object-detection 2024.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyOuSJoumSP0DPVCEQvEKHyX",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/muhd360/tf-lite-object_det/blob/main/object-detection%202024.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TId_s-2g-x_X",
        "outputId": "d4f5ac10-e78b-4642-ca26-9f5afedfa78d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: Cython 3.0.10\n",
            "Uninstalling Cython-3.0.10:\n",
            "  Successfully uninstalled Cython-3.0.10\n"
          ]
        }
      ],
      "source": [
        "!pip uninstall Cython -y"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone --depth 1 https://github.com/tensorflow/models"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U6pBEmbV_wpP",
        "outputId": "82e7c1e5-6d2a-4793-9150-fc03471bef08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'models' already exists and is not an empty directory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "cd models/research/\n",
        "protoc object_detection/protos/*.proto --python_out=."
      ],
      "metadata": {
        "id": "CelwiNbE_wr2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import re\n",
        "with open('/content/models/research/object_detection/packages/tf2/setup.py') as f:\n",
        "    s = f.read()\n",
        "\n",
        "with open('/content/models/research/setup.py', 'w') as f:\n",
        "    s = re.sub('tf-models-official>=2.5.1',\n",
        "               'tf-models-official==2.8.0', s)\n",
        "    f.write(s)"
      ],
      "metadata": {
        "id": "r9OPs9qK_wug"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyyaml==5.3\n",
        "!pip install /content/models/research/\n",
        "!pip install tensorflow==2.8.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SzANeoK6_wxp",
        "outputId": "58927bb0-90a8-49ec-eb76-a1efb5d47fcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pyyaml==5.3 in /usr/local/lib/python3.10/dist-packages (5.3)\n",
            "Processing ./models/research\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Collecting Cython (from object-detection==0.1)\n",
            "  Using cached Cython-3.0.10-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)\n",
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            "Building wheels for collected packages: object-detection\n",
            "  Building wheel for object-detection (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for object-detection: filename=object_detection-0.1-py3-none-any.whl size=1697352 sha256=d8f95d30392e16936e8cbf28834099e792299ebd64560d704d877ffa16c00902\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-2gayt6i0/wheels/53/dd/70/2de274d6c443c69d367bd6a5606f95e5a6df61aacf1435ec0d\n",
            "Successfully built object-detection\n",
            "Installing collected packages: Cython, object-detection\n",
            "  Attempting uninstall: object-detection\n",
            "    Found existing installation: object-detection 0.1\n",
            "    Uninstalling object-detection-0.1:\n",
            "      Successfully uninstalled object-detection-0.1\n",
            "Successfully installed Cython-3.0.10 object-detection-0.1\n",
            "Collecting tensorflow==2.8.0\n",
            "  Using cached tensorflow-2.8.0-cp310-cp310-manylinux2010_x86_64.whl (497.6 MB)\n",
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            "Collecting tf-estimator-nightly==2.8.0.dev2021122109 (from tensorflow==2.8.0)\n",
            "  Using cached tf_estimator_nightly-2.8.0.dev2021122109-py2.py3-none-any.whl (462 kB)\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow==2.8.0) (3.2.2)\n",
            "Installing collected packages: tf-estimator-nightly, tensorflow\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.8.1\n",
            "    Uninstalling tensorflow-2.8.1:\n",
            "      Successfully uninstalled tensorflow-2.8.1\n",
            "Successfully installed tensorflow-2.8.0 tf-estimator-nightly-2.8.0.dev2021122109\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python /content/models/research/object_detection/builders/model_builder_tf2_test.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7kUK3UTe_w0s",
        "outputId": "e5e8673f-ae20-4d0f-f6e4-c2973f0af2f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2024-04-15 14:43:44.330598: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 14:43:44.330645: 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",
            "/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\n",
            "caused by: ['/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl5mutex6unlockEv']\n",
            "  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n",
            "/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/libtensorflow_io.so']\n",
            "caused by: ['/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZNK10tensorflow4data15DatasetOpKernel11TraceStringB5cxx11ERKNS_15OpKernelContextEb']\n",
            "  warnings.warn(f\"file system plugins are not loaded: {e}\")\n",
            "2024-04-15 14:43:49.899615: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2024-04-15 14:43:49.899990: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 14:43:49.900204: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 14:43:49.900372: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 14:43:49.900550: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 14:43:50.458157: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 14:43:50.462707: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] 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",
            "Running tests under Python 3.10.12: /usr/bin/python3\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_deepmac\n",
            "2024-04-15 14:43:50.483542: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
            "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "W0415 14:43:50.711777 140445001265792 model_builder.py:1112] Building experimental DeepMAC meta-arch. Some features may be omitted.\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 0.65s\n",
            "I0415 14:43:51.122882 140445001265792 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 0.65s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_center_net_deepmac\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 0.53s\n",
            "I0415 14:43:51.654131 140445001265792 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 0.53s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.26s\n",
            "I0415 14:43:51.911058 140445001265792 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.26s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.27s\n",
            "I0415 14:43:52.180393 140445001265792 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.27s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 2.24s\n",
            "I0415 14:43:54.420618 140445001265792 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 2.24s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_experimental_model\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n",
            "I0415 14:43:54.421996 140445001265792 test_util.py:2373] 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",
            "I0415 14:43:54.449835 140445001265792 test_util.py:2373] 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",
            "I0415 14:43:54.465656 140445001265792 test_util.py:2373] 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",
            "I0415 14:43:54.486333 140445001265792 test_util.py:2373] 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): 0.12s\n",
            "I0415 14:43:54.602978 140445001265792 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.12s\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): 0.1s\n",
