Stenosis_Detection_Faster_RCNN_Inception_ResNetV2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qjQpt5_9s6B-"
      },
      "source": [
        "## Mounting google drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kbar1w2xs40-",
        "outputId": "e685f095-05e3-4090-e79e-499cba8c0dfa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "!rm -rf /content/sample_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g6WqbU13xbdg"
      },
      "source": [
        "## Cloning repository"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h12Ymu0lxCrO",
        "outputId": "5fca7042-2a29-4708-a35e-28afa215bdc9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'coronary-artery-stenosis-detection'...\n",
            "remote: Enumerating objects: 24, done.\u001b[K\n",
            "remote: Counting objects: 100% (24/24), done.\u001b[K\n",
            "remote: Compressing objects: 100% (22/22), done.\u001b[K\n",
            "remote: Total 24 (delta 8), reused 10 (delta 2), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (24/24), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/arrafi-musabbir/coronary-artery-stenosis-detection.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iu6MusGH_RlV"
      },
      "source": [
        "## Installing necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Sa0D1KyFY-jL",
        "outputId": "462a5bc3-729d-4878-d728-e5620919f770"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
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            "Uninstalling opencv-python-4.1.2.30:\n",
            "  Successfully uninstalled opencv-python-4.1.2.30\n",
            "Found existing installation: opencv-contrib-python 4.1.2.30\n",
            "Uninstalling opencv-contrib-python-4.1.2.30:\n",
            "  Successfully uninstalled opencv-contrib-python-4.1.2.30\n",
            "Found existing installation: imgaug 0.2.9\n",
            "Uninstalling imgaug-0.2.9:\n",
            "  Successfully uninstalled imgaug-0.2.9\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Collecting opencv-python>=4.1.0.25\n",
            "  Downloading opencv_python-4.6.0.66-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (60.9 MB)\n",
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            "Building wheels for collected packages: imgaug\n",
            "  Building wheel for imgaug (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for imgaug: filename=imgaug-0.2.5-py3-none-any.whl size=561438 sha256=3fd79ff4702766b9a50264d6cf163888c016f4ca9364a60257bc1c8016494d9b\n",
            "  Stored in directory: /root/.cache/pip/wheels/60/dd/38/d1dc2cad2b6a66dc0249261004990bccb0f27985c74ba26e49\n",
            "Successfully built imgaug\n",
            "Installing collected packages: opencv-python, opencv-python-headless, lvis, imgaug\n",
            "Successfully installed imgaug-0.2.5 lvis-0.5.3 opencv-python-4.6.0.66 opencv-python-headless-4.1.2.30\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "cv2"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Building wheels for collected packages: py-cpuinfo, seqeval\n",
            "  Building wheel for py-cpuinfo (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for py-cpuinfo: filename=py_cpuinfo-8.0.0-py3-none-any.whl size=22257 sha256=a3e838193d1e4f08cf05e1abc4641fb5f4a22e069a3335de9c07ed52daebdb63\n",
            "  Stored in directory: /root/.cache/pip/wheels/d2/f1/1f/041add21dc9c4220157f1bd2bd6afe1f1a49524c3396b94401\n",
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            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16180 sha256=e12f0526c10f228314748cdc78fc97354939dbf67ea659098b35953643e8a9b1\n",
            "  Stored in directory: /root/.cache/pip/wheels/05/96/ee/7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7\n",
            "Successfully built py-cpuinfo seqeval\n",
            "Installing collected packages: portalocker, colorama, tf-slim, tf-estimator-nightly, tensorflow-text, tensorflow-model-optimization, tensorflow-addons, seqeval, sentencepiece, sacrebleu, pyyaml, py-cpuinfo, tf-models-official, tensorflow-gpu\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed colorama-0.4.4 portalocker-2.4.0 py-cpuinfo-8.0.0 pyyaml-5.4.1 sacrebleu-2.1.0 sentencepiece-0.1.96 seqeval-1.2.2 tensorflow-addons-0.17.0 tensorflow-gpu-2.8.0 tensorflow-model-optimization-0.7.2 tensorflow-text-2.8.2 tf-estimator-nightly-2.8.0.dev2021122109 tf-models-official-2.8.0 tf-slim-1.1.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "tensorflow"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow.io\n",
            "  Downloading tensorflow_io-0.26.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (25.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 25.9 MB 2.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tensorflow-io-gcs-filesystem==0.26.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow.io) (0.26.0)\n",
            "Installing collected packages: tensorflow.io\n",
            "Successfully installed tensorflow.io-0.26.0\n",
            "Selecting previously unselected package python-bs4.\n",
            "(Reading database ... 155632 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",
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            "Setting up python-pil:amd64 (5.1.0-1ubuntu0.7) ...\n",
            "Setting up python-webencodings (0.5-2) ...\n",
            "Setting up python-chardet (3.0.4-1) ...\n",
            "Setting up python-html5lib (0.999999999-1) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'apt autoremove' to remove it.\n",
            "The following packages will be REMOVED:\n",
            "  libcudnn8-dev\n",
            "The following held packages will be changed:\n",
            "  libcudnn8\n",
            "The following packages will be upgraded:\n",
            "  libcudnn8\n",
            "1 upgraded, 0 newly installed, 1 to remove and 43 not upgraded.\n",
            "Need to get 430 MB of archives.\n",
            "After this operation, 3,139 MB disk space will be freed.\n",
            "Get:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  libcudnn8 8.1.0.77-1+cuda11.2 [430 MB]\n",
            "Fetched 430 MB in 8s (52.6 MB/s)\n",
            "(Reading database ... 156017 files and directories currently installed.)\n",
            "Removing libcudnn8-dev (8.0.5.39-1+cuda11.1) ...\n",
            "(Reading database ... 155995 files and directories currently installed.)\n",
            "Preparing to unpack .../libcudnn8_8.1.0.77-1+cuda11.2_amd64.deb ...\n",
            "Unpacking libcudnn8 (8.1.0.77-1+cuda11.2) over (8.0.5.39-1+cuda11.1) ...\n",
            "Setting up libcudnn8 (8.1.0.77-1+cuda11.2) ...\n",
            "Installed Tensorflow version >>>  2.8.2\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "%cd /content\n",
        "\n",
        "!pip uninstall opencv-python opencv-contrib-python imgaug -y\n",
        "!pip install imgaug==0.2.5 opencv-python-headless==4.1.2.30 lvis \n",
        "\n",
        "!pip install tf-models-official==2.8 tensorflow-gpu==2.8 keras==2.8 tensorboard==2.8 tensorflow-estimator==2.8\n",
        "!pip install tensorflow.io\n",
        "\n",
        "!apt-get install -qq protobuf-compiler python-pil python-lxml python-tk\n",
        "!apt install --allow-change-held-packages libcudnn8=8.1.0.77-1+cuda11.2\n",
        "\n",
        "!pip install -q Cython contextlib2 pillow lxml matplotlib\n",
        "!pip install -q pycocotools\n",
        "\n",
        "import tensorflow as tf\n",
        "print(\"Installed Tensorflow version >>> \", tf.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4EI4gMGGpUFs"
      },
      "source": [
        "## Downloading the TensorFlow Model Garden"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T_gcYeGmpVX6",
        "outputId": "c2bfc0fd-c2f2-4625-969a-17145cbefb67"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/models/research\n"
          ]
        }
      ],
      "source": [
        "!git clone --quiet https://github.com/tensorflow/models.git\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",
        "\n",
        "!python /content/models/research/object_detection/builders/model_builder_test.py"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kOWkEZS3pzDH"
      },
      "source": [
        "## Protobuf Installation/Compilation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VH33wmkSpf9l",
        "outputId": "3ad911f9-9bba-4003-ee12-d7543aac3062"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/models/research\n"
          ]
        }
      ],
      "source": [
        "%cd /content/models/research\n",
        "!protoc object_detection/protos/*.proto --python_out=."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TusbFq4-p3Ft"
      },
      "source": [
        "## COCO API installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lZSI_tlwpk-E",
        "outputId": "410fcf75-567d-426c-f8b5-02c8e3018e23"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'cocoapi'...\n",
            "remote: Enumerating objects: 975, done.\u001b[K\n",
            "remote: Total 975 (delta 0), reused 0 (delta 0), pack-reused 975\u001b[K\n",
            "Receiving objects: 100% (975/975), 11.72 MiB | 17.84 MiB/s, done.\n",
            "Resolving deltas: 100% (576/576), done.\n",
            "/content/models/research/cocoapi/PythonAPI\n",
            "python setup.py build_ext --inplace\n",
            "running build_ext\n",
            "cythoning pycocotools/_mask.pyx to pycocotools/_mask.c\n",
            "/usr/local/lib/python3.7/dist-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /content/models/research/cocoapi/PythonAPI/pycocotools/_mask.pyx\n",
            "  tree = Parsing.p_module(s, pxd, full_module_name)\n",
            "building 'pycocotools._mask' extension\n",
            "creating build\n",
            "creating build/common\n",
            "creating build/temp.linux-x86_64-3.7\n",
            "creating build/temp.linux-x86_64-3.7/pycocotools\n",
            "x86_64-linux-gnu-gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.7/dist-packages/numpy/core/include -I../common -I/usr/include/python3.7m -c ../common/maskApi.c -o build/temp.linux-x86_64-3.7/../common/maskApi.o -Wno-cpp -Wno-unused-function -std=c99\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleDecode\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:46:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "       \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; v=!v; }}\n",
            "       \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:46:49:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n",
            "       for( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; \u001b[01;36m\u001b[Kv\u001b[m\u001b[K=!v; }}\n",
            "                                                 \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrPoly\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:166:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "   \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); x[k]=x[0];\n",
            "   \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:166:54:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n",
            "   for(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); \u001b[01;36m\u001b[Kx\u001b[m\u001b[K[k]=x[0];\n",
            "                                                      \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:167:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "   \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); y[k]=y[0];\n",
            "   \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:167:54:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n",
            "   for(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); \u001b[01;36m\u001b[Ky\u001b[m\u001b[K[k]=y[0];\n",
            "                                                      \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleToString\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:212:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "       \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(more) c |= 0x20; c+=48; s[p++]=c;\n",
            "       \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:212:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            "       if(more) c |= 0x20; \u001b[01;36m\u001b[Kc\u001b[m\u001b[K+=48; s[p++]=c;\n",
            "                           \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrString\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:220:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kwhile\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "   \u001b[01;35m\u001b[Kwhile\u001b[m\u001b[K( s[m] ) m++; cnts=malloc(sizeof(uint)*m); m=0;\n",
            "   \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:220:22:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kwhile\u001b[m\u001b[K’\n",
            "   while( s[m] ) m++; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K=malloc(sizeof(uint)*m); m=0;\n",
            "                      \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:228:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(m>2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x;\n",
            "     \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:228:34:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            "     if(m>2) x+=(long) cnts[m-2]; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K[m++]=(uint) x;\n",
            "                                  \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleToBbox\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:141:31:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kxp\u001b[m\u001b[K’ may be used uninitialized in this function [\u001b[01;35m\u001b[K-Wmaybe-uninitialized\u001b[m\u001b[K]\n",
            "       if(j%2==0) xp=x; else if\u001b[01;35m\u001b[K(\u001b[m\u001b[Kxp<x) { ys=0; ye=h-1; }\n",
            "                               \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "x86_64-linux-gnu-gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.7/dist-packages/numpy/core/include -I../common -I/usr/include/python3.7m -c pycocotools/_mask.c -o build/temp.linux-x86_64-3.7/pycocotools/_mask.o -Wno-cpp -Wno-unused-function -std=c99\n",
            "creating build/lib.linux-x86_64-3.7\n",
            "creating build/lib.linux-x86_64-3.7/pycocotools\n",
            "x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -g -fwrapv -O2 -Wl,-Bsymbolic-functions -g -fwrapv -O2 -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.7/../common/maskApi.o build/temp.linux-x86_64-3.7/pycocotools/_mask.o -o build/lib.linux-x86_64-3.7/pycocotools/_mask.cpython-37m-x86_64-linux-gnu.so\n",
            "copying build/lib.linux-x86_64-3.7/pycocotools/_mask.cpython-37m-x86_64-linux-gnu.so -> pycocotools\n",
            "rm -rf build\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/cocodataset/cocoapi.git\n",
        "%cd cocoapi/PythonAPI\n",
        "!make\n",
        "%cp -r pycocotools /content/models/research"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sKWlS6e8qD_C"
      },
      "source": [
        "## Test object detection API"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1E7-F34DqgE5",
        "outputId": "5484239a-a9f7-4f78-a786-488b0a3b0685"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/models/research\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Processing /content/models/research\n",
            "\u001b[33m  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n",
            "   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\u001b[0m\n",
            "Collecting avro-python3\n",
            "  Downloading avro-python3-1.10.2.tar.gz (38 kB)\n",
            "Collecting apache-beam\n",
            "  Downloading apache_beam-2.39.0-cp37-cp37m-manylinux2010_x86_64.whl (10.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 10.3 MB 6.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (7.1.2)\n",
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            "Collecting pyparsing==2.4.7\n",
            "  Downloading pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)\n",
            "\u001b[K     |████████████████████████████████| 67 kB 5.1 MB/s \n",
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            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (0.6.1)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (1.3.1)\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow~=2.8.0->tf-models-official>=2.5.1->object-detection==0.1) (3.2.0)\n",
            "Requirement already satisfied: dm-tree~=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow-model-optimization>=0.4.1->tf-models-official>=2.5.1->object-detection==0.1) (0.1.7)\n",
            "Collecting fastavro<2,>=0.23.6\n",
            "  Downloading fastavro-1.5.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB)\n",
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            "\u001b[?25hCollecting dill<0.3.2,>=0.3.1.1\n",
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            "Collecting hdfs<3.0.0,>=2.1.0\n",
            "  Downloading hdfs-2.7.0-py3-none-any.whl (34 kB)\n",
            "Collecting pymongo<4.0.0,>=3.8.0\n",
            "  Downloading pymongo-3.12.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (508 kB)\n",
            "\u001b[K     |████████████████████████████████| 508 kB 43.7 MB/s \n",
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            "Collecting cloudpickle<3,>=2.0.0\n",
            "  Downloading cloudpickle-2.1.0-py3-none-any.whl (25 kB)\n",
            "Collecting proto-plus<2,>=1.7.1\n",
