LPE_U_Net.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LPE_U-Net.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1lVPW-mMOWt13Id6M1o5sWRWX124P66Fh",
      "authorship_tag": "ABX9TyMWtESLL/BVL2hhq8nHKsVO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sonochy/UoA_school_mission-12/blob/master/LPE_U_Net.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q5du9G3uLR-I",
        "colab_type": "text"
      },
      "source": [
        "## [惑星探査育英会　第十二回実習会](https://www.cps-jp.org/~tansaku/wiki/top/?school_mission-12)\n",
        "U-Netを用いたsemantic segmentation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N7_SPw3bLbna",
        "colab_type": "text"
      },
      "source": [
        "## 参考URL\n",
        "\n",
        "\n",
        "\n",
        "*   [Colaboratory へようこそ](https://colab.research.google.com/notebooks/welcome.ipynb?hl=ja#scrollTo=-Rh3-Vt9Nev9)\n",
        "*   [Google Colaboratory での Terminal 操作](https://qiita.com/shirobu2400/items/2fad1d24053b7cb35547)\n",
        "*   [Google Colaboratoryをもっと便利に使いたい](https://karaage.hatenadiary.jp/entry/2018/12/17/073000)\n",
        "*   [Image Segmentation with tf.keras](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/outreach/blogs/segmentation_blogpost/image_segmentation.ipynb#scrollTo=cl79rk4KKol8)\n",
        "*   [U-Net: Convolutional Networks for Biomedical\n",
        "Image Segmentation](https://arxiv.org/pdf/1505.04597.pdf)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lgD4PS58LzQE",
        "colab_type": "text"
      },
      "source": [
        "## スペック確認"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8bvF3XPvLzTc",
        "colab_type": "text"
      },
      "source": [
        "カレントディレクトリ確認"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nAhS9oBqLKaA",
        "colab_type": "code",
        "outputId": "203dcf3a-3eb2-4c3b-f7e4-ad55ac84d7f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "!pwd\n",
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "drive  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3eOC4HhhMqgC",
        "colab_type": "text"
      },
      "source": [
        "OS確認"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91oCKaGoMlSJ",
        "colab_type": "code",
        "outputId": "098a22a1-8995-42a5-9e81-1d7f5fda7e7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "!cat /etc/issue"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ubuntu 18.04.3 LTS \\n \\l\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bsrb1AYjM2wT",
        "colab_type": "text"
      },
      "source": [
        "メモリ確認"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Z-nunTbM4i0",
        "colab_type": "code",
        "outputId": "30a9c8e4-7e7f-40d3-f7cc-0d473f4a1fd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "!free -h"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              total        used        free      shared  buff/cache   available\n",
            "Mem:            12G        571M         10G        904K        2.1G         11G\n",
            "Swap:            0B          0B          0B\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fSG9aRtLefXm",
        "colab_type": "code",
        "outputId": "bcdcaade-7d63-4864-c466-b35a8b56d56c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        }
      },
      "source": [
        "!cat /proc/cpuinfo "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "processor\t: 0\n",
            "vendor_id\t: GenuineIntel\n",
            "cpu family\t: 6\n",
            "model\t\t: 79\n",
            "model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n",
            "stepping\t: 0\n",
            "microcode\t: 0x1\n",
            "cpu MHz\t\t: 2200.000\n",
            "cache size\t: 56320 KB\n",
            "physical id\t: 0\n",
            "siblings\t: 2\n",
            "core id\t\t: 0\n",
            "cpu cores\t: 1\n",
            "apicid\t\t: 0\n",
            "initial apicid\t: 0\n",
            "fpu\t\t: yes\n",
            "fpu_exception\t: yes\n",
            "cpuid level\t: 13\n",
            "wp\t\t: yes\n",
            "flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n",
            "bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs\n",
            "bogomips\t: 4400.00\n",
            "clflush size\t: 64\n",
            "cache_alignment\t: 64\n",
            "address sizes\t: 46 bits physical, 48 bits virtual\n",
            "power management:\n",
            "\n",
            "processor\t: 1\n",
            "vendor_id\t: GenuineIntel\n",
            "cpu family\t: 6\n",
            "model\t\t: 79\n",
            "model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n",
            "stepping\t: 0\n",
            "microcode\t: 0x1\n",
            "cpu MHz\t\t: 2200.000\n",
            "cache size\t: 56320 KB\n",
            "physical id\t: 0\n",
            "siblings\t: 2\n",
            "core id\t\t: 0\n",
            "cpu cores\t: 1\n",
            "apicid\t\t: 1\n",
            "initial apicid\t: 1\n",
            "fpu\t\t: yes\n",
            "fpu_exception\t: yes\n",
            "cpuid level\t: 13\n",
            "wp\t\t: yes\n",
            "flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n",
            "bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs\n",
            "bogomips\t: 4400.00\n",
            "clflush size\t: 64\n",
            "cache_alignment\t: 64\n",
            "address sizes\t: 46 bits physical, 48 bits virtual\n",
            "power management:\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xsfp81NdE5_l",
        "colab_type": "text"
      },
      "source": [
        " GPUのスペック確認"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "niVkodvLeofW",
        "colab_type": "code",
        "outputId": "d5007d12-edfd-4bce-8b0f-f98c205952a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "!cat /proc/driver/nvidia/gpus/0000:00:04.0/information"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \t\t Tesla K80\n",
