Number_Plate_Character_Recognition_Transfer_Learning.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-7IB3jPOGYHG"
      },
      "source": [
        "## Prepare Directory "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-evefEl8-8_1"
      },
      "source": [
        "mkdir TensorFlow"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G4fj0WjA_IRF",
        "outputId": "921baa2a-6c93-4736-daee-3b70a0a351ff"
      },
      "source": [
        "cd /content/TensorFlow"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/TensorFlow\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Ky1zTOaoYtL",
        "outputId": "8f33a891-cfb4-42ac-a57d-8e307a79971a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "se_8DGYB_MBB"
      },
      "source": [
        "!mkdir scripts\n",
        "!mkdir workspace"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "ttAH1NN1AV_t",
        "outputId": "887a191a-d210-4bf4-9fb2-457924ee19c6"
      },
      "source": [
        "pwd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/TensorFlow'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJ9tTUeW_cZz"
      },
      "source": [
        "%%bash\n",
        "cd scripts \n",
        "mkdir preprocessing"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ySoOPK9Avzf"
      },
      "source": [
        "%%bash\n",
        "cd workspace\n",
        "mkdir training_demo\n",
        "cd training_demo\n",
        "mkdir annotations\n",
        "mkdir models\n",
        "mkdir exported-models\n",
        "mkdir images\n",
        "mkdir pre-trained-models\n",
        "cd images\n",
        "mkdir train\n",
        "mkdir test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PD6aHq0BG3Dj"
      },
      "source": [
        "## Clone repository "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IW6lTFCjB3rz",
        "outputId": "db85b4dd-d7a0-4567-8e8e-1e2b2604b6f9"
      },
      "source": [
        "#download generate.py file to convert imgs into tfrec format\n",
        "%%bash \n",
        "cd /content/TensorFlow/scripts/preprocessing/\n",
        "curl -o generate_tfrecord.py https://raw.githubusercontent.com/sglvladi/TensorFlowObjectDetectionTutorial/master/docs/source/scripts/generate_tfrecord.py\n",
        "#curl -o partition_dataset.py https://drive.google.com/file/d/1YvFSF1D8h6L3T8P5V9M4s7IoBCcKUXIS/view?usp=sharing"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100  6410  100  6410    0     0  19662      0 --:--:-- --:--:-- --:--:-- 19662\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0dLOC-_kDHPr",
        "outputId": "a770952e-352f-4bf3-eca1-2cf79b15b9a1"
      },
      "source": [
        "#Download pretrained dataset\n",
        "%%bash \n",
        "cd /content/TensorFlow/workspace/training_demo/pre-trained-models/\n",
        "curl -o ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8.tar.gz http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8.tar.gz"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r  4 86.2M    4 4136k    0     0  3939k      0  0:00:22  0:00:01  0:00:21 3939k\r 18 86.2M   18 16.0M    0     0  7911k      0  0:00:11  0:00:02  0:00:09 7911k\r 64 86.2M   64 56.0M    0     0  17.9M      0  0:00:04  0:00:03  0:00:01 17.9M\r100 86.2M  100 86.2M    0     0  26.9M      0  0:00:03  0:00:03 --:--:-- 26.9M\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5cj-mMMVvSSJ",
        "outputId": "02ff30d8-e88f-4ffc-99bc-f9f722275544"
      },
      "source": [
        "#Extract pretrained dataset\n",
        "%%bash\n",
        "cd /content/TensorFlow/workspace/training_demo/pre-trained-models/\n",
        "tar -xvzf ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8.tar.gz"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/\n",
            "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/checkpoint/\n",
            "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/checkpoint/ckpt-0.data-00000-of-00001\n",
            "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/checkpoint/checkpoint\n",
            "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/checkpoint/ckpt-0.index\n",
            "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/pipeline.config\n",
            "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/saved_model/\n",
            "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/saved_model/saved_model.pb\n",
            "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/saved_model/variables/\n",
            "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/saved_model/variables/variables.data-00000-of-00001\n",
            "ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/saved_model/variables/variables.index\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H7-sWvqfxr7e",
        "outputId": "4790bb94-1421-45ef-a926-19c48b07ddab"
      },
      "source": [
        "%%bash\n",
        "cd /content/TensorFlow/workspace/training_demo/models\n",
        "mkdir my_ssd_mobilenet_v1_fpn\n",
        "cd ..\n",
        "cp -v pre-trained-models/ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/pipeline.config  models/my_ssd_mobilenet_v1_fpn/pipeline.config"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'pre-trained-models/ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8/pipeline.config' -> 'models/my_ssd_mobilenet_v1_fpn/pipeline.config'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KfeWYiT6aNDa"
      },
      "source": [
        "**TensorFlow 2 Object Detection API with Google Colab**\n",
        "\n",
        "[Medium](https://medium.com/@nisargkapkar/tensorflow-2-object-detection-api-with-google-colab-b2af171e81cc?source=friends_link&sk=0bb205df0e1c29a2e78c28671ddf4494)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0dW4lm8371iZ",
        "outputId": "1cc8567b-1b04-4b43-dd30-0fe632297f65"
      },
      "source": [
        "#cd into the TensorFlow directory in your Google Drive\n",
        "%cd '/content/TensorFlow'\n",
        "\n",
        "#and clone the TensorFlow Model Garden repository\n",
        "!git clone https://github.com/tensorflow/models.git\n",
        "\n",
        "#using a older version of repo (21 Sept 2020)\n",
        "%cd '/content/TensorFlow/models'\n",
        "!git checkout -f e04dafd04d69053d3733bb91d47d0d95bc2c8199"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/TensorFlow\n",
            "Cloning into 'models'...\n",
            "remote: Enumerating objects: 81917, done.\u001b[K\n",
            "remote: Counting objects: 100% (384/384), done.\u001b[K\n",
            "remote: Compressing objects: 100% (168/168), done.\u001b[K\n",
            "remote: Total 81917 (delta 235), reused 344 (delta 216), pack-reused 81533\u001b[K\n",
            "Receiving objects: 100% (81917/81917), 596.34 MiB | 15.97 MiB/s, done.\n",
            "Resolving deltas: 100% (58434/58434), done.\n",
            "/content/TensorFlow/models\n",
            "Note: switching to 'e04dafd04d69053d3733bb91d47d0d95bc2c8199'.\n",
            "\n",
            "You are in 'detached HEAD' state. You can look around, make experimental\n",
            "changes and commit them, and you can discard any commits you make in this\n",
            "state without impacting any branches by switching back to a branch.\n",
            "\n",
            "If you want to create a new branch to retain commits you create, you may\n",
            "do so (now or later) by using -c with the switch command. Example:\n",
            "\n",
            "  git switch -c <new-branch-name>\n",
            "\n",
            "Or undo this operation with:\n",
            "\n",
            "  git switch -\n",
            "\n",
            "Turn off this advice by setting config variable advice.detachedHead to false\n",
            "\n",
            "HEAD is now at e04dafd04 Add copybara presumbit metadata, and move files to root dir.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "So0uPn6C8j-Z",
        "outputId": "2b28e9f6-ce80-464b-aa38-1c12a3a9c3b0"
      },
      "source": [
        "#Step 6- Install some required libraries and tools.\n",
        "\n",
        "!apt-get install protobuf-compiler python-lxml python-pil\n",
        "!pip install Cython pandas tf-slim lvis"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "protobuf-compiler is already the newest version (3.6.1.3-2ubuntu5).\n",
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            "  libimagequant0 libpython2-stdlib python-backports.functools-lru-cache\n",
            "  python-bs4 python-chardet python-html5lib python-olefile\n",
            "  python-pkg-resources python-six python-soupsieve python-webencodings python2\n",
            "  python2-minimal\n",
            "Suggested packages:\n",
            "  python-genshi python-lxml-dbg python-lxml-doc python-pil-doc python-pil-dbg\n",
            "  python-setuptools python2-doc python-tk\n",
            "The following NEW packages will be installed:\n",
            "  libimagequant0 libpython2-stdlib python-backports.functools-lru-cache\n",
            "  python-bs4 python-chardet python-html5lib python-lxml python-olefile\n",
            "  python-pil python-pkg-resources python-six python-soupsieve\n",
            "  python-webencodings python2 python2-minimal\n",
            "0 upgraded, 15 newly installed, 0 to remove and 22 not upgraded.\n",
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            "Requirement already satisfied: pyparsing>=2.4.0 in /usr/local/lib/python3.8/dist-packages (from lvis) (3.0.9)\n",
            "Requirement already satisfied: matplotlib>=3.1.1 in /usr/local/lib/python3.8/dist-packages (from lvis) (3.5.3)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.8/dist-packages (from lvis) (1.15.0)\n",
            "Requirement already satisfied: kiwisolver>=1.1.0 in /usr/local/lib/python3.8/dist-packages (from lvis) (1.4.4)\n",
            "Requirement already satisfied: cycler>=0.10.0 in /usr/local/lib/python3.8/dist-packages (from lvis) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.1.1->lvis) (4.38.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.1.1->lvis) (23.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.1.1->lvis) (8.4.0)\n",
            "Installing collected packages: tf-slim, lvis\n",
            "Successfully installed lvis-0.5.3 tf-slim-1.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0BI9kNtN9Hca",
        "outputId": "1a31c2f7-e4b8-47d9-ca1f-45a2cfefb6f0"
      },
      "source": [
        "#Step 7- Compile the Protobuf libraries.\n",
        "\n",
        "#cd into 'TensorFlow/models/research'\n",
        "%cd '/content/TensorFlow/models/research/'\n",
        "!protoc object_detection/protos/*.proto --python_out=."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/TensorFlow/models/research\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wEAg3ksA9Pih",
        "outputId": "e5b06a35-a573-4a4b-e2de-b6f252167c1c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Step 8- Set the environment.\n",
        "\n",
        "import os\n",
        "import sys\n",
        "!pip install tensorflow==2.7\n",
        "os.environ['PYTHONPATH']+=\":/content/TensorFlow/models\"\n",
        "sys.path.append(\"/content/TensorFlow/models/research\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow==2.7\n",
            "  Downloading tensorflow-2.7.0-cp38-cp38-manylinux2010_x86_64.whl (489.6 MB)\n",
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            "\u001b[?25hCollecting flatbuffers<3.0,>=1.12\n",
            "  Downloading flatbuffers-2.0.7-py2.py3-none-any.whl (26 kB)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow==2.7) (1.15.0)\n",
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            "Collecting keras-preprocessing>=1.1.1\n",
            "  Downloading Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.6/42.6 KB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Collecting tensorflow-estimator<2.8,~=2.7.0rc0\n",
            "  Downloading tensorflow_estimator-2.7.0-py2.py3-none-any.whl (463 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m463.1/463.1 KB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Collecting keras<2.8,>=2.7.0rc0\n",
            "  Downloading keras-2.7.0-py2.py3-none-any.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m77.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.8/dist-packages (from tensorboard~=2.6->tensorflow==2.7) (57.4.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.8/dist-packages (from tensorboard~=2.6->tensorflow==2.7) (0.6.1)\n",
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            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.8/dist-packages (from google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow==2.7) (4.9)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow==2.7) (5.3.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.8/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow==2.7) (1.3.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.8/dist-packages (from markdown>=2.6.8->tensorboard~=2.6->tensorflow==2.7) (6.0.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow==2.7) (2022.12.7)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow==2.7) (2.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow==2.7) (1.26.14)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow==2.7) (4.0.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.8/dist-packages (from werkzeug>=1.0.1->tensorboard~=2.6->tensorflow==2.7) (2.1.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard~=2.6->tensorflow==2.7) (3.15.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.8/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow==2.7) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow==2.7) (3.2.2)\n",
            "Installing collected packages: tensorflow-estimator, keras, flatbuffers, keras-preprocessing, tensorflow\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.11.0\n",
            "    Uninstalling tensorflow-estimator-2.11.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.11.0\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.11.0\n",
            "    Uninstalling keras-2.11.0:\n",
            "      Successfully uninstalled keras-2.11.0\n",
            "  Attempting uninstall: flatbuffers\n",
            "    Found existing installation: flatbuffers 23.1.21\n",
            "    Uninstalling flatbuffers-23.1.21:\n",
            "      Successfully uninstalled flatbuffers-23.1.21\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.11.0\n",
            "    Uninstalling tensorflow-2.11.0:\n",
            "      Successfully uninstalled tensorflow-2.11.0\n",
            "Successfully installed flatbuffers-2.0.7 keras-2.7.0 keras-preprocessing-1.1.2 tensorflow-2.7.0 tensorflow-estimator-2.7.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UT9-acjE9d2K",
        "outputId": "10c9ef60-d8b9-48bf-a91f-9e0d67087394"
      },
      "source": [
        "#Step 9- Build and Install setup.py.\n",
        "#%cd /content/TensorFlow/models/research\n",
        "!python setup.py build\n",
        "!python setup.py install"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "running build\n",
            "running build_py\n",
            "creating build\n",
            "creating build/lib\n",
            "creating build/lib/object_detection\n",
            "copying object_detection/model_lib_tf2_test.py -> build/lib/object_detection\n",
            "copying object_detection/eval_util_test.py -> build/lib/object_detection\n",
            "copying object_detection/inputs.py -> build/lib/object_detection\n",
            "copying object_detection/export_tflite_ssd_graph_lib.py -> build/lib/object_detection\n",
            "copying object_detection/export_tflite_graph_tf2.py -> build/lib/object_detection\n",
            "copying object_detection/export_tflite_graph_lib_tf2.py -> build/lib/object_detection\n",
            "copying object_detection/exporter.py -> build/lib/object_detection\n",
            "copying object_detection/exporter_tf1_test.py -> build/lib/object_detection\n",
            "copying object_detection/export_inference_graph.py -> build/lib/object_detection\n",
            "copying object_detection/model_lib_tf1_test.py -> build/lib/object_detection\n",
            "copying object_detection/model_tpu_main.py -> build/lib/object_detection\n",
            "copying object_detection/exporter_lib_tf2_test.py -> build/lib/object_detection\n",
            "copying object_detection/model_main_tf2.py -> build/lib/object_detection\n",
            "copying object_detection/eval_util.py -> build/lib/object_detection\n",
            "copying object_detection/exporter_main_v2.py -> build/lib/object_detection\n",
            "copying object_detection/export_tflite_graph_lib_tf2_test.py -> build/lib/object_detection\n",
            "copying object_detection/inputs_test.py -> build/lib/object_detection\n",
            "copying object_detection/model_hparams.py -> build/lib/object_detection\n",
            "copying object_detection/export_tflite_ssd_graph_lib_tf1_test.py -> build/lib/object_detection\n",
            "copying object_detection/__init__.py -> build/lib/object_detection\n",
            "copying object_detection/model_lib_v2.py -> build/lib/object_detection\n",
            "copying object_detection/export_tflite_ssd_graph.py -> build/lib/object_detection\n",
            "copying object_detection/model_lib.py -> build/lib/object_detection\n",
            "copying object_detection/exporter_lib_v2.py -> build/lib/object_detection\n",
            "copying object_detection/model_main.py -> build/lib/object_detection\n",
            "creating build/lib/object_detection/legacy\n",
            "copying object_detection/legacy/trainer_tf1_test.py -> build/lib/object_detection/legacy\n",
            "copying object_detection/legacy/eval.py -> build/lib/object_detection/legacy\n",
            "copying object_detection/legacy/trainer.py -> build/lib/object_detection/legacy\n",
            "copying object_detection/legacy/evaluator.py -> build/lib/object_detection/legacy\n",
            "copying object_detection/legacy/__init__.py -> build/lib/object_detection/legacy\n",
            "copying object_detection/legacy/train.py -> build/lib/object_detection/legacy\n",
            "creating build/lib/object_detection/inference\n",
            "copying object_detection/inference/detection_inference_tf1_test.py -> build/lib/object_detection/inference\n",
            "copying object_detection/inference/detection_inference.py -> build/lib/object_detection/inference\n",
            "copying object_detection/inference/__init__.py -> build/lib/object_detection/inference\n",
            "copying object_detection/inference/infer_detections.py -> build/lib/object_detection/inference\n",
            "creating build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/create_coco_tf_record_test.py -> build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/create_ava_actions_tf_record.py -> build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/create_kitti_tf_record.py -> build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/create_pet_tf_record.py -> build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/tf_record_creation_util.py -> build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/oid_tfrecord_creation.py -> build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/oid_hierarchical_labels_expansion.py -> build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/create_pascal_tf_record_test.py -> build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/oid_tfrecord_creation_test.py -> build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/tf_record_creation_util_test.py -> build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/create_kitti_tf_record_test.py -> build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/create_coco_tf_record.py -> build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/__init__.py -> build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/oid_hierarchical_labels_expansion_test.py -> build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/create_pascal_tf_record.py -> build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/seq_example_util.py -> build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/seq_example_util_test.py -> build/lib/object_detection/dataset_tools\n",
