Train-Tensorflow-Object-Detection-API-in-Colab.ipynb

train-tensorflow-object-detection-api-in-colab.ipynb

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  "metadata": {
    "colab": {
      "name": "Train-Tensorflow-Object-Detection-API-in-Colab.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/rakesh4real/cd1dc82790cc61b62ad5722cc52683d3/train-tensorflow-object-detection-api-in-colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iK0-LhCcwWkf",
        "colab_type": "text"
      },
      "source": [
        "**Source:** *https://medium.com/analytics-vidhya/training-an-object-detection-model-with-tensorflow-api-using-google-colab-4f9a688d5e8b*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ka7I0KtgrgY2",
        "colab_type": "text"
      },
      "source": [
        "Enable GPU in Colab\n",
        "```\n",
        "Menu Bar > Edit > Notebook Settings > Hardware Accelerator > GPU\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z3iDGi7uooyZ",
        "colab_type": "code",
        "outputId": "4702d0a2-706d-469d-afbb-e45c2f08ec1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "%tensorflow_version 1.14 \n",
        "# Explicitly mention version as  \n",
        "# colab will use v2.x as default\n",
        "\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "\n",
        "print('Found GPU at: {}'.format(device_name)) # Note: \"Found GPU at:\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "`%tensorflow_version` only switches the major version: `1.x` or `2.x`.\n",
            "You set: `1.14`. This will be interpreted as: `1.x`.\n",
            "\n",
            "\n",
            "TensorFlow is already loaded. Please restart the runtime to change versions.\n",
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n8TnE2GvrcLX",
        "colab_type": "code",
        "outputId": "51f2935e-27ef-4381-8ec0-77a51c22f293",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "# Mount google drive by pasting the access link\n",
        "# Make sure you are logged in in correct gmail account\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cfxfOdWYtKYT",
        "colab_type": "code",
        "outputId": "98f3456b-ea83-4215-f0fd-401d54603523",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "%cd '/content/gdrive/My Drive/IEDC/'\n",
        "\n",
        "# Clone the Tensorflow models repository. \n",
        "# Also you may clone the COCO repository and install the \n",
        "# COCO object detection API for evaluation purpose.\n",
        "!git clone https://github.com/tensorflow/models.git # change this to your repo\n",
        "!git clone https://github.com/cocodataset/cocoapi.git # change this to your cloned repo\n",
        "!cd cocoapi/PythonAPI; make; cp -r pycocotools /content/models/research/\n",
        "\n",
        "# --------------------------------------------------------------------------------\n",
        "# fatal: destination path 'models' already exists and is not an empty directory.\n",
        "# fatal: destination path 'cocoapi' already exists and is not an empty directory.\n",
        "\n",
        "# The above fatal exceptions occur as folder already exist (cloned already)\n",
        "# Our data and scripts that we have stored in `object_detection` folder will\n",
        "# be safe and wont be rewritten as nothing is being cloned."
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/IEDC\n",
            "fatal: destination path 'models' already exists and is not an empty directory.\n",
            "fatal: destination path 'cocoapi' already exists and is not an empty directory.\n",
            "python setup.py build_ext --inplace\n",
            "running build_ext\n",
            "skipping 'pycocotools/_mask.c' Cython extension (up-to-date)\n",
            "building 'pycocotools._mask' extension\n",
            "creating build\n",
            "creating build/common\n",
            "creating build/temp.linux-x86_64-3.6\n",
            "creating build/temp.linux-x86_64-3.6/pycocotools\n",
            "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.6/dist-packages/numpy/core/include -I../common -I/usr/include/python3.6m -c ../common/maskApi.c -o build/temp.linux-x86_64-3.6/../common/maskApi.o -Wno-cpp -Wno-unused-function -std=c99\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleDecode\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:46:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "       \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; v=!v; }}\n",
            "       \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:46:49:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n",
            "       for( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; \u001b[01;36m\u001b[Kv\u001b[m\u001b[K=!v; }}\n",
            "                                                 \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrPoly\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:166:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "   \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); x[k]=x[0];\n",
            "   \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:166:54:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n",
            "   for(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); \u001b[01;36m\u001b[Kx\u001b[m\u001b[K[k]=x[0];\n",
            "                                                      \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:167:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "   \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); y[k]=y[0];\n",
            "   \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:167:54:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n",
            "   for(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); \u001b[01;36m\u001b[Ky\u001b[m\u001b[K[k]=y[0];\n",
            "                                                      \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleToString\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:212:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "       \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(more) c |= 0x20; c+=48; s[p++]=c;\n",
            "       \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:212:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            "       if(more) c |= 0x20; \u001b[01;36m\u001b[Kc\u001b[m\u001b[K+=48; s[p++]=c;\n",
            "                           \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrString\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:220:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kwhile\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "   \u001b[01;35m\u001b[Kwhile\u001b[m\u001b[K( s[m] ) m++; cnts=malloc(sizeof(uint)*m); m=0;\n",
            "   \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:220:22:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kwhile\u001b[m\u001b[K’\n",
            "   while( s[m] ) m++; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K=malloc(sizeof(uint)*m); m=0;\n",
            "                      \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:228:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(m>2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x;\n",
            "     \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:228:34:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            "     if(m>2) x+=(long) cnts[m-2]; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K[m++]=(uint) x;\n",
            "                                  \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleToBbox\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:141:31:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kxp\u001b[m\u001b[K’ may be used uninitialized in this function [\u001b[01;35m\u001b[K-Wmaybe-uninitialized\u001b[m\u001b[K]\n",
            "       if(j%2==0) xp=x; else if\u001b[01;35m\u001b[K(\u001b[m\u001b[Kxp<x) { ys=0; ye=h-1; }\n",
            "                               \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.6/dist-packages/numpy/core/include -I../common -I/usr/include/python3.6m -c pycocotools/_mask.c -o build/temp.linux-x86_64-3.6/pycocotools/_mask.o -Wno-cpp -Wno-unused-function -std=c99\n",
            "creating build/lib.linux-x86_64-3.6\n",
            "creating build/lib.linux-x86_64-3.6/pycocotools\n",
            "x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.6/../common/maskApi.o build/temp.linux-x86_64-3.6/pycocotools/_mask.o -o build/lib.linux-x86_64-3.6/pycocotools/_mask.cpython-36m-x86_64-linux-gnu.so\n",
            "copying build/lib.linux-x86_64-3.6/pycocotools/_mask.cpython-36m-x86_64-linux-gnu.so -> pycocotools\n",
            "rm -rf build\n",
            "cp: cannot create directory '/content/models/research/': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N4JLWrcJvdkt",
        "colab_type": "code",
        "outputId": "5cfd9aa0-d4fc-47a1-9398-a281367ec557",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Some handy tools\n",
        "!apt-get install protobuf-compiler python-pil python-lxml python-tk\n",
        "!pip install Cython"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "protobuf-compiler is already the newest version (3.0.0-9.1ubuntu1).\n",
            "python-tk is already the newest version (2.7.17-1~18.04).\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-430\n",
            "Use 'apt autoremove' to remove it.\n",
            "The following additional packages will be installed:\n",
            "  python-bs4 python-chardet python-html5lib python-olefile\n",
            "  python-pkg-resources python-six python-webencodings\n",
            "Suggested packages:\n",
            "  python-genshi python-lxml-dbg python-lxml-doc python-pil-doc python-pil-dbg\n",
            "  python-setuptools\n",
            "The following NEW packages will be installed:\n",
            "  python-bs4 python-chardet python-html5lib python-lxml python-olefile\n",
            "  python-pil python-pkg-resources python-six python-webencodings\n",
            "0 upgraded, 9 newly installed, 0 to remove and 7 not upgraded.\n",
            "Need to get 1,818 kB of archives.\n",
            "After this operation, 7,688 kB of additional disk space will be used.\n",
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            "Get:8 http://archive.ubuntu.com/ubuntu bionic/main amd64 python-olefile all 0.45.1-1 [33.2 kB]\n",
            "Get:9 http://archive.ubuntu.com/ubuntu bionic/main amd64 python-pil amd64 5.1.0-1 [328 kB]\n",
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            "Setting up python-pkg-resources (39.0.1-2) ...\n",
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            "Setting up python-olefile (0.45.1-1) ...\n",
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            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Requirement already satisfied: Cython in /usr/local/lib/python3.6/dist-packages (0.29.14)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BEFH7ipBvdcd",
        "colab_type": "code",
        "outputId": "ea6fb44d-c5f4-4ce7-ed50-f2bd8476d0e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "# Compile the model definition. Build protoc files\n",
        "%cd '/content/gdrive/My Drive/IEDC/models/research/'\n",
        "!protoc object_detection/protos/*.proto --python_out=."
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/IEDC/models/research\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INFlNT-QvdSV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# set the environment\n",
        "import os\n",
        "os.environ['PYTHONPATH'] += ':/content/gdrive/My Drive/IEDC/models/research/:/content/gdrive/My Drive/IEDC/models/research/slim'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EpJPurE_t8c1",
        "colab_type": "code",
        "outputId": "0ab01c90-9258-436b-fac9-4fa967e6cf0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#########################################################\n",
        "# Always run this👇 every restart of session\n",
        "#########################################################\n",
        "!python setup.py build\n",
        "!python setup.py install"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "running build\n",
            "running build_py\n",
            "copying object_detection/xml_to_csv.py -> build/lib/object_detection\n",
            "copying object_detection/generate_tfrecord2.py -> build/lib/object_detection\n",
            "copying object_detection/protos/box_coder_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/anchor_generator_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/bipartite_matcher_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/box_predictor_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/model_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/argmax_matcher_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/calibration_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/multiscale_anchor_generator_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/eval_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/optimizer_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/faster_rcnn_box_coder_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/pipeline_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/faster_rcnn_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/post_processing_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/flexible_grid_anchor_generator_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/preprocessor_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/graph_rewriter_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/region_similarity_calculator_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/grid_anchor_generator_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/square_box_coder_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/hyperparams_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/ssd_anchor_generator_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/image_resizer_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/ssd_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/input_reader_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/string_int_label_map_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/keypoint_box_coder_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/target_assigner_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/losses_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/train_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/matcher_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/mean_stddev_box_coder_pb2.py -> build/lib/object_detection/protos\n",
            "running egg_info\n",
            "writing object_detection.egg-info/PKG-INFO\n",
            "writing dependency_links to object_detection.egg-info/dependency_links.txt\n",
            "writing requirements to object_detection.egg-info/requires.txt\n",
            "writing top-level names to object_detection.egg-info/top_level.txt\n",
            "writing manifest file 'object_detection.egg-info/SOURCES.txt'\n",
            "running install\n",
            "running bdist_egg\n",
            "running egg_info\n",
            "writing object_detection.egg-info/PKG-INFO\n",
            "writing dependency_links to object_detection.egg-info/dependency_links.txt\n",
            "writing requirements to object_detection.egg-info/requires.txt\n",
            "writing top-level names to object_detection.egg-info/top_level.txt\n",
            "writing manifest file 'object_detection.egg-info/SOURCES.txt'\n",
            "installing library code to build/bdist.linux-x86_64/egg\n",
            "running install_lib\n",
            "running build_py\n",
            "creating build/bdist.linux-x86_64/egg\n",
            "creating build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/README.md -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/CONTRIBUTING.md -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/__init__.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/model_hparams.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/export_tflite_ssd_graph_lib.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/inputs.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/model_lib_test.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/export_tflite_ssd_graph_lib_test.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/exporter_test.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/model_lib.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/eval_util.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/model_lib_v2_test.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/eval_util_test.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/export_inference_graph.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/export_tflite_ssd_graph.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/exporter.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/inputs_test.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/model_lib_v2.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/object_detection_tutorial.ipynb -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/model_main.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/model_tpu_main.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "creating build/bdist.linux-x86_64/egg/object_detection/anchor_generators\n",
            "copying build/lib/object_detection/anchor_generators/multiple_grid_anchor_generator_test.py -> build/bdist.linux-x86_64/egg/object_detection/anchor_generators\n",
            "copying build/lib/object_detection/anchor_generators/multiple_grid_anchor_generator.py -> build/bdist.linux-x86_64/egg/object_detection/anchor_generators\n",
            "copying build/lib/object_detection/anchor_generators/multiscale_grid_anchor_generator.py -> build/bdist.linux-x86_64/egg/object_detection/anchor_generators\n",
            "copying build/lib/object_detection/anchor_generators/grid_anchor_generator_test.py -> build/bdist.linux-x86_64/egg/object_detection/anchor_generators\n",
            "copying build/lib/object_detection/anchor_generators/flexible_grid_anchor_generator.py -> build/bdist.linux-x86_64/egg/object_detection/anchor_generators\n",
            "copying build/lib/object_detection/anchor_generators/flexible_grid_anchor_generator_test.py -> build/bdist.linux-x86_64/egg/object_detection/anchor_generators\n",
            "copying build/lib/object_detection/anchor_generators/grid_anchor_generator.py -> build/bdist.linux-x86_64/egg/object_detection/anchor_generators\n",
            "copying build/lib/object_detection/anchor_generators/__init__.py -> build/bdist.linux-x86_64/egg/object_detection/anchor_generators\n",
            "copying build/lib/object_detection/anchor_generators/multiscale_grid_anchor_generator_test.py -> build/bdist.linux-x86_64/egg/object_detection/anchor_generators\n",
            "creating build/bdist.linux-x86_64/egg/object_detection/box_coders\n",
            "copying build/lib/object_detection/box_coders/square_box_coder.py -> build/bdist.linux-x86_64/egg/object_detection/box_coders\n",
            "copying build/lib/object_detection/box_coders/__init__.py -> build/bdist.linux-x86_64/egg/object_detection/box_coders\n",
            "copying build/lib/object_detection/box_coders/square_box_coder_test.py -> build/bdist.linux-x86_64/egg/object_detection/box_coders\n",
            "copying build/lib/object_detection/box_coders/faster_rcnn_box_coder.py -> build/bdist.linux-x86_64/egg/object_detection/box_coders\n",
            "copying build/lib/object_detection/box_coders/keypoint_box_coder.py -> build/bdist.linux-x86_64/egg/object_detection/box_coders\n",
            "copying build/lib/object_detection/box_coders/keypoint_box_coder_test.py -> build/bdist.linux-x86_64/egg/object_detection/box_coders\n",
            "copying build/lib/object_detection/box_coders/mean_stddev_box_coder_test.py -> build/bdist.linux-x86_64/egg/object_detection/box_coders\n",
            "copying build/lib/object_detection/box_coders/faster_rcnn_box_coder_test.py -> build/bdist.linux-x86_64/egg/object_detection/box_coders\n",
            "copying build/lib/object_detection/box_coders/mean_stddev_box_coder.py -> build/bdist.linux-x86_64/egg/object_detection/box_coders\n",
            "creating build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/box_coder_builder_test.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/anchor_generator_builder.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/__init__.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/anchor_generator_builder_test.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/box_coder_builder.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/calibration_builder_test.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/box_predictor_builder_test.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/calibration_builder.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/post_processing_builder_test.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/input_reader_builder.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/dataset_builder_test.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/optimizer_builder_test.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/matcher_builder.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/model_builder_test.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/hyperparams_builder_test.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/input_reader_builder_test.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/graph_rewriter_builder.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/region_similarity_calculator_builder.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/graph_rewriter_builder_test.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
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            "copying build/lib/object_detection/builders/box_predictor_builder.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "copying build/lib/object_detection/builders/target_assigner_builder.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
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            "copying build/lib/object_detection/builders/image_resizer_builder.py -> build/bdist.linux-x86_64/egg/object_detection/builders\n",
            "creating build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/box_predictor.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/freezable_batch_norm_test.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/preprocessor_cache.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/prefetcher_test.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
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            "copying build/lib/object_detection/core/freezable_batch_norm.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
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            "copying build/lib/object_detection/core/target_assigner_test.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
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            "copying build/lib/object_detection/core/multiclass_nms_test.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/data_parser.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/prefetcher.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
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            "copying build/lib/object_detection/core/batcher_test.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
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            "copying build/lib/object_detection/core/preprocessor.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/keypoint_ops_test.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/minibatch_sampler_test.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/batch_multiclass_nms_test.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/losses.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/box_list_test.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/data_decoder.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/region_similarity_calculator_test.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/keypoint_ops.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/balanced_positive_negative_sampler_test.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/__init__.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/class_agnostic_nms_test.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
            "copying build/lib/object_detection/core/box_coder_test.py -> build/bdist.linux-x86_64/egg/object_detection/core\n",
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            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/tpu_exporters/faster_rcnn.py to faster_rcnn.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/tpu_exporters/ssd.py to ssd.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/tpu_exporters/utils.py to utils.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/tpu_exporters/export_saved_model_tpu_lib_test.py to export_saved_model_tpu_lib_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/tpu_exporters/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/tpu_exporters/testdata/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/variables_helper.py to variables_helper.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/context_manager_test.py to context_manager_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/np_box_mask_list_ops.py to np_box_mask_list_ops.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/shape_utils.py to shape_utils.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/spatial_transform_ops_test.py to spatial_transform_ops_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/test_utils.py to test_utils.cpython-36.pyc\n",
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            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/test_case.py to test_case.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/object_detection_evaluation.py to object_detection_evaluation.cpython-36.pyc\n",
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            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/per_image_vrd_evaluation_test.py to per_image_vrd_evaluation_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/np_box_mask_list_test.py to np_box_mask_list_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/config_util_test.py to config_util_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/shape_utils_test.py to shape_utils_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/learning_schedules.py to learning_schedules.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/label_map_util_test.py to label_map_util_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/np_box_ops_test.py to np_box_ops_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/patch_ops_test.py to patch_ops_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/object_detection_evaluation_test.py to object_detection_evaluation_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/np_box_list.py to np_box_list.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/np_box_mask_list.py to np_box_mask_list.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/np_box_list_test.py to np_box_list_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/per_image_vrd_evaluation.py to per_image_vrd_evaluation.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/static_shape.py to static_shape.cpython-36.pyc\n",
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            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/metrics_test.py to metrics_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/np_box_mask_list_ops_test.py to np_box_mask_list_ops_test.cpython-36.pyc\n",
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            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/json_utils.py to json_utils.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/np_mask_ops.py to np_mask_ops.cpython-36.pyc\n",
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            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/model_util_test.py to model_util_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/per_image_evaluation.py to per_image_evaluation.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/dataset_util.py to dataset_util.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/np_box_list_ops.py to np_box_list_ops.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/category_util_test.py to category_util_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/visualization_utils.py to visualization_utils.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/variables_helper_test.py to variables_helper_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/vrd_evaluation.py to vrd_evaluation.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/vrd_evaluation_test.py to vrd_evaluation_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/utils/visualization_utils_test.py to visualization_utils_test.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/xml_to_csv.py to xml_to_csv.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/object_detection/generate_tfrecord2.py to generate_tfrecord2.cpython-36.pyc\n",
            "creating build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying object_detection.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying object_detection.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying object_detection.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying object_detection.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying object_detection.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "zip_safe flag not set; analyzing archive contents...\n",
