Model Training and Predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c1298553-b879-4483-8d3e-4d621f28f1f8",
   "metadata": {},
   "source": [
    "# 1) Imorting Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2096cdb7-5e4e-4627-9c00-7af08eee48f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import wget\n",
    "import object_detection\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from object_detection.utils import config_util\n",
    "from object_detection.protos import pipeline_pb2\n",
    "from google.protobuf import text_format\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "764bca26-9107-4046-9dc0-55a99e4fc63a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print(tf.config.list_physical_devices('GPU'))\n",
    "print(tf.test.is_built_with_cuda())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b625b6f-d3cf-4d60-aa52-a7fb071f0089",
   "metadata": {},
   "source": [
    "# 2) Defining Paths"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a0cfcb0-267a-458d-b0a6-71974c4ab169",
   "metadata": {},
   "source": [
    "URL for model: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8604c952-0207-4737-9960-bb1ec3819a64",
   "metadata": {},
   "outputs": [],
   "source": [
    "CUSTOM_MODEL_NAME = 'my_ssd_mobnet' \n",
    "PRETRAINED_MODEL_NAME = 'ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8'\n",
    "PRETRAINED_MODEL_URL = 'http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz'\n",
    "TF_RECORD_SCRIPT_NAME = 'generate_tfrecord.py'\n",
    "LABEL_MAP_NAME = 'label_map.pbtxt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bece0fdc-3a60-4192-8168-807e76c66bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "paths = {\n",
    "    'WORKSPACE_PATH': os.path.join('Tensorflow', 'workspace'), # It will have annotations,models folder, and tf record files\n",
    "    'SCRIPTS_PATH': os.path.join('Tensorflow','scripts'),\n",
    "    'APIMODEL_PATH': os.path.join('Tensorflow','models'),\n",
    "    'ANNOTATION_PATH': os.path.join('Tensorflow', 'workspace','annotations'),\n",
    "    'IMAGE_PATH': os.path.join('Tensorflow', 'workspace','images'),\n",
    "    'MODEL_PATH': os.path.join('Tensorflow', 'workspace','models'),\n",
    "    'PRETRAINED_MODEL_PATH': os.path.join('Tensorflow', 'workspace','pre-trained-models'),\n",
    "    'CHECKPOINT_PATH': os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME), \n",
    "    'OUTPUT_PATH': os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME, 'export'), \n",
    "    'PROTOC_PATH':os.path.join('Tensorflow','protoc')\n",
    " }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c5255b79-dfdf-4839-8673-da244dcbfa7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "files = {\n",
    "    'PIPELINE_CONFIG':os.path.join('Tensorflow', 'workspace','models', CUSTOM_MODEL_NAME, 'pipeline.config'),\n",
    "    'TF_RECORD_SCRIPT': os.path.join(paths['SCRIPTS_PATH'], TF_RECORD_SCRIPT_NAME), \n",
    "    'LABELMAP': os.path.join(paths['ANNOTATION_PATH'], LABEL_MAP_NAME)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a22c5fa-6237-4d86-b246-a2ac54a83279",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the folders on specified paths\n",
    "for path in paths.values():\n",
    "    if not os.path.exists(path):\n",
    "        if os.name == 'posix':\n",
    "            !mkdir -p {path}\n",
    "        if os.name == 'nt':\n",
    "            !mkdir {path}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb3746a2-75d5-4f2a-ac9c-662b8dfd6861",
   "metadata": {},
   "source": [
    "# 3) Download TF Models Pretrained Models from Tensorflow Model Zoo and Install TFOD (Tensorflow Object Detection)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23f75a69-3253-4a5b-ac4e-7390eff6035d",
   "metadata": {},
   "source": [
    "Cloning Tensorflow model APIs from https://github.com/tensorflow/models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2ce4b258-1b42-41cf-9e7e-916f11e81912",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'Tensorflow\\models' already exists and is not an empty directory.\n",
      "Cloning into 'Tensorflow\\models'...\n",
      "error: Untracked working tree file '.github/ISSUE_TEMPLATE/00-official-bug-report-issue.md' would be overwritten by merge.\n",
      "fatal: unable to checkout working tree\n",
      "warning: Clone succeeded, but checkout failed.\n",
      "You can inspect what was checked out with 'git status'\n",
      "and retry with 'git restore --source=HEAD :/'\n",
      "\n"
     ]
    }
   ],
   "source": [
    "if not os.path.exists(os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection')):\n",
    "    !git clone https://github.com/tensorflow/models {paths['APIMODEL_PATH']}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e54c2182-e669-4e8a-ac94-89579773cdf3",
   "metadata": {},
   "source": [
    "In next cell protoc is installed which is used by Tensorflow Object Detection and then install tensorflow object detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "22d07ede-0ae7-4868-9efd-2eeb2816e4a9",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% [..........................................................................] 1468733 / 1468733        1 file(s) moved.\n",
      "        1 file(s) copied.\n",
      "running build\n",
      "running build_py\n",
      "copying object_detection\\protos\\anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\argmax_matcher_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\bipartite_matcher_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\box_coder_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\box_predictor_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\calibration_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\center_net_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\eval_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\faster_rcnn_box_coder_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\faster_rcnn_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\flexible_grid_anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\fpn_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\graph_rewriter_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\grid_anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\hyperparams_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\image_resizer_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\input_reader_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\keypoint_box_coder_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\losses_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\matcher_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\mean_stddev_box_coder_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\model_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\multiscale_anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\optimizer_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\pipeline_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\post_processing_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\preprocessor_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\region_similarity_calculator_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\square_box_coder_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\ssd_anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\ssd_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\string_int_label_map_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\target_assigner_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\train_pb2.py -> build\\lib\\object_detection\\protos\n",
      "running egg_info\n",
      "writing object_detection.egg-info\\PKG-INFO\n",
      "writing dependency_links to object_detection.egg-info\\dependency_links.txt\n",
      "writing requirements to object_detection.egg-info\\requires.txt\n",
      "writing top-level names to object_detection.egg-info\\top_level.txt\n",
      "reading manifest file 'object_detection.egg-info\\SOURCES.txt'\n",
      "writing manifest file 'object_detection.egg-info\\SOURCES.txt'\n",
      "running install\n",
      "running bdist_egg\n",
      "running egg_info\n",
      "writing object_detection.egg-info\\PKG-INFO\n",
      "writing dependency_links to object_detection.egg-info\\dependency_links.txt\n",
      "writing requirements to object_detection.egg-info\\requires.txt\n",
      "writing top-level names to object_detection.egg-info\\top_level.txt\n",
      "reading manifest file 'object_detection.egg-info\\SOURCES.txt'\n",
      "writing manifest file 'object_detection.egg-info\\SOURCES.txt'\n",
      "installing library code to build\\bdist.win-amd64\\egg\n",
      "running install_lib\n",
      "running build_py\n",
      "creating build\\bdist.win-amd64\\egg\n",
      "creating build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\build_imagenet_data.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\cifar10.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\dataset_factory.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\dataset_utils.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\download_and_convert_cifar10.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\download_and_convert_flowers.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\download_and_convert_mnist.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\download_and_convert_visualwakewords.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\download_and_convert_visualwakewords_lib.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\flowers.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\imagenet.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\mnist.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\preprocess_imagenet_validation_data.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\process_bounding_boxes.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\visualwakewords.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\__init__.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "creating build\\bdist.win-amd64\\egg\\deployment\n",
