1_model_training_SSD_MobileNet/1_train_model_SSD_MobileNet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4e1cd147",
   "metadata": {},
   "source": [
    "# Tensorflow Object Detection API and AWS Sagemaker"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85592c17",
   "metadata": {},
   "source": [
    "In this notebook, you will train and evaluate different models using the [Tensorflow Object Detection API](https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/) and [AWS Sagemaker](https://aws.amazon.com/sagemaker/). \n",
    "\n",
    "If you ever feel stuck, you can refer to this [tutorial](https://aws.amazon.com/blogs/machine-learning/training-and-deploying-models-using-tensorflow-2-with-the-object-detection-api-on-amazon-sagemaker/).\n",
    "\n",
    "## Dataset\n",
    "\n",
    "We are using the [Waymo Open Dataset](https://waymo.com/open/) for this project. The dataset has already been exported using the tfrecords format. The files have been created following the format described [here](https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html#create-tensorflow-records). You can find data stored on [AWS S3](https://aws.amazon.com/s3/), AWS Object Storage. The images are saved with a resolution of 640x640."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dcc1d114",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%pip install tensorflow_io sagemaker -U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "96f55350",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sagemaker\n",
    "from sagemaker.estimator import Estimator\n",
    "from framework import CustomFramework"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccde6fd1",
   "metadata": {},
   "source": [
    "Save the IAM role in a variable called `role`. This would be useful when training the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0ab6b13b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arn:aws:iam::476375380884:role/service-role/AmazonSageMaker-ExecutionRole-20230227T161354\n"
     ]
    }
   ],
   "source": [
    "role = sagemaker.get_execution_role()\n",
    "print(role)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae64e29f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The train and val paths below are public S3 buckets created by Udacity for this project\n",
    "inputs = {'train': 's3://cd2688-object-detection-tf2/train/', \n",
    "        'val': 's3://cd2688-object-detection-tf2/val/'} \n",
    "\n",
    "# Insert path of a folder in your personal S3 bucket to store tensorboard logs.\n",
    "tensorboard_s3_prefix = 's3://object-detection-project-jckuri/logs/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc16a825",
   "metadata": {},
   "source": [
    "## Container\n",
    "\n",
    "To train the model, you will first need to build a [docker](https://www.docker.com/) container with all the dependencies required by the TF Object Detection API. The code below does the following:\n",
    "* clone the Tensorflow models repository\n",
    "* get the exporter and training scripts from the the repository\n",
    "* build the docker image and push it \n",
    "* print the container name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1ad5ac8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'docker/models' already exists and is not an empty directory.\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "\n",
    "# clone the repo and get the scripts\n",
    "git clone https://github.com/tensorflow/models.git docker/models\n",
    "\n",
    "# get model_main and exporter_main files from TF2 Object Detection GitHub repository\n",
    "cp docker/models/research/object_detection/exporter_main_v2.py source_dir \n",
    "cp docker/models/research/object_detection/model_main_tf2.py source_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b2dab3f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.\n",
      "Configure a credential helper to remove this warning. See\n",
      "https://docs.docker.com/engine/reference/commandline/login/#credentials-store\n",
      "\n",
      "Login Succeeded\n",
      "Building image with name tf2-object-detection\n",
      "Sending build context to Docker daemon  722.9MB\n",
      "Step 1/17 : FROM tensorflow/tensorflow:2.9.0-gpu\n",
      " ---> c8d9ee2a0ff4\n",
      "Step 2/17 : ARG DEBIAN_FRONTEND=noninteractive\n",
      " ---> Running in ca21e76d1416\n",
      "Removing intermediate container ca21e76d1416\n",
      " ---> 063a581ec0fe\n",
      "Step 3/17 : RUN rm /etc/apt/sources.list.d/cuda.list\n",
      " ---> Running in 4463e992a51e\n",
      "Removing intermediate container 4463e992a51e\n",
      " ---> 8b94f7292444\n",
      "Step 4/17 : RUN apt-key del 7fa2af80\n",
      " ---> Running in acc61013afc4\n",
      "OK\n",
      "Removing intermediate container acc61013afc4\n",
      " ---> 32004679cc8b\n",
      "Step 5/17 : RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub\n",
      " ---> Running in 7f62030de8b7\n",
      "\u001b[91mWarning: apt-key output should not be parsed (stdout is not a terminal)\n",
      "\u001b[0mExecuting: /tmp/apt-key-gpghome.nSpnYDwjjk/gpg.1.sh --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub\n",
      "\u001b[91mgpg: requesting key from 'https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub'\n",
      "\u001b[0m\u001b[91mgpg: key A4B469963BF863CC: \"cudatools <cudatools@nvidia.com>\" not changed\n",
      "gpg: Total number processed: 1\n",
      "gpg:              unchanged: 1\n",
      "\u001b[0mRemoving intermediate container 7f62030de8b7\n",
      " ---> 4aa8a4beb63d\n",
      "Step 6/17 : RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub\n",
      " ---> Running in 0dd9fce268ee\n",
      "\u001b[91mWarning: apt-key output should not be parsed (stdout is not a terminal)\n",
      "\u001b[0mExecuting: /tmp/apt-key-gpghome.lRN3A5dUwj/gpg.1.sh --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub\n",
      "\u001b[91mgpg: requesting key from 'https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub'\n",
      "\u001b[0m\u001b[91mgpg: key F60F4B3D7FA2AF80: public key \"cudatools <cudatools@nvidia.com>\" imported\n",
      "\u001b[0m\u001b[91mgpg: Total number processed: 1\u001b[0m\u001b[91m\n",
      "gpg:               imported: 1\n",
      "\u001b[0mRemoving intermediate container 0dd9fce268ee\n",
      " ---> d8821b04a961\n",
      "Step 7/17 : RUN apt-get update && apt-get install -y     git     gpg-agent     python3-cairocffi     protobuf-compiler     python3-pil     python3-lxml     python3-tk     libgl1-mesa-dev     wget\n",
      " ---> Running in aeb0d058bdbf\n",
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      "  libbsd0 libcairo2 libcbor0.6 libcurl3-gnutls libdrm-amdgpu1 libdrm-common\n",
      "  libdrm-intel1 libdrm-nouveau2 libdrm-radeon1 libdrm2 libedit2 libegl-dev\n",
      "  libegl-mesa0 libegl1 liberror-perl libfido2-1 libfontconfig1 libgbm1\n",
      "  libgdk-pixbuf2.0-0 libgdk-pixbuf2.0-bin libgdk-pixbuf2.0-common libgl-dev\n",
      "  libgl1 libgl1-mesa-dri libglapi-mesa libgles-dev libgles1 libgles2\n",
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      "  libjbig0 liblcms2-2 libllvm12 libopengl-dev libopengl0 libpciaccess0\n",
      "  libpixman-1-0 libprotobuf-dev libprotobuf-lite17 libprotobuf17 libprotoc17\n",
      "  libpthread-stubs0-dev libsensors-config libsensors5 libtcl8.6 libtiff5\n",
      "  libtk8.6 libvulkan1 libwayland-client0 libwayland-server0 libwebp6\n",
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      "Suggested packages:\n",
      "  blt-demo pinentry-gnome3 tor gettext-base git-daemon-run\n",
      "  | git-daemon-sysvinit git-doc git-el git-email git-gui gitk gitweb git-cvs\n",
      "  git-mediawiki git-svn parcimonie xloadimage scdaemon liblcms2-utils pciutils\n",
      "  lm-sensors tcl8.6 tk8.6 libx11-doc libxcb-doc keychain libpam-ssh\n",
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      "  blt fontconfig-config fonts-dejavu-core git git-man less libbsd0 libcairo2\n",
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      "  libdrm-nouveau2 libdrm-radeon1 libdrm2 libedit2 libegl-dev libegl-mesa0\n",
      "  libegl1 liberror-perl libfido2-1 libfontconfig1 libgbm1 libgdk-pixbuf2.0-0\n",
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      "  libjbig0 liblcms2-2 libllvm12 libopengl-dev libopengl0 libpciaccess0\n",
      "  libpixman-1-0 libprotobuf-dev libprotobuf-lite17 libprotobuf17 libprotoc17\n",
      "  libpthread-stubs0-dev libsensors-config libsensors5 libtcl8.6 libtiff5\n",
      "  libtk8.6 libvulkan1 libwayland-client0 libwayland-server0 libwebp6\n",
      "  libwebpdemux2 libwebpmux3 libx11-6 libx11-data libx11-dev libx11-xcb1\n",
      "  libxau-dev libxau6 libxcb-dri2-0 libxcb-dri3-0 libxcb-glx0 libxcb-present0\n",
      "  libxcb-randr0 libxcb-render0 libxcb-shm0 libxcb-sync1 libxcb-xfixes0 libxcb1\n",
      "  libxcb1-dev libxdmcp-dev libxdmcp6 libxext6 libxfixes3 libxft2 libxmuu1\n",
      "  libxrender1 libxshmfence1 libxslt1.1 libxss1 libxxf86vm1 mesa-vulkan-drivers\n",
      "  openssh-client protobuf-compiler python3-bs4 python3-cairocffi python3-cffi\n",
      "  python3-cffi-backend python3-html5lib python3-lxml python3-olefile\n",
      "  python3-pil python3-ply python3-pycparser python3-soupsieve python3-tk\n",
      "  python3-webencodings python3-xcffib shared-mime-info tk8.6-blt2.5 ucf wget\n",
      "  x11-common x11proto-core-dev x11proto-dev xauth xorg-sgml-doctools\n",
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      "  gpg-wks-server gpgconf gpgsm gpgv\n",
      "11 upgraded, 119 newly installed, 0 to remove and 109 not upgraded.\n",
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      "Get:52 http://archive.ubuntu.com/ubuntu focal/main amd64 libpciaccess0 amd64 0.16-0ubuntu1 [17.9 kB]\n",
      "Get:53 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-intel1 amd64 2.4.107-8ubuntu1~20.04.2 [60.3 kB]\n",
      "Get:54 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-nouveau2 amd64 2.4.107-8ubuntu1~20.04.2 [16.6 kB]\n",
      "Get:55 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-radeon1 amd64 2.4.107-8ubuntu1~20.04.2 [19.7 kB]\n",
      "Get:56 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglvnd0 amd64 1.3.2-1~ubuntu0.20.04.2 [48.1 kB]\n",
      "Get:57 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwayland-server0 amd64 1.18.0-1ubuntu0.1 [31.3 kB]\n",
      "Get:58 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgbm1 amd64 21.2.6-0ubuntu0.1~20.04.2 [29.2 kB]\n",
      "Get:59 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglapi-mesa amd64 21.2.6-0ubuntu0.1~20.04.2 [27.4 kB]\n",
      "Get:60 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwayland-client0 amd64 1.18.0-1ubuntu0.1 [23.9 kB]\n",
      "Get:61 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libx11-xcb1 amd64 2:1.6.9-2ubuntu1.2 [9372 B]\n",
      "Get:62 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-dri2-0 amd64 1.14-2 [6920 B]\n",
      "Get:63 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-dri3-0 amd64 1.14-2 [6552 B]\n",
      "Get:64 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-present0 amd64 1.14-2 [5560 B]\n",
      "Get:65 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-sync1 amd64 1.14-2 [8884 B]\n",
      "Get:66 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-xfixes0 amd64 1.14-2 [9296 B]\n",
      "Get:67 http://archive.ubuntu.com/ubuntu focal/main amd64 libxshmfence1 amd64 1.3-1 [5028 B]\n",
      "Get:68 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libegl-mesa0 amd64 21.2.6-0ubuntu0.1~20.04.2 [96.3 kB]\n",
      "Get:69 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libegl1 amd64 1.3.2-1~ubuntu0.20.04.2 [31.9 kB]\n",
      "Get:70 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-glx0 amd64 1.14-2 [22.1 kB]\n",
      "Get:71 http://archive.ubuntu.com/ubuntu focal/main amd64 libxfixes3 amd64 1:5.0.3-2 [10.9 kB]\n",
      "Get:72 http://archive.ubuntu.com/ubuntu focal/main amd64 libxxf86vm1 amd64 1:1.1.4-1build1 [10.2 kB]\n",
      "Get:73 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libllvm12 amd64 1:12.0.0-3ubuntu1~20.04.5 [18.8 MB]\n",
      "Get:74 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsensors-config all 1:3.6.0-2ubuntu1.1 [6052 B]\n",
      "Get:75 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsensors5 amd64 1:3.6.0-2ubuntu1.1 [27.2 kB]\n",
      "Get:76 http://archive.ubuntu.com/ubuntu focal/main amd64 libvulkan1 amd64 1.2.131.2-1 [93.3 kB]\n",
      "Get:77 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgl1-mesa-dri amd64 21.2.6-0ubuntu0.1~20.04.2 [11.0 MB]\n",
      "Get:78 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglx-mesa0 amd64 21.2.6-0ubuntu0.1~20.04.2 [137 kB]\n",
      "Get:79 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglx0 amd64 1.3.2-1~ubuntu0.20.04.2 [32.5 kB]\n",
      "Get:80 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgl1 amd64 1.3.2-1~ubuntu0.20.04.2 [85.8 kB]\n",
      "Get:81 http://archive.ubuntu.com/ubuntu focal/main amd64 xorg-sgml-doctools all 1:1.11-1 [12.9 kB]\n",
      "Get:82 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-dev all 2019.2-1ubuntu1 [594 kB]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Get:83 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-core-dev all 2019.2-1ubuntu1 [2620 B]\n",
      "Get:84 http://archive.ubuntu.com/ubuntu focal/main amd64 libxau-dev amd64 1:1.0.9-0ubuntu1 [9552 B]\n",
      "Get:85 http://archive.ubuntu.com/ubuntu focal/main amd64 libxdmcp-dev amd64 1:1.1.3-0ubuntu1 [25.3 kB]\n",
      "Get:86 http://archive.ubuntu.com/ubuntu focal/main amd64 xtrans-dev all 1.4.0-1 [68.9 kB]\n",
      "Get:87 http://archive.ubuntu.com/ubuntu focal/main amd64 libpthread-stubs0-dev amd64 0.4-1 [5384 B]\n",
      "Get:88 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb1-dev amd64 1.14-2 [80.5 kB]\n",
      "Get:89 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libx11-dev amd64 2:1.6.9-2ubuntu1.2 [647 kB]\n",
      "Get:90 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglx-dev amd64 1.3.2-1~ubuntu0.20.04.2 [14.0 kB]\n",
      "Get:91 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgl-dev amd64 1.3.2-1~ubuntu0.20.04.2 [97.8 kB]\n",
      "Get:92 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libegl-dev amd64 1.3.2-1~ubuntu0.20.04.2 [17.2 kB]\n",
      "Get:93 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libjbig0 amd64 2.1-3.1ubuntu0.20.04.1 [27.3 kB]\n",
      "Get:94 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwebp6 amd64 0.6.1-2ubuntu0.20.04.1 [185 kB]\n",
      "Get:95 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libtiff5 amd64 4.1.0+git191117-2ubuntu0.20.04.8 [163 kB]\n",
      "Get:96 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgdk-pixbuf2.0-common all 2.40.0+dfsg-3ubuntu0.4 [4592 B]\n",
      "Get:97 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgdk-pixbuf2.0-0 amd64 2.40.0+dfsg-3ubuntu0.4 [168 kB]\n",
      "Get:98 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgdk-pixbuf2.0-bin amd64 2.40.0+dfsg-3ubuntu0.4 [14.1 kB]\n",
      "Get:99 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgles1 amd64 1.3.2-1~ubuntu0.20.04.2 [10.3 kB]\n",
      "Get:100 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgles2 amd64 1.3.2-1~ubuntu0.20.04.2 [15.6 kB]\n",
      "Get:101 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgles-dev amd64 1.3.2-1~ubuntu0.20.04.2 [47.9 kB]\n",
      "Get:102 http://archive.ubuntu.com/ubuntu focal/main amd64 libimagequant0 amd64 2.12.2-1.1 [31.4 kB]\n",
      "Get:103 http://archive.ubuntu.com/ubuntu focal/main amd64 liblcms2-2 amd64 2.9-4 [140 kB]\n",
      "Get:104 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libopengl0 amd64 1.3.2-1~ubuntu0.20.04.2 [29.2 kB]\n",
      "Get:105 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libopengl-dev amd64 1.3.2-1~ubuntu0.20.04.2 [3584 B]\n",
      "Get:106 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libprotobuf-lite17 amd64 3.6.1.3-2ubuntu5.2 [132 kB]\n",
      "Get:107 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libprotobuf17 amd64 3.6.1.3-2ubuntu5.2 [798 kB]\n",
      "Get:108 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libprotoc17 amd64 3.6.1.3-2ubuntu5.2 [646 kB]\n",
      "Get:109 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwebpdemux2 amd64 0.6.1-2ubuntu0.20.04.1 [9576 B]\n",
      "Get:110 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwebpmux3 amd64 0.6.1-2ubuntu0.20.04.1 [19.5 kB]\n",
      "Get:111 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-randr0 amd64 1.14-2 [16.3 kB]\n",
      "Get:112 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxslt1.1 amd64 1.1.34-4ubuntu0.20.04.1 [151 kB]\n",