            "I0415 14:43:54.706665 140445001265792 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.1s\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): 0.1s\n",
            "I0415 14:43:54.802812 140445001265792 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.1s\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): 0.1s\n",
            "I0415 14:43:54.905780 140445001265792 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.1s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.09s\n",
            "I0415 14:43:54.996147 140445001265792 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.09s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.03s\n",
            "I0415 14:43:55.025465 140445001265792 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.03s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n",
            "I0415 14:43:55.201454 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:161] EfficientDet EfficientNet backbone version: efficientnet-b0\n",
            "I0415 14:43:55.201648 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:163] EfficientDet BiFPN num filters: 64\n",
            "I0415 14:43:55.201735 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:164] EfficientDet BiFPN num iterations: 3\n",
            "I0415 14:43:55.204293 140445001265792 efficientnet_model.py:144] round_filter input=32 output=32\n",
            "I0415 14:43:55.221052 140445001265792 efficientnet_model.py:144] round_filter input=32 output=32\n",
            "I0415 14:43:55.221206 140445001265792 efficientnet_model.py:144] round_filter input=16 output=16\n",
            "I0415 14:43:55.283570 140445001265792 efficientnet_model.py:144] round_filter input=16 output=16\n",
            "I0415 14:43:55.283767 140445001265792 efficientnet_model.py:144] round_filter input=24 output=24\n",
            "I0415 14:43:55.509000 140445001265792 efficientnet_model.py:144] round_filter input=24 output=24\n",
            "I0415 14:43:55.509209 140445001265792 efficientnet_model.py:144] round_filter input=40 output=40\n",
            "I0415 14:43:55.738373 140445001265792 efficientnet_model.py:144] round_filter input=40 output=40\n",
            "I0415 14:43:55.738576 140445001265792 efficientnet_model.py:144] round_filter input=80 output=80\n",
            "I0415 14:43:56.081451 140445001265792 efficientnet_model.py:144] round_filter input=80 output=80\n",
            "I0415 14:43:56.081699 140445001265792 efficientnet_model.py:144] round_filter input=112 output=112\n",
            "I0415 14:43:56.432425 140445001265792 efficientnet_model.py:144] round_filter input=112 output=112\n",
            "I0415 14:43:56.432679 140445001265792 efficientnet_model.py:144] round_filter input=192 output=192\n",
            "I0415 14:43:56.974365 140445001265792 efficientnet_model.py:144] round_filter input=192 output=192\n",
            "I0415 14:43:56.974629 140445001265792 efficientnet_model.py:144] round_filter input=320 output=320\n",
            "I0415 14:43:57.118189 140445001265792 efficientnet_model.py:144] round_filter input=1280 output=1280\n",
            "I0415 14:43:57.211980 140445001265792 efficientnet_model.py:454] 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",
            "I0415 14:43:57.297900 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:161] EfficientDet EfficientNet backbone version: efficientnet-b1\n",
            "I0415 14:43:57.298131 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:163] EfficientDet BiFPN num filters: 88\n",
            "I0415 14:43:57.298227 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:164] EfficientDet BiFPN num iterations: 4\n",
            "I0415 14:43:57.300916 140445001265792 efficientnet_model.py:144] round_filter input=32 output=32\n",
            "I0415 14:43:57.328124 140445001265792 efficientnet_model.py:144] round_filter input=32 output=32\n",
            "I0415 14:43:57.329434 140445001265792 efficientnet_model.py:144] round_filter input=16 output=16\n",
            "I0415 14:43:57.526845 140445001265792 efficientnet_model.py:144] round_filter input=16 output=16\n",
            "I0415 14:43:57.527063 140445001265792 efficientnet_model.py:144] round_filter input=24 output=24\n",
            "I0415 14:43:57.916873 140445001265792 efficientnet_model.py:144] round_filter input=24 output=24\n",
            "I0415 14:43:57.917073 140445001265792 efficientnet_model.py:144] round_filter input=40 output=40\n",
            "I0415 14:43:58.168539 140445001265792 efficientnet_model.py:144] round_filter input=40 output=40\n",
            "I0415 14:43:58.168707 140445001265792 efficientnet_model.py:144] round_filter input=80 output=80\n",
            "I0415 14:43:58.502502 140445001265792 efficientnet_model.py:144] round_filter input=80 output=80\n",
            "I0415 14:43:58.502710 140445001265792 efficientnet_model.py:144] round_filter input=112 output=112\n",
            "I0415 14:43:59.097517 140445001265792 efficientnet_model.py:144] round_filter input=112 output=112\n",
            "I0415 14:43:59.097737 140445001265792 efficientnet_model.py:144] round_filter input=192 output=192\n",
            "I0415 14:43:59.557031 140445001265792 efficientnet_model.py:144] round_filter input=192 output=192\n",
            "I0415 14:43:59.557207 140445001265792 efficientnet_model.py:144] round_filter input=320 output=320\n",
            "I0415 14:43:59.772925 140445001265792 efficientnet_model.py:144] round_filter input=1280 output=1280\n",
            "I0415 14:43:59.828104 140445001265792 efficientnet_model.py:454] 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",
            "I0415 14:43:59.891433 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:161] EfficientDet EfficientNet backbone version: efficientnet-b2\n",
            "I0415 14:43:59.891616 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:163] EfficientDet BiFPN num filters: 112\n",
            "I0415 14:43:59.891696 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:164] EfficientDet BiFPN num iterations: 5\n",
            "I0415 14:43:59.893430 140445001265792 efficientnet_model.py:144] round_filter input=32 output=32\n",
            "I0415 14:43:59.909775 140445001265792 efficientnet_model.py:144] round_filter input=32 output=32\n",
            "I0415 14:43:59.909914 140445001265792 efficientnet_model.py:144] round_filter input=16 output=16\n",
            "I0415 14:44:00.040763 140445001265792 efficientnet_model.py:144] round_filter input=16 output=16\n",
            "I0415 14:44:00.040946 140445001265792 efficientnet_model.py:144] round_filter input=24 output=24\n",
            "I0415 14:44:00.284322 140445001265792 efficientnet_model.py:144] round_filter input=24 output=24\n",
            "I0415 14:44:00.284504 140445001265792 efficientnet_model.py:144] round_filter input=40 output=48\n",
            "I0415 14:44:00.535247 140445001265792 efficientnet_model.py:144] round_filter input=40 output=48\n",
            "I0415 14:44:00.535439 140445001265792 efficientnet_model.py:144] round_filter input=80 output=88\n",
            "I0415 14:44:00.889508 140445001265792 efficientnet_model.py:144] round_filter input=80 output=88\n",
            "I0415 14:44:00.889696 140445001265792 efficientnet_model.py:144] round_filter input=112 output=120\n",
            "I0415 14:44:01.230489 140445001265792 efficientnet_model.py:144] round_filter input=112 output=120\n",
            "I0415 14:44:01.230699 140445001265792 efficientnet_model.py:144] round_filter input=192 output=208\n",
            "I0415 14:44:01.695583 140445001265792 efficientnet_model.py:144] round_filter input=192 output=208\n",
            "I0415 14:44:01.695761 140445001265792 efficientnet_model.py:144] round_filter input=320 output=352\n",
            "I0415 14:44:01.925976 140445001265792 efficientnet_model.py:144] round_filter input=1280 output=1408\n",
