MultiDetectionTraining.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MultiDetectionTraining.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k74PrgRn_j1E",
        "outputId": "c13f268f-c4c3-4d87-c37a-bc6e9e200d62"
      },
      "source": [
        "!pip install yolov4==2.1.0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting yolov4==2.1.0\n",
            "  Downloading https://files.pythonhosted.org/packages/c4/15/bc77345b4728beec780fc86e56fc4ba69f301e2d26d76421bdabc585b135/yolov4-2.1.0.tar.gz\n",
            "Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.7/dist-packages (from yolov4==2.1.0) (1.19.5)\n",
            "Requirement already satisfied: easydict in /usr/local/lib/python3.7/dist-packages (from yolov4==2.1.0) (1.9)\n",
            "Building wheels for collected packages: yolov4\n",
            "  Building wheel for yolov4 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yolov4: filename=yolov4-2.1.0-cp37-none-any.whl size=39936 sha256=cb162cba769333a02ab3e3a0d3f1036f7fc521bb82ea97e39d8518a4bbb34309\n",
            "  Stored in directory: /root/.cache/pip/wheels/7e/14/b8/fa302e43edc362a5cc332216fcda60d8f7742958308379f71e\n",
            "Successfully built yolov4\n",
            "Installing collected packages: yolov4\n",
            "Successfully installed yolov4-2.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WjBY_HGj9OAU"
      },
      "source": [
        "import os\n",
        "\n",
        "with open('/content/drive/MyDrive/MultiDetectionTraining/damage/train.txt', 'w') as out:\n",
        "  for img in [f for f in os.listdir('/content/drive/MyDrive/MultiDetectionTraining/damage/train') if f.endswith('jpg')]:\n",
        "    out.write('/content/drive/MyDrive/MultiDetectionTraining/obj/' + img + '\\n')\n",
        "  out.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-Pf45KD9rlc"
      },
      "source": [
        "import os\n",
        "\n",
        "with open('/content/drive/MyDrive/MultiDetectionTraining/damage/valid.txt', 'w') as out:\n",
        "  for img in [f for f in os.listdir('/content/drive/MyDrive/MultiDetectionTraining/damage/valid') if f.endswith('jpg')]:\n",
        "    out.write('/content/drive/MyDrive/MultiDetectionTraining/obj/' + img + '\\n')\n",
        "  out.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rRLbeM9549VP"
      },
      "source": [
        "import tensorflow\n",
        "physicalDevices = tensorflow.config.list_physical_devices('GPU')\n",
        "tensorflow.config.experimental.set_memory_growth(physicalDevices[0], True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bT2eh0eD9uql",
        "outputId": "ff53660d-e442-41ad-8217-2689f01b75a5"
      },
      "source": [
        "# Object Detection Training\n",
        "\n",
        "from tensorflow.keras import callbacks, optimizers\n",
        "from yolov4.tf import SaveWeightsCallback, YOLOv4\n",
        "import time\n",
        "\n",
        "yolo = YOLOv4()\n",
        "yolo.classes = \"/content/drive/MyDrive/MultiDetectionTraining/obj.names\"\n",
        "yolo.input_size = (640, 480)\n",
        "yolo.batch_size = 4\n",
        "\n",
        "yolo.make_model()\n",
        "yolo.load_weights(\n",
        "    \"/content/drive/MyDrive/MultiDetectionTraining/yolov4.conv.137\",\n",
        "    weights_type=\"yolo\"\n",
        "    )\n",
        "\n",
        "train_data_set = yolo.load_dataset(\n",
        "    \"/content/drive/MyDrive/MultiDetectionTraining/damage/train.txt\",\n",
