Dronedetector.ipynb

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  "metadata": {
    "colab": {
      "name": "new.ipynb",
      "provenance": [],
      "mount_file_id": "1FLHtHY8BX1sWrpta2ksdHhAqNbNa8soU",
      "authorship_tag": "ABX9TyMbFBP/gQPDOS4XLQaC3l1Y",
      "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/github/srivatsan18/Drone/blob/master/new.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lcLY0XnjLFjc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "MODELS_CONFIG = {\n",
        "    'ssd_mobilenet_v2': {\n",
        "        'model_name': 'ssd_mobilenet_v2_coco_2018_03_29',\n",
        "        'pipeline_file': 'ssd_mobilenet_v2_coco.config',\n",
        "    },\n",
        "    'faster_rcnn_inception_v2': {\n",
        "        'model_name': 'faster_rcnn_inception_v2_coco_2018_01_28',\n",
        "        'pipeline_file': 'faster_rcnn_inception_v2_pets.config',\n",
        "    },\n",
        "    'rfcn_resnet101': {\n",
        "        'model_name': 'rfcn_resnet101_coco_2018_01_28',\n",
        "        'pipeline_file': 'rfcn_resnet101_pets.config',\n",
        "    }\n",
        "}\n",
        "\n",
        "# Select a model in `MODELS_CONFIG`.\n",
        "# I chose ssd_mobilenet_v2 for this project, you could choose any\n",
        "selected_model = 'ssd_mobilenet_v2'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UFlrkRiILPa4",
        "colab_type": "code",
        "outputId": "20fcb875-07c3-43a9-b548-7608cf069ea8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        }
      },
      "source": [
        "!apt-get install -qq protobuf-compiler python-pil python-lxml python-tk\n",
        "\n",
        "!pip install -qq Cython contextlib2 pillow lxml matplotlib\n",
        "\n",
        "!pip install -qq pycocotools"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Selecting previously unselected package python-bs4.\n",
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            "Preparing to unpack .../0-python-bs4_4.6.0-1_all.deb ...\n",
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            "Preparing to unpack .../4-python-webencodings_0.5-2_all.deb ...\n",
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            "Preparing to unpack .../8-python-pil_5.1.0-1ubuntu0.2_amd64.deb ...\n",
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            "Setting up python-pkg-resources (39.0.1-2) ...\n",
            "Setting up python-six (1.11.0-2) ...\n",
            "Setting up python-bs4 (4.6.0-1) ...\n",
            "Setting up python-lxml:amd64 (4.2.1-1ubuntu0.1) ...\n",
            "Setting up python-olefile (0.45.1-1) ...\n",
            "Setting up python-pil:amd64 (5.1.0-1ubuntu0.2) ...\n",
            "Setting up python-webencodings (0.5-2) ...\n",
            "Setting up python-chardet (3.0.4-1) ...\n",
            "Setting up python-html5lib (0.999999999-1) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXhTQRl7LYQ6",
        "colab_type": "code",
        "outputId": "1184b7fa-03fb-4744-d406-a27adbe819e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 62
        }
      },
      "source": [
        "from __future__ import division, print_function, absolute_import\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import csv\n",
        "import re\n",
        "import cv2 \n",
        "import os\n",
        "import glob\n",
        "import xml.etree.ElementTree as ET\n",
        "\n",
        "import io\n",
        "import tensorflow.compat.v1 as tf\n",
        "\n",
        "from PIL import Image\n",
        "from collections import namedtuple, OrderedDict\n",
        "\n",
        "import shutil\n",
        "import urllib.request\n",
        "import tarfile\n",
        "\n",
        "from google.colab import files"
      ],
      "execution_count": 3,
      "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": []
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gSwDKbZbm9_h",
        "colab_type": "code",
        "outputId": "c096602f-e921-4f4f-fa17-2c383f0d930e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd /content/drive/My Drive"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ffX9IAFnLhz5",
        "colab_type": "code",
        "outputId": "97b6b19c-41f9-49e1-fd03-0780d0b11c63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "!git clone https://github.com/srivatsan18/Drone.git"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Drone'...\n",
            "remote: Enumerating objects: 1929, done.\u001b[K\n",
            "remote: Total 1929 (delta 0), reused 0 (delta 0), pack-reused 1929\u001b[K\n",
            "Receiving objects: 100% (1929/1929), 76.34 MiB | 32.29 MiB/s, done.\n",
            "Resolving deltas: 100% (964/964), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wXNXv_L8LmvR",
        "colab_type": "code",
        "outputId": "9677825b-463f-4eb3-995a-54d0a015fd11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd Drone"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Drone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4-Yi54kLrC1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgAY4ZISMYIz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir data/images data/train_labels data/test_labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3RCwuXVEMbi0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mv images/* data/images"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMski3z5MfhE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mv annotations/* data/train_labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQ9cgAWXMi9o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!ls data/train_labels/* | sort -R | head -250 | xargs -I{} mv {} data/test_labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHhUyGmcMlwP",
        "colab_type": "code",
        "outputId": "14c3a361-0717-4f2a-9670-e466979579c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ls -1 data/train_labels/ | wc -l"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "724\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ngdiEcIMqAZ",
        "colab_type": "code",
        "outputId": "0ea4ca3e-e2ca-4bc6-bd18-1332e604d974",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ls -1 data/test_labels/ | wc -l"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "250\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXQqcHUtMv5y",
        "colab_type": "code",
        "outputId": "d073615d-a006-4b9c-ab8b-f797fb8f8271",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd /content/Drone/data"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Drone/data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LhML1J--M0yl",
        "colab_type": "code",
        "outputId": "de2a90b0-2b2e-4934-c2bc-7acd0c7efbff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# images extension\n",
        "images_extension = ''\n",
        "\n",
        "# takes the path of a directory that contains xml files and converts\n",
        "#  them to one csv file.\n",
        "\n",
        "# returns a csv file that contains: image name, width, height, class, xmin, ymin, xmax, ymax.\n",
        "# note: if the xml file contains more than one box/label, it will create more than one row for the same image. each row contains the info for an individual box. \n",
        "def xml_to_csv(path):\n",
        "  classes_names = []\n",
        "  xml_list = []\n",
        "\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",
        "      classes_names.append(member[0].text)\n",
        "      value = (root.find('filename').text + images_extension,\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",
        "      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",
        "  classes_names = list(set(classes_names))\n",
        "  classes_names.sort()\n",
        "  return xml_df, classes_names\n",
        "\n",
        "# for both the train_labels and test_labels csv files, it runs the xml_to_csv() above.\n",
        "for label_path in ['train_labels', 'test_labels']:\n",
        "  image_path = os.path.join(os.getcwd(), label_path)\n",
        "  xml_df, classes = xml_to_csv(label_path)\n",
        "  xml_df.to_csv(f'{label_path}.csv', index=None)\n",
        "  print(f'Successfully converted {label_path} xml to csv.')\n",
        "\n",
        "# Creating the `label_map.pbtxt` file\n",
        "label_map_path = os.path.join(\"label_map.pbtxt\")\n",
        "\n",
        "pbtxt_content = \"\"\n",
        "\n",
        "#creats a pbtxt file the has the class names.\n",
        "for i, class_name in enumerate(classes):\n",
        "    # display_name is optional.\n",
        "    pbtxt_content = (\n",
        "        pbtxt_content\n",
        "        + \"item {{\\n    id: {0}\\n    name: '{1}'\\n    display_name: 'Drone'\\n }}\\n\\n\".format(i + 1, class_name)\n",
        "    )\n",
        "pbtxt_content = pbtxt_content.strip()\n",
        "with open(label_map_path, \"w\") as f:\n",
        "    f.write(pbtxt_content)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully converted train_labels xml to csv.\n",
            "Successfully converted test_labels xml to csv.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QvHlc7zxM-ip",
        "colab_type": "code",
        "outputId": "b2ceea51-9a99-4b85-bbcf-4b1a29affb25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "!cat label_map.pbtxt"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "item {\n",
            "    id: 1\n",
            "    name: 'd'\n",
            "    display_name: 'Drone'\n",
            " }"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8I2Gf7dNNEqf",
        "colab_type": "code",
        "outputId": "70c56be3-0070-4f05-9466-6a391267e205",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 722
        }
      },
      "source": [
        "#checks if the images box position is placed within the image.\n",
        "\n",
        "#note: while this doesn't checks if the boxes/annotatoins are correctly\n",
        "# placed around the object, Tensorflow will through an error if this occured.\n",
        "%cd /content/Drone/data\n",
        "# path to images\n",
        "images_path = 'images'\n",
        "\n",
        "#loops over both train_labels and test_labels csv files to do the check\n",
        "# returns the image name where an error is found \n",
        "# return the incorrect attributes; xmin, ymin, xmax, ymax.\n",
        "for CSV_FILE in ['train_labels.csv', 'test_labels.csv']:\n",
        "  with open(CSV_FILE, 'r') as fid:  \n",
        "      print('[*] Checking file:', CSV_FILE) \n",
        "      file = csv.reader(fid, delimiter=',')\n",
        "      first = True \n",
        "      cnt = 0\n",
        "      error_cnt = 0\n",
        "      error = False\n",
        "      for row in file:\n",
        "          if error == True:\n",
        "              error_cnt += 1\n",
        "              error = False         \n",
        "          if first == True:\n",
        "              first = False\n",
        "              continue     \n",
        "          cnt += 1      \n",
        "          name, width, height, xmin, ymin, xmax, ymax = row[0], int(row[1]), int(row[2]), int(row[4]), int(row[5]), int(row[6]), int(row[7])     \n",
        "          path = os.path.join(images_path, name)\n",
        "          img = cv2.imread(path)         \n",
        "          if type(img) == type(None):\n",
        "              error = True\n",
        "              print('Could not read image', img)\n",
        "              continue     \n",
        "          org_height, org_width = img.shape[:2]     \n",
        "          if org_width != width:\n",
        "              error = True\n",
        "              print('Width mismatch for image: ', name, width, '!=', org_width)     \n",
        "          if org_height != height:\n",
        "              error = True\n",
        "              print('Height mismatch for image: ', name, height, '!=', org_height) \n",
        "          if xmin > org_width:\n",
        "              error = True\n",
        "              print('XMIN > org_width for file', name)  \n",
        "          if xmax > org_width:\n",
        "              error = True\n",
        "              print('XMAX > org_width for file', name)\n",
        "          if ymin > org_height:\n",
        "              error = True\n",
        "              print('YMIN > org_height for file', name)\n",
        "          if ymax > org_height:\n",
        "              error = True\n",
        "              print('YMAX > org_height for file', name)\n",
        "          if error == True:\n",
        "              print('Error for file: %s' % name)\n",
        "              print()\n",
        "      print()\n",
        "      print('Checked %d files and realized %d errors' % (cnt, error_cnt))\n",
        "      print(\"-----\")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Drone/data\n",
            "[*] Checking file: train_labels.csv\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "\n",
            "Checked 769 files and realized 26 errors\n",
            "-----\n",
            "[*] Checking file: test_labels.csv\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "Could not read image None\n",
            "\n",
            "Checked 289 files and realized 7 errors\n",
            "-----\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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        "id": "S0JI_p4bNVXy",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Downlaods Tenorflow\n",
        "%cd /content/Drone/\n",
        "!git clone --q https://github.com/tensorflow/models.git"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Drone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEIyjStvOKLX",
        "colab_type": "code",
        "outputId": "edd3691f-3b3d-4a21-bb0b-250fa54f2cdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd /content/Drone/models/research\n",
        "#compiling the proto buffers (not important to understand for this project but you can learn more about them here: https://developers.google.com/protocol-buffers/)\n",
        "!protoc object_detection/protos/*.proto --python_out=.\n",
        "\n",
        "# exports the PYTHONPATH environment variable with the reasearch and slim folders' paths\n",
        "os.environ['PYTHONPATH'] += ':/content/Drone/models/research/:/content/Drone/models/research/slim/'"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Drone/models/research\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wG-P-bw2OPJY",
        "colab_type": "code",
        "outputId": "ce161587-59d9-478d-ff67-9619d3e356c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 806
        }
      },
      "source": [
        "!python3 object_detection/builders/model_builder_test.py"
      ],
      "execution_count": 24,
      "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",