      "Get:113 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 mesa-vulkan-drivers amd64 21.2.6-0ubuntu0.1~20.04.2 [5788 kB]\n",
      "Get:114 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-soupsieve all 1.9.5+dfsg-1 [29.1 kB]\n",
      "Get:115 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-bs4 all 4.8.2-1 [83.0 kB]\n",
      "Get:116 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-ply all 3.11-3ubuntu0.1 [46.3 kB]\n",
      "Get:117 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-pycparser all 2.19-1ubuntu1 [71.0 kB]\n",
      "Get:118 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-cffi all 1.14.0-1build1 [70.3 kB]\n",
      "Get:119 http://archive.ubuntu.com/ubuntu focal/universe amd64 python3-xcffib amd64 0.8.1-0.8 [61.9 kB]\n",
      "Get:120 http://archive.ubuntu.com/ubuntu focal/universe amd64 python3-cairocffi all 0.9.0-4 [67.1 kB]\n",
      "Get:121 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-webencodings all 0.5.1-1ubuntu1 [11.0 kB]\n",
      "Get:122 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-html5lib all 1.0.1-2 [84.3 kB]\n",
      "Get:123 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-lxml amd64 4.5.0-1ubuntu0.5 [1384 kB]\n",
      "Get:124 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-olefile all 0.46-2 [33.7 kB]\n",
      "Get:125 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-pil amd64 7.0.0-4ubuntu0.7 [366 kB]\n",
      "Get:126 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-tk amd64 3.8.10-0ubuntu1~20.04 [104 kB]\n",
      "Get:127 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglvnd-dev amd64 1.3.2-1~ubuntu0.20.04.2 [11.6 kB]\n",
      "Get:128 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgl1-mesa-dev amd64 21.2.6-0ubuntu0.1~20.04.2 [6420 B]\n",
      "Get:129 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libprotobuf-dev amd64 3.6.1.3-2ubuntu5.2 [1156 kB]\n",
      "Get:130 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 protobuf-compiler amd64 3.6.1.3-2ubuntu5.2 [27.6 kB]\n",
      "\u001b[91mdebconf: delaying package configuration, since apt-utils is not installed\n",
      "\u001b[0mFetched 59.5 MB in 12s (5122 kB/s)\n",
      "(Reading database ... 19687 files and directories currently installed.)\n",
      "Preparing to unpack .../00-gpg-wks-client_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking gpg-wks-client (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../01-dirmngr_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking dirmngr (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../02-gpg-wks-server_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking gpg-wks-server (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../03-gnupg-utils_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking gnupg-utils (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../04-gpg-agent_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking gpg-agent (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../05-gpg_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking gpg (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../06-gpgconf_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking gpgconf (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../07-gnupg-l10n_2.2.19-3ubuntu2.2_all.deb ...\n",
      "Unpacking gnupg-l10n (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../08-gnupg_2.2.19-3ubuntu2.2_all.deb ...\n",
      "Unpacking gnupg (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../09-gpgsm_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking gpgsm (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../10-gpgv_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking gpgv (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Setting up gpgv (2.2.19-3ubuntu2.2) ...\n",
      "Selecting previously unselected package less.\n",
      "(Reading database ... 19687 files and directories currently installed.)\n",
      "Preparing to unpack .../000-less_551-1ubuntu0.1_amd64.deb ...\n",
      "Unpacking less (551-1ubuntu0.1) ...\n",
      "Selecting previously unselected package libbsd0:amd64.\n",
      "Preparing to unpack .../001-libbsd0_0.10.0-1_amd64.deb ...\n",
      "Unpacking libbsd0:amd64 (0.10.0-1) ...\n",
      "Selecting previously unselected package python3-cffi-backend.\n",
      "Preparing to unpack .../002-python3-cffi-backend_1.14.0-1build1_amd64.deb ...\n",
      "Unpacking python3-cffi-backend (1.14.0-1build1) ...\n",
      "Selecting previously unselected package shared-mime-info.\n",
      "Preparing to unpack .../003-shared-mime-info_1.15-1_amd64.deb ...\n",
      "Unpacking shared-mime-info (1.15-1) ...\n",
      "Selecting previously unselected package ucf.\n",
      "Preparing to unpack .../004-ucf_3.0038+nmu1_all.deb ...\n",
      "Moving old data out of the way\n",
      "Unpacking ucf (3.0038+nmu1) ...\n",
      "Selecting previously unselected package libcbor0.6:amd64.\n",
      "Preparing to unpack .../005-libcbor0.6_0.6.0-0ubuntu1_amd64.deb ...\n",
      "Unpacking libcbor0.6:amd64 (0.6.0-0ubuntu1) ...\n",
      "Selecting previously unselected package libdrm-common.\n",
      "Preparing to unpack .../006-libdrm-common_2.4.107-8ubuntu1~20.04.2_all.deb ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unpacking libdrm-common (2.4.107-8ubuntu1~20.04.2) ...\n",
      "Selecting previously unselected package libdrm2:amd64.\n",
      "Preparing to unpack .../007-libdrm2_2.4.107-8ubuntu1~20.04.2_amd64.deb ...\n",
      "Unpacking libdrm2:amd64 (2.4.107-8ubuntu1~20.04.2) ...\n",
      "Selecting previously unselected package libedit2:amd64.\n",
      "Preparing to unpack .../008-libedit2_3.1-20191231-1_amd64.deb ...\n",
      "Unpacking libedit2:amd64 (3.1-20191231-1) ...\n",
      "Selecting previously unselected package libfido2-1:amd64.\n",
      "Preparing to unpack .../009-libfido2-1_1.3.1-1ubuntu2_amd64.deb ...\n",
      "Unpacking libfido2-1:amd64 (1.3.1-1ubuntu2) ...\n",
      "Selecting previously unselected package libxau6:amd64.\n",
      "Preparing to unpack .../010-libxau6_1%3a1.0.9-0ubuntu1_amd64.deb ...\n",
      "Unpacking libxau6:amd64 (1:1.0.9-0ubuntu1) ...\n",
      "Selecting previously unselected package libxdmcp6:amd64.\n",
      "Preparing to unpack .../011-libxdmcp6_1%3a1.1.3-0ubuntu1_amd64.deb ...\n",
      "Unpacking libxdmcp6:amd64 (1:1.1.3-0ubuntu1) ...\n",
      "Selecting previously unselected package libxcb1:amd64.\n",
      "Preparing to unpack .../012-libxcb1_1.14-2_amd64.deb ...\n",
      "Unpacking libxcb1:amd64 (1.14-2) ...\n",
      "Selecting previously unselected package libx11-data.\n",
      "Preparing to unpack .../013-libx11-data_2%3a1.6.9-2ubuntu1.2_all.deb ...\n",
      "Unpacking libx11-data (2:1.6.9-2ubuntu1.2) ...\n",
      "Selecting previously unselected package libx11-6:amd64.\n",
      "Preparing to unpack .../014-libx11-6_2%3a1.6.9-2ubuntu1.2_amd64.deb ...\n",
      "Unpacking libx11-6:amd64 (2:1.6.9-2ubuntu1.2) ...\n",
      "Selecting previously unselected package libxext6:amd64.\n",
      "Preparing to unpack .../015-libxext6_2%3a1.3.4-0ubuntu1_amd64.deb ...\n",
      "Unpacking libxext6:amd64 (2:1.3.4-0ubuntu1) ...\n",
      "Selecting previously unselected package libxmuu1:amd64.\n",
      "Preparing to unpack .../016-libxmuu1_2%3a1.1.3-0ubuntu1_amd64.deb ...\n",
      "Unpacking libxmuu1:amd64 (2:1.1.3-0ubuntu1) ...\n",
      "Selecting previously unselected package openssh-client.\n",
      "Preparing to unpack .../017-openssh-client_1%3a8.2p1-4ubuntu0.5_amd64.deb ...\n",
      "Unpacking openssh-client (1:8.2p1-4ubuntu0.5) ...\n",
      "Selecting previously unselected package wget.\n",
      "Preparing to unpack .../018-wget_1.20.3-1ubuntu2_amd64.deb ...\n",
      "Unpacking wget (1.20.3-1ubuntu2) ...\n",
      "Selecting previously unselected package xauth.\n",
      "Preparing to unpack .../019-xauth_1%3a1.1-0ubuntu1_amd64.deb ...\n",
      "Unpacking xauth (1:1.1-0ubuntu1) ...\n",
      "Selecting previously unselected package libtcl8.6:amd64.\n",
      "Preparing to unpack .../020-libtcl8.6_8.6.10+dfsg-1_amd64.deb ...\n",
      "Unpacking libtcl8.6:amd64 (8.6.10+dfsg-1) ...\n",
      "Selecting previously unselected package fonts-dejavu-core.\n",
      "Preparing to unpack .../021-fonts-dejavu-core_2.37-1_all.deb ...\n",
      "Unpacking fonts-dejavu-core (2.37-1) ...\n",
      "Selecting previously unselected package fontconfig-config.\n",
      "Preparing to unpack .../022-fontconfig-config_2.13.1-2ubuntu3_all.deb ...\n",
      "Unpacking fontconfig-config (2.13.1-2ubuntu3) ...\n",
      "Selecting previously unselected package libfontconfig1:amd64.\n",
      "Preparing to unpack .../023-libfontconfig1_2.13.1-2ubuntu3_amd64.deb ...\n",
      "Unpacking libfontconfig1:amd64 (2.13.1-2ubuntu3) ...\n",
      "Selecting previously unselected package libxrender1:amd64.\n",
      "Preparing to unpack .../024-libxrender1_1%3a0.9.10-1_amd64.deb ...\n",
      "Unpacking libxrender1:amd64 (1:0.9.10-1) ...\n",
      "Selecting previously unselected package libxft2:amd64.\n",
      "Preparing to unpack .../025-libxft2_2.3.3-0ubuntu1_amd64.deb ...\n",
      "Unpacking libxft2:amd64 (2.3.3-0ubuntu1) ...\n",
      "Selecting previously unselected package x11-common.\n",
      "Preparing to unpack .../026-x11-common_1%3a7.7+19ubuntu14_all.deb ...\n",
      "dpkg-query: no packages found matching nux-tools\n",
      "Unpacking x11-common (1:7.7+19ubuntu14) ...\n",
      "Selecting previously unselected package libxss1:amd64.\n",
      "Preparing to unpack .../027-libxss1_1%3a1.2.3-1_amd64.deb ...\n",
      "Unpacking libxss1:amd64 (1:1.2.3-1) ...\n",
      "Selecting previously unselected package libtk8.6:amd64.\n",
      "Preparing to unpack .../028-libtk8.6_8.6.10-1_amd64.deb ...\n",
      "Unpacking libtk8.6:amd64 (8.6.10-1) ...\n",
      "Selecting previously unselected package tk8.6-blt2.5.\n",
      "Preparing to unpack .../029-tk8.6-blt2.5_2.5.3+dfsg-4_amd64.deb ...\n",
      "Unpacking tk8.6-blt2.5 (2.5.3+dfsg-4) ...\n",
      "Selecting previously unselected package blt.\n",
      "Preparing to unpack .../030-blt_2.5.3+dfsg-4_amd64.deb ...\n",
      "Unpacking blt (2.5.3+dfsg-4) ...\n",
      "Selecting previously unselected package libcurl3-gnutls:amd64.\n",
      "Preparing to unpack .../031-libcurl3-gnutls_7.68.0-1ubuntu2.18_amd64.deb ...\n",
      "Unpacking libcurl3-gnutls:amd64 (7.68.0-1ubuntu2.18) ...\n",
      "Selecting previously unselected package liberror-perl.\n",
      "Preparing to unpack .../032-liberror-perl_0.17029-1_all.deb ...\n",
      "Unpacking liberror-perl (0.17029-1) ...\n",
      "Selecting previously unselected package git-man.\n",
      "Preparing to unpack .../033-git-man_1%3a2.25.1-1ubuntu3.10_all.deb ...\n",
      "Unpacking git-man (1:2.25.1-1ubuntu3.10) ...\n",
      "Selecting previously unselected package git.\n",
      "Preparing to unpack .../034-git_1%3a2.25.1-1ubuntu3.10_amd64.deb ...\n",
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     ]
    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Selecting previously unselected package libxslt1.1:amd64.\n",
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      "Setting up libpixman-1-0:amd64 (0.38.4-0ubuntu2.1) ...\n",
      "Setting up libwayland-server0:amd64 (1.18.0-1ubuntu0.1) ...\n",
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     "text": [
      "Setting up libdrm-nouveau2:amd64 (2.4.107-8ubuntu1~20.04.2) ...\n",
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      "Setting up libgl-dev:amd64 (1.3.2-1~ubuntu0.20.04.2) ...\n",
      "Setting up tk8.6-blt2.5 (2.5.3+dfsg-4) ...\n",
      "Setting up libegl-dev:amd64 (1.3.2-1~ubuntu0.20.04.2) ...\n",
      "Setting up blt (2.5.3+dfsg-4) ...\n",
      "Setting up python3-tk:amd64 (3.8.10-0ubuntu1~20.04) ...\n",
      "Setting up libgles-dev:amd64 (1.3.2-1~ubuntu0.20.04.2) ...\n",
      "Setting up libglvnd-dev:amd64 (1.3.2-1~ubuntu0.20.04.2) ...\n",
      "Setting up libgl1-mesa-dev:amd64 (21.2.6-0ubuntu0.1~20.04.2) ...\n",
      "Processing triggers for libc-bin (2.31-0ubuntu9.7) ...\n",
      "Processing triggers for systemd (245.4-4ubuntu3.17) ...\n",
      "Processing triggers for mime-support (3.64ubuntu1) ...\n",
      "Removing intermediate container aeb0d058bdbf\n",
      " ---> 3d2a2cbb2192\n",
      "Step 8/17 : COPY models/research/object_detection /home/tensorflow/models/research/object_detection\n",
      " ---> 3eca1fbd2c51\n",
      "Step 9/17 : RUN (cd /home/tensorflow/models/research/ && protoc object_detection/protos/*.proto --python_out=.)\n",
      " ---> Running in 9136300061f7\n",
      "Removing intermediate container 9136300061f7\n",
      " ---> 34fca0eb95af\n",
      "Step 10/17 : WORKDIR /home/tensorflow/models/research/\n",
      " ---> Running in 0c0020f553b0\n",
      "Removing intermediate container 0c0020f553b0\n",
      " ---> 4cb45c036696\n",
      "Step 11/17 : RUN cp object_detection/packages/tf2/setup.py ./\n",
      " ---> Running in ec5c4d7b02eb\n",
      "Removing intermediate container ec5c4d7b02eb\n",
      " ---> 925cb4864f34\n",
      "Step 12/17 : ENV PATH=\"/home/tensorflow/.local/bin:${PATH}\"\n",
      " ---> Running in e63a8fad3547\n",
      "Removing intermediate container e63a8fad3547\n",
      " ---> e4117e5fec74\n",
      "Step 13/17 : RUN python -m pip install -U pip\n",
      " ---> Running in 3373ec2204ef\n",
      "Collecting pip\n",
      "  Downloading pip-23.0.1-py3-none-any.whl (2.1 MB)\n",
      "Installing collected packages: pip\n",
      "  Attempting uninstall: pip\n",
      "    Found existing installation: pip 20.2.4\n",
      "    Uninstalling pip-20.2.4:\n",
      "      Successfully uninstalled pip-20.2.4\n",
      "Successfully installed pip-23.0.1\n",
      "Removing intermediate container 3373ec2204ef\n",
      " ---> 46affc1f96c1\n",
      "Step 14/17 : RUN python -m pip install .\n",
      " ---> Running in dfcb03a33638\n",
      "Processing /home/tensorflow/models/research\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Collecting avro-python3\n",
      "  Downloading avro-python3-1.10.2.tar.gz (38 kB)\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Collecting apache-beam\n",
      "  Downloading apache_beam-2.46.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (14.8 MB)\n",
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      "Requirement already satisfied: pillow in /usr/lib/python3/dist-packages (from object-detection==0.1) (7.0.0)\n",
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      "  Downloading pycocotools-2.0.6.tar.gz (24 kB)\n",
      "  Installing build dependencies: started\n",
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      "  Getting requirements to build wheel: started\n",
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      "  Preparing metadata (pyproject.toml): started\n",
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      "Requirement already satisfied: keras in /usr/local/lib/python3.8/dist-packages (from object-detection==0.1) (2.9.0)\n",
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      "  Downloading pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)\n",
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      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from sacrebleu<=2.2.0->object-detection==0.1) (1.22.3)\n",
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      "  Downloading py_cpuinfo-9.0.0-py3-none-any.whl (22 kB)\n",
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      "  Preparing metadata (setup.py): started\n",
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     ]
    },
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     "name": "stdout",
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      "Building wheels for collected packages: object-detection, avro-python3, pycocotools, crcmod, dill, kaggle, seqeval, docopt, promise\n",
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      "  Building wheel for pycocotools (pyproject.toml): finished with status 'done'\n",
      "  Created wheel for pycocotools: filename=pycocotools-2.0.6-cp38-cp38-linux_x86_64.whl size=423704 sha256=698bc59abe237512633d41479015d816ab2768765227250eb3e1d2ed38550684\n",
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      "  Created wheel for dill: filename=dill-0.3.1.1-py3-none-any.whl size=78543 sha256=6d518dc7c82d5bcaaacfc4c9d1c642b7ebc16b9946e58def53fb9a544d8ad0d3\n",
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      "  Created wheel for docopt: filename=docopt-0.6.2-py2.py3-none-any.whl size=13706 sha256=8021d8f75f52e2a49004c9dab1c9a076c8e8b1e8df08a20cf8176488cd57c6ab\n",
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      "  Building wheel for promise (setup.py): started\n",
