deepLabV3+.ipynb

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    "colab": {
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RS3sWReBG7iW"
      },
      "source": [
        "# **1.1.** Prepare data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g178kBPiGJ3v"
      },
      "source": [
        "%%capture \n",
        "!pip install fashionpedia\n",
        "!pip install split-folders\n",
        "!$ yes | python -m pip uninstall matplotlib\n",
        "!$ yes | pip install matplotlib==3.1.3\n",
        "#!pip install albumentations==0.3.0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JMGugBiJh_yk"
      },
      "source": [
        "\n",
        "\n",
        "> **Importing Modules**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vgneF-1ch_7t"
      },
      "source": [
        "import os\n",
        "import shutil\n",
        "import random\n",
        "import itertools\n",
        "import splitfolders\n",
        "import functools\n",
        "import cv2\n",
        "import json\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "from skimage.io import imread\n",
        "from skimage.transform import resize\n",
        "from skimage.measure import find_contours, regionprops, label\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.utils.data import Dataset as BaseDataset\n",
        "import tensorflow as tf\n",
        "from keras import backend as K\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from keras.callbacks import ModelCheckpoint, LearningRateScheduler\n",
        "from tensorflow.python.keras.utils import data_utils\n",
        "import h5py\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.utils import class_weight\n",
        "from sklearn.metrics import confusion_matrix, multilabel_confusion_matrix\n",
        "from fashionpedia.fp import Fashionpedia\n",
        "from tensorflow.keras.preprocessing.image import load_img, img_to_array, array_to_img\n",
        "import colorsys\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import patches,  lines\n",
        "from matplotlib.patches import Polygon\n",
        "import IPython.display"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ycBvcWaJGsUc",
        "outputId": "f00f8df6-d61a-4848-ee87-4c254afd5aad"
      },
      "source": [
        "!wget https://s3.amazonaws.com/ifashionist-dataset/annotations/instances_attributes_train2020.json"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-08-19 21:17:52--  https://s3.amazonaws.com/ifashionist-dataset/annotations/instances_attributes_train2020.json\n",
            "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.141.142\n",
            "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.141.142|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 542193045 (517M) [application/json]\n",
            "Saving to: ‘instances_attributes_train2020.json’\n",
            "\n",
            "instances_attribute 100%[===================>] 517.08M  35.7MB/s    in 15s     \n",
            "\n",
            "2021-08-19 21:18:08 (33.8 MB/s) - ‘instances_attributes_train2020.json’ saved [542193045/542193045]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZvh34h8ejyv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d9c9b16-d4e1-400f-dae5-4c17b3b717df"
      },
      "source": [
        "!wget https://s3.amazonaws.com/ifashionist-dataset/images/train2020.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-08-19 21:18:08--  https://s3.amazonaws.com/ifashionist-dataset/images/train2020.zip\n",
            "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.92.54\n",
            "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.92.54|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3344364592 (3.1G) [application/zip]\n",
            "Saving to: ‘train2020.zip’\n",
            "\n",
            "train2020.zip       100%[===================>]   3.11G  36.2MB/s    in 95s     \n",
            "\n",
            "2021-08-19 21:19:44 (33.4 MB/s) - ‘train2020.zip’ saved [3344364592/3344364592]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M7UGpIWHer9S"
      },
      "source": [
        "%%capture \n",
        "#Simply adding &> /dev/null to the tail of any command will silence its output outside of any errors that may arise.\n",
        "!unzip /content/train2020.zip && rm /content/train2020.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zaa65mI3Hxst"
      },
      "source": [
        "#!wget https://s3.amazonaws.com/ifashionist-dataset/annotations/instances_attributes_val2020.json"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "csLUXzFbHrat"
      },
      "source": [
        "#!wget https://s3.amazonaws.com/ifashionist-dataset/images/val_test2020.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2MG8iZ48HtjK"
      },
      "source": [
        "#!unzip /content/val_test2020.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w1EzV3p2MqdY"
      },
      "source": [
        "\n",
        "**Dataset Taxonomy**\n",
        "```\n",
        "+---------+-----------------------------------------+----------------+\n",
        "| ClassId |                   Name                  | SuperCategory  |\n",
        "+---------+-----------------------------------------+----------------+\n",
        "|    0    |              shirt, blouse              |   upperbody    |\n",
        "|    1    |         top, t-shirt, sweatshirt        |   upperbody    |\n",
        "|    2    |                 sweater                 |   upperbody    |\n",
        "|    3    |                 cardigan                |   upperbody    |\n",
        "|    4    |                  jacket                 |   upperbody    |\n",
        "|    5    |                   vest                  |   upperbody    |\n",
        "|    6    |                  pants                  |   lowerbody    |\n",
        "|    7    |                  shorts                 |   lowerbody    |\n",
        "|    8    |                  skirt                  |   lowerbody    |\n",
        "|    9    |                   coat                  |   wholebody    |\n",
        "|    10   |                  dress                  |   wholebody    |\n",
        "|    11   |                 jumpsuit                |   wholebody    |\n",
        "|    12   |                   cape                  |   wholebody    |\n",
        "|    13   |                 glasses                 |      head      |\n",
        "|    14   |                   hat                   |      head      |\n",
        "|    15   | headband, head covering, hair accessory |      head      |\n",
        "|    16   |                   tie                   |      neck      |\n",
        "|    17   |                  glove                  | arms and hands |\n",
        "|    18   |                  watch                  | arms and hands |\n",
        "|    19   |                   belt                  |     waist      |\n",
        "|    20   |                leg warmer               | legs and feet  |\n",
        "|    21   |            tights, stockings            | legs and feet  |\n",
        "|    22   |                   sock                  | legs and feet  |\n",
        "|    23   |                   shoe                  | legs and feet  |\n",
        "|    24   |               bag, wallet               |     others     |\n",
        "|    25   |                  scarf                  |     others     |\n",
        "|    26   |                 umbrella                |     others     |\n",
        "|    27   |                   hood                  | garment parts  |\n",
        "|    28   |                  collar                 | garment parts  |\n",
        "|    29   |                  lapel                  | garment parts  |\n",
        "|    30   |                epaulette                | garment parts  |\n",
        "|    31   |                  sleeve                 | garment parts  |\n",
        "|    32   |                  pocket                 | garment parts  |\n",
        "|    33   |                 neckline                | garment parts  |\n",
        "|    34   |                  buckle                 |    closures    |\n",
        "|    35   |                  zipper                 |    closures    |\n",
        "|    36   |                 applique                |  decorations   |\n",
        "|    37   |                   bead                  |  decorations   |\n",
        "|    38   |                   bow                   |  decorations   |\n",
        "|    39   |                  flower                 |  decorations   |\n",
        "|    40   |                  fringe                 |  decorations   |\n",
        "|    41   |                  ribbon                 |  decorations   |\n",
        "|    42   |                  rivet                  |  decorations   |\n",
        "|    43   |                  ruffle                 |  decorations   |\n",
        "|    44   |                  sequin                 |  decorations   |\n",
        "|    45   |                  tassel                 |  decorations   |\n",
        "+---------+-----------------------------------------+----------------+\n",
        "```\n",
        "[Data Link](https://www.kaggle.com/c/imaterialist-fashion-2020-fgvc7/overview/evaluation)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hpMgB6DRwOHN"
      },
      "source": [
        "def get_masks(root_dir, ann_dir):\n",
        "    \"\"\"\n",
        "    Get the masks from annotated json file data.\n",
        "    Parameters\n",
        "    ----------\n",
        "    root_dir : string\n",
        "        File path to save masks.\n",
        "    ann_dir : string\n",
        "        Annotations path folder.\n",
        "    Return\n",
        "    ----------\n",
        "    img_ids : ndarray\n",
        "        Used image index numbers.\n",
        "    Atts\n",
        "    ---------- \n",
        "    labels = [[0, 1, 2, 3, 4, 5, 28, 29, 30, 33], \n",
        "            [6, 7, 8], \n",
        "            [9, 10, 11, 12], \n",
        "            [20, 21, 22, 23],\n",
        "            [13, 14, 15, 16, 17, 18, 19, 24, 25, 26, 34, 37, 38]]\n",
        "    \n",
        "        1 'upperbody' -> 0, 1, 2, 3, 4, 5, 27, 28, 29, 30, 31, 33, 9, 10, 11, 12\n",
        "        2 'lowerbody' -> 6, 7, 8, 21, 32\n",
        "        3 'feet' -> 20, 22, 23\n",
        "        4 'accessories' -> 13, 14, 15, 16, 17, 18, 19, 24, 25, 26, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45\n",
        "\n",
        "        34 ve 35 fermuar ve kemer tokası\n",
        "    \"\"\"\n",
        "    mask_root=os.path.join(root_dir, 'masks')\n",
        "    images_root=os.path.join(root_dir, 'images')\n",
        "    if not os.path.exists(mask_root):\n",
        "        os.makedirs(mask_root)\n",
        "    if not os.path.exists(images_root):\n",
        "        os.makedirs(images_root)\n",
        "\n",
        "    fp = Fashionpedia(ann_dir)\n",
        "    img_ids = sorted(fp.getImgIds())\n",
        "    annotations = fp.loadAnns(ids=img_ids)\n",
        "    cat_ids = fp.getCatIds()\n",
        "    labels = [[0, 1, 2, 3, 4, 5], #upperbody\n",
        "            [6, 7, 8], #lowerbody\n",
        "            [9, 10, 11], #wholebody\n",