            "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 0.179s\n",
            "\n",
            "OK (skipped=1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMZ6kz-aOUjZ",
        "colab_type": "code",
        "outputId": "b72d570b-7c75-41ae-f498-d9b1ee86f35a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "#adjusted from: https://github.com/datitran/raccoon_dataset\n",
        "\n",
        "# converts the csv files for training and testing data to two TFRecords files.\n",
        "# places the output in the same directory as the input\n",
        "\n",
        "\n",
        "from object_detection.utils import dataset_util\n",
        "%cd /content/Drone/models/\n",
        "\n",
        "DATA_BASE_PATH = '/content/Drone/data/'\n",
        "image_dir = DATA_BASE_PATH +'images/'\n",
        "\n",
        "def class_text_to_int(row_label):\n",
        "\t\tif row_label == 'd':\n",
        "\t\t\t\treturn 1\n",
        "\t\telse:\n",
        "\t\t\t\tNone\n",
        "\n",
        "\n",
        "def split(df, group):\n",
        "\t\tdata = namedtuple('data', ['filename', 'object'])\n",
        "\t\tgb = df.groupby(group)\n",
        "\t\treturn [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)]\n",
        "\n",
        "def create_tf_example(group, path):\n",
        "\t\twith tf.io.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:\n",
        "\t\t\t\tencoded_jpg = fid.read()\n",
        "\t\tencoded_jpg_io = io.BytesIO(encoded_jpg)\n",
        "\t\timage = Image.open(encoded_jpg_io)\n",
        "\t\twidth, height = image.size\n",
        "\n",
        "\t\tfilename = group.filename.encode('utf8')\n",
        "\t\timage_format = b'jpg'\n",
        "\t\txmins = []\n",
        "\t\txmaxs = []\n",
        "\t\tymins = []\n",
        "\t\tymaxs = []\n",
        "\t\tclasses_text = []\n",
        "\t\tclasses = []\n",
        "\n",
        "\t\tfor index, row in group.object.iterrows():\n",
        "\t\t\t\txmins.append(row['xmin'] / width)\n",
        "\t\t\t\txmaxs.append(row['xmax'] / width)\n",
        "\t\t\t\tymins.append(row['ymin'] / height)\n",
        "\t\t\t\tymaxs.append(row['ymax'] / height)\n",
        "\t\t\t\tclasses_text.append(row['class'].encode('utf8'))\n",
        "\t\t\t\tclasses.append(class_text_to_int(row['class']))\n",
        "\n",
        "\t\ttf_example = tf.train.Example(features=tf.train.Features(feature={\n",
        "\t\t\t\t'image/height': dataset_util.int64_feature(height),\n",
        "\t\t\t\t'image/width': dataset_util.int64_feature(width),\n",
        "\t\t\t\t'image/filename': dataset_util.bytes_feature(filename),\n",
        "\t\t\t\t'image/source_id': dataset_util.bytes_feature(filename),\n",
        "\t\t\t\t'image/encoded': dataset_util.bytes_feature(encoded_jpg),\n",
        "\t\t\t\t'image/format': dataset_util.bytes_feature(image_format),\n",
        "\t\t\t\t'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),\n",
        "\t\t\t\t'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),\n",
        "\t\t\t\t'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),\n",
        "\t\t\t\t'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),\n",
        "\t\t\t\t'image/object/class/text': dataset_util.bytes_list_feature(classes_text),\n",
        "\t\t\t\t'image/object/class/label': dataset_util.int64_list_feature(classes),\n",
        "\t\t}))\n",
        "\t\treturn tf_example\n",
        "\n",
        "for csv in ['train_labels', 'test_labels']:\n",
        "  writer = tf.io.TFRecordWriter(DATA_BASE_PATH + csv + '.record')\n",
        "  path = os.path.join(image_dir)\n",
        "  examples = pd.read_csv(DATA_BASE_PATH + csv + '.csv')\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(), DATA_BASE_PATH + csv + '.record')\n",
        "  print('Successfully created the TFRecords: {}'.format(DATA_BASE_PATH +csv + '.record'))"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Drone/models\n",
            "Successfully created the TFRecords: /content/Drone/data/train_labels.record\n",
            "Successfully created the TFRecords: /content/Drone/data/test_labels.record\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d4a2oTtwOfkm",
        "colab_type": "code",
        "outputId": "e926eea1-d951-4cba-8b81-edd8b382bb51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "|#we have only one image with incorrect box position, we could just remove it \n",
        "#removing the image \n",
        "rm images/'934.jpg'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove 'images/934.jpg': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rlf8U_E8OuMn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#removing the entry for it in the csv for that image as well\n",
        "\n",
        "#because we did a random split for the data, we dont know if it ended up being in training or testing\n",
        "# we will remove the image from both.\n",
        "\n",
        "#training\n",
        "#reading the training csv\n",
        "df = pd.read_csv('/content/Drone/data/train_labels.csv')\n",
        "# removing armas (2815).jpg\n",
        "df = df[df['filename'] != '924.png']\n",
        "#reseting the index\n",
        "df.reset_index(drop=True, inplace=True)\n",
        "#saving the df\n",
        "df.to_csv('/content/Drone/data/train_labels.csv')\n",
        "\n",
        "\n",
        "#testing\n",
        "#reading the testing csv\n",
        "df = pd.read_csv('/content/Drone/data/test_labels.csv')\n",
        "# removing armas (2815).jpg\n",
        "df = df[df['filename'] != '924.png']\n",
        "#reseting the index\n",
        "df.reset_index(drop=True, inplace=True)\n",
        "#saving the df\n",
        "df.to_csv('/content/Drone/data/test_labels.csv')\n",
        "\n",
        "# Just for the memory\n",
        "df = None\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NnW6Ms8PD1b",
        "colab_type": "code",
        "outputId": "7354a23d-74f2-440b-ee3e-cc0eca802e21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "ls -lX /content/Drone/data/"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 78216\n",
            "drwxr-xr-x 2 root root    20480 Mar 16 15:39 \u001b[0m\u001b[01;34mimages\u001b[0m/\n",
            "drwxr-xr-x 2 root root     4096 Mar 16 15:39 \u001b[01;34mtest_labels\u001b[0m/\n",
            "drwxr-xr-x 2 root root    24576 Mar 16 15:39 \u001b[01;34mtrain_labels\u001b[0m/\n",
            "-rw-r--r-- 1 root root    48844 Mar 16 16:01 test_labels.csv\n",
            "-rw-r--r-- 1 root root   132424 Mar 16 16:01 train_labels.csv\n",
            "-rw-r--r-- 1 root root       59 Mar 16 15:40 label_map.pbtxt\n",
            "-rw-r--r-- 1 root root 21600684 Mar 16 16:01 test_labels.record\n",
            "-rw-r--r-- 1 root root 58253255 Mar 16 16:01 train_labels.record\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELfHStonUAe_",
        "colab_type": "code",
        "outputId": "8e904d64-baff-40a0-8ca7-bc90584d8418",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd /content/Drone/models/research\n",
        "\n",
        "# Name of the object detection model to use.\n",
        "MODEL = MODELS_CONFIG[selected_model]['model_name']\n",
        "\n",
        "# Name of the pipline file in tensorflow object detection API.\n",
        "pipeline_file = MODELS_CONFIG[selected_model]['pipeline_file']\n",
        "\n",
        "#selecting the model\n",
        "MODEL_FILE = MODEL + '.tar.gz'\n",
        "\n",
        "#creating the downlaod link for the model selected\n",
        "DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'\n",
        "\n",
        "#the distination folder where the model will be saved\n",
        "fine_tune_dir = '/content/Drone/models/research/pretrained_model'\n",
        "\n",
        "#checks if the model has already been downloaded\n",
        "if not (os.path.exists(MODEL_FILE)):\n",
        "    urllib.request.urlretrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)\n",
        "\n",
        "#unzipping the file and extracting its content\n",
        "tar = tarfile.open(MODEL_FILE)\n",
        "tar.extractall()\n",
        "tar.close()\n",
        "os.remove(MODEL_FILE)\n",
        "if (os.path.exists(fine_tune_dir)):\n",
        "    shutil.rmtree(fine_tune_dir)\n",
        "os.rename(MODEL, fine_tune_dir)\n"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Drone/models/research\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsh1CgFkUW8E",
        "colab_type": "code",
        "outputId": "028213d4-dd73-4ce6-ffa5-25b2f06d0a8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "#checking the content of the pretrained model.\n",
        "# this is the directory of the \"fine_tune_checkpoint\" that is used in the config file.\n",
        "!echo {fine_tune_dir}\n",
        "!ls -alh {fine_tune_dir}"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Drone/models/research/pretrained_model\n",
            "total 135M\n",
            "drwxr-xr-x  3 345018 89939 4.0K Mar 30  2018 .\n",
            "drwxr-xr-x 64 root   root  4.0K Mar 16 16:02 ..\n",
            "-rw-r--r--  1 345018 89939   77 Mar 30  2018 checkpoint\n",
            "-rw-r--r--  1 345018 89939  67M Mar 30  2018 frozen_inference_graph.pb\n",
            "-rw-r--r--  1 345018 89939  65M Mar 30  2018 model.ckpt.data-00000-of-00001\n",
            "-rw-r--r--  1 345018 89939  15K Mar 30  2018 model.ckpt.index\n",
            "-rw-r--r--  1 345018 89939 3.4M Mar 30  2018 model.ckpt.meta\n",
            "-rw-r--r--  1 345018 89939 4.2K Mar 30  2018 pipeline.config\n",
            "drwxr-xr-x  3 345018 89939 4.0K Mar 30  2018 saved_model\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQLBnxMMUaeJ",
        "colab_type": "code",
        "outputId": "434550a9-d64c-426c-f299-7d75d3ce2cc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#the path to the folder containing all the sample config files\n",
        "CONFIG_BASE = \"/content/Drone/models/research/object_detection/samples/configs/\"\n",
        "\n",
        "#path to the specified model's config file\n",
        "model_pipline = os.path.join(CONFIG_BASE, pipeline_file)\n",
        "model_pipline"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/Drone/models/research/object_detection/samples/configs/ssd_mobilenet_v2_coco.config'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EnKFeD2mUh45",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "72354c19-f02e-47d6-9500-1c2e3e088869"
      },
      "source": [
        "#check the sample config file that is provided by the tf model\n",
        "!cat /content/Drone/models/research/object_detection/samples/configs/ssd_mobilenet_v2_coco.config"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "# SSD with Mobilenet v2 configuration for MSCOCO Dataset.\n",
            "# Users should configure the fine_tune_checkpoint field in the train config as\n",
            "# well as the label_map_path and input_path fields in the train_input_reader and\n",
            "# eval_input_reader. Search for \"PATH_TO_BE_CONFIGURED\" to find the fields that\n",
            "# should be configured.\n",
            "\n",
            "model {\n",
            "  ssd {\n",
            "    num_classes: 90\n",
            "    box_coder {\n",
            "      faster_rcnn_box_coder {\n",
            "        y_scale: 10.0\n",
            "        x_scale: 10.0\n",
            "        height_scale: 5.0\n",
            "        width_scale: 5.0\n",
            "      }\n",
            "    }\n",
            "    matcher {\n",
            "      argmax_matcher {\n",
            "        matched_threshold: 0.5\n",
            "        unmatched_threshold: 0.5\n",
            "        ignore_thresholds: false\n",
            "        negatives_lower_than_unmatched: true\n",
            "        force_match_for_each_row: true\n",
            "      }\n",
            "    }\n",
            "    similarity_calculator {\n",
            "      iou_similarity {\n",
            "      }\n",
            "    }\n",
            "    anchor_generator {\n",
            "      ssd_anchor_generator {\n",
            "        num_layers: 6\n",
            "        min_scale: 0.2\n",
            "        max_scale: 0.95\n",
            "        aspect_ratios: 1.0\n",
            "        aspect_ratios: 2.0\n",
            "        aspect_ratios: 0.5\n",
            "        aspect_ratios: 3.0\n",
            "        aspect_ratios: 0.3333\n",
            "      }\n",
            "    }\n",
            "    image_resizer {\n",
            "      fixed_shape_resizer {\n",
            "        height: 300\n",
            "        width: 300\n",
            "      }\n",
            "    }\n",
            "    box_predictor {\n",
            "      convolutional_box_predictor {\n",
            "        min_depth: 0\n",
            "        max_depth: 0\n",
            "        num_layers_before_predictor: 0\n",
            "        use_dropout: false\n",
            "        dropout_keep_probability: 0.8\n",
            "        kernel_size: 1\n",
            "        box_code_size: 4\n",
            "        apply_sigmoid_to_scores: false\n",
            "        conv_hyperparams {\n",
            "          activation: RELU_6,\n",
            "          regularizer {\n",
            "            l2_regularizer {\n",
            "              weight: 0.00004\n",
            "            }\n",
            "          }\n",
            "          initializer {\n",
            "            truncated_normal_initializer {\n",
            "              stddev: 0.03\n",
            "              mean: 0.0\n",
            "            }\n",
            "          }\n",
            "          batch_norm {\n",
            "            train: true,\n",
            "            scale: true,\n",
            "            center: true,\n",
            "            decay: 0.9997,\n",
            "            epsilon: 0.001,\n",
            "          }\n",
            "        }\n",
            "      }\n",
            "    }\n",
            "    feature_extractor {\n",
            "      type: 'ssd_mobilenet_v2'\n",
            "      min_depth: 16\n",
            "      depth_multiplier: 1.0\n",
            "      conv_hyperparams {\n",
            "        activation: RELU_6,\n",
            "        regularizer {\n",
            "          l2_regularizer {\n",
            "            weight: 0.00004\n",
            "          }\n",
            "        }\n",
            "        initializer {\n",
            "          truncated_normal_initializer {\n",
            "            stddev: 0.03\n",
            "            mean: 0.0\n",
            "          }\n",
            "        }\n",
            "        batch_norm {\n",
            "          train: true,\n",
            "          scale: true,\n",
            "          center: true,\n",
            "          decay: 0.9997,\n",