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      "  Created wheel for promise: filename=promise-2.3-py3-none-any.whl size=21485 sha256=2872055cf795a08a66b6a7f535743e98ab8bc38f2f90c9607061538366dfc800\n",
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      "Successfully built object-detection avro-python3 pycocotools crcmod dill kaggle seqeval docopt promise\n",
      "Installing collected packages: text-unidecode, sentencepiece, pytz, py-cpuinfo, gin-config, flatbuffers, docopt, dm-tree, crcmod, zstandard, uritemplate, typing-extensions, tqdm, toml, threadpoolctl, tensorflow-model-optimization, tensorflow-io-gcs-filesystem, tensorflow-estimator, tabulate, scipy, regex, pyyaml, python-slugify, python-dateutil, pyparsing, pymongo, pyarrow, psutil, protobuf, promise, portalocker, orjson, opencv-python-headless, opencv-python, objsize, kiwisolver, keras, joblib, importlib-resources, immutabledict, fonttools, fasteners, fastavro, etils, dill, Cython, cycler, contourpy, contextlib2, colorama, cloudpickle, click, charset-normalizer, avro-python3, typeguard, tf-slim, tensorflow_io, tensorflow-hub, scikit-learn, sacrebleu, requests, pydot, proto-plus, pandas, httplib2, googleapis-common-protos, google-auth, tensorflow-metadata, tensorflow-addons, seqeval, oauth2client, matplotlib, kaggle, hdfs, google-auth-httplib2, google-api-core, tensorflow-datasets, pycocotools, lvis, google-api-python-client, apache-beam, tensorboard, tensorflow, tensorflow-text, tf-models-official, object-detection\n",
      "  Attempting uninstall: flatbuffers\n",
      "    Found existing installation: flatbuffers 1.12\n",
      "    Uninstalling flatbuffers-1.12:\n",
      "      Successfully uninstalled flatbuffers-1.12\n",
      "  Attempting uninstall: typing-extensions\n",
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      "  Attempting uninstall: protobuf\n",
      "    Found existing installation: protobuf 3.20.1\n",
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      "      Successfully uninstalled protobuf-3.20.1\n",
      "  Attempting uninstall: keras\n",
      "    Found existing installation: keras 2.9.0\n",
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      "      Successfully uninstalled keras-2.9.0\n",
      "  Attempting uninstall: requests\n",
      "    Found existing installation: requests 2.22.0\n",
      "    Uninstalling requests-2.22.0:\n",
      "      Successfully uninstalled requests-2.22.0\n",
      "  Attempting uninstall: google-auth\n",
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      "    Uninstalling google-auth-2.6.6:\n",
      "      Successfully uninstalled google-auth-2.6.6\n",
      "  Attempting uninstall: tensorboard\n",
      "    Found existing installation: tensorboard 2.9.0\n",
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      "      Successfully uninstalled tensorboard-2.9.0\n",
      "  Attempting uninstall: tensorflow\n",
      "    Found existing installation: tensorflow 2.9.0\n",
      "    Uninstalling tensorflow-2.9.0:\n",
      "      Successfully uninstalled tensorflow-2.9.0\n",
      "Successfully installed Cython-0.29.33 apache-beam-2.46.0 avro-python3-1.10.2 charset-normalizer-3.1.0 click-8.1.3 cloudpickle-2.2.1 colorama-0.4.6 contextlib2-21.6.0 contourpy-1.0.7 crcmod-1.7 cycler-0.11.0 dill-0.3.1.1 dm-tree-0.1.8 docopt-0.6.2 etils-1.1.1 fastavro-1.7.3 fasteners-0.18 flatbuffers-23.3.3 fonttools-4.39.2 gin-config-0.5.0 google-api-core-2.11.0 google-api-python-client-2.82.0 google-auth-2.16.3 google-auth-httplib2-0.1.0 googleapis-common-protos-1.59.0 hdfs-2.7.0 httplib2-0.21.0 immutabledict-2.2.3 importlib-resources-5.12.0 joblib-1.2.0 kaggle-1.5.13 keras-2.11.0 kiwisolver-1.4.4 lvis-0.5.3 matplotlib-3.7.1 oauth2client-4.1.3 object-detection-0.1 objsize-0.6.1 opencv-python-4.7.0.72 opencv-python-headless-4.7.0.72 orjson-3.8.8 pandas-1.5.3 portalocker-2.7.0 promise-2.3 proto-plus-1.22.2 protobuf-3.19.6 psutil-5.9.4 py-cpuinfo-9.0.0 pyarrow-9.0.0 pycocotools-2.0.6 pydot-1.4.2 pymongo-3.13.0 pyparsing-2.4.7 python-dateutil-2.8.2 python-slugify-8.0.1 pytz-2023.2 pyyaml-5.4.1 regex-2023.3.23 requests-2.28.2 sacrebleu-2.2.0 scikit-learn-1.2.2 scipy-1.10.1 sentencepiece-0.1.97 seqeval-1.2.2 tabulate-0.9.0 tensorboard-2.11.2 tensorflow-2.11.1 tensorflow-addons-0.19.0 tensorflow-datasets-4.8.3 tensorflow-estimator-2.11.0 tensorflow-hub-0.13.0 tensorflow-io-gcs-filesystem-0.31.0 tensorflow-metadata-1.12.0 tensorflow-model-optimization-0.7.3 tensorflow-text-2.11.0 tensorflow_io-0.31.0 text-unidecode-1.3 tf-models-official-2.11.5 tf-slim-1.1.0 threadpoolctl-3.1.0 toml-0.10.2 tqdm-4.65.0 typeguard-3.0.2 typing-extensions-4.5.0 uritemplate-4.1.1 zstandard-0.20.0\n",
      "\u001b[91mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n",
      "\u001b[0mRemoving intermediate container dfcb03a33638\n",
      " ---> a75bbba69f8e\n",
      "Step 15/17 : ENV TF_CPP_MIN_LOG_LEVEL 3\n",
      " ---> Running in 4f4ead7e0e12\n",
      "Removing intermediate container 4f4ead7e0e12\n",
      " ---> ca73f43343f5\n",
      "Step 16/17 : RUN pip3 install sagemaker-training\n",
      " ---> Running in 15e50b58cae6\n",
      "Collecting sagemaker-training\n",
      "  Downloading sagemaker_training-4.4.8.tar.gz (57 kB)\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "Collecting boto3\n",
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      "  Downloading inotify_simple-1.2.1.tar.gz (7.9 kB)\n",
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      "Building wheels for collected packages: sagemaker-training, inotify_simple\n",
      "  Building wheel for sagemaker-training (setup.py): started\n",
      "  Building wheel for sagemaker-training (setup.py): finished with status 'done'\n",
      "  Created wheel for sagemaker-training: filename=sagemaker_training-4.4.8-cp38-cp38-linux_x86_64.whl size=92186 sha256=08e289bedcb6c6db3ec2f18d33bab71461323822206fe35ae8b03307b84d23f0\n",
      "  Stored in directory: /root/.cache/pip/wheels/2d/c8/75/3a93f047986d48b64d2b799442ddad0eaa9e7565a77e7fe823\n",
      "  Building wheel for inotify_simple (setup.py): started\n",
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      "  Created wheel for inotify_simple: filename=inotify_simple-1.2.1-py3-none-any.whl size=8201 sha256=e7af5a383e70225d2633a7e130c20679d7863975f2697b5e42d68e7434c4bc7a\n",
      "  Stored in directory: /root/.cache/pip/wheels/8b/2d/c2/46bac8503a2469925f6f463615984b3d1fe472e729363a28d3\n",
      "Successfully built sagemaker-training inotify_simple\n",
      "Installing collected packages: inotify_simple, zope.interface, zope.event, retrying, pynacl, jmespath, greenlet, cryptography, bcrypt, paramiko, gevent, botocore, s3transfer, boto3, sagemaker-training\n",
      "Successfully installed bcrypt-4.0.1 boto3-1.26.100 botocore-1.29.100 cryptography-40.0.1 gevent-22.10.2 greenlet-2.0.2 inotify_simple-1.2.1 jmespath-1.0.1 paramiko-3.1.0 pynacl-1.5.0 retrying-1.3.4 s3transfer-0.6.0 sagemaker-training-4.4.8 zope.event-4.6 zope.interface-6.0\n",
      "\u001b[91mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n",
      "\u001b[0mRemoving intermediate container 15e50b58cae6\n",
      " ---> 6815a52ce273\n",
      "Step 17/17 : RUN pip install \"opencv-python-headless<4.3\"\n",
      " ---> Running in 41898a1589a0\n",
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      "  Downloading opencv_python_headless-4.2.0.34-cp38-cp38-manylinux1_x86_64.whl (21.6 MB)\n",
      "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 21.6/21.6 MB 66.8 MB/s eta 0:00:00\n",
      "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from opencv-python-headless<4.3) (1.22.3)\n",
      "Installing collected packages: opencv-python-headless\n",
      "  Attempting uninstall: opencv-python-headless\n",
      "    Found existing installation: opencv-python-headless 4.7.0.72\n",
      "    Uninstalling opencv-python-headless-4.7.0.72:\n",
      "      Successfully uninstalled opencv-python-headless-4.7.0.72\n",
      "Successfully installed opencv-python-headless-4.2.0.34\n",
      "\u001b[91mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n",
      "\u001b[0mRemoving intermediate container 41898a1589a0\n",
      " ---> 68df94a034b2\n",
      "Successfully built 68df94a034b2\n",
      "Successfully tagged tf2-object-detection:latest\n",
      "Pushing image to ECR 476375380884.dkr.ecr.us-east-1.amazonaws.com/tf2-object-detection:20230328055858\n",
      "The push refers to repository [476375380884.dkr.ecr.us-east-1.amazonaws.com/tf2-object-detection]\n",
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "\u001b[24Ba77cab5: Pushed   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digest: sha256:a6c80c3059c050a20b2ba6324b8e319f33884d2c88199b5415e29bd7317a0d83 size: 5782\n",
      "Saving ECR image URI into ecr_image_fullname.txt\n"
     ]
    }
   ],
   "source": [
    "# build and push the docker image. This code can be commented after being ran once.\n",
    "# This will take around 10 mins.\n",
    "image_name = 'tf2-object-detection'\n",
    "!sh ./docker/build_and_push.sh $image_name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e62b3562",
   "metadata": {},
   "source": [
    "To verify that the image was correctly pushed to the [Elastic Container Registry](https://aws.amazon.com/ecr/), you can look at it in the AWS webapp. For example, below you can see that three different images have been pushed to ECR. You should only see one, called `tf2-object-detection`.\n",
    "![ECR Example](../data/example_ecr.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0310b6a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "476375380884.dkr.ecr.us-east-1.amazonaws.com/tf2-object-detection:20230328055858\n"
     ]
    }
   ],
   "source": [
    "# display the container name\n",
    "with open (os.path.join('docker', 'ecr_image_fullname.txt'), 'r') as f:\n",
    "    container = f.readlines()[0][:-1]\n",
    "\n",
    "print(container)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b2a754",
   "metadata": {},
   "source": [
    "## Pre-trained model from model zoo\n",
    "\n",
    "As often, we are not training from scratch and we will be using a pretrained model from the TF Object Detection model zoo. You can find pretrained checkpoints [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md). Because your time is limited for this project, we recommend to only experiment with the following models:\n",
    "* SSD MobileNet V2 FPNLite 640x640\t\n",
    "* SSD ResNet50 V1 FPN 640x640 (RetinaNet50)\t\n",
    "* Faster R-CNN ResNet50 V1 640x640\t\n",
    "* EfficientDet D1 640x640\t\n",
    "* Faster R-CNN ResNet152 V1 640x640\t\n",
    "\n",
    "In the code below, the EfficientDet D1 model is downloaded and extracted. This code should be ajusted if you were to experiment with other architectures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c4b1d46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/checkpoint/ckpt-0.data-00000-of-00001\n",
      "ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/checkpoint/checkpoint\n",
      "ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/checkpoint/ckpt-0.index\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘/tmp/checkpoint’: File exists\n",
      "mkdir: cannot create directory ‘source_dir/checkpoint’: File exists\n",
      "--2023-03-28 06:06:50--  http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.tar.gz\n",
      "Resolving download.tensorflow.org (download.tensorflow.org)... 172.253.63.128, 2607:f8b0:4004:c17::80\n",
      "Connecting to download.tensorflow.org (download.tensorflow.org)|172.253.63.128|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 20518283 (20M) [application/x-tar]\n",
      "Saving to: ‘/tmp/ssd_mobilenet.tar.gz’\n",
      "\n",
      "     0K .......... .......... .......... .......... ..........  0% 12.7M 2s\n",
      "    50K .......... .......... .......... .......... ..........  0% 25.1M 1s\n",
      "   100K .......... .......... .......... .......... ..........  0% 22.8M 1s\n",
      "   150K .......... .......... .......... .......... ..........  0% 19.4M 1s\n",
      "   200K .......... .......... .......... .......... ..........  1% 24.5M 1s\n",
      "   250K .......... .......... .......... .......... ..........  1% 26.1M 1s\n",
      "   300K .......... .......... .......... .......... ..........  1% 21.0M 1s\n",
      "   350K .......... .......... .......... .......... ..........  1% 74.4M 1s\n",
      "   400K .......... .......... .......... .......... ..........  2% 24.1M 1s\n",
      "   450K .......... .......... .......... .......... ..........  2% 27.7M 1s\n",
      "   500K .......... .......... .......... .......... ..........  2% 50.5M 1s\n",
      "   550K .......... .......... .......... .......... ..........  2% 28.2M 1s\n",
      "   600K .......... .......... .......... .......... ..........  3% 34.8M 1s\n",
      "   650K .......... .......... .......... .......... ..........  3% 38.5M 1s\n",
      "   700K .......... .......... .......... .......... ..........  3% 37.7M 1s\n",
      "   750K .......... .......... .......... .......... ..........  3% 31.6M 1s\n",
      "   800K .......... .......... .......... .......... ..........  4% 44.0M 1s\n",
      "   850K .......... .......... .......... .......... ..........  4% 39.1M 1s\n",
      "   900K .......... .......... .......... .......... ..........  4% 43.7M 1s\n",
      "   950K .......... .......... .......... .......... ..........  4% 49.6M 1s\n",
      "  1000K .......... .......... .......... .......... ..........  5% 45.2M 1s\n",
      "  1050K .......... .......... .......... .......... ..........  5% 34.0M 1s\n",
      "  1100K .......... .......... .......... .......... ..........  5% 45.0M 1s\n",
      "  1150K .......... .......... .......... .......... ..........  5% 50.8M 1s\n",
      "  1200K .......... .......... .......... .......... ..........  6% 42.7M 1s\n",
      "  1250K .......... .......... .......... .......... ..........  6% 52.6M 1s\n",
      "  1300K .......... .......... .......... .......... ..........  6% 46.0M 1s\n",
      "  1350K .......... .......... .......... .......... ..........  6% 46.6M 1s\n",
      "  1400K .......... .......... .......... .......... ..........  7% 67.1M 1s\n",
      "  1450K .......... .......... .......... .......... ..........  7% 43.6M 1s\n",
      "  1500K .......... .......... .......... .......... ..........  7% 48.7M 1s\n",
      "  1550K .......... .......... .......... .......... ..........  7% 71.6M 1s\n",
      "  1600K .......... .......... .......... .......... ..........  8% 45.4M 1s\n",
      "  1650K .......... .......... .......... .......... ..........  8%  230M 1s\n",
      "  1700K .......... .......... .......... .......... ..........  8% 39.7M 1s\n",
      "  1750K .......... .......... .......... .......... ..........  8% 61.5M 0s\n",
      "  1800K .......... .......... .......... .......... ..........  9% 56.5M 0s\n",
      "  1850K .......... .......... .......... .......... ..........  9% 67.7M 0s\n",
      "  1900K .......... .......... .......... .......... ..........  9% 87.5M 0s\n",
      "  1950K .......... .......... .......... .......... ..........  9% 76.9M 0s\n",
      "  2000K .......... .......... .......... .......... .......... 10% 39.6M 0s\n",
      "  2050K .......... .......... .......... .......... .......... 10% 93.5M 0s\n",
      "  2100K .......... .......... .......... .......... .......... 10% 56.1M 0s\n",
      "  2150K .......... .......... .......... .......... .......... 10%  211M 0s\n",
      "  2200K .......... .......... .......... .......... .......... 11% 53.4M 0s\n",
      "  2250K .......... .......... .......... .......... .......... 11% 53.1M 0s\n",
      "  2300K .......... .......... .......... .......... .......... 11% 59.1M 0s\n",
      "  2350K .......... .......... .......... .......... .......... 11% 61.4M 0s\n",
      "  2400K .......... .......... .......... .......... .......... 12%  225M 0s\n",
      "  2450K .......... .......... .......... .......... .......... 12% 61.2M 0s\n",
      "  2500K .......... .......... .......... .......... .......... 12% 53.6M 0s\n",
      "  2550K .......... .......... .......... .......... .......... 12% 68.3M 0s\n",
      "  2600K .......... .......... .......... .......... .......... 13% 63.7M 0s\n",
      "  2650K .......... .......... .......... .......... .......... 13%  193M 0s\n",
      "  2700K .......... .......... .......... .......... .......... 13% 91.1M 0s\n",
      "  2750K .......... .......... .......... .......... .......... 13% 81.1M 0s\n",
      "  2800K .......... .......... .......... .......... .......... 14% 81.2M 0s\n",
      "  2850K .......... .......... .......... .......... .......... 14% 76.4M 0s\n",