        "    dataset_type=\"yolo\",\n",
        "    image_path_prefix=\"\",\n",
        "    label_smoothing=0.05\n",
        ")\n",
        "\n",
        "val_data_set = yolo.load_dataset(\n",
        "    \"/content/drive/MyDrive/MultiDetectionTraining/damage/valid.txt\",\n",
        "    dataset_type=\"yolo\",\n",
        "    image_path_prefix=\"\",\n",
        "    training=False\n",
        ")\n",
        "\n",
        "epochs = 200\n",
        "lr = 1e-4\n",
        "\n",
        "optimizer = optimizers.Adam(learning_rate=lr)\n",
        "yolo.compile(optimizer=optimizer, loss_iou_type=\"ciou\")\n",
        "\n",
        "def lr_scheduler(epoch):\n",
        "    if epoch < int(epochs * 0.5):\n",
        "        return lr\n",
        "    if epoch < int(epochs * 0.8):\n",
        "        return lr * 0.5\n",
        "    if epoch < int(epochs * 0.9):\n",
        "        return lr * 0.1\n",
        "    return lr * 0.01\n",
        "\n",
        "_callbacks = [\n",
        "    callbacks.LearningRateScheduler(lr_scheduler),\n",
        "    callbacks.TerminateOnNaN(),\n",
        "    callbacks.TensorBoard(\n",
        "        log_dir=\"/content/drive/MyDrive/MultiDetectionTraining/logs\",\n",
        "    ),\n",
        "    SaveWeightsCallback(\n",
        "        yolo=yolo, dir_path=\"/content/drive/MyDrive/MultiDetectionTraining/trained\",\n",
        "        weights_type=\"yolo\", epoch_per_save=10\n",
        "    ),\n",
        "]\n",
        "\n",
        "yolo.fit(\n",
        "    train_data_set,\n",
        "    epochs=epochs,\n",
        "    callbacks=_callbacks,\n",
        "    validation_data=val_data_set,\n",
        "    validation_steps=100,\n",
        "    validation_freq=5,\n",
        "    steps_per_epoch=100,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "grid: 60*80 iou_loss: 0.0461077876 conf_loss: 0.0290912427 prob_loss: 0 total_loss 0.0751990303\n",
            "grid: 30*40 iou_loss: 0.815849125 conf_loss: 0.0600292794 prob_loss: 0.000215807435 total_loss 0.876094222\n",
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            "grid: 60*80 iou_loss: 0 conf_loss: 0.0235186201 prob_loss: 0 total_loss 0.0235186201\n",
            "grid: 30*40 iou_loss: 0.348076671 conf_loss: 0.0774890929 prob_loss: 1.13547567e-05 total_loss 0.425577104\n",
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            "100/100 - 48s - loss: 50.7725 - output_1_loss: 1.0936 - output_2_loss: 3.8575 - output_3_loss: 2.6564\n",
            "Epoch 188/200\n",
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            "100/100 - 48s - loss: 49.7021 - output_1_loss: 0.8042 - output_2_loss: 3.2209 - output_3_loss: 2.5133\n",
            "Epoch 189/200\n",
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            "100/100 - 48s - loss: 49.6972 - output_1_loss: 0.9323 - output_2_loss: 3.0502 - output_3_loss: 2.5520\n",
            "Epoch 190/200\n",
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            "100/100 - 72s - loss: 48.7442 - output_1_loss: 0.7152 - output_2_loss: 2.4716 - output_3_loss: 2.3960 - val_loss: 89.2952 - val_output_1_loss: 2.9570 - val_output_2_loss: 22.9218 - val_output_3_loss: 20.2555\n",
            "Epoch 191/200\n",
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            "100/100 - 47s - loss: 48.1282 - output_1_loss: 0.5599 - output_2_loss: 2.4436 - output_3_loss: 1.9644\n",