            "object_detection.core.__pycache__.preprocessor.cpython-36: module MAY be using inspect.stack\n",
            "object_detection.utils.__pycache__.autoaugment_utils.cpython-36: module MAY be using inspect.stack\n",
            "creating 'dist/object_detection-0.1-py3.6.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n",
            "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n",
            "Processing object_detection-0.1-py3.6.egg\n",
            "creating /usr/local/lib/python3.6/dist-packages/object_detection-0.1-py3.6.egg\n",
            "Extracting object_detection-0.1-py3.6.egg to /usr/local/lib/python3.6/dist-packages\n",
            "Adding object-detection 0.1 to easy-install.pth file\n",
            "\n",
            "Installed /usr/local/lib/python3.6/dist-packages/object_detection-0.1-py3.6.egg\n",
            "Processing dependencies for object-detection==0.1\n",
            "Searching for Cython==0.29.14\n",
            "Best match: Cython 0.29.14\n",
            "Adding Cython 0.29.14 to easy-install.pth file\n",
            "Installing cygdb script to /usr/local/bin\n",
            "Installing cython script to /usr/local/bin\n",
            "Installing cythonize script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for matplotlib==3.1.2\n",
            "Best match: matplotlib 3.1.2\n",
            "Adding matplotlib 3.1.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for Pillow==4.3.0\n",
            "Best match: Pillow 4.3.0\n",
            "Adding Pillow 4.3.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for python-dateutil==2.6.1\n",
            "Best match: python-dateutil 2.6.1\n",
            "Adding python-dateutil 2.6.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for numpy==1.17.4\n",
            "Best match: numpy 1.17.4\n",
            "Adding numpy 1.17.4 to easy-install.pth file\n",
            "Installing f2py script to /usr/local/bin\n",
            "Installing f2py3 script to /usr/local/bin\n",
            "Installing f2py3.6 script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for pyparsing==2.4.5\n",
            "Best match: pyparsing 2.4.5\n",
            "Adding pyparsing 2.4.5 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for cycler==0.10.0\n",
            "Best match: cycler 0.10.0\n",
            "Adding cycler 0.10.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for kiwisolver==1.1.0\n",
            "Best match: kiwisolver 1.1.0\n",
            "Adding kiwisolver 1.1.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for olefile==0.46\n",
            "Best match: olefile 0.46\n",
            "Adding olefile 0.46 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for six==1.12.0\n",
            "Best match: six 1.12.0\n",
            "Adding six 1.12.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Searching for setuptools==42.0.2\n",
            "Best match: setuptools 42.0.2\n",
            "Adding setuptools 42.0.2 to easy-install.pth file\n",
            "Installing easy_install script to /usr/local/bin\n",
            "Installing easy_install-3.8 script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.6/dist-packages\n",
            "Finished processing dependencies for object-detection==0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7AadWO-vQq9",
        "colab_type": "code",
        "outputId": "54faf7d0-bbef-4a55-8c47-d43a1f851852",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "##############################################################\n",
        "# Note: if you wish to know the remaining hours you have \n",
        "# for your colab session run the copy and run the code below.\n",
        "##############################################################\n",
        "import time, psutil\n",
        "Start = time.time()- psutil.boot_time()\n",
        "Left= 12*3600 - Start\n",
        "print('Time remaining for this session is: ', Left/3600, 'Hrs')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time remaining for this session is:  11.863848649395837 Hrs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Ico3vAYxIFt",
        "colab_type": "code",
        "outputId": "0daf699b-26ec-4953-ea22-296ce54d7621",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 846
        }
      },
      "source": [
        "# Test to see if all we need for the training has been installed\n",
        "# rember the last CD you did in order to specify the directory.\n",
        "\n",
        "!python object_detection/builders/model_builder_test.py"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:\n",
            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/slim/nets/inception_resnet_v2.py:374: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/slim/nets/mobilenet/mobilenet.py:397: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n",
            "\n",
            "Running tests under Python 3.6.9: /usr/bin/python3\n",
            "[ RUN      ] ModelBuilderTest.test_create_experimental_model\n",
            "[       OK ] ModelBuilderTest.test_create_experimental_model\n",
            "[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_model_from_config_with_example_miner\n",
            "[       OK ] ModelBuilderTest.test_create_faster_rcnn_model_from_config_with_example_miner\n",
            "[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
            "[       OK ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
            "[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
            "[       OK ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
            "[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
            "[       OK ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
            "[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
            "[       OK ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
            "[ RUN      ] ModelBuilderTest.test_create_rfcn_model_from_config\n",
            "[       OK ] ModelBuilderTest.test_create_rfcn_model_from_config\n",
            "[ RUN      ] ModelBuilderTest.test_create_ssd_fpn_model_from_config\n",
            "[       OK ] ModelBuilderTest.test_create_ssd_fpn_model_from_config\n",
            "[ RUN      ] ModelBuilderTest.test_create_ssd_models_from_config\n",
            "[       OK ] ModelBuilderTest.test_create_ssd_models_from_config\n",
            "[ RUN      ] ModelBuilderTest.test_invalid_faster_rcnn_batchnorm_update\n",
            "[       OK ] ModelBuilderTest.test_invalid_faster_rcnn_batchnorm_update\n",
            "[ RUN      ] ModelBuilderTest.test_invalid_first_stage_nms_iou_threshold\n",
            "[       OK ] ModelBuilderTest.test_invalid_first_stage_nms_iou_threshold\n",
            "[ RUN      ] ModelBuilderTest.test_invalid_model_config_proto\n",
            "[       OK ] ModelBuilderTest.test_invalid_model_config_proto\n",
            "[ RUN      ] ModelBuilderTest.test_invalid_second_stage_batch_size\n",
            "[       OK ] ModelBuilderTest.test_invalid_second_stage_batch_size\n",
            "[ RUN      ] ModelBuilderTest.test_session\n",
            "[  SKIPPED ] ModelBuilderTest.test_session\n",
            "[ RUN      ] ModelBuilderTest.test_unknown_faster_rcnn_feature_extractor\n",
            "[       OK ] ModelBuilderTest.test_unknown_faster_rcnn_feature_extractor\n",
            "[ RUN      ] ModelBuilderTest.test_unknown_meta_architecture\n",
            "[       OK ] ModelBuilderTest.test_unknown_meta_architecture\n",
            "[ RUN      ] ModelBuilderTest.test_unknown_ssd_feature_extractor\n",
            "[       OK ] ModelBuilderTest.test_unknown_ssd_feature_extractor\n",
            "----------------------------------------------------------------------\n",
            "Ran 17 tests in 0.176s\n",
            "\n",
            "OK (skipped=1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KqFE-8usTwci",
        "colab_type": "text"
      },
      "source": [
        "All set up and done!\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lx8SnGgyTnky",
        "colab_type": "text"
      },
      "source": [
        "# Generate TFRecords"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ebcDgNPSyPUl",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "---------------\n",
        "**NOTE:** \n",
        "\n",
        "Make sure you have folders named `training`, `data` and `Images` in `object detection` folder. Also, let the data-sets be in a folder called images and in this `images` folder, split the data-sets into two folders named `train` and `test` folders. \n",
        "\n",
        "\n",
        "`train_labels.csv` and `test_labels.csv` will be generated in `data` folder.\n",
        "\n",
        "\n",
        "- As of this point you should have a folder in the `object detection` directory that contains your `train` and `test` images` with respective xml file` of each images. \n",
        "\n",
        "- We need to create a `tensorflow record` file from the `xml file` we have.\n",
        "\n",
        "---------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WQ4YAgrU0kQ_",
        "colab_type": "text"
      },
      "source": [
        "- create `xml_to_csv.py` in `object_detection/` dir \n",
        "\n",
        "`xml_to_csv.py` :\n",
        "```\n",
        "import os\n",
        "import glob\n",
        "import pandas as pd\n",
        "import xml.etree.ElementTree as ET\n",
        "def xml_to_csv(path):\n",
        "    xml_list = []\n",
        "    for xml_file in glob.glob(path + '/*.xml'):\n",
        "        tree = ET.parse(xml_file)\n",
        "        root = tree.getroot()\n",
        "        for member in root.findall('object'):\n",
        "            value = (root.find('filename').text,\n",
        "                     int(root.find('size')[0].text),\n",
        "                     int(root.find('size')[1].text),\n",
        "                     member[0].text,\n",
        "                     int(member[4][0].text),\n",
        "                     int(member[4][1].text),\n",
        "                     int(member[4][2].text),\n",
        "                     int(member[4][3].text)\n",
        "                     )\n",
        "            xml_list.append(value)\n",
        "    column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax']\n",
        "    xml_df = pd.DataFrame(xml_list, columns=column_name)\n",
        "    return xml_df\n",
        "def main(directory_list):\n",
        "    for Image_cat in directory_list:\n",
        "        image_path = os.path.join(os.getcwd(), 'images/{}'.format(Image_cat))\n",
        "        xml_df = xml_to_csv(image_path) \n",
        "        xml_df.to_csv('data/{}_labels.csv'.format(Image_cat), index=None)\n",
        "        print('Successfully converted xml to csv.')\n",
        "main(['train','test'])\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJB94r_gx3RV",
        "colab_type": "code",
        "outputId": "d6347a45-cb5c-48eb-b43b-3f64ee656444",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        }
      },
      "source": [
        "%cd '/content/gdrive/My Drive/IEDC/models/research/object_detection'\n",
        "!python xml_to_csv.py"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/IEDC/models/research/object_detection\n",
            "Successfully converted xml to csv.\n",
            "Successfully converted xml to csv.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZHxHdOb7GLSl",
        "colab_type": "text"
      },
      "source": [
        "Create a python file named `generate_tfrecord.py` in `object_detection` folder then *copy, edit the necessary parts* or simply paste the code below in the python file then upload it into the object detection directory if the same config applies to you or download the generate_tfrecord.py file\n",
        "\n",
        "**Note:** Never use `return 0` as `0` is reserved for background\n",
        "\n",
        "```\n",
        "# Edit flags as well\n",
        "\n",
        "# Note: id 0 is reserved for background\n",
        "\n",
        "# TO-DO replace this with label map\n",
        "def class_text_to_int(row_label):\n",
        "  if row_label == ‘The label you used’:\n",
        "    return 1\n",
        "  else:\n",
        "    None\n",
        "\n",
        "# Note if you have more than one label create as many ‘if ‘ statements to return 1 then 2…e.g.\n",
        "if row_label == ‘label 1’:\n",
        "  return 1\n",
        "elif row_label == ‘label 2’:\n",
        "  return 2\n",
        "else:\n",
        "  None\n",
        "```\n",
        "\n",
        "`generate_tfrecord2.py` :\n",
        "```\n",
        "\"\"\"\n",
        "\"\"\"\n",
        "Usage:\n",
        "\n",
        "# Create train data:\n",
        "python generate_tfrecord.py --label=<LABEL> --csv_input=<PATH_TO_ANNOTATIONS_FOLDER>/train_labels.csv  --output_path=<PATH_TO_ANNOTATIONS_FOLDER>/train.record\n",
        "\n",
        "# Create test data:\n",
        "python generate_tfrecord.py --label=<LABEL> --csv_input=<PATH_TO_ANNOTATIONS_FOLDER>/test_labels.csv  --output_path=<PATH_TO_ANNOTATIONS_FOLDER>/test.record\n",
        "\"\"\"\n",
        "\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "from __future__ import absolute_import\n",
        "\n",
        "import os\n",
        "import io\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import sys\n",
        "sys.path.append(\"../../models/research\")\n",
        "\n",
        "from PIL import Image\n",
        "from object_detection.utils import dataset_util\n",
        "from collections import namedtuple, OrderedDict\n",
        "\n",
        "flags = tf.app.flags\n",
        "flags.DEFINE_string('csv_input', '', 'Path to the CSV input')\n",
        "flags.DEFINE_string('output_path', '', 'Path to output TFRecord')\n",
        "flags.DEFINE_string('label1', '', 'Name of class1 label')\n",
        "flags.DEFINE_string('label2', '', 'Name of class2 label')\n",
        "flags.DEFINE_string('label3', '', 'Name of class3 label')\n",
        "# if your image has more labels input them as\n",
        "# flags.DEFINE_string('label0', '', 'Name of class[0] label')\n",
        "# flags.DEFINE_string('label1', '', 'Name of class[1] label')\n",
        "# and so on.\n",
        "flags.DEFINE_string('img_path', '', 'Path to images')\n",
        "FLAGS = flags.FLAGS\n",
        "\n",
        "\n",
        "# TO-DO replace this with label map\n",
        "# for multiple labels add more else if statements\n",
        "def class_text_to_int(row_label):\n",
        "    if row_label == FLAGS.label1:  # 'ship':\n",
        "        return 1\n",
        "    elif row_label == FLAGS.label2:\n",
        "        return 2\n",
        "    elif row_label == FLAGS.label3:\n",
        "        return 3\n",
        "    # comment upper if statement and uncomment these statements for multiple labelling\n",
        "    # if row_label == FLAGS.label0:\n",
        "    #   return 1\n",
        "    # elif row_label == FLAGS.label1:\n",
        "    #   return 0\n",
        "    else:\n",
        "        None\n",
        "\n",
        "\n",
        "def split(df, group):\n",
        "    data = namedtuple('data', ['filename', 'object'])\n",
        "    gb = df.groupby(group)\n",
        "    return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)]\n",
        "\n",
        "\n",
        "def create_tf_example(group, path):\n",
        "    with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:\n",
        "        encoded_jpg = fid.read()\n",
        "    encoded_jpg_io = io.BytesIO(encoded_jpg)\n",
        "    image = Image.open(encoded_jpg_io)\n",
        "    width, height = image.size\n",
        "\n",
        "    filename = group.filename.encode('utf8')\n",
        "    image_format = b'jpg'\n",
        "    # check if the image format is matching with your images.\n",
        "    xmins = []\n",
        "    xmaxs = []\n",
        "    ymins = []\n",
        "    ymaxs = []\n",
        "    classes_text = []\n",
        "    classes = []\n",
        "\n",
        "    for index, row in group.object.iterrows():\n",
        "        xmins.append(row['xmin'] / width)\n",
        "        xmaxs.append(row['xmax'] / width)\n",
        "        ymins.append(row['ymin'] / height)\n",
        "        ymaxs.append(row['ymax'] / height)\n",
        "        classes_text.append(row['class'].encode('utf8'))\n",
        "        classes.append(class_text_to_int(row['class']))\n",
        "\n",
        "    tf_example = tf.train.Example(features=tf.train.Features(feature={\n",
        "        'image/height': dataset_util.int64_feature(height),\n",
        "        'image/width': dataset_util.int64_feature(width),\n",
        "        'image/filename': dataset_util.bytes_feature(filename),\n",
        "        'image/source_id': dataset_util.bytes_feature(filename),\n",
        "        'image/encoded': dataset_util.bytes_feature(encoded_jpg),\n",
        "        'image/format': dataset_util.bytes_feature(image_format),\n",
        "        'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),\n",
        "        'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),\n",
        "        'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),\n",
        "        'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),\n",
        "        'image/object/class/text': dataset_util.bytes_list_feature(classes_text),\n",
        "        'image/object/class/label': dataset_util.int64_list_feature(classes),\n",
        "    }))\n",
        "    return tf_example\n",
        "\n",
        "\n",
        "def main(_):\n",
        "    writer = tf.python_io.TFRecordWriter(FLAGS.output_path)\n",
        "    path = os.path.join(os.getcwd(), FLAGS.img_path)\n",
        "    examples = pd.read_csv(FLAGS.csv_input)\n",
        "    grouped = split(examples, 'filename')\n",
        "    for group in grouped:\n",
        "        tf_example = create_tf_example(group, path)\n",
        "        writer.write(tf_example.SerializeToString())\n",
        "\n",
        "    writer.close()\n",
        "    output_path = os.path.join(os.getcwd(), FLAGS.output_path)\n",
        "    print('Successfully created the TFRecords: {}'.format(output_path))\n",
        "\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    tf.app.run()\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fwTarmlf1dpw",
        "colab_type": "code",
        "outputId": "b680e9f3-0c9c-4549-ea6a-57a8a4795fd8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "#In the object detection directory, run the codes below\n",
        "%cd '/content/gdrive/My Drive/IEDC/models/research/object_detection'\n",
        "\n",
        "# for test data\n",
        "!python generate_tfrecord2.py --label1=COW --label2=ELEPHANT --label3=PIG --csv_input=data/train_labels.csv --img_path=images/train --output_path=data/train.record\n",
        "# for train data\n",
        "!python generate_tfrecord2.py --label1=COW --label2=ELEPHANT --label3=PIG --csv_input=data/test_labels.csv --img_path=images/test --output_path=data/test.record"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/IEDC/models/research/object_detection\n",
            "WARNING:tensorflow:From generate_tfrecord2.py:121: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.\n",
            "\n",
            "WARNING:tensorflow:From generate_tfrecord2.py:107: The name tf.python_io.TFRecordWriter is deprecated. Please use tf.io.TFRecordWriter instead.\n",
            "\n",
            "W0101 16:49:52.409576 140026964658048 module_wrapper.py:139] From generate_tfrecord2.py:107: The name tf.python_io.TFRecordWriter is deprecated. Please use tf.io.TFRecordWriter instead.\n",
            "\n",
            "WARNING:tensorflow:From generate_tfrecord2.py:65: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
            "\n",
            "W0101 16:49:52.875359 140026964658048 module_wrapper.py:139] From generate_tfrecord2.py:65: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
            "\n",
            "Successfully created the TFRecords: /content/gdrive/My Drive/IEDC/models/research/object_detection/data/train.record\n",
            "WARNING:tensorflow:From generate_tfrecord2.py:121: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.\n",
            "\n",
            "WARNING:tensorflow:From generate_tfrecord2.py:107: The name tf.python_io.TFRecordWriter is deprecated. Please use tf.io.TFRecordWriter instead.\n",
            "\n",
            "W0101 16:51:18.748074 140221192898432 module_wrapper.py:139] From generate_tfrecord2.py:107: The name tf.python_io.TFRecordWriter is deprecated. Please use tf.io.TFRecordWriter instead.\n",
            "\n",
            "WARNING:tensorflow:From generate_tfrecord2.py:65: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
            "\n",
            "W0101 16:51:19.091598 140221192898432 module_wrapper.py:139] From generate_tfrecord2.py:65: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
            "\n",
            "Successfully created the TFRecords: /content/gdrive/My Drive/IEDC/models/research/object_detection/data/test.record\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dSRCKFQd6rmv",
        "colab_type": "text"
      },
      "source": [
        "# Create Label Map"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Wouyzrm672A",
        "colab_type": "text"
      },
      "source": [
        "A label map file called `object-detection.pbtxt` must be created and saved in both `data`, and `training` folders. the label map will look like below code\n",
        "```\n",
        "item {\n",
        "  id: 1\n",
        "  name: 'your Label'#the label from annotation in my case Arduino Device\n",
        "}\n",
        "\n",
        "''' You can create more items like\n",
        "item {\n",
        "  id: 2\n",
        "  name: 'Raspberry Devices'\n",
        "}\n",
        "and so on\n",
        "'''\n",
        "```\n",
        "\n",
        "`object_detection/data/object-detection.pbtxt` and `object_detection/training/object-detection.pbtxt` :\n",
        "```\n",
        "item {\n",
        "  id: 1\n",
        "  name: 'COW'\n",
        "}\n",
        "\n",
        "item {\n",
        "  id: 2\n",
        "  name: 'ELEPHANT'\n",
        "}\n",
        "\n",
        "item {\n",
        "  id: 3\n",
        "  name: 'PIG'\n",
        "}\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7aDeXBdTUWkf",
        "colab_type": "text"
      },
      "source": [
        "# Get the pre-trained Object detection model from tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UY7os5n4PPSj",
        "colab_type": "code",
        "outputId": "cff3d5d4-8461-410e-b85a-acf47f502469",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "#In the object detection directory,\n",
        "%cd '/content/gdrive/My Drive/IEDC/models/research/object_detection'\n",
        "\n",
        "# download suitable model from models zoo, as we will be using transfer learning\n",
        "!curl -O http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03.tar.gz\n",
        "!tar -xvf ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03.tar.gz"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/IEDC/models/research/object_detection\n",
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100  138M  100  138M    0     0  41.1M      0  0:00:03  0:00:03 --:--:-- 41.1M\n",
            "ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/\n",
            "ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/model.ckpt.data-00000-of-00001\n",
            "ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/model.ckpt.index\n",
            "ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/model.ckpt.meta\n",
            "ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/pipeline.config\n",
            "ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/tflite_graph.pb\n",
            "ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/tflite_graph.pbtxt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G26AJj35PPPx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Also get the respective config file which you might need \n",
        "# to edit. It can be found in 'object_detection/samples/configs'.\n",
        "cp '/content/gdrive/My Drive/IEDC/models/research/object_detection/samples/configs/ssd_mobilenet_v2_quantized_300x300_coco.config' ssd_mobilenet_v2_quantized_300x300_coco.config"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LbF25fbM4sLx",
        "colab_type": "text"
      },
      "source": [
        "Make the following changes to the `ssd_mobilenet_v2_quantized_300x300_coco.config` file.\n",
        "\n",
        "**Note:** The paths must be entered with single forward slashes (NOT backslashes), or TensorFlow will give a file path error when trying to train the model! Also, the paths must be in double quotation marks ( \" ), not single quotation marks ( ' ).\n",
        "\n",
        "---\n",
        "**Line 9.** Change `num_classes` to the number of different objects you want the classifier to detect. For my bird/squirrel/raccoon detector example, there are three classes, so I set num_classes: 3\n",
        "\n",
        "**Line 141.** Change `batch_size: 24` to `batch_size: 6` . The smaller batch size will prevent OOM (Out of Memory) errors during training.\n",
        "\n",
        "**Line 156.** Change `fine_tune_checkpoint` to: \"C:/tensorflow1/models/research/object_detection/ ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/model.ckpt\"\n",
        "\n",
        "**Line 175.** Change `input_path` to: \"C:/tensorflow1/models/research/object_detection/train.record\"\n",
        "\n",
        "**Line 177.** Change `label_map_path` to: \"C:/tensorflow1/models/research/object_detection/training/labelmap.pbtxt\"\n",
        "\n",
        "**Line 181.** Change `num_examples` to the number of images you have in the \\images\\test` directory. For my bird/squirrel/raccoon detector example, there are 582 test images, so I set num_examples: 582.\n",
        "\n",
        "**Line 189.** Change `input_path` to: \"C:/tensorflow1/models/research/object_detection/test.record\"\n",
        "\n",
        "**Line 191.** Change `label_map_path` to: \"C:/tensorflow1/models/research/object_detection/training/labelmap.pbtxt\"\n",
        "\n",
        "---\n",
        "\n",
        "Save and exit the training file after the changes have been made.\n",
        "\n",
        "**Note Line 162:**\n",
        "```\n",
        "  # Note: The below line limits the training process to 200K steps, which we\n",
        "  # empirically found to be sufficient enough to train the pets dataset. This\n",
        "  # effectively bypasses the learning rate schedule (the learning rate will\n",
        "  # never decay). Remove the below line to train indefinitely.\n",
        "  num_steps: 200000\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FpPgheTMHQaK",
        "colab_type": "text"
      },
      "source": [
        "# Monitoring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRqeSGNheYst",
        "colab_type": "code",
        "outputId": "eb0d3a74-0bcc-4904-f70a-9b531574a635",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        }
      },
      "source": [
        "##############################################################################\n",
        "# In order to do background track of your training check points, run the code \n",
        "# cell I found on DLology blog below and click the link that it returns. you \n",
        "# should notice a new tab will open named Tensorboard. You can use this to \n",
        "# track while training or Use it after training.\n",
        "##############################################################################\n",
        "\n",
        "LOG_DIR = 'training'\n",
        "get_ipython().system_raw(\n",
        "    'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'\n",
        "    .format(LOG_DIR)\n",
        ")\n",
        "# Install\n",
        "!npm install -g localtunnel\n",
        "!npm i -g npm\n",
        "# Tunnel port 6006 (TensorBoard assumed running)\n",
        "get_ipython().system_raw('lt --port 6006 >> url1.txt 2>&1 &')\n",
        "# Get url\n",
        "!cat url1.txt"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K\u001b[?25h/tools/node/bin/lt -> /tools/node/lib/node_modules/localtunnel/bin/lt.js\n",
            "+ localtunnel@2.0.0\n",
            "updated 1 package in 1.235s\n",
            "\u001b[K\u001b[?25h/tools/node/bin/npm -> /tools/node/lib/node_modules/npm/bin/npm-cli.js\n",
            "/tools/node/bin/npx -> /tools/node/lib/node_modules/npm/bin/npx-cli.js\n",
            "\u001b[K\u001b[?25h+ npm@6.13.4\n",
            "updated 1 package in 5.858s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wAJxZNkbIx5J",
        "colab_type": "text"
      },
      "source": [
        "# The TRAINING PROPER!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MxG25CI8eYoX",
        "colab_type": "code",
        "outputId": "3092fc35-2116-415c-ed4b-2b691125a9af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# copy or move the `train.py` file into the object detection folder\n",
        "# from legacy folder\n",
        "!cp '/content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/train.py' train.py\n",
        "\n",
        "# Upload .config file in `training/` dir and,\n",
        "# Finally, train\n",
        "!python train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v2_quantized_300x300_coco.config\n",
        "\n",
        "# when your loss is less than 1 you can stop the training with CTRL + C. \n",
        "# Note you might have to restart run-time before the next step can execute."