            "IRQ:   \t\t 35\n",
            "GPU UUID: \t GPU-62edca11-9752-e909-970d-4cff46a0052f\n",
            "Video BIOS: \t 80.21.25.00.01\n",
            "Bus Type: \t PCI\n",
            "DMA Size: \t 40 bits\n",
            "DMA Mask: \t 0xffffffffff\n",
            "Bus Location: \t 0000:00:04.0\n",
            "Device Minor: \t 0\n",
            "Blacklisted:\t No\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yyevdqgBE8Pg",
        "colab_type": "text"
      },
      "source": [
        "Google Colabの起動してからの時間確認"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFQSrVO6fLFP",
        "colab_type": "code",
        "outputId": "ba4427d9-86b2-4a5c-8170-982d4184b915",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!cat /proc/uptime | awk '{print $1 /60 /60 /24 \"days (\" $1 \"sec)\"}'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0201639days (1742.16sec)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R32JYT8msXva",
        "colab_type": "text"
      },
      "source": [
        "## ライブラリ Install"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pAPhkcN9Nh7v",
        "colab_type": "code",
        "outputId": "8234a0fb-c7f6-4485-b9a9-7d146822c29e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        }
      },
      "source": [
        "pip install tensorflow-gpu==1.13.1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-gpu==1.13.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7b/b1/0ad4ae02e17ddd62109cd54c291e311c4b5fd09b4d0678d3d6ce4159b0f0/tensorflow_gpu-1.13.1-cp36-cp36m-manylinux1_x86_64.whl (345.2MB)\n",
            "\u001b[K     |████████████████████████████████| 345.2MB 49kB/s \n",
            "\u001b[?25hRequirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (1.1.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (1.1.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (1.27.1)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (1.12.0)\n",
            "Collecting tensorflow-estimator<1.14.0rc0,>=1.13.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bb/48/13f49fc3fa0fdf916aa1419013bb8f2ad09674c275b4046d5ee669a46873/tensorflow_estimator-1.13.0-py2.py3-none-any.whl (367kB)\n",
            "\u001b[K     |████████████████████████████████| 368kB 34.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (0.8.1)\n",
            "Requirement already satisfied: absl-py>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (0.9.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (3.10.0)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (0.2.2)\n",
            "Collecting tensorboard<1.14.0,>=1.13.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0f/39/bdd75b08a6fba41f098b6cb091b9e8c7a80e1b4d679a581a0ccd17b10373/tensorboard-1.13.1-py3-none-any.whl (3.2MB)\n",
            "\u001b[K     |████████████████████████████████| 3.2MB 32.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (1.17.5)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (1.0.8)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (0.34.2)\n",
            "Collecting mock>=2.0.0\n",
            "  Downloading https://files.pythonhosted.org/packages/30/6a/9bde648117ec7087c89a45de0a8b25aba21d54d3defd08cb24eacded875f/mock-4.0.1-py3-none-any.whl\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-gpu==1.13.1) (45.2.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow-gpu==1.13.1) (3.2.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow-gpu==1.13.1) (1.0.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==1.13.1) (2.8.0)\n",
            "\u001b[31mERROR: tensorflow 1.15.0 has requirement tensorboard<1.16.0,>=1.15.0, but you'll have tensorboard 1.13.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 1.15.0 has requirement tensorflow-estimator==1.15.1, but you'll have tensorflow-estimator 1.13.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: mock, tensorflow-estimator, tensorboard, tensorflow-gpu\n",
            "  Found existing installation: tensorflow-estimator 1.15.1\n",
            "    Uninstalling tensorflow-estimator-1.15.1:\n",
            "      Successfully uninstalled tensorflow-estimator-1.15.1\n",
            "  Found existing installation: tensorboard 1.15.0\n",
            "    Uninstalling tensorboard-1.15.0:\n",
            "      Successfully uninstalled tensorboard-1.15.0\n",
            "Successfully installed mock-4.0.1 tensorboard-1.13.1 tensorflow-estimator-1.13.0 tensorflow-gpu-1.13.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ryxYd2Lg5r5V",
        "colab_type": "text"
      },
      "source": [
        "## tensorflowのversion確認"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sbMMsbV-vc2v",
        "colab_type": "code",
        "outputId": "6f46962d-bc89-487b-aea6-8d6b7fbdb778",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "1.13.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1WT5rMftqEC7",
        "colab_type": "text"
      },
      "source": [
        "## googleドライブをマウント\n",
        "colaboratory上でDriveのファイルにアクセスをできるようする"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9OoP6xz2NvLr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x5JonCx36LEG",
        "colab_type": "text"
      },
      "source": [
        "ファイル確認"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PIYC2tzrOBJC",
        "colab_type": "code",
        "outputId": "5881e205-d815-44e5-9251-92de15f12d7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "drive  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0g-yrpOs6N1b",
        "colab_type": "text"
      },
      "source": [
        "My Drive上に移動"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ba2rJW4vZmLZ",
        "colab_type": "code",
        "outputId": "f4e52c89-7847-4c68-938d-97a2c5d0b922",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "% cd drive/'My Drive'/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZvuicXgW6aXS",
