ODTA_SejongBHAJ_OneClick(for_Marking).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ODTA_SejongBHAJ_OneClick(for Marking)",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kM93m6IOZg3w",
        "colab_type": "text"
      },
      "source": [
        "# 일괄실행용 코드\n",
        "\n",
        "## 개발자용은 다른 링크를 참고해주세요.\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ElOUFzdCALTQ",
        "colab_type": "text"
      },
      "source": [
        "# 세종대학교 인공지능 텀프로젝트 - 휴학과 자퇴사이\n",
        "## Sejong Univ. AI Term Proejct - SejongBHAJ\n",
        "## Situation Classifier\n",
        "\n",
        "\n",
        "### Object Detection with Tensorflow API (ODTA)\n",
        "\n",
        "---\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MI8BLGdrM92J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "002dc5b4-3477-45b3-9cb2-7249a116884b"
      },
      "source": [
        "from google.colab import drive\n",
        "import os\n",
        "\n",
        "if not os.path.exists('/content/gdrive'):\n",
        "  drive.mount('/content/gdrive')\n",
        "  print('mounted')\n",
        "else:\n",
        "  print('already mounted')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n",
            "mounted\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Amt9fAZD7FD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "repo_url = 'https://github.com/Tony607/object_detection_demo'\n",
        "\n",
        "num_steps = 10000\n",
        "num_eval_steps = 50\n",
        "\n",
        "MODELS_CONFIG = {\n",
        "    'ssd_mobilenet_v2': {\n",
        "        'model_name': 'ssd_mobilenet_v2_coco_2018_03_29',\n",
        "        'pipeline_file': 'ssd_mobilenet_v2_coco.config',\n",
        "        'batch_size': 12\n",
        "    },\n",
        "    'faster_rcnn_inception_v2': {\n",
        "        'model_name': 'faster_rcnn_inception_v2_coco_2018_01_28',\n",
        "        'pipeline_file': 'faster_rcnn_inception_v2_pets.config',\n",
        "        'batch_size': 12\n",
        "    },\n",
        "    'rfcn_resnet101': {\n",
        "        'model_name': 'rfcn_resnet101_coco_2018_01_28',\n",
        "        'pipeline_file': 'rfcn_resnet101_pets.config',\n",
        "        'batch_size': 8\n",
        "    },\n",
        "    \n",
        "    'faster_rcnn_inception_resnet_v2_atrous': {\n",
        "        'model_name': 'faster_rcnn_inception_resnet_v2_atrous_coco_2018_01_28',\n",
        "        'pipeline_file': 'faster_rcnn_inception_resnet_v2_atrous_coco.config',\n",
        "        'batch_size': 8\n",
        "    },\n",
        "    \n",
        "    'ssd_resnet50_v1': {\n",
        "        'model_name': 'ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03',\n",
        "        'pipeline_file': 'ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync.config',\n",
        "        'batch_size': 1\n",
        "    }\n",
        "}\n",
        "\n",
        "\n",
        "\n",
        "selected_model = 'ssd_mobilenet_v2'\n",
        "    \n",
        "MODEL = MODELS_CONFIG[selected_model]['model_name']\n",
        "pipeline_file = MODELS_CONFIG[selected_model]['pipeline_file']    \n",
        "batch_size = MODELS_CONFIG[selected_model]['batch_size']    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NmmuEZBzE_JX",
        "colab_type": "code",
        "outputId": "f83dcd03-c01d-4fbf-cebf-3f9d90e330e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1621
        }
      },
      "source": [
        "import os\n",
        "%cd /content\n",
        "!git clone --quiet https://github.com/tensorflow/models.git\n",
        "!apt-get install -qq protobuf-compiler python-pil python-lxml python-tk\n",
        "\n",
        "!pip install -q Cython contextlib2 pillow lxml matplotlib\n",
        "!pip install -q pycocotools\n",
        "\n",
        "%cd /content/models/research\n",
        "!protoc object_detection/protos/*.proto --python_out=.\n",
        "\n",
        "os.environ['PYTHONPATH'] += ':/content/models/research/:/content/models/research/slim/'\n",
        "!python object_detection/builders/model_builder_test.py"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "Selecting previously unselected package python-bs4.\n",
            "(Reading database ... 130942 files and directories currently installed.)\n",
            "Preparing to unpack .../0-python-bs4_4.6.0-1_all.deb ...\n",
            "Unpacking python-bs4 (4.6.0-1) ...\n",
            "Selecting previously unselected package python-pkg-resources.\n",
            "Preparing to unpack .../1-python-pkg-resources_39.0.1-2_all.deb ...\n",
            "Unpacking python-pkg-resources (39.0.1-2) ...\n",
            "Selecting previously unselected package python-chardet.\n",
            "Preparing to unpack .../2-python-chardet_3.0.4-1_all.deb ...\n",
            "Unpacking python-chardet (3.0.4-1) ...\n",
            "Selecting previously unselected package python-six.\n",
            "Preparing to unpack .../3-python-six_1.11.0-2_all.deb ...\n",
            "Unpacking python-six (1.11.0-2) ...\n",
            "Selecting previously unselected package python-webencodings.\n",
            "Preparing to unpack .../4-python-webencodings_0.5-2_all.deb ...\n",
            "Unpacking python-webencodings (0.5-2) ...\n",
            "Selecting previously unselected package python-html5lib.\n",
            "Preparing to unpack .../5-python-html5lib_0.999999999-1_all.deb ...\n",
            "Unpacking python-html5lib (0.999999999-1) ...\n",
            "Selecting previously unselected package python-lxml:amd64.\n",
            "Preparing to unpack .../6-python-lxml_4.2.1-1ubuntu0.1_amd64.deb ...\n",
            "Unpacking python-lxml:amd64 (4.2.1-1ubuntu0.1) ...\n",
            "Selecting previously unselected package python-olefile.\n",
            "Preparing to unpack .../7-python-olefile_0.45.1-1_all.deb ...\n",
            "Unpacking python-olefile (0.45.1-1) ...\n",
            "Selecting previously unselected package python-pil:amd64.\n",
            "Preparing to unpack .../8-python-pil_5.1.0-1_amd64.deb ...\n",
            "Unpacking python-pil:amd64 (5.1.0-1) ...\n",
            "Setting up python-pkg-resources (39.0.1-2) ...\n",
            "Setting up python-six (1.11.0-2) ...\n",
            "Setting up python-bs4 (4.6.0-1) ...\n",
            "Setting up python-lxml:amd64 (4.2.1-1ubuntu0.1) ...\n",
            "Setting up python-olefile (0.45.1-1) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Setting up python-pil:amd64 (5.1.0-1) ...\n",
            "Setting up python-webencodings (0.5-2) ...\n",
            "Setting up python-chardet (3.0.4-1) ...\n",
            "Setting up python-html5lib (0.999999999-1) ...\n",
            "/content/models/research\n",
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0623 21:40:38.410171 140554367031168 lazy_loader.py:50] \n",
            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "W0623 21:40:38.631685 140554367031168 deprecation_wrapper.py:119] From /content/models/research/slim/nets/inception_resnet_v2.py:373: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
            "\n",
            "W0623 21:40:38.685621 140554367031168 deprecation_wrapper.py:119] From /content/models/research/slim/nets/mobilenet/mobilenet.py:397: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n",
            "\n",
            "Running tests under Python 3.6.8: /usr/bin/python3\n",
            "[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_model_from_config_with_example_miner\n",
            "[       OK ] ModelBuilderTest.test_create_faster_rcnn_model_from_config_with_example_miner\n",
            "[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
            "[       OK ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
            "[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
            "[       OK ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
            "[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
            "[       OK ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
            "[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
            "[       OK ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
            "[ RUN      ] ModelBuilderTest.test_create_rfcn_model_from_config\n",
            "[       OK ] ModelBuilderTest.test_create_rfcn_model_from_config\n",
            "[ RUN      ] ModelBuilderTest.test_create_ssd_fpn_model_from_config\n",
            "[       OK ] ModelBuilderTest.test_create_ssd_fpn_model_from_config\n",
            "[ RUN      ] ModelBuilderTest.test_create_ssd_models_from_config\n",
            "[       OK ] ModelBuilderTest.test_create_ssd_models_from_config\n",
            "[ RUN      ] ModelBuilderTest.test_invalid_faster_rcnn_batchnorm_update\n",
            "[       OK ] ModelBuilderTest.test_invalid_faster_rcnn_batchnorm_update\n",
            "[ RUN      ] ModelBuilderTest.test_invalid_first_stage_nms_iou_threshold\n",
            "[       OK ] ModelBuilderTest.test_invalid_first_stage_nms_iou_threshold\n",
            "[ RUN      ] ModelBuilderTest.test_invalid_model_config_proto\n",
            "[       OK ] ModelBuilderTest.test_invalid_model_config_proto\n",
            "[ RUN      ] ModelBuilderTest.test_invalid_second_stage_batch_size\n",
            "[       OK ] ModelBuilderTest.test_invalid_second_stage_batch_size\n",
            "[ RUN      ] ModelBuilderTest.test_session\n",
            "[  SKIPPED ] ModelBuilderTest.test_session\n",
            "[ RUN      ] ModelBuilderTest.test_unknown_faster_rcnn_feature_extractor\n",
            "[       OK ] ModelBuilderTest.test_unknown_faster_rcnn_feature_extractor\n",
            "[ RUN      ] ModelBuilderTest.test_unknown_meta_architecture\n",
            "[       OK ] ModelBuilderTest.test_unknown_meta_architecture\n",
            "[ RUN      ] ModelBuilderTest.test_unknown_ssd_feature_extractor\n",
            "[       OK ] ModelBuilderTest.test_unknown_ssd_feature_extractor\n",
            "----------------------------------------------------------------------\n",
            "Ran 16 tests in 0.230s\n",
            "\n",
            "OK (skipped=1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nne841FZEoD7",
        "colab_type": "code",
        "outputId": "668078ba-1bd6-4a1b-bf16-fcf29b0d31ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        }
      },
      "source": [
        "%cd /content\n",
        "repo_dir_path = os.path.abspath(os.path.join('.', os.path.basename(repo_url)))\n",
        "!git clone {repo_url}\n",
        "%cd {repo_dir_path}\n",
        "!git pull\n",
        "\n",
        "!rm data/images/train/*\n",
        "!rm data/images/test/*"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "Cloning into 'object_detection_demo'...\n",
            "remote: Enumerating objects: 124, done.\u001b[K\n",
            "remote: Total 124 (delta 0), reused 0 (delta 0), pack-reused 124\n",
            "Receiving objects: 100% (124/124), 11.16 MiB | 12.14 MiB/s, done.\n",
            "Resolving deltas: 100% (55/55), done.\n",
            "/content/object_detection_demo\n",
            "Already up to date.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MxbUmZsEjfSi",
        "colab_type": "text"
      },
      "source": [
        "## dataset download\n",
        "\n",
        "### structure\n",
        "```\n",
        "data.tar(1GB)\n",
        "\n",
        "ㄴtrain.tar(964MB)\n",
        "\n",
        "    ㄴSejong.mp4#t=0.tfrecord(1.25MB)\n",
        "        ㄴSejong.mp4#t=0.jpg\n",
        "        ㄴSejong.mp4#t=0.xml\n",
        "        \n",
        "    ㄴSejong.mp4#t=0.5.tfrecord(1.25MB)\n",
        "    ...\n",
        "    \n",
        "ㄴtest.tar(62.1MB)\n",
        "   ㄴSejong.mp4#t=445.tfrecord(1.25MB)\n",
        "   ...\n",
        "   ㄴSejong.mp4#t=469.tfrecord(1.25MB)\n",
        "   \n",
        "ㄴlabel_map.pbtxt(1.15KB)\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z0qMID_GjO66",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "2dbb3dad-388d-42e6-f8b9-8de99131356c"
      },
      "source": [
        "!mkdir -p data/annotations\n",
        "!mkdir -p data/annotations/train\n",
        "!mkdir -p data/annotations/test\n",
        "%cd '/content/object_detection_demo/data/annotations/'\n",
        "\n",
        "!wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1Ib2USZdv1nJcoGJQXNlAercKyZnnw-ZH' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1Ib2USZdv1nJcoGJQXNlAercKyZnnw-ZH\" -O data.tar && rm -rf /tmp/cookies.txt\n",
        "\n",
        "import tarfile\n",
        "\n",
        "tar=tarfile.open(\"data.tar\")\n",
        "tar.extract(\"train.tar\")\n",
        "tar.extract(\"test.tar\")\n",
        "tar.extract(\"label_map.pbtxt\")\n",
        "tar.close()\n",
        "\n",
        "!mv train.tar \"/content/object_detection_demo/data/annotations/train/train.tar\"\n",
        "!mv test.tar \"/content/object_detection_demo/data/annotations/test/test.tar\"\n",
        "\n",
        "%cd '/content/object_detection_demo/data/annotations/train'\n",
        "tar=tarfile.open(\"train.tar\")\n",
        "tar.extractall()\n",
        "tar.close()\n",
        "!rm train.tar\n",
        "\n",
        "%cd '/content/object_detection_demo/data/annotations/test'\n",
        "tar=tarfile.open(\"test.tar\")\n",
        "tar.extractall()\n",
        "tar.close()\n",
        "!rm test.tar\n",
        "\n",
        "%cd ../../../"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/object_detection_demo/data/annotations\n",
            "--2019-06-23 21:41:22--  https://docs.google.com/uc?export=download&confirm=BIyV&id=1Ib2USZdv1nJcoGJQXNlAercKyZnnw-ZH\n",
            "Resolving docs.google.com (docs.google.com)... 108.177.119.113, 108.177.119.101, 108.177.119.100, ...\n",
            "Connecting to docs.google.com (docs.google.com)|108.177.119.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-0s-4o-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/allrpttqnmrn0psbu97vjrjhr07iabt7/1561320000000/09621136847049687709/*/1Ib2USZdv1nJcoGJQXNlAercKyZnnw-ZH?e=download [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2019-06-23 21:41:22--  https://doc-0s-4o-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/allrpttqnmrn0psbu97vjrjhr07iabt7/1561320000000/09621136847049687709/*/1Ib2USZdv1nJcoGJQXNlAercKyZnnw-ZH?e=download\n",
            "Resolving doc-0s-4o-docs.googleusercontent.com (doc-0s-4o-docs.googleusercontent.com)... 108.177.127.132, 2a00:1450:4013:c07::84\n",
            "Connecting to doc-0s-4o-docs.googleusercontent.com (doc-0s-4o-docs.googleusercontent.com)|108.177.127.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [application/x-tar]\n",
            "Saving to: ‘data.tar’\n",
            "\n",
            "data.tar                [             <=>    ]   1.00G  56.5MB/s    in 19s     \n",
            "\n",
            "2019-06-23 21:41:42 (53.4 MB/s) - ‘data.tar’ saved [1076065792]\n",
            "\n",
            "/content/object_detection_demo/data/annotations/train\n",
            "/content/object_detection_demo/data/annotations/test\n",
            "/content/object_detection_demo\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "366cFOqeSMSB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "single='\"{}\"' #jpgxml\n",
        "multi=\"{}\" #tfrecord\n",
        "#print(single, multi)\n",
        "\n",
        "bracket_train=''\n",
        "bracket_test=''"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sT-Nou9tL7Tv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_record_fname_dir ='/content/object_detection_demo/data/annotations/train/'\n",
        "path=train_record_fname_dir\n",
        "file_list_tfrecord = []\n",
        "for file in os.listdir(path):\n",
        "  if file.endswith(\".tfrecord\"):\n",
        "    file_list_tfrecord.append(path+file)\n",
        "train_record_fname=file_list_tfrecord\n",
        "#print(train_record_fname)\n",
        "\n",
        "bracket_train=multi\n",
        "\n",
        "test_record_fname_dir ='/content/object_detection_demo/data/annotations/test/'\n",
        "path=test_record_fname_dir\n",
        "file_list_tfrecord = []\n",
        "for file in os.listdir(path):\n",
        "  if file.endswith(\".tfrecord\"):\n",
        "    file_list_tfrecord.append(path+file)\n",
        "test_record_fname=file_list_tfrecord\n",
        "#print(test_record_fname)\n",
        "\n",
        "bracket_test=multi"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Poa_0pm2vnbN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_map_pbtxt_fname ='/content/object_detection_demo/data/annotations/label_map.pbtxt'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3b_cMd95pP6s",
        "colab_type": "code",
        "outputId": "56f571d6-568d-4528-cfba-704071d959f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd /content/models/research\n",
        "\n",
        "import os\n",
        "import shutil\n",
        "import glob\n",
        "import urllib.request\n",
        "import tarfile\n",
        "MODEL_FILE = MODEL + '.tar.gz'\n",
        "DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'\n",
        "DEST_DIR = '/content/models/research/pretrained_model'\n",
        "\n",
        "if not (os.path.exists(MODEL_FILE)):\n",
        "    urllib.request.urlretrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)\n",
        "\n",
        "tar = tarfile.open(MODEL_FILE)\n",
        "tar.extractall()\n",
        "tar.close()\n",
        "\n",
        "os.remove(MODEL_FILE)\n",
        "if (os.path.exists(DEST_DIR)):\n",
        "    shutil.rmtree(DEST_DIR)\n",
        "os.rename(MODEL, DEST_DIR)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/models/research\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ht9pTMTpjip",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fine_tune_checkpoint = os.path.join(DEST_DIR, \"model.ckpt\")\n",
        "#fine_tune_checkpoint"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D42N60v0sdF8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "pipeline_fname = os.path.join('/content/models/research/object_detection/samples/configs/', pipeline_file)\n",
        "\n",
        "assert os.path.isfile(pipeline_fname), '`{}` not exist'.format(pipeline_fname)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PDZcRSHsszi3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_num_classes(pbtxt_fname):\n",
        "    from object_detection.utils import label_map_util\n",
        "    label_map = label_map_util.load_labelmap(pbtxt_fname)\n",
        "    categories = label_map_util.convert_label_map_to_categories(\n",
        "        label_map, max_num_classes=90, use_display_name=True)\n",
        "    category_index = label_map_util.create_category_index(categories)\n",
        "    return len(category_index.keys())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3jBpXzTQVrZl",
        "colab_type": "text"
      },
      "source": [
        "# 오류를 대비한 파이프라인 리셋버튼(백업)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_D05AStjZEz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp {pipeline_fname} {pipeline_fname}.bak"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YKY-d0dPCGZn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#!cp {pipeline_fname}.bak {pipeline_fname}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f0gsC01rN7br",
        "colab_type": "text"
      },
      "source": [
        "# !경고!\n",
        "\n",
        "## 데이터셋 업로드 여러 개의 tfrecord 파일을 업로드하였다면\n",
        "## 정규표현식 대입 수정이 필요합니다.\n",
        "\n",
        "### 1개일 경우:\n",
        "> input_path: \"{}\" (큰따음표)\n",
        "\n",
        "### 여러 개일 경우\n",
        "> input_path: '{}' (작은따음표)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zIPAVDYs1AP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "9d784748-6f9e-4468-c2a7-a8a49d574181"
      },
      "source": [
        "import re\n",
        "\n",
        "num_classes = get_num_classes(label_map_pbtxt_fname)\n",
        "\n",
        "#ERROR ! The number of calss of each label map pbtxt are different! You must have the same class to train.\n",
        "\n",
        "with open(pipeline_fname) as f:\n",
        "    s = f.read()\n",
        "with open(pipeline_fname, 'w') as f:\n",
        "    \n",
        "    # fine_tune_checkpoint\n",
        "    s = re.sub('fine_tune_checkpoint: \".*?\"',\n",
        "               'fine_tune_checkpoint: \"{}\"'.format(fine_tune_checkpoint), s)\n",
        "    \n",
        "    # tfrecord files train and test.\n",
        "    s = re.sub(\n",
        "        '(input_path: \".*?)(train.record)(.*?\")', 'input_path: '+bracket_train.format(train_record_fname), s)\n",
        "    s = re.sub(\n",
        "        '(input_path: \".*?)(val.record)(.*?\")', ('input_path: '+bracket_test).format(test_record_fname), s)\n",
        "\n",
        "    # label_map_path\n",
        "    s = re.sub(\n",
        "        'label_map_path: \".*?\"', 'label_map_path: \"{}\"'.format(label_map_pbtxt_fname), s)\n",
        "\n",
        "    # Set training batch_size.\n",
        "    s = re.sub('batch_size: [0-9]+',\n",
        "               'batch_size: {}'.format(batch_size), s)\n",
        "\n",
        "    # Set training steps, num_steps\n",
        "    s = re.sub('num_steps: [0-9]+',\n",
        "               'num_steps: {}'.format(num_steps), s)\n",
        "    \n",
        "    # Set number of classes num_classes.\n",
        "    s = re.sub('num_classes: [0-9]+',\n",
        "               'num_classes: {}'.format(num_classes), s)\n",
        "    \n",
        "    #s = re.sub('initial_learning_rate: 0.004', 'initial_learning_rate: 0.01', s)\n",
        "    f.write(s)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0623 21:42:32.275906 139690344122240 deprecation_wrapper.py:119] From /content/models/research/object_detection/utils/label_map_util.py:132: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FbteaNj4u_SI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "07caf80a-ca28-4789-a73b-3a82f1bc298f"
      },
      "source": [
        "%cd /\n",
        "model_dir = 'training/'\n",
        "# Optionally remove content in output model directory to fresh start.\n",
        "!rm -rf {model_dir}\n",
        "os.makedirs(model_dir, exist_ok=True)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7ghTBWhwrQw",
        "colab_type": "code",
        "outputId": "1165cd34-d47d-4aaa-ceeb-27d7585e1ff9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        }
      },
      "source": [
        "!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
        "!unzip -o ngrok-stable-linux-amd64.zip"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-06-23 21:43:01--  https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
            "Resolving bin.equinox.io (bin.equinox.io)... 34.224.236.142, 54.174.228.92, 34.226.180.131, ...\n",
            "Connecting to bin.equinox.io (bin.equinox.io)|34.224.236.142|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 17556757 (17M) [application/octet-stream]\n",
            "Saving to: ‘ngrok-stable-linux-amd64.zip’\n",
            "\n",
            "ngrok-stable-linux- 100%[===================>]  16.74M  16.6MB/s    in 1.0s    \n",
            "\n",
            "2019-06-23 21:43:03 (16.6 MB/s) - ‘ngrok-stable-linux-amd64.zip’ saved [17556757/17556757]\n",
            "\n",
            "Archive:  ngrok-stable-linux-amd64.zip\n",
            "  inflating: ngrok                   \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "itOR-8ICwuXH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "LOG_DIR = model_dir\n",
        "get_ipython().system_raw(\n",
        "    'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'\n",
        "    .format(LOG_DIR)\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mmwSRrQowvqP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "get_ipython().system_raw('./ngrok http 6006 &')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OyXXo9eBwzan",
