UNET_plusplus.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X9i0wvFZpjF7"
   },
   "source": [
    "## 9. UNET++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "GeEDd3r5Pr1q"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import os\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "L2auScnCrGkC"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, Dropout, MaxPooling2D, Conv2DTranspose\n",
    "from tensorflow.keras.layers import Concatenate, Add\n",
    "from tensorflow.keras import Input, Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.losses import binary_crossentropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Jb4Ta6cHprof",
    "outputId": "81e9d420-ed64-4c19-a1fc-e65c4ebaeb8a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NvElpRtlpjGM"
   },
   "source": [
    "### 9.1 Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "RxDfZ84IPx2P"
   },
   "outputs": [],
   "source": [
    "path = '/content/drive/MyDrive/cs2/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "bgPN6cVgP1uG",
    "outputId": "276a29cd-5301-4dc1-ea91-9327b23656e4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageId</th>\n",
       "      <th>rle_1</th>\n",
       "      <th>rle_2</th>\n",
       "      <th>rle_3</th>\n",
       "      <th>rle_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0002cc93b.jpg</td>\n",
       "      <td>29102 12 29346 24 29602 24 29858 24 30114 24 3...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00031f466.jpg</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000418bfc.jpg</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000789191.jpg</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0007a71bf.jpg</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>18661 28 18863 82 19091 110 19347 110 19603 11...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ImageId  ... rle_4\n",
       "0  0002cc93b.jpg  ...      \n",
       "1  00031f466.jpg  ...      \n",
       "2  000418bfc.jpg  ...      \n",
       "3  000789191.jpg  ...      \n",
       "4  0007a71bf.jpg  ...      \n",
       "\n",
       "[5 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data = pd.read_csv(path+'data/full_data.csv').fillna('')\n",
    "full_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "YK9fL3MpQKdJ"
   },
   "outputs": [],
   "source": [
    "#loading train and validation data\n",
    "train_data = pd.read_csv(path + \"data/train_data.csv\").fillna('')\n",
    "validtn_data = pd.read_csv(path + \"data/validtn_data.csv\").fillna('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n8fSRL7TpjGS"
   },
   "source": [
    "### 9.2 Data Generator Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "IYpZvFq2P4-e"
   },
   "outputs": [],
   "source": [
    "# Implementing custom data generator\n",
    "#https://towardsdatascience.com/implementing-custom-data-generators-in-keras-de56f013581c\n",
    "class DataGenerator(tf.keras.utils.Sequence):\n",
    "    def __init__(self, dataframe, batch_size=16, num_classes=None, shuffle=True, preprocess=None):\n",
    "        self.batch_size = batch_size\n",
    "        self.df = dataframe\n",
    "        self.indices = self.df.index.tolist()\n",
    "        self.preprocess = preprocess\n",
    "        self.shuffle = shuffle\n",
    "        self.on_epoch_end()\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.indices) // (self.batch_size)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        index = self.index[index * self.batch_size:(index + 1) * self.batch_size]\n",
    "        batch = [self.indices[k] for k in index]\n",
    "        \n",
    "        X, y = self.__get_data(batch)\n",
    "        return X, y\n",
    "\n",
    "    def on_epoch_end(self):\n",
    "        self.index = np.arange(len(self.indices))\n",
    "        if self.shuffle == True:\n",
    "            np.random.shuffle(self.index)\n",
    "\n",
    "    def __get_data(self, batch):\n",
    "        train_datagen = ImageDataGenerator()\n",
    "        param = {'flip_horizontal':True, 'samplewise_std_normalization' : True,\n",
    "                 'width_shift_range':0.1, 'height_shift_range':0.1,}\n",
    "        X = np.empty((self.batch_size,256,1600,3),dtype=np.float32) # image place-holders\n",
    "        Y = np.empty((self.batch_size,256,1600,4),dtype=np.float32)# 4 masks place-holders\n",
    "              \n",
    "        for i, id in enumerate(batch):\n",
    "          img = Image.open(path+'data/train_images/' + str(self.df['ImageId'].loc[id]))\n",
    "          X[i,] = train_datagen.apply_transform(x = img, transform_parameters = param)#input image\n",
    "          for j in range(4): #looping for each class\n",
    "                mask = rle2mask(self.df['rle_'+str(j+1)].loc[id])\n",
    "                Y[i,:,:,j] = train_datagen.apply_transform(x = mask, transform_parameters = param)#mask for each class\n",
    "                \n",
    "        # preprocess input\n",
    "        if self.preprocess!=None: X = self.preprocess(X)\n",
    "\n",
    "        return X, Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HmRVAXGlpjGU"
   },
   "source": [
    "### 9.3 Utility Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "zUZbT_k2QF5U"
   },
   "outputs": [],
   "source": [
    "#https://www.kaggle.com/titericz/building-and-visualizing-masks\n",
    "#https://www.kaggle.com/paulorzp/rle-functions-run-lenght-encode-decode\n",
    "\n",
    "#defining function for converting EncodedPixels(rle: run length encoding) to mask\n",
    "def rle2mask(rle_string, img_shape=(256,1600)):\n",
    "    '''\n",
    "    input: EncodedPixels (run-length-encoded) string & image shape:-(width,height)\n",
    "    output: mask in numpy.ndarray format with shape (256,1600)\n",
    "    '''\n",
    "    rle_array = np.array([int(s)for s in rle_string.split()])\n",
    "    starts_array = rle_array[::2]-1\n",
    "    lengths_array = rle_array[1::2]\n",
    "    mask_array = np.zeros(img_shape[0]*img_shape[1],dtype=np.uint8)\n",
    "    #print(starts_array,lengths_array)\n",
    "    for i in range(len(starts_array)):\n",
    "        mask_array[starts_array[i]:starts_array[i]+lengths_array[i]] = 1\n",
    "    #order='F' because encoded pixels are numbered from top to bottom, then left to right\n",
    "    return mask_array.reshape(img_shape, order = 'F')\n",
    "\n",
    "#defining function for converting given mask to EncodedPixels(rle: run length encoding)\n",
    "def mask2rle(mask_array):\n",
    "    '''\n",
    "    input: mask in numpy.ndarray format\n",
    "    output: EncodedPixels (run-length-encoded) string\n",
    "    '''\n",
    "    mask_array = mask_array.T.flatten()\n",
    "    mask_array = np.concatenate([[0], mask_array, [0]])\n",
    "    rle_array = np.where(mask_array[1:]!=mask_array[:-1])[0]+1\n",
    "    rle_array[1::2] -= rle_array[::2]\n",
    "    rle_string = ' '.join(map(str,rle_array))\n",
    "    return rle_string\n",
    "\n",
    "#defining function for calculation of metric dice coefficient\n",
    "def dice_coefficient(y_true, y_pred):\n",
    "    y_true_f = tf.reshape(y_true, [-1])\n",
    "    y_pred_f = tf.reshape(y_pred, [-1])\n",
    "    intersection = tf.math.reduce_sum(y_true_f * y_pred_f)\n",
    "    smoothing_const = 1e-9\n",
    "    return (2. * intersection + smoothing_const) / (tf.math.reduce_sum(y_true_f) + tf.math.reduce_sum(y_pred_f) + smoothing_const)\n",
    "\n",
    "#defining function for calculation of dice coefficient\n",
    "def dice_loss(y_true, y_pred):\n",