        "colab_type": "text"
      },
      "source": [
        "## githubにあるU-Netをclone"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXE1bOeKaAvp",
        "colab_type": "code",
        "outputId": "f4ab19f8-95b5-49d5-8250-1392100627b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "! git clone https://github.com/IsHYuhi/LPE_U-Net.git"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'LPE_U-Net'...\n",
            "remote: Enumerating objects: 28, done.\u001b[K\n",
            "remote: Counting objects: 100% (28/28), done.\u001b[K\n",
            "remote: Compressing objects: 100% (20/20), done.\u001b[K\n",
            "remote: Total 28 (delta 7), reused 25 (delta 4), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (28/28), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHiZQpNo6fhs",
        "colab_type": "text"
      },
      "source": [
        "cloneしたディレクトリへ移動"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dJ-zNVBRcbrV",
        "colab_type": "code",
        "outputId": "d66aea73-667f-4e9d-8ff7-7747f926851f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "% cd LPE_U-Net/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/LPE_U-Net\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-T6TtMjR6lUf",
        "colab_type": "text"
      },
      "source": [
        "## 今回扱うデータセット&pre-trained modelをzip形式でダウンロード"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AN-m69YS6vdC",
        "colab_type": "text"
      },
      "source": [
        "data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z4xHb-FeblYv",
        "colab_type": "code",
        "outputId": "20a0f6ce-dedd-48dd-9641-3c75889acb52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "%%shell\n",
        "FILE_ID=1FhUPqiTR1VF0_hXFtIEVuaP3W6JU4zsv\n",
        "FILE_NAME=data_set.zip\n",
        "curl -sc /tmp/cookie \"https://drive.google.com/uc?export=download&id=${FILE_ID}\" > /dev/null\n",
        "CODE=\"$(awk '/_warning_/ {print $NF}' /tmp/cookie)\"\n",
        "curl -Lb /tmp/cookie \"https://drive.google.com/uc?export=download&confirm=${CODE}&id=${FILE_ID}\" -o ${FILE_NAME}"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100   408    0   408    0     0   1350      0 --:--:-- --:--:-- --:--:--  1350\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
            "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
            "100  139M    0  139M    0     0  41.5M      0 --:--:--  0:00:03 --:--:-- 59.8M\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "13zYmkwn6yU-",
        "colab_type": "text"
      },
      "source": [
        "pre-trained model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-qJW0vcPZH1",
        "colab_type": "code",
        "outputId": "078b5a0a-4601-4a94-9583-1d9493179f44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "%%shell\n",
        "FILE_ID=1h-2UGjTifXJA7jyaBe085_3cPeAHEEda\n",
        "FILE_NAME=checkpoint.zip\n",
        "curl -sc /tmp/cookie \"https://drive.google.com/uc?export=download&id=${FILE_ID}\" > /dev/null\n",
        "CODE=\"$(awk '/_warning_/ {print $NF}' /tmp/cookie)\"\n",
        "curl -Lb /tmp/cookie \"https://drive.google.com/uc?export=download&confirm=${CODE}&id=${FILE_ID}\" -o ${FILE_NAME}"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100   408    0   408    0     0   1551      0 --:--:-- --:--:-- --:--:--  1551\n",
            "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
            "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
            "100  222M    0  222M    0     0  46.1M      0 --:--:--  0:00:04 --:--:-- 58.1M\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LS-fNSew65G8",
        "colab_type": "text"
      },
      "source": [
        "確認"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uthieeVUtX76",
        "colab_type": "code",
        "outputId": "60597672-136e-41ee-a18f-135dcf7c355e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "! ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "checkpoint.zip\tdata_set  data_set.zip\tmain.py  README.md  util\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1uFSFtVA6662",
        "colab_type": "text"
      },
      "source": [
        "## zipファイルの解凍&確認"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZClQyvegflb",
        "colab_type": "code",
        "outputId": "f2b0a742-9a92-4a84-dc22-1b635516e754",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "! unzip data_set.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  data_set.zip\n",
            "   creating: data_set/test_images/\n",
            "  inflating: __MACOSX/data_set/._test_images  \n",
            "   creating: data_set/train_annotations/\n",
            "  inflating: __MACOSX/data_set/._train_annotations  \n",
            "  inflating: data_set/.DS_Store      \n",
            "  inflating: __MACOSX/data_set/._.DS_Store  \n",
            "   creating: data_set/val_images/\n",
            "  inflating: __MACOSX/data_set/._val_images  \n",
            "   creating: data_set/val_annotations/\n",
            "  inflating: __MACOSX/data_set/._val_annotations  \n",
            "   creating: data_set/test_annotations/\n",
            "  inflating: __MACOSX/data_set/._test_annotations  \n",
            "   creating: data_set/train_images/\n",
            "  inflating: __MACOSX/data_set/._train_images  \n",
            "  inflating: data_set/test_images/TCm_MI_MI_MAPm04_N00E000S03E003SC_22_20.png  \n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eaj9RJ-Lu_zS",
        "colab_type": "code",
        "outputId": "4353f5c4-2b9c-494e-afc7-7c1aab8450b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!ls data_set/train_images\n",
        "!ls data_set/train_annotations"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZYgkLMTubaPp",
        "colab_type": "code",