        "colab_type": "code",
        "outputId": "46b6d501-dd4f-46ad-cef8-d4801699dc8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "! curl -s http://localhost:4040/api/tunnels | python3 -c \\\n",
        "    \"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\""
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "http://c519fd8d.ngrok.io\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hp2emd_l0KTc",
        "colab_type": "code",
        "outputId": "a66354ef-d614-40ed-b83f-79ab9f67308c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 20154
        }
      },
      "source": [
        "!python /content/models/research/object_detection/model_main.py \\\n",
        "    --pipeline_config_path={pipeline_fname} \\\n",
        "    --model_dir={model_dir} \\\n",
        "    --alsologtostderr \\\n",
        "    --num_train_steps={num_steps} \\\n",
        "    --num_eval_steps={num_eval_steps}"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0624 00:12:06.970629 140495266756480 lazy_loader.py:50] \n",
            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "W0624 00:12:06.995630 140495266756480 deprecation_wrapper.py:119] From /content/models/research/slim/nets/inception_resnet_v2.py:373: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
            "\n",
            "W0624 00:12:07.006050 140495266756480 deprecation_wrapper.py:119] From /content/models/research/slim/nets/mobilenet/mobilenet.py:397: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n",
            "\n",
            "W0624 00:12:07.017573 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/model_main.py:109: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.\n",
            "\n",
            "W0624 00:12:07.018591 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/utils/config_util.py:98: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
            "\n",
            "W0624 00:12:07.037469 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/model_lib.py:614: The name tf.logging.warning is deprecated. Please use tf.compat.v1.logging.warning instead.\n",
            "\n",
            "W0624 00:12:07.037589 140495266756480 model_lib.py:615] Forced number of epochs for all eval validations to be 1.\n",
            "W0624 00:12:07.037688 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/utils/config_util.py:484: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
            "\n",
            "I0624 00:12:07.037754 140495266756480 config_util.py:484] Maybe overwriting train_steps: 10000\n",
            "I0624 00:12:07.037825 140495266756480 config_util.py:484] Maybe overwriting sample_1_of_n_eval_examples: 1\n",
            "I0624 00:12:07.037894 140495266756480 config_util.py:484] Maybe overwriting use_bfloat16: False\n",
            "I0624 00:12:07.037958 140495266756480 config_util.py:484] Maybe overwriting eval_num_epochs: 1\n",
            "I0624 00:12:07.038025 140495266756480 config_util.py:484] Maybe overwriting load_pretrained: True\n",
            "I0624 00:12:07.038087 140495266756480 config_util.py:494] Ignoring config override key: load_pretrained\n",
            "W0624 00:12:07.038345 140495266756480 model_lib.py:631] Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.\n",
            "I0624 00:12:07.038442 140495266756480 model_lib.py:666] create_estimator_and_inputs: use_tpu False, export_to_tpu False\n",
            "I0624 00:12:07.038895 140495266756480 estimator.py:209] Using config: {'_model_dir': 'training/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fc74c4c0198>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
            "W0624 00:12:07.039091 140495266756480 model_fn.py:630] Estimator's model_fn (<function create_model_fn.<locals>.model_fn at 0x7fc74c4bb598>) includes params argument, but params are not passed to Estimator.\n",
            "I0624 00:12:07.039984 140495266756480 estimator_training.py:186] Not using Distribute Coordinator.\n",
            "I0624 00:12:07.040179 140495266756480 training.py:612] Running training and evaluation locally (non-distributed).\n",
            "I0624 00:12:07.040430 140495266756480 training.py:700] Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps None or save_checkpoints_secs 600.\n",
            "W0624 00:12:07.046416 140495266756480 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
            "W0624 00:12:07.059705 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/data_decoders/tf_example_decoder.py:177: The name tf.FixedLenFeature is deprecated. Please use tf.io.FixedLenFeature instead.\n",
            "\n",
            "W0624 00:12:07.059945 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/data_decoders/tf_example_decoder.py:192: The name tf.VarLenFeature is deprecated. Please use tf.io.VarLenFeature instead.\n",
            "\n",
            "W0624 00:12:07.081113 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/builders/dataset_builder.py:64: The name tf.gfile.Glob is deprecated. Please use tf.io.gfile.glob instead.\n",
            "\n",
            "W0624 00:12:10.265426 140495266756480 deprecation.py:323] From /content/models/research/object_detection/builders/dataset_builder.py:86: parallel_interleave (from tensorflow.contrib.data.python.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.experimental.parallel_interleave(...)`.\n",
            "W0624 00:12:10.265672 140495266756480 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/data/python/ops/interleave_ops.py:77: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_determinstic`.\n",
            "W0624 00:12:10.290801 140495266756480 deprecation.py:323] From /content/models/research/object_detection/builders/dataset_builder.py:155: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.map()\n",
            "W0624 00:12:10.461883 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/utils/ops.py:485: The name tf.is_nan is deprecated. Please use tf.math.is_nan instead.\n",
            "\n",
            "W0624 00:12:10.466188 140495266756480 deprecation.py:323] From /content/models/research/object_detection/utils/ops.py:487: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "W0624 00:12:10.517408 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/core/preprocessor.py:512: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W0624 00:12:10.574202 140495266756480 deprecation.py:323] From /content/models/research/object_detection/core/preprocessor.py:188: 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",
            "W0624 00:12:11.504434 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/core/preprocessor.py:2515: The name tf.image.resize_images is deprecated. Please use tf.image.resize instead.\n",
            "\n",
            "W0624 00:12:11.923192 140495266756480 deprecation.py:323] From /content/models/research/object_detection/builders/dataset_builder.py:158: batch_and_drop_remainder (from tensorflow.contrib.data.python.ops.batching) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.batch(..., drop_remainder=True)`.\n",
            "I0624 00:12:11.936965 140495266756480 estimator.py:1145] Calling model_fn.\n",
            "I0624 00:12:15.910511 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:12:15.950269 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:12:15.986980 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:12:16.023603 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:12:16.059923 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:12:16.272754 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "W0624 00:12:16.313089 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/utils/variables_helper.py:134: The name tf.train.NewCheckpointReader is deprecated. Please use tf.compat.v1.train.NewCheckpointReader instead.\n",
            "\n",
            "W0624 00:12:16.318114 140495266756480 variables_helper.py:149] Variable [FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 512]], model variable shape: [[3, 3, 256, 512]]. This variable will not be initialized from the checkpoint.\n",
            "W0624 00:12:16.318318 140495266756480 variables_helper.py:149] Variable [FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 128, 256]], model variable shape: [[3, 3, 128, 256]]. This variable will not be initialized from the checkpoint.\n",
            "W0624 00:12:16.318452 140495266756480 variables_helper.py:149] Variable [FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 128, 256]], model variable shape: [[3, 3, 128, 256]]. This variable will not be initialized from the checkpoint.\n",
            "W0624 00:12:16.318573 140495266756480 variables_helper.py:149] Variable [FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 64, 128]], model variable shape: [[3, 3, 64, 128]]. This variable will not be initialized from the checkpoint.\n",
            "W0624 00:12:16.318749 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/model_lib.py:346: The name tf.train.init_from_checkpoint is deprecated. Please use tf.compat.v1.train.init_from_checkpoint instead.\n",
            "\n",
            "W0624 00:12:19.687958 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/meta_architectures/ssd_meta_arch.py:1066: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.\n",
            "\n",
            "W0624 00:12:19.694721 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/core/losses.py:172: The name tf.losses.huber_loss is deprecated. Please use tf.compat.v1.losses.huber_loss instead.\n",
            "\n",
            "W0624 00:12:19.696023 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/core/losses.py:178: The name tf.losses.Reduction is deprecated. Please use tf.compat.v1.losses.Reduction instead.\n",
            "\n",
            "W0624 00:12:20.055831 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/model_lib.py:373: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.\n",
            "\n",
            "W0624 00:12:20.056172 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/utils/learning_schedules.py:61: The name tf.train.exponential_decay is deprecated. Please use tf.compat.v1.train.exponential_decay instead.\n",
            "\n",
            "W0624 00:12:20.066496 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/builders/optimizer_builder.py:47: The name tf.train.RMSPropOptimizer is deprecated. Please use tf.compat.v1.train.RMSPropOptimizer instead.\n",
            "\n",
            "W0624 00:12:22.363522 140495266756480 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/rmsprop.py:119: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "I0624 00:12:30.077341 140495266756480 estimator.py:1147] Done calling model_fn.\n",
            "I0624 00:12:30.078970 140495266756480 basic_session_run_hooks.py:541] Create CheckpointSaverHook.\n",
            "I0624 00:12:33.832801 140495266756480 monitored_session.py:240] Graph was finalized.\n",
            "2019-06-24 00:12:33.838331: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz\n",
            "2019-06-24 00:12:33.838578: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2317d40 executing computations on platform Host. Devices:\n",
            "2019-06-24 00:12:33.838611: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>\n",
            "2019-06-24 00:12:33.841001: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1\n",
            "2019-06-24 00:12:33.959800: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:12:33.960350: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2316680 executing computations on platform CUDA. Devices:\n",
            "2019-06-24 00:12:33.960382: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
            "2019-06-24 00:12:33.960661: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:12:33.960997: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 00:12:33.961386: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 00:12:33.962651: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 00:12:33.963876: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 00:12:33.964343: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 00:12:33.965935: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 00:12:33.967145: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 00:12:33.970633: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 00:12:33.970783: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:12:33.971209: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:12:33.971582: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 00:12:33.971695: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 00:12:33.972740: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-24 00:12:33.972768: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-24 00:12:33.972780: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-24 00:12:33.973091: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:12:33.973596: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:12:33.974113: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:40] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2019-06-24 00:12:33.974185: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12185 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "2019-06-24 00:12:38.900808: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set.  If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU.  To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.\n",
            "I0624 00:12:39.024994 140495266756480 session_manager.py:500] Running local_init_op.\n",
            "I0624 00:12:39.298661 140495266756480 session_manager.py:502] Done running local_init_op.\n",
            "I0624 00:12:50.396720 140495266756480 basic_session_run_hooks.py:606] Saving checkpoints for 0 into training/model.ckpt.\n",
            "2019-06-24 00:13:11.562556: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "I0624 00:13:14.747377 140495266756480 basic_session_run_hooks.py:262] loss = 105.80095, step = 0\n",
            "I0624 00:14:24.587238 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.43183\n",
            "I0624 00:14:24.588634 140495266756480 basic_session_run_hooks.py:260] loss = 8.554998, step = 100 (69.841 sec)\n",
            "I0624 00:15:30.066047 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.52721\n",
            "I0624 00:15:30.068359 140495266756480 basic_session_run_hooks.py:260] loss = 9.514282, step = 200 (65.480 sec)\n",
            "I0624 00:16:34.615533 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.5492\n",
            "I0624 00:16:34.616740 140495266756480 basic_session_run_hooks.py:260] loss = 6.805801, step = 300 (64.548 sec)\n",
            "I0624 00:17:39.014088 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.55283\n",
            "I0624 00:17:39.016342 140495266756480 basic_session_run_hooks.py:260] loss = 7.2064033, step = 400 (64.400 sec)\n",
            "I0624 00:18:43.284157 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.55593\n",
            "I0624 00:18:43.285420 140495266756480 basic_session_run_hooks.py:260] loss = 6.4567013, step = 500 (64.269 sec)\n",
            "I0624 00:19:47.729722 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.5517\n",
            "I0624 00:19:47.731811 140495266756480 basic_session_run_hooks.py:260] loss = 6.8269477, step = 600 (64.446 sec)\n",
            "I0624 00:20:51.767611 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.56158\n",
            "I0624 00:20:51.768935 140495266756480 basic_session_run_hooks.py:260] loss = 6.827889, step = 700 (64.037 sec)\n",
            "I0624 00:21:55.563485 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.5675\n",
            "I0624 00:21:55.565582 140495266756480 basic_session_run_hooks.py:260] loss = 6.1147356, step = 800 (63.797 sec)\n",
            "I0624 00:22:53.322229 140495266756480 basic_session_run_hooks.py:606] Saving checkpoints for 891 into training/model.ckpt.\n",
            "I0624 00:22:55.735069 140495266756480 estimator.py:1145] Calling model_fn.\n",
            "I0624 00:22:58.604364 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:22:58.640266 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:22:58.674383 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:22:58.708649 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:22:58.742856 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:22:58.776734 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "W0624 00:23:00.405161 140495266756480 deprecation.py:323] From /content/models/research/object_detection/eval_util.py:791: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0624 00:23:00.597440 140495266756480 deprecation.py:323] From /content/models/research/object_detection/utils/visualization_utils.py:492: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "tf.py_func is deprecated in TF V2. Instead, there are two\n",
            "    options available in V2.\n",
            "    - tf.py_function takes a python function which manipulates tf eager\n",
            "    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n",
            "    an ndarray (just call tensor.numpy()) but having access to eager tensors\n",
            "    means `tf.py_function`s can use accelerators such as GPUs as well as\n",
            "    being differentiable using a gradient tape.\n",
            "    - tf.numpy_function maintains the semantics of the deprecated tf.py_func\n",
            "    (it is not differentiable, and manipulates numpy arrays). It drops the\n",
            "    stateful argument making all functions stateful.\n",
            "    \n",
            "W0624 00:23:00.767196 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/utils/visualization_utils.py:1005: The name tf.summary.image is deprecated. Please use tf.compat.v1.summary.image instead.\n",
            "\n",
            "W0624 00:23:00.858964 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/model_lib.py:473: The name tf.metrics.mean is deprecated. Please use tf.compat.v1.metrics.mean instead.\n",
            "\n",
            "I0624 00:23:01.193911 140495266756480 estimator.py:1147] Done calling model_fn.\n",
            "I0624 00:23:01.227599 140495266756480 evaluation.py:255] Starting evaluation at 2019-06-24T00:23:01Z\n",
            "I0624 00:23:01.784669 140495266756480 monitored_session.py:240] Graph was finalized.\n",
            "2019-06-24 00:23:01.786048: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:23:01.786410: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 00:23:01.786513: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 00:23:01.786539: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 00:23:01.786561: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 00:23:01.786581: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 00:23:01.786602: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 00:23:01.786622: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 00:23:01.786644: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 00:23:01.786753: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:23:01.787112: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:23:01.787385: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 00:23:01.787429: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-24 00:23:01.787443: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-24 00:23:01.787453: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-24 00:23:01.787687: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:23:01.788026: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:23:01.788320: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12185 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "W0624 00:23:01.788469 140495266756480 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use standard file APIs to check for files with this prefix.\n",
            "I0624 00:23:01.790481 140495266756480 saver.py:1280] Restoring parameters from training/model.ckpt-891\n",
            "I0624 00:23:02.799803 140495266756480 session_manager.py:500] Running local_init_op.\n",
            "I0624 00:23:02.914289 140495266756480 session_manager.py:502] Done running local_init_op.\n",
            "I0624 00:23:09.708567 140493495654144 coco_evaluation.py:200] Performing evaluation on 47 images.\n",
            "creating index...\n",
            "index created!\n",
            "I0624 00:23:09.709460 140493495654144 coco_tools.py:109] Loading and preparing annotation results...\n",
            "I0624 00:23:09.712494 140493495654144 coco_tools.py:131] DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.38s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.09s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.012\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.039\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.104\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.005\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.039\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.010\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.027\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.033\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.108\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.013\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.077\n",
            "I0624 00:23:10.977273 140495266756480 evaluation.py:275] Finished evaluation at 2019-06-24-00:23:10\n",
            "I0624 00:23:10.977590 140495266756480 estimator.py:2039] Saving dict for global step 891: DetectionBoxes_Precision/mAP = 0.011635579, DetectionBoxes_Precision/mAP (large) = 0.038581483, DetectionBoxes_Precision/mAP (medium) = 0.0050977645, DetectionBoxes_Precision/mAP (small) = 0.104290426, DetectionBoxes_Precision/mAP@.50IOU = 0.03850205, DetectionBoxes_Precision/mAP@.75IOU = 0.00035894348, DetectionBoxes_Recall/AR@1 = 0.0102427285, DetectionBoxes_Recall/AR@10 = 0.026998326, DetectionBoxes_Recall/AR@100 = 0.032778826, DetectionBoxes_Recall/AR@100 (large) = 0.07729592, DetectionBoxes_Recall/AR@100 (medium) = 0.013092672, DetectionBoxes_Recall/AR@100 (small) = 0.108333334, Loss/classification_loss = 9.1142235, Loss/localization_loss = 3.5691211, Loss/regularization_loss = 0.26099846, Loss/total_loss = 12.944343, global_step = 891, learning_rate = 0.004, loss = 12.944343\n",
            "I0624 00:23:11.955868 140495266756480 estimator.py:2099] Saving 'checkpoint_path' summary for global step 891: training/model.ckpt-891\n",
            "I0624 00:23:18.428448 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.20678\n",
            "I0624 00:23:18.429545 140495266756480 basic_session_run_hooks.py:260] loss = 5.6931915, step = 900 (82.864 sec)\n",
            "I0624 00:24:22.964054 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54953\n",
            "I0624 00:24:22.966225 140495266756480 basic_session_run_hooks.py:260] loss = 5.6832385, step = 1000 (64.537 sec)\n",
            "I0624 00:25:27.274480 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.55496\n",
            "I0624 00:25:27.275685 140495266756480 basic_session_run_hooks.py:260] loss = 5.667531, step = 1100 (64.309 sec)\n",
            "I0624 00:26:31.653405 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.5533\n",
            "I0624 00:26:31.655789 140495266756480 basic_session_run_hooks.py:260] loss = 5.6886883, step = 1200 (64.380 sec)\n",
            "I0624 00:27:35.498922 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.56628\n",
            "I0624 00:27:35.500231 140495266756480 basic_session_run_hooks.py:260] loss = 6.0941763, step = 1300 (63.844 sec)\n",
            "I0624 00:28:39.678500 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.55813\n",
            "I0624 00:28:39.680867 140495266756480 basic_session_run_hooks.py:260] loss = 5.637447, step = 1400 (64.181 sec)\n",
            "I0624 00:29:44.263013 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54836\n",
            "I0624 00:29:44.264127 140495266756480 basic_session_run_hooks.py:260] loss = 4.804488, step = 1500 (64.583 sec)\n",