    "    y_true_f = tf.reshape(y_true, [-1])\n",
    "    y_pred_f = tf.reshape(y_pred, [-1])\n",
    "    return (1-dice_coefficient(y_true, y_pred))\n",
    "\n",
    "#defining function for calculation of loss function: binary cross entropy + dice loss\n",
    "def bce_dice_loss(y_true, y_pred):\n",
    "    y_true_f = tf.reshape(y_true, [-1])\n",
    "    y_pred_f = tf.reshape(y_pred, [-1])\n",
    "    return binary_crossentropy(y_true, y_pred) + (1-dice_coefficient(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2zntebldpjGV"
   },
   "source": [
    "### 9.4 Defining Unet++ Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "r20XtGhJuJAF"
   },
   "outputs": [],
   "source": [
    "# defining Unet++ architecture\n",
    "# https://arxiv.org/abs/1807.10165\n",
    "# https://towardsdatascience.com/biomedical-image-segmentation-unet-991d075a3a4b\n",
    "# https://gist.github.com/jinglescode/9d9ed6027e62e389e3165b59209e838e#file-unet-architecture-py\n",
    "\n",
    "def conv2D_block(input_tensor, n_filters, kernel_size = 3, batchnorm = True ):\n",
    "    \"\"\"function to pass Input_tensor through 2- Conv2D layers configured as per the input parameters\"\"\"\n",
    "    # first Conv2D layer\n",
    "    x = Conv2D(filters = n_filters, kernel_size = (kernel_size, kernel_size),\\\n",
    "            kernel_initializer = 'he_normal', padding = 'same')(input_tensor)\n",
    "    if batchnorm:\n",
    "        x = BatchNormalization()(x)\n",
    "        x = Activation('relu')(x)\n",
    "\n",
    "    # second Conv2D layer\n",
    "    x = Conv2D(filters = n_filters, kernel_size = (kernel_size, kernel_size),\\\n",
    "            kernel_initializer = 'he_normal', padding = 'same')(x)\n",
    "    if batchnorm:\n",
    "        x = BatchNormalization()(x)\n",
    "        x = Activation('relu')(x)\n",
    "\n",
    "    return x\n",
    "\n",
    "\n",
    "def UnetPP_Model(input_image, n_filters = 16, dropout = 0.1, batchnorm = True):\n",
    "    # Encoder (Contraction Path)\n",
    "    E1 = conv2D_block(input_img, n_filters * 1, kernel_size = 3, batchnorm = batchnorm)\n",
    "    p1 = MaxPooling2D((2, 2))(E1)\n",
    "    p1 = Dropout(dropout)(p1)\n",
    "\n",
    "    E2 = conv2D_block(p1, n_filters * 2, kernel_size = 3, batchnorm = batchnorm)\n",
    "    p2 = MaxPooling2D((2, 2))(E2)\n",
    "    p2 = Dropout(dropout)(p2)\n",
    "\n",
    "    E3 = conv2D_block(p2, n_filters * 4, kernel_size = 3, batchnorm = batchnorm)\n",
    "    p3 = MaxPooling2D((2, 2))(E3)\n",
    "    p3 = Dropout(dropout)(p3)\n",
    "\n",
    "    E4 = conv2D_block(p3, n_filters * 8, kernel_size = 3, batchnorm = batchnorm)\n",
    "    p4 = MaxPooling2D((2, 2))(E4)\n",
    "    p4 = Dropout(dropout)(p4)\n",
    "\n",
    "    E5 = conv2D_block(p4, n_filters = n_filters * 16, kernel_size = 3, batchnorm = batchnorm)\n",
    "\n",
    "    #Di1\n",
    "    D_1_1 = Conv2DTranspose(n_filters * 1, kernel_size = (3, 3), strides = (2, 2), padding = 'same')(E2)\n",
    "    D_1_1 = Concatenate()([D_1_1, E1])\n",
    "    D_1_1 = Dropout(dropout)(D_1_1)\n",
    "    D_1_1 = conv2D_block(D_1_1, n_filters * 1, kernel_size = 3, batchnorm = batchnorm)\n",
    "    \n",
    "    D_2_1 = Conv2DTranspose(n_filters * 2, kernel_size = (3, 3), strides = (2, 2), padding = 'same')(E3)\n",
    "    D_2_1 = Concatenate()([D_2_1, E2])\n",
    "    D_2_1 = Dropout(dropout)(D_2_1)\n",
    "    D_2_1 = conv2D_block(D_2_1, n_filters * 2, kernel_size = 3, batchnorm = batchnorm)\n",
    "    \n",
    "    D_3_1 = Conv2DTranspose(n_filters * 4, kernel_size = (3, 3), strides = (2, 2), padding = 'same')(E4)\n",
    "    D_3_1 = Concatenate()([D_3_1, E3])\n",
    "    D_3_1 = Dropout(dropout)(D_3_1)\n",
    "    D_3_1 = conv2D_block(D_3_1, n_filters * 4, kernel_size = 3, batchnorm = batchnorm)\n",
    "    \n",
    "    D_4_1 = Conv2DTranspose(n_filters * 8, kernel_size = (3, 3), strides = (2, 2), padding = 'same')(E5)\n",
    "    D_4_1 = Concatenate()([D_4_1, E4])\n",
    "    D_4_1 = Dropout(dropout)(D_4_1)\n",
    "    D_4_1 = conv2D_block(D_4_1, n_filters * 8, kernel_size = 3, batchnorm = batchnorm)\n",
    "    \n",
    "    #Di2\n",
    "    D_1_2 = Conv2DTranspose(n_filters * 1, kernel_size = (3, 3), strides = (2, 2), padding = 'same')(D_2_1)\n",
    "    D_1_2 = Concatenate()([D_1_2, D_1_1, E1])\n",
    "    D_1_2 = Dropout(dropout)(D_1_2)\n",
    "    D_1_2 = conv2D_block(D_1_2, n_filters * 1, kernel_size = 3, batchnorm = batchnorm)\n",
    "    \n",
    "    D_2_2 = Conv2DTranspose(n_filters * 2, kernel_size = (3, 3), strides = (2, 2), padding = 'same')(D_3_1)\n",
    "    D_2_2 = Concatenate()([D_2_2, D_2_1, E2])\n",
    "    D_2_2 = Dropout(dropout)(D_2_2)\n",
    "    D_2_2 = conv2D_block(D_2_2, n_filters * 2, kernel_size = 3, batchnorm = batchnorm)\n",
    "    \n",
    "    D_3_2 = Conv2DTranspose(n_filters * 4, kernel_size = (3, 3), strides = (2, 2), padding = 'same')(D_4_1)\n",
    "    D_3_2 = Concatenate()([D_3_2, D_3_1, E3])\n",
    "    D_3_2 = Dropout(dropout)(D_3_2)\n",
    "    D_3_2 = conv2D_block(D_3_2, n_filters * 4, kernel_size = 3, batchnorm = batchnorm)\n",
    "    \n",
    "    #Di3\n",
    "    D_1_3 = Conv2DTranspose(n_filters * 1, kernel_size = (3, 3), strides = (2, 2), padding = 'same')(D_2_2)\n",
    "    D_1_3 = Concatenate()([D_1_3, D_1_2, D_1_1, E1])\n",
    "    D_1_3 = Dropout(dropout)(D_1_3)\n",
    "    D_1_3 = conv2D_block(D_1_3, n_filters * 1, kernel_size = 3, batchnorm = batchnorm)\n",
    "    \n",
    "    D_2_3 = Conv2DTranspose(n_filters * 2, kernel_size = (3, 3), strides = (2, 2), padding = 'same')(D_3_2)\n",
    "    D_2_3 = Concatenate()([D_2_3, D_2_2, D_2_1, E2])\n",
    "    D_2_3 = Dropout(dropout)(D_2_3)\n",
    "    D_2_3 = conv2D_block(D_2_3, n_filters * 2, kernel_size = 3, batchnorm = batchnorm)\n",
    "    \n",
    "    #Di4\n",
    "    D_1_4 = Conv2DTranspose(n_filters * 1, kernel_size = (3, 3), strides = (2, 2), padding = 'same')(D_2_3)\n",
    "    D_1_4 = Concatenate()([D_1_4, D_1_3, D_1_2, D_1_1, E1])\n",
    "    D_1_4 = Dropout(dropout)(D_1_4)\n",
    "    D_1_4 = conv2D_block(D_1_4, n_filters * 1, kernel_size = 3, batchnorm = batchnorm)\n",
    "    \n",
    "    outputs = Conv2D(4, (1, 1), activation='sigmoid')(D_1_4)\n",
    "    model = Model(inputs=[input_img], outputs=[outputs])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iI96Ze93e_pF",