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:\n",
            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/slim/nets/inception_resnet_v2.py:374: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/slim/nets/mobilenet/mobilenet.py:397: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n",
            "\n",
            "WARNING:tensorflow:From train.py:56: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.\n",
            "\n",
            "WARNING:tensorflow:From train.py:56: The name tf.logging.INFO is deprecated. Please use tf.compat.v1.logging.INFO instead.\n",
            "\n",
            "WARNING:tensorflow:From train.py:185: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/absl/app.py:250: main (from __main__) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use object_detection/model_main.py.\n",
            "W0101 17:30:23.226784 140038727088000 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/absl/app.py:250: main (from __main__) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use object_detection/model_main.py.\n",
            "WARNING:tensorflow:From train.py:91: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.\n",
            "\n",
            "W0101 17:30:23.227034 140038727088000 module_wrapper.py:139] From train.py:91: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/utils/config_util.py:102: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
            "\n",
            "W0101 17:30:23.227479 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/utils/config_util.py:102: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
            "\n",
            "WARNING:tensorflow:From train.py:96: The name tf.gfile.Copy is deprecated. Please use tf.io.gfile.copy instead.\n",
            "\n",
            "W0101 17:30:23.609114 140038727088000 module_wrapper.py:139] From train.py:96: The name tf.gfile.Copy is deprecated. Please use tf.io.gfile.copy instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:267: create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please switch to tf.train.create_global_step\n",
            "W0101 17:30:23.620816 140038727088000 deprecation.py:323] From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:267: create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please switch to tf.train.create_global_step\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/data_decoders/tf_example_decoder.py:182: The name tf.FixedLenFeature is deprecated. Please use tf.io.FixedLenFeature instead.\n",
            "\n",
            "W0101 17:30:23.626834 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/data_decoders/tf_example_decoder.py:182: The name tf.FixedLenFeature is deprecated. Please use tf.io.FixedLenFeature instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/data_decoders/tf_example_decoder.py:197: The name tf.VarLenFeature is deprecated. Please use tf.io.VarLenFeature instead.\n",
            "\n",
            "W0101 17:30:23.627126 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/data_decoders/tf_example_decoder.py:197: The name tf.VarLenFeature is deprecated. Please use tf.io.VarLenFeature instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/dataset_builder.py:64: The name tf.gfile.Glob is deprecated. Please use tf.io.gfile.glob instead.\n",
            "\n",
            "W0101 17:30:24.091053 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/dataset_builder.py:64: The name tf.gfile.Glob is deprecated. Please use tf.io.gfile.glob instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/dataset_builder.py:71: The name tf.logging.warning is deprecated. Please use tf.compat.v1.logging.warning instead.\n",
            "\n",
            "W0101 17:30:24.096266 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/dataset_builder.py:71: The name tf.logging.warning is deprecated. Please use tf.compat.v1.logging.warning instead.\n",
            "\n",
            "WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.\n",
            "W0101 17:30:24.096457 140038727088000 dataset_builder.py:72] num_readers has been reduced to 1 to match input file shards.\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/dataset_builder.py:86: parallel_interleave (from tensorflow.contrib.data.python.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.experimental.parallel_interleave(...)`.\n",
            "W0101 17:30:24.103462 140038727088000 deprecation.py:323] From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/dataset_builder.py:86: parallel_interleave (from tensorflow.contrib.data.python.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.experimental.parallel_interleave(...)`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/data/python/ops/interleave_ops.py:77: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_determinstic`.\n",
            "W0101 17:30:24.103784 140038727088000 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/data/python/ops/interleave_ops.py:77: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_determinstic`.\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/dataset_builder.py:155: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.map()\n",
            "W0101 17:30:24.127880 140038727088000 deprecation.py:323] From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/dataset_builder.py:155: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.map()\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/dataset_builder.py:43: DatasetV1.make_initializable_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_initializable_iterator(dataset)`.\n",
            "W0101 17:30:24.681957 140038727088000 deprecation.py:323] From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/dataset_builder.py:43: DatasetV1.make_initializable_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_initializable_iterator(dataset)`.\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/dataset_builder.py:44: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.\n",
            "\n",
            "W0101 17:30:24.688670 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/dataset_builder.py:44: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/preprocessor.py:627: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W0101 17:30:24.695372 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/preprocessor.py:627: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/preprocessor.py:197: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.\n",
            "W0101 17:30:24.749099 140038727088000 deprecation.py:323] From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/preprocessor.py:197: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/box_list_ops.py:206: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "W0101 17:30:24.758718 140038727088000 deprecation.py:323] From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/box_list_ops.py:206: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/batcher.py:101: batch (from tensorflow.python.training.input) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.batch(batch_size)` (or `padded_batch(...)` if `dynamic_pad=True`).\n",
            "W0101 17:30:25.415620 140038727088000 deprecation.py:323] From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/batcher.py:101: batch (from tensorflow.python.training.input) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.batch(batch_size)` (or `padded_batch(...)` if `dynamic_pad=True`).\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/input.py:752: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "To construct input pipelines, use the `tf.data` module.\n",
            "W0101 17:30:25.419188 140038727088000 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/input.py:752: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "To construct input pipelines, use the `tf.data` module.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/input.py:752: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "To construct input pipelines, use the `tf.data` module.\n",
            "W0101 17:30:25.420390 140038727088000 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/input.py:752: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "To construct input pipelines, use the `tf.data` module.\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/prefetcher.py:51: The name tf.PaddingFIFOQueue is deprecated. Please use tf.queue.PaddingFIFOQueue instead.\n",
            "\n",
            "W0101 17:30:25.425442 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/prefetcher.py:51: The name tf.PaddingFIFOQueue is deprecated. Please use tf.queue.PaddingFIFOQueue instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/prefetcher.py:58: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.\n",
            "\n",
            "W0101 17:30:25.428784 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/prefetcher.py:58: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:286: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.\n",
            "\n",
            "W0101 17:30:25.431664 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:286: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/slim/deployment/model_deploy.py:192: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
            "\n",
            "W0101 17:30:25.432048 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/slim/deployment/model_deploy.py:192: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/slim/deployment/model_deploy.py:192: The name tf.get_variable_scope is deprecated. Please use tf.compat.v1.get_variable_scope instead.\n",
            "\n",
            "W0101 17:30:25.432173 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/slim/deployment/model_deploy.py:192: The name tf.get_variable_scope is deprecated. Please use tf.compat.v1.get_variable_scope instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/preprocessor.py:2937: The name tf.image.resize_images is deprecated. Please use tf.image.resize instead.\n",
            "\n",
            "W0101 17:30:26.202270 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/preprocessor.py:2937: The name tf.image.resize_images is deprecated. Please use tf.image.resize instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/layers/python/layers/layers.py:1057: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "W0101 17:30:26.475415 140038727088000 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/layers/python/layers/layers.py:1057: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/anchor_generator.py:171: The name tf.assert_equal is deprecated. Please use tf.compat.v1.assert_equal instead.\n",
            "\n",
            "W0101 17:30:29.044354 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/anchor_generator.py:171: The name tf.assert_equal is deprecated. Please use tf.compat.v1.assert_equal instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/predictors/convolutional_box_predictor.py:150: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
            "\n",
            "W0101 17:30:29.054925 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/predictors/convolutional_box_predictor.py:150: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
            "\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0101 17:30:29.055094 140038727088000 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0101 17:30:29.085760 140038727088000 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0101 17:30:29.116344 140038727088000 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0101 17:30:29.145216 140038727088000 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0101 17:30:29.173791 140038727088000 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0101 17:30:29.203441 140038727088000 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/box_coders/faster_rcnn_box_coder.py:82: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "W0101 17:30:29.371122 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/box_coders/faster_rcnn_box_coder.py:82: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/losses.py:79: The name tf.is_nan is deprecated. Please use tf.math.is_nan instead.\n",
            "\n",
            "W0101 17:30:33.003380 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/losses.py:79: The name tf.is_nan is deprecated. Please use tf.math.is_nan instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/losses.py:177: The name tf.losses.huber_loss is deprecated. Please use tf.compat.v1.losses.huber_loss instead.\n",
            "\n",
            "W0101 17:30:33.004654 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/losses.py:177: The name tf.losses.huber_loss is deprecated. Please use tf.compat.v1.losses.huber_loss instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/losses.py:183: The name tf.losses.Reduction is deprecated. Please use tf.compat.v1.losses.Reduction instead.\n",
            "\n",
            "W0101 17:30:33.005852 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/losses.py:183: The name tf.losses.Reduction is deprecated. Please use tf.compat.v1.losses.Reduction instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:209: The name tf.losses.add_loss is deprecated. Please use tf.compat.v1.losses.add_loss instead.\n",
            "\n",
            "W0101 17:30:33.679600 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:209: The name tf.losses.add_loss is deprecated. Please use tf.compat.v1.losses.add_loss instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/graph_rewriter_builder.py:36: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "W0101 17:30:33.680129 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/graph_rewriter_builder.py:36: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/Conv/add_fold\n",
            "I0101 17:30:42.348943 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/Conv/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv/depthwise/add_fold\n",
            "I0101 17:30:42.349490 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_1/expand/add_fold\n",
            "I0101 17:30:42.350119 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_1/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/add_fold\n",
            "I0101 17:30:42.350471 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_2/expand/add_fold\n",
            "I0101 17:30:42.351078 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_2/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/add_fold\n",
            "I0101 17:30:42.351415 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_3/expand/add_fold\n",
            "I0101 17:30:42.351991 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_3/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/add_fold\n",
            "I0101 17:30:42.352377 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_4/expand/add_fold\n",
            "I0101 17:30:42.352935 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_4/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/add_fold\n",
            "I0101 17:30:42.353298 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_5/expand/add_fold\n",
            "I0101 17:30:42.353928 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_5/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/add_fold\n",
            "I0101 17:30:42.354284 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_6/expand/add_fold\n",
            "I0101 17:30:42.354866 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_6/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/add_fold\n",
            "I0101 17:30:42.355260 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_7/expand/add_fold\n",
            "I0101 17:30:42.355805 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_7/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/add_fold\n",
            "I0101 17:30:42.356223 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_8/expand/add_fold\n",
            "I0101 17:30:42.356801 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_8/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/add_fold\n",
            "I0101 17:30:42.357122 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_9/expand/add_fold\n",
            "I0101 17:30:42.357636 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_9/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/add_fold\n",
            "I0101 17:30:42.357960 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_10/expand/add_fold\n",
            "I0101 17:30:42.358479 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_10/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise/add_fold\n",
            "I0101 17:30:42.358797 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_11/expand/add_fold\n",
            "I0101 17:30:42.359346 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_11/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/add_fold\n",
            "I0101 17:30:42.359669 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_12/expand/add_fold\n",
            "I0101 17:30:42.360242 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_12/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/add_fold\n",
            "I0101 17:30:42.360611 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_13/expand/add_fold\n",
            "I0101 17:30:42.361161 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_13/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/add_fold\n",
            "I0101 17:30:42.361489 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_14/expand/add_fold\n",
            "I0101 17:30:42.362129 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_14/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/add_fold\n",
            "I0101 17:30:42.362452 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_15/expand/add_fold\n",
            "I0101 17:30:42.362980 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_15/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/add_fold\n",
            "I0101 17:30:42.363327 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_16/expand/add_fold\n",
            "I0101 17:30:42.363838 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_16/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/add_fold\n",
            "I0101 17:30:42.364173 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/Conv_1/add_fold\n",
            "I0101 17:30:42.364724 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/Conv_1/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/add_fold\n",
            "I0101 17:30:42.365080 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/add_fold\n",
            "I0101 17:30:42.365406 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/add_fold\n",
            "I0101 17:30:42.365771 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/add_fold\n",
            "I0101 17:30:42.366112 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/add_fold\n",
            "I0101 17:30:42.366443 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/add_fold\n",
            "I0101 17:30:42.366770 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/add_fold\n",
            "I0101 17:30:42.367105 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/add_fold\n",
            "I0101 17:30:42.367434 140038727088000 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/add_fold\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/optimizer_builder.py:157: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.\n",
            "\n",
            "W0101 17:30:42.384367 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/optimizer_builder.py:157: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/utils/learning_schedules.py:66: The name tf.train.exponential_decay is deprecated. Please use tf.compat.v1.train.exponential_decay instead.\n",
            "\n",
            "W0101 17:30:42.384656 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/utils/learning_schedules.py:66: The name tf.train.exponential_decay is deprecated. Please use tf.compat.v1.train.exponential_decay instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/optimizer_builder.py:47: The name tf.train.RMSPropOptimizer is deprecated. Please use tf.compat.v1.train.RMSPropOptimizer instead.\n",
            "\n",
            "W0101 17:30:42.393192 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/optimizer_builder.py:47: The name tf.train.RMSPropOptimizer is deprecated. Please use tf.compat.v1.train.RMSPropOptimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:323: The name tf.check_numerics is deprecated. Please use tf.debugging.check_numerics instead.\n",
            "\n",
            "W0101 17:30:50.545930 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:323: The name tf.check_numerics is deprecated. Please use tf.debugging.check_numerics instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/rmsprop.py:119: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "W0101 17:30:50.548221 140038727088000 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/rmsprop.py:119: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/moving_averages.py:433: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
            "W0101 17:30:52.258368 140038727088000 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/moving_averages.py:433: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:354: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.\n",
            "\n",
            "W0101 17:30:56.054461 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:354: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:356: The name tf.losses.get_losses is deprecated. Please use tf.compat.v1.losses.get_losses instead.\n",
            "\n",
            "W0101 17:30:56.336818 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:356: The name tf.losses.get_losses is deprecated. Please use tf.compat.v1.losses.get_losses instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:360: The name tf.losses.get_total_loss is deprecated. Please use tf.compat.v1.losses.get_total_loss instead.\n",
            "\n",
            "W0101 17:30:56.338780 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:360: The name tf.losses.get_total_loss is deprecated. Please use tf.compat.v1.losses.get_total_loss instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:369: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.\n",
            "\n",
            "W0101 17:30:56.342069 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:369: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:372: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "W0101 17:30:56.349273 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:372: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:377: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
            "\n",
            "W0101 17:30:56.349500 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py:377: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/utils/variables_helper.py:179: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "W0101 17:30:57.500715 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/utils/variables_helper.py:179: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/utils/variables_helper.py:139: The name tf.train.NewCheckpointReader is deprecated. Please use tf.compat.v1.train.NewCheckpointReader instead.\n",