            "I0624 00:30:49.042599 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.5437\n",
            "I0624 00:30:49.044731 140495266756480 basic_session_run_hooks.py:260] loss = 6.1186447, step = 1600 (64.781 sec)\n",
            "I0624 00:31:53.329787 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.55552\n",
            "I0624 00:31:53.331027 140495266756480 basic_session_run_hooks.py:260] loss = 5.1824226, step = 1700 (64.286 sec)\n",
            "I0624 00:32:53.332338 140495266756480 basic_session_run_hooks.py:606] Saving checkpoints for 1795 into training/model.ckpt.\n",
            "I0624 00:32:56.365517 140495266756480 estimator.py:1145] Calling model_fn.\n",
            "I0624 00:32:58.823211 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:32:58.858418 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:32:58.893324 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:32:58.928374 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:32:58.963414 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:32:58.997510 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:33:01.876046 140495266756480 estimator.py:1147] Done calling model_fn.\n",
            "I0624 00:33:01.899017 140495266756480 evaluation.py:255] Starting evaluation at 2019-06-24T00:33:01Z\n",
            "I0624 00:33:02.442989 140495266756480 monitored_session.py:240] Graph was finalized.\n",
            "2019-06-24 00:33:02.443715: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:33:02.444066: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 00:33:02.444172: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 00:33:02.444196: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 00:33:02.444218: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 00:33:02.444239: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 00:33:02.444276: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 00:33:02.444296: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 00:33:02.444317: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 00:33:02.444421: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:33:02.444770: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:33:02.445031: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 00:33:02.445073: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-24 00:33:02.445087: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-24 00:33:02.445097: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-24 00:33:02.445346: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:33:02.445715: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:33:02.445997: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12185 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "I0624 00:33:02.448145 140495266756480 saver.py:1280] Restoring parameters from training/model.ckpt-1795\n",
            "I0624 00:33:03.424306 140495266756480 session_manager.py:500] Running local_init_op.\n",
            "I0624 00:33:03.542753 140495266756480 session_manager.py:502] Done running local_init_op.\n",
            "I0624 00:33:10.244676 140492249577216 coco_evaluation.py:200] Performing evaluation on 47 images.\n",
            "creating index...\n",
            "index created!\n",
            "I0624 00:33:10.245877 140492249577216 coco_tools.py:109] Loading and preparing annotation results...\n",
            "I0624 00:33:10.248854 140492249577216 coco_tools.py:131] DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.49s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.10s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.036\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.116\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.017\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.102\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.010\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.139\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.059\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.108\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.118\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.100\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.039\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.315\n",
            "I0624 00:33:11.583087 140495266756480 evaluation.py:275] Finished evaluation at 2019-06-24-00:33:11\n",
            "I0624 00:33:11.583420 140495266756480 estimator.py:2039] Saving dict for global step 1795: DetectionBoxes_Precision/mAP = 0.03557153, DetectionBoxes_Precision/mAP (large) = 0.13924009, DetectionBoxes_Precision/mAP (medium) = 0.010125985, DetectionBoxes_Precision/mAP (small) = 0.101980194, DetectionBoxes_Precision/mAP@.50IOU = 0.11596379, DetectionBoxes_Precision/mAP@.75IOU = 0.016879031, DetectionBoxes_Recall/AR@1 = 0.05941078, DetectionBoxes_Recall/AR@10 = 0.107761465, DetectionBoxes_Recall/AR@100 = 0.118025124, DetectionBoxes_Recall/AR@100 (large) = 0.31465185, DetectionBoxes_Recall/AR@100 (medium) = 0.039475575, DetectionBoxes_Recall/AR@100 (small) = 0.1, Loss/classification_loss = 6.5037336, Loss/localization_loss = 2.3298364, Loss/regularization_loss = 0.26306182, Loss/total_loss = 9.096631, global_step = 1795, learning_rate = 0.004, loss = 9.096631\n",
            "I0624 00:33:11.595605 140495266756480 estimator.py:2099] Saving 'checkpoint_path' summary for global step 1795: training/model.ckpt-1795\n",
            "I0624 00:33:15.533430 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.21649\n",
            "I0624 00:33:15.534447 140495266756480 basic_session_run_hooks.py:260] loss = 5.488206, step = 1800 (82.203 sec)\n",
            "I0624 00:34:19.294985 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.56834\n",
            "I0624 00:34:19.296129 140495266756480 basic_session_run_hooks.py:260] loss = 5.162644, step = 1900 (63.762 sec)\n",
            "I0624 00:35:23.224611 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.56422\n",
            "I0624 00:35:23.226710 140495266756480 basic_session_run_hooks.py:260] loss = 5.680831, step = 2000 (63.931 sec)\n",
            "I0624 00:36:27.032827 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.5672\n",
            "I0624 00:36:27.034331 140495266756480 basic_session_run_hooks.py:260] loss = 5.3970785, step = 2100 (63.808 sec)\n",
            "I0624 00:37:30.754064 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.56933\n",
            "I0624 00:37:30.756150 140495266756480 basic_session_run_hooks.py:260] loss = 6.046177, step = 2200 (63.722 sec)\n",
            "I0624 00:38:34.141625 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.5776\n",
            "I0624 00:38:34.142770 140495266756480 basic_session_run_hooks.py:260] loss = 6.7149615, step = 2300 (63.387 sec)\n",
            "I0624 00:39:37.866358 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.56925\n",
            "I0624 00:39:37.868754 140495266756480 basic_session_run_hooks.py:260] loss = 4.4026675, step = 2400 (63.726 sec)\n",
            "I0624 00:40:41.499494 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.57151\n",
            "I0624 00:40:41.500671 140495266756480 basic_session_run_hooks.py:260] loss = 5.533706, step = 2500 (63.632 sec)\n",
            "I0624 00:41:45.306267 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.56723\n",
            "I0624 00:41:45.308624 140495266756480 basic_session_run_hooks.py:260] loss = 6.6113777, step = 2600 (63.808 sec)\n",
            "I0624 00:42:48.944274 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.57139\n",
            "I0624 00:42:48.945297 140495266756480 basic_session_run_hooks.py:260] loss = 5.7838645, step = 2700 (63.637 sec)\n",
            "I0624 00:42:53.344917 140495266756480 basic_session_run_hooks.py:606] Saving checkpoints for 2708 into training/model.ckpt.\n",
            "I0624 00:42:55.821725 140495266756480 estimator.py:1145] Calling model_fn.\n",
            "I0624 00:42:58.877481 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:42:58.913183 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:42:58.948008 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:42:58.983554 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:42:59.017513 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:42:59.051780 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:43:01.435078 140495266756480 estimator.py:1147] Done calling model_fn.\n",
            "I0624 00:43:01.454018 140495266756480 evaluation.py:255] Starting evaluation at 2019-06-24T00:43:01Z\n",
            "I0624 00:43:01.940222 140495266756480 monitored_session.py:240] Graph was finalized.\n",
            "2019-06-24 00:43:01.941007: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:43:01.941375: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 00:43:01.941488: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 00:43:01.941542: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 00:43:01.941566: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 00:43:01.941590: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 00:43:01.941611: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 00:43:01.941630: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 00:43:01.941651: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 00:43:01.941771: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:43:01.942130: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:43:01.942417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 00:43:01.942499: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-24 00:43:01.942515: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-24 00:43:01.942541: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-24 00:43:01.942801: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:43:01.943433: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:43:01.943736: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12185 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "I0624 00:43:01.945991 140495266756480 saver.py:1280] Restoring parameters from training/model.ckpt-2708\n",
            "I0624 00:43:03.062419 140495266756480 session_manager.py:500] Running local_init_op.\n",
            "I0624 00:43:03.197861 140495266756480 session_manager.py:502] Done running local_init_op.\n",
            "I0624 00:43:10.213652 140493495654144 coco_evaluation.py:200] Performing evaluation on 47 images.\n",
            "creating index...\n",
            "index created!\n",
            "I0624 00:43:10.214523 140493495654144 coco_tools.py:109] Loading and preparing annotation results...\n",
            "I0624 00:43:10.217855 140493495654144 coco_tools.py:131] DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.48s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.11s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.067\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.201\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.014\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.163\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.029\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.159\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.107\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.155\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.160\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.167\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.069\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.333\n",
            "I0624 00:43:11.542992 140495266756480 evaluation.py:275] Finished evaluation at 2019-06-24-00:43:11\n",
            "I0624 00:43:11.543345 140495266756480 estimator.py:2039] Saving dict for global step 2708: DetectionBoxes_Precision/mAP = 0.06666328, DetectionBoxes_Precision/mAP (large) = 0.15873104, DetectionBoxes_Precision/mAP (medium) = 0.028701887, DetectionBoxes_Precision/mAP (small) = 0.16285628, DetectionBoxes_Precision/mAP@.50IOU = 0.20092063, DetectionBoxes_Precision/mAP@.75IOU = 0.014085182, DetectionBoxes_Recall/AR@1 = 0.10688281, DetectionBoxes_Recall/AR@10 = 0.15457702, DetectionBoxes_Recall/AR@100 = 0.15972976, DetectionBoxes_Recall/AR@100 (large) = 0.33327428, DetectionBoxes_Recall/AR@100 (medium) = 0.06932471, DetectionBoxes_Recall/AR@100 (small) = 0.16666667, Loss/classification_loss = 5.660824, Loss/localization_loss = 1.8190078, Loss/regularization_loss = 0.26495525, Loss/total_loss = 7.744785, global_step = 2708, learning_rate = 0.004, loss = 7.744785\n",
            "I0624 00:43:11.553857 140495266756480 estimator.py:2099] Saving 'checkpoint_path' summary for global step 2708: training/model.ckpt-2708\n",
            "I0624 00:44:10.746370 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.22246\n",
            "I0624 00:44:10.748293 140495266756480 basic_session_run_hooks.py:260] loss = 5.206538, step = 2800 (81.803 sec)\n",
            "I0624 00:45:14.444263 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.56991\n",
            "I0624 00:45:14.445507 140495266756480 basic_session_run_hooks.py:260] loss = 5.0591054, step = 2900 (63.697 sec)\n",
            "I0624 00:46:18.628174 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.55802\n",
            "I0624 00:46:18.630432 140495266756480 basic_session_run_hooks.py:260] loss = 7.6280136, step = 3000 (64.185 sec)\n",
            "I0624 00:47:22.952723 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.55462\n",
            "I0624 00:47:22.953941 140495266756480 basic_session_run_hooks.py:260] loss = 4.538428, step = 3100 (64.324 sec)\n",
            "I0624 00:48:26.404800 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.57599\n",
            "I0624 00:48:26.407184 140495266756480 basic_session_run_hooks.py:260] loss = 4.6835318, step = 3200 (63.453 sec)\n",
            "I0624 00:49:30.825209 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.5523\n",
            "I0624 00:49:30.826462 140495266756480 basic_session_run_hooks.py:260] loss = 5.1720366, step = 3300 (64.419 sec)\n",
            "I0624 00:50:35.932467 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.53593\n",
            "I0624 00:50:35.934936 140495266756480 basic_session_run_hooks.py:260] loss = 5.1832247, step = 3400 (65.108 sec)\n",
            "I0624 00:51:40.675871 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54456\n",
            "I0624 00:51:40.677070 140495266756480 basic_session_run_hooks.py:260] loss = 3.8179312, step = 3500 (64.742 sec)\n",
            "I0624 00:52:45.462065 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54354\n",
            "I0624 00:52:45.464390 140495266756480 basic_session_run_hooks.py:260] loss = 4.943626, step = 3600 (64.787 sec)\n",
            "I0624 00:52:53.871154 140495266756480 basic_session_run_hooks.py:606] Saving checkpoints for 3614 into training/model.ckpt.\n",
            "I0624 00:52:56.530911 140495266756480 estimator.py:1145] Calling model_fn.\n",
            "I0624 00:52:59.671545 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:52:59.715706 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:52:59.752022 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:52:59.788117 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:52:59.823668 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:52:59.858651 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 00:53:02.760217 140495266756480 estimator.py:1147] Done calling model_fn.\n",
            "I0624 00:53:02.780925 140495266756480 evaluation.py:255] Starting evaluation at 2019-06-24T00:53:02Z\n",
            "I0624 00:53:03.302929 140495266756480 monitored_session.py:240] Graph was finalized.\n",
            "2019-06-24 00:53:03.303645: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:53:03.303998: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 00:53:03.304087: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 00:53:03.304111: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 00:53:03.304132: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 00:53:03.304154: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 00:53:03.304173: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 00:53:03.304192: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 00:53:03.304212: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 00:53:03.304343: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:53:03.304701: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:53:03.304964: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 00:53:03.305009: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-24 00:53:03.305022: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-24 00:53:03.305032: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-24 00:53:03.305280: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:53:03.305635: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 00:53:03.305921: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12185 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "I0624 00:53:03.308457 140495266756480 saver.py:1280] Restoring parameters from training/model.ckpt-3614\n",
            "I0624 00:53:04.343015 140495266756480 session_manager.py:500] Running local_init_op.\n",
            "I0624 00:53:04.479463 140495266756480 session_manager.py:502] Done running local_init_op.\n",
            "I0624 00:53:11.849504 140492249577216 coco_evaluation.py:200] Performing evaluation on 47 images.\n",
            "creating index...\n",
            "index created!\n",
            "I0624 00:53:11.850352 140492249577216 coco_tools.py:109] Loading and preparing annotation results...\n",
            "I0624 00:53:11.854797 140492249577216 coco_tools.py:131] DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.49s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.12s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.088\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.216\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.048\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.118\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.033\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.197\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.131\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.184\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.187\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.117\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.099\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.358\n",
            "I0624 00:53:13.229706 140495266756480 evaluation.py:275] Finished evaluation at 2019-06-24-00:53:13\n",
            "I0624 00:53:13.230024 140495266756480 estimator.py:2039] Saving dict for global step 3614: DetectionBoxes_Precision/mAP = 0.08848852, DetectionBoxes_Precision/mAP (large) = 0.19688065, DetectionBoxes_Precision/mAP (medium) = 0.033450462, DetectionBoxes_Precision/mAP (small) = 0.11782178, DetectionBoxes_Precision/mAP@.50IOU = 0.21597098, DetectionBoxes_Precision/mAP@.75IOU = 0.047687538, DetectionBoxes_Recall/AR@1 = 0.1312209, DetectionBoxes_Recall/AR@10 = 0.18404256, DetectionBoxes_Recall/AR@100 = 0.18739578, DetectionBoxes_Recall/AR@100 (large) = 0.35830736, DetectionBoxes_Recall/AR@100 (medium) = 0.09911997, DetectionBoxes_Recall/AR@100 (small) = 0.11666667, Loss/classification_loss = 6.402751, Loss/localization_loss = 1.8952384, Loss/regularization_loss = 0.26667377, Loss/total_loss = 8.564664, global_step = 3614, learning_rate = 0.004, loss = 8.564664\n",
            "I0624 00:53:13.241387 140495266756480 estimator.py:2099] Saving 'checkpoint_path' summary for global step 3614: training/model.ckpt-3614\n",
            "I0624 00:54:09.317384 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.19253\n",
            "I0624 00:54:09.318404 140495266756480 basic_session_run_hooks.py:260] loss = 4.882463, step = 3700 (83.854 sec)\n",
            "I0624 00:55:13.799052 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.55083\n",
            "I0624 00:55:13.801340 140495266756480 basic_session_run_hooks.py:260] loss = 5.799382, step = 3800 (64.483 sec)\n",
            "I0624 00:56:19.904458 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.51274\n",
            "I0624 00:56:19.905615 140495266756480 basic_session_run_hooks.py:260] loss = 4.4549522, step = 3900 (66.104 sec)\n",
            "I0624 00:57:24.702579 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54325\n",
            "I0624 00:57:24.704982 140495266756480 basic_session_run_hooks.py:260] loss = 4.7656493, step = 4000 (64.799 sec)\n",
            "I0624 00:58:29.557862 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54189\n",
            "I0624 00:58:29.558965 140495266756480 basic_session_run_hooks.py:260] loss = 6.0657477, step = 4100 (64.854 sec)\n",
            "I0624 00:59:34.322778 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54405\n",
            "I0624 00:59:34.325058 140495266756480 basic_session_run_hooks.py:260] loss = 5.6044264, step = 4200 (64.766 sec)\n",
            "I0624 01:00:39.567194 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.5327\n",
            "I0624 01:00:39.568266 140495266756480 basic_session_run_hooks.py:260] loss = 5.4968824, step = 4300 (65.243 sec)\n",
            "I0624 01:01:44.889790 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.53086\n",
            "I0624 01:01:44.891770 140495266756480 basic_session_run_hooks.py:260] loss = 5.2271605, step = 4400 (65.324 sec)\n",
            "I0624 01:02:50.024487 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.53528\n",
            "I0624 01:02:50.025599 140495266756480 basic_session_run_hooks.py:260] loss = 5.67261, step = 4500 (65.134 sec)\n",
            "I0624 01:02:54.516333 140495266756480 basic_session_run_hooks.py:606] Saving checkpoints for 4508 into training/model.ckpt.\n",
            "W0624 01:02:54.789924 140495266756480 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:960: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use standard file APIs to delete files with this prefix.\n",
            "I0624 01:02:57.207159 140495266756480 estimator.py:1145] Calling model_fn.\n",
            "I0624 01:02:59.721258 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:02:59.757485 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:02:59.792454 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:03:00.446070 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:03:00.482021 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:03:00.516962 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:03:03.065605 140495266756480 estimator.py:1147] Done calling model_fn.\n",
            "I0624 01:03:03.085894 140495266756480 evaluation.py:255] Starting evaluation at 2019-06-24T01:03:03Z\n",
            "I0624 01:03:03.581088 140495266756480 monitored_session.py:240] Graph was finalized.\n",
            "2019-06-24 01:03:03.581798: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:03:03.582153: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 01:03:03.582268: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 01:03:03.582321: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 01:03:03.582345: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 01:03:03.582373: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 01:03:03.582393: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 01:03:03.582413: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 01:03:03.582434: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 01:03:03.582540: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:03:03.582898: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:03:03.583179: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 01:03:03.583224: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-24 01:03:03.583238: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-24 01:03:03.583265: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-24 01:03:03.583520: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:03:03.583876: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:03:03.584156: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12185 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "I0624 01:03:03.586658 140495266756480 saver.py:1280] Restoring parameters from training/model.ckpt-4508\n",
