Training & Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QUANWN3rpfC9"
   },
   "source": [
    "# Setup Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "146BB11JpfDA"
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "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": 5,
   "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": 6,
   "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wget in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (3.2)\n"
     ]
    }
   ],
   "source": [
    "if os.name=='nt':\n",
    "    !pip install wget\n",
    "    import wget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "iA1DIq5OpfDE"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cloning into 'Tensorflow\\models'...\n",
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     ]
    }
   ],
   "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": [
      "100% [..........................................................................] 1468733 / 1468733        1 file(s) moved.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "zip_safe flag not set; analyzing archive contents...\n",
      "object_detection.core.__pycache__.densepose_ops.cpython-37: module references __file__\n",
      "object_detection.core.__pycache__.preprocessor.cpython-37: module MAY be using inspect.stack\n",
      "object_detection.utils.__pycache__.autoaugment_utils.cpython-37: module MAY be using inspect.stack\n",
      "c:\\windows\\temp\\easy_install-qkum3x8e\\pycocotools-2.0.2\\.eggs\\cython-0.29.23-py3.7-win-amd64.egg\\Cython\\Compiler\\Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: C:\\WINDOWS\\Temp\\easy_install-qkum3x8e\\pycocotools-2.0.2\\pycocotools\\_mask.pyx\n",
      "  tree = Parsing.p_module(s, pxd, full_module_name)\n",
      "zip_safe flag not set; analyzing archive contents...\n",
      "pycocotools.__pycache__._mask.cpython-37: module references __file__\n",
      "error: [WinError 5] Access is denied: 'C:\\\\WINDOWS\\\\Temp\\\\easy_install-qkum3x8e\\\\pycocotools-2.0.2\\\\.eggs\\\\Cython-0.29.23-py3.7-win-amd64.egg\\\\Cython\\\\Compiler\\\\FlowControl.cp37-win_amd64.pyd'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        1 file(s) copied.\n",
      "running build\n",
      "running build_py\n",
      "creating build\n",
      "creating build\\lib\n",
      "creating build\\lib\\object_detection\n",
      "copying object_detection\\eval_util.py -> build\\lib\\object_detection\n",
      "copying object_detection\\eval_util_test.py -> build\\lib\\object_detection\n",
      "copying object_detection\\exporter.py -> build\\lib\\object_detection\n",
      "copying object_detection\\exporter_lib_tf2_test.py -> build\\lib\\object_detection\n",
      "copying object_detection\\exporter_lib_v2.py -> build\\lib\\object_detection\n",
      "copying object_detection\\exporter_main_v2.py -> build\\lib\\object_detection\n",
      "copying object_detection\\exporter_tf1_test.py -> build\\lib\\object_detection\n",
      "copying object_detection\\export_inference_graph.py -> build\\lib\\object_detection\n",
      "copying object_detection\\export_tflite_graph_lib_tf2.py -> build\\lib\\object_detection\n",
      "copying object_detection\\export_tflite_graph_lib_tf2_test.py -> build\\lib\\object_detection\n",
      "copying object_detection\\export_tflite_graph_tf2.py -> build\\lib\\object_detection\n",
      "copying object_detection\\export_tflite_ssd_graph.py -> build\\lib\\object_detection\n",
      "copying object_detection\\export_tflite_ssd_graph_lib.py -> build\\lib\\object_detection\n",
      "copying object_detection\\export_tflite_ssd_graph_lib_tf1_test.py -> build\\lib\\object_detection\n",
      "copying object_detection\\inputs.py -> build\\lib\\object_detection\n",
      "copying object_detection\\inputs_test.py -> build\\lib\\object_detection\n",
      "copying object_detection\\model_hparams.py -> build\\lib\\object_detection\n",
      "copying object_detection\\model_lib.py -> build\\lib\\object_detection\n",
      "copying object_detection\\model_lib_tf1_test.py -> build\\lib\\object_detection\n",
      "copying object_detection\\model_lib_tf2_test.py -> build\\lib\\object_detection\n",
      "copying object_detection\\model_lib_v2.py -> build\\lib\\object_detection\n",
      "copying object_detection\\model_main.py -> build\\lib\\object_detection\n",
      "copying object_detection\\model_main_tf2.py -> build\\lib\\object_detection\n",
      "copying object_detection\\model_tpu_main.py -> build\\lib\\object_detection\n",
      "copying object_detection\\__init__.py -> build\\lib\\object_detection\n",
      "creating build\\lib\\object_detection\\anchor_generators\n",
      "copying object_detection\\anchor_generators\\flexible_grid_anchor_generator.py -> build\\lib\\object_detection\\anchor_generators\n",
      "copying object_detection\\anchor_generators\\flexible_grid_anchor_generator_test.py -> build\\lib\\object_detection\\anchor_generators\n",
      "copying object_detection\\anchor_generators\\grid_anchor_generator.py -> build\\lib\\object_detection\\anchor_generators\n",
      "copying object_detection\\anchor_generators\\grid_anchor_generator_test.py -> build\\lib\\object_detection\\anchor_generators\n",
      "copying object_detection\\anchor_generators\\multiple_grid_anchor_generator.py -> build\\lib\\object_detection\\anchor_generators\n",
      "copying object_detection\\anchor_generators\\multiple_grid_anchor_generator_test.py -> build\\lib\\object_detection\\anchor_generators\n",
      "copying object_detection\\anchor_generators\\multiscale_grid_anchor_generator.py -> build\\lib\\object_detection\\anchor_generators\n",
      "copying object_detection\\anchor_generators\\multiscale_grid_anchor_generator_test.py -> build\\lib\\object_detection\\anchor_generators\n",
      "copying object_detection\\anchor_generators\\__init__.py -> build\\lib\\object_detection\\anchor_generators\n",
      "creating build\\lib\\object_detection\\box_coders\n",
      "copying object_detection\\box_coders\\faster_rcnn_box_coder.py -> build\\lib\\object_detection\\box_coders\n",
      "copying object_detection\\box_coders\\faster_rcnn_box_coder_test.py -> build\\lib\\object_detection\\box_coders\n",
      "copying object_detection\\box_coders\\keypoint_box_coder.py -> build\\lib\\object_detection\\box_coders\n",
      "copying object_detection\\box_coders\\keypoint_box_coder_test.py -> build\\lib\\object_detection\\box_coders\n",
      "copying object_detection\\box_coders\\mean_stddev_box_coder.py -> build\\lib\\object_detection\\box_coders\n",
      "copying object_detection\\box_coders\\mean_stddev_box_coder_test.py -> build\\lib\\object_detection\\box_coders\n",
      "copying object_detection\\box_coders\\square_box_coder.py -> build\\lib\\object_detection\\box_coders\n",
      "copying object_detection\\box_coders\\square_box_coder_test.py -> build\\lib\\object_detection\\box_coders\n",
      "copying object_detection\\box_coders\\__init__.py -> build\\lib\\object_detection\\box_coders\n",
      "creating build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\anchor_generator_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\anchor_generator_builder_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\box_coder_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\box_coder_builder_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\box_predictor_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\box_predictor_builder_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\calibration_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\calibration_builder_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\dataset_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\dataset_builder_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\decoder_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\decoder_builder_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\graph_rewriter_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\graph_rewriter_builder_tf1_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\hyperparams_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\hyperparams_builder_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\image_resizer_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\image_resizer_builder_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\input_reader_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\input_reader_builder_tf1_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\losses_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\losses_builder_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\matcher_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\matcher_builder_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\model_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\model_builder_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\model_builder_tf1_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\model_builder_tf2_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\optimizer_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\optimizer_builder_tf1_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\optimizer_builder_tf2_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\post_processing_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\post_processing_builder_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\preprocessor_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\preprocessor_builder_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\region_similarity_calculator_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\region_similarity_calculator_builder_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\target_assigner_builder.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\target_assigner_builder_test.py -> build\\lib\\object_detection\\builders\n",
      "copying object_detection\\builders\\__init__.py -> build\\lib\\object_detection\\builders\n",
      "creating build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\anchor_generator.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\balanced_positive_negative_sampler.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\balanced_positive_negative_sampler_test.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\batcher.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\batcher_tf1_test.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\batch_multiclass_nms_test.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\box_coder.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\box_coder_test.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\box_list.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\box_list_ops.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\box_list_ops_test.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\box_list_test.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\box_predictor.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\class_agnostic_nms_test.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\data_decoder.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\data_parser.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\densepose_ops.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\densepose_ops_test.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\freezable_batch_norm.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\freezable_batch_norm_tf2_test.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\freezable_sync_batch_norm.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\keypoint_ops.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\keypoint_ops_test.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\losses.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\losses_test.py -> build\\lib\\object_detection\\core\n",
      "copying object_detection\\core\\matcher.py -> build\\lib\\object_detection\\core\n",
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      "copying object_detection\\core\\__init__.py -> build\\lib\\object_detection\\core\n",
      "creating build\\lib\\object_detection\\dataset_tools\n",
      "copying object_detection\\dataset_tools\\create_ava_actions_tf_record.py -> build\\lib\\object_detection\\dataset_tools\n",
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      "copying object_detection\\dataset_tools\\create_oid_tf_record.py -> build\\lib\\object_detection\\dataset_tools\n",
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      "creating build\\lib\\object_detection\\data_decoders\n",
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      "creating build\\lib\\object_detection\\inference\n",
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      "creating build\\lib\\object_detection\\legacy\n",
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      "creating build\\lib\\object_detection\\matchers\n",
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      "creating build\\lib\\object_detection\\meta_architectures\n",
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      "creating build\\lib\\object_detection\\metrics\n",
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      "creating build\\lib\\object_detection\\models\n",
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      "creating build\\lib\\object_detection\\protos\n",
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      "copying object_detection\\protos\\bipartite_matcher_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\box_coder_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\box_predictor_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\calibration_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\center_net_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\eval_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\faster_rcnn_box_coder_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\faster_rcnn_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\flexible_grid_anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\fpn_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\graph_rewriter_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\grid_anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\hyperparams_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\image_resizer_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\input_reader_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\keypoint_box_coder_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\losses_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\matcher_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\mean_stddev_box_coder_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\model_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\multiscale_anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\optimizer_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\pipeline_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\post_processing_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\preprocessor_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\region_similarity_calculator_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\square_box_coder_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\ssd_anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\ssd_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\string_int_label_map_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\target_assigner_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\train_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\__init__.py -> build\\lib\\object_detection\\protos\n",
      "creating build\\lib\\object_detection\\tpu_exporters\n",
      "copying object_detection\\tpu_exporters\\export_saved_model_tpu.py -> build\\lib\\object_detection\\tpu_exporters\n",
      "copying object_detection\\tpu_exporters\\export_saved_model_tpu_lib.py -> build\\lib\\object_detection\\tpu_exporters\n",
      "copying object_detection\\tpu_exporters\\export_saved_model_tpu_lib_tf1_test.py -> build\\lib\\object_detection\\tpu_exporters\n",
      "copying object_detection\\tpu_exporters\\faster_rcnn.py -> build\\lib\\object_detection\\tpu_exporters\n",
      "copying object_detection\\tpu_exporters\\ssd.py -> build\\lib\\object_detection\\tpu_exporters\n",
      "copying object_detection\\tpu_exporters\\utils.py -> build\\lib\\object_detection\\tpu_exporters\n",
      "copying object_detection\\tpu_exporters\\utils_test.py -> build\\lib\\object_detection\\tpu_exporters\n",
      "copying object_detection\\tpu_exporters\\__init__.py -> build\\lib\\object_detection\\tpu_exporters\n",
      "creating build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\autoaugment_utils.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\bifpn_utils.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\category_util.py -> build\\lib\\object_detection\\utils\n",
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      "copying object_detection\\utils\\model_util.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\model_util_tf2_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\np_box_list.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\np_box_list_ops.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\np_box_list_ops_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\np_box_list_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\np_box_mask_list.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\np_box_mask_list_ops.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\np_box_mask_list_ops_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\np_box_mask_list_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\np_box_ops.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\np_box_ops_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\np_mask_ops.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\np_mask_ops_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\object_detection_evaluation.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\object_detection_evaluation_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\ops.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\ops_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\patch_ops.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\patch_ops_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\per_image_evaluation.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\per_image_evaluation_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\per_image_vrd_evaluation.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\per_image_vrd_evaluation_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\shape_utils.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\shape_utils_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\spatial_transform_ops.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\spatial_transform_ops_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\static_shape.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\static_shape_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\target_assigner_utils.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\target_assigner_utils_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\test_case.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\test_case_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\test_utils.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\test_utils_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\tf_version.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\variables_helper.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\variables_helper_tf1_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\visualization_utils.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\visualization_utils_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\vrd_evaluation.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\vrd_evaluation_test.py -> build\\lib\\object_detection\\utils\n",