            "I0415 14:44:01.983563 140445001265792 efficientnet_model.py:454] 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",
            "I0415 14:44:02.046694 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:161] EfficientDet EfficientNet backbone version: efficientnet-b3\n",
            "I0415 14:44:02.046862 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:163] EfficientDet BiFPN num filters: 160\n",
            "I0415 14:44:02.046939 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:164] EfficientDet BiFPN num iterations: 6\n",
            "I0415 14:44:02.048543 140445001265792 efficientnet_model.py:144] round_filter input=32 output=40\n",
            "I0415 14:44:02.065512 140445001265792 efficientnet_model.py:144] round_filter input=32 output=40\n",
            "I0415 14:44:02.065678 140445001265792 efficientnet_model.py:144] round_filter input=16 output=24\n",
            "I0415 14:44:02.195273 140445001265792 efficientnet_model.py:144] round_filter input=16 output=24\n",
            "I0415 14:44:02.195443 140445001265792 efficientnet_model.py:144] round_filter input=24 output=32\n",
            "I0415 14:44:02.440938 140445001265792 efficientnet_model.py:144] round_filter input=24 output=32\n",
            "I0415 14:44:02.441154 140445001265792 efficientnet_model.py:144] round_filter input=40 output=48\n",
            "I0415 14:44:02.687482 140445001265792 efficientnet_model.py:144] round_filter input=40 output=48\n",
            "I0415 14:44:02.687709 140445001265792 efficientnet_model.py:144] round_filter input=80 output=96\n",
            "I0415 14:44:03.119785 140445001265792 efficientnet_model.py:144] round_filter input=80 output=96\n",
            "I0415 14:44:03.119962 140445001265792 efficientnet_model.py:144] round_filter input=112 output=136\n",
            "I0415 14:44:03.560559 140445001265792 efficientnet_model.py:144] round_filter input=112 output=136\n",
            "I0415 14:44:03.560737 140445001265792 efficientnet_model.py:144] round_filter input=192 output=232\n",
            "I0415 14:44:04.153641 140445001265792 efficientnet_model.py:144] round_filter input=192 output=232\n",
            "I0415 14:44:04.153806 140445001265792 efficientnet_model.py:144] round_filter input=320 output=384\n",
            "I0415 14:44:04.382637 140445001265792 efficientnet_model.py:144] round_filter input=1280 output=1536\n",
            "I0415 14:44:04.446816 140445001265792 efficientnet_model.py:454] 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",
            "I0415 14:44:04.527692 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:161] EfficientDet EfficientNet backbone version: efficientnet-b4\n",
            "I0415 14:44:04.527867 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:163] EfficientDet BiFPN num filters: 224\n",
            "I0415 14:44:04.527951 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:164] EfficientDet BiFPN num iterations: 7\n",
            "I0415 14:44:04.529652 140445001265792 efficientnet_model.py:144] round_filter input=32 output=48\n",
            "I0415 14:44:04.548039 140445001265792 efficientnet_model.py:144] round_filter input=32 output=48\n",
            "I0415 14:44:04.548192 140445001265792 efficientnet_model.py:144] round_filter input=16 output=24\n",
            "I0415 14:44:04.677751 140445001265792 efficientnet_model.py:144] round_filter input=16 output=24\n",
            "I0415 14:44:04.677932 140445001265792 efficientnet_model.py:144] round_filter input=24 output=32\n",
            "I0415 14:44:05.329449 140445001265792 efficientnet_model.py:144] round_filter input=24 output=32\n",
            "I0415 14:44:05.329652 140445001265792 efficientnet_model.py:144] round_filter input=40 output=56\n",
            "I0415 14:44:05.671035 140445001265792 efficientnet_model.py:144] round_filter input=40 output=56\n",
            "I0415 14:44:05.671224 140445001265792 efficientnet_model.py:144] round_filter input=80 output=112\n",
            "I0415 14:44:06.197493 140445001265792 efficientnet_model.py:144] round_filter input=80 output=112\n",
            "I0415 14:44:06.197677 140445001265792 efficientnet_model.py:144] round_filter input=112 output=160\n",
            "I0415 14:44:06.726293 140445001265792 efficientnet_model.py:144] round_filter input=112 output=160\n",
            "I0415 14:44:06.726489 140445001265792 efficientnet_model.py:144] round_filter input=192 output=272\n",
            "I0415 14:44:07.566369 140445001265792 efficientnet_model.py:144] round_filter input=192 output=272\n",
            "I0415 14:44:07.566569 140445001265792 efficientnet_model.py:144] round_filter input=320 output=448\n",
            "I0415 14:44:07.825078 140445001265792 efficientnet_model.py:144] round_filter input=1280 output=1792\n",
            "I0415 14:44:07.909987 140445001265792 efficientnet_model.py:454] 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",
            "I0415 14:44:08.054048 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:161] EfficientDet EfficientNet backbone version: efficientnet-b5\n",
            "I0415 14:44:08.054272 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:163] EfficientDet BiFPN num filters: 288\n",
            "I0415 14:44:08.054376 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:164] EfficientDet BiFPN num iterations: 7\n",
            "I0415 14:44:08.056951 140445001265792 efficientnet_model.py:144] round_filter input=32 output=48\n",
            "I0415 14:44:08.078980 140445001265792 efficientnet_model.py:144] round_filter input=32 output=48\n",
            "I0415 14:44:08.079177 140445001265792 efficientnet_model.py:144] round_filter input=16 output=24\n",
            "I0415 14:44:08.361661 140445001265792 efficientnet_model.py:144] round_filter input=16 output=24\n",
            "I0415 14:44:08.361873 140445001265792 efficientnet_model.py:144] round_filter input=24 output=40\n",
            "I0415 14:44:08.922811 140445001265792 efficientnet_model.py:144] round_filter input=24 output=40\n",
            "I0415 14:44:08.923022 140445001265792 efficientnet_model.py:144] round_filter input=40 output=64\n",
            "I0415 14:44:09.524753 140445001265792 efficientnet_model.py:144] round_filter input=40 output=64\n",
            "I0415 14:44:09.524986 140445001265792 efficientnet_model.py:144] round_filter input=80 output=128\n",
            "I0415 14:44:10.451398 140445001265792 efficientnet_model.py:144] round_filter input=80 output=128\n",
            "I0415 14:44:10.451577 140445001265792 efficientnet_model.py:144] round_filter input=112 output=176\n",
            "I0415 14:44:11.094266 140445001265792 efficientnet_model.py:144] round_filter input=112 output=176\n",
            "I0415 14:44:11.094446 140445001265792 efficientnet_model.py:144] round_filter input=192 output=304\n",
            "I0415 14:44:12.112504 140445001265792 efficientnet_model.py:144] round_filter input=192 output=304\n",
            "I0415 14:44:12.112720 140445001265792 efficientnet_model.py:144] round_filter input=320 output=512\n",
            "I0415 14:44:12.591323 140445001265792 efficientnet_model.py:144] round_filter input=1280 output=2048\n",
            "I0415 14:44:12.669014 140445001265792 efficientnet_model.py:454] 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",
            "I0415 14:44:13.051812 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:161] EfficientDet EfficientNet backbone version: efficientnet-b6\n",
            "I0415 14:44:13.052008 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:163] EfficientDet BiFPN num filters: 384\n",
            "I0415 14:44:13.052093 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:164] EfficientDet BiFPN num iterations: 8\n",
            "I0415 14:44:13.053788 140445001265792 efficientnet_model.py:144] round_filter input=32 output=56\n",
            "I0415 14:44:13.073193 140445001265792 efficientnet_model.py:144] round_filter input=32 output=56\n",