      "copying build\\lib\\deployment\\model_deploy.py -> build\\bdist.win-amd64\\egg\\deployment\n",
      "copying build\\lib\\deployment\\model_deploy_test.py -> build\\bdist.win-amd64\\egg\\deployment\n",
      "copying build\\lib\\deployment\\__init__.py -> build\\bdist.win-amd64\\egg\\deployment\n",
      "creating build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\alexnet.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\alexnet_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\cifarnet.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\cyclegan.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\cyclegan_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\dcgan.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\dcgan_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\i3d.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\i3d_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\i3d_utils.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_resnet_v2.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_resnet_v2_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_utils.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_v1.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_v1_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_v2.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_v2_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_v3.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_v3_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_v4.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_v4_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\lenet.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "creating build\\bdist.win-amd64\\egg\\nets\\mobilenet\n",
      "copying build\\lib\\nets\\mobilenet\\conv_blocks.py -> build\\bdist.win-amd64\\egg\\nets\\mobilenet\n",
      "copying build\\lib\\nets\\mobilenet\\mobilenet.py -> build\\bdist.win-amd64\\egg\\nets\\mobilenet\n",
      "copying build\\lib\\nets\\mobilenet\\mobilenet_v2.py -> build\\bdist.win-amd64\\egg\\nets\\mobilenet\n",
      "copying build\\lib\\nets\\mobilenet\\mobilenet_v2_test.py -> build\\bdist.win-amd64\\egg\\nets\\mobilenet\n",
      "copying build\\lib\\nets\\mobilenet\\mobilenet_v3.py -> build\\bdist.win-amd64\\egg\\nets\\mobilenet\n",
      "copying build\\lib\\nets\\mobilenet\\mobilenet_v3_test.py -> build\\bdist.win-amd64\\egg\\nets\\mobilenet\n",
      "copying build\\lib\\nets\\mobilenet\\__init__.py -> build\\bdist.win-amd64\\egg\\nets\\mobilenet\n",
      "copying build\\lib\\nets\\mobilenet_v1.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\mobilenet_v1_eval.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\mobilenet_v1_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\mobilenet_v1_train.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "creating build\\bdist.win-amd64\\egg\\nets\\nasnet\n",
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      "copying build\\lib\\nets\\nets_factory_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\overfeat.py -> build\\bdist.win-amd64\\egg\\nets\n",
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      "copying build\\lib\\nets\\post_training_quantization.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\resnet_utils.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\resnet_v1.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\resnet_v1_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\resnet_v2.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\resnet_v2_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\s3dg.py -> build\\bdist.win-amd64\\egg\\nets\n",
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      "copying build\\lib\\nets\\vgg_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\__init__.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "creating build\\bdist.win-amd64\\egg\\object_detection\n",
      "creating build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\flexible_grid_anchor_generator.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\flexible_grid_anchor_generator_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\grid_anchor_generator.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\grid_anchor_generator_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\multiple_grid_anchor_generator.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\multiple_grid_anchor_generator_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\multiscale_grid_anchor_generator.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\multiscale_grid_anchor_generator_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\__init__.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "creating build\\bdist.win-amd64\\egg\\object_detection\\box_coders\n",
      "copying build\\lib\\object_detection\\box_coders\\faster_rcnn_box_coder.py -> build\\bdist.win-amd64\\egg\\object_detection\\box_coders\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "zip_safe flag not set; analyzing archive contents...\n",
      "object_detection.core.__pycache__.densepose_ops.cpython-37: module references __file__\n",
      "object_detection.core.__pycache__.preprocessor.cpython-37: module MAY be using inspect.stack\n",
      "object_detection.utils.__pycache__.autoaugment_utils.cpython-37: module MAY be using inspect.stack\n",
      "error: The read operation timed out\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "byte-compiling build\\bdist.win-amd64\\egg\\preprocessing\\__init__.py to __init__.cpython-37.pyc\n",
      "creating build\\bdist.win-amd64\\egg\\EGG-INFO\n",
      "copying object_detection.egg-info\\PKG-INFO -> build\\bdist.win-amd64\\egg\\EGG-INFO\n",
      "copying object_detection.egg-info\\SOURCES.txt -> build\\bdist.win-amd64\\egg\\EGG-INFO\n",
      "copying object_detection.egg-info\\dependency_links.txt -> build\\bdist.win-amd64\\egg\\EGG-INFO\n",
      "copying object_detection.egg-info\\requires.txt -> build\\bdist.win-amd64\\egg\\EGG-INFO\n",
      "copying object_detection.egg-info\\top_level.txt -> build\\bdist.win-amd64\\egg\\EGG-INFO\n",
      "creating 'dist\\object_detection-0.1-py3.7.egg' and adding 'build\\bdist.win-amd64\\egg' to it\n",
      "removing 'build\\bdist.win-amd64\\egg' (and everything under it)\n",
      "Processing object_detection-0.1-py3.7.egg\n",
      "creating d:\\hand sign prediction using tensorflow object detection\\hsptod\\lib\\site-packages\\object_detection-0.1-py3.7.egg\n",
      "Extracting object_detection-0.1-py3.7.egg to d:\\hand sign prediction using tensorflow object detection\\hsptod\\lib\\site-packages\n",
      "Adding object-detection 0.1 to easy-install.pth file\n",
      "\n",
      "Installed d:\\hand sign prediction using tensorflow object detection\\hsptod\\lib\\site-packages\\object_detection-0.1-py3.7.egg\n",
      "Processing dependencies for object-detection==0.1\n",
      "Searching for tf-models-official\n",
      "Reading https://pypi.org/simple/tf-models-official/\n",
      "Downloading https://files.pythonhosted.org/packages/96/08/81bbc275e8e9c6d1e03dd26daec3a67f45e6322804cbce3d51f93eae1961/tf_models_official-2.5.0-py2.py3-none-any.whl#sha256=fdbb6d9eb1f02548b3669cd0cc9b12746a37663ee759f43e3bfc00a84c4c9241\n",
      "Obtaining file:///D:/Hand%20Sign%20Prediction%20Using%20Tensorflow%20Object%20Detection/Tensorflow/models/research/slim\n",
      "Requirement already satisfied: six in d:\\hand sign prediction using tensorflow object detection\\hsptod\\lib\\site-packages (from slim==0.1) (1.15.0)\n",
      "Collecting tf-slim>=1.1\n",
      "  Downloading tf_slim-1.1.0-py2.py3-none-any.whl (352 kB)\n",
      "Requirement already satisfied: absl-py>=0.2.2 in d:\\hand sign prediction using tensorflow object detection\\hsptod\\lib\\site-packages (from tf-slim>=1.1->slim==0.1) (0.13.0)\n",
      "Installing collected packages: tf-slim, slim\n",
      "  Running setup.py develop for slim\n",
      "Successfully installed slim-0.1 tf-slim-1.1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "object-detection 0.1 requires apache-beam, which is not installed.\n",
      "object-detection 0.1 requires avro-python3, which is not installed.\n",
      "object-detection 0.1 requires contextlib2, which is not installed.\n",
      "object-detection 0.1 requires lvis, which is not installed.\n",
      "object-detection 0.1 requires tf-models-official, which is not installed.\n"
     ]
    }
   ],
   "source": [
    "# Installing tensorflow object detection\n",
    "if os.name=='posix':  \n",
    "    !apt-get install protobuf-compiler\n",
    "    !cd Tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=. && cp object_detection/packages/tf2/setup.py . && python -m pip install . \n",
    "    \n",
    "if os.name=='nt':\n",
    "    # Installing Protoc\n",
    "    url=\"https://github.com/protocolbuffers/protobuf/releases/download/v3.15.6/protoc-3.15.6-win64.zip\"\n",
    "    wget.download(url)\n",
    "    !move protoc-3.15.6-win64.zip {paths['PROTOC_PATH']}\n",
    "    !cd {paths['PROTOC_PATH']} && tar -xf protoc-3.15.6-win64.zip\n",
    "    os.environ['PATH'] += os.pathsep + os.path.abspath(os.path.join(paths['PROTOC_PATH'], 'bin')) \n",
    "    # Install Tensorflow object detection API\n",
    "    !cd Tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=. && copy object_detection\\\\packages\\\\tf2\\\\setup.py setup.py && python setup.py build && python setup.py install\n",
    "    !cd Tensorflow/models/research/slim && pip install -e ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "48f25b24-c8db-484e-b868-4f773e8deabe",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-07-12 01:00:01.451265: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll\n",
      "Running tests under Python 3.7.4: D:\\Hand Sign Prediction Using Tensorflow Object Detection\\HSPtod\\Scripts\\python.exe\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_deepmac\n",
      "2021-07-12 01:00:04.411977: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library nvcuda.dll\n",