            "          epsilon: 0.001,\n",
            "        }\n",
            "      }\n",
            "    }\n",
            "    loss {\n",
            "      classification_loss {\n",
            "        weighted_sigmoid {\n",
            "        }\n",
            "      }\n",
            "      localization_loss {\n",
            "        weighted_smooth_l1 {\n",
            "        }\n",
            "      }\n",
            "      hard_example_miner {\n",
            "        num_hard_examples: 3000\n",
            "        iou_threshold: 0.99\n",
            "        loss_type: CLASSIFICATION\n",
            "        max_negatives_per_positive: 3\n",
            "        min_negatives_per_image: 3\n",
            "      }\n",
            "      classification_weight: 1.0\n",
            "      localization_weight: 1.0\n",
            "    }\n",
            "    normalize_loss_by_num_matches: true\n",
            "    post_processing {\n",
            "      batch_non_max_suppression {\n",
            "        score_threshold: 1e-8\n",
            "        iou_threshold: 0.6\n",
            "        max_detections_per_class: 100\n",
            "        max_total_detections: 100\n",
            "      }\n",
            "      score_converter: SIGMOID\n",
            "    }\n",
            "  }\n",
            "}\n",
            "\n",
            "train_config: {\n",
            "  batch_size: 24\n",
            "  optimizer {\n",
            "    rms_prop_optimizer: {\n",
            "      learning_rate: {\n",
            "        exponential_decay_learning_rate {\n",
            "          initial_learning_rate: 0.004\n",
            "          decay_steps: 800720\n",
            "          decay_factor: 0.95\n",
            "        }\n",
            "      }\n",
            "      momentum_optimizer_value: 0.9\n",
            "      decay: 0.9\n",
            "      epsilon: 1.0\n",
            "    }\n",
            "  }\n",
            "  fine_tune_checkpoint: \"PATH_TO_BE_CONFIGURED/model.ckpt\"\n",
            "  fine_tune_checkpoint_type:  \"detection\"\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",
            "  data_augmentation_options {\n",
            "    random_horizontal_flip {\n",
            "    }\n",
            "  }\n",
            "  data_augmentation_options {\n",
            "    ssd_random_crop {\n",
            "    }\n",
            "  }\n",
            "}\n",
            "\n",
            "train_input_reader: {\n",
            "  tf_record_input_reader {\n",
            "    input_path: \"PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100\"\n",
            "  }\n",
            "  label_map_path: \"PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt\"\n",
            "}\n",
            "\n",
            "eval_config: {\n",
            "  num_examples: 8000\n",
            "  # Note: The below line limits the evaluation process to 10 evaluations.\n",
            "  # Remove the below line to evaluate indefinitely.\n",
            "  max_evals: 10\n",
            "}\n",
            "\n",
            "eval_input_reader: {\n",
            "  tf_record_input_reader {\n",
            "    input_path: \"PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010\"\n",
            "  }\n",
            "  label_map_path: \"PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt\"\n",
            "  shuffle: false\n",
            "  num_readers: 1\n",
            "}"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gfgkrUOhU5vO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kfsl5CsDGY3-",
        "colab_type": "code",
        "outputId": "070b2059-1431-4f58-ee3c-ed63cd933d9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#editing the configuration file to add the path for the TFRecords files, pbtxt,batch_size,num_steps,num_classes.\n",
        "# any image augmentation, hyperparemeter tunning (drop out, batch normalization... etc) would be editted here\n",
        "\n",
        "%%writefile {model_pipline}\n",
        "model {\n",
        "  ssd {\n",
        "    num_classes: 1 # number of classes to be detected\n",
        "    box_coder {\n",
        "      faster_rcnn_box_coder {\n",
        "        y_scale: 10.0\n",
        "        x_scale: 10.0\n",
        "        height_scale: 5.0\n",
        "        width_scale: 5.0\n",
        "      }\n",
        "    }\n",
        "    matcher {\n",
        "      argmax_matcher {\n",
        "        matched_threshold: 0.5\n",
        "        unmatched_threshold: 0.5\n",
        "        ignore_thresholds: false\n",
        "        negatives_lower_than_unmatched: true\n",
        "        force_match_for_each_row: true\n",
        "      }\n",
        "    }\n",
        "    similarity_calculator {\n",
        "      iou_similarity {\n",
        "      }\n",
        "    }\n",
        "    anchor_generator {\n",
        "      ssd_anchor_generator {\n",
        "        num_layers: 6\n",
        "        min_scale: 0.2\n",
        "        max_scale: 0.95\n",
        "        aspect_ratios: 1.0\n",
        "        aspect_ratios: 2.0\n",
        "        aspect_ratios: 0.5\n",
        "        aspect_ratios: 3.0\n",
        "        aspect_ratios: 0.3333\n",
        "      }\n",
        "    }\n",
        "    # all images will be resized to the below W x H.\n",
        "    image_resizer { \n",
        "      fixed_shape_resizer {\n",
        "        height: 300\n",
        "        width: 300\n",
        "      }\n",
        "    }\n",
        "    box_predictor {\n",
        "      convolutional_box_predictor {\n",
        "        min_depth: 0\n",
        "        max_depth: 0\n",
        "        num_layers_before_predictor: 0\n",
        "        #use_dropout: false\n",
        "        use_dropout: true # to counter over fitting. you can also try tweaking its probability below\n",
        "        dropout_keep_probability: 0.8\n",
        "        kernel_size: 1\n",
        "        box_code_size: 4\n",
        "        apply_sigmoid_to_scores: false\n",
        "        conv_hyperparams {\n",
        "          activation: RELU_6,\n",
        "          regularizer {\n",
        "            l2_regularizer {\n",
        "            # weight: 0.00004\n",
        "            weight: 0.001 # higher regularizition to counter overfitting\n",
        "          }\n",
        "          }\n",
        "          initializer {\n",
        "            truncated_normal_initializer {\n",
        "              stddev: 0.03\n",
        "              mean: 0.0\n",
        "            }\n",
        "          }\n",
        "          batch_norm {\n",
        "            train: true,\n",
        "            scale: true,\n",
        "            center: true,\n",
        "            decay: 0.9997,\n",
        "            epsilon: 0.001,\n",
        "          }\n",
        "        }\n",
        "      }\n",
        "    }\n",
        "    feature_extractor {\n",
        "      type: 'ssd_mobilenet_v2'\n",
        "      min_depth: 16\n",
        "      depth_multiplier: 1.0\n",
        "      conv_hyperparams {\n",
        "        activation: RELU_6,\n",
        "        regularizer {\n",
        "          l2_regularizer {\n",
        "            # weight: 0.00004\n",
        "            weight: 0.001 # higher regularizition to counter overfitting\n",
        "          }\n",
        "        }\n",
        "        initializer {\n",
        "          truncated_normal_initializer {\n",
        "            stddev: 0.03\n",
        "            mean: 0.0\n",
        "          }\n",
        "        }\n",
        "        batch_norm {\n",
        "          train: true,\n",
        "          scale: true,\n",
        "          center: true,\n",
        "          decay: 0.9997,\n",
        "          epsilon: 0.001,\n",
        "        }\n",
        "      }\n",
        "    }\n",
        "    loss {\n",
        "      classification_loss {\n",
        "        weighted_sigmoid {\n",
        "        }\n",
        "      }\n",
        "      localization_loss {\n",
        "        weighted_smooth_l1 {\n",
        "        }\n",
        "      }\n",
        "      hard_example_miner {\n",
        "        num_hard_examples: 3000 \n",
        "        iou_threshold: 0.95\n",
        "        loss_type: CLASSIFICATION\n",
        "        max_negatives_per_positive: 3\n",
        "        min_negatives_per_image: 3\n",
        "      }\n",
        "      classification_weight: 1.0\n",
        "      localization_weight: 1.0\n",
        "    }\n",
        "    normalize_loss_by_num_matches: true\n",
        "    post_processing {\n",
        "      batch_non_max_suppression {\n",
        "        score_threshold: 1e-8\n",
        "        iou_threshold: 0.6\n",
        "        \n",
        "        #adjust this to the max number of objects per class. \n",
        "        # ex, in my case, i have one pistol in most of the images.\n",
        "        # . there are some images with more than one up to 16.\n",
        "        max_detections_per_class: 16\n",
        "        # max number of detections among all classes. I have 1 class only so\n",
        "        max_total_detections: 16\n",
        "      }\n",
        "      score_converter: SIGMOID\n",
        "    }\n",
        "  }\n",
        "}\n",
        "\n",
        "train_config: {\n",
        "  batch_size: 16 # training batch size\n",
        "  optimizer {\n",
        "    rms_prop_optimizer: {\n",
        "      learning_rate: {\n",
        "        exponential_decay_learning_rate {\n",
        "          initial_learning_rate: 0.003\n",
        "          decay_steps: 800720\n",
        "          decay_factor: 0.95\n",
        "        }\n",
        "      }\n",
        "      momentum_optimizer_value: 0.9\n",
        "      decay: 0.9\n",
        "      epsilon: 1.0\n",
        "    }\n",
        "  }\n",
        "\n",
        "  #the path to the pretrained model. \n",
        "  fine_tune_checkpoint: \"/content/Drone/models/research/pretrained_model/model.ckpt\"\n",
        "  fine_tune_checkpoint_type:  \"detection\"\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: 2000 \n",
        "  \n",
        "\n",
        "  #data augmentaion is done here, you can remove or add more.\n",
        "  # They will help the model generalize but the training time will increase greatly by using more data augmentation.\n",
        "  # Check this link to add more image augmentation: https://github.com/tensorflow/models/blob/master/research/object_detection/protos/preprocessor.proto\n",
        "  \n",
        "  data_augmentation_options {\n",
        "    random_horizontal_flip {\n",
        "    }\n",
        "  }\n",
        "  data_augmentation_options {\n",
        "    random_adjust_contrast {\n",
        "    }\n",
        "  }\n",
        "  data_augmentation_options {\n",
        "    ssd_random_crop {\n",
        "    }\n",
        "  }\n",
        "}\n",
        "\n",
        "train_input_reader: {\n",
        "  tf_record_input_reader {\n",
        "    #path to the training TFRecord\n",
        "    input_path: \"/content/Drone/data/train_labels.record\"\n",
        "  }\n",
        "  #path to the label map \n",
        "  label_map_path: \"/content/Drone/data/label_map.pbtxt\"\n",
        "}\n",
        "\n",
        "eval_config: {\n",
        "  # the number of images in your \"testing\" data (was 600 but we removed one above :) )\n",
        "  num_examples: 137\n",
        "  # the number of images to disply in Tensorboard while training\n",
        "  num_visualizations: 20\n",
        "\n",
        "  # Note: The below line limits the evaluation process to 10 evaluations.\n",
        "  # Remove the below line to evaluate indefinitely.\n",
        "  #max_evals: 10\n",
        "}\n",
        "\n",
        "eval_input_reader: {\n",
        "  tf_record_input_reader {\n",
        "      \n",
        "    #path to the testing TFRecord\n",
        "    input_path: \"/content/Drone/data/test_labels.record\"\n",
        "  }\n",
        "  #path to the label map \n",
        "  label_map_path: \"/content/Drone/data/label_map.pbtxt\"\n",
        "  shuffle: false\n",
        "  num_readers: 1\n",
        "}"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Overwriting /content/Drone/models/research/object_detection/samples/configs/ssd_mobilenet_v2_coco.config\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S7Kyv9J3VN_X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# where the model will be saved at each checkpoint while training \n",
        "model_dir = 'training/'\n",
        "\n",
        "# Optionally: remove content in output model directory to fresh start.\n",
        "!rm -rf {model_dir}\n",
        "os.makedirs(model_dir, exist_ok=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ihC3HbCTVSY0",
        "colab_type": "code",
        "outputId": "dd42c88a-05d8-40b4-f501-31d46b693954",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "#downlaoding ngrok to be able to access tensorboard on google colab\n",
        "!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
        "!unzip -o ngrok-stable-linux-amd64.zip"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-03-16 16:05:44--  https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
            "Resolving bin.equinox.io (bin.equinox.io)... 34.192.215.160, 34.196.12.177, 52.72.59.23, ...\n",
            "Connecting to bin.equinox.io (bin.equinox.io)|34.192.215.160|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 13773305 (13M) [application/octet-stream]\n",
            "Saving to: ‘ngrok-stable-linux-amd64.zip’\n",
            "\n",
            "ngrok-stable-linux- 100%[===================>]  13.13M  14.0MB/s    in 0.9s    \n",
            "\n",
            "2020-03-16 16:05:46 (14.0 MB/s) - ‘ngrok-stable-linux-amd64.zip’ saved [13773305/13773305]\n",
            "\n",
            "Archive:  ngrok-stable-linux-amd64.zip\n",
            "  inflating: ngrok                   \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5c1FO-HKVYhp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#the logs that are created while training \n",
        "LOG_DIR = model_dir\n",
        "get_ipython().system_raw(\n",
        "    'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'\n",
        "    .format(LOG_DIR)\n",
        ")\n",
        "get_ipython().system_raw('./ngrok http 6006 &')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYhwqY2OVreS",
        "colab_type": "code",
        "outputId": "0a9f5bd5-4137-4a30-8228-57467f4a32b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#The link to tensorboard.\n",
        "#works after the training starts.\n",
        "\n",
        "### note: if you didnt get a link as output, rerun this cell and the one above\n",
        "!curl -s http://localhost:4040/api/tunnels | python3 -c \\\n",