            "copying object_detection/dataset_tools/create_oid_tf_record.py -> build/lib/object_detection/dataset_tools\n",
            "creating build/lib/object_detection/meta_architectures\n",
            "copying object_detection/meta_architectures/context_rcnn_meta_arch_test.py -> build/lib/object_detection/meta_architectures\n",
            "copying object_detection/meta_architectures/ssd_meta_arch.py -> build/lib/object_detection/meta_architectures\n",
            "copying object_detection/meta_architectures/center_net_meta_arch_tf2_test.py -> build/lib/object_detection/meta_architectures\n",
            "copying object_detection/meta_architectures/ssd_meta_arch_test.py -> build/lib/object_detection/meta_architectures\n",
            "copying object_detection/meta_architectures/context_rcnn_meta_arch.py -> build/lib/object_detection/meta_architectures\n",
            "copying object_detection/meta_architectures/faster_rcnn_meta_arch_test_lib.py -> build/lib/object_detection/meta_architectures\n",
            "copying object_detection/meta_architectures/faster_rcnn_meta_arch_test.py -> build/lib/object_detection/meta_architectures\n",
            "copying object_detection/meta_architectures/rfcn_meta_arch_test.py -> build/lib/object_detection/meta_architectures\n",
            "copying object_detection/meta_architectures/context_rcnn_lib_tf2_test.py -> build/lib/object_detection/meta_architectures\n",
            "copying object_detection/meta_architectures/context_rcnn_lib_tf2.py -> build/lib/object_detection/meta_architectures\n",
            "copying object_detection/meta_architectures/context_rcnn_lib.py -> build/lib/object_detection/meta_architectures\n",
            "copying object_detection/meta_architectures/context_rcnn_lib_tf1_test.py -> build/lib/object_detection/meta_architectures\n",
            "copying object_detection/meta_architectures/faster_rcnn_meta_arch.py -> build/lib/object_detection/meta_architectures\n",
            "copying object_detection/meta_architectures/center_net_meta_arch.py -> build/lib/object_detection/meta_architectures\n",
            "copying object_detection/meta_architectures/__init__.py -> build/lib/object_detection/meta_architectures\n",
            "copying object_detection/meta_architectures/rfcn_meta_arch.py -> build/lib/object_detection/meta_architectures\n",
            "copying object_detection/meta_architectures/ssd_meta_arch_test_lib.py -> build/lib/object_detection/meta_architectures\n",
            "creating build/lib/object_detection/predictors\n",
            "copying object_detection/predictors/rfcn_keras_box_predictor_tf2_test.py -> build/lib/object_detection/predictors\n",
            "copying object_detection/predictors/mask_rcnn_keras_box_predictor_tf2_test.py -> build/lib/object_detection/predictors\n",
            "copying object_detection/predictors/rfcn_keras_box_predictor.py -> build/lib/object_detection/predictors\n",
            "copying object_detection/predictors/convolutional_keras_box_predictor_tf2_test.py -> build/lib/object_detection/predictors\n",
            "copying object_detection/predictors/mask_rcnn_box_predictor_tf1_test.py -> build/lib/object_detection/predictors\n",
            "copying object_detection/predictors/rfcn_box_predictor.py -> build/lib/object_detection/predictors\n",
            "copying object_detection/predictors/mask_rcnn_box_predictor.py -> build/lib/object_detection/predictors\n",
            "copying object_detection/predictors/mask_rcnn_keras_box_predictor.py -> build/lib/object_detection/predictors\n",
            "copying object_detection/predictors/rfcn_box_predictor_tf1_test.py -> build/lib/object_detection/predictors\n",
            "copying object_detection/predictors/__init__.py -> build/lib/object_detection/predictors\n",
            "copying object_detection/predictors/convolutional_keras_box_predictor.py -> build/lib/object_detection/predictors\n",
            "copying object_detection/predictors/convolutional_box_predictor.py -> build/lib/object_detection/predictors\n",
            "copying object_detection/predictors/convolutional_box_predictor_tf1_test.py -> build/lib/object_detection/predictors\n",
            "creating build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/calibration_metrics.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/coco_tools.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/io_utils.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/coco_evaluation.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/calibration_evaluation.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/coco_tools_test.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/calibration_evaluation_tf1_test.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/coco_evaluation_test.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/oid_vrd_challenge_evaluation_utils_test.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/tf_example_parser_test.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/offline_eval_map_corloc.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/offline_eval_map_corloc_test.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/calibration_metrics_tf1_test.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/oid_challenge_evaluation_utils_test.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/oid_vrd_challenge_evaluation_utils.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/__init__.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/oid_vrd_challenge_evaluation.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/oid_challenge_evaluation_utils.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/tf_example_parser.py -> build/lib/object_detection/metrics\n",
            "copying object_detection/metrics/oid_challenge_evaluation.py -> build/lib/object_detection/metrics\n",
            "creating build/lib/object_detection/utils\n",
            "copying object_detection/utils/label_map_util.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/test_case_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/metrics.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/config_util.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/ops_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/json_utils.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/np_box_list_ops.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/label_map_util_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/test_case.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/metrics_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/static_shape.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/np_box_mask_list_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/vrd_evaluation_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/object_detection_evaluation_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/json_utils_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/config_util_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/ops.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/target_assigner_utils_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/tf_version.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/patch_ops_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/np_box_ops_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/np_box_mask_list_ops.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/np_box_ops.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/context_manager.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/shape_utils_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/dataset_util.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/target_assigner_utils.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/test_utils.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/np_box_mask_list_ops_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/learning_schedules_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/patch_ops.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/bifpn_utils.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/np_box_mask_list.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/category_util_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/context_manager_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/np_box_list.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/colab_utils.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/shape_utils.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/vrd_evaluation.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/category_util.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/per_image_evaluation.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/learning_schedules.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/variables_helper_tf1_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/test_utils_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/__init__.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/np_mask_ops_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/model_util_tf2_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/dataset_util_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/per_image_vrd_evaluation.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/spatial_transform_ops_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/per_image_vrd_evaluation_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/np_box_list_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/visualization_utils.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/object_detection_evaluation.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/autoaugment_utils.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/static_shape_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/variables_helper.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/model_util.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/visualization_utils_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/np_box_list_ops_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/spatial_transform_ops.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/per_image_evaluation_test.py -> build/lib/object_detection/utils\n",
            "copying object_detection/utils/np_mask_ops.py -> build/lib/object_detection/utils\n",
            "creating build/lib/object_detection/protos\n",
            "copying object_detection/protos/post_processing_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/string_int_label_map_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/graph_rewriter_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/image_resizer_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/eval_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/ssd_anchor_generator_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/grid_anchor_generator_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/multiscale_anchor_generator_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/box_predictor_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/center_net_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/bipartite_matcher_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/preprocessor_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/target_assigner_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/train_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/matcher_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/input_reader_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/argmax_matcher_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/flexible_grid_anchor_generator_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/optimizer_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/__init__.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/fpn_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/losses_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/calibration_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/box_coder_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/square_box_coder_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/mean_stddev_box_coder_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/faster_rcnn_box_coder_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/ssd_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/region_similarity_calculator_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/pipeline_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/anchor_generator_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/hyperparams_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/keypoint_box_coder_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/faster_rcnn_pb2.py -> build/lib/object_detection/protos\n",
            "copying object_detection/protos/model_pb2.py -> build/lib/object_detection/protos\n",
            "creating build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_efficientnet_bifpn_feature_extractor_tf2_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_inception_v2_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v3_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_inception_v3_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_resnet_v1_fpn_feature_extractor_tf2_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_resnet_v1_fpn_feature_extractor_testbase.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_edgetpu_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_mobilenet_v1_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/center_net_resnet_v1_fpn_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v2_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v1_fpn_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v2_keras_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_inception_v2_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_resnet_v1_fpn_keras_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_resnet_v1_ppn_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v2_fpn_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/bidirectional_feature_pyramid_generators_tf2_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_inception_resnet_v2_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/center_net_mobilenet_v2_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v2_fpn_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v3_feature_extractor_testbase.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_inception_v3_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_resnet_v1_fpn_keras_feature_extractor_tf2_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_nas_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobiledet_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v2_fpn_keras_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v2_fpn_feature_extractor_tf2_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_inception_resnet_v2_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/center_net_mobilenet_v2_fpn_feature_extractor_tf2_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_nas_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_pnas_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_resnet_v1_fpn_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v1_ppn_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_inception_resnet_v2_keras_feature_extractor_tf2_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_resnet_keras_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v1_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/embedded_ssd_mobilenet_v1_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_inception_v2_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/embedded_ssd_mobilenet_v1_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/center_net_hourglass_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/center_net_resnet_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/center_net_resnet_v1_fpn_feature_extractor_tf2_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_pnasnet_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_edgetpu_feature_extractor_testbase.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_efficientnet_bifpn_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_resnet_v1_fpn_keras_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v1_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_feature_extractor_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_pnasnet_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/__init__.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/feature_map_generators.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v2_feature_extractor_tf2_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_edgetpu_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/feature_map_generators_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobiledet_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/center_net_mobilenet_v2_fpn_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_resnet_v1_ppn_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_mobilenet_v1_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/center_net_resnet_feature_extractor_tf2_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_inception_resnet_v2_keras_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_pnas_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v2_mnasfpn_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_resnet_v1_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v2_mnasfpn_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/bidirectional_feature_pyramid_generators.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_inception_v2_feature_extractor_tf1_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v1_keras_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v1_ppn_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v1_fpn_feature_extractor_tf2_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v1_fpn_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v1_fpn_keras_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_resnet_keras_feature_extractor_tf2_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/faster_rcnn_resnet_v1_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v2_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_resnet_v1_fpn_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/center_net_hourglass_feature_extractor_tf2_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v1_feature_extractor_tf2_test.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_mobilenet_v3_feature_extractor.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/ssd_resnet_v1_ppn_feature_extractor_testbase.py -> build/lib/object_detection/models\n",
            "copying object_detection/models/center_net_mobilenet_v2_feature_extractor_tf2_test.py -> build/lib/object_detection/models\n",
            "creating build/lib/object_detection/anchor_generators\n",
            "copying object_detection/anchor_generators/grid_anchor_generator.py -> build/lib/object_detection/anchor_generators\n",
            "copying object_detection/anchor_generators/grid_anchor_generator_test.py -> build/lib/object_detection/anchor_generators\n",
            "copying object_detection/anchor_generators/multiscale_grid_anchor_generator_test.py -> build/lib/object_detection/anchor_generators\n",
            "copying object_detection/anchor_generators/__init__.py -> build/lib/object_detection/anchor_generators\n",
            "copying object_detection/anchor_generators/multiple_grid_anchor_generator_test.py -> build/lib/object_detection/anchor_generators\n",
            "copying object_detection/anchor_generators/flexible_grid_anchor_generator.py -> build/lib/object_detection/anchor_generators\n",
            "copying object_detection/anchor_generators/multiple_grid_anchor_generator.py -> build/lib/object_detection/anchor_generators\n",
            "copying object_detection/anchor_generators/flexible_grid_anchor_generator_test.py -> build/lib/object_detection/anchor_generators\n",
            "copying object_detection/anchor_generators/multiscale_grid_anchor_generator.py -> build/lib/object_detection/anchor_generators\n",
            "creating build/lib/object_detection/tpu_exporters\n",
            "copying object_detection/tpu_exporters/utils.py -> build/lib/object_detection/tpu_exporters\n",