        "            [20, 22, 23], #feet\n",
        "            [13, 14, 15, 16, 17, 18, 19, 24, 25, 26, 34, 37]] #accessories\n",
        "    \n",
        "    images=[]\n",
        "    img_ids=[]\n",
        "    for ann in annotations:\n",
        "        img_ids.append(ann['image_id'])\n",
        "\n",
        "    img_ids = np.asarray(img_ids)\n",
        "    img_ids = np.unique(img_ids)\n",
        "\n",
        "    for img_id in img_ids:\n",
        "        anns_ids = fp.getAnnIds(imgIds=img_id, catIds=cat_ids)\n",
        "        anns = fp.loadAnns(anns_ids)\n",
        "        mask = np.zeros_like(fp.annToMask(anns[0]))\n",
        "        img = fp.loadImgs(anns[0]['image_id'])[0]\n",
        "        images.append(os.path.join('/content/train', img['file_name']))\n",
        "        path = os.path.join(mask_root, img['file_name'])\n",
        "        path = path.split('.')[0] + '_mask.png'\n",
        "        for ann in anns:\n",
        "            if ann['category_id'] in labels[0]:#upperbody\n",
        "                label = 1\n",
        "            elif ann['category_id'] in labels[1]:#lowerboy\n",
        "                label = 2\n",
        "            elif ann['category_id'] in labels[2]:#wholebody\n",
        "                label = 3\n",
        "            elif ann['category_id'] in labels[3]:#feet\n",
        "                label = 4\n",
        "            elif ann['category_id'] in labels[4]:#accessories\n",
        "                label = 5\n",
        "            else:\n",
        "                label = 0\n",
        "            mask = np.maximum(mask, fp.annToMask(ann)*label) # mask += fp.annToMask(ann)*label\n",
        "        cv2.imwrite(path, mask)\n",
        "    \n",
        "    for f in images:\n",
        "        shutil.move(f, images_root)\n",
        "\n",
        "    return img_ids"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s0sNC4tSSsXM",
        "outputId": "038a8c95-bfda-43d7-ae22-503806495668"
      },
      "source": [
        "img_ids = get_masks(root_dir='/content/data/', ann_dir='/content/instances_attributes_train2020.json')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loading annotations into memory...\n",
            "Done (t=24.69s)\n",
            "creating index...\n",
            "index created!\n",
            "loading attributes...\n",
            "attributes index created!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjSixwRJB7w3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b380184-ebb6-486e-e7fe-a23ae29a9c41"
      },
      "source": [
        "shutil.rmtree(\"/content/train\")\n",
        "input_folder = '/content/data/'\n",
        "\n",
        "# Split with a ratio.\n",
        "# To only split into training and validation set, set a tuple to `ratio`, i.e, `(.8, .2)`.\n",
        "splitfolders.ratio(input_folder, output=\"datas\", seed=1337, ratio=(.8, .1, .1), group_prefix=None) # default values\n",
        "shutil.rmtree(\"/content/data\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Copying files: 13520 files [00:05, 2673.55 files/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-Ijy4faHG-N"
      },
      "source": [
        "#img_ids = get_masks(root_dir='/content/val_masks', ann_dir='/content/instances_attributes_val2020.json')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bpkrpim-Egf9"
      },
      "source": [
        "#%%capture \n",
        "#!zip -r mask_train.zip /content/masks"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EPV2RZs-vdzq"
      },
      "source": [
        "#**1.2.** DeepLabV3+"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c3f8T6Q_Khww"
      },
      "source": [
        "[Link](https://arxiv.org/pdf/1802.02611.pdf)\n",
        "\n",
        "<div>\n",
        "<img src=\"https://paperswithcode.com/media/models/Screen_Shot_2021-02-21_at_10.34.37_AM_kvOFts0.png\" width=\"690\"/>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jF31h1CXBiBz"
      },
      "source": [
        "### **DataGenerator**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y4iv6okuA-U8"
      },
      "source": [
        "class DataGenerator(data_utils.Sequence): \n",
        "    \"\"\"\n",
        "    Generates data for Keras Sequence based data generator. \n",
        "    Suitable for building data generator for training and prediction.\n",
        "    This class for a multiclass segmentation and superclass from\n",
        "    tensorflow.keras.utils.Sequence\n",
        "    Parameters\n",
        "    ----------\n",
        "    data_dir : string\n",
        "        The parent name of the directory where the data is located.\n",
        "        (Path to images and masks location.)\n",
        "    to_fit : boolean, default=True\n",
        "        Create a data generator based on this parameter \n",
        "        for training or prediction. True to return X and y, \n",
        "        False to return X only.\n",
        "    batch_size : int, default=32\n",
        "        Batch size at each iteration.\n",
        "    dim : tuple or list, default=(256, 256)\n",
        "        Tuple indicating for image dimension.\n",
        "    n_channels : int, default=3\n",
        "        Number of image channels.\n",
        "    n_classes : int, default=1\n",
        "        Number of classes.\n",
        "    shuffle : boolean, default=True\n",
        "        True to shuffle label indexes after every epoch.\n",
        "    seed : int, seed instance or None, default=None\n",
        "        Determines random number generation for shuffle label indexes. \n",
        "        Use an int to make the randomness deterministic.\n",
        "    preprocessing_function : default=None \n",
        "        Data preprocessing (e.g. normalization, shape manipulation, etc.). \n",
        "        function that will be applied on each input.\n",
        "        The function will run after the image is resized and augmented.\n",
        "        The function should take one argument:\n",
        "        Argument:\n",
        "            image : ndarray\n",
        "                input image for preprocessing. (*dim, 3)\n",
        "    interpolation : string, default='nearest'\n",
        "         Interpolation method used to resample the image if the target \n",
        "         size is different from that of the loaded image. \n",
        "         Supported methods are \"nearest\", \"bilinear\", and \"bicubic\". \n",
        "         If PIL version 1.1.3 or newer is installed, \"lanczos\" is also supported. \n",
        "         If PIL version 3.4.0 or newer is installed, \"box\" and \"hamming\" \n",
        "         are also supported. By default, \"nearest\" is used.\n",
        "    \"\"\"\n",
        "    def __init__(self, data_dir, to_fit=True, batch_size=32, \n",
        "                 dim=(256, 256), dtype=np.uint8, n_channels=3, \n",
        "                 n_classes=1, shuffle=True, seed=None, \n",
        "                 preprocessing_function=None, interpolation='nearest'):\n",
        "        self.data_dir = data_dir\n",
        "        self.to_fit = to_fit\n",
        "        self.batch_size = batch_size\n",
        "        self.dim = dim\n",
        "        self.dtype = dtype\n",
        "        self.n_channels = n_channels\n",
        "        self.interpolation = interpolation\n",
        "        self.n_classes = n_classes\n",
        "        self.preprocessing_function=preprocessing_function\n",
        "        self.seed = seed\n",
        "        self.shuffle = shuffle\n",
        "        self._n_batch = 0\n",
        "        self._initialize_dirs(self.data_dir)\n",
        "        self.on_epoch_end()\n",
        "\n",
        "    def __len__(self):\n",
        "        \"\"\"\n",
        "        Denotes the number of batches per epoch.\n",
        "        Returns\n",
        "        -------    \n",
        "        num_batches : int    \n",
        "            Number of batches per epoch.\n",
        "        \"\"\"\n",
        "        return int(np.floor(len(self.list_IDs) / self.batch_size))\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        \"\"\"\n",
        "        Generate one batch of data.\n",
        "        Parameters\n",
        "        ----------\n",
        "        index : int \n",
        "            Index of the batch.\n",
        "        Returns\n",
        "        ----------\n",
        "        X : list of ndarray shape (batch_size, *dim, n_channels)\n",
        "            Number of channels image up to batch size.\n",
        "        y : list of ndarray shape (batch_size, *dim, n_classes)  \n",
        "            Number of classes image up to batch size.\n",
        "        X and y when fitting. X only when predicting.\n",
        "        \"\"\"\n",
        "        if self._n_batch >= self.__len__():\n",
        "            self.on_epoch_end\n",
        "            self._n_batch = 0\n",
        "\n",
        "        indexes = self.indexes[index * self.batch_size:(index + 1) * self.batch_size]\n",
        "        list_IDs_temp = [self.list_IDs[idx] for idx in indexes]\n",
        "\n",
        "        X = self._generate_X(list_IDs_temp)\n",
        "        self._n_batch += 1\n",
        "        if self.to_fit:\n",
        "            y = self._generate_y(list_IDs_temp)\n",
        "            return X, y\n",
        "        return X            \n",
        "\n",
        "    def on_epoch_end(self):\n",
        "        \"\"\"\n",
        "        Updates indexes after each epoch.\n",
        "        Attributes\n",
        "        ----------\n",
        "        indexes : list of ndarray shape (len(filenames[1]),)\n",
        "            It keeps the indices as high as the size of the masks.\n",
        "            If shuffle is True, shuffle the data indices and updates\n",
        "            after each epoch.\n",
        "        \"\"\"\n",
        "        self.indexes = np.arange(len(self.list_IDs))\n",
        "        if self.shuffle == True:\n",
        "            if self.seed is not None:\n",
        "                np.random.seed(seed)\n",
        "            np.random.shuffle(self.indexes)\n",
        "\n",
        "    def __iter__(self):\n",
        "        \"\"\"\n",
        "        Create a generator that iterate over the DataGenerator.\n",
        "        \"\"\"\n",
        "        while True:\n",
        "            for item in (self[i] for i in range(len(self))):\n",
        "                yield item\n",
        "\n",
        "    def __next__(self):\n",
        "        \"\"\"\n",
        "        Function returns the next batches in a DataGenerator.\n",
        "        \"\"\"\n",
        "        batches = self.__getitem__(self._n_batch)\n",
        "        return batches\n",
        "    \n",
        "    def _initialize_dirs(self, directory):\n",
        "        \"\"\"\n",
        "        Initialize the all data directories.\n",
        "        The directory structure should be as follows.:\n",
        "            data:\n",
        "                test:\n",
        "                    images:\n",
        "                    masks:\n",
        "                train:\n",
        "                    images:\n",
        "                    masks:\n",
