mask_detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "MaskDetection.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ROLBVz0W-b4"
      },
      "source": [
        "FaceMask Detection on edgetpu using mobiledet ssdlite with kaggle dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jom9_jxIW-3c",
        "outputId": "e7eefde4-19f5-4c16-d424-cf93b87deacf"
      },
      "source": [
        "# Import tensorflow 1.x and install tf_slim\n",
        "%tensorflow_version 1.x\n",
        "!pip install tf_slim\n",
        "!pip show tensorflow"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tf_slim in /usr/local/lib/python3.6/dist-packages (1.1.0)\n",
            "Requirement already satisfied: absl-py>=0.2.2 in /usr/local/lib/python3.6/dist-packages (from tf_slim) (0.10.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from absl-py>=0.2.2->tf_slim) (1.15.0)\n",
            "Name: tensorflow\n",
            "Version: 1.15.2\n",
            "Summary: TensorFlow is an open source machine learning framework for everyone.\n",
            "Home-page: https://www.tensorflow.org/\n",
            "Author: Google Inc.\n",
            "Author-email: packages@tensorflow.org\n",
            "License: Apache 2.0\n",
            "Location: /tensorflow-1.15.2/python3.6\n",
            "Requires: keras-preprocessing, absl-py, six, tensorflow-estimator, keras-applications, astor, protobuf, google-pasta, opt-einsum, tensorboard, grpcio, gast, wheel, termcolor, wrapt, numpy\n",
            "Required-by: stable-baselines, magenta, fancyimpute\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SnSBXH75XMcD",
        "outputId": "b6ab3fd9-355f-477b-d44b-5cb7609fbe92"
      },
      "source": [
        "# Install protobuf-compiler and object detection API.\n",
        "!apt-get install protobuf-compiler imagemagick\n",
        "!git clone https://github.com/tensorflow/models.git\n",
        "\n",
        "import os\n",
        "os.environ['PYTHONPATH'] += ':/content/models/research/'\n",
        "os.environ['PYTHONPATH'] += ':/content/models/research/slim/'\n",
        "os.environ['PYTHONPATH'] += ':/content/models/research/object_detection/utils/'\n",
        "os.environ['PYTHONPATH'] += ':/content/models/research/object_detection'\n",
        "\n",
        "%cd models/research\n",
        "# Compile all the protobuf dependencies.\n",
        "!protoc object_detection/protos/*.proto --python_out=.\n",
        "# Set up and install the object detection API.\n",
        "!cp object_detection/packages/tf1/setup.py .\n",
        "!python -m pip install .\n",
        "# Run a test to make sure setup is correct.\n",
        "!python object_detection/builders/model_builder_test.py"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "protobuf-compiler is already the newest version (3.0.0-9.1ubuntu1).\n",
            "imagemagick is already the newest version (8:6.9.7.4+dfsg-16ubuntu6.8).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 14 not upgraded.\n",
            "Cloning into 'models'...\n",
            "remote: Enumerating objects: 10, done.\u001b[K\n",
            "remote: Counting objects: 100% (10/10), done.\u001b[K\n",
            "remote: Compressing objects: 100% (10/10), done.\u001b[K\n",
            "remote: Total 47579 (delta 2), reused 8 (delta 0), pack-reused 47569\u001b[K\n",
            "Receiving objects: 100% (47579/47579), 551.84 MiB | 19.98 MiB/s, done.\n",
            "Resolving deltas: 100% (32773/32773), done.\n",
            "/content/models/research/models/research\n",
            "Processing /content/models/research/models/research\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from object-detection==0.1) (7.0.0)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.6/dist-packages (from object-detection==0.1) (4.2.6)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from object-detection==0.1) (3.2.2)\n",
            "Requirement already satisfied: Cython in /usr/local/lib/python3.6/dist-packages (from object-detection==0.1) (0.29.21)\n",
            "Requirement already satisfied: contextlib2 in /usr/local/lib/python3.6/dist-packages (from object-detection==0.1) (0.5.5)\n",
            "Requirement already satisfied: tf-slim in /usr/local/lib/python3.6/dist-packages (from object-detection==0.1) (1.1.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from object-detection==0.1) (1.15.0)\n",
            "Requirement already satisfied: pycocotools in /usr/local/lib/python3.6/dist-packages (from object-detection==0.1) (2.0.2)\n",
            "Requirement already satisfied: lvis in /usr/local/lib/python3.6/dist-packages (from object-detection==0.1) (0.5.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from object-detection==0.1) (1.4.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from object-detection==0.1) (1.1.4)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->object-detection==0.1) (2.8.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->object-detection==0.1) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->object-detection==0.1) (1.3.1)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.6/dist-packages (from matplotlib->object-detection==0.1) (1.18.5)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->object-detection==0.1) (0.10.0)\n",
            "Requirement already satisfied: absl-py>=0.2.2 in /usr/local/lib/python3.6/dist-packages (from tf-slim->object-detection==0.1) (0.10.0)\n",
            "Requirement already satisfied: setuptools>=18.0 in /usr/local/lib/python3.6/dist-packages (from pycocotools->object-detection==0.1) (50.3.2)\n",
            "Requirement already satisfied: opencv-python>=4.1.0.25 in /usr/local/lib/python3.6/dist-packages (from lvis->object-detection==0.1) (4.1.2.30)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->object-detection==0.1) (2018.9)\n",
            "Building wheels for collected packages: object-detection\n",
            "  Building wheel for object-detection (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for object-detection: filename=object_detection-0.1-cp36-none-any.whl size=1599232 sha256=1d161f8a50381dc249b6318cb9eb5326547463a01a856b26b0f9af5abb995e87\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-m0saji21/wheels/9d/3f/b5/750377a9d856e5965353c5f355a6824953172c693becb777c3\n",
            "Successfully built object-detection\n",
            "Installing collected packages: object-detection\n",
            "  Found existing installation: object-detection 0.1\n",
            "    Uninstalling object-detection-0.1:\n",
            "      Successfully uninstalled object-detection-0.1\n",
            "Successfully installed object-detection-0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hFcNqnifoi2G",
        "outputId": "670bfaa2-7108-44f7-807f-4629cf003001"
      },
      "source": [
        "# Download the Mask Dataset from MakeML: https://makeml.app/datasets/mask\n",
        "# License: Public Domain Mark\n",
        "!mkdir /content/dataset\n",
        "%cd /content/dataset\n",
        "!wget https://arcraftimages.s3-accelerate.amazonaws.com/Datasets/Mask/MaskPascalVOC.zip\n",
        "!unzip MaskPascalVOC.zip\n",
        "!rm -r __* # remove MacOS cache"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/dataset\n",
            "--2020-11-23 14:18:14--  https://arcraftimages.s3-accelerate.amazonaws.com/Datasets/Mask/MaskPascalVOC.zip\n",
            "Resolving arcraftimages.s3-accelerate.amazonaws.com (arcraftimages.s3-accelerate.amazonaws.com)... 13.32.141.175\n",
            "Connecting to arcraftimages.s3-accelerate.amazonaws.com (arcraftimages.s3-accelerate.amazonaws.com)|13.32.141.175|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 417606530 (398M) [application/zip]\n",
            "Saving to: ‘MaskPascalVOC.zip’\n",
            "\n",
            "MaskPascalVOC.zip   100%[===================>] 398.26M  52.6MB/s    in 8.5s    \n",
            "\n",
            "2020-11-23 14:18:23 (47.1 MB/s) - ‘MaskPascalVOC.zip’ saved [417606530/417606530]\n",
            "\n",
            "Archive:  MaskPascalVOC.zip\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h7iv7gu0s2nM",
        "outputId": "1c5e5e85-11f6-48e2-8764-6ff0ec89102a"
      },
      "source": [
        "# Lets create a mask_label_map.pbtxt file\n",
        "%%file mask_label_map.pbtxt\n",
        "item {\n",
        "  id: 1\n",
        "  name: 'with_mask'\n",
        "}\n",
        "\n",
        "item {\n",
        "  id: 2\n",
        "  name: 'without_mask'\n",
        "}\n",
        "\n",
        "item {\n",
        "  id: 3\n",
        "  name: 'mask_weared_incorrect'\n",
        "}"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Writing mask_label_map.pbtxt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ByNzySFl-TV",
        "outputId": "bd14de7a-f971-4617-832e-3eb956c69cc8"
      },
      "source": [
        "# Here we plit the images and labels to test vs train\n",
        "!mkdir /content/dataset/test\n",
        "!mkdir /content/dataset/train\n",
        "%cd /content/dataset/images\n",
        "# Move first 25 images to test\n",
        "!for i in {0..100}; do mv maksssksksss$i.png /content/dataset/test; done\n",
        "# Move the rest to train\n",
        "!mv mak* /content/dataset/train\n",
        "\n",
        "# Same thing for annotations\n",
        "%cd /content/dataset/annotations\n",
        "# Move first 25 annotations to test\n",
        "!for i in {0..100}; do mv maksssksksss$i.xml /content/dataset/test; done\n",
        "# Move the rest to train\n",
        "!mv mak* /content/dataset/train"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/dataset/images\n",
            "/content/dataset/annotations\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f5v347uRtKzF",
        "outputId": "b0e94864-a429-4b47-a609-d69af721be75"
      },
      "source": [
        "# Now let's download our ssdlite mobiledet pretrained model from tensorflow's model zoo.\n",
        "!mkdir /content/pretrained_model\n",
        "%cd /content/pretrained_model\n",
        "!wget http://download.tensorflow.org/models/object_detection/ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19.tar.gz\n",