            "\n",
            "W0101 17:30:57.504676 140038727088000 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/utils/variables_helper.py:139: The name tf.train.NewCheckpointReader is deprecated. Please use tf.compat.v1.train.NewCheckpointReader instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/slim/python/slim/learning.py:742: Supervisor.__init__ (from tensorflow.python.training.supervisor) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please switch to tf.train.MonitoredTrainingSession\n",
            "W0101 17:30:59.366712 140038727088000 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/slim/python/slim/learning.py:742: Supervisor.__init__ (from tensorflow.python.training.supervisor) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please switch to tf.train.MonitoredTrainingSession\n",
            "2020-01-01 17:31:01.575489: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200000000 Hz\n",
            "2020-01-01 17:31:01.575680: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x25697f80 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-01-01 17:31:01.575711: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-01-01 17:31:01.577699: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-01-01 17:31:01.667544: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:31:01.668230: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x25697dc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-01-01 17:31:01.668269: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n",
            "2020-01-01 17:31:01.668440: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:31:01.668955: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n",
            "name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
            "pciBusID: 0000:00:04.0\n",
            "2020-01-01 17:31:01.669298: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-01-01 17:31:01.670973: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-01-01 17:31:01.672577: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-01-01 17:31:01.672975: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-01-01 17:31:01.674668: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-01-01 17:31:01.675401: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-01-01 17:31:01.678772: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-01-01 17:31:01.678884: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:31:01.679475: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:31:01.680013: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2020-01-01 17:31:01.680094: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-01-01 17:31:01.681306: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-01-01 17:31:01.681330: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2020-01-01 17:31:01.681340: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2020-01-01 17:31:01.681442: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:31:01.682066: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:31:01.682615: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-01-01 17:31:01.682648: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14974 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
            "INFO:tensorflow:Restoring parameters from /content/gdrive/My Drive/IEDC/models/research/object_detection/ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/model.ckpt\n",
            "I0101 17:31:06.769023 140038727088000 saver.py:1284] Restoring parameters from /content/gdrive/My Drive/IEDC/models/research/object_detection/ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/model.ckpt\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "I0101 17:31:08.902019 140038727088000 session_manager.py:500] Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "I0101 17:31:09.731205 140038727088000 session_manager.py:502] Done running local_init_op.\n",
            "INFO:tensorflow:Starting Session.\n",
            "I0101 17:31:25.520073 140038727088000 learning.py:754] Starting Session.\n",
            "INFO:tensorflow:Saving checkpoint to path training/model.ckpt\n",
            "I0101 17:31:26.391366 140034936944384 supervisor.py:1117] Saving checkpoint to path training/model.ckpt\n",
            "INFO:tensorflow:Starting Queues.\n",
            "I0101 17:31:26.394215 140038727088000 learning.py:768] Starting Queues.\n",
            "INFO:tensorflow:global_step/sec: 0\n",
            "I0101 17:31:56.807361 140034945337088 supervisor.py:1099] global_step/sec: 0\n",
            "2020-01-01 17:32:00.049077: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-01-01 17:32:05.639225: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "INFO:tensorflow:Recording summary at step 0.\n",
            "I0101 17:32:12.670754 140034970515200 supervisor.py:1050] Recording summary at step 0.\n",
            "INFO:tensorflow:global step 1: loss = 22.8228 (47.081 sec/step)\n",
            "I0101 17:32:14.273818 140038727088000 learning.py:507] global step 1: loss = 22.8228 (47.081 sec/step)\n",
            "INFO:tensorflow:global step 2: loss = 19.0355 (3.244 sec/step)\n",
            "I0101 17:32:18.265514 140038727088000 learning.py:507] global step 2: loss = 19.0355 (3.244 sec/step)\n",
            "INFO:tensorflow:global step 3: loss = 18.0541 (3.161 sec/step)\n",
            "I0101 17:32:21.428700 140038727088000 learning.py:507] global step 3: loss = 18.0541 (3.161 sec/step)\n",
            "INFO:tensorflow:global step 4: loss = 16.8601 (3.061 sec/step)\n",
            "I0101 17:32:24.491660 140038727088000 learning.py:507] global step 4: loss = 16.8601 (3.061 sec/step)\n",
            "INFO:tensorflow:global step 5: loss = 16.0976 (3.074 sec/step)\n",
            "I0101 17:32:27.567717 140038727088000 learning.py:507] global step 5: loss = 16.0976 (3.074 sec/step)\n",
            "INFO:tensorflow:global step 6: loss = 15.4309 (3.009 sec/step)\n",
            "I0101 17:32:30.578192 140038727088000 learning.py:507] global step 6: loss = 15.4309 (3.009 sec/step)\n",
            "INFO:tensorflow:global step 7: loss = 14.4456 (3.044 sec/step)\n",
            "I0101 17:32:33.624004 140038727088000 learning.py:507] global step 7: loss = 14.4456 (3.044 sec/step)\n",
            "INFO:tensorflow:global step 8: loss = 13.7330 (3.066 sec/step)\n",
            "I0101 17:32:36.692530 140038727088000 learning.py:507] global step 8: loss = 13.7330 (3.066 sec/step)\n",
            "INFO:tensorflow:global step 9: loss = 12.7268 (3.014 sec/step)\n",
            "I0101 17:32:39.708309 140038727088000 learning.py:507] global step 9: loss = 12.7268 (3.014 sec/step)\n",
            "INFO:tensorflow:global step 10: loss = 12.3959 (3.065 sec/step)\n",
            "I0101 17:32:42.775121 140038727088000 learning.py:507] global step 10: loss = 12.3959 (3.065 sec/step)\n",
            "INFO:tensorflow:global step 11: loss = 11.4331 (2.995 sec/step)\n",
            "I0101 17:32:45.771841 140038727088000 learning.py:507] global step 11: loss = 11.4331 (2.995 sec/step)\n",
            "INFO:tensorflow:global step 12: loss = 11.2082 (3.038 sec/step)\n",
            "I0101 17:32:48.811960 140038727088000 learning.py:507] global step 12: loss = 11.2082 (3.038 sec/step)\n",
            "INFO:tensorflow:global step 13: loss = 10.5293 (3.034 sec/step)\n",
            "I0101 17:32:51.847795 140038727088000 learning.py:507] global step 13: loss = 10.5293 (3.034 sec/step)\n",
            "INFO:tensorflow:global step 14: loss = 10.5153 (3.064 sec/step)\n",
            "I0101 17:32:54.915055 140038727088000 learning.py:507] global step 14: loss = 10.5153 (3.064 sec/step)\n",
            "INFO:tensorflow:global step 15: loss = 10.5338 (3.092 sec/step)\n",
            "I0101 17:32:58.010500 140038727088000 learning.py:507] global step 15: loss = 10.5338 (3.092 sec/step)\n",
            "INFO:tensorflow:global step 16: loss = 9.9668 (3.094 sec/step)\n",
            "I0101 17:33:01.106968 140038727088000 learning.py:507] global step 16: loss = 9.9668 (3.094 sec/step)\n",
            "INFO:tensorflow:global step 17: loss = 9.5422 (3.085 sec/step)\n",
            "I0101 17:33:04.193373 140038727088000 learning.py:507] global step 17: loss = 9.5422 (3.085 sec/step)\n",
            "INFO:tensorflow:global step 18: loss = 9.4768 (3.112 sec/step)\n",
            "I0101 17:33:07.306885 140038727088000 learning.py:507] global step 18: loss = 9.4768 (3.112 sec/step)\n",
            "INFO:tensorflow:global step 19: loss = 9.2325 (3.042 sec/step)\n",
            "I0101 17:33:10.350918 140038727088000 learning.py:507] global step 19: loss = 9.2325 (3.042 sec/step)\n",
            "INFO:tensorflow:global step 20: loss = 8.6567 (3.048 sec/step)\n",
            "I0101 17:33:13.400407 140038727088000 learning.py:507] global step 20: loss = 8.6567 (3.048 sec/step)\n",
            "INFO:tensorflow:global step 21: loss = 8.8344 (3.027 sec/step)\n",
            "I0101 17:33:16.428940 140038727088000 learning.py:507] global step 21: loss = 8.8344 (3.027 sec/step)\n",
            "INFO:tensorflow:global step 22: loss = 9.1582 (3.096 sec/step)\n",
            "I0101 17:33:19.526211 140038727088000 learning.py:507] global step 22: loss = 9.1582 (3.096 sec/step)\n",
            "INFO:tensorflow:global step 23: loss = 8.6817 (3.009 sec/step)\n",
            "I0101 17:33:22.536850 140038727088000 learning.py:507] global step 23: loss = 8.6817 (3.009 sec/step)\n",
            "INFO:tensorflow:global step 24: loss = 8.2737 (3.016 sec/step)\n",
            "I0101 17:33:25.555068 140038727088000 learning.py:507] global step 24: loss = 8.2737 (3.016 sec/step)\n",
            "INFO:tensorflow:global step 25: loss = 7.7413 (4.876 sec/step)\n",
            "I0101 17:33:30.432779 140038727088000 learning.py:507] global step 25: loss = 7.7413 (4.876 sec/step)\n",
            "INFO:tensorflow:Recording summary at step 25.\n",
            "I0101 17:33:31.151492 140034970515200 supervisor.py:1050] Recording summary at step 25.\n",
            "INFO:tensorflow:global step 26: loss = 8.3267 (3.277 sec/step)\n",
            "I0101 17:33:33.711492 140038727088000 learning.py:507] global step 26: loss = 8.3267 (3.277 sec/step)\n",
            "INFO:tensorflow:global_step/sec: 0.265484\n",
            "I0101 17:33:34.741608 140034945337088 supervisor.py:1099] global_step/sec: 0.265484\n",
            "INFO:tensorflow:global step 27: loss = 8.1345 (3.026 sec/step)\n",
            "I0101 17:33:36.739295 140038727088000 learning.py:507] global step 27: loss = 8.1345 (3.026 sec/step)\n",
            "INFO:tensorflow:global step 28: loss = 8.4821 (3.083 sec/step)\n",
            "I0101 17:33:39.824127 140038727088000 learning.py:507] global step 28: loss = 8.4821 (3.083 sec/step)\n",
            "INFO:tensorflow:global step 29: loss = 7.7431 (2.998 sec/step)\n",
            "I0101 17:33:42.824146 140038727088000 learning.py:507] global step 29: loss = 7.7431 (2.998 sec/step)\n",
            "INFO:tensorflow:global step 30: loss = 7.9500 (2.978 sec/step)\n",
            "I0101 17:33:45.804082 140038727088000 learning.py:507] global step 30: loss = 7.9500 (2.978 sec/step)\n",
            "INFO:tensorflow:global step 31: loss = 7.2623 (2.978 sec/step)\n",
            "I0101 17:33:48.784314 140038727088000 learning.py:507] global step 31: loss = 7.2623 (2.978 sec/step)\n",
            "INFO:tensorflow:global step 32: loss = 7.9245 (3.015 sec/step)\n",
            "I0101 17:33:51.800654 140038727088000 learning.py:507] global step 32: loss = 7.9245 (3.015 sec/step)\n",
            "INFO:tensorflow:global step 33: loss = 7.6394 (2.973 sec/step)\n",
            "I0101 17:33:54.775549 140038727088000 learning.py:507] global step 33: loss = 7.6394 (2.973 sec/step)\n",
            "INFO:tensorflow:global step 34: loss = 7.7305 (2.984 sec/step)\n",
            "I0101 17:33:57.761695 140038727088000 learning.py:507] global step 34: loss = 7.7305 (2.984 sec/step)\n",
            "INFO:tensorflow:global step 35: loss = 7.2921 (2.983 sec/step)\n",
            "I0101 17:34:00.746539 140038727088000 learning.py:507] global step 35: loss = 7.2921 (2.983 sec/step)\n",
            "INFO:tensorflow:global step 36: loss = 7.2196 (2.972 sec/step)\n",
            "I0101 17:34:03.720165 140038727088000 learning.py:507] global step 36: loss = 7.2196 (2.972 sec/step)\n",
            "INFO:tensorflow:global step 37: loss = 7.4056 (3.017 sec/step)\n",
            "I0101 17:34:06.738837 140038727088000 learning.py:507] global step 37: loss = 7.4056 (3.017 sec/step)\n",
            "INFO:tensorflow:global step 38: loss = 7.3713 (2.986 sec/step)\n",
            "I0101 17:34:09.726356 140038727088000 learning.py:507] global step 38: loss = 7.3713 (2.986 sec/step)\n",
            "INFO:tensorflow:global step 39: loss = 6.8832 (2.943 sec/step)\n",
            "I0101 17:34:12.670994 140038727088000 learning.py:507] global step 39: loss = 6.8832 (2.943 sec/step)\n",
            "INFO:tensorflow:global step 40: loss = 7.0579 (2.975 sec/step)\n",
            "I0101 17:34:15.647979 140038727088000 learning.py:507] global step 40: loss = 7.0579 (2.975 sec/step)\n",
            "INFO:tensorflow:global step 41: loss = 6.4204 (2.967 sec/step)\n",
            "I0101 17:34:18.616512 140038727088000 learning.py:507] global step 41: loss = 6.4204 (2.967 sec/step)\n",
            "INFO:tensorflow:global step 42: loss = 6.8508 (3.054 sec/step)\n",
            "I0101 17:34:21.673707 140038727088000 learning.py:507] global step 42: loss = 6.8508 (3.054 sec/step)\n",
            "INFO:tensorflow:global step 43: loss = 6.9968 (3.038 sec/step)\n",
            "I0101 17:34:24.713405 140038727088000 learning.py:507] global step 43: loss = 6.9968 (3.038 sec/step)\n",
            "INFO:tensorflow:global step 44: loss = 7.3580 (3.062 sec/step)\n",
            "I0101 17:34:27.776693 140038727088000 learning.py:507] global step 44: loss = 7.3580 (3.062 sec/step)\n",
            "INFO:tensorflow:global step 45: loss = 6.6372 (2.999 sec/step)\n",
            "I0101 17:34:30.777163 140038727088000 learning.py:507] global step 45: loss = 6.6372 (2.999 sec/step)\n",
            "INFO:tensorflow:global step 46: loss = 7.3382 (2.972 sec/step)\n",
            "I0101 17:34:33.750699 140038727088000 learning.py:507] global step 46: loss = 7.3382 (2.972 sec/step)\n",
            "INFO:tensorflow:global step 47: loss = 6.2769 (2.999 sec/step)\n",
            "I0101 17:34:36.751919 140038727088000 learning.py:507] global step 47: loss = 6.2769 (2.999 sec/step)\n",
            "INFO:tensorflow:global step 48: loss = 6.8993 (2.980 sec/step)\n",
            "I0101 17:34:39.733936 140038727088000 learning.py:507] global step 48: loss = 6.8993 (2.980 sec/step)\n",
            "INFO:tensorflow:global step 49: loss = 7.0942 (2.976 sec/step)\n",
            "I0101 17:34:42.711397 140038727088000 learning.py:507] global step 49: loss = 7.0942 (2.976 sec/step)\n",
            "INFO:tensorflow:global step 50: loss = 6.3588 (2.954 sec/step)\n",
            "I0101 17:34:45.666569 140038727088000 learning.py:507] global step 50: loss = 6.3588 (2.954 sec/step)\n",
            "INFO:tensorflow:global step 51: loss = 7.0106 (2.977 sec/step)\n",
            "I0101 17:34:48.645756 140038727088000 learning.py:507] global step 51: loss = 7.0106 (2.977 sec/step)\n",
            "INFO:tensorflow:global step 52: loss = 6.9799 (2.935 sec/step)\n",
            "I0101 17:34:51.582369 140038727088000 learning.py:507] global step 52: loss = 6.9799 (2.935 sec/step)\n",
            "INFO:tensorflow:global step 53: loss = 6.2326 (2.971 sec/step)\n",
            "I0101 17:34:54.554779 140038727088000 learning.py:507] global step 53: loss = 6.2326 (2.971 sec/step)\n",
            "INFO:tensorflow:global step 54: loss = 5.6998 (2.964 sec/step)\n",
            "I0101 17:34:57.520975 140038727088000 learning.py:507] global step 54: loss = 5.6998 (2.964 sec/step)\n",
            "INFO:tensorflow:global step 55: loss = 6.6898 (2.996 sec/step)\n",
            "I0101 17:35:00.518579 140038727088000 learning.py:507] global step 55: loss = 6.6898 (2.996 sec/step)\n",
            "INFO:tensorflow:global step 56: loss = 6.9270 (2.953 sec/step)\n",
            "I0101 17:35:03.473316 140038727088000 learning.py:507] global step 56: loss = 6.9270 (2.953 sec/step)\n",
            "INFO:tensorflow:global step 57: loss = 6.4946 (2.927 sec/step)\n",
            "I0101 17:35:06.402112 140038727088000 learning.py:507] global step 57: loss = 6.4946 (2.927 sec/step)\n",
            "INFO:tensorflow:global step 58: loss = 6.6311 (2.975 sec/step)\n",
            "I0101 17:35:09.378769 140038727088000 learning.py:507] global step 58: loss = 6.6311 (2.975 sec/step)\n",
            "INFO:tensorflow:global step 59: loss = 6.0995 (2.932 sec/step)\n",
            "I0101 17:35:12.312256 140038727088000 learning.py:507] global step 59: loss = 6.0995 (2.932 sec/step)\n",
            "INFO:tensorflow:global step 60: loss = 6.5017 (2.970 sec/step)\n",
            "I0101 17:35:15.284502 140038727088000 learning.py:507] global step 60: loss = 6.5017 (2.970 sec/step)\n",
            "INFO:tensorflow:global step 61: loss = 6.3264 (2.942 sec/step)\n",
            "I0101 17:35:18.227690 140038727088000 learning.py:507] global step 61: loss = 6.3264 (2.942 sec/step)\n",
            "INFO:tensorflow:global step 62: loss = 6.2603 (2.938 sec/step)\n",
            "I0101 17:35:21.167541 140038727088000 learning.py:507] global step 62: loss = 6.2603 (2.938 sec/step)\n",
            "INFO:tensorflow:global step 63: loss = 6.2623 (2.996 sec/step)\n",
            "I0101 17:35:24.165448 140038727088000 learning.py:507] global step 63: loss = 6.2623 (2.996 sec/step)\n",
            "INFO:tensorflow:global step 64: loss = 5.8047 (3.929 sec/step)\n",
            "I0101 17:35:28.114434 140038727088000 learning.py:507] global step 64: loss = 5.8047 (3.929 sec/step)\n",
            "INFO:tensorflow:Recording summary at step 64.\n",
            "I0101 17:35:31.443034 140034970515200 supervisor.py:1050] Recording summary at step 64.\n",
            "INFO:tensorflow:global step 65: loss = 5.9637 (4.003 sec/step)\n",
            "I0101 17:35:32.137703 140038727088000 learning.py:507] global step 65: loss = 5.9637 (4.003 sec/step)\n",
            "INFO:tensorflow:global_step/sec: 0.324994\n",
            "I0101 17:35:34.744090 140034945337088 supervisor.py:1099] global_step/sec: 0.324994\n",
            "INFO:tensorflow:global step 66: loss = 5.8909 (2.991 sec/step)\n",
            "I0101 17:35:35.130774 140038727088000 learning.py:507] global step 66: loss = 5.8909 (2.991 sec/step)\n",
            "INFO:tensorflow:global step 67: loss = 5.4621 (2.933 sec/step)\n",
            "I0101 17:35:38.065751 140038727088000 learning.py:507] global step 67: loss = 5.4621 (2.933 sec/step)\n",
            "INFO:tensorflow:global step 68: loss = 5.9944 (3.091 sec/step)\n",
            "I0101 17:35:41.159022 140038727088000 learning.py:507] global step 68: loss = 5.9944 (3.091 sec/step)\n",
            "INFO:tensorflow:global step 69: loss = 5.4703 (3.020 sec/step)\n",
            "I0101 17:35:44.180251 140038727088000 learning.py:507] global step 69: loss = 5.4703 (3.020 sec/step)\n",
            "INFO:tensorflow:global step 70: loss = 5.2971 (2.990 sec/step)\n",
            "I0101 17:35:47.171870 140038727088000 learning.py:507] global step 70: loss = 5.2971 (2.990 sec/step)\n",
            "INFO:tensorflow:global step 71: loss = 6.1211 (2.999 sec/step)\n",
            "I0101 17:35:50.172197 140038727088000 learning.py:507] global step 71: loss = 6.1211 (2.999 sec/step)\n",
            "INFO:tensorflow:global step 72: loss = 5.6891 (2.976 sec/step)\n",
            "I0101 17:35:53.149749 140038727088000 learning.py:507] global step 72: loss = 5.6891 (2.976 sec/step)\n",
            "INFO:tensorflow:global step 73: loss = 5.4884 (2.960 sec/step)\n",
            "I0101 17:35:56.111259 140038727088000 learning.py:507] global step 73: loss = 5.4884 (2.960 sec/step)\n",
            "INFO:tensorflow:global step 74: loss = 5.7992 (2.966 sec/step)\n",
            "I0101 17:35:59.078874 140038727088000 learning.py:507] global step 74: loss = 5.7992 (2.966 sec/step)\n",
            "INFO:tensorflow:global step 75: loss = 6.2851 (2.954 sec/step)\n",
            "I0101 17:36:02.034367 140038727088000 learning.py:507] global step 75: loss = 6.2851 (2.954 sec/step)\n",
            "INFO:tensorflow:global step 76: loss = 5.0621 (2.946 sec/step)\n",
            "I0101 17:36:04.981957 140038727088000 learning.py:507] global step 76: loss = 5.0621 (2.946 sec/step)\n",
            "INFO:tensorflow:global step 77: loss = 6.1395 (2.974 sec/step)\n",
            "I0101 17:36:07.957265 140038727088000 learning.py:507] global step 77: loss = 6.1395 (2.974 sec/step)\n",
            "INFO:tensorflow:global step 78: loss = 5.6905 (2.959 sec/step)\n",
            "I0101 17:36:10.918371 140038727088000 learning.py:507] global step 78: loss = 5.6905 (2.959 sec/step)\n",
            "INFO:tensorflow:global step 79: loss = 5.8352 (2.956 sec/step)\n",
            "I0101 17:36:13.876097 140038727088000 learning.py:507] global step 79: loss = 5.8352 (2.956 sec/step)\n",
            "INFO:tensorflow:global step 80: loss = 6.1509 (2.941 sec/step)\n",
            "I0101 17:36:16.818371 140038727088000 learning.py:507] global step 80: loss = 6.1509 (2.941 sec/step)\n",
            "INFO:tensorflow:global step 81: loss = 5.1659 (2.932 sec/step)\n",
            "I0101 17:36:19.752048 140038727088000 learning.py:507] global step 81: loss = 5.1659 (2.932 sec/step)\n",
            "INFO:tensorflow:global step 82: loss = 6.1239 (2.914 sec/step)\n",
            "I0101 17:36:22.667132 140038727088000 learning.py:507] global step 82: loss = 6.1239 (2.914 sec/step)\n",
            "INFO:tensorflow:global step 83: loss = 5.4162 (2.949 sec/step)\n",
            "I0101 17:36:25.617761 140038727088000 learning.py:507] global step 83: loss = 5.4162 (2.949 sec/step)\n",
            "INFO:tensorflow:global step 84: loss = 5.5527 (3.000 sec/step)\n",
            "I0101 17:36:28.619574 140038727088000 learning.py:507] global step 84: loss = 5.5527 (3.000 sec/step)\n",
            "INFO:tensorflow:global step 85: loss = 5.6803 (2.987 sec/step)\n",
            "I0101 17:36:31.608056 140038727088000 learning.py:507] global step 85: loss = 5.6803 (2.987 sec/step)\n",
            "INFO:tensorflow:global step 86: loss = 5.3108 (2.946 sec/step)\n",
            "I0101 17:36:34.555443 140038727088000 learning.py:507] global step 86: loss = 5.3108 (2.946 sec/step)\n",
            "INFO:tensorflow:global step 87: loss = 5.7423 (2.920 sec/step)\n",
            "I0101 17:36:37.477647 140038727088000 learning.py:507] global step 87: loss = 5.7423 (2.920 sec/step)\n",
            "INFO:tensorflow:global step 88: loss = 5.8227 (2.930 sec/step)\n",
            "I0101 17:36:40.408975 140038727088000 learning.py:507] global step 88: loss = 5.8227 (2.930 sec/step)\n",
            "INFO:tensorflow:global step 89: loss = 5.6754 (2.937 sec/step)\n",
            "I0101 17:36:43.347132 140038727088000 learning.py:507] global step 89: loss = 5.6754 (2.937 sec/step)\n",
            "INFO:tensorflow:global step 90: loss = 5.2367 (2.931 sec/step)\n",