      "2021-07-12 01:00:04.443517: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1650 computeCapability: 7.5\n",
      "coreClock: 1.515GHz coreCount: 14 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 178.84GiB/s\n",
      "2021-07-12 01:00:04.443542: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll\n",
      "2021-07-12 01:00:04.447740: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cublas64_11.dll\n",
      "2021-07-12 01:00:04.447760: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cublasLt64_11.dll\n",
      "2021-07-12 01:00:04.449786: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cufft64_10.dll\n",
      "2021-07-12 01:00:04.450554: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library curand64_10.dll\n",
      "2021-07-12 01:00:04.452536: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cusolver64_11.dll\n",
      "2021-07-12 01:00:04.454078: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cusparse64_11.dll\n",
      "2021-07-12 01:00:04.454623: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudnn64_8.dll\n",
      "2021-07-12 01:00:04.454729: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
      "2021-07-12 01:00:04.455039: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2021-07-12 01:00:04.455444: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1650 computeCapability: 7.5\n",
      "coreClock: 1.515GHz coreCount: 14 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 178.84GiB/s\n",
      "2021-07-12 01:00:04.455483: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
      "2021-07-12 01:00:04.984823: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-07-12 01:00:04.984841: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      0 \n",
      "2021-07-12 01:00:04.984846: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0:   N \n",
      "2021-07-12 01:00:04.985072: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2147 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5)\n",
      "D:\\Hand Sign Prediction Using Tensorflow Object Detection\\HSPtod\\lib\\site-packages\\object_detection-0.1-py3.7.egg\\object_detection\\builders\\model_builder.py:1088: DeprecationWarning: The 'warn' function is deprecated, use 'warning' instead\n",
      "  logging.warn(('Building experimental DeepMAC meta-arch.'\n",
      "W0712 01:00:05.235370 16840 model_builder.py:1088] Building experimental DeepMAC meta-arch. Some features may be omitted.\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 1.01s\n",
      "I0712 01:00:05.421515 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 1.01s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_center_net_deepmac\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 0.41s\n",
      "I0712 01:00:05.831269 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 0.41s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.21s\n",
      "I0712 01:00:06.037944 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.21s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.19s\n",
      "I0712 01:00:06.222954 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.19s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet\n",
      "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
      "W0712 01:00:06.229954 16840 mobilenet_v2.py:296] `input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 1.45s\n",
      "I0712 01:00:07.668503 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 1.45s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_experimental_model\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n",
      "I0712 01:00:07.669467 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_experimental_model\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.02s\n",
      "I0712 01:00:07.687525 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.02s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.02s\n",
      "I0712 01:00:07.703032 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.02s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.01s\n",
      "I0712 01:00:07.716997 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.01s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.1s\n",
      "I0712 01:00:07.820069 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.1s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.08s\n",
      "I0712 01:00:07.901137 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.08s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.09s\n",
      "I0712 01:00:07.993889 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.09s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.08s\n",
      "I0712 01:00:08.070988 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.08s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.07s\n",
      "I0712 01:00:08.139596 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.07s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.02s\n",
      "I0712 01:00:08.159617 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.02s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n",
      "I0712 01:00:08.284929 16840 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b0\n",
      "I0712 01:00:08.284929 16840 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 64\n",
      "I0712 01:00:08.284929 16840 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 3\n",
      "I0712 01:00:08.285937 16840 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0712 01:00:08.296890 16840 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0712 01:00:08.296890 16840 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0712 01:00:08.335921 16840 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0712 01:00:08.335921 16840 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0712 01:00:08.435239 16840 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0712 01:00:08.435239 16840 efficientnet_model.py:147] round_filter input=40 output=40\n",
      "I0712 01:00:08.533969 16840 efficientnet_model.py:147] round_filter input=40 output=40\n",
      "I0712 01:00:08.533969 16840 efficientnet_model.py:147] round_filter input=80 output=80\n",
      "I0712 01:00:08.690075 16840 efficientnet_model.py:147] round_filter input=80 output=80\n",
      "I0712 01:00:08.690075 16840 efficientnet_model.py:147] round_filter input=112 output=112\n",
      "I0712 01:00:08.838100 16840 efficientnet_model.py:147] round_filter input=112 output=112\n",
      "I0712 01:00:08.838100 16840 efficientnet_model.py:147] round_filter input=192 output=192\n",
      "I0712 01:00:09.116866 16840 efficientnet_model.py:147] round_filter input=192 output=192\n",
      "I0712 01:00:09.116866 16840 efficientnet_model.py:147] round_filter input=320 output=320\n",
      "I0712 01:00:09.167376 16840 efficientnet_model.py:147] round_filter input=1280 output=1280\n",
      "I0712 01:00:09.186026 16840 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.0, resolution=224, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0712 01:00:09.223861 16840 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b1\n",
      "I0712 01:00:09.223861 16840 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 88\n",
      "I0712 01:00:09.223861 16840 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 4\n",
      "I0712 01:00:09.224859 16840 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0712 01:00:09.234832 16840 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0712 01:00:09.234832 16840 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0712 01:00:09.312663 16840 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0712 01:00:09.313660 16840 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0712 01:00:09.499723 16840 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0712 01:00:09.499723 16840 efficientnet_model.py:147] round_filter input=40 output=40\n",
      "I0712 01:00:09.676836 16840 efficientnet_model.py:147] round_filter input=40 output=40\n",
      "I0712 01:00:09.676836 16840 efficientnet_model.py:147] round_filter input=80 output=80\n",
      "I0712 01:00:09.909061 16840 efficientnet_model.py:147] round_filter input=80 output=80\n",
      "I0712 01:00:09.909061 16840 efficientnet_model.py:147] round_filter input=112 output=112\n",
      "I0712 01:00:10.163857 16840 efficientnet_model.py:147] round_filter input=112 output=112\n",
      "I0712 01:00:10.163857 16840 efficientnet_model.py:147] round_filter input=192 output=192\n",
      "I0712 01:00:10.422695 16840 efficientnet_model.py:147] round_filter input=192 output=192\n",
      "I0712 01:00:10.422695 16840 efficientnet_model.py:147] round_filter input=320 output=320\n",
      "I0712 01:00:10.520874 16840 efficientnet_model.py:147] round_filter input=1280 output=1280\n",
      "I0712 01:00:10.538311 16840 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.1, resolution=240, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0712 01:00:10.583153 16840 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b2\n",
      "I0712 01:00:10.583153 16840 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 112\n",
      "I0712 01:00:10.583153 16840 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 5\n",
      "I0712 01:00:10.585145 16840 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0712 01:00:10.595118 16840 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0712 01:00:10.595118 16840 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0712 01:00:10.677768 16840 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0712 01:00:10.677768 16840 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0712 01:00:10.842726 16840 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0712 01:00:10.842726 16840 efficientnet_model.py:147] round_filter input=40 output=48\n",
      "I0712 01:00:11.004214 16840 efficientnet_model.py:147] round_filter input=40 output=48\n",
      "I0712 01:00:11.004214 16840 efficientnet_model.py:147] round_filter input=80 output=88\n",
      "I0712 01:00:11.220764 16840 efficientnet_model.py:147] round_filter input=80 output=88\n",