    "outputId": "fb73bf34-abe7-4e81-b92e-6be2a692f1f4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "img (InputLayer)                [(None, 256, 1600, 3 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 256, 1600, 4) 112         img[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 256, 1600, 4) 16          conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 256, 1600, 4) 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 256, 1600, 4) 148         activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 256, 1600, 4) 16          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 256, 1600, 4) 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 128, 800, 4)  0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 128, 800, 4)  0           max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 128, 800, 8)  296         dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 128, 800, 8)  32          conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 128, 800, 8)  0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 128, 800, 8)  584         activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 128, 800, 8)  32          conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 128, 800, 8)  0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 64, 400, 8)   0           activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 64, 400, 8)   0           max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 64, 400, 16)  1168        dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 64, 400, 16)  64          conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 64, 400, 16)  0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 64, 400, 16)  2320        activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 64, 400, 16)  64          conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 64, 400, 16)  0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 32, 200, 16)  0           activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 32, 200, 16)  0           max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 32, 200, 32)  4640        dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 32, 200, 32)  128         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 32, 200, 32)  0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 32, 200, 32)  9248        activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 32, 200, 32)  128         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 32, 200, 32)  0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 16, 100, 32)  0           activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 16, 100, 32)  0           max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 16, 100, 64)  18496       dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 16, 100, 64)  256         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 16, 100, 64)  0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 16, 100, 64)  36928       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 16, 100, 64)  256         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 16, 100, 64)  0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTrans (None, 32, 200, 32)  18464       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 32, 200, 64)  0           conv2d_transpose_3[0][0]         \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTrans (None, 64, 400, 16)  4624        activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_7 (Dropout)             (None, 32, 200, 64)  0           concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 64, 400, 32)  0           conv2d_transpose_2[0][0]         \n",
      "                                                                 activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTrans (None, 128, 800, 8)  1160        activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 32, 200, 32)  18464       dropout_7[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_6 (Dropout)             (None, 64, 400, 32)  0           concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 128, 800, 16) 0           conv2d_transpose_1[0][0]         \n",
      "                                                                 activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose (Conv2DTranspo (None, 256, 1600, 4) 292         activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 32, 200, 32)  128         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 64, 400, 16)  4624        dropout_6[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_5 (Dropout)             (None, 128, 800, 16) 0           concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 256, 1600, 8) 0           conv2d_transpose[0][0]           \n",
      "                                                                 activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 32, 200, 32)  0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 64, 400, 16)  64          conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 128, 800, 8)  1160        dropout_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, 256, 1600, 8) 0           concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 32, 200, 32)  9248        activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 64, 400, 16)  0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 128, 800, 8)  32          conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 256, 1600, 4) 292         dropout_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 32, 200, 32)  128         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 64, 400, 16)  2320        activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 128, 800, 8)  0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 256, 1600, 4) 16          conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 32, 200, 32)  0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 64, 400, 16)  64          conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 128, 800, 8)  584         activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 256, 1600, 4) 0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_6 (Conv2DTrans (None, 64, 400, 16)  4624        activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 64, 400, 16)  0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 128, 800, 8)  32          conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 256, 1600, 4) 148         activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_6 (Concatenate)     (None, 64, 400, 48)  0           conv2d_transpose_6[0][0]         \n",
      "                                                                 activation_15[0][0]              \n",
      "                                                                 activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_5 (Conv2DTrans (None, 128, 800, 8)  1160        activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 128, 800, 8)  0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 256, 1600, 4) 16          conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_10 (Dropout)            (None, 64, 400, 48)  0           concatenate_6[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_5 (Concatenate)     (None, 128, 800, 24) 0           conv2d_transpose_5[0][0]         \n",
      "                                                                 activation_13[0][0]              \n",
      "                                                                 activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_4 (Conv2DTrans (None, 256, 1600, 4) 292         activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 256, 1600, 4) 0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 64, 400, 16)  6928        dropout_10[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout_9 (Dropout)             (None, 128, 800, 24) 0           concatenate_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 256, 1600, 12 0           conv2d_transpose_4[0][0]         \n",
      "                                                                 activation_11[0][0]              \n",