            "copying object_detection/tpu_exporters/export_saved_model_tpu_lib_tf1_test.py -> build/lib/object_detection/tpu_exporters\n",
            "copying object_detection/tpu_exporters/ssd.py -> build/lib/object_detection/tpu_exporters\n",
            "copying object_detection/tpu_exporters/utils_test.py -> build/lib/object_detection/tpu_exporters\n",
            "copying object_detection/tpu_exporters/export_saved_model_tpu.py -> build/lib/object_detection/tpu_exporters\n",
            "copying object_detection/tpu_exporters/faster_rcnn.py -> build/lib/object_detection/tpu_exporters\n",
            "copying object_detection/tpu_exporters/export_saved_model_tpu_lib.py -> build/lib/object_detection/tpu_exporters\n",
            "copying object_detection/tpu_exporters/__init__.py -> build/lib/object_detection/tpu_exporters\n",
            "creating build/lib/object_detection/core\n",
            "copying object_detection/core/model.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/box_coder.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/preprocessor_cache.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/target_assigner.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/matcher_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/box_coder_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/prefetcher.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/model_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/box_list_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/box_predictor.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/minibatch_sampler_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/losses.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/balanced_positive_negative_sampler.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/class_agnostic_nms_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/box_list.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/keypoint_ops_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/region_similarity_calculator_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/data_parser.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/prefetcher_tf1_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/region_similarity_calculator.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/batch_multiclass_nms_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/losses_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/anchor_generator.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/multiclass_nms_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/target_assigner_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/balanced_positive_negative_sampler_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/data_decoder.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/standard_fields.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/__init__.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/keypoint_ops.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/minibatch_sampler.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/preprocessor.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/densepose_ops_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/box_list_ops_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/preprocessor_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/batcher_tf1_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/matcher.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/densepose_ops.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/batcher.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/post_processing.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/box_list_ops.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/freezable_batch_norm_tf2_test.py -> build/lib/object_detection/core\n",
            "copying object_detection/core/freezable_batch_norm.py -> build/lib/object_detection/core\n",
            "creating build/lib/object_detection/data_decoders\n",
            "copying object_detection/data_decoders/tf_example_decoder_test.py -> build/lib/object_detection/data_decoders\n",
            "copying object_detection/data_decoders/tf_sequence_example_decoder_test.py -> build/lib/object_detection/data_decoders\n",
            "copying object_detection/data_decoders/__init__.py -> build/lib/object_detection/data_decoders\n",
            "copying object_detection/data_decoders/tf_sequence_example_decoder.py -> build/lib/object_detection/data_decoders\n",
            "copying object_detection/data_decoders/tf_example_decoder.py -> build/lib/object_detection/data_decoders\n",
            "creating build/lib/object_detection/box_coders\n",
            "copying object_detection/box_coders/faster_rcnn_box_coder.py -> build/lib/object_detection/box_coders\n",
            "copying object_detection/box_coders/mean_stddev_box_coder_test.py -> build/lib/object_detection/box_coders\n",
            "copying object_detection/box_coders/keypoint_box_coder_test.py -> build/lib/object_detection/box_coders\n",
            "copying object_detection/box_coders/square_box_coder_test.py -> build/lib/object_detection/box_coders\n",
            "copying object_detection/box_coders/faster_rcnn_box_coder_test.py -> build/lib/object_detection/box_coders\n",
            "copying object_detection/box_coders/square_box_coder.py -> build/lib/object_detection/box_coders\n",
            "copying object_detection/box_coders/__init__.py -> build/lib/object_detection/box_coders\n",
            "copying object_detection/box_coders/mean_stddev_box_coder.py -> build/lib/object_detection/box_coders\n",
            "copying object_detection/box_coders/keypoint_box_coder.py -> build/lib/object_detection/box_coders\n",
            "creating build/lib/object_detection/builders\n",
            "copying object_detection/builders/input_reader_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/dataset_builder_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/box_coder_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/model_builder_tf1_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/region_similarity_calculator_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/target_assigner_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/input_reader_builder_tf1_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/hyperparams_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/optimizer_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/preprocessor_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/model_builder_tf2_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/post_processing_builder_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/optimizer_builder_tf1_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/preprocessor_builder_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/dataset_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/losses_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/region_similarity_calculator_builder_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/graph_rewriter_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/image_resizer_builder_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/box_coder_builder_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/hyperparams_builder_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/graph_rewriter_builder_tf1_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/decoder_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/__init__.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/anchor_generator_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/matcher_builder_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/anchor_generator_builder_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/calibration_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/losses_builder_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/box_predictor_builder_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/post_processing_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/model_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/target_assigner_builder_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/decoder_builder_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/box_predictor_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/optimizer_builder_tf2_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/image_resizer_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/matcher_builder.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/calibration_builder_test.py -> build/lib/object_detection/builders\n",
            "copying object_detection/builders/model_builder_test.py -> build/lib/object_detection/builders\n",
            "creating build/lib/object_detection/matchers\n",
            "copying object_detection/matchers/hungarian_matcher.py -> build/lib/object_detection/matchers\n",
            "copying object_detection/matchers/bipartite_matcher_tf1_test.py -> build/lib/object_detection/matchers\n",
            "copying object_detection/matchers/hungarian_matcher_tf2_test.py -> build/lib/object_detection/matchers\n",
            "copying object_detection/matchers/argmax_matcher_test.py -> build/lib/object_detection/matchers\n",
            "copying object_detection/matchers/__init__.py -> build/lib/object_detection/matchers\n",
            "copying object_detection/matchers/argmax_matcher.py -> build/lib/object_detection/matchers\n",
            "copying object_detection/matchers/bipartite_matcher.py -> build/lib/object_detection/matchers\n",
            "creating build/lib/object_detection/dataset_tools/context_rcnn\n",
            "copying object_detection/dataset_tools/context_rcnn/generate_detection_data.py -> build/lib/object_detection/dataset_tools/context_rcnn\n",
            "copying object_detection/dataset_tools/context_rcnn/generate_embedding_data.py -> build/lib/object_detection/dataset_tools/context_rcnn\n",
            "copying object_detection/dataset_tools/context_rcnn/add_context_to_examples.py -> build/lib/object_detection/dataset_tools/context_rcnn\n",
            "copying object_detection/dataset_tools/context_rcnn/create_cococameratraps_tfexample_tf2_test.py -> build/lib/object_detection/dataset_tools/context_rcnn\n",
            "copying object_detection/dataset_tools/context_rcnn/add_context_to_examples_tf2_test.py -> build/lib/object_detection/dataset_tools/context_rcnn\n",
            "copying object_detection/dataset_tools/context_rcnn/__init__.py -> build/lib/object_detection/dataset_tools/context_rcnn\n",
            "copying object_detection/dataset_tools/context_rcnn/create_cococameratraps_tfexample_main.py -> build/lib/object_detection/dataset_tools/context_rcnn\n",
            "copying object_detection/dataset_tools/context_rcnn/generate_embedding_data_tf2_test.py -> build/lib/object_detection/dataset_tools/context_rcnn\n",
            "copying object_detection/dataset_tools/context_rcnn/generate_detection_data_tf2_test.py -> build/lib/object_detection/dataset_tools/context_rcnn\n",
            "creating build/lib/object_detection/predictors/heads\n",
            "copying object_detection/predictors/heads/keras_class_head_tf2_test.py -> build/lib/object_detection/predictors/heads\n",
            "copying object_detection/predictors/heads/class_head_tf1_test.py -> build/lib/object_detection/predictors/heads\n",
            "copying object_detection/predictors/heads/mask_head_tf1_test.py -> build/lib/object_detection/predictors/heads\n",
            "copying object_detection/predictors/heads/box_head.py -> build/lib/object_detection/predictors/heads\n",
            "copying object_detection/predictors/heads/keras_class_head.py -> build/lib/object_detection/predictors/heads\n",
            "copying object_detection/predictors/heads/head.py -> build/lib/object_detection/predictors/heads\n",
            "copying object_detection/predictors/heads/keypoint_head_tf1_test.py -> build/lib/object_detection/predictors/heads\n",
            "copying object_detection/predictors/heads/keypoint_head.py -> build/lib/object_detection/predictors/heads\n",
            "copying object_detection/predictors/heads/keras_box_head.py -> build/lib/object_detection/predictors/heads\n",
            "copying object_detection/predictors/heads/class_head.py -> build/lib/object_detection/predictors/heads\n",
            "copying object_detection/predictors/heads/keras_box_head_tf2_test.py -> build/lib/object_detection/predictors/heads\n",
            "copying object_detection/predictors/heads/keras_mask_head_tf2_test.py -> build/lib/object_detection/predictors/heads\n",
            "copying object_detection/predictors/heads/__init__.py -> build/lib/object_detection/predictors/heads\n",
            "copying object_detection/predictors/heads/mask_head.py -> build/lib/object_detection/predictors/heads\n",
            "copying object_detection/predictors/heads/box_head_tf1_test.py -> build/lib/object_detection/predictors/heads\n",
            "copying object_detection/predictors/heads/keras_mask_head.py -> build/lib/object_detection/predictors/heads\n",
            "creating build/lib/object_detection/models/keras_models\n",
            "copying object_detection/models/keras_models/inception_resnet_v2.py -> build/lib/object_detection/models/keras_models\n",
            "copying object_detection/models/keras_models/mobilenet_v2_tf2_test.py -> build/lib/object_detection/models/keras_models\n",
            "copying object_detection/models/keras_models/convert_keras_models.py -> build/lib/object_detection/models/keras_models\n",
            "copying object_detection/models/keras_models/mobilenet_v1.py -> build/lib/object_detection/models/keras_models\n",
            "copying object_detection/models/keras_models/test_utils.py -> build/lib/object_detection/models/keras_models\n",
            "copying object_detection/models/keras_models/resnet_v1_tf2_test.py -> build/lib/object_detection/models/keras_models\n",
            "copying object_detection/models/keras_models/resnet_v1.py -> build/lib/object_detection/models/keras_models\n",
            "copying object_detection/models/keras_models/hourglass_network_tf2_test.py -> build/lib/object_detection/models/keras_models\n",
            "copying object_detection/models/keras_models/inception_resnet_v2_tf2_test.py -> build/lib/object_detection/models/keras_models\n",
            "copying object_detection/models/keras_models/mobilenet_v1_tf2_test.py -> build/lib/object_detection/models/keras_models\n",
            "copying object_detection/models/keras_models/__init__.py -> build/lib/object_detection/models/keras_models\n",
            "copying object_detection/models/keras_models/model_utils.py -> build/lib/object_detection/models/keras_models\n",
            "copying object_detection/models/keras_models/mobilenet_v2.py -> build/lib/object_detection/models/keras_models\n",
            "copying object_detection/models/keras_models/hourglass_network.py -> build/lib/object_detection/models/keras_models\n",
            "creating build/lib/object_detection/tpu_exporters/testdata\n",
            "copying object_detection/tpu_exporters/testdata/__init__.py -> build/lib/object_detection/tpu_exporters/testdata\n",
            "running egg_info\n",
            "creating object_detection.egg-info\n",
            "writing object_detection.egg-info/PKG-INFO\n",
            "writing dependency_links to object_detection.egg-info/dependency_links.txt\n",
            "writing requirements to object_detection.egg-info/requires.txt\n",
            "writing top-level names to object_detection.egg-info/top_level.txt\n",
            "writing manifest file 'object_detection.egg-info/SOURCES.txt'\n",
            "reading manifest file 'object_detection.egg-info/SOURCES.txt'\n",
            "writing manifest file 'object_detection.egg-info/SOURCES.txt'\n",
            "running install\n",
            "running bdist_egg\n",
            "running egg_info\n",
            "writing object_detection.egg-info/PKG-INFO\n",
            "writing dependency_links to object_detection.egg-info/dependency_links.txt\n",
            "writing requirements to object_detection.egg-info/requires.txt\n",
            "writing top-level names to object_detection.egg-info/top_level.txt\n",
            "reading manifest file 'object_detection.egg-info/SOURCES.txt'\n",
            "writing manifest file 'object_detection.egg-info/SOURCES.txt'\n",
            "installing library code to build/bdist.linux-x86_64/egg\n",
            "running install_lib\n",
            "running build_py\n",
            "creating build/bdist.linux-x86_64\n",
            "creating build/bdist.linux-x86_64/egg\n",
            "creating build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/model_lib_tf2_test.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/eval_util_test.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "copying build/lib/object_detection/inputs.py -> build/bdist.linux-x86_64/egg/object_detection\n",
            "creating build/bdist.linux-x86_64/egg/object_detection/legacy\n",
            "copying build/lib/object_detection/legacy/trainer_tf1_test.py -> build/bdist.linux-x86_64/egg/object_detection/legacy\n",
            "copying build/lib/object_detection/legacy/eval.py -> build/bdist.linux-x86_64/egg/object_detection/legacy\n",
            "copying build/lib/object_detection/legacy/trainer.py -> build/bdist.linux-x86_64/egg/object_detection/legacy\n",
            "copying build/lib/object_detection/legacy/evaluator.py -> build/bdist.linux-x86_64/egg/object_detection/legacy\n",
            "copying build/lib/object_detection/legacy/__init__.py -> build/bdist.linux-x86_64/egg/object_detection/legacy\n",
            "copying build/lib/object_detection/legacy/train.py -> build/bdist.linux-x86_64/egg/object_detection/legacy\n",
            "creating build/bdist.linux-x86_64/egg/object_detection/inference\n",
            "copying build/lib/object_detection/inference/detection_inference_tf1_test.py -> build/bdist.linux-x86_64/egg/object_detection/inference\n",
            "copying build/lib/object_detection/inference/detection_inference.py -> build/bdist.linux-x86_64/egg/object_detection/inference\n",
            "copying build/lib/object_detection/inference/__init__.py -> build/bdist.linux-x86_64/egg/object_detection/inference\n",
            "copying build/lib/object_detection/inference/infer_detections.py -> build/bdist.linux-x86_64/egg/object_detection/inference\n",
            "creating build/bdist.linux-x86_64/egg/object_detection/dataset_tools\n",
            "copying build/lib/object_detection/dataset_tools/create_coco_tf_record_test.py -> build/bdist.linux-x86_64/egg/object_detection/dataset_tools\n",
            "copying build/lib/object_detection/dataset_tools/create_ava_actions_tf_record.py -> build/bdist.linux-x86_64/egg/object_detection/dataset_tools\n",
            "copying build/lib/object_detection/dataset_tools/create_kitti_tf_record.py -> build/bdist.linux-x86_64/egg/object_detection/dataset_tools\n",
            "creating build/bdist.linux-x86_64/egg/object_detection/dataset_tools/context_rcnn\n",
            "copying build/lib/object_detection/dataset_tools/context_rcnn/generate_detection_data.py -> build/bdist.linux-x86_64/egg/object_detection/dataset_tools/context_rcnn\n",
            "copying build/lib/object_detection/dataset_tools/context_rcnn/generate_embedding_data.py -> build/bdist.linux-x86_64/egg/object_detection/dataset_tools/context_rcnn\n",
            "copying build/lib/object_detection/dataset_tools/context_rcnn/add_context_to_examples.py -> build/bdist.linux-x86_64/egg/object_detection/dataset_tools/context_rcnn\n",
            "copying build/lib/object_detection/dataset_tools/context_rcnn/create_cococameratraps_tfexample_tf2_test.py -> build/bdist.linux-x86_64/egg/object_detection/dataset_tools/context_rcnn\n",
            "copying build/lib/object_detection/dataset_tools/context_rcnn/add_context_to_examples_tf2_test.py -> build/bdist.linux-x86_64/egg/object_detection/dataset_tools/context_rcnn\n",
            "copying build/lib/object_detection/dataset_tools/context_rcnn/__init__.py -> build/bdist.linux-x86_64/egg/object_detection/dataset_tools/context_rcnn\n",