        "                val:\n",
        "                    images:\n",
        "                    masks:\n",
        "        Attributes\n",
        "        ----------\n",
        "        filenames : list, size (2,)\n",
        "            A list that keeps the full directories of images and masks.\n",
        "            Index 0 is images. Index 1 is masks.\n",
        "        list_IDs : list of ndarray shape (len(filenames[1]),)\n",
        "            It keeps the indices as high as the size of the masks.\n",
        "        \"\"\"\n",
        "        classes = []\n",
        "        for subdir in sorted(os.listdir(directory)):\n",
        "            if os.path.isdir(os.path.join(directory, subdir)):\n",
        "                classes.append(subdir)\n",
        "\n",
        "        filenames = []\n",
        "        for dirpath in (os.path.join(directory, subdir) for subdir in classes):\n",
        "            for roots,dirs,files in os.walk(dirpath):               \n",
        "                if files:\n",
        "                    sub_dirs = sorted([roots + os.sep + file for file in  files])\n",
        "                    filenames.append(sub_dirs)\n",
        "        \n",
        "        self.filenames = filenames\n",
        "        self.list_IDs = np.arange(len(filenames[0]))\n",
        "        \n",
        "    def _generate_X(self, list_IDs_temp):\n",
        "        \"\"\"\n",
        "        Generates data containing batch_size images.        \n",
        "        Parameters\n",
        "        ----------\n",
        "        list_IDs_temp : list\n",
        "            List of masks ids to load.\n",
        "        Returns\n",
        "        ----------\n",
        "        X : list of ndarray shape (batch_size, *dim, n_channels)\n",
        "            Batch of images.\n",
        "        \"\"\"\n",
        "        X = np.empty((self.batch_size, *self.dim, self.n_channels), dtype=self.dtype)\n",
        "\n",
        "        for i, ID in enumerate(list_IDs_temp):\n",
        "            X[i,] = self._load_rgb_image(self.filenames[0][ID])\n",
        "        return X\n",
        "\n",
        "    def _generate_y(self, list_IDs_temp):\n",
        "        \"\"\"\n",
        "        Generates data containing batch_size masks.\n",
        "        Parameters\n",
        "        ----------\n",
        "        list_IDs_temp : list\n",
        "            List of masks ids to load.\n",
        "        Returns\n",
        "        ----------\n",
        "        y : list of ndarray shape (batch_size, *dim, n_classes)\n",
        "            Batch of masks.\n",
        "        \"\"\"\n",
        "        y = np.empty((self.batch_size, *self.dim, self.n_classes), dtype=self.dtype)\n",
        "\n",
        "        for i, ID in enumerate(list_IDs_temp):\n",
        "            mask = self._load_grayscale_image(self.filenames[1][ID])\n",
        "            if self.n_classes is not 1:\n",
        "                mask = to_categorical(mask, num_classes=self.n_classes, dtype=self.dtype)\n",
        "            y[i,] = mask\n",
        "        return y\n",
        "\n",
        "    def _load_rgb_image(self, image_path):\n",
        "        \"\"\"\n",
        "        Load RGB image from given directory.\n",
        "        Supported image file formats:\n",
        "        ('png', 'jpg', 'jpeg', 'bmp', 'ppm', 'tif', 'tiff')\n",
        "        Parameters\n",
        "        ----------\n",
        "        image_path : string\n",
        "            Path to image to load.\n",
        "        Returns\n",
        "        ----------\n",
        "        img : ndarray, shape of (*dim, n_channels)\n",
        "            Loaded image.\n",
        "        \"\"\"\n",
        "        img = load_img(image_path, \n",
        "                       target_size=self.dim, \n",
        "                       interpolation=self.interpolation)\n",
        "        img = img_to_array(img, dtype=self.dtype)\n",
        "        if self.preprocessing_function is not None:\n",
        "            img = self.preprocessing_function(img)\n",
        "        return img\n",
        "\n",
        "    def _load_grayscale_image(self, image_path):\n",
        "        \"\"\"\n",
        "        Load grayscale image from given directory.\n",
        "        Supported image file formats:\n",
        "        ('png', 'jpg', 'jpeg', 'bmp', 'ppm', 'tif', 'tiff')\n",
        "        Parameters\n",
        "        ----------\n",
        "        image_path : string\n",
        "            Path to image to load.\n",
        "        Returns\n",
        "        ----------\n",
        "        img : ndarray, shape of (*dim, 1)\n",
        "            Loaded image.\n",
        "        \"\"\"\n",
        "        img = load_img(image_path, \n",
        "                       color_mode='grayscale', \n",
        "                       target_size=self.dim, \n",
        "                       interpolation=self.interpolation)\n",
        "        img = img_to_array(img, dtype=self.dtype)\n",
        "        return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CydWyZTAA-U9"
      },
      "source": [
        "\n",
        "\n",
        "> **Preprocessing Function**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vch3V6aYA-U9"
      },
      "source": [
        "scaler = MinMaxScaler()\n",
        "def preprocessing(img=None):\n",
        "    if img is not None:\n",
        "        img = scaler.fit_transform(img.reshape(-1, img.shape[-1])).reshape(img.shape)\n",
        "        return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OFKoSIHyA-U9"
      },
      "source": [
        "\n",
        "\n",
        "> **Initialize the Parameters**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MyLdxyUmA-U9"
      },
      "source": [
        "train_directory = '/content/datas/train'\n",
        "val_directory = '/content/datas/val'\n",
        "test_directory = '/content/datas/test'\n",
        "backbone = 'inceptionresnetv2'\n",
        "classes = ['background', 'upperbody', 'lowerbody', 'wholebody', 'feet', 'accessories']\n",
        "n_classes = len(classes)\n",
        "activation = 'sigmoid' if n_classes == 1 else 'softmax'\n",
        "num_epoch = 35\n",
        "LR = 1e-3\n",
        "batch_size = 8\n",
        "IMG_WIDTH, IMG_HEIGHT, IMG_CHANNELS = 256, 256, 3\n",
        "seed = None \n",
        "\n",
        "params = {'to_fit':True,\n",
        "          'batch_size': batch_size,\n",
        "          'dim': (IMG_WIDTH,IMG_HEIGHT),\n",
        "          'dtype':'float32',\n",
        "          'n_channels': IMG_CHANNELS,\n",
        "          'n_classes': n_classes,\n",
        "          'shuffle': True,\n",
        "          'seed':seed,\n",
        "          'interpolation':'nearest',\n",
        "          'preprocessing_function': preprocessing}\n",
        "\n",
        "train_gen = DataGenerator(train_directory, **params)\n",
        "val_gen = DataGenerator(val_directory, **params)\n",
        "test_gen = DataGenerator(test_directory, **params)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajl_zc00JwHc"
      },
      "source": [
        "### **Model**\n",
        "\n",
        "[Source](https://github.com/bonlime/keras-deeplab-v3-plus)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MCKJdzp1vgyn"
      },
      "source": [
        "\"\"\" Deeplabv3+ model for Keras.\n",
        "This model is based on TF repo:\n",
        "https://github.com/tensorflow/models/tree/master/research/deeplab\n",
        "On Pascal VOC, original model gets to 84.56% mIOU\n",
        "MobileNetv2 backbone is based on this repo:\n",
        "https://github.com/JonathanCMitchell/mobilenet_v2_keras\n",
        "# Reference\n",
        "- [Encoder-Decoder with Atrous Separable Convolution\n",
        "    for Semantic Image Segmentation](https://arxiv.org/pdf/1802.02611.pdf)\n",
        "- [Xception: Deep Learning with Depthwise Separable Convolutions]\n",
        "    (https://arxiv.org/abs/1610.02357)\n",
        "- [Inverted Residuals and Linear Bottlenecks: Mobile Networks for\n",
        "    Classification, Detection and Segmentation](https://arxiv.org/abs/1801.04381)\n",
        "\"\"\"\n",
        "\n",
        "from __future__ import absolute_import\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.python.keras.models import Model\n",
        "from tensorflow.python.keras import layers\n",
        "from tensorflow.python.keras.layers import Input\n",
        "from tensorflow.python.keras.layers import Reshape\n",
        "from tensorflow.python.keras.layers import Activation\n",
        "from tensorflow.python.keras.layers import Concatenate\n",
        "from tensorflow.python.keras.layers import Add\n",
        "from tensorflow.python.keras.layers import Dropout\n",
        "from tensorflow.python.keras.layers import BatchNormalization\n",
        "from tensorflow.python.keras.layers import Conv2D\n",
        "from tensorflow.python.keras.layers import DepthwiseConv2D\n",
        "from tensorflow.python.keras.layers import ZeroPadding2D\n",
        "from tensorflow.python.keras.layers import GlobalAveragePooling2D\n",
        "from tensorflow.python.keras.utils.layer_utils import get_source_inputs\n",
        "from tensorflow.python.keras.utils.data_utils import get_file\n",
        "from tensorflow.python.keras import backend as K\n",
        "from tensorflow.python.keras.applications.imagenet_utils import preprocess_input\n",
        "\n",
        "WEIGHTS_PATH_X = \"https://github.com/bonlime/keras-deeplab-v3-plus/releases/download/1.1/deeplabv3_xception_tf_dim_ordering_tf_kernels.h5\"\n",
        "WEIGHTS_PATH_MOBILE = \"https://github.com/bonlime/keras-deeplab-v3-plus/releases/download/1.1/deeplabv3_mobilenetv2_tf_dim_ordering_tf_kernels.h5\"\n",
        "WEIGHTS_PATH_X_CS = \"https://github.com/bonlime/keras-deeplab-v3-plus/releases/download/1.2/deeplabv3_xception_tf_dim_ordering_tf_kernels_cityscapes.h5\"\n",
        "WEIGHTS_PATH_MOBILE_CS = \"https://github.com/bonlime/keras-deeplab-v3-plus/releases/download/1.2/deeplabv3_mobilenetv2_tf_dim_ordering_tf_kernels_cityscapes.h5\"\n",
        "\n",
        "\n",
        "def SepConv_BN(x, filters, prefix, stride=1, kernel_size=3, rate=1, depth_activation=False, epsilon=1e-3):\n",
        "    \"\"\" SepConv with BN between depthwise & pointwise. Optionally add activation after BN\n",
        "        Implements right \"same\" padding for even kernel sizes\n",
        "        Args:\n",
        "            x: input tensor\n",
        "            filters: num of filters in pointwise convolution\n",
        "            prefix: prefix before name\n",
        "            stride: stride at depthwise conv\n",
        "            kernel_size: kernel size for depthwise convolution\n",
        "            rate: atrous rate for depthwise convolution\n",
        "            depth_activation: flag to use activation between depthwise & poinwise convs\n",
        "            epsilon: epsilon to use in BN layer\n",
        "    \"\"\"\n",
        "\n",
        "    if stride == 1:\n",
        "        depth_padding = 'same'\n",
        "    else:\n",
        "        kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)\n",
