{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "pneumonia.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4CA8nCoY_13W",
        "colab_type": "text"
      },
      "source": [
        "## Kaggle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9DLWwVC_VrG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir -p ~/.kaggle/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WTMssRja_0HR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mv kaggle.json ~/.kaggle/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqu4mZA9_1Qv",
        "colab_type": "code",
        "outputId": "51eb040f-14d6-4861-d441-5670ed11820a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "!kaggle competitions download -c rsna-pneumonia-detection-challenge"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "Downloading stage_2_detailed_class_info.csv.zip to /content\n",
            "\r  0% 0.00/583k [00:00<?, ?B/s]\n",
            "100% 583k/583k [00:00<00:00, 80.4MB/s]\n",
            "Downloading stage_2_sample_submission.csv to /content\n",
            "  0% 0.00/155k [00:00<?, ?B/s]\n",
            "100% 155k/155k [00:00<00:00, 46.5MB/s]\n",
            "Downloading stage_2_train_labels.csv.zip to /content\n",
            "  0% 0.00/661k [00:00<?, ?B/s]\n",
            "100% 661k/661k [00:00<00:00, 95.9MB/s]\n",
            "Downloading stage_2_test_images.zip to /content\n",
            " 95% 360M/377M [00:01<00:00, 222MB/s]\n",
            "100% 377M/377M [00:01<00:00, 211MB/s]\n",
            "Downloading stage_2_train_images.zip to /content\n",
            "100% 3.29G/3.29G [00:23<00:00, 127MB/s]\n",
            "100% 3.29G/3.29G [00:23<00:00, 148MB/s]\n",
            "Downloading GCP%20Credits%20Request%20Link%20-%20RSNA.txt to /content\n",
            "  0% 0.00/55.0 [00:00<?, ?B/s]\n",
            "100% 55.0/55.0 [00:00<00:00, 48.5kB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9RP-8hBoBhxH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip -q stage_2_train_labels.csv.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3AvN49wv4pT0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir images"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-pO7JID34h4z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip -q stage_2_train_images.zip -d images"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0NgArHlNXd7r",
        "colab_type": "code",
        "outputId": "207d151a-05e2-4632-9ff8-48f0df742fd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "!pip install pydicom"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pydicom\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/43/88/d3c419ab2e753e7651510882a53219373e78fb55294cb247dffd3934ea55/pydicom-1.2.2-py2.py3-none-any.whl (7.0MB)\n",
            "\u001b[K     |████████████████████████████████| 7.0MB 2.7MB/s \n",
            "\u001b[?25hInstalling collected packages: pydicom\n",
            "Successfully installed pydicom-1.2.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHfXv_StAH1H",
        "colab_type": "code",
        "outputId": "b033ecd7-ae15-4b18-abe0-ce9e8ecbb734",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "from tensorflow.train import AdamOptimizer\n",
        "\n",
        "from tensorflow.python.keras.utils.data_utils import Sequence\n",
        "\n",
        "from tensorflow.keras.layers import Flatten, Dropout, BatchNormalization, Reshape, GlobalAveragePooling2D\n",
        "from tensorflow.keras.layers import Dense, Dropout, Input, Conv2D, MaxPooling2D\n",
        "from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2, preprocess_input\n",
        "\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, LearningRateScheduler\n",
        "\n",
        "from keras.preprocessing import image\n",
        "\n",
        "import itertools\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "import matplotlib.image as mpimg\n",
        "import numpy as np\n",
        "import shutil\n",
        "import os\n",
        "import pandas as pd\n",
        "import pylab\n",
        "import pydicom\n",
        "\n",
        "import re"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y8A5a_E1W3qM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf.enable_eager_execution()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTa7yFz6BRfn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_labels = pd.read_csv(\"stage_2_train_labels.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C09LJ_TMBnsI",
        "colab_type": "code",
        "outputId": "6615ab24-5a5b-4aec-d615-f7da3c1dd7dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "train_labels.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>patientId</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0004cfab-14fd-4e49-80ba-63a80b6bddd6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>00313ee0-9eaa-42f4-b0ab-c148ed3241cd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>00322d4d-1c29-4943-afc9-b6754be640eb</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>003d8fa0-6bf1-40ed-b54c-ac657f8495c5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>00436515-870c-4b36-a041-de91049b9ab4</td>\n",
              "      <td>264.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>379.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                              patientId      x      y  width  height  Target\n",
              "0  0004cfab-14fd-4e49-80ba-63a80b6bddd6    NaN    NaN    NaN     NaN       0\n",
              "1  00313ee0-9eaa-42f4-b0ab-c148ed3241cd    NaN    NaN    NaN     NaN       0\n",
              "2  00322d4d-1c29-4943-afc9-b6754be640eb    NaN    NaN    NaN     NaN       0\n",
              "3  003d8fa0-6bf1-40ed-b54c-ac657f8495c5    NaN    NaN    NaN     NaN       0\n",
              "4  00436515-870c-4b36-a041-de91049b9ab4  264.0  152.0  213.0   379.0       1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OMeOZES2R5P",
        "colab_type": "code",
        "outputId": "f2f8210f-8786-48de-8a22-a7888233dcd8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_labels[\"patientId\"].unique().size"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26684"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GrDwc89E92hh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "images_paths = os.listdir(\"images\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qzs8skFiKCAR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = [f'images/{image_path}' for image_path in images_paths]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u9i7XiXW975y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "original_image_width = 1024\n",
        "original_image_height = 1024\n",
        "\n",
        "y = []\n",
        "boxes = []\n",
        "\n",
        "for image_path in images_paths:\n",
        "  image_name = image_path[:-4]\n",
        "  patient_data = train_labels[train_labels[\"patientId\"] == image_name]\n",
        "  image_class = patient_data.iloc[0][\"Target\"]\n",
        "  \n",
        "  patient_boxes = []\n",
        "  \n",
        "  for patient in patient_data.values:\n",
        "    box_width = patient[3]\n",
        "    box_height = patient[4]\n",
        "    \n",
        "    if np.isnan(box_width):\n",
        "      patient_boxes.append([0, 0, 0, 0])\n",
        "    else:\n",
        "      x_min = patient[1] / original_image_width\n",
        "      y_min = patient[2] / original_image_height\n",
        "      x_max = (patient[1] + box_width) / original_image_width\n",
        "      y_max = (patient[2] + box_height) / original_image_height\n",
        "      patient_boxes.append([x_min, y_min, x_max, y_max])\n",
        "  \n",
        "  boxes.append(patient_boxes)\n",
        "  y.append(image_class)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4aTkfAj3YZut",
        "colab_type": "code",