            "  Downloading proto_plus-1.20.5-py3-none-any.whl (46 kB)\n",
            "\u001b[K     |████████████████████████████████| 46 kB 3.9 MB/s \n",
            "\u001b[?25hCollecting orjson<4.0\n",
            "  Downloading orjson-3.7.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (256 kB)\n",
            "\u001b[K     |████████████████████████████████| 256 kB 47.2 MB/s \n",
            "\u001b[?25hCollecting requests<3.0.0dev,>=2.18.0\n",
            "  Downloading requests-2.28.0-py3-none-any.whl (62 kB)\n",
            "\u001b[K     |████████████████████████████████| 62 kB 1.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: docopt in /usr/local/lib/python3.7/dist-packages (from hdfs<3.0.0,>=2.1.0->apache-beam->object-detection==0.1) (0.6.2)\n",
            "Collecting protobuf<4.0.0dev,>=3.12.0\n",
            "  Downloading protobuf-3.19.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n",
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            "Requirement already satisfied: tensorflow-metadata in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets->tf-models-official>=2.5.1->object-detection==0.1) (1.8.0)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets->tf-models-official>=2.5.1->object-detection==0.1) (0.16.0)\n",
            "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets->tf-models-official>=2.5.1->object-detection==0.1) (5.7.1)\n",
            "Building wheels for collected packages: object-detection, dill, avro-python3\n",
            "  Building wheel for object-detection (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for object-detection: filename=object_detection-0.1-py3-none-any.whl size=1693282 sha256=24786121de291261437dd68ce4d47692c74d5240f75d6b26039d55f097da0fdf\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-dez7mmuz/wheels/fa/a4/d2/e9a5057e414fd46c8e543d2706cd836d64e1fcd9eccceb2329\n",
            "  Building wheel for dill (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dill: filename=dill-0.3.1.1-py3-none-any.whl size=78544 sha256=5c595ed99ea5f62e467594d72b6a6708025f0b2ada820d6f6631bb7c4eedabb2\n",
            "  Stored in directory: /root/.cache/pip/wheels/a4/61/fd/c57e374e580aa78a45ed78d5859b3a44436af17e22ca53284f\n",
            "  Building wheel for avro-python3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for avro-python3: filename=avro_python3-1.10.2-py3-none-any.whl size=44010 sha256=601ef50474d30fc85aa300b2ed259204e18a0f172805fc8571440f88c74bb971\n",
            "  Stored in directory: /root/.cache/pip/wheels/d6/e5/b1/6b151d9b535ee50aaa6ab27d145a0104b6df02e5636f0376da\n",
            "Successfully built object-detection dill avro-python3\n",
            "Installing collected packages: requests, pyparsing, protobuf, dill, pymongo, proto-plus, orjson, hdfs, fastavro, cloudpickle, avro-python3, apache-beam, object-detection\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Attempting uninstall: pyparsing\n",
            "    Found existing installation: pyparsing 3.0.9\n",
            "    Uninstalling pyparsing-3.0.9:\n",
            "      Successfully uninstalled pyparsing-3.0.9\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.17.3\n",
            "    Uninstalling protobuf-3.17.3:\n",
            "      Successfully uninstalled protobuf-3.17.3\n",
            "  Attempting uninstall: dill\n",
            "    Found existing installation: dill 0.3.5.1\n",
            "    Uninstalling dill-0.3.5.1:\n",
            "      Successfully uninstalled dill-0.3.5.1\n",
            "  Attempting uninstall: pymongo\n",
            "    Found existing installation: pymongo 4.1.1\n",
            "    Uninstalling pymongo-4.1.1:\n",
            "      Successfully uninstalled pymongo-4.1.1\n",
            "  Attempting uninstall: cloudpickle\n",
            "    Found existing installation: cloudpickle 1.3.0\n",
            "    Uninstalling cloudpickle-1.3.0:\n",
            "      Successfully uninstalled cloudpickle-1.3.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "multiprocess 0.70.13 requires dill>=0.3.5.1, but you have dill 0.3.1.1 which is incompatible.\n",
            "gym 0.17.3 requires cloudpickle<1.7.0,>=1.2.0, but you have cloudpickle 2.1.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.28.0 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed apache-beam-2.39.0 avro-python3-1.10.2 cloudpickle-2.1.0 dill-0.3.1.1 fastavro-1.5.1 hdfs-2.7.0 object-detection-0.1 orjson-3.7.2 proto-plus-1.20.5 protobuf-3.19.4 pymongo-3.12.3 pyparsing-2.4.7 requests-2.28.0\n",
            "2022-06-11 15:08:23.967981: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
            "Running tests under Python 3.7.13: /usr/bin/python3\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_deepmac\n",
            "W0611 15:08:24.275437 140425303955328 model_builder.py:1102] Building experimental DeepMAC meta-arch. Some features may be omitted.\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 0.76s\n",
            "I0611 15:08:24.734752 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 0.76s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_center_net_deepmac\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 0.64s\n",
            "I0611 15:08:25.371570 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 0.64s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.34s\n",
            "I0611 15:08:25.711513 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.34s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.29s\n",
            "I0611 15:08:26.003000 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.29s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 2.74s\n",
            "I0611 15:08:28.742858 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 2.74s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_experimental_model\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n",
            "I0611 15:08:28.744565 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_experimental_model\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.04s\n",
            "I0611 15:08:28.780895 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.04s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.02s\n",
            "I0611 15:08:28.802270 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.02s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.02s\n",
            "I0611 15:08:28.823805 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.02s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.14s\n",
            "I0611 15:08:28.961079 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.14s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.12s\n",
            "I0611 15:08:29.084168 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.12s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.13s\n",
            "I0611 15:08:29.213184 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.13s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.12s\n",
            "I0611 15:08:29.332602 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.12s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.12s\n",
            "I0611 15:08:29.449033 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.12s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.03s\n",
            "I0611 15:08:29.483229 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.03s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n",
            "I0611 15:08:29.695114 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b0\n",
            "I0611 15:08:29.695316 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 64\n",
            "I0611 15:08:29.695380 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 3\n",
            "I0611 15:08:29.698418 140425303955328 efficientnet_model.py:144] round_filter input=32 output=32\n",
            "I0611 15:08:29.719785 140425303955328 efficientnet_model.py:144] round_filter input=32 output=32\n",
            "I0611 15:08:29.720007 140425303955328 efficientnet_model.py:144] round_filter input=16 output=16\n",
            "I0611 15:08:29.798232 140425303955328 efficientnet_model.py:144] round_filter input=16 output=16\n",
            "I0611 15:08:29.798440 140425303955328 efficientnet_model.py:144] round_filter input=24 output=24\n",
            "I0611 15:08:30.000763 140425303955328 efficientnet_model.py:144] round_filter input=24 output=24\n",
            "I0611 15:08:30.000994 140425303955328 efficientnet_model.py:144] round_filter input=40 output=40\n",
            "I0611 15:08:30.211238 140425303955328 efficientnet_model.py:144] round_filter input=40 output=40\n",
            "I0611 15:08:30.211464 140425303955328 efficientnet_model.py:144] round_filter input=80 output=80\n",
            "I0611 15:08:30.518070 140425303955328 efficientnet_model.py:144] round_filter input=80 output=80\n",
            "I0611 15:08:30.518287 140425303955328 efficientnet_model.py:144] round_filter input=112 output=112\n",
            "I0611 15:08:31.040559 140425303955328 efficientnet_model.py:144] round_filter input=112 output=112\n",
            "I0611 15:08:31.040784 140425303955328 efficientnet_model.py:144] round_filter input=192 output=192\n",
            "I0611 15:08:31.486113 140425303955328 efficientnet_model.py:144] round_filter input=192 output=192\n",
            "I0611 15:08:31.486337 140425303955328 efficientnet_model.py:144] round_filter input=320 output=320\n",
            "I0611 15:08:31.598967 140425303955328 efficientnet_model.py:144] round_filter input=1280 output=1280\n",
            "I0611 15:08:31.668040 140425303955328 efficientnet_model.py:454] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.0, resolution=224, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0611 15:08:31.732595 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b1\n",
            "I0611 15:08:31.732799 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 88\n",
            "I0611 15:08:31.732875 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 4\n",
            "I0611 15:08:31.734767 140425303955328 efficientnet_model.py:144] round_filter input=32 output=32\n",
            "I0611 15:08:31.759151 140425303955328 efficientnet_model.py:144] round_filter input=32 output=32\n",
            "I0611 15:08:31.759378 140425303955328 efficientnet_model.py:144] round_filter input=16 output=16\n",
            "I0611 15:08:31.910021 140425303955328 efficientnet_model.py:144] round_filter input=16 output=16\n",
            "I0611 15:08:31.910224 140425303955328 efficientnet_model.py:144] round_filter input=24 output=24\n",
            "I0611 15:08:32.210465 140425303955328 efficientnet_model.py:144] round_filter input=24 output=24\n",
            "I0611 15:08:32.210676 140425303955328 efficientnet_model.py:144] round_filter input=40 output=40\n",
            "I0611 15:08:32.496552 140425303955328 efficientnet_model.py:144] round_filter input=40 output=40\n",
            "I0611 15:08:32.496772 140425303955328 efficientnet_model.py:144] round_filter input=80 output=80\n",
            "I0611 15:08:32.909101 140425303955328 efficientnet_model.py:144] round_filter input=80 output=80\n",
            "I0611 15:08:32.909371 140425303955328 efficientnet_model.py:144] round_filter input=112 output=112\n",
            "I0611 15:08:33.338367 140425303955328 efficientnet_model.py:144] round_filter input=112 output=112\n",
            "I0611 15:08:33.338590 140425303955328 efficientnet_model.py:144] round_filter input=192 output=192\n",
            "I0611 15:08:33.920634 140425303955328 efficientnet_model.py:144] round_filter input=192 output=192\n",
            "I0611 15:08:33.920909 140425303955328 efficientnet_model.py:144] round_filter input=320 output=320\n",
            "I0611 15:08:34.187666 140425303955328 efficientnet_model.py:144] round_filter input=1280 output=1280\n",
            "I0611 15:08:34.269649 140425303955328 efficientnet_model.py:454] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.1, resolution=240, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0611 15:08:34.343415 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b2\n",
            "I0611 15:08:34.343637 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 112\n",
            "I0611 15:08:34.343703 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 5\n",
            "I0611 15:08:34.345885 140425303955328 efficientnet_model.py:144] round_filter input=32 output=32\n",
            "I0611 15:08:34.365924 140425303955328 efficientnet_model.py:144] round_filter input=32 output=32\n",
            "I0611 15:08:34.366124 140425303955328 efficientnet_model.py:144] round_filter input=16 output=16\n",
            "I0611 15:08:34.514814 140425303955328 efficientnet_model.py:144] round_filter input=16 output=16\n",
            "I0611 15:08:34.515051 140425303955328 efficientnet_model.py:144] round_filter input=24 output=24\n",
            "I0611 15:08:34.822529 140425303955328 efficientnet_model.py:144] round_filter input=24 output=24\n",
            "I0611 15:08:34.822773 140425303955328 efficientnet_model.py:144] round_filter input=40 output=48\n",
            "I0611 15:08:35.144468 140425303955328 efficientnet_model.py:144] round_filter input=40 output=48\n",
            "I0611 15:08:35.144744 140425303955328 efficientnet_model.py:144] round_filter input=80 output=88\n",
            "I0611 15:08:35.604409 140425303955328 efficientnet_model.py:144] round_filter input=80 output=88\n",
            "I0611 15:08:35.604678 140425303955328 efficientnet_model.py:144] round_filter input=112 output=120\n",
            "I0611 15:08:36.071397 140425303955328 efficientnet_model.py:144] round_filter input=112 output=120\n",
            "I0611 15:08:36.071997 140425303955328 efficientnet_model.py:144] round_filter input=192 output=208\n",
            "I0611 15:08:36.897236 140425303955328 efficientnet_model.py:144] round_filter input=192 output=208\n",
            "I0611 15:08:36.897464 140425303955328 efficientnet_model.py:144] round_filter input=320 output=352\n",
            "I0611 15:08:37.178263 140425303955328 efficientnet_model.py:144] round_filter input=1280 output=1408\n",
            "I0611 15:08:37.257673 140425303955328 efficientnet_model.py:454] Building model efficientnet with params ModelConfig(width_coefficient=1.1, depth_coefficient=1.2, resolution=260, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0611 15:08:37.339381 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b3\n",
            "I0611 15:08:37.339629 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 160\n",
            "I0611 15:08:37.339718 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 6\n",
            "I0611 15:08:37.341927 140425303955328 efficientnet_model.py:144] round_filter input=32 output=40\n",
            "I0611 15:08:37.362767 140425303955328 efficientnet_model.py:144] round_filter input=32 output=40\n",
            "I0611 15:08:37.362986 140425303955328 efficientnet_model.py:144] round_filter input=16 output=24\n",
            "I0611 15:08:37.513072 140425303955328 efficientnet_model.py:144] round_filter input=16 output=24\n",
            "I0611 15:08:37.513373 140425303955328 efficientnet_model.py:144] round_filter input=24 output=32\n",
            "I0611 15:08:37.809551 140425303955328 efficientnet_model.py:144] round_filter input=24 output=32\n",
            "I0611 15:08:37.809791 140425303955328 efficientnet_model.py:144] round_filter input=40 output=48\n",
            "I0611 15:08:38.123256 140425303955328 efficientnet_model.py:144] round_filter input=40 output=48\n",
            "I0611 15:08:38.123497 140425303955328 efficientnet_model.py:144] round_filter input=80 output=96\n",
            "I0611 15:08:38.653096 140425303955328 efficientnet_model.py:144] round_filter input=80 output=96\n",
            "I0611 15:08:38.653310 140425303955328 efficientnet_model.py:144] round_filter input=112 output=136\n",
            "I0611 15:08:39.180978 140425303955328 efficientnet_model.py:144] round_filter input=112 output=136\n",
            "I0611 15:08:39.181208 140425303955328 efficientnet_model.py:144] round_filter input=192 output=232\n",
            "I0611 15:08:39.923595 140425303955328 efficientnet_model.py:144] round_filter input=192 output=232\n",
            "I0611 15:08:39.923838 140425303955328 efficientnet_model.py:144] round_filter input=320 output=384\n",
            "I0611 15:08:40.212469 140425303955328 efficientnet_model.py:144] round_filter input=1280 output=1536\n",
            "I0611 15:08:40.290474 140425303955328 efficientnet_model.py:454] Building model efficientnet with params ModelConfig(width_coefficient=1.2, depth_coefficient=1.4, resolution=300, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0611 15:08:40.369499 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b4\n",
            "I0611 15:08:40.369723 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 224\n",
            "I0611 15:08:40.369813 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 7\n",
            "I0611 15:08:40.371821 140425303955328 efficientnet_model.py:144] round_filter input=32 output=48\n",
            "I0611 15:08:40.399067 140425303955328 efficientnet_model.py:144] round_filter input=32 output=48\n",
            "I0611 15:08:40.399272 140425303955328 efficientnet_model.py:144] round_filter input=16 output=24\n",
            "I0611 15:08:40.560248 140425303955328 efficientnet_model.py:144] round_filter input=16 output=24\n",
            "I0611 15:08:40.560484 140425303955328 efficientnet_model.py:144] round_filter input=24 output=32\n",
            "I0611 15:08:40.958291 140425303955328 efficientnet_model.py:144] round_filter input=24 output=32\n",