      "                                                                 activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 64, 400, 16)  64          conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 128, 800, 8)  1736        dropout_9[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_8 (Dropout)             (None, 256, 1600, 12 0           concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 64, 400, 16)  0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 128, 800, 8)  32          conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 256, 1600, 4) 436         dropout_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 64, 400, 16)  2320        activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 128, 800, 8)  0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 256, 1600, 4) 16          conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 64, 400, 16)  64          conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 128, 800, 8)  584         activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 256, 1600, 4) 0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 64, 400, 16)  0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 128, 800, 8)  32          conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 256, 1600, 4) 148         activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_8 (Conv2DTrans (None, 128, 800, 8)  1160        activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 128, 800, 8)  0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 256, 1600, 4) 16          conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_8 (Concatenate)     (None, 128, 800, 32) 0           conv2d_transpose_8[0][0]         \n",
      "                                                                 activation_21[0][0]              \n",
      "                                                                 activation_13[0][0]              \n",
      "                                                                 activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_7 (Conv2DTrans (None, 256, 1600, 4) 292         activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 256, 1600, 4) 0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_12 (Dropout)            (None, 128, 800, 32) 0           concatenate_8[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_7 (Concatenate)     (None, 256, 1600, 16 0           conv2d_transpose_7[0][0]         \n",
      "                                                                 activation_19[0][0]              \n",
      "                                                                 activation_11[0][0]              \n",
      "                                                                 activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 128, 800, 8)  2312        dropout_12[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout_11 (Dropout)            (None, 256, 1600, 16 0           concatenate_7[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 128, 800, 8)  32          conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 256, 1600, 4) 580         dropout_11[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 128, 800, 8)  0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 256, 1600, 4) 16          conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 128, 800, 8)  584         activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 256, 1600, 4) 0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 128, 800, 8)  32          conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 256, 1600, 4) 148         activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 128, 800, 8)  0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 256, 1600, 4) 16          conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_9 (Conv2DTrans (None, 256, 1600, 4) 292         activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 256, 1600, 4) 0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_9 (Concatenate)     (None, 256, 1600, 20 0           conv2d_transpose_9[0][0]         \n",
      "                                                                 activation_25[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "                                                                 activation_11[0][0]              \n",
      "                                                                 activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_13 (Dropout)            (None, 256, 1600, 20 0           concatenate_9[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 256, 1600, 4) 724         dropout_13[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 256, 1600, 4) 16          conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 256, 1600, 4) 0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 256, 1600, 4) 148         activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 256, 1600, 4) 16          conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 256, 1600, 4) 0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 256, 1600, 4) 20          activation_29[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 161,632\n",
      "Trainable params: 160,720\n",
      "Non-trainable params: 912\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#configuring input tensor\n",
    "input_img = Input((256, 1600, 3), name='img')\n",
    "\n",
    "model = UnetPP_Model(input_img, n_filters=4, dropout=0.2, batchnorm=True)\n",
    "model.compile(optimizer=Adam(), loss=bce_dice_loss, metrics=[dice_coefficient])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "KGEmyotzQaU2"
   },
   "outputs": [],
   "source": [
    "#loading train & validation data using DataGenerator class defined\n",
    "train_batches = DataGenerator(train_data,shuffle=True)\n",
    "validtn_batches = DataGenerator(validtn_data,shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "CswfbyWNQfxU"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard\n",
    "import datetime, os\n",
    "\n",
    "# #tensorboard for visualizing loss & metric summary \n",
    "# logdir = path+\"logs/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "# tensorboard_callback = TensorBoard(logdir)\n",
    "\n",
    "#defining ModelCheckpoint to make a checkpoint when validation dice coefficient improves\n",
    "checkpoints = ModelCheckpoint(path+'unet++.h5', \\\n",
    "                              monitor='val_dice_coefficient', verbose=1, save_best_only=True, mode='max')\n",
    "\n",
    "#adding ModelCheckpoint & TensorBoard to callback functions list\n",
    "callbacks_list = [checkpoints]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OB0xRZcUpjGh"
   },
   "source": [
    "### 9.5 Traing Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZpStn8GyQjPz",
    "outputId": "3f9ff137-d22f-4ef2-b665-9bb9ee5188fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "667/667 [==============================] - 3285s 5s/step - loss: 1.4661 - dice_coefficient: 0.0185 - val_loss: 1.0317 - val_dice_coefficient: 0.0578\n",
      "\n",
      "Epoch 00001: val_dice_coefficient improved from -inf to 0.05776, saving model to /content/drive/MyDrive/cs2/unet++.h5\n",
      "Epoch 2/30\n",
      "667/667 [==============================] - 853s 1s/step - loss: 0.9042 - dice_coefficient: 0.1602 - val_loss: 1.0049 - val_dice_coefficient: 0.1859\n",
      "\n",
      "Epoch 00002: val_dice_coefficient improved from 0.05776 to 0.18591, saving model to /content/drive/MyDrive/cs2/unet++.h5\n",
      "Epoch 3/30\n",
      "667/667 [==============================] - 853s 1s/step - loss: 0.6025 - dice_coefficient: 0.4390 - val_loss: 0.5573 - val_dice_coefficient: 0.4859\n",
      "\n",
      "Epoch 00003: val_dice_coefficient improved from 0.18591 to 0.48587, saving model to /content/drive/MyDrive/cs2/unet++.h5\n",
      "Epoch 4/30\n",
      "667/667 [==============================] - 852s 1s/step - loss: 0.5625 - dice_coefficient: 0.4790 - val_loss: 0.5401 - val_dice_coefficient: 0.5062\n",
      "\n",