      "copying object_detection\\utils\\__init__.py -> build\\lib\\object_detection\\utils\n",
      "creating build\\lib\\object_detection\\dataset_tools\\context_rcnn\n",
      "copying object_detection\\dataset_tools\\context_rcnn\\add_context_to_examples.py -> build\\lib\\object_detection\\dataset_tools\\context_rcnn\n",
      "copying object_detection\\dataset_tools\\context_rcnn\\add_context_to_examples_tf2_test.py -> build\\lib\\object_detection\\dataset_tools\\context_rcnn\n",
      "copying object_detection\\dataset_tools\\context_rcnn\\create_cococameratraps_tfexample_main.py -> build\\lib\\object_detection\\dataset_tools\\context_rcnn\n",
      "copying object_detection\\dataset_tools\\context_rcnn\\create_cococameratraps_tfexample_tf2_test.py -> build\\lib\\object_detection\\dataset_tools\\context_rcnn\n",
      "copying object_detection\\dataset_tools\\context_rcnn\\generate_detection_data.py -> build\\lib\\object_detection\\dataset_tools\\context_rcnn\n",
      "copying object_detection\\dataset_tools\\context_rcnn\\generate_detection_data_tf2_test.py -> build\\lib\\object_detection\\dataset_tools\\context_rcnn\n",
      "copying object_detection\\dataset_tools\\context_rcnn\\generate_embedding_data.py -> build\\lib\\object_detection\\dataset_tools\\context_rcnn\n",
      "copying object_detection\\dataset_tools\\context_rcnn\\generate_embedding_data_tf2_test.py -> build\\lib\\object_detection\\dataset_tools\\context_rcnn\n",
      "copying object_detection\\dataset_tools\\context_rcnn\\__init__.py -> build\\lib\\object_detection\\dataset_tools\\context_rcnn\n",
      "creating build\\lib\\object_detection\\models\\keras_models\n",
      "copying object_detection\\models\\keras_models\\convert_keras_models.py -> build\\lib\\object_detection\\models\\keras_models\n",
      "copying object_detection\\models\\keras_models\\hourglass_network.py -> build\\lib\\object_detection\\models\\keras_models\n",
      "copying object_detection\\models\\keras_models\\hourglass_network_tf2_test.py -> build\\lib\\object_detection\\models\\keras_models\n",
      "copying object_detection\\models\\keras_models\\inception_resnet_v2.py -> build\\lib\\object_detection\\models\\keras_models\n",
      "copying object_detection\\models\\keras_models\\inception_resnet_v2_tf2_test.py -> build\\lib\\object_detection\\models\\keras_models\n",
      "copying object_detection\\models\\keras_models\\mobilenet_v1.py -> build\\lib\\object_detection\\models\\keras_models\n",
      "copying object_detection\\models\\keras_models\\mobilenet_v1_tf2_test.py -> build\\lib\\object_detection\\models\\keras_models\n",
      "copying object_detection\\models\\keras_models\\mobilenet_v2.py -> build\\lib\\object_detection\\models\\keras_models\n",
      "copying object_detection\\models\\keras_models\\mobilenet_v2_tf2_test.py -> build\\lib\\object_detection\\models\\keras_models\n",
      "copying object_detection\\models\\keras_models\\model_utils.py -> build\\lib\\object_detection\\models\\keras_models\n",
      "copying object_detection\\models\\keras_models\\resnet_v1.py -> build\\lib\\object_detection\\models\\keras_models\n",
      "copying object_detection\\models\\keras_models\\resnet_v1_tf2_test.py -> build\\lib\\object_detection\\models\\keras_models\n",
      "copying object_detection\\models\\keras_models\\test_utils.py -> build\\lib\\object_detection\\models\\keras_models\n",
      "copying object_detection\\models\\keras_models\\__init__.py -> build\\lib\\object_detection\\models\\keras_models\n",
      "creating build\\lib\\object_detection\\predictors\\heads\n",
      "copying object_detection\\predictors\\heads\\box_head.py -> build\\lib\\object_detection\\predictors\\heads\n",
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      "copying object_detection\\predictors\\heads\\keras_box_head.py -> build\\lib\\object_detection\\predictors\\heads\n",
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      "creating build\\lib\\object_detection\\tpu_exporters\\testdata\n",
      "copying object_detection\\tpu_exporters\\testdata\\__init__.py -> build\\lib\\object_detection\\tpu_exporters\\testdata\n",
      "creating build\\lib\\datasets\n",
      "copying slim\\datasets\\build_imagenet_data.py -> build\\lib\\datasets\n",
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      "creating build\\lib\\deployment\n",
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      "creating build\\lib\\nets\n",
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      "copying slim\\nets\\__init__.py -> build\\lib\\nets\n",
      "creating build\\lib\\preprocessing\n",
      "copying slim\\preprocessing\\cifarnet_preprocessing.py -> build\\lib\\preprocessing\n",
      "copying slim\\preprocessing\\inception_preprocessing.py -> build\\lib\\preprocessing\n",
      "copying slim\\preprocessing\\lenet_preprocessing.py -> build\\lib\\preprocessing\n",
      "copying slim\\preprocessing\\preprocessing_factory.py -> build\\lib\\preprocessing\n",
      "copying slim\\preprocessing\\vgg_preprocessing.py -> build\\lib\\preprocessing\n",
      "copying slim\\preprocessing\\__init__.py -> build\\lib\\preprocessing\n",
      "creating build\\lib\\nets\\mobilenet\n",
      "copying slim\\nets\\mobilenet\\conv_blocks.py -> build\\lib\\nets\\mobilenet\n",
      "copying slim\\nets\\mobilenet\\mobilenet.py -> build\\lib\\nets\\mobilenet\n",
      "copying slim\\nets\\mobilenet\\mobilenet_v2.py -> build\\lib\\nets\\mobilenet\n",
      "copying slim\\nets\\mobilenet\\mobilenet_v2_test.py -> build\\lib\\nets\\mobilenet\n",
      "copying slim\\nets\\mobilenet\\mobilenet_v3.py -> build\\lib\\nets\\mobilenet\n",
      "copying slim\\nets\\mobilenet\\mobilenet_v3_test.py -> build\\lib\\nets\\mobilenet\n",
      "copying slim\\nets\\mobilenet\\__init__.py -> build\\lib\\nets\\mobilenet\n",
      "creating build\\lib\\nets\\nasnet\n",
      "copying slim\\nets\\nasnet\\nasnet.py -> build\\lib\\nets\\nasnet\n",
      "copying slim\\nets\\nasnet\\nasnet_test.py -> build\\lib\\nets\\nasnet\n",
      "copying slim\\nets\\nasnet\\nasnet_utils.py -> build\\lib\\nets\\nasnet\n",
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      "copying slim\\nets\\nasnet\\pnasnet.py -> build\\lib\\nets\\nasnet\n",
      "copying slim\\nets\\nasnet\\pnasnet_test.py -> build\\lib\\nets\\nasnet\n",
      "copying slim\\nets\\nasnet\\__init__.py -> build\\lib\\nets\\nasnet\n",
      "running egg_info\n",
      "creating object_detection.egg-info\n",
      "writing object_detection.egg-info\\PKG-INFO\n",
      "writing dependency_links to object_detection.egg-info\\dependency_links.txt\n",
      "writing requirements to object_detection.egg-info\\requires.txt\n",
      "writing top-level names to object_detection.egg-info\\top_level.txt\n",
      "writing manifest file 'object_detection.egg-info\\SOURCES.txt'\n",
      "reading manifest file 'object_detection.egg-info\\SOURCES.txt'\n",
      "writing manifest file 'object_detection.egg-info\\SOURCES.txt'\n",
      "running install\n",
      "running bdist_egg\n",
      "running egg_info\n",
      "writing object_detection.egg-info\\PKG-INFO\n",
      "writing dependency_links to object_detection.egg-info\\dependency_links.txt\n",
      "writing requirements to object_detection.egg-info\\requires.txt\n",
      "writing top-level names to object_detection.egg-info\\top_level.txt\n",
      "reading manifest file 'object_detection.egg-info\\SOURCES.txt'\n",
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      "Extracting object_detection-0.1-py3.7.egg to c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages\n",
      "Adding object-detection 0.1 to easy-install.pth file\n",
      "\n",
      "Installed c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages\\object_detection-0.1-py3.7.egg\n",
      "Processing dependencies for object-detection==0.1\n",
      "Searching for tf-models-official\n",
      "Reading https://pypi.org/simple/tf-models-official/\n",
      "Downloading https://files.pythonhosted.org/packages/57/4a/23a08f8fd2747867ee223612e219eeb0d11c36116601d99b55ef3c72e707/tf_models_official-2.4.0-py2.py3-none-any.whl#sha256=e3d60e0feab1d4f759223cb7ffc2f2c44d0a16925c12c2db317ae2b4e0727cd8\n",
      "Best match: tf-models-official 2.4.0\n",
      "Processing tf_models_official-2.4.0-py2.py3-none-any.whl\n",
      "Installing tf_models_official-2.4.0-py2.py3-none-any.whl to c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages\n",
      "Adding tf-models-official 2.4.0 to easy-install.pth file\n",
      "\n",
      "Installed c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages\\tf_models_official-2.4.0-py3.7.egg\n",
      "Searching for pandas\n",
      "Reading https://pypi.org/simple/pandas/\n",
      "Downloading https://files.pythonhosted.org/packages/74/8c/9cf2e5304f4466dbc759a799b97bfd75cd3dc93b00d49558ca93bfc29173/pandas-1.2.4-cp37-cp37m-win_amd64.whl#sha256=2111c25e69fa9365ba80bbf4f959400054b2771ac5d041ed19415a8b488dc70a\n",
      "Best match: pandas 1.2.4\n",
      "Processing pandas-1.2.4-cp37-cp37m-win_amd64.whl\n",
      "Installing pandas-1.2.4-cp37-cp37m-win_amd64.whl to c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages\n",
      "Adding pandas 1.2.4 to easy-install.pth file\n",
      "\n",
      "Installed c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages\\pandas-1.2.4-py3.7-win-amd64.egg\n",
      "Searching for scipy\n",
      "Reading https://pypi.org/simple/scipy/\n",
      "Downloading https://files.pythonhosted.org/packages/e1/8b/d05bd3bcd0057954f08f61472db95f4ac71c3f0bf5432abe651694025396/scipy-1.6.3-cp37-cp37m-win_amd64.whl#sha256=10dbcc7de03b8d635a1031cb18fd3eaa997969b64fdf78f99f19ac163a825445\n",
      "Best match: scipy 1.6.3\n",
      "Processing scipy-1.6.3-cp37-cp37m-win_amd64.whl\n",
      "Installing scipy-1.6.3-cp37-cp37m-win_amd64.whl to c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages\n",
      "Adding scipy 1.6.3 to easy-install.pth file\n",
      "\n",
      "Installed c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages\\scipy-1.6.3-py3.7-win-amd64.egg\n",
      "Searching for lvis\n",
      "Reading https://pypi.org/simple/lvis/\n",
      "Downloading https://files.pythonhosted.org/packages/72/b6/1992240ab48310b5360bfdd1d53163f43bb97d90dc5dc723c67d41c38e78/lvis-0.5.3-py3-none-any.whl#sha256=4f07153330df342b3161fafb46641ce7c02864113a8ddf0d6ffab6b02407bef0\n",
      "Best match: lvis 0.5.3\n",
      "Processing lvis-0.5.3-py3-none-any.whl\n",
      "Installing lvis-0.5.3-py3-none-any.whl to c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages\n",
      "Adding lvis 0.5.3 to easy-install.pth file\n",
      "\n",
      "Installed c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages\\lvis-0.5.3-py3.7.egg\n",
      "Searching for pycocotools\n",
      "Reading https://pypi.org/simple/pycocotools/\n",
      "Downloading https://files.pythonhosted.org/packages/de/df/056875d697c45182ed6d2ae21f62015896fdb841906fe48e7268e791c467/pycocotools-2.0.2.tar.gz#sha256=24717a12799b4471c2e54aa210d642e6cd4028826a1d49fcc2b0e3497e041f1a\n",
      "Best match: pycocotools 2.0.2\n",
      "Processing pycocotools-2.0.2.tar.gz\n",
      "Writing C:\\WINDOWS\\Temp\\easy_install-qkum3x8e\\pycocotools-2.0.2\\setup.cfg\n",
      "Running pycocotools-2.0.2\\setup.py -q bdist_egg --dist-dir C:\\WINDOWS\\Temp\\easy_install-qkum3x8e\\pycocotools-2.0.2\\egg-dist-tmp-krr4sx82\n",
      "maskApi.c\n",
      "_mask.c\n",
      "c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages\\numpy\\core\\include\\numpy\\npy_1_7_deprecated_api.h(14) : Warning Msg: Using deprecated NumPy API, disable it with #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\n",
      "pycocotools\\_mask.c(3293): warning C4244: '=': conversion from 'Py_ssize_t' to 'siz', possible loss of data\n",
      "pycocotools\\_mask.c(3703): warning C4244: 'function': conversion from 'npy_intp' to 'siz', possible loss of data\n",
      "pycocotools\\_mask.c(6710): warning C4244: '=': conversion from 'npy_intp' to 'siz', possible loss of data\n",
      "pycocotools\\_mask.c(7468): warning C4244: '=': conversion from 'Py_ssize_t' to 'siz', possible loss of data\n",
      "   Creating library build\\temp.win-amd64-3.7\\Release\\./common\\_mask.cp37-win_amd64.lib and object build\\temp.win-amd64-3.7\\Release\\./common\\_mask.cp37-win_amd64.exp\n",
      "Generating code\n",
      "Finished generating code\n",
      "creating c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages\\pycocotools-2.0.2-py3.7-win-amd64.egg\n",
      "Extracting pycocotools-2.0.2-py3.7-win-amd64.egg to c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages\n",
      "Adding pycocotools 2.0.2 to easy-install.pth file\n",
      "\n",
      "Installed c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages\\pycocotools-2.0.2-py3.7-win-amd64.egg\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining file:///C:/Users/ralha/OneDrive/Desktop/IIITB/TFOD/TFODCourse/Tensorflow/models/research/slim\n",
      "Requirement already satisfied: six in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from slim==0.1) (1.15.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "object-detection 0.1 requires apache-beam, which is not installed.\n",
      "object-detection 0.1 requires avro-python3, which is not installed.\n",
      "object-detection 0.1 requires contextlib2, which is not installed.\n",
      "object-detection 0.1 requires Cython, which is not installed.\n",
      "object-detection 0.1 requires matplotlib, which is not installed.\n",
      "object-detection 0.1 requires pillow, which is not installed.\n",
      "tf-models-official 2.4.0 requires Cython, which is not installed.\n",
      "tf-models-official 2.4.0 requires dataclasses, which is not installed.\n",
      "tf-models-official 2.4.0 requires gin-config, which is not installed.\n",
      "tf-models-official 2.4.0 requires google-api-python-client>=1.6.7, which is not installed.\n",
      "tf-models-official 2.4.0 requires google-cloud-bigquery>=0.31.0, which is not installed.\n",
      "tf-models-official 2.4.0 requires kaggle>=1.3.9, which is not installed.\n",
      "tf-models-official 2.4.0 requires matplotlib, which is not installed.\n",
      "tf-models-official 2.4.0 requires oauth2client, which is not installed.\n",
      "tf-models-official 2.4.0 requires opencv-python-headless, which is not installed.\n",
      "tf-models-official 2.4.0 requires Pillow, which is not installed.\n",
      "tf-models-official 2.4.0 requires psutil>=5.4.3, which is not installed.\n",
      "tf-models-official 2.4.0 requires py-cpuinfo>=3.3.0, which is not installed."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tf-slim>=1.1\n",
      "  Downloading tf_slim-1.1.0-py2.py3-none-any.whl (352 kB)\n",
      "Collecting absl-py>=0.2.2\n",
      "  Using cached absl_py-0.12.0-py3-none-any.whl (129 kB)\n",
      "Installing collected packages: absl-py, tf-slim, slim\n",
      "  Running setup.py develop for slim\n",
      "Successfully installed absl-py-0.12.0 slim-0.1 tf-slim-1.1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "tf-models-official 2.4.0 requires pyyaml>=5.1, which is not installed.\n",
      "tf-models-official 2.4.0 requires sentencepiece, which is not installed.\n",
      "tf-models-official 2.4.0 requires seqeval, which is not installed.\n",
      "tf-models-official 2.4.0 requires tensorflow>=2.4.0, which is not installed.\n",
      "tf-models-official 2.4.0 requires tensorflow-addons, which is not installed.\n",
      "tf-models-official 2.4.0 requires tensorflow-datasets, which is not installed.\n",
      "tf-models-official 2.4.0 requires tensorflow-hub>=0.6.0, which is not installed.\n",
      "tf-models-official 2.4.0 requires tensorflow-model-optimization>=0.4.1, which is not installed.\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": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-04-30 01:25:27.930119: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\n",
      "2021-04-30 01:25:27.930149: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "Running tests under Python 3.7.7: C:\\Users\\ralha\\OneDrive\\Desktop\\IIITB\\TFOD\\TFODCourse\\env\\Scripts\\python.exe\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model\n",
      "2021-04-30 01:25:30.896205: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2021-04-30 01:25:30.898515: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll\n",
      "2021-04-30 01:25:31.993992: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1050 Ti computeCapability: 6.1\n",
      "coreClock: 1.62GHz coreCount: 6 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 104.43GiB/s\n",
      "2021-04-30 01:25:31.996043: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\n",
      "2021-04-30 01:25:31.997910: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublas64_11.dll'; dlerror: cublas64_11.dll not found\n",
      "2021-04-30 01:25:31.999545: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublasLt64_11.dll'; dlerror: cublasLt64_11.dll not found\n",