            "I0415 14:44:13.073348 140445001265792 efficientnet_model.py:144] round_filter input=16 output=32\n",
            "I0415 14:44:13.292331 140445001265792 efficientnet_model.py:144] round_filter input=16 output=32\n",
            "I0415 14:44:13.292511 140445001265792 efficientnet_model.py:144] round_filter input=24 output=40\n",
            "I0415 14:44:13.792992 140445001265792 efficientnet_model.py:144] round_filter input=24 output=40\n",
            "I0415 14:44:13.793200 140445001265792 efficientnet_model.py:144] round_filter input=40 output=72\n",
            "I0415 14:44:14.321918 140445001265792 efficientnet_model.py:144] round_filter input=40 output=72\n",
            "I0415 14:44:14.322102 140445001265792 efficientnet_model.py:144] round_filter input=80 output=144\n",
            "I0415 14:44:15.025005 140445001265792 efficientnet_model.py:144] round_filter input=80 output=144\n",
            "I0415 14:44:15.025186 140445001265792 efficientnet_model.py:144] round_filter input=112 output=200\n",
            "I0415 14:44:15.794604 140445001265792 efficientnet_model.py:144] round_filter input=112 output=200\n",
            "I0415 14:44:15.794778 140445001265792 efficientnet_model.py:144] round_filter input=192 output=344\n",
            "I0415 14:44:17.150186 140445001265792 efficientnet_model.py:144] round_filter input=192 output=344\n",
            "I0415 14:44:17.150369 140445001265792 efficientnet_model.py:144] round_filter input=320 output=576\n",
            "I0415 14:44:17.679120 140445001265792 efficientnet_model.py:144] round_filter input=1280 output=2304\n",
            "I0415 14:44:17.761545 140445001265792 efficientnet_model.py:454] 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",
            "I0415 14:44:17.862868 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:161] EfficientDet EfficientNet backbone version: efficientnet-b7\n",
            "I0415 14:44:17.863040 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:163] EfficientDet BiFPN num filters: 384\n",
            "I0415 14:44:17.863123 140445001265792 ssd_efficientnet_bifpn_feature_extractor.py:164] EfficientDet BiFPN num iterations: 8\n",
            "I0415 14:44:17.865150 140445001265792 efficientnet_model.py:144] round_filter input=32 output=64\n",
            "I0415 14:44:17.881272 140445001265792 efficientnet_model.py:144] round_filter input=32 output=64\n",
            "I0415 14:44:17.881435 140445001265792 efficientnet_model.py:144] round_filter input=16 output=32\n",
            "I0415 14:44:18.150066 140445001265792 efficientnet_model.py:144] round_filter input=16 output=32\n",
            "I0415 14:44:18.150253 140445001265792 efficientnet_model.py:144] round_filter input=24 output=48\n",
            "I0415 14:44:18.752784 140445001265792 efficientnet_model.py:144] round_filter input=24 output=48\n",
            "I0415 14:44:18.752984 140445001265792 efficientnet_model.py:144] round_filter input=40 output=80\n",
            "I0415 14:44:19.347881 140445001265792 efficientnet_model.py:144] round_filter input=40 output=80\n",
            "I0415 14:44:19.348073 140445001265792 efficientnet_model.py:144] round_filter input=80 output=160\n",
            "I0415 14:44:20.239194 140445001265792 efficientnet_model.py:144] round_filter input=80 output=160\n",
            "I0415 14:44:20.239393 140445001265792 efficientnet_model.py:144] round_filter input=112 output=224\n",
            "I0415 14:44:22.127956 140445001265792 efficientnet_model.py:144] round_filter input=112 output=224\n",
            "I0415 14:44:22.128184 140445001265792 efficientnet_model.py:144] round_filter input=192 output=384\n",
            "I0415 14:44:24.100895 140445001265792 efficientnet_model.py:144] round_filter input=192 output=384\n",
            "I0415 14:44:24.101078 140445001265792 efficientnet_model.py:144] round_filter input=320 output=640\n",
            "I0415 14:44:24.938182 140445001265792 efficientnet_model.py:144] round_filter input=1280 output=2560\n",
            "I0415 14:44:25.036268 140445001265792 efficientnet_model.py:454] 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): 30.14s\n",
            "I0415 14:44:25.164638 140445001265792 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 30.14s\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",
            "I0415 14:44:25.177365 140445001265792 test_util.py:2373] 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",
            "I0415 14:44:25.179183 140445001265792 test_util.py:2373] 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",
            "I0415 14:44:25.179723 140445001265792 test_util.py:2373] 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",
            "I0415 14:44:25.181225 140445001265792 test_util.py:2373] 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",
            "I0415 14:44:25.182583 140445001265792 test_util.py:2373] 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",
            "I0415 14:44:25.183013 140445001265792 test_util.py:2373] 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",
            "I0415 14:44:25.183982 140445001265792 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n",
            "----------------------------------------------------------------------\n",
            "Ran 24 tests in 34.717s\n",
            "\n",
            "OK (skipped=1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir /content/images\n",
        "!unzip -q images.zip -d /content/images/all\n",
        "!mkdir /content/images/train; mkdir /content/images/validation; mkdir /content/images/test"
      ],
      "metadata": {
        "id": "7OeIk1eK_w3U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/master/util_scripts/train_val_test_split.py\n",
        "!python train_val_test_split.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43m-NdYq_w6I",
        "outputId": "a936c5c1-87bb-4fdf-b8d6-92935461a7c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-04-15 14:48:45--  https://raw.githubusercontent.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/master/util_scripts/train_val_test_split.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3100 (3.0K) [text/plain]\n",
            "Saving to: ‘train_val_test_split.py’\n",
            "\n",
            "train_val_test_spli 100%[===================>]   3.03K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-04-15 14:48:45 (42.8 MB/s) - ‘train_val_test_split.py’ saved [3100/3100]\n",
            "\n",
            "Total images: 395\n",
            "Images moving to train: 316\n",
            "Images moving to validation: 39\n",
            "Images moving to test: 40\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### This creates a a \"labelmap.txt\" file with a list of classes the object detection model will detect.\n",
        "%%bash\n",
        "cat <<EOF >> /content/labelmap.txt\n",
        "Auto\n",
        "barrier\n",
        "Bus\n",
        "Car\n",
        "crosswalk\n",
        "Dog\n",
        "Footpath\n",
        "Traffic Lights\n",
        "Tree\n",
        "Vehicle\n",
        "Vendor\n",
        "Vendor cart\n",
        "barrier\n",
        "junk\n",
        "motorcycle\n",
        "obstacle\n",
        "pole\n",
        "pothole\n",
        "Pedestrian\n",
        "signboard\n",
        "stairs\n",
        "Obstacle\n",
        "person\n",
        "EOF"
      ],
      "metadata": {
        "id": "LH7KJBI__w88"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Download data conversion scripts\n",
        "! wget https://raw.githubusercontent.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/master/util_scripts/create_csv.py\n",
        "! wget https://raw.githubusercontent.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/master/util_scripts/create_tfrecord.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VmO3bNmoEwRX",