      "I0712 01:00:11.220764 16840 efficientnet_model.py:147] round_filter input=112 output=120\n",
      "I0712 01:00:11.513025 16840 efficientnet_model.py:147] round_filter input=112 output=120\n",
      "I0712 01:00:11.513025 16840 efficientnet_model.py:147] round_filter input=192 output=208\n",
      "I0712 01:00:11.784902 16840 efficientnet_model.py:147] round_filter input=192 output=208\n",
      "I0712 01:00:11.784902 16840 efficientnet_model.py:147] round_filter input=320 output=352\n",
      "I0712 01:00:11.888094 16840 efficientnet_model.py:147] round_filter input=1280 output=1408\n",
      "I0712 01:00:11.908156 16840 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.1, depth_coefficient=1.2, resolution=260, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0712 01:00:11.952181 16840 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b3\n",
      "I0712 01:00:11.952181 16840 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 160\n",
      "I0712 01:00:11.952181 16840 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 6\n",
      "I0712 01:00:11.954175 16840 efficientnet_model.py:147] round_filter input=32 output=40\n",
      "I0712 01:00:11.964152 16840 efficientnet_model.py:147] round_filter input=32 output=40\n",
      "I0712 01:00:11.964152 16840 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0712 01:00:12.041061 16840 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0712 01:00:12.041061 16840 efficientnet_model.py:147] round_filter input=24 output=32\n",
      "I0712 01:00:12.195939 16840 efficientnet_model.py:147] round_filter input=24 output=32\n",
      "I0712 01:00:12.196937 16840 efficientnet_model.py:147] round_filter input=40 output=48\n",
      "I0712 01:00:12.359905 16840 efficientnet_model.py:147] round_filter input=40 output=48\n",
      "I0712 01:00:12.359905 16840 efficientnet_model.py:147] round_filter input=80 output=96\n",
      "I0712 01:00:12.631955 16840 efficientnet_model.py:147] round_filter input=80 output=96\n",
      "I0712 01:00:12.631955 16840 efficientnet_model.py:147] round_filter input=112 output=136\n",
      "I0712 01:00:12.906897 16840 efficientnet_model.py:147] round_filter input=112 output=136\n",
      "I0712 01:00:12.906897 16840 efficientnet_model.py:147] round_filter input=192 output=232\n",
      "I0712 01:00:13.216415 16840 efficientnet_model.py:147] round_filter input=192 output=232\n",
      "I0712 01:00:13.216415 16840 efficientnet_model.py:147] round_filter input=320 output=384\n",
      "I0712 01:00:13.314203 16840 efficientnet_model.py:147] round_filter input=1280 output=1536\n",
      "I0712 01:00:13.332036 16840 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.2, depth_coefficient=1.4, resolution=300, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0712 01:00:13.382904 16840 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b4\n",
      "I0712 01:00:13.382904 16840 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 224\n",
      "I0712 01:00:13.382904 16840 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7\n",
      "I0712 01:00:13.384898 16840 efficientnet_model.py:147] round_filter input=32 output=48\n",
      "I0712 01:00:13.395960 16840 efficientnet_model.py:147] round_filter input=32 output=48\n",
      "I0712 01:00:13.395960 16840 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0712 01:00:13.478912 16840 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0712 01:00:13.478912 16840 efficientnet_model.py:147] round_filter input=24 output=32\n",
      "I0712 01:00:13.694865 16840 efficientnet_model.py:147] round_filter input=24 output=32\n",
      "I0712 01:00:13.694865 16840 efficientnet_model.py:147] round_filter input=40 output=56\n",
      "I0712 01:00:14.038908 16840 efficientnet_model.py:147] round_filter input=40 output=56\n",
      "I0712 01:00:14.038908 16840 efficientnet_model.py:147] round_filter input=80 output=112\n",
      "I0712 01:00:14.342594 16840 efficientnet_model.py:147] round_filter input=80 output=112\n",
      "I0712 01:00:14.342594 16840 efficientnet_model.py:147] round_filter input=112 output=160\n",
      "I0712 01:00:14.636526 16840 efficientnet_model.py:147] round_filter input=112 output=160\n",
      "I0712 01:00:14.636526 16840 efficientnet_model.py:147] round_filter input=192 output=272\n",
      "I0712 01:00:15.042054 16840 efficientnet_model.py:147] round_filter input=192 output=272\n",
      "I0712 01:00:15.042054 16840 efficientnet_model.py:147] round_filter input=320 output=448\n",
      "I0712 01:00:15.150100 16840 efficientnet_model.py:147] round_filter input=1280 output=1792\n",
      "I0712 01:00:15.168995 16840 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.4, depth_coefficient=1.8, resolution=380, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0712 01:00:15.227849 16840 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b5\n",
      "I0712 01:00:15.227849 16840 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 288\n",
      "I0712 01:00:15.227849 16840 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7\n",
      "I0712 01:00:15.228881 16840 efficientnet_model.py:147] round_filter input=32 output=48\n",
      "I0712 01:00:15.238805 16840 efficientnet_model.py:147] round_filter input=32 output=48\n",
      "I0712 01:00:15.238805 16840 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0712 01:00:15.355419 16840 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0712 01:00:15.356338 16840 efficientnet_model.py:147] round_filter input=24 output=40\n",
      "I0712 01:00:15.616938 16840 efficientnet_model.py:147] round_filter input=24 output=40\n",
      "I0712 01:00:15.616938 16840 efficientnet_model.py:147] round_filter input=40 output=64\n",
      "I0712 01:00:15.876411 16840 efficientnet_model.py:147] round_filter input=40 output=64\n",
      "I0712 01:00:15.876411 16840 efficientnet_model.py:147] round_filter input=80 output=128\n",
      "I0712 01:00:16.247113 16840 efficientnet_model.py:147] round_filter input=80 output=128\n",
      "I0712 01:00:16.247113 16840 efficientnet_model.py:147] round_filter input=112 output=176\n",
      "I0712 01:00:16.607177 16840 efficientnet_model.py:147] round_filter input=112 output=176\n",
      "I0712 01:00:16.607177 16840 efficientnet_model.py:147] round_filter input=192 output=304\n",
      "I0712 01:00:17.222870 16840 efficientnet_model.py:147] round_filter input=192 output=304\n",
      "I0712 01:00:17.222870 16840 efficientnet_model.py:147] round_filter input=320 output=512\n",
      "I0712 01:00:17.382844 16840 efficientnet_model.py:147] round_filter input=1280 output=2048\n",
      "I0712 01:00:17.403036 16840 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.6, depth_coefficient=2.2, resolution=456, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0712 01:00:17.473057 16840 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b6\n",
      "I0712 01:00:17.473057 16840 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384\n",
      "I0712 01:00:17.473057 16840 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8\n",
      "I0712 01:00:17.474054 16840 efficientnet_model.py:147] round_filter input=32 output=56\n",
      "I0712 01:00:17.485024 16840 efficientnet_model.py:147] round_filter input=32 output=56\n",
      "I0712 01:00:17.485024 16840 efficientnet_model.py:147] round_filter input=16 output=32\n",
      "I0712 01:00:17.617722 16840 efficientnet_model.py:147] round_filter input=16 output=32\n",
      "I0712 01:00:17.618719 16840 efficientnet_model.py:147] round_filter input=24 output=40\n",
      "I0712 01:00:17.934518 16840 efficientnet_model.py:147] round_filter input=24 output=40\n",
      "I0712 01:00:17.934518 16840 efficientnet_model.py:147] round_filter input=40 output=72\n",
      "I0712 01:00:18.243663 16840 efficientnet_model.py:147] round_filter input=40 output=72\n",
      "I0712 01:00:18.243663 16840 efficientnet_model.py:147] round_filter input=80 output=144\n",
      "I0712 01:00:18.658141 16840 efficientnet_model.py:147] round_filter input=80 output=144\n",
      "I0712 01:00:18.658141 16840 efficientnet_model.py:147] round_filter input=112 output=200\n",
      "I0712 01:00:19.062400 16840 efficientnet_model.py:147] round_filter input=112 output=200\n",
      "I0712 01:00:19.062400 16840 efficientnet_model.py:147] round_filter input=192 output=344\n",
      "I0712 01:00:19.626961 16840 efficientnet_model.py:147] round_filter input=192 output=344\n",
      "I0712 01:00:19.626961 16840 efficientnet_model.py:147] round_filter input=320 output=576\n",
      "I0712 01:00:19.928889 16840 efficientnet_model.py:147] round_filter input=1280 output=2304\n",
      "I0712 01:00:19.947667 16840 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.8, depth_coefficient=2.6, resolution=528, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0712 01:00:20.026655 16840 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b7\n",
      "I0712 01:00:20.026655 16840 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384\n",
      "I0712 01:00:20.026655 16840 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8\n",
      "I0712 01:00:20.027652 16840 efficientnet_model.py:147] round_filter input=32 output=64\n",
      "I0712 01:00:20.037628 16840 efficientnet_model.py:147] round_filter input=32 output=64\n",
      "I0712 01:00:20.038625 16840 efficientnet_model.py:147] round_filter input=16 output=32\n",
      "I0712 01:00:20.197994 16840 efficientnet_model.py:147] round_filter input=16 output=32\n",
      "I0712 01:00:20.197994 16840 efficientnet_model.py:147] round_filter input=24 output=48\n",
      "I0712 01:00:20.561966 16840 efficientnet_model.py:147] round_filter input=24 output=48\n",
      "I0712 01:00:20.561966 16840 efficientnet_model.py:147] round_filter input=40 output=80\n",
      "I0712 01:00:20.920069 16840 efficientnet_model.py:147] round_filter input=40 output=80\n",
      "I0712 01:00:20.921063 16840 efficientnet_model.py:147] round_filter input=80 output=160\n",