        "outputId": "2e5ca672-17e0-44b8-ce72-7c5c55aefc5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fri Apr 26 02:04:22 2019       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 418.56       Driver Version: 410.79       CUDA Version: 10.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   45C    P0    26W /  70W |   8813MiB / 15079MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IMKT2_EqZGNs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cat /proc/meminfo"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fbN4qVPJAJ8",
        "colab_type": "text"
      },
      "source": [
        "## Create new csv"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ykr7-fIYE40G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "training_information = pd.DataFrame({'X': X, 'y': y, 'boxes': boxes})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEy3JutZcvZU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "training_information.to_csv(\"training_information.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dh-hAFYBcxFC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "training_information = pd.read_csv(\"training_information.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELgd16ISGCEb",
        "colab_type": "code",
        "outputId": "3094a56a-0f09-4d7f-caec-3cef44807fe5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "training_information.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>X</th>\n",
              "      <th>boxes</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>images/b89bd111-9093-46cd-a75c-c58c58b80824.dcm</td>\n",
              "      <td>[[0, 0, 0, 0]]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>images/651cd9f4-24c3-4ed0-9139-bb1595b18522.dcm</td>\n",
              "      <td>[[0, 0, 0, 0]]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>images/764ae03e-966a-48d2-ad73-bdbe10274eea.dcm</td>\n",
              "      <td>[[0, 0, 0, 0]]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>images/91b0043a-35bd-4cf9-b338-1cbc9b05b76d.dcm</td>\n",
              "      <td>[[0, 0, 0, 0]]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>images/7aea6188-dc10-4758-9a8e-b4d3de1dbc2d.dcm</td>\n",
              "      <td>[[0, 0, 0, 0]]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                                                X  \\\n",
              "0           0  images/b89bd111-9093-46cd-a75c-c58c58b80824.dcm   \n",
              "1           1  images/651cd9f4-24c3-4ed0-9139-bb1595b18522.dcm   \n",
              "2           2  images/764ae03e-966a-48d2-ad73-bdbe10274eea.dcm   \n",
              "3           3  images/91b0043a-35bd-4cf9-b338-1cbc9b05b76d.dcm   \n",
              "4           4  images/7aea6188-dc10-4758-9a8e-b4d3de1dbc2d.dcm   \n",
              "\n",
              "            boxes  y  \n",
              "0  [[0, 0, 0, 0]]  0  \n",
              "1  [[0, 0, 0, 0]]  0  \n",
              "2  [[0, 0, 0, 0]]  0  \n",
              "3  [[0, 0, 0, 0]]  0  \n",
              "4  [[0, 0, 0, 0]]  0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5943tiMMdJgb",
        "colab_type": "text"
      },
      "source": [
        "## Images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11r8-xNGGG98",
        "colab_type": "code",
        "outputId": "e5e4d1fb-e2ca-4c22-85e8-02b919c53f08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "positive_images = training_information[training_information[\"y\"] == 1][\"X\"][0:3]\n",
        "negative_images = training_information[training_information[\"y\"] == 0][\"X\"][0:3]\n",
        "\n",
        "def plot_images(image_index, image_path, title):\n",
        "  sp = figure.add_subplot(2, 3, image_index + 1)\n",
        "  sp.axis('Off')\n",
        "  dcm_data = pydicom.read_file(image_path)\n",
        "  image_loaded = dcm_data.pixel_array\n",
        "  sp.set_title(title, fontsize=16)\n",
        "  plt.imshow(image_loaded, cmap=pylab.cm.gist_gray)\n",
        "\n",
        "figure = plt.figure(figsize=(12, 6))\n",
        "\n",
        "for image_index, image_path in enumerate(positive_images):\n",
        "  plot_images(image_index, image_path, \"Positive\")\n",
        "\n",
        "for image_index, image_path in enumerate(negative_images):\n",
        "  plot_images(image_index + 3, image_path, \"Negative\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0220AW0CPgI7Blz01TgClwRRNp7kpVaFpcMbf6cD4Xi7e/Jvb7RYgR5Bqfx8wC56n06mA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026K4f73fRa+injowSg0saw+gMlaochBI0z2c6YSRvGjTQzn5HP5xNaE9q0sIB2dnZ0fHwc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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x432 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0i-bVduK4j45",
        "colab_type": "text"
      },
      "source": [
        "## Match Boxes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rE-p8mZX4jdu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def iou(box, anchor_box):  \n",
        "  x_min = np.maximum(box[0], anchor_box[0])\n",
        "  y_min = np.maximum(box[1], anchor_box[1])\n",
        "  x_max = np.maximum(box[2], anchor_box[2])\n",
        "  y_max = np.maximum(box[3], anchor_box[3])\n",
        "  \n",
        "  overlap_area = np.maximum(0.0, x_max - x_min + 1) * np.maximum(0.0, y_max - y_min + 1)\n",
        "  \n",
        "  true_boxes_area = (box[2] - box[0] + 1) * (box[3] - box[1] + 1)\n",
        "  \n",
        "  anchor_boxes_area = (anchor_box[2] - anchor_box[0] + 1) * (anchor_box[3] - anchor_box[1] + 1)\n",
        "  \n",
        "  union_area = (true_boxes_area + anchor_boxes_area - overlap_area)\n",
        "  \n",
        "  return overlap_area / union_area"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g4F3reDKNS2w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "grid_size = 5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUsvIP5wMkBK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_anchor(box):\n",
        "  max_iou = 0.0 \n",
        "  best_anchor = [0, 0, 0, 0]\n",
        "  best_anchor_index = (0, 0)\n",
        "  \n",
        "  column = 0\n",
        "  row = 0\n",
        "  \n",
        "  cell_width, cell_height = (1 / grid_size, 1 / grid_size)\n",
        "  \n",
        "  for cell_x_position in np.linspace(0, 1, grid_size + 1)[:-1]:\n",
        "    row = 0 \n",
        "    \n",
        "    for cell_y_position in np.linspace(0, 1, grid_size + 1)[:-1]:\n",
        "      x_min = cell_x_position\n",
        "      y_min = cell_y_position\n",
        "      \n",
        "      x_max = (cell_x_position + cell_width)\n",
        "      y_max = (cell_y_position + cell_height)\n",
        "      \n",
        "      anchor_box = [x_min, y_min, x_max, y_max]\n",
        "      current_iou = iou(box, anchor_box)\n",
        "      \n",
        "      if current_iou > max_iou:\n",
        "        best_anchor = anchor_box\n",
        "        max_iou = current_iou\n",
        "        best_anchor_index = (column, row)\n",
        "      \n",
        "      row += 1\n",
        "    column += 1\n",
        "    \n",
        "  return best_anchor, best_anchor_index"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0O2wTltBOtF6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_volume(boxes):\n",
        "  grid_volume = np.zeros((grid_size, grid_size, 5))\n",
        "  \n",
        "  for box in boxes:\n",
        "    if max(box) == 0:\n",
        "      continue\n",
        "    _, (column, row) = get_anchor(box)\n",
        "    grid_volume[column, row, :] = [1, *box]\n",
        "    \n",
        "  return grid_volume"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mEUzOgNYYgUr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "positive_information = training_information[training_information[\"y\"] == 1]\n",
        "negative_information = training_information[training_information[\"y\"] == 0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cf9cDNifZZYi",
        "colab_type": "code",