      "  2900K .......... .......... .......... .......... .......... 14%  115M 0s\n",
      "  2950K .......... .......... .......... .......... .......... 14% 87.1M 0s\n",
      "  3000K .......... .......... .......... .......... .......... 15% 66.0M 0s\n",
      "  3050K .......... .......... .......... .......... .......... 15% 67.5M 0s\n",
      "  3100K .......... .......... .......... .......... .......... 15% 70.1M 0s\n",
      "  3150K .......... .......... .......... .......... .......... 15%  194M 0s\n",
      "  3200K .......... .......... .......... .......... .......... 16% 64.8M 0s\n",
      "  3250K .......... .......... .......... .......... .......... 16% 83.8M 0s\n",
      "  3300K .......... .......... .......... .......... .......... 16% 89.4M 0s\n",
      "  3350K .......... .......... .......... .......... .......... 16%  330M 0s\n",
      "  3400K .......... .......... .......... .......... .......... 17% 72.1M 0s\n",
      "  3450K .......... .......... .......... .......... .......... 17% 76.2M 0s\n",
      "  3500K .......... .......... .......... .......... .......... 17% 88.7M 0s\n",
      "  3550K .......... .......... .......... .......... .......... 17% 88.1M 0s\n",
      "  3600K .......... .......... .......... .......... .......... 18%  284M 0s\n",
      "  3650K .......... .......... .......... .......... .......... 18% 80.7M 0s\n",
      "  3700K .......... .......... .......... .......... .......... 18% 74.7M 0s\n",
      "  3750K .......... .......... .......... .......... .......... 18% 92.3M 0s\n",
      "  3800K .......... .......... .......... .......... .......... 19% 97.2M 0s\n",
      "  3850K .......... .......... .......... .......... .......... 19%  213M 0s\n",
      "  3900K .......... .......... .......... .......... .......... 19% 82.9M 0s\n",
      "  3950K .......... .......... .......... .......... .......... 19% 92.9M 0s\n",
      "  4000K .......... .......... .......... .......... .......... 20% 83.5M 0s\n",
      "  4050K .......... .......... .......... .......... .......... 20% 6.70M 0s\n",
      "  4100K .......... .......... .......... .......... .......... 20%  100M 0s\n",
      "  4150K .......... .......... .......... .......... .......... 20%  146M 0s\n",
      "  4200K .......... .......... .......... .......... .......... 21% 97.8M 0s\n",
      "  4250K .......... .......... .......... .......... .......... 21%  131M 0s\n",
      "  4300K .......... .......... .......... .......... .......... 21% 91.4M 0s\n",
      "  4350K .......... .......... .......... .......... .......... 21% 95.7M 0s\n",
      "  4400K .......... .......... .......... .......... .......... 22%  199M 0s\n",
      "  4450K .......... .......... .......... .......... .......... 22% 93.0M 0s\n",
      "  4500K .......... .......... .......... .......... .......... 22%  104M 0s\n",
      "  4550K .......... .......... .......... .......... .......... 22%  103M 0s\n",
      "  4600K .......... .......... .......... .......... .......... 23% 73.6M 0s\n",
      "  4650K .......... .......... .......... .......... .......... 23%  199M 0s\n",
      "  4700K .......... .......... .......... .......... .......... 23%  159M 0s\n",
      "  4750K .......... .......... .......... .......... .......... 23% 90.8M 0s\n",
      "  4800K .......... .......... .......... .......... .......... 24% 79.1M 0s\n",
      "  4850K .......... .......... .......... .......... .......... 24%  365M 0s\n",
      "  4900K .......... .......... .......... .......... .......... 24%  124M 0s\n",
      "  4950K .......... .......... .......... .......... .......... 24%  103M 0s\n",
      "  5000K .......... .......... .......... .......... .......... 25%  101M 0s\n",
      "  5050K .......... .......... .......... .......... .......... 25%  102M 0s\n",
      "  5100K .......... .......... .......... .......... .......... 25%  342M 0s\n",
      "  5150K .......... .......... .......... .......... .......... 25% 73.1M 0s\n",
      "  5200K .......... .......... .......... .......... .......... 26%  181M 0s\n",
      "  5250K .......... .......... .......... .......... .......... 26% 70.0M 0s\n",
      "  5300K .......... .......... .......... .......... .......... 26% 52.6M 0s\n",
      "  5350K .......... .......... .......... .......... .......... 26%  229M 0s\n",
      "  5400K .......... .......... .......... .......... .......... 27%  274M 0s\n",
      "  5450K .......... .......... .......... .......... .......... 27%  217M 0s\n",
      "  5500K .......... .......... .......... .......... .......... 27%  276M 0s\n",
      "  5550K .......... .......... .......... .......... .......... 27%  260M 0s\n",
      "  5600K .......... .......... .......... .......... .......... 28%  257M 0s\n",
      "  5650K .......... .......... .......... .......... .......... 28% 45.1M 0s\n",
      "  5700K .......... .......... .......... .......... .......... 28% 54.5M 0s\n",
      "  5750K .......... .......... .......... .......... .......... 28% 50.0M 0s\n",
      "  5800K .......... .......... .......... .......... .......... 29% 52.3M 0s\n",
      "  5850K .......... .......... .......... .......... .......... 29%  330M 0s\n",
      "  5900K .......... .......... .......... .......... .......... 29%  194M 0s\n",
      "  5950K .......... .......... .......... .......... .......... 29%  351M 0s\n",
      "  6000K .......... .......... .......... .......... .......... 30%  166M 0s\n",
      "  6050K .......... .......... .......... .......... .......... 30%  350M 0s\n",
      "  6100K .......... .......... .......... .......... .......... 30%  190M 0s\n",
      "  6150K .......... .......... .......... .......... .......... 30%  325M 0s\n",
      "  6200K .......... .......... .......... .......... .......... 31%  182M 0s\n",
      "  6250K .......... .......... .......... .......... .......... 31%  353M 0s\n",
      "  6300K .......... .......... .......... .......... .......... 31%  233M 0s\n",
      "  6350K .......... .......... .......... .......... .......... 31%  307M 0s\n",
      "  6400K .......... .......... .......... .......... .......... 32%  311M 0s\n",
      "  6450K .......... .......... .......... .......... .......... 32%  206M 0s\n",
      "  6500K .......... .......... .......... .......... .......... 32%  163M 0s\n",
      "  6550K .......... .......... .......... .......... .......... 32% 47.6M 0s\n",
      "  6600K .......... .......... .......... .......... .......... 33%  114M 0s\n",
      "  6650K .......... .......... .......... .......... .......... 33%  162M 0s\n",
      "  6700K .......... .......... .......... .......... .......... 33% 59.5M 0s\n",
      "  6750K .......... .......... .......... .......... .......... 33% 43.6M 0s\n",
      "  6800K .......... .......... .......... .......... .......... 34% 47.5M 0s\n",
      "  6850K .......... .......... .......... .......... .......... 34% 48.8M 0s\n",
      "  6900K .......... .......... .......... .......... .......... 34%  212M 0s\n",
      "  6950K .......... .......... .......... .......... .......... 34%  200M 0s\n",
      "  7000K .......... .......... .......... .......... .......... 35%  165M 0s\n",
      "  7050K .......... .......... .......... .......... .......... 35%  175M 0s\n",
      "  7100K .......... .......... .......... .......... .......... 35%  207M 0s\n",
      "  7150K .......... .......... .......... .......... .......... 35%  328M 0s\n",
      "  7200K .......... .......... .......... .......... .......... 36%  205M 0s\n",
      "  7250K .......... .......... .......... .......... .......... 36%  167M 0s\n",
      "  7300K .......... .......... .......... .......... .......... 36%  208M 0s\n",
      "  7350K .......... .......... .......... .......... .......... 36%  210M 0s\n",
      "  7400K .......... .......... .......... .......... .......... 37%  401M 0s\n",
      "  7450K .......... .......... .......... .......... .......... 37%  222M 0s\n",
      "  7500K .......... .......... .......... .......... .......... 37%  181M 0s\n",
      "  7550K .......... .......... .......... .......... .......... 37%  326M 0s\n",
      "  7600K .......... .......... .......... .......... .......... 38%  270M 0s\n",
      "  7650K .......... .......... .......... .......... .......... 38%  242M 0s\n",
      "  7700K .......... .......... .......... .......... .......... 38%  256M 0s\n",
      "  7750K .......... .......... .......... .......... .......... 38%  209M 0s\n",
      "  7800K .......... .......... .......... .......... .......... 39%  294M 0s\n",
      "  7850K .......... .......... .......... .......... .......... 39%  224M 0s\n",
      "  7900K .......... .......... .......... .......... .......... 39%  266M 0s\n",
      "  7950K .......... .......... .......... .......... .......... 39%  356M 0s\n",
      "  8000K .......... .......... .......... .......... .......... 40%  254M 0s\n",
      "  8050K .......... .......... .......... .......... .......... 40%  212M 0s\n",
      "  8100K .......... .......... .......... .......... .......... 40%  316M 0s\n",
      "  8150K .......... .......... .......... .......... .......... 40%  208M 0s\n",
      "  8200K .......... .......... .......... .......... .......... 41%  323M 0s\n",
      "  8250K .......... .......... .......... .......... .......... 41%  195M 0s\n",
      "  8300K .......... .......... .......... .......... .......... 41%  269M 0s\n",
      "  8350K .......... .......... .......... .......... .......... 41%  220M 0s\n",
      "  8400K .......... .......... .......... .......... .......... 42%  216M 0s\n",
      "  8450K .......... .......... .......... .......... .......... 42%  289M 0s\n",
      "  8500K .......... .......... .......... .......... .......... 42%  208M 0s\n",
      "  8550K .......... .......... .......... .......... .......... 42%  221M 0s\n",
      "  8600K .......... .......... .......... .......... .......... 43%  251M 0s\n",
      "  8650K .......... .......... .......... .......... .......... 43%  184M 0s\n",
      "  8700K .......... .......... .......... .......... .......... 43%  208M 0s\n",
      "  8750K .......... .......... .......... .......... .......... 43%  362M 0s\n",
      "  8800K .......... .......... .......... .......... .......... 44% 82.6M 0s\n",
      "  8850K .......... .......... .......... .......... .......... 44%  166M 0s\n",
      "  8900K .......... .......... .......... .......... .......... 44%  143M 0s\n",
      "  8950K .......... .......... .......... .......... .......... 44%  170M 0s\n",
      "  9000K .......... .......... .......... .......... .......... 45%  261M 0s\n",
      "  9050K .......... .......... .......... .......... .......... 45%  100M 0s\n",
      "  9100K .......... .......... .......... .......... .......... 45% 91.3M 0s\n",
      "  9150K .......... .......... .......... .......... .......... 45%  165M 0s\n",
      "  9200K .......... .......... .......... .......... .......... 46%  119M 0s\n",
      "  9250K .......... .......... .......... .......... .......... 46% 84.6M 0s\n",
      "  9300K .......... .......... .......... .......... .......... 46%  151M 0s\n",
      "  9350K .......... .......... .......... .......... .......... 46% 80.5M 0s\n",
      "  9400K .......... .......... .......... .......... .......... 47%  177M 0s\n",
      "  9450K .......... .......... .......... .......... .......... 47% 80.3M 0s\n",
      "  9500K .......... .......... .......... .......... .......... 47%  120M 0s\n",
      "  9550K .......... .......... .......... .......... .......... 47%  123M 0s\n",
      "  9600K .......... .......... .......... .......... .......... 48% 36.8M 0s\n",
      "  9650K .......... .......... .......... .......... .......... 48% 34.0M 0s\n",
      "  9700K .......... .......... .......... .......... .......... 48%  144M 0s\n",
      "  9750K .......... .......... .......... .......... .......... 48%  148M 0s\n",
      "  9800K .......... .......... .......... .......... .......... 49%  272M 0s\n",
      "  9850K .......... .......... .......... .......... .......... 49%  281M 0s\n",
      "  9900K .......... .......... .......... .......... .......... 49%  222M 0s\n",
      "  9950K .......... .......... .......... .......... .......... 49%  257M 0s\n",
      " 10000K .......... .......... .......... .......... .......... 50%  298M 0s\n",
      " 10050K .......... .......... .......... .......... .......... 50%  299M 0s\n",
      " 10100K .......... .......... .......... .......... .......... 50%  463M 0s\n",
      " 10150K .......... .......... .......... .......... .......... 50%  230M 0s\n",
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      " 10300K .......... .......... .......... .......... .......... 51%  232M 0s\n",
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      " 20000K .......... .......... .......... .......              100% 69.5M=0.2s\n",
      "\n",
      "2023-03-28 06:06:50 (92.4 MB/s) - ‘/tmp/ssd_mobilenet.tar.gz’ saved [20518283/20518283]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "mkdir /tmp/checkpoint\n",
    "mkdir source_dir/checkpoint\n",
    "\n",
    "#wget -O /tmp/efficientdet.tar.gz http://download.tensorflow.org/models/object_detection/tf2/20200711/efficientdet_d1_coco17_tpu-32.tar.gz\n",
    "wget -O /tmp/ssd_mobilenet.tar.gz http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.tar.gz\n",
    "\n",
    "#tar -zxvf /tmp/efficientdet.tar.gz --strip-components 2 --directory source_dir/checkpoint efficientdet_d1_coco17_tpu-32/checkpoint\n",
    "tar -zxvf /tmp/ssd_mobilenet.tar.gz --strip-components 2 --directory source_dir/checkpoint ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/checkpoint\n",
    "\n",
    "#tar -zxvf /tmp/ssd_mobilenet.tar.gz --strip-components 2 --directory . ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/pipeline.config\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1e04a98",
   "metadata": {},
   "source": [
    "## Edit pipeline.config file\n",
    "\n",
    "The [`pipeline.config`](source_dir/pipeline.config) in the `source_dir` folder should be updated when you experiment with different models. The different config files are available [here](https://github.com/tensorflow/models/tree/master/research/object_detection/configs/tf2).\n",
    "\n",
    ">Note: The provided `pipeline.config` file works well with the `EfficientDet` model. You would need to modify it when working with other models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47483545",
   "metadata": {},
   "source": [
    "## Launch Training Job\n",
    "\n",
    "Now that we have a dataset, a docker image and some pretrained model weights, we can launch the training job. To do so, we create a [Sagemaker Framework](https://sagemaker.readthedocs.io/en/stable/frameworks/index.html), where we indicate the container name, name of the config file, number of training steps etc.\n",
    "\n",
    "The `run_training.sh` script does the following:\n",
    "* train the model for `num_train_steps` \n",
    "* evaluate over the val dataset\n",
    "* export the model\n",
    "\n",
    "Different metrics will be displayed during the evaluation phase, including the mean average precision. These metrics can be used to quantify your model performances and compare over the different iterations.\n",
    "\n",
    "You can also monitor the training progress by navigating to **Training -> Training Jobs** from the Amazon Sagemaker dashboard in the Web UI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0c7175cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: tf2-object-detection-2023-03-28-06-06-51-015\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-03-28 06:06:53 Starting - Starting the training job...\n",
      "2023-03-28 06:07:32 Starting - Preparing the instances for training......\n",
      "2023-03-28 06:08:30 Downloading - Downloading input data...\n",
      "2023-03-28 06:08:49 Training - Downloading the training image...............\n",
      "2023-03-28 06:11:25 Training - Training image download completed. Training in progress....\u001b[34m2023-03-28 06:11:51,785 sagemaker-training-toolkit INFO     No Neurons detected (normal if no neurons installed)\u001b[0m\n",
      "\u001b[34m2023-03-28 06:11:51,816 sagemaker-training-toolkit INFO     No Neurons detected (normal if no neurons installed)\u001b[0m\n",
      "\u001b[34m2023-03-28 06:11:51,847 sagemaker-training-toolkit INFO     No Neurons detected (normal if no neurons installed)\u001b[0m\n",
      "\u001b[34m2023-03-28 06:11:51,861 sagemaker-training-toolkit INFO     Invoking user script\u001b[0m\n",
      "\u001b[34mTraining Env:\u001b[0m\n",
      "\u001b[34m{\n",
      "    \"additional_framework_parameters\": {},\n",