            "I0624 01:03:04.650169 140495266756480 session_manager.py:500] Running local_init_op.\n",
            "I0624 01:03:04.797937 140495266756480 session_manager.py:502] Done running local_init_op.\n",
            "I0624 01:03:12.182157 140492249577216 coco_evaluation.py:200] Performing evaluation on 47 images.\n",
            "creating index...\n",
            "index created!\n",
            "I0624 01:03:12.183019 140492249577216 coco_tools.py:109] Loading and preparing annotation results...\n",
            "I0624 01:03:12.186028 140492249577216 coco_tools.py:131] DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.48s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.11s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.072\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.221\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.041\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.088\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.026\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.248\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.105\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.167\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.174\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.225\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.080\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.370\n",
            "I0624 01:03:13.526662 140495266756480 evaluation.py:275] Finished evaluation at 2019-06-24-01:03:13\n",
            "I0624 01:03:13.526984 140495266756480 estimator.py:2039] Saving dict for global step 4508: DetectionBoxes_Precision/mAP = 0.07221996, DetectionBoxes_Precision/mAP (large) = 0.24832761, DetectionBoxes_Precision/mAP (medium) = 0.025680203, DetectionBoxes_Precision/mAP (small) = 0.087788776, DetectionBoxes_Precision/mAP@.50IOU = 0.22137305, DetectionBoxes_Precision/mAP@.75IOU = 0.04060727, DetectionBoxes_Recall/AR@1 = 0.10505071, DetectionBoxes_Recall/AR@10 = 0.16737786, DetectionBoxes_Recall/AR@100 = 0.17424123, DetectionBoxes_Recall/AR@100 (large) = 0.3695224, DetectionBoxes_Recall/AR@100 (medium) = 0.079597704, DetectionBoxes_Recall/AR@100 (small) = 0.225, Loss/classification_loss = 6.2806954, Loss/localization_loss = 1.7902116, Loss/regularization_loss = 0.2683219, Loss/total_loss = 8.339229, global_step = 4508, learning_rate = 0.004, loss = 8.339229\n",
            "I0624 01:03:13.538505 140495266756480 estimator.py:2099] Saving 'checkpoint_path' summary for global step 4508: training/model.ckpt-4508\n",
            "I0624 01:04:13.909491 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.19211\n",
            "I0624 01:04:13.911776 140495266756480 basic_session_run_hooks.py:260] loss = 4.5583606, step = 4600 (83.886 sec)\n",
            "I0624 01:05:18.857754 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.53969\n",
            "I0624 01:05:18.859078 140495266756480 basic_session_run_hooks.py:260] loss = 3.9544234, step = 4700 (64.947 sec)\n",
            "I0624 01:06:23.853302 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.53857\n",
            "I0624 01:06:23.855728 140495266756480 basic_session_run_hooks.py:260] loss = 4.780046, step = 4800 (64.997 sec)\n",
            "I0624 01:07:29.258686 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.52893\n",
            "I0624 01:07:29.260341 140495266756480 basic_session_run_hooks.py:260] loss = 3.811152, step = 4900 (65.405 sec)\n",
            "I0624 01:08:34.476329 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.53333\n",
            "I0624 01:08:34.478565 140495266756480 basic_session_run_hooks.py:260] loss = 4.6547008, step = 5000 (65.218 sec)\n",
            "I0624 01:09:39.928848 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.52782\n",
            "I0624 01:09:39.929953 140495266756480 basic_session_run_hooks.py:260] loss = 5.3349805, step = 5100 (65.451 sec)\n",
            "I0624 01:10:45.130915 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.53369\n",
            "I0624 01:10:45.132979 140495266756480 basic_session_run_hooks.py:260] loss = 4.5554442, step = 5200 (65.203 sec)\n",
            "I0624 01:11:50.366698 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.5329\n",
            "I0624 01:11:50.367955 140495266756480 basic_session_run_hooks.py:260] loss = 4.295329, step = 5300 (65.235 sec)\n",
            "I0624 01:12:54.826027 140495266756480 basic_session_run_hooks.py:606] Saving checkpoints for 5400 into training/model.ckpt.\n",
            "I0624 01:12:57.428698 140495266756480 estimator.py:1145] Calling model_fn.\n",
            "I0624 01:13:00.594919 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:13:00.632224 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:13:00.668369 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:13:00.703953 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:13:00.739758 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:13:00.774721 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:13:03.733299 140495266756480 estimator.py:1147] Done calling model_fn.\n",
            "I0624 01:13:03.752849 140495266756480 evaluation.py:255] Starting evaluation at 2019-06-24T01:13:03Z\n",
            "I0624 01:13:04.242273 140495266756480 monitored_session.py:240] Graph was finalized.\n",
            "2019-06-24 01:13:04.243014: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:13:04.243409: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 01:13:04.243522: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 01:13:04.243547: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 01:13:04.243572: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 01:13:04.243592: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 01:13:04.243612: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 01:13:04.243634: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 01:13:04.243658: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 01:13:04.243769: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:13:04.244129: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:13:04.244407: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 01:13:04.244452: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-24 01:13:04.244466: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-24 01:13:04.244476: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-24 01:13:04.244714: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:13:04.245068: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:13:04.245360: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12185 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "I0624 01:13:04.247797 140495266756480 saver.py:1280] Restoring parameters from training/model.ckpt-5400\n",
            "I0624 01:13:05.293852 140495266756480 session_manager.py:500] Running local_init_op.\n",
            "I0624 01:13:05.435148 140495266756480 session_manager.py:502] Done running local_init_op.\n",
            "I0624 01:13:12.784744 140493495654144 coco_evaluation.py:200] Performing evaluation on 47 images.\n",
            "creating index...\n",
            "index created!\n",
            "I0624 01:13:12.785367 140493495654144 coco_tools.py:109] Loading and preparing annotation results...\n",
            "I0624 01:13:12.788582 140493495654144 coco_tools.py:131] DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.52s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.11s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.116\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.280\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.078\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.093\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.055\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.295\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.138\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.189\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.200\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.092\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.094\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.471\n",
            "I0624 01:13:14.168939 140495266756480 evaluation.py:275] Finished evaluation at 2019-06-24-01:13:14\n",
            "I0624 01:13:14.169279 140495266756480 estimator.py:2039] Saving dict for global step 5400: DetectionBoxes_Precision/mAP = 0.115551345, DetectionBoxes_Precision/mAP (large) = 0.29471517, DetectionBoxes_Precision/mAP (medium) = 0.055449, DetectionBoxes_Precision/mAP (small) = 0.09306931, DetectionBoxes_Precision/mAP@.50IOU = 0.2796918, DetectionBoxes_Precision/mAP@.75IOU = 0.0781701, DetectionBoxes_Recall/AR@1 = 0.13775052, DetectionBoxes_Recall/AR@10 = 0.18852209, DetectionBoxes_Recall/AR@100 = 0.20008308, DetectionBoxes_Recall/AR@100 (large) = 0.47051683, DetectionBoxes_Recall/AR@100 (medium) = 0.09358836, DetectionBoxes_Recall/AR@100 (small) = 0.09166667, Loss/classification_loss = 5.7108517, Loss/localization_loss = 1.722372, Loss/regularization_loss = 0.2699254, Loss/total_loss = 7.703148, global_step = 5400, learning_rate = 0.004, loss = 7.703148\n",
            "I0624 01:13:14.180667 140495266756480 estimator.py:2099] Saving 'checkpoint_path' summary for global step 5400: training/model.ckpt-5400\n",
            "I0624 01:13:14.937901 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.18244\n",
            "I0624 01:13:14.939104 140495266756480 basic_session_run_hooks.py:260] loss = 5.082289, step = 5400 (84.571 sec)\n",
            "I0624 01:14:20.061723 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.53554\n",
            "I0624 01:14:20.062927 140495266756480 basic_session_run_hooks.py:260] loss = 4.327912, step = 5500 (65.124 sec)\n",
            "I0624 01:15:24.327728 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.55603\n",
            "I0624 01:15:24.330004 140495266756480 basic_session_run_hooks.py:260] loss = 4.5181913, step = 5600 (64.267 sec)\n",
            "I0624 01:16:29.418083 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.53633\n",
            "I0624 01:16:29.419320 140495266756480 basic_session_run_hooks.py:260] loss = 3.826172, step = 5700 (65.089 sec)\n",
            "I0624 01:17:34.632720 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.5334\n",
            "I0624 01:17:34.634685 140495266756480 basic_session_run_hooks.py:260] loss = 4.009103, step = 5800 (65.215 sec)\n",
            "I0624 01:18:39.714722 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.53652\n",
            "I0624 01:18:39.715975 140495266756480 basic_session_run_hooks.py:260] loss = 5.1562357, step = 5900 (65.081 sec)\n",
            "I0624 01:19:44.235657 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54988\n",
            "I0624 01:19:44.237893 140495266756480 basic_session_run_hooks.py:260] loss = 4.8029633, step = 6000 (64.522 sec)\n",
            "I0624 01:20:48.859991 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.5474\n",
            "I0624 01:20:48.861310 140495266756480 basic_session_run_hooks.py:260] loss = 4.361598, step = 6100 (64.623 sec)\n",
            "I0624 01:21:53.584257 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54502\n",
            "I0624 01:21:53.586562 140495266756480 basic_session_run_hooks.py:260] loss = 4.2808104, step = 6200 (64.725 sec)\n",
            "I0624 01:22:54.979537 140495266756480 basic_session_run_hooks.py:606] Saving checkpoints for 6296 into training/model.ckpt.\n",
            "I0624 01:22:57.523904 140495266756480 estimator.py:1145] Calling model_fn.\n",
            "I0624 01:23:00.046456 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:23:00.082268 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:23:00.117799 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:23:00.153435 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:23:00.188030 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:23:00.229706 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:23:03.331059 140495266756480 estimator.py:1147] Done calling model_fn.\n",
            "I0624 01:23:03.354142 140495266756480 evaluation.py:255] Starting evaluation at 2019-06-24T01:23:03Z\n",
            "I0624 01:23:03.856944 140495266756480 monitored_session.py:240] Graph was finalized.\n",
            "2019-06-24 01:23:03.857738: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:23:03.858094: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 01:23:03.858184: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 01:23:03.858208: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 01:23:03.858229: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 01:23:03.858267: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 01:23:03.858295: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 01:23:03.858314: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 01:23:03.858334: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 01:23:03.858441: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:23:03.858788: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:23:03.859066: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 01:23:03.859111: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-24 01:23:03.859124: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-24 01:23:03.859134: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-24 01:23:03.859398: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:23:03.859737: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:23:03.860014: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12185 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "I0624 01:23:03.862557 140495266756480 saver.py:1280] Restoring parameters from training/model.ckpt-6296\n",
            "I0624 01:23:04.945939 140495266756480 session_manager.py:500] Running local_init_op.\n",
            "I0624 01:23:05.081475 140495266756480 session_manager.py:502] Done running local_init_op.\n",
            "I0624 01:23:12.575914 140492249577216 coco_evaluation.py:200] Performing evaluation on 47 images.\n",
            "creating index...\n",
            "index created!\n",
            "I0624 01:23:12.576916 140492249577216 coco_tools.py:109] Loading and preparing annotation results...\n",
            "I0624 01:23:12.579770 140492249577216 coco_tools.py:131] DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.50s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.11s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.076\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.223\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.036\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.103\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.032\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.204\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.086\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.162\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.172\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.183\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.090\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.343\n",
            "I0624 01:23:13.961612 140495266756480 evaluation.py:275] Finished evaluation at 2019-06-24-01:23:13\n",
            "I0624 01:23:13.961935 140495266756480 estimator.py:2039] Saving dict for global step 6296: DetectionBoxes_Precision/mAP = 0.07622389, DetectionBoxes_Precision/mAP (large) = 0.20424467, DetectionBoxes_Precision/mAP (medium) = 0.032347728, DetectionBoxes_Precision/mAP (small) = 0.10338284, DetectionBoxes_Precision/mAP@.50IOU = 0.22284125, DetectionBoxes_Precision/mAP@.75IOU = 0.035601296, DetectionBoxes_Recall/AR@1 = 0.08627925, DetectionBoxes_Recall/AR@10 = 0.16182311, DetectionBoxes_Recall/AR@100 = 0.17209199, DetectionBoxes_Recall/AR@100 (large) = 0.34320438, DetectionBoxes_Recall/AR@100 (medium) = 0.09001437, DetectionBoxes_Recall/AR@100 (small) = 0.18333334, Loss/classification_loss = 6.258385, Loss/localization_loss = 1.710828, Loss/regularization_loss = 0.27143076, Loss/total_loss = 8.240644, global_step = 6296, learning_rate = 0.004, loss = 8.240644\n",
            "I0624 01:23:13.972914 140495266756480 estimator.py:2099] Saving 'checkpoint_path' summary for global step 6296: training/model.ckpt-6296\n",
            "I0624 01:23:17.344551 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.19388\n",
            "I0624 01:23:17.345688 140495266756480 basic_session_run_hooks.py:260] loss = 4.445612, step = 6300 (83.759 sec)\n",
            "I0624 01:24:22.292451 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.5397\n",
            "I0624 01:24:22.294716 140495266756480 basic_session_run_hooks.py:260] loss = 3.696401, step = 6400 (64.949 sec)\n",
            "I0624 01:25:27.051073 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.5442\n",
            "I0624 01:25:27.052358 140495266756480 basic_session_run_hooks.py:260] loss = 4.927076, step = 6500 (64.758 sec)\n",
            "I0624 01:26:31.250376 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.55765\n",
            "I0624 01:26:31.252582 140495266756480 basic_session_run_hooks.py:260] loss = 3.9462152, step = 6600 (64.200 sec)\n",
            "I0624 01:27:35.950927 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54558\n",
            "I0624 01:27:35.952210 140495266756480 basic_session_run_hooks.py:260] loss = 3.8454769, step = 6700 (64.700 sec)\n",
            "I0624 01:28:40.441792 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.55061\n",
            "I0624 01:28:40.444052 140495266756480 basic_session_run_hooks.py:260] loss = 5.999677, step = 6800 (64.492 sec)\n",
            "I0624 01:29:45.183718 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54459\n",
            "I0624 01:29:45.184874 140495266756480 basic_session_run_hooks.py:260] loss = 3.7404904, step = 6900 (64.741 sec)\n",
            "I0624 01:30:49.530500 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.55408\n",
            "I0624 01:30:49.532498 140495266756480 basic_session_run_hooks.py:260] loss = 3.6383774, step = 7000 (64.348 sec)\n",
            "I0624 01:31:54.155485 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54739\n",
            "I0624 01:31:54.156663 140495266756480 basic_session_run_hooks.py:260] loss = 4.1235113, step = 7100 (64.624 sec)\n",
            "I0624 01:32:55.166152 140495266756480 basic_session_run_hooks.py:606] Saving checkpoints for 7195 into training/model.ckpt.\n",
            "I0624 01:32:57.719663 140495266756480 estimator.py:1145] Calling model_fn.\n",
            "I0624 01:33:00.925268 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:33:00.962069 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:33:00.997215 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:33:01.033305 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:33:01.068953 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:33:01.103965 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:33:03.591686 140495266756480 estimator.py:1147] Done calling model_fn.\n",
            "I0624 01:33:03.612819 140495266756480 evaluation.py:255] Starting evaluation at 2019-06-24T01:33:03Z\n",
            "I0624 01:33:04.162448 140495266756480 monitored_session.py:240] Graph was finalized.\n",
            "2019-06-24 01:33:04.163141: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:33:04.163513: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 01:33:04.163617: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 01:33:04.163642: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 01:33:04.163666: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 01:33:04.163690: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 01:33:04.163711: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 01:33:04.163733: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 01:33:04.163763: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 01:33:04.163869: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:33:04.164232: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:33:04.164521: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 01:33:04.164569: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-24 01:33:04.164583: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-24 01:33:04.164593: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-24 01:33:04.164850: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:33:04.165271: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:33:04.165609: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12185 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "I0624 01:33:04.168554 140495266756480 saver.py:1280] Restoring parameters from training/model.ckpt-7195\n",
            "I0624 01:33:05.298897 140495266756480 session_manager.py:500] Running local_init_op.\n",
            "I0624 01:33:05.437620 140495266756480 session_manager.py:502] Done running local_init_op.\n",
            "I0624 01:33:13.081713 140493495654144 coco_evaluation.py:200] Performing evaluation on 47 images.\n",
            "creating index...\n",
            "index created!\n",
            "I0624 01:33:13.082550 140493495654144 coco_tools.py:109] Loading and preparing annotation results...\n",
            "I0624 01:33:13.085581 140493495654144 coco_tools.py:131] DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.53s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.12s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.080\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.207\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.048\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.130\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.040\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.214\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.092\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.175\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.189\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.225\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.088\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.430\n",
            "I0624 01:33:14.467635 140495266756480 evaluation.py:275] Finished evaluation at 2019-06-24-01:33:14\n",
            "I0624 01:33:14.467956 140495266756480 estimator.py:2039] Saving dict for global step 7195: DetectionBoxes_Precision/mAP = 0.08017448, DetectionBoxes_Precision/mAP (large) = 0.21369188, DetectionBoxes_Precision/mAP (medium) = 0.040316574, DetectionBoxes_Precision/mAP (small) = 0.13013908, DetectionBoxes_Precision/mAP@.50IOU = 0.2072086, DetectionBoxes_Precision/mAP@.75IOU = 0.048475802, DetectionBoxes_Recall/AR@1 = 0.09203938, DetectionBoxes_Recall/AR@10 = 0.17546195, DetectionBoxes_Recall/AR@100 = 0.18894188, DetectionBoxes_Recall/AR@100 (large) = 0.43033588, DetectionBoxes_Recall/AR@100 (medium) = 0.08836207, DetectionBoxes_Recall/AR@100 (small) = 0.225, Loss/classification_loss = 6.17922, Loss/localization_loss = 1.5223782, Loss/regularization_loss = 0.2729377, Loss/total_loss = 7.9745355, global_step = 7195, learning_rate = 0.004, loss = 7.9745355\n",
            "I0624 01:33:14.480521 140495266756480 estimator.py:2099] Saving 'checkpoint_path' summary for global step 7195: training/model.ckpt-7195\n",
            "I0624 01:33:18.491638 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.18573\n",
            "I0624 01:33:18.493163 140495266756480 basic_session_run_hooks.py:260] loss = 4.184851, step = 7200 (84.337 sec)\n",
            "I0624 01:34:23.170483 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.5461\n",
            "I0624 01:34:23.171669 140495266756480 basic_session_run_hooks.py:260] loss = 5.5877814, step = 7300 (64.679 sec)\n",
            "I0624 01:35:28.299744 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.53541\n",