        "!tar xvf ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19.tar.gz"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/pretrained_model\n",
            "--2020-11-23 14:18:31--  http://download.tensorflow.org/models/object_detection/ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19.tar.gz\n",
            "Resolving download.tensorflow.org (download.tensorflow.org)... 74.125.133.128, 2a00:1450:400c:c02::80\n",
            "Connecting to download.tensorflow.org (download.tensorflow.org)|74.125.133.128|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 156413934 (149M) [application/x-tar]\n",
            "Saving to: ‘ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19.tar.gz’\n",
            "\n",
            "ssdlite_mobiledet_e 100%[===================>] 149.17M  31.6MB/s    in 4.7s    \n",
            "\n",
            "2020-11-23 14:18:37 (31.6 MB/s) - ‘ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19.tar.gz’ saved [156413934/156413934]\n",
            "\n",
            "ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/\n",
            "ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/fp32/\n",
            "ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/fp32/model.ckpt-400000.data-00000-of-00001\n",
            "ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/fp32/model.ckpt-400000.index\n",
            "ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/fp32/model.ckpt-400000.meta\n",
            "ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/fp32/tflite_graph.pbtxt\n",
            "ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/fp32/tflite_graph.pb\n",
            "ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/fp32/pipeline.config\n",
            "ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/fp32/model.tflite\n",
            "ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/uint8/\n",
            "ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/uint8/model.ckpt-400000.data-00000-of-00001\n",
            "ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/uint8/model.ckpt-400000.index\n",
            "ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/uint8/model.ckpt-400000.meta\n",
            "ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/uint8/tflite_graph.pbtxt\n",
            "ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/uint8/tflite_graph.pb\n",
            "ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/uint8/pipeline.config\n",
            "ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/uint8/model.tflite\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qdF1oqYWuL4R",
        "outputId": "68fd131a-2958-44f9-da1a-d37cb4adc95c"
      },
      "source": [
        "# This script will turn our xml files from each image to a single label file.\n",
        "import os\n",
        "import glob\n",
        "import pandas as pd\n",
        "import xml.etree.ElementTree as ET\n",
        "\n",
        "def xml_to_csv(path):\n",
        "  xml_list = []\n",
        "  for xml_file in glob.glob(path + '/*.xml'):\n",
        "    tree = ET.parse(xml_file)\n",
        "    root = tree.getroot()\n",
        "    for member in root.findall('object'):\n",
        "      value = (root.find('filename').text,\n",
        "        int(root.find('size')[0].text),\n",
        "        int(root.find('size')[1].text),\n",
        "        member.find('name').text,\n",
        "        int(member.find('bndbox')[0].text),\n",
        "        int(member.find('bndbox')[1].text),\n",
        "        int(member.find('bndbox')[2].text),\n",
        "        int(member.find('bndbox')[3].text)\n",
        "        )\n",
        "      xml_list.append(value)\n",
        "  column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax']\n",
        "  xml_df = pd.DataFrame(xml_list, columns=column_name)\n",
        "  return xml_df\n",
        "\n",
        "\n",
        "def get_labels_from_image_dir():\n",
        "  for name in ['test', 'train']:\n",
        "    full_dir = '/content/dataset/' + name + '/'\n",
        "    label_file = '/content/dataset/' + name + '_labels.csv'\n",
        "    image_path = os.path.join(os.getcwd(), full_dir)\n",
        "    xml_df = xml_to_csv(image_path)\n",
        "    xml_df.to_csv(label_file, index=None)\n",
        "\n",
        "get_labels_from_image_dir()\n",
        "\n",
        "print('Training labels:')\n",
        "!head /content/dataset/train_labels.csv\n",
        "print('Testing labels:')\n",
        "!head /content/dataset/test_labels.csv"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training labels:\n",
            "filename,width,height,class,xmin,ymin,xmax,ymax\n",
            "maksssksksss493.png,301,400,with_mask,62,165,182,315\n",
            "maksssksksss264.png,400,267,with_mask,21,54,90,119\n",
            "maksssksksss264.png,400,267,with_mask,165,18,194,47\n",
            "maksssksksss264.png,400,267,with_mask,145,89,195,139\n",
            "maksssksksss264.png,400,267,with_mask,110,218,233,267\n",
            "maksssksksss264.png,400,267,with_mask,275,88,320,136\n",
            "maksssksksss264.png,400,267,with_mask,244,18,274,48\n",
            "maksssksksss264.png,400,267,with_mask,338,12,374,48\n",
            "maksssksksss365.png,400,240,with_mask,129,118,171,167\n",
            "Testing labels:\n",
            "filename,width,height,class,xmin,ymin,xmax,ymax\n",
            "maksssksksss88.png,301,400,with_mask,42,176,151,304\n",
            "maksssksksss9.png,267,400,mask_weared_incorrect,148,75,201,133\n",
            "maksssksksss9.png,267,400,without_mask,27,78,56,106\n",
            "maksssksksss79.png,400,400,with_mask,60,121,87,148\n",
            "maksssksksss79.png,400,400,with_mask,183,117,208,146\n",
            "maksssksksss79.png,400,400,with_mask,239,165,256,183\n",
            "maksssksksss79.png,400,400,with_mask,282,149,300,169\n",
            "maksssksksss79.png,400,400,with_mask,330,100,356,128\n",
            "maksssksksss66.png,380,400,with_mask,179,81,267,173\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yj4Pw-W6zKEq"
      },
      "source": [
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "from __future__ import absolute_import\n",
        "\n",
        "import os\n",
        "import io\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "\n",
        "from PIL import Image\n",
        "from object_detection.utils import dataset_util\n",
        "from collections import namedtuple, OrderedDict\n",
        "\n",
        "\n",
        "def class_text_to_int(row_label):\n",
        "    if row_label == 'with_mask':\n",
        "        return 1\n",
        "    elif row_label == 'without_mask':\n",
        "        return 2\n",
        "    elif row_label == 'mask_weared_incorrect':\n",
        "        return 3\n",
        "    else:\n",
        "        None\n",
        "\n",
        "\n",
        "def split(df, group):\n",
        "    data = namedtuple('data', ['filename', 'object'])\n",
        "    gb = df.groupby(group)\n",
        "    return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)]\n",
        "\n",
        "\n",
        "def create_tf_example(group, path):\n",
        "    with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:\n",
        "        encoded_jpg = fid.read()\n",
        "    encoded_jpg_io = io.BytesIO(encoded_jpg)\n",
        "    image = Image.open(encoded_jpg_io)\n",
        "    width, height = image.size\n",
        "\n",
        "    filename = group.filename.encode('utf8')\n",
        "    image_format = b'jpg'\n",
        "    xmins = []\n",
        "    xmaxs = []\n",
        "    ymins = []\n",
        "    ymaxs = []\n",
        "    classes_text = [] \n",
        "    classes = []\n",
        "\n",
        "    for index, row in group.object.iterrows():\n",
        "        xmins.append(row['xmin'] / width)\n",
        "        xmaxs.append(row['xmax'] / width)\n",
        "        ymins.append(row['ymin'] / height)\n",
        "        ymaxs.append(row['ymax'] / height)\n",
        "        classes_text.append(row['class'].encode('utf8'))\n",
        "        classes.append(class_text_to_int(row['class']))\n",
        "\n",
        "    tf_example = tf.train.Example(features=tf.train.Features(feature={\n",
        "        'image/height': dataset_util.int64_feature(height),\n",
        "        'image/width': dataset_util.int64_feature(width),\n",
        "        'image/filename': dataset_util.bytes_feature(filename),\n",
        "        'image/source_id': dataset_util.bytes_feature(filename),\n",
        "        'image/encoded': dataset_util.bytes_feature(encoded_jpg),\n",
        "        'image/format': dataset_util.bytes_feature(image_format),\n",
        "        'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),\n",
        "        'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),\n",
        "        'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),\n",
        "        'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),\n",
        "        'image/object/class/text': dataset_util.bytes_list_feature(classes_text),\n",
        "        'image/object/class/label': dataset_util.int64_list_feature(classes),\n",
        "    }))\n",
        "    return tf_example\n",
        "\n",
        "\n",
        "def main():\n",
        "  for name in ['test', 'train']:\n",
        "    writer = tf.python_io.TFRecordWriter('/content/dataset/' + name + '.record')\n",
        "    path = os.path.join('/content/dataset/'+ name)\n",
        "    examples = pd.read_csv('/content/dataset/' + name + '_labels.csv')\n",
        "    grouped = split(examples, 'filename')\n",
        "    for group in grouped:\n",
        "      tf_example = create_tf_example(group, path)\n",
        "      writer.write(tf_example.SerializeToString())\n",
        "    writer.close()\n",
        "\n",
        "main()"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mg1C8UwStK7i",
        "outputId": "e96a1fbe-60e7-43ed-da63-95a5c72e660f"
      },
      "source": [
        "# Edit Pipeline config to load in our new tfrecord that we just created.\n",
        "import tensorflow as tf\n",
        "from google.protobuf import text_format\n",