            "copying build/lib/object_detection/dataset_tools/context_rcnn/create_cococameratraps_tfexample_main.py -> build/bdist.linux-x86_64/egg/object_detection/dataset_tools/context_rcnn\n",
            "copying build/lib/object_detection/dataset_tools/context_rcnn/generate_embedding_data_tf2_test.py -> build/bdist.linux-x86_64/egg/object_detection/dataset_tools/context_rcnn\n",
            "copying build/lib/object_detection/dataset_tools/context_rcnn/generate_detection_data_tf2_test.py -> build/bdist.linux-x86_64/egg/object_detection/dataset_tools/context_rcnn\n",
            "copying build/lib/object_detection/dataset_tools/create_pet_tf_record.py -> build/bdist.linux-x86_64/egg/object_detection/dataset_tools\n",
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            "creating build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying object_detection.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying object_detection.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying object_detection.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying object_detection.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying object_detection.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "zip_safe flag not set; analyzing archive contents...\n",
            "object_detection.core.__pycache__.densepose_ops.cpython-38: module references __file__\n",
            "object_detection.core.__pycache__.preprocessor.cpython-38: module MAY be using inspect.stack\n",
            "object_detection.utils.__pycache__.autoaugment_utils.cpython-38: module MAY be using inspect.stack\n",
            "creating dist\n",
            "creating 'dist/object_detection-0.1-py3.8.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n",
            "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n",
            "Processing object_detection-0.1-py3.8.egg\n",
            "creating /usr/local/lib/python3.8/dist-packages/object_detection-0.1-py3.8.egg\n",
            "Extracting object_detection-0.1-py3.8.egg to /usr/local/lib/python3.8/dist-packages\n",
            "Adding object-detection 0.1 to easy-install.pth file\n",
            "\n",
            "Installed /usr/local/lib/python3.8/dist-packages/object_detection-0.1-py3.8.egg\n",
            "Processing dependencies for object-detection==0.1\n",
            "Searching for Cython==0.29.33\n",
            "Best match: Cython 0.29.33\n",
            "Adding Cython 0.29.33 to easy-install.pth file\n",
            "Installing cygdb script to /usr/local/bin\n",
            "Installing cython script to /usr/local/bin\n",
            "Installing cythonize script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.8/dist-packages\n",
            "Searching for matplotlib==3.5.3\n",
            "Best match: matplotlib 3.5.3\n",
            "Adding matplotlib 3.5.3 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.8/dist-packages\n",
            "Searching for Pillow==8.4.0\n",
            "Best match: Pillow 8.4.0\n",
            "Adding Pillow 8.4.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.8/dist-packages\n",
            "Searching for cycler==0.11.0\n",
            "Best match: cycler 0.11.0\n",
            "Adding cycler 0.11.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.8/dist-packages\n",
            "Searching for numpy==1.22.4\n",
            "Best match: numpy 1.22.4\n",
            "Adding numpy 1.22.4 to easy-install.pth file\n",
            "Installing f2py script to /usr/local/bin\n",
            "Installing f2py3 script to /usr/local/bin\n",
            "Installing f2py3.8 script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.8/dist-packages\n",
            "Searching for pyparsing==3.0.9\n",
            "Best match: pyparsing 3.0.9\n",
            "Adding pyparsing 3.0.9 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.8/dist-packages\n",
            "Searching for kiwisolver==1.4.4\n",
            "Best match: kiwisolver 1.4.4\n",
            "Adding kiwisolver 1.4.4 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.8/dist-packages\n",
            "Searching for python-dateutil==2.8.2\n",
            "Best match: python-dateutil 2.8.2\n",
            "Adding python-dateutil 2.8.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.8/dist-packages\n",
            "Searching for fonttools==4.38.0\n",
            "Best match: fonttools 4.38.0\n",
            "Adding fonttools 4.38.0 to easy-install.pth file\n",
            "Installing fonttools script to /usr/local/bin\n",
            "Installing pyftmerge script to /usr/local/bin\n",
            "Installing pyftsubset script to /usr/local/bin\n",
            "Installing ttx script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.8/dist-packages\n",
            "Searching for packaging==23.0\n",
            "Best match: packaging 23.0\n",
            "Adding packaging 23.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.8/dist-packages\n",
            "Searching for six==1.15.0\n",
            "Best match: six 1.15.0\n",
            "Adding six 1.15.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.8/dist-packages\n",
            "Finished processing dependencies for object-detection==0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7M5qUba89u3S",
        "outputId": "075f0cda-c4b3-4607-e662-c06ab0f5d3f2"
      },
      "source": [
        "#Step 10- Test the installation.\n",
        "\n",
        "#cd into 'TensorFlow/models/research/object_detection/builders/'\n",
        "%cd '/content/TensorFlow/models/research/object_detection/builders/'\n",
        "!python model_builder_tf2_test.py\n",
        "from object_detection.utils import label_map_util\n",
        "from object_detection.utils import visualization_utils as viz_utils\n",
        "print('Done')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/TensorFlow/models/research/object_detection/builders\n",
            "Running tests under Python 3.8.10: /usr/bin/python3\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model\n",
            "2023-03-08 09:21:08.403844: 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",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model): 4.97s\n",
            "I0308 09:21:11.896066 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_create_center_net_model): 4.97s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_center_net_model\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_experimental_model\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n",
            "I0308 09:21:11.897417 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_experimental_model\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.02s\n",
            "I0308 09:21:11.917738 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.02s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.01s\n",
            "I0308 09:21:11.930007 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.01s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.01s\n",
            "I0308 09:21:11.943327 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.01s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.08s\n",
            "I0308 09:21:12.025470 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.08s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.08s\n",
            "I0308 09:21:12.107953 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.08s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.09s\n",
            "I0308 09:21:12.197517 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.09s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.08s\n",
            "I0308 09:21:12.280433 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.08s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.09s\n",
            "I0308 09:21:12.367239 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.09s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.02s\n",
            "I0308 09:21:12.391546 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.02s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n",
            "I0308 09:21:12.542019 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b0\n",
            "I0308 09:21:12.542176 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 64\n",
            "I0308 09:21:12.542245 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 3\n",
            "I0308 09:21:12.544145 140584800134976 efficientnet_model.py:147] round_filter input=32 output=32\n",
            "I0308 09:21:12.560681 140584800134976 efficientnet_model.py:147] round_filter input=32 output=32\n",
            "I0308 09:21:12.560808 140584800134976 efficientnet_model.py:147] round_filter input=16 output=16\n",
            "I0308 09:21:12.614641 140584800134976 efficientnet_model.py:147] round_filter input=16 output=16\n",
            "I0308 09:21:12.614788 140584800134976 efficientnet_model.py:147] round_filter input=24 output=24\n",
            "I0308 09:21:12.748689 140584800134976 efficientnet_model.py:147] round_filter input=24 output=24\n",
            "I0308 09:21:12.748865 140584800134976 efficientnet_model.py:147] round_filter input=40 output=40\n",
            "I0308 09:21:12.880153 140584800134976 efficientnet_model.py:147] round_filter input=40 output=40\n",
            "I0308 09:21:12.880326 140584800134976 efficientnet_model.py:147] round_filter input=80 output=80\n",
            "I0308 09:21:13.083807 140584800134976 efficientnet_model.py:147] round_filter input=80 output=80\n",
            "I0308 09:21:13.083982 140584800134976 efficientnet_model.py:147] round_filter input=112 output=112\n",
            "I0308 09:21:13.288644 140584800134976 efficientnet_model.py:147] round_filter input=112 output=112\n",
            "I0308 09:21:13.288805 140584800134976 efficientnet_model.py:147] round_filter input=192 output=192\n",
            "I0308 09:21:13.572843 140584800134976 efficientnet_model.py:147] round_filter input=192 output=192\n",
            "I0308 09:21:13.573011 140584800134976 efficientnet_model.py:147] round_filter input=320 output=320\n",
            "I0308 09:21:13.638941 140584800134976 efficientnet_model.py:147] round_filter input=1280 output=1280\n",
            "I0308 09:21:13.665925 140584800134976 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.0, resolution=224, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0308 09:21:13.712503 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b1\n",
            "I0308 09:21:13.712664 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 88\n",
            "I0308 09:21:13.712734 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 4\n",
            "I0308 09:21:13.714234 140584800134976 efficientnet_model.py:147] round_filter input=32 output=32\n",
            "I0308 09:21:13.729627 140584800134976 efficientnet_model.py:147] round_filter input=32 output=32\n",
            "I0308 09:21:13.729741 140584800134976 efficientnet_model.py:147] round_filter input=16 output=16\n",
            "I0308 09:21:13.838485 140584800134976 efficientnet_model.py:147] round_filter input=16 output=16\n",
            "I0308 09:21:13.838665 140584800134976 efficientnet_model.py:147] round_filter input=24 output=24\n",
            "I0308 09:21:14.042460 140584800134976 efficientnet_model.py:147] round_filter input=24 output=24\n",
            "I0308 09:21:14.042629 140584800134976 efficientnet_model.py:147] round_filter input=40 output=40\n",
            "I0308 09:21:14.449310 140584800134976 efficientnet_model.py:147] round_filter input=40 output=40\n",
            "I0308 09:21:14.449482 140584800134976 efficientnet_model.py:147] round_filter input=80 output=80\n",
            "I0308 09:21:14.713328 140584800134976 efficientnet_model.py:147] round_filter input=80 output=80\n",
            "I0308 09:21:14.713507 140584800134976 efficientnet_model.py:147] round_filter input=112 output=112\n",
            "I0308 09:21:14.992160 140584800134976 efficientnet_model.py:147] round_filter input=112 output=112\n",
            "I0308 09:21:14.992325 140584800134976 efficientnet_model.py:147] round_filter input=192 output=192\n",
            "I0308 09:21:15.328826 140584800134976 efficientnet_model.py:147] round_filter input=192 output=192\n",
            "I0308 09:21:15.329009 140584800134976 efficientnet_model.py:147] round_filter input=320 output=320\n",
            "I0308 09:21:15.470231 140584800134976 efficientnet_model.py:147] round_filter input=1280 output=1280\n",
            "I0308 09:21:15.494655 140584800134976 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.1, resolution=240, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0308 09:21:15.544168 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b2\n",
            "I0308 09:21:15.544317 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 112\n",
            "I0308 09:21:15.544392 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 5\n",
            "I0308 09:21:15.545842 140584800134976 efficientnet_model.py:147] round_filter input=32 output=32\n",
            "I0308 09:21:15.562957 140584800134976 efficientnet_model.py:147] round_filter input=32 output=32\n",
            "I0308 09:21:15.563072 140584800134976 efficientnet_model.py:147] round_filter input=16 output=16\n",
            "I0308 09:21:15.667613 140584800134976 efficientnet_model.py:147] round_filter input=16 output=16\n",
            "I0308 09:21:15.667792 140584800134976 efficientnet_model.py:147] round_filter input=24 output=24\n",
            "I0308 09:21:15.872600 140584800134976 efficientnet_model.py:147] round_filter input=24 output=24\n",
            "I0308 09:21:15.872764 140584800134976 efficientnet_model.py:147] round_filter input=40 output=48\n",
            "I0308 09:21:16.077427 140584800134976 efficientnet_model.py:147] round_filter input=40 output=48\n",
            "I0308 09:21:16.077589 140584800134976 efficientnet_model.py:147] round_filter input=80 output=88\n",
            "I0308 09:21:16.378992 140584800134976 efficientnet_model.py:147] round_filter input=80 output=88\n",
            "I0308 09:21:16.379159 140584800134976 efficientnet_model.py:147] round_filter input=112 output=120\n",
            "I0308 09:21:16.650664 140584800134976 efficientnet_model.py:147] round_filter input=112 output=120\n",
            "I0308 09:21:16.650843 140584800134976 efficientnet_model.py:147] round_filter input=192 output=208\n",
            "I0308 09:21:16.986009 140584800134976 efficientnet_model.py:147] round_filter input=192 output=208\n",
            "I0308 09:21:16.986183 140584800134976 efficientnet_model.py:147] round_filter input=320 output=352\n",
            "I0308 09:21:17.118975 140584800134976 efficientnet_model.py:147] round_filter input=1280 output=1408\n",
            "I0308 09:21:17.143552 140584800134976 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.1, depth_coefficient=1.2, resolution=260, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0308 09:21:17.201980 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b3\n",
            "I0308 09:21:17.202128 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 160\n",
            "I0308 09:21:17.202195 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 6\n",
            "I0308 09:21:17.203571 140584800134976 efficientnet_model.py:147] round_filter input=32 output=40\n",
            "I0308 09:21:17.217733 140584800134976 efficientnet_model.py:147] round_filter input=32 output=40\n",
            "I0308 09:21:17.217847 140584800134976 efficientnet_model.py:147] round_filter input=16 output=24\n",
            "I0308 09:21:17.335131 140584800134976 efficientnet_model.py:147] round_filter input=16 output=24\n",
            "I0308 09:21:17.335337 140584800134976 efficientnet_model.py:147] round_filter input=24 output=32\n",
            "I0308 09:21:17.545539 140584800134976 efficientnet_model.py:147] round_filter input=24 output=32\n",
            "I0308 09:21:17.545712 140584800134976 efficientnet_model.py:147] round_filter input=40 output=48\n",
            "I0308 09:21:17.743124 140584800134976 efficientnet_model.py:147] round_filter input=40 output=48\n",
            "I0308 09:21:17.743289 140584800134976 efficientnet_model.py:147] round_filter input=80 output=96\n",
            "I0308 09:21:18.104642 140584800134976 efficientnet_model.py:147] round_filter input=80 output=96\n",
            "I0308 09:21:18.104865 140584800134976 efficientnet_model.py:147] round_filter input=112 output=136\n",
            "I0308 09:21:18.526096 140584800134976 efficientnet_model.py:147] round_filter input=112 output=136\n",
            "I0308 09:21:18.526278 140584800134976 efficientnet_model.py:147] round_filter input=192 output=232\n",
            "I0308 09:21:19.179221 140584800134976 efficientnet_model.py:147] round_filter input=192 output=232\n",
            "I0308 09:21:19.179390 140584800134976 efficientnet_model.py:147] round_filter input=320 output=384\n",
            "I0308 09:21:19.329907 140584800134976 efficientnet_model.py:147] round_filter input=1280 output=1536\n",
            "I0308 09:21:19.356086 140584800134976 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.2, depth_coefficient=1.4, resolution=300, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0308 09:21:19.411913 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b4\n",
            "I0308 09:21:19.412058 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 224\n",
            "I0308 09:21:19.412124 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7\n",
            "I0308 09:21:19.413472 140584800134976 efficientnet_model.py:147] round_filter input=32 output=48\n",
            "I0308 09:21:19.432025 140584800134976 efficientnet_model.py:147] round_filter input=32 output=48\n",
            "I0308 09:21:19.432161 140584800134976 efficientnet_model.py:147] round_filter input=16 output=24\n",
            "I0308 09:21:19.542215 140584800134976 efficientnet_model.py:147] round_filter input=16 output=24\n",
            "I0308 09:21:19.542367 140584800134976 efficientnet_model.py:147] round_filter input=24 output=32\n",
            "I0308 09:21:19.818453 140584800134976 efficientnet_model.py:147] round_filter input=24 output=32\n",
            "I0308 09:21:19.818618 140584800134976 efficientnet_model.py:147] round_filter input=40 output=56\n",
            "I0308 09:21:20.100224 140584800134976 efficientnet_model.py:147] round_filter input=40 output=56\n",
            "I0308 09:21:20.100397 140584800134976 efficientnet_model.py:147] round_filter input=80 output=112\n",
            "I0308 09:21:20.515487 140584800134976 efficientnet_model.py:147] round_filter input=80 output=112\n",
            "I0308 09:21:20.515672 140584800134976 efficientnet_model.py:147] round_filter input=112 output=160\n",
            "I0308 09:21:20.929236 140584800134976 efficientnet_model.py:147] round_filter input=112 output=160\n",
            "I0308 09:21:20.929413 140584800134976 efficientnet_model.py:147] round_filter input=192 output=272\n",
            "I0308 09:21:21.707202 140584800134976 efficientnet_model.py:147] round_filter input=192 output=272\n",
            "I0308 09:21:21.707404 140584800134976 efficientnet_model.py:147] round_filter input=320 output=448\n",
            "I0308 09:21:21.885023 140584800134976 efficientnet_model.py:147] round_filter input=1280 output=1792\n",
            "I0308 09:21:21.922894 140584800134976 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.4, depth_coefficient=1.8, resolution=380, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0308 09:21:22.045142 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b5\n",
            "I0308 09:21:22.045348 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 288\n",
            "I0308 09:21:22.045425 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7\n",
            "I0308 09:21:22.047586 140584800134976 efficientnet_model.py:147] round_filter input=32 output=48\n",
            "I0308 09:21:22.068095 140584800134976 efficientnet_model.py:147] round_filter input=32 output=48\n",
            "I0308 09:21:22.068223 140584800134976 efficientnet_model.py:147] round_filter input=16 output=24\n",
            "I0308 09:21:22.390942 140584800134976 efficientnet_model.py:147] round_filter input=16 output=24\n",
            "I0308 09:21:22.391940 140584800134976 efficientnet_model.py:147] round_filter input=24 output=40\n",