        "outputId": "e46fa36a-cd49-4159-f178-4e90825f00cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "negative_information[\"X\"].size, positive_information[\"X\"].size"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20672, 6012)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GYulIepiZzO_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_data_from_files(information):\n",
        "  data = {}\n",
        "  for file in information.values:\n",
        "    image_path = file[1]\n",
        "    boxes_text = file[2]\n",
        "    boxes_list = re.findall(r'\\[(.*?)\\]', boxes_text[1:-1])\n",
        "    ground_truth_boxes = [np.fromstring(box, dtype=float, sep=', ') for box in boxes_list]\n",
        "\n",
        "    data[image_path] = create_volume(ground_truth_boxes)\n",
        "  \n",
        "  return data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E_ruAXzaPnds",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "positive_data = get_data_from_files(positive_information)\n",
        "negative_data = get_data_from_files(negative_information)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NET8NACuYzv-",
        "colab_type": "code",
        "outputId": "24aab9f6-2738-4592-c7f9-c1aebd6c4a7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(negative_data.values()), len(positive_data.values())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20672, 6012)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E6sfXkEW-_u2",
        "colab_type": "text"
      },
      "source": [
        "## Training / Validation splits"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QbrtpnW7Xw9I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "positive_images_paths = list(positive_data.keys())\n",
        "positive_tensor_volumes = list(positive_data.values())\n",
        "\n",
        "negative_images_paths = list(negative_data.keys())\n",
        "negative_tensor_volumes = list(negative_data.values())\n",
        "\n",
        "positive_data_size = len(positive_images_paths)\n",
        "negative_data_size = len(negative_images_paths)\n",
        "\n",
        "positive_validation_size = int(positive_data_size * 0.20)\n",
        "negative_validation_size = int(negative_data_size * 0.20)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y5mpaBCzbVTm",
        "colab_type": "code",
        "outputId": "f17c6991-4ad4-4f98-a1b4-451279f48416",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "negative_validation_size, positive_validation_size"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4134, 1202)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fE1qUoqJbRxo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = [*positive_images_paths[positive_validation_size:], *negative_images_paths[negative_validation_size:]]\n",
        "X_val = [*positive_images_paths[:positive_validation_size], *negative_images_paths[:negative_validation_size]]\n",
        "\n",
        "y_train = [*positive_tensor_volumes[positive_validation_size:], *negative_tensor_volumes[negative_validation_size:]]\n",
        "y_val = [*positive_tensor_volumes[:positive_validation_size], *negative_tensor_volumes[:negative_validation_size]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "30l6_789JDdI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = np.array(X_train)\n",
        "X_val = np.array(X_val)\n",
        "\n",
        "y_train = np.array(y_train)\n",
        "y_val = np.array(y_val)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m973GGsnX481",
        "colab_type": "code",
        "outputId": "88e1f539-d896-478b-8ed7-c11c12f4e447",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(X_train), len(X_val)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21348, 5336)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hs02RfvGcgfD",
        "colab_type": "code",
        "outputId": "df592df8-9260-41cd-8893-f0cc8862c35d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(y_train), len(y_val)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21348, 5336)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n_4JTvP7TM2f",
        "colab_type": "text"
      },
      "source": [
        "## Generators"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1lR-5i4miD4b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class DataGenerator(Sequence):\n",
        "    def __init__(self, images_paths, tensor_volumes, batch_size=32, predict=False, shuffle=True):\n",
        "      self.batch_size = batch_size\n",
        "      self.tensor_volumes = tensor_volumes\n",
        "      self.images_paths = images_paths\n",
        "      self.shuffle = shuffle\n",
        "      self.predict = predict\n",
        "      self.on_epoch_end()\n",
        "\n",
        "    def __len__(self):\n",
        "      return int(np.floor(len(self.images_paths) / self.batch_size))\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "      from_range = index * self.batch_size\n",
        "      to_range = (index + 1) * self.batch_size\n",
        "      indexes = self.indexes[from_range:to_range]\n",
        "      \n",
        "      if self.predict:\n",
        "        X = self.__load_predict__(indexes)\n",
        "        return X\n",
        "      \n",
        "      else:\n",
        "        X, y = self.__load__(indexes)\n",
        "        return X, y\n",
        "\n",
        "    def on_epoch_end(self):\n",
        "      self.indexes = np.arange(len(self.images_paths))\n",
        "      \n",
        "      if self.shuffle == True:\n",
        "          np.random.shuffle(self.indexes)\n",
        "          \n",
        "    def __load_image__(self, index):\n",
        "      image_path = self.images_paths[index]\n",
        "      tensor_volume = self.tensor_volumes[index]\n",
        "\n",
        "      dcm_data = pydicom.read_file(image_path)\n",
        "      image_loaded = dcm_data.pixel_array\n",
        "\n",
        "      img = np.stack([image_loaded.copy()] * 3, axis=2)\n",
        "      img = image.array_to_img(img, scale=False)\n",
        "      img = img.resize((img_size, img_size))\n",
        "      img = image.img_to_array(img)\n",
        "      img = preprocess_input(img)\n",
        "      \n",
        "      return img\n",
        "    \n",
        "    def __load_predict__(self, indexes):\n",
        "      X = []\n",
        "      \n",
        "      for index in indexes:\n",
        "        img = self.__load_image__(index)\n",
        "        X.append(img)\n",
        "\n",
        "      return np.array(X)\n",
        "    \n",
        "    def __load__(self, indexes):\n",
        "      X = []\n",
        "      y = []\n",
        "      \n",
        "      for index in indexes:\n",
        "        img = self.__load_image__(index)\n",
        "        tensor_volume = self.tensor_volumes[index]\n",
        "        \n",
        "        X.append(img)\n",
        "        y.append(tensor_volume)\n",
        "\n",
        "      return np.array(X), np.array(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_1kWjAqCSF-0",
        "colab_type": "text"
      },
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PjmcrAKySCl6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_model():\n",
        "  mobile_model = MobileNetV2(\n",
        "       weights=None,\n",
        "       input_shape=input_img_size,\n",
        "       alpha=1.5,\n",
        "       include_top=False)\n",
        "  \n",
        "  for layer in mobile_model.layers:\n",
        "    layer.trainable = True\n",
        "  \n",
        "  mobile_model_output = mobile_model.output\n",
        "\n",
        "  feature_map = Conv2D(5, 3, padding='valid', activation='sigmoid')(mobile_model_output)\n",
        "\n",
        "  model = Model(inputs=mobile_model.input, outputs=feature_map)\n",
        "  \n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FM2oVoI0zgrg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def custom_loss(y_true, y_pred):\n",