        "outputId": "06cf180f-bb72-4d2c-8c2d-0b581a48e64d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-04-15 15:12:23--  https://raw.githubusercontent.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/master/util_scripts/create_csv.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1348 (1.3K) [text/plain]\n",
            "Saving to: ‘create_csv.py’\n",
            "\n",
            "create_csv.py       100%[===================>]   1.32K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-04-15 15:12:23 (74.1 MB/s) - ‘create_csv.py’ saved [1348/1348]\n",
            "\n",
            "--2024-04-15 15:12:23--  https://raw.githubusercontent.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/master/util_scripts/create_tfrecord.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4414 (4.3K) [text/plain]\n",
            "Saving to: ‘create_tfrecord.py’\n",
            "\n",
            "create_tfrecord.py  100%[===================>]   4.31K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-04-15 15:12:24 (55.6 MB/s) - ‘create_tfrecord.py’ saved [4414/4414]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create CSV data files and TFRecord files\n",
        "!python3 amir.py\n",
        "!python3 create_tfrecord.py --csv_input=images/train_labels.csv --labelmap=labelmap.txt --image_dir=images/train --output_path=train.tfrecord\n",
        "!python3 create_tfrecord.py --csv_input=images/validation_labels.csv --labelmap=labelmap.txt --image_dir=images/validation --output_path=val.tfrecord"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3crib86E8a5",
        "outputId": "41c89359-1acb-4c5a-9915-31e25d55255f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Successfully converted XML to CSV for folder: train\n",
            "Successfully converted XML to CSV for folder: validation\n",
            "2024-04-15 15:13:55.456064: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 15:13:55.456098: 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",
            "2024-04-15 15:13:57.627749: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2024-04-15 15:13:57.628080: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 15:13:57.628210: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 15:13:57.628329: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 15:13:57.628436: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 15:13:57.675678: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 15:13:57.676015: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] 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",
            "Successfully created the TFRecords: /content/train.tfrecord\n",
            "2024-04-15 15:13:59.383882: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 15:13:59.383922: 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",
            "2024-04-15 15:14:02.392612: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2024-04-15 15:14:02.392960: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 15:14:02.393084: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 15:14:02.393190: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 15:14:02.393296: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 15:14:02.437906: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 15:14:02.438308: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] 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",
            "Successfully created the TFRecords: /content/val.tfrecord\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_record_fname = '/content/train.tfrecord'\n",
        "val_record_fname = '/content/val.tfrecord'\n",
        "label_map_pbtxt_fname = '/content/labelmap.pbtxt'"
      ],
      "metadata": {
        "id": "akD15H2S_w_v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = 'ssd_mobilenet_v2_320x320_coco17_tpu-8'\n",
        "pretrained_checkpoint = 'ssd_mobilenet_v2_320x320_coco17_tpu-8.tar.gz'\n",
        "base_pipeline_file = 'ssd_mobilenet_v2_320x320_coco17_tpu-8.config'"
      ],
      "metadata": {
        "id": "Jrbv1HtY_xCt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%mkdir /content/models/mymodel/\n",
        "%cd /content/models/mymodel/\n",
        "\n",
        "import tarfile\n",
        "download_tar = 'http://download.tensorflow.org/models/object_detection/tf2/20200711/' + pretrained_checkpoint\n",
        "!wget {download_tar}\n",
        "tar = tarfile.open(pretrained_checkpoint)\n",
        "tar.extractall()\n",
        "tar.close()\n",
        "\n",
        "download_config = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/configs/tf2/' + base_pipeline_file\n",
        "!wget {download_config}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rmgkXteJ_xFw",
        "outputId": "f09f26ca-98ff-491c-da17-5665cd252d05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/models/mymodel\n",
            "--2024-04-15 15:17:12--  http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_320x320_coco17_tpu-8.tar.gz\n",
            "Resolving download.tensorflow.org (download.tensorflow.org)... 142.250.4.207, 172.217.194.207, 142.251.12.207, ...\n",
            "Connecting to download.tensorflow.org (download.tensorflow.org)|142.250.4.207|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 46042990 (44M) [application/x-tar]\n",
            "Saving to: ‘ssd_mobilenet_v2_320x320_coco17_tpu-8.tar.gz’\n",
            "\n",
            "ssd_mobilenet_v2_32 100%[===================>]  43.91M  12.7MB/s    in 3.7s    \n",
            "\n",
            "2024-04-15 15:17:16 (11.9 MB/s) - ‘ssd_mobilenet_v2_320x320_coco17_tpu-8.tar.gz’ saved [46042990/46042990]\n",
            "\n",
            "--2024-04-15 15:17:16--  https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/configs/tf2/ssd_mobilenet_v2_320x320_coco17_tpu-8.config\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.111.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4484 (4.4K) [text/plain]\n",
            "Saving to: ‘ssd_mobilenet_v2_320x320_coco17_tpu-8.config’\n",
            "\n",
            "ssd_mobilenet_v2_32 100%[===================>]   4.38K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-04-15 15:17:17 (57.9 MB/s) - ‘ssd_mobilenet_v2_320x320_coco17_tpu-8.config’ saved [4484/4484]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_steps = 30000\n",
        "batch_size = 16"
      ],
      "metadata": {
        "id": "cm2cy7mp_xJK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "pipeline_fname = '/content/models/mymodel/' + base_pipeline_file\n",
        "fine_tune_checkpoint = '/content/models/mymodel/' + model_name + '/checkpoint/ckpt-0'\n",
        "\n",
        "def get_num_classes(pbtxt_fname):\n",
        "    from object_detection.utils import label_map_util\n",
        "    label_map = label_map_util.load_labelmap(pbtxt_fname)\n",
        "    categories = label_map_util.convert_label_map_to_categories(\n",
        "        label_map, max_num_classes=90, use_display_name=True)\n",
        "    category_index = label_map_util.create_category_index(categories)\n",
        "    return len(category_index.keys())\n",
        "num_classes = get_num_classes(label_map_pbtxt_fname)\n",
        "print('Total classes:', num_classes)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Uw-0uONJ_XM",
        "outputId": "78ab4fc9-b5a6-4553-9d29-2a0d6c7a3666"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total classes: 23\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "%cd /content/models/mymodel\n",
        "print('writing custom configuration file')\n",
        "\n",
        "with open(pipeline_fname) as f:\n",
        "    s = f.read()\n",
        "with open('pipeline_file.config', 'w') as f:\n",
        "\n",
        "    s = re.sub('fine_tune_checkpoint: \".*?\"',\n",
        "               'fine_tune_checkpoint: \"{}\"'.format(fine_tune_checkpoint), s)\n",
        "    s = re.sub(\n",