        "outputId": "21a8895f-d674-40f7-839a-29faeffa09d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "! unzip checkpoint.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  checkpoint.zip\n",
            "   creating: pre_model/\n",
            "  inflating: __MACOSX/._pre_model    \n",
            "  inflating: pre_model/pre_model.ckpt.meta  \n",
            "  inflating: __MACOSX/pre_model/._pre_model.ckpt.meta  \n",
            "  inflating: pre_model/pre_model.ckpt.data-00000-of-00001  \n",
            "  inflating: __MACOSX/pre_model/._pre_model.ckpt.data-00000-of-00001  \n",
            "  inflating: pre_model/pre_model.ckpt.index  \n",
            "  inflating: __MACOSX/pre_model/._pre_model.ckpt.index  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nx0d_WDPQUOg",
        "colab_type": "code",
        "outputId": "8f42f04d-9759-4121-9d52-98d26081e405",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!ls pre_model/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "pre_model.ckpt.data-00000-of-00001  pre_model.ckpt.index  pre_model.ckpt.meta\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LqSDmGA49qB2",
        "colab_type": "text"
      },
      "source": [
        "# 訓練データの学習\n",
        "\n",
        "\n",
        "|省略オプション|オプション|使用方法|デフォルト|\n",
        "|---|---|---|---|\n",
        "|-g|--gpu| -g でgpu使用|False|\n",
        "|-e|--epoch| -e　N でエポック数指定|50|\n",
        "|-b|--batch| -b N でバッチサイズ指定|4|\n",
        "|-t|--trainrate| -t 0.0~1.0 で訓練データの割合指定|0.7|\n",
        "|-l|--l2lreg| -l N でregularizer指定|0.0001|\n",
        "|-n|--num| -n N で画像の枚数指定|None|\n",
        "|-s|--size| -s 128 128 で画像のサイズ指定|(128, 128)|\n",
        "|-r|--restore| -r CHECKPOINT_NAME でチェックポイントから学習再開|None|\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zS1YQm9V7QYi",
        "colab_type": "text"
      },
      "source": [
        "※defaultのepoch=50では学習が不十分な場合があります。  \n",
        "※image sizeは自由に変更できますが、メモリに乗り切らない場合があります。batch_sizeもその都度変更してください。またimage sizeの変更を行う場合は__*ランタイム*__->__*ランタイムを出荷時にリセット*__を行うようにしてください。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEp3hUkFvLbT",
        "colab_type": "code",
        "outputId": "b3511e57-12cc-402a-ae7f-ae1f8126f9b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "! python main.py -g -e 50 -b 4 -t 0.7 -l 0.0001 -n 504 -s 128 128"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
            "Loading original images..... Completed\n",
            "Loading segmented images..... Completed\n",
            "Casting to one-hot encoding... Done\n",
            "True\n",
            "\n",
            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "WARNING:tensorflow:From /content/drive/My Drive/LPE_U-Net/util/model.py:69: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.conv2d instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /content/drive/My Drive/LPE_U-Net/util/model.py:85: batch_normalization (from tensorflow.python.layers.normalization) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.batch_normalization instead.\n",
            "WARNING:tensorflow:From /content/drive/My Drive/LPE_U-Net/util/model.py:91: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.max_pooling2d instead.\n",
            "WARNING:tensorflow:From /content/drive/My Drive/LPE_U-Net/util/model.py:107: conv2d_transpose (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.conv2d_transpose instead.\n",
            "WARNING:tensorflow:From main.py:54: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "\n",
            "Future major versions of TensorFlow will allow gradients to flow\n",
            "into the labels input on backprop by default.\n",
            "\n",
            "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
            "\n",
            "2020-03-09 01:25:23.406366: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
            "2020-03-09 01:25:23.482328: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] 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",
            "2020-03-09 01:25:23.483244: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x2c0e3c0 executing computations on platform CUDA. Devices:\n",
            "2020-03-09 01:25:23.483284: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): Tesla K80, Compute Capability 3.7\n",
            "2020-03-09 01:25:23.485048: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200000000 Hz\n",
            "2020-03-09 01:25:23.485753: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x2c0e260 executing computations on platform Host. Devices:\n",
            "2020-03-09 01:25:23.485807: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>\n",
            "2020-03-09 01:25:23.485966: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: \n",
            "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
            "pciBusID: 0000:00:04.0\n",
            "totalMemory: 11.17GiB freeMemory: 11.11GiB\n",
            "2020-03-09 01:25:23.486010: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0\n",
            "2020-03-09 01:25:23.487086: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-03-09 01:25:23.487121: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 \n",
            "2020-03-09 01:25:23.487139: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N \n",
            "2020-03-09 01:25:23.487279: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8008 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
            "gpu mode\n",
            "2020-03-09 01:25:26.933832: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally\n",
            "2020-03-09 01:25:50.296606: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.28GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
            "2020-03-09 01:25:51.929200: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.06GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
            "2020-03-09 01:25:52.698864: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.06GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