            "I0611 15:08:40.958518 140425303955328 efficientnet_model.py:144] round_filter input=40 output=56\n",
            "I0611 15:08:41.359414 140425303955328 efficientnet_model.py:144] round_filter input=40 output=56\n",
            "I0611 15:08:41.359640 140425303955328 efficientnet_model.py:144] round_filter input=80 output=112\n",
            "I0611 15:08:41.988334 140425303955328 efficientnet_model.py:144] round_filter input=80 output=112\n",
            "I0611 15:08:41.988570 140425303955328 efficientnet_model.py:144] round_filter input=112 output=160\n",
            "I0611 15:08:42.663881 140425303955328 efficientnet_model.py:144] round_filter input=112 output=160\n",
            "I0611 15:08:42.664157 140425303955328 efficientnet_model.py:144] round_filter input=192 output=272\n",
            "I0611 15:08:43.790436 140425303955328 efficientnet_model.py:144] round_filter input=192 output=272\n",
            "I0611 15:08:43.790734 140425303955328 efficientnet_model.py:144] round_filter input=320 output=448\n",
            "I0611 15:08:44.603237 140425303955328 efficientnet_model.py:144] round_filter input=1280 output=1792\n",
            "I0611 15:08:44.706938 140425303955328 efficientnet_model.py:454] Building model efficientnet with params ModelConfig(width_coefficient=1.4, depth_coefficient=1.8, resolution=380, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0611 15:08:44.807612 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b5\n",
            "I0611 15:08:44.807905 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 288\n",
            "I0611 15:08:44.808004 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 7\n",
            "I0611 15:08:44.810216 140425303955328 efficientnet_model.py:144] round_filter input=32 output=48\n",
            "I0611 15:08:44.831367 140425303955328 efficientnet_model.py:144] round_filter input=32 output=48\n",
            "I0611 15:08:44.831669 140425303955328 efficientnet_model.py:144] round_filter input=16 output=24\n",
            "I0611 15:08:45.081089 140425303955328 efficientnet_model.py:144] round_filter input=16 output=24\n",
            "I0611 15:08:45.081377 140425303955328 efficientnet_model.py:144] round_filter input=24 output=40\n",
            "I0611 15:08:45.624742 140425303955328 efficientnet_model.py:144] round_filter input=24 output=40\n",
            "I0611 15:08:45.625047 140425303955328 efficientnet_model.py:144] round_filter input=40 output=64\n",
            "I0611 15:08:46.175498 140425303955328 efficientnet_model.py:144] round_filter input=40 output=64\n",
            "I0611 15:08:46.175812 140425303955328 efficientnet_model.py:144] round_filter input=80 output=128\n",
            "I0611 15:08:46.932062 140425303955328 efficientnet_model.py:144] round_filter input=80 output=128\n",
            "I0611 15:08:46.932342 140425303955328 efficientnet_model.py:144] round_filter input=112 output=176\n",
            "I0611 15:08:47.785870 140425303955328 efficientnet_model.py:144] round_filter input=112 output=176\n",
            "I0611 15:08:47.786142 140425303955328 efficientnet_model.py:144] round_filter input=192 output=304\n",
            "I0611 15:08:49.042842 140425303955328 efficientnet_model.py:144] round_filter input=192 output=304\n",
            "I0611 15:08:49.043113 140425303955328 efficientnet_model.py:144] round_filter input=320 output=512\n",
            "I0611 15:08:49.600287 140425303955328 efficientnet_model.py:144] round_filter input=1280 output=2048\n",
            "I0611 15:08:49.722681 140425303955328 efficientnet_model.py:454] Building model efficientnet with params ModelConfig(width_coefficient=1.6, depth_coefficient=2.2, resolution=456, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0611 15:08:49.842418 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b6\n",
            "I0611 15:08:49.842686 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 384\n",
            "I0611 15:08:49.842796 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 8\n",
            "I0611 15:08:49.844979 140425303955328 efficientnet_model.py:144] round_filter input=32 output=56\n",
            "I0611 15:08:49.866409 140425303955328 efficientnet_model.py:144] round_filter input=32 output=56\n",
            "I0611 15:08:49.866664 140425303955328 efficientnet_model.py:144] round_filter input=16 output=32\n",
            "I0611 15:08:50.111344 140425303955328 efficientnet_model.py:144] round_filter input=16 output=32\n",
            "I0611 15:08:50.111625 140425303955328 efficientnet_model.py:144] round_filter input=24 output=40\n",
            "I0611 15:08:50.714545 140425303955328 efficientnet_model.py:144] round_filter input=24 output=40\n",
            "I0611 15:08:50.714805 140425303955328 efficientnet_model.py:144] round_filter input=40 output=72\n",
            "I0611 15:08:51.292066 140425303955328 efficientnet_model.py:144] round_filter input=40 output=72\n",
            "I0611 15:08:51.292292 140425303955328 efficientnet_model.py:144] round_filter input=80 output=144\n",
            "I0611 15:08:52.509959 140425303955328 efficientnet_model.py:144] round_filter input=80 output=144\n",
            "I0611 15:08:52.510179 140425303955328 efficientnet_model.py:144] round_filter input=112 output=200\n",
            "I0611 15:08:53.391092 140425303955328 efficientnet_model.py:144] round_filter input=112 output=200\n",
            "I0611 15:08:53.391313 140425303955328 efficientnet_model.py:144] round_filter input=192 output=344\n",
            "I0611 15:08:55.060110 140425303955328 efficientnet_model.py:144] round_filter input=192 output=344\n",
            "I0611 15:08:55.060411 140425303955328 efficientnet_model.py:144] round_filter input=320 output=576\n",
            "I0611 15:08:55.703320 140425303955328 efficientnet_model.py:144] round_filter input=1280 output=2304\n",
            "I0611 15:08:55.811017 140425303955328 efficientnet_model.py:454] Building model efficientnet with params ModelConfig(width_coefficient=1.8, depth_coefficient=2.6, resolution=528, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0611 15:08:55.933122 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b7\n",
            "I0611 15:08:55.933346 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 384\n",
            "I0611 15:08:55.933441 140425303955328 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 8\n",
            "I0611 15:08:55.935473 140425303955328 efficientnet_model.py:144] round_filter input=32 output=64\n",
            "I0611 15:08:55.955035 140425303955328 efficientnet_model.py:144] round_filter input=32 output=64\n",
            "I0611 15:08:55.955244 140425303955328 efficientnet_model.py:144] round_filter input=16 output=32\n",
            "I0611 15:08:56.272087 140425303955328 efficientnet_model.py:144] round_filter input=16 output=32\n",
            "I0611 15:08:56.272316 140425303955328 efficientnet_model.py:144] round_filter input=24 output=48\n",
            "I0611 15:08:57.032918 140425303955328 efficientnet_model.py:144] round_filter input=24 output=48\n",
            "I0611 15:08:57.033210 140425303955328 efficientnet_model.py:144] round_filter input=40 output=80\n",
            "I0611 15:08:57.888520 140425303955328 efficientnet_model.py:144] round_filter input=40 output=80\n",
            "I0611 15:08:57.888817 140425303955328 efficientnet_model.py:144] round_filter input=80 output=160\n",
            "I0611 15:08:59.130038 140425303955328 efficientnet_model.py:144] round_filter input=80 output=160\n",
            "I0611 15:08:59.130321 140425303955328 efficientnet_model.py:144] round_filter input=112 output=224\n",
            "I0611 15:09:00.396079 140425303955328 efficientnet_model.py:144] round_filter input=112 output=224\n",
            "I0611 15:09:00.396351 140425303955328 efficientnet_model.py:144] round_filter input=192 output=384\n",
            "I0611 15:09:02.916960 140425303955328 efficientnet_model.py:144] round_filter input=192 output=384\n",
            "I0611 15:09:02.917225 140425303955328 efficientnet_model.py:144] round_filter input=320 output=640\n",
            "I0611 15:09:03.925970 140425303955328 efficientnet_model.py:144] round_filter input=1280 output=2560\n",
            "I0611 15:09:04.049540 140425303955328 efficientnet_model.py:454] Building model efficientnet with params ModelConfig(width_coefficient=2.0, depth_coefficient=3.1, resolution=600, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 34.71s\n",
            "I0611 15:09:04.191176 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 34.71s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n",
            "[ RUN      ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s\n",
            "I0611 15:09:04.200320 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update\n",
            "[ RUN      ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s\n",
            "I0611 15:09:04.202309 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold\n",
            "[ RUN      ] ModelBuilderTF2Test.test_invalid_model_config_proto\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s\n",
            "I0611 15:09:04.202900 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_invalid_model_config_proto\n",
            "[ RUN      ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s\n",
            "I0611 15:09:04.204603 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size\n",
            "[ RUN      ] ModelBuilderTF2Test.test_session\n",
            "[  SKIPPED ] ModelBuilderTF2Test.test_session\n",
            "[ RUN      ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s\n",
            "I0611 15:09:04.206005 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor\n",
            "[ RUN      ] ModelBuilderTF2Test.test_unknown_meta_architecture\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n",
            "I0611 15:09:04.206440 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_unknown_meta_architecture\n",
            "[ RUN      ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n",
            "I0611 15:09:04.207462 140425303955328 test_util.py:2374] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n",
            "----------------------------------------------------------------------\n",
            "Ran 24 tests in 40.236s\n",
            "\n",
            "OK (skipped=1)\n"
          ]
        }
      ],
      "source": [
        "%cd /content/models/research\n",
        "%cp object_detection/packages/tf2/setup.py .\n",
        "!python -m pip install .\n",
        "!python object_detection/builders/model_builder_tf2_test.py"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJUzavGDcbMi"
      },
      "source": [
        "## Downloading dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Pf2QHy8lAQE",
        "outputId": "d2b288c9-1ea6-4e5e-ae32-3eb69314199c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n",
            "Are you sure? Y/N :N\n",
            "Moving on ...\n",
            "\n",
            "Execution time: 0.0518347183863322  minutes\n"
          ]
        }
      ],
      "source": [
        "from tqdm import tqdm\n",
        "import zipfile\n",
        "import time\n",
        "\n",
        "%cd /content/\n",
        "st = time.time()\n",
        "q = input(\"Are you sure? Y/N :\")\n",
        "if q == 'Y':\n",
        "    !wget https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/ydrm75xywg-1.zip\n",
        "    with zipfile.ZipFile('/content/ydrm75xywg-1.zip') as zf:\n",
        "        for member in tqdm(zf.infolist(), desc='Extracting preliminary'):\n",
        "            try:\n",
        "                zf.extract(member, \"/content/dataset\")\n",
        "            except zipfile.error as e:\n",
        "                pass\n",
        "    !rm /content/ydrm75xywg-1.zip\n",
        "    with zipfile.ZipFile('/content/dataset/ydrm75xywg-1/Stenosis detection.zip') as zf:\n",
        "        for member in tqdm(zf.infolist(), desc='Extracting final dataset'):\n",
        "            try:\n",
        "                zf.extract(member, \"/content/stenosis_dataset\")\n",
        "            except zipfile.error as e:\n",
        "                pass\n",
        "    print(\"Cleaning up after downlaod and extraction\")\n",
        "    !rm -rf /content/dataset\n",
        "    !rm -rf /content/stenosis_dataset/video_test\n",
        "    !rm -rf /content/stenosis_dataset/video_val\n",
        "    print(\"Download & extraction completed\")\n",
        "else:\n",
        "    print(\"Moving on ...\")\n",
        "\n",
        "et = time.time()\n",
        "elapsed_time = et - st\n",
        "print('\\nExecution time:', elapsed_time/60, ' minutes')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NvzvK5qXrMdN"
      },
      "source": [
        "## Creating folder structure"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ewt_-f7NrL0j",
        "outputId": "02d18c16-b085-4089-e988-5b7aecb70e19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "workspace directory already exists: creating rest of the structure inside workspace\n",
            "directory structure already exists: Will be using existing directories\n"
          ]
        }
      ],
      "source": [
        "import os \n",
        "workspace_path = '/content/drive/MyDrive/workspace/'\n",
        "workspace_model_path = workspace_path + 'stenosis_detection_FRCNN/'\n",
        "annotation_path = workspace_model_path + 'annotations/'\n",
        "exported_model_path = workspace_model_path + 'exported-models/'\n",
        "images_path = workspace_model_path + 'images/'\n",
        "trained_model_path = workspace_model_path + 'trained_model/'\n",
        "pretrained_model_path = workspace_model_path + 'pre-trained-models/'\n",
        "\n",
        "try:\n",
        "    os.mkdir(workspace_path)\n",
        "    print(\"Created workspace\")\n",
        "except FileExistsError :\n",
        "    print(\"workspace directory already exists: creating rest of the structure inside workspace\")\n",
        "try:\n",
        "    os.mkdir(workspace_model_path)\n",
        "    os.mkdir(annotation_path)\n",
        "    os.mkdir(exported_model_path)\n",
        "    os.mkdir(images_path)\n",
        "    os.mkdir(trained_model_path)\n",
        "    os.mkdir(pretrained_model_path)\n",
        "    print(\"full directory structure creation done\")\n",
        "except FileExistsError :\n",
        "    print(\"directory structure already exists: Will be using existing directories\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YCpTSsXxtsb1"
      },
      "source": [
        "## Creating labelmap.pbtxt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ztFm385BsSx0"
      },
      "outputs": [],
      "source": [
        "labels = [{'name':'Stenosis', 'id':1}]\n",
        "with open(annotation_path + \"label_map.pbtxt\", 'w') as f:\n",
        "    for label in labels:\n",
        "        f.write('item { \\n')\n",
        "        f.write('\\tname:\\'{}\\'\\n'.format(label['name']))\n",
        "        f.write('\\tid:{}\\n'.format(label['id']))\n",
        "        f.write('}\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qrq9IslwOgER"
      },
      "source": [
        "## Partitioning the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lSTT5auUOf0p",
        "outputId": "a79522c5-9784-4d91-910e-7e61ee4eb5bf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Are you sure? Y/N :Y\n",
            "/content/coronary-artery-stenosis-detection/partition_dataset.py:35: DeprecationWarning: Flags not at the start of the expression '([a-zA-Z0-9\\\\s_\\\\\\\\.\\\\-\\\\' (truncated)\n",
            "  if re.search(r'([a-zA-Z0-9\\s_\\\\.\\-\\(\\):])+(?i)(.jpg|.jpeg|.png|.bmp)$', f)]\n",
            "Dataset split into Train & Test\n",
            "Cleaning up after partitioning dataset\n",
            "Execution time: 4.6686864773432415 minutes\n"
          ]
        }
      ],
      "source": [
        "st = time.time()\n",
        "q = input(\"Are you sure? Y/N :\")\n",
        "if q == 'Y':\n",
        "    if os.path.isdir(\"/content/stenosis_dataset/dataset\"):\n",
        "        !rm -rf /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/images/\n",
        "        os.mkdir(images_path)\n",
        "\n",
        "        !python /content/coronary-artery-stenosis-detection/partition_dataset.py -x -i /content/stenosis_dataset/dataset -o /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/images -r 0.1\n",
        "\n",
        "        print(\"Dataset split into Train & Test\")\n",
        "        print(\"Cleaning up after partitioning dataset\")\n",
        "        !rm -rf /content/stenosis_dataset\n",
        "    else:\n",
        "      print(\"Dataset doesn't exists\")\n",
        "else:\n",
        "    print('Moving on ...')\n",
        "et = time.time()\n",
        "elapsed_time = et - st\n",
        "print('\\nExecution time:', elapsed_time/60 , 'minutes')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wbbucESLUD-7"
      },
      "source": [
        "## Generating TF records"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MXtO29cXZ2pc"
      },
      "source": [
        "### train record"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "McKDzMmNhY7k",
        "outputId": "8af859b1-0b37-439a-a460-a75fbbbd91ec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Successfully created the TFRecord file: /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/annotations/train.record\n",
            "\n",
            " Generated train.record successfully\n",
            "Execution time: 6.282655191421509 minutes\n"
          ]
        }
      ],
      "source": [
        "st = time.time()\n",
        "q = input(\"Are you sure? Y/N :\")\n",
        "if q == 'Y':\n",
