2. Training and Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QUANWN3rpfC9"
   },
   "source": [
    "# 0. Setup Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "146BB11JpfDA"
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "42hJEdo_pfDB"
   },
   "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": 3,
   "metadata": {
    "id": "hbPhYVy_pfDB"
   },
   "outputs": [],
   "source": [
    "paths = {\n",
    "    'WORKSPACE_PATH': os.path.join('Tensorflow', 'workspace'),\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",
    "    'TFJS_PATH':os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME, 'tfjsexport'), \n",
    "    'TFLITE_PATH':os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME, 'tfliteexport'), \n",
    "    'PROTOC_PATH':os.path.join('Tensorflow','protoc')\n",
    " }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "LwhWZMI0pfDC"
   },
   "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": 5,
   "metadata": {
    "id": "HR-TfDGrpfDC"
   },
   "outputs": [],
   "source": [
    "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",
   "metadata": {
    "id": "OLU-rs_ipfDE"
   },
   "source": [
    "# 1. Download TF Models Pretrained Models from Tensorflow Model Zoo and Install TFOD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.tensorflow.org/install/source_windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "K-Cmz2edpfDE",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wget in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (3.2)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -pencv-python (d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -pencv-python (d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -pencv-python (d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -pencv-python (d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -pencv-python (d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -pencv-python (d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "if os.name=='nt':\n",
    "    !pip install wget\n",
    "    import wget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "iA1DIq5OpfDE"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cloning into 'Tensorflow\\models'...\n",
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      "Updating files: 100% (3101/3101), done.\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": "code",
   "execution_count": 10,
   "metadata": {
    "id": "rJjMHbnDs3Tv"
   },
   "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",
      "copying build\\lib\\nets\\nasnet\\nasnet.py -> build\\bdist.win-amd64\\egg\\nets\\nasnet\n",
      "copying build\\lib\\nets\\nasnet\\nasnet_test.py -> build\\bdist.win-amd64\\egg\\nets\\nasnet\n",
      "copying build\\lib\\nets\\nasnet\\nasnet_utils.py -> build\\bdist.win-amd64\\egg\\nets\\nasnet\n",
      "copying build\\lib\\nets\\nasnet\\nasnet_utils_test.py -> build\\bdist.win-amd64\\egg\\nets\\nasnet\n",
      "copying build\\lib\\nets\\nasnet\\pnasnet.py -> build\\bdist.win-amd64\\egg\\nets\\nasnet\n",
      "copying build\\lib\\nets\\nasnet\\pnasnet_test.py -> build\\bdist.win-amd64\\egg\\nets\\nasnet\n",
      "copying build\\lib\\nets\\nasnet\\__init__.py -> build\\bdist.win-amd64\\egg\\nets\\nasnet\n",
      "copying build\\lib\\nets\\nets_factory.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "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",
      "copying build\\lib\\nets\\overfeat_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\pix2pix.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\pix2pix_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "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",
      "copying build\\lib\\nets\\s3dg_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
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      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\variables_helper.py to variables_helper.cpython-39.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\variables_helper_tf1_test.py to variables_helper_tf1_test.cpython-39.pyc\n",
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      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\visualization_utils_test.py to visualization_utils_test.cpython-39.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\vrd_evaluation.py to vrd_evaluation.cpython-39.pyc\n",
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      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\__init__.py to __init__.cpython-39.pyc\n",
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      "byte-compiling build\\bdist.win-amd64\\egg\\preprocessing\\cifarnet_preprocessing.py to cifarnet_preprocessing.cpython-39.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\preprocessing\\inception_preprocessing.py to inception_preprocessing.cpython-39.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\preprocessing\\lenet_preprocessing.py to lenet_preprocessing.cpython-39.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\preprocessing\\preprocessing_factory.py to preprocessing_factory.cpython-39.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\preprocessing\\vgg_preprocessing.py to vgg_preprocessing.cpython-39.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\preprocessing\\__init__.py to __init__.cpython-39.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-py3.9.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-py3.9.egg\n",
      "removing 'd:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\\object_detection-0.1-py3.9.egg' (and everything under it)\n",
      "creating d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\\object_detection-0.1-py3.9.egg\n",
      "Extracting object_detection-0.1-py3.9.egg to d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\n",
      "object-detection 0.1 is already the active version in easy-install.pth\n",
      "\n",
      "Installed d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\\object_detection-0.1-py3.9.egg\n",
      "Processing dependencies for object-detection==0.1\n",
      "Searching for pycocotools\n",
      "Reading https://pypi.org/simple/pycocotools/\n",
      "Downloading https://files.pythonhosted.org/packages/75/5c/ac61ea715d7a89ecc31c090753bde28810238225ca8b71778dfe3e6a68bc/pycocotools-2.0.4.tar.gz#sha256=2ab586aa389b9657b6d73c2b9a827a3681f8d00f36490c2e8ab05902e3fd9e93\n",
      "Best match: pycocotools 2.0.4\n",
      "Processing pycocotools-2.0.4.tar.gz\n",
      "Writing C:\\Users\\ADITYA~1\\AppData\\Local\\Temp\\easy_install-s8bilcd5\\pycocotools-2.0.4\\setup.cfg\n",
      "Running pycocotools-2.0.4\\setup.py -q bdist_egg --dist-dir C:\\Users\\ADITYA~1\\AppData\\Local\\Temp\\easy_install-s8bilcd5\\pycocotools-2.0.4\\egg-dist-tmp-q9dxxik5\n",
      "maskApi.c\n",
      "./common/maskApi.c(135): warning C4101: 'xp': unreferenced local variable\n",
      "_mask.c\n",
      "D:\\Technology\\Python Projects\\Computer Vision Project\\TFODCourse-main\\tfod\\lib\\site-packages\\numpy\\core\\include\\numpy\\npy_1_7_deprecated_api.h(14) : Warning Msg: Using deprecated NumPy API, disable it with #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\n",
      "pycocotools/_mask.c(3288): warning C4244: '=': conversion from 'Py_ssize_t' to 'siz', possible loss of data\n",
      "pycocotools/_mask.c(3698): warning C4244: 'function': conversion from 'npy_intp' to 'siz', possible loss of data\n",
      "pycocotools/_mask.c(3698): warning C4244: 'function': conversion from 'npy_intp' to 'siz', possible loss of data\n",
      "pycocotools/_mask.c(3698): warning C4244: 'function': conversion from 'npy_intp' to 'siz', possible loss of data\n",
      "pycocotools/_mask.c(6723): warning C4244: '=': conversion from 'npy_intp' to 'siz', possible loss of data\n",
      "pycocotools/_mask.c(7481): warning C4244: '=': conversion from 'Py_ssize_t' to 'siz', possible loss of data\n",
      "   Creating library build\\temp.win-amd64-3.9\\Release\\./common\\_mask.cp39-win_amd64.lib and object build\\temp.win-amd64-3.9\\Release\\./common\\_mask.cp39-win_amd64.exp\n",
      "Generating code\n",
      "Finished generating code\n",
      "creating d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\\pycocotools-2.0.4-py3.9-win-amd64.egg\n",
      "Extracting pycocotools-2.0.4-py3.9-win-amd64.egg to d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\n",
      "Adding pycocotools 2.0.4 to easy-install.pth file\n",
      "\n",
      "Installed d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\\pycocotools-2.0.4-py3.9-win-amd64.egg\n",
      "Searching for contextlib2\n",
      "Reading https://pypi.org/simple/contextlib2/\n",
      "Downloading https://files.pythonhosted.org/packages/76/56/6d6872f79d14c0cb02f1646cbb4592eef935857c0951a105874b7b62a0c3/contextlib2-21.6.0-py2.py3-none-any.whl#sha256=3fbdb64466afd23abaf6c977627b75b6139a5a3e8ce38405c5b413aed7a0471f\n",
      "Best match: contextlib2 21.6.0\n",
      "Processing contextlib2-21.6.0-py2.py3-none-any.whl\n",
      "Installing contextlib2-21.6.0-py2.py3-none-any.whl to d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\n",
      "Adding contextlib2 21.6.0 to easy-install.pth file\n",
      "\n",
      "Installed d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\\contextlib2-21.6.0-py3.9.egg\n",
      "Searching for Cython\n",
      "Reading https://pypi.org/simple/Cython/\n",
      "Downloading https://files.pythonhosted.org/packages/b6/50/97fec65cbde210f6bf71db1970ce8f6461a0594c619b6c6834d03742f1bd/Cython-3.0.0a11-py2.py3-none-any.whl#sha256=939d5d252b242445933c078f3e6a7661436574d6eb50fca08842eedc7167eefe\n",
      "Best match: Cython 3.0.0a11\n",
      "Processing Cython-3.0.0a11-py2.py3-none-any.whl\n",
      "Installing Cython-3.0.0a11-py2.py3-none-any.whl to d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\n",
      "Adding Cython 3.0.0a11 to easy-install.pth file\n",
      "Installing cygdb-script.py script to D:\\Technology\\Python Projects\\Computer Vision Project\\TFODCourse-main\\tfod\\Scripts\n",
      "Installing cygdb.exe script to D:\\Technology\\Python Projects\\Computer Vision Project\\TFODCourse-main\\tfod\\Scripts\n",
      "Installing cython-script.py script to D:\\Technology\\Python Projects\\Computer Vision Project\\TFODCourse-main\\tfod\\Scripts\n",
      "Installing cython.exe script to D:\\Technology\\Python Projects\\Computer Vision Project\\TFODCourse-main\\tfod\\Scripts\n",
      "Installing cythonize-script.py script to D:\\Technology\\Python Projects\\Computer Vision Project\\TFODCourse-main\\tfod\\Scripts\n",
      "Installing cythonize.exe script to D:\\Technology\\Python Projects\\Computer Vision Project\\TFODCourse-main\\tfod\\Scripts\n",
      "\n",
      "Installed d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\\cython-3.0.0a11-py3.9.egg\n",
      "Searching for apache-beam\n",
      "Reading https://pypi.org/simple/apache-beam/\n",
      "Downloading https://files.pythonhosted.org/packages/fb/f7/4a945189f7e645c0e0e9d0359fb1c2bbcd4edc9e009260297fbe16dd861d/apache_beam-2.40.0-cp39-cp39-win_amd64.whl#sha256=95fd05de546933b3f181c2e304ac771e22a04b8da8337f3b2c463a87be3d5223\n",
      "Best match: apache-beam 2.40.0\n",
      "Processing apache_beam-2.40.0-cp39-cp39-win_amd64.whl\n",
      "Installing apache_beam-2.40.0-cp39-cp39-win_amd64.whl to d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\n",
      "Adding apache-beam 2.40.0 to easy-install.pth file\n",
      "\n",
      "Installed d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\\apache_beam-2.40.0-py3.9-win-amd64.egg\n",
      "Searching for avro-python3\n",
