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": 7,
   "metadata": {
    "id": "K-Cmz2edpfDE",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "if os.name=='nt':\n",
    "    !pip install wget\n",
    "    import wget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "iA1DIq5OpfDE"
   },
   "outputs": [],
   "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": 9,
   "metadata": {
    "id": "rJjMHbnDs3Tv"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E: Could not open lock file /var/lib/dpkg/lock-frontend - open (13: Permission denied)\n",
      "E: Unable to acquire the dpkg frontend lock (/var/lib/dpkg/lock-frontend), are you root?\n",
      "Processing /home/ffubuntu/Documents/GHRepos/TFODVanilla/Tensorflow/models/research\n",
      "\u001b[33m  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n",
      "   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\u001b[0m\n",
      "Requirement already satisfied: Cython in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from object-detection==0.1) (0.29.23)\n",
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     "name": "stdout",
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      "Requirement already satisfied: astunparse~=1.6.3 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (1.6.3)\n",
      "Requirement already satisfied: opt-einsum~=3.3.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (3.3.0)\n",
      "Requirement already satisfied: termcolor~=1.1.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (1.1.0)\n",
      "Requirement already satisfied: flatbuffers~=1.12.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (1.12)\n",
      "Requirement already satisfied: h5py~=3.1.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (3.1.0)\n",
      "Requirement already satisfied: google-pasta~=0.2 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (0.2.0)\n",
      "Requirement already satisfied: absl-py~=0.10 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (0.12.0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (1.8.0)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (0.4.4)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (0.6.1)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (2.0.1)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (3.3.4)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (1.3.0)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow>=2.5.0->tf-models-official->object-detection==0.1) (3.1.0)\n",
      "Requirement already satisfied: dm-tree~=0.1.1 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow-model-optimization>=0.4.1->tf-models-official->object-detection==0.1) (0.1.6)\n",
      "Requirement already satisfied: text-unidecode>=1.3 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from python-slugify->kaggle>=1.3.9->tf-models-official->object-detection==0.1) (1.3)\n",
      "Requirement already satisfied: portalocker==2.0.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from sacrebleu->tf-models-official->object-detection==0.1) (2.0.0)\n",
      "Requirement already satisfied: scikit-learn>=0.21.3 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from seqeval->tf-models-official->object-detection==0.1) (0.24.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from scikit-learn>=0.21.3->seqeval->tf-models-official->object-detection==0.1) (2.1.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from scikit-learn>=0.21.3->seqeval->tf-models-official->object-detection==0.1) (1.0.1)\n",
      "Requirement already satisfied: typeguard>=2.7 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow-addons->tf-models-official->object-detection==0.1) (2.12.0)\n",
      "Requirement already satisfied: tensorflow-metadata in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow-datasets->tf-models-official->object-detection==0.1) (1.0.0)\n",
      "Requirement already satisfied: promise in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow-datasets->tf-models-official->object-detection==0.1) (2.3)\n",
      "Requirement already satisfied: importlib-resources in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow-datasets->tf-models-official->object-detection==0.1) (5.1.4)\n",
      "Requirement already satisfied: attrs>=18.1.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow-datasets->tf-models-official->object-detection==0.1) (21.2.0)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from importlib-resources->tensorflow-datasets->tf-models-official->object-detection==0.1) (3.4.1)\n",
      "Building wheels for collected packages: object-detection\n",
      "  Building wheel for object-detection (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for object-detection: filename=object_detection-0.1-py3-none-any.whl size=1650353 sha256=135d469594b9b6f8b4a006d00f7bff883211faf3398608164563c80ae7ca74f0\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-b8_8_2u4/wheels/b3/b5/41/0f4ddac82e4d576c29f77e0a58b72a6f5fa5d95bc9a37502bd\n",
      "Successfully built object-detection\n",
      "Installing collected packages: object-detection\n",
      "  Attempting uninstall: object-detection\n",
      "    Found existing installation: object-detection 0.1\n",
      "    Uninstalling object-detection-0.1:\n",
      "      Successfully uninstalled object-detection-0.1\n",
      "Successfully installed object-detection-0.1\n"
     ]
    }
   ],
   "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": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-06-02 22:20:04.610269: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
      "Running tests under Python 3.8.5: /home/ffubuntu/Desktop/TFODCourse/tfod/bin/python\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_deepmac\n",
      "2021-06-02 22:20:06.036114: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n",
      "2021-06-02 22:20:06.078305: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1060 6GB computeCapability: 6.1\n",
      "coreClock: 1.7085GHz coreCount: 10 deviceMemorySize: 5.93GiB deviceMemoryBandwidth: 178.99GiB/s\n",
      "2021-06-02 22:20:06.078344: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
      "2021-06-02 22:20:06.111685: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n",
      "2021-06-02 22:20:06.111785: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n",
      "2021-06-02 22:20:06.121878: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcufft.so.10\n",
      "2021-06-02 22:20:06.127063: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcurand.so.10\n",
      "2021-06-02 22:20:06.131885: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusolver.so.11\n",
      "2021-06-02 22:20:06.139597: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusparse.so.11\n",
      "2021-06-02 22:20:06.140872: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n",
      "2021-06-02 22:20:06.141462: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
      "2021-06-02 22:20:06.141683: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2021-06-02 22:20:06.142202: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1060 6GB computeCapability: 6.1\n",
      "coreClock: 1.7085GHz coreCount: 10 deviceMemorySize: 5.93GiB deviceMemoryBandwidth: 178.99GiB/s\n",
      "2021-06-02 22:20:06.142732: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
      "2021-06-02 22:20:06.142769: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
      "2021-06-02 22:20:06.539749: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-06-02 22:20:06.539780: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      0 \n",
      "2021-06-02 22:20:06.539789: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0:   N \n",
      "2021-06-02 22:20:06.540698: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5170 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)\n",
      "/home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/object_detection/builders/model_builder.py:1061: DeprecationWarning: The 'warn' function is deprecated, use 'warning' instead\n",
      "  logging.warn(('Building experimental DeepMAC meta-arch.'\n",
      "W0602 22:20:06.803265 139771779716928 model_builder.py:1061] Building experimental DeepMAC meta-arch. Some features may be omitted.\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 0.94s\n",
      "I0602 22:20:06.966057 139771779716928 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 0.94s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_center_net_deepmac\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 0.33s\n",
      "I0602 22:20:07.292647 139771779716928 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 0.33s\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)): 0.17s\n",
      "I0602 22:20:07.464079 139771779716928 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.17s\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): 0.21s\n",
      "I0602 22:20:07.676766 139771779716928 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.21s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet\n",
      "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
      "W0602 22:20:07.678266 139771779716928 mobilenet_v2.py:296] `input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 1.29s\n",
      "I0602 22:20:08.971663 139771779716928 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 1.29s\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",
      "I0602 22:20:08.972458 139771779716928 test_util.py:2102] 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)): 0.01s\n",
      "I0602 22:20:08.986567 139771779716928 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.01s\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",
      "I0602 22:20:08.997386 139771779716928 test_util.py:2102] 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",
      "I0602 22:20:09.007667 139771779716928 test_util.py:2102] 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): 0.07s\n",
      "I0602 22:20:09.075030 139771779716928 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.07s\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.06s\n",
      "I0602 22:20:09.137482 139771779716928 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.06s\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): 0.07s\n",
      "I0602 22:20:09.207225 139771779716928 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.07s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.08s\n",
      "I0602 22:20:09.283213 139771779716928 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.08s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.06s\n",
      "I0602 22:20:09.345925 139771779716928 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.06s\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",
      "I0602 22:20:09.363293 139771779716928 test_util.py:2102] 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",
      "I0602 22:20:09.560560 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:142] EfficientDet EfficientNet backbone version: efficientnet-b0\n",
      "I0602 22:20:09.560665 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 64\n",
      "I0602 22:20:09.560707 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num iterations: 3\n",
      "I0602 22:20:09.562114 139771779716928 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0602 22:20:09.574534 139771779716928 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0602 22:20:09.574651 139771779716928 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0602 22:20:09.617535 139771779716928 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0602 22:20:09.617644 139771779716928 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0602 22:20:09.724411 139771779716928 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0602 22:20:09.724518 139771779716928 efficientnet_model.py:147] round_filter input=40 output=40\n",
      "I0602 22:20:09.832110 139771779716928 efficientnet_model.py:147] round_filter input=40 output=40\n",
      "I0602 22:20:09.832232 139771779716928 efficientnet_model.py:147] round_filter input=80 output=80\n",
      "I0602 22:20:09.996500 139771779716928 efficientnet_model.py:147] round_filter input=80 output=80\n",
      "I0602 22:20:09.996616 139771779716928 efficientnet_model.py:147] round_filter input=112 output=112\n",
      "I0602 22:20:10.159847 139771779716928 efficientnet_model.py:147] round_filter input=112 output=112\n",
      "I0602 22:20:10.159971 139771779716928 efficientnet_model.py:147] round_filter input=192 output=192\n",
      "I0602 22:20:10.378577 139771779716928 efficientnet_model.py:147] round_filter input=192 output=192\n",
      "I0602 22:20:10.378690 139771779716928 efficientnet_model.py:147] round_filter input=320 output=320\n",
      "I0602 22:20:10.432076 139771779716928 efficientnet_model.py:147] round_filter input=1280 output=1280\n",
      "I0602 22:20:10.456062 139771779716928 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.0, resolution=224, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0602 22:20:10.503008 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:142] EfficientDet EfficientNet backbone version: efficientnet-b1\n",
      "I0602 22:20:10.503139 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 88\n",
      "I0602 22:20:10.503196 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num iterations: 4\n",
      "I0602 22:20:10.504742 139771779716928 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0602 22:20:10.515270 139771779716928 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0602 22:20:10.515404 139771779716928 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0602 22:20:10.599910 139771779716928 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0602 22:20:10.600020 139771779716928 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0602 22:20:10.761691 139771779716928 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0602 22:20:10.761802 139771779716928 efficientnet_model.py:147] round_filter input=40 output=40\n",
      "I0602 22:20:10.918568 139771779716928 efficientnet_model.py:147] round_filter input=40 output=40\n",
      "I0602 22:20:10.918677 139771779716928 efficientnet_model.py:147] round_filter input=80 output=80\n",
      "I0602 22:20:11.139923 139771779716928 efficientnet_model.py:147] round_filter input=80 output=80\n",
      "I0602 22:20:11.140029 139771779716928 efficientnet_model.py:147] round_filter input=112 output=112\n",
      "I0602 22:20:11.357984 139771779716928 efficientnet_model.py:147] round_filter input=112 output=112\n",
      "I0602 22:20:11.358087 139771779716928 efficientnet_model.py:147] round_filter input=192 output=192\n",
      "I0602 22:20:11.621643 139771779716928 efficientnet_model.py:147] round_filter input=192 output=192\n",
      "I0602 22:20:11.621753 139771779716928 efficientnet_model.py:147] round_filter input=320 output=320\n",
      "I0602 22:20:11.799740 139771779716928 efficientnet_model.py:147] round_filter input=1280 output=1280\n",
      "I0602 22:20:11.819458 139771779716928 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.1, resolution=240, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I0602 22:20:11.861611 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:142] EfficientDet EfficientNet backbone version: efficientnet-b2\n",
      "I0602 22:20:11.861720 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 112\n",
      "I0602 22:20:11.861767 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num iterations: 5\n",
      "I0602 22:20:11.862890 139771779716928 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0602 22:20:11.873745 139771779716928 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0602 22:20:11.873845 139771779716928 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0602 22:20:11.963582 139771779716928 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0602 22:20:11.963690 139771779716928 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0602 22:20:12.132859 139771779716928 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0602 22:20:12.132968 139771779716928 efficientnet_model.py:147] round_filter input=40 output=48\n",
      "I0602 22:20:12.298650 139771779716928 efficientnet_model.py:147] round_filter input=40 output=48\n",
      "I0602 22:20:12.298760 139771779716928 efficientnet_model.py:147] round_filter input=80 output=88\n",
      "I0602 22:20:12.519315 139771779716928 efficientnet_model.py:147] round_filter input=80 output=88\n",
      "I0602 22:20:12.519425 139771779716928 efficientnet_model.py:147] round_filter input=112 output=120\n",
      "I0602 22:20:12.743064 139771779716928 efficientnet_model.py:147] round_filter input=112 output=120\n",
      "I0602 22:20:12.743170 139771779716928 efficientnet_model.py:147] round_filter input=192 output=208\n",
      "I0602 22:20:13.017075 139771779716928 efficientnet_model.py:147] round_filter input=192 output=208\n",
      "I0602 22:20:13.017183 139771779716928 efficientnet_model.py:147] round_filter input=320 output=352\n",
      "I0602 22:20:13.132083 139771779716928 efficientnet_model.py:147] round_filter input=1280 output=1408\n",