        "        pad_total = kernel_size_effective - 1\n",
        "        pad_beg = pad_total // 2\n",
        "        pad_end = pad_total - pad_beg\n",
        "        x = ZeroPadding2D((pad_beg, pad_end))(x)\n",
        "        depth_padding = 'valid'\n",
        "\n",
        "    if not depth_activation:\n",
        "        x = Activation(tf.nn.relu)(x)\n",
        "    x = DepthwiseConv2D((kernel_size, kernel_size), strides=(stride, stride), dilation_rate=(rate, rate),\n",
        "                        padding=depth_padding, use_bias=False, name=prefix + '_depthwise')(x)\n",
        "    x = BatchNormalization(name=prefix + '_depthwise_BN', epsilon=epsilon)(x)\n",
        "    if depth_activation:\n",
        "        x = Activation(tf.nn.relu)(x)\n",
        "    x = Conv2D(filters, (1, 1), padding='same',\n",
        "               use_bias=False, name=prefix + '_pointwise')(x)\n",
        "    x = BatchNormalization(name=prefix + '_pointwise_BN', epsilon=epsilon)(x)\n",
        "    if depth_activation:\n",
        "        x = Activation(tf.nn.relu)(x)\n",
        "\n",
        "    return x\n",
        "\n",
        "\n",
        "def _conv2d_same(x, filters, prefix, stride=1, kernel_size=3, rate=1):\n",
        "    \"\"\"Implements right 'same' padding for even kernel sizes\n",
        "        Without this there is a 1 pixel drift when stride = 2\n",
        "        Args:\n",
        "            x: input tensor\n",
        "            filters: num of filters in pointwise convolution\n",
        "            prefix: prefix before name\n",
        "            stride: stride at depthwise conv\n",
        "            kernel_size: kernel size for depthwise convolution\n",
        "            rate: atrous rate for depthwise convolution\n",
        "    \"\"\"\n",
        "    if stride == 1:\n",
        "        return Conv2D(filters,\n",
        "                      (kernel_size, kernel_size),\n",
        "                      strides=(stride, stride),\n",
        "                      padding='same', use_bias=False,\n",
        "                      dilation_rate=(rate, rate),\n",
        "                      name=prefix)(x)\n",
        "    else:\n",
        "        kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)\n",
        "        pad_total = kernel_size_effective - 1\n",
        "        pad_beg = pad_total // 2\n",
        "        pad_end = pad_total - pad_beg\n",
        "        x = ZeroPadding2D((pad_beg, pad_end))(x)\n",
        "        return Conv2D(filters,\n",
        "                      (kernel_size, kernel_size),\n",
        "                      strides=(stride, stride),\n",
        "                      padding='valid', use_bias=False,\n",
        "                      dilation_rate=(rate, rate),\n",
        "                      name=prefix)(x)\n",
        "\n",
        "\n",
        "def _xception_block(inputs, depth_list, prefix, skip_connection_type, stride,\n",
        "                    rate=1, depth_activation=False, return_skip=False):\n",
        "    \"\"\" Basic building block of modified Xception network\n",
        "        Args:\n",
        "            inputs: input tensor\n",
        "            depth_list: number of filters in each SepConv layer. len(depth_list) == 3\n",
        "            prefix: prefix before name\n",
        "            skip_connection_type: one of {'conv','sum','none'}\n",
        "            stride: stride at last depthwise conv\n",
        "            rate: atrous rate for depthwise convolution\n",
        "            depth_activation: flag to use activation between depthwise & pointwise convs\n",
        "            return_skip: flag to return additional tensor after 2 SepConvs for decoder\n",
        "            \"\"\"\n",
        "    residual = inputs\n",
        "    for i in range(3):\n",
        "        residual = SepConv_BN(residual,\n",
        "                              depth_list[i],\n",
        "                              prefix + '_separable_conv{}'.format(i + 1),\n",
        "                              stride=stride if i == 2 else 1,\n",
        "                              rate=rate,\n",
        "                              depth_activation=depth_activation)\n",
        "        if i == 1:\n",
        "            skip = residual\n",
        "    if skip_connection_type == 'conv':\n",
        "        shortcut = _conv2d_same(inputs, depth_list[-1], prefix + '_shortcut',\n",
        "                                kernel_size=1,\n",
        "                                stride=stride)\n",
        "        shortcut = BatchNormalization(name=prefix + '_shortcut_BN')(shortcut)\n",
        "        outputs = layers.add([residual, shortcut])\n",
        "    elif skip_connection_type == 'sum':\n",
        "        outputs = layers.add([residual, inputs])\n",
        "    elif skip_connection_type == 'none':\n",
        "        outputs = residual\n",
        "    if return_skip:\n",
        "        return outputs, skip\n",
        "    else:\n",
        "        return outputs\n",
        "\n",
        "\n",
        "def _make_divisible(v, divisor, min_value=None):\n",
        "    if min_value is None:\n",
        "        min_value = divisor\n",
        "    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)\n",
        "    # Make sure that round down does not go down by more than 10%.\n",
        "    if new_v < 0.9 * v:\n",
        "        new_v += divisor\n",
        "    return new_v\n",
        "\n",
        "\n",
        "def _inverted_res_block(inputs, expansion, stride, alpha, filters, block_id, skip_connection, rate=1):\n",
        "    in_channels = inputs.shape[-1]  # inputs._keras_shape[-1]\n",
        "    pointwise_conv_filters = int(filters * alpha)\n",
        "    pointwise_filters = _make_divisible(pointwise_conv_filters, 8)\n",
        "    x = inputs\n",
        "    prefix = 'expanded_conv_{}_'.format(block_id)\n",
        "    if block_id:\n",
        "        # Expand\n",
        "\n",
        "        x = Conv2D(expansion * in_channels, kernel_size=1, padding='same',\n",
        "                   use_bias=False, activation=None,\n",
        "                   name=prefix + 'expand')(x)\n",
        "        x = BatchNormalization(epsilon=1e-3, momentum=0.999,\n",
        "                               name=prefix + 'expand_BN')(x)\n",
        "        x = Activation(tf.nn.relu6, name=prefix + 'expand_relu')(x)\n",
        "    else:\n",
        "        prefix = 'expanded_conv_'\n",
        "    # Depthwise\n",
        "    x = DepthwiseConv2D(kernel_size=3, strides=stride, activation=None,\n",
        "                        use_bias=False, padding='same', dilation_rate=(rate, rate),\n",
        "                        name=prefix + 'depthwise')(x)\n",
        "    x = BatchNormalization(epsilon=1e-3, momentum=0.999,\n",
        "                           name=prefix + 'depthwise_BN')(x)\n",
        "\n",
        "    x = Activation(tf.nn.relu6, name=prefix + 'depthwise_relu')(x)\n",
        "\n",
        "    # Project\n",
        "    x = Conv2D(pointwise_filters,\n",
        "               kernel_size=1, padding='same', use_bias=False, activation=None,\n",
        "               name=prefix + 'project')(x)\n",
        "    x = BatchNormalization(epsilon=1e-3, momentum=0.999,\n",
        "                           name=prefix + 'project_BN')(x)\n",
        "\n",
        "    if skip_connection:\n",
        "        return Add(name=prefix + 'add')([inputs, x])\n",
        "\n",
        "    # if in_channels == pointwise_filters and stride == 1:\n",
        "    #    return Add(name='res_connect_' + str(block_id))([inputs, x])\n",
        "\n",
        "    return x\n",
        "\n",
        "\n",
        "def Deeplabv3(weights='pascal_voc', input_tensor=None, input_shape=(512, 512, 3), classes=21, backbone='mobilenetv2',\n",
        "              OS=16, alpha=1., activation=None):\n",
        "    \"\"\" Instantiates the Deeplabv3+ architecture\n",
        "    Optionally loads weights pre-trained\n",
        "    on PASCAL VOC or Cityscapes. This model is available for TensorFlow only.\n",
        "    # Arguments\n",
        "        weights: one of 'pascal_voc' (pre-trained on pascal voc),\n",
        "            'cityscapes' (pre-trained on cityscape) or None (random initialization)\n",
        "        input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)\n",
        "            to use as image input for the model.\n",
        "        input_shape: shape of input image. format HxWxC\n",
        "            PASCAL VOC model was trained on (512,512,3) images. None is allowed as shape/width\n",
        "        classes: number of desired classes. PASCAL VOC has 21 classes, Cityscapes has 19 classes.\n",
        "            If number of classes not aligned with the weights used, last layer is initialized randomly\n",
        "        backbone: backbone to use. one of {'xception','mobilenetv2'}\n",
        "        activation: optional activation to add to the top of the network.\n",
        "            One of 'softmax', 'sigmoid' or None\n",
        "        OS: determines input_shape/feature_extractor_output ratio. One of {8,16}.\n",
        "            Used only for xception backbone.\n",
        "        alpha: controls the width of the MobileNetV2 network. This is known as the\n",
        "            width multiplier in the MobileNetV2 paper.\n",
        "                - If `alpha` < 1.0, proportionally decreases the number\n",
        "                    of filters in each layer.\n",
        "                - If `alpha` > 1.0, proportionally increases the number\n",
        "                    of filters in each layer.\n",
        "                - If `alpha` = 1, default number of filters from the paper\n",
        "                    are used at each layer.\n",
        "            Used only for mobilenetv2 backbone. Pretrained is only available for alpha=1.\n",
        "    # Returns\n",
        "        A Keras model instance.\n",
        "    # Raises\n",
        "        RuntimeError: If attempting to run this model with a\n",
        "            backend that does not support separable convolutions.\n",
        "        ValueError: in case of invalid argument for `weights` or `backbone`\n",
        "    \"\"\"\n",
        "\n",
        "    if not (weights in {'pascal_voc', 'cityscapes', None}):\n",
        "        raise ValueError('The `weights` argument should be either '\n",
        "                         '`None` (random initialization), `pascal_voc`, or `cityscapes` '\n",
        "                         '(pre-trained on PASCAL VOC)')\n",
        "\n",
        "    if not (backbone in {'xception', 'mobilenetv2'}):\n",
        "        raise ValueError('The `backbone` argument should be either '\n",
        "                         '`xception`  or `mobilenetv2` ')\n",
        "\n",
        "    if input_tensor is None:\n",
        "        img_input = Input(shape=input_shape)\n",
        "    else:\n",
        "        img_input = input_tensor\n",
        "\n",
        "    if backbone == 'xception':\n",
        "        if OS == 8:\n",
        "            entry_block3_stride = 1\n",