            "I0308 09:21:23.490439 140584800134976 efficientnet_model.py:147] round_filter input=24 output=40\n",
            "I0308 09:21:23.499945 140584800134976 efficientnet_model.py:147] round_filter input=40 output=64\n",
            "I0308 09:21:24.823248 140584800134976 efficientnet_model.py:147] round_filter input=40 output=64\n",
            "I0308 09:21:24.823698 140584800134976 efficientnet_model.py:147] round_filter input=80 output=128\n",
            "I0308 09:21:26.336735 140584800134976 efficientnet_model.py:147] round_filter input=80 output=128\n",
            "I0308 09:21:26.336989 140584800134976 efficientnet_model.py:147] round_filter input=112 output=176\n",
            "I0308 09:21:27.898146 140584800134976 efficientnet_model.py:147] round_filter input=112 output=176\n",
            "I0308 09:21:27.898383 140584800134976 efficientnet_model.py:147] round_filter input=192 output=304\n",
            "I0308 09:21:30.470824 140584800134976 efficientnet_model.py:147] round_filter input=192 output=304\n",
            "I0308 09:21:30.471049 140584800134976 efficientnet_model.py:147] round_filter input=320 output=512\n",
            "I0308 09:21:30.989738 140584800134976 efficientnet_model.py:147] round_filter input=1280 output=2048\n",
            "I0308 09:21:31.029731 140584800134976 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.6, depth_coefficient=2.2, resolution=456, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0308 09:21:31.200116 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b6\n",
            "I0308 09:21:31.200410 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 384\n",
            "I0308 09:21:31.200522 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8\n",
            "I0308 09:21:31.202807 140584800134976 efficientnet_model.py:147] round_filter input=32 output=56\n",
            "I0308 09:21:31.231371 140584800134976 efficientnet_model.py:147] round_filter input=32 output=56\n",
            "I0308 09:21:31.231540 140584800134976 efficientnet_model.py:147] round_filter input=16 output=32\n",
            "I0308 09:21:31.601703 140584800134976 efficientnet_model.py:147] round_filter input=16 output=32\n",
            "I0308 09:21:31.601930 140584800134976 efficientnet_model.py:147] round_filter input=24 output=40\n",
            "I0308 09:21:32.379040 140584800134976 efficientnet_model.py:147] round_filter input=24 output=40\n",
            "I0308 09:21:32.379259 140584800134976 efficientnet_model.py:147] round_filter input=40 output=72\n",
            "I0308 09:21:33.147123 140584800134976 efficientnet_model.py:147] round_filter input=40 output=72\n",
            "I0308 09:21:33.147349 140584800134976 efficientnet_model.py:147] round_filter input=80 output=144\n",
            "I0308 09:21:34.168319 140584800134976 efficientnet_model.py:147] round_filter input=80 output=144\n",
            "I0308 09:21:34.168555 140584800134976 efficientnet_model.py:147] round_filter input=112 output=200\n",
            "I0308 09:21:35.240909 140584800134976 efficientnet_model.py:147] round_filter input=112 output=200\n",
            "I0308 09:21:35.241135 140584800134976 efficientnet_model.py:147] round_filter input=192 output=344\n",
            "I0308 09:21:36.834401 140584800134976 efficientnet_model.py:147] round_filter input=192 output=344\n",
            "I0308 09:21:36.834600 140584800134976 efficientnet_model.py:147] round_filter input=320 output=576\n",
            "I0308 09:21:37.221714 140584800134976 efficientnet_model.py:147] round_filter input=1280 output=2304\n",
            "I0308 09:21:37.259591 140584800134976 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.8, depth_coefficient=2.6, resolution=528, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0308 09:21:37.403920 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b7\n",
            "I0308 09:21:37.404124 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 384\n",
            "I0308 09:21:37.404219 140584800134976 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8\n",
            "I0308 09:21:37.406407 140584800134976 efficientnet_model.py:147] round_filter input=32 output=64\n",
            "I0308 09:21:37.426776 140584800134976 efficientnet_model.py:147] round_filter input=32 output=64\n",
            "I0308 09:21:37.426931 140584800134976 efficientnet_model.py:147] round_filter input=16 output=32\n",
            "I0308 09:21:37.773090 140584800134976 efficientnet_model.py:147] round_filter input=16 output=32\n",
            "I0308 09:21:37.773293 140584800134976 efficientnet_model.py:147] round_filter input=24 output=48\n",
            "I0308 09:21:38.822129 140584800134976 efficientnet_model.py:147] round_filter input=24 output=48\n",
            "I0308 09:21:38.822349 140584800134976 efficientnet_model.py:147] round_filter input=40 output=80\n",
            "I0308 09:21:41.449944 140584800134976 efficientnet_model.py:147] round_filter input=40 output=80\n",
            "I0308 09:21:41.450137 140584800134976 efficientnet_model.py:147] round_filter input=80 output=160\n",
            "I0308 09:21:43.622934 140584800134976 efficientnet_model.py:147] round_filter input=80 output=160\n",
            "I0308 09:21:43.623125 140584800134976 efficientnet_model.py:147] round_filter input=112 output=224\n",
            "I0308 09:21:44.589262 140584800134976 efficientnet_model.py:147] round_filter input=112 output=224\n",
            "I0308 09:21:44.589472 140584800134976 efficientnet_model.py:147] round_filter input=192 output=384\n",
            "I0308 09:21:45.924871 140584800134976 efficientnet_model.py:147] round_filter input=192 output=384\n",
            "I0308 09:21:45.925077 140584800134976 efficientnet_model.py:147] round_filter input=320 output=640\n",
            "I0308 09:21:46.334352 140584800134976 efficientnet_model.py:147] round_filter input=1280 output=2560\n",
            "I0308 09:21:46.386466 140584800134976 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=2.0, depth_coefficient=3.1, resolution=600, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 34.17s\n",
            "I0308 09:21:46.563126 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 34.17s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n",
            "[ RUN      ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.01s\n",
            "I0308 09:21:46.583182 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.01s\n",
            "[       OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update\n",
            "[ RUN      ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s\n",
            "I0308 09:21:46.585611 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold\n",
            "[ RUN      ] ModelBuilderTF2Test.test_invalid_model_config_proto\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s\n",
            "I0308 09:21:46.586216 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_invalid_model_config_proto\n",
            "[ RUN      ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s\n",
            "I0308 09:21:46.588275 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size\n",
            "[ RUN      ] ModelBuilderTF2Test.test_session\n",
            "[  SKIPPED ] ModelBuilderTF2Test.test_session\n",
            "[ RUN      ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s\n",
            "I0308 09:21:46.590084 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor\n",
            "[ RUN      ] ModelBuilderTF2Test.test_unknown_meta_architecture\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n",
            "I0308 09:21:46.590587 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_unknown_meta_architecture\n",
            "[ RUN      ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n",
            "I0308 09:21:46.591900 140584800134976 test_util.py:2308] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n",
            "----------------------------------------------------------------------\n",
            "Ran 20 tests in 39.671s\n",
            "\n",
            "OK (skipped=1)\n",
            "Done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ql7XBP2VOhcV"
      },
      "source": [
        "Step 11- Copying some files\n",
        "\n",
        "\n",
        "*   Copy the \"model_main_tf2.py\" file from   \"TensorFlow\\models\\research\\object_detection\" and paste it into training_demo. We will need this file for training the model.\n",
        "\n",
        "*   Copy the \"exporter_main_v2.py\" file from \"TensorFlow\\models\\research\\object_detection\" and paste it into training_demo.\n",
        "We will need this file to export the trained model\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QuQDaTpb2Yea",
        "outputId": "547adb97-563e-4f05-c2af-8d39f96a91e2"
      },
      "source": [
        "%%bash\n",
        "cd /content/TensorFlow/\n",
        "cp -v models/research/object_detection/model_main_tf2.py workspace/training_demo/model_main_tf2.py\n",
        "cp -v models/research/object_detection/exporter_main_v2.py workspace/training_demo/exporter_main_v2.py "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'models/research/object_detection/model_main_tf2.py' -> 'workspace/training_demo/model_main_tf2.py'\n",
            "'models/research/object_detection/exporter_main_v2.py' -> 'workspace/training_demo/exporter_main_v2.py'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BNeZ5ytb9IUm"
      },
      "source": [
        "## Upload data\n",
        "train file with xml files in training_demo/images/train folder\n",
        "\n",
        "test file with xml files in training_demo/images/test folder\n",
        "\n",
        "upload label_map.pdtxt in trainig_demo/annotations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-O3ZOrb-EwH",
        "outputId": "afe3858c-a806-4733-ee56-8b10a26ff16b"
      },
      "source": [
        "#Mount Google Drive.\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3l-a8PBTBJvT"
      },
      "source": [
        "### Copying dataset from your drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jl7_1Clw9k6I"
      },
      "source": [
        "%%bash\n",
        "cp -r /content/drive/MyDrive/dataset_split/train /content/TensorFlow/workspace/training_demo/images\n",
        "cp -r /content/drive/MyDrive/dataset_split/test /content/TensorFlow/workspace/training_demo/images"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vQ9iPmLFEQJi"
      },
      "source": [
        "NOTE:\n",
        "\n",
        "You should have the images in test and train folder (with their corresponding XML files) and label_map.pbtxt file ready in respective directories.\n",
        "\n",
        "You should also have the generate_tfrecord.py in your preprocessing directory.\n",
        "\n",
        "If you don't have these files ready, go back to Step 1 and finish downloading required files."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5C5-PCp-elV",
        "outputId": "2b426b03-bcbf-4523-aed7-792de466542c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Step 12- Generate TFrecords.\n",
        "\n",
        "#cd into preprocessing directory\n",
        "%cd '/content/TensorFlow/scripts/preprocessing'\n",
        "\n",
        "#run the cell to generate test.record and train.record\n",
        "!python generate_tfrecord.py -x '/content/TensorFlow/workspace/training_demo/images/train' -l '/content/TensorFlow/workspace/training_demo/annotations/label_map.pbtxt' -o '/content/TensorFlow/workspace/training_demo/annotations/train.record'\n",
        "!python generate_tfrecord.py -x '/content/TensorFlow/workspace/training_demo/images/test' -l '/content/TensorFlow/workspace/training_demo/annotations/label_map.pbtxt' -o '/content/TensorFlow/workspace/training_demo/annotations/test.record'\n",
        "\n",
        "# !python generate_tfrecord.py -x '[path_to_train_folder]' -l '[path_to_annotations_folder]/label_map.pbtxt' -o '[path_to_annotations_folder]/train.record'\n",
        "# !python generate_tfrecord.py -x '[path_to_test_folder]' -l '[path_to_annotations_folder]/label_map.pbtxt' -o '[path_to_annotations_folder]/test.record'\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/TensorFlow/scripts/preprocessing\n",
            "Successfully created the TFRecord file: /content/TensorFlow/workspace/training_demo/annotations/train.record\n",
            "Successfully created the TFRecord file: /content/TensorFlow/workspace/training_demo/annotations/test.record\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Q5vNzgCJNDa"
      },
      "source": [
        "##Step 13- Configure the pipeline file.\n",
        "\n",
        "Refer the mentioned [Medium article](https://medium.com/@nisargkapkar/tensorflow-2-object-detection-api-with-google-colab-b2af171e81cc?source=friends_link&sk=0bb205df0e1c29a2e78c28671ddf4494) for more details!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7JISAAfK-inS"
      },
      "source": [
        "#Step 14- Start Tensorboard.\n",
        "\n",
        "#cd into training_demo\n",
        "%cd '/content/TensorFlow/workspace/training_demo'\n",
        "\n",
        "#start the Tensorboard\n",
        "%reload_ext tensorboard\n",
        "%tensorboard --logdir=models/my_ssd_mobilenet_v1_fpn\n",
        "\n",
        "# %load_ext tensorboard\n",
        "# %tensorboard --logdir=models/[name_of_pre-trained-model_you_downloaded]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_j6sPRggKbug"
      },
      "source": [
        "## Train Model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd '/content/TensorFlow/workspace/training_demo'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vSfaOD9urTI3",
        "outputId": "5984cb39-3722-46f3-a7ea-a0a9558b3fb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/TensorFlow/workspace/training_demo\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "yovSLWc_rT61",
        "outputId": "580f2d15-f5ec-4a6c-a934-ac554165ff13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/TensorFlow/workspace/training_demo'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yFLP5SUS-vYa",
        "outputId": "cf2642eb-6595-463f-ad0e-9e5e1088cd67",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Step 15- Train the model.\n",
        "\n",
        "#%cd '/content/TensorFlow/workspace/training_demo'\n",
        "#run the cell to start model training \n",
        "!python model_main_tf2.py --model_dir=models/my_ssd_mobilenet_v1_fpn --pipeline_config_path=models/my_ssd_mobilenet_v1_fpn/pipeline.config\n",
        "\n",
        "#!python model_main_tf2.py --model_dir=models/[name_of_pre-trained-model_you_downloaded] --pipeline_config_path=models/[name_of_pre-trained-model_you_downloaded]/pipeline.config"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-03-08 09:25:19.012903: 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",
            "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n",
            "I0308 09:25:19.017641 140191288239936 mirrored_strategy.py:376] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n",
            "INFO:tensorflow:Maybe overwriting train_steps: None\n",
            "I0308 09:25:19.021979 140191288239936 config_util.py:552] Maybe overwriting train_steps: None\n",
            "INFO:tensorflow:Maybe overwriting use_bfloat16: False\n",
            "I0308 09:25:19.022113 140191288239936 config_util.py:552] Maybe overwriting use_bfloat16: False\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/object_detection-0.1-py3.8.egg/object_detection/model_lib_v2.py:525: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "rename to distribute_datasets_from_function\n",
            "W0308 09:25:19.043877 140191288239936 deprecation.py:341] From /usr/local/lib/python3.8/dist-packages/object_detection-0.1-py3.8.egg/object_detection/model_lib_v2.py:525: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "rename to distribute_datasets_from_function\n",
            "WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.\n",
            "W0308 09:25:19.053275 140191288239936 dataset_builder.py:82] num_readers has been reduced to 1 to match input file shards.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/object_detection-0.1-py3.8.egg/object_detection/builders/dataset_builder.py:96: 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.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.\n",
            "W0308 09:25:19.055684 140191288239936 deprecation.py:341] From /usr/local/lib/python3.8/dist-packages/object_detection-0.1-py3.8.egg/object_detection/builders/dataset_builder.py:96: 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.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/object_detection-0.1-py3.8.egg/object_detection/builders/dataset_builder.py:174: 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",
            "W0308 09:25:19.078532 140191288239936 deprecation.py:341] From /usr/local/lib/python3.8/dist-packages/object_detection-0.1-py3.8.egg/object_detection/builders/dataset_builder.py:174: 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/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1096: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
            "W0308 09:25:24.348805 140191288239936 deprecation.py:341] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1096: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1096: 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",
            "W0308 09:25:26.783670 140191288239936 deprecation.py:341] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1096: 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/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:465: 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",
            "W0308 09:25:28.571798 140191288239936 deprecation.py:341] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:465: 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",
            "2023-03-08 09:25:32.896441: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 23970816 exceeds 10% of free system memory.\n",
            "2023-03-08 09:25:33.182236: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 23970816 exceeds 10% of free system memory.\n",
            "2023-03-08 09:25:33.420438: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at multi_device_iterator_ops.cc:789 : NOT_FOUND: Resource AnonymousMultiDeviceIterator/AnonymousMultiDeviceIterator0/N10tensorflow4data12_GLOBAL__N_119MultiDeviceIteratorE does not exist.\n",
            "2023-03-08 09:25:33.720794: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 95883264 exceeds 10% of free system memory.\n",
            "2023-03-08 09:25:33.905891: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 75057060 exceeds 10% of free system memory.\n",
            "2023-03-08 09:25:34.114679: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 75057060 exceeds 10% of free system memory.\n",
            "/usr/local/lib/python3.8/dist-packages/keras/backend.py:414: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n",
            "  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._groundtruth_lists\n",
            "W0308 09:26:03.590975 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._groundtruth_lists\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor\n",
            "W0308 09:26:03.591235 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._batched_prediction_tensor_names\n",
            "W0308 09:26:03.591316 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._batched_prediction_tensor_names\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head\n",
            "W0308 09:26:03.591385 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head\n",
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            "W0308 09:26:03.593729 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1\n",
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            "W0308 09:26:03.594489 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.8\n",
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            "W0308 09:26:03.594545 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10\n",
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            "W0308 09:26:03.594601 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.11\n",
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            "W0308 09:26:03.594657 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1\n",