        "from object_detection.protos import pipeline_pb2\n",
        "\n",
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "print(gpu_info)\n",
        "\n",
        "pipeline = pipeline_pb2.TrainEvalPipelineConfig()                                                                                                                                                                                                          \n",
        "config_path = '/content/models/research/object_detection/samples/configs/ssdlite_mobiledet_edgetpu_320x320_coco_sync_4x4.config'\n",
        "with tf.gfile.GFile(config_path, \"r\") as f:                                                                                                                                                                                                                     \n",
        "    proto_str = f.read()                                                                                                                                                                                                                                          \n",
        "    text_format.Merge(proto_str, pipeline)\n",
        "\n",
        "pipeline.train_input_reader.tf_record_input_reader.input_path[:] = ['/content/dataset/train.record'] \n",
        "pipeline.train_input_reader.label_map_path = '/content/dataset/mask_label_map.pbtxt'\n",
        "pipeline.eval_input_reader[0].tf_record_input_reader.input_path[:] = ['/content/dataset/test.record'] \n",
        "pipeline.eval_input_reader[0].label_map_path = '/content/dataset/mask_label_map.pbtxt'\n",
        "pipeline.train_config.fine_tune_checkpoint = '/content/pretrained_model/ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/uint8/model.ckpt'\n",
        "pipeline.model.ssd.num_classes = 3\n",
        "pipeline.model.ssd.image_resizer.fixed_shape_resizer.height = 320\n",
        "pipeline.model.ssd.image_resizer.fixed_shape_resizer.width = 320\n",
        "\n",
        "pipeline.train_config.batch_size = 64\n",
        "pipeline.train_config.num_steps = 2000\n",
        "pipeline.eval_config.num_examples = 100\n",
        "# Quantization Aware training\n",
        "pipeline.graph_rewriter.quantization.delay = 0\n",
        "pipeline.graph_rewriter.quantization.weight_bits = 8\n",
        "pipeline.graph_rewriter.quantization.activation_bits = 8\n",
        "# Shuffle testing data\n",
        "pipeline.eval_input_reader[0].shuffle = True\n",
        "\n",
        "config_text = text_format.MessageToString(pipeline)                                                                                                                                                                                                        \n",
        "with tf.gfile.Open(config_path, \"wb\") as f:                                                                                                                                                                                                                       \n",
        "    f.write(config_text)\n",
        "\n",
        "# This is out config after modifying.\n",
        "!cat /content/models/research/object_detection/samples/configs/ssdlite_mobiledet_edgetpu_320x320_coco_sync_4x4.config"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mon Nov 23 14:23:41 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 455.38       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   69C    P8    31W / 149W |      0MiB / 11441MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n",
            "model {\n",
            "  ssd {\n",
            "    num_classes: 3\n",
            "    image_resizer {\n",
            "      fixed_shape_resizer {\n",
            "        height: 320\n",
            "        width: 320\n",
            "      }\n",
            "    }\n",
            "    feature_extractor {\n",
            "      type: \"ssd_mobiledet_edgetpu\"\n",
            "      depth_multiplier: 1.0\n",
            "      min_depth: 16\n",
            "      conv_hyperparams {\n",
            "        regularizer {\n",
            "          l2_regularizer {\n",
            "            weight: 4e-05\n",
            "          }\n",
            "        }\n",
            "        initializer {\n",
            "          truncated_normal_initializer {\n",
            "            mean: 0.0\n",
            "            stddev: 0.03\n",
            "          }\n",
            "        }\n",
            "        activation: RELU_6\n",
            "        batch_norm {\n",
            "          decay: 0.97\n",
            "          center: true\n",
            "          scale: true\n",
            "          epsilon: 0.001\n",
            "          train: true\n",
            "        }\n",
            "      }\n",
            "      use_depthwise: true\n",
            "      override_base_feature_extractor_hyperparams: false\n",
            "    }\n",
            "    box_coder {\n",
            "      faster_rcnn_box_coder {\n",
            "        y_scale: 10.0\n",
            "        x_scale: 10.0\n",
            "        height_scale: 5.0\n",
            "        width_scale: 5.0\n",
            "      }\n",
            "    }\n",
            "    matcher {\n",
            "      argmax_matcher {\n",
            "        matched_threshold: 0.5\n",
            "        unmatched_threshold: 0.5\n",
            "        ignore_thresholds: false\n",
            "        negatives_lower_than_unmatched: true\n",
            "        force_match_for_each_row: true\n",
            "        use_matmul_gather: true\n",
            "      }\n",
            "    }\n",
            "    similarity_calculator {\n",
            "      iou_similarity {\n",
            "      }\n",
            "    }\n",
            "    box_predictor {\n",
            "      convolutional_box_predictor {\n",
            "        conv_hyperparams {\n",
            "          regularizer {\n",
            "            l2_regularizer {\n",
            "              weight: 4e-05\n",
            "            }\n",
            "          }\n",
            "          initializer {\n",
            "            random_normal_initializer {\n",
            "              mean: 0.0\n",
            "              stddev: 0.03\n",
            "            }\n",
            "          }\n",
            "          activation: RELU_6\n",
            "          batch_norm {\n",
            "            decay: 0.97\n",
            "            center: true\n",
            "            scale: true\n",
            "            epsilon: 0.001\n",
            "            train: true\n",
            "          }\n",
            "        }\n",
            "        min_depth: 0\n",
            "        max_depth: 0\n",
            "        num_layers_before_predictor: 0\n",
            "        use_dropout: false\n",
            "        dropout_keep_probability: 0.8\n",
            "        kernel_size: 3\n",
            "        box_code_size: 4\n",
            "        apply_sigmoid_to_scores: false\n",
            "        class_prediction_bias_init: -4.6\n",
            "        use_depthwise: true\n",
            "      }\n",
            "    }\n",
            "    anchor_generator {\n",
            "      ssd_anchor_generator {\n",
            "        num_layers: 6\n",
            "        min_scale: 0.2\n",
            "        max_scale: 0.95\n",
            "        aspect_ratios: 1.0\n",
            "        aspect_ratios: 2.0\n",
            "        aspect_ratios: 0.5\n",
            "        aspect_ratios: 3.0\n",
            "        aspect_ratios: 0.3333\n",
            "      }\n",
            "    }\n",
            "    post_processing {\n",
            "      batch_non_max_suppression {\n",
            "        score_threshold: 1e-08\n",
            "        iou_threshold: 0.6\n",
            "        max_detections_per_class: 100\n",
            "        max_total_detections: 100\n",
            "        use_static_shapes: true\n",
            "      }\n",
            "      score_converter: SIGMOID\n",
            "    }\n",
            "    normalize_loss_by_num_matches: true\n",
            "    loss {\n",
            "      localization_loss {\n",
            "        weighted_smooth_l1 {\n",
            "          delta: 1.0\n",
            "        }\n",
            "      }\n",
            "      classification_loss {\n",
            "        weighted_sigmoid_focal {\n",
            "          gamma: 2.0\n",
            "          alpha: 0.75\n",
            "        }\n",
            "      }\n",
            "      classification_weight: 1.0\n",
            "      localization_weight: 1.0\n",
            "    }\n",
            "    encode_background_as_zeros: true\n",
            "    normalize_loc_loss_by_codesize: true\n",
            "    inplace_batchnorm_update: true\n",
            "    freeze_batchnorm: false\n",
            "  }\n",
            "}\n",
            "train_config {\n",
            "  batch_size: 64\n",
            "  data_augmentation_options {\n",
            "    random_horizontal_flip {\n",
            "    }\n",
            "  }\n",
            "  data_augmentation_options {\n",
            "    ssd_random_crop {\n",
            "    }\n",
            "  }\n",
            "  sync_replicas: true\n",
            "  optimizer {\n",
            "    momentum_optimizer {\n",
            "      learning_rate {\n",
            "        cosine_decay_learning_rate {\n",
            "          learning_rate_base: 0.8\n",
            "          total_steps: 400000\n",
            "          warmup_learning_rate: 0.13333\n",
            "          warmup_steps: 2000\n",
            "        }\n",
            "      }\n",
            "      momentum_optimizer_value: 0.9\n",
            "    }\n",
            "    use_moving_average: false\n",
            "  }\n",
            "  fine_tune_checkpoint: \"/content/pretrained_model/ssdlite_mobiledet_edgetpu_320x320_coco_2020_05_19/uint8/model.ckpt\"\n",
            "  num_steps: 2000\n",
            "  startup_delay_steps: 0.0\n",
            "  replicas_to_aggregate: 32\n",
            "  max_number_of_boxes: 100\n",
            "  unpad_groundtruth_tensors: false\n",
