n",
        "    elapsed_time = []\n",
        "\n",
        "    for i in range(N_warmup_run):\n",
        "      labeling = infer(batched_input)\n",
        "\n",
        "    for i in range(N_run):\n",
        "      start_time = time.time()\n",
        "      labeling = infer(batched_input)\n",
        "      #prob = labeling['probs'].numpy()\n",
        "      end_time = time.time()\n",
        "      elapsed_time = np.append(elapsed_time, end_time - start_time)\n",
        "      if i % 50 == 0:\n",
        "        print('Step {}: {:4.1f}ms'.format(i, (elapsed_time[-50:].mean()) * 1000))\n",
        "\n",
        "    print('Throughput: {:.0f} images/s'.format(N_run * batch_size / elapsed_time.sum()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2mM9D3BTEzQS",
        "colab": {}
      },
      "source": [
        "benchmark_tftrt('resnet50_saved_model_TFTRT_INT8')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "I13snJ9VkVQh"
      },
      "source": [
        "## Conclusion\n",
        "In this notebook, we have demonstrated the process of creating TF-TRT FP32, FP16 and INT8 inference models from an original Keras FP32 model, as well as verify their speed and accuracy. \n",
        "\n",
        "### What's next\n",
        "Try TF-TRT on your own model and data, and experience the simplicity and speed up it offers."
      ]
    }
  ]
}