        "  mask = y_true[..., 0]\n",
        "  true_boxes = tf.boolean_mask(y_true, mask)\n",
        "  predicted_boxes = tf.boolean_mask(y_pred, mask)\n",
        "  \n",
        "  prediction_loss = tf.keras.losses.binary_crossentropy(y_true[..., 0], y_pred[..., 0])\n",
        "\n",
        "  detection_loss = tf.losses.absolute_difference(true_boxes[..., 1:], predicted_boxes[..., 1:]) \n",
        "  \n",
        "  return tf.reduce_mean(prediction_loss) + 10 * detection_loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_QghiAmi-_RX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def custom_accuracy(y_true, y_pred):\n",
        "  return tf.keras.metrics.binary_accuracy(y_true[..., 0], y_pred[..., 0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8US0irXki6F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def mean_iou(y_true, y_pred):\n",
        "  mask = y_true[..., 0]\n",
        "  true_boxes = tf.boolean_mask(y_true, mask)[..., 1:]\n",
        "  predicted_boxes = tf.boolean_mask(y_pred, mask)[..., 1:]\n",
        "  \n",
        "  x_min = tf.math.maximum(true_boxes[..., 0], predicted_boxes[..., 0])\n",
        "  y_min = tf.math.maximum(true_boxes[..., 1], predicted_boxes[..., 1])\n",
        "  x_max = tf.math.maximum(true_boxes[..., 2], predicted_boxes[..., 2])\n",
        "  y_max = tf.math.maximum(true_boxes[..., 3], predicted_boxes[..., 3])\n",
        "  \n",
        "  overlap_area = tf.math.maximum(0.0, x_max - x_min + 1) * tf.math.maximum(0.0, y_max - y_min + 1)\n",
        "  \n",
        "  true_boxes_area = (true_boxes[..., 2] - true_boxes[..., 0] + 1) * (true_boxes[..., 3] - true_boxes[..., 1] + 1)\n",
        "  \n",
        "  predicted_boxes_area = (predicted_boxes[..., 2] - predicted_boxes[..., 0] + 1) * (predicted_boxes[..., 3] - predicted_boxes[..., 1] + 1)\n",
        "  \n",
        "  union_area = (true_boxes_area + predicted_boxes_area - overlap_area)\n",
        "  \n",
        "  return tf.math.reduce_mean(overlap_area / union_area)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aBhft3fdTdci",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 32\n",
        "img_size = 224\n",
        "input_img_size = (224, 224, 3)\n",
        "\n",
        "learning_rate = 3e-4\n",
        "epochs = 50"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zgu-t5fVTfB5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def polynomial_decay(epoch):\n",
        "\tpower = 1.0\n",
        " \n",
        "\talpha = learning_rate * (1 - (epoch / float(epochs))) ** power\n",
        "\treturn alpha"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78qamJmXTgH4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "weights_name = \"epoch={epoch:02d}|loss={loss:.4f}|mean_iou={mean_iou:.4f}.h5\"\n",
        "\n",
        "checkpoint = ModelCheckpoint(weights_name, monitor=\"loss\", verbose=1, save_best_only=True,\n",
        "                                 save_weights_only=True, mode=\"min\", period=1)\n",
        "\n",
        "lr_decay = LearningRateScheduler(polynomial_decay)\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(lr=learning_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3UCCHp2WToE8",
        "colab_type": "text"
      },
      "source": [
        "## Loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upKLK49DsnEC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_steps = int(len(X_train) / batch_size)\n",
        "val_steps = int( len(X_val) / batch_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J0CZDHYvswCi",
        "colab_type": "code",
        "outputId": "61c80750-3f42-4ade-ad32-de69957a298e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_steps, val_steps"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(667, 166)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mo3NZpsTThxX",
        "colab_type": "code",
        "outputId": "d38435cd-e945-4e8d-ad94-f7a54d5f0271",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "model = create_model()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:642: 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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ptNF4gfNuY5p",
        "colab_type": "code",
        "outputId": "6626eb17-f069-4280-d1cd-380924e5aade",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "model.compile(loss=custom_loss, metrics=[custom_accuracy, mean_iou], optimizer=optimizer)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/losses/losses_impl.py:277: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aR76EQpWBhym",
        "colab_type": "code",
        "outputId": "1d9a9f38-8e5f-412d-e737-a3c633ffe0c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.metrics_names"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['loss', 'custom_accuracy', 'mean_iou']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XbL6SWO0k9E8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_generator = DataGenerator(X_train, y_train, batch_size)\n",
        "val_generator = DataGenerator(X_val, y_val, batch_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aL09FPiMTvS7",
        "colab_type": "code",
        "outputId": "665006b8-5873-46b4-957c-1212c018468d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 4372
        }
      },
      "source": [
        "trained_model = model.fit_generator(train_generator,\n",
        "                        epochs=epochs,\n",
        "                        steps_per_epoch=train_steps,\n",
        "                        callbacks=[checkpoint, lr_decay],\n",
        "                        validation_data=val_generator,\n",
        "                        validation_steps=val_steps,\n",
        "                        verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gradients_impl.py:110: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
            "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "166/166 [==============================] - 98s 593ms/step - loss: 1.6360 - custom_accuracy: 0.9857 - mean_iou: 1.0002\n",
            "\n",
            "Epoch 00001: loss improved from inf to 1.20958, saving model to epoch=01|loss=1.2096|mean_iou=1.0118.h5\n",
            "667/667 [==============================] - 644s 965ms/step - loss: 1.2096 - custom_accuracy: 0.9851 - mean_iou: 1.0118 - val_loss: 1.6360 - val_custom_accuracy: 0.9857 - val_mean_iou: 1.0002\n",
            "Epoch 2/50\n",
            "166/166 [==============================] - 98s 589ms/step - loss: 1.6043 - custom_accuracy: 0.9857 - mean_iou: 0.9997\n",
            "\n",
            "Epoch 00002: loss improved from 1.20958 to 0.66199, saving model to epoch=02|loss=0.6620|mean_iou=0.9991.h5\n",
            "667/667 [==============================] - 610s 914ms/step - loss: 0.6620 - custom_accuracy: 0.9857 - mean_iou: 0.9991 - val_loss: 1.6043 - val_custom_accuracy: 0.9857 - val_mean_iou: 0.9997\n",
            "Epoch 3/50\n",
            "166/166 [==============================] - 97s 587ms/step - loss: 1.6035 - custom_accuracy: 0.9857 - mean_iou: 0.9913\n",
            "\n",
            "Epoch 00003: loss improved from 0.66199 to 0.62514, saving model to epoch=03|loss=0.6251|mean_iou=0.9989.h5\n",
            "667/667 [==============================] - 611s 916ms/step - loss: 0.6251 - custom_accuracy: 0.9857 - mean_iou: 0.9989 - val_loss: 1.6035 - val_custom_accuracy: 0.9857 - val_mean_iou: 0.9913\n",
            "Epoch 4/50\n",
            "166/166 [==============================] - 98s 592ms/step - loss: 1.6554 - custom_accuracy: 0.9858 - mean_iou: 1.0143\n",
            "\n",
            "Epoch 00004: loss improved from 0.62514 to 0.59481, saving model to epoch=04|loss=0.5948|mean_iou=0.9980.h5\n",
            "667/667 [==============================] - 608s 911ms/step - loss: 0.5948 - custom_accuracy: 0.9857 - mean_iou: 0.9980 - val_loss: 1.6554 - val_custom_accuracy: 0.9858 - val_mean_iou: 1.0143\n",
            "Epoch 5/50\n",