        "            middle_block_rate = 2  # ! Not mentioned in paper, but required\n",
        "            exit_block_rates = (2, 4)\n",
        "            atrous_rates = (12, 24, 36)\n",
        "        else:\n",
        "            entry_block3_stride = 2\n",
        "            middle_block_rate = 1\n",
        "            exit_block_rates = (1, 2)\n",
        "            atrous_rates = (6, 12, 18)\n",
        "\n",
        "        x = Conv2D(32, (3, 3), strides=(2, 2),\n",
        "                   name='entry_flow_conv1_1', use_bias=False, padding='same')(img_input)\n",
        "        x = BatchNormalization(name='entry_flow_conv1_1_BN')(x)\n",
        "        x = Activation(tf.nn.relu)(x)\n",
        "\n",
        "        x = _conv2d_same(x, 64, 'entry_flow_conv1_2', kernel_size=3, stride=1)\n",
        "        x = BatchNormalization(name='entry_flow_conv1_2_BN')(x)\n",
        "        x = Activation(tf.nn.relu)(x)\n",
        "\n",
        "        x = _xception_block(x, [128, 128, 128], 'entry_flow_block1',\n",
        "                            skip_connection_type='conv', stride=2,\n",
        "                            depth_activation=False)\n",
        "        x, skip1 = _xception_block(x, [256, 256, 256], 'entry_flow_block2',\n",
        "                                   skip_connection_type='conv', stride=2,\n",
        "                                   depth_activation=False, return_skip=True)\n",
        "\n",
        "        x = _xception_block(x, [728, 728, 728], 'entry_flow_block3',\n",
        "                            skip_connection_type='conv', stride=entry_block3_stride,\n",
        "                            depth_activation=False)\n",
        "        for i in range(16):\n",
        "            x = _xception_block(x, [728, 728, 728], 'middle_flow_unit_{}'.format(i + 1),\n",
        "                                skip_connection_type='sum', stride=1, rate=middle_block_rate,\n",
        "                                depth_activation=False)\n",
        "\n",
        "        x = _xception_block(x, [728, 1024, 1024], 'exit_flow_block1',\n",
        "                            skip_connection_type='conv', stride=1, rate=exit_block_rates[0],\n",
        "                            depth_activation=False)\n",
        "        x = _xception_block(x, [1536, 1536, 2048], 'exit_flow_block2',\n",
        "                            skip_connection_type='none', stride=1, rate=exit_block_rates[1],\n",
        "                            depth_activation=True)\n",
        "\n",
        "    else:\n",
        "        OS = 8\n",
        "        first_block_filters = _make_divisible(32 * alpha, 8)\n",
        "        x = Conv2D(first_block_filters,\n",
        "                   kernel_size=3,\n",
        "                   strides=(2, 2), padding='same', use_bias=False,\n",
        "                   name='Conv' if input_shape[2] == 3 else 'Conv_')(img_input)\n",
        "        x = BatchNormalization(\n",
        "            epsilon=1e-3, momentum=0.999, name='Conv_BN')(x)\n",
        "        x = Activation(tf.nn.relu6, name='Conv_Relu6')(x)\n",
        "\n",
        "        x = _inverted_res_block(x, filters=16, alpha=alpha, stride=1,\n",
        "                                expansion=1, block_id=0, skip_connection=False)\n",
        "\n",
        "        x = _inverted_res_block(x, filters=24, alpha=alpha, stride=2,\n",
        "                                expansion=6, block_id=1, skip_connection=False)\n",
        "        x = _inverted_res_block(x, filters=24, alpha=alpha, stride=1,\n",
        "                                expansion=6, block_id=2, skip_connection=True)\n",
        "\n",
        "        x = _inverted_res_block(x, filters=32, alpha=alpha, stride=2,\n",
        "                                expansion=6, block_id=3, skip_connection=False)\n",
        "        x = _inverted_res_block(x, filters=32, alpha=alpha, stride=1,\n",
        "                                expansion=6, block_id=4, skip_connection=True)\n",
        "        x = _inverted_res_block(x, filters=32, alpha=alpha, stride=1,\n",
        "                                expansion=6, block_id=5, skip_connection=True)\n",
        "\n",
        "        # stride in block 6 changed from 2 -> 1, so we need to use rate = 2\n",
        "        x = _inverted_res_block(x, filters=64, alpha=alpha, stride=1,  # 1!\n",
        "                                expansion=6, block_id=6, skip_connection=False)\n",
        "        x = _inverted_res_block(x, filters=64, alpha=alpha, stride=1, rate=2,\n",
        "                                expansion=6, block_id=7, skip_connection=True)\n",
        "        x = _inverted_res_block(x, filters=64, alpha=alpha, stride=1, rate=2,\n",
        "                                expansion=6, block_id=8, skip_connection=True)\n",
        "        x = _inverted_res_block(x, filters=64, alpha=alpha, stride=1, rate=2,\n",
        "                                expansion=6, block_id=9, skip_connection=True)\n",
        "\n",
        "        x = _inverted_res_block(x, filters=96, alpha=alpha, stride=1, rate=2,\n",
        "                                expansion=6, block_id=10, skip_connection=False)\n",
        "        x = _inverted_res_block(x, filters=96, alpha=alpha, stride=1, rate=2,\n",
        "                                expansion=6, block_id=11, skip_connection=True)\n",
        "        x = _inverted_res_block(x, filters=96, alpha=alpha, stride=1, rate=2,\n",
        "                                expansion=6, block_id=12, skip_connection=True)\n",
        "\n",
        "        x = _inverted_res_block(x, filters=160, alpha=alpha, stride=1, rate=2,  # 1!\n",
        "                                expansion=6, block_id=13, skip_connection=False)\n",
        "        x = _inverted_res_block(x, filters=160, alpha=alpha, stride=1, rate=4,\n",
        "                                expansion=6, block_id=14, skip_connection=True)\n",
        "        x = _inverted_res_block(x, filters=160, alpha=alpha, stride=1, rate=4,\n",
        "                                expansion=6, block_id=15, skip_connection=True)\n",
        "\n",
        "        x = _inverted_res_block(x, filters=320, alpha=alpha, stride=1, rate=4,\n",
        "                                expansion=6, block_id=16, skip_connection=False)\n",
        "\n",
        "    # end of feature extractor\n",
        "\n",
        "    # branching for Atrous Spatial Pyramid Pooling\n",
        "\n",
        "    # Image Feature branch\n",
        "    shape_before = tf.shape(x)\n",
        "    b4 = GlobalAveragePooling2D()(x)\n",
        "    b4_shape = tf.keras.backend.int_shape(b4)\n",
        "    # from (b_size, channels)->(b_size, 1, 1, channels)\n",
        "    b4 = Reshape((1, 1, b4_shape[1]))(b4)\n",
        "    b4 = Conv2D(256, (1, 1), padding='same',\n",
        "                use_bias=False, name='image_pooling')(b4)\n",
        "    b4 = BatchNormalization(name='image_pooling_BN', epsilon=1e-5)(b4)\n",
        "    b4 = Activation(tf.nn.relu)(b4)\n",
        "    # upsample. have to use compat because of the option align_corners\n",
        "    size_before = tf.keras.backend.int_shape(x)\n",
        "    b4 = tf.keras.layers.experimental.preprocessing.Resizing(\n",
        "            *size_before[1:3], interpolation=\"bilinear\"\n",
        "        )(b4)\n",
        "    # simple 1x1\n",
        "    b0 = Conv2D(256, (1, 1), padding='same', use_bias=False, name='aspp0')(x)\n",
        "    b0 = BatchNormalization(name='aspp0_BN', epsilon=1e-5)(b0)\n",
        "    b0 = Activation(tf.nn.relu, name='aspp0_activation')(b0)\n",
        "\n",
        "    # there are only 2 branches in mobilenetV2. not sure why\n",
        "    if backbone == 'xception':\n",
        "        # rate = 6 (12)\n",
        "        b1 = SepConv_BN(x, 256, 'aspp1',\n",
        "                        rate=atrous_rates[0], depth_activation=True, epsilon=1e-5)\n",
        "        # rate = 12 (24)\n",
        "        b2 = SepConv_BN(x, 256, 'aspp2',\n",
        "                        rate=atrous_rates[1], depth_activation=True, epsilon=1e-5)\n",
        "        # rate = 18 (36)\n",
        "        b3 = SepConv_BN(x, 256, 'aspp3',\n",
        "                        rate=atrous_rates[2], depth_activation=True, epsilon=1e-5)\n",
        "\n",
        "        # concatenate ASPP branches & project\n",
        "        x = Concatenate()([b4, b0, b1, b2, b3])\n",
        "    else:\n",
        "        x = Concatenate()([b4, b0])\n",
        "\n",
        "    x = Conv2D(256, (1, 1), padding='same',\n",
        "               use_bias=False, name='concat_projection')(x)\n",
        "    x = BatchNormalization(name='concat_projection_BN', epsilon=1e-5)(x)\n",
        "    x = Activation(tf.nn.relu)(x)\n",
        "    x = Dropout(0.1)(x)\n",
        "    # DeepLab v.3+ decoder\n",
        "\n",
        "    if backbone == 'xception':\n",
        "        # Feature projection\n",
        "        # x4 (x2) block\n",
        "        skip_size = tf.keras.backend.int_shape(skip1)\n",
        "        x = tf.keras.layers.experimental.preprocessing.Resizing(\n",
        "                *skip_size[1:3], interpolation=\"bilinear\"\n",
        "            )(x)\n",
        "        dec_skip1 = Conv2D(48, (1, 1), padding='same',\n",
        "                           use_bias=False, name='feature_projection0')(skip1)\n",
        "        dec_skip1 = BatchNormalization(\n",
        "            name='feature_projection0_BN', epsilon=1e-5)(dec_skip1)\n",
        "        dec_skip1 = Activation(tf.nn.relu)(dec_skip1)\n",
        "        x = Concatenate()([x, dec_skip1])\n",
        "        x = SepConv_BN(x, 256, 'decoder_conv0',\n",
        "                       depth_activation=True, epsilon=1e-5)\n",
        "        x = SepConv_BN(x, 256, 'decoder_conv1',\n",
        "                       depth_activation=True, epsilon=1e-5)\n",
        "\n",
        "    # you can use it with arbitary number of classes\n",
        "    if (weights == 'pascal_voc' and classes == 21) or (weights == 'cityscapes' and classes == 19):\n",
        "        last_layer_name = 'logits_semantic'\n",
        "    else:\n",
        "        last_layer_name = 'custom_logits_semantic'\n",
        "\n",
        "    x = Conv2D(classes, (1, 1), padding='same', name=last_layer_name)(x)\n",
        "    size_before3 = tf.keras.backend.int_shape(img_input)\n",
        "    x = tf.keras.layers.experimental.preprocessing.Resizing(\n",
        "            *size_before3[1:3], interpolation=\"bilinear\"\n",
        "        )(x)\n",
        "    # Ensure that the model takes into account\n",
        "    # any potential predecessors of `input_tensor`.\n",
        "    if input_tensor is not None:\n",
        "        inputs = get_source_inputs(input_tensor)\n",
        "    else:\n",
        "        inputs = img_input\n",
        "\n",
        "    if activation in {'softmax', 'sigmoid'}:\n",
        "        x = tf.keras.layers.Activation(activation)(x)\n",
        "\n",
        "    model = Model(inputs, x, name='deeplabv3plus')\n",
        "\n",
        "    # load weights\n",
        "\n",
        "    if weights == 'pascal_voc':\n",