      "I0712 01:00:21.457513 16840 efficientnet_model.py:147] round_filter input=80 output=160\n",
      "I0712 01:00:21.457513 16840 efficientnet_model.py:147] round_filter input=112 output=224\n",
      "I0712 01:00:22.008300 16840 efficientnet_model.py:147] round_filter input=112 output=224\n",
      "I0712 01:00:22.008300 16840 efficientnet_model.py:147] round_filter input=192 output=384\n",
      "I0712 01:00:22.711558 16840 efficientnet_model.py:147] round_filter input=192 output=384\n",
      "I0712 01:00:22.711558 16840 efficientnet_model.py:147] round_filter input=320 output=640\n",
      "I0712 01:00:22.920404 16840 efficientnet_model.py:147] round_filter input=1280 output=2560\n",
      "I0712 01:00:22.939321 16840 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=2.0, depth_coefficient=3.1, resolution=600, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 15.03s\n",
      "I0712 01:00:23.187046 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 15.03s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n",
      "[ RUN      ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s\n",
      "I0712 01:00:23.191038 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update\n",
      "[ RUN      ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s\n",
      "I0712 01:00:23.192033 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold\n",
      "[ RUN      ] ModelBuilderTF2Test.test_invalid_model_config_proto\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s\n",
      "I0712 01:00:23.193030 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_invalid_model_config_proto\n",
      "[ RUN      ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s\n",
      "I0712 01:00:23.194031 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size\n",
      "[ RUN      ] ModelBuilderTF2Test.test_session\n",
      "[  SKIPPED ] ModelBuilderTF2Test.test_session\n",
      "[ RUN      ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s\n",
      "I0712 01:00:23.195029 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor\n",
      "[ RUN      ] ModelBuilderTF2Test.test_unknown_meta_architecture\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n",
      "I0712 01:00:23.195029 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_unknown_meta_architecture\n",
      "[ RUN      ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n",
      "I0712 01:00:23.196025 16840 test_util.py:2103] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n",
      "----------------------------------------------------------------------\n",
      "Ran 24 tests in 18.788s\n",
      "\n",
      "OK (skipped=1)\n"
     ]
    }
   ],
   "source": [
    "# Verification Script to check whether installation was proper or not\n",
    "VERIFICATION_SCRIPT = os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection', 'builders', 'model_builder_tf2_test.py')\n",
    "# Verify Installation\n",
    "!python {VERIFICATION_SCRIPT}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "855332e2-88a2-48be-86f8-4a525dfe7177",
   "metadata": {},
   "source": [
    "Downloading SSD MobileNet V2 FPNLite 320x320 model from Tesndorflow Model Zoo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "259c69f5-6b8c-493c-8809-b76fff297ae1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% [........................................................................] 20515344 / 20515344        1 file(s) moved.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0.data-00000-of-00001\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/checkpoint\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0.index\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/pipeline.config\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/saved_model.pb\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/variables/\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/variables/variables.data-00000-of-00001\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/variables/variables.index\n"
     ]
    }
   ],
   "source": [
    "if os.name =='posix':\n",
    "    !wget {PRETRAINED_MODEL_URL}\n",
    "    !mv {PRETRAINED_MODEL_NAME+'.tar.gz'} {paths['PRETRAINED_MODEL_PATH']}\n",
    "    !cd {paths['PRETRAINED_MODEL_PATH']} && tar -zxvf {PRETRAINED_MODEL_NAME+'.tar.gz'}\n",
    "if os.name == 'nt':\n",
    "    wget.download(PRETRAINED_MODEL_URL)\n",
    "    !move {PRETRAINED_MODEL_NAME+'.tar.gz'} {paths['PRETRAINED_MODEL_PATH']}\n",
    "    !cd {paths['PRETRAINED_MODEL_PATH']} && tar -zxvf {PRETRAINED_MODEL_NAME+'.tar.gz'}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68a9d234-cf56-4447-b18f-574b47fa6861",
   "metadata": {},
   "source": [
    "# 4) Creating Label Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fac2f6e5-86f2-4822-8dbf-8b5355c47fbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = [{\"name\": \"Ok\" ,\"id\": 1},\n",
    "          {\"name\": \"NotOk\" ,\"id\": 2},\n",
    "          {\"name\": \"ThankYou\" ,\"id\": 3},\n",
    "          {\"name\": \"LiveLong\" , \"id\": 4},\n",
    "          {\"name\": \"Name\" , \"id\": 5},\n",
    "          {\"name\": \"What\" , \"id\": 6},\n",
    "          {\"name\": \"You\" , \"id\": 7},\n",
    "          {\"name\": \"ILoveYou\" , \"id\": 8},\n",
    "          {\"name\": \"Nice\" , \"id\": 9},\n",
    "          {\"name\": \"Love\", \"id\":10}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4ef3c44c-7e24-4d34-b949-fa18144e5c71",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(files['LABELMAP'], 'w') as f:\n",
    "    for label in labels:\n",
    "        f.write('item { \\n')\n",
    "        f.write('\\tname:\\'{}\\'\\n'.format(label['name']))\n",
    "        f.write('\\tid:{}\\n'.format(label['id']))\n",
    "        f.write('}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "138400c7-7c59-47e3-a370-32694bc97866",
   "metadata": {},
   "source": [
    "# 5) Creating TF Records"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d71b2121-af60-4dc5-8982-a80dad2c2c73",
   "metadata": {},
   "source": [
    "TF Records are a binary file format for storing data.<br>\n",
    "Using it helps speed up our training for cusotm Object Detection Model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6dee5e10-5645-4004-b702-425ac06e2176",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cloning into 'Tensorflow\\scripts'...\n"
     ]
    }
   ],
   "source": [
    "if not os.path.exists(files['TF_RECORD_SCRIPT']):\n",
    "    !git clone https://github.com/nicknochnack/GenerateTFRecord {paths['SCRIPTS_PATH']}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1bee963-6fb1-4391-92ec-e6effcb46a15",
   "metadata": {},
   "source": [
    "The above clone file will help in converting our images and annotations into TF Records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "97805f7c-f878-46eb-a865-7bb5c6fa85d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully created the TFRecord file: Tensorflow\\workspace\\annotations\\train.record\n",
      "Successfully created the TFRecord file: Tensorflow\\workspace\\annotations\\test.record\n"
     ]
    }
   ],
   "source": [
    "!python {files['TF_RECORD_SCRIPT']} -x {os.path.join(paths['IMAGE_PATH'], 'train')} -l {files['LABELMAP']} -o {os.path.join(paths['ANNOTATION_PATH'], 'train.record')} \n",
    "!python {files['TF_RECORD_SCRIPT']} -x {os.path.join(paths['IMAGE_PATH'], 'test')} -l {files['LABELMAP']} -o {os.path.join(paths['ANNOTATION_PATH'], 'test.record')}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e69523bd-03de-46e1-8170-6d57bb1bae47",
   "metadata": {},
   "source": [
    "# 6) Copy Model Config to Training Folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8beb2c04-37b6-4898-80ab-c32b2c684352",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        1 file(s) copied.\n"
     ]
    }
   ],
   "source": [
    "if os.name =='posix':\n",
    "    !cp {os.path.join(paths['PRETRAINED_MODEL_PATH'], PRETRAINED_MODEL_NAME, 'pipeline.config')} {os.path.join(paths['CHECKPOINT_PATH'])}\n",
    "if os.name == 'nt':\n",
    "    !copy {os.path.join(paths['PRETRAINED_MODEL_PATH'], PRETRAINED_MODEL_NAME, 'pipeline.config')} {os.path.join(paths['CHECKPOINT_PATH'])}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba03f80e-8548-4da5-b9c0-43d1f0d745a3",
   "metadata": {},
   "source": [
    "# 7) Update Config For Transfer Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8e50ded3-fdd6-49b9-b6a7-871c22718484",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model': ssd {\n",
       "   num_classes: 90\n",
       "   image_resizer {\n",
       "     fixed_shape_resizer {\n",
       "       height: 320\n",
       "       width: 320\n",
       "     }\n",
       "   }\n",
       "   feature_extractor {\n",
       "     type: \"ssd_mobilenet_v2_fpn_keras\"\n",
       "     depth_multiplier: 1.0\n",
       "     min_depth: 16\n",
       "     conv_hyperparams {\n",
       "       regularizer {\n",
       "         l2_regularizer {\n",
       "           weight: 3.9999998989515007e-05\n",
       "         }\n",
       "       }\n",
       "       initializer {\n",
       "         random_normal_initializer {\n",
       "           mean: 0.0\n",
       "           stddev: 0.009999999776482582\n",
       "         }\n",
       "       }\n",
       "       activation: RELU_6\n",
       "       batch_norm {\n",
       "         decay: 0.996999979019165\n",
       "         scale: true\n",
       "         epsilon: 0.0010000000474974513\n",
       "       }\n",
       "     }\n",
       "     use_depthwise: true\n",
       "     override_base_feature_extractor_hyperparams: true\n",
       "     fpn {\n",
       "       min_level: 3\n",
       "       max_level: 7\n",
       "       additional_layer_depth: 128\n",
       "     }\n",
       "   }\n",
       "   box_coder {\n",
       "     faster_rcnn_box_coder {\n",
       "       y_scale: 10.0\n",
       "       x_scale: 10.0\n",
       "       height_scale: 5.0\n",
       "       width_scale: 5.0\n",
       "     }\n",
       "   }\n",