      "2021-04-30 01:25:32.003971: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll\n",
      "2021-04-30 01:25:32.005702: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll\n",
      "2021-04-30 01:25:32.010403: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll\n",
      "2021-04-30 01:25:32.012183: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusparse64_11.dll'; dlerror: cusparse64_11.dll not found\n",
      "2021-04-30 01:25:32.013763: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudnn64_8.dll'; dlerror: cudnn64_8.dll not found\n",
      "2021-04-30 01:25:32.013778: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2021-04-30 01:25:32.014027: 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",
      "2021-04-30 01:25:32.015916: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-04-30 01:25:32.015965: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      \n",
      "2021-04-30 01:25:32.015986: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model): 1.77s\n",
      "I0430 01:25:32.413887  4888 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_center_net_model): 1.77s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_center_net_model\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.47s\n",
      "I0430 01:25:32.887037  4888 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.47s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_experimental_model\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n",
      "I0430 01:25:32.887037  4888 test_util.py:2076] 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)): 0.03s\n",
      "I0430 01:25:32.920686  4888 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.03s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.02s\n",
      "I0430 01:25:32.940566  4888 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.02s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.02s\n",
      "I0430 01:25:32.960741  4888 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.02s\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): 0.14s\n",
      "I0430 01:25:33.100090  4888 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.14s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.14s\n",
      "I0430 01:25:33.245733  4888 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.14s\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): 0.17s\n",
      "I0430 01:25:33.416340  4888 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.17s\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): 0.16s\n",
      "I0430 01:25:33.577668  4888 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.16s\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): 0.16s\n",
      "I0430 01:25:33.743142  4888 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.16s\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): 0.05s\n",
      "I0430 01:25:33.791184  4888 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.05s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n",
      "I0430 01:25:34.099738  4888 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b0\n",
      "I0430 01:25:34.099738  4888 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 64\n",
      "I0430 01:25:34.099738  4888 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 3\n",
      "I0430 01:25:34.105773  4888 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0430 01:25:34.125175  4888 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0430 01:25:34.125175  4888 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0430 01:25:34.194493  4888 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0430 01:25:34.194493  4888 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0430 01:25:34.350931  4888 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0430 01:25:34.350931  4888 efficientnet_model.py:147] round_filter input=40 output=40\n",
      "I0430 01:25:34.533925  4888 efficientnet_model.py:147] round_filter input=40 output=40\n",
      "I0430 01:25:34.533925  4888 efficientnet_model.py:147] round_filter input=80 output=80\n",
      "I0430 01:25:34.806792  4888 efficientnet_model.py:147] round_filter input=80 output=80\n",
      "I0430 01:25:34.807792  4888 efficientnet_model.py:147] round_filter input=112 output=112\n",
      "I0430 01:25:35.058454  4888 efficientnet_model.py:147] round_filter input=112 output=112\n",
      "I0430 01:25:35.058454  4888 efficientnet_model.py:147] round_filter input=192 output=192\n",
      "I0430 01:25:35.508673  4888 efficientnet_model.py:147] round_filter input=192 output=192\n",
      "I0430 01:25:35.508673  4888 efficientnet_model.py:147] round_filter input=320 output=320\n",
      "I0430 01:25:35.605765  4888 efficientnet_model.py:147] round_filter input=1280 output=1280\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0430 01:25:35.655971  4888 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",
      "I0430 01:25:35.732563  4888 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b1\n",
      "I0430 01:25:35.733564  4888 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 88\n",
      "I0430 01:25:35.733564  4888 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 4\n",
      "I0430 01:25:35.739086  4888 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0430 01:25:35.754546  4888 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0430 01:25:35.754546  4888 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0430 01:25:35.945854  4888 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0430 01:25:35.945854  4888 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0430 01:25:36.453893  4888 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0430 01:25:36.453893  4888 efficientnet_model.py:147] round_filter input=40 output=40\n",
      "I0430 01:25:36.952195  4888 efficientnet_model.py:147] round_filter input=40 output=40\n",
      "I0430 01:25:36.952863  4888 efficientnet_model.py:147] round_filter input=80 output=80\n",
      "I0430 01:25:37.617475  4888 efficientnet_model.py:147] round_filter input=80 output=80\n",
      "I0430 01:25:37.617475  4888 efficientnet_model.py:147] round_filter input=112 output=112\n",
      "I0430 01:25:38.323867  4888 efficientnet_model.py:147] round_filter input=112 output=112\n",
      "I0430 01:25:38.323867  4888 efficientnet_model.py:147] round_filter input=192 output=192\n",
      "I0430 01:25:39.206796  4888 efficientnet_model.py:147] round_filter input=192 output=192\n",
      "I0430 01:25:39.206796  4888 efficientnet_model.py:147] round_filter input=320 output=320\n",
      "I0430 01:25:39.583828  4888 efficientnet_model.py:147] round_filter input=1280 output=1280\n",
      "I0430 01:25:39.666822  4888 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",
      "I0430 01:25:39.842794  4888 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b2\n",
      "I0430 01:25:39.842794  4888 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 112\n",
      "I0430 01:25:39.842794  4888 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 5\n",
      "I0430 01:25:39.851791  4888 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0430 01:25:39.884907  4888 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0430 01:25:39.884907  4888 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0430 01:25:40.288824  4888 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0430 01:25:40.289824  4888 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0430 01:25:40.793149  4888 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0430 01:25:40.793149  4888 efficientnet_model.py:147] round_filter input=40 output=48\n",
      "I0430 01:25:41.292142  4888 efficientnet_model.py:147] round_filter input=40 output=48\n",
      "I0430 01:25:41.292142  4888 efficientnet_model.py:147] round_filter input=80 output=88\n",
      "I0430 01:25:41.992184  4888 efficientnet_model.py:147] round_filter input=80 output=88\n",
      "I0430 01:25:41.992451  4888 efficientnet_model.py:147] round_filter input=112 output=120\n",
      "I0430 01:25:42.664419  4888 efficientnet_model.py:147] round_filter input=112 output=120\n",
      "I0430 01:25:42.665419  4888 efficientnet_model.py:147] round_filter input=192 output=208\n",
      "I0430 01:25:43.564678  4888 efficientnet_model.py:147] round_filter input=192 output=208\n",
      "I0430 01:25:43.564678  4888 efficientnet_model.py:147] round_filter input=320 output=352\n",
      "I0430 01:25:43.953150  4888 efficientnet_model.py:147] round_filter input=1280 output=1408\n",
      "I0430 01:25:44.039980  4888 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",
      "I0430 01:25:44.214956  4888 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b3\n",
      "I0430 01:25:44.214956  4888 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 160\n",
      "I0430 01:25:44.214956  4888 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 6\n",
      "I0430 01:25:44.224948  4888 efficientnet_model.py:147] round_filter input=32 output=40\n",
      "I0430 01:25:44.257991  4888 efficientnet_model.py:147] round_filter input=32 output=40\n",
      "I0430 01:25:44.257991  4888 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0430 01:25:44.521404  4888 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0430 01:25:44.521404  4888 efficientnet_model.py:147] round_filter input=24 output=32\n",
      "I0430 01:25:45.001291  4888 efficientnet_model.py:147] round_filter input=24 output=32\n",
      "I0430 01:25:45.001291  4888 efficientnet_model.py:147] round_filter input=40 output=48\n",
      "I0430 01:25:45.495292  4888 efficientnet_model.py:147] round_filter input=40 output=48\n",
      "I0430 01:25:45.495292  4888 efficientnet_model.py:147] round_filter input=80 output=96\n",
      "I0430 01:25:46.332542  4888 efficientnet_model.py:147] round_filter input=80 output=96\n",
      "I0430 01:25:46.332542  4888 efficientnet_model.py:147] round_filter input=112 output=136\n",
      "I0430 01:25:47.373626  4888 efficientnet_model.py:147] round_filter input=112 output=136\n",
      "I0430 01:25:47.373626  4888 efficientnet_model.py:147] round_filter input=192 output=232\n",
      "I0430 01:25:48.475535  4888 efficientnet_model.py:147] round_filter input=192 output=232\n",
      "I0430 01:25:48.476535  4888 efficientnet_model.py:147] round_filter input=320 output=384\n",
      "I0430 01:25:48.883572  4888 efficientnet_model.py:147] round_filter input=1280 output=1536\n",
      "I0430 01:25:48.966574  4888 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",
      "I0430 01:25:49.149164  4888 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b4\n",
      "I0430 01:25:49.149164  4888 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 224\n",
      "I0430 01:25:49.149580  4888 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7\n",
      "I0430 01:25:49.158607  4888 efficientnet_model.py:147] round_filter input=32 output=48\n",
      "I0430 01:25:49.189606  4888 efficientnet_model.py:147] round_filter input=32 output=48\n",
      "I0430 01:25:49.190618  4888 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0430 01:25:49.439614  4888 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0430 01:25:49.439614  4888 efficientnet_model.py:147] round_filter input=24 output=32\n",
      "I0430 01:25:50.090382  4888 efficientnet_model.py:147] round_filter input=24 output=32\n",
      "I0430 01:25:50.090382  4888 efficientnet_model.py:147] round_filter input=40 output=56\n",
      "I0430 01:25:50.741911  4888 efficientnet_model.py:147] round_filter input=40 output=56\n",
      "I0430 01:25:50.742912  4888 efficientnet_model.py:147] round_filter input=80 output=112\n",
      "I0430 01:25:51.751315  4888 efficientnet_model.py:147] round_filter input=80 output=112\n",
      "I0430 01:25:51.752315  4888 efficientnet_model.py:147] round_filter input=112 output=160\n",
      "I0430 01:25:52.780638  4888 efficientnet_model.py:147] round_filter input=112 output=160\n",
      "I0430 01:25:52.780638  4888 efficientnet_model.py:147] round_filter input=192 output=272\n",
      "I0430 01:25:54.344231  4888 efficientnet_model.py:147] round_filter input=192 output=272\n",
      "I0430 01:25:54.345232  4888 efficientnet_model.py:147] round_filter input=320 output=448\n",
      "I0430 01:25:54.835808  4888 efficientnet_model.py:147] round_filter input=1280 output=1792\n",
      "I0430 01:25:54.926416  4888 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",
      "I0430 01:25:55.325448  4888 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b5\n",
      "I0430 01:25:55.325448  4888 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 288\n",
      "I0430 01:25:55.326442  4888 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7\n",
      "I0430 01:25:55.333463  4888 efficientnet_model.py:147] round_filter input=32 output=48\n",
      "I0430 01:25:55.358441  4888 efficientnet_model.py:147] round_filter input=32 output=48\n",
      "I0430 01:25:55.359440  4888 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0430 01:25:55.737769  4888 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0430 01:25:55.738739  4888 efficientnet_model.py:147] round_filter input=24 output=40\n",
      "I0430 01:25:56.575821  4888 efficientnet_model.py:147] round_filter input=24 output=40\n",
      "I0430 01:25:56.576781  4888 efficientnet_model.py:147] round_filter input=40 output=64\n",
      "I0430 01:25:57.383821  4888 efficientnet_model.py:147] round_filter input=40 output=64\n",
      "I0430 01:25:57.384821  4888 efficientnet_model.py:147] round_filter input=80 output=128\n",
      "I0430 01:25:58.570822  4888 efficientnet_model.py:147] round_filter input=80 output=128\n",
      "I0430 01:25:58.571821  4888 efficientnet_model.py:147] round_filter input=112 output=176\n",
      "I0430 01:25:59.780155  4888 efficientnet_model.py:147] round_filter input=112 output=176\n",
      "I0430 01:25:59.781253  4888 efficientnet_model.py:147] round_filter input=192 output=304\n",
      "I0430 01:26:01.546346  4888 efficientnet_model.py:147] round_filter input=192 output=304\n",
      "I0430 01:26:01.546346  4888 efficientnet_model.py:147] round_filter input=320 output=512\n",
      "I0430 01:26:02.251187  4888 efficientnet_model.py:147] round_filter input=1280 output=2048\n",
      "I0430 01:26:02.357191  4888 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",
      "I0430 01:26:02.589461  4888 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b6\n",
      "I0430 01:26:02.589461  4888 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384\n",
      "I0430 01:26:02.590317  4888 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8\n",
      "I0430 01:26:02.599459  4888 efficientnet_model.py:147] round_filter input=32 output=56\n",
      "I0430 01:26:02.634315  4888 efficientnet_model.py:147] round_filter input=32 output=56\n",
      "I0430 01:26:02.634315  4888 efficientnet_model.py:147] round_filter input=16 output=32\n",
      "I0430 01:26:03.020317  4888 efficientnet_model.py:147] round_filter input=16 output=32\n",
      "I0430 01:26:03.020317  4888 efficientnet_model.py:147] round_filter input=24 output=40\n",
      "I0430 01:26:04.339740  4888 efficientnet_model.py:147] round_filter input=24 output=40\n",
      "I0430 01:26:04.340731  4888 efficientnet_model.py:147] round_filter input=40 output=72\n",
      "I0430 01:26:05.330851  4888 efficientnet_model.py:147] round_filter input=40 output=72\n",
      "I0430 01:26:05.330851  4888 efficientnet_model.py:147] round_filter input=80 output=144\n",
      "I0430 01:26:06.690859  4888 efficientnet_model.py:147] round_filter input=80 output=144\n",
      "I0430 01:26:06.691856  4888 efficientnet_model.py:147] round_filter input=112 output=200\n",
      "I0430 01:26:08.097858  4888 efficientnet_model.py:147] round_filter input=112 output=200\n",
      "I0430 01:26:08.098859  4888 efficientnet_model.py:147] round_filter input=192 output=344\n",
      "I0430 01:26:10.298993  4888 efficientnet_model.py:147] round_filter input=192 output=344\n",
      "I0430 01:26:10.298993  4888 efficientnet_model.py:147] round_filter input=320 output=576\n",
      "I0430 01:26:11.062185  4888 efficientnet_model.py:147] round_filter input=1280 output=2304\n",
      "I0430 01:26:11.174947  4888 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",
      "I0430 01:26:11.444980  4888 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b7\n",
      "I0430 01:26:11.444980  4888 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384\n",
      "I0430 01:26:11.445987  4888 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8\n",
      "I0430 01:26:11.454986  4888 efficientnet_model.py:147] round_filter input=32 output=64\n",
      "I0430 01:26:11.485946  4888 efficientnet_model.py:147] round_filter input=32 output=64\n",
      "I0430 01:26:11.486987  4888 efficientnet_model.py:147] round_filter input=16 output=32\n",
      "I0430 01:26:12.233657  4888 efficientnet_model.py:147] round_filter input=16 output=32\n",
      "I0430 01:26:12.233657  4888 efficientnet_model.py:147] round_filter input=24 output=48\n",
      "I0430 01:26:13.386198  4888 efficientnet_model.py:147] round_filter input=24 output=48\n",
      "I0430 01:26:13.386198  4888 efficientnet_model.py:147] round_filter input=40 output=80\n",
      "I0430 01:26:14.547331  4888 efficientnet_model.py:147] round_filter input=40 output=80\n",
      "I0430 01:26:14.547331  4888 efficientnet_model.py:147] round_filter input=80 output=160\n",
      "I0430 01:26:16.277536  4888 efficientnet_model.py:147] round_filter input=80 output=160\n",
      "I0430 01:26:16.277536  4888 efficientnet_model.py:147] round_filter input=112 output=224\n",
      "I0430 01:26:18.136256  4888 efficientnet_model.py:147] round_filter input=112 output=224\n",
      "I0430 01:26:18.137255  4888 efficientnet_model.py:147] round_filter input=192 output=384\n",
      "I0430 01:26:20.711018  4888 efficientnet_model.py:147] round_filter input=192 output=384\n",
      "I0430 01:26:20.711018  4888 efficientnet_model.py:147] round_filter input=320 output=640\n",
      "I0430 01:26:21.819911  4888 efficientnet_model.py:147] round_filter input=1280 output=2560\n",
      "I0430 01:26:21.947912  4888 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): 48.46s\n",
      "I0430 01:26:22.250049  4888 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 48.46s\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",
      "I0430 01:26:22.261441  4888 test_util.py:2076] 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",
      "I0430 01:26:22.265745  4888 test_util.py:2076] 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",