            "Epoch 192/200\n",
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            "100/100 - 48s - loss: 49.2054 - output_1_loss: 0.6632 - output_2_loss: 2.8649 - output_3_loss: 2.5181\n",
            "Epoch 193/200\n",
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            "grid: 15*20 iou_loss: 0.397684932 conf_loss: 0.0475822687 prob_loss: 3.70447378e-05 total_loss 0.445304245\n",
            "100/100 - 48s - loss: 49.3072 - output_1_loss: 0.5315 - output_2_loss: 2.7795 - output_3_loss: 2.8382\n",
            "Epoch 194/200\n",
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            "100/100 - 48s - loss: 49.3281 - output_1_loss: 0.9155 - output_2_loss: 2.8827 - output_3_loss: 2.3731\n",
            "Epoch 195/200\n",
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            "grid: 15*20 iou_loss: 9.88607407 conf_loss: 19.5726204 prob_loss: 0.00178320124 total_loss 29.4604778\n",
            "grid: 60*80 iou_loss: 5.79966116 conf_loss: 32.5015488 prob_loss: 0.0119468793 total_loss 38.3131561\n",
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            "grid: 15*20 iou_loss: 8.08380604 conf_loss: 28.6886864 prob_loss: 0.00122078951 total_loss 36.7737122\n",
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            "100/100 - 72s - loss: 50.4632 - output_1_loss: 0.7736 - output_2_loss: 3.4032 - output_3_loss: 3.1308 - val_loss: 91.1288 - val_output_1_loss: 3.4970 - val_output_2_loss: 24.5288 - val_output_3_loss: 19.9481\n",
            "Epoch 196/200\n",
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            "grid: 15*20 iou_loss: 0.562698722 conf_loss: 0.174601436 prob_loss: 7.32564513e-05 total_loss 0.737373412\n",
            "100/100 - 48s - loss: 50.9779 - output_1_loss: 0.8536 - output_2_loss: 3.6548 - output_3_loss: 3.3150\n",
            "Epoch 197/200\n",
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            "100/100 - 48s - loss: 49.6418 - output_1_loss: 1.0784 - output_2_loss: 2.9221 - output_3_loss: 2.4881\n",
            "Epoch 198/200\n",
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            "100/100 - 47s - loss: 49.1283 - output_1_loss: 0.5808 - output_2_loss: 2.8590 - output_3_loss: 2.5365\n",
            "Epoch 199/200\n",
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            "grid: 30*40 iou_loss: 2.35925126 conf_loss: 3.70409513 prob_loss: 0.000838793465 total_loss 6.06418514\n",
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            "grid: 15*20 iou_loss: 0.989217103 conf_loss: 0.0628134236 prob_loss: 9.92617e-05 total_loss 1.05212975\n",
            "100/100 - 49s - loss: 49.7598 - output_1_loss: 1.0876 - output_2_loss: 2.9949 - output_3_loss: 2.5264\n",
            "Epoch 200/200\n",
            "grid: 60*80 iou_loss: 0 conf_loss: 0.0267553683 prob_loss: 0 total_loss 0.0267553683\n",
            "grid: 30*40 iou_loss: 3.63195944 conf_loss: 6.96495628 prob_loss: 0.00103486609 total_loss 10.59795\n",
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            "grid: 30*40 iou_loss: 14.7668009 conf_loss: 37.8936615 prob_loss: 0.00248534558 total_loss 52.6629486\n",
            "grid: 15*20 iou_loss: 9.90659714 conf_loss: 18.8256607 prob_loss: 0.00155027467 total_loss 28.7338085\n",
            "grid: 60*80 iou_loss: 0 conf_loss: 0.0351440683 prob_loss: 0 total_loss 0.0351440683\n",
            "grid: 30*40 iou_loss: 4.96298885 conf_loss: 20.2037601 prob_loss: 0.00863627 total_loss 25.1753845\n",