        "    \"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\""
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "https://ffd61d5e.ngrok.io\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-eQQvuACVxbf",
        "colab_type": "code",
        "outputId": "ce7b241b-575d-4203-988c-5c00db3377e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!python3 /content/Drone/models/research/object_detection/model_main.py \\\n",
        "    --pipeline_config_path={model_pipline}\\\n",
        "    --model_dir={model_dir} \\\n",
        "    --alsologtostderr \\"
      ],
      "execution_count": 108,
      "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/Drone/models/research/object_detection/model_main.py:109: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/Drone/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",
            "W0316 16:06:41.792927 139922412312448 module_wrapper.py:139] From /content/Drone/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 /content/Drone/models/research/object_detection/model_lib.py:628: The name tf.logging.warning is deprecated. Please use tf.compat.v1.logging.warning instead.\n",
            "\n",
            "W0316 16:06:41.797023 139922412312448 module_wrapper.py:139] From /content/Drone/models/research/object_detection/model_lib.py:628: The name tf.logging.warning is deprecated. Please use tf.compat.v1.logging.warning instead.\n",
            "\n",
            "WARNING:tensorflow:Forced number of epochs for all eval validations to be 1.\n",
            "W0316 16:06:41.797147 139922412312448 model_lib.py:629] Forced number of epochs for all eval validations to be 1.\n",
            "WARNING:tensorflow:From /content/Drone/models/research/object_detection/utils/config_util.py:488: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
            "\n",
            "W0316 16:06:41.797233 139922412312448 module_wrapper.py:139] From /content/Drone/models/research/object_detection/utils/config_util.py:488: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
            "\n",
            "INFO:tensorflow:Maybe overwriting train_steps: None\n",
            "I0316 16:06:41.797296 139922412312448 config_util.py:488] Maybe overwriting train_steps: None\n",
            "INFO:tensorflow:Maybe overwriting use_bfloat16: False\n",
            "I0316 16:06:41.797348 139922412312448 config_util.py:488] Maybe overwriting use_bfloat16: False\n",
            "INFO:tensorflow:Maybe overwriting sample_1_of_n_eval_examples: 1\n",
            "I0316 16:06:41.797397 139922412312448 config_util.py:488] Maybe overwriting sample_1_of_n_eval_examples: 1\n",
            "INFO:tensorflow:Maybe overwriting eval_num_epochs: 1\n",
            "I0316 16:06:41.797446 139922412312448 config_util.py:488] Maybe overwriting eval_num_epochs: 1\n",
            "INFO:tensorflow:Maybe overwriting load_pretrained: True\n",
            "I0316 16:06:41.797492 139922412312448 config_util.py:488] Maybe overwriting load_pretrained: True\n",
            "INFO:tensorflow:Ignoring config override key: load_pretrained\n",
            "I0316 16:06:41.797559 139922412312448 config_util.py:498] Ignoring config override key: load_pretrained\n",
            "WARNING:tensorflow:Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.\n",
            "W0316 16:06:41.798088 139922412312448 model_lib.py:645] Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.\n",
            "INFO:tensorflow:create_estimator_and_inputs: use_tpu False, export_to_tpu False\n",
            "I0316 16:06:41.798161 139922412312448 model_lib.py:680] create_estimator_and_inputs: use_tpu False, export_to_tpu False\n",
            "INFO:tensorflow:Using config: {'_model_dir': 'training/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f41d1bd2240>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
            "I0316 16:06:41.798506 139922412312448 estimator.py:212] Using config: {'_model_dir': 'training/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f41d1bd2240>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
            "WARNING:tensorflow:Estimator's model_fn (<function create_model_fn.<locals>.model_fn at 0x7f41d1bcf6a8>) includes params argument, but params are not passed to Estimator.\n",
            "W0316 16:06:41.798725 139922412312448 model_fn.py:630] Estimator's model_fn (<function create_model_fn.<locals>.model_fn at 0x7f41d1bcf6a8>) includes params argument, but params are not passed to Estimator.\n",
            "INFO:tensorflow:Not using Distribute Coordinator.\n",
            "I0316 16:06:41.799305 139922412312448 estimator_training.py:186] Not using Distribute Coordinator.\n",
            "INFO:tensorflow:Running training and evaluation locally (non-distributed).\n",
            "I0316 16:06:41.799457 139922412312448 training.py:612] Running training and evaluation locally (non-distributed).\n",
            "INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps None or save_checkpoints_secs 600.\n",
            "I0316 16:06:41.799664 139922412312448 training.py:700] Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps None or save_checkpoints_secs 600.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/training_util.py:236: 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",
            "W0316 16:06:41.806670 139922412312448 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/training_util.py:236: 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/Drone/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",
            "W0316 16:06:41.816601 139922412312448 module_wrapper.py:139] From /content/Drone/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/Drone/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",
            "W0316 16:06:41.816797 139922412312448 module_wrapper.py:139] From /content/Drone/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/Drone/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",
            "W0316 16:06:41.828076 139922412312448 module_wrapper.py:139] From /content/Drone/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:num_readers has been reduced to 1 to match input file shards.\n",
            "W0316 16:06:41.828985 139922412312448 dataset_builder.py:72] num_readers has been reduced to 1 to match input file shards.\n",
            "WARNING:tensorflow:From /content/Drone/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",
            "W0316 16:06:41.833629 139922412312448 deprecation.py:323] From /content/Drone/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",
            "W0316 16:06:41.833776 139922412312448 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/Drone/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",
            "W0316 16:06:41.852910 139922412312448 deprecation.py:323] From /content/Drone/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 /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.logging.warn is deprecated. Please use tf.compat.v1.logging.warn instead.\n",
            "\n",
            "W0316 16:06:43.013269 139922412312448 module_wrapper.py:139] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.logging.warn is deprecated. Please use tf.compat.v1.logging.warn instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.is_nan is deprecated. Please use tf.math.is_nan instead.\n",
            "\n",
            "W0316 16:06:49.842003 139922412312448 module_wrapper.py:139] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.is_nan is deprecated. Please use tf.math.is_nan instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/Drone/models/research/object_detection/utils/ops.py:493: 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",
            "W0316 16:06:49.915745 139922412312448 deprecation.py:323] From /content/Drone/models/research/object_detection/utils/ops.py:493: 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 /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W0316 16:06:51.907709 139922412312448 module_wrapper.py:139] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/autograph/operators/control_flow.py:1004: 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",
            "W0316 16:06:55.215003 139922412312448 api.py:332] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/autograph/operators/control_flow.py:1004: 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 /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.image.resize_images is deprecated. Please use tf.image.resize instead.\n",
            "\n",
            "W0316 16:06:58.239122 139922412312448 module_wrapper.py:139] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/autograph/converters/directives.py:119: 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/python/autograph/converters/directives.py:119: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\n",
            "\n",
            "W0316 16:06:58.240153 139922412312448 module_wrapper.py:139] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/Drone/models/research/object_detection/inputs.py:166: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0316 16:06:58.615986 139922412312448 deprecation.py:323] From /content/Drone/models/research/object_detection/inputs.py:166: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.string_to_hash_bucket_fast is deprecated. Please use tf.strings.to_hash_bucket_fast instead.\n",
            "\n",
            "W0316 16:07:00.226541 139922412312448 module_wrapper.py:139] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/autograph/converters/directives.py:119: The name tf.string_to_hash_bucket_fast is deprecated. Please use tf.strings.to_hash_bucket_fast instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/Drone/models/research/object_detection/builders/dataset_builder.py:158: batch_and_drop_remainder (from tensorflow.contrib.data.python.ops.batching) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.batch(..., drop_remainder=True)`.\n",
            "W0316 16:07:00.672053 139922412312448 deprecation.py:323] From /content/Drone/models/research/object_detection/builders/dataset_builder.py:158: batch_and_drop_remainder (from tensorflow.contrib.data.python.ops.batching) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.batch(..., drop_remainder=True)`.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "I0316 16:07:00.684244 139922412312448 estimator.py:1148] Calling model_fn.\n",
            "WARNING:tensorflow:From /content/Drone/models/research/object_detection/meta_architectures/ssd_meta_arch.py:589: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
            "\n",
            "W0316 16:07:00.888819 139922412312448 module_wrapper.py:139] From /content/Drone/models/research/object_detection/meta_architectures/ssd_meta_arch.py:589: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/Drone/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",
            "W0316 16:07:00.889067 139922412312448 module_wrapper.py:139] From /content/Drone/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",
            "W0316 16:07:00.892396 139922412312448 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/Drone/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",
            "W0316 16:07:03.495787 139922412312448 module_wrapper.py:139] From /content/Drone/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",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0316 16:07:03.505230 139922412312448 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",
            "I0316 16:07:03.545200 139922412312448 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",
            "I0316 16:07:03.584870 139922412312448 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",
            "I0316 16:07:03.621970 139922412312448 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",
            "I0316 16:07:03.657813 139922412312448 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",
            "I0316 16:07:03.696900 139922412312448 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "WARNING:tensorflow:From /content/Drone/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",
            "W0316 16:07:03.738608 139922412312448 module_wrapper.py:139] From /content/Drone/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/Drone/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",
            "W0316 16:07:03.739551 139922412312448 module_wrapper.py:139] From /content/Drone/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",
            "W0316 16:07:03.743774 139922412312448 variables_helper.py:154] Variable [FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 512]], model variable shape: [[3, 3, 256, 512]]. This variable will not be initialized from the checkpoint.\n",
            "W0316 16:07:03.743928 139922412312448 variables_helper.py:154] Variable [FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 128, 256]], model variable shape: [[3, 3, 128, 256]]. This variable will not be initialized from the checkpoint.\n",
            "W0316 16:07:03.744021 139922412312448 variables_helper.py:154] Variable [FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 128, 256]], model variable shape: [[3, 3, 128, 256]]. This variable will not be initialized from the checkpoint.\n",
            "W0316 16:07:03.744104 139922412312448 variables_helper.py:154] Variable [FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 64, 128]], model variable shape: [[3, 3, 64, 128]]. This variable will not be initialized from the checkpoint.\n",
            "WARNING:tensorflow:From /content/Drone/models/research/object_detection/model_lib.py:353: The name tf.train.init_from_checkpoint is deprecated. Please use tf.compat.v1.train.init_from_checkpoint instead.\n",
            "\n",
            "W0316 16:07:03.744266 139922412312448 module_wrapper.py:139] From /content/Drone/models/research/object_detection/model_lib.py:353: The name tf.train.init_from_checkpoint is deprecated. Please use tf.compat.v1.train.init_from_checkpoint instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/Drone/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",
            "W0316 16:07:04.609056 139922412312448 module_wrapper.py:139] From /content/Drone/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/Drone/models/research/object_detection/meta_architectures/ssd_meta_arch.py:1163: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.\n",
            "\n",
            "W0316 16:07:06.974376 139922412312448 module_wrapper.py:139] From /content/Drone/models/research/object_detection/meta_architectures/ssd_meta_arch.py:1163: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/Drone/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",
            "W0316 16:07:06.979835 139922412312448 module_wrapper.py:139] From /content/Drone/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/Drone/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",