            "}\n",
            "train_input_reader {\n",
            "  label_map_path: \"/content/dataset/mask_label_map.pbtxt\"\n",
            "  tf_record_input_reader {\n",
            "    input_path: \"/content/dataset/train.record\"\n",
            "  }\n",
            "}\n",
            "eval_config {\n",
            "  num_examples: 100\n",
            "  metrics_set: \"coco_detection_metrics\"\n",
            "  use_moving_averages: false\n",
            "}\n",
            "eval_input_reader {\n",
            "  label_map_path: \"/content/dataset/mask_label_map.pbtxt\"\n",
            "  shuffle: true\n",
            "  num_epochs: 1\n",
            "  tf_record_input_reader {\n",
            "    input_path: \"/content/dataset/test.record\"\n",
            "  }\n",
            "}\n",
            "graph_rewriter {\n",
            "  quantization {\n",
            "    delay: 0\n",
            "    weight_bits: 8\n",
            "    activation_bits: 8\n",
            "  }\n",
            "}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "whIM6gZz2DEn",
        "outputId": "9fb1ca17-6aef-4525-805c-e3f16f6fbb79"
      },
      "source": [
        "# Before we start training, let's start tensorboard so we can track the progress.\n",
        "# More info on tensorflow can be found here: https://www.tensorflow.org/tutorials\n",
        "%cd /content\n",
        "!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
        "!unzip -o ngrok-stable-linux-amd64.zip"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "--2020-11-23 14:23:47--  https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
            "Resolving bin.equinox.io (bin.equinox.io)... 52.20.238.4, 107.21.11.91, 54.175.121.11, ...\n",
            "Connecting to bin.equinox.io (bin.equinox.io)|52.20.238.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 13773305 (13M) [application/octet-stream]\n",
            "Saving to: ‘ngrok-stable-linux-amd64.zip.1’\n",
            "\n",
            "ngrok-stable-linux- 100%[===================>]  13.13M  14.3MB/s    in 0.9s    \n",
            "\n",
            "2020-11-23 14:23:49 (14.3 MB/s) - ‘ngrok-stable-linux-amd64.zip.1’ saved [13773305/13773305]\n",
            "\n",
            "Archive:  ngrok-stable-linux-amd64.zip\n",
            "  inflating: ngrok                   \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XvJ9X0qBfSIX",
        "outputId": "68f698c0-b642-4d47-a4c5-d7eda73dacf0"
      },
      "source": [
        "%cd /content\n",
        "# Starts tensorboard.\n",
        "get_ipython().system_raw(\n",
        "    'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'\n",
        "    .format('/content/train')\n",
        ")\n",
        "get_ipython().system_raw('./ngrok http 6006 &')\n",
        "\n",
        "print('Click on link below to track progress:')\n",
        "import time\n",
        "time.sleep(1)\n",
        "!curl -s http://localhost:4040/api/tunnels | python3 -c \\\n",
        "    \"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\""
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "Click on link below to track progress:\n",
            "https://85a0a74d97a0.ngrok.io\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Idw8OgZQtLKG",
        "outputId": "d172b5da-7782-4933-ee33-0457ea26e122"
      },
      "source": [
        "# Change into the research directory and begin training.\n",
        "%cd /content/models/research/\n",
        "!python3 object_detection/model_main.py \\\n",
        "    --logtostderr=true \\\n",
        "    --model_dir=/content/train \\\n",
        "    --pipeline_config_path=/content/models/research/object_detection/samples/configs/ssdlite_mobiledet_edgetpu_320x320_coco_sync_4x4.config"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/models/research\n",
            "WARNING:tensorflow:Forced number of epochs for all eval validations to be 1.\n",
            "W1123 14:25:24.151241 140282653464448 model_lib.py:793] Forced number of epochs for all eval validations to be 1.\n",
            "INFO:tensorflow:Maybe overwriting train_steps: None\n",
            "I1123 14:25:24.151627 140282653464448 config_util.py:552] Maybe overwriting train_steps: None\n",
            "INFO:tensorflow:Maybe overwriting use_bfloat16: False\n",
            "I1123 14:25:24.151814 140282653464448 config_util.py:552] Maybe overwriting use_bfloat16: False\n",
            "INFO:tensorflow:Maybe overwriting sample_1_of_n_eval_examples: 1\n",
            "I1123 14:25:24.151953 140282653464448 config_util.py:552] Maybe overwriting sample_1_of_n_eval_examples: 1\n",
            "INFO:tensorflow:Maybe overwriting eval_num_epochs: 1\n",
            "I1123 14:25:24.152095 140282653464448 config_util.py:552] Maybe overwriting eval_num_epochs: 1\n",
            "WARNING:tensorflow:Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.\n",
            "W1123 14:25:24.152256 140282653464448 model_lib.py:809] Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.\n",
            "INFO:tensorflow:create_estimator_and_inputs: use_tpu False, export_to_tpu None\n",
            "I1123 14:25:24.152413 140282653464448 model_lib.py:846] create_estimator_and_inputs: use_tpu False, export_to_tpu None\n",
            "INFO:tensorflow:Using config: {'_model_dir': '/content/train', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f95a55bb8d0>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
            "I1123 14:25:24.152978 140282653464448 estimator.py:212] Using config: {'_model_dir': '/content/train', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f95a55bb8d0>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
            "WARNING:tensorflow:Estimator's model_fn (<function create_model_fn.<locals>.model_fn at 0x7f95a55d0268>) includes params argument, but params are not passed to Estimator.\n",
            "W1123 14:25:24.153256 140282653464448 model_fn.py:630] Estimator's model_fn (<function create_model_fn.<locals>.model_fn at 0x7f95a55d0268>) includes params argument, but params are not passed to Estimator.\n",
            "INFO:tensorflow:Not using Distribute Coordinator.\n",
            "I1123 14:25:24.154306 140282653464448 estimator_training.py:186] Not using Distribute Coordinator.\n",
            "INFO:tensorflow:Running training and evaluation locally (non-distributed).\n",
            "I1123 14:25:24.154566 140282653464448 training.py:612] Running training and evaluation locally (non-distributed).\n",
            "INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps None or save_checkpoints_secs 600.\n",
            "I1123 14:25:24.154875 140282653464448 training.py:700] Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps None or save_checkpoints_secs 600.\n",
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
            "W1123 14:25:24.161253 140282653464448 deprecation.py:323] From /tensorflow-1.15.2/python3.6/tensorflow_core/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
            "INFO:tensorflow:Reading unweighted datasets: ['/content/dataset/train.record']\n",
            "I1123 14:25:24.199969 140282653464448 dataset_builder.py:148] Reading unweighted datasets: ['/content/dataset/train.record']\n",
            "INFO:tensorflow:Reading record datasets for input file: ['/content/dataset/train.record']\n",
            "I1123 14:25:24.201202 140282653464448 dataset_builder.py:77] Reading record datasets for input file: ['/content/dataset/train.record']\n",
            "INFO:tensorflow:Number of filenames to read: 1\n",
            "I1123 14:25:24.201378 140282653464448 dataset_builder.py:78] Number of filenames to read: 1\n",
            "WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.\n",
            "W1123 14:25:24.201524 140282653464448 dataset_builder.py:86] 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:103: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_determinstic`.\n",
            "W1123 14:25:24.207942 140282653464448 deprecation.py:323] From /content/models/research/object_detection/builders/dataset_builder.py:103: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_determinstic`.\n",
            "WARNING:tensorflow:From /content/models/research/object_detection/builders/dataset_builder.py:222: 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",
            "W1123 14:25:24.233135 140282653464448 deprecation.py:323] From /content/models/research/object_detection/builders/dataset_builder.py:222: 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:Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f95a55cb358>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
            "W1123 14:25:24.272799 140282653464448 ag_logging.py:146] Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f95a55cb358>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
            "WARNING:tensorflow:Entity <function train_input.<locals>.transform_and_pad_input_data_fn at 0x7f95cd8706a8> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
            "W1123 14:25:24.519000 140282653464448 ag_logging.py:146] Entity <function train_input.<locals>.transform_and_pad_input_data_fn at 0x7f95cd8706a8> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
            "WARNING:tensorflow:From /content/models/research/object_detection/inputs.py:110: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "W1123 14:25:24.520536 140282653464448 deprecation.py:323] From /content/models/research/object_detection/inputs.py:110: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /content/models/research/object_detection/inputs.py:96: 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",
            "W1123 14:25:24.530125 140282653464448 deprecation.py:323] From /content/models/research/object_detection/inputs.py:96: 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 /content/models/research/object_detection/core/preprocessor.py:199: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.\n",
            "W1123 14:25:24.659917 140282653464448 deprecation.py:323] From /content/models/research/object_detection/core/preprocessor.py:199: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.\n",
            "WARNING:tensorflow:From /content/models/research/object_detection/inputs.py:283: 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",