      "I0602 22:20:13.151623 139771779716928 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.1, depth_coefficient=1.2, resolution=260, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0602 22:20:13.197950 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:142] EfficientDet EfficientNet backbone version: efficientnet-b3\n",
      "I0602 22:20:13.198056 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 160\n",
      "I0602 22:20:13.198100 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num iterations: 6\n",
      "I0602 22:20:13.199352 139771779716928 efficientnet_model.py:147] round_filter input=32 output=40\n",
      "I0602 22:20:13.209971 139771779716928 efficientnet_model.py:147] round_filter input=32 output=40\n",
      "I0602 22:20:13.210082 139771779716928 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0602 22:20:13.297533 139771779716928 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0602 22:20:13.297641 139771779716928 efficientnet_model.py:147] round_filter input=24 output=32\n",
      "I0602 22:20:13.456317 139771779716928 efficientnet_model.py:147] round_filter input=24 output=32\n",
      "I0602 22:20:13.456423 139771779716928 efficientnet_model.py:147] round_filter input=40 output=48\n",
      "I0602 22:20:13.617246 139771779716928 efficientnet_model.py:147] round_filter input=40 output=48\n",
      "I0602 22:20:13.617351 139771779716928 efficientnet_model.py:147] round_filter input=80 output=96\n",
      "I0602 22:20:13.889349 139771779716928 efficientnet_model.py:147] round_filter input=80 output=96\n",
      "I0602 22:20:13.889462 139771779716928 efficientnet_model.py:147] round_filter input=112 output=136\n",
      "I0602 22:20:14.183555 139771779716928 efficientnet_model.py:147] round_filter input=112 output=136\n",
      "I0602 22:20:14.183684 139771779716928 efficientnet_model.py:147] round_filter input=192 output=232\n",
      "I0602 22:20:14.679282 139771779716928 efficientnet_model.py:147] round_filter input=192 output=232\n",
      "I0602 22:20:14.679490 139771779716928 efficientnet_model.py:147] round_filter input=320 output=384\n",
      "I0602 22:20:14.799453 139771779716928 efficientnet_model.py:147] round_filter input=1280 output=1536\n",
      "I0602 22:20:14.820331 139771779716928 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.2, depth_coefficient=1.4, resolution=300, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0602 22:20:14.868789 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:142] EfficientDet EfficientNet backbone version: efficientnet-b4\n",
      "I0602 22:20:14.868893 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 224\n",
      "I0602 22:20:14.868938 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num iterations: 7\n",
      "I0602 22:20:14.870158 139771779716928 efficientnet_model.py:147] round_filter input=32 output=48\n",
      "I0602 22:20:14.881362 139771779716928 efficientnet_model.py:147] round_filter input=32 output=48\n",
      "I0602 22:20:14.881460 139771779716928 efficientnet_model.py:147] round_filter input=16 output=24\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I0602 22:20:14.973565 139771779716928 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0602 22:20:14.973669 139771779716928 efficientnet_model.py:147] round_filter input=24 output=32\n",
      "I0602 22:20:15.207686 139771779716928 efficientnet_model.py:147] round_filter input=24 output=32\n",
      "I0602 22:20:15.207791 139771779716928 efficientnet_model.py:147] round_filter input=40 output=56\n",
      "I0602 22:20:15.424334 139771779716928 efficientnet_model.py:147] round_filter input=40 output=56\n",
      "I0602 22:20:15.424438 139771779716928 efficientnet_model.py:147] round_filter input=80 output=112\n",
      "I0602 22:20:15.753072 139771779716928 efficientnet_model.py:147] round_filter input=80 output=112\n",
      "I0602 22:20:15.753179 139771779716928 efficientnet_model.py:147] round_filter input=112 output=160\n",
      "I0602 22:20:16.068201 139771779716928 efficientnet_model.py:147] round_filter input=112 output=160\n",
      "I0602 22:20:16.068311 139771779716928 efficientnet_model.py:147] round_filter input=192 output=272\n",
      "I0602 22:20:16.492257 139771779716928 efficientnet_model.py:147] round_filter input=192 output=272\n",
      "I0602 22:20:16.492372 139771779716928 efficientnet_model.py:147] round_filter input=320 output=448\n",
      "I0602 22:20:16.603206 139771779716928 efficientnet_model.py:147] round_filter input=1280 output=1792\n",
      "I0602 22:20:16.623583 139771779716928 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.4, depth_coefficient=1.8, resolution=380, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0602 22:20:16.673717 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:142] EfficientDet EfficientNet backbone version: efficientnet-b5\n",
      "I0602 22:20:16.673821 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 288\n",
      "I0602 22:20:16.673871 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num iterations: 7\n",
      "I0602 22:20:16.675025 139771779716928 efficientnet_model.py:147] round_filter input=32 output=48\n",
      "I0602 22:20:16.685406 139771779716928 efficientnet_model.py:147] round_filter input=32 output=48\n",
      "I0602 22:20:16.685513 139771779716928 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0602 22:20:16.813783 139771779716928 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0602 22:20:16.813893 139771779716928 efficientnet_model.py:147] round_filter input=24 output=40\n",
      "I0602 22:20:17.078814 139771779716928 efficientnet_model.py:147] round_filter input=24 output=40\n",
      "I0602 22:20:17.078924 139771779716928 efficientnet_model.py:147] round_filter input=40 output=64\n",
      "I0602 22:20:17.353589 139771779716928 efficientnet_model.py:147] round_filter input=40 output=64\n",
      "I0602 22:20:17.353715 139771779716928 efficientnet_model.py:147] round_filter input=80 output=128\n",
      "I0602 22:20:17.877959 139771779716928 efficientnet_model.py:147] round_filter input=80 output=128\n",
      "I0602 22:20:17.878071 139771779716928 efficientnet_model.py:147] round_filter input=112 output=176\n",
      "I0602 22:20:18.270467 139771779716928 efficientnet_model.py:147] round_filter input=112 output=176\n",
      "I0602 22:20:18.270573 139771779716928 efficientnet_model.py:147] round_filter input=192 output=304\n",
      "I0602 22:20:18.792022 139771779716928 efficientnet_model.py:147] round_filter input=192 output=304\n",
      "I0602 22:20:18.792138 139771779716928 efficientnet_model.py:147] round_filter input=320 output=512\n",
      "I0602 22:20:18.959725 139771779716928 efficientnet_model.py:147] round_filter input=1280 output=2048\n",
      "I0602 22:20:18.980883 139771779716928 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.6, depth_coefficient=2.2, resolution=456, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0602 22:20:19.046898 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:142] EfficientDet EfficientNet backbone version: efficientnet-b6\n",
      "I0602 22:20:19.047030 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384\n",
      "I0602 22:20:19.047087 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num iterations: 8\n",
      "I0602 22:20:19.048626 139771779716928 efficientnet_model.py:147] round_filter input=32 output=56\n",
      "I0602 22:20:19.060380 139771779716928 efficientnet_model.py:147] round_filter input=32 output=56\n",
      "I0602 22:20:19.060492 139771779716928 efficientnet_model.py:147] round_filter input=16 output=32\n",
      "I0602 22:20:19.194046 139771779716928 efficientnet_model.py:147] round_filter input=16 output=32\n",
      "I0602 22:20:19.194152 139771779716928 efficientnet_model.py:147] round_filter input=24 output=40\n",
      "I0602 22:20:19.510154 139771779716928 efficientnet_model.py:147] round_filter input=24 output=40\n",
      "I0602 22:20:19.510279 139771779716928 efficientnet_model.py:147] round_filter input=40 output=72\n",
      "I0602 22:20:19.828900 139771779716928 efficientnet_model.py:147] round_filter input=40 output=72\n",
      "I0602 22:20:19.829012 139771779716928 efficientnet_model.py:147] round_filter input=80 output=144\n",
      "I0602 22:20:20.271418 139771779716928 efficientnet_model.py:147] round_filter input=80 output=144\n",
      "I0602 22:20:20.271528 139771779716928 efficientnet_model.py:147] round_filter input=112 output=200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I0602 22:20:20.843379 139771779716928 efficientnet_model.py:147] round_filter input=112 output=200\n",
      "I0602 22:20:20.843492 139771779716928 efficientnet_model.py:147] round_filter input=192 output=344\n",
      "I0602 22:20:21.468291 139771779716928 efficientnet_model.py:147] round_filter input=192 output=344\n",
      "I0602 22:20:21.468397 139771779716928 efficientnet_model.py:147] round_filter input=320 output=576\n",
      "I0602 22:20:21.636720 139771779716928 efficientnet_model.py:147] round_filter input=1280 output=2304\n",
      "I0602 22:20:21.656801 139771779716928 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.8, depth_coefficient=2.6, resolution=528, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0602 22:20:21.738338 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:142] EfficientDet EfficientNet backbone version: efficientnet-b7\n",
      "I0602 22:20:21.738448 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384\n",
      "I0602 22:20:21.738496 139771779716928 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num iterations: 8\n",
      "I0602 22:20:21.739593 139771779716928 efficientnet_model.py:147] round_filter input=32 output=64\n",
      "I0602 22:20:21.750872 139771779716928 efficientnet_model.py:147] round_filter input=32 output=64\n",
      "I0602 22:20:21.750978 139771779716928 efficientnet_model.py:147] round_filter input=16 output=32\n",
      "I0602 22:20:21.931331 139771779716928 efficientnet_model.py:147] round_filter input=16 output=32\n",
      "I0602 22:20:21.931438 139771779716928 efficientnet_model.py:147] round_filter input=24 output=48\n",
      "I0602 22:20:22.314954 139771779716928 efficientnet_model.py:147] round_filter input=24 output=48\n",
      "I0602 22:20:22.315083 139771779716928 efficientnet_model.py:147] round_filter input=40 output=80\n",
      "I0602 22:20:22.693266 139771779716928 efficientnet_model.py:147] round_filter input=40 output=80\n",
      "I0602 22:20:22.693379 139771779716928 efficientnet_model.py:147] round_filter input=80 output=160\n",
      "I0602 22:20:23.311833 139771779716928 efficientnet_model.py:147] round_filter input=80 output=160\n",
      "I0602 22:20:23.311966 139771779716928 efficientnet_model.py:147] round_filter input=112 output=224\n",
      "I0602 22:20:24.044548 139771779716928 efficientnet_model.py:147] round_filter input=112 output=224\n",
      "I0602 22:20:24.044687 139771779716928 efficientnet_model.py:147] round_filter input=192 output=384\n",
      "I0602 22:20:25.207069 139771779716928 efficientnet_model.py:147] round_filter input=192 output=384\n",
      "I0602 22:20:25.207205 139771779716928 efficientnet_model.py:147] round_filter input=320 output=640\n",
      "I0602 22:20:25.507104 139771779716928 efficientnet_model.py:147] round_filter input=1280 output=2560\n",
      "I0602 22:20:25.535697 139771779716928 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=2.0, depth_coefficient=3.1, resolution=600, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 16.31s\n",
      "I0602 22:20:25.672208 139771779716928 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 16.31s\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",
      "I0602 22:20:25.683959 139771779716928 test_util.py:2102] 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",
      "I0602 22:20:25.685821 139771779716928 test_util.py:2102] 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",
      "I0602 22:20:25.686318 139771779716928 test_util.py:2102] 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",
      "I0602 22:20:25.687971 139771779716928 test_util.py:2102] 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",
      "I0602 22:20:25.689454 139771779716928 test_util.py:2102] 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",
      "I0602 22:20:25.689885 139771779716928 test_util.py:2102] 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",
      "I0602 22:20:25.690914 139771779716928 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n",
      "----------------------------------------------------------------------\n",
      "Ran 24 tests in 19.666s\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": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (2.5.0)\n",
      "Requirement already satisfied: astunparse~=1.6.3 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (1.6.3)\n",
      "Requirement already satisfied: google-pasta~=0.2 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (0.2.0)\n",
      "Requirement already satisfied: numpy~=1.19.2 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (1.19.5)\n",
      "Requirement already satisfied: wheel~=0.35 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (0.36.2)\n",
      "Requirement already satisfied: gast==0.4.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (0.4.0)\n",
      "Requirement already satisfied: wrapt~=1.12.1 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (1.12.1)\n",
      "Requirement already satisfied: opt-einsum~=3.3.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (3.3.0)\n",
      "Requirement already satisfied: typing-extensions~=3.7.4 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (3.7.4.3)\n",
      "Requirement already satisfied: keras-nightly~=2.5.0.dev in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (2.5.0.dev2021032900)\n",
      "Requirement already satisfied: termcolor~=1.1.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (1.1.0)\n",
      "Requirement already satisfied: h5py~=3.1.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (3.1.0)\n",
      "Requirement already satisfied: tensorflow-estimator<2.6.0,>=2.5.0rc0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (2.5.0)\n",
      "Requirement already satisfied: absl-py~=0.10 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (0.12.0)\n",
      "Requirement already satisfied: tensorboard~=2.5 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (2.5.0)\n",
      "Requirement already satisfied: flatbuffers~=1.12.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (1.12)\n",
      "Requirement already satisfied: protobuf>=3.9.2 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (3.17.1)\n",
      "Requirement already satisfied: grpcio~=1.34.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (1.34.1)\n",
      "Requirement already satisfied: six~=1.15.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (1.15.0)\n",
      "Requirement already satisfied: keras-preprocessing~=1.1.2 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorflow) (1.1.2)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow) (44.0.0)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow) (0.4.4)\n",
      "Requirement already satisfied: google-auth<2,>=1.6.3 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow) (1.30.1)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow) (3.3.4)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow) (1.8.0)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow) (2.25.1)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow) (0.6.1)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from tensorboard~=2.5->tensorflow) (2.0.1)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow) (4.2.2)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow) (4.7.2)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow) (0.2.8)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow) (1.3.0)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow) (0.4.8)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow) (2021.5.30)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow) (2.10)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow) (4.0.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow) (1.26.5)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow) (3.1.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found existing installation: protobuf 3.17.1\n",
      "Uninstalling protobuf-3.17.1:\n",
      "  Successfully uninstalled protobuf-3.17.1\n",
      "Found existing installation: matplotlib 3.2.0\n",
      "Uninstalling matplotlib-3.2.0:\n",
      "  Successfully uninstalled matplotlib-3.2.0\n",
      "Collecting protobuf\n",
      "  Downloading protobuf-3.17.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.0 MB)\n",
      "\u001b[K     |████████████████████████████████| 1.0 MB 1.6 MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting matplotlib==3.2\n",
      "  Using cached matplotlib-3.2.0-cp38-cp38-manylinux1_x86_64.whl (12.4 MB)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from matplotlib==3.2) (2.4.7)\n",