            "W0316 16:07:06.980957 139922412312448 module_wrapper.py:139] From /content/Drone/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/Drone/models/research/object_detection/meta_architectures/ssd_meta_arch.py:1275: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.\n",
            "\n",
            "W0316 16:07:07.344201 139922412312448 module_wrapper.py:139] From /content/Drone/models/research/object_detection/meta_architectures/ssd_meta_arch.py:1275: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/Drone/models/research/object_detection/model_lib.py:380: 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",
            "W0316 16:07:07.346670 139922412312448 module_wrapper.py:139] From /content/Drone/models/research/object_detection/model_lib.py:380: 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/Drone/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",
            "W0316 16:07:07.346933 139922412312448 module_wrapper.py:139] From /content/Drone/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/Drone/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",
            "W0316 16:07:07.354935 139922412312448 module_wrapper.py:139] From /content/Drone/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/Drone/models/research/object_detection/model_lib.py:398: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n",
            "\n",
            "W0316 16:07:07.355150 139922412312448 module_wrapper.py:139] From /content/Drone/models/research/object_detection/model_lib.py:398: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables 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",
            "W0316 16:07:09.185231 139922412312448 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 /content/Drone/models/research/object_detection/model_lib.py:515: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
            "\n",
            "W0316 16:07:14.399866 139922412312448 module_wrapper.py:139] From /content/Drone/models/research/object_detection/model_lib.py:515: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/Drone/models/research/object_detection/model_lib.py:519: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.\n",
            "\n",
            "W0316 16:07:15.072958 139922412312448 module_wrapper.py:139] From /content/Drone/models/research/object_detection/model_lib.py:519: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/Drone/models/research/object_detection/model_lib.py:520: The name tf.train.Scaffold is deprecated. Please use tf.compat.v1.train.Scaffold instead.\n",
            "\n",
            "W0316 16:07:15.073249 139922412312448 module_wrapper.py:139] From /content/Drone/models/research/object_detection/model_lib.py:520: The name tf.train.Scaffold is deprecated. Please use tf.compat.v1.train.Scaffold instead.\n",
            "\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "I0316 16:07:15.073795 139922412312448 estimator.py:1150] Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "I0316 16:07:15.075019 139922412312448 basic_session_run_hooks.py:541] Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "I0316 16:07:18.522751 139922412312448 monitored_session.py:240] Graph was finalized.\n",
            "2020-03-16 16:07:18.523181: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX512F\n",
            "2020-03-16 16:07:18.536316: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2000155000 Hz\n",
            "2020-03-16 16:07:18.539821: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x25d3100 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-03-16 16:07:18.539858: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-03-16 16:07:18.544985: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-03-16 16:07:18.715196: 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",
            "2020-03-16 16:07:18.715999: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x25d3800 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-03-16 16:07:18.716031: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n",
            "2020-03-16 16:07:18.717196: 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",
            "2020-03-16 16:07:18.717824: 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",
            "2020-03-16 16:07:18.732390: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-03-16 16:07:18.992083: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-03-16 16:07:19.112321: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-03-16 16:07:19.138629: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-03-16 16:07:19.419507: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-03-16 16:07:19.444399: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-03-16 16:07:19.952233: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-03-16 16:07:19.952441: 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",
            "2020-03-16 16:07:19.953248: 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",
            "2020-03-16 16:07:19.953852: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2020-03-16 16:07:19.960208: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-03-16 16:07:19.961666: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-03-16 16:07:19.961714: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2020-03-16 16:07:19.961726: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2020-03-16 16:07:19.962992: 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",
            "2020-03-16 16:07:19.963631: 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",
            "2020-03-16 16:07:19.964188: 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",
            "2020-03-16 16:07:19.964225: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15216 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:Running local_init_op.\n",
            "I0316 16:07:30.264014 139922412312448 session_manager.py:500] Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "I0316 16:07:30.593757 139922412312448 session_manager.py:502] Done running local_init_op.\n",
            "INFO:tensorflow:Saving checkpoints for 0 into training/model.ckpt.\n",
            "I0316 16:07:39.516813 139922412312448 basic_session_run_hooks.py:606] Saving checkpoints for 0 into training/model.ckpt.\n",
            "2020-03-16 16:07:50.648480: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-03-16 16:07:55.077218: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "INFO:tensorflow:loss = 13.558943, step = 0\n",
            "I0316 16:07:57.360795 139922412312448 basic_session_run_hooks.py:262] loss = 13.558943, step = 0\n",
            "INFO:tensorflow:global_step/sec: 2.08205\n",
            "I0316 16:08:45.389717 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.08205\n",
            "INFO:tensorflow:loss = 7.885797, step = 100 (48.030 sec)\n",
            "I0316 16:08:45.390957 139922412312448 basic_session_run_hooks.py:260] loss = 7.885797, step = 100 (48.030 sec)\n",
            "INFO:tensorflow:global_step/sec: 2.31499\n",
            "I0316 16:09:28.586393 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.31499\n",
            "INFO:tensorflow:loss = 8.264233, step = 200 (43.196 sec)\n",
            "I0316 16:09:28.587400 139922412312448 basic_session_run_hooks.py:260] loss = 8.264233, step = 200 (43.196 sec)\n",
            "INFO:tensorflow:global_step/sec: 2.32038\n",
            "I0316 16:10:11.682792 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.32038\n",
            "INFO:tensorflow:loss = 8.203926, step = 300 (43.096 sec)\n",
            "I0316 16:10:11.683806 139922412312448 basic_session_run_hooks.py:260] loss = 8.203926, step = 300 (43.096 sec)\n",
            "INFO:tensorflow:global_step/sec: 2.32255\n",
            "I0316 16:10:54.738988 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.32255\n",
            "INFO:tensorflow:loss = 7.9129744, step = 400 (43.056 sec)\n",
            "I0316 16:10:54.740071 139922412312448 basic_session_run_hooks.py:260] loss = 7.9129744, step = 400 (43.056 sec)\n",
            "INFO:tensorflow:global_step/sec: 2.33078\n",
            "I0316 16:11:37.642970 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.33078\n",
            "INFO:tensorflow:loss = 8.14499, step = 500 (42.904 sec)\n",
            "I0316 16:11:37.644008 139922412312448 basic_session_run_hooks.py:260] loss = 8.14499, step = 500 (42.904 sec)\n",
            "INFO:tensorflow:global_step/sec: 2.32698\n",
            "I0316 16:12:20.617176 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.32698\n",
            "INFO:tensorflow:loss = 8.163713, step = 600 (42.974 sec)\n",
            "I0316 16:12:20.618229 139922412312448 basic_session_run_hooks.py:260] loss = 8.163713, step = 600 (42.974 sec)\n",
            "INFO:tensorflow:global_step/sec: 2.29827\n",
            "I0316 16:13:04.128292 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.29827\n",
            "INFO:tensorflow:loss = 7.906437, step = 700 (43.511 sec)\n",
            "I0316 16:13:04.129191 139922412312448 basic_session_run_hooks.py:260] loss = 7.906437, step = 700 (43.511 sec)\n",
            "INFO:tensorflow:global_step/sec: 2.2983\n",
            "I0316 16:13:47.638797 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.2983\n",
            "INFO:tensorflow:loss = 7.8751054, step = 800 (43.511 sec)\n",
            "I0316 16:13:47.639950 139922412312448 basic_session_run_hooks.py:260] loss = 7.8751054, step = 800 (43.511 sec)\n",
            "INFO:tensorflow:global_step/sec: 2.25839\n",
            "I0316 16:14:31.918064 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.25839\n",
            "INFO:tensorflow:loss = 7.474257, step = 900 (44.279 sec)\n",
            "I0316 16:14:31.919132 139922412312448 basic_session_run_hooks.py:260] loss = 7.474257, step = 900 (44.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 2.23916\n",
            "I0316 16:15:16.577730 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.23916\n",
            "INFO:tensorflow:loss = 7.560547, step = 1000 (44.660 sec)\n",
            "I0316 16:15:16.578716 139922412312448 basic_session_run_hooks.py:260] loss = 7.560547, step = 1000 (44.660 sec)\n",
            "INFO:tensorflow:global_step/sec: 2.25621\n",
            "I0316 16:16:00.899914 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.25621\n",
            "INFO:tensorflow:loss = 7.462542, step = 1100 (44.322 sec)\n",
            "I0316 16:16:00.900947 139922412312448 basic_session_run_hooks.py:260] loss = 7.462542, step = 1100 (44.322 sec)\n",
            "INFO:tensorflow:global_step/sec: 2.25319\n",
            "I0316 16:16:45.281445 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.25319\n",
            "INFO:tensorflow:loss = 7.2114983, step = 1200 (44.381 sec)\n",
            "I0316 16:16:45.282438 139922412312448 basic_session_run_hooks.py:260] loss = 7.2114983, step = 1200 (44.381 sec)\n",
            "INFO:tensorflow:global_step/sec: 2.25791\n",
            "I0316 16:17:29.570153 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.25791\n",
            "INFO:tensorflow:loss = 6.8528748, step = 1300 (44.289 sec)\n",
            "I0316 16:17:29.571263 139922412312448 basic_session_run_hooks.py:260] loss = 6.8528748, step = 1300 (44.289 sec)\n",
            "INFO:tensorflow:Saving checkpoints for 1329 into training/model.ckpt.\n",
            "I0316 16:17:41.787248 139922412312448 basic_session_run_hooks.py:606] Saving checkpoints for 1329 into training/model.ckpt.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "I0316 16:17:44.062665 139922412312448 estimator.py:1148] Calling model_fn.\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0316 16:17:46.138656 139922412312448 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",
            "I0316 16:17:46.169876 139922412312448 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",
            "I0316 16:17:46.201813 139922412312448 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",
            "I0316 16:17:46.234099 139922412312448 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",
            "I0316 16:17:46.266131 139922412312448 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",
            "I0316 16:17:46.297176 139922412312448 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "WARNING:tensorflow:From /content/Drone/models/research/object_detection/eval_util.py:796: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0316 16:17:46.979719 139922412312448 deprecation.py:323] From /content/Drone/models/research/object_detection/eval_util.py:796: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "WARNING:tensorflow:From /content/Drone/models/research/object_detection/utils/visualization_utils.py:498: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "tf.py_func is deprecated in TF V2. Instead, there are two\n",
            "    options available in V2.\n",
            "    - tf.py_function takes a python function which manipulates tf eager\n",
            "    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n",
            "    an ndarray (just call tensor.numpy()) but having access to eager tensors\n",
            "    means `tf.py_function`s can use accelerators such as GPUs as well as\n",
            "    being differentiable using a gradient tape.\n",
            "    - tf.numpy_function maintains the semantics of the deprecated tf.py_func\n",
            "    (it is not differentiable, and manipulates numpy arrays). It drops the\n",
            "    stateful argument making all functions stateful.\n",
            "    \n",
            "W0316 16:17:47.385106 139922412312448 deprecation.py:323] From /content/Drone/models/research/object_detection/utils/visualization_utils.py:498: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "tf.py_func is deprecated in TF V2. Instead, there are two\n",
            "    options available in V2.\n",
            "    - tf.py_function takes a python function which manipulates tf eager\n",
            "    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n",
            "    an ndarray (just call tensor.numpy()) but having access to eager tensors\n",
            "    means `tf.py_function`s can use accelerators such as GPUs as well as\n",
            "    being differentiable using a gradient tape.\n",
            "    - tf.numpy_function maintains the semantics of the deprecated tf.py_func\n",
            "    (it is not differentiable, and manipulates numpy arrays). It drops the\n",
            "    stateful argument making all functions stateful.\n",
            "    \n",
            "WARNING:tensorflow:From /content/Drone/models/research/object_detection/utils/visualization_utils.py:1044: The name tf.summary.image is deprecated. Please use tf.compat.v1.summary.image instead.\n",
            "\n",