      "I0430 01:26:22.266744  4888 test_util.py:2076] 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",
      "I0430 01:26:22.269968  4888 test_util.py:2076] 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",
      "I0430 01:26:22.272871  4888 test_util.py:2076] 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",
      "I0430 01:26:22.274556  4888 test_util.py:2076] 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",
      "I0430 01:26:22.275876  4888 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n",
      "----------------------------------------------------------------------\n",
      "Ran 21 tests in 51.630s\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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow\n",
      "  Using cached tensorflow-2.4.1-cp37-cp37m-win_amd64.whl (370.7 MB)\n",
      "Requirement already satisfied: six~=1.15.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow) (1.15.0)\n",
      "Requirement already satisfied: wheel~=0.35 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow) (0.36.2)\n",
      "Collecting gast==0.3.3\n",
      "  Using cached gast-0.3.3-py2.py3-none-any.whl (9.7 kB)\n",
      "Requirement already satisfied: numpy~=1.19.2 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow) (1.19.5)\n",
      "Collecting termcolor~=1.1.0\n",
      "  Using cached termcolor-1.1.0-py3-none-any.whl\n",
      "Requirement already satisfied: absl-py~=0.10 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow) (0.12.0)\n",
      "Collecting h5py~=2.10.0\n",
      "  Using cached h5py-2.10.0-cp37-cp37m-win_amd64.whl (2.5 MB)\n",
      "Requirement already satisfied: typing-extensions~=3.7.4 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow) (3.7.4.3)\n",
      "Collecting tensorboard~=2.4\n",
      "  Using cached tensorboard-2.5.0-py3-none-any.whl (6.0 MB)\n",
      "Collecting grpcio~=1.32.0\n",
      "  Using cached grpcio-1.32.0-cp37-cp37m-win_amd64.whl (2.5 MB)\n",
      "Collecting google-pasta~=0.2\n",
      "  Using cached google_pasta-0.2.0-py3-none-any.whl (57 kB)\n",
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      "  Using cached flatbuffers-1.12-py2.py3-none-any.whl (15 kB)\n",
      "Collecting keras-preprocessing~=1.1.2\n",
      "  Using cached Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB)\n",
      "Collecting wrapt~=1.12.1\n",
      "  Using cached wrapt-1.12.1-cp37-cp37m-win_amd64.whl\n",
      "Collecting opt-einsum~=3.3.0\n",
      "  Using cached opt_einsum-3.3.0-py3-none-any.whl (65 kB)\n",
      "Collecting tensorflow-estimator<2.5.0,>=2.4.0\n",
      "  Using cached tensorflow_estimator-2.4.0-py2.py3-none-any.whl (462 kB)\n",
      "Collecting astunparse~=1.6.3\n",
      "  Using cached astunparse-1.6.3-py2.py3-none-any.whl (12 kB)\n",
      "Requirement already satisfied: protobuf>=3.9.2 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow) (3.15.8)\n",
      "Collecting markdown>=2.6.8\n",
      "  Using cached Markdown-3.3.4-py3-none-any.whl (97 kB)\n",
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      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorboard~=2.4->tensorflow) (0.6.0)\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "tf-models-official 2.4.0 requires Cython, which is not installed.\n",
      "tf-models-official 2.4.0 requires dataclasses, which is not installed.\n",
      "tf-models-official 2.4.0 requires gin-config, which is not installed.\n",
      "tf-models-official 2.4.0 requires google-api-python-client>=1.6.7, which is not installed.\n",
      "tf-models-official 2.4.0 requires google-cloud-bigquery>=0.31.0, which is not installed.\n",
      "tf-models-official 2.4.0 requires kaggle>=1.3.9, which is not installed.\n",
      "tf-models-official 2.4.0 requires matplotlib, which is not installed.\n",
      "tf-models-official 2.4.0 requires oauth2client, which is not installed.\n",
      "tf-models-official 2.4.0 requires opencv-python-headless, which is not installed.\n",
      "tf-models-official 2.4.0 requires Pillow, which is not installed.\n",
      "tf-models-official 2.4.0 requires psutil>=5.4.3, which is not installed.\n",
      "tf-models-official 2.4.0 requires py-cpuinfo>=3.3.0, which is not installed.\n",
      "tf-models-official 2.4.0 requires pyyaml>=5.1, which is not installed.\n",
      "tf-models-official 2.4.0 requires sentencepiece, which is not installed.\n",
      "tf-models-official 2.4.0 requires seqeval, which is not installed.\n",
      "tf-models-official 2.4.0 requires tensorflow-addons, which is not installed.\n",
      "tf-models-official 2.4.0 requires tensorflow-datasets, which is not installed.\n",
      "tf-models-official 2.4.0 requires tensorflow-hub>=0.6.0, which is not installed.\n",
      "tf-models-official 2.4.0 requires tensorflow-model-optimization>=0.4.1, which is not installed.\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorboard~=2.4->tensorflow) (2.25.1)\n",
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      "Collecting google-auth-oauthlib<0.5,>=0.4.1\n",
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      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow) (0.2.8)\n",
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      "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow) (2.10)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow) (4.0.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow) (2020.12.5)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow) (3.1.0)\n",
      "Requirement already satisfied: zipp>=0.5 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from importlib-metadata->markdown>=2.6.8->tensorboard~=2.4->tensorflow) (3.4.1)\n",
      "Installing collected packages: markdown, grpcio, google-auth-oauthlib, wrapt, termcolor, tensorflow-estimator, tensorboard, opt-einsum, keras-preprocessing, h5py, google-pasta, gast, flatbuffers, astunparse, tensorflow\n",
      "Successfully installed astunparse-1.6.3 flatbuffers-1.12 gast-0.3.3 google-auth-oauthlib-0.4.4 google-pasta-0.2.0 grpcio-1.32.0 h5py-2.10.0 keras-preprocessing-1.1.2 markdown-3.3.4 opt-einsum-3.3.0 tensorboard-2.5.0 tensorflow-2.4.1 tensorflow-estimator-2.4.0 termcolor-1.1.0 wrapt-1.12.1\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found existing installation: protobuf 3.15.8\n",
      "Uninstalling protobuf-3.15.8:\n",
      "  Successfully uninstalled protobuf-3.15.8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Skipping matplotlib as it is not installed.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting protobuf\n",
      "  Using cached protobuf-3.15.8-cp37-cp37m-win_amd64.whl (904 kB)\n",
      "Collecting matplotlib==3.2\n",
      "  Downloading matplotlib-3.2.0-cp37-cp37m-win_amd64.whl (9.2 MB)\n",
      "Collecting kiwisolver>=1.0.1\n",
      "  Using cached kiwisolver-1.3.1-cp37-cp37m-win_amd64.whl (51 kB)\n",
      "Collecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1\n",
      "  Using cached pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)\n",
      "Requirement already satisfied: numpy>=1.11 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from matplotlib==3.2) (1.19.5)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from matplotlib==3.2) (2.8.1)\n",
      "Collecting cycler>=0.10\n",
      "  Using cached cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)\n",
      "Requirement already satisfied: six in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from cycler>=0.10->matplotlib==3.2) (1.15.0)\n",
      "Installing collected packages: pyparsing, kiwisolver, cycler, protobuf, matplotlib\n",
      "Successfully installed cycler-0.10.0 kiwisolver-1.3.1 matplotlib-3.2.0 protobuf-3.15.8 pyparsing-2.4.7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "object-detection 0.1 requires apache-beam, which is not installed.\n",
      "object-detection 0.1 requires avro-python3, which is not installed.\n",
      "object-detection 0.1 requires contextlib2, which is not installed.\n",
      "object-detection 0.1 requires Cython, which is not installed.\n",
      "object-detection 0.1 requires pillow, which is not installed.\n",
      "tf-models-official 2.4.0 requires Cython, which is not installed.\n",
      "tf-models-official 2.4.0 requires dataclasses, which is not installed.\n",
      "tf-models-official 2.4.0 requires gin-config, which is not installed.\n",
      "tf-models-official 2.4.0 requires google-api-python-client>=1.6.7, which is not installed.\n",
      "tf-models-official 2.4.0 requires google-cloud-bigquery>=0.31.0, which is not installed.\n",
      "tf-models-official 2.4.0 requires kaggle>=1.3.9, which is not installed.\n",
      "tf-models-official 2.4.0 requires oauth2client, which is not installed.\n",
      "tf-models-official 2.4.0 requires opencv-python-headless, which is not installed.\n",
      "tf-models-official 2.4.0 requires Pillow, which is not installed.\n",
      "tf-models-official 2.4.0 requires psutil>=5.4.3, which is not installed.\n",
      "tf-models-official 2.4.0 requires py-cpuinfo>=3.3.0, which is not installed.\n",
      "tf-models-official 2.4.0 requires pyyaml>=5.1, which is not installed.\n",
      "tf-models-official 2.4.0 requires sentencepiece, which is not installed.\n",
      "tf-models-official 2.4.0 requires seqeval, which is not installed.\n",
      "tf-models-official 2.4.0 requires tensorflow-addons, which is not installed.\n",
      "tf-models-official 2.4.0 requires tensorflow-datasets, which is not installed.\n",
      "tf-models-official 2.4.0 requires tensorflow-hub>=0.6.0, which is not installed.\n",
      "tf-models-official 2.4.0 requires tensorflow-model-optimization>=0.4.1, which is not installed.\n",
      "lvis 0.5.3 requires Cython>=0.29.12, which is not installed.\n",
      "pycocotools 2.0.2 requires cython>=0.27.3, which is not installed.\n"
     ]
    }
   ],
   "source": [
    "!pip uninstall protobuf matplotlib -y\n",
    "!pip install protobuf matplotlib==3.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import object_detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                 Version   Location\n",
      "----------------------- --------- -------------------------------------------------------------------------------------\n",
      "absl-py                 0.12.0\n",
      "astunparse              1.6.3\n",
      "backcall                0.2.0\n",
      "cachetools              4.2.2\n",
      "certifi                 2020.12.5\n",
      "chardet                 4.0.0\n",
      "colorama                0.4.4\n",
      "cycler                  0.10.0\n",
      "Cython                  0.29.23\n",
      "decorator               5.0.7\n",
      "flatbuffers             1.12\n",
      "gast                    0.3.3\n",
      "gin                     0.1.6\n",
      "gin-config              0.1.1\n",
      "google-auth             1.30.0\n",
      "google-auth-oauthlib    0.4.4\n",
      "google-pasta            0.2.0\n",
      "grpcio                  1.32.0\n",
      "h5py                    2.10.0\n",
      "idna                    2.10\n",
      "importlib-metadata      4.0.1\n",
      "ipykernel               5.5.3\n",
      "ipython                 7.22.0\n",
      "ipython-genutils        0.2.0\n",
      "jedi                    0.18.0\n",
      "jupyter-client          6.1.12\n",
      "jupyter-core            4.7.1\n",
      "Keras-Preprocessing     1.1.2\n",
      "kiwisolver              1.3.1\n",
      "lvis                    0.5.3\n",
      "lxml                    4.6.3\n",
      "Markdown                3.3.4\n",
      "matplotlib              3.2.0\n",
      "numpy                   1.19.5\n",
      "oauthlib                3.1.0\n",
      "object-detection        0.1\n",
      "opencv-python           4.5.1.48\n",
      "opt-einsum              3.3.0\n",
      "pandas                  1.2.4\n",
      "parso                   0.8.2\n",
      "pickleshare             0.7.5\n",
      "Pillow                  8.2.0\n",
      "pip                     21.1\n",
      "prompt-toolkit          3.0.18\n",
      "protobuf                3.15.8\n",
      "pyasn1                  0.4.8\n",
      "pyasn1-modules          0.2.8\n",
      "pycocotools             2.0.2\n",
      "Pygments                2.8.1\n",
      "pyparsing               2.4.7\n",
      "PyQt5                   5.15.4\n",
      "PyQt5-Qt5               5.15.2\n",
      "PyQt5-sip               12.8.1\n",
      "python-dateutil         2.8.1\n",
      "pytz                    2021.1\n",
      "pywin32                 300\n",
      "PyYAML                  5.4.1\n",
      "pyzmq                   22.0.3\n",
      "requests                2.25.1\n",
      "requests-oauthlib       1.3.0\n",
      "rsa                     4.7.2\n",
      "scipy                   1.6.3\n",
      "setuptools              46.4.0\n",
      "six                     1.15.0\n",
      "slim                    0.1       c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\tensorflow\\models\\research\\slim\n",
      "tensorboard             2.5.0\n",
      "tensorboard-data-server 0.6.0\n",
      "tensorboard-plugin-wit  1.8.0\n",
      "tensorflow              2.4.1\n",
      "tensorflow-addons       0.12.1\n",
      "tensorflow-estimator    2.4.0\n",
      "termcolor               1.1.0\n",
      "tf-models-official      2.4.0\n",
      "tf-slim                 1.1.0\n",
      "tornado                 6.1\n",
      "traitlets               5.0.5\n",
      "typeguard               2.12.0\n",
      "typing-extensions       3.7.4.3\n",
      "urllib3                 1.26.4\n",
      "wcwidth                 0.2.5\n",
      "Werkzeug                1.0.1\n",
      "wget                    3.2\n",
      "wheel                   0.36.2\n",
      "wrapt                   1.12.1\n",
      "zipp                    3.4.1\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"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% [........................................................................] 20515344 / 20515344        1 file(s) moved.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0.data-00000-of-00001\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/checkpoint\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0.index\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/pipeline.config\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/saved_model.pb\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/variables/\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/variables/variables.data-00000-of-00001\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/variables/variables.index\n"
     ]
    }
   ],
   "source": [
    "if os.name =='posix':\n",
    "    !wget {PRETRAINED_MODEL_URL}\n",
    "    !mv {PRETRAINED_MODEL_NAME+'.tar.gz'} {paths['PRETRAINED_MODEL_PATH']}\n",
    "    !cd {paths['PRETRAINED_MODEL_PATH']} && tar -zxvf {PRETRAINED_MODEL_NAME+'.tar.gz'}\n",
    "if os.name == 'nt':\n",
    "    wget.download(PRETRAINED_MODEL_URL)\n",
    "    !move {PRETRAINED_MODEL_NAME+'.tar.gz'} {paths['PRETRAINED_MODEL_PATH']}\n",
    "    !cd {paths['PRETRAINED_MODEL_PATH']} && tar -zxvf {PRETRAINED_MODEL_NAME+'.tar.gz'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M5KJTnkfpfDC"
   },
   "source": [
    "# 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": [
    "# Create TF records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kvf5WccwrFGq",
    "outputId": "49902aeb-0bd7-4298-e1a0-5b4a64eb2064"
   },
   "outputs": [],
   "source": [
    "# OPTIONAL IF RUNNING ON COLAB\n",
    "ARCHIVE_FILES = os.path.join(paths['IMAGE_PATH'], 'archive.tar.gz')\n",
    "if os.path.exists(ARCHIVE_FILES):\n",
    "  !tar -zxvf {ARCHIVE_FILES}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KWpb_BVUpfDD",
    "outputId": "56ce2a3f-3933-4ee6-8a9d-d5ec65f7d73c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cloning into 'Tensorflow\\scripts'...\n"
     ]
    }
   ],
   "source": [
    "if not os.path.exists(files['TF_RECORD_SCRIPT']):\n",
    "    !git clone https://github.com/nicknochnack/GenerateTFRecord {paths['SCRIPTS_PATH']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "# Copy Model Config to Training Folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "cOjuTFbwpfDF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        1 file(s) copied.\n"
     ]
    }
   ],
   "source": [
    "if os.name =='posix':\n",
    "    !cp {os.path.join(paths['PRETRAINED_MODEL_PATH'], PRETRAINED_MODEL_NAME, 'pipeline.config')} {os.path.join(paths['CHECKPOINT_PATH'])}\n",
    "if os.name == 'nt':\n",
    "    !copy {os.path.join(paths['PRETRAINED_MODEL_PATH'], PRETRAINED_MODEL_NAME, 'pipeline.config')} {os.path.join(paths['CHECKPOINT_PATH'])}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ga8gpNslpfDF"
   },
   "source": [
    "# Update Config For Transfer Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "metadata": {
    "id": "c2A0mn4ipfDF"
   },
   "outputs": [],
   "source": [
    "config = config_util.get_configs_from_pipeline_file(files['PIPELINE_CONFIG'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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: 90\n",
       "   image_resizer {\n",
       "     fixed_shape_resizer {\n",
       "       height: 320\n",
       "       width: 320\n",
       "     }\n",
       "   }\n",
       "   feature_extractor {\n",
       "     type: \"ssd_mobilenet_v2_fpn_keras\"\n",
       "     depth_multiplier: 1.0\n",
       "     min_depth: 16\n",
       "     conv_hyperparams {\n",
       "       regularizer {\n",
       "         l2_regularizer {\n",
       "           weight: 3.9999998989515007e-05\n",
       "         }\n",
       "       }\n",
       "       initializer {\n",
       "         random_normal_initializer {\n",
       "           mean: 0.0\n",
       "           stddev: 0.009999999776482582\n",
       "         }\n",
       "       }\n",
       "       activation: RELU_6\n",
       "       batch_norm {\n",
       "         decay: 0.996999979019165\n",
       "         scale: true\n",
       "         epsilon: 0.0010000000474974513\n",
       "       }\n",
       "     }\n",
       "     use_depthwise: true\n",
       "     override_base_feature_extractor_hyperparams: true\n",
       "     fpn {\n",
       "       min_level: 3\n",
       "       max_level: 7\n",
       "       additional_layer_depth: 128\n",
       "     }\n",
       "   }\n",
       "   box_coder {\n",
       "     faster_rcnn_box_coder {\n",