      "Requirement already satisfied: numpy>=1.11 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from matplotlib==3.2) (1.19.5)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from matplotlib==3.2) (1.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from matplotlib==3.2) (2.8.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from matplotlib==3.2) (0.10.0)\n",
      "Requirement already satisfied: six in /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages (from cycler>=0.10->matplotlib==3.2) (1.15.0)\n",
      "Installing collected packages: protobuf, matplotlib\n",
      "Successfully installed matplotlib-3.2.0 protobuf-3.17.2\n"
     ]
    }
   ],
   "source": [
    "!pip uninstall protobuf matplotlib -y\n",
    "!pip install protobuf matplotlib==3.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import object_detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                       Version\r\n",
      "----------------------------- -------------------\r\n",
      "absl-py                       0.12.0\r\n",
      "apache-beam                   2.29.0\r\n",
      "argon2-cffi                   20.1.0\r\n",
      "astunparse                    1.6.3\r\n",
      "async-generator               1.10\r\n",
      "attrs                         21.2.0\r\n",
      "avro-python3                  1.9.2.1\r\n",
      "backcall                      0.2.0\r\n",
      "bleach                        3.3.0\r\n",
      "cachetools                    4.2.2\r\n",
      "certifi                       2021.5.30\r\n",
      "cffi                          1.14.5\r\n",
      "chardet                       4.0.0\r\n",
      "contextlib2                   0.6.0.post1\r\n",
      "crcmod                        1.7\r\n",
      "cycler                        0.10.0\r\n",
      "Cython                        0.29.23\r\n",
      "decorator                     5.0.9\r\n",
      "defusedxml                    0.7.1\r\n",
      "dill                          0.3.1.1\r\n",
      "dm-tree                       0.1.6\r\n",
      "docopt                        0.6.2\r\n",
      "entrypoints                   0.3\r\n",
      "fastavro                      1.4.1\r\n",
      "flatbuffers                   1.12\r\n",
      "future                        0.18.2\r\n",
      "gast                          0.4.0\r\n",
      "gin-config                    0.4.0\r\n",
      "google-api-core               1.28.0\r\n",
      "google-api-python-client      2.6.0\r\n",
      "google-auth                   1.30.1\r\n",
      "google-auth-httplib2          0.1.0\r\n",
      "google-auth-oauthlib          0.4.4\r\n",
      "google-cloud-bigquery         2.17.0\r\n",
      "google-cloud-core             1.6.0\r\n",
      "google-crc32c                 1.1.2\r\n",
      "google-pasta                  0.2.0\r\n",
      "google-resumable-media        1.3.0\r\n",
      "googleapis-common-protos      1.53.0\r\n",
      "grpcio                        1.34.1\r\n",
      "h5py                          3.1.0\r\n",
      "hdfs                          2.6.0\r\n",
      "httplib2                      0.17.4\r\n",
      "idna                          2.10\r\n",
      "importlib-resources           5.1.4\r\n",
      "install                       1.3.4\r\n",
      "ipykernel                     5.5.5\r\n",
      "ipython                       7.24.0\r\n",
      "ipython-genutils              0.2.0\r\n",
      "ipywidgets                    7.6.3\r\n",
      "jedi                          0.18.0\r\n",
      "Jinja2                        3.0.1\r\n",
      "joblib                        1.0.1\r\n",
      "jsonschema                    3.2.0\r\n",
      "jupyter                       1.0.0\r\n",
      "jupyter-client                6.1.12\r\n",
      "jupyter-console               6.4.0\r\n",
      "jupyter-core                  4.7.1\r\n",
      "jupyterlab-pygments           0.1.2\r\n",
      "jupyterlab-widgets            1.0.0\r\n",
      "kaggle                        1.5.12\r\n",
      "keras-nightly                 2.5.0.dev2021032900\r\n",
      "Keras-Preprocessing           1.1.2\r\n",
      "kiwisolver                    1.3.1\r\n",
      "lvis                          0.5.3\r\n",
      "lxml                          4.6.3\r\n",
      "Markdown                      3.3.4\r\n",
      "MarkupSafe                    2.0.1\r\n",
      "matplotlib                    3.2.0\r\n",
      "matplotlib-inline             0.1.2\r\n",
      "mistune                       0.8.4\r\n",
      "nbclient                      0.5.3\r\n",
      "nbconvert                     6.0.7\r\n",
      "nbformat                      5.1.3\r\n",
      "nest-asyncio                  1.5.1\r\n",
      "notebook                      6.4.0\r\n",
      "numpy                         1.19.5\r\n",
      "oauth2client                  4.1.3\r\n",
      "oauthlib                      3.1.0\r\n",
      "object-detection              0.1\r\n",
      "opencv-contrib-python         4.5.2.52\r\n",
      "opencv-python                 4.5.2.52\r\n",
      "opencv-python-headless        4.5.2.52\r\n",
      "opt-einsum                    3.3.0\r\n",
      "packaging                     20.9\r\n",
      "pandas                        1.2.4\r\n",
      "pandocfilters                 1.4.3\r\n",
      "parso                         0.8.2\r\n",
      "pexpect                       4.8.0\r\n",
      "pickleshare                   0.7.5\r\n",
      "Pillow                        8.2.0\r\n",
      "pip                           21.1.2\r\n",
      "pkg-resources                 0.0.0\r\n",
      "portalocker                   2.0.0\r\n",
      "prometheus-client             0.10.1\r\n",
      "promise                       2.3\r\n",
      "prompt-toolkit                3.0.18\r\n",
      "proto-plus                    1.18.1\r\n",
      "protobuf                      3.17.2\r\n",
      "psutil                        5.8.0\r\n",
      "ptyprocess                    0.7.0\r\n",
      "py-cpuinfo                    8.0.0\r\n",
      "pyarrow                       3.0.0\r\n",
      "pyasn1                        0.4.8\r\n",
      "pyasn1-modules                0.2.8\r\n",
      "pycocotools                   2.0.2\r\n",
      "pycparser                     2.20\r\n",
      "pydot                         1.4.2\r\n",
      "Pygments                      2.9.0\r\n",
      "pymongo                       3.11.4\r\n",
      "pyparsing                     2.4.7\r\n",
      "PyQt5                         5.15.4\r\n",
      "PyQt5-Qt5                     5.15.2\r\n",
      "PyQt5-sip                     12.9.0\r\n",
      "pyrsistent                    0.17.3\r\n",
      "python-dateutil               2.8.1\r\n",
      "python-slugify                5.0.2\r\n",
      "pytz                          2021.1\r\n",
      "PyYAML                        5.4.1\r\n",
      "pyzmq                         22.1.0\r\n",
      "qtconsole                     5.1.0\r\n",
      "QtPy                          1.9.0\r\n",
      "requests                      2.25.1\r\n",
      "requests-oauthlib             1.3.0\r\n",
      "rsa                           4.7.2\r\n",
      "sacrebleu                     1.5.1\r\n",
      "scikit-learn                  0.24.2\r\n",
      "scipy                         1.6.3\r\n",
      "Send2Trash                    1.5.0\r\n",
      "sentencepiece                 0.1.95\r\n",
      "seqeval                       1.2.2\r\n",
      "setuptools                    44.0.0\r\n",
      "six                           1.15.0\r\n",
      "tensorboard                   2.5.0\r\n",
      "tensorboard-data-server       0.6.1\r\n",
      "tensorboard-plugin-wit        1.8.0\r\n",
      "tensorflow                    2.5.0\r\n",
      "tensorflow-addons             0.13.0\r\n",
      "tensorflow-datasets           4.3.0\r\n",
      "tensorflow-estimator          2.5.0\r\n",
      "tensorflow-gpu                2.5.0\r\n",
      "tensorflow-hub                0.12.0\r\n",
      "tensorflow-metadata           1.0.0\r\n",
      "tensorflow-model-optimization 0.5.0\r\n",
      "termcolor                     1.1.0\r\n",
      "terminado                     0.10.0\r\n",
      "testpath                      0.5.0\r\n",
      "text-unidecode                1.3\r\n",
      "tf-models-official            2.5.0\r\n",
      "tf-slim                       1.1.0\r\n",
      "threadpoolctl                 2.1.0\r\n",
      "tornado                       6.1\r\n",
      "tqdm                          4.61.0\r\n",
      "traitlets                     5.0.5\r\n",
      "typeguard                     2.12.0\r\n",
      "typing-extensions             3.7.4.3\r\n",
      "uritemplate                   3.0.1\r\n",
      "urllib3                       1.26.5\r\n",
      "wcwidth                       0.2.5\r\n",
      "webencodings                  0.5.1\r\n",
      "Werkzeug                      2.0.1\r\n",
      "wheel                         0.36.2\r\n",
      "widgetsnbextension            3.5.1\r\n",
      "wrapt                         1.12.1\r\n",
      "zipp                          3.4.1\r\n"
     ]
    }
   ],
   "source": [
    "!pip list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "csofht2npfDE",
    "outputId": "ff5471b2-bed2-43f2-959c-327a706527b6",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-06-02 22:20:33--  http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz\n",
      "Resolving download.tensorflow.org (download.tensorflow.org)... 2607:f8b0:4023:1002::80, 142.250.115.128\n",
      "Connecting to download.tensorflow.org (download.tensorflow.org)|2607:f8b0:4023:1002::80|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 20515344 (20M) [application/x-tar]\n",
      "Saving to: ‘ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz’\n",
      "\n",
      "ssd_mobilenet_v2_fp 100%[===================>]  19.56M  5.46MB/s    in 3.9s    \n",
      "\n",
      "2021-06-02 22:20:38 (5.00 MB/s) - ‘ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz’ saved [20515344/20515344]\n",
      "\n",
      "ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/\n",
      "ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/\n",
      "ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0.data-00000-of-00001\n",
      "ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/checkpoint\n",
      "ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0.index\n",
      "ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/pipeline.config\n",
      "ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/\n",
      "ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/saved_model.pb\n",
      "ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/variables/\n",
      "ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/variables/variables.data-00000-of-00001\n",
      "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": 16,
   "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": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kvf5WccwrFGq",
    "outputId": "49902aeb-0bd7-4298-e1a0-5b4a64eb2064"
   },
   "outputs": [],
   "source": [
    "# OPTIONAL - USE 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": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KWpb_BVUpfDD",
    "outputId": "56ce2a3f-3933-4ee6-8a9d-d5ec65f7d73c"
   },
   "outputs": [],
   "source": [
    "if not os.path.exists(files['TF_RECORD_SCRIPT']):\n",
    "    !git clone https://github.com/nicknochnack/GenerateTFRecord {paths['SCRIPTS_PATH']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "metadata": {
    "id": "cOjuTFbwpfDF"
   },
   "outputs": [],
   "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": 21,
   "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": 30,
   "metadata": {
    "id": "c2A0mn4ipfDF"
   },
   "outputs": [],
   "source": [
    "config = config_util.get_configs_from_pipeline_file(files['PIPELINE_CONFIG'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uQA13-afpfDF",
    "outputId": "907496a4-a39d-4b13-8c2c-e5978ecb1f10"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model': ssd {\n",
       "   num_classes: 4\n",
       "   image_resizer {\n",
       "     fixed_shape_resizer {\n",
       "       height: 320\n",
       "       width: 320\n",
       "     }\n",
       "   }\n",
       "   feature_extractor {\n",
       "     type: \"ssd_mobilenet_v2_fpn_keras\"\n",
       "     depth_multiplier: 1.0\n",
       "     min_depth: 16\n",
       "     conv_hyperparams {\n",
       "       regularizer {\n",
       "         l2_regularizer {\n",
       "           weight: 3.9999998989515007e-05\n",
       "         }\n",
       "       }\n",
       "       initializer {\n",
       "         random_normal_initializer {\n",
       "           mean: 0.0\n",
       "           stddev: 0.009999999776482582\n",
       "         }\n",
       "       }\n",
       "       activation: RELU_6\n",
       "       batch_norm {\n",
       "         decay: 0.996999979019165\n",
       "         scale: true\n",
       "         epsilon: 0.0010000000474974513\n",
       "       }\n",
       "     }\n",
       "     use_depthwise: true\n",
       "     override_base_feature_extractor_hyperparams: true\n",
       "     fpn {\n",
       "       min_level: 3\n",
       "       max_level: 7\n",
       "       additional_layer_depth: 128\n",
       "     }\n",
       "   }\n",
       "   box_coder {\n",
       "     faster_rcnn_box_coder {\n",
       "       y_scale: 10.0\n",
       "       x_scale: 10.0\n",
       "       height_scale: 5.0\n",
       "       width_scale: 5.0\n",
       "     }\n",
       "   }\n",
       "   matcher {\n",
       "     argmax_matcher {\n",
       "       matched_threshold: 0.5\n",
       "       unmatched_threshold: 0.5\n",
       "       ignore_thresholds: false\n",
       "       negatives_lower_than_unmatched: true\n",
       "       force_match_for_each_row: true\n",
       "       use_matmul_gather: true\n",
       "     }\n",
       "   }\n",
       "   similarity_calculator {\n",
       "     iou_similarity {\n",
       "     }\n",
       "   }\n",
       "   box_predictor {\n",
       "     weight_shared_convolutional_box_predictor {\n",
       "       conv_hyperparams {\n",
       "         regularizer {\n",
       "           l2_regularizer {\n",
       "             weight: 3.9999998989515007e-05\n",
       "           }\n",
       "         }\n",
       "         initializer {\n",
       "           random_normal_initializer {\n",
       "             mean: 0.0\n",
       "             stddev: 0.009999999776482582\n",
       "           }\n",
       "         }\n",
       "         activation: RELU_6\n",
       "         batch_norm {\n",
       "           decay: 0.996999979019165\n",
       "           scale: true\n",
       "           epsilon: 0.0010000000474974513\n",
       "         }\n",
       "       }\n",
       "       depth: 128\n",
       "       num_layers_before_predictor: 4\n",
       "       kernel_size: 3\n",
       "       class_prediction_bias_init: -4.599999904632568\n",
       "       share_prediction_tower: true\n",
       "       use_depthwise: true\n",
       "     }\n",
       "   }\n",
       "   anchor_generator {\n",
       "     multiscale_anchor_generator {\n",
       "       min_level: 3\n",
       "       max_level: 7\n",
       "       anchor_scale: 4.0\n",
       "       aspect_ratios: 1.0\n",
       "       aspect_ratios: 2.0\n",
       "       aspect_ratios: 0.5\n",
       "       scales_per_octave: 2\n",
       "     }\n",
       "   }\n",
       "   post_processing {\n",
       "     batch_non_max_suppression {\n",
       "       score_threshold: 9.99999993922529e-09\n",
       "       iou_threshold: 0.6000000238418579\n",
       "       max_detections_per_class: 100\n",
       "       max_total_detections: 100\n",
       "       use_static_shapes: false\n",
       "     }\n",
       "     score_converter: SIGMOID\n",
       "   }\n",
       "   normalize_loss_by_num_matches: true\n",
       "   loss {\n",
       "     localization_loss {\n",
       "       weighted_smooth_l1 {\n",
       "       }\n",
       "     }\n",
       "     classification_loss {\n",
       "       weighted_sigmoid_focal {\n",
       "         gamma: 2.0\n",
       "         alpha: 0.25\n",
       "       }\n",
       "     }\n",
       "     classification_weight: 1.0\n",
       "     localization_weight: 1.0\n",
       "   }\n",
       "   encode_background_as_zeros: true\n",
       "   normalize_loc_loss_by_codesize: true\n",
       "   inplace_batchnorm_update: true\n",
       "   freeze_batchnorm: false\n",
       " },\n",
       " 'train_config': batch_size: 4\n",
       " data_augmentation_options {\n",
       "   random_horizontal_flip {\n",
       "   }\n",
       " }\n",
       " data_augmentation_options {\n",
       "   random_crop_image {\n",
       "     min_object_covered: 0.0\n",
       "     min_aspect_ratio: 0.75\n",
       "     max_aspect_ratio: 3.0\n",
       "     min_area: 0.75\n",
       "     max_area: 1.0\n",
       "     overlap_thresh: 0.0\n",
       "   }\n",
       " }\n",
       " sync_replicas: true\n",
       " optimizer {\n",
       "   momentum_optimizer {\n",
       "     learning_rate {\n",
       "       cosine_decay_learning_rate {\n",