            "I0624 01:35:28.302466 140495266756480 basic_session_run_hooks.py:260] loss = 4.765779, step = 7400 (65.131 sec)\n",
            "I0624 01:36:33.238138 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.53992\n",
            "I0624 01:36:33.239496 140495266756480 basic_session_run_hooks.py:260] loss = 4.260319, step = 7500 (64.937 sec)\n",
            "I0624 01:37:37.884333 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54688\n",
            "I0624 01:37:37.886337 140495266756480 basic_session_run_hooks.py:260] loss = 4.3623047, step = 7600 (64.647 sec)\n",
            "I0624 01:38:42.620056 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54474\n",
            "I0624 01:38:42.621459 140495266756480 basic_session_run_hooks.py:260] loss = 4.815087, step = 7700 (64.735 sec)\n",
            "I0624 01:39:47.376238 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54425\n",
            "I0624 01:39:47.378637 140495266756480 basic_session_run_hooks.py:260] loss = 4.041305, step = 7800 (64.757 sec)\n",
            "I0624 01:40:52.079054 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54553\n",
            "I0624 01:40:52.080462 140495266756480 basic_session_run_hooks.py:260] loss = 5.180723, step = 7900 (64.702 sec)\n",
            "I0624 01:41:56.693775 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54763\n",
            "I0624 01:41:56.696118 140495266756480 basic_session_run_hooks.py:260] loss = 4.8424716, step = 8000 (64.616 sec)\n",
            "I0624 01:42:55.358766 140495266756480 basic_session_run_hooks.py:606] Saving checkpoints for 8092 into training/model.ckpt.\n",
            "I0624 01:42:58.450984 140495266756480 estimator.py:1145] Calling model_fn.\n",
            "I0624 01:43:00.946828 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:43:00.982287 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:43:01.017192 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:43:01.052344 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:43:01.087854 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:43:01.122549 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:43:03.992819 140495266756480 estimator.py:1147] Done calling model_fn.\n",
            "I0624 01:43:04.011634 140495266756480 evaluation.py:255] Starting evaluation at 2019-06-24T01:43:04Z\n",
            "I0624 01:43:04.532858 140495266756480 monitored_session.py:240] Graph was finalized.\n",
            "2019-06-24 01:43:04.533854: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:43:04.534203: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 01:43:04.534316: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 01:43:04.534344: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 01:43:04.534366: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 01:43:04.534386: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 01:43:04.534406: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 01:43:04.534425: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 01:43:04.534445: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 01:43:04.534551: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:43:04.534920: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:43:04.535189: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 01:43:04.535284: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-24 01:43:04.535299: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-24 01:43:04.535309: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-24 01:43:04.535553: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:43:04.535908: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:43:04.536183: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12185 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "I0624 01:43:04.541170 140495266756480 saver.py:1280] Restoring parameters from training/model.ckpt-8092\n",
            "I0624 01:43:05.693921 140495266756480 session_manager.py:500] Running local_init_op.\n",
            "I0624 01:43:05.841937 140495266756480 session_manager.py:502] Done running local_init_op.\n",
            "I0624 01:43:13.914410 140493495654144 coco_evaluation.py:200] Performing evaluation on 47 images.\n",
            "creating index...\n",
            "index created!\n",
            "I0624 01:43:13.915537 140493495654144 coco_tools.py:109] Loading and preparing annotation results...\n",
            "I0624 01:43:13.920160 140493495654144 coco_tools.py:131] DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.54s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.13s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.131\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.277\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.118\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.077\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.347\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.161\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.234\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.241\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.025\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.146\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.508\n",
            "I0624 01:43:15.308446 140495266756480 evaluation.py:275] Finished evaluation at 2019-06-24-01:43:15\n",
            "I0624 01:43:15.308812 140495266756480 estimator.py:2039] Saving dict for global step 8092: DetectionBoxes_Precision/mAP = 0.1313922, DetectionBoxes_Precision/mAP (large) = 0.34717497, DetectionBoxes_Precision/mAP (medium) = 0.07697638, DetectionBoxes_Precision/mAP (small) = 0.0066831685, DetectionBoxes_Precision/mAP@.50IOU = 0.2770482, DetectionBoxes_Precision/mAP@.75IOU = 0.11761155, DetectionBoxes_Recall/AR@1 = 0.16116256, DetectionBoxes_Recall/AR@10 = 0.2342621, DetectionBoxes_Recall/AR@100 = 0.24138178, DetectionBoxes_Recall/AR@100 (large) = 0.5083829, DetectionBoxes_Recall/AR@100 (medium) = 0.14572558, DetectionBoxes_Recall/AR@100 (small) = 0.025, Loss/classification_loss = 6.068773, Loss/localization_loss = 1.6068013, Loss/regularization_loss = 0.27440366, Loss/total_loss = 7.9499784, global_step = 8092, learning_rate = 0.004, loss = 7.9499784\n",
            "I0624 01:43:15.320497 140495266756480 estimator.py:2099] Saving 'checkpoint_path' summary for global step 8092: training/model.ckpt-8092\n",
            "I0624 01:43:21.374238 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.18091\n",
            "I0624 01:43:21.375673 140495266756480 basic_session_run_hooks.py:260] loss = 4.301814, step = 8100 (84.680 sec)\n",
            "I0624 01:44:26.135591 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54413\n",
            "I0624 01:44:26.137858 140495266756480 basic_session_run_hooks.py:260] loss = 3.6391783, step = 8200 (64.762 sec)\n",
            "I0624 01:45:30.769809 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54717\n",
            "I0624 01:45:30.771008 140495266756480 basic_session_run_hooks.py:260] loss = 4.189775, step = 8300 (64.633 sec)\n",
            "I0624 01:46:35.125300 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.55387\n",
            "I0624 01:46:35.127600 140495266756480 basic_session_run_hooks.py:260] loss = 5.591038, step = 8400 (64.357 sec)\n",
            "I0624 01:47:39.660398 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54954\n",
            "I0624 01:47:39.661614 140495266756480 basic_session_run_hooks.py:260] loss = 3.247426, step = 8500 (64.534 sec)\n",
            "I0624 01:48:44.500568 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54225\n",
            "I0624 01:48:44.502458 140495266756480 basic_session_run_hooks.py:260] loss = 3.7865903, step = 8600 (64.841 sec)\n",
            "I0624 01:49:49.083799 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54839\n",
            "I0624 01:49:49.085020 140495266756480 basic_session_run_hooks.py:260] loss = 3.8204806, step = 8700 (64.583 sec)\n",
            "I0624 01:50:53.565737 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.55082\n",
            "I0624 01:50:53.567966 140495266756480 basic_session_run_hooks.py:260] loss = 4.662012, step = 8800 (64.483 sec)\n",
            "I0624 01:51:57.973074 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.55262\n",
            "I0624 01:51:57.974456 140495266756480 basic_session_run_hooks.py:260] loss = 4.757192, step = 8900 (64.406 sec)\n",
            "I0624 01:52:55.588535 140495266756480 basic_session_run_hooks.py:606] Saving checkpoints for 8990 into training/model.ckpt.\n",
            "I0624 01:52:58.078304 140495266756480 estimator.py:1145] Calling model_fn.\n",
            "I0624 01:53:01.178289 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:53:01.214483 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:53:01.249078 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:53:01.285113 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:53:01.319072 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:53:01.355147 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 01:53:03.817678 140495266756480 estimator.py:1147] Done calling model_fn.\n",
            "I0624 01:53:03.839535 140495266756480 evaluation.py:255] Starting evaluation at 2019-06-24T01:53:03Z\n",
            "I0624 01:53:04.351911 140495266756480 monitored_session.py:240] Graph was finalized.\n",
            "2019-06-24 01:53:04.352716: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:53:04.353060: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 01:53:04.353147: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 01:53:04.353172: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 01:53:04.353193: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 01:53:04.353217: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 01:53:04.353237: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 01:53:04.353275: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 01:53:04.353297: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 01:53:04.353407: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:53:04.353775: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:53:04.354036: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 01:53:04.354080: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-24 01:53:04.354093: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-24 01:53:04.354103: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-24 01:53:04.354345: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:53:04.354678: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 01:53:04.354958: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12185 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "I0624 01:53:04.357825 140495266756480 saver.py:1280] Restoring parameters from training/model.ckpt-8990\n",
            "I0624 01:53:05.462292 140495266756480 session_manager.py:500] Running local_init_op.\n",
            "I0624 01:53:05.602431 140495266756480 session_manager.py:502] Done running local_init_op.\n",
            "I0624 01:53:13.278450 140493495654144 coco_evaluation.py:200] Performing evaluation on 47 images.\n",
            "creating index...\n",
            "index created!\n",
            "I0624 01:53:13.279336 140493495654144 coco_tools.py:109] Loading and preparing annotation results...\n",
            "I0624 01:53:13.282596 140493495654144 coco_tools.py:131] DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.49s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.12s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.094\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.248\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.058\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.023\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.065\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.220\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.133\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.190\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.199\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.092\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.118\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.446\n",
            "I0624 01:53:14.626843 140495266756480 evaluation.py:275] Finished evaluation at 2019-06-24-01:53:14\n",
            "I0624 01:53:14.627159 140495266756480 estimator.py:2039] Saving dict for global step 8990: DetectionBoxes_Precision/mAP = 0.09384192, DetectionBoxes_Precision/mAP (large) = 0.22038065, DetectionBoxes_Precision/mAP (medium) = 0.064696796, DetectionBoxes_Precision/mAP (small) = 0.022984935, DetectionBoxes_Precision/mAP@.50IOU = 0.24835514, DetectionBoxes_Precision/mAP@.75IOU = 0.057509784, DetectionBoxes_Recall/AR@1 = 0.13258632, DetectionBoxes_Recall/AR@10 = 0.190286, DetectionBoxes_Recall/AR@100 = 0.19883904, DetectionBoxes_Recall/AR@100 (large) = 0.44581443, DetectionBoxes_Recall/AR@100 (medium) = 0.11778017, DetectionBoxes_Recall/AR@100 (small) = 0.09166667, Loss/classification_loss = 7.6935706, Loss/localization_loss = 1.7869782, Loss/regularization_loss = 0.27586338, Loss/total_loss = 9.756413, global_step = 8990, learning_rate = 0.004, loss = 9.756413\n",
            "I0624 01:53:14.638165 140495266756480 estimator.py:2099] Saving 'checkpoint_path' summary for global step 8990: training/model.ckpt-8990\n",
            "I0624 01:53:21.867492 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.19197\n",
            "I0624 01:53:21.868767 140495266756480 basic_session_run_hooks.py:260] loss = 4.1941705, step = 9000 (83.894 sec)\n",
            "I0624 01:54:26.670614 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54314\n",
            "I0624 01:54:26.671770 140495266756480 basic_session_run_hooks.py:260] loss = 4.1230855, step = 9100 (64.803 sec)\n",
            "I0624 01:55:31.547923 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54137\n",
            "I0624 01:55:31.550221 140495266756480 basic_session_run_hooks.py:260] loss = 3.7453241, step = 9200 (64.878 sec)\n",
            "I0624 01:56:36.449839 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.54079\n",
            "I0624 01:56:36.451150 140495266756480 basic_session_run_hooks.py:260] loss = 4.5281615, step = 9300 (64.901 sec)\n",
            "I0624 01:57:41.418360 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.53921\n",
            "I0624 01:57:41.421066 140495266756480 basic_session_run_hooks.py:260] loss = 4.507952, step = 9400 (64.970 sec)\n",
            "I0624 01:58:46.672017 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.53248\n",
            "I0624 01:58:46.673335 140495266756480 basic_session_run_hooks.py:260] loss = 4.0735774, step = 9500 (65.252 sec)\n",
            "I0624 01:59:51.699227 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.53782\n",
            "I0624 01:59:51.701460 140495266756480 basic_session_run_hooks.py:260] loss = 3.5794342, step = 9600 (65.028 sec)\n",
            "I0624 02:00:57.079550 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.52951\n",
            "I0624 02:00:57.080659 140495266756480 basic_session_run_hooks.py:260] loss = 4.7703958, step = 9700 (65.379 sec)\n",
            "I0624 02:02:02.017132 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.53994\n",
            "I0624 02:02:02.019424 140495266756480 basic_session_run_hooks.py:260] loss = 3.8254254, step = 9800 (64.939 sec)\n",
            "I0624 02:02:56.108719 140495266756480 basic_session_run_hooks.py:606] Saving checkpoints for 9884 into training/model.ckpt.\n",
            "I0624 02:02:59.191446 140495266756480 estimator.py:1145] Calling model_fn.\n",
            "I0624 02:03:01.776888 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:03:01.814408 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:03:01.858610 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:03:01.895440 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:03:01.931325 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:03:01.966466 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:03:04.941227 140495266756480 estimator.py:1147] Done calling model_fn.\n",
            "I0624 02:03:04.969044 140495266756480 evaluation.py:255] Starting evaluation at 2019-06-24T02:03:04Z\n",
            "I0624 02:03:05.483300 140495266756480 monitored_session.py:240] Graph was finalized.\n",
            "2019-06-24 02:03:05.484003: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:03:05.484397: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 02:03:05.484490: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 02:03:05.484514: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 02:03:05.484535: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 02:03:05.484555: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 02:03:05.484575: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 02:03:05.484594: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 02:03:05.484614: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 02:03:05.484725: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:03:05.485071: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:03:05.485363: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 02:03:05.485410: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-24 02:03:05.485423: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-24 02:03:05.485433: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-24 02:03:05.485666: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:03:05.486089: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:03:05.486408: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12185 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "I0624 02:03:05.488848 140495266756480 saver.py:1280] Restoring parameters from training/model.ckpt-9884\n",
            "I0624 02:03:06.539736 140495266756480 session_manager.py:500] Running local_init_op.\n",
            "I0624 02:03:06.678589 140495266756480 session_manager.py:502] Done running local_init_op.\n",
            "I0624 02:03:14.348879 140493495654144 coco_evaluation.py:200] Performing evaluation on 47 images.\n",
            "creating index...\n",
            "index created!\n",
            "I0624 02:03:14.350824 140493495654144 coco_tools.py:109] Loading and preparing annotation results...\n",
            "I0624 02:03:14.355075 140493495654144 coco_tools.py:131] DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.46s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.12s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.120\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.246\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.106\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.009\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.058\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.294\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.147\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.210\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.217\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.083\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.126\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.384\n",
            "I0624 02:03:15.693005 140495266756480 evaluation.py:275] Finished evaluation at 2019-06-24-02:03:15\n",
            "I0624 02:03:15.693360 140495266756480 estimator.py:2039] Saving dict for global step 9884: DetectionBoxes_Precision/mAP = 0.12037623, DetectionBoxes_Precision/mAP (large) = 0.2939972, DetectionBoxes_Precision/mAP (medium) = 0.058033828, DetectionBoxes_Precision/mAP (small) = 0.008726437, DetectionBoxes_Precision/mAP@.50IOU = 0.24639241, DetectionBoxes_Precision/mAP@.75IOU = 0.10592532, DetectionBoxes_Recall/AR@1 = 0.14711021, DetectionBoxes_Recall/AR@10 = 0.21000856, DetectionBoxes_Recall/AR@100 = 0.21706024, DetectionBoxes_Recall/AR@100 (large) = 0.38376796, DetectionBoxes_Recall/AR@100 (medium) = 0.12602371, DetectionBoxes_Recall/AR@100 (small) = 0.083333336, Loss/classification_loss = 6.600097, Loss/localization_loss = 1.6202891, Loss/regularization_loss = 0.27725956, Loss/total_loss = 8.497645, global_step = 9884, learning_rate = 0.004, loss = 8.497645\n",
            "I0624 02:03:15.704297 140495266756480 estimator.py:2099] Saving 'checkpoint_path' summary for global step 9884: training/model.ckpt-9884\n",
            "I0624 02:03:26.984430 140495266756480 basic_session_run_hooks.py:692] global_step/sec: 1.17692\n",
            "I0624 02:03:26.985739 140495266756480 basic_session_run_hooks.py:260] loss = 4.817923, step = 9900 (84.966 sec)\n",
            "I0624 02:04:31.475531 140495266756480 basic_session_run_hooks.py:606] Saving checkpoints for 10000 into training/model.ckpt.\n",
            "I0624 02:04:33.202584 140495266756480 training.py:527] Skip the current checkpoint eval due to throttle secs (600 secs).\n",
            "I0624 02:04:34.003369 140495266756480 estimator.py:1145] Calling model_fn.\n",
            "I0624 02:04:37.183900 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:04:37.221362 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:04:37.256463 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:04:37.292452 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:04:37.327909 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:04:37.362509 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:04:39.922152 140495266756480 estimator.py:1147] Done calling model_fn.\n",
            "I0624 02:04:39.941492 140495266756480 evaluation.py:255] Starting evaluation at 2019-06-24T02:04:39Z\n",
            "I0624 02:04:40.437643 140495266756480 monitored_session.py:240] Graph was finalized.\n",
            "2019-06-24 02:04:40.438462: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:04:40.438808: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 02:04:40.438920: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 02:04:40.438953: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 02:04:40.438976: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 02:04:40.439000: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 02:04:40.439022: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 02:04:40.439041: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 02:04:40.439061: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 02:04:40.439154: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:04:40.439598: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:04:40.439885: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 02:04:40.439932: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-24 02:04:40.439946: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-24 02:04:40.439956: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-24 02:04:40.440192: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:04:40.440573: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:04:40.440864: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12185 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "I0624 02:04:40.443552 140495266756480 saver.py:1280] Restoring parameters from training/model.ckpt-10000\n",
            "I0624 02:04:41.500346 140495266756480 session_manager.py:500] Running local_init_op.\n",
            "I0624 02:04:41.646631 140495266756480 session_manager.py:502] Done running local_init_op.\n",
            "I0624 02:04:49.590105 140492249577216 coco_evaluation.py:200] Performing evaluation on 47 images.\n",
            "creating index...\n",
            "index created!\n",
            "I0624 02:04:49.591164 140492249577216 coco_tools.py:109] Loading and preparing annotation results...\n",