       "       y_scale: 10.0\n",
       "       x_scale: 10.0\n",
       "       height_scale: 5.0\n",
       "       width_scale: 5.0\n",
       "     }\n",
       "   }\n",
       "   matcher {\n",
       "     argmax_matcher {\n",
       "       matched_threshold: 0.5\n",
       "       unmatched_threshold: 0.5\n",
       "       ignore_thresholds: false\n",
       "       negatives_lower_than_unmatched: true\n",
       "       force_match_for_each_row: true\n",
       "       use_matmul_gather: true\n",
       "     }\n",
       "   }\n",
       "   similarity_calculator {\n",
       "     iou_similarity {\n",
       "     }\n",
       "   }\n",
       "   box_predictor {\n",
       "     weight_shared_convolutional_box_predictor {\n",
       "       conv_hyperparams {\n",
       "         regularizer {\n",
       "           l2_regularizer {\n",
       "             weight: 3.9999998989515007e-05\n",
       "           }\n",
       "         }\n",
       "         initializer {\n",
       "           random_normal_initializer {\n",
       "             mean: 0.0\n",
       "             stddev: 0.009999999776482582\n",
       "           }\n",
       "         }\n",
       "         activation: RELU_6\n",
       "         batch_norm {\n",
       "           decay: 0.996999979019165\n",
       "           scale: true\n",
       "           epsilon: 0.0010000000474974513\n",
       "         }\n",
       "       }\n",
       "       depth: 128\n",
       "       num_layers_before_predictor: 4\n",
       "       kernel_size: 3\n",
       "       class_prediction_bias_init: -4.599999904632568\n",
       "       share_prediction_tower: true\n",
       "       use_depthwise: true\n",
       "     }\n",
       "   }\n",
       "   anchor_generator {\n",
       "     multiscale_anchor_generator {\n",
       "       min_level: 3\n",
       "       max_level: 7\n",
       "       anchor_scale: 4.0\n",
       "       aspect_ratios: 1.0\n",
       "       aspect_ratios: 2.0\n",
       "       aspect_ratios: 0.5\n",
       "       scales_per_octave: 2\n",
       "     }\n",
       "   }\n",
       "   post_processing {\n",
       "     batch_non_max_suppression {\n",
       "       score_threshold: 9.99999993922529e-09\n",
       "       iou_threshold: 0.6000000238418579\n",
       "       max_detections_per_class: 100\n",
       "       max_total_detections: 100\n",
       "       use_static_shapes: false\n",
       "     }\n",
       "     score_converter: SIGMOID\n",
       "   }\n",
       "   normalize_loss_by_num_matches: true\n",
       "   loss {\n",
       "     localization_loss {\n",
       "       weighted_smooth_l1 {\n",
       "       }\n",
       "     }\n",
       "     classification_loss {\n",
       "       weighted_sigmoid_focal {\n",
       "         gamma: 2.0\n",
       "         alpha: 0.25\n",
       "       }\n",
       "     }\n",
       "     classification_weight: 1.0\n",
       "     localization_weight: 1.0\n",
       "   }\n",
       "   encode_background_as_zeros: true\n",
       "   normalize_loc_loss_by_codesize: true\n",
       "   inplace_batchnorm_update: true\n",
       "   freeze_batchnorm: false\n",
       " },\n",
       " 'train_config': batch_size: 128\n",
       " data_augmentation_options {\n",
       "   random_horizontal_flip {\n",
       "   }\n",
       " }\n",
       " data_augmentation_options {\n",
       "   random_crop_image {\n",
       "     min_object_covered: 0.0\n",
       "     min_aspect_ratio: 0.75\n",
       "     max_aspect_ratio: 3.0\n",
       "     min_area: 0.75\n",
       "     max_area: 1.0\n",
       "     overlap_thresh: 0.0\n",
       "   }\n",
       " }\n",
       " sync_replicas: true\n",
       " optimizer {\n",
       "   momentum_optimizer {\n",
       "     learning_rate {\n",
       "       cosine_decay_learning_rate {\n",
       "         learning_rate_base: 0.07999999821186066\n",
       "         total_steps: 50000\n",
       "         warmup_learning_rate: 0.026666000485420227\n",
       "         warmup_steps: 1000\n",
       "       }\n",
       "     }\n",
       "     momentum_optimizer_value: 0.8999999761581421\n",
       "   }\n",
       "   use_moving_average: false\n",
       " }\n",
       " fine_tune_checkpoint: \"PATH_TO_BE_CONFIGURED\"\n",
       " num_steps: 50000\n",
       " startup_delay_steps: 0.0\n",
       " replicas_to_aggregate: 8\n",
       " max_number_of_boxes: 100\n",
       " unpad_groundtruth_tensors: false\n",
       " fine_tune_checkpoint_type: \"classification\"\n",
       " fine_tune_checkpoint_version: V2,\n",
       " 'train_input_config': label_map_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " tf_record_input_reader {\n",
       "   input_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " },\n",
       " 'eval_config': metrics_set: \"coco_detection_metrics\"\n",
       " use_moving_averages: false,\n",
       " 'eval_input_configs': [label_map_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " shuffle: false\n",
       " num_epochs: 1\n",
       " tf_record_input_reader {\n",
       "   input_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " }\n",
       " ],\n",
       " 'eval_input_config': label_map_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " shuffle: false\n",
       " num_epochs: 1\n",
       " tf_record_input_reader {\n",
       "   input_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " }}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 26,
   "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": 27,
   "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": [
    "# Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 29,
   "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": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A4OXXi-ApfDH",
    "outputId": "117a0e83-012b-466e-b7a6-ccaa349ac5ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python Tensorflow\\models\\research\\object_detection\\model_main_tf2.py --model_dir=Tensorflow\\workspace\\models\\my_ssd_mobnet --pipeline_config_path=Tensorflow\\workspace\\models\\my_ssd_mobnet\\pipeline.config --num_train_steps=2000\n"
     ]
    }
   ],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i3ZsJR-qpfDH",
    "outputId": "cabec5e1-45e6-4f2f-d9cf-297d9c1d0225"
   },
   "outputs": [],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4_YRZu7npfDH"
   },
   "source": [
    "# Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lYsgEPx9pfDH",
    "outputId": "8632d48b-91d2-45d9-bcb8-c1b172bf6eed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python Tensorflow\\models\\research\\object_detection\\model_main_tf2.py --model_dir=Tensorflow\\workspace\\models\\my_ssd_mobnet --pipeline_config_path=Tensorflow\\workspace\\models\\my_ssd_mobnet\\pipeline.config --checkpoint_dir=Tensorflow\\workspace\\models\\my_ssd_mobnet\n"
     ]
    }
   ],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "lqTV2jGBpfDH"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n"
     ]
    }
   ],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "orvRk02UpfDI"
   },
   "source": [
    "# Load Train Model From Checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "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-12')).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": [
    "# Detect from an Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {
    "id": "cBDbIhNapfDI"
   },
   "outputs": [],
   "source": [
    "category_index = label_map_util.create_category_index_from_labelmap(files['LABELMAP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "Lx3crOhOzITB"
   },
   "outputs": [],
   "source": [
    "IMAGE_PATH = os.path.join(paths['IMAGE_PATH'], 'test', 'livelong.a20808fe-a8f7-11eb-b980-48ba4e43290e.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "# Real Time Detections from your Webcam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip uninstall opencv-python-headless -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "o_grs6OGpfDJ"
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-32471f95dba4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0minput_tensor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage_np\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m     \u001b[0mdetections\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdetect_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[0mnum_detections\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdetections\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'num_detections'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\OneDrive\\Desktop\\IIITB\\TFOD\\TFODCourse\\env\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    826\u001b[0m     \u001b[0mtracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    827\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtm\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 828\u001b[1;33m       \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    829\u001b[0m       \u001b[0mcompiler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"xla\"\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_experimental_compile\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m\"nonXla\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    830\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\OneDrive\\Desktop\\IIITB\\TFOD\\TFODCourse\\env\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    853\u001b[0m       \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    854\u001b[0m       \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 855\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    856\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    857\u001b[0m       \u001b[1;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\OneDrive\\Desktop\\IIITB\\TFOD\\TFODCourse\\env\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2941\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m   2942\u001b[0m     return graph_function._call_flat(\n\u001b[1;32m-> 2943\u001b[1;33m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[0;32m   2944\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2945\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\OneDrive\\Desktop\\IIITB\\TFOD\\TFODCourse\\env\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1917\u001b[0m       \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1918\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[1;32m-> 1919\u001b[1;33m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[0;32m   1920\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[0;32m   1921\u001b[0m         \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\OneDrive\\Desktop\\IIITB\\TFOD\\TFODCourse\\env\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    558\u001b[0m               \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    559\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 560\u001b[1;33m               ctx=ctx)\n\u001b[0m\u001b[0;32m    561\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    562\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[1;32m~\\OneDrive\\Desktop\\IIITB\\TFOD\\TFODCourse\\env\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[1;32m---> 60\u001b[1;33m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[0;32m     61\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "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": [
    "# Freezing the Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "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": 10,
   "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": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1Sw1ULgHpfDJ",
    "outputId": "6fd441e1-9fc9-4889-d072-3395c21e40b6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-04-30 03:32:42.676307: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\n",
      "2021-04-30 03:32:42.676358: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2021-04-30 03:32:45.848579: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2021-04-30 03:32:45.849434: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll\n",
      "2021-04-30 03:32:46.919729: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1050 Ti computeCapability: 6.1\n",
      "coreClock: 1.62GHz coreCount: 6 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 104.43GiB/s\n",
      "2021-04-30 03:32:46.922979: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\n",
      "2021-04-30 03:32:46.924998: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublas64_11.dll'; dlerror: cublas64_11.dll not found\n",
      "2021-04-30 03:32:46.926669: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublasLt64_11.dll'; dlerror: cublasLt64_11.dll not found\n",
      "2021-04-30 03:32:46.932049: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll\n",
      "2021-04-30 03:32:46.934090: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll\n",
      "2021-04-30 03:32:46.940396: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll\n",
      "2021-04-30 03:32:46.942255: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusparse64_11.dll'; dlerror: cusparse64_11.dll not found\n",
      "2021-04-30 03:32:46.944526: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudnn64_8.dll'; dlerror: cudnn64_8.dll not found\n",
      "2021-04-30 03:32:46.944549: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2021-04-30 03:32:46.944832: 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",
      "2021-04-30 03:32:46.947039: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-04-30 03:32:46.947085: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      \n",
      "2021-04-30 03:32:46.947110: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "WARNING:tensorflow:From C:\\Users\\ralha\\OneDrive\\Desktop\\IIITB\\TFOD\\TFODCourse\\env\\lib\\site-packages\\object_detection-0.1-py3.7.egg\\object_detection\\exporter_lib_v2.py:106: 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",
      "W0430 03:32:47.177856 12592 deprecation.py:604] From C:\\Users\\ralha\\OneDrive\\Desktop\\IIITB\\TFOD\\TFODCourse\\env\\lib\\site-packages\\object_detection-0.1-py3.7.egg\\object_detection\\exporter_lib_v2.py:106: 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 0x00000269C418C448>, because it is not built.\n",
      "W0430 03:33:04.118168 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x00000269C418C448>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x00000269CC2C1F88>, because it is not built.\n",
      "W0430 03:33:05.118174 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x00000269CC2C1F88>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD2B0808>, because it is not built.\n",
      "W0430 03:33:05.119169 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD2B0808>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD2BCE48>, because it is not built.\n",
      "W0430 03:33:05.119169 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD2BCE48>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x00000269CD2BC648>, because it is not built.\n",
      "W0430 03:33:05.119169 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x00000269CD2BC648>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CC049148>, because it is not built.\n",
      "W0430 03:33:05.119169 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CC049148>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CC138788>, because it is not built.\n",
      "W0430 03:33:05.119169 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CC138788>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x00000269CC138AC8>, because it is not built.\n",
      "W0430 03:33:05.119169 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x00000269CC138AC8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CC016BC8>, because it is not built.\n",
      "W0430 03:33:05.119169 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CC016BC8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CC03DD88>, because it is not built.\n",
      "W0430 03:33:05.120171 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CC03DD88>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x00000269CC007648>, because it is not built.\n",
      "W0430 03:33:05.120171 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x00000269CC007648>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CC162908>, because it is not built.\n",
      "W0430 03:33:05.120171 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CC162908>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CC175A88>, because it is not built.\n",
      "W0430 03:33:05.120171 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CC175A88>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CC2383C8>, because it is not built.\n",
      "W0430 03:33:05.120171 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CC2383C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CC2AC408>, because it is not built.\n",
      "W0430 03:33:05.120171 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CC2AC408>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD2C6548>, because it is not built.\n",
      "W0430 03:33:05.120171 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD2C6548>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD2C6C88>, because it is not built.\n",
      "W0430 03:33:05.120171 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD2C6C88>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD321848>, because it is not built.\n",
      "W0430 03:33:05.120171 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD321848>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD2C0FC8>, because it is not built.\n",
      "W0430 03:33:05.120171 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD2C0FC8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD2C0D08>, because it is not built.\n",
      "W0430 03:33:05.120171 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD2C0D08>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD2C0A48>, because it is not built.\n",
      "W0430 03:33:05.121169 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD2C0A48>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD2B77C8>, because it is not built.\n",
      "W0430 03:33:05.121169 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD2B77C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CC24C208>, because it is not built.\n",
      "W0430 03:33:05.121169 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CC24C208>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CC24C6C8>, because it is not built.\n",