            "W0316 16:17:47.539521 139922412312448 module_wrapper.py:139] From /content/Drone/models/research/object_detection/utils/visualization_utils.py:1044: The name tf.summary.image is deprecated. Please use tf.compat.v1.summary.image instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/Drone/models/research/object_detection/model_lib.py:484: The name tf.metrics.mean is deprecated. Please use tf.compat.v1.metrics.mean instead.\n",
            "\n",
            "W0316 16:17:47.694192 139922412312448 module_wrapper.py:139] From /content/Drone/models/research/object_detection/model_lib.py:484: The name tf.metrics.mean is deprecated. Please use tf.compat.v1.metrics.mean instead.\n",
            "\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "I0316 16:17:47.990501 139922412312448 estimator.py:1150] Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2020-03-16T16:17:48Z\n",
            "I0316 16:17:48.007085 139922412312448 evaluation.py:255] Starting evaluation at 2020-03-16T16:17:48Z\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "I0316 16:17:48.423599 139922412312448 monitored_session.py:240] Graph was finalized.\n",
            "2020-03-16 16:17:48.424642: 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",
            "2020-03-16 16:17:48.425163: 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",
            "2020-03-16 16:17:48.425262: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-03-16 16:17:48.425279: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-03-16 16:17:48.425295: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-03-16 16:17:48.425309: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-03-16 16:17:48.425325: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-03-16 16:17:48.425339: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-03-16 16:17:48.425355: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-03-16 16:17:48.425418: 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",
            "2020-03-16 16:17:48.425973: 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",
            "2020-03-16 16:17:48.426435: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2020-03-16 16:17:48.426480: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-03-16 16:17:48.426491: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2020-03-16 16:17:48.426501: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2020-03-16 16:17:48.426591: 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",
            "2020-03-16 16:17:48.427146: 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",
            "2020-03-16 16:17:48.427639: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15216 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-1329\n",
            "I0316 16:17:48.428737 139922412312448 saver.py:1284] Restoring parameters from training/model.ckpt-1329\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "I0316 16:17:49.312628 139922412312448 session_manager.py:500] Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "I0316 16:17:49.432829 139922412312448 session_manager.py:502] Done running local_init_op.\n",
            "INFO:tensorflow:Performing evaluation on 243 images.\n",
            "I0316 16:18:01.201919 139918679246592 coco_evaluation.py:205] Performing evaluation on 243 images.\n",
            "creating index...\n",
            "index created!\n",
            "INFO:tensorflow:Loading and preparing annotation results...\n",
            "I0316 16:18:01.202836 139918679246592 coco_tools.py:115] Loading and preparing annotation results...\n",
            "INFO:tensorflow:DONE (t=0.00s)\n",
            "I0316 16:18:01.204988 139918679246592 coco_tools.py:137] DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.51s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.09s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.022\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.072\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.009\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.033\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.067\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.081\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.085\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.130\n",
            "INFO:tensorflow:Finished evaluation at 2020-03-16-16:18:03\n",
            "I0316 16:18:03.106360 139922412312448 evaluation.py:275] Finished evaluation at 2020-03-16-16:18:03\n",
            "INFO:tensorflow:Saving dict for global step 1329: DetectionBoxes_Precision/mAP = 0.022197962, DetectionBoxes_Precision/mAP (large) = 0.032940257, DetectionBoxes_Precision/mAP (medium) = 0.0, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/mAP@.50IOU = 0.07218658, DetectionBoxes_Precision/mAP@.75IOU = 0.0085854465, DetectionBoxes_Recall/AR@1 = 0.06666667, DetectionBoxes_Recall/AR@10 = 0.08120567, DetectionBoxes_Recall/AR@100 = 0.08510638, DetectionBoxes_Recall/AR@100 (large) = 0.13043478, DetectionBoxes_Recall/AR@100 (medium) = 0.0, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 5.6572933, Loss/localization_loss = 3.2462356, Loss/regularization_loss = 1.0457908, Loss/total_loss = 9.949319, global_step = 1329, learning_rate = 0.003, loss = 9.949319\n",
            "I0316 16:18:03.106723 139922412312448 estimator.py:2049] Saving dict for global step 1329: DetectionBoxes_Precision/mAP = 0.022197962, DetectionBoxes_Precision/mAP (large) = 0.032940257, DetectionBoxes_Precision/mAP (medium) = 0.0, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/mAP@.50IOU = 0.07218658, DetectionBoxes_Precision/mAP@.75IOU = 0.0085854465, DetectionBoxes_Recall/AR@1 = 0.06666667, DetectionBoxes_Recall/AR@10 = 0.08120567, DetectionBoxes_Recall/AR@100 = 0.08510638, DetectionBoxes_Recall/AR@100 (large) = 0.13043478, DetectionBoxes_Recall/AR@100 (medium) = 0.0, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 5.6572933, Loss/localization_loss = 3.2462356, Loss/regularization_loss = 1.0457908, Loss/total_loss = 9.949319, global_step = 1329, learning_rate = 0.003, loss = 9.949319\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1329: training/model.ckpt-1329\n",
            "I0316 16:18:03.894197 139922412312448 estimator.py:2109] Saving 'checkpoint_path' summary for global step 1329: training/model.ckpt-1329\n",
            "INFO:tensorflow:global_step/sec: 1.51397\n",
            "I0316 16:18:35.621428 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 1.51397\n",
            "INFO:tensorflow:loss = 7.252416, step = 1400 (66.051 sec)\n",
            "I0316 16:18:35.622297 139922412312448 basic_session_run_hooks.py:260] loss = 7.252416, step = 1400 (66.051 sec)\n",
            "INFO:tensorflow:global_step/sec: 2.20396\n",
            "I0316 16:19:20.994282 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.20396\n",
            "INFO:tensorflow:loss = 6.7012544, step = 1500 (45.373 sec)\n",
            "I0316 16:19:20.995602 139922412312448 basic_session_run_hooks.py:260] loss = 6.7012544, step = 1500 (45.373 sec)\n",
            "INFO:tensorflow:global_step/sec: 2.18856\n",
            "I0316 16:20:06.686355 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.18856\n",
            "INFO:tensorflow:loss = 7.662503, step = 1600 (45.692 sec)\n",
            "I0316 16:20:06.687499 139922412312448 basic_session_run_hooks.py:260] loss = 7.662503, step = 1600 (45.692 sec)\n",
            "INFO:tensorflow:global_step/sec: 2.17851\n",
            "I0316 16:20:52.589277 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.17851\n",
            "INFO:tensorflow:loss = 7.2487783, step = 1700 (45.903 sec)\n",
            "I0316 16:20:52.590562 139922412312448 basic_session_run_hooks.py:260] loss = 7.2487783, step = 1700 (45.903 sec)\n",
            "INFO:tensorflow:global_step/sec: 2.18741\n",
            "I0316 16:21:38.305433 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.18741\n",
            "INFO:tensorflow:loss = 7.667349, step = 1800 (45.716 sec)\n",
            "I0316 16:21:38.306490 139922412312448 basic_session_run_hooks.py:260] loss = 7.667349, step = 1800 (45.716 sec)\n",
            "INFO:tensorflow:global_step/sec: 2.22393\n",
            "I0316 16:22:23.270810 139922412312448 basic_session_run_hooks.py:692] global_step/sec: 2.22393\n",
            "INFO:tensorflow:loss = 7.3170605, step = 1900 (44.965 sec)\n",
            "I0316 16:22:23.271987 139922412312448 basic_session_run_hooks.py:260] loss = 7.3170605, step = 1900 (44.965 sec)\n",
            "INFO:tensorflow:Saving checkpoints for 2000 into training/model.ckpt.\n",
            "I0316 16:23:07.473273 139922412312448 basic_session_run_hooks.py:606] Saving checkpoints for 2000 into training/model.ckpt.\n",
            "INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).\n",
            "I0316 16:23:08.941710 139922412312448 training.py:527] Skip the current checkpoint eval due to throttle secs (600 secs).\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "I0316 16:23:09.622565 139922412312448 estimator.py:1148] Calling model_fn.\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0316 16:23:11.742213 139922412312448 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",
            "I0316 16:23:11.774617 139922412312448 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",
            "I0316 16:23:11.805449 139922412312448 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",
            "I0316 16:23:11.836834 139922412312448 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",
            "I0316 16:23:11.867228 139922412312448 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",
            "I0316 16:23:11.897362 139922412312448 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "I0316 16:23:13.355702 139922412312448 estimator.py:1150] Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2020-03-16T16:23:13Z\n",
            "I0316 16:23:13.371644 139922412312448 evaluation.py:255] Starting evaluation at 2020-03-16T16:23:13Z\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "I0316 16:23:13.772488 139922412312448 monitored_session.py:240] Graph was finalized.\n",
            "2020-03-16 16:23:13.773197: 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",
            "2020-03-16 16:23:13.773794: 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",
            "2020-03-16 16:23:13.773938: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-03-16 16:23:13.773967: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-03-16 16:23:13.773992: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-03-16 16:23:13.774014: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-03-16 16:23:13.774036: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-03-16 16:23:13.774058: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-03-16 16:23:13.774083: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-03-16 16:23:13.774165: 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",
            "2020-03-16 16:23:13.774787: 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",
            "2020-03-16 16:23:13.775264: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2020-03-16 16:23:13.775314: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-03-16 16:23:13.775328: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2020-03-16 16:23:13.775339: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2020-03-16 16:23:13.775433: 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",
            "2020-03-16 16:23:13.775944: 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",
            "2020-03-16 16:23:13.776396: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15216 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-2000\n",
            "I0316 16:23:13.777510 139922412312448 saver.py:1284] Restoring parameters from training/model.ckpt-2000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "I0316 16:23:14.708832 139922412312448 session_manager.py:500] Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "I0316 16:23:14.856626 139922412312448 session_manager.py:502] Done running local_init_op.\n",
            "INFO:tensorflow:Performing evaluation on 243 images.\n",
            "I0316 16:23:26.795158 139918687639296 coco_evaluation.py:205] Performing evaluation on 243 images.\n",
            "creating index...\n",
            "index created!\n",
            "INFO:tensorflow:Loading and preparing annotation results...\n",
            "I0316 16:23:26.796195 139918687639296 coco_tools.py:115] Loading and preparing annotation results...\n",
            "INFO:tensorflow:DONE (t=0.00s)\n",
            "I0316 16:23:26.799560 139918687639296 coco_tools.py:137] DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.49s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.09s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.014\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.040\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.008\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.020\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.058\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.086\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.096\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.147\n",
            "INFO:tensorflow:Finished evaluation at 2020-03-16-16:23:28\n",
            "I0316 16:23:28.710425 139922412312448 evaluation.py:275] Finished evaluation at 2020-03-16-16:23:28\n",
            "INFO:tensorflow:Saving dict for global step 2000: DetectionBoxes_Precision/mAP = 0.01356067, DetectionBoxes_Precision/mAP (large) = 0.020198671, DetectionBoxes_Precision/mAP (medium) = 0.0, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/mAP@.50IOU = 0.039957974, DetectionBoxes_Precision/mAP@.75IOU = 0.007529359, DetectionBoxes_Recall/AR@1 = 0.05815603, DetectionBoxes_Recall/AR@10 = 0.08617021, DetectionBoxes_Recall/AR@100 = 0.09609929, DetectionBoxes_Recall/AR@100 (large) = 0.14728262, DetectionBoxes_Recall/AR@100 (medium) = 0.0, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 6.0439353, Loss/localization_loss = 3.1440024, Loss/regularization_loss = 1.0149199, Loss/total_loss = 10.202858, global_step = 2000, learning_rate = 0.003, loss = 10.202858\n",
            "I0316 16:23:28.710759 139922412312448 estimator.py:2049] Saving dict for global step 2000: DetectionBoxes_Precision/mAP = 0.01356067, DetectionBoxes_Precision/mAP (large) = 0.020198671, DetectionBoxes_Precision/mAP (medium) = 0.0, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/mAP@.50IOU = 0.039957974, DetectionBoxes_Precision/mAP@.75IOU = 0.007529359, DetectionBoxes_Recall/AR@1 = 0.05815603, DetectionBoxes_Recall/AR@10 = 0.08617021, DetectionBoxes_Recall/AR@100 = 0.09609929, DetectionBoxes_Recall/AR@100 (large) = 0.14728262, DetectionBoxes_Recall/AR@100 (medium) = 0.0, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 6.0439353, Loss/localization_loss = 3.1440024, Loss/regularization_loss = 1.0149199, Loss/total_loss = 10.202858, global_step = 2000, learning_rate = 0.003, loss = 10.202858\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: training/model.ckpt-2000\n",