      "Reading https://pypi.org/simple/avro-python3/\n",
      "Downloading https://files.pythonhosted.org/packages/cc/97/7a6970380ca8db9139a3cc0b0e3e0dd3e4bc584fb3644e1d06e71e1a55f0/avro-python3-1.10.2.tar.gz#sha256=3b63f24e6b04368c3e4a6f923f484be0230d821aad65ac36108edbff29e9aaab\n",
      "Best match: avro-python3 1.10.2\n",
      "Processing avro-python3-1.10.2.tar.gz\n",
      "Writing C:\\Users\\ADITYA~1\\AppData\\Local\\Temp\\easy_install-axkl7v6d\\avro-python3-1.10.2\\setup.cfg\n",
      "Running avro-python3-1.10.2\\setup.py -q bdist_egg --dist-dir C:\\Users\\ADITYA~1\\AppData\\Local\\Temp\\easy_install-axkl7v6d\\avro-python3-1.10.2\\egg-dist-tmp-vu0a4e0j\n",
      "Copying avro_python3-1.10.2-py3.9.egg to d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\n",
      "Adding avro-python3 1.10.2 to easy-install.pth file\n",
      "Installing avro script to D:\\Technology\\Python Projects\\Computer Vision Project\\TFODCourse-main\\tfod\\Scripts\n",
      "\n",
      "Installed d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\\avro_python3-1.10.2-py3.9.egg\n",
      "Searching for tensorflow-text~=2.9.0\n",
      "Reading https://pypi.org/simple/tensorflow-text/\n",
      "Downloading https://files.pythonhosted.org/packages/74/44/14eca9c4a148a419d50ab5badd82dd790f06664c6759d19c1bb1e9250bea/tensorflow_text-2.9.0-cp39-cp39-win_amd64.whl#sha256=f449feeff2368bd84cfb33f34e6bf5436010e91c97f1e1d70db4b00797653078\n",
      "Best match: tensorflow-text 2.9.0\n",
      "Processing tensorflow_text-2.9.0-cp39-cp39-win_amd64.whl\n",
      "Installing tensorflow_text-2.9.0-cp39-cp39-win_amd64.whl to d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\n",
      "Adding tensorflow-text 2.9.0 to easy-install.pth file\n",
      "\n",
      "Installed d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\\tensorflow_text-2.9.0-py3.9-win-amd64.egg\n",
      "Searching for tensorflow-model-optimization>=0.4.1\n",
      "Reading https://pypi.org/simple/tensorflow-model-optimization/\n",
      "Downloading https://files.pythonhosted.org/packages/9c/0f/f592fb793fc83370fe84a43f8ba072b419bf2aa921e6dd7edebc3c82ba8a/tensorflow_model_optimization-0.7.3-py2.py3-none-any.whl#sha256=5452804f99edcf93390a1e6d84949022e008e5ea2b593365694bc768f973186b\n",
      "Best match: tensorflow-model-optimization 0.7.3\n",
      "Processing tensorflow_model_optimization-0.7.3-py2.py3-none-any.whl\n",
      "Installing tensorflow_model_optimization-0.7.3-py2.py3-none-any.whl to d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "zip_safe flag not set; analyzing archive contents...\n",
      "object_detection.core.__pycache__.densepose_ops.cpython-39: module references __file__\n",
      "object_detection.core.__pycache__.preprocessor.cpython-39: module MAY be using inspect.stack\n",
      "object_detection.utils.__pycache__.autoaugment_utils.cpython-39: module MAY be using inspect.stack\n",
      "zip_safe flag not set; analyzing archive contents...\n",
      "pycocotools.__pycache__._mask.cpython-39: module references __file__\n",
      "error: [WinError 206] The filename or extension is too long: 'd:\\\\technology\\\\python projects\\\\computer vision project\\\\tfodcourse-main\\\\tfod\\\\lib\\\\site-packages\\\\tensorflow_model_optimization-0.7.3-py3.9.egg\\\\tensorflow_model_optimization\\\\python\\\\core\\\\api\\\\quantization\\\\keras\\\\experimental\\\\default_n_bit\\\\default_n_bit_transforms'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining file:///D:/Technology/Python%20Projects/Computer%20Vision%20Project/TFODCourse-main/Tensorflow/models/research/slim\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Requirement already satisfied: six in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from slim==0.1) (1.16.0)\n",
      "Requirement already satisfied: tf-slim>=1.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from slim==0.1) (1.1.0)\n",
      "Requirement already satisfied: absl-py>=0.2.2 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tf-slim>=1.1->slim==0.1) (1.2.0)\n",
      "Installing collected packages: slim\n",
      "  Attempting uninstall: slim\n",
      "    Found existing installation: slim 0.1\n",
      "    Uninstalling slim-0.1:\n",
      "      Successfully uninstalled slim-0.1\n",
      "  Running setup.py develop for slim\n",
      "Successfully installed slim-0.1\n"
     ]
    }
   ],
   "source": [
    "# Install 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",
    "    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",
    "    !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": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Running tests under Python 3.9.7: D:\\Technology\\Python Projects\\Computer Vision Project\\TFODCourse-main\\tfod\\Scripts\\python.exe\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_deepmac\n",
      "2022-08-03 20:03:51.220533: I tensorflow/core/platform/cpu_feature_guard.cc:193] 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",
      "2022-08-03 20:03:51.895005: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2776 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1\n",
      "D:\\Technology\\Python Projects\\Computer Vision Project\\TFODCourse-main\\tfod\\lib\\site-packages\\object_detection-0.1-py3.9.egg\\object_detection\\builders\\model_builder.py:1102: DeprecationWarning: The 'warn' function is deprecated, use 'warning' instead\n",
      "  logging.warn(('Building experimental DeepMAC meta-arch.'\n",
      "W0803 20:03:52.231946  1260 model_builder.py:1102] Building experimental DeepMAC meta-arch. Some features may be omitted.\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 1.39s\n",
      "I0803 20:03:52.589003  1260 test_util.py:2458] time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 1.39s\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)): 2.25s\n",
      "I0803 20:03:54.837040  1260 test_util.py:2458] time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 2.25s\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)): 0.31s\n",
      "I0803 20:03:55.147211  1260 test_util.py:2458] time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.31s\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): 0.26s\n",
      "I0803 20:03:55.412397  1260 test_util.py:2458] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.26s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 1.84s\n",
      "I0803 20:03:57.256546  1260 test_util.py:2458] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 1.84s\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",
      "I0803 20:03:57.257544  1260 test_util.py:2458] 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",
      "I0803 20:03:57.278462  1260 test_util.py:2458] 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)): 0.01s\n",
      "I0803 20:03:57.292454  1260 test_util.py:2458] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.01s\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",
      "I0803 20:03:57.306413  1260 test_util.py:2458] 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): 0.09s\n",
      "I0803 20:03:57.395177  1260 test_util.py:2458] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.09s\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",
      "I0803 20:03:57.480947  1260 test_util.py:2458] 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",
      "I0803 20:03:57.569709  1260 test_util.py:2458] 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): 0.09s\n",
      "I0803 20:03:57.657474  1260 test_util.py:2458] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.09s\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): 0.09s\n",
      "I0803 20:03:57.745240  1260 test_util.py:2458] time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.09s\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",
      "I0803 20:03:57.771171  1260 test_util.py:2458] 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",
      "I0803 20:03:57.938723  1260 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet EfficientNet backbone version: efficientnet-b0\n",
      "I0803 20:03:57.938723  1260 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 64\n",
      "I0803 20:03:57.938723  1260 ssd_efficientnet_bifpn_feature_extractor.py:148] EfficientDet BiFPN num iterations: 3\n",
      "I0803 20:03:57.940718  1260 efficientnet_model.py:143] round_filter input=32 output=32\n",
      "I0803 20:03:57.956675  1260 efficientnet_model.py:143] round_filter input=32 output=32\n",
      "I0803 20:03:57.956675  1260 efficientnet_model.py:143] round_filter input=16 output=16\n",
      "I0803 20:03:58.015492  1260 efficientnet_model.py:143] round_filter input=16 output=16\n",
      "I0803 20:03:58.016488  1260 efficientnet_model.py:143] round_filter input=24 output=24\n",
      "I0803 20:03:58.167114  1260 efficientnet_model.py:143] round_filter input=24 output=24\n",
      "I0803 20:03:58.167114  1260 efficientnet_model.py:143] round_filter input=40 output=40\n",
      "I0803 20:03:58.318681  1260 efficientnet_model.py:143] round_filter input=40 output=40\n",
      "I0803 20:03:58.318681  1260 efficientnet_model.py:143] round_filter input=80 output=80\n",
      "I0803 20:03:58.545102  1260 efficientnet_model.py:143] round_filter input=80 output=80\n",
      "I0803 20:03:58.545102  1260 efficientnet_model.py:143] round_filter input=112 output=112\n",
      "I0803 20:03:58.771320  1260 efficientnet_model.py:143] round_filter input=112 output=112\n",
      "I0803 20:03:58.771320  1260 efficientnet_model.py:143] round_filter input=192 output=192\n",
      "I0803 20:03:59.069094  1260 efficientnet_model.py:143] round_filter input=192 output=192\n",
      "I0803 20:03:59.069094  1260 efficientnet_model.py:143] round_filter input=320 output=320\n",
      "I0803 20:03:59.142897  1260 efficientnet_model.py:143] round_filter input=1280 output=1280\n",
      "I0803 20:03:59.172817  1260 efficientnet_model.py:453] 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",
      "I0803 20:03:59.222683  1260 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet EfficientNet backbone version: efficientnet-b1\n",
      "I0803 20:03:59.222683  1260 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 88\n",
      "I0803 20:03:59.222683  1260 ssd_efficientnet_bifpn_feature_extractor.py:148] EfficientDet BiFPN num iterations: 4\n",
      "I0803 20:03:59.224681  1260 efficientnet_model.py:143] round_filter input=32 output=32\n",
      "I0803 20:03:59.239646  1260 efficientnet_model.py:143] round_filter input=32 output=32\n",
      "I0803 20:03:59.239646  1260 efficientnet_model.py:143] round_filter input=16 output=16\n",
      "I0803 20:03:59.358322  1260 efficientnet_model.py:143] round_filter input=16 output=16\n",
      "I0803 20:03:59.358322  1260 efficientnet_model.py:143] round_filter input=24 output=24\n",
      "I0803 20:03:59.669588  1260 efficientnet_model.py:143] round_filter input=24 output=24\n",
      "I0803 20:03:59.669588  1260 efficientnet_model.py:143] round_filter input=40 output=40\n",
      "I0803 20:03:59.891993  1260 efficientnet_model.py:143] round_filter input=40 output=40\n",
      "I0803 20:03:59.891993  1260 efficientnet_model.py:143] round_filter input=80 output=80\n",
      "I0803 20:04:00.253029  1260 efficientnet_model.py:143] round_filter input=80 output=80\n",
      "I0803 20:04:00.253029  1260 efficientnet_model.py:143] round_filter input=112 output=112\n",
      "I0803 20:04:00.567188  1260 efficientnet_model.py:143] round_filter input=112 output=112\n",
      "I0803 20:04:00.568190  1260 efficientnet_model.py:143] round_filter input=192 output=192\n",