            "I0101 17:36:46.279774 140038727088000 learning.py:507] global step 90: loss = 5.2367 (2.931 sec/step)\n",
            "INFO:tensorflow:global step 91: loss = 5.4836 (2.984 sec/step)\n",
            "I0101 17:36:49.265366 140038727088000 learning.py:507] global step 91: loss = 5.4836 (2.984 sec/step)\n",
            "INFO:tensorflow:global step 92: loss = 5.0041 (2.949 sec/step)\n",
            "I0101 17:36:52.215571 140038727088000 learning.py:507] global step 92: loss = 5.0041 (2.949 sec/step)\n",
            "INFO:tensorflow:global step 93: loss = 5.0155 (2.982 sec/step)\n",
            "I0101 17:36:55.199184 140038727088000 learning.py:507] global step 93: loss = 5.0155 (2.982 sec/step)\n",
            "INFO:tensorflow:global step 94: loss = 4.9095 (2.941 sec/step)\n",
            "I0101 17:36:58.141927 140038727088000 learning.py:507] global step 94: loss = 4.9095 (2.941 sec/step)\n",
            "INFO:tensorflow:global step 95: loss = 5.3713 (2.980 sec/step)\n",
            "I0101 17:37:01.123864 140038727088000 learning.py:507] global step 95: loss = 5.3713 (2.980 sec/step)\n",
            "INFO:tensorflow:global step 96: loss = 4.8526 (2.979 sec/step)\n",
            "I0101 17:37:04.104810 140038727088000 learning.py:507] global step 96: loss = 4.8526 (2.979 sec/step)\n",
            "INFO:tensorflow:global step 97: loss = 4.9669 (2.972 sec/step)\n",
            "I0101 17:37:07.078694 140038727088000 learning.py:507] global step 97: loss = 4.9669 (2.972 sec/step)\n",
            "INFO:tensorflow:global step 98: loss = 5.3383 (2.957 sec/step)\n",
            "I0101 17:37:10.037732 140038727088000 learning.py:507] global step 98: loss = 5.3383 (2.957 sec/step)\n",
            "INFO:tensorflow:global step 99: loss = 5.4079 (2.922 sec/step)\n",
            "I0101 17:37:12.961434 140038727088000 learning.py:507] global step 99: loss = 5.4079 (2.922 sec/step)\n",
            "INFO:tensorflow:global step 100: loss = 5.4061 (2.992 sec/step)\n",
            "I0101 17:37:15.954942 140038727088000 learning.py:507] global step 100: loss = 5.4061 (2.992 sec/step)\n",
            "INFO:tensorflow:global step 101: loss = 6.0478 (2.963 sec/step)\n",
            "I0101 17:37:18.919722 140038727088000 learning.py:507] global step 101: loss = 6.0478 (2.963 sec/step)\n",
            "INFO:tensorflow:global step 102: loss = 4.9759 (2.921 sec/step)\n",
            "I0101 17:37:21.843372 140038727088000 learning.py:507] global step 102: loss = 4.9759 (2.921 sec/step)\n",
            "INFO:tensorflow:global step 103: loss = 4.8435 (2.964 sec/step)\n",
            "I0101 17:37:24.808961 140038727088000 learning.py:507] global step 103: loss = 4.8435 (2.964 sec/step)\n",
            "INFO:tensorflow:global step 104: loss = 5.1344 (4.619 sec/step)\n",
            "I0101 17:37:29.429823 140038727088000 learning.py:507] global step 104: loss = 5.1344 (4.619 sec/step)\n",
            "INFO:tensorflow:Recording summary at step 104.\n",
            "I0101 17:37:31.521546 140034970515200 supervisor.py:1050] Recording summary at step 104.\n",
            "INFO:tensorflow:global step 105: loss = 5.2493 (3.343 sec/step)\n",
            "I0101 17:37:32.774826 140038727088000 learning.py:507] global step 105: loss = 5.2493 (3.343 sec/step)\n",
            "INFO:tensorflow:global_step/sec: 0.33334\n",
            "I0101 17:37:34.741605 140034945337088 supervisor.py:1099] global_step/sec: 0.33334\n",
            "INFO:tensorflow:global step 106: loss = 4.9244 (3.041 sec/step)\n",
            "I0101 17:37:35.816965 140038727088000 learning.py:507] global step 106: loss = 4.9244 (3.041 sec/step)\n",
            "INFO:tensorflow:global step 107: loss = 5.7395 (2.930 sec/step)\n",
            "I0101 17:37:38.747961 140038727088000 learning.py:507] global step 107: loss = 5.7395 (2.930 sec/step)\n",
            "INFO:tensorflow:global step 108: loss = 5.7413 (2.908 sec/step)\n",
            "I0101 17:37:41.657645 140038727088000 learning.py:507] global step 108: loss = 5.7413 (2.908 sec/step)\n",
            "INFO:tensorflow:global step 109: loss = 3.9486 (2.948 sec/step)\n",
            "I0101 17:37:44.606854 140038727088000 learning.py:507] global step 109: loss = 3.9486 (2.948 sec/step)\n",
            "INFO:tensorflow:global step 110: loss = 5.5701 (2.914 sec/step)\n",
            "I0101 17:37:47.522967 140038727088000 learning.py:507] global step 110: loss = 5.5701 (2.914 sec/step)\n",
            "INFO:tensorflow:global step 111: loss = 5.7878 (2.958 sec/step)\n",
            "I0101 17:37:50.482440 140038727088000 learning.py:507] global step 111: loss = 5.7878 (2.958 sec/step)\n",
            "INFO:tensorflow:global step 112: loss = 4.5800 (2.927 sec/step)\n",
            "I0101 17:37:53.411208 140038727088000 learning.py:507] global step 112: loss = 4.5800 (2.927 sec/step)\n",
            "INFO:tensorflow:global step 113: loss = 4.9238 (2.973 sec/step)\n",
            "I0101 17:37:56.385783 140038727088000 learning.py:507] global step 113: loss = 4.9238 (2.973 sec/step)\n",
            "INFO:tensorflow:global step 114: loss = 4.6528 (2.968 sec/step)\n",
            "I0101 17:37:59.354767 140038727088000 learning.py:507] global step 114: loss = 4.6528 (2.968 sec/step)\n",
            "INFO:tensorflow:global step 115: loss = 5.9118 (2.920 sec/step)\n",
            "I0101 17:38:02.276145 140038727088000 learning.py:507] global step 115: loss = 5.9118 (2.920 sec/step)\n",
            "INFO:tensorflow:global step 116: loss = 5.2822 (2.975 sec/step)\n",
            "I0101 17:38:05.252977 140038727088000 learning.py:507] global step 116: loss = 5.2822 (2.975 sec/step)\n",
            "INFO:tensorflow:global step 117: loss = 4.7516 (2.987 sec/step)\n",
            "I0101 17:38:08.241624 140038727088000 learning.py:507] global step 117: loss = 4.7516 (2.987 sec/step)\n",
            "INFO:tensorflow:global step 118: loss = 4.5529 (2.969 sec/step)\n",
            "I0101 17:38:11.212513 140038727088000 learning.py:507] global step 118: loss = 4.5529 (2.969 sec/step)\n",
            "INFO:tensorflow:global step 119: loss = 4.8469 (2.985 sec/step)\n",
            "I0101 17:38:14.199635 140038727088000 learning.py:507] global step 119: loss = 4.8469 (2.985 sec/step)\n",
            "INFO:tensorflow:global step 120: loss = 5.7578 (3.017 sec/step)\n",
            "I0101 17:38:17.218357 140038727088000 learning.py:507] global step 120: loss = 5.7578 (3.017 sec/step)\n",
            "INFO:tensorflow:global step 121: loss = 4.5495 (3.013 sec/step)\n",
            "I0101 17:38:20.232826 140038727088000 learning.py:507] global step 121: loss = 4.5495 (3.013 sec/step)\n",
            "INFO:tensorflow:global step 122: loss = 5.2192 (2.962 sec/step)\n",
            "I0101 17:38:23.196441 140038727088000 learning.py:507] global step 122: loss = 5.2192 (2.962 sec/step)\n",
            "INFO:tensorflow:global step 123: loss = 4.7200 (2.975 sec/step)\n",
            "I0101 17:38:26.172682 140038727088000 learning.py:507] global step 123: loss = 4.7200 (2.975 sec/step)\n",
            "INFO:tensorflow:global step 124: loss = 4.9767 (3.058 sec/step)\n",
            "I0101 17:38:29.231755 140038727088000 learning.py:507] global step 124: loss = 4.9767 (3.058 sec/step)\n",
            "INFO:tensorflow:global step 125: loss = 4.1562 (2.926 sec/step)\n",
            "I0101 17:38:32.159794 140038727088000 learning.py:507] global step 125: loss = 4.1562 (2.926 sec/step)\n",
            "INFO:tensorflow:global step 126: loss = 4.7160 (2.958 sec/step)\n",
            "I0101 17:38:35.120149 140038727088000 learning.py:507] global step 126: loss = 4.7160 (2.958 sec/step)\n",
            "INFO:tensorflow:global step 127: loss = 4.6043 (2.974 sec/step)\n",
            "I0101 17:38:38.096986 140038727088000 learning.py:507] global step 127: loss = 4.6043 (2.974 sec/step)\n",
            "INFO:tensorflow:global step 128: loss = 5.4603 (2.903 sec/step)\n",
            "I0101 17:38:41.001920 140038727088000 learning.py:507] global step 128: loss = 5.4603 (2.903 sec/step)\n",
            "INFO:tensorflow:global step 129: loss = 4.4017 (2.947 sec/step)\n",
            "I0101 17:38:43.951292 140038727088000 learning.py:507] global step 129: loss = 4.4017 (2.947 sec/step)\n",
            "INFO:tensorflow:global step 130: loss = 5.2553 (2.938 sec/step)\n",
            "I0101 17:38:46.890599 140038727088000 learning.py:507] global step 130: loss = 5.2553 (2.938 sec/step)\n",
            "INFO:tensorflow:global step 131: loss = 4.9089 (2.952 sec/step)\n",
            "I0101 17:38:49.843998 140038727088000 learning.py:507] global step 131: loss = 4.9089 (2.952 sec/step)\n",
            "INFO:tensorflow:global step 132: loss = 3.8629 (2.907 sec/step)\n",
            "I0101 17:38:52.752237 140038727088000 learning.py:507] global step 132: loss = 3.8629 (2.907 sec/step)\n",
            "INFO:tensorflow:global step 133: loss = 5.0479 (2.937 sec/step)\n",
            "I0101 17:38:55.691223 140038727088000 learning.py:507] global step 133: loss = 5.0479 (2.937 sec/step)\n",
            "INFO:tensorflow:global step 134: loss = 5.0041 (2.952 sec/step)\n",
            "I0101 17:38:58.644843 140038727088000 learning.py:507] global step 134: loss = 5.0041 (2.952 sec/step)\n",
            "INFO:tensorflow:global step 135: loss = 4.7902 (2.953 sec/step)\n",
            "I0101 17:39:01.599705 140038727088000 learning.py:507] global step 135: loss = 4.7902 (2.953 sec/step)\n",
            "INFO:tensorflow:global step 136: loss = 4.7810 (2.947 sec/step)\n",
            "I0101 17:39:04.548115 140038727088000 learning.py:507] global step 136: loss = 4.7810 (2.947 sec/step)\n",
            "INFO:tensorflow:global step 137: loss = 4.9325 (2.917 sec/step)\n",
            "I0101 17:39:07.466890 140038727088000 learning.py:507] global step 137: loss = 4.9325 (2.917 sec/step)\n",
            "INFO:tensorflow:global step 138: loss = 5.0637 (2.957 sec/step)\n",
            "I0101 17:39:10.426093 140038727088000 learning.py:507] global step 138: loss = 5.0637 (2.957 sec/step)\n",
            "INFO:tensorflow:global step 139: loss = 4.9301 (2.884 sec/step)\n",
            "I0101 17:39:13.311890 140038727088000 learning.py:507] global step 139: loss = 4.9301 (2.884 sec/step)\n",
            "INFO:tensorflow:global step 140: loss = 3.9730 (2.908 sec/step)\n",
            "I0101 17:39:16.221156 140038727088000 learning.py:507] global step 140: loss = 3.9730 (2.908 sec/step)\n",
            "INFO:tensorflow:global step 141: loss = 4.3044 (2.972 sec/step)\n",
            "I0101 17:39:19.194843 140038727088000 learning.py:507] global step 141: loss = 4.3044 (2.972 sec/step)\n",
            "INFO:tensorflow:global step 142: loss = 4.7991 (2.946 sec/step)\n",
            "I0101 17:39:22.142544 140038727088000 learning.py:507] global step 142: loss = 4.7991 (2.946 sec/step)\n",
            "INFO:tensorflow:global step 143: loss = 4.6813 (2.932 sec/step)\n",
            "I0101 17:39:25.075633 140038727088000 learning.py:507] global step 143: loss = 4.6813 (2.932 sec/step)\n",
            "INFO:tensorflow:global step 144: loss = 4.5891 (4.747 sec/step)\n",
            "I0101 17:39:29.826089 140038727088000 learning.py:507] global step 144: loss = 4.5891 (4.747 sec/step)\n",
            "INFO:tensorflow:Recording summary at step 144.\n",
            "I0101 17:39:31.082702 140034970515200 supervisor.py:1050] Recording summary at step 144.\n",
            "INFO:tensorflow:global step 145: loss = 4.5852 (3.119 sec/step)\n",
            "I0101 17:39:32.949048 140038727088000 learning.py:507] global step 145: loss = 4.5852 (3.119 sec/step)\n",
            "INFO:tensorflow:global_step/sec: 0.333332\n",
            "I0101 17:39:34.742270 140034945337088 supervisor.py:1099] global_step/sec: 0.333332\n",
            "INFO:tensorflow:global step 146: loss = 5.1077 (2.951 sec/step)\n",
            "I0101 17:39:35.900990 140038727088000 learning.py:507] global step 146: loss = 5.1077 (2.951 sec/step)\n",
            "INFO:tensorflow:global step 147: loss = 4.5892 (3.011 sec/step)\n",
            "I0101 17:39:38.913676 140038727088000 learning.py:507] global step 147: loss = 4.5892 (3.011 sec/step)\n",
            "INFO:tensorflow:global step 148: loss = 5.1614 (3.046 sec/step)\n",
            "I0101 17:39:41.961412 140038727088000 learning.py:507] global step 148: loss = 5.1614 (3.046 sec/step)\n",
            "INFO:tensorflow:global step 149: loss = 4.8599 (2.975 sec/step)\n",
            "I0101 17:39:44.937578 140038727088000 learning.py:507] global step 149: loss = 4.8599 (2.975 sec/step)\n",
            "INFO:tensorflow:global step 150: loss = 4.1675 (2.948 sec/step)\n",
            "I0101 17:39:47.886727 140038727088000 learning.py:507] global step 150: loss = 4.1675 (2.948 sec/step)\n",
            "INFO:tensorflow:global step 151: loss = 5.1808 (2.958 sec/step)\n",
            "I0101 17:39:50.846491 140038727088000 learning.py:507] global step 151: loss = 5.1808 (2.958 sec/step)\n",
            "INFO:tensorflow:global step 152: loss = 4.9250 (2.946 sec/step)\n",
            "I0101 17:39:53.794006 140038727088000 learning.py:507] global step 152: loss = 4.9250 (2.946 sec/step)\n",
            "INFO:tensorflow:global step 153: loss = 4.1387 (2.997 sec/step)\n",
            "I0101 17:39:56.792169 140038727088000 learning.py:507] global step 153: loss = 4.1387 (2.997 sec/step)\n",
            "INFO:tensorflow:global step 154: loss = 4.2347 (2.946 sec/step)\n",
            "I0101 17:39:59.739700 140038727088000 learning.py:507] global step 154: loss = 4.2347 (2.946 sec/step)\n",
            "INFO:tensorflow:global step 155: loss = 3.7558 (2.915 sec/step)\n",
            "I0101 17:40:02.656239 140038727088000 learning.py:507] global step 155: loss = 3.7558 (2.915 sec/step)\n",
            "INFO:tensorflow:global step 156: loss = 4.1855 (3.032 sec/step)\n",
            "I0101 17:40:05.690258 140038727088000 learning.py:507] global step 156: loss = 4.1855 (3.032 sec/step)\n",
            "INFO:tensorflow:global step 157: loss = 5.1578 (2.930 sec/step)\n",
            "I0101 17:40:08.622307 140038727088000 learning.py:507] global step 157: loss = 5.1578 (2.930 sec/step)\n",
            "INFO:tensorflow:global step 158: loss = 4.5711 (2.978 sec/step)\n",
            "I0101 17:40:11.602353 140038727088000 learning.py:507] global step 158: loss = 4.5711 (2.978 sec/step)\n",
            "INFO:tensorflow:global step 159: loss = 4.1425 (2.892 sec/step)\n",
            "I0101 17:40:14.495874 140038727088000 learning.py:507] global step 159: loss = 4.1425 (2.892 sec/step)\n",
            "INFO:tensorflow:global step 160: loss = 5.1825 (2.914 sec/step)\n",
            "I0101 17:40:17.411555 140038727088000 learning.py:507] global step 160: loss = 5.1825 (2.914 sec/step)\n",
            "INFO:tensorflow:global step 161: loss = 3.7843 (2.967 sec/step)\n",
            "I0101 17:40:20.380419 140038727088000 learning.py:507] global step 161: loss = 3.7843 (2.967 sec/step)\n",
            "INFO:tensorflow:global step 162: loss = 4.2088 (2.922 sec/step)\n",
            "I0101 17:40:23.303869 140038727088000 learning.py:507] global step 162: loss = 4.2088 (2.922 sec/step)\n",
            "INFO:tensorflow:global step 163: loss = 4.5584 (2.947 sec/step)\n",
            "I0101 17:40:26.252440 140038727088000 learning.py:507] global step 163: loss = 4.5584 (2.947 sec/step)\n",
            "INFO:tensorflow:global step 164: loss = 4.6918 (2.938 sec/step)\n",
            "I0101 17:40:29.191734 140038727088000 learning.py:507] global step 164: loss = 4.6918 (2.938 sec/step)\n",
            "INFO:tensorflow:global step 165: loss = 5.5010 (2.921 sec/step)\n",
            "I0101 17:40:32.114221 140038727088000 learning.py:507] global step 165: loss = 5.5010 (2.921 sec/step)\n",
            "INFO:tensorflow:global step 166: loss = 3.9358 (2.907 sec/step)\n",
            "I0101 17:40:35.022570 140038727088000 learning.py:507] global step 166: loss = 3.9358 (2.907 sec/step)\n",
            "INFO:tensorflow:global step 167: loss = 4.5484 (2.968 sec/step)\n",
            "I0101 17:40:37.992231 140038727088000 learning.py:507] global step 167: loss = 4.5484 (2.968 sec/step)\n",
            "INFO:tensorflow:global step 168: loss = 3.6224 (2.973 sec/step)\n",
            "I0101 17:40:40.966708 140038727088000 learning.py:507] global step 168: loss = 3.6224 (2.973 sec/step)\n",
            "INFO:tensorflow:global step 169: loss = 4.7513 (2.963 sec/step)\n",
            "I0101 17:40:43.931236 140038727088000 learning.py:507] global step 169: loss = 4.7513 (2.963 sec/step)\n",
            "INFO:tensorflow:global step 170: loss = 4.8790 (2.938 sec/step)\n",
            "I0101 17:40:46.870337 140038727088000 learning.py:507] global step 170: loss = 4.8790 (2.938 sec/step)\n",
            "INFO:tensorflow:global step 171: loss = 5.0207 (2.963 sec/step)\n",
            "I0101 17:40:49.835520 140038727088000 learning.py:507] global step 171: loss = 5.0207 (2.963 sec/step)\n",
            "INFO:tensorflow:global step 172: loss = 4.2239 (2.917 sec/step)\n",
            "I0101 17:40:52.754163 140038727088000 learning.py:507] global step 172: loss = 4.2239 (2.917 sec/step)\n",
            "INFO:tensorflow:global step 173: loss = 4.0434 (2.970 sec/step)\n",
            "I0101 17:40:55.725275 140038727088000 learning.py:507] global step 173: loss = 4.0434 (2.970 sec/step)\n",
            "INFO:tensorflow:global step 174: loss = 4.4283 (2.955 sec/step)\n",
            "I0101 17:40:58.681698 140038727088000 learning.py:507] global step 174: loss = 4.4283 (2.955 sec/step)\n",
            "INFO:tensorflow:global step 175: loss = 4.1045 (2.966 sec/step)\n",
            "I0101 17:41:01.649178 140038727088000 learning.py:507] global step 175: loss = 4.1045 (2.966 sec/step)\n",
            "INFO:tensorflow:global step 176: loss = 4.4198 (2.984 sec/step)\n",
            "I0101 17:41:04.634359 140038727088000 learning.py:507] global step 176: loss = 4.4198 (2.984 sec/step)\n",
            "INFO:tensorflow:global step 177: loss = 4.0935 (2.958 sec/step)\n",
            "I0101 17:41:07.594307 140038727088000 learning.py:507] global step 177: loss = 4.0935 (2.958 sec/step)\n",
            "INFO:tensorflow:global step 178: loss = 4.7017 (2.984 sec/step)\n",
            "I0101 17:41:10.580449 140038727088000 learning.py:507] global step 178: loss = 4.7017 (2.984 sec/step)\n",
            "INFO:tensorflow:global step 179: loss = 4.2584 (2.922 sec/step)\n",
            "I0101 17:41:13.503971 140038727088000 learning.py:507] global step 179: loss = 4.2584 (2.922 sec/step)\n",
            "INFO:tensorflow:global step 180: loss = 4.3632 (2.952 sec/step)\n",
            "I0101 17:41:16.457536 140038727088000 learning.py:507] global step 180: loss = 4.3632 (2.952 sec/step)\n",
            "INFO:tensorflow:global step 181: loss = 4.7173 (2.949 sec/step)\n",
            "I0101 17:41:19.408433 140038727088000 learning.py:507] global step 181: loss = 4.7173 (2.949 sec/step)\n",
            "INFO:tensorflow:global step 182: loss = 4.3028 (2.905 sec/step)\n",
            "I0101 17:41:22.314920 140038727088000 learning.py:507] global step 182: loss = 4.3028 (2.905 sec/step)\n",
            "INFO:tensorflow:global step 183: loss = 4.4796 (2.939 sec/step)\n",
            "I0101 17:41:25.256069 140038727088000 learning.py:507] global step 183: loss = 4.4796 (2.939 sec/step)\n",
            "INFO:tensorflow:Saving checkpoint to path training/model.ckpt\n",
            "I0101 17:41:26.392219 140034936944384 supervisor.py:1117] Saving checkpoint to path training/model.ckpt\n",
            "INFO:tensorflow:Recording summary at step 184.\n",
            "I0101 17:41:31.813287 140034970515200 supervisor.py:1050] Recording summary at step 184.\n",
            "INFO:tensorflow:global step 184: loss = 3.5282 (6.556 sec/step)\n",
            "I0101 17:41:31.818600 140038727088000 learning.py:507] global step 184: loss = 3.5282 (6.556 sec/step)\n",
            "INFO:tensorflow:global_step/sec: 0.324993\n",
            "I0101 17:41:34.744693 140034945337088 supervisor.py:1099] global_step/sec: 0.324993\n",
            "INFO:tensorflow:global step 185: loss = 5.0865 (3.303 sec/step)\n",
            "I0101 17:41:35.132076 140038727088000 learning.py:507] global step 185: loss = 5.0865 (3.303 sec/step)\n",
            "INFO:tensorflow:global step 186: loss = 4.0358 (3.105 sec/step)\n",
            "I0101 17:41:38.238700 140038727088000 learning.py:507] global step 186: loss = 4.0358 (3.105 sec/step)\n",
            "INFO:tensorflow:global step 187: loss = 4.6497 (3.025 sec/step)\n",
            "I0101 17:41:41.265218 140038727088000 learning.py:507] global step 187: loss = 4.6497 (3.025 sec/step)\n",
            "INFO:tensorflow:global step 188: loss = 3.9629 (3.006 sec/step)\n",
            "I0101 17:41:44.272309 140038727088000 learning.py:507] global step 188: loss = 3.9629 (3.006 sec/step)\n",
            "INFO:tensorflow:global step 189: loss = 3.8465 (2.990 sec/step)\n",
            "I0101 17:41:47.263390 140038727088000 learning.py:507] global step 189: loss = 3.8465 (2.990 sec/step)\n",
            "INFO:tensorflow:global step 190: loss = 5.3449 (2.952 sec/step)\n",
            "I0101 17:41:50.218113 140038727088000 learning.py:507] global step 190: loss = 5.3449 (2.952 sec/step)\n",
            "INFO:tensorflow:global step 191: loss = 4.3366 (2.929 sec/step)\n",
            "I0101 17:41:53.149347 140038727088000 learning.py:507] global step 191: loss = 4.3366 (2.929 sec/step)\n",
            "INFO:tensorflow:global step 192: loss = 3.5316 (2.938 sec/step)\n",
            "I0101 17:41:56.088562 140038727088000 learning.py:507] global step 192: loss = 3.5316 (2.938 sec/step)\n",
            "INFO:tensorflow:global step 193: loss = 4.5438 (2.935 sec/step)\n",
            "I0101 17:41:59.025326 140038727088000 learning.py:507] global step 193: loss = 4.5438 (2.935 sec/step)\n",
            "INFO:tensorflow:global step 194: loss = 4.7353 (2.963 sec/step)\n",
            "I0101 17:42:01.990000 140038727088000 learning.py:507] global step 194: loss = 4.7353 (2.963 sec/step)\n",
            "INFO:tensorflow:global step 195: loss = 3.2780 (2.895 sec/step)\n",
            "I0101 17:42:04.886639 140038727088000 learning.py:507] global step 195: loss = 3.2780 (2.895 sec/step)\n",
            "INFO:tensorflow:global step 196: loss = 4.3779 (2.906 sec/step)\n",
            "I0101 17:42:07.793891 140038727088000 learning.py:507] global step 196: loss = 4.3779 (2.906 sec/step)\n",
            "INFO:tensorflow:global step 197: loss = 4.4770 (2.922 sec/step)\n",
            "I0101 17:42:10.717833 140038727088000 learning.py:507] global step 197: loss = 4.4770 (2.922 sec/step)\n",
            "INFO:tensorflow:global step 198: loss = 3.6238 (2.937 sec/step)\n",
            "I0101 17:42:13.656635 140038727088000 learning.py:507] global step 198: loss = 3.6238 (2.937 sec/step)\n",
            "INFO:tensorflow:global step 199: loss = 3.7497 (2.962 sec/step)\n",
            "I0101 17:42:16.619882 140038727088000 learning.py:507] global step 199: loss = 3.7497 (2.962 sec/step)\n",
            "Traceback (most recent call last):\n",
            "  File \"train.py\", line 185, in <module>\n",