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            "W0308 09:26:03.594713 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.2\n",
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            "W0308 09:26:03.594770 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4\n",
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            "W0308 09:26:03.594842 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.5\n",
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            "W0308 09:26:03.641154 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.11\n",
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            "W0308 09:26:03.641234 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1\n",
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            "W0308 09:26:03.641329 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.2\n",
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            "W0308 09:26:03.641412 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4\n",
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            "W0308 09:26:03.641493 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.5\n",
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            "W0308 09:26:03.641573 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7\n",
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            "W0308 09:26:03.641652 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.8\n",
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            "W0308 09:26:03.641731 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10\n",
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            "W0308 09:26:03.641812 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.11\n",
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            "W0308 09:26:03.641910 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1\n",
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            "W0308 09:26:03.641993 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.2\n",
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            "W0308 09:26:03.642073 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4\n",
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            "W0308 09:26:03.642152 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.5\n",
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            "W0308 09:26:03.642231 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7\n",
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            "W0308 09:26:03.642320 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.8\n",
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            "W0308 09:26:03.642402 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.10\n",
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            "W0308 09:26:03.642483 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.11\n",
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            "W0308 09:26:03.642561 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1\n",
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            "W0308 09:26:03.642639 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.2\n",
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            "W0308 09:26:03.642718 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4\n",
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            "W0308 09:26:03.642798 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.5\n",
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            "W0308 09:26:03.642894 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7\n",
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            "W0308 09:26:03.642976 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.8\n",
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            "W0308 09:26:03.643054 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.10\n",
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            "W0308 09:26:03.643132 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.11\n",
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            "W0308 09:26:03.643211 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1\n",
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            "W0308 09:26:03.643298 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.2\n",
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            "W0308 09:26:03.643382 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4\n",
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            "W0308 09:26:03.643470 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.5\n",
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            "W0308 09:26:03.643562 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7\n",
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            "W0308 09:26:03.643649 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.8\n",
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            "W0308 09:26:03.643723 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.10\n",
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            "W0308 09:26:03.643798 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.11\n",
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            "W0308 09:26:03.643874 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1\n",
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            "W0308 09:26:03.643965 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.2\n",
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            "W0308 09:26:03.644041 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4\n",
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            "W0308 09:26:03.644115 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.5\n",
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            "W0308 09:26:03.644211 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7\n",
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            "W0308 09:26:03.644294 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.8\n",
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            "W0308 09:26:03.644360 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.10\n",
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            "W0308 09:26:03.644418 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.11\n",
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            "W0308 09:26:03.644474 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1\n",
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            "W0308 09:26:03.644527 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.2\n",
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            "W0308 09:26:03.644585 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4\n",
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            "W0308 09:26:03.644652 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.5\n",
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            "W0308 09:26:03.644722 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7\n",
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            "W0308 09:26:03.644795 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.8\n",
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            "W0308 09:26:03.644886 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10\n",
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            "W0308 09:26:03.644968 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.11\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.0.kernel\n",
            "W0308 09:26:03.645046 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.0.kernel\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.1.kernel\n",
            "W0308 09:26:03.645126 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.1.kernel\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.2.kernel\n",
            "W0308 09:26:03.645205 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.2.kernel\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.3.kernel\n",
            "W0308 09:26:03.645284 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.3.kernel\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.0.kernel\n",
            "W0308 09:26:03.645378 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.0.kernel\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.1.kernel\n",
            "W0308 09:26:03.645458 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.1.kernel\n",
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            "W0308 09:26:03.645537 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.2.kernel\n",
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            "W0308 09:26:03.645615 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.3.kernel\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.kernel\n",
            "W0308 09:26:03.645699 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.kernel\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.bias\n",
            "W0308 09:26:03.645780 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.bias\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.axis\n",
            "W0308 09:26:03.645874 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.gamma\n",
            "W0308 09:26:03.645958 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.beta\n",
            "W0308 09:26:03.646017 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.moving_mean\n",
            "W0308 09:26:03.646075 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.moving_variance\n",
            "W0308 09:26:03.646135 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.axis\n",
            "W0308 09:26:03.646193 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.gamma\n",
            "W0308 09:26:03.646254 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.beta\n",
            "W0308 09:26:03.646324 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.moving_mean\n",
            "W0308 09:26:03.646383 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.moving_variance\n",
            "W0308 09:26:03.646447 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.axis\n",
            "W0308 09:26:03.646514 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.gamma\n",
            "W0308 09:26:03.646581 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.beta\n",
            "W0308 09:26:03.646649 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.moving_mean\n",
            "W0308 09:26:03.646718 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.moving_variance\n",
            "W0308 09:26:03.646805 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.axis\n",
            "W0308 09:26:03.646902 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.gamma\n",
            "W0308 09:26:03.646979 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.beta\n",
            "W0308 09:26:03.647053 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.moving_mean\n",
            "W0308 09:26:03.647125 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.moving_variance\n",
            "W0308 09:26:03.647197 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.axis\n",
            "W0308 09:26:03.647270 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.gamma\n",
            "W0308 09:26:03.647354 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.beta\n",
            "W0308 09:26:03.647429 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.moving_mean\n",
            "W0308 09:26:03.647501 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.moving_variance\n",
            "W0308 09:26:03.647574 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.axis\n",
            "W0308 09:26:03.647645 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.gamma\n",
            "W0308 09:26:03.647718 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.beta\n",
            "W0308 09:26:03.647790 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.moving_mean\n",
            "W0308 09:26:03.647879 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.moving_variance\n",
            "W0308 09:26:03.647955 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.axis\n",
            "W0308 09:26:03.648029 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.gamma\n",
            "W0308 09:26:03.648101 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.beta\n",
            "W0308 09:26:03.648173 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.moving_mean\n",
            "W0308 09:26:03.648252 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.moving_variance\n",
            "W0308 09:26:03.648346 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.axis\n",
            "W0308 09:26:03.648420 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.gamma\n",
            "W0308 09:26:03.648492 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.beta\n",
            "W0308 09:26:03.648565 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.moving_mean\n",
            "W0308 09:26:03.648635 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.moving_variance\n",
            "W0308 09:26:03.648707 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.axis\n",
            "W0308 09:26:03.648778 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.gamma\n",
            "W0308 09:26:03.648852 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.beta\n",
            "W0308 09:26:03.648939 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.moving_mean\n",
            "W0308 09:26:03.649013 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.moving_variance\n",
            "W0308 09:26:03.649086 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.axis\n",
            "W0308 09:26:03.649157 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.gamma\n",
            "W0308 09:26:03.649230 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.beta\n",
            "W0308 09:26:03.649311 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.moving_mean\n",
            "W0308 09:26:03.649386 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.moving_variance\n",
            "W0308 09:26:03.649457 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.axis\n",
            "W0308 09:26:03.649538 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.gamma\n",
            "W0308 09:26:03.649604 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.beta\n",
            "W0308 09:26:03.649672 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.moving_mean\n",
            "W0308 09:26:03.649739 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.moving_variance\n",
            "W0308 09:26:03.649807 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.axis\n",
            "W0308 09:26:03.649888 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.gamma\n",
            "W0308 09:26:03.649959 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.beta\n",
            "W0308 09:26:03.650027 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.moving_mean\n",
            "W0308 09:26:03.650093 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.moving_variance\n",
            "W0308 09:26:03.659106 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.axis\n",
            "W0308 09:26:03.659261 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.gamma\n",
            "W0308 09:26:03.659359 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.beta\n",
            "W0308 09:26:03.659443 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.moving_mean\n",
            "W0308 09:26:03.659522 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.moving_variance\n",
            "W0308 09:26:03.659601 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.axis\n",
            "W0308 09:26:03.659679 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.gamma\n",
            "W0308 09:26:03.659756 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.beta\n",
            "W0308 09:26:03.659845 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.moving_mean\n",
            "W0308 09:26:03.659942 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.moving_variance\n",
            "W0308 09:26:03.660020 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.axis\n",
            "W0308 09:26:03.660096 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.gamma\n",
            "W0308 09:26:03.660171 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.beta\n",
            "W0308 09:26:03.660253 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.moving_mean\n",
            "W0308 09:26:03.660356 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.moving_variance\n",
            "W0308 09:26:03.660437 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.axis\n",
            "W0308 09:26:03.660516 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.gamma\n",
            "W0308 09:26:03.660594 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.beta\n",
            "W0308 09:26:03.660674 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.moving_mean\n",
            "W0308 09:26:03.660753 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.moving_variance\n",
            "W0308 09:26:03.660846 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.axis\n",
            "W0308 09:26:03.660953 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.gamma\n",
            "W0308 09:26:03.661036 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.beta\n",
            "W0308 09:26:03.661115 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.moving_mean\n",
            "W0308 09:26:03.661195 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.moving_variance\n",
            "W0308 09:26:03.661273 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.axis\n",
            "W0308 09:26:03.661354 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.gamma\n",
            "W0308 09:26:03.661441 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.beta\n",
            "W0308 09:26:03.661514 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.moving_mean\n",
            "W0308 09:26:03.661586 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.moving_variance\n",
            "W0308 09:26:03.661661 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.axis\n",
            "W0308 09:26:03.661734 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.gamma\n",
            "W0308 09:26:03.661815 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.beta\n",
            "W0308 09:26:03.661910 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.moving_mean\n",
            "W0308 09:26:03.661986 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.moving_variance\n",
            "W0308 09:26:03.662060 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.axis\n",
            "W0308 09:26:03.662134 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.gamma\n",
            "W0308 09:26:03.662207 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.beta\n",
            "W0308 09:26:03.662281 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.moving_mean\n",
            "W0308 09:26:03.662357 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.moving_variance\n",
            "W0308 09:26:03.662432 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.axis\n",
            "W0308 09:26:03.662507 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.gamma\n",
            "W0308 09:26:03.662581 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.beta\n",
            "W0308 09:26:03.662656 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.moving_mean\n",
            "W0308 09:26:03.662730 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.moving_variance\n",
            "W0308 09:26:03.662814 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.axis\n",
            "W0308 09:26:03.662911 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.gamma\n",
            "W0308 09:26:03.662989 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.beta\n",
            "W0308 09:26:03.663065 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.moving_mean\n",