        "    !python /content/coronary-artery-stenosis-detection/generate_tfrecord.py -x /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/images/train -l /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/annotations/label_map.pbtxt -o /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/annotations/train.record\n",
        "else:\n",
        "    print('Moving on ...')\n",
        "et = time.time()\n",
        "elapsed_time = et - st\n",
        "print('\\nGenerated train.record successfully')\n",
        "print('Execution time:', elapsed_time/60 , 'minutes')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "txmgYN0IZ64Y"
      },
      "source": [
        "### test record"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0XoWiZZYKflp",
        "outputId": "bc225af9-96a5-44cf-becc-3836f51bdbea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Successfully created the TFRecord file: /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/annotations/test.record\n",
            "\n",
            " Generated train.record successfully\n",
            "Execution time: 4.545587809880574 minutes\n"
          ]
        }
      ],
      "source": [
        "st = time.time()\n",
        "q = input(\"Are you sure? Y/N :\")\n",
        "if q == 'Y':\n",
        "    !python /content/coronary-artery-stenosis-detection/generate_tfrecord.py -x /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/images/test -l /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/annotations/label_map.pbtxt -o /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/annotations/test.record\n",
        "else:\n",
        "    print('Moving on ...')\n",
        "et = time.time()\n",
        "elapsed_time = et - st\n",
        "print('\\nGenerated train.record successfully')\n",
        "print('Execution time:', elapsed_time/60 , 'minutes')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hSQlO8cRvwyR"
      },
      "source": [
        "## Download & extract pretrained model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DUC_mXhgvoxx",
        "outputId": "d59bf11e-d0a0-4aaf-8439-c5ff70b6a3f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/.shortcut-targets-by-id/19rcwKUZQHYVyynGSgm5qlA-3QgO0RMOl/workspace/stenosis_detection_FRCNN/pre-trained-models\n",
            "--2022-06-11 15:09:07--  http://download.tensorflow.org/models/object_detection/tf2/20200711/faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8.tar.gz\n",
            "Resolving download.tensorflow.org (download.tensorflow.org)... 74.125.142.128, 2607:f8b0:400e:c08::80\n",
            "Connecting to download.tensorflow.org (download.tensorflow.org)|74.125.142.128|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 447961769 (427M) [application/x-tar]\n",
            "Saving to: ‘faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8.tar.gz’\n",
            "\n",
            "faster_rcnn_incepti 100%[===================>] 427.21M  56.3MB/s    in 9.0s    \n",
            "\n",
            "2022-06-11 15:09:16 (47.7 MB/s) - ‘faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8.tar.gz’ saved [447961769/447961769]\n",
            "\n",
            "faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8/\n",
            "faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8/checkpoint/\n",
            "faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8/checkpoint/ckpt-0.data-00000-of-00001\n",
            "faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8/checkpoint/checkpoint\n",
            "faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8/checkpoint/ckpt-0.index\n",
            "faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8/pipeline.config\n",
            "faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8/saved_model/\n",
            "faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8/saved_model/saved_model.pb\n",
            "faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8/saved_model/variables/\n",
            "faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8/saved_model/variables/variables.data-00000-of-00001\n",
            "faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8/saved_model/variables/variables.index\n",
            "\n",
            "Download & extraction completed\n"
          ]
        }
      ],
      "source": [
        "%cd /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/pre-trained-models\n",
        "!wget http://download.tensorflow.org/models/object_detection/tf2/20200711/faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8.tar.gz\n",
        "!tar -xzvf /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/pre-trained-models/faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8.tar.gz\n",
        "!rm /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/pre-trained-models/faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8.tar.gz\n",
        "print(\"\\nDownload & extraction completed\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4iWbtrTmwldK"
      },
      "source": [
        "## Configure training pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yetwxMVRx2Pr",
        "outputId": "f620a6e6-aaf0-4c9f-9946-07ae0d691ff3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "%cd /content/\n",
        "import tensorflow as tf\n",
        "from object_detection.utils import config_util\n",
        "from object_detection.protos import pipeline_pb2\n",
        "from google.protobuf import text_format"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z_KLoDyiwxmi"
      },
      "outputs": [],
      "source": [
        "my_model_path = trained_model_path + \"my_FRCNN/\"\n",
        "CONFIG_PATH = my_model_path + \"pipeline.config\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s38AaLRWmKpb",
        "outputId": "8f51da5c-fecd-4541-d8e9-a81add6077ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "my FRCNN already exists\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "    os.mkdir(my_model_path)\n",
        "except FileExistsError :\n",
        "    print(\"my FRCNN already exists\")\n",
        "!cp {\"/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/pre-trained-models/faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8/pipeline.config\"} {my_model_path}\n",
        "config = config_util.get_configs_from_pipeline_file(CONFIG_PATH)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "EUuao_JIpHNw",
        "outputId": "06181606-7a58-4bd4-94b6-64c60931ff74"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/trained_model/my_FRCNN/pipeline.config'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "config = config_util.get_configs_from_pipeline_file(CONFIG_PATH)\n",
        "CONFIG_PATH"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oj9ELnfnw9d7"
      },
      "outputs": [],
      "source": [
        "pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()\n",
        "with tf.io.gfile.GFile(CONFIG_PATH, \"r\") as f:                                                                                                                                                                                                                     \n",
        "    proto_str = f.read()                                                                                                                                                                                                                                          \n",
        "    text_format.Merge(proto_str, pipeline_config)  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SRu-uUO-xQ-3"
      },
      "outputs": [],
      "source": [
        "pipeline_config.model.faster_rcnn.num_classes = 1\n",
        "pipeline_config.train_config.batch_size = 1\n",
        "pipeline_config.train_config.fine_tune_checkpoint = pretrained_model_path + \"faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8/checkpoint/ckpt-0\"\n",
        "pipeline_config.train_config.fine_tune_checkpoint_type = \"detection\"\n",
        "pipeline_config.train_config.use_bfloat16 = False\n",
        "pipeline_config.train_input_reader.label_map_path= annotation_path + \"label_map.pbtxt\"\n",
        "pipeline_config.train_input_reader.tf_record_input_reader.input_path[:] = [annotation_path + \"train.record\"]\n",
        "pipeline_config.eval_input_reader[0].label_map_path = annotation_path + \"label_map.pbtxt\"\n",
        "pipeline_config.eval_input_reader[0].tf_record_input_reader.input_path[:] = [annotation_path + \"test.record\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q2LyrXGnYbFF"
      },
      "outputs": [],
      "source": [
        "config_text = text_format.MessageToString(pipeline_config)                                                                                                                                                                                                        \n",
        "with tf.io.gfile.GFile(CONFIG_PATH, \"wb\") as f:                                                                                                                                                                                                                     \n",
        "    f.write(config_text)   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnedeabXu_JN",
        "outputId": "8964864f-68cd-45ad-b94e-f255cdd36050"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_config': metrics_set: \"coco_detection_metrics\"\n",
              " use_moving_averages: false\n",
              " batch_size: 1,\n",
              " 'eval_input_config': label_map_path: \"/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/annotations/label_map.pbtxt\"\n",
              " shuffle: false\n",
              " num_epochs: 1\n",
              " tf_record_input_reader {\n",
              "   input_path: \"/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/annotations/test.record\"\n",
              " },\n",
              " 'eval_input_configs': [label_map_path: \"/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/annotations/label_map.pbtxt\"\n",
              " shuffle: false\n",
              " num_epochs: 1\n",
              " tf_record_input_reader {\n",
              "   input_path: \"/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/annotations/test.record\"\n",
              " }\n",
              " ],\n",
              " 'model': faster_rcnn {\n",
              "   num_classes: 1\n",
              "   image_resizer {\n",
              "     keep_aspect_ratio_resizer {\n",
              "       min_dimension: 800\n",
              "       max_dimension: 1333\n",
              "       pad_to_max_dimension: true\n",
              "     }\n",
              "   }\n",
              "   feature_extractor {\n",
              "     type: \"faster_rcnn_inception_resnet_v2_keras\"\n",
              "   }\n",
              "   first_stage_anchor_generator {\n",
              "     grid_anchor_generator {\n",
              "       height_stride: 16\n",
              "       width_stride: 16\n",
              "       scales: 0.25\n",
              "       scales: 0.5\n",
              "       scales: 1.0\n",
              "       scales: 2.0\n",
              "       aspect_ratios: 0.5\n",
              "       aspect_ratios: 1.0\n",
              "       aspect_ratios: 2.0\n",
              "     }\n",
              "   }\n",
              "   first_stage_box_predictor_conv_hyperparams {\n",
              "     op: CONV\n",
              "     regularizer {\n",
              "       l2_regularizer {\n",
              "         weight: 0.0\n",
              "       }\n",
              "     }\n",
              "     initializer {\n",
              "       truncated_normal_initializer {\n",
              "         stddev: 0.009999999776482582\n",
              "       }\n",
              "     }\n",
              "   }\n",
              "   first_stage_nms_score_threshold: 0.0\n",
              "   first_stage_nms_iou_threshold: 0.699999988079071\n",
              "   first_stage_max_proposals: 300\n",
              "   first_stage_localization_loss_weight: 2.0\n",
              "   first_stage_objectness_loss_weight: 1.0\n",
              "   initial_crop_size: 17\n",
              "   maxpool_kernel_size: 1\n",
              "   maxpool_stride: 1\n",
              "   second_stage_box_predictor {\n",
              "     mask_rcnn_box_predictor {\n",
              "       fc_hyperparams {\n",
              "         op: FC\n",
              "         regularizer {\n",
              "           l2_regularizer {\n",
              "             weight: 0.0\n",
              "           }\n",
              "         }\n",
              "         initializer {\n",
              "           variance_scaling_initializer {\n",
              "             factor: 1.0\n",
              "             uniform: true\n",
              "             mode: FAN_AVG\n",
              "           }\n",
              "         }\n",
              "       }\n",
              "       use_dropout: false\n",
              "       dropout_keep_probability: 1.0\n",
              "     }\n",
              "   }\n",
              "   second_stage_post_processing {\n",
              "     batch_non_max_suppression {\n",
              "       score_threshold: 0.0\n",
              "       iou_threshold: 0.6000000238418579\n",
              "       max_detections_per_class: 100\n",
              "       max_total_detections: 100\n",
              "     }\n",
              "     score_converter: SOFTMAX\n",
              "   }\n",
              "   second_stage_localization_loss_weight: 2.0\n",
              "   second_stage_classification_loss_weight: 1.0\n",
              " },\n",
              " 'train_config': batch_size: 1\n",
              " data_augmentation_options {\n",
              "   random_horizontal_flip {\n",
              "   }\n",
              " }\n",
              " data_augmentation_options {\n",
              "   random_adjust_hue {\n",
              "   }\n",
              " }\n",
              " data_augmentation_options {\n",
              "   random_adjust_contrast {\n",
              "   }\n",
              " }\n",
              " data_augmentation_options {\n",
              "   random_adjust_saturation {\n",
              "   }\n",
              " }\n",
              " data_augmentation_options {\n",
              "   random_square_crop_by_scale {\n",
              "     scale_min: 0.6000000238418579\n",
              "     scale_max: 1.2999999523162842\n",
              "   }\n",
              " }\n",
              " optimizer {\n",
              "   momentum_optimizer {\n",
              "     learning_rate {\n",
              "       cosine_decay_learning_rate {\n",
              "         learning_rate_base: 0.00800000037997961\n",
              "         total_steps: 200000\n",
              "         warmup_learning_rate: 0.0\n",
              "         warmup_steps: 5000\n",
              "       }\n",
              "     }\n",
              "     momentum_optimizer_value: 0.8999999761581421\n",
              "   }\n",
              "   use_moving_average: false\n",
              " }\n",
              " gradient_clipping_by_norm: 10.0\n",
              " fine_tune_checkpoint: \"/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/pre-trained-models/faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8/checkpoint/ckpt-0\"\n",
              " num_steps: 200000\n",
              " fine_tune_checkpoint_type: \"detection\"\n",
              " use_bfloat16: false\n",
              " fine_tune_checkpoint_version: V2,\n",
              " 'train_input_config': label_map_path: \"/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/annotations/label_map.pbtxt\"\n",
              " tf_record_input_reader {\n",
              "   input_path: \"/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/annotations/train.record\"\n",
              " }}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "updated_config_path = my_model_path + \"pipeline.config\"\n",
        "updated_config = config_util.get_configs_from_pipeline_file(updated_config_path)\n",
        "updated_config"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "erBzTh0czIGj"
      },
      "source": [
        "## Training the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "009mx3AjxGUO"
      },
      "source": [
        "### Configuring GPU"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "id": "GiezjrT_KkqC",
        "outputId": "2f9ff57f-2fa0-4e28-898b-d95845e11ff7"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-9c2a271a1655>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mgpus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlist_physical_devices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"GPU\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_memory_growth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpus\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mgpus\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: list index out of range"
          ]
        }
      ],
      "source": [
        "gpus = tf.config.experimental.list_physical_devices(\"GPU\")\n",
        "tf.config.experimental.set_memory_growth(gpus[0], True)\n",
        "gpus"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZ0dERHvxIvS"
      },
      "source": [
        "### Training model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mNKLoiCczNuY",
        "outputId": "19040394-69ac-48df-acfd-c83db5e3c901"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-06-11 07:30:53.105232: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n",
            "I0611 07:30:53.176881 139937817839488 mirrored_strategy.py:374] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n",