      "Epoch 00004: val_dice_coefficient improved from 0.48587 to 0.50619, saving model to /content/drive/MyDrive/cs2/unet++.h5\n",
      "Epoch 5/30\n",
      "667/667 [==============================] - 852s 1s/step - loss: 0.5497 - dice_coefficient: 0.4925 - val_loss: 0.5815 - val_dice_coefficient: 0.4715\n",
      "\n",
      "Epoch 00005: val_dice_coefficient did not improve from 0.50619\n",
      "Epoch 6/30\n",
      "667/667 [==============================] - 853s 1s/step - loss: 0.5417 - dice_coefficient: 0.5001 - val_loss: 0.5423 - val_dice_coefficient: 0.5053\n",
      "\n",
      "Epoch 00006: val_dice_coefficient did not improve from 0.50619\n",
      "Epoch 7/30\n",
      "667/667 [==============================] - 853s 1s/step - loss: 0.5460 - dice_coefficient: 0.4962 - val_loss: 0.5467 - val_dice_coefficient: 0.4998\n",
      "\n",
      "Epoch 00007: val_dice_coefficient did not improve from 0.50619\n",
      "Epoch 8/30\n",
      "667/667 [==============================] - 853s 1s/step - loss: 0.5292 - dice_coefficient: 0.5114 - val_loss: 0.5351 - val_dice_coefficient: 0.5088\n",
      "\n",
      "Epoch 00008: val_dice_coefficient improved from 0.50619 to 0.50877, saving model to /content/drive/MyDrive/cs2/unet++.h5\n",
      "Epoch 9/30\n",
      "667/667 [==============================] - 852s 1s/step - loss: 0.5220 - dice_coefficient: 0.5185 - val_loss: 0.5287 - val_dice_coefficient: 0.5181\n",
      "\n",
      "Epoch 00009: val_dice_coefficient improved from 0.50877 to 0.51806, saving model to /content/drive/MyDrive/cs2/unet++.h5\n",
      "Epoch 10/30\n",
      "667/667 [==============================] - 851s 1s/step - loss: 0.5304 - dice_coefficient: 0.5096 - val_loss: 0.5425 - val_dice_coefficient: 0.5032\n",
      "\n",
      "Epoch 00010: val_dice_coefficient did not improve from 0.51806\n",
      "Epoch 11/30\n",
      "667/667 [==============================] - 851s 1s/step - loss: 0.5099 - dice_coefficient: 0.5284 - val_loss: 0.5476 - val_dice_coefficient: 0.4976\n",
      "\n",
      "Epoch 00011: val_dice_coefficient did not improve from 0.51806\n",
      "Epoch 12/30\n",
      "667/667 [==============================] - 851s 1s/step - loss: 0.5156 - dice_coefficient: 0.5215 - val_loss: 0.5298 - val_dice_coefficient: 0.5160\n",
      "\n",
      "Epoch 00012: val_dice_coefficient did not improve from 0.51806\n",
      "Epoch 13/30\n",
      "667/667 [==============================] - 851s 1s/step - loss: 0.5151 - dice_coefficient: 0.5237 - val_loss: 0.5148 - val_dice_coefficient: 0.5301\n",
      "\n",
      "Epoch 00013: val_dice_coefficient improved from 0.51806 to 0.53008, saving model to /content/drive/MyDrive/cs2/unet++.h5\n",
      "Epoch 14/30\n",
      "667/667 [==============================] - 851s 1s/step - loss: 0.5119 - dice_coefficient: 0.5264 - val_loss: 0.5127 - val_dice_coefficient: 0.5302\n",
      "\n",
      "Epoch 00014: val_dice_coefficient improved from 0.53008 to 0.53017, saving model to /content/drive/MyDrive/cs2/unet++.h5\n",
      "Epoch 15/30\n",
      "667/667 [==============================] - 852s 1s/step - loss: 0.5043 - dice_coefficient: 0.5326 - val_loss: 0.5177 - val_dice_coefficient: 0.5273\n",
      "\n",
      "Epoch 00015: val_dice_coefficient did not improve from 0.53017\n",
      "Epoch 16/30\n",
      "667/667 [==============================] - 853s 1s/step - loss: 0.5088 - dice_coefficient: 0.5280 - val_loss: 0.5143 - val_dice_coefficient: 0.5290\n",
      "\n",
      "Epoch 00016: val_dice_coefficient did not improve from 0.53017\n",
      "Epoch 17/30\n",
      "667/667 [==============================] - 853s 1s/step - loss: 0.5057 - dice_coefficient: 0.5320 - val_loss: 0.5129 - val_dice_coefficient: 0.5294\n",
      "\n",
      "Epoch 00017: val_dice_coefficient did not improve from 0.53017\n",
      "Epoch 18/30\n",
      "667/667 [==============================] - 853s 1s/step - loss: 0.4941 - dice_coefficient: 0.5427 - val_loss: 0.5206 - val_dice_coefficient: 0.5247\n",
      "\n",
      "Epoch 00018: val_dice_coefficient did not improve from 0.53017\n",
      "Epoch 19/30\n",
      "667/667 [==============================] - 852s 1s/step - loss: 0.4998 - dice_coefficient: 0.5368 - val_loss: 0.5276 - val_dice_coefficient: 0.5160\n",
      "\n",
      "Epoch 00019: val_dice_coefficient did not improve from 0.53017\n",
      "Epoch 20/30\n",
      "667/667 [==============================] - 853s 1s/step - loss: 0.4927 - dice_coefficient: 0.5423 - val_loss: 0.4987 - val_dice_coefficient: 0.5437\n",
      "\n",
      "Epoch 00020: val_dice_coefficient improved from 0.53017 to 0.54367, saving model to /content/drive/MyDrive/cs2/unet++.h5\n",
      "Epoch 21/30\n",
      "667/667 [==============================] - 853s 1s/step - loss: 0.4824 - dice_coefficient: 0.5535 - val_loss: 0.4970 - val_dice_coefficient: 0.5427\n",
      "\n",
      "Epoch 00021: val_dice_coefficient did not improve from 0.54367\n",
      "Epoch 22/30\n",
      "667/667 [==============================] - 852s 1s/step - loss: 0.4892 - dice_coefficient: 0.5472 - val_loss: 0.5168 - val_dice_coefficient: 0.5280\n",
      "\n",
      "Epoch 00022: val_dice_coefficient did not improve from 0.54367\n",
      "Epoch 23/30\n",
      "667/667 [==============================] - 852s 1s/step - loss: 0.4906 - dice_coefficient: 0.5466 - val_loss: 0.5134 - val_dice_coefficient: 0.5302\n",
      "\n",
      "Epoch 00023: val_dice_coefficient did not improve from 0.54367\n",
      "Epoch 24/30\n",
      "667/667 [==============================] - 852s 1s/step - loss: 0.4889 - dice_coefficient: 0.5471 - val_loss: 0.4773 - val_dice_coefficient: 0.5623\n",
      "\n",
      "Epoch 00024: val_dice_coefficient improved from 0.54367 to 0.56234, saving model to /content/drive/MyDrive/cs2/unet++.h5\n",
      "Epoch 25/30\n",
      "667/667 [==============================] - 851s 1s/step - loss: 0.4846 - dice_coefficient: 0.5508 - val_loss: 0.5022 - val_dice_coefficient: 0.5410\n",
      "\n",
      "Epoch 00025: val_dice_coefficient did not improve from 0.56234\n",
      "Epoch 26/30\n",
      "667/667 [==============================] - 851s 1s/step - loss: 0.4933 - dice_coefficient: 0.5421 - val_loss: 0.4882 - val_dice_coefficient: 0.5521\n",
      "\n",
      "Epoch 00026: val_dice_coefficient did not improve from 0.56234\n",
      "Epoch 27/30\n",
      "667/667 [==============================] - 851s 1s/step - loss: 0.4732 - dice_coefficient: 0.5610 - val_loss: 0.5162 - val_dice_coefficient: 0.5281\n",
      "\n",
      "Epoch 00027: val_dice_coefficient did not improve from 0.56234\n",
      "Epoch 28/30\n",
      "667/667 [==============================] - 851s 1s/step - loss: 0.4777 - dice_coefficient: 0.5574 - val_loss: 0.4819 - val_dice_coefficient: 0.5577\n",
      "\n",
      "Epoch 00028: val_dice_coefficient did not improve from 0.56234\n",
      "Epoch 29/30\n",
      "667/667 [==============================] - 851s 1s/step - loss: 0.4825 - dice_coefficient: 0.5527 - val_loss: 0.4834 - val_dice_coefficient: 0.5569\n",
      "\n",
      "Epoch 00029: val_dice_coefficient did not improve from 0.56234\n",
      "Epoch 30/30\n",
      "667/667 [==============================] - 851s 1s/step - loss: 0.4743 - dice_coefficient: 0.5592 - val_loss: 0.5374 - val_dice_coefficient: 0.5087\n",
      "\n",
      "Epoch 00030: val_dice_coefficient did not improve from 0.56234\n"
     ]
    }
   ],
   "source": [
    "# fit model\n",
    "history = model.fit(train_batches,validation_data=validtn_batches, epochs = 30, callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "uWroPuJCUVvC",