            "W1123 14:25:25.621193 140282653464448 deprecation.py:323] From /content/models/research/object_detection/inputs.py:283: 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",
            "INFO:tensorflow:Calling model_fn.\n",
            "I1123 14:25:26.257996 140282653464448 estimator.py:1148] Calling model_fn.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tf_slim/layers/layers.py:1089: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "W1123 14:25:26.278293 140282653464448 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tf_slim/layers/layers.py:1089: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I1123 14:25:29.559906 140282653464448 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I1123 14:25:29.673532 140282653464448 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I1123 14:25:29.786674 140282653464448 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I1123 14:25:29.902368 140282653464448 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I1123 14:25:30.018379 140282653464448 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I1123 14:25:30.140372 140282653464448 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "I1123 14:26:10.932050 140282653464448 estimator.py:1150] Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "I1123 14:26:10.933978 140282653464448 basic_session_run_hooks.py:541] Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "I1123 14:26:18.669316 140282653464448 monitored_session.py:240] Graph was finalized.\n",
            "2020-11-23 14:26:18.674968: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz\n",
            "2020-11-23 14:26:18.675214: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x264f1800 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-11-23 14:26:18.675250: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-11-23 14:26:18.677386: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-11-23 14:26:18.744466: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-23 14:26:18.745424: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x264f1640 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-11-23 14:26:18.745457: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla K80, Compute Capability 3.7\n",
            "2020-11-23 14:26:18.745668: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-23 14:26:18.746447: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
            "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
            "pciBusID: 0000:00:04.0\n",
            "2020-11-23 14:26:18.746944: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-23 14:26:18.748764: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-11-23 14:26:18.750575: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-11-23 14:26:18.751157: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-11-23 14:26:18.753089: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-11-23 14:26:18.754076: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-11-23 14:26:18.757737: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-11-23 14:26:18.757873: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-23 14:26:18.758707: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-23 14:26:18.759431: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
            "2020-11-23 14:26:18.759512: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-23 14:26:18.761252: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-11-23 14:26:18.761288: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186]      0 \n",
            "2020-11-23 14:26:18.761339: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0:   N \n",
            "2020-11-23 14:26:18.761643: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-23 14:26:18.762424: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-23 14:26:18.763245: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-11-23 14:26:18.763298: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10805 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "I1123 14:26:25.355456 140282653464448 session_manager.py:500] Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "I1123 14:26:26.178385 140282653464448 session_manager.py:502] Done running local_init_op.\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /content/train/model.ckpt.\n",
            "I1123 14:26:48.985391 140282653464448 basic_session_run_hooks.py:606] Saving checkpoints for 0 into /content/train/model.ckpt.\n",
            "2020-11-23 14:27:26.605894: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-11-23 14:27:39.990499: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:27:41.303979: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:27:43.334542: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "INFO:tensorflow:loss = 46.742203, step = 0\n",
            "I1123 14:27:56.710690 140282653464448 basic_session_run_hooks.py:262] loss = 46.742203, step = 0\n",
            "2020-11-23 14:29:02.370394: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:29:39.075103: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:30:40.737521: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:31:30.995831: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:32:32.847781: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:32:43.141741: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:33:01.745604: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:33:05.193095: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:33:16.019098: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:33:30.691021: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "INFO:tensorflow:global_step/sec: 0.255716\n",
            "I1123 14:34:27.768775 140282653464448 basic_session_run_hooks.py:692] global_step/sec: 0.255716\n",
            "INFO:tensorflow:loss = 3.159631, step = 100 (391.060 sec)\n",
            "I1123 14:34:27.770411 140282653464448 basic_session_run_hooks.py:260] loss = 3.159631, step = 100 (391.060 sec)\n",
            "2020-11-23 14:34:39.040510: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "INFO:tensorflow:Saving checkpoints for 142 into /content/train/model.ckpt.\n",
            "I1123 14:36:55.569632 140282653464448 basic_session_run_hooks.py:606] Saving checkpoints for 142 into /content/train/model.ckpt.\n",
            "INFO:tensorflow:Reading unweighted datasets: ['/content/dataset/test.record']\n",
            "I1123 14:36:58.684206 140282653464448 dataset_builder.py:148] Reading unweighted datasets: ['/content/dataset/test.record']\n",
            "INFO:tensorflow:Reading record datasets for input file: ['/content/dataset/test.record']\n",
            "I1123 14:36:58.686257 140282653464448 dataset_builder.py:77] Reading record datasets for input file: ['/content/dataset/test.record']\n",
            "INFO:tensorflow:Number of filenames to read: 1\n",
            "I1123 14:36:58.686462 140282653464448 dataset_builder.py:78] Number of filenames to read: 1\n",
            "WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.\n",
            "W1123 14:36:58.686599 140282653464448 dataset_builder.py:86] num_readers has been reduced to 1 to match input file shards.\n",
            "WARNING:tensorflow:Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f95904f1c18>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
            "W1123 14:36:58.743143 140282653464448 ag_logging.py:146] Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f95904f1c18>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
            "WARNING:tensorflow:Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f95992f4f28> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
            "W1123 14:36:58.976852 140282653464448 ag_logging.py:146] Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f95992f4f28> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "I1123 14:36:59.683749 140282653464448 estimator.py:1148] Calling model_fn.\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I1123 14:37:02.501390 140282653464448 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I1123 14:37:02.599099 140282653464448 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I1123 14:37:02.700515 140282653464448 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I1123 14:37:02.804908 140282653464448 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I1123 14:37:02.908521 140282653464448 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I1123 14:37:03.330261 140282653464448 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/Conv/add_fold\n",
            "I1123 14:37:06.525639 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/Conv/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/TuckerConv/Conv/add_fold\n",
            "I1123 14:37:06.526032 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/TuckerConv/Conv/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/TuckerConv/Conv_1/add_fold\n",
            "I1123 14:37:06.526289 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/TuckerConv/Conv_1/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/FusedConv/Conv/add_fold\n",
            "I1123 14:37:06.526672 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/FusedConv/Conv/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/FusedConv_1/Conv/add_fold\n",
            "I1123 14:37:06.527013 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/FusedConv_1/Conv/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/FusedConv_2/Conv/add_fold\n",
            "I1123 14:37:06.527403 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/FusedConv_2/Conv/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/FusedConv_3/Conv/add_fold\n",
            "I1123 14:37:06.527724 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/FusedConv_3/Conv/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/FusedConv_4/Conv/add_fold\n",
            "I1123 14:37:06.528105 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/FusedConv_4/Conv/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/FusedConv_5/Conv/add_fold\n",
            "I1123 14:37:06.528437 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/FusedConv_5/Conv/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/FusedConv_6/Conv/add_fold\n",