            "166/166 [==============================] - 99s 594ms/step - loss: 1.5775 - custom_accuracy: 0.9858 - mean_iou: 1.0033\n",
            "\n",
            "Epoch 00005: loss improved from 0.59481 to 0.57673, saving model to epoch=05|loss=0.5767|mean_iou=nan.h5\n",
            "667/667 [==============================] - 614s 921ms/step - loss: 0.5767 - custom_accuracy: 0.9857 - mean_iou: nan - val_loss: 1.5775 - val_custom_accuracy: 0.9858 - val_mean_iou: 1.0033\n",
            "Epoch 6/50\n",
            "166/166 [==============================] - 97s 585ms/step - loss: 1.4732 - custom_accuracy: 0.9857 - mean_iou: 0.9965\n",
            "\n",
            "Epoch 00006: loss improved from 0.57673 to 0.55884, saving model to epoch=06|loss=0.5588|mean_iou=0.9979.h5\n",
            "667/667 [==============================] - 613s 919ms/step - loss: 0.5588 - custom_accuracy: 0.9857 - mean_iou: 0.9979 - val_loss: 1.4732 - val_custom_accuracy: 0.9857 - val_mean_iou: 0.9965\n",
            "Epoch 7/50\n",
            "166/166 [==============================] - 97s 586ms/step - loss: 1.2823 - custom_accuracy: 0.9858 - mean_iou: 0.9896\n",
            "\n",
            "Epoch 00007: loss improved from 0.55884 to 0.53841, saving model to epoch=07|loss=0.5384|mean_iou=0.9956.h5\n",
            "667/667 [==============================] - 617s 925ms/step - loss: 0.5384 - custom_accuracy: 0.9857 - mean_iou: 0.9956 - val_loss: 1.2823 - val_custom_accuracy: 0.9858 - val_mean_iou: 0.9896\n",
            "Epoch 8/50\n",
            "166/166 [==============================] - 99s 594ms/step - loss: 1.0434 - custom_accuracy: 0.9857 - mean_iou: 0.9855\n",
            "\n",
            "Epoch 00008: loss improved from 0.53841 to 0.52544, saving model to epoch=08|loss=0.5254|mean_iou=0.9959.h5\n",
            "667/667 [==============================] - 618s 927ms/step - loss: 0.5254 - custom_accuracy: 0.9857 - mean_iou: 0.9959 - val_loss: 1.0434 - val_custom_accuracy: 0.9857 - val_mean_iou: 0.9855\n",
            "Epoch 9/50\n",
            "166/166 [==============================] - 97s 582ms/step - loss: 0.8220 - custom_accuracy: 0.9857 - mean_iou: 0.9803\n",
            "\n",
            "Epoch 00009: loss improved from 0.52544 to 0.51854, saving model to epoch=09|loss=0.5185|mean_iou=nan.h5\n",
            "667/667 [==============================] - 616s 923ms/step - loss: 0.5185 - custom_accuracy: 0.9857 - mean_iou: nan - val_loss: 0.8220 - val_custom_accuracy: 0.9857 - val_mean_iou: 0.9803\n",
            "Epoch 10/50\n",
            "166/166 [==============================] - 98s 590ms/step - loss: 0.6328 - custom_accuracy: 0.9858 - mean_iou: 0.9844\n",
            "\n",
            "Epoch 00010: loss improved from 0.51854 to 0.50182, saving model to epoch=10|loss=0.5018|mean_iou=0.9950.h5\n",
            "667/667 [==============================] - 616s 923ms/step - loss: 0.5018 - custom_accuracy: 0.9858 - mean_iou: 0.9950 - val_loss: 0.6328 - val_custom_accuracy: 0.9858 - val_mean_iou: 0.9844\n",
            "Epoch 11/50\n",
            "166/166 [==============================] - 97s 586ms/step - loss: 0.6287 - custom_accuracy: 0.9857 - mean_iou: 0.9939\n",
            "\n",
            "Epoch 00011: loss improved from 0.50182 to 0.49507, saving model to epoch=11|loss=0.4951|mean_iou=0.9944.h5\n",
            "667/667 [==============================] - 618s 926ms/step - loss: 0.4951 - custom_accuracy: 0.9857 - mean_iou: 0.9944 - val_loss: 0.6287 - val_custom_accuracy: 0.9857 - val_mean_iou: 0.9939\n",
            "Epoch 12/50\n",
            "166/166 [==============================] - 96s 580ms/step - loss: 0.5860 - custom_accuracy: 0.9857 - mean_iou: 0.9915\n",
            "\n",
            "Epoch 00012: loss improved from 0.49507 to 0.49168, saving model to epoch=12|loss=0.4917|mean_iou=0.9953.h5\n",
            "667/667 [==============================] - 613s 920ms/step - loss: 0.4917 - custom_accuracy: 0.9858 - mean_iou: 0.9953 - val_loss: 0.5860 - val_custom_accuracy: 0.9857 - val_mean_iou: 0.9915\n",
            "Epoch 13/50\n",
            "166/166 [==============================] - 97s 583ms/step - loss: 0.5467 - custom_accuracy: 0.9860 - mean_iou: 0.9894\n",
            "\n",
            "Epoch 00013: loss improved from 0.49168 to 0.47869, saving model to epoch=13|loss=0.4787|mean_iou=0.9949.h5\n",
            "667/667 [==============================] - 613s 918ms/step - loss: 0.4787 - custom_accuracy: 0.9857 - mean_iou: 0.9949 - val_loss: 0.5467 - val_custom_accuracy: 0.9860 - val_mean_iou: 0.9894\n",
            "Epoch 14/50\n",
            "166/166 [==============================] - 93s 561ms/step - loss: 0.6184 - custom_accuracy: 0.9858 - mean_iou: 0.9933\n",
            "\n",
            "Epoch 00014: loss improved from 0.47869 to 0.46922, saving model to epoch=14|loss=0.4692|mean_iou=0.9952.h5\n",
            "667/667 [==============================] - 604s 905ms/step - loss: 0.4692 - custom_accuracy: 0.9858 - mean_iou: 0.9952 - val_loss: 0.6184 - val_custom_accuracy: 0.9858 - val_mean_iou: 0.9933\n",
            "Epoch 15/50\n",
            "166/166 [==============================] - 95s 574ms/step - loss: 0.5580 - custom_accuracy: 0.9861 - mean_iou: 0.9870\n",
            "\n",
            "Epoch 00015: loss improved from 0.46922 to 0.46137, saving model to epoch=15|loss=0.4614|mean_iou=0.9948.h5\n",
            "667/667 [==============================] - 617s 925ms/step - loss: 0.4614 - custom_accuracy: 0.9859 - mean_iou: 0.9948 - val_loss: 0.5580 - val_custom_accuracy: 0.9861 - val_mean_iou: 0.9870\n",
            "Epoch 16/50\n",
            "166/166 [==============================] - 105s 630ms/step - loss: 0.5394 - custom_accuracy: 0.9859 - mean_iou: 0.9929\n",
            "\n",
            "Epoch 00016: loss improved from 0.46137 to 0.44691, saving model to epoch=16|loss=0.4469|mean_iou=0.9938.h5\n",
            "667/667 [==============================] - 657s 985ms/step - loss: 0.4469 - custom_accuracy: 0.9859 - mean_iou: 0.9938 - val_loss: 0.5394 - val_custom_accuracy: 0.9859 - val_mean_iou: 0.9929\n",
            "Epoch 17/50\n",
            "166/166 [==============================] - 106s 637ms/step - loss: 0.5312 - custom_accuracy: 0.9860 - mean_iou: 0.9914\n",
            "\n",
            "Epoch 00017: loss improved from 0.44691 to 0.43889, saving model to epoch=17|loss=0.4389|mean_iou=0.9948.h5\n",
            "667/667 [==============================] - 653s 979ms/step - loss: 0.4389 - custom_accuracy: 0.9859 - mean_iou: 0.9948 - val_loss: 0.5312 - val_custom_accuracy: 0.9860 - val_mean_iou: 0.9914\n",
            "Epoch 18/50\n",
            "166/166 [==============================] - 106s 639ms/step - loss: 0.5312 - custom_accuracy: 0.9859 - mean_iou: 0.9966\n",
            "\n",
            "Epoch 00018: loss improved from 0.43889 to 0.41748, saving model to epoch=18|loss=0.4175|mean_iou=0.9948.h5\n",
            "667/667 [==============================] - 658s 986ms/step - loss: 0.4175 - custom_accuracy: 0.9859 - mean_iou: 0.9948 - val_loss: 0.5312 - val_custom_accuracy: 0.9859 - val_mean_iou: 0.9966\n",
            "Epoch 19/50\n",
            "166/166 [==============================] - 107s 644ms/step - loss: 0.5397 - custom_accuracy: 0.9861 - mean_iou: 0.9960\n",
            "\n",
            "Epoch 00019: loss improved from 0.41748 to 0.40982, saving model to epoch=19|loss=0.4098|mean_iou=0.9948.h5\n",
            "667/667 [==============================] - 656s 984ms/step - loss: 0.4098 - custom_accuracy: 0.9860 - mean_iou: 0.9948 - val_loss: 0.5397 - val_custom_accuracy: 0.9861 - val_mean_iou: 0.9960\n",
            "Epoch 20/50\n",
            "166/166 [==============================] - 107s 644ms/step - loss: 0.5114 - custom_accuracy: 0.9862 - mean_iou: 0.9901\n",
            "\n",
            "Epoch 00020: loss improved from 0.40982 to 0.38965, saving model to epoch=20|loss=0.3897|mean_iou=0.9950.h5\n",