       "         learning_rate_base: 0.07999999821186066\n",
       "         total_steps: 50000\n",
       "         warmup_learning_rate: 0.026666000485420227\n",
       "         warmup_steps: 1000\n",
       "       }\n",
       "     }\n",
       "     momentum_optimizer_value: 0.8999999761581421\n",
       "   }\n",
       "   use_moving_average: false\n",
       " }\n",
       " fine_tune_checkpoint: \"Tensorflow/workspace/pre-trained-models/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0\"\n",
       " num_steps: 50000\n",
       " startup_delay_steps: 0.0\n",
       " replicas_to_aggregate: 8\n",
       " max_number_of_boxes: 100\n",
       " unpad_groundtruth_tensors: false\n",
       " fine_tune_checkpoint_type: \"detection\"\n",
       " fine_tune_checkpoint_version: V2,\n",
       " 'train_input_config': label_map_path: \"Tensorflow/workspace/annotations/label_map.pbtxt\"\n",
       " tf_record_input_reader {\n",
       "   input_path: \"Tensorflow/workspace/annotations/train.record\"\n",
       " },\n",
       " 'eval_config': metrics_set: \"coco_detection_metrics\"\n",
       " use_moving_averages: false,\n",
       " 'eval_input_configs': [label_map_path: \"Tensorflow/workspace/annotations/label_map.pbtxt\"\n",
       " shuffle: false\n",
       " num_epochs: 1\n",
       " tf_record_input_reader {\n",
       "   input_path: \"Tensorflow/workspace/annotations/test.record\"\n",
       " }\n",
       " ],\n",
       " 'eval_input_config': label_map_path: \"Tensorflow/workspace/annotations/label_map.pbtxt\"\n",
       " shuffle: false\n",
       " num_epochs: 1\n",
       " tf_record_input_reader {\n",
       "   input_path: \"Tensorflow/workspace/annotations/test.record\"\n",
       " }}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "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": 26,
   "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": 27,
   "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": 28,
   "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": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A4OXXi-ApfDH",
    "outputId": "117a0e83-012b-466e-b7a6-ccaa349ac5ab",
    "scrolled": true
   },
   "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": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i3ZsJR-qpfDH",
    "outputId": "cabec5e1-45e6-4f2f-d9cf-297d9c1d0225",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-06-02 22:21:36.843820: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
      "2021-06-02 22:21:39.022859: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n",
      "2021-06-02 22:21:39.067372: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1060 6GB computeCapability: 6.1\n",
      "coreClock: 1.7085GHz coreCount: 10 deviceMemorySize: 5.93GiB deviceMemoryBandwidth: 178.99GiB/s\n",
      "2021-06-02 22:21:39.067427: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
      "2021-06-02 22:21:39.075280: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n",
      "2021-06-02 22:21:39.080474: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n",
      "2021-06-02 22:21:39.081601: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcufft.so.10\n",
      "2021-06-02 22:21:39.081979: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcurand.so.10\n",
      "2021-06-02 22:21:39.082565: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusolver.so.11\n",
      "2021-06-02 22:21:39.083456: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusparse.so.11\n",
      "2021-06-02 22:21:39.083612: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n",
      "2021-06-02 22:21:39.092256: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
      "2021-06-02 22:21:39.092557: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2021-06-02 22:21:39.093174: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1060 6GB computeCapability: 6.1\n",
      "coreClock: 1.7085GHz coreCount: 10 deviceMemorySize: 5.93GiB deviceMemoryBandwidth: 178.99GiB/s\n",
      "2021-06-02 22:21:39.093882: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
      "2021-06-02 22:21:39.093920: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
      "2021-06-02 22:21:39.568451: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-06-02 22:21:39.568480: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      0 \n",
      "2021-06-02 22:21:39.568489: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0:   N \n",
      "2021-06-02 22:21:39.569656: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5174 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)\n",
      "WARNING:tensorflow:Collective ops is not configured at program startup. Some performance features may not be enabled.\n",
      "W0602 22:21:39.571210 140444164183872 mirrored_strategy.py:379] Collective ops is not configured at program startup. Some performance features may not be enabled.\n",
      "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n",
      "I0602 22:21:39.651237 140444164183872 mirrored_strategy.py:369] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n",
      "INFO:tensorflow:Maybe overwriting train_steps: 2000\n",
      "I0602 22:21:39.655704 140444164183872 config_util.py:552] Maybe overwriting train_steps: 2000\n",
      "INFO:tensorflow:Maybe overwriting use_bfloat16: False\n",
      "I0602 22:21:39.655860 140444164183872 config_util.py:552] Maybe overwriting use_bfloat16: False\n",
      "WARNING:tensorflow:From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/object_detection/model_lib_v2.py:550: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "rename to distribute_datasets_from_function\n",
      "W0602 22:21:39.677107 140444164183872 deprecation.py:330] From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/object_detection/model_lib_v2.py:550: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "rename to distribute_datasets_from_function\n",
      "INFO:tensorflow:Reading unweighted datasets: ['Tensorflow/workspace/annotations/train.record']\n",
      "I0602 22:21:39.680757 140444164183872 dataset_builder.py:163] Reading unweighted datasets: ['Tensorflow/workspace/annotations/train.record']\n",
      "INFO:tensorflow:Reading record datasets for input file: ['Tensorflow/workspace/annotations/train.record']\n",
      "I0602 22:21:39.680971 140444164183872 dataset_builder.py:80] Reading record datasets for input file: ['Tensorflow/workspace/annotations/train.record']\n",
      "INFO:tensorflow:Number of filenames to read: 1\n",
      "I0602 22:21:39.681046 140444164183872 dataset_builder.py:81] Number of filenames to read: 1\n",
      "WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.\n",
      "W0602 22:21:39.681097 140444164183872 dataset_builder.py:87] num_readers has been reduced to 1 to match input file shards.\n",
      "WARNING:tensorflow:From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/object_detection/builders/dataset_builder.py:101: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.\n",
      "W0602 22:21:39.683038 140444164183872 deprecation.py:330] From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/object_detection/builders/dataset_builder.py:101: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.\n",
      "WARNING:tensorflow:From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/object_detection/builders/dataset_builder.py:236: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.map()\n",
      "W0602 22:21:39.707969 140444164183872 deprecation.py:330] From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/object_detection/builders/dataset_builder.py:236: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.map()\n",
      "WARNING:tensorflow:From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:206: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
      "W0602 22:21:45.338989 140444164183872 deprecation.py:330] From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:206: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:206: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.\n",
      "W0602 22:21:47.440625 140444164183872 deprecation.py:330] From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:206: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.\n",
      "WARNING:tensorflow:From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:464: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.cast` instead.\n",
      "W0602 22:21:48.530393 140444164183872 deprecation.py:330] From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:464: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.cast` instead.\n",
      "2021-06-02 22:21:49.993787: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2021-06-02 22:21:50.018275: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 4200000000 Hz\n",
      "/home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/keras/backend.py:435: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n",
      "  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '\n",
      "2021-06-02 22:22:03.904167: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n",
      "2021-06-02 22:22:04.490849: I tensorflow/stream_executor/cuda/cuda_dnn.cc:359] Loaded cuDNN version 8200\n",
      "2021-06-02 22:22:05.164522: E tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2021-06-02 22:22:05.164552: W tensorflow/stream_executor/gpu/asm_compiler.cc:56] Couldn't invoke ptxas --version\n",
      "2021-06-02 22:22:05.164833: E tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2021-06-02 22:22:05.164876: W tensorflow/stream_executor/gpu/redzone_allocator.cc:314] Internal: Failed to launch ptxas\n",
      "Relying on driver to perform ptx compilation. \n",
      "Modify $PATH to customize ptxas location.\n",
      "This message will be only logged once.\n",
      "2021-06-02 22:22:05.185006: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n",
      "2021-06-02 22:22:05.996051: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._groundtruth_lists\n",
      "W0602 22:22:06.925543 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._groundtruth_lists\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor\n",
      "W0602 22:22:06.925712 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._batched_prediction_tensor_names\n",
      "W0602 22:22:06.925763 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._batched_prediction_tensor_names\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head\n",
      "W0602 22:22:06.925800 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads\n",
      "W0602 22:22:06.925834 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._sorted_head_names\n",
      "W0602 22:22:06.925866 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._sorted_head_names\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers\n",
      "W0602 22:22:06.925897 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads\n",
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      "W0602 22:22:06.929841 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.0.depthwise_kernel\n",
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      "W0602 22:22:06.930587 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.gamma\n",
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      "W0602 22:22:06.930620 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.beta\n",
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      "W0602 22:22:06.930654 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.moving_mean\n",
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      "W0602 22:22:06.930687 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.moving_variance\n",
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      "W0602 22:22:06.930721 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.axis\n",
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      "W0602 22:22:06.930754 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.gamma\n",
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      "W0602 22:22:06.930787 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.beta\n",
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      "W0602 22:22:06.930821 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.moving_mean\n",
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      "W0602 22:22:06.930854 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.moving_variance\n",
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      "W0602 22:22:06.930888 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.axis\n",
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      "W0602 22:22:06.930922 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.gamma\n",
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      "W0602 22:22:06.930955 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.beta\n",
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      "W0602 22:22:06.930988 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.moving_mean\n",
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      "W0602 22:22:06.931022 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.moving_variance\n",
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      "W0602 22:22:06.931055 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.axis\n",
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      "W0602 22:22:06.931088 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.gamma\n",
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      "W0602 22:22:06.931121 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.beta\n",
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      "W0602 22:22:06.931154 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.moving_mean\n",
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      "W0602 22:22:06.931188 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.moving_variance\n",
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      "W0602 22:22:06.931221 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.axis\n",
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      "W0602 22:22:06.931254 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.gamma\n",
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      "W0602 22:22:06.931288 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.beta\n",
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      "W0602 22:22:06.931321 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.moving_mean\n",
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      "W0602 22:22:06.931354 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.moving_variance\n",
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      "W0602 22:22:06.931388 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.axis\n",
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      "W0602 22:22:06.931421 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.gamma\n",
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      "W0602 22:22:06.931455 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.beta\n",
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      "W0602 22:22:06.931487 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.moving_mean\n",
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      "W0602 22:22:06.931517 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.moving_variance\n",
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      "W0602 22:22:06.931547 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.axis\n",
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      "W0602 22:22:06.931576 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.gamma\n",
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      "W0602 22:22:06.931606 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.beta\n",
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      "W0602 22:22:06.931636 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.moving_mean\n",
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      "W0602 22:22:06.931665 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.moving_variance\n",
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      "W0602 22:22:06.931695 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.axis\n",
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      "W0602 22:22:06.931725 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.gamma\n",
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      "W0602 22:22:06.931755 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.beta\n",
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      "W0602 22:22:06.931785 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.moving_mean\n",
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      "W0602 22:22:06.931815 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.moving_variance\n",
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      "W0602 22:22:06.931845 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.axis\n",
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      "W0602 22:22:06.931875 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.gamma\n",
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      "W0602 22:22:06.931905 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.beta\n",