            "I1123 14:37:06.528777 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/FusedConv_6/Conv/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/FusedConv_7/Conv/add_fold\n",
            "I1123 14:37:06.529170 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/FusedConv_7/Conv/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN/Conv/add_fold\n",
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            "I1123 14:37:06.531402 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_2/Conv/add_fold\n",
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            "I1123 14:37:06.531630 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_2/SeparableConv2d/add_fold\n",
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            "I1123 14:37:06.531996 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_3/Conv/add_fold\n",
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            "I1123 14:37:06.532269 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_3/SeparableConv2d/add_fold\n",
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            "I1123 14:37:06.532655 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_4/Conv/add_fold\n",
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            "I1123 14:37:06.532944 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_4/SeparableConv2d/add_fold\n",
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            "I1123 14:37:06.533251 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_5/Conv/add_fold\n",
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            "I1123 14:37:06.533580 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_5/SeparableConv2d/add_fold\n",
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            "I1123 14:37:06.533983 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_6/Conv/add_fold\n",
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            "I1123 14:37:06.534236 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_6/SeparableConv2d/add_fold\n",
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            "I1123 14:37:06.534611 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_7/Conv/add_fold\n",
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            "I1123 14:37:06.534884 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_7/SeparableConv2d/add_fold\n",
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            "I1123 14:37:06.535216 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_8/Conv/add_fold\n",
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            "I1123 14:37:06.535469 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_8/SeparableConv2d/add_fold\n",
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            "I1123 14:37:06.535809 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_9/Conv/add_fold\n",
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            "I1123 14:37:06.536142 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_9/SeparableConv2d/add_fold\n",
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            "I1123 14:37:06.536680 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_10/Conv/add_fold\n",
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            "I1123 14:37:06.537147 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/IBN_10/SeparableConv2d/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_1_Conv2d_2_1x1_256/add_fold\n",
            "I1123 14:37:06.537624 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_1_Conv2d_2_1x1_256/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_2_Conv2d_2_3x3_s2_512_depthwise/add_fold\n",
            "I1123 14:37:06.537916 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_2_Conv2d_2_3x3_s2_512_depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_2_Conv2d_2_3x3_s2_512/add_fold\n",
            "I1123 14:37:06.538176 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_2_Conv2d_2_3x3_s2_512/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_1_Conv2d_3_1x1_128/add_fold\n",
            "I1123 14:37:06.538451 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_1_Conv2d_3_1x1_128/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_2_Conv2d_3_3x3_s2_256_depthwise/add_fold\n",
            "I1123 14:37:06.538722 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_2_Conv2d_3_3x3_s2_256_depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_2_Conv2d_3_3x3_s2_256/add_fold\n",
            "I1123 14:37:06.538987 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_2_Conv2d_3_3x3_s2_256/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_1_Conv2d_4_1x1_128/add_fold\n",
            "I1123 14:37:06.539240 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_1_Conv2d_4_1x1_128/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_2_Conv2d_4_3x3_s2_256_depthwise/add_fold\n",
            "I1123 14:37:06.539504 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_2_Conv2d_4_3x3_s2_256_depthwise/add_fold\n",
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            "I1123 14:37:06.539736 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_2_Conv2d_4_3x3_s2_256/add_fold\n",
            "INFO:tensorflow:Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_1_Conv2d_5_1x1_64/add_fold\n",
            "I1123 14:37:06.539994 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_1_Conv2d_5_1x1_64/add_fold\n",
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            "I1123 14:37:06.540243 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_2_Conv2d_5_3x3_s2_128_depthwise/add_fold\n",
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            "I1123 14:37:06.540496 140282653464448 quantize.py:299] Skipping quant after FeatureExtractor/MobileDetEdgeTPU/C5_2_Conv2d_5_3x3_s2_128/add_fold\n",
            "INFO:tensorflow:Skipping quant after BoxPredictor_0/BoxEncodingPredictor_depthwise/add_fold\n",
            "I1123 14:37:06.540746 140282653464448 quantize.py:299] Skipping quant after BoxPredictor_0/BoxEncodingPredictor_depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after BoxPredictor_0/ClassPredictor_depthwise/add_fold\n",
            "I1123 14:37:06.541001 140282653464448 quantize.py:299] Skipping quant after BoxPredictor_0/ClassPredictor_depthwise/add_fold\n",
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            "INFO:tensorflow:Skipping quant after BoxPredictor_1/ClassPredictor_depthwise/add_fold\n",
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            "I1123 14:37:06.541858 140282653464448 quantize.py:299] Skipping quant after BoxPredictor_2/BoxEncodingPredictor_depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after BoxPredictor_2/ClassPredictor_depthwise/add_fold\n",
            "I1123 14:37:06.542092 140282653464448 quantize.py:299] Skipping quant after BoxPredictor_2/ClassPredictor_depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after BoxPredictor_3/BoxEncodingPredictor_depthwise/add_fold\n",
            "I1123 14:37:06.542426 140282653464448 quantize.py:299] Skipping quant after BoxPredictor_3/BoxEncodingPredictor_depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after BoxPredictor_3/ClassPredictor_depthwise/add_fold\n",
            "I1123 14:37:06.542689 140282653464448 quantize.py:299] Skipping quant after BoxPredictor_3/ClassPredictor_depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after BoxPredictor_4/BoxEncodingPredictor_depthwise/add_fold\n",
            "I1123 14:37:06.542989 140282653464448 quantize.py:299] Skipping quant after BoxPredictor_4/BoxEncodingPredictor_depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after BoxPredictor_4/ClassPredictor_depthwise/add_fold\n",
            "I1123 14:37:06.543237 140282653464448 quantize.py:299] Skipping quant after BoxPredictor_4/ClassPredictor_depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after BoxPredictor_5/BoxEncodingPredictor_depthwise/add_fold\n",
            "I1123 14:37:06.543503 140282653464448 quantize.py:299] Skipping quant after BoxPredictor_5/BoxEncodingPredictor_depthwise/add_fold\n",
            "INFO:tensorflow:Skipping quant after BoxPredictor_5/ClassPredictor_depthwise/add_fold\n",
            "I1123 14:37:06.543742 140282653464448 quantize.py:299] Skipping quant after BoxPredictor_5/ClassPredictor_depthwise/add_fold\n",
            "WARNING:tensorflow:From /content/models/research/object_detection/eval_util.py:927: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W1123 14:37:06.579814 140282653464448 deprecation.py:323] From /content/models/research/object_detection/eval_util.py:927: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "WARNING:tensorflow:From /content/models/research/object_detection/utils/visualization_utils.py:618: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "tf.py_func is deprecated in TF V2. Instead, there are two\n",
            "    options available in V2.\n",
            "    - tf.py_function takes a python function which manipulates tf eager\n",
            "    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n",
            "    an ndarray (just call tensor.numpy()) but having access to eager tensors\n",
            "    means `tf.py_function`s can use accelerators such as GPUs as well as\n",
            "    being differentiable using a gradient tape.\n",
            "    - tf.numpy_function maintains the semantics of the deprecated tf.py_func\n",
            "    (it is not differentiable, and manipulates numpy arrays). It drops the\n",
            "    stateful argument making all functions stateful.\n",
            "    \n",
            "W1123 14:37:06.858312 140282653464448 deprecation.py:323] From /content/models/research/object_detection/utils/visualization_utils.py:618: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "tf.py_func is deprecated in TF V2. Instead, there are two\n",
            "    options available in V2.\n",
            "    - tf.py_function takes a python function which manipulates tf eager\n",
            "    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n",
            "    an ndarray (just call tensor.numpy()) but having access to eager tensors\n",