            "I0624 02:04:49.595780 140492249577216 coco_tools.py:131] DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.53s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.12s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.106\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.279\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.036\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.105\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.059\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.297\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.121\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.206\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.212\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.250\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.116\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.458\n",
            "I0624 02:04:50.977958 140495266756480 evaluation.py:275] Finished evaluation at 2019-06-24-02:04:50\n",
            "I0624 02:04:50.978314 140495266756480 estimator.py:2039] Saving dict for global step 10000: DetectionBoxes_Precision/mAP = 0.10611799, DetectionBoxes_Precision/mAP (large) = 0.29678518, DetectionBoxes_Precision/mAP (medium) = 0.058738828, DetectionBoxes_Precision/mAP (small) = 0.105102815, DetectionBoxes_Precision/mAP@.50IOU = 0.27911484, DetectionBoxes_Precision/mAP@.75IOU = 0.035950944, DetectionBoxes_Recall/AR@1 = 0.12111425, DetectionBoxes_Recall/AR@10 = 0.20557538, DetectionBoxes_Recall/AR@100 = 0.21170636, DetectionBoxes_Recall/AR@100 (large) = 0.45790344, DetectionBoxes_Recall/AR@100 (medium) = 0.115625, DetectionBoxes_Recall/AR@100 (small) = 0.25, Loss/classification_loss = 6.6623645, Loss/localization_loss = 1.5766988, Loss/regularization_loss = 0.27742675, Loss/total_loss = 8.516489, global_step = 10000, learning_rate = 0.004, loss = 8.516489\n",
            "I0624 02:04:50.990153 140495266756480 estimator.py:2099] Saving 'checkpoint_path' summary for global step 10000: training/model.ckpt-10000\n",
            "I0624 02:04:50.991209 140495266756480 exporter.py:410] Performing the final export in the end of training.\n",
            "I0624 02:04:51.209079 140495266756480 estimator.py:1145] Calling model_fn.\n",
            "I0624 02:04:54.305396 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:04:54.343512 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:04:54.378718 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:04:54.414692 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:04:54.450389 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:04:54.485137 140495266756480 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "W0624 02:04:55.862261 140495266756480 deprecation_wrapper.py:119] From /content/models/research/object_detection/model_lib.py:419: The name tf.saved_model.signature_constants.PREDICT_METHOD_NAME is deprecated. Please use tf.saved_model.PREDICT_METHOD_NAME instead.\n",
            "\n",
            "I0624 02:04:56.149132 140495266756480 estimator.py:1147] Done calling model_fn.\n",
            "W0624 02:04:56.149454 140495266756480 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
            "I0624 02:04:56.150271 140495266756480 export_utils.py:170] Signatures INCLUDED in export for Classify: None\n",
            "I0624 02:04:56.150401 140495266756480 export_utils.py:170] Signatures INCLUDED in export for Regress: None\n",
            "I0624 02:04:56.150470 140495266756480 export_utils.py:170] Signatures INCLUDED in export for Predict: ['tensorflow/serving/predict', 'serving_default']\n",
            "I0624 02:04:56.150534 140495266756480 export_utils.py:170] Signatures INCLUDED in export for Train: None\n",
            "I0624 02:04:56.150590 140495266756480 export_utils.py:170] Signatures INCLUDED in export for Eval: None\n",
            "2019-06-24 02:04:56.151447: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:04:56.151778: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 02:04:56.151879: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 02:04:56.151904: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 02:04:56.151929: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 02:04:56.151950: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 02:04:56.151971: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 02:04:56.151993: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 02:04:56.152015: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 02:04:56.152118: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:04:56.152538: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:04:56.152818: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 02:04:56.152860: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-24 02:04:56.152873: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-24 02:04:56.152883: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-24 02:04:56.153106: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:04:56.153461: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:04:56.153834: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12185 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "I0624 02:04:56.158130 140495266756480 saver.py:1280] Restoring parameters from training/model.ckpt-10000\n",
            "I0624 02:04:56.718982 140495266756480 builder_impl.py:661] Assets added to graph.\n",
            "I0624 02:04:56.719221 140495266756480 builder_impl.py:456] No assets to write.\n",
            "I0624 02:04:57.680844 140495266756480 builder_impl.py:421] SavedModel written to: training/export/Servo/temp-b'1561341890'/saved_model.pb\n",
            "I0624 02:04:58.180581 140495266756480 estimator.py:368] Loss for final step: 3.406697.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2kVUqQCOFRGK",
        "colab_type": "code",
        "outputId": "3ee10ebe-2fb7-4a69-8c6c-bcfb98751092",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 12691
        }
      },
      "source": [
        "import re\n",
        "import numpy as np\n",
        "\n",
        "output_directory = './fine_tuned_model'\n",
        "\n",
        "lst = os.listdir(model_dir)\n",
        "lst = [l for l in lst if 'model.ckpt-' in l and '.meta' in l]\n",
        "steps=np.array([int(re.findall('\\d+', l)[0]) for l in lst])\n",
        "last_model = lst[steps.argmax()].replace('.meta', '')\n",
        "\n",
        "last_model_path = os.path.join(model_dir, last_model)\n",
        "print(last_model_path)\n",
        "!python /content/models/research/object_detection/export_inference_graph.py \\\n",
        "    --input_type=image_tensor \\\n",
        "    --pipeline_config_path={pipeline_fname} \\\n",
        "    --output_directory={output_directory} \\\n",
        "    --trained_checkpoint_prefix={last_model_path}"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training/model.ckpt-10000\n",
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0624 02:08:14.458543 140362188785536 deprecation_wrapper.py:119] From /content/models/research/slim/nets/inception_resnet_v2.py:373: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
            "\n",
            "W0624 02:08:14.469620 140362188785536 deprecation_wrapper.py:119] From /content/models/research/slim/nets/mobilenet/mobilenet.py:397: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n",
            "\n",
            "W0624 02:08:14.480847 140362188785536 deprecation_wrapper.py:119] From /content/models/research/object_detection/export_inference_graph.py:156: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.\n",
            "\n",
            "W0624 02:08:14.481548 140362188785536 deprecation_wrapper.py:119] From /content/models/research/object_detection/export_inference_graph.py:139: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
            "\n",
            "W0624 02:08:14.501060 140362188785536 deprecation_wrapper.py:119] From /content/models/research/object_detection/exporter.py:367: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.\n",
            "\n",
            "W0624 02:08:14.511913 140362188785536 deprecation_wrapper.py:119] From /content/models/research/object_detection/exporter.py:110: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0624 02:08:14.547406 140362188785536 deprecation_wrapper.py:119] From /content/models/research/object_detection/core/preprocessor.py:2515: The name tf.image.resize_images is deprecated. Please use tf.image.resize instead.\n",
            "\n",
            "W0624 02:08:14.710640 140362188785536 deprecation_wrapper.py:119] From /content/models/research/object_detection/meta_architectures/ssd_meta_arch.py:566: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
            "\n",
            "W0624 02:08:17.672805 140362188785536 deprecation_wrapper.py:119] From /content/models/research/object_detection/core/anchor_generator.py:149: The name tf.assert_equal is deprecated. Please use tf.compat.v1.assert_equal instead.\n",
            "\n",
            "W0624 02:08:17.686007 140362188785536 deprecation_wrapper.py:119] From /content/models/research/object_detection/predictors/convolutional_box_predictor.py:150: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
            "\n",
            "I0624 02:08:17.686180 140362188785536 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:08:17.729675 140362188785536 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:08:17.773006 140362188785536 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:08:17.815459 140362188785536 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:08:17.858572 140362188785536 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "I0624 02:08:17.900896 140362188785536 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0\n",
            "W0624 02:08:18.169712 140362188785536 deprecation.py:323] From /content/models/research/object_detection/core/post_processing.py:255: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "W0624 02:08:19.555355 140362188785536 deprecation_wrapper.py:119] From /content/models/research/object_detection/exporter.py:246: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.\n",
            "\n",
            "W0624 02:08:19.555658 140362188785536 deprecation.py:323] From /content/models/research/object_detection/exporter.py:348: get_or_create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please switch to tf.train.get_or_create_global_step\n",
            "W0624 02:08:19.559071 140362188785536 deprecation.py:323] From /content/models/research/object_detection/exporter.py:504: print_model_analysis (from tensorflow.contrib.tfprof.model_analyzer) is deprecated and will be removed after 2018-01-01.\n",
            "Instructions for updating:\n",
            "Use `tf.profiler.profile(graph, run_meta, op_log, cmd, options)`. Build `options` with `tf.profiler.ProfileOptionBuilder`. See README.md for details\n",
            "W0624 02:08:19.560265 140362188785536 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/profiler/internal/flops_registry.py:142: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n",
            "230 ops no flops stats due to incomplete shapes.\n",
            "Parsing Inputs...\n",
            "Incomplete shape.\n",
            "\n",
            "=========================Options=============================\n",
            "-max_depth                  10000\n",
            "-min_bytes                  0\n",
            "-min_peak_bytes             0\n",
            "-min_residual_bytes         0\n",
            "-min_output_bytes           0\n",
            "-min_micros                 0\n",
            "-min_accelerator_micros     0\n",
            "-min_cpu_micros             0\n",
            "-min_params                 0\n",
            "-min_float_ops              0\n",
            "-min_occurrence             0\n",
            "-step                       -1\n",
            "-order_by                   name\n",
            "-account_type_regexes       _trainable_variables\n",
            "-start_name_regexes         .*\n",
            "-trim_name_regexes          .*BatchNorm.*\n",
            "-show_name_regexes          .*\n",
            "-hide_name_regexes          \n",
            "-account_displayed_op_only  true\n",
            "-select                     params\n",
            "-output                     stdout:\n",
            "\n",
            "==================Model Analysis Report======================\n",
            "Incomplete shape.\n",
            "\n",
            "Doc:\n",
            "scope: The nodes in the model graph are organized by their names, which is hierarchical like filesystem.\n",
            "param: Number of parameters (in the Variable).\n",
            "\n",
            "Profile:\n",
            "node name | # parameters\n",
            "_TFProfRoot (--/5.03m params)\n",
            "  BoxPredictor_0 (--/58.85k params)\n",
            "    BoxPredictor_0/BoxEncodingPredictor (--/6.92k params)\n",
            "      BoxPredictor_0/BoxEncodingPredictor/biases (12, 12/12 params)\n",
            "      BoxPredictor_0/BoxEncodingPredictor/weights (1x1x576x12, 6.91k/6.91k params)\n",
            "    BoxPredictor_0/ClassPredictor (--/51.93k params)\n",
            "      BoxPredictor_0/ClassPredictor/biases (90, 90/90 params)\n",
            "      BoxPredictor_0/ClassPredictor/weights (1x1x576x90, 51.84k/51.84k params)\n",
            "  BoxPredictor_1 (--/261.32k params)\n",
            "    BoxPredictor_1/BoxEncodingPredictor (--/30.74k params)\n",
            "      BoxPredictor_1/BoxEncodingPredictor/biases (24, 24/24 params)\n",
            "      BoxPredictor_1/BoxEncodingPredictor/weights (1x1x1280x24, 30.72k/30.72k params)\n",
            "    BoxPredictor_1/ClassPredictor (--/230.58k params)\n",
            "      BoxPredictor_1/ClassPredictor/biases (180, 180/180 params)\n",
            "      BoxPredictor_1/ClassPredictor/weights (1x1x1280x180, 230.40k/230.40k params)\n",
            "  BoxPredictor_2 (--/104.65k params)\n",
            "    BoxPredictor_2/BoxEncodingPredictor (--/12.31k params)\n",
            "      BoxPredictor_2/BoxEncodingPredictor/biases (24, 24/24 params)\n",
            "      BoxPredictor_2/BoxEncodingPredictor/weights (1x1x512x24, 12.29k/12.29k params)\n",
            "    BoxPredictor_2/ClassPredictor (--/92.34k params)\n",
            "      BoxPredictor_2/ClassPredictor/biases (180, 180/180 params)\n",
            "      BoxPredictor_2/ClassPredictor/weights (1x1x512x180, 92.16k/92.16k params)\n",
            "  BoxPredictor_3 (--/52.43k params)\n",
            "    BoxPredictor_3/BoxEncodingPredictor (--/6.17k params)\n",
            "      BoxPredictor_3/BoxEncodingPredictor/biases (24, 24/24 params)\n",
            "      BoxPredictor_3/BoxEncodingPredictor/weights (1x1x256x24, 6.14k/6.14k params)\n",
            "    BoxPredictor_3/ClassPredictor (--/46.26k params)\n",
            "      BoxPredictor_3/ClassPredictor/biases (180, 180/180 params)\n",
            "      BoxPredictor_3/ClassPredictor/weights (1x1x256x180, 46.08k/46.08k params)\n",
            "  BoxPredictor_4 (--/52.43k params)\n",
            "    BoxPredictor_4/BoxEncodingPredictor (--/6.17k params)\n",
            "      BoxPredictor_4/BoxEncodingPredictor/biases (24, 24/24 params)\n",
            "      BoxPredictor_4/BoxEncodingPredictor/weights (1x1x256x24, 6.14k/6.14k params)\n",
            "    BoxPredictor_4/ClassPredictor (--/46.26k params)\n",
            "      BoxPredictor_4/ClassPredictor/biases (180, 180/180 params)\n",
            "      BoxPredictor_4/ClassPredictor/weights (1x1x256x180, 46.08k/46.08k params)\n",
            "  BoxPredictor_5 (--/26.32k params)\n",
            "    BoxPredictor_5/BoxEncodingPredictor (--/3.10k params)\n",
            "      BoxPredictor_5/BoxEncodingPredictor/biases (24, 24/24 params)\n",
            "      BoxPredictor_5/BoxEncodingPredictor/weights (1x1x128x24, 3.07k/3.07k params)\n",
            "    BoxPredictor_5/ClassPredictor (--/23.22k params)\n",
            "      BoxPredictor_5/ClassPredictor/biases (180, 180/180 params)\n",
            "      BoxPredictor_5/ClassPredictor/weights (1x1x128x180, 23.04k/23.04k params)\n",
            "  FeatureExtractor (--/4.48m params)\n",
            "    FeatureExtractor/MobilenetV2 (--/4.48m params)\n",
            "      FeatureExtractor/MobilenetV2/Conv (--/864 params)\n",
            "        FeatureExtractor/MobilenetV2/Conv/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/Conv/weights (3x3x3x32, 864/864 params)\n",
            "      FeatureExtractor/MobilenetV2/Conv_1 (--/409.60k params)\n",
            "        FeatureExtractor/MobilenetV2/Conv_1/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/Conv_1/weights (1x1x320x1280, 409.60k/409.60k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv (--/800 params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv/depthwise (--/288 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv/depthwise/depthwise_weights (3x3x32x1, 288/288 params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv/project (--/512 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv/project/weights (1x1x32x16, 512/512 params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_1 (--/4.70k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise (--/864 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/depthwise_weights (3x3x96x1, 864/864 params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_1/expand (--/1.54k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_1/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_1/expand/weights (1x1x16x96, 1.54k/1.54k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_1/project (--/2.30k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_1/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_1/project/weights (1x1x96x24, 2.30k/2.30k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_10 (--/64.90k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise (--/3.46k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise/depthwise_weights (3x3x384x1, 3.46k/3.46k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_10/expand (--/24.58k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_10/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_10/expand/weights (1x1x64x384, 24.58k/24.58k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_10/project (--/36.86k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_10/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_10/project/weights (1x1x384x96, 36.86k/36.86k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_11 (--/115.78k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise (--/5.18k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/depthwise_weights (3x3x576x1, 5.18k/5.18k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_11/expand (--/55.30k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_11/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_11/expand/weights (1x1x96x576, 55.30k/55.30k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_11/project (--/55.30k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_11/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_11/project/weights (1x1x576x96, 55.30k/55.30k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_12 (--/115.78k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise (--/5.18k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/depthwise_weights (3x3x576x1, 5.18k/5.18k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_12/expand (--/55.30k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_12/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_12/expand/weights (1x1x96x576, 55.30k/55.30k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_12/project (--/55.30k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_12/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_12/project/weights (1x1x576x96, 55.30k/55.30k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_13 (--/152.64k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise (--/5.18k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/depthwise_weights (3x3x576x1, 5.18k/5.18k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_13/expand (--/55.30k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_13/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_13/expand/weights (1x1x96x576, 55.30k/55.30k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_13/project (--/92.16k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_13/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_13/project/weights (1x1x576x160, 92.16k/92.16k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_14 (--/315.84k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise (--/8.64k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/depthwise_weights (3x3x960x1, 8.64k/8.64k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_14/expand (--/153.60k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_14/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_14/expand/weights (1x1x160x960, 153.60k/153.60k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_14/project (--/153.60k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_14/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_14/project/weights (1x1x960x160, 153.60k/153.60k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_15 (--/315.84k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise (--/8.64k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/depthwise_weights (3x3x960x1, 8.64k/8.64k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_15/expand (--/153.60k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_15/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_15/expand/weights (1x1x160x960, 153.60k/153.60k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_15/project (--/153.60k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_15/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_15/project/weights (1x1x960x160, 153.60k/153.60k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_16 (--/469.44k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise (--/8.64k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/depthwise_weights (3x3x960x1, 8.64k/8.64k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_16/expand (--/153.60k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_16/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_16/expand/weights (1x1x160x960, 153.60k/153.60k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_16/project (--/307.20k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_16/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_16/project/weights (1x1x960x320, 307.20k/307.20k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_2 (--/8.21k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise (--/1.30k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/depthwise_weights (3x3x144x1, 1.30k/1.30k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_2/expand (--/3.46k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_2/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_2/expand/weights (1x1x24x144, 3.46k/3.46k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_2/project (--/3.46k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_2/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_2/project/weights (1x1x144x24, 3.46k/3.46k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_3 (--/9.36k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise (--/1.30k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/depthwise_weights (3x3x144x1, 1.30k/1.30k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_3/expand (--/3.46k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_3/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_3/expand/weights (1x1x24x144, 3.46k/3.46k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_3/project (--/4.61k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_3/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_3/project/weights (1x1x144x32, 4.61k/4.61k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_4 (--/14.02k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise (--/1.73k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/depthwise_weights (3x3x192x1, 1.73k/1.73k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_4/expand (--/6.14k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_4/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_4/expand/weights (1x1x32x192, 6.14k/6.14k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_4/project (--/6.14k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_4/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_4/project/weights (1x1x192x32, 6.14k/6.14k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_5 (--/14.02k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise (--/1.73k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/depthwise_weights (3x3x192x1, 1.73k/1.73k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_5/expand (--/6.14k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_5/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_5/expand/weights (1x1x32x192, 6.14k/6.14k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_5/project (--/6.14k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_5/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_5/project/weights (1x1x192x32, 6.14k/6.14k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_6 (--/20.16k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise (--/1.73k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/depthwise_weights (3x3x192x1, 1.73k/1.73k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_6/expand (--/6.14k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_6/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_6/expand/weights (1x1x32x192, 6.14k/6.14k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_6/project (--/12.29k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_6/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_6/project/weights (1x1x192x64, 12.29k/12.29k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_7 (--/52.61k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise (--/3.46k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/depthwise_weights (3x3x384x1, 3.46k/3.46k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_7/expand (--/24.58k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_7/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_7/expand/weights (1x1x64x384, 24.58k/24.58k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_7/project (--/24.58k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_7/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_7/project/weights (1x1x384x64, 24.58k/24.58k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_8 (--/52.61k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise (--/3.46k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/depthwise_weights (3x3x384x1, 3.46k/3.46k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_8/expand (--/24.58k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_8/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_8/expand/weights (1x1x64x384, 24.58k/24.58k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_8/project (--/24.58k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_8/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_8/project/weights (1x1x384x64, 24.58k/24.58k params)\n",