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      "W0602 22:22:06.931935 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.moving_mean\n",
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      "W0602 22:22:06.931964 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.moving_variance\n",
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      "W0602 22:22:06.931994 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.axis\n",
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      "W0602 22:22:06.932024 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.gamma\n",
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      "W0602 22:22:06.932054 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.beta\n",
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      "W0602 22:22:06.932084 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.moving_mean\n",
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      "W0602 22:22:06.932114 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.moving_variance\n",
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      "W0602 22:22:06.932144 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.axis\n",
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      "W0602 22:22:06.932174 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.gamma\n",
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      "W0602 22:22:06.932230 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.beta\n",
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      "W0602 22:22:06.932266 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.moving_mean\n",
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      "W0602 22:22:06.932297 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.moving_variance\n",
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      "W0602 22:22:06.932328 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.axis\n",
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      "W0602 22:22:06.932359 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.gamma\n",
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      "W0602 22:22:06.932390 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.beta\n",
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      "W0602 22:22:06.932420 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.moving_mean\n",
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      "W0602 22:22:06.932450 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.moving_variance\n",
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      "W0602 22:22:06.932481 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.axis\n",
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      "W0602 22:22:06.932511 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.gamma\n",
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      "W0602 22:22:06.932541 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.beta\n",
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      "W0602 22:22:06.932571 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.moving_mean\n",
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      "W0602 22:22:06.932601 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.moving_variance\n",
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      "W0602 22:22:06.932631 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.axis\n",
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      "W0602 22:22:06.932662 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.gamma\n",
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      "W0602 22:22:06.932693 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.beta\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.moving_mean\n",
      "W0602 22:22:06.932723 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.moving_mean\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.moving_variance\n",
      "W0602 22:22:06.932753 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.moving_variance\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.axis\n",
      "W0602 22:22:06.932783 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.axis\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.gamma\n",
      "W0602 22:22:06.932813 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.gamma\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.beta\n",
      "W0602 22:22:06.932843 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.beta\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.moving_mean\n",
      "W0602 22:22:06.932873 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.moving_mean\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.moving_variance\n",
      "W0602 22:22:06.932903 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.moving_variance\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.axis\n",
      "W0602 22:22:06.932933 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.axis\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.gamma\n",
      "W0602 22:22:06.932963 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.gamma\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.beta\n",
      "W0602 22:22:06.932993 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.beta\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.moving_mean\n",
      "W0602 22:22:06.933023 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.moving_mean\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.moving_variance\n",
      "W0602 22:22:06.933053 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.moving_variance\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.axis\n",
      "W0602 22:22:06.933083 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.axis\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.gamma\n",
      "W0602 22:22:06.933113 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.gamma\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.beta\n",
      "W0602 22:22:06.933143 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.beta\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.moving_mean\n",
      "W0602 22:22:06.933174 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.moving_mean\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.moving_variance\n",
      "W0602 22:22:06.933203 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.moving_variance\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.axis\n",
      "W0602 22:22:06.933233 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.axis\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.gamma\n",
      "W0602 22:22:06.933263 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.gamma\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.beta\n",
      "W0602 22:22:06.933293 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.beta\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.moving_mean\n",
      "W0602 22:22:06.933322 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.moving_mean\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.moving_variance\n",
      "W0602 22:22:06.933352 140444164183872 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.moving_variance\n",
      "WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.\n",
      "W0602 22:22:06.933385 140444164183872 util.py:169] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.iter\r\n",
      "W0602 22:22:07.974900 140444164183872 util.py:161] Unresolved object in checkpoint: (root).optimizer.iter\r\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.decay\r\n",
      "W0602 22:22:07.975054 140444164183872 util.py:161] Unresolved object in checkpoint: (root).optimizer.decay\r\n",
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.momentum\r\n",
      "W0602 22:22:07.975106 140444164183872 util.py:161] Unresolved object in checkpoint: (root).optimizer.momentum\r\n",
      "WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.\r\n",
      "W0602 22:22:07.975201 140444164183872 util.py:169] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.\r\n"
     ]
    }
   ],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4_YRZu7npfDH"
   },
   "source": [
    "# 7. Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "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": null,
   "metadata": {
    "id": "lqTV2jGBpfDH",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-06-02 22:22:23.536405: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
      "WARNING:tensorflow:Forced number of epochs for all eval validations to be 1.\n",
      "W0602 22:22:25.136996 139932287952704 model_lib_v2.py:1063] Forced number of epochs for all eval validations to be 1.\n",
      "INFO:tensorflow:Maybe overwriting sample_1_of_n_eval_examples: None\n",
      "I0602 22:22:25.137147 139932287952704 config_util.py:552] Maybe overwriting sample_1_of_n_eval_examples: None\n",
      "INFO:tensorflow:Maybe overwriting use_bfloat16: False\n",
      "I0602 22:22:25.137196 139932287952704 config_util.py:552] Maybe overwriting use_bfloat16: False\n",
      "INFO:tensorflow:Maybe overwriting eval_num_epochs: 1\n",
      "I0602 22:22:25.137239 139932287952704 config_util.py:552] Maybe overwriting eval_num_epochs: 1\n",
      "WARNING:tensorflow:Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.\n",
      "W0602 22:22:25.137315 139932287952704 model_lib_v2.py:1081] Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.\n",
      "2021-06-02 22:22:25.140161: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n",
      "2021-06-02 22:22:25.169920: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1060 6GB computeCapability: 6.1\n",
      "coreClock: 1.7085GHz coreCount: 10 deviceMemorySize: 5.93GiB deviceMemoryBandwidth: 178.99GiB/s\n",
      "2021-06-02 22:22:25.169954: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
      "2021-06-02 22:22:25.173017: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n",
      "2021-06-02 22:22:25.173086: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n",
      "2021-06-02 22:22:25.173895: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcufft.so.10\n",
      "2021-06-02 22:22:25.174324: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcurand.so.10\n",
      "2021-06-02 22:22:25.175057: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusolver.so.11\n",
      "2021-06-02 22:22:25.175800: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusparse.so.11\n",
      "2021-06-02 22:22:25.175951: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n",
      "2021-06-02 22:22:25.176556: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
      "2021-06-02 22:22:25.176757: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2021-06-02 22:22:25.177226: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1060 6GB computeCapability: 6.1\n",
      "coreClock: 1.7085GHz coreCount: 10 deviceMemorySize: 5.93GiB deviceMemoryBandwidth: 178.99GiB/s\n",
      "2021-06-02 22:22:25.177739: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
      "2021-06-02 22:22:25.177773: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
      "2021-06-02 22:22:25.570216: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-06-02 22:22:25.570245: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      0 \n",
      "2021-06-02 22:22:25.570254: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0:   N \n",
      "2021-06-02 22:22:25.571269: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5173 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)\n",
      "INFO:tensorflow:Reading unweighted datasets: ['Tensorflow/workspace/annotations/test.record']\n",
      "I0602 22:22:25.659262 139932287952704 dataset_builder.py:163] Reading unweighted datasets: ['Tensorflow/workspace/annotations/test.record']\n",
      "INFO:tensorflow:Reading record datasets for input file: ['Tensorflow/workspace/annotations/test.record']\n",
      "I0602 22:22:25.659436 139932287952704 dataset_builder.py:80] Reading record datasets for input file: ['Tensorflow/workspace/annotations/test.record']\n",
      "INFO:tensorflow:Number of filenames to read: 1\n",
      "I0602 22:22:25.659497 139932287952704 dataset_builder.py:81] Number of filenames to read: 1\n",
      "WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.\n",
      "W0602 22:22:25.659547 139932287952704 dataset_builder.py:87] num_readers has been reduced to 1 to match input file shards.\n",
      "WARNING:tensorflow:From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/object_detection/builders/dataset_builder.py:101: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.\n",
      "W0602 22:22:25.661024 139932287952704 deprecation.py:330] From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/object_detection/builders/dataset_builder.py:101: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.\n",
      "WARNING:tensorflow:From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/object_detection/builders/dataset_builder.py:236: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.map()\n",
      "W0602 22:22:25.683673 139932287952704 deprecation.py:330] From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/object_detection/builders/dataset_builder.py:236: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.map()\n",
      "WARNING:tensorflow:From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:206: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
      "W0602 22:22:28.007903 139932287952704 deprecation.py:330] From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:206: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
      "WARNING:tensorflow:From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:464: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.cast` instead.\n",
      "W0602 22:22:28.684787 139932287952704 deprecation.py:330] From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:464: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.cast` instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Waiting for new checkpoint at Tensorflow/workspace/models/my_ssd_mobnet\n",
      "I0602 22:22:30.412683 139932287952704 checkpoint_utils.py:140] Waiting for new checkpoint at Tensorflow/workspace/models/my_ssd_mobnet\n",
      "INFO:tensorflow:Found new checkpoint at Tensorflow/workspace/models/my_ssd_mobnet/ckpt-3\n",
      "I0602 22:22:30.413525 139932287952704 checkpoint_utils.py:149] Found new checkpoint at Tensorflow/workspace/models/my_ssd_mobnet/ckpt-3\n",
      "/home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/keras/backend.py:435: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n",
      "  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '\n",
      "2021-06-02 22:22:30.515516: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2021-06-02 22:22:30.534286: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 4200000000 Hz\n",
      "2021-06-02 22:22:45.937218: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n",
      "2021-06-02 22:22:46.170780: I tensorflow/stream_executor/cuda/cuda_dnn.cc:359] Loaded cuDNN version 8200\n",
      "2021-06-02 22:22:46.192699: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n",
      "2021-06-02 22:22:46.605096: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n",
      "2021-06-02 22:22:46.637375: E tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2021-06-02 22:22:46.637405: W tensorflow/stream_executor/gpu/asm_compiler.cc:56] Couldn't invoke ptxas --version\n",
      "2021-06-02 22:22:46.637823: E tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2021-06-02 22:22:46.638424: W tensorflow/stream_executor/gpu/redzone_allocator.cc:314] Internal: Failed to launch ptxas\n",
      "Relying on driver to perform ptx compilation. \n",
      "Modify $PATH to customize ptxas location.\n",
      "This message will be only logged once.\n",
      "WARNING:tensorflow:From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/object_detection/eval_util.py:929: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.cast` instead.\n",
      "W0602 22:22:47.028496 139932287952704 deprecation.py:330] From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/object_detection/eval_util.py:929: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.cast` instead.\n",
      "INFO:tensorflow:Finished eval step 0\n",
      "I0602 22:22:47.033449 139932287952704 model_lib_v2.py:940] Finished eval step 0\n",
      "WARNING:tensorflow:From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:464: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "tf.py_func is deprecated in TF V2. Instead, there are two\n",
      "    options available in V2.\n",
      "    - tf.py_function takes a python function which manipulates tf eager\n",
      "    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n",
      "    an ndarray (just call tensor.numpy()) but having access to eager tensors\n",
      "    means `tf.py_function`s can use accelerators such as GPUs as well as\n",
      "    being differentiable using a gradient tape.\n",
      "    - tf.numpy_function maintains the semantics of the deprecated tf.py_func\n",