      "I0803 20:04:00.962106  1260 efficientnet_model.py:143] round_filter input=192 output=192\n",
      "I0803 20:04:00.962106  1260 efficientnet_model.py:143] round_filter input=320 output=320\n",
      "I0803 20:04:01.107743  1260 efficientnet_model.py:143] round_filter input=1280 output=1280\n",
      "I0803 20:04:01.136666  1260 efficientnet_model.py:453] 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",
      "I0803 20:04:01.197622  1260 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet EfficientNet backbone version: efficientnet-b2\n",
      "I0803 20:04:01.197622  1260 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 112\n",
      "I0803 20:04:01.197622  1260 ssd_efficientnet_bifpn_feature_extractor.py:148] EfficientDet BiFPN num iterations: 5\n",
      "I0803 20:04:01.199590  1260 efficientnet_model.py:143] round_filter input=32 output=32\n",
      "I0803 20:04:01.214577  1260 efficientnet_model.py:143] round_filter input=32 output=32\n",
      "I0803 20:04:01.215575  1260 efficientnet_model.py:143] round_filter input=16 output=16\n",
      "I0803 20:04:01.346198  1260 efficientnet_model.py:143] round_filter input=16 output=16\n",
      "I0803 20:04:01.346198  1260 efficientnet_model.py:143] round_filter input=24 output=24\n",
      "I0803 20:04:01.587580  1260 efficientnet_model.py:143] round_filter input=24 output=24\n",
      "I0803 20:04:01.587580  1260 efficientnet_model.py:143] round_filter input=40 output=48\n",
      "I0803 20:04:01.814944  1260 efficientnet_model.py:143] round_filter input=40 output=48\n",
      "I0803 20:04:01.814944  1260 efficientnet_model.py:143] round_filter input=80 output=88\n",
      "I0803 20:04:02.118476  1260 efficientnet_model.py:143] round_filter input=80 output=88\n",
      "I0803 20:04:02.118476  1260 efficientnet_model.py:143] round_filter input=112 output=120\n",
      "I0803 20:04:02.427624  1260 efficientnet_model.py:143] round_filter input=112 output=120\n",
      "I0803 20:04:02.427624  1260 efficientnet_model.py:143] round_filter input=192 output=208\n",
      "I0803 20:04:02.812595  1260 efficientnet_model.py:143] round_filter input=192 output=208\n",
      "I0803 20:04:02.812595  1260 efficientnet_model.py:143] round_filter input=320 output=352\n",
      "I0803 20:04:02.960226  1260 efficientnet_model.py:143] round_filter input=1280 output=1408\n",
      "I0803 20:04:02.988154  1260 efficientnet_model.py:453] 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",
      "I0803 20:04:03.049961  1260 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet EfficientNet backbone version: efficientnet-b3\n",
      "I0803 20:04:03.049961  1260 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 160\n",
      "I0803 20:04:03.049961  1260 ssd_efficientnet_bifpn_feature_extractor.py:148] EfficientDet BiFPN num iterations: 6\n",
      "I0803 20:04:03.051955  1260 efficientnet_model.py:143] round_filter input=32 output=40\n",
      "I0803 20:04:03.068940  1260 efficientnet_model.py:143] round_filter input=32 output=40\n",
      "I0803 20:04:03.068940  1260 efficientnet_model.py:143] round_filter input=16 output=24\n",
      "I0803 20:04:03.193605  1260 efficientnet_model.py:143] round_filter input=16 output=24\n",
      "I0803 20:04:03.193605  1260 efficientnet_model.py:143] round_filter input=24 output=32\n",
      "I0803 20:04:03.452884  1260 efficientnet_model.py:143] round_filter input=24 output=32\n",
      "I0803 20:04:03.452884  1260 efficientnet_model.py:143] round_filter input=40 output=48\n",
      "I0803 20:04:03.705238  1260 efficientnet_model.py:143] round_filter input=40 output=48\n",
      "I0803 20:04:03.705238  1260 efficientnet_model.py:143] round_filter input=80 output=96\n",
      "I0803 20:04:04.195923  1260 efficientnet_model.py:143] round_filter input=80 output=96\n",
      "I0803 20:04:04.196937  1260 efficientnet_model.py:143] round_filter input=112 output=136\n",
      "I0803 20:04:04.572915  1260 efficientnet_model.py:143] round_filter input=112 output=136\n",
      "I0803 20:04:04.572915  1260 efficientnet_model.py:143] round_filter input=192 output=232\n",
      "I0803 20:04:05.040666  1260 efficientnet_model.py:143] round_filter input=192 output=232\n",
      "I0803 20:04:05.041663  1260 efficientnet_model.py:143] round_filter input=320 output=384\n",
      "I0803 20:04:05.205199  1260 efficientnet_model.py:143] round_filter input=1280 output=1536\n",
      "I0803 20:04:05.233150  1260 efficientnet_model.py:453] 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",
      "I0803 20:04:05.297976  1260 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet EfficientNet backbone version: efficientnet-b4\n",
      "I0803 20:04:05.297976  1260 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 224\n",
      "I0803 20:04:05.297976  1260 ssd_efficientnet_bifpn_feature_extractor.py:148] EfficientDet BiFPN num iterations: 7\n",
      "I0803 20:04:05.298974  1260 efficientnet_model.py:143] round_filter input=32 output=48\n",
      "I0803 20:04:05.314931  1260 efficientnet_model.py:143] round_filter input=32 output=48\n",
      "I0803 20:04:05.314931  1260 efficientnet_model.py:143] round_filter input=16 output=24\n",
      "I0803 20:04:05.432617  1260 efficientnet_model.py:143] round_filter input=16 output=24\n",
      "I0803 20:04:05.433615  1260 efficientnet_model.py:143] round_filter input=24 output=32\n",
      "I0803 20:04:05.802637  1260 efficientnet_model.py:143] round_filter input=24 output=32\n",
      "I0803 20:04:05.802637  1260 efficientnet_model.py:143] round_filter input=40 output=56\n",
      "I0803 20:04:06.101828  1260 efficientnet_model.py:143] round_filter input=40 output=56\n",
      "I0803 20:04:06.101828  1260 efficientnet_model.py:143] round_filter input=80 output=112\n",
      "I0803 20:04:06.553620  1260 efficientnet_model.py:143] round_filter input=80 output=112\n",
      "I0803 20:04:06.553620  1260 efficientnet_model.py:143] round_filter input=112 output=160\n",
      "I0803 20:04:07.016384  1260 efficientnet_model.py:143] round_filter input=112 output=160\n",
      "I0803 20:04:07.016384  1260 efficientnet_model.py:143] round_filter input=192 output=272\n",
      "I0803 20:04:07.802085  1260 efficientnet_model.py:143] round_filter input=192 output=272\n",
      "I0803 20:04:07.802085  1260 efficientnet_model.py:143] round_filter input=320 output=448\n",
      "I0803 20:04:07.979610  1260 efficientnet_model.py:143] round_filter input=1280 output=1792\n",
      "I0803 20:04:08.007562  1260 efficientnet_model.py:453] 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",
      "I0803 20:04:08.081364  1260 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet EfficientNet backbone version: efficientnet-b5\n",
      "I0803 20:04:08.081364  1260 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 288\n",
      "I0803 20:04:08.081364  1260 ssd_efficientnet_bifpn_feature_extractor.py:148] EfficientDet BiFPN num iterations: 7\n",
      "I0803 20:04:08.083359  1260 efficientnet_model.py:143] round_filter input=32 output=48\n",
      "I0803 20:04:08.097323  1260 efficientnet_model.py:143] round_filter input=32 output=48\n",
      "I0803 20:04:08.097323  1260 efficientnet_model.py:143] round_filter input=16 output=24\n",
      "I0803 20:04:08.278839  1260 efficientnet_model.py:143] round_filter input=16 output=24\n",
      "I0803 20:04:08.279835  1260 efficientnet_model.py:143] round_filter input=24 output=40\n",
      "I0803 20:04:08.862277  1260 efficientnet_model.py:143] round_filter input=24 output=40\n",
      "I0803 20:04:08.863274  1260 efficientnet_model.py:143] round_filter input=40 output=64\n",
      "I0803 20:04:09.349946  1260 efficientnet_model.py:143] round_filter input=40 output=64\n",
      "I0803 20:04:09.349946  1260 efficientnet_model.py:143] round_filter input=80 output=128\n",
      "I0803 20:04:10.286445  1260 efficientnet_model.py:143] round_filter input=80 output=128\n",
      "I0803 20:04:10.286445  1260 efficientnet_model.py:143] round_filter input=112 output=176\n",
      "I0803 20:04:11.030317  1260 efficientnet_model.py:143] round_filter input=112 output=176\n",
      "I0803 20:04:11.030317  1260 efficientnet_model.py:143] round_filter input=192 output=304\n",
      "I0803 20:04:11.870546  1260 efficientnet_model.py:143] round_filter input=192 output=304\n",
      "I0803 20:04:11.871543  1260 efficientnet_model.py:143] round_filter input=320 output=512\n",
      "I0803 20:04:12.151363  1260 efficientnet_model.py:143] round_filter input=1280 output=2048\n",
      "I0803 20:04:12.188291  1260 efficientnet_model.py:453] 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",
      "I0803 20:04:12.325453  1260 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet EfficientNet backbone version: efficientnet-b6\n",
      "I0803 20:04:12.325453  1260 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 384\n",
      "I0803 20:04:12.325453  1260 ssd_efficientnet_bifpn_feature_extractor.py:148] EfficientDet BiFPN num iterations: 8\n",
      "I0803 20:04:12.328444  1260 efficientnet_model.py:143] round_filter input=32 output=56\n",
      "I0803 20:04:12.349901  1260 efficientnet_model.py:143] round_filter input=32 output=56\n",
      "I0803 20:04:12.350866  1260 efficientnet_model.py:143] round_filter input=16 output=32\n",
      "I0803 20:04:12.576273  1260 efficientnet_model.py:143] round_filter input=16 output=32\n",
      "I0803 20:04:12.576273  1260 efficientnet_model.py:143] round_filter input=24 output=40\n",
      "I0803 20:04:13.113826  1260 efficientnet_model.py:143] round_filter input=24 output=40\n",
      "I0803 20:04:13.113826  1260 efficientnet_model.py:143] round_filter input=40 output=72\n",
      "I0803 20:04:13.724921  1260 efficientnet_model.py:143] round_filter input=40 output=72\n",
      "I0803 20:04:13.724921  1260 efficientnet_model.py:143] round_filter input=80 output=144\n",
      "I0803 20:04:14.569242  1260 efficientnet_model.py:143] round_filter input=80 output=144\n",
      "I0803 20:04:14.569242  1260 efficientnet_model.py:143] round_filter input=112 output=200\n",
      "I0803 20:04:15.546638  1260 efficientnet_model.py:143] round_filter input=112 output=200\n",
      "I0803 20:04:15.546638  1260 efficientnet_model.py:143] round_filter input=192 output=344\n",
      "I0803 20:04:16.616258  1260 efficientnet_model.py:143] round_filter input=192 output=344\n",
      "I0803 20:04:16.617236  1260 efficientnet_model.py:143] round_filter input=320 output=576\n",
      "I0803 20:04:16.955245  1260 efficientnet_model.py:143] round_filter input=1280 output=2304\n",
      "I0803 20:04:17.004115  1260 efficientnet_model.py:453] 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",
      "I0803 20:04:17.144738  1260 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet EfficientNet backbone version: efficientnet-b7\n",
      "I0803 20:04:17.144738  1260 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 384\n",
      "I0803 20:04:17.144738  1260 ssd_efficientnet_bifpn_feature_extractor.py:148] EfficientDet BiFPN num iterations: 8\n",
      "I0803 20:04:17.146733  1260 efficientnet_model.py:143] round_filter input=32 output=64\n",
      "I0803 20:04:17.164685  1260 efficientnet_model.py:143] round_filter input=32 output=64\n",
      "I0803 20:04:17.164685  1260 efficientnet_model.py:143] round_filter input=16 output=32\n",
      "I0803 20:04:17.582568  1260 efficientnet_model.py:143] round_filter input=16 output=32\n",
      "I0803 20:04:17.583564  1260 efficientnet_model.py:143] round_filter input=24 output=48\n",