            "2020-03-09 01:25:53.557275: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.06GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
            "Epoch: 0\n",
            "[Train] Loss: 0.07960179  Accuracy: 0.9809841\n",
            "[Test]  Loss: 0.057199884 Accuracy: 0.9875529\n",
            "Epoch: 1\n",
            "[Train] Loss: 0.063961424  Accuracy: 0.98118824\n",
            "[Test]  Loss: 0.05503326 Accuracy: 0.985225\n",
            "Epoch: 2\n",
            "[Train] Loss: 0.0836048  Accuracy: 0.98037535\n",
            "[Test]  Loss: 0.05680097 Accuracy: 0.9879813\n",
            "Epoch: 3\n",
            "[Train] Loss: 0.07188572  Accuracy: 0.9803932\n",
            "[Test]  Loss: 0.063776806 Accuracy: 0.9873172\n",
            "Epoch: 4\n",
            "[Train] Loss: 0.0674255  Accuracy: 0.9805815\n",
            "[Test]  Loss: 0.052137952 Accuracy: 0.9878249\n",
            "Epoch: 5\n",
            "[Train] Loss: 0.06482965  Accuracy: 0.9805682\n",
            "[Test]  Loss: 0.048263047 Accuracy: 0.9876568\n",
            "Epoch: 6\n",
            "[Train] Loss: 0.06263187  Accuracy: 0.98065794\n",
            "[Test]  Loss: 0.050451446 Accuracy: 0.9858314\n",
            "Epoch: 7\n",
            "[Train] Loss: 0.06764488  Accuracy: 0.9803685\n",
            "[Test]  Loss: 0.049245268 Accuracy: 0.9879813\n",
            "Epoch: 8\n",
            "[Train] Loss: 0.06590202  Accuracy: 0.98055404\n",
            "[Test]  Loss: 0.04868539 Accuracy: 0.98794174\n",
            "Epoch: 9\n",
            "[Train] Loss: 0.05894687  Accuracy: 0.98072743\n",
            "[Test]  Loss: 0.045639597 Accuracy: 0.9857101\n",
            "Epoch: 10\n",
            "[Train] Loss: 0.06988465  Accuracy: 0.98038185\n",
            "[Test]  Loss: 0.050479893 Accuracy: 0.98798215\n",
            "Epoch: 11\n",
            "[Train] Loss: 0.06951201  Accuracy: 0.98042226\n",
            "[Test]  Loss: 0.04886219 Accuracy: 0.98798335\n",
            "Epoch: 12\n",
            "[Train] Loss: 0.06644006  Accuracy: 0.98039395\n",
            "[Test]  Loss: 0.047713228 Accuracy: 0.9879882\n",
            "Epoch: 13\n",
            "[Train] Loss: 0.07720004  Accuracy: 0.98042065\n",
            "[Test]  Loss: 0.055141833 Accuracy: 0.98798656\n",
            "Epoch: 14\n",
            "[Train] Loss: 0.05840218  Accuracy: 0.9824886\n",
            "[Test]  Loss: 0.04979424 Accuracy: 0.98771983\n",
            "Epoch: 15\n",
            "[Train] Loss: 0.057231747  Accuracy: 0.98268825\n",
            "[Test]  Loss: 0.043158147 Accuracy: 0.987964\n",
            "Epoch: 16\n",
            "[Train] Loss: 0.073848955  Accuracy: 0.980744\n",
            "[Test]  Loss: 0.054557584 Accuracy: 0.9880011\n",
            "Epoch: 17\n",
            "[Train] Loss: 0.057330966  Accuracy: 0.98072016\n",
            "[Test]  Loss: 0.04594652 Accuracy: 0.9875529\n",
            "Epoch: 18\n",
            "[Train] Loss: 0.061500967  Accuracy: 0.9808277\n",
            "[Test]  Loss: 0.045351177 Accuracy: 0.98681235\n",
            "Epoch: 19\n",
            "[Train] Loss: 0.08020142  Accuracy: 0.9804603\n",
            "[Test]  Loss: 0.05991705 Accuracy: 0.98796475\n",
            "Epoch: 20\n",
            "[Train] Loss: 0.059939202  Accuracy: 0.98090976\n",
            "[Test]  Loss: 0.04591566 Accuracy: 0.9879797\n",
            "Epoch: 21\n",
            "[Train] Loss: 0.09134251  Accuracy: 0.98093116\n",
            "[Test]  Loss: 0.06374557 Accuracy: 0.98796594\n",
            "Epoch: 22\n",
            "[Train] Loss: 0.054066956  Accuracy: 0.98215467\n",
            "[Test]  Loss: 0.043239146 Accuracy: 0.988183\n",
            "Epoch: 23\n",
            "[Train] Loss: 0.051974244  Accuracy: 0.9815791\n",
            "[Test]  Loss: 0.04040499 Accuracy: 0.9875286\n",
            "Epoch: 24\n",
            "[Train] Loss: 0.062557586  Accuracy: 0.9814793\n",
            "[Test]  Loss: 0.046967153 Accuracy: 0.9876276\n",
            "Epoch: 25\n",
            "[Train] Loss: 0.058583796  Accuracy: 0.98262477\n",
            "[Test]  Loss: 0.04643964 Accuracy: 0.9882388\n",
            "Epoch: 26\n",
            "[Train] Loss: 0.05490132  Accuracy: 0.9827226\n",
            "[Test]  Loss: 0.051192343 Accuracy: 0.98402697\n",
            "Epoch: 27\n",
            "[Train] Loss: 0.059469793  Accuracy: 0.9813968\n",
            "[Test]  Loss: 0.046786055 Accuracy: 0.98813653\n",
            "Epoch: 28\n",
            "[Train] Loss: 0.05756814  Accuracy: 0.98316926\n",
            "[Test]  Loss: 0.051328845 Accuracy: 0.9880856\n",
            "Epoch: 29\n",
            "[Train] Loss: 0.055110425  Accuracy: 0.9820888\n",
            "[Test]  Loss: 0.047246207 Accuracy: 0.98807955\n",
            "Epoch: 30\n",
            "[Train] Loss: 0.06675934  Accuracy: 0.9810601\n",
            "[Test]  Loss: 0.050333448 Accuracy: 0.9881143\n",
            "Epoch: 31\n",
            "[Train] Loss: 0.08095986  Accuracy: 0.980643\n",
            "[Test]  Loss: 0.06371578 Accuracy: 0.9879607\n",
            "Epoch: 32\n",
            "[Train] Loss: 0.06650761  Accuracy: 0.9816017\n",
            "[Test]  Loss: 0.049445968 Accuracy: 0.9881406\n",
            "Epoch: 33\n",
            "[Train] Loss: 0.069022655  Accuracy: 0.9812998\n",
            "[Test]  Loss: 0.05408318 Accuracy: 0.98768103\n",
            "Epoch: 34\n",
            "[Train] Loss: 0.0531686  Accuracy: 0.98148733\n",
            "[Test]  Loss: 0.041273355 Accuracy: 0.9881527\n",
            "Epoch: 35\n",
            "[Train] Loss: 0.05429017  Accuracy: 0.9823855\n",
            "[Test]  Loss: 0.045710202 Accuracy: 0.9881406\n",
            "Epoch: 36\n",
            "[Train] Loss: 0.052206885  Accuracy: 0.9824655\n",
            "[Test]  Loss: 0.04349683 Accuracy: 0.9884559\n",
            "Epoch: 37\n",