        "        if backbone == 'xception':\n",
        "            weights_path = get_file('deeplabv3_xception_tf_dim_ordering_tf_kernels.h5',\n",
        "                                    WEIGHTS_PATH_X,\n",
        "                                    cache_subdir='models')\n",
        "        else:\n",
        "            weights_path = get_file('deeplabv3_mobilenetv2_tf_dim_ordering_tf_kernels.h5',\n",
        "                                    WEIGHTS_PATH_MOBILE,\n",
        "                                    cache_subdir='models')\n",
        "        model.load_weights(weights_path, by_name=True)\n",
        "    elif weights == 'cityscapes':\n",
        "        if backbone == 'xception':\n",
        "            weights_path = get_file('deeplabv3_xception_tf_dim_ordering_tf_kernels_cityscapes.h5',\n",
        "                                    WEIGHTS_PATH_X_CS,\n",
        "                                    cache_subdir='models')\n",
        "        else:\n",
        "            weights_path = get_file('deeplabv3_mobilenetv2_tf_dim_ordering_tf_kernels_cityscapes.h5',\n",
        "                                    WEIGHTS_PATH_MOBILE_CS,\n",
        "                                    cache_subdir='models')\n",
        "        model.load_weights(weights_path, by_name=True)\n",
        "    return model\n",
        "\n",
        "def preprocess_input(x):\n",
        "    \"\"\"Preprocesses a numpy array encoding a batch of images.\n",
        "    # Arguments\n",
        "        x: a 4D numpy array consists of RGB values within [0, 255].\n",
        "    # Returns\n",
        "        Input array scaled to [-1.,1.]\n",
        "    \"\"\"\n",
        "    return preprocess_input(x, mode='tf')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fohP44B6JzGg"
      },
      "source": [
        "###**Load Weights**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oEzEBwNkKFvS"
      },
      "source": [
        "MODEL_DIR = 'models'\n",
        "\n",
        "for backbone in ['mobilenetv2', 'xception']:\n",
        "    print('Instantiating an empty Deeplabv3+ model...')\n",
        "    model = Deeplabv3(input_shape=(512, 512, 3),\n",
        "                      classes=21, backbone=backbone, weights=None)\n",
        "\n",
        "    WEIGHTS_DIR = 'weights/' + backbone\n",
        "    print('Loading weights from', WEIGHTS_DIR)\n",
        "    for layer in tqdm(model.layers):\n",
        "        if layer.weights:\n",
        "            weights = []\n",
        "            for w in layer.weights:\n",
        "                weight_name = os.path.basename(w.name).replace(':0', '')\n",
        "                weight_file = layer.name + '_' + weight_name + '.npy'\n",
        "                weight_arr = np.load(os.path.join(WEIGHTS_DIR, weight_file))\n",
        "                weights.append(weight_arr)\n",
        "            layer.set_weights(weights)\n",
        "\n",
        "    print('Saving model weights...')\n",
        "    OUTPUT_WEIGHT_FILENAME = 'deeplabv3_' + \\\n",
        "        backbone + '_tf_dim_ordering_tf_kernels.h5'\n",
        "    if not os.path.exists(MODEL_DIR):\n",
        "        os.makedirs(MODEL_DIR)\n",
        "    model.save_weights(os.path.join(MODEL_DIR, OUTPUT_WEIGHT_FILENAME))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cGfjTtxWKJ2D"
      },
      "source": [
        "###**Extract Weights**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-yHsMrGKNKc"
      },
      "source": [
        "def get_xception_filename(key):\n",
        "    \"\"\"Rename tensor name to the corresponding Keras layer weight name.\n",
        "    # Arguments\n",
        "        key: tensor name in TF (determined by tf.variable_scope)\n",
        "    \"\"\"\n",
        "    filename = str(key)\n",
        "    filename = filename.replace('/', '_')\n",
        "    filename = filename.replace('xception_65_', '')\n",
        "    filename = filename.replace('decoder_', '', 1)\n",
        "    filename = filename.replace('BatchNorm', 'BN')\n",
        "    if 'Momentum' in filename:\n",
        "        return None\n",
        "    if 'entry_flow' in filename or 'exit_flow' in filename:\n",
        "        filename = filename.replace('_unit_1_xception_module', '')\n",
        "    elif 'middle_flow' in filename:\n",
        "        filename = filename.replace('_block1', '')\n",
        "        filename = filename.replace('_xception_module', '')\n",
        "\n",
        "    # from TF to Keras naming\n",
        "    filename = filename.replace('_weights', '_kernel')\n",
        "    filename = filename.replace('_biases', '_bias')\n",
        "\n",
        "    return filename + '.npy'\n",
        "\n",
        "\n",
        "def get_mobilenetv2_filename(key):\n",
        "    \"\"\"Rename tensor name to the corresponding Keras layer weight name.\n",
        "    # Arguments\n",
        "        key: tensor name in TF (determined by tf.variable_scope)\n",
        "    \"\"\"\n",
        "    filename = str(key)\n",
        "    filename = filename.replace('/', '_')\n",
        "    filename = filename.replace('MobilenetV2_', '')\n",
        "    filename = filename.replace('BatchNorm', 'BN')\n",
        "    if 'Momentum' in filename:\n",
        "        return None\n",
        "\n",
        "    # from TF to Keras naming\n",
        "    filename = filename.replace('_weights', '_kernel')\n",
        "    filename = filename.replace('_biases', '_bias')\n",
        "\n",
        "    return filename + '.npy'\n",
        "\n",
        "\n",
        "def extract_tensors_from_checkpoint_file(filename, output_folder='weights', net_name=None):\n",
        "    \"\"\"Extract tensors from a TF checkpoint file.\n",
        "    # Arguments\n",
        "        filename: TF checkpoint file\n",
        "        output_folder: where to save the output numpy array files\n",
        "    \"\"\"\n",
        "    if not os.path.exists(output_folder):\n",
        "        os.makedirs(output_folder)\n",
        "\n",
        "    reader = tf.train.NewCheckpointReader(filename)\n",
        "\n",
        "    for key in reader.get_variable_to_shape_map():\n",
        "        # convert tensor name into the corresponding Keras layer weight name and save\n",
        "        if net_name == 'xception':\n",
        "            filename = get_xception_filename(key)\n",
        "        elif net_name == 'mobilenetv2':\n",
        "            filename = get_mobilenetv2_filename(key)\n",
        "        if filename:\n",
        "            path = os.path.join(output_folder, filename)\n",
        "            arr = reader.get_tensor(key)\n",
        "            np.save(path, arr)\n",
        "            print(\"tensor_name: \", key)\n",
        "\n",
        "CKPT_URL = 'http://download.tensorflow.org/models/deeplabv3_pascal_trainval_2018_01_04.tar.gz'\n",
        "CKPT_URL_MOBILE = 'http://download.tensorflow.org/models/deeplabv3_mnv2_pascal_trainval_2018_01_29.tar.gz'\n",
        "MODEL_DIR = 'models'\n",
        "MODEL_SUBDIR = 'deeplabv3_pascal_trainval'\n",
        "MODEL_SUBDIR_MOBILE = 'deeplabv3_mnv2_pascal_trainval'\n",
        "\n",
        "if not os.path.exists(MODEL_DIR):\n",
        "    os.makedirs(MODEL_DIR)\n",
        "\n",
        "checkpoint_tar = data_utils.get_file(\n",
        "    'deeplabv3_pascal_trainval_2018_01_04.tar.gz',\n",
        "    CKPT_URL,\n",
        "    extract=True,\n",
        "    cache_subdir='',\n",
        "    cache_dir=MODEL_DIR)\n",
        "\n",
        "checkpoint_tar_mobile = data_utils.get_file(\n",
        "    'deeplabv3_mnv2_pascal_trainval_2018_01_29.tar.gz',\n",
        "    CKPT_URL_MOBILE,\n",
        "    extract=True,\n",
        "    cache_subdir='',\n",
        "    cache_dir=MODEL_DIR)\n",
        "\n",
        "checkpoint_file = os.path.join(MODEL_DIR, MODEL_SUBDIR, 'model.ckpt')\n",
        "extract_tensors_from_checkpoint_file(\n",
        "    checkpoint_file, net_name='xception', output_folder='weights/xception')\n",
        "\n",
        "checkpoint_file = os.path.join(\n",
        "    MODEL_DIR, MODEL_SUBDIR_MOBILE, 'model.ckpt-30000')\n",
        "extract_tensors_from_checkpoint_file(\n",
        "    checkpoint_file, net_name='mobilenetv2', output_folder='weights/mobilenetv2')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "orbk4FkqMBsy"
      },
      "source": [
        "###**Train the Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WM4Q-EOPEnqk",
        "outputId": "250286b1-797a-4b0b-d270-4833b5373c76"
      },
      "source": [
        "history = model.fit(train_gen,\n",
        "                    validation_data=val_gen,\n",
        "                    steps_per_epoch=len(train_gen),\n",
        "                    validation_steps=len(val_gen),\n",
        "                    epochs=num_epoch,\n",
        "                    callbacks=callbacks,\n",
        "                    verbose = 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/35\n",
            "676/676 [==============================] - 331s 436ms/step - loss: 0.5184 - iou_score: 0.4022 - f1-score: 0.5068 - val_loss: 0.7936 - val_iou_score: 0.2299 - val_f1-score: 0.3113\n",
            "\n",
            "Epoch 00001: loss improved from inf to 0.51839, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 2/35\n",
            "676/676 [==============================] - 295s 436ms/step - loss: 0.4465 - iou_score: 0.4604 - f1-score: 0.5728 - val_loss: 0.6548 - val_iou_score: 0.3083 - val_f1-score: 0.4016\n",
            "\n",
            "Epoch 00002: loss improved from 0.51839 to 0.44653, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 3/35\n",
            "676/676 [==============================] - 294s 434ms/step - loss: 0.4202 - iou_score: 0.4843 - f1-score: 0.5974 - val_loss: 0.6793 - val_iou_score: 0.2920 - val_f1-score: 0.3837\n",
            "\n",
            "Epoch 00003: loss improved from 0.44653 to 0.42025, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 4/35\n",
            "676/676 [==============================] - 295s 436ms/step - loss: 0.4019 - iou_score: 0.5024 - f1-score: 0.6153 - val_loss: 0.6388 - val_iou_score: 0.3198 - val_f1-score: 0.4232\n",
            "\n",
            "Epoch 00004: loss improved from 0.42025 to 0.40187, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 5/35\n",
            "676/676 [==============================] - 295s 436ms/step - loss: 0.3873 - iou_score: 0.5159 - f1-score: 0.6292 - val_loss: 0.6167 - val_iou_score: 0.3317 - val_f1-score: 0.4366\n",
            "\n",
            "Epoch 00005: loss improved from 0.40187 to 0.38731, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 6/35\n",
            "676/676 [==============================] - 295s 437ms/step - loss: 0.3737 - iou_score: 0.5292 - f1-score: 0.6423 - val_loss: 0.4945 - val_iou_score: 0.4287 - val_f1-score: 0.5338\n",
            "\n",
            "Epoch 00006: loss improved from 0.38731 to 0.37375, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 7/35\n",
            "676/676 [==============================] - 294s 435ms/step - loss: 0.3737 - iou_score: 0.5293 - f1-score: 0.6425 - val_loss: 0.5002 - val_iou_score: 0.4167 - val_f1-score: 0.5285\n",
            "\n",