            "INFO:tensorflow:Maybe overwriting train_steps: 20000\n",
            "I0611 07:30:53.189944 139937817839488 config_util.py:552] Maybe overwriting train_steps: 20000\n",
            "INFO:tensorflow:Maybe overwriting use_bfloat16: False\n",
            "I0611 07:30:53.190916 139937817839488 config_util.py:552] Maybe overwriting use_bfloat16: False\n",
            "WARNING:tensorflow:From /content/models/research/object_detection/model_lib_v2.py:564: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "rename to distribute_datasets_from_function\n",
            "W0611 07:30:53.758241 139937817839488 deprecation.py:343] From /content/models/research/object_detection/model_lib_v2.py:564: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "rename to distribute_datasets_from_function\n",
            "INFO:tensorflow:Reading unweighted datasets: ['/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/annotations/train.record']\n",
            "I0611 07:30:53.811166 139937817839488 dataset_builder.py:162] Reading unweighted datasets: ['/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/annotations/train.record']\n",
            "INFO:tensorflow:Reading record datasets for input file: ['/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/annotations/train.record']\n",
            "I0611 07:30:53.812067 139937817839488 dataset_builder.py:79] Reading record datasets for input file: ['/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/annotations/train.record']\n",
            "INFO:tensorflow:Number of filenames to read: 1\n",
            "I0611 07:30:53.812241 139937817839488 dataset_builder.py:80] Number of filenames to read: 1\n",
            "WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.\n",
            "W0611 07:30:53.812333 139937817839488 dataset_builder.py:87] num_readers has been reduced to 1 to match input file shards.\n",
            "WARNING:tensorflow:From /content/models/research/object_detection/builders/dataset_builder.py:104: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.\n",
            "W0611 07:30:53.824984 139937817839488 deprecation.py:343] From /content/models/research/object_detection/builders/dataset_builder.py:104: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.\n",
            "WARNING:tensorflow:From /content/models/research/object_detection/builders/dataset_builder.py:236: 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",
            "W0611 07:30:53.929588 139937817839488 deprecation.py:343] From /content/models/research/object_detection/builders/dataset_builder.py:236: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.map()\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
            "W0611 07:31:06.067576 139937817839488 deprecation.py:343] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0611 07:31:10.406405 139937817839488 deprecation.py:343] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "/usr/local/lib/python3.7/dist-packages/keras/backend.py:450: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n",
            "  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0611 07:31:57.741283 139932874487552 convolutional_keras_box_predictor.py:153] depth of additional conv before box predictor: 0\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "box_ind is deprecated, use box_indices instead\n",
            "W0611 07:32:03.600240 139932874487552 deprecation.py:547] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "box_ind is deprecated, use box_indices instead\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:459: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use ref() instead.\n",
            "W0611 07:32:04.055670 139932874487552 deprecation.py:343] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:459: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use ref() instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "\n",
            "Future major versions of TensorFlow will allow gradients to flow\n",
            "into the labels input on backprop by default.\n",
            "\n",
            "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
            "\n",
            "W0611 07:32:09.027103 139932874487552 deprecation.py:343] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "\n",
            "Future major versions of TensorFlow will allow gradients to flow\n",
            "into the labels input on backprop by default.\n",
            "\n",
            "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:616: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use fn_output_signature instead\n",
            "W0611 07:32:54.614854 139932899665664 deprecation.py:547] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:616: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use fn_output_signature instead\n",
            "INFO:tensorflow:Step 14100 per-step time 2.023s\n",
            "I0611 07:36:16.520090 139937817839488 model_lib_v2.py:707] Step 14100 per-step time 2.023s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.2175468,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.114887364,\n",
            " 'Loss/RPNLoss/localization_loss': 0.037145022,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0013176375,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.37089685,\n",
            " 'learning_rate': 0.00795709}\n",
            "I0611 07:36:16.527123 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.2175468,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.114887364,\n",
            " 'Loss/RPNLoss/localization_loss': 0.037145022,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0013176375,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.37089685,\n",
            " 'learning_rate': 0.00795709}\n",
            "INFO:tensorflow:Step 14200 per-step time 1.099s\n",
            "I0611 07:38:06.405435 139937817839488 model_lib_v2.py:707] Step 14200 per-step time 1.099s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.36810857,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.19067752,\n",
            " 'Loss/RPNLoss/localization_loss': 0.009456917,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.014903619,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.58314663,\n",
            " 'learning_rate': 0.0079561435}\n",
            "I0611 07:38:06.405825 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.36810857,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.19067752,\n",
            " 'Loss/RPNLoss/localization_loss': 0.009456917,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.014903619,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.58314663,\n",
            " 'learning_rate': 0.0079561435}\n",
            "INFO:tensorflow:Step 14300 per-step time 1.114s\n",
            "I0611 07:39:57.848557 139937817839488 model_lib_v2.py:707] Step 14300 per-step time 1.114s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.18337579,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.3459984,\n",
            " 'Loss/RPNLoss/localization_loss': 0.010780409,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0010767862,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.54123133,\n",
            " 'learning_rate': 0.007955186}\n",
            "I0611 07:39:57.848943 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.18337579,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.3459984,\n",
            " 'Loss/RPNLoss/localization_loss': 0.010780409,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0010767862,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.54123133,\n",
            " 'learning_rate': 0.007955186}\n",
            "INFO:tensorflow:Step 14400 per-step time 1.115s\n",
            "I0611 07:41:49.329131 139937817839488 model_lib_v2.py:707] Step 14400 per-step time 1.115s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.09713017,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.21654814,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0087111285,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00039162516,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.3227811,\n",
            " 'learning_rate': 0.007954219}\n",
            "I0611 07:41:49.329509 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.09713017,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.21654814,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0087111285,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00039162516,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.3227811,\n",
            " 'learning_rate': 0.007954219}\n",
            "INFO:tensorflow:Step 14500 per-step time 1.115s\n",
            "I0611 07:43:40.824144 139937817839488 model_lib_v2.py:707] Step 14500 per-step time 1.115s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.07926112,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.24850585,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0073838467,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0010846355,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.33623543,\n",
            " 'learning_rate': 0.0079532415}\n",
            "I0611 07:43:40.824534 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.07926112,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.24850585,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0073838467,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0010846355,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.33623543,\n",
            " 'learning_rate': 0.0079532415}\n",
            "INFO:tensorflow:Step 14600 per-step time 1.113s\n",
            "I0611 07:45:32.130469 139937817839488 model_lib_v2.py:707] Step 14600 per-step time 1.113s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.05673054,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.30675077,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0011216076,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0050943624,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.36969727,\n",
            " 'learning_rate': 0.007952254}\n",
            "I0611 07:45:32.130864 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.05673054,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.30675077,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0011216076,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0050943624,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.36969727,\n",
            " 'learning_rate': 0.007952254}\n",
            "INFO:tensorflow:Step 14700 per-step time 1.114s\n",
            "I0611 07:47:23.497737 139937817839488 model_lib_v2.py:707] Step 14700 per-step time 1.114s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.09335574,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.18303283,\n",
            " 'Loss/RPNLoss/localization_loss': 0.016226497,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00022713021,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.29284218,\n",
            " 'learning_rate': 0.007951256}\n",
            "I0611 07:47:23.498153 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.09335574,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.18303283,\n",
            " 'Loss/RPNLoss/localization_loss': 0.016226497,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00022713021,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.29284218,\n",
            " 'learning_rate': 0.007951256}\n",
            "INFO:tensorflow:Step 14800 per-step time 1.114s\n",
            "I0611 07:49:14.918737 139937817839488 model_lib_v2.py:707] Step 14800 per-step time 1.114s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.08221236,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.13178599,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0054732575,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0011865627,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.22065817,\n",
            " 'learning_rate': 0.007950248}\n",
            "I0611 07:49:14.919134 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.08221236,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.13178599,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0054732575,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0011865627,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.22065817,\n",
            " 'learning_rate': 0.007950248}\n",
            "INFO:tensorflow:Step 14900 per-step time 1.115s\n",
            "I0611 07:51:06.398738 139937817839488 model_lib_v2.py:707] Step 14900 per-step time 1.115s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.1600757,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.4681864,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0042731613,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0061134463,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.63864875,\n",
            " 'learning_rate': 0.00794923}\n",
            "I0611 07:51:06.399116 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.1600757,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.4681864,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0042731613,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0061134463,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.63864875,\n",
            " 'learning_rate': 0.00794923}\n",
            "INFO:tensorflow:Step 15000 per-step time 1.114s\n",
            "I0611 07:52:57.778870 139937817839488 model_lib_v2.py:707] Step 15000 per-step time 1.114s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.11442846,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.20583743,\n",
            " 'Loss/RPNLoss/localization_loss': 0.025897395,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0017824826,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.34794575,\n",
            " 'learning_rate': 0.007948201}\n",
            "I0611 07:52:57.779249 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.11442846,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.20583743,\n",
            " 'Loss/RPNLoss/localization_loss': 0.025897395,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0017824826,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.34794575,\n",
            " 'learning_rate': 0.007948201}\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0611 07:52:58.120898 139937817839488 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0611 07:52:58.122226 139937817839488 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0611 07:52:58.124007 139937817839488 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0611 07:52:58.124925 139937817839488 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0611 07:52:58.126570 139937817839488 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0611 07:52:58.127422 139937817839488 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0611 07:52:58.129035 139937817839488 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0611 07:52:58.129933 139937817839488 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0611 07:52:58.132096 139937817839488 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0611 07:52:58.132955 139937817839488 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Step 15100 per-step time 1.148s\n",
            "I0611 07:54:52.584268 139937817839488 model_lib_v2.py:707] Step 15100 per-step time 1.148s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.11576688,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.15284565,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0064045023,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00033722588,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.27535424,\n",
            " 'learning_rate': 0.007947163}\n",
            "I0611 07:54:52.584679 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.11576688,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.15284565,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0064045023,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00033722588,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.27535424,\n",
            " 'learning_rate': 0.007947163}\n",
            "INFO:tensorflow:Step 15200 per-step time 1.110s\n",
            "I0611 07:56:43.619254 139937817839488 model_lib_v2.py:707] Step 15200 per-step time 1.110s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.05154986,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.4954637,\n",
            " 'Loss/RPNLoss/localization_loss': 0.00034158086,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00031721027,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.5476724,\n",
            " 'learning_rate': 0.007946113}\n",
            "I0611 07:56:43.619662 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.05154986,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.4954637,\n",
            " 'Loss/RPNLoss/localization_loss': 0.00034158086,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00031721027,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.5476724,\n",
            " 'learning_rate': 0.007946113}\n",
            "INFO:tensorflow:Step 15300 per-step time 1.113s\n",
            "I0611 07:58:34.910573 139937817839488 model_lib_v2.py:707] Step 15300 per-step time 1.113s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.07051209,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.44717574,\n",
            " 'Loss/RPNLoss/localization_loss': 0.011082903,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0015998138,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.53037053,\n",
            " 'learning_rate': 0.007945054}\n",
            "I0611 07:58:34.910960 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.07051209,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.44717574,\n",