            "    means `tf.py_function`s can use accelerators such as GPUs as well as\n",
            "    being differentiable using a gradient tape.\n",
            "    - tf.numpy_function maintains the semantics of the deprecated tf.py_func\n",
            "    (it is not differentiable, and manipulates numpy arrays). It drops the\n",
            "    stateful argument making all functions stateful.\n",
            "    \n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "I1123 14:37:07.985471 140282653464448 estimator.py:1150] Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2020-11-23T14:37:08Z\n",
            "I1123 14:37:08.007391 140282653464448 evaluation.py:255] Starting evaluation at 2020-11-23T14:37:08Z\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "I1123 14:37:09.332260 140282653464448 monitored_session.py:240] Graph was finalized.\n",
            "2020-11-23 14:37:09.333582: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-23 14:37:09.333944: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
            "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
            "pciBusID: 0000:00:04.0\n",
            "2020-11-23 14:37:09.334087: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-23 14:37:09.334148: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-11-23 14:37:09.334202: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-11-23 14:37:09.334252: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-11-23 14:37:09.334317: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-11-23 14:37:09.334389: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-11-23 14:37:09.334442: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-11-23 14:37:09.334551: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-23 14:37:09.334914: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-23 14:37:09.335214: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
            "2020-11-23 14:37:09.335305: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-11-23 14:37:09.335329: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186]      0 \n",
            "2020-11-23 14:37:09.335376: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0:   N \n",
            "2020-11-23 14:37:09.335508: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-23 14:37:09.335873: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-23 14:37:09.336204: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10805 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
            "INFO:tensorflow:Restoring parameters from /content/train/model.ckpt-142\n",
            "I1123 14:37:09.337465 140282653464448 saver.py:1284] Restoring parameters from /content/train/model.ckpt-142\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "I1123 14:37:11.416861 140282653464448 session_manager.py:500] Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "I1123 14:37:11.643656 140282653464448 session_manager.py:502] Done running local_init_op.\n",
            "INFO:tensorflow:Performing evaluation on 101 images.\n",
            "I1123 14:37:24.797655 140280356390656 coco_evaluation.py:293] Performing evaluation on 101 images.\n",
            "creating index...\n",
            "index created!\n",
            "INFO:tensorflow:Loading and preparing annotation results...\n",
            "I1123 14:37:24.799568 140280356390656 coco_tools.py:116] Loading and preparing annotation results...\n",
            "INFO:tensorflow:DONE (t=0.01s)\n",
            "I1123 14:37:24.809677 140280356390656 coco_tools.py:138] DONE (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.53s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.12s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.022\n",
            "INFO:tensorflow:Finished evaluation at 2020-11-23-14:37:26\n",
            "I1123 14:37:26.657053 140282653464448 evaluation.py:275] Finished evaluation at 2020-11-23-14:37:26\n",
            "INFO:tensorflow:Saving dict for global step 142: DetectionBoxes_Precision/mAP = 1.1312303e-06, DetectionBoxes_Precision/mAP (large) = 1.6908398e-05, DetectionBoxes_Precision/mAP (medium) = 0.0, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/mAP@.50IOU = 5.9326444e-06, DetectionBoxes_Precision/mAP@.75IOU = 0.0, DetectionBoxes_Recall/AR@1 = 0.0, DetectionBoxes_Recall/AR@10 = 0.0, DetectionBoxes_Recall/AR@100 = 0.0009852217, DetectionBoxes_Recall/AR@100 (large) = 0.022222223, DetectionBoxes_Recall/AR@100 (medium) = 0.0, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 124.19263, Loss/localization_loss = 3.6801658, Loss/regularization_loss = 1.0385877, Loss/total_loss = 128.91147, global_step = 142, learning_rate = 0.18066357, loss = 128.91147\n",
            "I1123 14:37:26.657446 140282653464448 estimator.py:2049] Saving dict for global step 142: DetectionBoxes_Precision/mAP = 1.1312303e-06, DetectionBoxes_Precision/mAP (large) = 1.6908398e-05, DetectionBoxes_Precision/mAP (medium) = 0.0, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/mAP@.50IOU = 5.9326444e-06, DetectionBoxes_Precision/mAP@.75IOU = 0.0, DetectionBoxes_Recall/AR@1 = 0.0, DetectionBoxes_Recall/AR@10 = 0.0, DetectionBoxes_Recall/AR@100 = 0.0009852217, DetectionBoxes_Recall/AR@100 (large) = 0.022222223, DetectionBoxes_Recall/AR@100 (medium) = 0.0, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 124.19263, Loss/localization_loss = 3.6801658, Loss/regularization_loss = 1.0385877, Loss/total_loss = 128.91147, global_step = 142, learning_rate = 0.18066357, loss = 128.91147\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 142: /content/train/model.ckpt-142\n",
            "I1123 14:37:28.124369 140282653464448 estimator.py:2109] Saving 'checkpoint_path' summary for global step 142: /content/train/model.ckpt-142\n",
            "2020-11-23 14:37:31.943031: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:38:30.831841: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:39:21.222092: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:39:21.336213: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:40:11.853679: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "INFO:tensorflow:global_step/sec: 0.253853\n",
            "I1123 14:41:01.698289 140282653464448 basic_session_run_hooks.py:692] global_step/sec: 0.253853\n",
            "INFO:tensorflow:loss = 2.4328368, step = 200 (393.929 sec)\n",
            "I1123 14:41:01.699689 140282653464448 basic_session_run_hooks.py:260] loss = 2.4328368, step = 200 (393.929 sec)\n",
            "2020-11-23 14:41:56.122471: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:42:40.264605: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:42:47.436540: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:43:52.235313: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n",
            "2020-11-23 14:43:55.519773: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: Not recognizing known sRGB profile that has been edited\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KicA9DZZmnKe",
        "outputId": "7041669e-c874-49cc-f0c4-3b744ebae1b9"
      },
      "source": [
        "# Make inference graph.\n",
        "!python3 /content/models/research/object_detection/export_inference_graph.py \\\n",
        "    --input_type=image_tensor \\\n",
        "    --pipeline_config_path=/content/models/research/object_detection/samples/configs/ssdlite_mobiledet_edgetpu_320x320_coco_sync_4x4.config \\\n",
        "    --output_directory=/content/fine_tuned_model \\\n",
        "    --trained_checkpoint_prefix=/content/train/model.ckpt-2000"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "python3: can't open file '/content/models/research/object_detection/export_inference_graph.py': [Errno 2] No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mhg-2OA-m-9n"
      },
      "source": [
        "# Do a Quick Evaluation on the graph model/\n",
        "import pathlib\n",
        "import numpy as np\n",
        "import os\n",
        "import six.moves.urllib as urllib\n",
        "import sys\n",
        "import tarfile\n",
        "import tensorflow as tf\n",
        "import zipfile\n",
        "\n",
        "from collections import defaultdict\n",
        "from io import StringIO\n",
        "from matplotlib import pyplot as plt\n",
        "from PIL import Image\n",
        "from IPython.display import display\n",
        "\n",
        "from object_detection.utils import ops as utils_ops\n",
        "from object_detection.utils import label_map_util\n",
        "from object_detection.utils import visualization_utils as vis_util\n",
        "# This is needed to display the images.\n",
        "%matplotlib inline\n",
        "\n",
        "PATH_TO_CKPT = '/content/fine_tuned_model/frozen_inference_graph.pb'\n",
        "PATH_TO_LABELS = '/content/dataset/mask_label_map.pbtxt'\n",
        "NUM_CLASSES = 3\n",
        "\n",
        "detection_graph = tf.Graph()\n",
        "with detection_graph.as_default():\n",
        "  od_graph_def = tf.GraphDef()\n",
        "  with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\n",
        "    serialized_graph = fid.read()\n",
        "    od_graph_def.ParseFromString(serialized_graph)\n",
        "    tf.import_graph_def(od_graph_def, name='')\n",
        "\n",
        "label_map = label_map_util.load_labelmap(PATH_TO_LABELS)\n",
        "categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)\n",
        "category_index = label_map_util.create_category_index(categories)\n",
        "print(category_index)\n",
        "\n",
        "\n",
        "PATH_TO_TEST_IMAGES_DIR = '/content/dataset/test'\n",
        "TEST_IMAGE_PATHS = [os.path.join(PATH_TO_TEST_IMAGES_DIR, 'maksssksksss{}.png'.format(i)) for i in range(0, 10)]\n",
        "\n",
        "def run_inference_for_single_image(image, graph):\n",
        "  with graph.as_default():\n",
        "    with tf.Session() as sess:\n",
        "      # Get handles to input and output tensors\n",
        "      ops = tf.get_default_graph().get_operations()\n",
        "      all_tensor_names = {output.name for op in ops for output in op.outputs}\n",
        "      tensor_dict = {}\n",
        "      for key in [\n",
        "          'num_detections', 'detection_boxes', 'detection_scores',\n",
        "          'detection_classes', 'detection_masks'\n",