      "    \"channel_input_dirs\": {\n",
      "        \"train\": \"/opt/ml/input/data/train\",\n",
      "        \"val\": \"/opt/ml/input/data/val\"\n",
      "    },\n",
      "    \"current_host\": \"algo-1\",\n",
      "    \"current_instance_group\": \"homogeneousCluster\",\n",
      "    \"current_instance_group_hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"current_instance_type\": \"ml.p3.2xlarge\",\n",
      "    \"distribution_hosts\": [],\n",
      "    \"distribution_instance_groups\": [],\n",
      "    \"framework_module\": null,\n",
      "    \"hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"hyperparameters\": {\n",
      "        \"model_dir\": \"/opt/training\",\n",
      "        \"num_train_steps\": \"10000\",\n",
      "        \"pipeline_config_path\": \"pipeline.config\",\n",
      "        \"sample_1_of_n_eval_examples\": \"1\"\n",
      "    },\n",
      "    \"input_config_dir\": \"/opt/ml/input/config\",\n",
      "    \"input_data_config\": {\n",
      "        \"train\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        },\n",
      "        \"val\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        }\n",
      "    },\n",
      "    \"input_dir\": \"/opt/ml/input\",\n",
      "    \"instance_groups\": [\n",
      "        \"homogeneousCluster\"\n",
      "    ],\n",
      "    \"instance_groups_dict\": {\n",
      "        \"homogeneousCluster\": {\n",
      "            \"instance_group_name\": \"homogeneousCluster\",\n",
      "            \"instance_type\": \"ml.p3.2xlarge\",\n",
      "            \"hosts\": [\n",
      "                \"algo-1\"\n",
      "            ]\n",
      "        }\n",
      "    },\n",
      "    \"is_hetero\": false,\n",
      "    \"is_master\": true,\n",
      "    \"is_modelparallel_enabled\": null,\n",
      "    \"is_smddpmprun_installed\": false,\n",
      "    \"job_name\": \"tf2-object-detection-2023-03-28-06-06-51-015\",\n",
      "    \"log_level\": 20,\n",
      "    \"master_hostname\": \"algo-1\",\n",
      "    \"model_dir\": \"/opt/ml/model\",\n",
      "    \"module_dir\": \"s3://sagemaker-us-east-1-476375380884/tf2-object-detection-2023-03-28-06-06-51-015/source/sourcedir.tar.gz\",\n",
      "    \"module_name\": \"run_training.sh\",\n",
      "    \"network_interface_name\": \"eth0\",\n",
      "    \"num_cpus\": 8,\n",
      "    \"num_gpus\": 1,\n",
      "    \"num_neurons\": 0,\n",
      "    \"output_data_dir\": \"/opt/ml/output/data\",\n",
      "    \"output_dir\": \"/opt/ml/output\",\n",
      "    \"output_intermediate_dir\": \"/opt/ml/output/intermediate\",\n",
      "    \"resource_config\": {\n",
      "        \"current_host\": \"algo-1\",\n",
      "        \"current_instance_type\": \"ml.p3.2xlarge\",\n",
      "        \"current_group_name\": \"homogeneousCluster\",\n",
      "        \"hosts\": [\n",
      "            \"algo-1\"\n",
      "        ],\n",
      "        \"instance_groups\": [\n",
      "            {\n",
      "                \"instance_group_name\": \"homogeneousCluster\",\n",
      "                \"instance_type\": \"ml.p3.2xlarge\",\n",
      "                \"hosts\": [\n",
      "                    \"algo-1\"\n",
      "                ]\n",
      "            }\n",
      "        ],\n",
      "        \"network_interface_name\": \"eth0\"\n",
      "    },\n",
      "    \"user_entry_point\": \"run_training.sh\"\u001b[0m\n",
      "\u001b[34m}\u001b[0m\n",
      "\u001b[34mEnvironment variables:\u001b[0m\n",
      "\u001b[34mSM_HOSTS=[\"algo-1\"]\u001b[0m\n",
      "\u001b[34mSM_NETWORK_INTERFACE_NAME=eth0\u001b[0m\n",
      "\u001b[34mSM_HPS={\"model_dir\":\"/opt/training\",\"num_train_steps\":\"10000\",\"pipeline_config_path\":\"pipeline.config\",\"sample_1_of_n_eval_examples\":\"1\"}\u001b[0m\n",
      "\u001b[34mSM_USER_ENTRY_POINT=run_training.sh\u001b[0m\n",
      "\u001b[34mSM_FRAMEWORK_PARAMS={}\u001b[0m\n",
      "\u001b[34mSM_RESOURCE_CONFIG={\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.p3.2xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.p3.2xlarge\"}],\"network_interface_name\":\"eth0\"}\u001b[0m\n",
      "\u001b[34mSM_INPUT_DATA_CONFIG={\"train\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"val\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}}\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\u001b[0m\n",
      "\u001b[34mSM_CHANNELS=[\"train\",\"val\"]\u001b[0m\n",
      "\u001b[34mSM_CURRENT_HOST=algo-1\u001b[0m\n",
      "\u001b[34mSM_CURRENT_INSTANCE_TYPE=ml.p3.2xlarge\u001b[0m\n",
      "\u001b[34mSM_CURRENT_INSTANCE_GROUP=homogeneousCluster\u001b[0m\n",
      "\u001b[34mSM_CURRENT_INSTANCE_GROUP_HOSTS=[\"algo-1\"]\u001b[0m\n",
      "\u001b[34mSM_INSTANCE_GROUPS=[\"homogeneousCluster\"]\u001b[0m\n",
      "\u001b[34mSM_INSTANCE_GROUPS_DICT={\"homogeneousCluster\":{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.p3.2xlarge\"}}\u001b[0m\n",
      "\u001b[34mSM_DISTRIBUTION_INSTANCE_GROUPS=[]\u001b[0m\n",
      "\u001b[34mSM_IS_HETERO=false\u001b[0m\n",
      "\u001b[34mSM_MODULE_NAME=run_training.sh\u001b[0m\n",
      "\u001b[34mSM_LOG_LEVEL=20\u001b[0m\n",
      "\u001b[34mSM_FRAMEWORK_MODULE=\u001b[0m\n",
      "\u001b[34mSM_INPUT_DIR=/opt/ml/input\u001b[0m\n",
      "\u001b[34mSM_INPUT_CONFIG_DIR=/opt/ml/input/config\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_DIR=/opt/ml/output\u001b[0m\n",
      "\u001b[34mSM_NUM_CPUS=8\u001b[0m\n",
      "\u001b[34mSM_NUM_GPUS=1\u001b[0m\n",
      "\u001b[34mSM_NUM_NEURONS=0\u001b[0m\n",
      "\u001b[34mSM_MODEL_DIR=/opt/ml/model\u001b[0m\n",
      "\u001b[34mSM_MODULE_DIR=s3://sagemaker-us-east-1-476375380884/tf2-object-detection-2023-03-28-06-06-51-015/source/sourcedir.tar.gz\u001b[0m\n",
      "\u001b[34mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{\"train\":\"/opt/ml/input/data/train\",\"val\":\"/opt/ml/input/data/val\"},\"current_host\":\"algo-1\",\"current_instance_group\":\"homogeneousCluster\",\"current_instance_group_hosts\":[\"algo-1\"],\"current_instance_type\":\"ml.p3.2xlarge\",\"distribution_hosts\":[],\"distribution_instance_groups\":[],\"framework_module\":null,\"hosts\":[\"algo-1\"],\"hyperparameters\":{\"model_dir\":\"/opt/training\",\"num_train_steps\":\"10000\",\"pipeline_config_path\":\"pipeline.config\",\"sample_1_of_n_eval_examples\":\"1\"},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{\"train\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"val\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}},\"input_dir\":\"/opt/ml/input\",\"instance_groups\":[\"homogeneousCluster\"],\"instance_groups_dict\":{\"homogeneousCluster\":{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.p3.2xlarge\"}},\"is_hetero\":false,\"is_master\":true,\"is_modelparallel_enabled\":null,\"is_smddpmprun_installed\":false,\"job_name\":\"tf2-object-detection-2023-03-28-06-06-51-015\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"s3://sagemaker-us-east-1-476375380884/tf2-object-detection-2023-03-28-06-06-51-015/source/sourcedir.tar.gz\",\"module_name\":\"run_training.sh\",\"network_interface_name\":\"eth0\",\"num_cpus\":8,\"num_gpus\":1,\"num_neurons\":0,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.p3.2xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.p3.2xlarge\"}],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"run_training.sh\"}\u001b[0m\n",
      "\u001b[34mSM_USER_ARGS=[\"--model_dir\",\"/opt/training\",\"--num_train_steps\",\"10000\",\"--pipeline_config_path\",\"pipeline.config\",\"--sample_1_of_n_eval_examples\",\"1\"]\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\u001b[0m\n",
      "\u001b[34mSM_CHANNEL_TRAIN=/opt/ml/input/data/train\u001b[0m\n",
      "\u001b[34mSM_CHANNEL_VAL=/opt/ml/input/data/val\u001b[0m\n",
      "\u001b[34mSM_HP_MODEL_DIR=/opt/training\u001b[0m\n",
      "\u001b[34mSM_HP_NUM_TRAIN_STEPS=10000\u001b[0m\n",
      "\u001b[34mSM_HP_PIPELINE_CONFIG_PATH=pipeline.config\u001b[0m\n",
      "\u001b[34mSM_HP_SAMPLE_1_OF_N_EVAL_EXAMPLES=1\u001b[0m\n",
      "\u001b[34mPYTHONPATH=/opt/ml/code:/usr/local/bin:/usr/lib/python38.zip:/usr/lib/python3.8:/usr/lib/python3.8/lib-dynload:/usr/local/lib/python3.8/dist-packages:/usr/lib/python3/dist-packages\u001b[0m\n",
      "\u001b[34mInvoking script with the following command:\u001b[0m\n",
      "\u001b[34m/bin/sh -c \"./run_training.sh --model_dir /opt/training --num_train_steps 10000 --pipeline_config_path pipeline.config --sample_1_of_n_eval_examples 1\"\u001b[0m\n",
      "\u001b[34m2023-03-28 06:11:51,862 sagemaker-training-toolkit INFO     Exceptions not imported for SageMaker Debugger as it is not installed.\u001b[0m\n",
      "\u001b[34m===TRAINING THE MODEL==\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\u001b[0m\n",
      "\u001b[34mI0328 06:12:00.057149 140083402434368 mirrored_strategy.py:374] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Maybe overwriting train_steps: 10000\u001b[0m\n",
      "\u001b[34mI0328 06:12:00.061558 140083402434368 config_util.py:552] Maybe overwriting train_steps: 10000\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Maybe overwriting use_bfloat16: False\u001b[0m\n",
      "\u001b[34mI0328 06:12:00.061692 140083402434368 config_util.py:552] Maybe overwriting use_bfloat16: False\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/object_detection/model_lib_v2.py:563: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mrename to distribute_datasets_from_function\u001b[0m\n",
      "\u001b[34mW0328 06:12:00.092616 140083402434368 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/object_detection/model_lib_v2.py:563: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mrename to distribute_datasets_from_function\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reading unweighted datasets: ['/opt/ml/input/data/train/*.tfrecord']\u001b[0m\n",
      "\u001b[34mI0328 06:12:00.100981 140083402434368 dataset_builder.py:162] Reading unweighted datasets: ['/opt/ml/input/data/train/*.tfrecord']\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reading record datasets for input file: ['/opt/ml/input/data/train/*.tfrecord']\u001b[0m\n",
      "\u001b[34mI0328 06:12:00.102638 140083402434368 dataset_builder.py:79] Reading record datasets for input file: ['/opt/ml/input/data/train/*.tfrecord']\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Number of filenames to read: 84\u001b[0m\n",
      "\u001b[34mI0328 06:12:00.102744 140083402434368 dataset_builder.py:80] Number of filenames to read: 84\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/object_detection/builders/dataset_builder.py:100: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.\u001b[0m\n",
      "\u001b[34mW0328 06:12:00.110701 140083402434368 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/object_detection/builders/dataset_builder.py:100: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/object_detection/builders/dataset_builder.py:235: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.data.Dataset.map()\u001b[0m\n",
      "\u001b[34mW0328 06:12:00.130904 140083402434368 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/object_detection/builders/dataset_builder.py:235: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.data.Dataset.map()\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mLambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\u001b[0m\n",
      "\u001b[34mW0328 06:12:00.644567 140083402434368 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mLambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mCreate a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\u001b[0m\n",
      "\u001b[34mW0328 06:12:07.401397 140083402434368 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mCreate a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mkeep_dims is deprecated, use keepdims instead\u001b[0m\n",
      "\u001b[34mW0328 06:12:10.100912 140083402434368 deprecation.py:554] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mkeep_dims is deprecated, use keepdims instead\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.cast` instead.\u001b[0m\n",
      "\u001b[34mW0328 06:12:10.787984 140083402434368 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.cast` instead.\u001b[0m\n",
      "\u001b[34m/usr/local/lib/python3.8/dist-packages/keras/backend.py:451: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n",
      "  warnings.warn(\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 06:12:41.267839 140083402434368 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 06:12:41.271147 140083402434368 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 06:12:41.272311 140083402434368 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 06:12:41.273379 140083402434368 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 06:12:41.277359 140083402434368 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 06:12:41.278469 140083402434368 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 06:12:41.279553 140083402434368 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 06:12:41.280636 140083402434368 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 06:12:41.285716 140083402434368 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 06:12:41.286780 140083402434368 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/deprecation.py:629: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse fn_output_signature instead\u001b[0m\n",
      "\u001b[34mW0328 06:12:42.586434 140060843337472 deprecation.py:554] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/deprecation.py:629: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse fn_output_signature instead\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 100 per-step time 0.626s\u001b[0m\n",
      "\u001b[34mI0328 06:13:44.848868 140083402434368 model_lib_v2.py:705] Step 100 per-step time 0.626s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.24476863,\n",
      " 'Loss/localization_loss': 0.45889825,\n",
      " 'Loss/regularization_loss': 0.14803497,\n",
      " 'Loss/total_loss': 0.85170186,\n",
      " 'learning_rate': 0.0005}\u001b[0m\n",
      "\u001b[34mI0328 06:13:44.849197 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.24476863,\n",
      " 'Loss/localization_loss': 0.45889825,\n",
      " 'Loss/regularization_loss': 0.14803497,\n",
      " 'Loss/total_loss': 0.85170186,\n",
      " 'learning_rate': 0.0005}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 200 per-step time 0.200s\u001b[0m\n",
      "\u001b[34mI0328 06:14:04.832009 140083402434368 model_lib_v2.py:705] Step 200 per-step time 0.200s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.21524966,\n",
      " 'Loss/localization_loss': 0.34028274,\n",
      " 'Loss/regularization_loss': 0.1467805,\n",
      " 'Loss/total_loss': 0.7023129,\n",
      " 'learning_rate': 0.0005}\u001b[0m\n",
      "\u001b[34mI0328 06:14:04.832303 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.21524966,\n",
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      "\u001b[34mINFO:tensorflow:Step 300 per-step time 0.198s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.23653822,\n",
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      "\u001b[34mI0328 06:14:24.639883 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.23653822,\n",
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      " 'learning_rate': 0.0005}\u001b[0m\n"
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     "output_type": "stream",
     "text": [
      "\u001b[34mINFO:tensorflow:Step 400 per-step time 0.197s\u001b[0m\n",
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      "\u001b[34mI0328 06:14:44.316056 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.20762832,\n",
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      "\u001b[34mI0328 06:15:04.263198 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.19174273,\n",
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      "\u001b[34mI0328 06:16:04.006964 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.20474581,\n",
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      "\u001b[34mI0328 06:16:43.634899 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.21180555,\n",
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     ]
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    {
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     "output_type": "stream",
     "text": [
      "\u001b[34mINFO:tensorflow:Step 1800 per-step time 0.198s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.13927726,\n",
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      "\u001b[34mI0328 06:22:03.760773 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.13927726,\n",
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      "\u001b[34mINFO:tensorflow:Step 2700 per-step time 0.197s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.14185762,\n",