            " 'Loss/RPNLoss/localization_loss': 0.011082903,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0015998138,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.53037053,\n",
            " 'learning_rate': 0.007945054}\n",
            "INFO:tensorflow:Step 15400 per-step time 1.114s\n",
            "I0611 08:00:26.278158 139937817839488 model_lib_v2.py:707] Step 15400 per-step time 1.114s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.15873602,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.48958558,\n",
            " 'Loss/RPNLoss/localization_loss': 0.006839336,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0017347573,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.6568957,\n",
            " 'learning_rate': 0.007943984}\n",
            "I0611 08:00:26.278546 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.15873602,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.48958558,\n",
            " 'Loss/RPNLoss/localization_loss': 0.006839336,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0017347573,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.6568957,\n",
            " 'learning_rate': 0.007943984}\n",
            "INFO:tensorflow:Step 15500 per-step time 1.114s\n",
            "I0611 08:02:17.648228 139937817839488 model_lib_v2.py:707] Step 15500 per-step time 1.114s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.08229624,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.16222645,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0019898075,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.000831392,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.2473439,\n",
            " 'learning_rate': 0.007942905}\n",
            "I0611 08:02:17.648692 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.08229624,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.16222645,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0019898075,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.000831392,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.2473439,\n",
            " 'learning_rate': 0.007942905}\n",
            "INFO:tensorflow:Step 15600 per-step time 1.112s\n",
            "I0611 08:04:08.834409 139937817839488 model_lib_v2.py:707] Step 15600 per-step time 1.112s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.08790978,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.22115004,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0054396777,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.002696811,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.3171963,\n",
            " 'learning_rate': 0.007941815}\n",
            "I0611 08:04:08.834781 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.08790978,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.22115004,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0054396777,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.002696811,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.3171963,\n",
            " 'learning_rate': 0.007941815}\n",
            "INFO:tensorflow:Step 15700 per-step time 1.114s\n",
            "I0611 08:06:00.261577 139937817839488 model_lib_v2.py:707] Step 15700 per-step time 1.114s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.090925746,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.12546864,\n",
            " 'Loss/RPNLoss/localization_loss': 0.020349137,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0022121323,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.23895568,\n",
            " 'learning_rate': 0.007940714}\n",
            "I0611 08:06:00.261969 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.090925746,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.12546864,\n",
            " 'Loss/RPNLoss/localization_loss': 0.020349137,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0022121323,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.23895568,\n",
            " 'learning_rate': 0.007940714}\n",
            "INFO:tensorflow:Step 15800 per-step time 1.113s\n",
            "I0611 08:07:51.572662 139937817839488 model_lib_v2.py:707] Step 15800 per-step time 1.113s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.09188225,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.23177958,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0054784482,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0014188447,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.33055913,\n",
            " 'learning_rate': 0.007939604}\n",
            "I0611 08:07:51.573031 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.09188225,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.23177958,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0054784482,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0014188447,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.33055913,\n",
            " 'learning_rate': 0.007939604}\n",
            "INFO:tensorflow:Step 15900 per-step time 1.115s\n",
            "I0611 08:09:43.060564 139937817839488 model_lib_v2.py:707] Step 15900 per-step time 1.115s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.07676345,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.1842838,\n",
            " 'Loss/RPNLoss/localization_loss': 0.004901473,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0041818554,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.27013057,\n",
            " 'learning_rate': 0.007938483}\n",
            "I0611 08:09:43.060945 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.07676345,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.1842838,\n",
            " 'Loss/RPNLoss/localization_loss': 0.004901473,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0041818554,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.27013057,\n",
            " 'learning_rate': 0.007938483}\n",
            "INFO:tensorflow:Step 16000 per-step time 1.113s\n",
            "I0611 08:11:34.351293 139937817839488 model_lib_v2.py:707] Step 16000 per-step time 1.113s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.10370946,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.20231321,\n",
            " 'Loss/RPNLoss/localization_loss': 0.008452966,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0036302824,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.3181059,\n",
            " 'learning_rate': 0.007937352}\n",
            "I0611 08:11:34.351684 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.10370946,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.20231321,\n",
            " 'Loss/RPNLoss/localization_loss': 0.008452966,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0036302824,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.3181059,\n",
            " 'learning_rate': 0.007937352}\n",
            "INFO:tensorflow:Step 16100 per-step time 1.153s\n",
            "I0611 08:13:29.683267 139937817839488 model_lib_v2.py:707] Step 16100 per-step time 1.153s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0666168,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.10409936,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0414078,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0052837357,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.2174077,\n",
            " 'learning_rate': 0.007936211}\n",
            "I0611 08:13:29.683679 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0666168,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.10409936,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0414078,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0052837357,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.2174077,\n",
            " 'learning_rate': 0.007936211}\n",
            "INFO:tensorflow:Step 16200 per-step time 1.112s\n",
            "I0611 08:15:20.933205 139937817839488 model_lib_v2.py:707] Step 16200 per-step time 1.112s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.005132175,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0002281534,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.0053603286,\n",
            " 'learning_rate': 0.007935059}\n",
            "I0611 08:15:20.933598 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.005132175,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0002281534,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.0053603286,\n",
            " 'learning_rate': 0.007935059}\n",
            "INFO:tensorflow:Step 16300 per-step time 1.115s\n",
            "I0611 08:17:12.428631 139937817839488 model_lib_v2.py:707] Step 16300 per-step time 1.115s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.053709522,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.13064817,\n",
            " 'Loss/RPNLoss/localization_loss': 0.020270426,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0025154173,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.20714353,\n",
            " 'learning_rate': 0.007933898}\n",
            "I0611 08:17:12.428993 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.053709522,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.13064817,\n",
            " 'Loss/RPNLoss/localization_loss': 0.020270426,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0025154173,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.20714353,\n",
            " 'learning_rate': 0.007933898}\n",
            "INFO:tensorflow:Step 16400 per-step time 1.113s\n",
            "I0611 08:19:03.705894 139937817839488 model_lib_v2.py:707] Step 16400 per-step time 1.113s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.19156674,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.25802702,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0053707333,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0024485325,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.45741302,\n",
            " 'learning_rate': 0.007932726}\n",
            "I0611 08:19:03.706326 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.19156674,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.25802702,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0053707333,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0024485325,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.45741302,\n",
            " 'learning_rate': 0.007932726}\n",
            "INFO:tensorflow:Step 16500 per-step time 1.114s\n",
            "I0611 08:20:55.068430 139937817839488 model_lib_v2.py:707] Step 16500 per-step time 1.114s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0045942687,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0015097652,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.006104034,\n",
            " 'learning_rate': 0.0079315435}\n",
            "I0611 08:20:55.068801 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0045942687,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0015097652,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.006104034,\n",
            " 'learning_rate': 0.0079315435}\n",
            "INFO:tensorflow:Step 16600 per-step time 1.115s\n",
            "I0611 08:22:46.610676 139937817839488 model_lib_v2.py:707] Step 16600 per-step time 1.115s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.007459155,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 7.1762915e-05,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.007530918,\n",
            " 'learning_rate': 0.007930351}\n",
            "I0611 08:22:46.611154 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.007459155,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 7.1762915e-05,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.007530918,\n",
            " 'learning_rate': 0.007930351}\n",
            "INFO:tensorflow:Step 16700 per-step time 1.115s\n",
            "I0611 08:24:38.087413 139937817839488 model_lib_v2.py:707] Step 16700 per-step time 1.115s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.13473779,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.19774754,\n",
            " 'Loss/RPNLoss/localization_loss': 0.004820046,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0025657814,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.33987114,\n",
            " 'learning_rate': 0.007929149}\n",
            "I0611 08:24:38.087803 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.13473779,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.19774754,\n",
            " 'Loss/RPNLoss/localization_loss': 0.004820046,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0025657814,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.33987114,\n",
            " 'learning_rate': 0.007929149}\n",
            "INFO:tensorflow:Step 16800 per-step time 1.114s\n",
            "I0611 08:26:29.536542 139937817839488 model_lib_v2.py:707] Step 16800 per-step time 1.114s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.028968938,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.029640503,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0017835918,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.060393035,\n",
            " 'learning_rate': 0.0079279365}\n",
            "I0611 08:26:29.536892 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.028968938,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.029640503,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0017835918,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.060393035,\n",
            " 'learning_rate': 0.0079279365}\n",
            "INFO:tensorflow:Step 16900 per-step time 1.114s\n",
            "I0611 08:28:20.927777 139937817839488 model_lib_v2.py:707] Step 16900 per-step time 1.114s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.015649404,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.04843635,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0021462128,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.066231966,\n",
            " 'learning_rate': 0.007926714}\n",
            "I0611 08:28:20.928145 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.015649404,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.04843635,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0021462128,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.066231966,\n",
            " 'learning_rate': 0.007926714}\n",
            "INFO:tensorflow:Step 17000 per-step time 1.115s\n",
            "I0611 08:30:12.397677 139937817839488 model_lib_v2.py:707] Step 17000 per-step time 1.115s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.017387914,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.04645932,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.007637474,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.07148471,\n",
            " 'learning_rate': 0.007925482}\n",
            "I0611 08:30:12.398075 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.017387914,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.04645932,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.007637474,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.07148471,\n",
            " 'learning_rate': 0.007925482}\n",
            "INFO:tensorflow:Step 17100 per-step time 1.147s\n",
            "I0611 08:32:07.063871 139937817839488 model_lib_v2.py:707] Step 17100 per-step time 1.147s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.05653745,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.26309785,\n",
            " 'Loss/RPNLoss/localization_loss': 0.02032908,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.001251153,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.34121555,\n",
            " 'learning_rate': 0.007924237}\n",
            "I0611 08:32:07.064329 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.05653745,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.26309785,\n",
            " 'Loss/RPNLoss/localization_loss': 0.02032908,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.001251153,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.34121555,\n",
            " 'learning_rate': 0.007924237}\n",
            "INFO:tensorflow:Step 17200 per-step time 1.111s\n",
            "I0611 08:33:58.159764 139937817839488 model_lib_v2.py:707] Step 17200 per-step time 1.111s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0032576793,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.4840051,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.04235698,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.52961975,\n",
            " 'learning_rate': 0.007922985}\n",
            "I0611 08:33:58.160140 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0032576793,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.4840051,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.04235698,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.52961975,\n",
            " 'learning_rate': 0.007922985}\n",
            "INFO:tensorflow:Step 17300 per-step time 1.113s\n",
            "I0611 08:35:49.476057 139937817839488 model_lib_v2.py:707] Step 17300 per-step time 1.113s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.10244224,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.19855116,\n",
            " 'Loss/RPNLoss/localization_loss': 0.016295347,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.002829145,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.3201179,\n",
            " 'learning_rate': 0.007921721}\n",
            "I0611 08:35:49.476427 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.10244224,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.19855116,\n",
            " 'Loss/RPNLoss/localization_loss': 0.016295347,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.002829145,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.3201179,\n",
            " 'learning_rate': 0.007921721}\n",