        "        '(input_path: \".*?)(PATH_TO_BE_CONFIGURED/train)(.*?\")', 'input_path: \"{}\"'.format(train_record_fname), s)\n",
        "    s = re.sub(\n",
        "        '(input_path: \".*?)(PATH_TO_BE_CONFIGURED/val)(.*?\")', 'input_path: \"{}\"'.format(val_record_fname), s)\n",
        "\n",
        "    s = re.sub(\n",
        "        'label_map_path: \".*?\"', 'label_map_path: \"{}\"'.format(label_map_pbtxt_fname), s)\n",
        "\n",
        "    s = re.sub('batch_size: [0-9]+',\n",
        "               'batch_size: {}'.format(batch_size), s)\n",
        "\n",
        "    s = re.sub('num_steps: [0-9]+',\n",
        "               'num_steps: {}'.format(num_steps), s)\n",
        "\n",
        "    s = re.sub('num_classes: [0-9]+',\n",
        "               'num_classes: {}'.format(num_classes), s)\n",
        "\n",
        "    s = re.sub(\n",
        "        'fine_tune_checkpoint_type: \"classification\"', 'fine_tune_checkpoint_type: \"{}\"'.format('detection'), s)\n",
        "\n",
        "    s = re.sub('learning_rate_base: .8','learning_rate_base: .08', s)\n",
        "\n",
        "    s = re.sub('warmup_learning_rate: 0.13333','warmup_learning_rate: .026666', s)\n",
        "\n",
        "    f.write(s)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zuMAXU32J_fw",
        "outputId": "d51dca62-ea97-4edd-9f13-81230be1ed09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/models/mymodel\n",
            "writing custom configuration file\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the path to the custom config file and the directory to store training checkpoints in\n",
        "pipeline_file = '/content/models/mymodel/pipeline_file.config'\n",
        "model_dir = '/content/training/'"
      ],
      "metadata": {
        "id": "9CCbhj8TJ_nj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Run training!\n",
        "!python /content/models/research/object_detection/model_main_tf2.py \\\n",
        "    --pipeline_config_path={pipeline_file} \\\n",
        "    --model_dir={model_dir} \\\n",
        "    --alsologtostderr \\\n",
        "    --num_train_steps={num_steps} \\\n",
        "    --sample_1_of_n_eval_examples=1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RB6xIqsKK2I9",
        "outputId": "453f683f-8191-4bb4-ca52-1649d2570a7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2024-04-15 15:23:01.306400: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\n",
            "caused by: ['/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl5mutex6unlockEv']\n",
            "  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n",
            "/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/libtensorflow_io.so']\n",
            "caused by: ['/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZNK10tensorflow4data15DatasetOpKernel11TraceStringB5cxx11ERKNS_15OpKernelContextEb']\n",
            "  warnings.warn(f\"file system plugins are not loaded: {e}\")\n",
            "/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
            "\n",
            "TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
            "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
            "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
            "\n",
            "For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/ensure_tf_install.py:53: UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.13.0 and strictly below 2.16.0 (nightly versions are not supported). \n",
            " The versions of TensorFlow you are currently using is 2.8.0 and is not supported. \n",
            "Some things might work, some things might not.\n",
            "If you were to encounter a bug, do not file an issue.\n",
            "If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version. \n",
            "You can find the compatibility matrix in TensorFlow Addon's readme:\n",
            "https://github.com/tensorflow/addons\n",
            "  warnings.warn(\n",
            "2024-04-15 15:23:05.202296: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.10/dist-packages/cv2/../../lib64:/usr/lib64-nvidia\n",
            "2024-04-15 15:23:05.202455: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.10/dist-packages/cv2/../../lib64:/usr/lib64-nvidia\n",
            "2024-04-15 15:23:05.202603: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.10/dist-packages/cv2/../../lib64:/usr/lib64-nvidia\n",
            "2024-04-15 15:23:05.202726: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.10/dist-packages/cv2/../../lib64:/usr/lib64-nvidia\n",
            "2024-04-15 15:23:05.256690: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.10/dist-packages/cv2/../../lib64:/usr/lib64-nvidia\n",
            "2024-04-15 15:23:05.257087: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] 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",
            "WARNING:tensorflow:There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.\n",
            "W0415 15:23:05.258801 135099133063808 cross_device_ops.py:1386] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.\n",
            "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0',)\n",
            "I0415 15:23:05.268632 135099133063808 mirrored_strategy.py:374] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0',)\n",
            "INFO:tensorflow:Maybe overwriting train_steps: 30000\n",
            "I0415 15:23:05.272138 135099133063808 config_util.py:552] Maybe overwriting train_steps: 30000\n",
            "INFO:tensorflow:Maybe overwriting use_bfloat16: False\n",
            "I0415 15:23:05.272302 135099133063808 config_util.py:552] Maybe overwriting use_bfloat16: False\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/object_detection/model_lib_v2.py:563: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "rename to distribute_datasets_from_function\n",
            "W0415 15:23:05.324443 135099133063808 deprecation.py:337] From /usr/local/lib/python3.10/dist-packages/object_detection/model_lib_v2.py:563: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "rename to distribute_datasets_from_function\n",
            "INFO:tensorflow:Reading unweighted datasets: ['/content/train.tfrecord']\n",
            "I0415 15:23:05.331841 135099133063808 dataset_builder.py:162] Reading unweighted datasets: ['/content/train.tfrecord']\n",
            "INFO:tensorflow:Reading record datasets for input file: ['/content/train.tfrecord']\n",
            "I0415 15:23:05.332081 135099133063808 dataset_builder.py:79] Reading record datasets for input file: ['/content/train.tfrecord']\n",
            "INFO:tensorflow:Number of filenames to read: 1\n",
            "I0415 15:23:05.332190 135099133063808 dataset_builder.py:80] Number of filenames to read: 1\n",
            "WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.\n",
            "W0415 15:23:05.332266 135099133063808 dataset_builder.py:86] num_readers has been reduced to 1 to match input file shards.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/object_detection/builders/dataset_builder.py:100: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.\n",
            "W0415 15:23:05.335153 135099133063808 deprecation.py:337] From /usr/local/lib/python3.10/dist-packages/object_detection/builders/dataset_builder.py:100: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/object_detection/builders/dataset_builder.py:235: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.map()\n",
            "W0415 15:23:05.360663 135099133063808 deprecation.py:337] From /usr/local/lib/python3.10/dist-packages/object_detection/builders/dataset_builder.py:235: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.map()\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/util/dispatch.py:1082: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