      "W0430 03:33:05.121169 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CC24C6C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CBFD8988>, because it is not built.\n",
      "W0430 03:33:05.121169 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CBFD8988>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CBFD8C88>, because it is not built.\n",
      "W0430 03:33:05.121169 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CBFD8C88>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD375288>, because it is not built.\n",
      "W0430 03:33:05.121169 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD375288>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD3759C8>, because it is not built.\n",
      "W0430 03:33:05.121169 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD3759C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CC2BD3C8>, because it is not built.\n",
      "W0430 03:33:05.121169 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CC2BD3C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CC057E88>, because it is not built.\n",
      "W0430 03:33:05.121169 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CC057E88>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CC057208>, because it is not built.\n",
      "W0430 03:33:05.121169 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CC057208>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD3C3708>, because it is not built.\n",
      "W0430 03:33:05.121169 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD3C3708>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD3C3048>, because it is not built.\n",
      "W0430 03:33:05.122137 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD3C3048>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD3A6408>, because it is not built.\n",
      "W0430 03:33:05.122137 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD3A6408>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD3A6848>, because it is not built.\n",
      "W0430 03:33:05.122137 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD3A6848>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD3A6D08>, because it is not built.\n",
      "W0430 03:33:05.122137 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD3A6D08>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CC0242C8>, because it is not built.\n",
      "W0430 03:33:05.122137 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CC0242C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD2C3C08>, because it is not built.\n",
      "W0430 03:33:05.122137 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD2C3C08>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD31E0C8>, because it is not built.\n",
      "W0430 03:33:05.122137 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD31E0C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD31EAC8>, because it is not built.\n",
      "W0430 03:33:05.122137 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD31EAC8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD318BC8>, because it is not built.\n",
      "W0430 03:33:05.122137 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD318BC8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD318408>, because it is not built.\n",
      "W0430 03:33:05.122137 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CD318408>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD318D08>, because it is not built.\n",
      "W0430 03:33:05.122137 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD318D08>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CC1264C8>, because it is not built.\n",
      "W0430 03:33:05.122137 12592 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x00000269CC1264C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD32A8C8>, because it is not built.\n",
      "W0430 03:33:05.122137 12592 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x00000269CD32A8C8>, because it is not built.\n",
      "2021-04-30 03:33:16.210561: 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",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B388>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B588>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:33:26.070321 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B388>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B588>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5E88>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5108>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D53C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:33:26.070321 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5E88>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5108>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D53C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546D508>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546DA08>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E5472208>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:33:26.070321 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546D508>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546DA08>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E5472208>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D3C8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D548>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:33:26.071321 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D3C8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D548>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546D508>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546DA08>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E5472208>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:33:31.268353 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546D508>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546DA08>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E5472208>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D3C8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D548>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:33:31.269323 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D3C8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D548>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B388>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B588>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:33:31.269323 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B388>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B588>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5E88>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5108>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D53C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:33:31.269323 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5E88>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5108>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D53C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B388>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B588>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:33:31.270321 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B388>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B588>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5E88>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5108>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D53C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0430 03:33:31.270321 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5E88>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5108>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D53C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "W0430 03:33:34.856854 12592 save.py:241] 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 155). These functions will not be directly callable after loading.\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B388>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B588>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:33:35.082853 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B388>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B588>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5E88>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5108>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D53C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:33:35.082853 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5E88>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5108>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D53C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546D508>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546DA08>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E5472208>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:33:35.082853 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546D508>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546DA08>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E5472208>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D3C8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D548>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:33:35.082853 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D3C8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D548>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546D508>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546DA08>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E5472208>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:33:35.402992 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546D508>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546DA08>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E5472208>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D3C8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D548>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:33:35.402992 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D3C8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D548>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B388>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B588>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:33:35.402992 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B388>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B588>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5E88>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5108>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D53C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:33:35.402992 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5E88>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5108>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D53C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B388>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B588>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:33:35.404014 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B388>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B588>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5E88>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5108>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D53C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:33:35.404014 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5E88>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5108>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D53C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "W0430 03:33:36.154960 12592 save.py:241] 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 155). These functions will not be directly callable after loading.\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B388>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B588>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:33:40.854992 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B388>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B588>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E541B288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5E88>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5108>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D53C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:33:40.855992 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5E88>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D5108>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E53D53C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546D508>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546DA08>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E5472208>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:33:40.855992 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546D508>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E546DA08>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E5472208>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D3C8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D548>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:33:40.856961 12592 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D3C8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D548>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x00000269E545D288>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Assets written to: Tensorflow\\workspace\\models\\my_ssd_mobnet\\export\\saved_model\\assets\n",
      "I0430 03:33:42.501673 12592 builder_impl.py:775] Assets written to: Tensorflow\\workspace\\models\\my_ssd_mobnet\\export\\saved_model\\assets\n",
      "INFO:tensorflow:Writing pipeline config file to Tensorflow\\workspace\\models\\my_ssd_mobnet\\export\\pipeline.config\n",
      "I0430 03:33:43.630028 12592 config_util.py:254] Writing pipeline config file to Tensorflow\\workspace\\models\\my_ssd_mobnet\\export\\pipeline.config\n"
     ]
    }
   ],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wTPmdqaXpfDK"
   },
   "source": [
    "# Conversion to TFJS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
      "  Downloading tensorflowjs-3.6.0-py3-none-any.whl (63 kB)\n",
      "Requirement already satisfied: tensorflow<3,>=2.1.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflowjs) (2.4.1)\n",
      "Collecting tensorflow-hub<0.10,>=0.7.0\n",
      "  Downloading tensorflow_hub-0.9.0-py2.py3-none-any.whl (103 kB)\n",
      "Requirement already satisfied: six<2,>=1.12.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflowjs) (1.15.0)\n",
      "Requirement already satisfied: numpy~=1.19.2 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.19.5)\n",
      "Requirement already satisfied: flatbuffers~=1.12.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.12)\n",
      "Requirement already satisfied: keras-preprocessing~=1.1.2 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.1.2)\n",
      "Requirement already satisfied: protobuf>=3.9.2 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (3.15.8)\n",
      "Requirement already satisfied: absl-py~=0.10 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.12.0)\n",
      "Requirement already satisfied: astunparse~=1.6.3 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.6.3)\n",
      "Requirement already satisfied: wrapt~=1.12.1 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.12.1)\n",
      "Requirement already satisfied: typing-extensions~=3.7.4 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (3.7.4.3)\n",
      "Requirement already satisfied: gast==0.3.3 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.3.3)\n",
      "Requirement already satisfied: grpcio~=1.32.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.32.0)\n",
      "Requirement already satisfied: tensorflow-estimator<2.5.0,>=2.4.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (2.4.0)\n",
      "Requirement already satisfied: google-pasta~=0.2 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.2.0)\n",
      "Requirement already satisfied: opt-einsum~=3.3.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (3.3.0)\n",
      "Requirement already satisfied: tensorboard~=2.4 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (2.5.0)\n",
      "Requirement already satisfied: wheel~=0.35 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.36.2)\n",
      "Requirement already satisfied: h5py~=2.10.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (2.10.0)\n",
      "Requirement already satisfied: termcolor~=1.1.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.1.0)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (0.6.0)\n",
      "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\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 c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (1.8.0)\n",
      "Requirement already satisfied: google-auth<2,>=1.6.3 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (1.30.0)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\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 c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (1.0.1)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (46.4.0)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (2.25.1)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\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: cachetools<5.0,>=2.0.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (4.2.2)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\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: requests-oauthlib>=0.7.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\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 c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from markdown>=2.6.8->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (4.0.1)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\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: urllib3<1.27,>=1.21.1 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\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: chardet<5,>=3.0.2 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\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: certifi>=2017.4.17 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\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: idna<3,>=2.5 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow<3,>=2.1.0->tensorflowjs) (2.10)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\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 c:\\users\\ralha\\onedrive\\desktop\\iiitb\\tfod\\tfodcourse\\env\\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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "tf-models-official 2.4.0 requires dataclasses, which is not installed.\n",