            "I0316 16:23:28.716645 139922412312448 estimator.py:2109] Saving 'checkpoint_path' summary for global step 2000: training/model.ckpt-2000\n",
            "INFO:tensorflow:Performing the final export in the end of training.\n",
            "I0316 16:23:28.717425 139922412312448 exporter.py:410] Performing the final export in the end of training.\n",
            "WARNING:tensorflow:From /content/Drone/models/research/object_detection/inputs.py:750: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0316 16:23:28.721925 139922412312448 module_wrapper.py:139] From /content/Drone/models/research/object_detection/inputs.py:750: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "I0316 16:23:28.926110 139922412312448 estimator.py:1148] Calling model_fn.\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0316 16:23:31.394634 139922412312448 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",
            "I0316 16:23:31.425768 139922412312448 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",
            "I0316 16:23:31.456261 139922412312448 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",
            "I0316 16:23:31.486506 139922412312448 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",
            "I0316 16:23:31.516067 139922412312448 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",
            "I0316 16:23:31.545677 139922412312448 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "WARNING:tensorflow:From /content/Drone/models/research/object_detection/model_lib.py:426: The name tf.saved_model.signature_constants.PREDICT_METHOD_NAME is deprecated. Please use tf.saved_model.PREDICT_METHOD_NAME instead.\n",
            "\n",
            "W0316 16:23:31.942818 139922412312448 module_wrapper.py:139] From /content/Drone/models/research/object_detection/model_lib.py:426: The name tf.saved_model.signature_constants.PREDICT_METHOD_NAME is deprecated. Please use tf.saved_model.PREDICT_METHOD_NAME instead.\n",
            "\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "I0316 16:23:32.189095 139922412312448 estimator.py:1150] Done calling model_fn.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/saved_model/signature_def_utils_impl.py:201: 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",
            "W0316 16:23:32.189411 139922412312448 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/saved_model/signature_def_utils_impl.py:201: 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",
            "INFO:tensorflow:Signatures INCLUDED in export for Classify: None\n",
            "I0316 16:23:32.190137 139922412312448 export_utils.py:170] Signatures INCLUDED in export for Classify: None\n",
            "INFO:tensorflow:Signatures INCLUDED in export for Regress: None\n",
            "I0316 16:23:32.190264 139922412312448 export_utils.py:170] Signatures INCLUDED in export for Regress: None\n",
            "INFO:tensorflow:Signatures INCLUDED in export for Predict: ['tensorflow/serving/predict', 'serving_default']\n",
            "I0316 16:23:32.190359 139922412312448 export_utils.py:170] Signatures INCLUDED in export for Predict: ['tensorflow/serving/predict', 'serving_default']\n",
            "INFO:tensorflow:Signatures INCLUDED in export for Train: None\n",
            "I0316 16:23:32.190439 139922412312448 export_utils.py:170] Signatures INCLUDED in export for Train: None\n",
            "INFO:tensorflow:Signatures INCLUDED in export for Eval: None\n",
            "I0316 16:23:32.190514 139922412312448 export_utils.py:170] Signatures INCLUDED in export for Eval: None\n",
            "2020-03-16 16:23:32.191062: 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",
            "2020-03-16 16:23:32.191634: 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",
            "2020-03-16 16:23:32.191733: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-03-16 16:23:32.191763: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-03-16 16:23:32.191790: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-03-16 16:23:32.191815: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-03-16 16:23:32.191840: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-03-16 16:23:32.191864: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-03-16 16:23:32.191888: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-03-16 16:23:32.191982: 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",
            "2020-03-16 16:23:32.192510: 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",
            "2020-03-16 16:23:32.192992: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2020-03-16 16:23:32.193036: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-03-16 16:23:32.193052: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2020-03-16 16:23:32.193062: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2020-03-16 16:23:32.193172: 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",
            "2020-03-16 16:23:32.193701: 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",
            "2020-03-16 16:23:32.194181: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15216 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-2000\n",
            "I0316 16:23:32.196759 139922412312448 saver.py:1284] Restoring parameters from training/model.ckpt-2000\n",
            "INFO:tensorflow:Assets added to graph.\n",
            "I0316 16:23:32.684239 139922412312448 builder_impl.py:665] Assets added to graph.\n",
            "INFO:tensorflow:No assets to write.\n",
            "I0316 16:23:32.684445 139922412312448 builder_impl.py:460] No assets to write.\n",
            "INFO:tensorflow:SavedModel written to: training/export/Servo/temp-b'1584375808'/saved_model.pb\n",
            "I0316 16:23:33.443815 139922412312448 builder_impl.py:425] SavedModel written to: training/export/Servo/temp-b'1584375808'/saved_model.pb\n",
            "INFO:tensorflow:Loss for final step: 7.709052.\n",
            "I0316 16:23:33.824516 139922412312448 estimator.py:371] Loss for final step: 7.709052.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "juhqX3ejV7yo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e941df3a-5a10-492b-cee8-0017411b8c48"
      },
      "source": [
        "#the location where the exported model will be saved in.\n",
        "output_directory = '/content/Drone/models/research/fine_tuned_model'\n",
        "\n",
        "# goes through the model is the training/ dir and gets the last one.\n",
        "# you could choose a specfic one instead of the last\n",
        "lst = os.listdir(model_dir)\n",
        "lst = [l for l in lst if 'model.ckpt-' in l and '.meta' in l]\n",
        "steps=np.array([int(re.findall('\\d+', l)[0]) for l in lst])\n",
        "last_model = lst[steps.argmax()].replace('.meta', '')\n",
        "last_model_path = os.path.join(model_dir, last_model)\n",
        "print(last_model_path)\n",
        "\n",
        "#exports the model specifed and inference graph\n",
        "!python /content/Drone/models/research/object_detection/export_inference_graph.py \\\n",
        "    --input_type=image_tensor \\\n",
        "    --pipeline_config_path={model_pipline} \\\n",
        "    --output_directory={output_directory} \\\n",
        "    --trained_checkpoint_prefix={last_model_path}"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training/model.ckpt-2000\n",
            "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/Drone/models/research/object_detection/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 /content/Drone/models/research/object_detection/export_inference_graph.py:145: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
            "\n",
            "W0316 16:24:28.978032 140296625141632 module_wrapper.py:139] From /content/Drone/models/research/object_detection/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/Drone/models/research/object_detection/exporter.py:402: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.\n",
            "\n",
            "W0316 16:24:28.984715 140296625141632 module_wrapper.py:139] From /content/Drone/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/Drone/models/research/object_detection/exporter.py:121: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0316 16:24:28.985027 140296625141632 module_wrapper.py:139] From /content/Drone/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/Drone/models/research/object_detection/core/preprocessor.py:2937: The name tf.image.resize_images is deprecated. Please use tf.image.resize instead.\n",
            "\n",
            "W0316 16:24:29.017118 140296625141632 module_wrapper.py:139] From /content/Drone/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/Drone/models/research/object_detection/meta_architectures/ssd_meta_arch.py:589: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
            "\n",
            "W0316 16:24:29.045845 140296625141632 module_wrapper.py:139] From /content/Drone/models/research/object_detection/meta_architectures/ssd_meta_arch.py:589: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/Drone/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",
            "W0316 16:24:29.046166 140296625141632 module_wrapper.py:139] From /content/Drone/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",
            "W0316 16:24:29.048776 140296625141632 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/Drone/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",
            "W0316 16:24:31.188938 140296625141632 module_wrapper.py:139] From /content/Drone/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/Drone/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",
            "W0316 16:24:31.199779 140296625141632 module_wrapper.py:139] From /content/Drone/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",
            "I0316 16:24:31.199943 140296625141632 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",
            "I0316 16:24:31.239764 140296625141632 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",
            "I0316 16:24:31.279520 140296625141632 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",
            "I0316 16:24:31.318433 140296625141632 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",
            "I0316 16:24:31.356994 140296625141632 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",
            "I0316 16:24:31.394511 140296625141632 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "WARNING:tensorflow:From /content/Drone/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",
            "W0316 16:24:31.765865 140296625141632 deprecation.py:323] From /content/Drone/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/Drone/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",
            "W0316 16:24:32.105791 140296625141632 module_wrapper.py:139] From /content/Drone/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/Drone/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",
            "W0316 16:24:32.106057 140296625141632 deprecation.py:323] From /content/Drone/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/Drone/models/research/object_detection/exporter.py:415: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "W0316 16:24:32.109301 140296625141632 module_wrapper.py:139] From /content/Drone/models/research/object_detection/exporter.py:415: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/Drone/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",
            "W0316 16:24:32.109498 140296625141632 deprecation.py:323] From /content/Drone/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",
            "W0316 16:24:32.110903 140296625141632 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",
            "133 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 (--/4.57m params)\n",
            "  BoxPredictor_0 (--/10.39k 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 (--/3.46k params)\n",
            "      BoxPredictor_0/ClassPredictor/biases (6, 6/6 params)\n",
            "      BoxPredictor_0/ClassPredictor/weights (1x1x576x6, 3.46k/3.46k params)\n",
            "  BoxPredictor_1 (--/46.12k 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 (--/15.37k params)\n",
            "      BoxPredictor_1/ClassPredictor/biases (12, 12/12 params)\n",
            "      BoxPredictor_1/ClassPredictor/weights (1x1x1280x12, 15.36k/15.36k params)\n",
            "  BoxPredictor_2 (--/18.47k 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 (--/6.16k params)\n",
            "      BoxPredictor_2/ClassPredictor/biases (12, 12/12 params)\n",
            "      BoxPredictor_2/ClassPredictor/weights (1x1x512x12, 6.14k/6.14k params)\n",
            "  BoxPredictor_3 (--/9.25k 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 (--/3.08k params)\n",
            "      BoxPredictor_3/ClassPredictor/biases (12, 12/12 params)\n",
            "      BoxPredictor_3/ClassPredictor/weights (1x1x256x12, 3.07k/3.07k params)\n",
            "  BoxPredictor_4 (--/9.25k 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 (--/3.08k params)\n",
            "      BoxPredictor_4/ClassPredictor/biases (12, 12/12 params)\n",
            "      BoxPredictor_4/ClassPredictor/weights (1x1x256x12, 3.07k/3.07k params)\n",
            "  BoxPredictor_5 (--/4.64k 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 (--/1.55k params)\n",
            "      BoxPredictor_5/ClassPredictor/biases (12, 12/12 params)\n",
            "      BoxPredictor_5/ClassPredictor/weights (1x1x128x12, 1.54k/1.54k 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",
            "133 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 (--/13.71k flops)\n",
            "  MultipleGridAnchorGenerator/sub (2.17k/2.17k flops)\n",
            "  MultipleGridAnchorGenerator/mul_20 (2.17k/2.17k flops)\n",
            "  MultipleGridAnchorGenerator/mul_19 (2.17k/2.17k 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",
            "  MultipleGridAnchorGenerator/mul_29 (600/600 flops)\n",
            "  MultipleGridAnchorGenerator/mul_36 (300/300 flops)\n",
            "  MultipleGridAnchorGenerator/mul_35 (300/300 flops)\n",
            "  MultipleGridAnchorGenerator/sub_2 (300/300 flops)\n",
            "  MultipleGridAnchorGenerator/mul_37 (150/150 flops)\n",
            "  MultipleGridAnchorGenerator/mul_43 (108/108 flops)\n",
            "  MultipleGridAnchorGenerator/mul_44 (108/108 flops)\n",
            "  MultipleGridAnchorGenerator/sub_3 (108/108 flops)\n",
            "  MultipleGridAnchorGenerator/mul_45 (54/54 flops)\n",
            "  MultipleGridAnchorGenerator/mul_52 (48/48 flops)\n",