            "    tf.app.run()\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/platform/app.py\", line 40, in run\n",
            "    _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/absl/app.py\", line 299, in run\n",
            "    _run_main(main, args)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/absl/app.py\", line 250, in _run_main\n",
            "    sys.exit(main(argv))\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/util/deprecation.py\", line 324, in new_func\n",
            "    return func(*args, **kwargs)\n",
            "  File \"train.py\", line 181, in main\n",
            "    graph_hook_fn=graph_rewriter_fn)\n",
            "  File \"/content/gdrive/My Drive/IEDC/models/research/object_detection/legacy/trainer.py\", line 417, in train\n",
            "    saver=saver)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/slim/python/slim/learning.py\", line 775, in train\n",
            "    train_step_kwargs)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/slim/python/slim/learning.py\", line 490, in train_step\n",
            "    run_metadata=run_metadata)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\", line 956, in run\n",
            "    run_metadata_ptr)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\", line 1180, in _run\n",
            "    feed_dict_tensor, options, run_metadata)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\", line 1359, in _do_run\n",
            "    run_metadata)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\", line 1365, in _do_call\n",
            "    return fn(*args)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\", line 1350, in _run_fn\n",
            "    target_list, run_metadata)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\", line 1443, in _call_tf_sessionrun\n",
            "    run_metadata)\n",
            "KeyboardInterrupt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R6cqZhu3LHd0",
        "colab_type": "text"
      },
      "source": [
        "-----------\n",
        "\n",
        "# Post training procedures"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-N-NdrCeYlu",
        "colab_type": "code",
        "outputId": "e4cd4555-ba64-4e35-9876-06af09e098d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Export your inference graph.\n",
        "# ----------------------------\n",
        "# Make sure you have `My_exp_graph` folder in your `object_detection` folder\n",
        "# and it is EMPTY\n",
        "!python export_inference_graph.py \\\n",
        "--input_type image_tensor \\\n",
        "--pipeline_config_path training/ssd_mobilenet_v2_quantized_300x300_coco.config \\\n",
        "--trained_checkpoint_prefix training/model.ckpt-183 \\\n",
        "--output_directory My_exp_graph/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:\n",
            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/slim/nets/inception_resnet_v2.py:374: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/slim/nets/mobilenet/mobilenet.py:397: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n",
            "\n",
            "WARNING:tensorflow:From export_inference_graph.py:162: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.\n",
            "\n",
            "WARNING:tensorflow:From export_inference_graph.py:145: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
            "\n",
            "W0101 17:50:40.465936 140331270162304 module_wrapper.py:139] From export_inference_graph.py:145: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:402: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.\n",
            "\n",
            "W0101 17:50:40.472173 140331270162304 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:402: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:121: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0101 17:50:40.472543 140331270162304 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:121: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/preprocessor.py:2937: The name tf.image.resize_images is deprecated. Please use tf.image.resize instead.\n",
            "\n",
            "W0101 17:50:40.508375 140331270162304 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/preprocessor.py:2937: The name tf.image.resize_images is deprecated. Please use tf.image.resize instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/meta_architectures/ssd_meta_arch.py:597: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
            "\n",
            "W0101 17:50:40.534950 140331270162304 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/meta_architectures/ssd_meta_arch.py:597: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/layers/python/layers/layers.py:1057: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "W0101 17:50:40.537263 140331270162304 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/layers/python/layers/layers.py:1057: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/anchor_generator.py:171: The name tf.assert_equal is deprecated. Please use tf.compat.v1.assert_equal instead.\n",
            "\n",
            "W0101 17:50:42.623241 140331270162304 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/anchor_generator.py:171: The name tf.assert_equal is deprecated. Please use tf.compat.v1.assert_equal instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/predictors/convolutional_box_predictor.py:150: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
            "\n",
            "W0101 17:50:42.633919 140331270162304 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/predictors/convolutional_box_predictor.py:150: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
            "\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0101 17:50:42.634082 140331270162304 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0101 17:50:42.672041 140331270162304 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0101 17:50:42.821245 140331270162304 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0101 17:50:42.860283 140331270162304 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0101 17:50:42.899016 140331270162304 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0101 17:50:42.936913 140331270162304 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/post_processing.py:581: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "W0101 17:50:43.198052 140331270162304 deprecation.py:323] From /content/gdrive/My Drive/IEDC/models/research/object_detection/core/post_processing.py:581: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:278: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.\n",
            "\n",
            "W0101 17:50:43.610483 140331270162304 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:278: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:383: get_or_create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please switch to tf.train.get_or_create_global_step\n",
            "W0101 17:50:43.610771 140331270162304 deprecation.py:323] From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:383: get_or_create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please switch to tf.train.get_or_create_global_step\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/graph_rewriter_builder.py:41: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "W0101 17:50:43.614054 140331270162304 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/builders/graph_rewriter_builder.py:41: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/Conv/add_fold\n",
            "I0101 17:50:45.039546 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/Conv/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv/depthwise/add_fold\n",
            "I0101 17:50:45.039888 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_1/expand/add_fold\n",
            "I0101 17:50:45.040199 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_1/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/add_fold\n",
            "I0101 17:50:45.040408 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_2/expand/add_fold\n",
            "I0101 17:50:45.040655 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_2/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/add_fold\n",
            "I0101 17:50:45.040853 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_3/expand/add_fold\n",
            "I0101 17:50:45.041152 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_3/expand/add_fold\n",
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            "I0101 17:50:45.041347 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/add_fold\n",
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            "I0101 17:50:45.041608 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_4/expand/add_fold\n",
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            "I0101 17:50:45.041801 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_5/expand/add_fold\n",
            "I0101 17:50:45.042064 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_5/expand/add_fold\n",
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            "I0101 17:50:45.042249 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/add_fold\n",
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            "I0101 17:50:45.042669 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/add_fold\n",
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            "I0101 17:50:45.042929 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_7/expand/add_fold\n",
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            "I0101 17:50:45.043157 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_8/expand/add_fold\n",
            "I0101 17:50:45.043441 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_8/expand/add_fold\n",
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            "I0101 17:50:45.043615 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_9/expand/add_fold\n",
            "I0101 17:50:45.043882 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_9/expand/add_fold\n",
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            "I0101 17:50:45.044097 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_10/expand/add_fold\n",
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            "I0101 17:50:45.044527 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise/add_fold\n",
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            "I0101 17:50:45.044765 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_11/expand/add_fold\n",
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            "I0101 17:50:45.045587 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_13/expand/add_fold\n",
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            "I0101 17:50:45.045780 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_14/expand/add_fold\n",
            "I0101 17:50:45.046042 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_14/expand/add_fold\n",
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            "I0101 17:50:45.046224 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_15/expand/add_fold\n",
            "I0101 17:50:45.046478 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_15/expand/add_fold\n",
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            "I0101 17:50:45.046642 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_16/expand/add_fold\n",
            "I0101 17:50:45.046919 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_16/expand/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/add_fold\n",
            "I0101 17:50:45.047092 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/Conv_1/add_fold\n",
            "I0101 17:50:45.047351 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/Conv_1/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/add_fold\n",
            "I0101 17:50:45.047519 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/add_fold\n",
            "I0101 17:50:45.047687 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/add_fold\n",
            "I0101 17:50:45.047856 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/add_fold\n",
            "I0101 17:50:45.048045 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/add_fold\n",
            "I0101 17:50:45.048234 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/add_fold\n",
            "I0101 17:50:45.048409 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/add_fold\n",
            "I0101 17:50:45.048582 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/add_fold\n",
            "I0101 17:50:45.048753 140331270162304 quantize.py:299] Skipping quant after FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/add_fold\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:539: print_model_analysis (from tensorflow.contrib.tfprof.model_analyzer) is deprecated and will be removed after 2018-01-01.\n",
            "Instructions for updating:\n",
            "Use `tf.profiler.profile(graph, run_meta, op_log, cmd, options)`. Build `options` with `tf.profiler.ProfileOptionBuilder`. See README.md for details\n",
            "W0101 17:50:45.051191 140331270162304 deprecation.py:323] From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:539: print_model_analysis (from tensorflow.contrib.tfprof.model_analyzer) is deprecated and will be removed after 2018-01-01.\n",
            "Instructions for updating:\n",
            "Use `tf.profiler.profile(graph, run_meta, op_log, cmd, options)`. Build `options` with `tf.profiler.ProfileOptionBuilder`. See README.md for details\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/profiler/internal/flops_registry.py:142: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n",
            "W0101 17:50:45.052218 140331270162304 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/profiler/internal/flops_registry.py:142: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n",
            "257 ops no flops stats due to incomplete shapes.\n",
            "Parsing Inputs...\n",
            "Incomplete shape.\n",
            "\n",
            "=========================Options=============================\n",
            "-max_depth                  10000\n",
            "-min_bytes                  0\n",
            "-min_peak_bytes             0\n",
            "-min_residual_bytes         0\n",
            "-min_output_bytes           0\n",
            "-min_micros                 0\n",
            "-min_accelerator_micros     0\n",
            "-min_cpu_micros             0\n",
            "-min_params                 0\n",
            "-min_float_ops              0\n",
            "-min_occurrence             0\n",
            "-step                       -1\n",
            "-order_by                   name\n",
            "-account_type_regexes       _trainable_variables\n",
            "-start_name_regexes         .*\n",
            "-trim_name_regexes          .*BatchNorm.*\n",
            "-show_name_regexes          .*\n",
            "-hide_name_regexes          \n",
            "-account_displayed_op_only  true\n",
            "-select                     params\n",
            "-output                     stdout:\n",
            "\n",
            "==================Model Analysis Report======================\n",
            "Incomplete shape.\n",
            "\n",
            "Doc:\n",
            "scope: The nodes in the model graph are organized by their names, which is hierarchical like filesystem.\n",
            "param: Number of parameters (in the Variable).\n",
            "\n",
            "Profile:\n",
            "node name | # parameters\n",
            "_TFProfRoot (--/4.61m params)\n",
            "  BoxPredictor_0 (--/13.85k params)\n",
            "    BoxPredictor_0/BoxEncodingPredictor (--/6.92k params)\n",
            "      BoxPredictor_0/BoxEncodingPredictor/biases (12, 12/12 params)\n",
            "      BoxPredictor_0/BoxEncodingPredictor/weights (1x1x576x12, 6.91k/6.91k params)\n",
            "    BoxPredictor_0/ClassPredictor (--/6.92k params)\n",
            "      BoxPredictor_0/ClassPredictor/biases (12, 12/12 params)\n",
            "      BoxPredictor_0/ClassPredictor/weights (1x1x576x12, 6.91k/6.91k params)\n",
            "  BoxPredictor_1 (--/61.49k params)\n",
            "    BoxPredictor_1/BoxEncodingPredictor (--/30.74k params)\n",
            "      BoxPredictor_1/BoxEncodingPredictor/biases (24, 24/24 params)\n",
            "      BoxPredictor_1/BoxEncodingPredictor/weights (1x1x1280x24, 30.72k/30.72k params)\n",
            "    BoxPredictor_1/ClassPredictor (--/30.74k params)\n",
            "      BoxPredictor_1/ClassPredictor/biases (24, 24/24 params)\n",
            "      BoxPredictor_1/ClassPredictor/weights (1x1x1280x24, 30.72k/30.72k params)\n",
            "  BoxPredictor_2 (--/24.62k params)\n",
            "    BoxPredictor_2/BoxEncodingPredictor (--/12.31k params)\n",
            "      BoxPredictor_2/BoxEncodingPredictor/biases (24, 24/24 params)\n",
            "      BoxPredictor_2/BoxEncodingPredictor/weights (1x1x512x24, 12.29k/12.29k params)\n",
            "    BoxPredictor_2/ClassPredictor (--/12.31k params)\n",
            "      BoxPredictor_2/ClassPredictor/biases (24, 24/24 params)\n",
            "      BoxPredictor_2/ClassPredictor/weights (1x1x512x24, 12.29k/12.29k params)\n",
            "  BoxPredictor_3 (--/12.34k params)\n",
            "    BoxPredictor_3/BoxEncodingPredictor (--/6.17k params)\n",
            "      BoxPredictor_3/BoxEncodingPredictor/biases (24, 24/24 params)\n",
            "      BoxPredictor_3/BoxEncodingPredictor/weights (1x1x256x24, 6.14k/6.14k params)\n",
            "    BoxPredictor_3/ClassPredictor (--/6.17k params)\n",
            "      BoxPredictor_3/ClassPredictor/biases (24, 24/24 params)\n",
            "      BoxPredictor_3/ClassPredictor/weights (1x1x256x24, 6.14k/6.14k params)\n",
            "  BoxPredictor_4 (--/12.34k params)\n",
            "    BoxPredictor_4/BoxEncodingPredictor (--/6.17k params)\n",
            "      BoxPredictor_4/BoxEncodingPredictor/biases (24, 24/24 params)\n",
            "      BoxPredictor_4/BoxEncodingPredictor/weights (1x1x256x24, 6.14k/6.14k params)\n",
            "    BoxPredictor_4/ClassPredictor (--/6.17k params)\n",
            "      BoxPredictor_4/ClassPredictor/biases (24, 24/24 params)\n",
            "      BoxPredictor_4/ClassPredictor/weights (1x1x256x24, 6.14k/6.14k params)\n",
            "  BoxPredictor_5 (--/6.19k params)\n",
            "    BoxPredictor_5/BoxEncodingPredictor (--/3.10k params)\n",
            "      BoxPredictor_5/BoxEncodingPredictor/biases (24, 24/24 params)\n",
            "      BoxPredictor_5/BoxEncodingPredictor/weights (1x1x128x24, 3.07k/3.07k params)\n",
            "    BoxPredictor_5/ClassPredictor (--/3.10k params)\n",
            "      BoxPredictor_5/ClassPredictor/biases (24, 24/24 params)\n",
            "      BoxPredictor_5/ClassPredictor/weights (1x1x128x24, 3.07k/3.07k params)\n",
            "  FeatureExtractor (--/4.48m params)\n",
            "    FeatureExtractor/MobilenetV2 (--/4.48m params)\n",
            "      FeatureExtractor/MobilenetV2/Conv (--/864 params)\n",
            "        FeatureExtractor/MobilenetV2/Conv/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/Conv/weights (3x3x3x32, 864/864 params)\n",
            "      FeatureExtractor/MobilenetV2/Conv_1 (--/409.60k params)\n",
            "        FeatureExtractor/MobilenetV2/Conv_1/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/Conv_1/weights (1x1x320x1280, 409.60k/409.60k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv (--/800 params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv/depthwise (--/288 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv/depthwise/depthwise_weights (3x3x32x1, 288/288 params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv/project (--/512 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv/project/weights (1x1x32x16, 512/512 params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_1 (--/4.70k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise (--/864 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/depthwise_weights (3x3x96x1, 864/864 params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_1/expand (--/1.54k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_1/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_1/expand/weights (1x1x16x96, 1.54k/1.54k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_1/project (--/2.30k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_1/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_1/project/weights (1x1x96x24, 2.30k/2.30k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_10 (--/64.90k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise (--/3.46k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise/depthwise_weights (3x3x384x1, 3.46k/3.46k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_10/expand (--/24.58k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_10/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_10/expand/weights (1x1x64x384, 24.58k/24.58k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_10/project (--/36.86k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_10/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_10/project/weights (1x1x384x96, 36.86k/36.86k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_11 (--/115.78k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise (--/5.18k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/depthwise_weights (3x3x576x1, 5.18k/5.18k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_11/expand (--/55.30k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_11/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_11/expand/weights (1x1x96x576, 55.30k/55.30k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_11/project (--/55.30k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_11/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_11/project/weights (1x1x576x96, 55.30k/55.30k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_12 (--/115.78k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise (--/5.18k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/depthwise_weights (3x3x576x1, 5.18k/5.18k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_12/expand (--/55.30k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_12/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_12/expand/weights (1x1x96x576, 55.30k/55.30k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_12/project (--/55.30k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_12/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_12/project/weights (1x1x576x96, 55.30k/55.30k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_13 (--/152.64k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise (--/5.18k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/depthwise_weights (3x3x576x1, 5.18k/5.18k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_13/expand (--/55.30k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_13/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_13/expand/weights (1x1x96x576, 55.30k/55.30k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_13/project (--/92.16k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_13/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_13/project/weights (1x1x576x160, 92.16k/92.16k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_14 (--/315.84k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise (--/8.64k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/depthwise_weights (3x3x960x1, 8.64k/8.64k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_14/expand (--/153.60k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_14/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_14/expand/weights (1x1x160x960, 153.60k/153.60k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_14/project (--/153.60k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_14/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_14/project/weights (1x1x960x160, 153.60k/153.60k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_15 (--/315.84k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise (--/8.64k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/depthwise_weights (3x3x960x1, 8.64k/8.64k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_15/expand (--/153.60k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_15/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_15/expand/weights (1x1x160x960, 153.60k/153.60k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_15/project (--/153.60k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_15/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_15/project/weights (1x1x960x160, 153.60k/153.60k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_16 (--/469.44k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise (--/8.64k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/depthwise_weights (3x3x960x1, 8.64k/8.64k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_16/expand (--/153.60k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_16/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_16/expand/weights (1x1x160x960, 153.60k/153.60k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_16/project (--/307.20k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_16/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_16/project/weights (1x1x960x320, 307.20k/307.20k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_2 (--/8.21k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise (--/1.30k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/depthwise_weights (3x3x144x1, 1.30k/1.30k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_2/expand (--/3.46k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_2/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_2/expand/weights (1x1x24x144, 3.46k/3.46k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_2/project (--/3.46k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_2/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_2/project/weights (1x1x144x24, 3.46k/3.46k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_3 (--/9.36k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise (--/1.30k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/depthwise_weights (3x3x144x1, 1.30k/1.30k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_3/expand (--/3.46k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_3/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_3/expand/weights (1x1x24x144, 3.46k/3.46k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_3/project (--/4.61k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_3/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_3/project/weights (1x1x144x32, 4.61k/4.61k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_4 (--/14.02k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise (--/1.73k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/depthwise_weights (3x3x192x1, 1.73k/1.73k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_4/expand (--/6.14k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_4/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_4/expand/weights (1x1x32x192, 6.14k/6.14k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_4/project (--/6.14k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_4/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_4/project/weights (1x1x192x32, 6.14k/6.14k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_5 (--/14.02k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise (--/1.73k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/depthwise_weights (3x3x192x1, 1.73k/1.73k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_5/expand (--/6.14k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_5/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_5/expand/weights (1x1x32x192, 6.14k/6.14k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_5/project (--/6.14k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_5/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_5/project/weights (1x1x192x32, 6.14k/6.14k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_6 (--/20.16k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise (--/1.73k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/depthwise_weights (3x3x192x1, 1.73k/1.73k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_6/expand (--/6.14k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_6/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_6/expand/weights (1x1x32x192, 6.14k/6.14k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_6/project (--/12.29k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_6/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_6/project/weights (1x1x192x64, 12.29k/12.29k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_7 (--/52.61k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise (--/3.46k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/depthwise_weights (3x3x384x1, 3.46k/3.46k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_7/expand (--/24.58k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_7/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_7/expand/weights (1x1x64x384, 24.58k/24.58k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_7/project (--/24.58k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_7/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_7/project/weights (1x1x384x64, 24.58k/24.58k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_8 (--/52.61k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise (--/3.46k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/depthwise_weights (3x3x384x1, 3.46k/3.46k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_8/expand (--/24.58k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_8/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_8/expand/weights (1x1x64x384, 24.58k/24.58k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_8/project (--/24.58k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_8/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_8/project/weights (1x1x384x64, 24.58k/24.58k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_9 (--/52.61k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise (--/3.46k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/depthwise_weights (3x3x384x1, 3.46k/3.46k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_9/expand (--/24.58k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_9/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_9/expand/weights (1x1x64x384, 24.58k/24.58k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_9/project (--/24.58k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_9/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_9/project/weights (1x1x384x64, 24.58k/24.58k params)\n",
            "      FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256 (--/327.68k params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/weights (1x1x1280x256, 327.68k/327.68k params)\n",
            "      FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128 (--/65.54k params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/weights (1x1x512x128, 65.54k/65.54k params)\n",
            "      FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128 (--/32.77k params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/weights (1x1x256x128, 32.77k/32.77k params)\n",
            "      FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64 (--/16.38k params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/weights (1x1x256x64, 16.38k/16.38k params)\n",
            "      FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512 (--/1.18m params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/weights (3x3x256x512, 1.18m/1.18m params)\n",
            "      FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256 (--/294.91k params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/weights (3x3x128x256, 294.91k/294.91k params)\n",
            "      FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256 (--/294.91k params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/weights (3x3x128x256, 294.91k/294.91k params)\n",
            "      FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128 (--/73.73k params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/weights (3x3x64x128, 73.73k/73.73k params)\n",
            "\n",
            "======================End of Report==========================\n",
            "257 ops no flops stats due to incomplete shapes.\n",
            "Parsing Inputs...\n",
            "Incomplete shape.\n",
            "\n",
            "=========================Options=============================\n",
            "-max_depth                  10000\n",
            "-min_bytes                  0\n",
            "-min_peak_bytes             0\n",
            "-min_residual_bytes         0\n",
            "-min_output_bytes           0\n",
            "-min_micros                 0\n",
            "-min_accelerator_micros     0\n",
            "-min_cpu_micros             0\n",
            "-min_params                 0\n",
            "-min_float_ops              1\n",
            "-min_occurrence             0\n",
            "-step                       -1\n",
            "-order_by                   float_ops\n",
            "-account_type_regexes       .*\n",
            "-start_name_regexes         .*\n",
            "-trim_name_regexes          .*BatchNorm.*,.*Initializer.*,.*Regularizer.*,.*BiasAdd.*\n",
            "-show_name_regexes          .*\n",
            "-hide_name_regexes          \n",
            "-account_displayed_op_only  true\n",
            "-select                     float_ops\n",
            "-output                     stdout:\n",
            "\n",
            "==================Model Analysis Report======================\n",
            "Incomplete shape.\n",
            "\n",
            "Doc:\n",
            "scope: The nodes in the model graph are organized by their names, which is hierarchical like filesystem.\n",
            "flops: Number of float operations. Note: Please read the implementation for the math behind it.\n",
            "\n",
            "Profile:\n",
            "node name | # float_ops\n",
            "_TFProfRoot (--/4.49m flops)\n",
            "  FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/mul_fold (1.18m/1.18m flops)\n",
            "  FeatureExtractor/MobilenetV2/Conv_1/mul_fold (409.60k/409.60k flops)\n",
            "  FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/mul_fold (327.68k/327.68k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_16/project/mul_fold (307.20k/307.20k flops)\n",
            "  FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/mul_fold (294.91k/294.91k flops)\n",
            "  FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/mul_fold (294.91k/294.91k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_16/expand/mul_fold (153.60k/153.60k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_15/project/mul_fold (153.60k/153.60k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_15/expand/mul_fold (153.60k/153.60k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_14/project/mul_fold (153.60k/153.60k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_14/expand/mul_fold (153.60k/153.60k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_13/project/mul_fold (92.16k/92.16k flops)\n",
            "  FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/mul_fold (73.73k/73.73k flops)\n",
            "  FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/mul_fold (65.54k/65.54k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_12/expand/mul_fold (55.30k/55.30k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_12/project/mul_fold (55.30k/55.30k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_13/expand/mul_fold (55.30k/55.30k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_11/project/mul_fold (55.30k/55.30k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_11/expand/mul_fold (55.30k/55.30k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_10/project/mul_fold (36.86k/36.86k flops)\n",
            "  FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/mul_fold (32.77k/32.77k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_9/expand/mul_fold (24.58k/24.58k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_7/expand/mul_fold (24.58k/24.58k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_7/project/mul_fold (24.58k/24.58k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_8/expand/mul_fold (24.58k/24.58k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_8/project/mul_fold (24.58k/24.58k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_9/project/mul_fold (24.58k/24.58k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_10/expand/mul_fold (24.58k/24.58k flops)\n",
            "  FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/mul_fold (16.38k/16.38k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_6/project/mul_fold (12.29k/12.29k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/mul_fold (8.64k/8.64k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/mul_fold (8.64k/8.64k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/mul_fold (8.64k/8.64k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_4/expand/mul_fold (6.14k/6.14k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_4/project/mul_fold (6.14k/6.14k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_5/expand/mul_fold (6.14k/6.14k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_5/project/mul_fold (6.14k/6.14k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_6/expand/mul_fold (6.14k/6.14k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/mul_fold (5.18k/5.18k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/mul_fold (5.18k/5.18k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/mul_fold (5.18k/5.18k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_3/project/mul_fold (4.61k/4.61k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_3/expand/mul_fold (3.46k/3.46k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/mul_fold (3.46k/3.46k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_2/project/mul_fold (3.46k/3.46k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/mul_fold (3.46k/3.46k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_2/expand/mul_fold (3.46k/3.46k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/mul_fold (3.46k/3.46k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise/mul_fold (3.46k/3.46k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_1/project/mul_fold (2.30k/2.30k flops)\n",
            "  MultipleGridAnchorGenerator/mul_19 (2.17k/2.17k flops)\n",
            "  MultipleGridAnchorGenerator/sub (2.17k/2.17k flops)\n",
            "  MultipleGridAnchorGenerator/mul_20 (2.17k/2.17k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/mul_fold (1.73k/1.73k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/mul_fold (1.73k/1.73k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/mul_fold (1.73k/1.73k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_1/expand/mul_fold (1.54k/1.54k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/mul_fold (1.30k/1.30k flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/mul_fold (1.30k/1.30k flops)\n",
            "  MultipleGridAnchorGenerator/mul_27 (1.20k/1.20k flops)\n",
            "  MultipleGridAnchorGenerator/mul_28 (1.20k/1.20k flops)\n",
            "  MultipleGridAnchorGenerator/sub_1 (1.20k/1.20k flops)\n",
            "  MultipleGridAnchorGenerator/mul_21 (1.08k/1.08k flops)\n",
            "  FeatureExtractor/MobilenetV2/Conv/mul_fold (864/864 flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/mul_fold (864/864 flops)\n",
            "  MultipleGridAnchorGenerator/mul_29 (600/600 flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv/project/mul_fold (512/512 flops)\n",
            "  MultipleGridAnchorGenerator/mul_36 (300/300 flops)\n",
            "  MultipleGridAnchorGenerator/mul_35 (300/300 flops)\n",
            "  MultipleGridAnchorGenerator/sub_2 (300/300 flops)\n",
            "  FeatureExtractor/MobilenetV2/expanded_conv/depthwise/mul_fold (288/288 flops)\n",
            "  MultipleGridAnchorGenerator/mul_37 (150/150 flops)\n",
            "  MultipleGridAnchorGenerator/mul_43 (108/108 flops)\n",
            "  MultipleGridAnchorGenerator/sub_3 (108/108 flops)\n",
            "  MultipleGridAnchorGenerator/mul_44 (108/108 flops)\n",
            "  MultipleGridAnchorGenerator/mul_45 (54/54 flops)\n",
            "  MultipleGridAnchorGenerator/mul_51 (48/48 flops)\n",
            "  MultipleGridAnchorGenerator/mul_52 (48/48 flops)\n",
            "  MultipleGridAnchorGenerator/sub_4 (48/48 flops)\n",
            "  MultipleGridAnchorGenerator/mul_53 (24/24 flops)\n",
            "  MultipleGridAnchorGenerator/mul_18 (19/19 flops)\n",
            "  MultipleGridAnchorGenerator/mul_17 (19/19 flops)\n",
            "  MultipleGridAnchorGenerator/sub_5 (12/12 flops)\n",
            "  MultipleGridAnchorGenerator/mul_60 (12/12 flops)\n",
            "  MultipleGridAnchorGenerator/mul_59 (12/12 flops)\n",
            "  MultipleGridAnchorGenerator/mul_25 (10/10 flops)\n",
            "  MultipleGridAnchorGenerator/mul_26 (10/10 flops)\n",
            "  MultipleGridAnchorGenerator/mul_55 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_16 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_46 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_47 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_48 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_19 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_18 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_17 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_54 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_56 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_61 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_15 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_30 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_31 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_40 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_32 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_39 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_24 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_38 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_23 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_22 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_33 (5/5 flops)\n",
            "  MultipleGridAnchorGenerator/mul_34 (5/5 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_14 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/mul_42 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/mul_15 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/mul_14 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/mul_41 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/mul_16 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/mul_50 (2/2 flops)\n",
            "  MultipleGridAnchorGenerator/mul_49 (2/2 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_8 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_9 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_7 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_6 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_5 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_4 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_3 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_2 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_1 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/SortByField/Equal (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_5 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_4 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_3 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_2 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_1 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/SortByField_1/Equal (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Less (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_19 (1/1 flops)\n",
            "  Preprocessor/map/while/Less_1 (1/1 flops)\n",
            "  Preprocessor/map/while/Less (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/ones/Less (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_9 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_8 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_7 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_6 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_5 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_4 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_3 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_2 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_18 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_17 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_16 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_15 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_14 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_13 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_12 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_11 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_10 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_1 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_7 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_10 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_1 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_3 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/Minimum (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/assert_equal_1/Equal (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_1 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_9 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_8 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_11 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_6 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_58 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_57 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_13 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_12 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_5 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_4 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_10 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_11 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_8 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_2 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_1 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Greater (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/ChangeCoordinateFrame/truediv_1 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/ChangeCoordinateFrame/truediv (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/ChangeCoordinateFrame/sub_1 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/ChangeCoordinateFrame/sub (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Less_1 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_9 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_3 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_7 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_6 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_5 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_4 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_3 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_2 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_2 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_13 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_12 (1/1 flops)\n",