            "Epoch 00007: loss improved from 0.37375 to 0.37370, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 8/35\n",
            "676/676 [==============================] - 295s 436ms/step - loss: 0.3539 - iou_score: 0.5491 - f1-score: 0.6611 - val_loss: 0.5204 - val_iou_score: 0.4061 - val_f1-score: 0.5181\n",
            "\n",
            "Epoch 00008: loss improved from 0.37370 to 0.35394, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 9/35\n",
            "676/676 [==============================] - 294s 435ms/step - loss: 0.3424 - iou_score: 0.5592 - f1-score: 0.6724 - val_loss: 0.4532 - val_iou_score: 0.4634 - val_f1-score: 0.5747\n",
            "\n",
            "Epoch 00009: loss improved from 0.35394 to 0.34241, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 10/35\n",
            "676/676 [==============================] - 295s 436ms/step - loss: 0.3323 - iou_score: 0.5715 - f1-score: 0.6820 - val_loss: 0.4394 - val_iou_score: 0.4725 - val_f1-score: 0.5860\n",
            "\n",
            "Epoch 00010: loss improved from 0.34241 to 0.33234, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 11/35\n",
            "676/676 [==============================] - 294s 435ms/step - loss: 0.3261 - iou_score: 0.5765 - f1-score: 0.6880 - val_loss: 0.4563 - val_iou_score: 0.4553 - val_f1-score: 0.5704\n",
            "\n",
            "Epoch 00011: loss improved from 0.33234 to 0.32609, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 12/35\n",
            "676/676 [==============================] - 294s 435ms/step - loss: 0.3223 - iou_score: 0.5810 - f1-score: 0.6916 - val_loss: 0.4769 - val_iou_score: 0.4400 - val_f1-score: 0.5501\n",
            "\n",
            "Epoch 00012: loss improved from 0.32609 to 0.32235, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 13/35\n",
            "676/676 [==============================] - 294s 434ms/step - loss: 0.3165 - iou_score: 0.5866 - f1-score: 0.6971 - val_loss: 0.4815 - val_iou_score: 0.4349 - val_f1-score: 0.5443\n",
            "\n",
            "Epoch 00013: loss improved from 0.32235 to 0.31651, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 14/35\n",
            "676/676 [==============================] - 295s 436ms/step - loss: 0.3162 - iou_score: 0.5866 - f1-score: 0.6975 - val_loss: 0.4958 - val_iou_score: 0.4294 - val_f1-score: 0.5408\n",
            "\n",
            "Epoch 00014: loss improved from 0.31651 to 0.31619, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 15/35\n",
            "676/676 [==============================] - 294s 435ms/step - loss: 0.2990 - iou_score: 0.6055 - f1-score: 0.7137 - val_loss: 0.4371 - val_iou_score: 0.4780 - val_f1-score: 0.5861\n",
            "\n",
            "Epoch 00015: loss improved from 0.31619 to 0.29903, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 16/35\n",
            "676/676 [==============================] - 295s 437ms/step - loss: 0.2962 - iou_score: 0.6082 - f1-score: 0.7167 - val_loss: 0.4382 - val_iou_score: 0.4740 - val_f1-score: 0.5866\n",
            "\n",
            "Epoch 00016: loss improved from 0.29903 to 0.29620, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 17/35\n",
            "676/676 [==============================] - 294s 435ms/step - loss: 0.2936 - iou_score: 0.6108 - f1-score: 0.7191 - val_loss: 0.4563 - val_iou_score: 0.4581 - val_f1-score: 0.5694\n",
            "\n",
            "Epoch 00017: loss improved from 0.29620 to 0.29359, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 18/35\n",
            "676/676 [==============================] - 295s 436ms/step - loss: 0.2811 - iou_score: 0.6238 - f1-score: 0.7312 - val_loss: 0.3898 - val_iou_score: 0.5160 - val_f1-score: 0.6275\n",
            "\n",
            "Epoch 00018: loss improved from 0.29359 to 0.28110, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 19/35\n",
            "676/676 [==============================] - 294s 435ms/step - loss: 0.2759 - iou_score: 0.6302 - f1-score: 0.7360 - val_loss: 0.4004 - val_iou_score: 0.5121 - val_f1-score: 0.6198\n",
            "\n",
            "Epoch 00019: loss improved from 0.28110 to 0.27589, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 20/35\n",
            "676/676 [==============================] - 295s 436ms/step - loss: 0.2749 - iou_score: 0.6314 - f1-score: 0.7374 - val_loss: 0.4467 - val_iou_score: 0.4673 - val_f1-score: 0.5806\n",
            "\n",
            "Epoch 00020: loss improved from 0.27589 to 0.27492, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 21/35\n",
            "676/676 [==============================] - 294s 435ms/step - loss: 0.2744 - iou_score: 0.6315 - f1-score: 0.7375 - val_loss: 0.5027 - val_iou_score: 0.4218 - val_f1-score: 0.5318\n",
            "\n",
            "Epoch 00021: loss improved from 0.27492 to 0.27440, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 22/35\n",
            "676/676 [==============================] - 295s 436ms/step - loss: 0.2661 - iou_score: 0.6408 - f1-score: 0.7455 - val_loss: 0.3910 - val_iou_score: 0.5176 - val_f1-score: 0.6284\n",
            "\n",
            "Epoch 00022: loss improved from 0.27440 to 0.26607, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 23/35\n",
            "676/676 [==============================] - 294s 435ms/step - loss: 0.2580 - iou_score: 0.6506 - f1-score: 0.7532 - val_loss: 0.4154 - val_iou_score: 0.4944 - val_f1-score: 0.6053\n",
            "\n",
            "Epoch 00023: loss improved from 0.26607 to 0.25802, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 24/35\n",
            "676/676 [==============================] - 295s 436ms/step - loss: 0.2508 - iou_score: 0.6579 - f1-score: 0.7603 - val_loss: 0.4377 - val_iou_score: 0.4757 - val_f1-score: 0.5859\n",
            "\n",
            "Epoch 00024: loss improved from 0.25802 to 0.25081, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 25/35\n",
            "676/676 [==============================] - 295s 435ms/step - loss: 0.2573 - iou_score: 0.6506 - f1-score: 0.7542 - val_loss: 0.4121 - val_iou_score: 0.4988 - val_f1-score: 0.6071\n",
            "\n",
            "Epoch 00025: loss did not improve from 0.25081\n",
            "Epoch 26/35\n",
            "676/676 [==============================] - 295s 436ms/step - loss: 0.2538 - iou_score: 0.6547 - f1-score: 0.7574 - val_loss: 0.3863 - val_iou_score: 0.5208 - val_f1-score: 0.6315\n",
            "\n",
            "Epoch 00026: loss did not improve from 0.25081\n",
            "Epoch 27/35\n",
            "676/676 [==============================] - 294s 435ms/step - loss: 0.2481 - iou_score: 0.6611 - f1-score: 0.7630 - val_loss: 0.4013 - val_iou_score: 0.5102 - val_f1-score: 0.6205\n",
            "\n",
            "Epoch 00027: loss improved from 0.25081 to 0.24807, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 28/35\n",
            "676/676 [==============================] - 294s 435ms/step - loss: 0.2448 - iou_score: 0.6651 - f1-score: 0.7662 - val_loss: 0.4052 - val_iou_score: 0.5039 - val_f1-score: 0.6130\n",
            "\n",
            "Epoch 00028: loss improved from 0.24807 to 0.24480, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 29/35\n",
            "676/676 [==============================] - 296s 437ms/step - loss: 0.2380 - iou_score: 0.6734 - f1-score: 0.7727 - val_loss: 0.3988 - val_iou_score: 0.5124 - val_f1-score: 0.6229\n",
            "\n",
            "Epoch 00029: loss improved from 0.24480 to 0.23797, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 30/35\n",
            "676/676 [==============================] - 297s 439ms/step - loss: 0.2444 - iou_score: 0.6657 - f1-score: 0.7669 - val_loss: 0.4116 - val_iou_score: 0.4985 - val_f1-score: 0.6100\n",
            "\n",
            "Epoch 00030: loss did not improve from 0.23797\n",
            "Epoch 31/35\n",
            "676/676 [==============================] - 295s 437ms/step - loss: 0.2343 - iou_score: 0.6768 - f1-score: 0.7762 - val_loss: 0.3810 - val_iou_score: 0.5277 - val_f1-score: 0.6370\n",
            "\n",
            "Epoch 00031: loss improved from 0.23797 to 0.23431, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 32/35\n",
            "676/676 [==============================] - 296s 438ms/step - loss: 0.2357 - iou_score: 0.6746 - f1-score: 0.7748 - val_loss: 0.4363 - val_iou_score: 0.4790 - val_f1-score: 0.5896\n",
            "\n",
            "Epoch 00032: loss did not improve from 0.23431\n",
            "Epoch 33/35\n",
            "676/676 [==============================] - 295s 437ms/step - loss: 0.2330 - iou_score: 0.6790 - f1-score: 0.7774 - val_loss: 0.3857 - val_iou_score: 0.5208 - val_f1-score: 0.6341\n",
            "\n",
            "Epoch 00033: loss improved from 0.23431 to 0.23297, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 34/35\n",
            "676/676 [==============================] - 297s 439ms/step - loss: 0.2240 - iou_score: 0.6891 - f1-score: 0.7861 - val_loss: 0.3764 - val_iou_score: 0.5306 - val_f1-score: 0.6415\n",
            "\n",
            "Epoch 00034: loss improved from 0.23297 to 0.22402, saving model to /content/clothingseg_deeplabv3+_256_35.h5\n",
            "Epoch 35/35\n",
            "676/676 [==============================] - 296s 438ms/step - loss: 0.2269 - iou_score: 0.6854 - f1-score: 0.7833 - val_loss: 0.3981 - val_iou_score: 0.5149 - val_f1-score: 0.6217\n",
            "\n",
            "Epoch 00035: loss did not improve from 0.22402\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "HFCEMlqWZte-",
        "outputId": "a1b57cb8-6e5b-4715-fd8e-de8497910b18"
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.download('/content/clothingseg_deeplabv3+_256_35.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_f7ae6f7c-b7ca-4361-9ef3-c82d3a74eb1d\", \"clothingseg_deeplabv3+_256_35.h5\", 8987640)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IZTsCqxKJkqJ"
      },
      "source": [
        "###**Model Evaluation**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zQTXRLdZfr4Z"
      },
      "source": [
        "\n",
        "\n",
        "> **Helper Functions for visualization**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rIzIeLRFuHX_"
      },
      "source": [
        "def visualize(**images):\n",
        "    \"\"\"\n",
        "    PLot images in one row.\n",
        "    Parameters\n",
        "    ----------\n",
        "    **images : ndarray\n",
        "        Images sent for plotting.\n",
        "        input image, ground_truth, generated_mask\n",
        "    \"\"\"\n",
        "    n = len(images)\n",
        "    plt.figure(figsize=(12, 5))\n",
        "    for i, (name, image) in enumerate(images.items()):\n",
        "        plt.subplot(1, n, i + 1)\n",
        "        plt.xticks([])\n",
        "        plt.yticks([]) \n",
        "        plt.title(' '.join(name.split('_')).title())\n",
        "        plt.imshow(image)\n",
        "        if i == n-1:\n",
        "            plt.imshow(images['image'])\n",
        "            plt.imshow(overlay, alpha = 0.4)\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BicqbCbVRW_R"
      },
      "source": [
        "def plot_history(history, metrics, losses):\n",
        "    \"\"\"\n",
        "    PLot loss and metrics evaluation on per epoch.\n",
        "    Parameters\n",