            "      FeatureExtractor/MobilenetV2/expanded_conv_9 (--/52.61k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise (--/3.46k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/depthwise_weights (3x3x384x1, 3.46k/3.46k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_9/expand (--/24.58k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_9/expand/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_9/expand/weights (1x1x64x384, 24.58k/24.58k params)\n",
            "        FeatureExtractor/MobilenetV2/expanded_conv_9/project (--/24.58k params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_9/project/BatchNorm (--/0 params)\n",
            "          FeatureExtractor/MobilenetV2/expanded_conv_9/project/weights (1x1x384x64, 24.58k/24.58k params)\n",
            "      FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256 (--/327.68k params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/weights (1x1x1280x256, 327.68k/327.68k params)\n",
            "      FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128 (--/65.54k params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/weights (1x1x512x128, 65.54k/65.54k params)\n",
            "      FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128 (--/32.77k params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/weights (1x1x256x128, 32.77k/32.77k params)\n",
            "      FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64 (--/16.38k params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/weights (1x1x256x64, 16.38k/16.38k params)\n",
            "      FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512 (--/1.18m params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/weights (3x3x256x512, 1.18m/1.18m params)\n",
            "      FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256 (--/294.91k params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/weights (3x3x128x256, 294.91k/294.91k params)\n",
            "      FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256 (--/294.91k params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/weights (3x3x128x256, 294.91k/294.91k params)\n",
            "      FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128 (--/73.73k params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/BatchNorm (--/0 params)\n",
            "        FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/weights (3x3x64x128, 73.73k/73.73k params)\n",
            "\n",
            "======================End of Report==========================\n",
            "230 ops no flops stats due to incomplete shapes.\n",
            "Parsing Inputs...\n",
            "Incomplete shape.\n",
            "\n",
            "=========================Options=============================\n",
            "-max_depth                  10000\n",
            "-min_bytes                  0\n",
            "-min_peak_bytes             0\n",
            "-min_residual_bytes         0\n",
            "-min_output_bytes           0\n",
            "-min_micros                 0\n",
            "-min_accelerator_micros     0\n",
            "-min_cpu_micros             0\n",
            "-min_params                 0\n",
            "-min_float_ops              1\n",
            "-min_occurrence             0\n",
            "-step                       -1\n",
            "-order_by                   float_ops\n",
            "-account_type_regexes       .*\n",
            "-start_name_regexes         .*\n",
            "-trim_name_regexes          .*BatchNorm.*,.*Initializer.*,.*Regularizer.*,.*BiasAdd.*\n",
            "-show_name_regexes          .*\n",
            "-hide_name_regexes          \n",
            "-account_displayed_op_only  true\n",
            "-select                     float_ops\n",
            "-output                     stdout:\n",
            "\n",
            "==================Model Analysis Report======================\n",
            "Incomplete shape.\n",
            "\n",
            "Doc:\n",
            "scope: The nodes in the model graph are organized by their names, which is hierarchical like filesystem.\n",
            "flops: Number of float operations. Note: Please read the implementation for the math behind it.\n",
            "\n",
            "Profile:\n",
            "node name | # float_ops\n",
            "_TFProfRoot (--/17.71k flops)\n",
            "  MultipleGridAnchorGenerator/sub (2.17k/2.17k flops)\n",
            "  MultipleGridAnchorGenerator/add_2 (2.17k/2.17k flops)\n",
            "  MultipleGridAnchorGenerator/mul_20 (2.17k/2.17k flops)\n",
            "  MultipleGridAnchorGenerator/mul_19 (2.17k/2.17k flops)\n",
            "  MultipleGridAnchorGenerator/mul_27 (1.20k/1.20k flops)\n",
            "  MultipleGridAnchorGenerator/sub_1 (1.20k/1.20k flops)\n",
            "  MultipleGridAnchorGenerator/add_5 (1.20k/1.20k flops)\n",
            "  MultipleGridAnchorGenerator/mul_28 (1.20k/1.20k flops)\n",
            "  MultipleGridAnchorGenerator/mul_21 (1.08k/1.08k flops)\n",
            "  MultipleGridAnchorGenerator/mul_29 (600/600 flops)\n",
            "  MultipleGridAnchorGenerator/add_8 (300/300 flops)\n",
            "  MultipleGridAnchorGenerator/mul_36 (300/300 flops)\n",
            "  MultipleGridAnchorGenerator/sub_2 (300/300 flops)\n",
            "  MultipleGridAnchorGenerator/mul_35 (300/300 flops)\n",
            "  MultipleGridAnchorGenerator/mul_37 (150/150 flops)\n",
            "  MultipleGridAnchorGenerator/sub_3 (108/108 flops)\n",
            "  MultipleGridAnchorGenerator/mul_44 (108/108 flops)\n",
            "  MultipleGridAnchorGenerator/mul_43 (108/108 flops)\n",
            "  MultipleGridAnchorGenerator/add_11 (108/108 flops)\n",
            "  MultipleGridAnchorGenerator/mul_45 (54/54 flops)\n",
            "  MultipleGridAnchorGenerator/sub_4 (48/48 flops)\n",
            "  MultipleGridAnchorGenerator/add_14 (48/48 flops)\n",
            "  MultipleGridAnchorGenerator/mul_51 (48/48 flops)\n",
            "  MultipleGridAnchorGenerator/mul_52 (48/48 flops)\n",
            "  MultipleGridAnchorGenerator/mul_53 (24/24 flops)\n",
            "  MultipleGridAnchorGenerator/add (19/19 flops)\n",
            "  MultipleGridAnchorGenerator/mul_18 (19/19 flops)\n",
            "  MultipleGridAnchorGenerator/mul_17 (19/19 flops)\n",
            "  MultipleGridAnchorGenerator/add_1 (19/19 flops)\n",
            "  MultipleGridAnchorGenerator/mul_59 (12/12 flops)\n",
            "  MultipleGridAnchorGenerator/mul_60 (12/12 flops)\n",
            "  MultipleGridAnchorGenerator/sub_5 (12/12 flops)\n",
            "  MultipleGridAnchorGenerator/add_17 (12/12 flops)\n",
            "  MultipleGridAnchorGenerator/add_4 (10/10 flops)\n",
            "  MultipleGridAnchorGenerator/add_3 (10/10 flops)\n",
            "  MultipleGridAnchorGenerator/mul_26 (10/10 flops)\n",
            "  MultipleGridAnchorGenerator/mul_25 (10/10 flops)\n",
            "  MultipleGridAnchorGenerator/mul_38 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_54 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_19 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_18 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_17 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_16 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_39 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_40 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_15 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_61 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_46 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_47 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_48 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_56 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_55 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_32 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_31 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_30 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_22 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_24 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_23 (6/6 flops)\n",
            "  MultipleGridAnchorGenerator/mul_33 (5/5 flops)\n",
            "  MultipleGridAnchorGenerator/add_6 (5/5 flops)\n",
            "  MultipleGridAnchorGenerator/add_7 (5/5 flops)\n",
            "  MultipleGridAnchorGenerator/mul_34 (5/5 flops)\n",
            "  MultipleGridAnchorGenerator/mul_41 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/mul_42 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/add_9 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/mul_16 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/mul_15 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/mul_14 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/add_10 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_14 (3/3 flops)\n",
            "  MultipleGridAnchorGenerator/add_13 (2/2 flops)\n",
            "  MultipleGridAnchorGenerator/add_12 (2/2 flops)\n",
            "  MultipleGridAnchorGenerator/mul_50 (2/2 flops)\n",
            "  MultipleGridAnchorGenerator/mul_49 (2/2 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_17 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_18 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_16 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_26 (1/1 flops)\n",
            "  Preprocessor/map/while/add_1 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_15 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_14 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_27 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_13 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_19 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_2 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_20 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_21 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_22 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_23 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_24 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_25 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_26 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_56 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_24 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_52 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_50 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_6 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_48 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_46 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_44 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_42 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_40 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_4 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_8 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_28 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_1 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_54 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_10 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_11 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_12 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_38 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_36 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_34 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_32 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_30 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_7 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_13 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_14 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_15 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_16 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_17 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_2 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_3 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_4 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_5 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_6 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_12 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_8 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_9 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/add (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/add_1 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/ones/Less (1/1 flops)\n",
            "  Preprocessor/map/while/Less (1/1 flops)\n",
            "  Preprocessor/map/while/Less_1 (1/1 flops)\n",
            "  Preprocessor/map/while/add (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/Minimum (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_3 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_3 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_4 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_5 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_6 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_7 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_8 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_9 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_1 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_2 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_28 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_4 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_5 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_6 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_7 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_8 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_1 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_10 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_11 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_2 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_6 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_7 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_8 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_9 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_1 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_10 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_11 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_12 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_13 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_58 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_3 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_4 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_5 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_6 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_7 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_8 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/truediv_9 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Less (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Less_1 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_1 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/add_15 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/add_16 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/add_18 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/add_19 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/add_20 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/add_21 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/add_22 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/add_23 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/assert_equal/Equal (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Greater (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_10 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_11 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_12 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_13 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_2 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_3 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_4 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_5 (1/1 flops)\n",
            "  MultipleGridAnchorGenerator/mul_57 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/SortByField/Equal (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_27 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_28 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_29 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_3 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_4 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_5 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_6 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_7 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_8 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_9 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_26 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/SortByField_1/Equal (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_10 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_12 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_14 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_16 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_18 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_2 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_20 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_17 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_1 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_10 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_11 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_12 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_13 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_14 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_15 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_16 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_22 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_18 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_19 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_2 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_20 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_21 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_22 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_23 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_24 (1/1 flops)\n",
            "  Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Minimum_25 (1/1 flops)\n",
            "\n",
            "======================End of Report==========================\n",
            "W0624 02:08:21.157602 140362188785536 deprecation_wrapper.py:119] From /content/models/research/object_detection/exporter.py:397: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
            "\n",
            "2019-06-24 02:08:22.333197: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1\n",
            "2019-06-24 02:08:22.338770: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:22.339140: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 02:08:22.343643: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 02:08:22.351572: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 02:08:22.357120: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 02:08:22.367021: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 02:08:22.375975: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 02:08:22.383630: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 02:08:22.398579: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 02:08:22.398743: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:22.399141: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:22.399502: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 02:08:22.404876: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz\n",
            "2019-06-24 02:08:22.405085: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1971c00 executing computations on platform Host. Devices:\n",
            "2019-06-24 02:08:22.405113: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>\n",
            "2019-06-24 02:08:22.536791: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:22.537286: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1972680 executing computations on platform CUDA. Devices:\n",
            "2019-06-24 02:08:22.537314: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