      "I0803 20:04:18.260270  1260 efficientnet_model.py:143] round_filter input=24 output=48\n",
      "I0803 20:04:18.260270  1260 efficientnet_model.py:143] round_filter input=40 output=80\n",
      "I0803 20:04:18.956070  1260 efficientnet_model.py:143] round_filter input=40 output=80\n",
      "I0803 20:04:18.956070  1260 efficientnet_model.py:143] round_filter input=80 output=160\n",
      "I0803 20:04:19.924676  1260 efficientnet_model.py:143] round_filter input=80 output=160\n",
      "I0803 20:04:19.924676  1260 efficientnet_model.py:143] round_filter input=112 output=224\n",
      "I0803 20:04:20.760741  1260 efficientnet_model.py:143] round_filter input=112 output=224\n",
      "I0803 20:04:20.761767  1260 efficientnet_model.py:143] round_filter input=192 output=384\n",
      "I0803 20:04:21.856576  1260 efficientnet_model.py:143] round_filter input=192 output=384\n",
      "I0803 20:04:21.856576  1260 efficientnet_model.py:143] round_filter input=320 output=640\n",
      "I0803 20:04:22.382905  1260 efficientnet_model.py:143] round_filter input=1280 output=2560\n",
      "I0803 20:04:22.418810  1260 efficientnet_model.py:453] 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): 24.79s\n",
      "I0803 20:04:22.558464  1260 test_util.py:2458] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 24.79s\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",
      "I0803 20:04:22.565445  1260 test_util.py:2458] 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",
      "I0803 20:04:22.566442  1260 test_util.py:2458] 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",
      "I0803 20:04:22.567413  1260 test_util.py:2458] 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",
      "I0803 20:04:22.569408  1260 test_util.py:2458] 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",
      "I0803 20:04:22.570433  1260 test_util.py:2458] 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",
      "I0803 20:04:22.571402  1260 test_util.py:2458] 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",
      "I0803 20:04:22.571402  1260 test_util.py:2458] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n",
      "----------------------------------------------------------------------\n",
      "Ran 24 tests in 31.368s\n",
      "\n",
      "OK (skipped=1)\n"
     ]
    }
   ],
   "source": [
    "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": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#install missing libraries (in case of module not found error) as required\n",
    "#!pip install pyyaml==5.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (2.9.1)\n",
      "Requirement already satisfied: keras-preprocessing>=1.1.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow) (1.1.2)\n",
      "Requirement already satisfied: keras<2.10.0,>=2.9.0rc0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow) (2.9.0)\n",
      "Requirement already satisfied: tensorflow-estimator<2.10.0,>=2.9.0rc0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow) (2.9.0)\n",
      "Requirement already satisfied: tensorboard<2.10,>=2.9 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow) (2.9.1)\n",
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      "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow) (0.4.0)\n",
      "Requirement already satisfied: protobuf<3.20,>=3.9.2 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow) (3.19.4)\n",
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      "Requirement already satisfied: packaging in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow) (21.3)\n",
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      "Requirement already satisfied: wrapt>=1.11.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow) (1.14.1)\n",
      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow) (0.26.0)\n",
      "Requirement already satisfied: six>=1.12.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow) (1.16.0)\n",
      "Requirement already satisfied: numpy>=1.20 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow) (1.23.1)\n",
      "Requirement already satisfied: setuptools in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow) (57.4.0)\n",
      "Requirement already satisfied: libclang>=13.0.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow) (14.0.6)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow) (1.2.0)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow) (0.2.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow) (4.3.0)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from astunparse>=1.6.0->tensorflow) (0.37.1)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (1.8.1)\n",
      "Requirement already satisfied: markdown>=2.6.8 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (3.4.1)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (2.28.1)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (0.6.1)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (2.2.1)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (0.4.6)\n",
      "Requirement already satisfied: google-auth<3,>=1.6.3 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (2.9.1)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from packaging->tensorflow) (3.0.9)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow) (0.2.8)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow) (4.9)\n",
      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow) (5.2.0)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow) (1.3.1)\n",
      "Requirement already satisfied: importlib-metadata>=4.4 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from markdown>=2.6.8->tensorboard<2.10,>=2.9->tensorflow) (4.12.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow) (1.26.11)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow) (2022.6.15)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow) (2.1.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow) (3.3)\n",
      "Requirement already satisfied: MarkupSafe>=2.1.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.10,>=2.9->tensorflow) (2.1.1)\n",
      "Requirement already satisfied: zipp>=0.5 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.10,>=2.9->tensorflow) (3.8.1)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow) (3.2.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip uninstall apache-beam avro-python3 contextlib2 Cython matplotlib pillow pycocotools gin-config google-api-python-client kaggle oauth2client opencv-python-headless py-cpuinfo pyyaml sacrebleu sentencepiece seqeval tensorflow-addons tensorflow-datasets tensorflow-hub tensorflow-model-optimization tensorflow-text pyparsing\n",
    "#!pip install apache-beam avro-python3 contextlib2 Cython matplotlib pillow pycocotools gin-config google-api-python-client==1.6.7 kaggle==1.3.9 oauth2client opencv-python-headless py-cpuinfo==3.3.0 pyyaml==5.1 sacrebleu sentencepiece seqeval tensorflow-addons tensorflow-datasets tensorflow-hub==0.6.0 tensorflow-model-optimization==0.4.1 tensorflow-text==2.9.0 pyparsing==3.0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found existing installation: protobuf 3.20.0\n",
      "Uninstalling protobuf-3.20.0:\n",
      "  Successfully uninstalled protobuf-3.20.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Skipping matplotlib as it is not installed.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting protobuf==3.20.0"
     ]
    },
    {
     "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 Cython, which is not installed.\n",
      "object-detection 0.1 requires pycocotools, which is not installed.\n",
      "tf-models-official 2.9.2 requires Cython, which is not installed.\n",
      "tf-models-official 2.9.2 requires gin-config, which is not installed.\n",
      "tf-models-official 2.9.2 requires google-api-python-client>=1.6.7, which is not installed.\n",
      "tf-models-official 2.9.2 requires kaggle>=1.3.9, which is not installed.\n",
      "tf-models-official 2.9.2 requires oauth2client, which is not installed.\n",
      "tf-models-official 2.9.2 requires opencv-python-headless, which is not installed.\n",
      "tf-models-official 2.9.2 requires py-cpuinfo>=3.3.0, which is not installed.\n",
      "tf-models-official 2.9.2 requires pycocotools, which is not installed.\n",
      "tf-models-official 2.9.2 requires pyyaml<6.0,>=5.1, which is not installed.\n",
      "tf-models-official 2.9.2 requires sacrebleu, which is not installed.\n",
      "tf-models-official 2.9.2 requires sentencepiece, which is not installed.\n",
      "tf-models-official 2.9.2 requires seqeval, which is not installed.\n",
      "tf-models-official 2.9.2 requires tensorflow-addons, which is not installed.\n",
      "tf-models-official 2.9.2 requires tensorflow-datasets, which is not installed.\n",
      "tf-models-official 2.9.2 requires tensorflow-hub>=0.6.0, which is not installed.\n",
      "tf-models-official 2.9.2 requires tensorflow-model-optimization>=0.4.1, which is not installed.\n",
      "tf-models-official 2.9.2 requires tensorflow-text~=2.9.0, which is not installed.\n",
      "lvis 0.5.3 requires Cython>=0.29.12, which is not installed.\n",
      "tensorflow 2.9.1 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.0 which is incompatible.\n",
      "tensorboard 2.9.1 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.0 which is incompatible.\n",
      "object-detection 0.1 requires pyparsing==2.4.7, but you have pyparsing 3.0.9 which is incompatible.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Using cached protobuf-3.20.0-cp39-cp39-win_amd64.whl (904 kB)\n",
      "Collecting matplotlib==3.3\n",
      "  Downloading matplotlib-3.3.0-cp39-cp39-win_amd64.whl (8.8 MB)\n",
      "     ---------------------------------------- 8.8/8.8 MB 4.1 MB/s eta 0:00:00\n",
      "Requirement already satisfied: numpy>=1.15 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from matplotlib==3.3) (1.23.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from matplotlib==3.3) (1.4.4)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from matplotlib==3.3) (3.0.9)\n",
      "Requirement already satisfied: cycler>=0.10 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from matplotlib==3.3) (0.11.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from matplotlib==3.3) (2.8.2)\n",
      "Collecting pillow>=6.2.0\n",
      "  Downloading Pillow-9.2.0-cp39-cp39-win_amd64.whl (3.3 MB)\n",
      "     ---------------------------------------- 3.3/3.3 MB 7.7 MB/s eta 0:00:00\n",
      "Requirement already satisfied: six>=1.5 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from python-dateutil>=2.1->matplotlib==3.3) (1.16.0)\n",
      "Installing collected packages: protobuf, pillow, matplotlib\n",
      "Successfully installed matplotlib-3.3.0 pillow-9.2.0 protobuf-3.20.0\n"
     ]
    }
   ],
   "source": [
    "!pip uninstall protobuf matplotlib -y\n",
    "!pip install protobuf==3.20.0 matplotlib==3.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import object_detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default GPU Device:/device:GPU:0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf \n",
    "\n",
    "if tf.test.gpu_device_name():\n",
    "    print('Default GPU Device:{}'.format(tf.test.gpu_device_name()))\n",
    "\n",
    "else:\n",
    "    print(\"Please install GPU version of TF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                       Version   Editable project location\n",
      "----------------------------- --------- -----------------------------------------------------------------------------------------------------\n",
      "absl-py                       1.2.0\n",