      "    (it is not differentiable, and manipulates numpy arrays). It drops the\n",
      "    stateful argument making all functions stateful.\n",
      "    \n",
      "W0602 22:22:47.135506 139932287952704 deprecation.py:330] From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:464: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "tf.py_func is deprecated in TF V2. Instead, there are two\n",
      "    options available in V2.\n",
      "    - tf.py_function takes a python function which manipulates tf eager\n",
      "    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n",
      "    an ndarray (just call tensor.numpy()) but having access to eager tensors\n",
      "    means `tf.py_function`s can use accelerators such as GPUs as well as\n",
      "    being differentiable using a gradient tape.\n",
      "    - tf.numpy_function maintains the semantics of the deprecated tf.py_func\n",
      "    (it is not differentiable, and manipulates numpy arrays). It drops the\n",
      "    stateful argument making all functions stateful.\n",
      "    \n",
      "INFO:tensorflow:Performing evaluation on 4 images.\n",
      "I0602 22:22:47.859295 139932287952704 coco_evaluation.py:293] Performing evaluation on 4 images.\n",
      "creating index...\n",
      "index created!\n",
      "INFO:tensorflow:Loading and preparing annotation results...\n",
      "I0602 22:22:47.859502 139932287952704 coco_tools.py:116] Loading and preparing annotation results...\n",
      "INFO:tensorflow:DONE (t=0.00s)\n",
      "I0602 22:22:47.861812 139932287952704 coco_tools.py:138] DONE (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.01s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.01s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.625\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.750\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.625\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.625\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.625\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.625\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.625\n",
      "INFO:tensorflow:Eval metrics at step 2000\n",
      "I0602 22:22:47.883322 139932287952704 model_lib_v2.py:989] Eval metrics at step 2000\n",
      "INFO:tensorflow:\t+ DetectionBoxes_Precision/mAP: 0.625000\n",
      "I0602 22:22:47.884599 139932287952704 model_lib_v2.py:992] \t+ DetectionBoxes_Precision/mAP: 0.625000\n",
      "INFO:tensorflow:\t+ DetectionBoxes_Precision/mAP@.50IOU: 1.000000\n",
      "I0602 22:22:47.885481 139932287952704 model_lib_v2.py:992] \t+ DetectionBoxes_Precision/mAP@.50IOU: 1.000000\n",
      "INFO:tensorflow:\t+ DetectionBoxes_Precision/mAP@.75IOU: 0.750000\n",
      "I0602 22:22:47.886196 139932287952704 model_lib_v2.py:992] \t+ DetectionBoxes_Precision/mAP@.75IOU: 0.750000\n",
      "INFO:tensorflow:\t+ DetectionBoxes_Precision/mAP (small): -1.000000\n",
      "I0602 22:22:47.890845 139932287952704 model_lib_v2.py:992] \t+ DetectionBoxes_Precision/mAP (small): -1.000000\n",
      "INFO:tensorflow:\t+ DetectionBoxes_Precision/mAP (medium): -1.000000\n",
      "I0602 22:22:47.891505 139932287952704 model_lib_v2.py:992] \t+ DetectionBoxes_Precision/mAP (medium): -1.000000\n",
      "INFO:tensorflow:\t+ DetectionBoxes_Precision/mAP (large): 0.625000\n",
      "I0602 22:22:47.892138 139932287952704 model_lib_v2.py:992] \t+ DetectionBoxes_Precision/mAP (large): 0.625000\n",
      "INFO:tensorflow:\t+ DetectionBoxes_Recall/AR@1: 0.625000\n",
      "I0602 22:22:47.893565 139932287952704 model_lib_v2.py:992] \t+ DetectionBoxes_Recall/AR@1: 0.625000\n",
      "INFO:tensorflow:\t+ DetectionBoxes_Recall/AR@10: 0.625000\n",
      "I0602 22:22:47.894412 139932287952704 model_lib_v2.py:992] \t+ DetectionBoxes_Recall/AR@10: 0.625000\n",
      "INFO:tensorflow:\t+ DetectionBoxes_Recall/AR@100: 0.625000\n",
      "I0602 22:22:47.895056 139932287952704 model_lib_v2.py:992] \t+ DetectionBoxes_Recall/AR@100: 0.625000\n",
      "INFO:tensorflow:\t+ DetectionBoxes_Recall/AR@100 (small): -1.000000\n",
      "I0602 22:22:47.895754 139932287952704 model_lib_v2.py:992] \t+ DetectionBoxes_Recall/AR@100 (small): -1.000000\n",
      "INFO:tensorflow:\t+ DetectionBoxes_Recall/AR@100 (medium): -1.000000\n",
      "I0602 22:22:47.896375 139932287952704 model_lib_v2.py:992] \t+ DetectionBoxes_Recall/AR@100 (medium): -1.000000\n",
      "INFO:tensorflow:\t+ DetectionBoxes_Recall/AR@100 (large): 0.625000\n",
      "I0602 22:22:47.896984 139932287952704 model_lib_v2.py:992] \t+ DetectionBoxes_Recall/AR@100 (large): 0.625000\n",
      "INFO:tensorflow:\t+ Loss/localization_loss: 0.152493\n",
      "I0602 22:22:47.897522 139932287952704 model_lib_v2.py:992] \t+ Loss/localization_loss: 0.152493\n",
      "INFO:tensorflow:\t+ Loss/classification_loss: 0.289429\n",
      "I0602 22:22:47.898087 139932287952704 model_lib_v2.py:992] \t+ Loss/classification_loss: 0.289429\n",
      "INFO:tensorflow:\t+ Loss/regularization_loss: 0.143942\n",
      "I0602 22:22:47.898638 139932287952704 model_lib_v2.py:992] \t+ Loss/regularization_loss: 0.143942\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:\t+ Loss/total_loss: 0.585865\n",
      "I0602 22:22:47.899191 139932287952704 model_lib_v2.py:992] \t+ Loss/total_loss: 0.585865\n",
      "INFO:tensorflow:Waiting for new checkpoint at Tensorflow/workspace/models/my_ssd_mobnet\n",
      "I0602 22:27:30.510244 139932287952704 checkpoint_utils.py:140] Waiting for new checkpoint at Tensorflow/workspace/models/my_ssd_mobnet\n"
     ]
    }
   ],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "orvRk02UpfDI"
   },
   "source": [
    "# 8. Load Train Model From Checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 35,
   "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-3')).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": 36,
   "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": 37,
   "metadata": {
    "id": "cBDbIhNapfDI"
   },
   "outputs": [],
   "source": [
    "category_index = label_map_util.create_category_index_from_labelmap(files['LABELMAP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "Lx3crOhOzITB"
   },
   "outputs": [],
   "source": [
    "IMAGE_PATH = os.path.join(paths['IMAGE_PATH'], 'test', '2021-05-31-150743.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Skipping opencv-python-headless as it is not installed.\u001b[0m\n",
      "\u001b[33mWARNING: Skipping opencv-python as it is not installed.\u001b[0m\n",
      "Requirement already satisfied: pip in ./tfod/lib/python3.8/site-packages (21.1.2)\n",
      "Requirement already satisfied: opencv-contrib-python in ./tfod/lib/python3.8/site-packages (4.5.2.52)\n",
      "Requirement already satisfied: numpy>=1.17.3 in ./tfod/lib/python3.8/site-packages (from opencv-contrib-python) (1.19.5)\n"
     ]
    }
   ],
   "source": [
    "!pip uninstall opencv-python-headless -y\n",
    "!pip uninstall opencv-python\n",
    "!pip install --upgrade pip\n",
    "!pip install opencv-contrib-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "o_grs6OGpfDJ"
   },
   "outputs": [
    {
     "ename": "error",
     "evalue": "OpenCV(4.5.2) /tmp/pip-req-build-dzetuct2/opencv/modules/highgui/src/window.cpp:679: error: (-2:Unspecified error) The function is not implemented. Rebuild the library with Windows, GTK+ 2.x or Cocoa support. If you are on Ubuntu or Debian, install libgtk2.0-dev and pkg-config, then re-run cmake or configure script in function 'cvShowImage'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-51-32471f95dba4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     32\u001b[0m                 agnostic_mode=False)\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m     \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'object detection'\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_np_with_detections\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m800\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m600\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaitKey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;36m0xFF\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mord\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'q'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31merror\u001b[0m: OpenCV(4.5.2) /tmp/pip-req-build-dzetuct2/opencv/modules/highgui/src/window.cpp:679: error: (-2:Unspecified error) The function is not implemented. Rebuild the library with Windows, GTK+ 2.x or Cocoa support. If you are on Ubuntu or Debian, install libgtk2.0-dev and pkg-config, then re-run cmake or configure script in function 'cvShowImage'\n"
     ]
    }
   ],
   "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": [
    "# 11. Freezing the Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": 53,
   "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": 54,
   "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": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1Sw1ULgHpfDJ",
    "outputId": "6fd441e1-9fc9-4889-d072-3395c21e40b6",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-06-01 18:01:42.642314: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
      "2021-06-01 18:01:43.932060: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n",
      "2021-06-01 18:01:43.953870: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n",
      "2021-06-01 18:01:43.953896: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: Ubuntu-Desktop\n",
      "2021-06-01 18:01:43.953902: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: Ubuntu-Desktop\n",
      "2021-06-01 18:01:43.953960: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 465.19.1\n",
      "2021-06-01 18:01:43.953978: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 465.19.1\n",
      "2021-06-01 18:01:43.953983: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 465.19.1\n",
      "2021-06-01 18:01:43.954172: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "WARNING:tensorflow:From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:463: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "back_prop=False is deprecated. Consider using tf.stop_gradient instead.\n",
      "Instead of:\n",
      "results = tf.map_fn(fn, elems, back_prop=False)\n",
      "Use:\n",
      "results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\n",
      "W0601 18:01:44.102623 139737505044288 deprecation.py:596] From /home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:463: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "back_prop=False is deprecated. Consider using tf.stop_gradient instead.\n",
      "Instead of:\n",
      "results = tf.map_fn(fn, elems, back_prop=False)\n",
      "Use:\n",
      "results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7f16b441a310>, because it is not built.\n",
      "W0601 18:01:55.000638 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7f16b441a310>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7f16b411acd0>, because it is not built.\n",
      "W0601 18:01:55.173697 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7f16b411acd0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16aca0c190>, because it is not built.\n",
      "W0601 18:01:55.173836 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16aca0c190>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16aca0c220>, because it is not built.\n",
      "W0601 18:01:55.173896 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16aca0c220>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7f16acc97b50>, because it is not built.\n",
      "W0601 18:01:55.173944 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7f16acc97b50>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16acf02ee0>, because it is not built.\n",
      "W0601 18:01:55.174012 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16acf02ee0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16acf02610>, because it is not built.\n",
      "W0601 18:01:55.174060 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16acf02610>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7f16acf02cd0>, because it is not built.\n",
      "W0601 18:01:55.174102 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7f16acf02cd0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16acf02730>, because it is not built.\n",
      "W0601 18:01:55.174143 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16acf02730>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16b40b6370>, because it is not built.\n",
      "W0601 18:01:55.174203 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16b40b6370>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7f16ac9fa910>, because it is not built.\n",
      "W0601 18:01:55.174248 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7f16ac9fa910>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac9fa3d0>, because it is not built.\n",
      "W0601 18:01:55.174289 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac9fa3d0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac9fa4c0>, because it is not built.\n",
      "W0601 18:01:55.174330 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac9fa4c0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac9a92b0>, because it is not built.\n",
      "W0601 18:01:55.174386 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac9a92b0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16acc8f970>, because it is not built.\n",
      "W0601 18:01:55.174431 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16acc8f970>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac941400>, because it is not built.\n",
      "W0601 18:01:55.174472 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac941400>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac941490>, because it is not built.\n",
      "W0601 18:01:55.174512 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac941490>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac941ac0>, because it is not built.\n",
      "W0601 18:01:55.174569 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac941ac0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac941460>, because it is not built.\n",
      "W0601 18:01:55.174613 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac941460>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac9419d0>, because it is not built.\n",
      "W0601 18:01:55.174654 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac9419d0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac941a00>, because it is not built.\n",
      "W0601 18:01:55.174695 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac941a00>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16b41433a0>, because it is not built.\n",
      "W0601 18:01:55.174753 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16b41433a0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16b40a3460>, because it is not built.\n",
      "W0601 18:01:55.174798 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16b40a3460>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac945eb0>, because it is not built.\n",
      "W0601 18:01:55.174839 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac945eb0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac945be0>, because it is not built.\n",
      "W0601 18:01:55.174879 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac945be0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac9b01c0>, because it is not built.\n",
      "W0601 18:01:55.174933 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac9b01c0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac9b0730>, because it is not built.\n",
      "W0601 18:01:55.174982 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac9b0730>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac9b0b50>, because it is not built.\n",
      "W0601 18:01:55.175026 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac9b0b50>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac9b0c40>, because it is not built.\n",
      "W0601 18:01:55.175067 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac9b0c40>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac9b6970>, because it is not built.\n",
      "W0601 18:01:55.175122 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac9b6970>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16acb8b9d0>, because it is not built.\n",
      "W0601 18:01:55.175169 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16acb8b9d0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16acb8b7c0>, because it is not built.\n",
      "W0601 18:01:55.175210 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16acb8b7c0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16acb8b880>, because it is not built.\n",
      "W0601 18:01:55.175251 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16acb8b880>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16acc8ef70>, because it is not built.\n",
      "W0601 18:01:55.175300 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16acc8ef70>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16acc8e790>, because it is not built.\n",