       "   matcher {\n",
       "     argmax_matcher {\n",
       "       matched_threshold: 0.5\n",
       "       unmatched_threshold: 0.5\n",
       "       ignore_thresholds: false\n",
       "       negatives_lower_than_unmatched: true\n",
       "       force_match_for_each_row: true\n",
       "       use_matmul_gather: true\n",
       "     }\n",
       "   }\n",
       "   similarity_calculator {\n",
       "     iou_similarity {\n",
       "     }\n",
       "   }\n",
       "   box_predictor {\n",
       "     weight_shared_convolutional_box_predictor {\n",
       "       conv_hyperparams {\n",
       "         regularizer {\n",
       "           l2_regularizer {\n",
       "             weight: 3.9999998989515007e-05\n",
       "           }\n",
       "         }\n",
       "         initializer {\n",
       "           random_normal_initializer {\n",
       "             mean: 0.0\n",
       "             stddev: 0.009999999776482582\n",
       "           }\n",
       "         }\n",
       "         activation: RELU_6\n",
       "         batch_norm {\n",
       "           decay: 0.996999979019165\n",
       "           scale: true\n",
       "           epsilon: 0.0010000000474974513\n",
       "         }\n",
       "       }\n",
       "       depth: 128\n",
       "       num_layers_before_predictor: 4\n",
       "       kernel_size: 3\n",
       "       class_prediction_bias_init: -4.599999904632568\n",
       "       share_prediction_tower: true\n",
       "       use_depthwise: true\n",
       "     }\n",
       "   }\n",
       "   anchor_generator {\n",
       "     multiscale_anchor_generator {\n",
       "       min_level: 3\n",
       "       max_level: 7\n",
       "       anchor_scale: 4.0\n",
       "       aspect_ratios: 1.0\n",
       "       aspect_ratios: 2.0\n",
       "       aspect_ratios: 0.5\n",
       "       scales_per_octave: 2\n",
       "     }\n",
       "   }\n",
       "   post_processing {\n",
       "     batch_non_max_suppression {\n",
       "       score_threshold: 9.99999993922529e-09\n",
       "       iou_threshold: 0.6000000238418579\n",
       "       max_detections_per_class: 100\n",
       "       max_total_detections: 100\n",
       "       use_static_shapes: false\n",
       "     }\n",
       "     score_converter: SIGMOID\n",
       "   }\n",
       "   normalize_loss_by_num_matches: true\n",
       "   loss {\n",
       "     localization_loss {\n",
       "       weighted_smooth_l1 {\n",
       "       }\n",
       "     }\n",
       "     classification_loss {\n",
       "       weighted_sigmoid_focal {\n",
       "         gamma: 2.0\n",
       "         alpha: 0.25\n",
       "       }\n",
       "     }\n",
       "     classification_weight: 1.0\n",
       "     localization_weight: 1.0\n",
       "   }\n",
       "   encode_background_as_zeros: true\n",
       "   normalize_loc_loss_by_codesize: true\n",
       "   inplace_batchnorm_update: true\n",
       "   freeze_batchnorm: false\n",
       " },\n",
       " 'train_config': batch_size: 128\n",
       " data_augmentation_options {\n",
       "   random_horizontal_flip {\n",
       "   }\n",
       " }\n",
       " data_augmentation_options {\n",
       "   random_crop_image {\n",
       "     min_object_covered: 0.0\n",
       "     min_aspect_ratio: 0.75\n",
       "     max_aspect_ratio: 3.0\n",
       "     min_area: 0.75\n",
       "     max_area: 1.0\n",
       "     overlap_thresh: 0.0\n",
       "   }\n",
       " }\n",
       " sync_replicas: true\n",
       " optimizer {\n",
       "   momentum_optimizer {\n",
       "     learning_rate {\n",
       "       cosine_decay_learning_rate {\n",
       "         learning_rate_base: 0.07999999821186066\n",
       "         total_steps: 50000\n",
       "         warmup_learning_rate: 0.026666000485420227\n",
       "         warmup_steps: 1000\n",
       "       }\n",
       "     }\n",
       "     momentum_optimizer_value: 0.8999999761581421\n",
       "   }\n",
       "   use_moving_average: false\n",
       " }\n",
       " fine_tune_checkpoint: \"PATH_TO_BE_CONFIGURED\"\n",
       " num_steps: 50000\n",
       " startup_delay_steps: 0.0\n",
       " replicas_to_aggregate: 8\n",
       " max_number_of_boxes: 100\n",
       " unpad_groundtruth_tensors: false\n",
       " fine_tune_checkpoint_type: \"classification\"\n",
       " fine_tune_checkpoint_version: V2,\n",
       " 'train_input_config': label_map_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " tf_record_input_reader {\n",
       "   input_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " },\n",
       " 'eval_config': metrics_set: \"coco_detection_metrics\"\n",
       " use_moving_averages: false,\n",
       " 'eval_input_configs': [label_map_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " shuffle: false\n",
       " num_epochs: 1\n",
       " tf_record_input_reader {\n",
       "   input_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " }\n",
       " ],\n",
       " 'eval_input_config': label_map_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " shuffle: false\n",
       " num_epochs: 1\n",
       " tf_record_input_reader {\n",
       "   input_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " }}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config = config_util.get_configs_from_pipeline_file(files['PIPELINE_CONFIG'])\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cf5f5435-4cee-4fa4-89fc-69482f45e258",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()\n",
    "with tf.io.gfile.GFile(files['PIPELINE_CONFIG'], \"r\") as f:\n",
    "    proto_str = f.read()\n",
    "    text_format.Merge(proto_str, pipeline_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8a8d520a-521f-47a8-9c1a-7cb84a963354",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_config.model.ssd.num_classes = len(labels)\n",
    "pipeline_config.train_config.batch_size = 4\n",
    "pipeline_config.train_config.fine_tune_checkpoint = os.path.join(paths['PRETRAINED_MODEL_PATH'], PRETRAINED_MODEL_NAME, 'checkpoint', 'ckpt-0')\n",
    "pipeline_config.train_config.fine_tune_checkpoint_type = \"detection\"\n",
    "pipeline_config.train_input_reader.label_map_path= files['LABELMAP']\n",
    "pipeline_config.train_input_reader.tf_record_input_reader.input_path[:] = [os.path.join(paths['ANNOTATION_PATH'], 'train.record')]\n",
    "pipeline_config.eval_input_reader[0].label_map_path = files['LABELMAP']\n",
    "pipeline_config.eval_input_reader[0].tf_record_input_reader.input_path[:] = [os.path.join(paths['ANNOTATION_PATH'], 'test.record')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b51628f8-788b-47c0-a9c8-5ce589eac9d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "config_text = text_format.MessageToString(pipeline_config)                                                                                                                       \n",
    "with tf.io.gfile.GFile(files['PIPELINE_CONFIG'], \"wb\") as f:\n",
    "    f.write(config_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc585fe4-0ffc-4c20-b5df-025440ed8727",
   "metadata": {},
   "source": [
    "# 8) Training Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d5b43fe1-83c6-4efb-9ba9-4b3e94a7d029",
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAINING_SCRIPT = os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection', 'model_main_tf2.py')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b2118410-b25c-446d-8633-2393413f5a29",
   "metadata": {},
   "outputs": [],
   "source": [
    "command = \"python {} --model_dir={} --pipeline_config_path={} --num_train_steps=10000\".format(TRAINING_SCRIPT, paths['CHECKPOINT_PATH'],files['PIPELINE_CONFIG'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "871e617f-efa5-46dc-86da-fbe5767b2b80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python Tensorflow\\models\\research\\object_detection\\model_main_tf2.py --model_dir=Tensorflow\\workspace\\models\\my_ssd_mobnet --pipeline_config_path=Tensorflow\\workspace\\models\\my_ssd_mobnet\\pipeline.config --num_train_steps=10000\n"
     ]
    }
   ],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "776f2b1c-b102-47e7-9abf-eeb26f07d031",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 9) Evaluating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "48ed0cfa-217b-4a2b-8634-ba9f9ed9caf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python Tensorflow\\models\\research\\object_detection\\model_main_tf2.py --model_dir=Tensorflow\\workspace\\models\\my_ssd_mobnet --pipeline_config_path=Tensorflow\\workspace\\models\\my_ssd_mobnet\\pipeline.config --checkpoint_dir=Tensorflow\\workspace\\models\\my_ssd_mobnet\n"
     ]
    }
   ],
   "source": [
    "command = \"python {} --model_dir={} --pipeline_config_path={} --checkpoint_dir={}\".format(TRAINING_SCRIPT, paths['CHECKPOINT_PATH'],files['PIPELINE_CONFIG'], paths['CHECKPOINT_PATH'])\n",
    "print(command)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19ae8149-c086-4458-85d7-64532494c9f3",
   "metadata": {},
   "source": [
    "# 10) Importing Model From Latest Checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "af788750-e8b7-4efb-b5c1-19cc576835c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from object_detection.utils import label_map_util\n",
    "from object_detection.utils import visualization_utils as viz_utils\n",
    "from object_detection.builders import model_builder\n",
    "from object_detection.utils import config_util"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "83fcbc6f-7505-4a3c-8fca-1a4a9b74c734",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load pipeline config and build a detection model\n",
    "configs = config_util.get_configs_from_pipeline_file(files['PIPELINE_CONFIG'])\n",
    "detection_model = model_builder.build(model_config=configs['model'], is_training=False)\n",
    "\n",
    "# Restore checkpoint\n",
    "ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)\n",
    "ckpt.restore(os.path.join(paths['CHECKPOINT_PATH'], 'ckpt-11')).expect_partial()\n",