      "apache-beam                   2.40.0\n",
      "asttokens                     2.0.5\n",
      "astunparse                    1.6.3\n",
      "avro-python3                  1.10.2\n",
      "backcall                      0.2.0\n",
      "cachetools                    5.2.0\n",
      "certifi                       2022.6.15\n",
      "charset-normalizer            2.1.0\n",
      "colorama                      0.4.5\n",
      "contextlib2                   21.6.0\n",
      "cycler                        0.11.0\n",
      "Cython                        3.0.0a11\n",
      "debugpy                       1.6.2\n",
      "decorator                     5.1.1\n",
      "dill                          0.3.5.1\n",
      "dm-tree                       0.1.7\n",
      "entrypoints                   0.4\n",
      "etils                         0.6.0\n",
      "executing                     0.9.1\n",
      "flatbuffers                   1.12\n",
      "gast                          0.4.0\n",
      "gin-config                    0.5.0\n",
      "google-api-python-client      1.6.7\n",
      "google-auth                   2.9.1\n",
      "google-auth-oauthlib          0.4.6\n",
      "google-pasta                  0.2.0\n",
      "googleapis-common-protos      1.56.4\n",
      "grpcio                        1.47.0\n",
      "h5py                          3.7.0\n",
      "httplib2                      0.20.4\n",
      "idna                          3.3\n",
      "importlib-metadata            4.12.0\n",
      "importlib-resources           5.9.0\n",
      "ipykernel                     6.15.1\n",
      "ipython                       8.4.0\n",
      "jedi                          0.18.1\n",
      "joblib                        1.1.0\n",
      "jupyter-client                7.3.4\n",
      "jupyter-core                  4.11.1\n",
      "kaggle                        1.3.9\n",
      "keras                         2.9.0\n",
      "Keras-Preprocessing           1.1.2\n",
      "kiwisolver                    1.4.4\n",
      "libclang                      14.0.6\n",
      "lvis                          0.5.3\n",
      "lxml                          4.9.1\n",
      "Markdown                      3.4.1\n",
      "MarkupSafe                    2.1.1\n",
      "matplotlib                    3.3.0\n",
      "matplotlib-inline             0.1.3\n",
      "nest-asyncio                  1.5.5\n",
      "numpy                         1.23.1\n",
      "oauth2client                  4.1.3\n",
      "oauthlib                      3.2.0\n",
      "object-detection              0.1\n",
      "opencv-python                 4.6.0.66\n",
      "opencv-python-headless        4.6.0.66\n",
      "opt-einsum                    3.3.0\n",
      "packaging                     20.9\n",
      "pandas                        1.4.3\n",
      "parso                         0.8.3\n",
      "pickleshare                   0.7.5\n",
      "Pillow                        9.2.0\n",
      "pip                           22.2.1\n",
      "portalocker                   2.5.1\n",
      "promise                       2.3\n",
      "prompt-toolkit                3.0.30\n",
      "protobuf                      3.20.0\n",
      "psutil                        5.9.1\n",
      "pure-eval                     0.2.2\n",
      "py-cpuinfo                    3.3.0\n",
      "pyasn1                        0.4.8\n",
      "pyasn1-modules                0.2.8\n",
      "pycocotools                   2.0.4\n",
      "Pygments                      2.12.0\n",
      "pyparsing                     2.4.7\n",
      "PyQt5                         5.15.7\n",
      "PyQt5-Qt5                     5.15.2\n",
      "PyQt5-sip                     12.11.0\n",
      "python-dateutil               2.8.2\n",
      "pytz                          2022.1\n",
      "pywin32                       304\n",
      "PyYAML                        5.4\n",
      "pyzmq                         23.2.0\n",
      "regex                         2022.7.25\n",
      "requests                      2.28.1\n",
      "requests-oauthlib             1.3.1\n",
      "rsa                           4.9\n",
      "sacrebleu                     2.2.0\n",
      "scikit-learn                  1.1.1\n",
      "scipy                         1.9.0\n",
      "sentencepiece                 0.1.96\n",
      "seqeval                       1.2.2\n",
      "setuptools                    57.4.0\n",
      "six                           1.16.0\n",
      "slim                          0.1       d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tensorflow\\models\\research\\slim\n",
      "stack-data                    0.3.0\n",
      "tabulate                      0.8.10\n",
      "tensorboard                   2.9.0\n",
      "tensorboard-data-server       0.6.1\n",
      "tensorboard-plugin-wit        1.8.1\n",
      "tensorflow                    2.9.0\n",
      "tensorflow-addons             0.17.1\n",
      "tensorflow-datasets           4.6.0\n",
      "tensorflow-estimator          2.9.0\n",
      "tensorflow-hub                0.8.0\n",
      "tensorflow-io                 0.26.0\n",
      "tensorflow-io-gcs-filesystem  0.26.0\n",
      "tensorflow-metadata           1.9.0\n",
      "tensorflow-model-optimization 0.4.1\n",
      "tensorflow-text               2.9.0\n",
      "tensorflowjs                  3.19.0\n",
      "termcolor                     1.1.0\n",
      "tf-models-official            2.9.2\n",
      "tf-slim                       1.1.0\n",
      "threadpoolctl                 3.1.0\n",
      "toml                          0.10.2\n",
      "tornado                       6.2\n",
      "tqdm                          4.64.0\n",
      "traitlets                     5.3.0\n",
      "typeguard                     2.13.3\n",
      "typing_extensions             4.3.0\n",
      "uritemplate                   3.0.1\n",
      "urllib3                       1.26.11\n",
      "wcwidth                       0.2.5\n",
      "Werkzeug                      2.2.1\n",
      "wget                          3.2\n",
      "wheel                         0.37.1\n",
      "wrapt                         1.14.1\n",
      "zipp                          3.8.1\n"
     ]
    }
   ],
   "source": [
    "!pip list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "csofht2npfDE",
    "outputId": "ff5471b2-bed2-43f2-959c-327a706527b6"
   },
   "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",
   "metadata": {
    "id": "M5KJTnkfpfDC"
   },
   "source": [
    "# 2. Create Label Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "p1BVDWo7pfDC"
   },
   "outputs": [],
   "source": [
    "labels = [{'name':'ThumbsUp', 'id':1}, {'name':'ThumbsDown', 'id':2}, {'name':'ThankYou', 'id':3}, {'name':'LiveLong', 'id':4}]\n",
    "\n",
    "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",
   "metadata": {
    "id": "C88zyVELpfDC"
   },
   "source": [
    "# 3. Create TF records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kvf5WccwrFGq",
    "outputId": "49902aeb-0bd7-4298-e1a0-5b4a64eb2064"
   },
   "outputs": [],
   "source": [
    "# OPTIONAL IF RUNNING ON COLAB\n",
    "ARCHIVE_FILES = os.path.join(paths['IMAGE_PATH'], 'archive.tar.gz')\n",
    "if os.path.exists(ARCHIVE_FILES):\n",
    "  !tar -zxvf {ARCHIVE_FILES}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KWpb_BVUpfDD",
    "outputId": "56ce2a3f-3933-4ee6-8a9d-d5ec65f7d73c"
   },
   "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/adityagoel08/GenerateTFRecord {paths['SCRIPTS_PATH']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install pytz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UPFToGZqpfDD",
    "outputId": "0ebb456f-aadc-4a1f-96e6-fbfec1923e1c"
   },
   "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",
   "metadata": {
    "id": "qT4QU7pLpfDE"
   },
   "source": [
    "# 4. Copy Model Config to Training Folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "cOjuTFbwpfDF"
   },
   "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",
   "metadata": {
    "id": "Ga8gpNslpfDF"
   },
   "source": [
    "# 5. Update Config For Transfer Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "Z9hRrO_ppfDF"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from object_detection.utils import config_util\n",
    "from object_detection.protos import pipeline_pb2\n",
    "from google.protobuf import text_format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "c2A0mn4ipfDF"
   },
   "outputs": [],
   "source": [
    "config = config_util.get_configs_from_pipeline_file(files['PIPELINE_CONFIG'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uQA13-afpfDF",
    "outputId": "907496a4-a39d-4b13-8c2c-e5978ecb1f10"
   },
   "outputs": [],
   "source": [
    "#config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "9vK5lotDpfDF"
   },
   "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": 23,
   "metadata": {
    "id": "rP43Ph0JpfDG"
   },
   "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": 24,
   "metadata": {
    "id": "oJvfgwWqpfDG"
   },
   "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",
   "metadata": {
    "id": "Zr3ON7xMpfDG"
   },
   "source": [
    "# 6. Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "B-Y2UQmQpfDG"
   },
   "outputs": [],
   "source": [
    "TRAINING_SCRIPT = os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection', 'model_main_tf2.py')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "jMP2XDfQpfDH"
   },
   "outputs": [],
   "source": [
    "command = \"python {} --model_dir={} --pipeline_config_path={} --num_train_steps=2000\".format(TRAINING_SCRIPT, paths['CHECKPOINT_PATH'],files['PIPELINE_CONFIG'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A4OXXi-ApfDH",
    "outputId": "117a0e83-012b-466e-b7a6-ccaa349ac5ab"
   },
   "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=2000\n"
     ]
    }
   ],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i3ZsJR-qpfDH",
    "outputId": "cabec5e1-45e6-4f2f-d9cf-297d9c1d0225"
   },
   "outputs": [],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4_YRZu7npfDH"
   },
   "source": [
    "# 7. Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "80L7-fdPpfDH"
   },
   "outputs": [],
   "source": [
    "command = \"python {} --model_dir={} --pipeline_config_path={} --checkpoint_dir={}\".format(TRAINING_SCRIPT, paths['CHECKPOINT_PATH'],files['PIPELINE_CONFIG'], paths['CHECKPOINT_PATH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lYsgEPx9pfDH",
    "outputId": "8632d48b-91d2-45d9-bcb8-c1b172bf6eed"
   },
   "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": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "lqTV2jGBpfDH"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n"
     ]
    }
   ],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "orvRk02UpfDI"
   },
   "source": [
    "# 8. Load Train Model From Checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "8TYk4_oIpfDI"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import tensorflow as tf\n",
    "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": 54,
   "metadata": {
    "id": "tDnQg-cYpfDI"
   },
   "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-5')).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",
   "metadata": {
    "id": "0EmsmbBZpfDI"
   },
   "source": [
    "# 9. Detect from an Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "Y_MKiuZ4pfDI"
   },
   "outputs": [],
   "source": [
    "import cv2 \n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "cBDbIhNapfDI"
   },
   "outputs": [],
   "source": [