    "outputId": "3017393f-b210-41df-8e3c-0fa494fb6983"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing history for dice_coefficient\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.plot(history.history['dice_coefficient'])\n",
    "plt.plot(history.history['val_dice_coefficient'])\n",
    "plt.title('model dice_coefficient')\n",
    "plt.ylabel('dice_coefficient')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'Validation'], loc='upper left')\n",
    "plt.show()\n",
    "# Visualizing history for loss\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('bce_dice_loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'Validation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6ZxIZeU0U0WS"
   },
   "outputs": [],
   "source": [
    "# saving history data for first 30 epochs in csv file\n",
    "history1 = pd.DataFrame(history.history)\n",
    "history1.to_csv(path + \"data/history1-unet++.csv\", index=True)\n",
    "# saving model(weights upto 30 epochs)\n",
    "model.save(path+'unet++-30epochs.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h3eoOjI1s_ZY"
   },
   "source": [
    "### 9.6 Traing Model (31-60 epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "idhWTsH5q7pY",
    "outputId": "ea882483-72da-4fd4-ee6c-8396ee523131"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "667/667 [==============================] - 3265s 5s/step - loss: 0.4774 - dice_coefficient: 0.5565 - val_loss: 0.4867 - val_dice_coefficient: 0.5532\n",
      "\n",
      "Epoch 00001: val_dice_coefficient improved from -inf to 0.55318, saving model to /content/drive/MyDrive/cs2/unet++.h5\n",
      "Epoch 2/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4692 - dice_coefficient: 0.5642 - val_loss: 0.4771 - val_dice_coefficient: 0.5623\n",
      "\n",
      "Epoch 00002: val_dice_coefficient improved from 0.55318 to 0.56232, saving model to /content/drive/MyDrive/cs2/unet++.h5\n",
      "Epoch 3/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4623 - dice_coefficient: 0.5711 - val_loss: 0.4787 - val_dice_coefficient: 0.5616\n",
      "\n",
      "Epoch 00003: val_dice_coefficient did not improve from 0.56232\n",
      "Epoch 4/30\n",
      "667/667 [==============================] - 857s 1s/step - loss: 0.4759 - dice_coefficient: 0.5573 - val_loss: 0.4823 - val_dice_coefficient: 0.5572\n",
      "\n",
      "Epoch 00004: val_dice_coefficient did not improve from 0.56232\n",
      "Epoch 5/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4670 - dice_coefficient: 0.5663 - val_loss: 0.4825 - val_dice_coefficient: 0.5570\n",
      "\n",
      "Epoch 00005: val_dice_coefficient did not improve from 0.56232\n",
      "Epoch 6/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4657 - dice_coefficient: 0.5666 - val_loss: 0.4680 - val_dice_coefficient: 0.5690\n",
      "\n",
      "Epoch 00006: val_dice_coefficient improved from 0.56232 to 0.56898, saving model to /content/drive/MyDrive/cs2/unet++.h5\n",
      "Epoch 7/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4702 - dice_coefficient: 0.5625 - val_loss: 0.4934 - val_dice_coefficient: 0.5484\n",
      "\n",
      "Epoch 00007: val_dice_coefficient did not improve from 0.56898\n",
      "Epoch 8/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4836 - dice_coefficient: 0.5506 - val_loss: 0.4784 - val_dice_coefficient: 0.5608\n",
      "\n",
      "Epoch 00008: val_dice_coefficient did not improve from 0.56898\n",
      "Epoch 9/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4605 - dice_coefficient: 0.5740 - val_loss: 0.4864 - val_dice_coefficient: 0.5558\n",
      "\n",
      "Epoch 00009: val_dice_coefficient did not improve from 0.56898\n",
      "Epoch 10/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4579 - dice_coefficient: 0.5746 - val_loss: 0.4921 - val_dice_coefficient: 0.5484\n",
      "\n",
      "Epoch 00010: val_dice_coefficient did not improve from 0.56898\n",
      "Epoch 11/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4683 - dice_coefficient: 0.5651 - val_loss: 0.4749 - val_dice_coefficient: 0.5633\n",
      "\n",
      "Epoch 00011: val_dice_coefficient did not improve from 0.56898\n",
      "Epoch 12/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4653 - dice_coefficient: 0.5680 - val_loss: 0.4675 - val_dice_coefficient: 0.5730\n",
      "\n",
      "Epoch 00012: val_dice_coefficient improved from 0.56898 to 0.57296, saving model to /content/drive/MyDrive/cs2/unet++.h5\n",
      "Epoch 13/30\n",
      "667/667 [==============================] - 855s 1s/step - loss: 0.4436 - dice_coefficient: 0.5884 - val_loss: 0.4998 - val_dice_coefficient: 0.5378\n",
      "\n",
      "Epoch 00013: val_dice_coefficient did not improve from 0.57296\n",
      "Epoch 14/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4602 - dice_coefficient: 0.5727 - val_loss: 0.4837 - val_dice_coefficient: 0.5576\n",
      "\n",
      "Epoch 00014: val_dice_coefficient did not improve from 0.57296\n",
      "Epoch 15/30\n",
      "667/667 [==============================] - 855s 1s/step - loss: 0.4540 - dice_coefficient: 0.5786 - val_loss: 0.4932 - val_dice_coefficient: 0.5500\n",
      "\n",
      "Epoch 00015: val_dice_coefficient did not improve from 0.57296\n",
      "Epoch 16/30\n",
      "667/667 [==============================] - 855s 1s/step - loss: 0.4567 - dice_coefficient: 0.5757 - val_loss: 0.5173 - val_dice_coefficient: 0.5276\n",
      "\n",
      "Epoch 00016: val_dice_coefficient did not improve from 0.57296\n",
      "Epoch 17/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4703 - dice_coefficient: 0.5620 - val_loss: 0.4791 - val_dice_coefficient: 0.5595\n",
      "\n",
      "Epoch 00017: val_dice_coefficient did not improve from 0.57296\n",
      "Epoch 18/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4607 - dice_coefficient: 0.5714 - val_loss: 0.4744 - val_dice_coefficient: 0.5655\n",
      "\n",
      "Epoch 00018: val_dice_coefficient did not improve from 0.57296\n",
      "Epoch 19/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4555 - dice_coefficient: 0.5771 - val_loss: 0.4686 - val_dice_coefficient: 0.5689\n",
      "\n",
      "Epoch 00019: val_dice_coefficient did not improve from 0.57296\n",
      "Epoch 20/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4504 - dice_coefficient: 0.5817 - val_loss: 0.4642 - val_dice_coefficient: 0.5734\n",
      "\n",
      "Epoch 00020: val_dice_coefficient improved from 0.57296 to 0.57341, saving model to /content/drive/MyDrive/cs2/unet++.h5\n",
      "Epoch 21/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4540 - dice_coefficient: 0.5782 - val_loss: 0.4678 - val_dice_coefficient: 0.5708\n",
      "\n",
      "Epoch 00021: val_dice_coefficient did not improve from 0.57341\n",
      "Epoch 22/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4603 - dice_coefficient: 0.5718 - val_loss: 0.4756 - val_dice_coefficient: 0.5616\n",
      "\n",
      "Epoch 00022: val_dice_coefficient did not improve from 0.57341\n",
      "Epoch 23/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4431 - dice_coefficient: 0.5889 - val_loss: 0.4714 - val_dice_coefficient: 0.5668\n",
      "\n",
      "Epoch 00023: val_dice_coefficient did not improve from 0.57341\n",
      "Epoch 24/30\n",
      "667/667 [==============================] - 855s 1s/step - loss: 0.4449 - dice_coefficient: 0.5859 - val_loss: 0.4774 - val_dice_coefficient: 0.5634\n",
      "\n",