      "W0601 18:01:55.175351 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16acc8e790>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16acee2f70>, because it is not built.\n",
      "W0601 18:01:55.175391 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16acee2f70>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16acee2ee0>, because it is not built.\n",
      "W0601 18:01:55.175433 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16acee2ee0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac9ffc10>, because it is not built.\n",
      "W0601 18:01:55.175482 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac9ffc10>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac9a3340>, because it is not built.\n",
      "W0601 18:01:55.175538 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac9a3340>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac942340>, because it is not built.\n",
      "W0601 18:01:55.175579 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac942340>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac942460>, because it is not built.\n",
      "W0601 18:01:55.175630 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac942460>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac9426a0>, because it is not built.\n",
      "W0601 18:01:55.175685 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac9426a0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac942580>, because it is not built.\n",
      "W0601 18:01:55.175726 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac942580>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac942d00>, because it is not built.\n",
      "W0601 18:01:55.175766 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f16ac942d00>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac942b20>, because it is not built.\n",
      "W0601 18:01:55.175810 139737505044288 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f16ac942b20>, because it is not built.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-06-01 18:02:01.424138: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n",
      "W0601 18:02:12.986324 139737505044288 save.py:238] Found untraced functions such as WeightSharedConvolutionalBoxPredictor_layer_call_fn, WeightSharedConvolutionalBoxPredictor_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxHead_layer_call_fn, WeightSharedConvolutionalBoxHead_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxPredictor_layer_call_fn while saving (showing 5 of 260). These functions will not be directly callable after loading.\n",
      "/home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/keras/utils/generic_utils.py:494: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  warnings.warn('Custom mask layers require a config and must override '\n",
      "WARNING:tensorflow:FOR KERAS USERS: The object that you are saving contains one or more Keras models or layers. If you are loading the SavedModel with `tf.keras.models.load_model`, continue reading (otherwise, you may ignore the following instructions). Please change your code to save with `tf.keras.models.save_model` or `model.save`, and confirm that the file \"keras.metadata\" exists in the export directory. In the future, Keras will only load the SavedModels that have this file. In other words, `tf.saved_model.save` will no longer write SavedModels that can be recovered as Keras models (this will apply in TF 2.5).\n",
      "\n",
      "FOR DEVS: If you are overwriting _tracking_metadata in your class, this property has been used to save metadata in the SavedModel. The metadta field will be deprecated soon, so please move the metadata to a different file.\n",
      "W0601 18:02:16.118519 139737505044288 save.py:1239] FOR KERAS USERS: The object that you are saving contains one or more Keras models or layers. If you are loading the SavedModel with `tf.keras.models.load_model`, continue reading (otherwise, you may ignore the following instructions). Please change your code to save with `tf.keras.models.save_model` or `model.save`, and confirm that the file \"keras.metadata\" exists in the export directory. In the future, Keras will only load the SavedModels that have this file. In other words, `tf.saved_model.save` will no longer write SavedModels that can be recovered as Keras models (this will apply in TF 2.5).\n",
      "\n",
      "FOR DEVS: If you are overwriting _tracking_metadata in your class, this property has been used to save metadata in the SavedModel. The metadta field will be deprecated soon, so please move the metadata to a different file.\n",
      "INFO:tensorflow:Assets written to: Tensorflow/workspace/models/my_ssd_mobnet/export/saved_model/assets\n",
      "I0601 18:02:16.356036 139737505044288 builder_impl.py:774] 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",
      "I0601 18:02:16.956558 139737505044288 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": [
    "# 12. Conversion to TFJS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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-3.3.0-py3-none-any.whl (63 kB)\n",
      "Requirement already satisfied: six<2,>=1.12.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflowjs) (1.15.0)\n",
      "Collecting tensorflow-hub<0.10,>=0.7.0\n",
      "  Using cached tensorflow_hub-0.9.0-py2.py3-none-any.whl (103 kB)\n",
      "Requirement already satisfied: h5py<3,>=2.8.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflowjs) (2.10.0)\n",
      "Requirement already satisfied: tensorflow<3,>=2.1.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflowjs) (2.4.0)\n",
      "Requirement already satisfied: numpy>=1.7 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from h5py<3,>=2.8.0->tensorflowjs) (1.19.5)\n",
      "Requirement already satisfied: flatbuffers~=1.12.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.12)\n",
      "Requirement already satisfied: gast==0.3.3 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.3.3)\n",
      "Requirement already satisfied: wrapt~=1.12.1 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.12.1)\n",
      "Requirement already satisfied: tensorboard~=2.4 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (2.4.1)\n",
      "Requirement already satisfied: termcolor~=1.1.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.1.0)\n",
      "Requirement already satisfied: protobuf>=3.9.2 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (3.15.7)\n",
      "Requirement already satisfied: wheel~=0.35 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.36.2)\n",
      "Requirement already satisfied: tensorflow-estimator<2.5.0,>=2.4.0rc0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (2.4.0)\n",
      "Requirement already satisfied: absl-py~=0.10 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.12.0)\n",
      "Requirement already satisfied: opt-einsum~=3.3.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (3.3.0)\n",
      "Requirement already satisfied: grpcio~=1.32.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.32.0)\n",
      "Requirement already satisfied: keras-preprocessing~=1.1.2 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.1.2)\n",
      "Requirement already satisfied: astunparse~=1.6.3 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.6.3)\n",
      "Requirement already satisfied: typing-extensions~=3.7.4 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\typing_extensions-3.7.4.3-py3.7.egg (from tensorflow<3,>=2.1.0->tensorflowjs) (3.7.4.3)\n",
      "Requirement already satisfied: google-pasta~=0.2 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.2.0)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (0.4.4)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (1.0.1)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\requests-2.25.1-py3.7.egg (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (2.25.1)\n",
      "Requirement already satisfied: google-auth<2,>=1.6.3 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (1.28.0)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (54.2.0)\n",
      "Requirement already satisfied: markdown>=2.6.8 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (3.3.4)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (1.8.0)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (4.7.2)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (0.2.8)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (4.2.1)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (1.3.0)\n",
      "Requirement already satisfied: importlib-metadata in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from markdown>=2.6.8->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (3.10.0)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (0.4.8)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (2020.12.5)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (4.0.0)\n",
      "Requirement already satisfied: idna<3,>=2.5 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (2.10)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (1.26.4)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (3.1.0)\n",
      "Requirement already satisfied: zipp>=0.5 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from importlib-metadata->markdown>=2.6.8->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (3.4.1)\n",
      "Installing collected packages: tensorflow-hub, tensorflowjs\n",
      "  Attempting uninstall: tensorflow-hub\n",
      "    Found existing installation: tensorflow-hub 0.11.0\n",
      "    Uninstalling tensorflow-hub-0.11.0:\n",
      "      Successfully uninstalled tensorflow-hub-0.11.0\n",
      "Successfully installed tensorflow-hub-0.9.0 tensorflowjs-3.3.0\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflowjs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "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": 22,
   "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": [
      "2021-04-03 11:54:23.153051: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\n",
      "2021-04-03 11:54:25.644887: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2021-04-03 11:54:25.645576: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll\n",
      "2021-04-03 11:54:25.667969: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
      "pciBusID: 0000:2b:00.0 name: GeForce RTX 2070 SUPER computeCapability: 7.5\n",
      "coreClock: 1.785GHz coreCount: 40 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s\n",
      "2021-04-03 11:54:25.668001: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\n",
      "2021-04-03 11:54:25.671400: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll\n",
      "2021-04-03 11:54:25.671416: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll\n",
      "2021-04-03 11:54:25.673240: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll\n",
      "2021-04-03 11:54:25.673772: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll\n",
      "2021-04-03 11:54:25.677306: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll\n",
      "2021-04-03 11:54:25.678684: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll\n",
      "2021-04-03 11:54:25.679228: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll\n",
      "2021-04-03 11:54:25.679291: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n",
      "2021-04-03 11:54:25.679494: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2021-04-03 11:54:25.680122: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
      "pciBusID: 0000:2b:00.0 name: GeForce RTX 2070 SUPER computeCapability: 7.5\n",
      "coreClock: 1.785GHz coreCount: 40 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s\n",
      "2021-04-03 11:54:25.680135: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\n",
      "2021-04-03 11:54:25.680141: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll\n",
      "2021-04-03 11:54:25.680148: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll\n",
      "2021-04-03 11:54:25.680152: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll\n",
      "2021-04-03 11:54:25.680158: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll\n",
      "2021-04-03 11:54:25.680163: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll\n",
      "2021-04-03 11:54:25.680167: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll\n",
      "2021-04-03 11:54:25.680171: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll\n",
      "2021-04-03 11:54:25.680197: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n",
      "2021-04-03 11:54:26.114383: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-04-03 11:54:26.114403: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n",
      "2021-04-03 11:54:26.114407: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n",
      "2021-04-03 11:54:26.114533: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6611 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2070 SUPER, pci bus id: 0000:2b:00.0, compute capability: 7.5)\n",
      "2021-04-03 11:54:26.114935: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2021-04-03 11:54:34.068925: I tensorflow/core/grappler/devices.cc:69] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1\n",
      "2021-04-03 11:54:34.069068: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session\n",
      "2021-04-03 11:54:34.070081: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
      "pciBusID: 0000:2b:00.0 name: GeForce RTX 2070 SUPER computeCapability: 7.5\n",
      "coreClock: 1.785GHz coreCount: 40 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s\n",
      "2021-04-03 11:54:34.070099: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\n",
      "2021-04-03 11:54:34.070106: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll\n",
      "2021-04-03 11:54:34.070112: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll\n",
      "2021-04-03 11:54:34.070119: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll\n",
      "2021-04-03 11:54:34.070123: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll\n",
      "2021-04-03 11:54:34.070130: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll\n",
      "2021-04-03 11:54:34.070134: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll\n",
      "2021-04-03 11:54:34.070141: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll\n",
      "2021-04-03 11:54:34.070164: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n",
      "2021-04-03 11:54:34.070202: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-04-03 11:54:34.070208: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n",
      "2021-04-03 11:54:34.070211: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n",
      "2021-04-03 11:54:34.070267: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6611 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2070 SUPER, pci bus id: 0000:2b:00.0, compute capability: 7.5)\n",
      "2021-04-03 11:54:34.070284: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2021-04-03 11:54:34.396918: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:928] Optimization results for grappler item: graph_to_optimize\n",
      "  function_optimizer: Graph size after: 4000 nodes (3591), 8430 edges (8014), time = 217.05ms.\n",
      "  function_optimizer: function_optimizer did nothing. time = 4.085ms.\n",
      "\n",
      "2021-04-03 11:54:37.417793: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:928] Optimization results for grappler item: graph_to_optimize\n",
      "  debug_stripper: Graph size after: 3683 nodes (0), 8201 edges (0), time = 79.922ms.\n",
      "  model_pruner: Graph size after: 3232 nodes (-451), 7750 edges (-451), time = 125.865ms.\n",
      "  constant_folding: Graph size after: 1551 nodes (-1681), 5834 edges (-1916), time = 199.089ms.\n",
      "  arithmetic_optimizer: Graph size after: 1551 nodes (0), 5834 edges (0), time = 33.234ms.\n",
      "  dependency_optimizer: Graph size after: 1453 nodes (-98), 1650 edges (-4184), time = 22.074ms.\n",
      "  model_pruner: Graph size after: 1453 nodes (0), 1650 edges (0), time = 9.534ms.\n",
      "  constant_folding: Graph size after: 1453 nodes (0), 1650 edges (0), time = 29.71ms.\n",
      "  arithmetic_optimizer: Graph size after: 1453 nodes (0), 1650 edges (0), time = 22.603ms.\n",
      "  dependency_optimizer: Graph size after: 1453 nodes (0), 1650 edges (0), time = 14.027ms.\n",
      "  debug_stripper: debug_stripper did nothing. time = 1.378ms.\n",
      "  model_pruner: Graph size after: 1453 nodes (0), 1650 edges (0), time = 7.504ms.\n",
      "  constant_folding: Graph size after: 1453 nodes (0), 1650 edges (0), time = 29.06ms.\n",
      "  arithmetic_optimizer: Graph size after: 1453 nodes (0), 1650 edges (0), time = 23.745ms.\n",
      "  dependency_optimizer: Graph size after: 1453 nodes (0), 1650 edges (0), time = 12.714ms.\n",
      "  model_pruner: Graph size after: 1453 nodes (0), 1650 edges (0), time = 8.842ms.\n",
      "  constant_folding: Graph size after: 1453 nodes (0), 1650 edges (0), time = 29.59ms.\n",
      "  arithmetic_optimizer: Graph size after: 1453 nodes (0), 1650 edges (0), time = 23.085ms.\n",
      "  dependency_optimizer: Graph size after: 1453 nodes (0), 1650 edges (0), time = 14.073ms.\n",
      "\n",