            "[Train] Loss: 0.05279434  Accuracy: 0.982624\n",
            "[Test]  Loss: 0.045916453 Accuracy: 0.9881895\n",
            "Epoch: 38\n",
            "[Train] Loss: 0.055492938  Accuracy: 0.9825011\n",
            "[Test]  Loss: 0.04397072 Accuracy: 0.98819274\n",
            "Epoch: 39\n",
            "[Train] Loss: 0.075422764  Accuracy: 0.9810884\n",
            "[Test]  Loss: 0.05860476 Accuracy: 0.9880088\n",
            "Epoch: 40\n",
            "[Train] Loss: 0.05006142  Accuracy: 0.98322016\n",
            "[Test]  Loss: 0.042644285 Accuracy: 0.98795426\n",
            "Epoch: 41\n",
            "[Train] Loss: 0.055767868  Accuracy: 0.9828463\n",
            "[Test]  Loss: 0.045335263 Accuracy: 0.9878944\n",
            "Epoch: 42\n",
            "[Train] Loss: 0.054655157  Accuracy: 0.9825213\n",
            "[Test]  Loss: 0.046379413 Accuracy: 0.9883847\n",
            "Epoch: 43\n",
            "[Train] Loss: 0.055018198  Accuracy: 0.9823204\n",
            "[Test]  Loss: 0.042410776 Accuracy: 0.98800313\n",
            "Epoch: 44\n",
            "[Train] Loss: 0.054050434  Accuracy: 0.9825108\n",
            "[Test]  Loss: 0.042969674 Accuracy: 0.98837703\n",
            "Epoch: 45\n",
            "[Train] Loss: 0.06979489  Accuracy: 0.9815581\n",
            "[Test]  Loss: 0.054663196 Accuracy: 0.9878475\n",
            "Epoch: 46\n",
            "[Train] Loss: 0.063960835  Accuracy: 0.981276\n",
            "[Test]  Loss: 0.052980542 Accuracy: 0.98780227\n",
            "Epoch: 47\n",
            "[Train] Loss: 0.05276813  Accuracy: 0.9835767\n",
            "[Test]  Loss: 0.044832494 Accuracy: 0.9877016\n",
            "Epoch: 48\n",
            "[Train] Loss: 0.050806783  Accuracy: 0.9822982\n",
            "[Test]  Loss: 0.04189934 Accuracy: 0.9878358\n",
            "Epoch: 49\n",
            "[Train] Loss: 0.07071066  Accuracy: 0.9810981\n",
            "[Test]  Loss: 0.058117956 Accuracy: 0.98754436\n",
            "Result\n",
            "[Test]  Loss: 0.058117956 Accuracy: 0.98754436\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WNfjNFVQEEjS",
        "colab_type": "text"
      },
      "source": [
        "## LossとAccuracyのグラフ表示"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n58akzfhFIOb",
        "colab_type": "text"
      },
      "source": [
        "※性能指標としてAccuracyを採用しているため、高精度に見える(背景が大部分を占めるため)が本来はタスクに合わせた性能評価が必要になる。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M7OZlXCwEORk",
        "colab_type": "text"
      },
      "source": [
        "XXXX:作成されたresultのディレクトリ名"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xonT5Jt0EZ0v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!ls result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1R0eMYUw95FT",
        "colab_type": "code",
        "outputId": "7aa361f6-a46e-450d-f6e0-22920cc2eeca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        }
      },
      "source": [
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "date = \"20200309_XXXX\"\n",
        "Loss_path = \"./result/\"+date+\"/learning/Loss.png\"\n",
        "Accuracy_path = \"./result/\"+date+\"/learning/Accuracy.png\"\n",
        "image_L = Image.open(Loss_path)\n",
        "image_A = Image.open(Accuracy_path)\n",
        "\n",
        "data = [image_L, image_A]\n",
        "\n",
        "fig = plt.figure(figsize=(20,20))\n",
        "ax1 = fig.add_subplot(1,2,1)\n",
        "ax1.imshow(data[0])\n",
        "ax2 = fig.add_subplot(1,2,2)\n",
        "ax2.imshow(data[1])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rr4/R+tQa+uLjz4OPli3/AhhIs7PiOWf/bCLnD2dPwlCS0n8kuuA465MncTS7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8f7J598gmuuuSbFoyIIgiASQSgUenLZsh2hUIi+vr6cCdGORHr+iidSy7n97K1A6BWEus6kbjxh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cj42N5eabb/bBbygiIq1Vax+DKSDkZ7t37yY6OhrDMLy6TmFhId27d6810JHa1EfOqY+cUx85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\nP5sMBa6AU6Q0AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3wG_idql9hiQ",
        "colab_type": "text"
      },
      "source": [
        "作成されたチェックポイントの確認"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3MCZBy4MbDM3",
        "colab_type": "code",
        "outputId": "c6a3712d-31e2-4364-9436-5ea321f54160",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "!ls checkpoint"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "checkpoint\n",
            "save_model_done.ckpt.data-00000-of-00001\n",
            "save_model_done.ckpt.index\n",
            "save_model_done.ckpt.meta\n",
            "save_model_epoch_0_.ckpt.data-00000-of-00001\n",
            "save_model_epoch_0_.ckpt.index\n",
            "save_model_epoch_0_.ckpt.meta\n",
            "save_model_epoch_10_.ckpt.data-00000-of-00001\n",
            "save_model_epoch_10_.ckpt.index\n",
            "save_model_epoch_10_.ckpt.meta\n",
            "save_model_epoch_20_.ckpt.data-00000-of-00001\n",
            "save_model_epoch_20_.ckpt.index\n",
            "save_model_epoch_20_.ckpt.meta\n",
            "save_model_epoch_30_.ckpt.data-00000-of-00001\n",
            "save_model_epoch_30_.ckpt.index\n",
            "save_model_epoch_30_.ckpt.meta\n",
            "save_model_epoch_40_.ckpt.data-00000-of-00001\n",
            "save_model_epoch_40_.ckpt.index\n",
            "save_model_epoch_40_.ckpt.meta\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hbxa5KMx79Cv",
        "colab_type": "text"
      },
      "source": [
        "## 推論用のLoader "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kY-8iZzHWPU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from PIL import Image\n",
        "import numpy as np\n",
        "import glob\n",
        "import os\n",
        "import random\n",
        "from natsort import natsorted\n",
        "\n",
        "\n",
        "class LoaderForPredict(object):\n",
        "    def __init__(self, dir_original, init_size=(128, 128)):\n",