            "grid: 15*20 iou_loss: 4.57954741 conf_loss: 8.60329 prob_loss: 0.00703847874 total_loss 13.1898746\n",
            "grid: 60*80 iou_loss: 0 conf_loss: 0.020059891 prob_loss: 0 total_loss 0.020059891\n",
            "grid: 30*40 iou_loss: 1.57520342 conf_loss: 7.82381153 prob_loss: 0.00052476034 total_loss 9.39954\n",
            "grid: 15*20 iou_loss: 4.14003801 conf_loss: 17.7991142 prob_loss: 0.000607273891 total_loss 21.9397583\n",
            "grid: 60*80 iou_loss: 0.597108841 conf_loss: 3.40407491 prob_loss: 0.000697098731 total_loss 4.00188065\n",
            "grid: 30*40 iou_loss: 4.19731665 conf_loss: 17.0903664 prob_loss: 0.00648364751 total_loss 21.2941666\n",
            "grid: 15*20 iou_loss: 4.80738592 conf_loss: 14.7567177 prob_loss: 0.00198301254 total_loss 19.5660858\n",
            "grid: 60*80 iou_loss: 0 conf_loss: 0.0505790934 prob_loss: 0 total_loss 0.0505790934\n",
            "grid: 30*40 iou_loss: 3.89210844 conf_loss: 14.8258228 prob_loss: 0.00107103016 total_loss 18.7190018\n",
            "grid: 15*20 iou_loss: 7.44413757 conf_loss: 26.7824707 prob_loss: 0.00129841454 total_loss 34.2279053\n",
            "grid: 60*80 iou_loss: 3.22267199 conf_loss: 16.3192387 prob_loss: 0.00839423575 total_loss 19.5503044\n",
            "grid: 30*40 iou_loss: 9.94276428 conf_loss: 24.7408123 prob_loss: 0.00138227781 total_loss 34.6849594\n",
            "grid: 15*20 iou_loss: 6.4366045 conf_loss: 18.1425056 prob_loss: 0.00143898767 total_loss 24.5805473\n",
            "grid: 60*80 iou_loss: 2.23253059 conf_loss: 19.292963 prob_loss: 0.00749498326 total_loss 21.5329895\n",
            "grid: 30*40 iou_loss: 20.9877529 conf_loss: 67.1068954 prob_loss: 0.0108472202 total_loss 88.1055\n",
            "grid: 15*20 iou_loss: 7.29582739 conf_loss: 14.0807447 prob_loss: 0.00667479401 total_loss 21.3832474\n",
            "grid: 60*80 iou_loss: 0 conf_loss: 0.0371710882 prob_loss: 0 total_loss 0.0371710882\n",
            "grid: 30*40 iou_loss: 7.17191887 conf_loss: 26.8035564 prob_loss: 0.00326867076 total_loss 33.9787445\n",
            "grid: 15*20 iou_loss: 5.00411415 conf_loss: 21.0418262 prob_loss: 0.00185276312 total_loss 26.0477924\n",
            "grid: 60*80 iou_loss: 0.840419769 conf_loss: 6.3169384 prob_loss: 0.00108745042 total_loss 7.15844584\n",
            "grid: 30*40 iou_loss: 7.65212345 conf_loss: 25.3604698 prob_loss: 0.00128550897 total_loss 33.0138779\n",
            "grid: 15*20 iou_loss: 6.00414944 conf_loss: 13.217392 prob_loss: 0.00108752854 total_loss 19.2226295\n",
            "grid: 60*80 iou_loss: 1.07493019 conf_loss: 6.2778759 prob_loss: 0.00134931901 total_loss 7.35415554\n",
            "grid: 30*40 iou_loss: 11.5552 conf_loss: 42.2255859 prob_loss: 0.00327535812 total_loss 53.7840614\n",
            "grid: 15*20 iou_loss: 7.32709837 conf_loss: 15.8373795 prob_loss: 0.000623670523 total_loss 23.165102\n",
            "grid: 60*80 iou_loss: 0 conf_loss: 0.0363594 prob_loss: 0 total_loss 0.0363594\n",
            "grid: 30*40 iou_loss: 5.75746536 conf_loss: 13.8998356 prob_loss: 0.00145827897 total_loss 19.6587601\n",
            "grid: 15*20 iou_loss: 3.65186048 conf_loss: 10.6820316 prob_loss: 0.000527963741 total_loss 14.3344202\n",
            "grid: 60*80 iou_loss: 0.251465261 conf_loss: 2.69871 prob_loss: 0.000384029496 total_loss 2.95055938\n",
            "grid: 30*40 iou_loss: 11.5122499 conf_loss: 39.1893845 prob_loss: 0.00351144793 total_loss 50.7051468\n",