      "Epoch 00024: val_dice_coefficient did not improve from 0.57341\n",
      "Epoch 25/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4580 - dice_coefficient: 0.5739 - val_loss: 0.4834 - val_dice_coefficient: 0.5553\n",
      "\n",
      "Epoch 00025: val_dice_coefficient did not improve from 0.57341\n",
      "Epoch 26/30\n",
      "667/667 [==============================] - 857s 1s/step - loss: 0.4712 - dice_coefficient: 0.5607 - val_loss: 0.4788 - val_dice_coefficient: 0.5608\n",
      "\n",
      "Epoch 00026: val_dice_coefficient did not improve from 0.57341\n",
      "Epoch 27/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4437 - dice_coefficient: 0.5883 - val_loss: 0.4760 - val_dice_coefficient: 0.5602\n",
      "\n",
      "Epoch 00027: val_dice_coefficient did not improve from 0.57341\n",
      "Epoch 28/30\n",
      "667/667 [==============================] - 856s 1s/step - loss: 0.4364 - dice_coefficient: 0.5943 - val_loss: 0.5498 - val_dice_coefficient: 0.4976\n",
      "\n",
      "Epoch 00028: val_dice_coefficient did not improve from 0.57341\n",
      "Epoch 29/30\n",
      "667/667 [==============================] - 855s 1s/step - loss: 0.4463 - dice_coefficient: 0.5846 - val_loss: 0.4821 - val_dice_coefficient: 0.5578\n",
      "\n",
      "Epoch 00029: val_dice_coefficient did not improve from 0.57341\n",
      "Epoch 30/30\n",
      "667/667 [==============================] - 855s 1s/step - loss: 0.4418 - dice_coefficient: 0.5896 - val_loss: 0.4735 - val_dice_coefficient: 0.5666\n",
      "\n",
      "Epoch 00030: val_dice_coefficient did not improve from 0.57341\n"
     ]
    }
   ],
   "source": [
    "#loading saved weights of model trained upto 30 epochs\n",
    "model.load_weights(path + 'unet++-30epochs.h5')\n",
    "# fit model\n",
    "history = model.fit(train_batches,validation_data=validtn_batches, epochs = 30, callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "3XrGNn46rI4v"
   },
   "outputs": [],
   "source": [
    "# saving history data for 31-60 epochs in csv file\n",
    "history2 = pd.DataFrame(history.history)\n",
    "history2.to_csv(path + \"data/history2-unet++.csv\", index=True)\n",
    "# saving model(weights upto 60 epochs)\n",
    "model.save(path+'unet++-31-60epochs.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "vxzq_XwRrSgA",
    "outputId": "5dd1a40d-9238-45be-9c65-10040530fb56"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#loading saved history of 1-30 epochs\n",
    "history1 = pd.read_csv(path + \"data/history1-unet++.csv\")\n",
    "#loading saved history of 30-60 epochs\n",
    "history2 = pd.read_csv(path + \"data/history2-unet++.csv\")\n",
    "\n",
    "# Visualizing history(1-60 epochs) for dice_coefficient\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.plot(history1['dice_coefficient'].tolist() + history2['dice_coefficient'].tolist())\n",
    "plt.plot(history1['val_dice_coefficient'].tolist() + history2['val_dice_coefficient'].tolist())\n",
    "plt.title('model dice_coefficient')\n",
    "plt.ylabel('dice_coefficient')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'Validation'], loc='upper left')\n",
    "plt.show()\n",
    "# Visualizing history(1-60 epochs) for loss\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.plot(history1['loss'].tolist() + history2['loss'].tolist())\n",
    "plt.plot(history1['val_loss'].tolist() + history2['val_loss'].tolist())\n",
    "plt.title('model loss')\n",
    "plt.ylabel('bce_dice_loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'Validation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZRILSfpdpjGk"
   },
   "source": [
    "### 9.7 Visualizing Model Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RC4JaxPEQqWK",
    "outputId": "80c2fa44-19c3-430f-aba0-18a7a8987333"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117/117 [==============================] - 44s 373ms/step - loss: 0.4642 - dice_coefficient: 0.5734\n",
      "loss: 0.46424591541290283 dice_coefficient: 0.5734127163887024\n"
     ]
    }
   ],
   "source": [
    "# evaluating model(best weights) with the validation batch\n",
    "model.load_weights(path + 'unet++.h5')\n",
    "loss, dc= model.evaluate(validtn_batches,verbose=1)\n",
    "print('loss:',loss, 'dice_coefficient:',dc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "m7kmLfigQwlb"
   },
   "outputs": [],
   "source": [
    "#defining function to display GroundTruth & Predicted mask images for a given imageId\n",
    "def visualize_model_prediction(img_id):\n",
    "    fig, axs = plt.subplots(4, 3, figsize=(16,8))\n",
    "    img_obj = cv2.imread(path+'data/train_images/'+ img_id)\n",
    "    masks_actual = full_data[full_data['ImageId'] == img_id]\n",
    "    x = np.empty((1,256,1600,3),dtype=np.float32) # image place-holders\n",
    "    x[0,] = Image.open(path+'data/train_images/' + img_id)\n",
    "    masks_predicted = model.predict(x)\n",
    "    for i in range(4):\n",
    "        axs[i,0].imshow(img_obj)\n",
    "        axs[i,0].set_title(img_id)\n",
    "        axs[i,1].imshow(rle2mask(masks_actual['rle_'+str(i+1)].iloc[0]))\n",
    "        axs[i,1].set_title(\"Actual mask for Class '{}'\".format(i+1) )\n",
    "        axs[i,2].imshow(masks_predicted[0,:,:,i])\n",
    "        axs[i,2].set_title(\"Predicted mask for Class '{}'\".format(i+1))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "id": "ORYvFHwKQzgE",
    "outputId": "9e5cc928-aa2e-421d-e776-8f821712a92d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model_prediction('0002cc93b.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "id": "07MuvASpQ8Oj",
    "outputId": "0e7736b0-2e62-4870-cdda-c774a5fe841d",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model_prediction('4d38c353e.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CHLq2NubpjGo"
   },
   "source": [
    "### 9.8 Preparing Data for submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "F5AK0b7aqRbk"
   },
   "outputs": [],
   "source": [
    "class PredictDataGenerator(tf.keras.utils.Sequence):\n",
    "    def __init__(self,dataframe, list_idcs, batch_size=32, ):\n",
    "        self.batch_size = batch_size\n",
    "        self.df = dataframe\n",
    "        self.list_idcs = list_idcs\n",
    "        self.indices = self.df.index.tolist()\n",
    "        self.rem = len(self.list_idcs) % (self.batch_size)\n",
    "        self.on_epoch_end()\n",
    "\n",
    "    def __len__(self):\n",
    "         return len(self.list_idcs) // (self.batch_size)\n",
    "#         if (self.rem) == 0:\n",
    "#             return len(self.list_idcs) // (self.batch_size)\n",
    "#         else:\n",
    "#             return (len(self.list_idcs) // (self.batch_size) )+1\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        index = self.indices[index * self.batch_size:(index + 1) * self.batch_size]\n",
    "#         if ((index + 1) * self.batch_size) < len(self.list_idcs):\n",
    "#             index = self.indices[index * self.batch_size:(index + 1) * self.batch_size]\n",
    "#         else:\n",
    "#             index = self.indices[index * self.batch_size: (index * self.batch_size)+ self.rem]\n",
    "        batch = [self.list_idcs[k] for k in index]\n",
    "        \n",
    "        X = self.__get_data(batch)\n",
    "         \n",
    "        return X\n",
    "    def on_epoch_end(self):\n",
    "        self.index = np.arange(len(self.indices))\n",
    "\n",
    "    def __get_data(self, batch):\n",
    "        X = np.empty((self.batch_size,256,1600,3),dtype=np.float32) # image place-holders\n",