            "  MultipleGridAnchorGenerator/mul_51 (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_46 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_40 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_54 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_17 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_16 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_15 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_24 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_47 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_48 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_61 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_39 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_38 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_19 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_55 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_56 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_32 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_31 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_30 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_18 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_22 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_23 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_34 (5/5 flops)\n",
            "  MultipleGridAnchorGenerator/mul_33 (5/5 flops)\n",
            "  MultipleGridAnchorGenerator/mul_42 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/mul_41 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/mul_16 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/mul_15 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/mul_14 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_14 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/mul_50 (2/2 flops)\n",
            "  MultipleGridAnchorGenerator/mul_49 (2/2 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_9 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_1 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_8 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_7 (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/PadOrClipBoxList/Greater (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/MultiClassNonMaxSuppression/SortByField/Equal (1/1 flops)\n",
            "  Preprocessor/map/while/Less_1 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_2 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/Minimum (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_1 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_19 (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",
            "  MultipleGridAnchorGenerator/mul_4 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_1 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_9 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_8 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_7 (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_5 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_10 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_3 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_2 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_13 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_12 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_11 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_10 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_1 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/assert_equal_1/Equal (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_7 (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",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Less (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_9 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_8 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum (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/truediv_13 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_12 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_11 (1/1 flops)\n",
            "\n",
            "======================End of Report==========================\n",
            "WARNING:tensorflow:From /content/Drone/models/research/object_detection/exporter.py:432: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
            "\n",
            "W0316 16:24:33.192862 140296625141632 module_wrapper.py:139] From /content/Drone/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/Drone/models/research/object_detection/exporter.py:342: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "W0316 16:24:33.904995 140296625141632 module_wrapper.py:139] From /content/Drone/models/research/object_detection/exporter.py:342: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "2020-03-16 16:24:33.906619: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-03-16 16:24:33.923134: 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",
            "2020-03-16 16:24:33.923754: 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",
            "2020-03-16 16:24:33.924028: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-03-16 16:24:33.925819: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-03-16 16:24:33.927483: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-03-16 16:24:33.927836: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-03-16 16:24:33.936977: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-03-16 16:24:33.937920: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-03-16 16:24:33.944432: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-03-16 16:24:33.944576: 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",
            "2020-03-16 16:24:33.945178: 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",
            "2020-03-16 16:24:33.945681: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2020-03-16 16:24:33.946126: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX512F\n",
            "2020-03-16 16:24:33.950944: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2000155000 Hz\n",
            "2020-03-16 16:24:33.951127: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x18ed100 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-03-16 16:24:33.951153: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-03-16 16:24:34.047283: 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",
            "2020-03-16 16:24:34.048015: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x18ed640 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-03-16 16:24:34.048045: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n",
            "2020-03-16 16:24:34.048229: 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",
            "2020-03-16 16:24:34.048804: 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",
            "2020-03-16 16:24:34.048878: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-03-16 16:24:34.048904: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-03-16 16:24:34.048925: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-03-16 16:24:34.048945: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-03-16 16:24:34.048971: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-03-16 16:24:34.048992: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-03-16 16:24:34.049012: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-03-16 16:24:34.049085: 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",
            "2020-03-16 16:24:34.049662: 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",
            "2020-03-16 16:24:34.050184: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2020-03-16 16:24:34.050252: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-03-16 16:24:34.051495: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-03-16 16:24:34.051525: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2020-03-16 16:24:34.051551: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2020-03-16 16:24:34.051672: 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",
            "2020-03-16 16:24:34.052251: 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",
            "2020-03-16 16:24:34.052818: 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",
            "2020-03-16 16:24:34.052858: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15216 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-2000\n",
            "I0316 16:24:34.054653 140296625141632 saver.py:1284] Restoring parameters from training/model.ckpt-2000\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",
            "W0316 16:24:35.789294 140296625141632 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",
            "2020-03-16 16:24:36.285416: 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",
            "2020-03-16 16:24:36.286094: 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",
            "2020-03-16 16:24:36.286192: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-03-16 16:24:36.286210: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-03-16 16:24:36.286224: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-03-16 16:24:36.286238: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-03-16 16:24:36.286250: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-03-16 16:24:36.286263: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-03-16 16:24:36.286277: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-03-16 16:24:36.286345: 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",
            "2020-03-16 16:24:36.286919: 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",
            "2020-03-16 16:24:36.287404: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2020-03-16 16:24:36.287437: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-03-16 16:24:36.287447: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2020-03-16 16:24:36.287456: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2020-03-16 16:24:36.287551: 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",
            "2020-03-16 16:24:36.288053: 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",
            "2020-03-16 16:24:36.288579: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15216 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-2000\n",
            "I0316 16:24:36.289860 140296625141632 saver.py:1284] Restoring parameters from training/model.ckpt-2000\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",
            "W0316 16:24:36.895439 140296625141632 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",
            "W0316 16:24:36.895738 140296625141632 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 324 variables.\n",
            "I0316 16:24:37.238616 140296625141632 graph_util_impl.py:334] Froze 324 variables.\n",
            "INFO:tensorflow:Converted 324 variables to const ops.\n",
            "I0316 16:24:37.313830 140296625141632 graph_util_impl.py:394] Converted 324 variables to const ops.\n",
            "2020-03-16 16:24:37.440009: 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",
            "2020-03-16 16:24:37.440695: 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",
            "2020-03-16 16:24:37.440786: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-03-16 16:24:37.440819: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-03-16 16:24:37.440835: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-03-16 16:24:37.440851: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-03-16 16:24:37.440866: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-03-16 16:24:37.440880: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-03-16 16:24:37.440896: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-03-16 16:24:37.440972: 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",
            "2020-03-16 16:24:37.441567: 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",
            "2020-03-16 16:24:37.442111: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2020-03-16 16:24:37.442146: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-03-16 16:24:37.442157: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2020-03-16 16:24:37.442167: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2020-03-16 16:24:37.442250: 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",
            "2020-03-16 16:24:37.442858: 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",
            "2020-03-16 16:24:37.443350: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15216 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/Drone/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",
            "W0316 16:24:37.818485 140296625141632 module_wrapper.py:139] From /content/Drone/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/Drone/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",
            "W0316 16:24:37.818965 140296625141632 deprecation.py:323] From /content/Drone/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/Drone/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",
            "W0316 16:24:37.819418 140296625141632 module_wrapper.py:139] From /content/Drone/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/Drone/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",
            "W0316 16:24:37.819684 140296625141632 module_wrapper.py:139] From /content/Drone/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/Drone/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",
            "W0316 16:24:37.820028 140296625141632 module_wrapper.py:139] From /content/Drone/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/Drone/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",
            "W0316 16:24:37.820176 140296625141632 module_wrapper.py:139] From /content/Drone/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",
            "I0316 16:24:37.820473 140296625141632 builder_impl.py:640] No assets to save.\n",
            "INFO:tensorflow:No assets to write.\n",
            "I0316 16:24:37.820630 140296625141632 builder_impl.py:460] No assets to write.\n",
            "INFO:tensorflow:SavedModel written to: /content/Drone/models/research/fine_tuned_model/saved_model/saved_model.pb\n",
            "I0316 16:24:38.235151 140296625141632 builder_impl.py:425] SavedModel written to: /content/Drone/models/research/fine_tuned_model/saved_model/saved_model.pb\n",
            "WARNING:tensorflow:From /content/Drone/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",
            "W0316 16:24:38.262066 140296625141632 module_wrapper.py:139] From /content/Drone/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 /content/Drone/models/research/fine_tuned_model/pipeline.config\n",
            "I0316 16:24:38.262289 140296625141632 config_util.py:190] Writing pipeline config file to /content/Drone/models/research/fine_tuned_model/pipeline.config\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hOk9EMgiKR0t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#downloads the frozen model that is needed for inference\n",
        "files.download(output_directory + '/frozen_inference_graph.pb')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GoD9mnQcKs_Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#downlaod the label map\n",
        "files.download(DATA_BASE_PATH + '/label_map.pbtxt')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oB00WNC6LonL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}