            "667/667 [==============================] - 652s 978ms/step - loss: 0.3897 - custom_accuracy: 0.9861 - mean_iou: 0.9950 - val_loss: 0.5114 - val_custom_accuracy: 0.9862 - val_mean_iou: 0.9901\n",
            "Epoch 21/50\n",
            "166/166 [==============================] - 105s 634ms/step - loss: 0.5572 - custom_accuracy: 0.9860 - mean_iou: 0.9901\n",
            "\n",
            "Epoch 00021: loss improved from 0.38965 to 0.37514, saving model to epoch=21|loss=0.3751|mean_iou=0.9955.h5\n",
            "667/667 [==============================] - 652s 978ms/step - loss: 0.3751 - custom_accuracy: 0.9861 - mean_iou: 0.9955 - val_loss: 0.5572 - val_custom_accuracy: 0.9860 - val_mean_iou: 0.9901\n",
            "Epoch 22/50\n",
            "166/166 [==============================] - 106s 636ms/step - loss: 0.5153 - custom_accuracy: 0.9862 - mean_iou: 0.9909\n",
            "\n",
            "Epoch 00022: loss improved from 0.37514 to 0.36302, saving model to epoch=22|loss=0.3630|mean_iou=0.9957.h5\n",
            "667/667 [==============================] - 653s 979ms/step - loss: 0.3630 - custom_accuracy: 0.9861 - mean_iou: 0.9957 - val_loss: 0.5153 - val_custom_accuracy: 0.9862 - val_mean_iou: 0.9909\n",
            "Epoch 23/50\n",
            "166/166 [==============================] - 106s 636ms/step - loss: 0.4980 - custom_accuracy: 0.9861 - mean_iou: 0.9912\n",
            "\n",
            "Epoch 00023: loss improved from 0.36302 to 0.34428, saving model to epoch=23|loss=0.3443|mean_iou=0.9961.h5\n",
            "667/667 [==============================] - 654s 981ms/step - loss: 0.3443 - custom_accuracy: 0.9862 - mean_iou: 0.9961 - val_loss: 0.4980 - val_custom_accuracy: 0.9861 - val_mean_iou: 0.9912\n",
            "Epoch 24/50\n",
            "166/166 [==============================] - 105s 635ms/step - loss: 0.4931 - custom_accuracy: 0.9863 - mean_iou: 0.9910\n",
            "\n",
            "Epoch 00024: loss improved from 0.34428 to 0.32694, saving model to epoch=24|loss=0.3269|mean_iou=nan.h5\n",
            "667/667 [==============================] - 652s 977ms/step - loss: 0.3269 - custom_accuracy: 0.9863 - mean_iou: nan - val_loss: 0.4931 - val_custom_accuracy: 0.9863 - val_mean_iou: 0.9910\n",
            "Epoch 25/50\n",
            "166/166 [==============================] - 95s 571ms/step - loss: 0.5048 - custom_accuracy: 0.9862 - mean_iou: 0.9907\n",
            "\n",
            "Epoch 00025: loss improved from 0.32694 to 0.31456, saving model to epoch=25|loss=0.3146|mean_iou=nan.h5\n",
            "667/667 [==============================] - 614s 920ms/step - loss: 0.3146 - custom_accuracy: 0.9863 - mean_iou: nan - val_loss: 0.5048 - val_custom_accuracy: 0.9862 - val_mean_iou: 0.9907\n",
            "Epoch 26/50\n",
            "166/166 [==============================] - 98s 593ms/step - loss: 0.5111 - custom_accuracy: 0.9862 - mean_iou: 0.9918\n",
            "\n",
            "Epoch 00026: loss improved from 0.31456 to 0.30083, saving model to epoch=26|loss=0.3008|mean_iou=0.9962.h5\n",
            "667/667 [==============================] - 616s 923ms/step - loss: 0.3008 - custom_accuracy: 0.9864 - mean_iou: 0.9962 - val_loss: 0.5111 - val_custom_accuracy: 0.9862 - val_mean_iou: 0.9918\n",
            "Epoch 27/50\n",
            "166/166 [==============================] - 98s 590ms/step - loss: 0.4973 - custom_accuracy: 0.9862 - mean_iou: 0.9917\n",
            "\n",
            "Epoch 00027: loss improved from 0.30083 to 0.28508, saving model to epoch=27|loss=0.2851|mean_iou=0.9969.h5\n",
            "667/667 [==============================] - 623s 934ms/step - loss: 0.2851 - custom_accuracy: 0.9865 - mean_iou: 0.9969 - val_loss: 0.4973 - val_custom_accuracy: 0.9862 - val_mean_iou: 0.9917\n",
            "Epoch 28/50\n",
            "166/166 [==============================] - 97s 582ms/step - loss: 0.5094 - custom_accuracy: 0.9861 - mean_iou: 0.9924\n",
            "\n",
            "Epoch 00028: loss improved from 0.28508 to 0.27412, saving model to epoch=28|loss=0.2741|mean_iou=0.9964.h5\n",
            "667/667 [==============================] - 627s 939ms/step - loss: 0.2741 - custom_accuracy: 0.9866 - mean_iou: 0.9964 - val_loss: 0.5094 - val_custom_accuracy: 0.9861 - val_mean_iou: 0.9924\n",
            "Epoch 29/50\n",
            "166/166 [==============================] - 97s 586ms/step - loss: 0.5102 - custom_accuracy: 0.9862 - mean_iou: 0.9917\n",
            "\n",
            "Epoch 00029: loss improved from 0.27412 to 0.26504, saving model to epoch=29|loss=0.2650|mean_iou=nan.h5\n",
            "667/667 [==============================] - 621s 930ms/step - loss: 0.2650 - custom_accuracy: 0.9866 - mean_iou: nan - val_loss: 0.5102 - val_custom_accuracy: 0.9862 - val_mean_iou: 0.9917\n",
            "Epoch 30/50\n",
            "166/166 [==============================] - 96s 580ms/step - loss: 0.4935 - custom_accuracy: 0.9864 - mean_iou: 0.9916\n",
            "\n",
            "Epoch 00030: loss improved from 0.26504 to 0.25096, saving model to epoch=30|loss=0.2510|mean_iou=0.9973.h5\n",
            "667/667 [==============================] - 622s 933ms/step - loss: 0.2510 - custom_accuracy: 0.9868 - mean_iou: 0.9973 - val_loss: 0.4935 - val_custom_accuracy: 0.9864 - val_mean_iou: 0.9916\n",
            "Epoch 31/50\n",
            "166/166 [==============================] - 96s 578ms/step - loss: 0.4985 - custom_accuracy: 0.9863 - mean_iou: 0.9932\n",
            "\n",
            "Epoch 00031: loss improved from 0.25096 to 0.24230, saving model to epoch=31|loss=0.2423|mean_iou=0.9973.h5\n",
            "667/667 [==============================] - 622s 932ms/step - loss: 0.2423 - custom_accuracy: 0.9869 - mean_iou: 0.9973 - val_loss: 0.4985 - val_custom_accuracy: 0.9863 - val_mean_iou: 0.9932\n",
            "Epoch 32/50\n",
            "166/166 [==============================] - 95s 572ms/step - loss: 0.4992 - custom_accuracy: 0.9863 - mean_iou: 0.9927\n",
            "\n",
            "Epoch 00032: loss improved from 0.24230 to 0.22928, saving model to epoch=32|loss=0.2293|mean_iou=0.9976.h5\n",
            "667/667 [==============================] - 617s 925ms/step - loss: 0.2293 - custom_accuracy: 0.9870 - mean_iou: 0.9976 - val_loss: 0.4992 - val_custom_accuracy: 0.9863 - val_mean_iou: 0.9927\n",
            "Epoch 33/50\n",
            "166/166 [==============================] - 94s 569ms/step - loss: 0.4855 - custom_accuracy: 0.9862 - mean_iou: 0.9907\n",
            "\n",
            "Epoch 00033: loss improved from 0.22928 to 0.21918, saving model to epoch=33|loss=0.2192|mean_iou=0.9980.h5\n",
            "667/667 [==============================] - 611s 916ms/step - loss: 0.2192 - custom_accuracy: 0.9870 - mean_iou: 0.9980 - val_loss: 0.4855 - val_custom_accuracy: 0.9862 - val_mean_iou: 0.9907\n",
            "Epoch 34/50\n",
            "166/166 [==============================] - 94s 568ms/step - loss: 0.4933 - custom_accuracy: 0.9863 - mean_iou: 0.9919\n",
            "\n",
            "Epoch 00034: loss improved from 0.21918 to 0.20760, saving model to epoch=34|loss=0.2076|mean_iou=0.9980.h5\n",
            "667/667 [==============================] - 611s 916ms/step - loss: 0.2076 - custom_accuracy: 0.9871 - mean_iou: 0.9980 - val_loss: 0.4933 - val_custom_accuracy: 0.9863 - val_mean_iou: 0.9919\n",
            "Epoch 35/50\n",
            "166/166 [==============================] - 95s 570ms/step - loss: 0.4896 - custom_accuracy: 0.9864 - mean_iou: 0.9906\n",
            "\n",
            "Epoch 00035: loss improved from 0.20760 to 0.20027, saving model to epoch=35|loss=0.2003|mean_iou=nan.h5\n",
            "667/667 [==============================] - 608s 912ms/step - loss: 0.2003 - custom_accuracy: 0.9873 - mean_iou: nan - val_loss: 0.4896 - val_custom_accuracy: 0.9864 - val_mean_iou: 0.9906\n",
            "Epoch 36/50\n",