            "W0308 09:26:03.663139 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.moving_variance\n",
            "W0308 09:26:03.663212 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.axis\n",
            "W0308 09:26:03.663287 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.gamma\n",
            "W0308 09:26:03.663364 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.beta\n",
            "W0308 09:26:03.663439 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.moving_mean\n",
            "W0308 09:26:03.663515 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.moving_variance\n",
            "W0308 09:26:03.663590 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.10.axis\n",
            "W0308 09:26:03.663666 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.10.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.10.gamma\n",
            "W0308 09:26:03.663740 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.10.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.10.beta\n",
            "W0308 09:26:03.663824 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.10.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.10.moving_mean\n",
            "W0308 09:26:03.663919 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.10.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.10.moving_variance\n",
            "W0308 09:26:03.663996 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.10.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.axis\n",
            "W0308 09:26:03.664073 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.gamma\n",
            "W0308 09:26:03.664148 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.beta\n",
            "W0308 09:26:03.664222 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.moving_mean\n",
            "W0308 09:26:03.664296 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.moving_variance\n",
            "W0308 09:26:03.664373 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.axis\n",
            "W0308 09:26:03.664448 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.gamma\n",
            "W0308 09:26:03.664522 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.beta\n",
            "W0308 09:26:03.664595 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.moving_mean\n",
            "W0308 09:26:03.664669 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.moving_variance\n",
            "W0308 09:26:03.664742 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.axis\n",
            "W0308 09:26:03.664827 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.gamma\n",
            "W0308 09:26:03.664921 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.beta\n",
            "W0308 09:26:03.664998 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.moving_mean\n",
            "W0308 09:26:03.665073 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.moving_variance\n",
            "W0308 09:26:03.665146 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.10.axis\n",
            "W0308 09:26:03.665222 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.10.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.10.gamma\n",
            "W0308 09:26:03.665296 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.10.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.10.beta\n",
            "W0308 09:26:03.665371 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.10.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.10.moving_mean\n",
            "W0308 09:26:03.665445 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.10.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.10.moving_variance\n",
            "W0308 09:26:03.665519 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.10.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.axis\n",
            "W0308 09:26:03.665594 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.gamma\n",
            "W0308 09:26:03.665668 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.beta\n",
            "W0308 09:26:03.665742 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.moving_mean\n",
            "W0308 09:26:03.665848 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.moving_variance\n",
            "W0308 09:26:03.665947 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.axis\n",
            "W0308 09:26:03.666024 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.gamma\n",
            "W0308 09:26:03.666097 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.beta\n",
            "W0308 09:26:03.666162 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.moving_mean\n",
            "W0308 09:26:03.666226 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.moving_variance\n",
            "W0308 09:26:03.666293 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.axis\n",
            "W0308 09:26:03.666367 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.gamma\n",
            "W0308 09:26:03.666441 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.beta\n",
            "W0308 09:26:03.666514 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.moving_mean\n",
            "W0308 09:26:03.666588 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.moving_variance\n",
            "W0308 09:26:03.666660 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.10.axis\n",
            "W0308 09:26:03.666736 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.10.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.10.gamma\n",
            "W0308 09:26:03.666819 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.10.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.10.beta\n",
            "W0308 09:26:03.666913 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.10.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.10.moving_mean\n",
            "W0308 09:26:03.666999 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.10.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.10.moving_variance\n",
            "W0308 09:26:03.667068 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.10.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.axis\n",
            "W0308 09:26:03.667138 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.gamma\n",
            "W0308 09:26:03.667206 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.beta\n",
            "W0308 09:26:03.667274 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.moving_mean\n",
            "W0308 09:26:03.667342 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.moving_variance\n",
            "W0308 09:26:03.667412 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.axis\n",
            "W0308 09:26:03.667479 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.gamma\n",
            "W0308 09:26:03.667548 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.beta\n",
            "W0308 09:26:03.667615 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.moving_mean\n",
            "W0308 09:26:03.667683 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.moving_variance\n",
            "W0308 09:26:03.667751 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.axis\n",
            "W0308 09:26:03.669107 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.gamma\n",
            "W0308 09:26:03.669235 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.beta\n",
            "W0308 09:26:03.669301 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.moving_mean\n",
            "W0308 09:26:03.669363 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.moving_variance\n",
            "W0308 09:26:03.669421 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.10.axis\n",
            "W0308 09:26:03.669476 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.10.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.10.gamma\n",
            "W0308 09:26:03.669531 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.10.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.10.beta\n",
            "W0308 09:26:03.669583 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.10.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.10.moving_mean\n",
            "W0308 09:26:03.669640 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.10.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.10.moving_variance\n",
            "W0308 09:26:03.669696 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.10.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.axis\n",
            "W0308 09:26:03.669751 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.gamma\n",
            "W0308 09:26:03.669817 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.beta\n",
            "W0308 09:26:03.669900 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.moving_mean\n",
            "W0308 09:26:03.669987 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.moving_variance\n",
            "W0308 09:26:03.670059 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.axis\n",
            "W0308 09:26:03.670132 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.gamma\n",
            "W0308 09:26:03.670207 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.beta\n",
            "W0308 09:26:03.683982 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.moving_mean\n",
            "W0308 09:26:03.684108 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.moving_variance\n",
            "W0308 09:26:03.684204 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.axis\n",
            "W0308 09:26:03.684295 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.gamma\n",
            "W0308 09:26:03.684383 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.beta\n",
            "W0308 09:26:03.684466 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.moving_mean\n",
            "W0308 09:26:03.684557 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.moving_variance\n",
            "W0308 09:26:03.684635 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.axis\n",
            "W0308 09:26:03.684721 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.gamma\n",
            "W0308 09:26:03.684819 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.beta\n",
            "W0308 09:26:03.684916 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.moving_mean\n",
            "W0308 09:26:03.684990 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.moving_variance\n",
            "W0308 09:26:03.685062 140191288239936 util.py:181] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.moving_variance\n",
            "WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.\n",
            "W0308 09:26:03.685139 140191288239936 util.py:189] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0308 09:26:04.319126 140191288239936 cross_device_ops.py:619] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0308 09:26:04.320562 140191288239936 cross_device_ops.py:619] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0308 09:26:04.322935 140191288239936 cross_device_ops.py:619] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0308 09:26:04.323944 140191288239936 cross_device_ops.py:619] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0308 09:26:04.326255 140191288239936 cross_device_ops.py:619] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0308 09:26:04.327300 140191288239936 cross_device_ops.py:619] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0308 09:26:04.329587 140191288239936 cross_device_ops.py:619] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0308 09:26:04.330630 140191288239936 cross_device_ops.py:619] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0308 09:26:04.332967 140191288239936 cross_device_ops.py:619] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0308 09:26:04.333972 140191288239936 cross_device_ops.py:619] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/deprecation.py:620: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use fn_output_signature instead\n",
            "W0308 09:26:12.362543 140185610200832 deprecation.py:545] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/deprecation.py:620: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use fn_output_signature instead\n",
            "INFO:tensorflow:Step 100 per-step time 1.122s loss=1.156\n",
            "I0308 09:28:22.493860 140191288239936 model_lib_v2.py:649] Step 100 per-step time 1.122s loss=1.156\n",
            "INFO:tensorflow:Step 200 per-step time 1.130s loss=0.964\n",
            "I0308 09:30:17.431616 140191288239936 model_lib_v2.py:649] Step 200 per-step time 1.130s loss=0.964\n",
            "INFO:tensorflow:Step 300 per-step time 1.073s loss=0.963\n",
            "I0308 09:32:13.117177 140191288239936 model_lib_v2.py:649] Step 300 per-step time 1.073s loss=0.963\n",
            "INFO:tensorflow:Step 400 per-step time 1.078s loss=0.928\n",
            "I0308 09:34:07.263554 140191288239936 model_lib_v2.py:649] Step 400 per-step time 1.078s loss=0.928\n",
            "INFO:tensorflow:Step 500 per-step time 1.075s loss=0.894\n",
            "I0308 09:36:02.023148 140191288239936 model_lib_v2.py:649] Step 500 per-step time 1.075s loss=0.894\n",
            "INFO:tensorflow:Step 600 per-step time 1.063s loss=0.966\n",
            "I0308 09:37:55.971838 140191288239936 model_lib_v2.py:649] Step 600 per-step time 1.063s loss=0.966\n",
            "INFO:tensorflow:Step 700 per-step time 1.116s loss=0.877\n",
            "I0308 09:39:50.319415 140191288239936 model_lib_v2.py:649] Step 700 per-step time 1.116s loss=0.877\n",
            "INFO:tensorflow:Step 800 per-step time 1.196s loss=0.907\n",
            "I0308 09:41:44.674450 140191288239936 model_lib_v2.py:649] Step 800 per-step time 1.196s loss=0.907\n",
            "INFO:tensorflow:Step 900 per-step time 1.139s loss=0.883\n",
            "I0308 09:43:38.670893 140191288239936 model_lib_v2.py:649] Step 900 per-step time 1.139s loss=0.883\n",
            "INFO:tensorflow:Step 1000 per-step time 1.166s loss=1.169\n",
            "I0308 09:45:31.793961 140191288239936 model_lib_v2.py:649] Step 1000 per-step time 1.166s loss=1.169\n",
            "INFO:tensorflow:Step 1100 per-step time 1.216s loss=0.891\n",
            "I0308 09:47:27.329355 140191288239936 model_lib_v2.py:649] Step 1100 per-step time 1.216s loss=0.891\n",
            "INFO:tensorflow:Step 1200 per-step time 1.256s loss=0.879\n",
            "I0308 09:49:21.562068 140191288239936 model_lib_v2.py:649] Step 1200 per-step time 1.256s loss=0.879\n",
            "INFO:tensorflow:Step 1300 per-step time 1.087s loss=1.099\n",
            "I0308 09:51:16.723065 140191288239936 model_lib_v2.py:649] Step 1300 per-step time 1.087s loss=1.099\n",
            "INFO:tensorflow:Step 1400 per-step time 1.135s loss=0.862\n",
            "I0308 09:53:10.586821 140191288239936 model_lib_v2.py:649] Step 1400 per-step time 1.135s loss=0.862\n",
            "INFO:tensorflow:Step 1500 per-step time 1.099s loss=0.850\n",
            "I0308 09:55:05.463871 140191288239936 model_lib_v2.py:649] Step 1500 per-step time 1.099s loss=0.850\n",
            "INFO:tensorflow:Step 1600 per-step time 1.074s loss=0.852\n",
            "I0308 09:56:59.341102 140191288239936 model_lib_v2.py:649] Step 1600 per-step time 1.074s loss=0.852\n",
            "INFO:tensorflow:Step 1700 per-step time 1.184s loss=0.874\n",
            "I0308 09:58:54.237246 140191288239936 model_lib_v2.py:649] Step 1700 per-step time 1.184s loss=0.874\n",
            "INFO:tensorflow:Step 1800 per-step time 1.147s loss=0.851\n",
            "I0308 10:00:48.718649 140191288239936 model_lib_v2.py:649] Step 1800 per-step time 1.147s loss=0.851\n",
            "INFO:tensorflow:Step 1900 per-step time 1.189s loss=0.877\n",
            "I0308 10:02:44.317665 140191288239936 model_lib_v2.py:649] Step 1900 per-step time 1.189s loss=0.877\n",
            "INFO:tensorflow:Step 2000 per-step time 1.068s loss=0.842\n",
            "I0308 10:04:37.640372 140191288239936 model_lib_v2.py:649] Step 2000 per-step time 1.068s loss=0.842\n",
            "INFO:tensorflow:Step 2100 per-step time 1.047s loss=0.835\n",
            "I0308 10:06:32.996970 140191288239936 model_lib_v2.py:649] Step 2100 per-step time 1.047s loss=0.835\n",
            "INFO:tensorflow:Step 2200 per-step time 1.117s loss=0.853\n",
            "I0308 10:08:26.271673 140191288239936 model_lib_v2.py:649] Step 2200 per-step time 1.117s loss=0.853\n",
            "INFO:tensorflow:Step 2300 per-step time 1.109s loss=0.830\n",
            "I0308 10:10:21.214118 140191288239936 model_lib_v2.py:649] Step 2300 per-step time 1.109s loss=0.830\n",
            "INFO:tensorflow:Step 2400 per-step time 1.307s loss=0.863\n",
            "I0308 10:12:15.232075 140191288239936 model_lib_v2.py:649] Step 2400 per-step time 1.307s loss=0.863\n",
            "INFO:tensorflow:Step 2500 per-step time 1.197s loss=0.831\n",
            "I0308 10:14:09.955772 140191288239936 model_lib_v2.py:649] Step 2500 per-step time 1.197s loss=0.831\n",
            "INFO:tensorflow:Step 2600 per-step time 1.200s loss=0.824\n",
            "I0308 10:16:04.257626 140191288239936 model_lib_v2.py:649] Step 2600 per-step time 1.200s loss=0.824\n",
            "INFO:tensorflow:Step 2700 per-step time 1.164s loss=0.813\n",
            "I0308 10:17:59.216186 140191288239936 model_lib_v2.py:649] Step 2700 per-step time 1.164s loss=0.813\n",
            "INFO:tensorflow:Step 2800 per-step time 1.150s loss=0.827\n",
            "I0308 10:19:53.547380 140191288239936 model_lib_v2.py:649] Step 2800 per-step time 1.150s loss=0.827\n",
            "INFO:tensorflow:Step 2900 per-step time 1.086s loss=0.807\n",
            "I0308 10:21:47.346215 140191288239936 model_lib_v2.py:649] Step 2900 per-step time 1.086s loss=0.807\n",
            "INFO:tensorflow:Step 3000 per-step time 1.094s loss=0.832\n",
            "I0308 10:23:42.643484 140191288239936 model_lib_v2.py:649] Step 3000 per-step time 1.094s loss=0.832\n",
            "INFO:tensorflow:Step 3100 per-step time 1.183s loss=0.809\n",
            "I0308 10:25:37.645979 140191288239936 model_lib_v2.py:649] Step 3100 per-step time 1.183s loss=0.809\n",
            "INFO:tensorflow:Step 3200 per-step time 1.434s loss=0.808\n",
            "I0308 10:27:32.955767 140191288239936 model_lib_v2.py:649] Step 3200 per-step time 1.434s loss=0.808\n",
            "INFO:tensorflow:Step 3300 per-step time 1.215s loss=0.798\n",
            "I0308 10:29:27.173668 140191288239936 model_lib_v2.py:649] Step 3300 per-step time 1.215s loss=0.798\n",
            "INFO:tensorflow:Step 3400 per-step time 1.226s loss=0.790\n",
            "I0308 10:31:22.236544 140191288239936 model_lib_v2.py:649] Step 3400 per-step time 1.226s loss=0.790\n",
            "INFO:tensorflow:Step 3500 per-step time 1.096s loss=0.785\n",
            "I0308 10:33:16.125773 140191288239936 model_lib_v2.py:649] Step 3500 per-step time 1.096s loss=0.785\n",
            "INFO:tensorflow:Step 3600 per-step time 1.095s loss=0.775\n",
            "I0308 10:35:11.037492 140191288239936 model_lib_v2.py:649] Step 3600 per-step time 1.095s loss=0.775\n",
            "INFO:tensorflow:Step 3700 per-step time 1.091s loss=0.781\n",
            "I0308 10:37:04.175921 140191288239936 model_lib_v2.py:649] Step 3700 per-step time 1.091s loss=0.781\n",
            "INFO:tensorflow:Step 3800 per-step time 1.103s loss=0.783\n",
            "I0308 10:38:58.188099 140191288239936 model_lib_v2.py:649] Step 3800 per-step time 1.103s loss=0.783\n",
            "INFO:tensorflow:Step 3900 per-step time 1.175s loss=0.782\n",
            "I0308 10:40:51.913734 140191288239936 model_lib_v2.py:649] Step 3900 per-step time 1.175s loss=0.782\n",
            "INFO:tensorflow:Step 4000 per-step time 1.099s loss=0.783\n",
            "I0308 10:42:45.875648 140191288239936 model_lib_v2.py:649] Step 4000 per-step time 1.099s loss=0.783\n",
            "INFO:tensorflow:Step 4100 per-step time 1.273s loss=0.783\n",
            "I0308 10:44:40.645958 140191288239936 model_lib_v2.py:649] Step 4100 per-step time 1.273s loss=0.783\n",
            "INFO:tensorflow:Step 4200 per-step time 1.089s loss=0.767\n",
            "I0308 10:46:35.620579 140191288239936 model_lib_v2.py:649] Step 4200 per-step time 1.089s loss=0.767\n",
            "INFO:tensorflow:Step 4300 per-step time 1.096s loss=0.762\n",