      "2021-04-03 11:54:45.020557: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:928] Optimization results for grappler item: graph_to_optimize\n",
      "  remapper: Graph size after: 1415 nodes (-114), 1308 edges (-114), time = 6.93ms.\n",
      "  constant_folding: Graph size after: 1111 nodes (-304), 1308 edges (0), time = 45.571ms.\n",
      "  arithmetic_optimizer: Graph size after: 1111 nodes (0), 1308 edges (0), time = 18.394ms.\n",
      "  dependency_optimizer: Graph size after: 1111 nodes (0), 1308 edges (0), time = 9.992ms.\n",
      "  remapper: Graph size after: 1111 nodes (0), 1308 edges (0), time = 5.143ms.\n",
      "  constant_folding: Graph size after: 1111 nodes (0), 1308 edges (0), time = 22.813ms.\n",
      "  arithmetic_optimizer: Graph size after: 1111 nodes (0), 1308 edges (0), time = 18.23ms.\n",
      "  dependency_optimizer: Graph size after: 1111 nodes (0), 1308 edges (0), time = 9.571ms.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "o8_hm-itpfDK"
   },
   "outputs": [],
   "source": [
    "# Test Code: https://github.com/nicknochnack/RealTimeSignLanguageDetectionwithTFJS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VtUw73FHpfDK"
   },
   "source": [
    "# 13. Conversion to TFLite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": 57,
   "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": 58,
   "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": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I-xWpHN8pfDL",
    "outputId": "7f6bacd8-d077-43b5-c131-5b081fba24a4",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-06-01 18:17:28.657695: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
      "2021-06-01 18:17:29.957756: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n",
      "2021-06-01 18:17:29.979976: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n",
      "2021-06-01 18:17:29.980006: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: Ubuntu-Desktop\n",
      "2021-06-01 18:17:29.980012: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: Ubuntu-Desktop\n",
      "2021-06-01 18:17:29.980090: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 465.19.1\n",
      "2021-06-01 18:17:29.980111: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 465.19.1\n",
      "2021-06-01 18:17:29.980118: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 465.19.1\n",
      "2021-06-01 18:17:29.980316: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2021-06-01 18:17:34.425821: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 4200000000 Hz\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7fdb4c6b4e80>, because it is not built.\n",
      "W0601 18:17:36.167938 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7fdb4c6b4e80>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7fdb4c662b20>, because it is not built.\n",
      "W0601 18:17:36.260370 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7fdb4c662b20>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb44557820>, because it is not built.\n",
      "W0601 18:17:36.260497 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb44557820>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb44557dc0>, because it is not built.\n",
      "W0601 18:17:36.260557 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb44557dc0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7fdb445572b0>, because it is not built.\n",
      "W0601 18:17:36.260609 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7fdb445572b0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb44557f40>, because it is not built.\n",
      "W0601 18:17:36.260663 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb44557f40>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb44557340>, because it is not built.\n",
      "W0601 18:17:36.260709 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb44557340>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7fdb44511ee0>, because it is not built.\n",
      "W0601 18:17:36.260758 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7fdb44511ee0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb44511490>, because it is not built.\n",
      "W0601 18:17:36.260803 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb44511490>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445114c0>, because it is not built.\n",
      "W0601 18:17:36.260847 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445114c0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7fdb44511730>, because it is not built.\n",
      "W0601 18:17:36.260895 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7fdb44511730>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb44511ac0>, because it is not built.\n",
      "W0601 18:17:36.260940 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb44511ac0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb4459bb80>, because it is not built.\n",
      "W0601 18:17:36.260983 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb4459bb80>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb4c60a3d0>, because it is not built.\n",
      "W0601 18:17:36.261029 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb4c60a3d0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb4450c520>, because it is not built.\n",
      "W0601 18:17:36.261072 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb4450c520>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445d5040>, because it is not built.\n",
      "W0601 18:17:36.261117 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445d5040>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445d5190>, because it is not built.\n",
      "W0601 18:17:36.261161 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445d5190>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445d5dc0>, because it is not built.\n",
      "W0601 18:17:36.261204 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445d5dc0>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445d5df0>, because it is not built.\n",
      "W0601 18:17:36.261249 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445d5df0>, because it is not built.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445d5f70>, because it is not built.\r\n",
      "W0601 18:17:36.261293 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445d5f70>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445d5820>, because it is not built.\r\n",
      "W0601 18:17:36.333583 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445d5820>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb4c60a400>, because it is not built.\r\n",
      "W0601 18:17:36.333684 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb4c60a400>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb447cbc70>, because it is not built.\r\n",
      "W0601 18:17:36.333776 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb447cbc70>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445a3b20>, because it is not built.\r\n",
      "W0601 18:17:36.333862 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445a3b20>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445a3430>, because it is not built.\r\n",
      "W0601 18:17:36.333944 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445a3430>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445a3b80>, because it is not built.\r\n",
      "W0601 18:17:36.334026 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445a3b80>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445a3d90>, because it is not built.\r\n",
      "W0601 18:17:36.334106 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445a3d90>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445a31c0>, because it is not built.\r\n",
      "W0601 18:17:36.334187 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445a31c0>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445a30d0>, because it is not built.\r\n",
      "W0601 18:17:36.334266 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445a30d0>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb4c60a430>, because it is not built.\r\n",
      "W0601 18:17:36.334344 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb4c60a430>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb444f0a30>, because it is not built.\r\n",
      "W0601 18:17:36.334423 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb444f0a30>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb444f28e0>, because it is not built.\r\n",
      "W0601 18:17:36.334504 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb444f28e0>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb444f27f0>, because it is not built.\r\n",
      "W0601 18:17:36.334585 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb444f27f0>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb444f2be0>, because it is not built.\r\n",
      "W0601 18:17:36.334666 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb444f2be0>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb444f2dc0>, because it is not built.\r\n",
      "W0601 18:17:36.334742 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb444f2dc0>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445e7790>, because it is not built.\r\n",
      "W0601 18:17:36.334795 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445e7790>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445e7ac0>, because it is not built.\r\n",
      "W0601 18:17:36.334841 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445e7ac0>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445055b0>, because it is not built.\r\n",
      "W0601 18:17:36.334885 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445055b0>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb44505f10>, because it is not built.\r\n",
      "W0601 18:17:36.334929 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb44505f10>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445cd280>, because it is not built.\r\n",
      "W0601 18:17:36.334971 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445cd280>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445cd4c0>, because it is not built.\r\n",
      "W0601 18:17:36.335011 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445cd4c0>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445cd700>, because it is not built.\r\n",
      "W0601 18:17:36.335053 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445cd700>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445cd220>, because it is not built.\r\n",
      "W0601 18:17:36.335093 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445cd220>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445addf0>, because it is not built.\r\n",
      "W0601 18:17:36.335134 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7fdb445addf0>, because it is not built.\r\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445ad280>, because it is not built.\r\n",
      "W0601 18:17:36.335177 140581854517056 save_impl.py:76] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7fdb445ad280>, because it is not built.\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-06-01 18:17:42.762468: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n",
      "W0601 18:17:53.700566 140581854517056 save.py:238] Found untraced functions such as WeightSharedConvolutionalBoxPredictor_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxPredictor_layer_call_fn, WeightSharedConvolutionalBoxHead_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxHead_layer_call_fn, WeightSharedConvolutionalBoxPredictor_layer_call_fn while saving (showing 5 of 260). These functions will not be directly callable after loading.\n",
      "/home/ffubuntu/Desktop/TFODCourse/tfod/lib/python3.8/site-packages/tensorflow/python/keras/utils/generic_utils.py:494: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  warnings.warn('Custom mask layers require a config and must override '\n",
      "WARNING:tensorflow:FOR KERAS USERS: The object that you are saving contains one or more Keras models or layers. If you are loading the SavedModel with `tf.keras.models.load_model`, continue reading (otherwise, you may ignore the following instructions). Please change your code to save with `tf.keras.models.save_model` or `model.save`, and confirm that the file \"keras.metadata\" exists in the export directory. In the future, Keras will only load the SavedModels that have this file. In other words, `tf.saved_model.save` will no longer write SavedModels that can be recovered as Keras models (this will apply in TF 2.5).\n",
      "\n",
      "FOR DEVS: If you are overwriting _tracking_metadata in your class, this property has been used to save metadata in the SavedModel. The metadta field will be deprecated soon, so please move the metadata to a different file.\n",
      "W0601 18:17:56.813444 140581854517056 save.py:1239] FOR KERAS USERS: The object that you are saving contains one or more Keras models or layers. If you are loading the SavedModel with `tf.keras.models.load_model`, continue reading (otherwise, you may ignore the following instructions). Please change your code to save with `tf.keras.models.save_model` or `model.save`, and confirm that the file \"keras.metadata\" exists in the export directory. In the future, Keras will only load the SavedModels that have this file. In other words, `tf.saved_model.save` will no longer write SavedModels that can be recovered as Keras models (this will apply in TF 2.5).\n",
      "\n",
      "FOR DEVS: If you are overwriting _tracking_metadata in your class, this property has been used to save metadata in the SavedModel. The metadta field will be deprecated soon, so please move the metadata to a different file.\n",
      "INFO:tensorflow:Assets written to: Tensorflow/workspace/models/my_ssd_mobnet/tfliteexport/saved_model/assets\n",
      "I0601 18:17:57.035759 140581854517056 builder_impl.py:774] Assets written to: Tensorflow/workspace/models/my_ssd_mobnet/tfliteexport/saved_model/assets\n"
     ]
    }
   ],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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": 63,
   "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": 64,
   "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": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nbd7gqHMpfDL",
    "outputId": "7c8fe6d5-2415-4641-8548-39d425c202f7",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-06-01 18:18:17.035343: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
      "2021-06-01 18:18:18.355116: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n",
      "2021-06-01 18:18:18.379677: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n",
      "2021-06-01 18:18:18.379702: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: Ubuntu-Desktop\n",
      "2021-06-01 18:18:18.379708: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: Ubuntu-Desktop\n",
      "2021-06-01 18:18:18.379766: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 465.19.1\n",
      "2021-06-01 18:18:18.379791: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 465.19.1\n",
      "2021-06-01 18:18:18.379796: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 465.19.1\n",
      "2021-06-01 18:18:18.379991: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2021-06-01 18:18:25.886397: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:345] Ignored output_format.\n",
      "2021-06-01 18:18:25.886424: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:348] Ignored drop_control_dependency.\n",
      "2021-06-01 18:18:25.886432: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:354] Ignored change_concat_input_ranges.\n",
      "2021-06-01 18:18:25.887396: I tensorflow/cc/saved_model/reader.cc:38] Reading SavedModel from: Tensorflow/workspace/models/my_ssd_mobnet/tfliteexport/saved_model\n",
      "2021-06-01 18:18:25.960212: I tensorflow/cc/saved_model/reader.cc:90] Reading meta graph with tags { serve }\n",
      "2021-06-01 18:18:25.960247: I tensorflow/cc/saved_model/reader.cc:132] Reading SavedModel debug info (if present) from: Tensorflow/workspace/models/my_ssd_mobnet/tfliteexport/saved_model\n",
      "2021-06-01 18:18:26.225972: I tensorflow/cc/saved_model/loader.cc:206] Restoring SavedModel bundle.\n",
      "2021-06-01 18:18:26.281791: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 4200000000 Hz\n",
      "2021-06-01 18:18:26.836994: I tensorflow/cc/saved_model/loader.cc:190] Running initialization op on SavedModel bundle at path: Tensorflow/workspace/models/my_ssd_mobnet/tfliteexport/saved_model\n",
      "2021-06-01 18:18:27.067823: I tensorflow/cc/saved_model/loader.cc:277] SavedModel load for tags { serve }; Status: success: OK. Took 1180429 microseconds.\n",
      "2021-06-01 18:18:27.889358: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:210] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n"
     ]
    }
   ],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5NQqZRdA21Uc"
   },
   "source": [
    "# 14. Zip and Export Models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "tTVTGCQp2ZJJ"
   },
   "outputs": [],
   "source": [
    "!tar -czf models.tar.gz {paths['CHECKPOINT_PATH']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}