    "\n",
    "@tf.function\n",
    "def detect_fn(image):\n",
    "    image, shapes = detection_model.preprocess(image)\n",
    "    prediction_dict = detection_model.predict(image, shapes)\n",
    "    detections = detection_model.postprocess(prediction_dict, shapes)\n",
    "    return detections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7dd759a-e4e3-4d94-ad3c-bca1557e7f3a",
   "metadata": {},
   "source": [
    "# 11) Detection using an Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bd7c1fef-c533-4f59-bfd4-3f526a8061fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "category_index = label_map_util.create_category_index_from_labelmap(files['LABELMAP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "336de196-fc10-4ebb-87ed-c71f4de966ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "IMAGE_PATH = os.path.join(paths['IMAGE_PATH'], 'test', 'iloveyou.27337f3e-de5b-11eb-a227-8c47be4b3dc0.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "df5709c3-14b8-4082-8cf8-477732791d40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = cv2.imread(IMAGE_PATH)\n",
    "image_np = np.array(img)\n",
    "\n",
    "input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)\n",
    "detections = detect_fn(input_tensor)\n",
    "\n",
    "num_detections = int(detections.pop('num_detections'))\n",
    "detections = {key: value[0, :num_detections].numpy()\n",
    "              for key, value in detections.items()}\n",
    "detections['num_detections'] = num_detections\n",
    "\n",
    "# detection_classes should be ints.\n",
    "detections['detection_classes'] = detections['detection_classes'].astype(np.int64)\n",
    "\n",
    "label_id_offset = 1\n",
    "image_np_with_detections = image_np.copy()\n",
    "\n",
    "viz_utils.visualize_boxes_and_labels_on_image_array(\n",
    "            image_np_with_detections,\n",
    "            detections['detection_boxes'],\n",
    "            detections['detection_classes']+label_id_offset,\n",
    "            detections['detection_scores'],\n",
    "            category_index,\n",
    "            use_normalized_coordinates=True,\n",
    "            max_boxes_to_draw=5,\n",
    "            min_score_thresh=.8,\n",
    "            agnostic_mode=False)\n",
    "\n",
    "plt.imshow(cv2.cvtColor(image_np_with_detections, cv2.COLOR_BGR2RGB))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6f422a9-bcce-4c63-b6fc-da19fa79336d",
   "metadata": {},
   "source": [
    "# 12) Real Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c0efeb60-c0b5-4e3b-9d60-9a9b7e3e8a03",
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
    "height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "\n",
    "while cap.isOpened(): \n",
    "    ret, frame = cap.read()\n",
    "    image_np = np.array(frame)\n",
    "    \n",
    "    input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)\n",
    "    detections = detect_fn(input_tensor)\n",
    "    \n",
    "    num_detections = int(detections.pop('num_detections'))\n",
    "    detections = {key: value[0, :num_detections].numpy()\n",
    "                  for key, value in detections.items()}\n",
    "    detections['num_detections'] = num_detections\n",
    "\n",
    "    # detection_classes should be ints.\n",
    "    detections['detection_classes'] = detections['detection_classes'].astype(np.int64)\n",
    "\n",
    "    label_id_offset = 1\n",
    "    image_np_with_detections = image_np.copy()\n",
    "\n",
    "    viz_utils.visualize_boxes_and_labels_on_image_array(\n",
    "                image_np_with_detections,\n",
    "                detections['detection_boxes'],\n",
    "                detections['detection_classes']+label_id_offset,\n",
    "                detections['detection_scores'],\n",
    "                category_index,\n",
    "                use_normalized_coordinates=True,\n",
    "                max_boxes_to_draw=5,\n",
    "                min_score_thresh=.8,\n",
    "                agnostic_mode=False)\n",
    "\n",
    "    cv2.imshow('object detection',  cv2.resize(image_np_with_detections, (800, 600)))\n",
    "    \n",
    "    if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "        cap.release()\n",
    "        cv2.destroyAllWindows()\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a2b1c92-782e-4fba-9935-9087bf9084b7",
   "metadata": {},
   "source": [
    "# 13) Freezing The Graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7933f98-127e-4f75-9433-993ac3a00320",
   "metadata": {},
   "source": [
    "In this section, a slice of the model is being saved for future use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b9f282dd-1cd3-4738-b1bc-f110174fc8ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "FREEZE_SCRIPT = os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection', 'exporter_main_v2.py ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c9d5edae-9522-4b20-b53e-4f29e6204e65",
   "metadata": {},
   "outputs": [],
   "source": [
    "command = \"python {} --input_type=image_tensor --pipeline_config_path={} --trained_checkpoint_dir={} --output_directory={}\".format(FREEZE_SCRIPT ,files['PIPELINE_CONFIG'], paths['CHECKPOINT_PATH'], paths['OUTPUT_PATH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "09c80b19-8584-493a-8f7b-28b49ea96110",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python Tensorflow\\models\\research\\object_detection\\exporter_main_v2.py  --input_type=image_tensor --pipeline_config_path=Tensorflow\\workspace\\models\\my_ssd_mobnet\\pipeline.config --trained_checkpoint_dir=Tensorflow\\workspace\\models\\my_ssd_mobnet --output_directory=Tensorflow\\workspace\\models\\my_ssd_mobnet\\export\n"
     ]
    }
   ],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7cf6653e-cb36-4f83-8eac-17524a1908c2",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-07-12 02:28:55.047833: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll\n",
      "2021-07-12 02:28:57.491785: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library nvcuda.dll\n",
      "2021-07-12 02:28:57.522252: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1650 computeCapability: 7.5\n",
      "coreClock: 1.515GHz coreCount: 14 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 178.84GiB/s\n",
      "2021-07-12 02:28:57.522274: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll\n",
      "2021-07-12 02:28:57.526622: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cublas64_11.dll\n",
      "2021-07-12 02:28:57.526644: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cublasLt64_11.dll\n",
      "2021-07-12 02:28:57.528790: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cufft64_10.dll\n",
      "2021-07-12 02:28:57.529596: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library curand64_10.dll\n",
      "2021-07-12 02:28:57.531672: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cusolver64_11.dll\n",
      "2021-07-12 02:28:57.533336: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cusparse64_11.dll\n",
      "2021-07-12 02:28:57.533919: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudnn64_8.dll\n",
      "2021-07-12 02:28:57.534038: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
      "2021-07-12 02:28:57.534272: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2021-07-12 02:28:57.534689: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1650 computeCapability: 7.5\n",
      "coreClock: 1.515GHz coreCount: 14 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 178.84GiB/s\n",
      "2021-07-12 02:28:57.534728: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
      "2021-07-12 02:28:57.998395: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-07-12 02:28:57.998414: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      0 \n",
      "2021-07-12 02:28:57.998419: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0:   N \n",
      "2021-07-12 02:28:57.998563: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2147 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5)\n",
      "WARNING:tensorflow:From D:\\Hand Sign Prediction Using Tensorflow Object Detection\\HSPtod\\lib\\site-packages\\tensorflow\\python\\autograph\\impl\\api.py:463: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "back_prop=False is deprecated. Consider using tf.stop_gradient instead.\n",
      "Instead of:\n",
      "results = tf.map_fn(fn, elems, back_prop=False)\n",
      "Use:\n",
      "results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\n",
      "W0712 02:28:58.528480  2316 deprecation.py:601] From D:\\Hand Sign Prediction Using Tensorflow Object Detection\\HSPtod\\lib\\site-packages\\tensorflow\\python\\autograph\\impl\\api.py:463: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "back_prop=False is deprecated. Consider using tf.stop_gradient instead.\n",
      "Instead of:\n",
      "results = tf.map_fn(fn, elems, back_prop=False)\n",
      "Use:\n",
      "results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x000001E9AC591288>, because it is not built.\n",
      "W0712 02:29:13.338243  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x000001E9AC591288>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x000001EA0517E248>, because it is not built.\n",
      "W0712 02:29:13.447513  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x000001EA0517E248>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA0514BDC8>, because it is not built.\n",
      "W0712 02:29:13.447513  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA0514BDC8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA05187808>, because it is not built.\n",
      "W0712 02:29:13.447513  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA05187808>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x000001EA051870C8>, because it is not built.\n",
      "W0712 02:29:13.447513  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x000001EA051870C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA0517A388>, because it is not built.\n",