      "tf-models-official 2.4.0 requires google-api-python-client>=1.6.7, which is not installed.\n",
      "tf-models-official 2.4.0 requires google-cloud-bigquery>=0.31.0, which is not installed.\n",
      "tf-models-official 2.4.0 requires kaggle>=1.3.9, which is not installed.\n",
      "tf-models-official 2.4.0 requires oauth2client, which is not installed.\n",
      "tf-models-official 2.4.0 requires opencv-python-headless, which is not installed.\n",
      "tf-models-official 2.4.0 requires psutil>=5.4.3, which is not installed.\n",
      "tf-models-official 2.4.0 requires py-cpuinfo>=3.3.0, which is not installed.\n",
      "tf-models-official 2.4.0 requires sentencepiece, which is not installed.\n",
      "tf-models-official 2.4.0 requires seqeval, which is not installed.\n",
      "tf-models-official 2.4.0 requires tensorflow-datasets, which is not installed.\n",
      "tf-models-official 2.4.0 requires tensorflow-model-optimization>=0.4.1, which is not installed.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully installed tensorflow-hub-0.9.0 tensorflowjs-3.6.0\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflowjs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "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": 15,
   "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": [
      "2021-04-30 03:35:33.174027: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\n",
      "2021-04-30 03:35:33.174056: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2021-04-30 03:35:37.353890: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2021-04-30 03:35:37.354766: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll\n",
      "2021-04-30 03:35:38.490428: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1050 Ti computeCapability: 6.1\n",
      "coreClock: 1.62GHz coreCount: 6 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 104.43GiB/s\n",
      "2021-04-30 03:35:38.492316: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\n",
      "2021-04-30 03:35:38.494209: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublas64_11.dll'; dlerror: cublas64_11.dll not found\n",
      "2021-04-30 03:35:38.495977: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublasLt64_11.dll'; dlerror: cublasLt64_11.dll not found\n",
      "2021-04-30 03:35:38.501758: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll\n",
      "2021-04-30 03:35:38.504660: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll\n",
      "2021-04-30 03:35:38.512147: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll\n",
      "2021-04-30 03:35:38.514420: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusparse64_11.dll'; dlerror: cusparse64_11.dll not found\n",
      "2021-04-30 03:35:38.516426: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudnn64_8.dll'; dlerror: cudnn64_8.dll not found\n",
      "2021-04-30 03:35:38.516462: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2021-04-30 03:35:38.517014: 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",
      "2021-04-30 03:35:38.519078: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-04-30 03:35:38.519104: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      \n",
      "2021-04-30 03:35:38.519134: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2021-04-30 03:35:52.424112: I tensorflow/core/grappler/devices.cc:69] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0\n",
      "2021-04-30 03:35:52.456373: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session\n",
      "2021-04-30 03:35:52.519052: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1050 Ti computeCapability: 6.1\n",
      "coreClock: 1.62GHz coreCount: 6 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 104.43GiB/s\n",
      "2021-04-30 03:35:52.521681: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\n",
      "2021-04-30 03:35:52.523565: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublas64_11.dll'; dlerror: cublas64_11.dll not found\n",
      "2021-04-30 03:35:52.525471: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublasLt64_11.dll'; dlerror: cublasLt64_11.dll not found\n",
      "2021-04-30 03:35:52.525503: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll\n",
      "2021-04-30 03:35:52.525522: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll\n",
      "2021-04-30 03:35:52.525539: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll\n",
      "2021-04-30 03:35:52.529649: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusparse64_11.dll'; dlerror: cusparse64_11.dll not found\n",
      "2021-04-30 03:35:52.532878: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudnn64_8.dll'; dlerror: cudnn64_8.dll not found\n",
      "2021-04-30 03:35:52.532926: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2021-04-30 03:35:52.778832: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-04-30 03:35:52.778895: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n",
      "2021-04-30 03:35:52.778906: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n",
      "2021-04-30 03:35:52.778921: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2021-04-30 03:35:53.332214: 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 = 307.248ms.\n",
      "  function_optimizer: function_optimizer did nothing. time = 4.457ms.\n",
      "\n",
      "2021-04-30 03:35:59.045684: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:928] Optimization results for grappler item: graph_to_optimize\n",
      "  model_pruner: Graph size after: 3255 nodes (-431), 7770 edges (-431), time = 95.321ms.\n",
      "  constant_folding: Graph size after: 1574 nodes (-1681), 5854 edges (-1916), time = 312.037ms.\n",
      "  arithmetic_optimizer: Graph size after: 1574 nodes (0), 5854 edges (0), time = 47.778ms.\n",
      "  dependency_optimizer: Graph size after: 1505 nodes (-69), 1709 edges (-4145), time = 39.715ms.\n",
      "  model_pruner: Graph size after: 1505 nodes (0), 1709 edges (0), time = 16.965ms.\n",
      "  constant_folding: Graph size after: 1505 nodes (0), 1709 edges (0), time = 48.757ms.\n",
      "  arithmetic_optimizer: Graph size after: 1505 nodes (0), 1709 edges (0), time = 39.724ms.\n",
      "  dependency_optimizer: Graph size after: 1505 nodes (0), 1709 edges (0), time = 23.768ms.\n",
      "  debug_stripper: Graph size after: 1505 nodes (0), 1709 edges (0), time = 7.517ms.\n",
      "  model_pruner: Graph size after: 1467 nodes (-38), 1671 edges (-38), time = 27.356ms.\n",
      "  constant_folding: Graph size after: 1467 nodes (0), 1671 edges (0), time = 49.66ms.\n",
      "  arithmetic_optimizer: Graph size after: 1467 nodes (0), 1671 edges (0), time = 39.679ms.\n",
      "  dependency_optimizer: Graph size after: 1456 nodes (-11), 1650 edges (-21), time = 21.853ms.\n",
      "  model_pruner: Graph size after: 1456 nodes (0), 1650 edges (0), time = 22.644ms.\n",
      "  constant_folding: Graph size after: 1456 nodes (0), 1650 edges (0), time = 49.717ms.\n",
      "  arithmetic_optimizer: Graph size after: 1456 nodes (0), 1650 edges (0), time = 39.881ms.\n",
      "  dependency_optimizer: Graph size after: 1456 nodes (0), 1650 edges (0), time = 20.062ms.\n",
      "  debug_stripper: debug_stripper did nothing. time = 3.021ms.\n",
      "\n",
      "2021-04-30 03:36:09.136264: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:928] Optimization results for grappler item: graph_to_optimize\n",
      "  remapper: Graph size after: 1418 nodes (-114), 1308 edges (-114), time = 13.962ms.\n",
      "  constant_folding: Graph size after: 1114 nodes (-304), 1308 edges (0), time = 88.507ms.\n",
      "  arithmetic_optimizer: Graph size after: 1114 nodes (0), 1308 edges (0), time = 29.631ms.\n",
      "  dependency_optimizer: Graph size after: 1114 nodes (0), 1308 edges (0), time = 14.793ms.\n",
      "  remapper: Graph size after: 1114 nodes (0), 1308 edges (0), time = 11.184ms.\n",
      "  constant_folding: Graph size after: 1114 nodes (0), 1308 edges (0), time = 38.294ms.\n",
      "  arithmetic_optimizer: Graph size after: 1114 nodes (0), 1308 edges (0), time = 31.774ms.\n",
      "  dependency_optimizer: Graph size after: 1114 nodes (0), 1308 edges (0), time = 15.623ms.\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": [
    "# Conversion to TFLite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 17,
   "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": 18,
   "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": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I-xWpHN8pfDL",
    "outputId": "7f6bacd8-d077-43b5-c131-5b081fba24a4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-04-30 03:37:24.883638: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\n",
      "2021-04-30 03:37:24.883703: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2021-04-30 03:37:28.580371: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2021-04-30 03:37:28.581249: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll\n",
      "2021-04-30 03:37:29.661572: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1050 Ti computeCapability: 6.1\n",
      "coreClock: 1.62GHz coreCount: 6 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 104.43GiB/s\n",
      "2021-04-30 03:37:29.664510: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\n",
      "2021-04-30 03:37:29.666497: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublas64_11.dll'; dlerror: cublas64_11.dll not found\n",
      "2021-04-30 03:37:29.668747: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublasLt64_11.dll'; dlerror: cublasLt64_11.dll not found\n",
      "2021-04-30 03:37:29.673725: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll\n",
      "2021-04-30 03:37:29.675824: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll\n",
      "2021-04-30 03:37:29.680630: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll\n",
      "2021-04-30 03:37:29.683337: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusparse64_11.dll'; dlerror: cusparse64_11.dll not found\n",
      "2021-04-30 03:37:29.686379: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudnn64_8.dll'; dlerror: cudnn64_8.dll not found\n",
      "2021-04-30 03:37:29.686428: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2021-04-30 03:37:29.686890: 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",
      "2021-04-30 03:37:29.688465: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-04-30 03:37:29.688500: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      \n",
      "2021-04-30 03:37:29.688541: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2021-04-30 03:37:38.069428: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-04-30 03:37:38.069448: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      \n",
      "2021-04-30 03:37:38.069456: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2021-04-30 03:37:38.172117: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:196] None of the MLIR optimization passes are enabled (registered 0 passes)\n",
      "2021-04-30 03:37:40.958428: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-04-30 03:37:40.958448: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      \n",
      "2021-04-30 03:37:40.958455: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x000002017B77D088>, because it is not built.\n",
      "W0430 03:37:41.709468 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x000002017B77D088>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x000002017CE721C8>, because it is not built.\n",
      "W0430 03:37:42.734857 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x000002017CE721C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E637148>, because it is not built.\n",
      "W0430 03:37:42.735837 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E637148>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017E637108>, because it is not built.\n",
      "W0430 03:37:42.735837 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017E637108>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x000002017BAD7EC8>, because it is not built.\n",
      "W0430 03:37:42.735837 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x000002017BAD7EC8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017BB55B08>, because it is not built.\n",
      "W0430 03:37:42.735837 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017BB55B08>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017D0ED588>, because it is not built.\n",
      "W0430 03:37:42.735837 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017D0ED588>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x000002017CDBD188>, because it is not built.\n",
      "W0430 03:37:42.735837 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x000002017CDBD188>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017BC2DF88>, because it is not built.\n",
      "W0430 03:37:42.735837 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017BC2DF88>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017CF60A48>, because it is not built.\n",
      "W0430 03:37:42.736836 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017CF60A48>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x000002017E671D88>, because it is not built.\n",
      "W0430 03:37:42.736836 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x000002017E671D88>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E671EC8>, because it is not built.\n",
      "W0430 03:37:42.736836 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E671EC8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017E5F7C48>, because it is not built.\n",
      "W0430 03:37:42.736836 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017E5F7C48>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017BCE4B88>, because it is not built.\n",
      "W0430 03:37:42.737847 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017BCE4B88>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017D469088>, because it is not built.\n",
      "W0430 03:37:42.737847 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017D469088>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E627EC8>, because it is not built.\n",
      "W0430 03:37:42.737847 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E627EC8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017E6279C8>, because it is not built.\n",
      "W0430 03:37:42.737847 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017E6279C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017CF6CAC8>, because it is not built.\n",
      "W0430 03:37:42.737847 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017CF6CAC8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017CEBB548>, because it is not built.\n",
      "W0430 03:37:42.738836 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017CEBB548>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017BB80C08>, because it is not built.\n",
      "W0430 03:37:42.738836 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017BB80C08>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017D283BC8>, because it is not built.\n",
      "W0430 03:37:42.738836 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017D283BC8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017BB5DC48>, because it is not built.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0430 03:37:42.738836 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017BB5DC48>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017D048DC8>, because it is not built.\n",
      "W0430 03:37:42.738836 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017D048DC8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017D048808>, because it is not built.\n",
      "W0430 03:37:42.739836 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017D048808>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017BCF8D48>, because it is not built.\n",
      "W0430 03:37:42.739836 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017BCF8D48>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017CEB3448>, because it is not built.\n",
      "W0430 03:37:42.739836 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017CEB3448>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017CFC6F48>, because it is not built.\n",
      "W0430 03:37:42.739836 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017CFC6F48>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017CFC67C8>, because it is not built.\n",
      "W0430 03:37:42.740848 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017CFC67C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017B8B3B48>, because it is not built.\n",
      "W0430 03:37:42.740848 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017B8B3B48>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E687848>, because it is not built.\n",
      "W0430 03:37:42.740848 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E687848>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017E628048>, because it is not built.\n",
      "W0430 03:37:42.740848 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017E628048>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E628B48>, because it is not built.\n",
      "W0430 03:37:42.740848 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E628B48>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017E628DC8>, because it is not built.\n",
      "W0430 03:37:42.740848 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017E628DC8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E6765C8>, because it is not built.\n",
      "W0430 03:37:42.741838 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E6765C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017E6B3108>, because it is not built.\n",
      "W0430 03:37:42.741838 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017E6B3108>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E6AE108>, because it is not built.\n",
      "W0430 03:37:42.741838 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E6AE108>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017E6AE708>, because it is not built.\n",
      "W0430 03:37:42.741838 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017E6AE708>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E6BB2C8>, because it is not built.\n",
      "W0430 03:37:42.741838 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E6BB2C8>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017E649788>, because it is not built.\n",