        "    ----------\n",
        "    history : tf.keras.callbacks.History\n",
        "        This callback is automatically applied to \n",
        "        every Keras model. The History object gets \n",
        "        returned by the fit method of models.\n",
        "    metrics : list\n",
        "        Is a list of metrics used to train the model.\n",
        "    losses : list\n",
        "        Is a list of losses used to train the model.\n",
        "    \"\"\"\n",
        "    plt.figure(figsize=(17, 5))\n",
        "    plt.subplot(121)\n",
        "    for metric in metrics:\n",
        "        plt.plot(history.history[metric], linewidth=3)\n",
        "    plt.ylabel(\"Metric\", fontsize=20)\n",
        "    plt.xlabel(\"Epoch\", fontsize=20)\n",
        "    plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "\n",
        "    plt.subplot(122)\n",
        "    for loss in losses:\n",
        "        plt.plot(history.history[loss], linewidth=3)\n",
        "    plt.suptitle(\"Model metrics & loss over epochs\", fontsize=20)\n",
        "    plt.ylabel(\"Loss\", fontsize=20)\n",
        "    plt.xlabel(\"Epoch\", fontsize=20)\n",
        "    plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "id": "kj4P8-xgNMZ_",
        "outputId": "89e175b8-cd97-4737-fba4-e0f83966f36f"
      },
      "source": [
        "plot_history(history, \n",
        "             metrics=[\"iou_score\", \"val_iou_score\"], \n",
        "             losses=[\"loss\",\"val_loss\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1224x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gq3-Iec5xgFh",
        "outputId": "8c3239ba-6c98-4db3-f781-5ce26dd9a49b"
      },
      "source": [
        "scores = model.evaluate(test_gen, steps=len(test_gen)*batch_size, verbose=0)\n",
        "\n",
        "print(\"Loss: {:.5}\".format(scores[0]))\n",
        "for metric, value in zip(metrics, scores[1:]):\n",
        "    print(\"mean {}: {:.5}\".format(metric.__name__, value))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 672 batches). You may need to use the repeat() function when building your dataset.\n",
            "Loss: 0.40149\n",
            "mean iou_score: 0.51699\n",
            "mean f1-score: 0.61926\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        },
        "id": "jKVoPNP8xXZI",
        "outputId": "76e5991d-e9c3-4851-e008-d704c983142a"
      },
      "source": [
        "num_images=3\n",
        "ids = np.random.choice(np.arange(batch_size-1), size=num_images, replace=False)\n",
        "x, y = next(test_gen)\n",
        "for i in ids:\n",
        "    image = x[i]\n",
        "    ground_truth = np.argmax(np.squeeze(y[i]), axis=2)\n",
        "    prediction = model.predict(np.asarray([x[i]]))\n",
        "    generated_mask = np.argmax(np.squeeze(prediction), axis=2)\n",
        "    overlay = np.ma.masked_where(np.squeeze(np.argmax(prediction, axis = 3)) == 0, np.squeeze(np.argmax(prediction, axis = 3)))\n",
        "\n",
        "    print(\" Dice Score : %2f \" % (metrics[0](y[i], prediction)))\n",
        "    cls_items = ''\n",
        "    for cls in  np.unique(ground_truth):\n",
        "        if cls != 0:\n",
        "            cls_items += classes[cls] + ', ' \n",
        "    print(\" Ground Truth Mask Classes: \", cls_items)\n",
        "    cls_items = ''\n",
        "    for cls in  np.unique(generated_mask):\n",
        "        if cls != 0:\n",
        "            cls_items += classes[cls] + ', ' \n",
        "    print(\" Generated Mask Classes: \", cls_items)\n",
        "    visualize(\n",
        "        image=image,\n",
        "        ground_truth=ground_truth,\n",
        "        generated_mask=generated_mask,\n",
        "        overlay=overlay,\n",
        "    )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Dice Score : 0.285750 \n",
            " Ground Truth Mask Classes:  wholebody, feet, accessories, \n",
            " Generated Mask Classes:  upperbody, lowerbody, wholebody, feet, accessories, \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 4 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            " Dice Score : 0.589583 \n",
            " Ground Truth Mask Classes:  upperbody, lowerbody, feet, accessories, \n",
            " Generated Mask Classes:  upperbody, lowerbody, feet, \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 4 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            " Dice Score : 0.704164 \n",
            " Ground Truth Mask Classes:  upperbody, lowerbody, feet, accessories, \n",
            " Generated Mask Classes:  upperbody, lowerbody, feet, \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 4 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "84lJHzj_CMxL"
      },
      "source": [
        "\n",
        "\n",
        "### **Visualization**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8fqJr1O_CS2y"
      },
      "source": [
        "def random_colors(N, bright=True):\n",
        "    \"\"\"\n",
        "    Generate random colors.\n",
        "    To get visually distinct colors, generate them in HSV space then\n",
        "    convert to RGB.\n",
        "    \"\"\"\n",
        "    brightness = 1.0 if bright else 0.7\n",
        "    hsv = [(i / N, 1, brightness) for i in range(N)]\n",
        "    colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))\n",
        "    random.shuffle(colors)\n",
        "    return colors\n",
        "\n",
        "\n",
        "def apply_mask(image, mask, color, alpha=0.4):\n",
        "    \"\"\"Apply the given mask to the image.\n",
        "    \"\"\"\n",
        "    for c in range(3):\n",
        "        image[:, :, c] = np.where(mask == 1,\n",
        "                                  image[:, :, c] *\n",
        "                                  (1 - alpha) + alpha * color[c] * 255,\n",
        "                                  image[:, :, c])\n",
        "    return image\n",
        "\n",
        "\n",
        "def expand_mask(mini_mask, image_shape):\n",
        "    \"\"\"Resizes mini masks back to image size.\n",
        "    \"\"\"\n",
        "    mask = np.zeros(image_shape[:2] + (mini_mask.shape[-1],), dtype=bool)\n",
        "    for i in range(mask.shape[-1]):\n",
        "        m = mini_mask[:, :, i]\n",
        "        # Resize with bilinear interpolation\n",
        "        m = resize(m, image_shape[:2])\n",
        "        mask[:, :, i] = np.around(m).astype(np.bool)\n",
        "    return mask\n",
        "\n",
        "\n",
        "def display_instances(image, masks, class_names,\n",
        "                      scores=None, title=\"\",\n",
        "                      figsize=(12, 12), ax=None, show_bbox=True,\n",
        "                      show_mask=True, colors=None, captions=None):\n",
        "    \"\"\"\n",
        "    masks: [height, width, num_instances]\n",
        "    class_names: list of class names of the dataset\n",
        "    scores: (optional) confidence scores for each box\n",
        "    title: (optional) Figure title\n",
        "    show_mask, show_bbox: To show masks and bounding boxes or not\n",
        "    figsize: (optional) the size of the image\n",
        "    colors: (optional) An array or colors to use with each object\n",
        "    captions: (optional) A list of strings to use as captions for each object\n",
        "    \"\"\"\n",
        "    # Number of instances\n",
        "    N = masks.shape[-1]\n",
        "\n",
        "    # If no axis is passed, create one and automatically call show()\n",
        "    auto_show = False\n",
        "    if not ax:\n",
        "        _, ax = plt.subplots(1, figsize=figsize)\n",
        "        auto_show = True\n",
        "\n",
        "    # Generate random colors\n",
        "    colors = colors or random_colors(N)\n",
        "    \n",
        "    # Show area outside image boundaries.\n",
        "    height, width = image.shape[:2]\n",
        "    ax.set_ylim(height + 10, -10)\n",
        "    ax.set_xlim(-10, width + 10)\n",
        "    ax.axis('off')\n",
        "    ax.set_title(title)\n",
        "\n",
        "    masked_image = image.astype(np.uint32).copy()\n",
        "    for i in range(1, N):\n",
        "        color = colors[i]\n",
        "\n",
        "        # Mask\n",
        "        mask = masks[:, :, i]\n",
        "        if show_mask:\n",
        "            masked_image = apply_mask(masked_image, mask, color)\n",
        "\n",
        "        # Mask Polygon\n",
        "        # Pad to ensure proper polygons for masks that touch image edges.\n",
        "        padded_mask = np.zeros(\n",
        "            (mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8)\n",
        "        padded_mask[1:-1, 1:-1] = mask\n",
        "        contours = find_contours(padded_mask, 0.5)\n",
        "        contour = []\n",
        "        for verts in contours:\n",
        "            # Subtract the padding and flip (y, x) to (x, y)\n",
        "            verts = np.fliplr(verts) - 1\n",
        "            contour.append(verts)\n",
        "            p = Polygon(verts, facecolor=\"none\", edgecolor=color)\n",
        "            ax.add_patch(p)\n",
        "        \n",
        "        # Bounding box\n",
        "        if contour:\n",
        "            labels = label(padded_mask)\n",
        "            props = regionprops(labels)\n",
        "            bboxes = [prop.bbox for prop in props]\n",
        "            for bbox in bboxes:\n",
        "                y1, x1, y2, x2 = bbox\n",
        "                if show_bbox:\n",
        "                    p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,\n",
        "                                        alpha=0.7, linestyle=\"dashed\",\n",
        "                                        edgecolor=color, facecolor='none')\n",
        "                    ax.add_patch(p)\n",
        "                \n",
        "            # Label\n",
        "            if not captions:\n",
        "                score = scores[i] if scores is not None else None\n",
        "                text_label = class_names[i]\n",
        "                caption = \"{} {:.3f}\".format(text_label, score) if score else text_label\n",
        "            else:\n",
        "                caption = captions[i]\n",
        "            ax.text(x1, y1 + 8, caption,\n",
        "                    color='w', size=11, backgroundcolor=\"none\")\n",
        "                \n",
        "    ax.imshow(masked_image.astype(np.uint8))\n",
        "    if auto_show:\n",
        "        plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "bMtpancncmg1",
        "outputId": "3be33985-372a-4d32-ca14-88b6279c70a5"
      },
      "source": [
        "img = load_img('/content/datas/test/images/07609b8383b7479d789ee3cef8fd5fa4.jpg')\n",
        "img = img_to_array(img, dtype='float32')\n",
        "in_img = resize(img, (IMG_HEIGHT, IMG_WIDTH))\n",
        "in_img = preprocessing(in_img)\n",
        "prediction = model.predict(np.asarray([in_img]))\n",
        "prediction = np.squeeze(prediction)\n",
        "masks = expand_mask(prediction, img.shape)\n",
        "display_instances(img, masks, classes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}