    "category_index = label_map_util.create_category_index_from_labelmap(files['LABELMAP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "Lx3crOhOzITB"
   },
   "outputs": [],
   "source": [
    "IMAGE_PATH = os.path.join(paths['IMAGE_PATH'], 'test', 'thumbsup.540f16d4-1272-11ed-9a00-c821587e8221.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "id": "Tpzn1SMry1yK",
    "outputId": "c392a2c5-10fe-4fc4-9998-a1d4c7db2bd3"
   },
   "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",
   "metadata": {
    "id": "IsNAaYAo0WVL"
   },
   "source": [
    "# 10. Real Time Detections from your Webcam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip uninstall opencv-python-headless -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip uninstall opencv-python -y\n",
    "# !pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "o_grs6OGpfDJ"
   },
   "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",
   "metadata": {
    "id": "rzlM4jt0pfDJ"
   },
   "source": [
    "# 10. Freezing the Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "n4olHB2npfDJ"
   },
   "outputs": [],
   "source": [
    "FREEZE_SCRIPT = os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection', 'exporter_main_v2.py ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "0AjO93QDpfDJ"
   },
   "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": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "F6Lsp3tCpfDJ",
    "outputId": "c3828529-bf06-4df5-d7f3-145890ec3edd"
   },
   "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": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1Sw1ULgHpfDJ",
    "outputId": "6fd441e1-9fc9-4889-d072-3395c21e40b6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-04 10:26:35.335061: I tensorflow/core/platform/cpu_feature_guard.cc:193] 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",
      "2022-08-04 10:26:36.249950: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2600 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1\n",
      "WARNING:tensorflow:From D:\\Technology\\Python Projects\\Computer Vision Project\\TFODCourse-main\\tfod\\lib\\site-packages\\tensorflow\\python\\autograph\\impl\\api.py:458: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "back_prop=False is deprecated. Consider using tf.stop_gradient instead.\n",
      "Instead of:\n",
      "results = tf.map_fn(fn, elems, back_prop=False)\n",
      "Use:\n",
      "results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\n",
      "W0804 10:26:37.508591 11964 deprecation.py:623] From D:\\Technology\\Python Projects\\Computer Vision Project\\TFODCourse-main\\tfod\\lib\\site-packages\\tensorflow\\python\\autograph\\impl\\api.py:458: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "back_prop=False is deprecated. Consider using tf.stop_gradient instead.\n",
      "Instead of:\n",
      "results = tf.map_fn(fn, elems, back_prop=False)\n",
      "Use:\n",
      "results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x00000287815F6580>, because it is not built.\n",
      "W0804 10:27:46.627743 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x00000287815F6580>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x000002878D2F34C0>, because it is not built.\n",
      "W0804 10:27:47.762784 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x000002878D2F34C0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD4FE580>, because it is not built.\n",
      "W0804 10:27:47.763425 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD4FE580>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x000002878FAE96A0>, because it is not built.\n",
      "W0804 10:27:47.763425 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x000002878FAE96A0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x000002878FAE9E50>, because it is not built.\n",
      "W0804 10:27:47.763746 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x000002878FAE9E50>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002878FAE9D90>, because it is not built.\n",
      "W0804 10:27:47.763746 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002878FAE9D90>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x000002878FAE9EE0>, because it is not built.\n",
      "W0804 10:27:47.764741 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x000002878FAE9EE0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x00000287CD6357F0>, because it is not built.\n",
      "W0804 10:27:47.764741 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x00000287CD6357F0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6352B0>, because it is not built.\n",
      "W0804 10:27:47.764741 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6352B0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD635BB0>, because it is not built.\n",
      "W0804 10:27:47.765920 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD635BB0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x00000287CD635A60>, because it is not built.\n",
      "W0804 10:27:47.765920 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x00000287CD635A60>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6354F0>, because it is not built.\n",
      "W0804 10:27:47.765920 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6354F0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD6DE700>, because it is not built.\n",
      "W0804 10:27:47.766735 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD6DE700>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002878D2F37F0>, because it is not built.\n",
      "W0804 10:27:47.766735 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002878D2F37F0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD6DEEB0>, because it is not built.\n",
      "W0804 10:27:47.766735 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD6DEEB0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6ECB80>, because it is not built.\n",
      "W0804 10:27:47.766735 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6ECB80>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD6EC970>, because it is not built.\n",
      "W0804 10:27:47.767742 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD6EC970>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6ECFA0>, because it is not built.\n",
      "W0804 10:27:47.767742 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6ECFA0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD6ECC70>, because it is not built.\n",
      "W0804 10:27:47.767742 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD6ECC70>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x0000028790036A30>, because it is not built.\n",
      "W0804 10:27:47.767742 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x0000028790036A30>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x0000028790061190>, because it is not built.\n",
      "W0804 10:27:47.768840 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x0000028790061190>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002878D2F3820>, because it is not built.\n",
      "W0804 10:27:47.768840 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002878D2F3820>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x000002878FFD8760>, because it is not built.\n",
      "W0804 10:27:47.768840 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x000002878FFD8760>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD514400>, because it is not built.\n",
      "W0804 10:27:47.769741 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD514400>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD514910>, because it is not built.\n",
      "W0804 10:27:47.770068 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD514910>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002878D3CF970>, because it is not built.\n",
      "W0804 10:27:47.770068 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002878D3CF970>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x000002878D3CF2B0>, because it is not built.\n",
      "W0804 10:27:47.770068 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x000002878D3CF2B0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD502E80>, because it is not built.\n",
      "W0804 10:27:47.770778 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD502E80>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD5023A0>, because it is not built.\n",
      "W0804 10:27:47.771093 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD5023A0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002878D2F3850>, because it is not built.\n",
      "W0804 10:27:47.771093 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002878D2F3850>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x000002878FFC34F0>, because it is not built.\n",
      "W0804 10:27:47.771093 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x000002878FFC34F0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6FAAF0>, because it is not built.\n",
      "W0804 10:27:47.771735 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6FAAF0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD6FA7F0>, because it is not built.\n",
      "W0804 10:27:47.771735 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD6FA7F0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6FAF10>, because it is not built.\n",
      "W0804 10:27:47.773730 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6FAF10>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x000002878D3D1AF0>, because it is not built.\n",
      "W0804 10:27:47.773730 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x000002878D3D1AF0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6FC250>, because it is not built.\n",
      "W0804 10:27:47.774729 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6FC250>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD6FC640>, because it is not built.\n",
      "W0804 10:27:47.774729 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD6FC640>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6FCC70>, because it is not built.\n",
      "W0804 10:27:47.774729 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6FCC70>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD7A1BE0>, because it is not built.\n",
      "W0804 10:27:47.775728 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD7A1BE0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD634B20>, because it is not built.\n",
      "W0804 10:27:47.775728 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD634B20>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD634DC0>, because it is not built.\n",
      "W0804 10:27:47.775728 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD634DC0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD634C40>, because it is not built.\n",
      "W0804 10:27:47.775728 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD634C40>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD634220>, because it is not built.\n",
      "W0804 10:27:47.776544 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD634220>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6340D0>, because it is not built.\n",
      "W0804 10:27:47.776544 11964 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000287CD6340D0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD634520>, because it is not built.\n",
      "W0804 10:27:47.777107 11964 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000287CD634520>, because it is not built.\n",