            "INFO:tensorflow:Step 17400 per-step time 1.114s\n",
            "I0611 08:37:40.919335 139937817839488 model_lib_v2.py:707] Step 17400 per-step time 1.114s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.085716635,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.19158188,\n",
            " 'Loss/RPNLoss/localization_loss': 0.015656643,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.016204124,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.30915928,\n",
            " 'learning_rate': 0.007920447}\n",
            "I0611 08:37:40.919735 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.085716635,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.19158188,\n",
            " 'Loss/RPNLoss/localization_loss': 0.015656643,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.016204124,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.30915928,\n",
            " 'learning_rate': 0.007920447}\n",
            "INFO:tensorflow:Step 17500 per-step time 1.113s\n",
            "I0611 08:39:32.216048 139937817839488 model_lib_v2.py:707] Step 17500 per-step time 1.113s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.089833856,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.06939329,\n",
            " 'Loss/RPNLoss/localization_loss': 0.017654087,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0038256422,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.18070689,\n",
            " 'learning_rate': 0.007919163}\n",
            "I0611 08:39:32.216456 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.089833856,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.06939329,\n",
            " 'Loss/RPNLoss/localization_loss': 0.017654087,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0038256422,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.18070689,\n",
            " 'learning_rate': 0.007919163}\n",
            "INFO:tensorflow:Step 17600 per-step time 1.112s\n",
            "I0611 08:41:23.385173 139937817839488 model_lib_v2.py:707] Step 17600 per-step time 1.112s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.13628063,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.3097664,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0065744473,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.003960357,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.45658186,\n",
            " 'learning_rate': 0.007917869}\n",
            "I0611 08:41:23.385560 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.13628063,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.3097664,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0065744473,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.003960357,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.45658186,\n",
            " 'learning_rate': 0.007917869}\n",
            "INFO:tensorflow:Step 17700 per-step time 1.113s\n",
            "I0611 08:43:14.726871 139937817839488 model_lib_v2.py:707] Step 17700 per-step time 1.113s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.012353935,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00079762854,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.013151564,\n",
            " 'learning_rate': 0.007916564}\n",
            "I0611 08:43:14.727239 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.012353935,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00079762854,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.013151564,\n",
            " 'learning_rate': 0.007916564}\n",
            "INFO:tensorflow:Step 17800 per-step time 1.110s\n",
            "I0611 08:45:05.739846 139937817839488 model_lib_v2.py:707] Step 17800 per-step time 1.110s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.28777432,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.29549748,\n",
            " 'Loss/RPNLoss/localization_loss': 0.014060339,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0037104099,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.6010425,\n",
            " 'learning_rate': 0.00791525}\n",
            "I0611 08:45:05.740211 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.28777432,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.29549748,\n",
            " 'Loss/RPNLoss/localization_loss': 0.014060339,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0037104099,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.6010425,\n",
            " 'learning_rate': 0.00791525}\n",
            "INFO:tensorflow:Step 17900 per-step time 1.113s\n",
            "I0611 08:46:56.995482 139937817839488 model_lib_v2.py:707] Step 17900 per-step time 1.113s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0017900708,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 9.6582145e-05,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.0018866529,\n",
            " 'learning_rate': 0.007913926}\n",
            "I0611 08:46:56.995864 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0017900708,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 9.6582145e-05,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.0018866529,\n",
            " 'learning_rate': 0.007913926}\n",
            "INFO:tensorflow:Step 18000 per-step time 1.115s\n",
            "I0611 08:48:48.466558 139937817839488 model_lib_v2.py:707] Step 18000 per-step time 1.115s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.11318307,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.29215887,\n",
            " 'Loss/RPNLoss/localization_loss': 0.008336242,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00047958834,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.41415778,\n",
            " 'learning_rate': 0.007912591}\n",
            "I0611 08:48:48.467203 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.11318307,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.29215887,\n",
            " 'Loss/RPNLoss/localization_loss': 0.008336242,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00047958834,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.41415778,\n",
            " 'learning_rate': 0.007912591}\n",
            "INFO:tensorflow:Step 18100 per-step time 1.149s\n",
            "I0611 08:50:43.346220 139937817839488 model_lib_v2.py:707] Step 18100 per-step time 1.149s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.029893767,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00073206745,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.030625835,\n",
            " 'learning_rate': 0.007911245}\n",
            "I0611 08:50:43.346630 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.029893767,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00073206745,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.030625835,\n",
            " 'learning_rate': 0.007911245}\n",
            "INFO:tensorflow:Step 18200 per-step time 1.110s\n",
            "I0611 08:52:34.379202 139937817839488 model_lib_v2.py:707] Step 18200 per-step time 1.110s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.17050819,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.34168777,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0018558027,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.010119917,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.52417165,\n",
            " 'learning_rate': 0.00790989}\n",
            "I0611 08:52:34.379635 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.17050819,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.34168777,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0018558027,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.010119917,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.52417165,\n",
            " 'learning_rate': 0.00790989}\n",
            "INFO:tensorflow:Step 18300 per-step time 1.112s\n",
            "I0611 08:54:25.580433 139937817839488 model_lib_v2.py:707] Step 18300 per-step time 1.112s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00064026855,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00042665168,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.0010669202,\n",
            " 'learning_rate': 0.007908526}\n",
            "I0611 08:54:25.580822 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00064026855,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00042665168,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.0010669202,\n",
            " 'learning_rate': 0.007908526}\n",
            "INFO:tensorflow:Step 18400 per-step time 1.112s\n",
            "I0611 08:56:16.737450 139937817839488 model_lib_v2.py:707] Step 18400 per-step time 1.112s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.12125518,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.26692453,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0041388716,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.002763094,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.39508167,\n",
            " 'learning_rate': 0.00790715}\n",
            "I0611 08:56:16.737838 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.12125518,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.26692453,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0041388716,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.002763094,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.39508167,\n",
            " 'learning_rate': 0.00790715}\n",
            "INFO:tensorflow:Step 18500 per-step time 1.114s\n",
            "I0611 08:58:08.115022 139937817839488 model_lib_v2.py:707] Step 18500 per-step time 1.114s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.07983341,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.08013405,\n",
            " 'Loss/RPNLoss/localization_loss': 0.01630419,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0071280403,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.18339969,\n",
            " 'learning_rate': 0.0079057645}\n",
            "I0611 08:58:08.115420 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.07983341,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.08013405,\n",
            " 'Loss/RPNLoss/localization_loss': 0.01630419,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0071280403,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.18339969,\n",
            " 'learning_rate': 0.0079057645}\n",
            "INFO:tensorflow:Step 18600 per-step time 1.111s\n",
            "I0611 08:59:59.182688 139937817839488 model_lib_v2.py:707] Step 18600 per-step time 1.111s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.040228426,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.06521231,\n",
            " 'Loss/RPNLoss/localization_loss': 0.015395077,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00035993342,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.12119574,\n",
            " 'learning_rate': 0.007904368}\n",
            "I0611 08:59:59.183050 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.040228426,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.06521231,\n",
            " 'Loss/RPNLoss/localization_loss': 0.015395077,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00035993342,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.12119574,\n",
            " 'learning_rate': 0.007904368}\n",
            "INFO:tensorflow:Step 18700 per-step time 1.108s\n",
            "I0611 09:01:50.021926 139937817839488 model_lib_v2.py:707] Step 18700 per-step time 1.108s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.27061185,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.12762454,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0057543274,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.021920148,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.42591086,\n",
            " 'learning_rate': 0.007902963}\n",
            "I0611 09:01:50.022312 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.27061185,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.12762454,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0057543274,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.021920148,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.42591086,\n",
            " 'learning_rate': 0.007902963}\n",
            "INFO:tensorflow:Step 18800 per-step time 1.111s\n",
            "I0611 09:03:41.070848 139937817839488 model_lib_v2.py:707] Step 18800 per-step time 1.111s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.23901506,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.3072067,\n",
            " 'Loss/RPNLoss/localization_loss': 0.015525399,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.004150963,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.5658981,\n",
            " 'learning_rate': 0.0079015475}\n",
            "I0611 09:03:41.071223 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.23901506,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.3072067,\n",
            " 'Loss/RPNLoss/localization_loss': 0.015525399,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.004150963,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.5658981,\n",
            " 'learning_rate': 0.0079015475}\n",
            "INFO:tensorflow:Step 18900 per-step time 1.111s\n",
            "I0611 09:05:32.193577 139937817839488 model_lib_v2.py:707] Step 18900 per-step time 1.111s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.11427668,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.005886656,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.12016334,\n",
            " 'learning_rate': 0.007900121}\n",
            "I0611 09:05:32.194026 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.11427668,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.005886656,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.12016334,\n",
            " 'learning_rate': 0.007900121}\n",
            "INFO:tensorflow:Step 19000 per-step time 1.110s\n",
            "I0611 09:07:23.238947 139937817839488 model_lib_v2.py:707] Step 19000 per-step time 1.110s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.10978748,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.152109,\n",
            " 'Loss/RPNLoss/localization_loss': 0.022647724,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.003347772,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.28789198,\n",
            " 'learning_rate': 0.007898685}\n",
            "I0611 09:07:23.239325 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.10978748,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.152109,\n",
            " 'Loss/RPNLoss/localization_loss': 0.022647724,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.003347772,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.28789198,\n",
            " 'learning_rate': 0.007898685}\n",
            "INFO:tensorflow:Step 19100 per-step time 1.146s\n",
            "I0611 09:09:17.837813 139937817839488 model_lib_v2.py:707] Step 19100 per-step time 1.146s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.10849029,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.23227702,\n",
            " 'Loss/RPNLoss/localization_loss': 0.00063299295,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0016711114,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.34307143,\n",
            " 'learning_rate': 0.007897239}\n",
            "I0611 09:09:17.838222 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.10849029,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.23227702,\n",
            " 'Loss/RPNLoss/localization_loss': 0.00063299295,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0016711114,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.34307143,\n",
            " 'learning_rate': 0.007897239}\n",
            "INFO:tensorflow:Step 19200 per-step time 1.109s\n",
            "I0611 09:11:08.696678 139937817839488 model_lib_v2.py:707] Step 19200 per-step time 1.109s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0039365212,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00044431287,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.0043808343,\n",
            " 'learning_rate': 0.007895783}\n",
            "I0611 09:11:08.697052 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0039365212,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.00044431287,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.0043808343,\n",
            " 'learning_rate': 0.007895783}\n",
            "INFO:tensorflow:Step 19300 per-step time 1.111s\n",
            "I0611 09:12:59.814666 139937817839488 model_lib_v2.py:707] Step 19300 per-step time 1.111s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.34594923,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.53705305,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0143574495,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0055669057,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.9029266,\n",
            " 'learning_rate': 0.007894316}\n",
            "I0611 09:12:59.815042 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.34594923,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.53705305,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0143574495,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0055669057,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.9029266,\n",
            " 'learning_rate': 0.007894316}\n",
            "INFO:tensorflow:Step 19400 per-step time 1.107s\n",
            "I0611 09:14:50.541968 139937817839488 model_lib_v2.py:707] Step 19400 per-step time 1.107s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.22020727,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.38768625,\n",
            " 'Loss/RPNLoss/localization_loss': 0.008127313,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0017312855,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.61775213,\n",