        "      ]:\n",
        "        tensor_name = key + ':0'\n",
        "        if tensor_name in all_tensor_names:\n",
        "          tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(\n",
        "              tensor_name)\n",
        "      if 'detection_masks' in tensor_dict:\n",
        "        # The following processing is only for single image\n",
        "        detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])\n",
        "        detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])\n",
        "        # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.\n",
        "        real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)\n",
        "        detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])\n",
        "        detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])\n",
        "        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(\n",
        "            detection_masks, detection_boxes, image.shape[0], image.shape[1])\n",
        "        detection_masks_reframed = tf.cast(\n",
        "            tf.greater(detection_masks_reframed, 0.5), tf.uint8)\n",
        "        # Follow the convention by adding back the batch dimension\n",
        "        tensor_dict['detection_masks'] = tf.expand_dims(\n",
        "            detection_masks_reframed, 0)\n",
        "      image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')\n",
        "\n",
        "      # Run inference\n",
        "      output_dict = sess.run(tensor_dict,\n",
        "                             feed_dict={image_tensor: np.expand_dims(image, 0)})\n",
        "\n",
        "      # all outputs are float32 numpy arrays, so convert types as appropriate\n",
        "      # output_dict['num_detections'] = int(output_dict['num_detections'][0])\n",
        "      output_dict['detection_classes'] = output_dict[\n",
        "          'detection_classes'][0].astype(np.uint8)\n",
        "      output_dict['detection_boxes'] = output_dict['detection_boxes'][0]\n",
        "      output_dict['detection_scores'] = output_dict['detection_scores'][0]\n",
        "      if 'detection_masks' in output_dict:\n",
        "        output_dict['detection_masks'] = output_dict['detection_masks'][0]\n",
        "  return output_dict\n",
        "\n",
        "for image_path in TEST_IMAGE_PATHS:\n",
        "  print('Evaluating:', image_path)\n",
        "  image = Image.open(image_path)\n",
        "  img_width, img_height = image.size\n",
        "  # Removing the Alpha Channel\n",
        "  if image.mode == 'RGBA':\n",
        "    image.load()\n",
        "    removed_A = Image.new('RGB', image.size, (255,255,255))\n",
        "    removed_A.paste(image, mask=image.split()[3])\n",
        "    data = np.asarray(removed_A.getdata())\n",
        "  else:  \n",
        "    data = np.asarray(image.getdata())\n",
        "  image_np = data.reshape((img_height, img_width, 3)).astype(np.uint8)\n",
        "  output_dict = run_inference_for_single_image(image_np, detection_graph)\n",
        "  # Visualization of the results of a detection.\n",
        "  vis_util.visualize_boxes_and_labels_on_image_array(\n",
        "      image_np,\n",
        "      output_dict['detection_boxes'],\n",
        "      output_dict['detection_classes'],\n",
        "      output_dict['detection_scores'],\n",
        "      category_index,\n",
        "      instance_masks=output_dict.get('detection_masks'),\n",
        "      use_normalized_coordinates=True,\n",
        "      line_thickness=5)\n",
        "  plt.figure(figsize=(12, 8))\n",
        "  plt.imshow(image_np)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZ406dcWSSK_"
      },
      "source": [
        "# Export tflite ssd graph\n",
        "%cd /content/models/research\n",
        "!mkdir /content/output_model\n",
        "!python3 object_detection/export_tflite_ssd_graph.py \\\n",
        "  --pipeline_config_path=/content/models/research/object_detection/samples/configs/ssdlite_mobiledet_edgetpu_320x320_coco_sync_4x4.config \\\n",
        "  --trained_checkpoint_prefix=/content/train/model.ckpt-2000 \\\n",
        "  --output_directory=/content/output_model \\\n",
        "  --add_postprocessing_op=true\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NYcwbhdEmBN"
      },
      "source": [
        "# Now we can convert this custom trained model to a CPU tflite model :)\n",
        "!tflite_convert \\\n",
        "  --output_file=\"/content/output_model/ssdlite_mobiledet_mask.tflite\" \\\n",
        "  --graph_def_file=\"/content/output_model/tflite_graph.pb\" \\\n",
        "  --inference_type=QUANTIZED_UINT8 \\\n",
        "  --input_arrays=\"normalized_input_image_tensor\" \\\n",
        "  --output_arrays=\"TFLite_Detection_PostProcess,TFLite_Detection_PostProcess:1,TFLite_Detection_PostProcess:2,TFLite_Detection_PostProcess:3\" \\\n",
        "  --mean_values=128 \\\n",
        "  --std_dev_values=128 \\\n",
        "  --input_shapes=1,320,320,3 \\\n",
        "  --change_concat_input_ranges=false \\\n",
        "  --allow_nudging_weights_to_use_fast_gemm_kernel=true \\\n",
        "  --allow_custom_ops"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "isTD5FWZ0K6_"
      },
      "source": [
        "# Now we install tflite_runtime package to evaluate the model.\n",
        "!pip3 install https://github.com/google-coral/pycoral/releases/download/release-frogfish/tflite_runtime-2.5.0-cp36-cp36m-linux_x86_64.whl  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_A-IYRpm-Ate"
      },
      "source": [
        "# Now we do evaluation on the tflite model.\n",
        "from tflite_runtime.interpreter import Interpreter\n",
        "from tflite_runtime.interpreter import load_delegate\n",
        "from PIL import Image\n",
        "from PIL import ImageDraw\n",
        "%matplotlib inline\n",
        "\n",
        "PATH_TO_MODEL = '/content/output_model/ssdlite_mobiledet_mask.tflite'\n",
        "labels = {0:'mask', 1:'no_mask', 2:'incorrect'}\n",
        "interpreter = Interpreter(PATH_TO_MODEL)\n",
        "\n",
        "interpreter.allocate_tensors()\n",
        "interpreter.invoke() # warmup\n",
        "input_details = interpreter.get_input_details()\n",
        "output_details = interpreter.get_output_details()\n",
        "width = input_details[0]['shape'][2]\n",
        "height = input_details[0]['shape'][1]\n",
        "\n",
        "PATH_TO_TEST_IMAGES_DIR = '/content/dataset/test'\n",
        "TEST_IMAGE_PATHS = [os.path.join(PATH_TO_TEST_IMAGES_DIR, 'maksssksksss{}.png'.format(i)) for i in range(0, 100)]\n",
        "\n",
        "def run_inference_for_single_image(image, interpreter):\n",
        "  interpreter.set_tensor(input_details[0]['index'], image)\n",
        "  interpreter.invoke()\n",
        "  boxes = interpreter.get_tensor(output_details[0]['index'])[0]\n",
        "  classes = interpreter.get_tensor(output_details[1]['index'])[0]\n",
        "  scores = interpreter.get_tensor(output_details[2]['index'])[0]\n",
        "  \n",
        "  return boxes, classes, scores\n",
        "\n",
        "colors = {0:(128, 255, 102), 1:(102, 255, 255), 2:(232, 123, 212)}\n",
        "for image_path in TEST_IMAGE_PATHS:\n",
        "  print('Evaluating:', image_path)\n",
        "  image = Image.open(image_path)\n",
        "  image_width, image_height = image.size\n",
        "  draw = ImageDraw.Draw(image)\n",
        "  # Removing the Alpha Channel\n",
        "  if image.mode == 'RGBA':\n",
        "    image.load()\n",
        "    removed_A = Image.new('RGB', image.size, (255,255,255))\n",
        "    removed_A.paste(image, mask=image.split()[3])\n",
        "    resized_image = removed_A.resize((width, height))\n",
        "  else:  \n",
        "    resized_image = image.resize((width, height))\n",
        "  np_image = np.asarray(resized_image)\n",
        "  image_np_expanded = np.expand_dims(np_image, axis=0)\n",
        "  # Actual detection.\n",
        "  boxes, classes, scores = run_inference_for_single_image(image_np_expanded, interpreter)\n",
        "  # Visualization of the results of a detection.\n",
        "  for i in range(len(boxes)):\n",
        "    if scores[i] > 0.5:\n",
        "      ymin = int(max(1, (boxes[i][0] * image_height)))\n",
        "      xmin = int(max(1, (boxes[i][1] * image_width)))\n",
        "      ymax = int(min(image_height, (boxes[i][2] * image_height)))\n",
        "      xmax = int(min(image_width, (boxes[i][3] * image_width)))\n",
        "      draw.rectangle((xmin, ymin, xmax, ymax), width=3, outline=colors[int(classes[i])])\n",
        "      draw.rectangle((xmin, ymin, xmax, ymin-10), fill=colors[int(classes[i])])\n",
        "      text = labels[int(classes[i])] + ' ' + str(scores[i]*100) + '%'\n",
        "      draw.text((xmin+2, ymin-10), text, fill=(0,0,0), width=2)\n",
        "  display(image)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jUy2l-pJFUfV"
      },
      "source": [
        "# Install the edgetpu compiler.\n",
        "!curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add -\n",
        "!echo \"deb https://packages.cloud.google.com/apt coral-edgetpu-stable main\" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list\n",
        "!sudo apt-get update\n",
        "!sudo apt-get install edgetpu-compiler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OW45-68aFbNd"
      },
      "source": [
        "# Compile our model and make a tarball of the finished trained model.\n",
        "%cd /content/output_model\n",
        "!edgetpu_compiler -s ssdlite_mobiledet_mask.tflite\n",
        "%cd /content/\n",
        "# Copy the checkpoints, inference graph, pipeline config, and the tflite models\n",
        "!cp -r /content/train/model.ckpt-2000* /content/output_model\n",
        "!cp -r /content/inference_graph/* /content/output_model\n",
        "!tar cvf facessd_mask.tar.gz output_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQ5utbgC4zRg"
      },
      "source": [
        "# Download model and you're done!\n",
        "from google.colab import files\n",
        "files.download('/content/facessd_mask.tar.gz')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}