      "W0430 03:37:42.741838 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017E649788>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E649F08>, because it is not built.\n",
      "W0430 03:37:42.741838 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017E649F08>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017D019888>, because it is not built.\n",
      "W0430 03:37:42.741838 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017D019888>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017CE95F48>, because it is not built.\n",
      "W0430 03:37:42.742835 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017CE95F48>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017CF73508>, because it is not built.\n",
      "W0430 03:37:42.742835 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017CF73508>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017D10C448>, because it is not built.\n",
      "W0430 03:37:42.742835 10420 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x000002017D10C448>, because it is not built.\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017BC55448>, because it is not built.\n",
      "W0430 03:37:42.742835 10420 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x000002017BC55448>, because it is not built.\n",
      "2021-04-30 03:37:54.276432: 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",
      "2021-04-30 03:37:56.531699: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-04-30 03:37:56.531755: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      \n",
      "2021-04-30 03:37:56.531763: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295EC8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295208>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295688>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:38:05.658871 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295EC8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295208>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295688>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DF788>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFD88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFA88>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:38:05.659849 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DF788>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFD88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFA88>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301208>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301A88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B3018C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:38:05.659849 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301208>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301A88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B3018C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31F708>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FDC8>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FAC8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:38:05.660850 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31F708>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FDC8>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FAC8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301208>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301A88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B3018C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:38:10.848649 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301208>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301A88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B3018C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31F708>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FDC8>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FAC8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:38:10.848649 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31F708>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FDC8>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FAC8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295EC8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295208>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295688>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:38:10.849647 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295EC8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295208>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295688>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DF788>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFD88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFA88>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:38:10.849647 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DF788>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFD88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFA88>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295EC8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295208>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295688>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:38:10.849647 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295EC8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295208>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295688>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DF788>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFD88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFA88>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:38:10.849647 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DF788>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFD88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFA88>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "W0430 03:38:14.663667 10420 save.py:241] 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 155). These functions will not be directly callable after loading.\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295EC8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295208>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295688>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:38:14.911656 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295EC8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295208>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295688>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DF788>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFD88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFA88>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:38:14.913650 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DF788>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFD88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFA88>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301208>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301A88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B3018C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:38:14.914654 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301208>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301A88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B3018C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31F708>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FDC8>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FAC8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:38:14.914654 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31F708>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FDC8>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FAC8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301208>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301A88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B3018C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:38:15.317676 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301208>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301A88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B3018C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31F708>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FDC8>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FAC8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:38:15.317676 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31F708>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FDC8>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FAC8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295EC8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295208>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295688>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:38:15.318674 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295EC8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295208>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295688>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DF788>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFD88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFA88>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:38:15.318674 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DF788>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFD88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFA88>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295EC8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295208>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295688>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:38:15.318674 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295EC8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295208>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295688>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DF788>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFD88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFA88>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:38:15.318674 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DF788>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFD88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFA88>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "W0430 03:38:16.115650 10420 save.py:241] 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 155). These functions will not be directly callable after loading.\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295EC8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295208>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295688>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:38:20.705120 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295EC8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295208>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A295688>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DF788>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFD88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFA88>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:38:20.705120 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DF788>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFD88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A2DFA88>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301208>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301A88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B3018C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "I0430 03:38:20.706094 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301208>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B301A88>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011B3018C8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
      "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31F708>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FDC8>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FAC8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "I0430 03:38:20.706094 10420 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31F708>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FDC8>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x000002011A31FAC8>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
      "INFO:tensorflow:Assets written to: Tensorflow\\workspace\\models\\my_ssd_mobnet\\tfliteexport\\saved_model\\assets\n",
      "I0430 03:38:22.341922 10420 builder_impl.py:775] Assets written to: Tensorflow\\workspace\\models\\my_ssd_mobnet\\tfliteexport\\saved_model\\assets\n"
     ]
    }
   ],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "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": 22,
   "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": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nbd7gqHMpfDL",
    "outputId": "7c8fe6d5-2415-4641-8548-39d425c202f7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-04-30 03:38:27.004132: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\n",
      "2021-04-30 03:38:27.004194: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2021-04-30 03:38:30.594505: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2021-04-30 03:38:30.595417: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll\n",
      "2021-04-30 03:38:31.183951: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1050 Ti computeCapability: 6.1\n",
      "coreClock: 1.62GHz coreCount: 6 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 104.43GiB/s\n",
      "2021-04-30 03:38:31.185947: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\n",
      "2021-04-30 03:38:31.188607: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublas64_11.dll'; dlerror: cublas64_11.dll not found\n",
      "2021-04-30 03:38:31.190660: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublasLt64_11.dll'; dlerror: cublasLt64_11.dll not found\n",
      "2021-04-30 03:38:31.197169: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll\n",
      "2021-04-30 03:38:31.199335: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll\n",
      "2021-04-30 03:38:31.205612: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll\n",
      "2021-04-30 03:38:31.207771: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusparse64_11.dll'; dlerror: cusparse64_11.dll not found\n",
      "2021-04-30 03:38:31.209644: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudnn64_8.dll'; dlerror: cudnn64_8.dll not found\n",
      "2021-04-30 03:38:31.209660: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2021-04-30 03:38:31.209900: 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",
      "2021-04-30 03:38:31.211503: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-04-30 03:38:31.211518: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      \n",
      "2021-04-30 03:38:31.211566: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2021-04-30 03:38:46.591622: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:316] Ignored output_format.\n",
      "2021-04-30 03:38:46.591645: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:319] Ignored drop_control_dependency.\n",
      "2021-04-30 03:38:46.591651: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:325] Ignored change_concat_input_ranges.\n",
      "2021-04-30 03:38:46.717117: I tensorflow/cc/saved_model/reader.cc:32] Reading SavedModel from: Tensorflow\\workspace\\models\\my_ssd_mobnet\\tfliteexport\\saved_model\n",
      "2021-04-30 03:38:46.888518: I tensorflow/cc/saved_model/reader.cc:55] Reading meta graph with tags { serve }\n",
      "2021-04-30 03:38:46.888595: I tensorflow/cc/saved_model/reader.cc:93] Reading SavedModel debug info (if present) from: Tensorflow\\workspace\\models\\my_ssd_mobnet\\tfliteexport\\saved_model\n",
      "2021-04-30 03:38:46.888802: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-04-30 03:38:46.888816: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      \n",
      "2021-04-30 03:38:46.888844: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2021-04-30 03:38:47.369850: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:196] None of the MLIR optimization passes are enabled (registered 0 passes)\n",
      "2021-04-30 03:38:47.428020: I tensorflow/cc/saved_model/loader.cc:206] Restoring SavedModel bundle.\n",
      "2021-04-30 03:38:48.706478: 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",
      "2021-04-30 03:38:49.154099: I tensorflow/cc/saved_model/loader.cc:277] SavedModel load for tags { serve }; Status: success: OK. Took 2436987 microseconds.\n",
      "2021-04-30 03:38:51.871287: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:194] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2021-04-30 03:38:54.038356: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
      "pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1050 Ti computeCapability: 6.1\n",
      "coreClock: 1.62GHz coreCount: 6 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 104.43GiB/s\n",
      "2021-04-30 03:38:54.040708: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\n",
      "2021-04-30 03:38:54.042674: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublas64_11.dll'; dlerror: cublas64_11.dll not found\n",
      "2021-04-30 03:38:54.044736: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublasLt64_11.dll'; dlerror: cublasLt64_11.dll not found\n",
      "2021-04-30 03:38:54.044769: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll\n",
      "2021-04-30 03:38:54.044778: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll\n",
      "2021-04-30 03:38:54.044785: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll\n",
      "2021-04-30 03:38:54.046714: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusparse64_11.dll'; dlerror: cusparse64_11.dll not found\n",
      "2021-04-30 03:38:54.050998: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudnn64_8.dll'; dlerror: cudnn64_8.dll not found\n",
      "2021-04-30 03:38:54.051038: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2021-04-30 03:38:54.387421: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-04-30 03:38:54.387443: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n",
      "2021-04-30 03:38:54.387449: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n",
      "2021-04-30 03:38:54.387457: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n"
     ]
    }
   ],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5NQqZRdA21Uc"
   },
   "source": [
    "# Zip and Export Models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "env",
   "language": "python",
   "name": "env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}