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      "\u001b[34mI0328 06:22:23.458512 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.14185762,\n",
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      "\u001b[34mINFO:tensorflow:Step 2800 per-step time 0.198s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.1253938,\n",
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      "\u001b[34mI0328 06:22:43.283566 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.1253938,\n",
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      "\u001b[34mINFO:tensorflow:Step 2900 per-step time 0.198s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.114503235,\n",
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      "\u001b[34mI0328 06:23:03.039424 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.114503235,\n",
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      "\u001b[34mINFO:tensorflow:Step 3000 per-step time 0.198s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.16103198,\n",
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      "\u001b[34mI0328 06:23:22.801836 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.16103198,\n",
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      "\u001b[34mINFO:tensorflow:Step 3100 per-step time 0.214s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.13288279,\n",
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      "\u001b[34mI0328 06:23:44.171678 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.13288279,\n",
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      " 'learning_rate': 1e-04}\u001b[0m\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mINFO:tensorflow:Step 3200 per-step time 0.198s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.12721327,\n",
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      "\u001b[34mI0328 06:24:04.004794 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.12721327,\n",
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      "\u001b[34mINFO:tensorflow:Step 3300 per-step time 0.198s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.10752064,\n",
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      "\u001b[34mI0328 06:24:23.792006 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.10752064,\n",
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      "\u001b[34mINFO:tensorflow:Step 3400 per-step time 0.197s\u001b[0m\n",
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      "\u001b[34mI0328 06:24:43.525863 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.1250873,\n",
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      "\u001b[34mI0328 06:25:03.350882 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.11763041,\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.11862395,\n",
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      "\u001b[34mI0328 06:25:23.191158 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.11862395,\n",
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      "\u001b[34mI0328 06:25:43.025831 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.121447936,\n",
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      "\u001b[34mI0328 06:26:02.724974 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.115895934,\n",
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      "\u001b[34mI0328 06:28:22.982294 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.113166116,\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "\u001b[34mINFO:tensorflow:Step 4600 per-step time 0.197s\u001b[0m\n",
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      "\u001b[34mI0328 06:28:42.718199 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.100091666,\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.12441511,\n",
      " 'Loss/localization_loss': 0.13244615,\n",
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      " 'learning_rate': 1e-04}\u001b[0m\n",
      "\u001b[34mI0328 06:29:42.207038 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.12441511,\n",
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      "\u001b[34mINFO:tensorflow:Step 5000 per-step time 0.198s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.102208704,\n",
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      "\u001b[34mI0328 06:30:02.039499 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.102208704,\n",
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      "\u001b[34mINFO:tensorflow:Step 5100 per-step time 0.214s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.11610595,\n",
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      "\u001b[34mI0328 06:30:23.394045 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.11610595,\n",
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      "\u001b[34mINFO:tensorflow:Step 5200 per-step time 0.198s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.089622326,\n",
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      "\u001b[34mI0328 06:30:43.238082 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.089622326,\n",
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      "\u001b[34mINFO:tensorflow:Step 5300 per-step time 0.199s\u001b[0m\n",
      "\u001b[34mI0328 06:31:03.141107 140083402434368 model_lib_v2.py:705] Step 5300 per-step time 0.199s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.10164692,\n",
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      "\u001b[34mI0328 06:31:03.141415 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.10164692,\n",
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      "\u001b[34mINFO:tensorflow:Step 5400 per-step time 0.198s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.12550238,\n",
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      "\u001b[34mI0328 06:31:22.973876 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.12550238,\n",
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      "\u001b[34mINFO:tensorflow:Step 5500 per-step time 0.198s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.10612437,\n",
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      "\u001b[34mI0328 06:31:42.729727 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.10612437,\n",
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      "\u001b[34mINFO:tensorflow:Step 5600 per-step time 0.199s\u001b[0m\n",
      "\u001b[34mI0328 06:32:02.639743 140083402434368 model_lib_v2.py:705] Step 5600 per-step time 0.199s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.09542818,\n",
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      "\u001b[34mI0328 06:32:02.640035 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.09542818,\n",
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      "\u001b[34mINFO:tensorflow:Step 5700 per-step time 0.198s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.08765979,\n",
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      "\u001b[34mI0328 06:32:22.470701 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.08765979,\n",
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      "\u001b[34mINFO:tensorflow:Step 5800 per-step time 0.197s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.10282505,\n",
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      "\u001b[34mI0328 06:32:42.172876 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.10282505,\n",
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      "\u001b[34mINFO:tensorflow:Step 5900 per-step time 0.197s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.082412496,\n",
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      "\u001b[34mI0328 06:33:01.918648 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.082412496,\n",
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      " 'learning_rate': 5e-05}\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mINFO:tensorflow:Step 6000 per-step time 0.198s\u001b[0m\n",
      "\u001b[34mI0328 06:33:21.738929 140083402434368 model_lib_v2.py:705] Step 6000 per-step time 0.198s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.10756228,\n",
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      "\u001b[34mI0328 06:33:21.739262 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.10756228,\n",
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      "\u001b[34mINFO:tensorflow:Step 6100 per-step time 0.215s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.108089484,\n",
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      " 'learning_rate': 5e-05}\u001b[0m\n",
      "\u001b[34mI0328 06:33:43.233988 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.108089484,\n",
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      "\u001b[34mINFO:tensorflow:Step 6200 per-step time 0.200s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.08391653,\n",
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      "\u001b[34mI0328 06:34:03.190734 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.08391653,\n",
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      "\u001b[34mINFO:tensorflow:Step 6300 per-step time 0.199s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.07188357,\n",
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      "\u001b[34mI0328 06:34:23.088821 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.07188357,\n",
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      "\u001b[34mINFO:tensorflow:Step 6400 per-step time 0.199s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.10734618,\n",
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      "\u001b[34mI0328 06:34:42.989519 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.10734618,\n",
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      "\u001b[34mINFO:tensorflow:Step 6500 per-step time 0.198s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.105060816,\n",
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      "\u001b[34mI0328 06:35:02.813765 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.105060816,\n",
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      "\u001b[34mINFO:tensorflow:Step 6600 per-step time 0.198s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.08356622,\n",
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      "\u001b[34mI0328 06:35:22.622053 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.08356622,\n",
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      "\u001b[34mINFO:tensorflow:Step 6700 per-step time 0.199s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.09979926,\n",
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      "\u001b[34mI0328 06:35:42.537655 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.09979926,\n",
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      "\u001b[34mI0328 06:36:02.549775 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.08169236,\n",
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      "\u001b[34mI0328 06:36:22.288320 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.09089319,\n",
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      "\u001b[34mI0328 06:36:42.096563 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.087540425,\n",
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      "\u001b[34mI0328 06:37:03.443123 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.094383016,\n",
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      "\u001b[34mI0328 06:37:23.374556 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.110768646,\n",
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      "\u001b[34mINFO:tensorflow:Step 7300 per-step time 0.197s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.09944536,\n",
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      "\u001b[34mI0328 06:37:43.079467 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.09944536,\n",
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      " 'learning_rate': 5e-05}\u001b[0m\n"
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     "name": "stdout",
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     "text": [
      "\u001b[34mINFO:tensorflow:Step 7400 per-step time 0.198s\u001b[0m\n",
      "\u001b[34mI0328 06:38:02.846490 140083402434368 model_lib_v2.py:705] Step 7400 per-step time 0.198s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.08257485,\n",
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      "\u001b[34mI0328 06:38:02.846834 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.08257485,\n",
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      "\u001b[34mINFO:tensorflow:Step 7500 per-step time 0.197s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.09261198,\n",
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      "\u001b[34mI0328 06:38:22.589524 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.09261198,\n",
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      "\u001b[34mINFO:tensorflow:Step 7600 per-step time 0.199s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.079624705,\n",
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      "\u001b[34mI0328 06:38:42.490374 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.079624705,\n",
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      "\u001b[34mINFO:tensorflow:Step 7700 per-step time 0.197s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.07906,\n",
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      "\u001b[34mI0328 06:39:02.209890 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.07906,\n",
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      "\u001b[34mINFO:tensorflow:Step 7800 per-step time 0.199s\u001b[0m\n",
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      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.09330241,\n",
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      "\u001b[34mI0328 06:39:22.112222 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.09330241,\n",
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      "\u001b[34mINFO:tensorflow:Step 7900 per-step time 0.198s\u001b[0m\n",
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      "\u001b[34mI0328 06:39:41.914400 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.07658799,\n",
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      "\u001b[34mINFO:tensorflow:Step 8000 per-step time 0.199s\u001b[0m\n",
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      "\u001b[34mI0328 06:40:01.847076 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.09531404,\n",
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      "\u001b[34mI0328 06:40:23.473521 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.08845184,\n",
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      "\u001b[34mI0328 06:40:43.253319 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.100558706,\n",
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      "\u001b[34mI0328 06:41:23.023095 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.082930826,\n",
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      "\u001b[34mI0328 06:41:42.950003 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.10136029,\n",
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      "\u001b[34mI0328 06:42:02.832331 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.08505704,\n",
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      "\u001b[34mI0328 06:42:22.674308 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.09176675,\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\u001b[34mINFO:tensorflow:Step 8800 per-step time 0.198s\u001b[0m\n",
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      "\u001b[34mI0328 06:42:42.427338 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.0946676,\n",
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      "\u001b[34mI0328 06:43:02.319877 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.06862148,\n",
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      "\u001b[34mI0328 06:43:22.060746 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.07781081,\n",
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      "\u001b[34mI0328 06:43:43.394207 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.10069044,\n",
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      "\u001b[34mI0328 06:44:03.224445 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.10259696,\n",
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      "\u001b[34mI0328 06:44:23.010681 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.076272756,\n",
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      "\u001b[34mI0328 06:44:42.890815 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.08906011,\n",