      "W0804 10:28:45.113345 11964 save.py:233] Found untraced functions such as WeightSharedConvolutionalBoxPredictor_layer_call_fn, WeightSharedConvolutionalBoxPredictor_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxHead_layer_call_fn, WeightSharedConvolutionalBoxHead_layer_call_and_return_conditional_losses, WeightSharedConvolutionalClassHead_layer_call_fn while saving (showing 5 of 173). These functions will not be directly callable after loading.\n",
      "INFO:tensorflow:Assets written to: Tensorflow\\workspace\\models\\my_ssd_mobnet\\export\\saved_model\\assets\n",
      "I0804 10:29:03.885181 11964 builder_impl.py:779] 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",
      "I0804 10:29:06.282208 11964 config_util.py:253] Writing pipeline config file to Tensorflow\\workspace\\models\\my_ssd_mobnet\\export\\pipeline.config\n"
     ]
    }
   ],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wTPmdqaXpfDK"
   },
   "source": [
    "# 11. Conversion to TFJS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gZ6UzY_fpfDK",
    "outputId": "0c84722e-1c2b-4002-d857-80827ade828a",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflowjs\n",
      "  Using cached tensorflowjs-3.19.0-py3-none-any.whl (78 kB)\n",
      "Requirement already satisfied: tensorflow-hub<0.13,>=0.7.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflowjs) (0.8.0)\n",
      "Requirement already satisfied: tensorflow<3,>=2.1.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflowjs) (2.9.0)\n",
      "Requirement already satisfied: six<2,>=1.12.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflowjs) (1.16.0)\n",
      "Requirement already satisfied: packaging~=20.9 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflowjs) (20.9)\n",
      "Requirement already satisfied: protobuf==3.20.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflowjs) (3.20.0)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages\\pyparsing-2.4.7-py3.9.egg (from packaging~=20.9->tensorflowjs) (2.4.7)\n",
      "Requirement already satisfied: numpy>=1.20 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.23.1)\n",
      "Requirement already satisfied: wrapt>=1.11.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.14.1)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.2.0)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.47.0)\n",
      "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.4.0)\n",
      "Requirement already satisfied: flatbuffers<2,>=1.12 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.12)\n",
      "Requirement already satisfied: tensorflow-estimator<2.10.0,>=2.9.0rc0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (2.9.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (4.3.0)\n",
      "Requirement already satisfied: h5py>=2.9.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (3.7.0)\n",
      "Requirement already satisfied: libclang>=13.0.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (14.0.6)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.6.3)\n",
      "Requirement already satisfied: keras<2.10.0,>=2.9.0rc0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (2.9.0)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.1.0)\n",
      "Requirement already satisfied: setuptools in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (57.4.0)\n",
      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.26.0)\n",
      "Requirement already satisfied: tensorboard<2.10,>=2.9 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (2.9.0)\n",
      "Requirement already satisfied: keras-preprocessing>=1.1.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.1.2)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.2.0)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (3.3.0)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from astunparse>=1.6.0->tensorflow<3,>=2.1.0->tensorflowjs) (0.37.1)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (1.8.1)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (2.28.1)\n",
      "Requirement already satisfied: google-auth<3,>=1.6.3 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (2.9.1)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (0.4.6)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (2.2.1)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (0.6.1)\n",
      "Requirement already satisfied: markdown>=2.6.8 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (3.4.1)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (0.2.8)\n",
      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (5.2.0)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (4.9)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (1.3.1)\n",
      "Requirement already satisfied: importlib-metadata>=4.4 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from markdown>=2.6.8->tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (4.12.0)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (2.1.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (1.26.11)\n",
      "Requirement already satisfied: idna<4,>=2.5 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (3.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (2022.6.15)\n",
      "Requirement already satisfied: MarkupSafe>=2.1.1 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (2.1.1)\n",
      "Requirement already satisfied: zipp>=0.5 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (3.8.1)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in d:\\technology\\python projects\\computer vision project\\tfodcourse-main\\tfod\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow<3,>=2.1.0->tensorflowjs) (3.2.0)\n",
      "Installing collected packages: tensorflowjs\n",
      "Successfully installed tensorflowjs-3.19.0\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflowjs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "0oxbVynHpfDK"
   },
   "outputs": [],
   "source": [
    "command = \"tensorflowjs_converter --input_format=tf_saved_model --output_node_names='detection_boxes,detection_classes,detection_features,detection_multiclass_scores,detection_scores,num_detections,raw_detection_boxes,raw_detection_scores' --output_format=tfjs_graph_model --signature_name=serving_default {} {}\".format(os.path.join(paths['OUTPUT_PATH'], 'saved_model'), paths['TFJS_PATH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DB2AGNmJpfDK",
    "outputId": "fbc9f747-f511-47e8-df8f-5ea65cef0374"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensorflowjs_converter --input_format=tf_saved_model --output_node_names='detection_boxes,detection_classes,detection_features,detection_multiclass_scores,detection_scores,num_detections,raw_detection_boxes,raw_detection_scores' --output_format=tfjs_graph_model --signature_name=serving_default Tensorflow\\workspace\\models\\my_ssd_mobnet\\export\\saved_model Tensorflow\\workspace\\models\\my_ssd_mobnet\\tfjsexport\n"
     ]
    }
   ],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K7rfT4-hpfDK",
    "outputId": "532707fd-6feb-4bc6-84a3-325b5d16303c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing weight file Tensorflow\\workspace\\models\\my_ssd_mobnet\\tfjsexport\\model.json...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-04 13:04:39.257239: I tensorflow/core/platform/cpu_feature_guard.cc:193] 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",
      "2022-08-04 13:04:39.969261: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2776 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1\n",
      "2022-08-04 13:04:51.545796: I tensorflow/core/grappler/devices.cc:66] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0\n",
      "2022-08-04 13:04:51.546062: I tensorflow/core/grappler/clusters/single_machine.cc:358] Starting new session\n",
      "2022-08-04 13:04:51.547806: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2776 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1\n"
     ]
    }
   ],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VtUw73FHpfDK"
   },
   "source": [
    "# 12. Conversion to TFLite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "XviMtewLpfDK"
   },
   "outputs": [],
   "source": [
    "TFLITE_SCRIPT = os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection', 'export_tflite_graph_tf2.py ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "us86cjC4pfDL"
   },
   "outputs": [],
   "source": [
    "command = \"python {} --pipeline_config_path={} --trained_checkpoint_dir={} --output_directory={}\".format(TFLITE_SCRIPT ,files['PIPELINE_CONFIG'], paths['CHECKPOINT_PATH'], paths['TFLITE_PATH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n1r5YO3rpfDL",
    "outputId": "5fcdf7a4-eee2-4365-f1ca-1751968379ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python Tensorflow\\models\\research\\object_detection\\export_tflite_graph_tf2.py  --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\\tfliteexport\n"
     ]
    }
   ],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I-xWpHN8pfDL",
    "outputId": "7f6bacd8-d077-43b5-c131-5b081fba24a4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-04 10:37:42.078000: I tensorflow/core/platform/cpu_feature_guard.cc:193] 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",
      "2022-08-04 10:37:43.053570: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2776 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1\n",
      "2022-08-04 10:38:01.108615: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2776 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1\n",
      "2022-08-04 10:38:01.164034: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled\n",
      "2022-08-04 10:38:06.039345: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2776 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1\n",
      "2022-08-04 10:38:12.457511: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2776 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x00000221D8D23940>, because it is not built.\n",
      "W0804 10:38:13.347981 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x00000221D8D23940>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x00000221D8DFA9D0>, because it is not built.\n",
      "W0804 10:38:14.047010 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x00000221D8DFA9D0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F5D450A0>, because it is not built.\n",
      "W0804 10:38:14.048002 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F5D450A0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F5D45B80>, because it is not built.\n",
      "W0804 10:38:14.048062 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F5D45B80>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x00000221F2338070>, because it is not built.\n",
      "W0804 10:38:14.048062 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x00000221F2338070>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221EFA35160>, because it is not built.\n",
      "W0804 10:38:14.048062 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221EFA35160>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221EFA35C40>, because it is not built.\n",
      "W0804 10:38:14.048062 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221EFA35C40>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x00000221EFA35220>, because it is not built.\n",
      "W0804 10:38:14.048997 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x00000221EFA35220>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221EFA35430>, because it is not built.\n",
      "W0804 10:38:14.048997 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221EFA35430>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F20CD460>, because it is not built.\n",
      "W0804 10:38:14.049246 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F20CD460>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x00000221F20CDDC0>, because it is not built.\n",