        "        self._data = LoaderForPredict.import_data(dir_original, init_size)\n",
        "\n",
        "    def get_dataset(self):\n",
        "        return self._data\n",
        "\n",
        "    @staticmethod\n",
        "    def import_data(dir_original, init_size=None):\n",
        "        paths_original = LoaderForPredict.generate_paths(dir_original)\n",
        "        # pathをファイル順にソートする\n",
        "        paths_original = natsorted(paths_original)\n",
        "\n",
        "        images_original, images_original_size = LoaderForPredict.extract_images(paths_original, init_size)\n",
        "\n",
        "        image_sample_palette = Image.open(paths_original[0])#一枚の画像からパレットを取り出す\n",
        "        #print(im.shape) #パレットのshapeを確認\n",
        "        image_sample_palette = image_sample_palette.convert(\"P\")\n",
        "        palette = image_sample_palette.getpalette()\n",
        "\n",
        "        #任意のパッレットカラーを割り当てる。今回のタスクは三クラスなので自分で割り当て\n",
        "        palette[3] = 255\n",
        "        palette[7] = 255\n",
        "\n",
        "        return DataSet(images_original, images_original_size, None, palette)\n",
        "\n",
        "    @staticmethod\n",
        "    def extract_images(paths_original, init_size):\n",
        "        images_original,images_original_size = [], []\n",
        "\n",
        "        # Load images\n",
        "        print(\"Loading original images\", end=\"\", flush=True)\n",
        "        for image, original_size in LoaderForPredict.image_generator(paths_original, init_size, antialias=True):\n",
        "            images_original.append(image)\n",
        "            images_original_size.append(original_size)\n",
        "            if len(images_original) % 100 == 0:\n",
        "                print(\".\", end=\"\", flush=True)\n",
        "        print(\" Completed\", flush=True)\n",
        "\n",
        "        # Cast to ndarray\n",
        "        images_original = np.asarray(images_original, dtype=np.float32)\n",
        "\n",
        "        return images_original, images_original_size\n",
        "\n",
        "    @staticmethod\n",
        "    def image_generator(file_paths, init_size=None, antialias=True, normalization=True):\n",
        "        for file_path in file_paths:\n",
        "            if file_path.endswith(\".png\") or file_path.endswith(\".jpg\"):\n",
        "\n",
        "                image = Image.open(file_path)\n",
        "                original_size = (image.width, image.height)\n",
        "\n",
        "                if antialias:\n",
        "                    image = image.resize(init_size, Image.ANTIALIAS)\n",
        "                else:\n",
        "                    image = image.resize(init_size)\n",
        "\n",
        "                # delete alpha channel\n",
        "                if image.mode == \"RGBA\":\n",
        "                    image = image.convert(\"RGB\")\n",
        "                image = np.asarray(image)\n",
        "                if normalization:\n",
        "                    image = image / 255.0\n",
        "                yield image, original_size\n",
        "\n",
        "    @staticmethod\n",
        "    def generate_paths(dir_original):\n",
        "        paths_original = glob.glob(dir_original + \"/*\")\n",
        "        filenames = list(map(lambda path: path.split(os.sep)[-1].split(\".\")[0], paths_original))#.以降を削除し、ファイルネームのみにする\n",
        "        paths_original = list(map(lambda filename: dir_original + \"/\" + filename + \".png\", filenames))#ファイルネーム.pngにする\n",
        "\n",
        "        return paths_original\n",
        "\n",
        "\n",
        "class DataSet(object):\n",
        "    def __init__(self, images_original, images_original_size, file_name, image_palette):\n",
        "        self._images_original = images_original\n",
        "        self._images_original_size = images_original_size\n",
        "        self._file_name = file_name\n",
        "        self._image_palette = image_palette\n",
        "\n",
        "    @property\n",
        "    def images_original(self):\n",
        "        return self._images_original\n",
        "\n",
        "    @property\n",
        "    def images_original_size(self):\n",
        "        return self._images_original_size\n",
        "\n",
        "    @property\n",
        "    def palette(self):\n",
        "        return self._image_palette\n",
        "\n",
        "    @property\n",
        "    def len(self):\n",
        "        return len(self._images_original)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "icqW9RAD8Fk8",
        "colab_type": "text"
      },
      "source": [
        "## ndarryからPILimageへ変換"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bziKOMKWbLiG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def cast_to_out_image(ndarray, size, palette):\n",
        "  res = np.argmax(ndarray, axis=2)\n",
        "  image = Image.fromarray(np.uint8(res), mode=\"P\")\n",
        "  image.putpalette(palette)\n",
        "  image = image.resize(size)\n",
        "  return image\n",
        "\n",
        "def cast_to_out_image_in(ndarray, size):\n",
        "  image = Image.fromarray(np.uint8(ndarray * 255), mode=\"RGB\")\n",
        "  image = image.resize(size)\n",
        "  return image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFYTvkT28O-4",
        "colab_type": "text"
      },
      "source": [
        "## 推論プログラム"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mv_rTM-UIt9o",
        "colab_type": "code",
        "outputId": "a52cec76-7f42-460b-c248-86d689c0b630",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "import random\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import os\n",
        "from PIL import Image\n",
        "import easydict\n",