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      "\u001b[34mI0328 06:45:02.864056 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.09042391,\n",
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      " 'Loss/regularization_loss': 0.1165901,\n",
      " 'Loss/total_loss': 0.31698704,\n",
      " 'learning_rate': 1e-05}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 9600 per-step time 0.199s\u001b[0m\n",
      "\u001b[34mI0328 06:45:22.737965 140083402434368 model_lib_v2.py:705] Step 9600 per-step time 0.199s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.07603133,\n",
      " 'Loss/localization_loss': 0.0726733,\n",
      " 'Loss/regularization_loss': 0.11656834,\n",
      " 'Loss/total_loss': 0.26527297,\n",
      " 'learning_rate': 1e-05}\u001b[0m\n",
      "\u001b[34mI0328 06:45:22.738256 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.07603133,\n",
      " 'Loss/localization_loss': 0.0726733,\n",
      " 'Loss/regularization_loss': 0.11656834,\n",
      " 'Loss/total_loss': 0.26527297,\n",
      " 'learning_rate': 1e-05}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 9700 per-step time 0.197s\u001b[0m\n",
      "\u001b[34mI0328 06:45:42.460453 140083402434368 model_lib_v2.py:705] Step 9700 per-step time 0.197s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.10561768,\n",
      " 'Loss/localization_loss': 0.12367955,\n",
      " 'Loss/regularization_loss': 0.11654676,\n",
      " 'Loss/total_loss': 0.34584397,\n",
      " 'learning_rate': 1e-05}\u001b[0m\n",
      "\u001b[34mI0328 06:45:42.460806 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.10561768,\n",
      " 'Loss/localization_loss': 0.12367955,\n",
      " 'Loss/regularization_loss': 0.11654676,\n",
      " 'Loss/total_loss': 0.34584397,\n",
      " 'learning_rate': 1e-05}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 9800 per-step time 0.197s\u001b[0m\n",
      "\u001b[34mI0328 06:46:02.182202 140083402434368 model_lib_v2.py:705] Step 9800 per-step time 0.197s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.086041786,\n",
      " 'Loss/localization_loss': 0.112305544,\n",
      " 'Loss/regularization_loss': 0.11652537,\n",
      " 'Loss/total_loss': 0.31487268,\n",
      " 'learning_rate': 1e-05}\u001b[0m\n",
      "\u001b[34mI0328 06:46:02.182567 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.086041786,\n",
      " 'Loss/localization_loss': 0.112305544,\n",
      " 'Loss/regularization_loss': 0.11652537,\n",
      " 'Loss/total_loss': 0.31487268,\n",
      " 'learning_rate': 1e-05}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 9900 per-step time 0.198s\u001b[0m\n",
      "\u001b[34mI0328 06:46:21.982601 140083402434368 model_lib_v2.py:705] Step 9900 per-step time 0.198s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.09930786,\n",
      " 'Loss/localization_loss': 0.11362833,\n",
      " 'Loss/regularization_loss': 0.11650403,\n",
      " 'Loss/total_loss': 0.32944024,\n",
      " 'learning_rate': 1e-05}\u001b[0m\n",
      "\u001b[34mI0328 06:46:21.982914 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.09930786,\n",
      " 'Loss/localization_loss': 0.11362833,\n",
      " 'Loss/regularization_loss': 0.11650403,\n",
      " 'Loss/total_loss': 0.32944024,\n",
      " 'learning_rate': 1e-05}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 10000 per-step time 0.197s\u001b[0m\n",
      "\u001b[34mI0328 06:46:41.710158 140083402434368 model_lib_v2.py:705] Step 10000 per-step time 0.197s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.08606551,\n",
      " 'Loss/localization_loss': 0.099971645,\n",
      " 'Loss/regularization_loss': 0.116482556,\n",
      " 'Loss/total_loss': 0.3025197,\n",
      " 'learning_rate': 1e-05}\u001b[0m\n",
      "\u001b[34mI0328 06:46:41.710464 140083402434368 model_lib_v2.py:708] {'Loss/classification_loss': 0.08606551,\n",
      " 'Loss/localization_loss': 0.099971645,\n",
      " 'Loss/regularization_loss': 0.116482556,\n",
      " 'Loss/total_loss': 0.3025197,\n",
      " 'learning_rate': 1e-05}\u001b[0m\n",
      "\u001b[34m==EVALUATING THE MODEL==\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Forced number of epochs for all eval validations to be 1.\u001b[0m\n",
      "\u001b[34mW0328 06:46:49.641895 139632741836608 model_lib_v2.py:1089] Forced number of epochs for all eval validations to be 1.\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Maybe overwriting sample_1_of_n_eval_examples: None\u001b[0m\n",
      "\u001b[34mI0328 06:46:49.642318 139632741836608 config_util.py:552] Maybe overwriting sample_1_of_n_eval_examples: None\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Maybe overwriting use_bfloat16: False\u001b[0m\n",
      "\u001b[34mI0328 06:46:49.642451 139632741836608 config_util.py:552] Maybe overwriting use_bfloat16: False\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Maybe overwriting eval_num_epochs: 1\u001b[0m\n",
      "\u001b[34mI0328 06:46:49.642590 139632741836608 config_util.py:552] Maybe overwriting eval_num_epochs: 1\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.\u001b[0m\n",
      "\u001b[34mW0328 06:46:49.642754 139632741836608 model_lib_v2.py:1106] Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reading unweighted datasets: ['/opt/ml/input/data/val/*.tfrecord']\u001b[0m\n",
      "\u001b[34mI0328 06:46:50.770012 139632741836608 dataset_builder.py:162] Reading unweighted datasets: ['/opt/ml/input/data/val/*.tfrecord']\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reading record datasets for input file: ['/opt/ml/input/data/val/*.tfrecord']\u001b[0m\n",
      "\u001b[34mI0328 06:46:50.771507 139632741836608 dataset_builder.py:79] Reading record datasets for input file: ['/opt/ml/input/data/val/*.tfrecord']\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Number of filenames to read: 13\u001b[0m\n",
      "\u001b[34mI0328 06:46:50.771620 139632741836608 dataset_builder.py:80] Number of filenames to read: 13\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:num_readers has been reduced to 13 to match input file shards.\u001b[0m\n",
      "\u001b[34mW0328 06:46:50.771747 139632741836608 dataset_builder.py:86] num_readers has been reduced to 13 to match input file shards.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:`shuffle` is false, but the input data stream is still slightly shuffled since `num_readers` > 1.\u001b[0m\n",
      "\u001b[34mW0328 06:46:50.773946 139632741836608 dataset_builder.py:93] `shuffle` is false, but the input data stream is still slightly shuffled since `num_readers` > 1.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/object_detection/builders/dataset_builder.py:100: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.\u001b[0m\n",
      "\u001b[34mW0328 06:46:50.776029 139632741836608 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/object_detection/builders/dataset_builder.py:100: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/object_detection/builders/dataset_builder.py:235: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.data.Dataset.map()\u001b[0m\n",
      "\u001b[34mW0328 06:46:50.796840 139632741836608 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/object_detection/builders/dataset_builder.py:235: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.data.Dataset.map()\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mLambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\u001b[0m\n",
      "\u001b[34mW0328 06:46:51.307074 139632741836608 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mLambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mCreate a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\u001b[0m\n",
      "\u001b[34mW0328 06:46:54.756629 139632741836608 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mCreate a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.cast` instead.\u001b[0m\n",
      "\u001b[34mW0328 06:46:55.884582 139632741836608 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.cast` instead.\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Waiting for new checkpoint at /opt/training\u001b[0m\n",
      "\u001b[34mI0328 06:46:58.552043 139632741836608 checkpoint_utils.py:140] Waiting for new checkpoint at /opt/training\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Found new checkpoint at /opt/training/ckpt-11\u001b[0m\n",
      "\u001b[34mI0328 06:46:58.552916 139632741836608 checkpoint_utils.py:149] Found new checkpoint at /opt/training/ckpt-11\u001b[0m\n",
      "\u001b[34m/usr/local/lib/python3.8/dist-packages/keras/backend.py:451: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n",
      "  warnings.warn(\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.cast` instead.\u001b[0m\n",
      "\u001b[34mW0328 06:47:23.774353 139632741836608 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.cast` instead.\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Finished eval step 0\u001b[0m\n",
      "\u001b[34mI0328 06:47:23.808919 139632741836608 model_lib_v2.py:966] Finished eval step 0\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:459: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mtf.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",
      "    \u001b[0m\n",
      "\u001b[34mW0328 06:47:23.962402 139632741836608 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:459: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mtf.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",
      "    \u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Finished eval step 100\u001b[0m\n",
      "\u001b[34mI0328 06:47:32.168632 139632741836608 model_lib_v2.py:966] Finished eval step 100\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Finished eval step 200\u001b[0m\n",
      "\u001b[34mI0328 06:47:37.491065 139632741836608 model_lib_v2.py:966] Finished eval step 200\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Performing evaluation on 258 images.\u001b[0m\n",
      "\u001b[34mI0328 06:47:40.360237 139632741836608 coco_evaluation.py:293] Performing evaluation on 258 images.\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Loading and preparing annotation results...\u001b[0m\n",
      "\u001b[34mI0328 06:47:40.365785 139632741836608 coco_tools.py:116] Loading and preparing annotation results...\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:DONE (t=0.02s)\u001b[0m\n",
      "\u001b[34mI0328 06:47:40.381635 139632741836608 coco_tools.py:138] DONE (t=0.02s)\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Eval metrics at step 10000\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.264510 139632741836608 model_lib_v2.py:1015] Eval metrics at step 10000\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Precision/mAP: 0.127604\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.275685 139632741836608 model_lib_v2.py:1018] #011+ DetectionBoxes_Precision/mAP: 0.127604\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Precision/mAP@.50IOU: 0.262165\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.277360 139632741836608 model_lib_v2.py:1018] #011+ DetectionBoxes_Precision/mAP@.50IOU: 0.262165\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Precision/mAP@.75IOU: 0.109342\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.278857 139632741836608 model_lib_v2.py:1018] #011+ DetectionBoxes_Precision/mAP@.75IOU: 0.109342\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Precision/mAP (small): 0.053764\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.280415 139632741836608 model_lib_v2.py:1018] #011+ DetectionBoxes_Precision/mAP (small): 0.053764\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Precision/mAP (medium): 0.435959\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.281942 139632741836608 model_lib_v2.py:1018] #011+ DetectionBoxes_Precision/mAP (medium): 0.435959\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Precision/mAP (large): 0.556335\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.283474 139632741836608 model_lib_v2.py:1018] #011+ DetectionBoxes_Precision/mAP (large): 0.556335\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Recall/AR@1: 0.028387\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.285100 139632741836608 model_lib_v2.py:1018] #011+ DetectionBoxes_Recall/AR@1: 0.028387\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Recall/AR@10: 0.129599\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.286616 139632741836608 model_lib_v2.py:1018] #011+ DetectionBoxes_Recall/AR@10: 0.129599\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Recall/AR@100: 0.183234\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.288196 139632741836608 model_lib_v2.py:1018] #011+ DetectionBoxes_Recall/AR@100: 0.183234\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Recall/AR@100 (small): 0.117098\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.289684 139632741836608 model_lib_v2.py:1018] #011+ DetectionBoxes_Recall/AR@100 (small): 0.117098\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Recall/AR@100 (medium): 0.515716\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.291176 139632741836608 model_lib_v2.py:1018] #011+ DetectionBoxes_Recall/AR@100 (medium): 0.515716\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Recall/AR@100 (large): 0.651331\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.292778 139632741836608 model_lib_v2.py:1018] #011+ DetectionBoxes_Recall/AR@100 (large): 0.651331\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ Loss/localization_loss: 0.418279\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.293933 139632741836608 model_lib_v2.py:1018] #011+ Loss/localization_loss: 0.418279\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ Loss/classification_loss: 0.305893\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.295172 139632741836608 model_lib_v2.py:1018] #011+ Loss/classification_loss: 0.305893\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ Loss/regularization_loss: 0.116482\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.296380 139632741836608 model_lib_v2.py:1018] #011+ Loss/regularization_loss: 0.116482\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ Loss/total_loss: 0.840655\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.297557 139632741836608 model_lib_v2.py:1018] #011+ Loss/total_loss: 0.840655\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Exiting evaluation at step 10000\u001b[0m\n",
      "\u001b[34mI0328 06:47:53.440448 139632741836608 model_lib_v2.py:1168] Exiting evaluation at step 10000\u001b[0m\n",
      "\u001b[34mcreating index...\u001b[0m\n",
      "\u001b[34mindex created!\u001b[0m\n",
      "\u001b[34mcreating index...\u001b[0m\n",
      "\u001b[34mindex created!\u001b[0m\n",
      "\u001b[34mRunning per image evaluation...\u001b[0m\n",
      "\u001b[34mEvaluate annotation type *bbox*\u001b[0m\n",
      "\u001b[34mDONE (t=12.56s).\u001b[0m\n",
      "\u001b[34mAccumulating evaluation results...\u001b[0m\n",
      "\u001b[34mDONE (t=0.28s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.128\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.262\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.109\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.054\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.436\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.556\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.028\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.130\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.183\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.117\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.516\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.651\u001b[0m\n",
      "\u001b[34m==EXPORTING THE MODEL==\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mLambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\u001b[0m\n",
      "\u001b[34mW0328 06:47:59.711834 140348559726400 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mLambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:458: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mback_prop=False is deprecated. Consider using tf.stop_gradient instead.\u001b[0m\n",
      "\u001b[34mInstead of:\u001b[0m\n",
      "\u001b[34mresults = tf.map_fn(fn, elems, back_prop=False)\u001b[0m\n",
      "\u001b[34mUse:\u001b[0m\n",
      "\u001b[34mresults = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\u001b[0m\n",
      "\u001b[34mW0328 06:47:59.790015 140348559726400 deprecation.py:623] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:458: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mback_prop=False is deprecated. Consider using tf.stop_gradient instead.\u001b[0m\n",
      "\u001b[34mInstead of:\u001b[0m\n",
      "\u001b[34mresults = tf.map_fn(fn, elems, back_prop=False)\u001b[0m\n",