      "W0804 10:38:14.049246 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.convolutional.separable_conv2d.SeparableConv2D object at 0x00000221F20CDDC0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F5C81430>, because it is not built.\n",
      "W0804 10:38:14.049246 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F5C81430>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F5C811C0>, because it is not built.\n",
      "W0804 10:38:14.049246 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F5C811C0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221D8DFAD00>, because it is not built.\n",
      "W0804 10:38:14.049996 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221D8DFAD00>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F5D0FC70>, because it is not built.\n",
      "W0804 10:38:14.049996 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F5D0FC70>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221EF9E79A0>, because it is not built.\n",
      "W0804 10:38:14.050384 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221EF9E79A0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221EF9E7AF0>, because it is not built.\n",
      "W0804 10:38:14.050384 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221EF9E7AF0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F23333A0>, because it is not built.\n",
      "W0804 10:38:14.050384 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F23333A0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F23330A0>, because it is not built.\n",
      "W0804 10:38:14.050384 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F23330A0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F2333250>, because it is not built.\n",
      "W0804 10:38:14.050384 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F2333250>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F23335B0>, because it is not built.\n",
      "W0804 10:38:14.050384 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F23335B0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221D8DFAD30>, because it is not built.\n",
      "W0804 10:38:14.050990 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221D8DFAD30>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F5C8D070>, because it is not built.\n",
      "W0804 10:38:14.050990 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F5C8D070>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221EF9C3EE0>, because it is not built.\n",
      "W0804 10:38:14.050990 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221EF9C3EE0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221EF9C36D0>, because it is not built.\n",
      "W0804 10:38:14.050990 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221EF9C36D0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221EF9C3FD0>, because it is not built.\n",
      "W0804 10:38:14.050990 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221EF9C3FD0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221EF9C3A00>, because it is not built.\n",
      "W0804 10:38:14.051480 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221EF9C3A00>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221E9CD75B0>, because it is not built.\n",
      "W0804 10:38:14.051480 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221E9CD75B0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221E9CD7B80>, because it is not built.\n",
      "W0804 10:38:14.051480 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221E9CD7B80>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221D8DFAD60>, because it is not built.\n",
      "W0804 10:38:14.051480 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221D8DFAD60>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F6047A30>, because it is not built.\n",
      "W0804 10:38:14.051480 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F6047A30>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F2359970>, because it is not built.\n",
      "W0804 10:38:14.051480 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F2359970>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F2359FA0>, because it is not built.\n",
      "W0804 10:38:14.051987 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F2359FA0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F2359EE0>, because it is not built.\n",
      "W0804 10:38:14.051987 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F2359EE0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F2359A00>, because it is not built.\n",
      "W0804 10:38:14.051987 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F2359A00>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F2359C40>, because it is not built.\n",
      "W0804 10:38:14.051987 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F2359C40>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F2359B50>, because it is not built.\n",
      "W0804 10:38:14.051987 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F2359B50>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F5D16400>, because it is not built.\n",
      "W0804 10:38:14.051987 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F5D16400>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F5D16820>, because it is not built.\n",
      "W0804 10:38:14.051987 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F5D16820>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F5D02130>, because it is not built.\n",
      "W0804 10:38:14.052575 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F5D02130>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F5D029A0>, because it is not built.\n",
      "W0804 10:38:14.052575 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F5D029A0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F5CEFAC0>, because it is not built.\n",
      "W0804 10:38:14.052575 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F5CEFAC0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F5CEF4C0>, because it is not built.\n",
      "W0804 10:38:14.052575 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F5CEF4C0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F5CEF2B0>, because it is not built.\n",
      "W0804 10:38:14.052575 20388 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000221F5CEF2B0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F5CEFD30>, because it is not built.\n",
      "W0804 10:38:14.052989 20388 save_impl.py:71] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x00000221F5CEFD30>, because it is not built.\n",
      "W0804 10:39:02.503681 20388 save.py:233] Found untraced functions such as WeightSharedConvolutionalBoxPredictor_layer_call_fn, WeightSharedConvolutionalBoxPredictor_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxHead_layer_call_fn, WeightSharedConvolutionalBoxHead_layer_call_and_return_conditional_losses, WeightSharedConvolutionalClassHead_layer_call_fn while saving (showing 5 of 173). These functions will not be directly callable after loading.\n",
      "INFO:tensorflow:Assets written to: Tensorflow\\workspace\\models\\my_ssd_mobnet\\tfliteexport\\saved_model\\assets\n",
      "I0804 10:39:18.701227 20388 builder_impl.py:779] Assets written to: Tensorflow\\workspace\\models\\my_ssd_mobnet\\tfliteexport\\saved_model\\assets\n"
     ]
    }
   ],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "iJfYMbN6pfDL"
   },
   "outputs": [],
   "source": [
    "FROZEN_TFLITE_PATH = os.path.join(paths['TFLITE_PATH'], 'saved_model')\n",
    "TFLITE_MODEL = os.path.join(paths['TFLITE_PATH'], 'saved_model', 'detect.tflite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "command = \"tflite_convert \\\n",
    "--saved_model_dir={} \\\n",
    "--output_file={} \\\n",
    "--input_shapes=1,300,300,3 \\\n",
    "--input_arrays=normalized_input_image_tensor \\\n",
    "--output_arrays='TFLite_Detection_PostProcess','TFLite_Detection_PostProcess:1','TFLite_Detection_PostProcess:2','TFLite_Detection_PostProcess:3' \\\n",
    "--inference_type=FLOAT \\\n",
    "--allow_custom_ops\".format(FROZEN_TFLITE_PATH, TFLITE_MODEL, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E8GwUeoFpfDL",
    "outputId": "fac43ea4-cc85-471b-a362-e994b06fd583"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tflite_convert --saved_model_dir=Tensorflow\\workspace\\models\\my_ssd_mobnet\\tfliteexport\\saved_model --output_file=Tensorflow\\workspace\\models\\my_ssd_mobnet\\tfliteexport\\saved_model\\detect.tflite --input_shapes=1,300,300,3 --input_arrays=normalized_input_image_tensor --output_arrays='TFLite_Detection_PostProcess','TFLite_Detection_PostProcess:1','TFLite_Detection_PostProcess:2','TFLite_Detection_PostProcess:3' --inference_type=FLOAT --allow_custom_ops\n"
     ]
    }
   ],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nbd7gqHMpfDL",
    "outputId": "7c8fe6d5-2415-4641-8548-39d425c202f7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated count of arithmetic ops: 1.710 G  ops, equivalently 0.855 G  MACs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-04 10:41:48.656781: I tensorflow/core/platform/cpu_feature_guard.cc:193] 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",
      "2022-08-04 10:41:49.490045: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2776 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1\n",
      "2022-08-04 10:42:24.676557: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.\n",
      "2022-08-04 10:42:24.676592: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.\n",
      "2022-08-04 10:42:24.678366: I tensorflow/cc/saved_model/reader.cc:43] Reading SavedModel from: Tensorflow\\workspace\\models\\my_ssd_mobnet\\tfliteexport\\saved_model\n",
      "2022-08-04 10:42:24.823815: I tensorflow/cc/saved_model/reader.cc:81] Reading meta graph with tags { serve }\n",
      "2022-08-04 10:42:24.823864: I tensorflow/cc/saved_model/reader.cc:122] Reading SavedModel debug info (if present) from: Tensorflow\\workspace\\models\\my_ssd_mobnet\\tfliteexport\\saved_model\n",
      "2022-08-04 10:42:25.387502: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled\n",
      "2022-08-04 10:42:25.506945: I tensorflow/cc/saved_model/loader.cc:228] Restoring SavedModel bundle.\n",
      "2022-08-04 10:42:28.394832: I tensorflow/cc/saved_model/loader.cc:212] Running initialization op on SavedModel bundle at path: Tensorflow\\workspace\\models\\my_ssd_mobnet\\tfliteexport\\saved_model\n",
      "2022-08-04 10:42:29.661125: I tensorflow/cc/saved_model/loader.cc:301] SavedModel load for tags { serve }; Status: success: OK. Took 4982746 microseconds.\n",
      "2022-08-04 10:42:32.502673: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:263] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2022-08-04 10:42:35.525509: I tensorflow/compiler/mlir/lite/flatbuffer_export.cc:1972] Estimated count of arithmetic ops: 1.710 G  ops, equivalently 0.855 G  MACs\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5NQqZRdA21Uc"
   },
   "source": [
    "# 13. Zip and Export Models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "tTVTGCQp2ZJJ"
   },
   "outputs": [],
   "source": [
    "!tar -czf models.tar.gz {paths['CHECKPOINT_PATH']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "whShhB0x3PYJ",
    "outputId": "b773201d-35c9-46a8-b893-4a76bd4d5d97"
   },
   "outputs": [],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "3. Training and Detection.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "tfod",
   "language": "python",
   "name": "tfod"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}