            "166/166 [==============================] - 94s 566ms/step - loss: 0.4862 - custom_accuracy: 0.9862 - mean_iou: 0.9925\n",
            "\n",
            "Epoch 00036: loss improved from 0.20027 to 0.18930, saving model to epoch=36|loss=0.1893|mean_iou=0.9984.h5\n",
            "667/667 [==============================] - 607s 911ms/step - loss: 0.1893 - custom_accuracy: 0.9875 - mean_iou: 0.9984 - val_loss: 0.4862 - val_custom_accuracy: 0.9862 - val_mean_iou: 0.9925\n",
            "Epoch 37/50\n",
            "166/166 [==============================] - 94s 568ms/step - loss: 0.4868 - custom_accuracy: 0.9861 - mean_iou: 0.9924\n",
            "\n",
            "Epoch 00037: loss improved from 0.18930 to 0.18190, saving model to epoch=37|loss=0.1819|mean_iou=0.9982.h5\n",
            "667/667 [==============================] - 608s 912ms/step - loss: 0.1819 - custom_accuracy: 0.9877 - mean_iou: 0.9982 - val_loss: 0.4868 - val_custom_accuracy: 0.9861 - val_mean_iou: 0.9924\n",
            "Epoch 38/50\n",
            "166/166 [==============================] - 94s 566ms/step - loss: 0.4905 - custom_accuracy: 0.9862 - mean_iou: nan\n",
            "\n",
            "Epoch 00038: loss improved from 0.18190 to 0.17232, saving model to epoch=38|loss=0.1723|mean_iou=0.9981.h5\n",
            "667/667 [==============================] - 606s 908ms/step - loss: 0.1723 - custom_accuracy: 0.9877 - mean_iou: 0.9981 - val_loss: 0.4905 - val_custom_accuracy: 0.9862 - val_mean_iou: nan\n",
            "Epoch 39/50\n",
            "166/166 [==============================] - 94s 564ms/step - loss: 0.4846 - custom_accuracy: 0.9860 - mean_iou: 0.9901\n",
            "\n",
            "Epoch 00039: loss improved from 0.17232 to 0.16364, saving model to epoch=39|loss=0.1636|mean_iou=nan.h5\n",
            "667/667 [==============================] - 608s 912ms/step - loss: 0.1636 - custom_accuracy: 0.9879 - mean_iou: nan - val_loss: 0.4846 - val_custom_accuracy: 0.9860 - val_mean_iou: 0.9901\n",
            "Epoch 40/50\n",
            "166/166 [==============================] - 93s 560ms/step - loss: 0.4837 - custom_accuracy: 0.9862 - mean_iou: 0.9910\n",
            "\n",
            "Epoch 00040: loss improved from 0.16364 to 0.15525, saving model to epoch=40|loss=0.1553|mean_iou=0.9985.h5\n",
            "667/667 [==============================] - 605s 907ms/step - loss: 0.1553 - custom_accuracy: 0.9881 - mean_iou: 0.9985 - val_loss: 0.4837 - val_custom_accuracy: 0.9862 - val_mean_iou: 0.9910\n",
            "Epoch 41/50\n",
            "166/166 [==============================] - 95s 570ms/step - loss: 0.4856 - custom_accuracy: 0.9861 - mean_iou: 0.9914\n",
            "\n",
            "Epoch 00041: loss improved from 0.15525 to 0.14682, saving model to epoch=41|loss=0.1468|mean_iou=0.9987.h5\n",
            "667/667 [==============================] - 611s 915ms/step - loss: 0.1468 - custom_accuracy: 0.9882 - mean_iou: 0.9987 - val_loss: 0.4856 - val_custom_accuracy: 0.9861 - val_mean_iou: 0.9914\n",
            "Epoch 42/50\n",
            "166/166 [==============================] - 96s 581ms/step - loss: 0.4854 - custom_accuracy: 0.9861 - mean_iou: 0.9914\n",
            "\n",
            "Epoch 00042: loss improved from 0.14682 to 0.14014, saving model to epoch=42|loss=0.1401|mean_iou=0.9987.h5\n",
            "667/667 [==============================] - 616s 924ms/step - loss: 0.1401 - custom_accuracy: 0.9883 - mean_iou: 0.9987 - val_loss: 0.4854 - val_custom_accuracy: 0.9861 - val_mean_iou: 0.9914\n",
            "Epoch 43/50\n",
            "166/166 [==============================] - 93s 562ms/step - loss: 0.4812 - custom_accuracy: 0.9860 - mean_iou: 0.9917\n",
            "\n",
            "Epoch 00043: loss improved from 0.14014 to 0.13173, saving model to epoch=43|loss=0.1317|mean_iou=0.9989.h5\n",
            "667/667 [==============================] - 613s 919ms/step - loss: 0.1317 - custom_accuracy: 0.9885 - mean_iou: 0.9989 - val_loss: 0.4812 - val_custom_accuracy: 0.9860 - val_mean_iou: 0.9917\n",
            "Epoch 44/50\n",
            "166/166 [==============================] - 96s 581ms/step - loss: 0.4782 - custom_accuracy: 0.9858 - mean_iou: 0.9912\n",
            "\n",
            "Epoch 00044: loss improved from 0.13173 to 0.12265, saving model to epoch=44|loss=0.1227|mean_iou=0.9990.h5\n",
            "667/667 [==============================] - 613s 919ms/step - loss: 0.1227 - custom_accuracy: 0.9888 - mean_iou: 0.9990 - val_loss: 0.4782 - val_custom_accuracy: 0.9858 - val_mean_iou: 0.9912\n",
            "Epoch 45/50\n",
            "166/166 [==============================] - 97s 586ms/step - loss: 0.4824 - custom_accuracy: 0.9861 - mean_iou: 0.9909\n",
            "\n",
            "Epoch 00045: loss improved from 0.12265 to 0.11457, saving model to epoch=45|loss=0.1146|mean_iou=0.9990.h5\n",
            "667/667 [==============================] - 620s 930ms/step - loss: 0.1146 - custom_accuracy: 0.9889 - mean_iou: 0.9990 - val_loss: 0.4824 - val_custom_accuracy: 0.9861 - val_mean_iou: 0.9909\n",
            "Epoch 46/50\n",
            "166/166 [==============================] - 96s 579ms/step - loss: 0.4805 - custom_accuracy: 0.9861 - mean_iou: 0.9913\n",
            "\n",
            "Epoch 00046: loss improved from 0.11457 to 0.10836, saving model to epoch=46|loss=0.1084|mean_iou=0.9991.h5\n",
            "667/667 [==============================] - 617s 925ms/step - loss: 0.1084 - custom_accuracy: 0.9892 - mean_iou: 0.9991 - val_loss: 0.4805 - val_custom_accuracy: 0.9861 - val_mean_iou: 0.9913\n",
            "Epoch 47/50\n",
            "166/166 [==============================] - 98s 590ms/step - loss: 0.4808 - custom_accuracy: 0.9861 - mean_iou: 0.9915\n",
            "\n",
            "Epoch 00047: loss improved from 0.10836 to 0.10227, saving model to epoch=47|loss=0.1023|mean_iou=nan.h5\n",
            "667/667 [==============================] - 618s 927ms/step - loss: 0.1023 - custom_accuracy: 0.9893 - mean_iou: nan - val_loss: 0.4808 - val_custom_accuracy: 0.9861 - val_mean_iou: 0.9915\n",
            "Epoch 48/50\n",
            "166/166 [==============================] - 97s 586ms/step - loss: 0.4795 - custom_accuracy: 0.9860 - mean_iou: 0.9897\n",
            "\n",
            "Epoch 00048: loss improved from 0.10227 to 0.09496, saving model to epoch=48|loss=0.0950|mean_iou=0.9993.h5\n",
            "667/667 [==============================] - 615s 923ms/step - loss: 0.0950 - custom_accuracy: 0.9894 - mean_iou: 0.9993 - val_loss: 0.4795 - val_custom_accuracy: 0.9860 - val_mean_iou: 0.9897\n",
            "Epoch 49/50\n",
            "166/166 [==============================] - 98s 587ms/step - loss: 0.4788 - custom_accuracy: 0.9861 - mean_iou: 0.9911\n",
            "\n",
            "Epoch 00049: loss improved from 0.09496 to 0.08859, saving model to epoch=49|loss=0.0886|mean_iou=nan.h5\n",
            "667/667 [==============================] - 616s 924ms/step - loss: 0.0886 - custom_accuracy: 0.9896 - mean_iou: nan - val_loss: 0.4788 - val_custom_accuracy: 0.9861 - val_mean_iou: 0.9911\n",
            "Epoch 50/50\n",
            "166/166 [==============================] - 96s 581ms/step - loss: 0.4780 - custom_accuracy: 0.9861 - mean_iou: 0.9909\n",
            "\n",
            "Epoch 00050: loss improved from 0.08859 to 0.08462, saving model to epoch=50|loss=0.0846|mean_iou=0.9992.h5\n",
            "667/667 [==============================] - 621s 930ms/step - loss: 0.0846 - custom_accuracy: 0.9897 - mean_iou: 0.9992 - val_loss: 0.4780 - val_custom_accuracy: 0.9861 - val_mean_iou: 0.9909\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMt-hhtWS_Mj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_validation_training(metric, trained_model):\n",
        "  validation_metric = trained_model.history[f'val_{metric}']\n",
        "  training_metric = trained_model.history[metric]\n",