            "W0415 15:23:12.913224 135099133063808 deprecation.py:337] From /usr/local/lib/python3.10/dist-packages/tensorflow/python/util/dispatch.py:1082: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/util/dispatch.py:1082: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.\n",
            "W0415 15:23:16.125911 135099133063808 deprecation.py:337] From /usr/local/lib/python3.10/dist-packages/tensorflow/python/util/dispatch.py:1082: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/util/dispatch.py:1082: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0415 15:23:19.027849 135099133063808 deprecation.py:337] From /usr/local/lib/python3.10/dist-packages/tensorflow/python/util/dispatch.py:1082: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "2024-04-15 15:23:22.853502: W tensorflow/core/framework/dataset.cc:768] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n",
            "/usr/local/lib/python3.10/dist-packages/keras/backend.py:450: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n",
            "  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '\n",
            "I0415 15:23:29.536971 135094161368640 api.py:441] feature_map_spatial_dims: [(19, 19), (10, 10), (5, 5), (3, 3), (2, 2), (1, 1)]\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0415 15:23:33.081983 135094161368640 convolutional_keras_box_predictor.py:152] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0415 15:23:33.082375 135094161368640 convolutional_keras_box_predictor.py:152] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0415 15:23:33.082610 135094161368640 convolutional_keras_box_predictor.py:152] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0415 15:23:33.082797 135094161368640 convolutional_keras_box_predictor.py:152] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0415 15:23:33.082973 135094161368640 convolutional_keras_box_predictor.py:152] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0415 15:23:33.083142 135094161368640 convolutional_keras_box_predictor.py:152] depth of additional conv before box predictor: 0\n",
            "I0415 15:23:42.649774 135094161368640 api.py:441] feature_map_spatial_dims: [(19, 19), (10, 10), (5, 5), (3, 3), (2, 2), (1, 1)]\n",
            "2024-04-15 15:23:51.216645: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 46080000 exceeds 10% of free system memory.\n",
            "2024-04-15 15:23:51.297901: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 46080000 exceeds 10% of free system memory.\n",
            "2024-04-15 15:23:51.371720: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 46080000 exceeds 10% of free system memory.\n",
            "2024-04-15 15:23:51.452868: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 138240000 exceeds 10% of free system memory.\n",
            "2024-04-15 15:23:51.694456: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 51840000 exceeds 10% of free system memory.\n",
            "2024-04-15 15:23:54.046696: W tensorflow/core/framework/dataset.cc:768] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/util/deprecation.py:616: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use fn_output_signature instead\n",
            "W0415 15:23:54.813658 135094557734464 deprecation.py:541] From /usr/local/lib/python3.10/dist-packages/tensorflow/python/util/deprecation.py:616: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use fn_output_signature instead\n",
            "I0415 15:23:55.825771 135094557734464 api.py:441] feature_map_spatial_dims: [(19, 19), (10, 10), (5, 5), (3, 3), (2, 2), (1, 1)]\n",
            "I0415 15:24:05.529867 135094557734464 api.py:441] feature_map_spatial_dims: [(19, 19), (10, 10), (5, 5), (3, 3), (2, 2), (1, 1)]\n",
            "I0415 15:24:14.536184 135094557734464 api.py:441] feature_map_spatial_dims: [(19, 19), (10, 10), (5, 5), (3, 3), (2, 2), (1, 1)]\n",
            "I0415 15:24:24.025484 135094557734464 api.py:441] feature_map_spatial_dims: [(19, 19), (10, 10), (5, 5), (3, 3), (2, 2), (1, 1)]\n",
            "INFO:tensorflow:Step 100 per-step time 6.832s\n",
            "I0415 15:35:17.596469 135099133063808 model_lib_v2.py:705] Step 100 per-step time 6.832s\n",
            "INFO:tensorflow:{'Loss/classification_loss': 0.7646681,\n",
            " 'Loss/localization_loss': 0.36503208,\n",
            " 'Loss/regularization_loss': 0.08764481,\n",
            " 'Loss/total_loss': 1.217345,\n",
            " 'learning_rate': 0.029332701}\n",
            "I0415 15:35:17.596880 135099133063808 model_lib_v2.py:708] {'Loss/classification_loss': 0.7646681,\n",
            " 'Loss/localization_loss': 0.36503208,\n",
            " 'Loss/regularization_loss': 0.08764481,\n",
            " 'Loss/total_loss': 1.217345,\n",
            " 'learning_rate': 0.029332701}\n",
            "INFO:tensorflow:Step 200 per-step time 6.189s\n",
            "I0415 15:45:36.499644 135099133063808 model_lib_v2.py:705] Step 200 per-step time 6.189s\n",
            "INFO:tensorflow:{'Loss/classification_loss': 0.7090742,\n",
            " 'Loss/localization_loss': 0.2903736,\n",
            " 'Loss/regularization_loss': 0.08860036,\n",
            " 'Loss/total_loss': 1.0880482,\n",
            " 'learning_rate': 0.0319994}\n",
            "I0415 15:45:36.500073 135099133063808 model_lib_v2.py:708] {'Loss/classification_loss': 0.7090742,\n",
            " 'Loss/localization_loss': 0.2903736,\n",
            " 'Loss/regularization_loss': 0.08860036,\n",
            " 'Loss/total_loss': 1.0880482,\n",
            " 'learning_rate': 0.0319994}\n",
            "INFO:tensorflow:Step 300 per-step time 6.172s\n",
            "I0415 15:55:53.722596 135099133063808 model_lib_v2.py:705] Step 300 per-step time 6.172s\n",
            "INFO:tensorflow:{'Loss/classification_loss': 0.74853665,\n",
            " 'Loss/localization_loss': 0.32288423,\n",
            " 'Loss/regularization_loss': 0.08951852,\n",
            " 'Loss/total_loss': 1.1609395,\n",
            " 'learning_rate': 0.0346661}\n",
            "I0415 15:55:53.722996 135099133063808 model_lib_v2.py:708] {'Loss/classification_loss': 0.74853665,\n",
            " 'Loss/localization_loss': 0.32288423,\n",
            " 'Loss/regularization_loss': 0.08951852,\n",
            " 'Loss/total_loss': 1.1609395,\n",
            " 'learning_rate': 0.0346661}\n",
            "INFO:tensorflow:Step 400 per-step time 6.131s\n",
            "I0415 16:06:06.854006 135099133063808 model_lib_v2.py:705] Step 400 per-step time 6.131s\n",
            "INFO:tensorflow:{'Loss/classification_loss': 0.8051527,\n",
            " 'Loss/localization_loss': 0.37667954,\n",
            " 'Loss/regularization_loss': 0.0905705,\n",
            " 'Loss/total_loss': 1.2724028,\n",
            " 'learning_rate': 0.0373328}\n",
            "I0415 16:06:06.854370 135099133063808 model_lib_v2.py:708] {'Loss/classification_loss': 0.8051527,\n",
            " 'Loss/localization_loss': 0.37667954,\n",
            " 'Loss/regularization_loss': 0.0905705,\n",
            " 'Loss/total_loss': 1.2724028,\n",
            " 'learning_rate': 0.0373328}\n",
            "INFO:tensorflow:Step 500 per-step time 6.073s\n",
            "I0415 16:16:14.144918 135099133063808 model_lib_v2.py:705] Step 500 per-step time 6.073s\n",
            "INFO:tensorflow:{'Loss/classification_loss': 0.7494157,\n",
            " 'Loss/localization_loss': 0.2158199,\n",
            " 'Loss/regularization_loss': 0.09165683,\n",
            " 'Loss/total_loss': 1.0568924,\n",
            " 'learning_rate': 0.0399995}\n",
            "I0415 16:16:14.145285 135099133063808 model_lib_v2.py:708] {'Loss/classification_loss': 0.7494157,\n",
            " 'Loss/localization_loss': 0.2158199,\n",
            " 'Loss/regularization_loss': 0.09165683,\n",
            " 'Loss/total_loss': 1.0568924,\n",
            " 'learning_rate': 0.0399995}\n",
            "INFO:tensorflow:Step 600 per-step time 6.057s\n",
            "I0415 16:26:19.890988 135099133063808 model_lib_v2.py:705] Step 600 per-step time 6.057s\n",