            "2019-06-24 02:08:22.537542: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:22.537869: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 02:08:22.537938: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 02:08:22.537964: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 02:08:22.537985: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 02:08:22.538009: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 02:08:22.538028: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 02:08:22.538047: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 02:08:22.538066: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 02:08:22.538151: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:22.538584: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:22.538889: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 02:08:22.538947: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 02:08:22.539928: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-24 02:08:22.539957: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-24 02:08:22.539971: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-24 02:08:22.540282: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:22.540699: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:22.541012: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:40] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2019-06-24 02:08:22.541053: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12185 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "W0624 02:08:22.541730 140362188785536 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use standard file APIs to check for files with this prefix.\n",
            "I0624 02:08:22.544470 140362188785536 saver.py:1280] Restoring parameters from training/model.ckpt-10000\n",
            "2019-06-24 02:08:25.464022: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:25.464445: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 02:08:25.464528: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 02:08:25.464552: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 02:08:25.464573: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 02:08:25.464593: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 02:08:25.464617: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 02:08:25.464637: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 02:08:25.464657: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 02:08:25.464758: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:25.465140: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:25.465456: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 02:08:25.465498: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-24 02:08:25.465512: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-24 02:08:25.465528: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-24 02:08:25.465789: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:25.466155: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:25.466484: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12185 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "I0624 02:08:25.468966 140362188785536 saver.py:1280] Restoring parameters from training/model.ckpt-10000\n",
            "W0624 02:08:26.180709 140362188785536 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/tools/freeze_graph.py:233: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
            "W0624 02:08:26.180960 140362188785536 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/graph_util_impl.py:270: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
            "I0624 02:08:26.540220 140362188785536 graph_util_impl.py:311] Froze 324 variables.\n",
            "I0624 02:08:26.639878 140362188785536 graph_util_impl.py:364] Converted 324 variables to const ops.\n",
            "2019-06-24 02:08:26.869556: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:26.869955: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-06-24 02:08:26.870045: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-06-24 02:08:26.870077: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-06-24 02:08:26.870101: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-06-24 02:08:26.870128: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-06-24 02:08:26.870152: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-06-24 02:08:26.870174: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-06-24 02:08:26.870214: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-06-24 02:08:26.870337: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:26.870725: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:26.871041: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2019-06-24 02:08:26.871083: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-06-24 02:08:26.871098: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2019-06-24 02:08:26.871108: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2019-06-24 02:08:26.871398: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:26.871770: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-06-24 02:08:26.872083: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12185 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "W0624 02:08:27.614063 140362188785536 deprecation_wrapper.py:119] From /content/models/research/object_detection/exporter.py:274: The name tf.saved_model.builder.SavedModelBuilder is deprecated. Please use tf.compat.v1.saved_model.builder.SavedModelBuilder instead.\n",
            "\n",
            "W0624 02:08:27.616919 140362188785536 deprecation.py:323] From /content/models/research/object_detection/exporter.py:277: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
            "W0624 02:08:27.617423 140362188785536 deprecation_wrapper.py:119] From /content/models/research/object_detection/exporter.py:283: The name tf.saved_model.signature_def_utils.build_signature_def is deprecated. Please use tf.compat.v1.saved_model.signature_def_utils.build_signature_def instead.\n",
            "\n",
            "W0624 02:08:27.617557 140362188785536 deprecation_wrapper.py:119] From /content/models/research/object_detection/exporter.py:286: The name tf.saved_model.signature_constants.PREDICT_METHOD_NAME is deprecated. Please use tf.saved_model.PREDICT_METHOD_NAME instead.\n",
            "\n",
            "W0624 02:08:27.617751 140362188785536 deprecation_wrapper.py:119] From /content/models/research/object_detection/exporter.py:291: The name tf.saved_model.tag_constants.SERVING is deprecated. Please use tf.saved_model.SERVING instead.\n",
            "\n",
            "W0624 02:08:27.617869 140362188785536 deprecation_wrapper.py:119] From /content/models/research/object_detection/exporter.py:293: The name tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY is deprecated. Please use tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY instead.\n",
            "\n",
            "I0624 02:08:27.618110 140362188785536 builder_impl.py:636] No assets to save.\n",
            "I0624 02:08:27.618191 140362188785536 builder_impl.py:456] No assets to write.\n",
            "I0624 02:08:28.048489 140362188785536 builder_impl.py:421] SavedModel written to: ./fine_tuned_model/saved_model/saved_model.pb\n",
            "W0624 02:08:28.112948 140362188785536 deprecation_wrapper.py:119] From /content/models/research/object_detection/utils/config_util.py:184: The name tf.gfile.Open is deprecated. Please use tf.io.gfile.GFile instead.\n",
            "\n",
            "I0624 02:08:28.113197 140362188785536 config_util.py:186] Writing pipeline config file to ./fine_tuned_model/pipeline.config\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S7HJ6hRdFfED",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "\n",
        "pb_fname = os.path.join(os.path.abspath(output_directory), \"frozen_inference_graph.pb\")\n",
        "assert os.path.isfile(pb_fname), '`{}` not exist'.format(pb_fname)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jcam6z4EYlUv",
        "colab_type": "text"
      },
      "source": [
        "# 테스트 이미지 업로드"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Iea7uH5FfnT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "f812aa97-1f14-4b7a-9def-d674371de331"
      },
      "source": [
        "import os\n",
        "import glob\n",
        "\n",
        "# Path to frozen detection graph. This is the actual model that is used for the object detection.\n",
        "PATH_TO_CKPT = pb_fname\n",
        "\n",
        "# List of the strings that is used to add correct label for each box.\n",
        "PATH_TO_LABELS = label_map_pbtxt_fname\n",
        "\n",
        "# If you want to test the code with your images, just add images files to the PATH_TO_TEST_IMAGES_DIR.\n",
        "PATH_TO_TEST_IMAGES_DIR =  os.path.join(repo_dir_path, \"test\")\n",
        "#PATH_TO_TEST_IMAGES_DIR = '/content/gdrive/\"My Drive\"/ai_project/data/'\n",
        "\n",
        "%cd {PATH_TO_TEST_IMAGES_DIR}\n",
        "#https://drive.google.com/open?id=1jBujyNnJs6twVLXHFGO7g84iMynFBOF1\n",
        "!rm -rf *\n",
        "!wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1jBujyNnJs6twVLXHFGO7g84iMynFBOF1' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1jBujyNnJs6twVLXHFGO7g84iMynFBOF1\" -O test_sejong.tar && rm -rf /tmp/cookies.txt\n",
        "tar=tarfile.open('test_sejong.tar')\n",
        "tar.extractall()\n",
        "!rm test_sejong.tar\n",
        "\n",
        "assert os.path.isfile(pb_fname)\n",
        "assert os.path.isfile(PATH_TO_LABELS)\n",
        "TEST_IMAGE_PATHS = glob.glob(os.path.join(PATH_TO_TEST_IMAGES_DIR, \"*.*\"))\n",
        "assert len(TEST_IMAGE_PATHS) > 0, 'No image found in `{}`.'.format(PATH_TO_TEST_IMAGES_DIR)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/object_detection_demo/test\n",
            "--2019-06-24 02:08:33--  https://docs.google.com/uc?export=download&confirm=LNgQ&id=1jBujyNnJs6twVLXHFGO7g84iMynFBOF1\n",
            "Resolving docs.google.com (docs.google.com)... 108.177.119.139, 108.177.119.101, 108.177.119.102, ...\n",
            "Connecting to docs.google.com (docs.google.com)|108.177.119.139|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-14-4o-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/hb3nmbbe39jk9vkfscfcik2j2uideofn/1561341600000/09621136847049687709/*/1jBujyNnJs6twVLXHFGO7g84iMynFBOF1?e=download [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2019-06-24 02:08:33--  https://doc-14-4o-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/hb3nmbbe39jk9vkfscfcik2j2uideofn/1561341600000/09621136847049687709/*/1jBujyNnJs6twVLXHFGO7g84iMynFBOF1?e=download\n",
            "Resolving doc-14-4o-docs.googleusercontent.com (doc-14-4o-docs.googleusercontent.com)... 108.177.127.132, 2a00:1450:4013:c07::84\n",
            "Connecting to doc-14-4o-docs.googleusercontent.com (doc-14-4o-docs.googleusercontent.com)|108.177.127.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [application/x-tar]\n",
            "Saving to: ‘test_sejong.tar’\n",
            "\n",
            "test_sejong.tar         [          <=>       ] 267.62M   131MB/s    in 2.0s    \n",
            "\n",
            "2019-06-24 02:08:35 (131 MB/s) - ‘test_sejong.tar’ saved [280616448]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFX6EEpJIZCt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#print(TEST_IMAGE_PATHS)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HPbGHmIBZyn1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "TEST_IMAGE_PATHS.sort()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PymCri5zGEnk",
        "colab_type": "code",
        "outputId": "36ee5678-7a48-4e25-b884-fbe1d3f4ca91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd /content/models/research/object_detection\n",
        "\n",
        "import numpy as np\n",
        "import os\n",
        "import six.moves.urllib as urllib\n",
        "import sys\n",
        "import tarfile\n",
        "import tensorflow as tf\n",
        "import zipfile\n",
        "\n",
        "from collections import defaultdict\n",
        "from io import StringIO\n",
        "from matplotlib import pyplot as plt\n",
        "from PIL import Image\n",
        "\n",
        "# This is needed since the notebook is stored in the object_detection folder.\n",
        "sys.path.append(\"..\")\n",
        "from object_detection.utils import ops as utils_ops\n",
        "\n",
        "\n",
        "# This is needed to display the images.\n",
        "%matplotlib inline\n",
        "\n",
        "\n",
        "from object_detection.utils import label_map_util\n",
        "\n",
        "from object_detection.utils import visualization_utils as vis_util\n",
        "\n",
        "\n",
        "detection_graph = tf.Graph()\n",
        "with detection_graph.as_default():\n",
        "    od_graph_def = tf.GraphDef()\n",
        "    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\n",
        "        serialized_graph = fid.read()\n",
        "        od_graph_def.ParseFromString(serialized_graph)\n",
        "        tf.import_graph_def(od_graph_def, name='')\n",
        "\n",
        "\n",
        "label_map = label_map_util.load_labelmap(PATH_TO_LABELS)\n",
        "categories = label_map_util.convert_label_map_to_categories(\n",
        "    label_map, max_num_classes=num_classes, use_display_name=True)\n",
        "category_index = label_map_util.create_category_index(categories)\n",
        "\n",
        "\n",
        "def load_image_into_numpy_array(image):\n",
        "    (im_width, im_height) = image.size\n",
        "    #(im_width, im_height, _) = image.shape\n",
        "    return np.array(image).reshape(\n",
        "        (im_height, im_width, 3)).astype(np.uint8)\n",
        "\n",
        "# Size, in inches, of the output images.\n",
        "#IMAGE_SIZE = (12, 8)\n",
        "\n",
        "def run_inference_for_single_image(image, graph):\n",
        "    with graph.as_default():\n",
        "        with tf.Session() as sess:\n",
        "            # Get handles to input and output tensors\n",
        "            ops = tf.get_default_graph().get_operations()\n",
        "            all_tensor_names = {\n",
        "                output.name for op in ops for output in op.outputs}\n",
        "            tensor_dict = {}\n",
        "            for key in [\n",
        "                'num_detections', 'detection_boxes', 'detection_scores',\n",
        "                'detection_classes', 'detection_masks'\n",
        "            ]:\n",
        "                tensor_name = key + ':0'\n",
        "                if tensor_name in all_tensor_names:\n",
        "                    tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(\n",
        "                        tensor_name)\n",
        "            if 'detection_masks' in tensor_dict:\n",
        "                # The following processing is only for single image\n",
        "                detection_boxes = tf.squeeze(\n",
        "                    tensor_dict['detection_boxes'], [0])\n",
        "                detection_masks = tf.squeeze(\n",
        "                    tensor_dict['detection_masks'], [0])\n",
        "                # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.\n",
        "                real_num_detection = tf.cast(\n",
        "                    tensor_dict['num_detections'][0], tf.int32)\n",
        "                detection_boxes = tf.slice(detection_boxes, [0, 0], [\n",
        "                                           real_num_detection, -1])\n",
        "                detection_masks = tf.slice(detection_masks, [0, 0, 0], [\n",
        "                                           real_num_detection, -1, -1])\n",
        "                detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(\n",
        "                    detection_masks, detection_boxes, image.shape[0], image.shape[1])\n",
        "                detection_masks_reframed = tf.cast(\n",
        "                    tf.greater(detection_masks_reframed, 0.5), tf.uint8)\n",
        "                # Follow the convention by adding back the batch dimension\n",
        "                tensor_dict['detection_masks'] = tf.expand_dims(\n",
        "                    detection_masks_reframed, 0)\n",
        "            image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')\n",
        "\n",
        "            # Run inference\n",
        "            output_dict = sess.run(tensor_dict,\n",
        "                                   feed_dict={image_tensor: np.expand_dims(image, 0)})\n",
        "\n",
        "            # all outputs are float32 numpy arrays, so convert types as appropriate\n",
        "            output_dict['num_detections'] = int(\n",
        "                output_dict['num_detections'][0])\n",
        "            output_dict['detection_classes'] = output_dict[\n",
        "                'detection_classes'][0].astype(np.uint8)\n",
        "            output_dict['detection_boxes'] = output_dict['detection_boxes'][0]\n",
        "            output_dict['detection_scores'] = output_dict['detection_scores'][0]\n",
        "            if 'detection_masks' in output_dict:\n",
        "                output_dict['detection_masks'] = output_dict['detection_masks'][0]\n",
        "    return output_dict"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/models/research/object_detection\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WzKkX-Q_Y2hr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "outputId": "25d18635-fc68-46b6-b8e6-a6ad93154a5a"
      },
      "source": [
        "!cd /; cd \"content/gdrive/My Drive/Colab Notebooks/\"; ls"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'[5-2]<선형분류>Linear Classification'\n",
            "'[5]<선형분류>[과제]과제2_Skeleton'\n",
            "'[6-2]<NN>tensorflow_Learning_Tips'\n",
            " checkpoint\n",
            "'인공지능 CNN 퀴즈 문제_17011832_김남훈.ipynb'\n",
            "'인공지능 CNN 퀴즈2 문제1_17011832_김남훈'\n",
            "'인공지능 CNN 퀴즈2 문제2_17011832_김남훈'\n",
            " data-zoo-v2.csv\n",
            " diabetes.csv\n",
            " object_detection_TensorflowAPI\n",
            " Object_detection_with_Tensorflow_API_at_colab_SejongBHAJ_Backup\n",
            " ODTA_SejongBHAJ.ipynb\n",
            "'ODTA_SejongBHAJ_OneClick(for Marking)'\n",
            " price_data_yk.csv\n",
            " saved.cpkt.data-00000-of-00001\n",
            " saved.cpkt.index\n",
            " saved.cpkt.meta\n",
            " SSD-Tensorflow\n",
            "'tensorflow-object-detection-training-colab.ipynb 개정판'\n",
            " Untitled0.ipynb\n",
            " Untitled10.ipynb\n",
            " Untitled11.ipynb\n",
            " Untitled12.ipynb\n",
            " Untitled13.ipynb\n",
            " Untitled14.ipynb\n",
            " Untitled15.ipynb\n",
            " Untitled16.ipynb\n",
            " Untitled1.ipynb\n",
            " Untitled2.ipynb\n",
            " Untitled3.ipynb\n",
            " Untitled4.ipynb\n",
            " Untitled5.ipynb\n",
            " Untitled6.ipynb\n",
            " Untitled7.ipynb\n",
            " Untitled8.ipynb\n",
            " Untitled9.ipynb\n",
            "'(Unused)Object_detction_TensorflowAPI_train.ipynb'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J0tRs_FUZPAW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "eed4f73b-85d2-4d56-dd46-5e7c56608272"
      },
      "source": [
        "!mkdir \"/content/gdrive/My Drive/samplefolder\""
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘/content/gdrive/My Drive/samplefolder’: File exists\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zXEmZcy5U3uz",
        "colab_type": "code",
        "outputId": "b5900fef-784e-4171-dc11-80adfd376f12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17722
        },
        "cellView": "both"
      },
      "source": [
        "#@title\n",
        "%matplotlib inline\n",
        "\n",
        "%cd \"/content/gdrive/My Drive/samplefolder\"\n",
        "\n",
        "import numpy as np\n",
        "import codecs\n",
        "import json\n",
        "from collections import OrderedDict\n",
        "import cv2\n",
        "print(cv2.__version__)\n",
        "\n",
        "\n",
        "i=0\n",
        "#Maximum: 20pics\n",
        "for image_path in TEST_IMAGE_PATHS:\n",
        "  with Image.open(image_path) as image:\n",
        "\n",
        "    # the array based representation of the image will be used later in order to prepare the\n",
        "    # result image with boxes and labels on it.\n",
        "    image_np = load_image_into_numpy_array(image)\n",
        "    #image_np=image\n",
        "    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n",
        "    image_np_expanded = np.expand_dims(image_np, axis=0)\n",
        "    \n",
        "    '''\n",
        "    #----------------------classification_to_toss_depth-------------\n",
        "    \n",
        "    # Actual detection.\n",
        "    output_dict = run_inference_for_single_image(image_np, detection_graph)\n",
        "\n",
        "    threshold=0.5\n",
        "    \n",
        "    f=OrderedDict()\n",
        "    f[\"num_detections\"]=output_dict[\"num_detections\"]      \n",
        "    \n",
        "    boxes=[]\n",
        "    scores=[]\n",
        "    classes=[]\n",
        "    \n",
        "    for i in range(output_dict[\"num_detections\"]):  \n",
        "      det_box=output_dict[\"detection_boxes\"][i]\n",
        "      det_score=output_dict[\"detection_scores\"][i]\n",
        "      det_class=output_dict[\"detection_classes\"][i]\n",
        "    \n",
        "      #threshold check to output filter\n",
        "      \n",
        "      if det_score<0.5:\n",
        "        continue\n",
        "      \n",
        "      boxes.append(det_box)\n",
        "      scores.append(det_score)\n",
        "      classes.append(det_class)\n",
        "      \n",
        "    f[\"detection_boxes\"]=boxes\n",
        "    f[\"detection_scores\"]=scores\n",
        "    f[\"detection_classes\"]=classes\n",
        "\n",
        "    json.dumps(f, ensure_ascii=False, indent=\"\\t\")\n",
        "    \n",
        "    #--------------------------------------------\n",
        "    '''\n",
        "    \n",
        "    # 감지 결과 json으로 저장\n",
        "    f=open(os.path.basename(image_path).split('.')[0]+\"_test.txt\", 'w')\n",
        "    f.write(str(output_dict[\"num_detections\"]))\n",
        "    f.write(\"\\n\")\n",
        "    f.write(str(output_dict[\"detection_boxes\"]))\n",
        "    f.write(\"\\n\")\n",
        "    f.write(str(output_dict[\"detection_scores\"]))\n",
        "    f.write(\"\\n\")\n",
        "    f.write(str(output_dict[\"detection_classes\"]))\n",
        "    f.write(\"\\n\")\n",
        "    f.close()\n",
        "    \n",
        "        \n",
        "    '''\n",
        "    #--------------dangerous classification with thickness and color------\n",
        "    flag=False\n",
        "    dangerous_classnum=[14, 18, 26] #person, bycicle, car\n",
        "    \n",
        "    if dangerous_classnum in output_dict['detection_classes']:\n",
        "      flag=True\n",
        "      line_thickness=10\n",
        "      groundtruth_box_visualization_color=\"red\"\n",
        "      \n",
        "    #-------------------------------------------------  \n",
        "    '''\n",
        "\n",
        "    # Visualization of the results of a detection.\n",
        "    vis_util.visualize_boxes_and_labels_on_image_array(\n",
        "        image_np,\n",
        "        output_dict['detection_boxes'],\n",
        "        output_dict['detection_classes'],\n",
        "        output_dict['detection_scores'],\n",
        "        category_index,\n",
        "        instance_masks=output_dict.get('detection_masks'),\n",
        "        use_normalized_coordinates=True,\n",
        "        line_thickness=5,\n",
        "    )\n",
        "    plt.imsave(os.path.basename(image_path).split('.')[0]+'_test.jpg', image_np)\n",
        "    print(str(round((i/2402*100), 2))+\"%\")\n",
        "    i=i+1\n",
        "    #plt.imshow(image_np); plt.show(); plt.close()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/samplefolder\n",
            "3.4.3\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5CjU95-OmmsI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 28230
        },
        "outputId": "bc24717c-419d-4f39-8ef3-e6d95cab17da"
      },
      "source": [
        "!"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "frame00000.json      frame00801_test.jpg  frame01603.json\n",
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}