      "W0712 02:29:13.447513  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA0517A388>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA0517A7C8>, because it is not built.\n",
      "W0712 02:29:13.447513  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA0517A7C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x000001EA0517AF88>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x000001EA0517AF88>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA051902C8>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA051902C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA05190188>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA05190188>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x000001E9C71BFFC8>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x000001E9C71BFFC8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001E9C71BFBC8>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001E9C71BFBC8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001E9C7256748>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001E9C7256748>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001E9C8324348>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001E9C8324348>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001E9C82C9A48>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001E9C82C9A48>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001E9C5B53B88>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001E9C5B53B88>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001E9B2BBBD08>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001E9B2BBBD08>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001E9C5BF6188>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001E9C5BF6188>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001E9C5BF67C8>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001E9C5BF67C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001E9C5BD0948>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001E9C5BD0948>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001E9C5BD0308>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001E9C5BD0308>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA05119088>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA05119088>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001E9B78FEE08>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001E9B78FEE08>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA0512F588>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA0512F588>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA0512FE08>, because it is not built.\n",
      "W0712 02:29:13.448510  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA0512FE08>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA0512F748>, because it is not built.\n",
      "W0712 02:29:13.449507  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA0512F748>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001E9C82F9C48>, because it is not built.\n",
      "W0712 02:29:13.449507  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001E9C82F9C48>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001E9C82F9C08>, because it is not built.\n",
      "W0712 02:29:13.449507  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001E9C82F9C08>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA0519F0C8>, because it is not built.\n",
      "W0712 02:29:13.449507  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA0519F0C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA051F1448>, because it is not built.\n",
      "W0712 02:29:13.449507  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA051F1448>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA0521AE88>, because it is not built.\n",
      "W0712 02:29:13.449507  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA0521AE88>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA0521ADC8>, because it is not built.\n",
      "W0712 02:29:13.449507  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA0521ADC8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA051F5508>, because it is not built.\n",
      "W0712 02:29:13.449507  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA051F5508>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA051F5D88>, because it is not built.\n",
      "W0712 02:29:13.449507  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA051F5D88>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA051F5888>, because it is not built.\n",
      "W0712 02:29:13.449507  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA051F5888>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA051A87C8>, because it is not built.\n",
      "W0712 02:29:13.449507  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA051A87C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA051A8408>, because it is not built.\n",
      "W0712 02:29:13.449507  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA051A8408>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA051F14C8>, because it is not built.\n",
      "W0712 02:29:13.449507  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA051F14C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA05174888>, because it is not built.\n",
      "W0712 02:29:13.449507  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA05174888>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA051743C8>, because it is not built.\n",
      "W0712 02:29:13.449507  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA051743C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001E9BB3DC4C8>, because it is not built.\n",
      "W0712 02:29:13.449507  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001E9BB3DC4C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA053E2F48>, because it is not built.\n",
      "W0712 02:29:13.449507  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA053E2F48>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA053E2D88>, because it is not built.\n",
      "W0712 02:29:13.450505  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA053E2D88>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA053E2288>, because it is not built.\n",
      "W0712 02:29:13.450505  2316 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000001EA053E2288>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA05207548>, because it is not built.\n",
      "W0712 02:29:13.450505  2316 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000001EA05207548>, because it is not built.\n",
      "2021-07-12 02:29:20.719411: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n",
      "W0712 02:29:35.633570  2316 save.py:243] Found untraced functions such as WeightSharedConvolutionalBoxPredictor_layer_call_fn, WeightSharedConvolutionalBoxPredictor_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxHead_layer_call_fn, WeightSharedConvolutionalBoxHead_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxPredictor_layer_call_fn while saving (showing 5 of 260). These functions will not be directly callable after loading.\n",
      "D:\\Hand Sign Prediction Using Tensorflow Object Detection\\HSPtod\\lib\\site-packages\\tensorflow\\python\\keras\\utils\\generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  category=CustomMaskWarning)\n",
      "WARNING:tensorflow:FOR KERAS USERS: The object that you are saving contains one or more Keras models or layers. If you are loading the SavedModel with `tf.keras.models.load_model`, continue reading (otherwise, you may ignore the following instructions). Please change your code to save with `tf.keras.models.save_model` or `model.save`, and confirm that the file \"keras.metadata\" exists in the export directory. In the future, Keras will only load the SavedModels that have this file. In other words, `tf.saved_model.save` will no longer write SavedModels that can be recovered as Keras models (this will apply in TF 2.5).\n",
      "\n",
      "FOR DEVS: If you are overwriting _tracking_metadata in your class, this property has been used to save metadata in the SavedModel. The metadta field will be deprecated soon, so please move the metadata to a different file.\n",
      "W0712 02:29:39.114362  2316 save.py:1240] FOR KERAS USERS: The object that you are saving contains one or more Keras models or layers. If you are loading the SavedModel with `tf.keras.models.load_model`, continue reading (otherwise, you may ignore the following instructions). Please change your code to save with `tf.keras.models.save_model` or `model.save`, and confirm that the file \"keras.metadata\" exists in the export directory. In the future, Keras will only load the SavedModels that have this file. In other words, `tf.saved_model.save` will no longer write SavedModels that can be recovered as Keras models (this will apply in TF 2.5).\n",
      "\n",
      "FOR DEVS: If you are overwriting _tracking_metadata in your class, this property has been used to save metadata in the SavedModel. The metadta field will be deprecated soon, so please move the metadata to a different file.\n",
      "INFO:tensorflow:Assets written to: Tensorflow\\workspace\\models\\my_ssd_mobnet\\export\\saved_model\\assets\n",
      "I0712 02:29:39.577739  2316 builder_impl.py:775] Assets written to: Tensorflow\\workspace\\models\\my_ssd_mobnet\\export\\saved_model\\assets\n",
      "INFO:tensorflow:Writing pipeline config file to Tensorflow\\workspace\\models\\my_ssd_mobnet\\export\\pipeline.config\n",
      "I0712 02:29:40.352613  2316 config_util.py:254] Writing pipeline config file to Tensorflow\\workspace\\models\\my_ssd_mobnet\\export\\pipeline.config\n"
     ]
    }
   ],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8fb5f37-c1e4-4792-9511-9b9e107f77df",
   "metadata": {},
   "source": [
    "# 14) Zipping And Saving The Model as .tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7beb65f5-5e58-4892-921b-37ef1f48ffb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "!tar -czf models.tar.gz {paths['CHECKPOINT_PATH']}"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}