    "              \n",
    "        for i, id in enumerate(batch):\n",
    "            img = Image.open(path + 'data/test_images/' + str(self.df['ImageId'].loc[id]))\n",
    "            X[i,] = img#input image\n",
    "\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "id": "w1ue3zm5qidV",
    "outputId": "a0d2991e-04d8-4fa2-db1d-363485c054ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5506\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d3dcedbd7.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>db7cff638.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d3764cee5.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d387d301f.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d221d2a3f.jpg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ImageId\n",
       "0  d3dcedbd7.jpg\n",
       "1  db7cff638.jpg\n",
       "2  d3764cee5.jpg\n",
       "3  d387d301f.jpg\n",
       "4  d221d2a3f.jpg"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_img_IDs = list(os.listdir(path + 'data/test_images/'))\n",
    "test_imgsIds_df = pd.DataFrame({'ImageId': test_img_IDs})\n",
    "print(len(test_imgsIds_df))\n",
    "test_imgsIds_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "4ABHK6oPqzlu"
   },
   "outputs": [],
   "source": [
    "SubmissionDf = pd.DataFrame(columns = ['ImageId','EncodedPixels','ClassId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "y3aI_xFzqujN",
    "outputId": "fea3b811-769f-4918-fe60-16905918eceb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "320it [00:03, 85.75it/s]\n",
      "320it [00:03, 85.44it/s]\n",
      "320it [00:03, 83.15it/s]\n",
      "320it [00:03, 83.40it/s]\n",
      "320it [00:03, 83.65it/s]\n",
      "320it [00:03, 82.02it/s]\n",
      "320it [00:03, 82.41it/s]\n",
      "320it [00:04, 78.65it/s]\n",
      "320it [00:04, 78.02it/s]\n",
      "320it [00:03, 82.68it/s]\n",
      "320it [00:03, 81.32it/s]\n",
      "320it [00:04, 79.56it/s]\n",
      "320it [00:03, 80.27it/s]\n",
      "320it [00:03, 81.17it/s]\n",
      "320it [00:04, 79.31it/s]\n",
      "320it [00:03, 80.40it/s]\n",
      "320it [00:03, 81.45it/s]\n",
      "66it [00:00, 81.35it/s]\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,len(test_imgsIds_df),320):\n",
    "    batch_idcs =  list(range(i, min(test_imgsIds_df.shape[0], i + 320)))#.iloc[batch_idcs]\n",
    "    if len(batch_idcs)== 320:        \n",
    "        test_subbatch = PredictDataGenerator(dataframe = test_imgsIds_df,\n",
    "                                             list_idcs = batch_idcs)\n",
    "    else:\n",
    "        test_subbatch = PredictDataGenerator(dataframe = test_imgsIds_df,\n",
    "                                             list_idcs = batch_idcs,\n",
    "                                             batch_size= len(batch_idcs))\n",
    "    #print(len(test_subbatch))\n",
    "    subbatch_pred_masks = model.predict(test_subbatch)\n",
    "    #print(len(subbatch_pred_masks))\n",
    "    #break\n",
    "    for j, idx in tqdm(enumerate(batch_idcs)):\n",
    "        filename = test_imgsIds_df['ImageId'].iloc[idx]\n",
    "        rle1 = mask2rle(subbatch_pred_masks[j,:,:,0].round().astype(int))\n",
    "        rle2 = mask2rle(subbatch_pred_masks[j,:,:,1].round().astype(int))\n",
    "        rle3 = mask2rle(subbatch_pred_masks[j,:,:,2].round().astype(int))\n",
    "        rle4 = mask2rle(subbatch_pred_masks[j,:,:,3].round().astype(int))\n",
    "        df = pd.DataFrame({'ImageId':[filename]*4,\n",
    "                      'EncodedPixels': [rle1,rle2,rle3,rle4],\n",
    "                      'ClassId':['1', '2', '3', '4']})\n",
    "        SubmissionDf = SubmissionDf.append(df,ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "id": "N-eL3mWjrJha",
    "outputId": "b5b54593-3baf-4ca5-c816-9c61759ca659"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(22024, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageId</th>\n",
       "      <th>EncodedPixels</th>\n",
       "      <th>ClassId</th>\n",
       "      <th>ImageId_ClassId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20016</th>\n",
       "      <td>0000f269f.jpg</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0000f269f.jpg_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20017</th>\n",
       "      <td>0000f269f.jpg</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>0000f269f.jpg_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20018</th>\n",
       "      <td>0000f269f.jpg</td>\n",
       "      <td>145303 13 145545 47 145798 60 145870 4 145875 ...</td>\n",
       "      <td>3</td>\n",
       "      <td>0000f269f.jpg_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20019</th>\n",
       "      <td>0000f269f.jpg</td>\n",
       "      <td></td>\n",
       "      <td>4</td>\n",
       "      <td>0000f269f.jpg_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20004</th>\n",
       "      <td>000ccc2ac.jpg</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>000ccc2ac.jpg_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20005</th>\n",
       "      <td>000ccc2ac.jpg</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>000ccc2ac.jpg_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20006</th>\n",
       "      <td>000ccc2ac.jpg</td>\n",
       "      <td></td>\n",
       "      <td>3</td>\n",
       "      <td>000ccc2ac.jpg_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20007</th>\n",
       "      <td>000ccc2ac.jpg</td>\n",
       "      <td></td>\n",
       "      <td>4</td>\n",
       "      <td>000ccc2ac.jpg_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20012</th>\n",
       "      <td>002451917.jpg</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>002451917.jpg_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20013</th>\n",
       "      <td>002451917.jpg</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>002451917.jpg_2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ImageId  ...  ImageId_ClassId\n",
       "20016  0000f269f.jpg  ...  0000f269f.jpg_1\n",
       "20017  0000f269f.jpg  ...  0000f269f.jpg_2\n",
       "20018  0000f269f.jpg  ...  0000f269f.jpg_3\n",
       "20019  0000f269f.jpg  ...  0000f269f.jpg_4\n",
       "20004  000ccc2ac.jpg  ...  000ccc2ac.jpg_1\n",
       "20005  000ccc2ac.jpg  ...  000ccc2ac.jpg_2\n",
       "20006  000ccc2ac.jpg  ...  000ccc2ac.jpg_3\n",
       "20007  000ccc2ac.jpg  ...  000ccc2ac.jpg_4\n",
       "20012  002451917.jpg  ...  002451917.jpg_1\n",
       "20013  002451917.jpg  ...  002451917.jpg_2\n",
       "\n",
       "[10 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SubmissionDf.sort_values(by=['ImageId', 'ClassId'], inplace=True)\n",
    "SubmissionDf['ImageId_ClassId'] = SubmissionDf['ImageId'] + '_' + SubmissionDf['ClassId']\n",
    "print(SubmissionDf.shape)\n",
    "SubmissionDf.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "4m4rVqpPrQF8"
   },
   "outputs": [],
   "source": [
    "SubmissionDf.to_csv(path+'submission-unet++.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dg0tEK7vpjGs"
   },
   "source": [
    "### 9.9 Kaggle Submission Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "nxS74h98pjGt",
    "outputId": "ae627cd6-af79-444a-a75f-9a8fb16050d2"
   },
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Kaggle Score\n",
    "from IPython.display import Image\n",
    "Image(filename='unet++.JPG') "
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "UNET++.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}