      "\u001b[34mUse:\u001b[0m\n",
      "\u001b[34mresults = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7fa4b0942ee0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.236810 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7fa4b0942ee0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x7fa49c274c10>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.520539 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x7fa49c274c10>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa49c1e2970>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.520746 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa49c1e2970>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa49c1e2a00>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.520869 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa49c1e2a00>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x7fa49c1e2e20>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.521046 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x7fa49c1e2e20>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa49c29d5b0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.521190 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa49c29d5b0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa49c29d850>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.521317 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa49c29d850>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x7fa49c29d790>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.521453 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x7fa49c29d790>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa49c29d670>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.521600 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa49c29d670>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa49c29dd00>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.521748 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa49c29dd00>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x7fa4b03e5d60>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.521878 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x7fa4b03e5d60>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b05ceaf0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.521996 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b05ceaf0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b066cee0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.522141 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b066cee0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b04d5c40>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.522258 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b04d5c40>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b01c4ee0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.522410 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b01c4ee0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b07bef40>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.522541 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b07bef40>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b07bebe0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.522673 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b07bebe0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b07befa0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.522815 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b07befa0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b07be610>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.522947 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b07be610>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b08013a0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.523076 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b08013a0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b00567f0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.523209 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b00567f0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b003bf70>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.523358 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b003bf70>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b061a5e0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.523468 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b061a5e0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b0040b50>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.523605 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b0040b50>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b0040550>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.523777 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b0040550>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b0040cd0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.523920 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b0040cd0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b0040b20>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.524052 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b0040b20>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b00ee3d0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.524196 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b00ee3d0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b00eee20>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.524314 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b00eee20>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b00ee910>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.524430 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b00ee910>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa49c1e8430>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.524541 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa49c1e8430>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa49c1e8b20>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.524643 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa49c1e8b20>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa49c1e8ac0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.524794 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa49c1e8ac0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa49c3682e0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.524921 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa49c3682e0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa49c368850>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.525082 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa49c368850>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa49c368520>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.525229 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa49c368520>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa49c3682b0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.525340 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa49c3682b0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa49c18f910>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.525474 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa49c18f910>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b00f22b0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.525579 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b00f22b0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b063d370>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.525676 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b063d370>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b063d2e0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.525788 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b063d2e0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b00ddfa0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.525898 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b00ddfa0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b00dd0a0>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.526012 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b00dd0a0>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b00dd220>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.526113 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fa4b00dd220>, because it is not built.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b00dd160>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 06:48:21.526228 140348559726400 save_impl.py:66] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7fa4b00dd160>, because it is not built.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mW0328 06:48:41.331298 140348559726400 save.py:271] Found untraced functions such as WeightSharedConvolutionalBoxPredictor_layer_call_fn, WeightSharedConvolutionalBoxPredictor_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxHead_layer_call_fn, WeightSharedConvolutionalBoxHead_layer_call_and_return_conditional_losses, WeightSharedConvolutionalClassHead_layer_call_fn while saving (showing 5 of 173). These functions will not be directly callable after loading.\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Assets written to: /tmp/exported/saved_model/assets\u001b[0m\n",
      "\u001b[34mI0328 06:48:47.651884 140348559726400 builder_impl.py:797] Assets written to: /tmp/exported/saved_model/assets\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Writing pipeline config file to /tmp/exported/pipeline.config\u001b[0m\n",
      "\u001b[34mI0328 06:48:49.053254 140348559726400 config_util.py:253] Writing pipeline config file to /tmp/exported/pipeline.config\u001b[0m\n",
      "\u001b[34m2023-03-28 06:48:50,453 sagemaker-training-toolkit INFO     Reporting training SUCCESS\u001b[0m\n",
      "\n",
      "2023-03-28 06:49:11 Uploading - Uploading generated training model\n",
      "2023-03-28 06:49:11 Completed - Training job completed\n",
      "Training seconds: 2441\n",
      "Billable seconds: 2441\n"
     ]
    }
   ],
   "source": [
    "tensorboard_output_config = sagemaker.debugger.TensorBoardOutputConfig(\n",
    "    s3_output_path=tensorboard_s3_prefix,\n",
    "    container_local_output_path='/opt/training/'\n",
    ")\n",
    "\n",
    "estimator = CustomFramework(\n",
    "    role=role,\n",
    "    image_uri=container,\n",
    "    entry_point='run_training.sh',\n",
    "    source_dir='source_dir/',\n",
    "    hyperparameters={\n",
    "        \"model_dir\":\"/opt/training\",        \n",
    "        \"pipeline_config_path\": \"pipeline.config\",\n",
    "        \"num_train_steps\": \"10000\",    \n",
    "        \"sample_1_of_n_eval_examples\": \"1\"\n",
    "    },\n",
    "    instance_count=1,\n",
    "    instance_type='ml.p3.2xlarge',\n",
    "    tensorboard_output_config=tensorboard_output_config,\n",
    "    disable_profiler=True,\n",
    "    base_job_name='tf2-object-detection'\n",
    ")\n",
    "\n",
    "estimator.fit(inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84545881",
   "metadata": {},
   "source": [
    "You should be able to see your model training in the AWS webapp as shown below:\n",
    "![ECR Example](../data/example_trainings.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cfe8a3a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:botocore.credentials:Found credentials from IAM Role: BaseNotebookInstanceEc2InstanceRole\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output path:\n",
      "s3://sagemaker-us-east-1-476375380884/\n",
      "\n",
      "Last model URI:\n",
      "s3://sagemaker-us-east-1-476375380884/tf2-object-detection-2023-03-28-06-06-51-015/output/model.tar.gz\n"
     ]
    }
   ],
   "source": [
    "# Inspired by: https://stackoverflow.com/questions/36209068/boto3-grabbing-only-selected-objects-from-the-s3-resource\n",
    "\n",
    "import boto3\n",
    "\n",
    "output_path = estimator.output_path\n",
    "print('Output path:\\n{}\\n'.format(output_path))\n",
    "\n",
    "bucket_name = output_path[5:-1]\n",
    "s3 = boto3.resource('s3')\n",
    "my_bucket = s3.Bucket(bucket_name)\n",
    "\n",
    "prefix = 'tf2-object-detection-'\n",
    "model_objects = my_bucket.objects.filter(Prefix=prefix)\n",
    "model_objects = [object_summary for object_summary in model_objects if object_summary.key.endswith('model.tar.gz')]\n",
    "model_objects = sorted(model_objects, key = lambda e: e.key)\n",
    "#for model_object in model_objects: print(model_object)\n",
    "last_model_object = model_objects[-1]\n",
    "last_model_uri = 's3://' + last_model_object.bucket_name + '/' + last_model_object.key\n",
    "print('Last model URI:\\n{}'.format(last_model_uri))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "500e3fab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aws_region=us-east-1\n"
     ]
    }
   ],
   "source": [
    "import sagemaker\n",
    "sagemaker_session = sagemaker.Session()\n",
    "aws_region = sagemaker_session.boto_region_name\n",
    "print('aws_region={}'.format(aws_region))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9e722407",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pip install 'tensorflow<2.4'\n",
      "pip install 'tensorflow-io<2.4'\n",
      "pip install 'tensorboard<2.4'\n",
      "AWS_REGION=us-east-1\n",
      "tensorboard --logdir=s3://object-detection-project-jckuri/logs/tf2-object-detection-2023-03-28-06-06-51-015/tensorboard-output\n"
     ]
    }
   ],
   "source": [
    "job_artifacts_path = estimator.latest_job_tensorboard_artifacts_path()\n",
    "\n",
    "!echo \"pip install 'tensorflow<2.4'\"\n",
    "!echo \"pip install 'tensorflow-io<2.4'\"\n",
    "!echo \"pip install 'tensorboard<2.4'\"\n",
    "!echo \"AWS_REGION={aws_region}\"\n",
    "!echo \"tensorboard --logdir={job_artifacts_path}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a8c6b6d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class URL:\n",
    "    \n",
    "    def __init__(self, url):\n",
    "        self.url = url\n",
    "    \n",
    "    def _repr_html_(self):\n",
    "        return '<a href=\"{}\">{}</a>'.format(self.url, self.url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "daed91bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=\"https://object-detection-project-a5lc.notebook.us-east-1.sagemaker.aws/proxy/6006/\">https://object-detection-project-a5lc.notebook.us-east-1.sagemaker.aws/proxy/6006/</a>"
      ],
      "text/plain": [
       "<__main__.URL at 0x7fc1145c9f90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://<notebook instance hostname>/proxy/6006/\n",
    "jupyter_notebook_url = 'https://object-detection-project-a5lc.notebook.us-east-1.sagemaker.aws'\n",
    "url = '{}/proxy/6006/'.format(jupyter_notebook_url)\n",
    "\n",
    "o = URL(url)\n",
    "o"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9844f25",
   "metadata": {},
   "source": [
    "## Improve on the initial model\n",
    "\n",
    "Most likely, this initial experiment did not yield optimal results. However, you can make multiple changes to the `pipeline.config` file to improve this model. One obvious change consists in improving the data augmentation strategy. The [`preprocessor.proto`](https://github.com/tensorflow/models/blob/master/research/object_detection/protos/preprocessor.proto) file contains the different data augmentation method available in the Tf Object Detection API. Justify your choices of augmentations in the writeup.\n",
    "\n",
    "Keep in mind that the following are also available:\n",
    "* experiment with the optimizer: type of optimizer, learning rate, scheduler etc\n",
    "* experiment with the architecture. The Tf Object Detection API model zoo offers many architectures. Keep in mind that the pipeline.config file is unique for each architecture and you will have to edit it.\n",
    "* visualize results on the test frames using the `2_deploy_model` notebook available in this repository.\n",
    "\n",
    "In the cell below, write down all the different approaches you have experimented with, why you have chosen them and what you would have done if you had more time and resources. Justify your choices using the tensorboard visualizations (take screenshots and insert them in your writeup), the metrics on the evaluation set and the generated animation you have created with [this tool](../2_run_inference/2_deploy_model.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "17284a60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# your writeup goes here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bda0883",
   "metadata": {},
   "source": [
    "**I preferred to write all my documentation in the file <big><big>[README.md](../README.md)</big></big>.<br/>\n",
    "Please, click on this file and read it because it explains all the items in the rubric.**"
   ]
  }
 ],
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