        "  epochs = range(len(training_metric))\n",
        "  plt.plot(epochs, training_metric, 'b', label=f'Training {metric}')\n",
        "  plt.plot(epochs, validation_metric, 'r', label=f'Validation {metric}')\n",
        "  plt.ylim(bottom=0)\n",
        "  plt.xlabel('Epochs ', fontsize=16)\n",
        "  plt.ylabel(metric, fontsize=16)\n",
        "  loc = 'upper right' if metric == \"loss\" else 'lower right'\n",
        "  plt.legend(loc=loc)\n",
        "  plt.title(f'Training and validation {metric}', fontsize = 20)\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Gx8yJFPTCnE",
        "colab_type": "code",
        "outputId": "b4bf968d-3401-4508-dbdb-03b01b2c38d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        }
      },
      "source": [
        "plot_validation_training(\"loss\", trained_model)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Kc+YkOyXOuQqsciI/TETGAQOAhiKyCrgNyARQ1X8CtwINgL+LCECeqmaXZZqa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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mEFaEhLkTJi8",
        "colab_type": "code",
        "outputId": "e853526e-8394-4b54-8d45-7f1eeae9525b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        }
      },
      "source": [
        "plot_validation_training(\"custom_accuracy\", trained_model)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ms3qF8Dm8YGb/JjQRnu7umRJ1wjWE+1SOB7qY2cuEncAxhFsBrnD3TA0Mas3dvzGzUwn3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\nIlJQlLhERKSgKHGJiEhB+f9GIqOK0AgJzgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9yUGnuyz09rN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.load_weights(\"epoch=50_loss=0.0846_mean_iou=0.9992.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RKw2ezeI4FN-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "    plt.tight_layout()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5b4GfYca4e4h",
        "colab_type": "code",
        "outputId": "9d4a1adc-45c1-40f6-d252-4f9ea3f0eb34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "predictions = model.predict_generator(val_generator, steps=val_steps, verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "166/166 [==============================] - 102s 615ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDMRXeLh4_P0",
        "colab_type": "code",
        "outputId": "602f1672-cc4b-40b7-fd00-338fbcd8ea0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(predictions)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5312"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wNT5e1hV4_My",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classes_predicted = predictions[..., 0].reshape(-1)\n",
        "classes_predicted = np.where(classes_predicted >= 0.5, 1, 0)\n",
        "\n",
        "real_classes = y_val[..., 0][:5312].reshape(-1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7M9lgjIA-vKB",
        "colab_type": "code",
        "outputId": "9efdcf75-780e-481a-cc57-f5be9c763b1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "classes_predicted.shape, real_classes.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((132800,), (132800,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbpk81PQ-fVh",
        "colab_type": "code",
        "outputId": "8668faa6-85c6-479b-f8a5-2ab1c66ba986",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(real_classes[real_classes == 1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1566"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-cWcUMsD-sbO",
        "colab_type": "code",
        "outputId": "664c7545-4621-4bf4-cfaa-77cab6a517e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(real_classes[real_classes == 0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "131234"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5j5d4dB6Za9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classes_names = [\"Negatives\", \"Positives\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p_Ai6xqm-ILq",
        "colab_type": "code",
        "outputId": "6210fd12-5de6-42e5-e192-35205201c9ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "cm = confusion_matrix(real_classes, classes_predicted, labels=range(2))\n",
        "plot_confusion_matrix(cm, classes_names)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Si+y0c0cAjv5Rf17/z2uUlJSwatUqpr7xBrvutnvO2p9NNZSTLc8XMQ1A/Lswln8GtE3U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\n5zLIg6xzzmXQ/wMq0f+xItAVmAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ggsBeI0lBMmr",
        "colab_type": "code",
        "outputId": "c42ab970-0fb3-45fd-f7c7-a989f85803c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "sensitivity = cm[0, 0] / (cm[0, 0] + cm[0, 1])\n",
        "print(f'sensitivity: {sensitivity}')\n",
        "\n",
        "specificity = cm[1, 1] / (cm[1, 1] + cm[1, 0])\n",
        "print(f'specificity: {specificity}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sensitivity: 0.9955346937531432\n",
            "specificity: 0.0006385696040868455\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l7ujgAz4S6zi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "42FTDbO2gMlO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_generator = DataGenerator(X_val[:10], y_val[:10], batch_size=10, shuffle=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ESkljZUqS7Xf",
        "colab_type": "code",
        "outputId": "bbc6804d-4537-413d-acd8-8634a6792045",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1220
        }
      },
      "source": [
        "font = cv2.FONT_HERSHEY_SIMPLEX\n",
        "\n",
        "for images, tensor_volumes in test_generator:\n",
        "  predictions = model.predict(images)\n",
        "  \n",
        "  plt.figure()\n",
        "  figure, axis = plt.subplots(5, 2, figsize=(8, 16))\n",
        "  plt.suptitle(\"Predictions\")\n",
        "  \n",
        "  plot_row_indeces_array = np.repeat([0, 1, 2, 3, 4], 2)\n",
        "  plot_column_indeces_array = np.tile([0, 1], 5)\n",
        "  \n",
        "  for index, (predicted_tensor, tensor_volume) in enumerate(zip(predictions, tensor_volumes)):\n",
        "    predicted_classes = predicted_tensor[..., 0]\n",
        "    predicted_boxes = predicted_tensor[..., 1:]\n",
        "\n",
        "    real_classes = tensor_volume[..., 0]\n",
        "    real_boxes = tensor_volume[..., 1:]\n",
        "    \n",
        "    plot_row_index = plot_row_indeces_array[index]\n",
        "    plot_column_index = plot_column_indeces_array[index]\n",
        "    \n",
        "    img = images[index]\n",
        "\n",
        "    for column_index, (column_predicted_classes, column_real_classes) in enumerate(zip(predicted_classes, real_classes)):\n",
        "      for row_index, (predicted_class, real_class) in enumerate(zip(column_predicted_classes, column_real_classes)):\n",
        "        if real_class > 0.5:\n",
        "          boxes = real_boxes[column_index][row_index] * img_size\n",
        "          img = cv2.rectangle(img, (int(boxes[0]), int(boxes[1])), (int(boxes[2]), int(boxes[3])), (0, 255, 0), 3) \n",
        "\n",
        "        if predicted_class > 0.5:\n",
        "          boxes = predicted_boxes[column_index][row_index] * img_size\n",
        "          img = cv2.rectangle(img, (int(boxes[0]), int(boxes[1])), (int(boxes[2]), int(boxes[3])), (255, 0, 0), 3) \n",
        "    \n",
        "    axis[plot_row_index, plot_column_index].imshow(img)\n",
        "  \n",
        "  break"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jzcPKBK/ulb0bp6en8eSTT+LNN9+MxcG4bha1LD9Fu16vY3V1FfPz81hZWXGW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hPVE+BMeK7Vpf3k+VCmci0u399LYhxxOlEsFjP55a2trRAq7PV6KhaLYS54fspFScjWkyTR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\nQm3AgAEDBtwZ/P+R2XTO5T3OLQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x1152 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p37BelcVaH49",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}