            " 'learning_rate': 0.00789284}\n",
            "I0611 09:14:50.542336 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.22020727,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.38768625,\n",
            " 'Loss/RPNLoss/localization_loss': 0.008127313,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0017312855,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.61775213,\n",
            " 'learning_rate': 0.00789284}\n",
            "INFO:tensorflow:Step 19500 per-step time 1.108s\n",
            "I0611 09:16:41.322194 139937817839488 model_lib_v2.py:707] Step 19500 per-step time 1.108s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.24621478,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.42020208,\n",
            " 'Loss/RPNLoss/localization_loss': 0.007106881,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.01577994,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.6893037,\n",
            " 'learning_rate': 0.007891352}\n",
            "I0611 09:16:41.322558 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.24621478,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.42020208,\n",
            " 'Loss/RPNLoss/localization_loss': 0.007106881,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.01577994,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.6893037,\n",
            " 'learning_rate': 0.007891352}\n",
            "INFO:tensorflow:Step 19600 per-step time 1.108s\n",
            "I0611 09:18:32.132991 139937817839488 model_lib_v2.py:707] Step 19600 per-step time 1.108s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.1748495,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.44397575,\n",
            " 'Loss/RPNLoss/localization_loss': 0.01151905,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0036036258,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.63394797,\n",
            " 'learning_rate': 0.007889856}\n",
            "I0611 09:18:32.133355 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.1748495,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.44397575,\n",
            " 'Loss/RPNLoss/localization_loss': 0.01151905,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0036036258,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.63394797,\n",
            " 'learning_rate': 0.007889856}\n",
            "INFO:tensorflow:Step 19700 per-step time 1.108s\n",
            "I0611 09:20:22.968385 139937817839488 model_lib_v2.py:707] Step 19700 per-step time 1.108s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00046452554,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 2.7949563e-05,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.0004924751,\n",
            " 'learning_rate': 0.007888349}\n",
            "I0611 09:20:22.968804 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00046452554,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 2.7949563e-05,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.0004924751,\n",
            " 'learning_rate': 0.007888349}\n",
            "INFO:tensorflow:Step 19800 per-step time 1.109s\n",
            "I0611 09:22:13.918323 139937817839488 model_lib_v2.py:707] Step 19800 per-step time 1.109s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.009973922,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0004947035,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.010468625,\n",
            " 'learning_rate': 0.007886832}\n",
            "I0611 09:22:13.918712 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.009973922,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0004947035,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.010468625,\n",
            " 'learning_rate': 0.007886832}\n",
            "INFO:tensorflow:Step 19900 per-step time 1.111s\n",
            "I0611 09:24:04.970542 139937817839488 model_lib_v2.py:707] Step 19900 per-step time 1.111s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.3119316,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.2395189,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0111774895,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.013145825,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.5757738,\n",
            " 'learning_rate': 0.007885305}\n",
            "I0611 09:24:04.970905 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.3119316,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.2395189,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0111774895,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.013145825,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.5757738,\n",
            " 'learning_rate': 0.007885305}\n",
            "INFO:tensorflow:Step 20000 per-step time 1.111s\n",
            "I0611 09:25:56.072111 139937817839488 model_lib_v2.py:707] Step 20000 per-step time 1.111s\n",
            "INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.12460114,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.35320833,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0030867623,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0026139985,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.48351026,\n",
            " 'learning_rate': 0.007883768}\n",
            "I0611 09:25:56.072496 139937817839488 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.12460114,\n",
            " 'Loss/BoxClassifierLoss/localization_loss': 0.35320833,\n",
            " 'Loss/RPNLoss/localization_loss': 0.0030867623,\n",
            " 'Loss/RPNLoss/objectness_loss': 0.0026139985,\n",
            " 'Loss/regularization_loss': 0.0,\n",
            " 'Loss/total_loss': 0.48351026,\n",
            " 'learning_rate': 0.007883768}\n"
          ]
        }
      ],
      "source": [
        "!python /content/models/research/object_detection/model_main_tf2.py --model_dir=/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/trained_model/my_FRCNN --pipeline_config_path=/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/trained_model/my_FRCNN/pipeline.config --num_train_steps=20000"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1oziokltZc5F"
      },
      "source": [
        "## Evaluating the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6dPk6eozZcN_"
      },
      "outputs": [],
      "source": [
        "!python /content/models/research/object_detection/model_main_tf2.py --model_dir=/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/trained_model/my_FRCNN --pipeline_config_path=/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/trained_model/my_FRCNN/pipeline.config --checkpoint_dir=/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/trained_model/my_FRCNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lyFl5rOXZiSa"
      },
      "source": [
        "## Exporting model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rYFVfvwJ3vB-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        },
        "outputId": "bb58ea9e-ff4b-4aeb-8da9-08452b860db6"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileExistsError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileExistsError\u001b[0m                           Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-5fd392733ab3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/exported-models/my_model_20000\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mFileExistsError\u001b[0m: [Errno 17] File exists: '/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/exported-models/my_model_20000'"
          ]
        }
      ],
      "source": [
        "os.mkdir(\"/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/exported-models/my_model_20000\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UdIT6xON3xfa",
        "outputId": "eb7c810e-8dfd-4f1d-ab30-55895c0a3c1c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-06-11 15:11:15.729609: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:458: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "back_prop=False is deprecated. Consider using tf.stop_gradient instead.\n",
            "Instead of:\n",
            "results = tf.map_fn(fn, elems, back_prop=False)\n",
            "Use:\n",
            "results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\n",
            "W0611 15:11:17.528967 140638064674688 deprecation.py:615] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:458: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "back_prop=False is deprecated. Consider using tf.stop_gradient instead.\n",
            "Instead of:\n",
            "results = tf.map_fn(fn, elems, back_prop=False)\n",
            "Use:\n",
            "results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0611 15:11:37.052408 140638064674688 convolutional_keras_box_predictor.py:153] depth of additional conv before box predictor: 0\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "box_ind is deprecated, use box_indices instead\n",
            "W0611 15:11:44.088586 140638064674688 deprecation.py:547] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "box_ind is deprecated, use box_indices instead\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:459: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use ref() instead.\n",
            "W0611 15:11:44.649422 140638064674688 deprecation.py:343] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:459: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use ref() instead.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.faster_rcnn_meta_arch.FasterRCNNMetaArch object at 0x7fe8590eff10>, because it is not built.\n",
            "W0611 15:12:05.042618 140638064674688 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.meta_architectures.faster_rcnn_meta_arch.FasterRCNNMetaArch object at 0x7fe8590eff10>, because it is not built.\n",
            "W0611 15:13:14.009943 140638064674688 save.py:265] Found untraced functions such as FirstStageBoxPredictor_layer_call_fn, FirstStageBoxPredictor_layer_call_and_return_conditional_losses, mask_rcnn_keras_box_predictor_layer_call_fn, mask_rcnn_keras_box_predictor_layer_call_and_return_conditional_losses, mask_rcnn_box_head_layer_call_fn while saving (showing 5 of 28). These functions will not be directly callable after loading.\n",
            "INFO:tensorflow:Assets written to: /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/exported-models/my_model_20000/saved_model/assets\n",
            "I0611 15:13:32.592448 140638064674688 builder_impl.py:780] Assets written to: /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/exported-models/my_model_20000/saved_model/assets\n",
            "INFO:tensorflow:Writing pipeline config file to /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/exported-models/my_model_20000/pipeline.config\n",
            "I0611 15:13:34.622046 140638064674688 config_util.py:254] Writing pipeline config file to /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/exported-models/my_model_20000/pipeline.config\n"
          ]
        }
      ],
      "source": [
        "!python /content/models/research/object_detection/exporter_main_v2.py --input_type image_tensor --pipeline_config_path /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/trained_model/my_FRCNN/pipeline.config --trained_checkpoint_dir /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/trained_model/my_FRCNN --output_directory /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/exported-models/my_model_20000"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3qBaZSsm5UFo"
      },
      "source": [
        "## Inferencing trained model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "spZJ4ms3FqRT",
        "outputId": "d12aeee1-4de9-4154-ba47-e28daf90f066"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading model...Done! Took 44.052143573760986 seconds\n"
          ]
        }
      ],
      "source": [
        "\"\"\"\n",
        "Object Detection (On Image) From TF2 Saved Model\n",
        "=====================================\n",
        "\"\"\"\n",
        "\n",
        "import os\n",
        "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'    # Suppress TensorFlow logging (1)\n",
        "import pathlib\n",
        "import tensorflow as tf\n",
        "import cv2\n",
        "import argparse\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "# Enable GPU dynamic memory allocation\n",
        "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
        "for gpu in gpus:\n",
        "    tf.config.experimental.set_memory_growth(gpu, True)\n",
        "\n",
        "\n",
        "# PROVIDE PATH TO MODEL DIRECTORY\n",
        "PATH_TO_MODEL_DIR = '/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/exported-models/my_model_20000'\n",
        "\n",
        "# PROVIDE PATH TO LABEL MAP\n",
        "PATH_TO_LABELS = '/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/annotations/label_map.pbtxt'\n",
        "\n",
        "# PROVIDE THE MINIMUM CONFIDENCE THRESHOLD\n",
        "MIN_CONF_THRESH = float(0.80)\n",
        "\n",
        "# LOAD THE MODEL\n",
        "\n",
        "import time\n",
        "from object_detection.utils import label_map_util\n",
        "from object_detection.utils import visualization_utils as viz_utils\n",
        "\n",
        "PATH_TO_SAVED_MODEL = \"/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/exported-models/my_model_20000/saved_model\"\n",
        "\n",
        "print('Loading model...', end='')\n",
        "start_time = time.time()\n",
        "\n",
        "# LOAD SAVED MODEL AND BUILD DETECTION FUNCTION\n",
        "detect_fn = tf.saved_model.load(PATH_TO_SAVED_MODEL)\n",
        "\n",
        "end_time = time.time()\n",
        "elapsed_time = end_time - start_time\n",
        "print('Done! Took {} seconds'.format(elapsed_time))\n",
        "\n",
        "# LOAD LABEL MAP DATA FOR PLOTTING\n",
        "\n",
        "category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,\n",
        "                                                                    use_display_name=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sduoJNqsvEXM"
      },
      "source": [
        "## Detecting stenosis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "iFalxOpU5Wy9",
        "outputId": "0aa51ac7-6b75-4cd7-d779-ef6bb49d576e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Running inference for /content/drive/MyDrive/workspace/stenosis_detection_FRCNN/images/test/14_002_5_0031.bmp... Done\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=720x540 at 0x7FC99ADF6F50>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# PROVIDE PATH TO IMAGE DIRECTORY\n",
        "IMAGE_PATHS = '/content/drive/MyDrive/workspace/stenosis_detection_FRCNN/images/test/14_007_10_0071.bmp'\n",
        "\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')   # Suppress Matplotlib warnings\n",
        "\n",
        "def load_image_into_numpy_array(path):\n",
        "    \"\"\"Load an image from file into a numpy array.\n",
        "    Puts image into numpy array to feed into tensorflow graph.\n",
        "    Note that by convention we put it into a numpy array with shape\n",
        "    (height, width, channels), where channels=3 for RGB.\n",
        "    Args:\n",
        "      path: the file path to the image\n",
        "    Returns:\n",
        "      uint8 numpy array with shape (img_height, img_width, 3)\n",
        "    \"\"\"\n",
        "    return np.array(Image.open(path))\n",
        "\n",
        "\n",
        "print('Running inference for {}... '.format(IMAGE_PATHS), end='')\n",
        "\n",
        "image = cv2.imread(IMAGE_PATHS)\n",
        "image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "image_expanded = np.expand_dims(image_rgb, axis=0)\n",
        "\n",
        "# The input needs to be a tensor, convert it using `tf.convert_to_tensor`.\n",
        "input_tensor = tf.convert_to_tensor(image)\n",
        "# The model expects a batch of images, so add an axis with `tf.newaxis`.\n",
        "input_tensor = input_tensor[tf.newaxis, ...]\n",
        "\n",
        "# input_tensor = np.expand_dims(image_np, 0)\n",
        "detections = detect_fn(input_tensor)\n",
        "\n",
        "# All outputs are batches tensors.\n",
        "# Convert to numpy arrays, and take index [0] to remove the batch dimension.\n",
        "# We're only interested in the first num_detections.\n",
        "num_detections = int(detections.pop('num_detections'))\n",
        "detections = {key: value[0, :num_detections].numpy()\n",
        "               for key, value in detections.items()}\n",
        "detections['num_detections'] = num_detections\n",
        "\n",
        "# detection_classes should be ints.\n",
        "detections['detection_classes'] = detections['detection_classes'].astype(np.int64)\n",
        "\n",
        "image_with_detections = image.copy()\n",
        "\n",
        "# SET MIN_SCORE_THRESH BASED ON YOU MINIMUM THRESHOLD FOR DETECTIONS\n",
        "viz_utils.visualize_boxes_and_labels_on_image_array(\n",
        "      image_with_detections,\n",
        "      detections['detection_boxes'],\n",
        "      detections['detection_classes'],\n",
        "      detections['detection_scores'],\n",
        "      category_index,\n",
        "      use_normalized_coordinates=True,\n",
        "      max_boxes_to_draw=1,\n",
        "      min_score_thresh=0.3,\n",
        "      agnostic_mode=False)\n",
        "\n",
        "print('Done')\n",
        "# DISPLAYS OUTPUT IMAGE\n",
        "imS = cv2.resize(image_with_detections, (720, 540)) \n",
        "cv2_imshow(imS)\n",
        "# CLOSES WINDOW ONCE KEY IS PRESSED"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "Stenosis Detection: Faster RCNN Inception ResNetV2",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}