            "grid: 15*20 iou_loss: 9.23712158 conf_loss: 23.685564 prob_loss: 0.00135845051 total_loss 32.9240417\n",
            "grid: 60*80 iou_loss: 0 conf_loss: 0.0419515371 prob_loss: 0 total_loss 0.0419515371\n",
            "grid: 30*40 iou_loss: 6.75040483 conf_loss: 23.0867596 prob_loss: 0.00179722253 total_loss 29.8389606\n",
            "grid: 15*20 iou_loss: 3.14256811 conf_loss: 7.61749 prob_loss: 0.000603656576 total_loss 10.7606621\n",
            "grid: 60*80 iou_loss: 0 conf_loss: 0.0315067768 prob_loss: 0 total_loss 0.0315067768\n",
            "grid: 30*40 iou_loss: 1.94571912 conf_loss: 8.38868618 prob_loss: 0.000457997201 total_loss 10.3348627\n",
            "grid: 15*20 iou_loss: 3.32722735 conf_loss: 11.5267773 prob_loss: 0.00109956937 total_loss 14.8551044\n",
            "grid: 60*80 iou_loss: 0 conf_loss: 0.0784447491 prob_loss: 0 total_loss 0.0784447491\n",
            "grid: 30*40 iou_loss: 2.62715077 conf_loss: 6.15125465 prob_loss: 0.000522591174 total_loss 8.7789278\n",
            "grid: 15*20 iou_loss: 5.77903128 conf_loss: 22.6123333 prob_loss: 0.000970241381 total_loss 28.3923359\n",
            "grid: 60*80 iou_loss: 0.624967754 conf_loss: 5.21026325 prob_loss: 0.00100656471 total_loss 5.83623743\n",
            "grid: 30*40 iou_loss: 3.32860422 conf_loss: 13.3308792 prob_loss: 0.00652545132 total_loss 16.666008\n",
            "grid: 15*20 iou_loss: 3.31386948 conf_loss: 12.5565758 prob_loss: 0.000400961959 total_loss 15.8708458\n",
            "grid: 60*80 iou_loss: 0 conf_loss: 0.0363454744 prob_loss: 0 total_loss 0.0363454744\n",
            "grid: 30*40 iou_loss: 3.15568256 conf_loss: 14.9414091 prob_loss: 0.0010749104 total_loss 18.0981674\n",
            "grid: 15*20 iou_loss: 5.00947857 conf_loss: 21.1377182 prob_loss: 0.00025692975 total_loss 26.1474533\n",
            "100/100 - 72s - loss: 49.3711 - output_1_loss: 0.7055 - output_2_loss: 3.0492 - output_3_loss: 2.4668 - val_loss: 90.7999 - val_output_1_loss: 3.0931 - val_output_2_loss: 23.9565 - val_output_3_loss: 20.6012\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHeEUTlP9yKF"
      },
      "source": [
        "from yolov4.tf import YOLOv4\n",
        "\n",
        "yolo = YOLOv4()\n",
        "\n",
        "yolo.classes = \"/content/drive/MyDrive/MultiDetectionTraining/obj.names\"\n",
        "\n",
        "yolo.make_model()\n",
        "yolo.load_weights(\"/content/drive/MyDrive/MultiDetectionTraining/trained/yolov4-final.weights\", weights_type=\"yolo\")\n",
        "\n",
        "dataset = yolo.load_dataset(\n",
        "    \"/content/drive/MyDrive/MultiDetectionTraining/damage/valid.txt\",\n",
        "    dataset_type=\"yolo\",\n",
        "    training=False,\n",
        "    image_path_prefix=\"\",\n",
        ")\n",
        "\n",
        "yolo.save_dataset_for_mAP(\"/content/drive/MyDrive/MultiDetectionTraining/mAP\", dataset)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bVyysA_Y90Jt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9fdc2bb-20aa-43ba-8b5e-bc90dd1eff4b"
      },
      "source": [
        "!python /content/drive/MyDrive/MultiDetectionTraining/mAP/main.py"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9.81% = Damage AP \n",
            "mAP = 9.81%\n",
            "<Figure size 640x480 with 1 Axes>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LBjNpocn9020"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}