        "from util import model\n",
        "from util import repoter as rp\n",
        "\n",
        "\n",
        "def load_dataset():\n",
        "    #you can replace path to data_set/test_images, data_set/val_images\n",
        "    loader = LoaderForPredict(dir_original=\"data_set/train_images\", init_size=(128, 128))\n",
        "    return loader.get_dataset()\n",
        "\n",
        "\n",
        "def implement(parser):\n",
        "    tf.reset_default_graph()\n",
        "    #image data\n",
        "    train = load_dataset()\n",
        "\n",
        "    #reporter\n",
        "    reporter = rp.Reporter(parser=parser)\n",
        "\n",
        "    # GPU\n",
        "    gpu = parser.gpu\n",
        "\n",
        "    #model\n",
        "    model_unet = model.UNet(size=(128, 128), l2_reg=parser.l2reg).model\n",
        "\n",
        "    # Initialize session\n",
        "    gpu_config = tf.ConfigProto(gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.7, visible_device_list=\"\", allow_growth=True), #device_count={'GPU': 0},\n",
        "                                log_device_placement=False, allow_soft_placement=True)\n",
        "    if gpu:\n",
        "        sess = tf.InteractiveSession(config=gpu_config)\n",
        "        print(\"gpu mode\")\n",
        "    else:\n",
        "        sess = tf.InteractiveSession()\n",
        "        print(\"cpu mode\")\n",
        "\n",
        "    tf.global_variables_initializer().run()\n",
        "\n",
        "\n",
        "    #Sarver\n",
        "    saver = tf.train.Saver()\n",
        "    #学習済みのモデルを指定してロード\n",
        "    saver.restore(sess, \"./pre_model/pre_model.ckpt\")\n",
        "    pred_images = []\n",
        "    in_images = []\n",
        "    for idx_train in range(train.len):\n",
        "      train_images_original = train.images_original\n",
        "      images_original_size = train.images_original_size\n",
        "      \n",
        "      outputs_train = sess.run(model_unet.outputs,\n",
        "                                         feed_dict={model_unet.inputs: [train_images_original[idx_train]],\n",
        "                                                    model_unet.is_training: False})\n",
        "\n",
        "\n",
        "      pred_image = cast_to_out_image(outputs_train[0], images_original_size[idx_train], train.palette)\n",
        "      in_image = cast_to_out_image_in(train_images_original[idx_train], images_original_size[idx_train])\n",
        "\n",
        "      pred_images.append(pred_image)\n",
        "      in_images.append(in_image)\n",
        "\n",
        "    print(\"Result\")\n",
        "\n",
        "    sess.close()\n",
        "    return pred_images, in_images\n",
        "\n",
        "\n",
        "def get_parser():\n",
        "\n",
        "    args = easydict.EasyDict({\n",
        "        \"batchsize\": 1,\n",
        "        \"epoch\": 300,\n",
        "        \"gpu\": True,\n",
        "        \"l2reg\":0.0001,\n",
        "    })\n",
        "    \n",
        "    return args\n",
        "\n",
        "\n",
        "parser = get_parser()\n",
        "pred_images, in_images = implement(parser)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading original images..... Completed\n",
            "\n",
            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "WARNING:tensorflow:From /content/drive/My Drive/LPE_U-Net/util/model.py:69: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.conv2d instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /content/drive/My Drive/LPE_U-Net/util/model.py:85: batch_normalization (from tensorflow.python.layers.normalization) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.batch_normalization instead.\n",
            "WARNING:tensorflow:From /content/drive/My Drive/LPE_U-Net/util/model.py:91: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.max_pooling2d instead.\n",
            "WARNING:tensorflow:From /content/drive/My Drive/LPE_U-Net/util/model.py:107: conv2d_transpose (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.conv2d_transpose instead.\n",
            "gpu mode\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use standard file APIs to check for files with this prefix.\n",
            "INFO:tensorflow:Restoring parameters from ./pre_model/pre_model.ckpt\n",
            "Result\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vn3DYjwp9ASE",
        "colab_type": "text"
      },
      "source": [
        "## 実際に推論した画像の確認"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n7gparrPkH5V",
        "colab_type": "code",
        "outputId": "d6d640e5-cfad-44ff-f268-4f183681f5d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        }
      },
      "source": [
        "pred_images[304]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAMAAADDpiTIAAADAFBMVEUAAAD/AAAA/wAAAAAAAAAA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\nAAAAAAAA1zae2wghba0RtgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<PIL.Image.Image image mode=P size=512x512 at 0x7FE6B06E8CF8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0usT9NQYaLZ",
        "colab_type": "code",
        "outputId": "cc59a92f-caf8-4d17-af63-880863005bd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        }
      },
      "source": [
        "in_images[304]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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H6pThKDc8kRJe8GlmTbJ/OuT64UYVan5icne1K7hdLZ3UIeir6Hj+9NPvqkXh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CAQeFHECyAQCAReFFN/4oGzDB6z3PDeoaZb/58ZnitDau76xcPbHqv1s2gG8Fn5ihd8PhWxM2p4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\n4UbPjX1L8zvZBggFa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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=512x512 at 0x7FE6B06E8D30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    }
  ]
}