            "INFO:tensorflow:{'Loss/classification_loss': 0.58720815,\n",
            " 'Loss/localization_loss': 0.2716209,\n",
            " 'Loss/regularization_loss': 0.0928455,\n",
            " 'Loss/total_loss': 0.9516746,\n",
            " 'learning_rate': 0.0426662}\n",
            "I0415 16:26:19.891366 135099133063808 model_lib_v2.py:708] {'Loss/classification_loss': 0.58720815,\n",
            " 'Loss/localization_loss': 0.2716209,\n",
            " 'Loss/regularization_loss': 0.0928455,\n",
            " 'Loss/total_loss': 0.9516746,\n",
            " 'learning_rate': 0.0426662}\n",
            "INFO:tensorflow:Step 700 per-step time 6.068s\n",
            "I0415 16:36:26.658847 135099133063808 model_lib_v2.py:705] Step 700 per-step time 6.068s\n",
            "INFO:tensorflow:{'Loss/classification_loss': 0.59945303,\n",
            " 'Loss/localization_loss': 0.28430986,\n",
            " 'Loss/regularization_loss': 0.094314724,\n",
            " 'Loss/total_loss': 0.97807765,\n",
            " 'learning_rate': 0.0453329}\n",
            "I0415 16:36:26.659224 135099133063808 model_lib_v2.py:708] {'Loss/classification_loss': 0.59945303,\n",
            " 'Loss/localization_loss': 0.28430986,\n",
            " 'Loss/regularization_loss': 0.094314724,\n",
            " 'Loss/total_loss': 0.97807765,\n",
            " 'learning_rate': 0.0453329}\n",
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir /content/custom_model_lite\n",
        "output_directory = '/content/custom_model_lite'\n",
        "\n",
        "# Path to training directory (the conversion script automatically chooses the highest checkpoint file)\n",
        "last_model_path = '/content/training'\n",
        "\n",
        "!python /content/models/research/object_detection/export_tflite_graph_tf2.py \\\n",
        "    --trained_checkpoint_dir {last_model_path} \\\n",
        "    --output_directory {output_directory} \\\n",
        "    --pipeline_config_path {pipeline_file}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jHCQNjYbK2Ps",
        "outputId": "a1ca7822-caad-41c2-ef50-7b8e8b640eff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2024-04-15 16:38:11.222615: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\n",
            "caused by: ['/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl5mutex6unlockEv']\n",
            "  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n",
            "/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/libtensorflow_io.so']\n",
            "caused by: ['/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZNK10tensorflow4data15DatasetOpKernel11TraceStringB5cxx11ERKNS_15OpKernelContextEb']\n",
            "  warnings.warn(f\"file system plugins are not loaded: {e}\")\n",
            "2024-04-15 16:38:15.559693: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 16:38:15.559841: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 16:38:15.559954: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 16:38:15.560067: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 16:38:15.610670: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
            "2024-04-15 16:38:15.611049: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] 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",
            "I0415 16:38:19.379828 135791824937600 api.py:441] feature_map_spatial_dims: [(19, 19), (10, 10), (5, 5), (3, 3), (2, 2), (1, 1)]\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0415 16:38:22.406954 135791824937600 convolutional_keras_box_predictor.py:152] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0415 16:38:22.407284 135791824937600 convolutional_keras_box_predictor.py:152] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0415 16:38:22.407441 135791824937600 convolutional_keras_box_predictor.py:152] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0415 16:38:22.407590 135791824937600 convolutional_keras_box_predictor.py:152] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0415 16:38:22.407724 135791824937600 convolutional_keras_box_predictor.py:152] depth of additional conv before box predictor: 0\n",
            "INFO:tensorflow:depth of additional conv before box predictor: 0\n",
            "I0415 16:38:22.408042 135791824937600 convolutional_keras_box_predictor.py:152] depth of additional conv before box predictor: 0\n",
            "I0415 16:38:23.940917 135791824937600 api.py:441] feature_map_spatial_dims: [(19, 19), (10, 10), (5, 5), (3, 3), (2, 2), (1, 1)]\n",
            "2024-04-15 16:38:25.279467: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n",
            "I0415 16:38:26.151329 135791824937600 api.py:441] feature_map_spatial_dims: [(19, 19), (10, 10), (5, 5), (3, 3), (2, 2), (1, 1)]\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7b800c95ded0>, because it is not built.\n",
            "W0415 16:38:27.474887 135791824937600 save_impl.py:71] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7b800c95ded0>, because it is not built.\n",
            "W0415 16:38:46.057488 135791824937600 save.py:260] Found untraced functions such as BoxPredictor_layer_call_fn, BoxPredictor_layer_call_and_return_conditional_losses, ConvolutionalBoxHead_0_layer_call_fn, ConvolutionalBoxHead_0_layer_call_and_return_conditional_losses, ConvolutionalBoxHead_1_layer_call_fn while saving (showing 5 of 50). These functions will not be directly callable after loading.\n",
            "INFO:tensorflow:Assets written to: /content/custom_model_lite/saved_model/assets\n",
            "I0415 16:38:50.292840 135791824937600 builder_impl.py:779] Assets written to: /content/custom_model_lite/saved_model/assets\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model('/content/custom_model_lite/saved_model')\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "with open('/content/custom_model_lite/detect.tflite', 'wb') as f:\n",
        "  f.write(tflite_model)"
      ],
      "metadata": {
        "id": "VC8-ZXV-cqae"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp /content/labelmap.txt /content/custom_model_lite\n",
        "!cp /content/labelmap.pbtxt /content/custom_model_lite\n",
        "!cp /content/models/mymodel/pipeline_file.config /content/custom_model_lite\n",
        "\n",
        "%cd /content\n",
        "!zip -r custom_model_lite.zip custom_model_lite"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sVeGLEXecqdK",
        "outputId": "8d27102d-e657-4d48-a194-7efc901f138e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "  adding: custom_model_lite/ (stored 0%)\n",
            "  adding: custom_model_lite/labelmap.pbtxt (deflated 72%)\n",
            "  adding: custom_model_lite/saved_model/ (stored 0%)\n",
            "  adding: custom_model_lite/saved_model/saved_model.pb (deflated 92%)\n",
            "  adding: custom_model_lite/saved_model/assets/ (stored 0%)\n",
            "  adding: custom_model_lite/saved_model/variables/ (stored 0%)\n",
            "  adding: custom_model_lite/saved_model/variables/variables.index (deflated 77%)\n",
            "  adding: custom_model_lite/saved_model/variables/variables.data-00000-of-00001 (deflated 8%)\n",
            "  adding: custom_model_lite/labelmap.txt (deflated 29%)\n",
            "  adding: custom_model_lite/pipeline_file.config (deflated 67%)\n",
            "  adding: custom_model_lite/detect.tflite (deflated 7%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.download('/content/custom_model_lite.zip')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "nvsWIBK0cqf4",
        "outputId": "37f673b9-a955-446e-d433-6384fb9e309f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_4409007a-5ea8-469d-a39d-433b9cfa1c92\", \"custom_model_lite.zip\", 37604549)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_UqHt9WmcqjW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}