            "I0308 10:48:30.608559 140191288239936 model_lib_v2.py:649] Step 4300 per-step time 1.096s loss=0.762\n",
            "INFO:tensorflow:Step 4400 per-step time 1.091s loss=0.764\n",
            "I0308 10:50:25.336939 140191288239936 model_lib_v2.py:649] Step 4400 per-step time 1.091s loss=0.764\n",
            "INFO:tensorflow:Step 4500 per-step time 1.132s loss=0.748\n",
            "I0308 10:52:19.142098 140191288239936 model_lib_v2.py:649] Step 4500 per-step time 1.132s loss=0.748\n",
            "INFO:tensorflow:Step 4600 per-step time 1.295s loss=0.766\n",
            "I0308 10:54:14.302294 140191288239936 model_lib_v2.py:649] Step 4600 per-step time 1.295s loss=0.766\n",
            "INFO:tensorflow:Step 4700 per-step time 1.147s loss=0.751\n",
            "I0308 10:56:08.284405 140191288239936 model_lib_v2.py:649] Step 4700 per-step time 1.147s loss=0.751\n",
            "INFO:tensorflow:Step 4800 per-step time 1.130s loss=0.744\n",
            "I0308 10:58:02.670045 140191288239936 model_lib_v2.py:649] Step 4800 per-step time 1.130s loss=0.744\n",
            "INFO:tensorflow:Step 4900 per-step time 1.088s loss=0.737\n",
            "I0308 10:59:56.360739 140191288239936 model_lib_v2.py:649] Step 4900 per-step time 1.088s loss=0.737\n",
            "INFO:tensorflow:Step 5000 per-step time 1.140s loss=0.743\n",
            "I0308 11:01:51.235159 140191288239936 model_lib_v2.py:649] Step 5000 per-step time 1.140s loss=0.743\n",
            "INFO:tensorflow:Step 5100 per-step time 1.186s loss=0.751\n",
            "I0308 11:03:45.765827 140191288239936 model_lib_v2.py:649] Step 5100 per-step time 1.186s loss=0.751\n",
            "INFO:tensorflow:Step 5200 per-step time 1.135s loss=0.773\n",
            "I0308 11:05:40.087906 140191288239936 model_lib_v2.py:649] Step 5200 per-step time 1.135s loss=0.773\n",
            "INFO:tensorflow:Step 5300 per-step time 1.299s loss=0.735\n",
            "I0308 11:07:34.516737 140191288239936 model_lib_v2.py:649] Step 5300 per-step time 1.299s loss=0.735\n",
            "INFO:tensorflow:Step 5400 per-step time 1.219s loss=0.734\n",
            "I0308 11:09:29.621692 140191288239936 model_lib_v2.py:649] Step 5400 per-step time 1.219s loss=0.734\n",
            "INFO:tensorflow:Step 5500 per-step time 1.069s loss=0.735\n",
            "I0308 11:11:23.257018 140191288239936 model_lib_v2.py:649] Step 5500 per-step time 1.069s loss=0.735\n",
            "INFO:tensorflow:Step 5600 per-step time 1.189s loss=0.726\n",
            "I0308 11:13:18.387677 140191288239936 model_lib_v2.py:649] Step 5600 per-step time 1.189s loss=0.726\n",
            "INFO:tensorflow:Step 5700 per-step time 1.139s loss=0.750\n",
            "I0308 11:15:12.187158 140191288239936 model_lib_v2.py:649] Step 5700 per-step time 1.139s loss=0.750\n",
            "INFO:tensorflow:Step 5800 per-step time 1.095s loss=0.715\n",
            "I0308 11:17:05.694606 140191288239936 model_lib_v2.py:649] Step 5800 per-step time 1.095s loss=0.715\n",
            "INFO:tensorflow:Step 5900 per-step time 1.247s loss=0.729\n",
            "I0308 11:19:00.654896 140191288239936 model_lib_v2.py:649] Step 5900 per-step time 1.247s loss=0.729\n",
            "INFO:tensorflow:Step 6000 per-step time 1.213s loss=0.709\n",
            "I0308 11:20:54.970110 140191288239936 model_lib_v2.py:649] Step 6000 per-step time 1.213s loss=0.709\n",
            "INFO:tensorflow:Step 6100 per-step time 1.192s loss=0.718\n",
            "I0308 11:22:50.738745 140191288239936 model_lib_v2.py:649] Step 6100 per-step time 1.192s loss=0.718\n",
            "INFO:tensorflow:Step 6200 per-step time 1.069s loss=0.708\n",
            "I0308 11:24:44.874540 140191288239936 model_lib_v2.py:649] Step 6200 per-step time 1.069s loss=0.708\n",
            "INFO:tensorflow:Step 6300 per-step time 1.189s loss=0.711\n",
            "I0308 11:26:40.310456 140191288239936 model_lib_v2.py:649] Step 6300 per-step time 1.189s loss=0.711\n",
            "INFO:tensorflow:Step 6400 per-step time 1.104s loss=0.701\n",
            "I0308 11:28:34.330177 140191288239936 model_lib_v2.py:649] Step 6400 per-step time 1.104s loss=0.701\n",
            "INFO:tensorflow:Step 6500 per-step time 1.224s loss=0.722\n",
            "I0308 11:30:28.861422 140191288239936 model_lib_v2.py:649] Step 6500 per-step time 1.224s loss=0.722\n",
            "INFO:tensorflow:Step 6600 per-step time 1.194s loss=0.700\n",
            "I0308 11:32:23.457143 140191288239936 model_lib_v2.py:649] Step 6600 per-step time 1.194s loss=0.700\n",
            "INFO:tensorflow:Step 6700 per-step time 1.291s loss=0.739\n",
            "I0308 11:34:18.588426 140191288239936 model_lib_v2.py:649] Step 6700 per-step time 1.291s loss=0.739\n",
            "INFO:tensorflow:Step 6800 per-step time 1.083s loss=0.726\n",
            "I0308 11:36:13.293772 140191288239936 model_lib_v2.py:649] Step 6800 per-step time 1.083s loss=0.726\n",
            "INFO:tensorflow:Step 6900 per-step time 1.115s loss=0.704\n",
            "I0308 11:38:07.925562 140191288239936 model_lib_v2.py:649] Step 6900 per-step time 1.115s loss=0.704\n",
            "INFO:tensorflow:Step 7000 per-step time 1.060s loss=0.693\n",
            "I0308 11:40:01.583317 140191288239936 model_lib_v2.py:649] Step 7000 per-step time 1.060s loss=0.693\n",
            "INFO:tensorflow:Step 7100 per-step time 1.137s loss=0.696\n",
            "I0308 11:41:56.588088 140191288239936 model_lib_v2.py:649] Step 7100 per-step time 1.137s loss=0.696\n",
            "INFO:tensorflow:Step 7200 per-step time 1.156s loss=0.688\n",
            "I0308 11:43:50.639903 140191288239936 model_lib_v2.py:649] Step 7200 per-step time 1.156s loss=0.688\n",
            "INFO:tensorflow:Step 7300 per-step time 1.137s loss=0.696\n",
            "I0308 11:45:45.410733 140191288239936 model_lib_v2.py:649] Step 7300 per-step time 1.137s loss=0.696\n",
            "INFO:tensorflow:Step 7400 per-step time 1.172s loss=0.695\n",
            "I0308 11:47:39.434103 140191288239936 model_lib_v2.py:649] Step 7400 per-step time 1.172s loss=0.695\n",
            "INFO:tensorflow:Step 7500 per-step time 1.129s loss=0.717\n",
            "I0308 11:49:33.628564 140191288239936 model_lib_v2.py:649] Step 7500 per-step time 1.129s loss=0.717\n",
            "INFO:tensorflow:Step 7600 per-step time 1.151s loss=0.678\n",
            "I0308 11:51:27.046953 140191288239936 model_lib_v2.py:649] Step 7600 per-step time 1.151s loss=0.678\n",
            "INFO:tensorflow:Step 7700 per-step time 1.166s loss=0.681\n",
            "I0308 11:53:21.753760 140191288239936 model_lib_v2.py:649] Step 7700 per-step time 1.166s loss=0.681\n",
            "INFO:tensorflow:Step 7800 per-step time 1.170s loss=0.680\n",
            "I0308 11:55:15.380548 140191288239936 model_lib_v2.py:649] Step 7800 per-step time 1.170s loss=0.680\n",
            "INFO:tensorflow:Step 7900 per-step time 1.093s loss=0.691\n",
            "I0308 11:57:09.572610 140191288239936 model_lib_v2.py:649] Step 7900 per-step time 1.093s loss=0.691\n",
            "INFO:tensorflow:Step 8000 per-step time 1.068s loss=0.687\n",
            "I0308 11:59:03.631338 140191288239936 model_lib_v2.py:649] Step 8000 per-step time 1.068s loss=0.687\n",
            "INFO:tensorflow:Step 8100 per-step time 1.212s loss=0.713\n",
            "I0308 12:00:59.143962 140191288239936 model_lib_v2.py:649] Step 8100 per-step time 1.212s loss=0.713\n",
            "INFO:tensorflow:Step 8200 per-step time 1.214s loss=0.683\n",
            "I0308 12:02:53.099915 140191288239936 model_lib_v2.py:649] Step 8200 per-step time 1.214s loss=0.683\n",
            "INFO:tensorflow:Step 8300 per-step time 1.237s loss=0.672\n",
            "I0308 12:04:47.630452 140191288239936 model_lib_v2.py:649] Step 8300 per-step time 1.237s loss=0.672\n",
            "INFO:tensorflow:Step 8400 per-step time 1.158s loss=0.671\n",
            "I0308 12:06:41.754487 140191288239936 model_lib_v2.py:649] Step 8400 per-step time 1.158s loss=0.671\n",
            "INFO:tensorflow:Step 8500 per-step time 1.249s loss=0.665\n",
            "I0308 12:08:35.425214 140191288239936 model_lib_v2.py:649] Step 8500 per-step time 1.249s loss=0.665\n",
            "INFO:tensorflow:Step 8600 per-step time 1.072s loss=0.659\n",
            "I0308 12:10:29.239828 140191288239936 model_lib_v2.py:649] Step 8600 per-step time 1.072s loss=0.659\n",
            "INFO:tensorflow:Step 8700 per-step time 1.076s loss=0.670\n",
            "I0308 12:12:22.782715 140191288239936 model_lib_v2.py:649] Step 8700 per-step time 1.076s loss=0.670\n",
            "INFO:tensorflow:Step 8800 per-step time 1.093s loss=0.658\n",
            "I0308 12:14:17.445203 140191288239936 model_lib_v2.py:649] Step 8800 per-step time 1.093s loss=0.658\n",
            "INFO:tensorflow:Step 8900 per-step time 1.232s loss=0.660\n",
            "I0308 12:16:11.215026 140191288239936 model_lib_v2.py:649] Step 8900 per-step time 1.232s loss=0.660\n",
            "INFO:tensorflow:Step 9000 per-step time 1.136s loss=0.668\n",
            "I0308 12:18:05.536758 140191288239936 model_lib_v2.py:649] Step 9000 per-step time 1.136s loss=0.668\n",
            "INFO:tensorflow:Step 9100 per-step time 1.252s loss=0.682\n",
            "I0308 12:19:59.901279 140191288239936 model_lib_v2.py:649] Step 9100 per-step time 1.252s loss=0.682\n",
            "INFO:tensorflow:Step 9200 per-step time 1.198s loss=0.665\n",
            "I0308 12:21:54.937921 140191288239936 model_lib_v2.py:649] Step 9200 per-step time 1.198s loss=0.665\n",
            "INFO:tensorflow:Step 9300 per-step time 1.032s loss=0.661\n",
            "I0308 12:23:48.701500 140191288239936 model_lib_v2.py:649] Step 9300 per-step time 1.032s loss=0.661\n",
            "INFO:tensorflow:Step 9400 per-step time 1.095s loss=0.645\n",
            "I0308 12:25:42.306077 140191288239936 model_lib_v2.py:649] Step 9400 per-step time 1.095s loss=0.645\n",
            "INFO:tensorflow:Step 9500 per-step time 1.097s loss=0.664\n",
            "I0308 12:27:36.071946 140191288239936 model_lib_v2.py:649] Step 9500 per-step time 1.097s loss=0.664\n",
            "INFO:tensorflow:Step 9600 per-step time 1.162s loss=0.666\n",
            "I0308 12:29:31.158539 140191288239936 model_lib_v2.py:649] Step 9600 per-step time 1.162s loss=0.666\n",
            "INFO:tensorflow:Step 9700 per-step time 1.239s loss=0.658\n",
            "I0308 12:31:25.879714 140191288239936 model_lib_v2.py:649] Step 9700 per-step time 1.239s loss=0.658\n",
            "INFO:tensorflow:Step 9800 per-step time 1.212s loss=0.654\n",
            "I0308 12:33:20.585509 140191288239936 model_lib_v2.py:649] Step 9800 per-step time 1.212s loss=0.654\n",
            "INFO:tensorflow:Step 9900 per-step time 1.089s loss=0.643\n",
            "I0308 12:35:14.270970 140191288239936 model_lib_v2.py:649] Step 9900 per-step time 1.089s loss=0.643\n",
            "INFO:tensorflow:Step 10000 per-step time 1.125s loss=0.653\n",
            "I0308 12:37:08.963011 140191288239936 model_lib_v2.py:649] Step 10000 per-step time 1.125s loss=0.653\n",
            "INFO:tensorflow:Step 10100 per-step time 1.055s loss=0.628\n",
            "I0308 12:39:03.210935 140191288239936 model_lib_v2.py:649] Step 10100 per-step time 1.055s loss=0.628\n",
            "INFO:tensorflow:Step 10200 per-step time 1.127s loss=0.630\n",
            "I0308 12:40:57.401336 140191288239936 model_lib_v2.py:649] Step 10200 per-step time 1.127s loss=0.630\n",
            "Traceback (most recent call last):\n",
            "  File \"model_main_tf2.py\", line 113, in <module>\n",
            "    tf.compat.v1.app.run()\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/app.py\", line 40, in run\n",
            "    _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/absl/app.py\", line 308, in run\n",
            "    _run_main(main, args)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/absl/app.py\", line 254, in _run_main\n",
            "    sys.exit(main(argv))\n",
            "  File \"model_main_tf2.py\", line 104, in main\n",
            "    model_lib_v2.train_loop(\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/object_detection-0.1-py3.8.egg/object_detection/model_lib_v2.py\", line 639, in train_loop\n",
            "    loss = _dist_train_step(train_input_iter)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/traceback_utils.py\", line 150, in error_handler\n",
            "    return fn(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\", line 910, in __call__\n",
            "    result = self._call(*args, **kwds)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\", line 942, in _call\n",
            "    return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\", line 3130, in __call__\n",
            "    return graph_function._call_flat(\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\", line 1959, in _call_flat\n",
            "    return self._build_call_outputs(self._inference_function.call(\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\", line 598, in call\n",
            "    outputs = execute.execute(\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py\", line 58, in quick_execute\n",
            "    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n",
            "KeyboardInterrupt\n",
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zDSirOAQ-0sy",
        "outputId": "3d25ada2-2d47-43f7-f97d-6adcf1dc4814",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Step 16- Export the Trained Model.\n",
        "#run the cell to start model training\n",
        "!python exporter_main_v2.py --input_type image_tensor --pipeline_config_path ./models/my_ssd_mobilenet_v1_fpn/pipeline.config --trained_checkpoint_dir ./models/my_ssd_mobilenet_v1_fpn/ --output_directory ./exported-models/my_model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-03-08 12:42:03.190411: 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",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7f3c600ac5b0>, because it is not built.\n",
            "W0308 12:42:29.916709 139899343013696 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7f3c600ac5b0>, because it is not built.\n",
            "2023-03-08 12:42:34.816239: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n",
            "W0308 12:42:51.464698 139899343013696 save.py:263] Found untraced functions such as WeightSharedConvolutionalBoxPredictor_layer_call_fn, WeightSharedConvolutionalBoxPredictor_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxHead_layer_call_fn, WeightSharedConvolutionalBoxHead_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxPredictor_layer_call_fn while saving (showing 5 of 520). These functions will not be directly callable after loading.\n",
            "INFO:tensorflow:Assets written to: ./exported-models/my_model/saved_model/assets\n",
            "I0308 12:42:55.585847 139899343013696 builder_impl.py:783] Assets written to: ./exported-models/my_model/saved_model/assets\n",
            "INFO:tensorflow:Writing pipeline config file to ./exported-models/my_model/pipeline.config\n",
            "I0308 12:42:56.145516 139899343013696 config_util.py:253] Writing pipeline config file to ./exported-models/my_model/pipeline.config\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tAQWLOON4dZt"
      },
      "source": [
        "## Testing the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nz2jiOgJ4mUK",
        "outputId": "603bd60a-0205-4732-b8fb-caccc2e64c43",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Step 17- Test the Model.\n",
        "\n",
        "#Loading the saved_model\n",
        "import tensorflow as tf\n",
        "import time\n",
        "from object_detection.utils import label_map_util\n",
        "from object_detection.utils import visualization_utils as viz_utils\n",
        "\n",
        "PATH_TO_SAVED_MODEL=\"/content/TensorFlow/workspace/training_demo/exported-models/my_model/saved_model\"\n",
        "\n",
        "print('Loading model...', end='')\n",
        "\n",
        "# Load saved model and build the detection function\n",
        "detect_fn=tf.saved_model.load(PATH_TO_SAVED_MODEL)\n",
        "\n",
        "print('Done!')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading model...Done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sX--5tHy4-S1"
      },
      "source": [
        "#Step 18- Testing the Model.\n",
        "\n",
        "#Loading the label_map\n",
        "category_index=label_map_util.create_category_index_from_labelmap(\"/content/TensorFlow/workspace/training_demo/annotations/label_map.pbtxt\",use_display_name=True)\n",
        "\n",
        "#category_index=label_map_util.create_category_index_from_labelmap([path_to_label_map],use_display_name=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UURD_H_c5grh",
        "outputId": "168141ec-af5f-46e6-adf1-dc1a93a40c13",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Step 19- Testing the Model.\n",
        "#download imag for testing\n",
        "#curl -o test.jpg https://images.thestar.com/FOfRxDY5GE4eTKn_ahGMn1CkWEo=/1200x798/smart/filters:cb(2700061000)/https://www.thestar.com/content/dam/thestar/life/food_wine/2013/11/04/apples_oranges_or_bananas_which_fruit_is_nutritionally_the_best/apple_orange_banana.jpg\n",
        "\n",
        "#Loading the image\n",
        "\n",
        "img=['/content/TensorFlow/workspace/training_demo/images/test/P9180018.jpg']\n",
        "print(img)\n",
        "\n",
        "#list containing paths of all the images"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['/content/TensorFlow/workspace/training_demo/images/test/P9180018.jpg']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tNLxnQNa54d_",
        "outputId": "68855cfd-162f-4572-e002-e7aa5c0271b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 923
        }
      },
      "source": [
        "img=['/content/t1.jpeg','/content/TensorFlow/workspace/training_demo/images/test/P9180018.jpg','/content/TensorFlow/workspace/training_demo/images/test/P9170028.jpg','/content/TensorFlow/workspace/training_demo/images/test/P9170016.jpg']\n",
        "\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "def load_image_into_numpy_array(path):\n",
        "    return np.array(Image.open(path))\n",
        "\n",
        "for image_path in img:\n",
        "    print('Running inference for {}... '.format(image_path), end='')\n",
        "    image_np=load_image_into_numpy_array(image_path)\n",
        "    input_tensor=tf.convert_to_tensor(image_np)\n",
        "    input_tensor=input_tensor[tf.newaxis, ...]\n",
        "    detections=detect_fn(input_tensor)\n",
        "    num_detections=int(detections.pop('num_detections'))\n",
        "    detections={key:value[0,:num_detections].numpy()\n",
        "                   for key,value in detections.items()}\n",
        "    detections['num_detections']=num_detections\n",
        "    detections['detection_classes']=detections['detection_classes'].astype(np.int64)\n",
        "    image_np_with_detections=image_np.copy()\n",
        "    viz_utils.visualize_boxes_and_labels_on_image_array(\n",
        "          image_np_with_detections,\n",
        "          detections['detection_boxes'],\n",
        "          detections['detection_classes'],\n",
        "          detections['detection_scores'],\n",
        "          category_index,\n",
        "          use_normalized_coordinates=True,\n",
        "          max_boxes_to_draw=100,     \n",
        "          min_score_thresh=.8,#0.0001      \n",
        "          agnostic_mode=False)\n",
        "    %matplotlib inline\n",
        "    plt.figure()\n",
        "    plt.imshow(image_np_with_detections)\n",
        "    print('Done')\n",
        "    plt.axis('off')\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running inference for /content/t1.jpeg... Done\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running inference for /content/TensorFlow/workspace/training_demo/images/test/P9180018.jpg... Done\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running inference for /content/TensorFlow/workspace/training_demo/images/test/P9170028.jpg... Done\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running inference for /content/TensorFlow/workspace/training_demo/images/test/P9170016.jpg... Done\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}