            "\n",
            "======================End of Report==========================\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:432: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
            "\n",
            "W0101 17:50:46.570867 140331270162304 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:432: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:342: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "W0101 17:50:47.845571 140331270162304 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:342: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "2020-01-01 17:50:47.847265: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-01-01 17:50:47.850691: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:47.851352: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n",
            "name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
            "pciBusID: 0000:00:04.0\n",
            "2020-01-01 17:50:47.851629: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-01-01 17:50:47.853247: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-01-01 17:50:47.854978: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-01-01 17:50:47.855343: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-01-01 17:50:47.857742: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-01-01 17:50:47.858873: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-01-01 17:50:47.863167: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-01-01 17:50:47.863270: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:47.863806: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:47.864308: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2020-01-01 17:50:47.869185: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200000000 Hz\n",
            "2020-01-01 17:50:47.869363: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x28f2d80 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-01-01 17:50:47.869389: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-01-01 17:50:47.959459: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:47.960184: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x28f3480 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-01-01 17:50:47.960215: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n",
            "2020-01-01 17:50:47.960389: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:47.960946: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n",
            "name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
            "pciBusID: 0000:00:04.0\n",
            "2020-01-01 17:50:47.961012: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-01-01 17:50:47.961027: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-01-01 17:50:47.961040: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-01-01 17:50:47.961053: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-01-01 17:50:47.961066: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-01-01 17:50:47.961077: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-01-01 17:50:47.961091: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-01-01 17:50:47.961144: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:47.961668: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:47.962333: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2020-01-01 17:50:47.962412: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-01-01 17:50:47.963617: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-01-01 17:50:47.963644: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2020-01-01 17:50:47.963654: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2020-01-01 17:50:47.963752: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:47.964349: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:47.964838: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-01-01 17:50:47.964871: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14974 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
            "INFO:tensorflow:Restoring parameters from training/model.ckpt-183\n",
            "I0101 17:50:47.966796 140331270162304 saver.py:1284] Restoring parameters from training/model.ckpt-183\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/tools/freeze_graph.py:127: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use standard file APIs to check for files with this prefix.\n",
            "W0101 17:50:50.522432 140331270162304 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/tools/freeze_graph.py:127: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use standard file APIs to check for files with this prefix.\n",
            "2020-01-01 17:50:51.240202: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:51.240793: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n",
            "name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
            "pciBusID: 0000:00:04.0\n",
            "2020-01-01 17:50:51.240866: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-01-01 17:50:51.240882: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-01-01 17:50:51.240942: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-01-01 17:50:51.240971: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-01-01 17:50:51.240993: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-01-01 17:50:51.241008: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-01-01 17:50:51.241023: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-01-01 17:50:51.241109: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:51.241668: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:51.242155: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2020-01-01 17:50:51.242189: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-01-01 17:50:51.242199: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2020-01-01 17:50:51.242208: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2020-01-01 17:50:51.242284: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:51.242770: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:51.243257: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14974 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
            "INFO:tensorflow:Restoring parameters from training/model.ckpt-183\n",
            "I0101 17:50:51.244847 140331270162304 saver.py:1284] Restoring parameters from training/model.ckpt-183\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/tools/freeze_graph.py:233: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
            "W0101 17:50:52.355343 140331270162304 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/tools/freeze_graph.py:233: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/graph_util_impl.py:277: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
            "W0101 17:50:52.355623 140331270162304 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/graph_util_impl.py:277: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
            "INFO:tensorflow:Froze 632 variables.\n",
            "I0101 17:50:52.977719 140331270162304 graph_util_impl.py:334] Froze 632 variables.\n",
            "INFO:tensorflow:Converted 632 variables to const ops.\n",
            "I0101 17:50:53.092002 140331270162304 graph_util_impl.py:394] Converted 632 variables to const ops.\n",
            "2020-01-01 17:50:53.311777: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:53.312451: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n",
            "name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
            "pciBusID: 0000:00:04.0\n",
            "2020-01-01 17:50:53.312576: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-01-01 17:50:53.312610: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-01-01 17:50:53.312636: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-01-01 17:50:53.312661: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-01-01 17:50:53.312685: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-01-01 17:50:53.312708: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-01-01 17:50:53.312731: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-01-01 17:50:53.312825: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:53.313458: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:53.314027: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2020-01-01 17:50:53.314069: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-01-01 17:50:53.314091: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2020-01-01 17:50:53.314102: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2020-01-01 17:50:53.314214: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:53.314784: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-01-01 17:50:53.315357: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14974 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:306: The name tf.saved_model.builder.SavedModelBuilder is deprecated. Please use tf.compat.v1.saved_model.builder.SavedModelBuilder instead.\n",
            "\n",
            "W0101 17:50:54.027639 140331270162304 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:306: The name tf.saved_model.builder.SavedModelBuilder is deprecated. Please use tf.compat.v1.saved_model.builder.SavedModelBuilder instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:309: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
            "W0101 17:50:54.029838 140331270162304 deprecation.py:323] From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:309: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:315: The name tf.saved_model.signature_def_utils.build_signature_def is deprecated. Please use tf.compat.v1.saved_model.signature_def_utils.build_signature_def instead.\n",
            "\n",
            "W0101 17:50:54.030326 140331270162304 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:315: The name tf.saved_model.signature_def_utils.build_signature_def is deprecated. Please use tf.compat.v1.saved_model.signature_def_utils.build_signature_def instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:318: The name tf.saved_model.signature_constants.PREDICT_METHOD_NAME is deprecated. Please use tf.saved_model.PREDICT_METHOD_NAME instead.\n",
            "\n",
            "W0101 17:50:54.030527 140331270162304 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:318: The name tf.saved_model.signature_constants.PREDICT_METHOD_NAME is deprecated. Please use tf.saved_model.PREDICT_METHOD_NAME instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:323: The name tf.saved_model.tag_constants.SERVING is deprecated. Please use tf.saved_model.SERVING instead.\n",
            "\n",
            "W0101 17:50:54.030766 140331270162304 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:323: The name tf.saved_model.tag_constants.SERVING is deprecated. Please use tf.saved_model.SERVING instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:325: The name tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY is deprecated. Please use tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY instead.\n",
            "\n",
            "W0101 17:50:54.030932 140331270162304 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/exporter.py:325: The name tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY is deprecated. Please use tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY instead.\n",
            "\n",
            "INFO:tensorflow:No assets to save.\n",
            "I0101 17:50:54.031250 140331270162304 builder_impl.py:640] No assets to save.\n",
            "INFO:tensorflow:No assets to write.\n",
            "I0101 17:50:54.031355 140331270162304 builder_impl.py:460] No assets to write.\n",
            "INFO:tensorflow:SavedModel written to: My_exp_graph/saved_model/saved_model.pb\n",
            "I0101 17:50:54.402521 140331270162304 builder_impl.py:425] SavedModel written to: My_exp_graph/saved_model/saved_model.pb\n",
            "WARNING:tensorflow:From /content/gdrive/My Drive/IEDC/models/research/object_detection/utils/config_util.py:188: The name tf.gfile.Open is deprecated. Please use tf.io.gfile.GFile instead.\n",
            "\n",
            "W0101 17:50:54.437597 140331270162304 module_wrapper.py:139] From /content/gdrive/My Drive/IEDC/models/research/object_detection/utils/config_util.py:188: The name tf.gfile.Open is deprecated. Please use tf.io.gfile.GFile instead.\n",
            "\n",
            "INFO:tensorflow:Writing pipeline config file to My_exp_graph/pipeline.config\n",
            "I0101 17:50:54.437840 140331270162304 config_util.py:190] Writing pipeline config file to My_exp_graph/pipeline.config\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfV1SEuzPPMO",
        "colab_type": "code",
        "outputId": "0d6d48e4-b84f-434f-df6b-f060d22616cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        }
      },
      "source": [
        "# Zip your inference\n",
        "!zip -r MY_exp_g.zip My_exp_graph"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "updating: My_exp_graph/ (stored 0%)\n",
            "  adding: My_exp_graph/model.ckpt.data-00000-of-00001 (deflated 7%)\n",
            "  adding: My_exp_graph/model.ckpt.index (deflated 67%)\n",
            "  adding: My_exp_graph/checkpoint (deflated 42%)\n",
            "  adding: My_exp_graph/model.ckpt.meta (deflated 94%)\n",
            "  adding: My_exp_graph/frozen_inference_graph.pb (deflated 11%)\n",
            "  adding: My_exp_graph/saved_model/ (stored 0%)\n",
            "  adding: My_exp_graph/saved_model/variables/ (stored 0%)\n",
            "  adding: My_exp_graph/saved_model/saved_model.pb (deflated 11%)\n",
            "  adding: My_exp_graph/pipeline.config (deflated 70%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p9atNZdYLdH4",
        "colab_type": "code",
        "outputId": "91f2df04-4ea6-4da7-ce12-2e34f4a45051",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "############################################################################\n",
        "############################################################################\n",
        "################# Check / test the trained model ###########################\n",
        "############################################################################\n",
        "############################################################################\n",
        "\n",
        "# Copy some 9 images to folder named ‘test_images’ and rename them to \n",
        "# image1.jpg, image2.jpg, . . . , image9.jpg then run this cell.\n",
        "# --------------------------------------------------------------------------\n",
        "\n",
        "# Set constants\n",
        "MODEL_NAME = 'My_exp_graph'\n",
        "PATH_TO_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph.pb'\n",
        "PATH_TO_LABELS = 'training/object-detection.pbtxt'\n",
        "NUM_CLASSES = 3 #remember number of objects you are training? cool.\n",
        "\n",
        "\n",
        "# Then, run this\n",
        "import numpy as np\n",
        "import os\n",
        "import six.moves.urllib as urllib\n",
        "import sys\n",
        "import tarfile\n",
        "import tensorflow as tf\n",
        "import zipfile\n",
        "from distutils.version import StrictVersion\n",
        "from collections import defaultdict\n",
        "from io import StringIO\n",
        "from matplotlib import pyplot as plt\n",
        "from PIL import Image\n",
        "# This is needed since the notebook is stored in the object_detection folder.\n",
        "sys.path.append(\"..\")\n",
        "from object_detection.utils import ops as utils_ops\n",
        "%matplotlib inline\n",
        "from utils import label_map_util\n",
        "from utils import visualization_utils as vis_util\n",
        "\n",
        "detection_graph = tf.Graph()\n",
        "with detection_graph.as_default():\n",
        "  od_graph_def = tf.GraphDef()\n",
        "  with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:\n",
        "    serialized_graph = fid.read()\n",
        "    od_graph_def.ParseFromString(serialized_graph)\n",
        "    tf.import_graph_def(od_graph_def, name='')\n",
        "\n",
        "# Loading label map\n",
        "category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)\n",
        "#Helper code\n",
        "def load_image_into_numpy_array(image):\n",
        "    (im_width, im_height) = image.size\n",
        "    return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)\n",
        "# Detection -------------------------------------------------------\n",
        "# For the sake of simplicity we will use only 2 images:\n",
        "# image1.jpg\n",
        "# image2.jpg\n",
        "# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.\n",
        "PATH_TO_TEST_IMAGES_DIR = 'test_images/'\n",
        "TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 10) ] # add only 9 images as 1 <= i < 10\n",
        "# Size, in inches, of the output images.\n",
        "IMAGE_SIZE = (40, 24)\n",
        "def run_inference_for_single_image(image, graph):\n",
        "  with graph.as_default():\n",
        "    with tf.Session() as sess:\n",
        "      # Get handles to input and output tensors\n",
        "      ops = tf.get_default_graph().get_operations()\n",
        "      all_tensor_names = {output.name for op in ops for output in op.outputs}\n",
        "      tensor_dict = {}\n",
        "      for key in ['num_detections', 'detection_boxes', 'detection_scores','detection_classes', 'detection_masks']:\n",
        "        tensor_name = key + ':0'\n",
        "        if tensor_name in all_tensor_names:\n",
        "          tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(tensor_name):\\\n",
        "          if 'detection_masks' in tensor_dict:\n",
        "        # The following processing is only for single image\n",
        "            detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])\n",
        "            detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])\n",
        "        # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.\n",
        "        real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)\n",
        "        detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])\n",
        "        detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])\n",
        "        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(detection_masks, detection_boxes, image.shape[1], image.shape[2])\n",
        "        detection_masks_reframed = tf.cast(tf.greater(detection_masks_reframed, 0.5), tf.uint8)\n",
        "        # Follow the convention by adding back the batch dimension\n",
        "        tensor_dict['detection_masks'] = tf.expand_dims(detection_masks_reframed, 0)\n",
        "        image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')\n",
        "        # Run inference\n",
        "        output_dict = sess.run(tensor_dict, feed_dict={image_tensor: image})\n",
        "        # all outputs are float32 numpy arrays, so convert types as appropriate\n",
        "        output_dict['num_detections'] = int(output_dict['num_detections'][0])\n",
        "        output_dict['detection_classes'] = output_dict['detection_classes'][0].astype(np.uint8)\n",
        "        output_dict['detection_boxes'] = output_dict['detection_boxes'][0]\n",
        "        output_dict['detection_scores'] = output_dict['detection_scores'][0]\n",
        "        if 'detection_masks' in output_dict:\n",
        "          output_dict['detection_masks'] = output_dict['detection_masks'][0]\n",
        "  return output_dict\n",
        "\n",
        "\n",
        "for image_path in TEST_IMAGE_PATHS:\n",
        "image = Image.open(image_path)\n",
        "    # the array based representation of the image will be used later in order to prepare the\n",
        "    # result image with boxes and labels on it.\n",
        "    image_np = load_image_into_numpy_array(image)\n",
        "    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n",
        "    image_np_expanded = np.expand_dims(image_np, axis=0)\n",
        "    # Actual detection.\n",
        "    output_dict = run_inference_for_single_image(image_np_expanded, detection_graph)\n",
        "    # Visualization of the results of a detection.\n",
        "vis_util.visualize_boxes_and_labels_on_image_array(\n",
        "        image_np,\n",
        "        output_dict['detection_boxes'],\n",
        "        output_dict['detection_classes'],\n",
        "        output_dict['detection_scores'],\n",
        "        category_index,\n",
        "        instance_masks=output_dict.get('detection_masks'),\n",
        "        use_normalized_coordinates=True,\n",
        "        line_thickness=3)\n",
        "plt.figure(figsize=IMAGE_SIZE)\n",
        "    plt.imshow(image_np)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-46-d5cc89efc1c4>\"\u001b[0;36m, line \u001b[0;32m60\u001b[0m\n\u001b[0;31m    tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(tensor_name):          if 'detection_masks' in tensor_dict:\u001b[0m\n\u001b[0m                                                                             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KyGuOiS5UxG8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# or try this\n",
        "# https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/\n",
        "# or this: https://github.com/rishab-sharma/object_detection/blob/master/object_detection_tutorial.py"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRLfE9qkNAxr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# save python requirements.txt\n",
        "# !pip freeze > '/content/gdrive/My Drive/IEDC/tf-api-requirements.txt'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "317T0YmCU7yZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PZEaXL_VjV5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# convert to tflie"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}